statistics assignment online

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AP®︎/College Statistics

Unit 1: exploring categorical data, unit 2: exploring one-variable quantitative data: displaying and describing, unit 3: exploring one-variable quantitative data: summary statistics, unit 4: exploring one-variable quantitative data: percentiles, z-scores, and the normal distribution, unit 5: exploring two-variable quantitative data, unit 6: collecting data, unit 7: probability, unit 8: random variables and probability distributions, unit 9: sampling distributions, unit 10: inference for categorical data: proportions, unit 11: inference for quantitative data: means, unit 12: inference for categorical data: chi-square, unit 13: inference for quantitative data: slopes, unit 14: prepare for the 2022 ap®︎ statistics exam.

Introduction to Statistics

(15 reviews)

David Lane, Rice University

Publisher: David Lane

Language: English

Formats Available

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Reviewed by Terri Torres, professor, Oregon Institute of Technology on 8/17/23

This author covers all the topics that would be covered in an introductory statistics course plus some. I could imagine using it for two courses at my university, which is on the quarter system. I would rather have the problem of too many topics... read more

Comprehensiveness rating: 5 see less

Content Accuracy rating: 5

Yes, Lane is both thorough and accurate.

Relevance/Longevity rating: 5

What is covered is what is usually covered in an introductory statistics book. The only topic I may, given sufficient time, cover is bootstrapping.

Clarity rating: 5

The book is clear and well-written. For the trickier topics, simulations are included to help with understanding.

Consistency rating: 5

All is organized in a way that is consistent with the previous topic.

Modularity rating: 5

The text is organized in a way that easily enables navigation.

Organization/Structure/Flow rating: 5

The text is organized like most statistics texts.

Interface rating: 5

Easy navigation.

Grammatical Errors rating: 5

I didn't see any grammatical errors.

Cultural Relevance rating: 5

Nothing is included that is culturally insensitive.

The videos that accompany this text are short and easy to watch and understand. Videos should be short enough to teach, but not so long that they are tiresome. This text includes almost everything: videos, simulations, case studies---all nicely organized in one spot. In addition, Lane has promised to send an instructor's manual and slide deck.

Reviewed by Professor Sandberg, Professor, Framingham State University on 6/29/21

This text covers all the usual topics in an Introduction to Statistics for college students. In addition, it has some additional topics that are useful. read more

This text covers all the usual topics in an Introduction to Statistics for college students. In addition, it has some additional topics that are useful.

I did not find any errors.

Some of the examples are dated. And the frequent use of male/female examples need updating in terms of current gender splits.

I found it was easy to read and understand and I expect that students would also find the writing clear and the explanations accessible.

Even with different authors of chapter, the writing is consistent.

The text is well organized into sections making it easy to assign individual topics and sections.

The topics are presented in the usual order. Regression comes later in the text but there is a difference of opinions about whether to present it early with descriptive statistics for bivariate data or later with inferential statistics.

I had no problem navigating the text online.

The writing is grammatical correct.

I saw no issues that would be offensive.

I did like this text. It seems like it would be a good choice for most introductory statistics courses. I liked that the Monty Hall problem was included in the probability section. The author offers to provide an instructor's manual, PowerPoint slides and additional questions. These additional resources are very helpful and not always available with online OER texts.

Reviewed by Emilio Vazquez, Associate Professor, Trine University on 4/23/21

This appears to be an excellent textbook for an Introductory Course in Statistics. It covers subjects in enough depth to fulfill the needs of a beginner in Statistics work yet is not so complex as to be overwhelming. read more

I found no errors in their discussions. Did not work out all of the questions and answers but my sampling did not reveal any errors.

Some of the examples may need updating depending on the times but the examples are still relevant at this time.

This is a Statistics text so a little dry. I found that the derivation of some of the formulas was not explained. However the background is there to allow the instructor to derive these in class if desired.

The text is consistent throughout using the same verbiage in various sections.

The text dose lend itself to reasonable reading assignments. For example the chapter (Chapter 3) on Summarizing Distributions covers Central Tendency and its associated components in an easy 20 pages with Measures of Variability making up most of the rest of the chapter and covering approximately another 20 pages. Exercises are available at the end of each chapter making it easy for the instructor to assign reading and exercises to be discussed in class.

The textbook flows easily from Descriptive to Inferential Statistics with chapters on Sampling and Estimation preceding chapters on hypothesis testing

I had no problems with navigation

All textbooks have a few errors but certainly nothing glaring or making text difficult

I saw no issues and I am part of a cultural minority in the US

Overall I found this to be a excellent in-depth overview of Statistical Theory, Concepts and Analysis. The length of the textbook appears to be more than adequate for a one-semester course in Introduction to Statistics. As I no longer teach a full statistics course but simply a few lectures as part of our Research Curriculum, I am recommending this book to my students as a good reference. Especially as it is available on-line and in Open Access.

Reviewed by Audrey Hickert, Assistant Professor, Southern Illinois University Carbondale on 3/29/21

All of the major topics of an introductory level statistics course for social science are covered. Background areas include levels of measurement and research design basics. Descriptive statistics include all major measures of central tendency and dispersion/variation. Building blocks for inferential statistics include sampling distributions, the standard normal curve (z scores), and hypothesis testing sections. Inferential statistics include how to calculate confidence intervals, as well as conduct tests of one-sample tests of the population mean (Z- and t-tests), two-sample tests of the difference in population means (Z- and t-tests), chi square test of independence, correlation, and regression. Doesn’t include full probability distribution tables (e.g., t or Z), but those can be easily found online in many places.

I did not find any errors or issues of inaccuracy. When a particular method or practice is debated in the field, the authors acknowledge it (and provide citations in some circumstances).

Relevance/Longevity rating: 4

Basic statistics are standard, so the core information will remain relevant in perpetuity. Some of the examples are dated (e.g., salaries from 1999), but not problematic.

Clarity rating: 4

All of the key terms, formulas, and logic for statistical tests are clearly explained. The book sometimes uses different notation than other entry-level books. For example, the variance formula uses "M" for mean, rather than x-bar.

The explanations are consistent and build from and relate to corresponding sections that are listed in each unit.

Modularity is a strength of this text in both the PDF and interactive online format. Students can easily navigate to the necessary sections and each starts with a “Prerequisites” list of other sections in the book for those who need the additional background material. Instructors could easily compile concise sub-sections of the book for readings.

The presentation of topics differs somewhat from the standard introductory social science statistics textbooks I have used before. However, the modularity allows the instructor and student to work through the discrete sections in the desired order.

Interface rating: 4

For the most part the display of all images/charts is good and navigation is straightforward. One concern is that the organization of the Table of Contents does not exactly match the organizational outline at the start of each chapter in the PDF version. For example, sometimes there are more detailed sub-headings at the start of chapter and occasionally slightly different section headings/titles. There are also inconsistencies in section listings at start of chapters vs. start of sub-sections.

The text is easy to read and free from any obvious grammatical errors.

Although some of the examples are outdated, I did not review any that were offensive. One example of an outdated reference is using descriptive data on “Men per 100 Women” in U.S. cities as “useful if we are looking for an opposite-sex partner”.

This is a good introduction level statistics text book if you have a course with students who may be intimated by longer texts with more detailed information. Just the core basics are provided here and it is easy to select the sections you need. It is a good text if you plan to supplement with an array of your own materials (lectures, practice, etc.) that are specifically tailored to your discipline (e.g., criminal justice and criminology). Be advised that some formulas use different notation than other standard texts, so you will need to point that out to students if they differ from your lectures or assessment materials.

Reviewed by Shahar Boneh, Professor, Metropolitan State University of Denver on 3/26/21, updated 4/22/21

The textbook is indeed quite comprehensive. It can accommodate any style of introductory statistics course. read more

The textbook is indeed quite comprehensive. It can accommodate any style of introductory statistics course.

The text seems to be statistically accurate.

It is a little too extensive, which requires instructors to cover it selectively, and has a potential to confuse the students.

It is written clearly.

Consistency rating: 4

The terminology is fairly consistent. There is room for some improvement.

By the nature of the subject, the topics have to be presented in a sequential and coherent order. However, the book breaks things down quite effectively.

Organization/Structure/Flow rating: 3

Some of the topics are interleaved and not presented in the order I would like to cover them.

Good interface.

The grammar is ok.

The book seems to be culturally neutral, and not offensive in any way.

I really liked the simulations that go with the book. Parts of the book are a little too advanced for students who are learning statistics for the first time.

Reviewed by Julie Gray, Adjunct Assistant Professor, University of Texas at Arlington on 2/26/21

The textbook is for beginner-level students. The concept development is appropriate--there is always room to grow to high higher level, but for an introduction, the basics are what is needed. This is a well-thought-through OER textbook project by... read more

I found all the material accurate.

Essentially, statistical concepts at the introductory level are accepted as universal. This suggests that the relevance of this textbook will continue for a long time.

The book is well written for introducing beginners to statistical concepts. The figures, tables, and animated examples reinforce the clarity of the written text.

Yes, the information is consistent; when it is introduced in early chapters it ties in well in later chapters that build on and add more understanding for the topic.

Modularity rating: 4

The book is well-written with attention to modularity where possible. Due to the nature of statistics, that is not always possible. The content is presented in the order that I usually teach these concepts.

The organization of the book is good, I particularly like the sample lecture slide presentations and the problem set with solutions for use in quizzes and exams. These are available by writing to the author. It is wonderful to have access to these helpful resources for instructors to use in preparation.

I did not find any interface issues.

The book is well written. In my reading I did not notice grammatical errors.

For this subject and in the examples given, I did not notice any cultural issues.

For the field of social work where qualitative data is as common as quantitative, the importance of giving students the rationale or the motivation to learn the quantitative side is understated. To use this text as an introductory statistics OER textbook in a social work curriculum, the instructor will want to bring in field-relevant examples to engage and motivate students. The field needs data-driven decision making and evidence-based practices to become more ubiquitous than not. Preparing future social workers by teaching introductory statistics is essential to meet that goal.

Reviewed by Mamata Marme, Assistant Professor, Augustana College on 6/25/19

This textbook offers a fairly comprehensive summary of what should be discussed in an introductory course in Statistics. The statistical literacy exercises are particularly interesting. It would be helpful to have the statistical tables... read more

Comprehensiveness rating: 4 see less

The terminology and notation used in the textbook is pretty standard. The content is accurate.

The statistical literacy example are up to date but will need to be updated fairly regularly to keep the textbook fresh. The applications within the chapter are accessible and can be used fairly easily over a couple of editions.

The textbook does not necessarily explain the derivation of some of the formulae and this will need to be augmented by the instructor in class discussion. What is beneficial is that there are multiple ways that a topic is discussed using graphs, calculations and explanations of the results. Statistics textbooks have to cover a wide variety of topics with a fair amount of depth. To do this concisely is difficult. There is a fine line between being concise and clear, which this textbook does well, and being somewhat dry. It may be up to the instructor to bring case studies into the readings we are going through the topics rather than wait until the end of the chapter.

The textbook uses standard notation and terminology. The heading section of each chapter is closely tied to topics that are covered. The end of chapter problems and the statistical literacy applications are closely tied to the material covered.

The authors have done a good job treating each chapter as if they stand alone. The lack of connection to a past reference may create a sense of disconnect between the topics discussed

The text's "modularity" does make the flow of the material a little disconnected. If would be better if there was accountability of what a student should already have learnt in a different section. The earlier material is easy to find but not consistently referred to in the text.

I had no problem with the interface. The online version is more visually interesting than the pdf version.

I did not see any grammatical errors.

Cultural Relevance rating: 4

I am not sure how to evaluate this. The examples are mostly based on the American experience and the data alluded to mostly domestic. However, I am not sure if that creates a problem in understanding the methodology.

Overall, this textbook will cover most of the topics in a survey of statistics course.

Reviewed by Alexandra Verkhovtseva, Professor, Anoka-Ramsey Community College on 6/3/19

This is a comprehensive enough text, considering that it is not easy to create a comprehensive statistics textbook. It is suitable for an introductory statistics course for non-math majors. It contains twenty-one chapters, covering the wide range... read more

The content is pretty accurate, I did not find any biases or errors.

The book contains fairly recent data presented in the form of exercises, examples and applications. The topics are up-to-date, and appropriate technology is used for examples, applications, and case studies.

The language is simple and clear, which is a good thing, since students are usually scared of this class, and instructors are looking for something to put them at ease. I would, however, try to make it a little more interesting, exciting, or may be even funny.

Consistency is good, the book has a great structure. I like how each chapter has prerequisites and learner outcomes, this gives students a good idea of what to expect. Material in this book is covered in good detail.

The text can be easily divided into sub-sections, some of which can be omitted if needed. The chapter on regression is covered towards the end (chapter 14), but part of it can be covered sooner in the course.

The book contains well organized chapters that makes reading through easy and understandable. The order of chapters and sections is clear and logical.

The online version has many functions and is easy to navigate. This book also comes with a PDF version. There is no distortion of images or charts. The text is clean and clear, the examples provided contain appropriate format of data presentation.

No grammatical errors found.

The text uses simple and clear language, which is helpful for non-native speakers. I would include more culturally-relevant examples and case studies. Overall, good text.

In all, this book is a good learning experience. It contains tools and techniques that free and easy to use and also easy to modify for both, students and instructors. I very much appreciate this opportunity to use this textbook at no cost for our students.

Reviewed by Dabrina Dutcher, Assistant Professor, Bucknell University on 3/4/19

This is a reasonably thorough first-semester statistics book for most classes. It would have worked well for the general statistics courses I have taught in the past but is not as suitable for specialized introductory statistics courses for engineers or business applications. That is OK, they have separate texts for that! The only sections that feel somewhat light in terms of content are the confidence intervals and ANOVA sections. Given that these topics are often sort of crammed in at the end of many introductory classes, that might not be problematic for many instructors. It should also be pointed out that while there are a couple of chapters on probability, this book spends presents most formulas as "black boxes" rather than worry about the derivation or origin of the formulas. The probability sections do not include any significant combinatorics work, which is sometimes included at this level.

I did not find any errors in the formulas presented but I did not work many end-of-chapter problems to gauge the accuracy of their answers.

There isn't much changing in the introductory stats world, so I have no concerns about the book becoming outdated rapidly. The examples and problems still feel relevant and reasonably modern. My only concern is that the statistical tool most often referenced in the book are TI-83/84 type calculators. As students increasingly buy TI-89s or Inspires, these sections of the book may lose relevance faster than other parts.

Solid. The book gives a list of key terms and their definitions at the end of each chapter which is a nice feature. It also has a formula review at the end of each chapter. I can imagine that these are heavily used by students when studying! Formulas are easy to find and read and are well defined. There are a few areas that I might have found frustrating as a student. For example, the explanation for the difference in formulas for a population vs sample standard deviation is quite weak. Again, this is a book that focuses on sort of a "black-box" approach but you may have to supplement such sections for some students.

I did not detect any problems with inconsistent symbol use or switches in terminology.

Modularity rating: 3

This low rating should not be taken as an indicator of an issue with this book but would be true of virtually any statistics book. Different books still use different variable symbols even for basic calculated statistics. So trying to use a chapter of this book without some sort of symbol/variable cheat-sheet would likely be frustrating to the students.

However, I think it would be possible to skip some chapters or use the chapters in a different order without any loss of functionality.

This book uses a very standard order for the material. The chapter on regressions comes later than it does in some texts but it doesn't really matter since that chapter never seems to fit smoothly anywhere.

There are numerous end of chapter problems, some with answers, available in this book. I'm vacillating on whether these problems would be more useful if they were distributed after each relevant section or are better clumped at the end of the whole chapter. That might be a matter of individual preference.

I did not detect any problems.

I found no errors. However, there were several sections where the punctuation seemed non-ideal. This did not affect the over-all useability of the book though

I'm not sure how well this book would work internationally as many of the examples contain domestic (American) references. However, I did not see anything offensive or biased in the book.

Reviewed by Ilgin Sager, Assistant Professor, University of Missouri - St. Louis on 1/14/19

As the title implies, this is a brief introduction textbook. It covers the fundamental of the introductory statistics, however not a comprehensive text on the subject. A teacher can use this book as the sole text of an introductory statistics. The prose format of definitions and theorems make theoretical concepts accessible to non-math major students. The textbook covers all chapters required in this level course.

It is accurate; the subject matter in the examples to be up to date, is timeless and wouldn't need to be revised in future editions; there is no error except a few typographical errors. There are no logic errors or incorrect explanations.

This text will remain up to date for a long time since it has timeless examples and exercises, it wouldn't be outdated. The information is presented clearly with a simple way and the exercises are beneficial to follow the information.

The material is presented in a clear, concise manner. The text is easy readable for the first time statistics student.

The structure of the text is very consistent. Topics are presented with examples, followed by exercises. Problem sets are appropriate for the level of learner.

When the earlier matters need to be referenced, it is easy to find; no trouble reading the book and finding results, it has a consistent scheme. This book is set very well in sections.

The text presents the information in a logical order.

The learner can easily follow up the material; there is no interface problem.

There is no logic errors and incorrect explanations, a few typographical errors is just to be ignored.

Not applicable for this textbook.

Reviewed by Suhwon Lee, Associate Teaching Professor, University of Missouri on 6/19/18

This book is pretty comprehensive for being a brief introductory book. This book covers all necessary content areas for an introduction to Statistics course for non-math majors. The text book provides an effective index, plenty of exercises, review questions, and practice tests. It provides references and case studies. The glossary and index section is very helpful for students and can be used as a great resource.

Content appears to be accurate throughout. Being an introductory book, the book is unbiased and straight to the point. The terminology is standard.

The content in textbook is up to date. It will be very easy to update it or make changes at any point in time because of the well-structured contents in the textbook.

The author does a great job of explaining nearly every new term or concept. The book is easy to follow, clear and concise. The graphics are good to follow. The language in the book is easily understandable. I found most instructions in the book to be very detailed and clear for students to follow.

Overall consistency is good. It is consistent in terms of terminology and framework. The writing is straightforward and standardized throughout the text and it makes reading easier.

The authors do a great job of partitioning the text and labeling sections with appropriate headings. The table of contents is well organized and easily divisible into reading sections and it can be assigned at different points within the course.

Organization/Structure/Flow rating: 4

Overall, the topics are arranged in an order that follows natural progression in a statistics course with some exception. They are addressed logically and given adequate coverage.

The text is free of any issues. There are no navigation problems nor any display issues.

The text contains no grammatical errors.

The text is not culturally insensitive or offensive in any way most of time. Some examples might need to consider citing the sources or use differently to reflect current inclusive teaching strategies.

Overall, it's well-written and good recourse to be an introduction to statistical methods. Some materials may not need to be covered in an one-semester course. Various examples and quizzes can be a great recourse for instructor.

Reviewed by Jenna Kowalski, Mathematics Instructor, Anoka-Ramsey Community College on 3/27/18

The text includes the introductory statistics topics covered in a college-level semester course. An effective index and glossary are included, with functional hyperlinks. read more

The text includes the introductory statistics topics covered in a college-level semester course. An effective index and glossary are included, with functional hyperlinks.

Content Accuracy rating: 3

The content of this text is accurate and error-free, based on a random sampling of various pages throughout the text. Several examples included information without formal citation, leading the reader to potential bias and discrimination. These examples should be corrected to reflect current values of inclusive teaching.

The text contains relevant information that is current and will not become outdated in the near future. The statistical formulas and calculations have been used for centuries. The examples are direct applications of the formulas and accurately assess the conceptual knowledge of the reader.

The text is very clear and direct with the language used. The jargon does require a basic mathematical and/or statistical foundation to interpret, but this foundational requirement should be met with course prerequisites and placement testing. Graphs, tables, and visual displays are clearly labeled.

The terminology and framework of the text is consistent. The hyperlinks are working effectively, and the glossary is valuable. Each chapter contains modules that begin with prerequisite information and upcoming learning objectives for mastery.

The modules are clearly defined and can be used in conjunction with other modules, or individually to exemplify a choice topic. With the prerequisite information stated, the reader understands what prior mathematical understanding is required to successfully use the module.

The topics are presented well, but I recommend placing Sampling Distributions, Advanced Graphs, and Research Design ahead of Probability in the text. I think this rearranged version of the index would better align with current Introductory Statistics texts. The structure is very organized with the prerequisite information stated and upcoming learner outcomes highlighted. Each module is well-defined.

Adding an option of returning to the previous page would be of great value to the reader. While progressing through the text systematically, this is not an issue, but when the reader chooses to skip modules and read select pages then returning to the previous state of information is not easily accessible.

No grammatical errors were found while reviewing select pages of this text at random.

Cultural Relevance rating: 3

Several examples contained data that were not formally cited. These examples need to be corrected to reflect current inclusive teaching strategies. For example, one question stated that “while men are XX times more likely to commit murder than women, …” This data should be cited, otherwise the information can be interpreted as biased and offensive.

An included solutions manual for the exercises would be valuable to educators who choose to use this text.

Reviewed by Zaki Kuruppalil, Associate Professor, Ohio University on 2/1/18

This is a comprehensive book on statistical methods, its settings and most importantly the interpretation of the results. With the advent of computers and software’s, complex statistical analysis can be done very easily. But the challenge is the knowledge of how to set the case, setting parameters (for example confidence intervals) and knowing its implication on the interpretation of the results. If not done properly this could lead to deceptive inferences, inadvertently or purposely. This book does a great job in explaining the above using many examples and real world case studies. If you are looking for a book to learn and apply statistical methods, this is a great one. I think the author could consider revising the title of the book to reflect the above, as it is more than just an introduction to statistics, may be include the word such as practical guide.

The contents of the book seems accurate. Some plots and calculations were randomly selected and checked for accuracy.

The book topics are up to date and in my opinion, will not be obsolete in the near future. I think the smartest thing the author has done is, not tied the book with any particular software such as minitab or spss . No matter what the software is, standard deviation is calculated the same way as it is always. The only noticeable exception in this case was using the Java Applet for calculating Z values in page 261 and in page 416 an excerpt of SPSS analysis is provided for ANOVA calculations.

The contents and examples cited are clear and explained in simple language. Data analysis and presentation of the results including mathematical calculations, graphical explanation using charts, tables, figures etc are presented with clarity.

Terminology is consistant. Framework for each chapter seems consistent with each chapter beginning with a set of defined topics, and each of the topic divided into modules with each module having a set of learning objectives and prerequisite chapters.

The text book is divided into chapters with each chapter further divided into modules. Each of the modules have detailed learning objectives and prerequisite required. So you can extract a portion of the book and use it as a standalone to teach certain topics or as a learning guide to apply a relevant topic.

Presentation of the topics are well thought and are presented in a logical fashion as if it would be introduced to someone who is learning the contents. However, there are some issues with table of contents and page numbers, for example chapter 17 starts in page 597 not 598. Also some tables and figures does not have a number, for instance the graph shown in page 114 does not have a number. Also it would have been better if the chapter number was included in table and figure identification, for example Figure 4-5 . Also in some cases, for instance page 109, the figures and titles are in two different pages.

No major issues. Only suggestion would be, since each chapter has several modules, any means such as a header to trace back where you are currently, would certainly help.

Grammatical Errors rating: 4

Easy to read and phrased correctly in most cases. Minor grammatical errors such as missing prepositions etc. In some cases the author seems to have the habbit of using a period after the decimal. For instance page 464, 467 etc. For X = 1, Y' = (0.425)(1) + 0.785 = 1.21. For X = 2, Y' = (0.425)(2) + 0.785 = 1.64.

However it contains some statements (even though given as examples) that could be perceived as subjective, which the author could consider citing the sources. For example from page 11: Statistics include numerical facts and figures. For instance: • The largest earthquake measured 9.2 on the Richter scale. • Men are at least 10 times more likely than women to commit murder. • One in every 8 South Africans is HIV positive. • By the year 2020, there will be 15 people aged 65 and over for every new baby born.

Solutions for the exercises would be a great teaching resource to have

Reviewed by Randy Vander Wal, Professor, The Pennsylvania State University on 2/1/18

As a text for an introductory course, standard topics are covered. It was nice to see some topics such as power, sampling, research design and distribution free methods covered, as these are often omitted in abbreviated texts. Each module introduces the topic, has appropriate graphics, illustration or worked example(s) as appropriate and concluding with many exercises. An instructor’s manual is available by contacting the author. A comprehensive glossary provides definitions for all the major terms and concepts. The case studies give examples of practical applications of statistical analyses. Many of the case studies contain the actual raw data. To note is that the on-line e-book provides several calculators for the essential distributions and tests. These are provided in lieu of printed tables which are not included in the pdf. (Such tables are readily available on the web.)

The content is accurate and error free. Notation is standard and terminology is used accurately, as are the videos and verbal explanations therein. Online links work properly as do all the calculators. The text appears neutral and unbiased in subject and content.

The text achieves contemporary relevance by ending each section with a Statistical Literacy example, drawn from contemporary headlines and issues. Of course, the core topics are time proven. There is no obvious material that may become “dated”.

The text is very readable. While the pdf text may appear “sparse” by absence varied colored and inset boxes, pictures etc., the essential illustrations and descriptions are provided. Meanwhile for this same content the on-line version appears streamlined, uncluttered, enhancing the value of the active links. Moreover, the videos provide nice short segments of “active” instruction that are clear and concise. Despite being a mathematical text, the text is not overly burdened by formulas and numbers but rather has “readable feel”.

This terminology and symbol use are consistent throughout the text and with common use in the field. The pdf text and online version are also consistent by content, but with the online e-book offering much greater functionality.

The chapters and topics may be used in a selective manner. Certain chapters have no pre-requisite chapter and in all cases, those required are listed at the beginning of each module. It would be straightforward to select portions of the text and reorganize as needed. The online version is highly modular offering students both ease of navigation and selection of topics.

Chapter topics are arranged appropriately. In an introductory statistics course, there is a logical flow given the buildup to the normal distribution, concept of sampling distributions, confidence intervals, hypothesis testing, regression and additional parametric and non-parametric tests. The normal distribution is central to an introductory course. Necessary precursor topics are covered in this text, while its use in significance and hypothesis testing follow, and thereafter more advanced topics, including multi-factor ANOVA.

Each chapter is structured with several modules, each beginning with pre-requisite chapter(s), learning objectives and concluding with Statistical Literacy sections providing a self-check question addressing the core concept, along with answer, followed by an extensive problem set. The clear and concise learning objectives will be of benefit to students and the course instructor. No solutions or answer key is provided to students. An instructor’s manual is available by request.

The on-line interface works well. In fact, I was pleasantly surprised by its options and functionality. The pdf appears somewhat sparse by comparison to publisher texts, lacking pictures, colored boxes, etc. But the on-line version has many active links providing definitions and graphic illustrations for key terms and topics. This can really facilitate learning as making such “refreshers” integral to the new material. Most sections also have short videos that are professionally done, with narration and smooth graphics. In this way, the text is interactive and flexible, offering varied tools for students. To note is that the interactive e-book works for both IOS and OS X.

The text in pdf form appeared to free of grammatical errors, as did the on-line version, text, graphics and videos.

This text contains no culturally insensitive or offensive content. The focus of the text is on concepts and explanation.

The text would be a great resource for students. The full content would be ambitious for a 1-semester course, such use would be unlikely. The text is clearly geared towards students with no statistics background nor calculus. The text could be used in two styles of course. For 1st year students early chapters on graphs and distributions would be the starting point, omitting later chapters on Chi-square, transformations, distribution-free and size effect chapters. Alternatively, for upper level students the introductory chapters could be bypassed with the latter chapters then covered to completion.

This text adopts a descriptive style of presentation with topics well and fully explained, much like the “Dummy series”. For this, it may seem a bit “wordy”, but this can well serve students and notably it complements powerpoint slides that are generally sparse on written content. This text could be used as the primary text, for regular lectures, or as reference for a “flipped” class. The e-book videos are an enabling tool if this approach is adopted.

Reviewed by David jabon, Associate Professor, DePaul University on 8/15/17

This text covers all the standard topics in a semester long introductory course in statistics. It is particularly well indexed and very easy to navigate. There is comprehensive hyperlinked glossary. read more

This text covers all the standard topics in a semester long introductory course in statistics. It is particularly well indexed and very easy to navigate. There is comprehensive hyperlinked glossary.

The material is completely accurate. There are no errors. The terminology is standard with one exception: the book calls what most people call the interquartile range, the H-spread in a number of places. Ideally, the term "interquartile range" would be used in place of every reference to "H-spread." "Interquartile range" is simply a better, more descriptive term of the concept that it describes. It is also more commonly used nowadays.

This book came out a number of years ago, but the material is still up to date. Some more recent case studies have been added.

The writing is very clear. There are also videos for almost every section. The section on boxplots uses a lot of technical terms that I don't find are very helpful for my students (hinge, H-spread, upper adjacent value).

The text is internally consistent with one exception that I noted (the use of the synonymous words "H-spread" and "interquartile range").

The text book is brokenly into very short sections, almost to a fault. Each section is at most two pages long. However at the end of each of these sections there are a few multiple choice questions to test yourself. These questions are a very appealing feature of the text.

The organization, in particular the ordering of the topics, is rather standard with a few exceptions. Boxplots are introduced in Chapter II before the discussion of measures of center and dispersion. Most books introduce them as part of discussion of summaries of data using measure of center and dispersion. Some statistics instructors may not like the way the text lumps all of the sampling distributions in a single chapter (sampling distribution of mean, sampling distribution for the difference of means, sampling distribution of a proportion, sampling distribution of r). I have tried this approach, and I now like this approach. But it is a very challenging chapter for students.

The book's interface has no features that distracted me. Overall the text is very clean and spare, with no additional distracting visual elements.

The book contains no grammatical errors.

The book's cultural relevance comes out in the case studies. As of this writing there are 33 such case studies, and they cover a wide range of issues from health to racial, ethnic, and gender disparity.

Each chapter as a nice set of exercises with selected answers. The thirty three case studies are excellent and can be supplement with some other online case studies. An instructor's manual and PowerPoint slides can be obtained by emailing the author. There are direct links to online simulations within the text. This text is very high quality textbook in every way.

1. Introduction
2. Graphing Distributions
3. Summarizing Distributions
4. Describing Bivariate Data
5. Probability
6. Research Design
7. Normal Distributions
8. Advanced Graphs
9. Sampling Distributions
10. Estimation
11. Logic of Hypothesis Testing
12. Testing Means
14. Regression
15. Analysis of Variance
16. Transformations
17. Chi Square
18. Distribution-Free Tests
19. Effect Size
20. Case Studies
21. Glossary

Ancillary Material

Ancillary materials are available by contacting the author or publisher .

About the Book

Introduction to Statistics is a resource for learning and teaching introductory statistics. This work is in the public domain. Therefore, it can be copied and reproduced without limitation. However, we would appreciate a citation where possible. Please cite as: Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University. Instructor's manual, PowerPoint Slides, and additional questions are available.

About the Contributors

David Lane is an Associate Professor in the Departments of Psychology, Statistics, and Management at the Rice University. Lane is the principal developer of this resource although many others have made substantial contributions. This site was developed at Rice University, University of Houston-Clear Lake, and Tufts University.

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Statistics Exercises

Teaching statistics grasple offers curated lessons and exercises on statistics, probability and research methods. get access and enhance your education..

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Below you find a list of all the lessons and exercises on statistics, probability and research methods, that are open or will be released with an open license. These materials have been created by Grasple over the past five years in collaboration with different universities. All of these will be released with an open Creative Commons license in the coming months. To access materials that are not yet CC licensed, you can create a free teacher account.

Introduction

10 Variables
27 Measurement Levels
10 Relative vs Absolute values

Descriptive Statistics

10 Symbols (reading math)
7 Symbols (indexing)
4 Measures of spread
5 Mean Deviation
5 Mean Absolute Deviation
5 Quartiles
6 Variance (conceptual)
5 Variance (calculate)
5 Standard Deviation (conceptual)
22 Standard Deviation
12 Coefficient of Variation

Graphs and Charts

10 Bar graph
10 Histogram
9 Pie chart
18 Box plot
10 Scatterplot
10 Line graph
13 Crosstabulations
9 Clustered bar graphs
25 When to choose which visualisation?

Inferential Statistics

20 Population and Sample
7 Sampling Variation
3 Sampling Distribution
8 Population, Sample, and Sampling Distribution
6 Sample Correction

Standard Error

11 Normal Distribution

Central Limit Theorem

Statistical Estimation

Point Versus Interval Estimates

Confidence Intervals for the Mean (conceptual)

Student's t-Distribution (conceptual)

t-Distribution

Hypothesis Testing

Hypothesis Testing (conceptual)

Type-I and Type-II Errors

P-Value (conceptual)

P-Value and Calculations

Choosing a Suitable Statistical Test

Statistical Tests

t-Test 1 Sample

t-Test 2 Samples

t-Test 2 Samples: Equal Variances

t-Test 2 Samples: Unequal Variances

t-Test 2 Samples: Paired Samples

ANOVA Calculations

Value to Probability

Probability to Value

Probability Intervals

Z-score to Z-test

Calculate confidence Intervals using Z-distribution

Relations (interval/ratio)

Pearson's r

Correlation vs Causation

Testing Pearson's r

Relations (nominal/ordinal)

Chi-Squared GOF

Chi-Squared for Independence

Linear Regression

Regression Equation

Testing Regression Coefficients

Regression Confidence Interval

SPSS: First Visualizations

Manual Data Entry in SPSS

Importing Data in SPSS

More Visualisations in SPSS

Frequency Tables in SPSS

Descriptive Statistics in SPSS

Chi-Squared for Independence in SPSS

Coding in SPSS

Recoding in SPSS

Evidential Statistics

Effect Size

Forest Plot

Clinical Measures

Minimal Detectable Change (MDC)

Minimal Clinically Important Difference (MCID)

Kolmogorov-Smirnov Test

Given Power, Calculate Sample Size

Prediction Intervals versus Confidence Intervals

NHST Procedure

Statistical Significance

Probability Trees

Conditional Probability

Take With or Without Replacement

Expected Value

Rules for Repetition

Probability Basics

Conceptual Understanding of Random Variables

Conceptual Understanding of Probabilities

Conceptual Understanding of Sample Space

Conceptual Understanding of (Probability) Distributions

Conceptual Understanding of Discrete vs Continuous Distributions

Conceptual Understanding of Probability Density Functions and Cumulative Density Functions

Probability Calculations & Rules

Conceptual Understanding of Marginal Probabilities

Conceptual Understanding of Joint Probabilities

Conceptual Understanding of Conditional Probabilities

Notation of Probabilities

Conceptual Understanding of Dependent and Independent Events

Conceptual Understanding of Probability Rules

Computing Probabilities Using Probability Rules

Counting and Expected Values

Conceptual Understanding of Expected Values

Computing Expected Values

Conceptual Understanding of Combinations

Conceptual understanding of Permutations

Computing Combinations

Computing Permutations

The Normal Distribution

Notation of Normal Distribution and Parameters

Conceptual Understanding of Standard Normal Distribution

Conceptual Understanding of Combining of Several Normally Distributed Variables

Computing Parameters of Combined Normally Distributed Variables

Binomial Distribution

Conceptual Understanding of Binomial Distribution

Notation of Binomial Distribution and Parameters

Properties of Binomial Distributions

Computing with Binomial Distributions

Poisson Distribution

Conceptual Understanding of Poisson Distribution

Notation of Poisson Distribution and Parameters

Properties of the Poisson Distribution

Computing with Poisson Distributions

t-distribution

Random variables.

Population and Sample - Conceptual explanation

Experiments - Conceptual Explanation

Random Variables - Conceptual Explanation

Realizations of Random Variables - Conceptual Explanation

Continuous versus Discrete Random Variables - Conceptual Explanation

Notation of Probabilities - Conceptual Explanation

Distribution Functions

Probability Distribution - Conceptual Explanation

Distribution Functions - Conceptual Explanation

Probability Density Functions - Conceptual Explanation

Probability Calculations Using Probability Density Functions

Probability Mass Functions - Conceptual Explanation

Probability Calculations Using Probability Mass Functions

Cumulative Distribution Functions - Conceptual Explanation

Probability Calculations Using Cumulative Distribution Functions

Relation and Difference Between Mass, Density and Cumulative Functions - Conceptual Explanation

Finding Probability Density Function Using the Cumulative Distribution Function

Well known distribution functions

Well Known Continuous Distribution Functions (List)

Center and Spread of Distributions

Expected Value - Conceptual Explanation

Calculating the Expected Value for Continuous Random Variables

Second Moment - Conceptual Explanation

Calculating the Second Moment for Continuous Random Variables

Variance - Conceptual Explanation

Calculating the Variance of a Random Variable

Conditional Probability - Conceptual Explanation

Conditional Distribution Functions - Conceptual Explanation

Calculations with Conditional Distribution Functions

Statistic versus Parameter - Conceptual Explanation

Statistical Models and Estimation - Conceptual Explanation

Regression Analysis - Conceptual Explanation

Finding Parameters of Linear Model Using Regression Analysis

Maximum Likelihood - Conceptual Explanation

Estimate Parameters of Distributions Using Maximum Likelihood Estimation

Research Cycle

17 Research Cycle
10 Phase 1: Problem analysis
10 Phase 2: Research design
10 Phase 5: Reporting
13 Analysis Schema

Qualitative research

Literature and APA referencing

17 Non-random sampling methods
21 SPI(C)E Acronym
9 Triangulation

Correlational research

Measurement levels

7 Operationalisation

More on Measurement levels and Operationalisation

17 Random (aselect) sampling methods
14 Validity and Reliability

Population and Sample

Correlation

Correlations in samples

Experimental research

Forming hypotheses

Randomisation

Indicates whether a lesson/explanation is available per subject 10 Indicates if and how many exercises are currently available per subject Content has an open Creative Commons license Content will be released with an open Creative Commons license in the near future

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Most Important Topics in Statistics

Analysis of Variance (ANOVA)
Bayes Estimators
Binomial Distribution
Completeness, tutorial statistics
Conditional Moments
Conditional Probability
Confidence Intervals
Cramer-Rao Inequality

Descriptive Statistics

Expectations and Variances
Independence
Inferential Statistics
Laws of Large Numbers
Minimax Estimators
Multivariate Multiple Regression Analysis
Neyman-Pearson Testing Hypotheses
Nonparametric Estimation
Normal Distribution
Order Statistics
Theorem of Bayes
Point Estimation
Poisson Distribution
Pólya’s Lemma
Probability Integral Transform,
Random Variables
Sampling Theory
Scatter Plots
Sequential Sampling
Stochastic Independence
The Central Limit Theorem
The Maximum Likelihood Principle
Unbiasedness
Univariate case

Sample Data

When studying population dynamics, it’s important to understand the process of sampling. Samples are small groups of individuals chosen randomly for research purposes. These samples are used to make inferences about the population’s characteristics and needs. Because a single person cannot represent the entire population, the sample data is a more accurate representation of the population. For example, a random number generator can choose a randomly chosen year or two, and all students in those two years are included in the sample.

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Statistics is a highly scientific subject. Students who don’t understand it are unlikely to succeed. Statistics homework is often difficult to complete, and there are a variety of reasons for this. Many students simply don’t have the time to complete the assignment on their own. Lack of knowledge about the subject and proper statistical instruments makes completing the assignments difficult. However, there’s no need to worry because there are professional statistics homework help services available online.

Correlation and regression are two examples of statistical tests. The former measures the relationship between two variables, while the latter uses statistical methods to estimate a random variable’s value in relation to a fixed variable. The beta distribution is an extremely versatile method of presenting outcomes for percentages and proportions. If you take a bus trip, there’s a 30% chance that you’ll see a bus every 20 minutes or an hour.

Statistical homework is challenging, requiring students to focus on data collection, analysis, and summary. Even one small inaccuracy can ruin an otherwise perfect grade. Students should consider statistics homework help before embarking on this difficult course. Statistics is a necessary part of our lives and we should all make time for learning it. When you’re studying for an advanced degree, you’ll be better equipped to face the challenges of statistics.

Statistics homework is important for many reasons. It helps students understand the subject better, and it’s the backbone of the process of analyzing multiple aspects of various fields. It’s also essential for our day-to-day activities. The ability to analyze multiple aspects of a subject is crucial in many fields, including the study of statistics. This is why students should consider statistics homework help as an opportunity and create it with meticulous zeal.

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Here, we have answered the most frequently asked questions from our students. If you have any further inquiries, please don't hesitate to reach out to us.

What is statistics homework help, and how can it benefit me?

Statistics homework help from TutorChamps is a service where experienced tutors provide assistance and guidance to students with their statistics assignments. It benefits students by offering personalized support, clarifying complex concepts, enhancing problem-solving skills, and improving overall understanding of statistics. With statistics homework help, students can overcome challenges, boost their confidence, and achieve better grades.

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Is statistics homework help available for all educational levels?

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Can TutorChamps assist with both basic and advanced statistics homework?

Absolutely! TutorChamps is well-equipped to assist with both basic and advanced statistics homework. Our expert tutors have a strong foundation in statistics and can guide you through a wide range of topics, including probability, statistical inference, regression analysis, data analysis, and more. We provide comprehensive support to help you understand and excel at all levels of statistics.

How can statistics homework help improve my understanding and grades?

Statistics homework help from TutorChamps can improve your understanding and grades in several ways. Our experienced tutors provide step-by-step explanations, clarify concepts, and offer insights into solving statistical problems. By receiving personalized guidance, you can enhance your understanding of statistical principles, develop effective problem-solving strategies, and build confidence in tackling statistics assignments. This improvement in understanding and skills can lead to better performance and improved grades.

Can TutorChamps help with specific statistics software like SPSS or Excel?

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Are the statistics tutors at TutorChamps qualified and experienced?

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Yes, absolutely! You can ask specific questions and seek clarification about your statistics homework from TutorChamps. We encourage active participation and engagement from students. Our tutors are here to address your specific queries, clarify doubts, and provide detailed explanations. We strive to ensure that you have a clear understanding of the concepts and can successfully complete your statistics homework.

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The Statistician Nurse

Florence Nightingale was a British nurse, social reformer, and statistician who is best known for her work during the Crimean War. She was born in 1820 into a wealthy family and was well-educated, but she felt a calling to nursing and started working at a hospital in London.

In 1854, when the Crimean War broke out, Nightingale was sent to the front lines to care for wounded soldiers. She arrived at the hospital to find that conditions were terrible, with unsanitary conditions, poor hygiene, and high mortality rates. Nightingale quickly took charge, improving hygiene practices, reorganizing the hospital, and improving the soldiers' diet.

One of Nightingale's most innovative contributions was her use of statistics to improve patient care. She collected data on the number of patients who were admitted, the number of patients who died, and the causes of death. She then used this data to identify patterns and make improvements. For example, she found that the majority of deaths were due to preventable diseases like typhoid, not battle wounds. She worked to improve sanitation practices and reduce the spread of disease, which helped to lower the mortality rate dramatically.

Nightingale also used her statistical analysis to advocate for healthcare reform. She published reports and presented her findings to government officials, calling for improved hospital conditions and better care for patients. Her work led to reforms in healthcare and improved conditions in hospitals around the world.

Nightingale's commitment to using data to improve patient care and her dedication to improving healthcare conditions made her an inspirational figure in the fields of nursing and statistics. Her legacy continues to inspire people today to use data and statistics to make a positive impact on the world.

Nightingale also had to face a lot of struggles and challenges.

As a woman in the 19th century, Nightingale faced societal expectations that women should marry and have children, rather than pursue a career. She faced resistance and opposition from her family and society for choosing a career in nursing.

The working conditions in the hospitals where Nightingale worked were often unsanitary and hazardous to her health. She faced exposure to disease and faced physical and emotional challenges in caring for patients.

Nightingale faced limited resources and inadequate funding for her work, which made it difficult for her to improve conditions for patients.

Nightingale faced political resistance from government officials who were reluctant to implement her recommendations for healthcare reform.

Nightingale suffered from poor health throughout her life, including a near-fatal bout of Crimean fever during the war, which made it difficult for her to continue her work.

Despite these challenges, Nightingale was driven by her passion for improving healthcare conditions and her dedication to helping others. Her tenacity and hard work led to significant improvements in healthcare and her legacy continues to inspire people today.

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Statistics Homework Help - Achieve A+

Are you facing challenges with your statistics homework? Do you find the concepts and calculations confusing? Look no further than TutorChamps for reliable and effective statistics homework help. In this article, we will introduce you to TutorChamps and explain how their expert tutors can assist you in mastering statistics.

At TutorChamps, we understand that statistics can be a complex subject for many students. The combination of theory, formulas, and data analysis can often lead to confusion and frustration. That's why we have assembled a team of experienced statistics tutors who are ...

Our expert tutors possess in-depth knowledge of statistics and have a passion for tutoring. Our tutors are well-versed in various statistical concepts, including probability, hypothesis testing, regression analysis, and more. Whether you are struggling with understanding the fundamentals or need assistance with advanced statistical techniques, TutorChamps has got you covered.

One of the key aspects that sets TutorChamps apart is our personalized approach to tutoring. We understand that every student has unique learning needs and goals. That's why our tutors tailor their lessons to suit your individual requirements. They take the time to understand your strengths and weaknesses, and then develop a customized learning plan that will help you grasp the concepts effectively.

What Topics Do TutorChamps Cover in Statistics Homework?

At TutorChamps, we cover a wide range of topics in statistics homework to provide comprehensive assistance to students. Our expert tutors are well-versed in various areas of statistics, ensuring that we can support you in understanding and completing assignments on different topics. Here are some of the key topics that TutorChamps covers in statistics homework:

Probability: We help students grasp the concepts of probability, including basic probability rules, conditional probability, probability distributions, and more. Our tutors can guide you through solving probability problems and understanding the principles behind them.

Statistical Inference: This topic focuses on drawing conclusions and making predictions based on sample data. We cover concepts such as estimation, hypothesis testing, confidence intervals, and p-values. Our tutors can assist you in understanding the logic and techniques involved in statistical inference.

Regression Analysis: Regression analysis involves examining the relationship between variables and making predictions. TutorChamps can help you with simple linear regression, multiple regression, logistic regression, and other regression techniques. We can guide you in interpreting regression models and using them for data analysis.

Data Analysis and Descriptive Statistics: Understanding how to analyze and summarize data is crucial in statistics. We cover descriptive statistics, measures of central tendency, variability, graphical representations, and data visualization techniques. Our tutors can assist you in interpreting and presenting data effectively.

Statistical Software: Many statistics assignments require the use of statistical software such as SPSS, R, or Excel. TutorChamps can help you navigate these software tools, providing guidance on data entry, data manipulation, conducting analyses, and interpreting output.

Experimental Design: If your assignment involves designing experiments, our tutors can guide you through the principles of experimental design. We cover topics such as randomization, control groups, factorial designs, and analysis of variance (ANOVA).

Time Series Analysis: Time series analysis deals with analyzing data collected over time. We can assist you in understanding concepts such as trend analysis, seasonality, forecasting, and smoothing techniques.

Please note that the above topics are not exhaustive, and TutorChamps can provide support in various other areas of statistics as well. Our goal is to help you build a solid foundation in statistics and develop the skills necessary to excel in your coursework.

Who Can Benefit From Statistics Homework Help?

Statistics is a complex subject that often poses challenges for students at all academic levels. Whether you're in high school, pursuing an undergraduate degree, or engaged in advanced studies, statistics homework help can be immensely beneficial. This article explores the advantages that students at all levels can gain from seeking expert assistance with their statistics assignments.

High School Students: Even at the high school level, statistics assignments can be demanding. Many high school students are introduced to statistical concepts and analysis for the first time, which can be overwhelming. Seeking statistics homework help allows these students to grasp the fundamentals, understand data interpretation, and develop problem-solving skills early on. By receiving personalized guidance and support, high school students can build a strong foundation in statistics and lay the groundwork for success in higher education.

Undergraduate Students: Statistics is a core subject in various undergraduate programs, such as mathematics, economics, psychology, and social sciences. Undergraduate students often encounter more complex statistical problems and data analysis tasks. Statistics homework help offers them the opportunity to deepen their understanding of advanced statistical concepts, refine their analytical skills, and gain expertise in statistical software usage. This assistance can significantly improve their performance in coursework, exams, and research projects.

Graduate Students: Graduate-level statistics can be highly specialized and rigorous. Students pursuing master's or doctoral degrees in fields like statistics, business analytics, or data science face advanced statistical theories, complex methodologies, and extensive research requirements. Seeking statistics homework help enables graduate students to receive expert guidance on statistical modeling, hypothesis testing, data visualization, and other advanced topics. This assistance not only aids in completing coursework but also supports the successful completion of research studies.

Non-Statistics Major Students: Not all students pursuing degrees in fields unrelated to statistics have a strong background in the subject. For example, students in disciplines like biology, sociology, or engineering may need to incorporate statistical analysis into their research or projects. Statistics homework help caters to the specific needs of non-statistics major students by providing targeted assistance in understanding statistical concepts, choosing appropriate statistical tests, and interpreting results accurately. This support ensures that these students can effectively apply statistical methods in their respective fields.

Exam Preparation: Statistics often appears in standardized tests, such as the SAT, ACT, GRE, or GMAT. Adequate preparation is essential to achieve high scores in these exams. Statistics homework help can offer targeted guidance and practice materials to help students strengthen their statistical knowledge, problem-solving abilities, and time management skills for the exams. By preparing effectively with the help of experts, students can enhance their chances of success and improve their overall performance.

Why is Taking Homework Help in Statistics Important for Students?

Seeking and taking homework help in statistics is important for students for several reasons. Here are some key reasons why students should consider seeking statistics homework help:

Concept Clarity: Statistics can be a complex subject with intricate concepts and formulas. Taking homework help allows students to gain a clearer understanding of these concepts. Expert tutors can explain difficult topics in a simplified manner, breaking them down into easily digestible parts. This clarity helps students grasp the fundamental principles of statistics and build a strong foundation for future learning.

Improved Performance: Statistics homework help can significantly improve a student's performance in the subject. By receiving guidance from experienced tutors, students can enhance their problem-solving skills and learn effective strategies for tackling statistical problems. This improvement in performance can lead to better grades, increased confidence, and a deeper appreciation for the subject.

Time Management: Statistics homework assignments can be time-consuming, especially when students are juggling multiple subjects and extracurricular activities. Taking homework help in statistics can help students manage their time more efficiently. Tutors can provide step-by-step guidance, reducing the time spent on trial and error. This allows students to complete their assignments more effectively and allocate time to other important tasks.

Personalized Learning: Each student has unique learning needs and preferences. Homework help in statistics offers personalized learning experiences tailored to individual students. Tutors can identify the areas where students struggle the most and provide targeted assistance. This personalized approach ensures that students receive the specific help they need to overcome their challenges and excel in statistics.

Confidence Building: Statistics homework help can boost a student's confidence in the subject. As students receive guidance and support, they gain a sense of assurance in their ability to understand and solve statistical problems. Increased confidence not only leads to improved performance but also fosters a positive attitude towards learning and future challenges.

Deeper Understanding: Homework help enables students to develop a deeper understanding of statistics. Tutors go beyond simply providing answers; they explain the underlying concepts, theories, and methodologies. This comprehensive understanding allows students to apply statistical knowledge to real-world scenarios, making connections and seeing the relevance of statistics in various fields.

Academic and Career Advancement: Statistics is a vital skill in numerous academic disciplines and professional fields. Taking homework help in statistics equips students with the knowledge and skills necessary for academic advancement and future career opportunities. A strong foundation in statistics can open doors to fields such as data analysis, market research, finance, and social sciences.

In conclusion, seeking homework help in statistics empowers students to overcome challenges, enhance their understanding, improve their performance, and develop essential skills for academic and professional success. It provides personalized guidance, builds confidence, and fosters a deeper appreciation for the subject, ultimately benefiting students in their educational journey and beyond.

Why TutorChamps Fulfils the Requirements of Students in Statistics Homework?

TutorChamps fulfills the requirements of students in statistics homework due to several factors that make our services stand out. Here's why TutorChamps is well-equipped to meet the needs of students seeking assistance with their statistics homework:

Expert Tutors: At TutorChamps, we have a team of experienced statistics tutors who possess in-depth knowledge of the subject. Our tutors are well-qualified and have a strong background in statistics, enabling them to provide accurate and reliable guidance. They stay updated with the latest developments in the field, ensuring that students receive the most relevant and comprehensive support.

Personalized Approach: We understand that each student has unique learning requirements. TutorChamps adopts a personalized approach to statistics homework help, tailoring our assistance to match the specific needs of individual students. Our tutors take the time to understand a student's strengths, weaknesses, and learning style, allowing them to provide targeted support and foster effective learning.

Comprehensive Coverage: We cover a wide range of statistics topics to cater to the diverse needs of students. Whether it's probability, statistical inference, regression analysis, data analysis, or any other area within statistics, TutorChamps offers comprehensive coverage. Our tutors are well-versed in various concepts and techniques, ensuring that students receive guidance on the specific topics they require assistance with.

Clarity and Simplification: Statistics can be a complex subject, but TutorChamps excels at breaking down complicated concepts into easily understandable terms. Our tutors have the ability to explain statistical theories, formulas, and methodologies in a clear and simplified manner. This approach helps students overcome confusion and develop a strong grasp of statistical concepts.

Step-by-Step Guidance: We provide step-by-step guidance to students, ensuring they understand the logical progression of solving statistical problems. Our tutors take students through the problem-solving process, explaining each step in detail. This approach helps students build their problem-solving skills, enhance their understanding, and gain confidence in applying statistical techniques.

Timely Assistance: Meeting deadlines is crucial when it comes to statistics homework. TutorChamps recognizes the importance of timely assistance and ensures that students receive the support they need within the specified time frame. Our tutors are committed to providing prompt responses and ensuring that students can complete and submit their assignments on time.

Continuous Support: TutorChamps offers ongoing support to students, fostering a long-term learning relationship. We are available to answer questions, clarify doubts, and provide guidance beyond immediate homework help. Our goal is to support students throughout their statistical journey, helping them build a solid foundation and excel in the subject.

In conclusion, TutorChamps fulfills the requirements of students in statistics homework by providing expert tutors, a personalized approach, comprehensive coverage, clarity in explanations, step-by-step guidance, timely assistance, and continuous support. With TutorChamps, students can gain confidence, improve their understanding, and achieve success in their statistics coursework.

Is Statistics Homework Help Available At TutorChamps For Urgent Assignments?

Yes, at TutorChamps, we understand that students may have urgent assignments and deadlines to meet. We offer statistics homework help for urgent assignments to ensure that students receive the necessary assistance in a timely manner.

Our team of expert tutors is equipped to handle urgent assignments and provide prompt support. When you have an urgent statistics homework assignment, simply reach out to us, and we will prioritize your request. Our tutors will work efficiently to assist you in completing the assignment within the given timeframe without compromising the quality of the work.

We recognize the importance of meeting deadlines and the potential stress that comes with time constraints. Therefore, we strive to provide timely assistance and ensure that students can submit their assignments on time. Our tutors are committed to delivering prompt responses and helping you meet your urgent statistics homework requirements.

Whether you need clarification on a specific concept, assistance with problem-solving, or guidance on data analysis, our tutors are ready to support you. They will work closely with you to understand the assignment requirements and provide the necessary help to ensure your success.

At TutorChamps, we value your academic progress and strive to be a reliable resource for your statistics homework needs, even in urgent situations. Our goal is to assist you in meeting your deadlines while maintaining the quality and accuracy of your work.

Can Someone Do My Statistics Homework For Me? Yes TutorChamps Here!

At TutorChamps, we provide comprehensive statistics homework help to assist students in completing their assignments. Our expert tutors are here to support you and guide you through the process of solving statistical problems. While we offer extensive assistance, it is important to note that our approach is focused on helping you understand and learn the concepts, rather than simply doing the homework for you.

Our tutors are dedicated to providing step-by-step guidance, explaining the logic behind each step, and helping you develop problem-solving skills. This approach ensures that you not only complete your statistics homework but also gain a deeper understanding of the subject.

We encourage active participation and engagement from students to maximize their learning experience. Rather than simply doing the homework on your behalf, our tutors aim to empower you with the knowledge and skills to solve similar problems independently in the future.

By working with TutorChamps, you will receive the necessary support, clarification, and guidance to complete your statistics homework successfully. Our tutors will provide explanations, offer insights, and help you build a solid foundation in statistics. Ultimately, our goal is to help you develop the confidence and competence to tackle statistics assignments effectively.

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Applied Probability and Statistics

Eas-305lr-z1, university at buffalo.

Cost & Fees
Financial Aid
Semester Summer 2024
Instructor Johnson Adebayo Fadeyi
Start Date 05-28-2024
Total Credits 4
Call to Register 716-645-5698

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Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data.

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Statistics Help Desk is a team of accredited and seasoned statisticians in the field of statistics committed to delivering valuable assistance to statistics students and research scholars worldwide. We have a team of dedicated experts in statistics, who are recognized by their academic degrees and have a background in statistics. We extend our services with an ultimate aim of providing help with statistics homework and coursework for students pursuing their bachelors or master’s degree in statistics. Whether it is for school, college or scholarly research, Statistics Help Desk has specialists and tutors who work round the clock ensuring timely support for those seeking last-minute assistance. Our services are designed for students not only to solve statistical problems with the right approach but also to increase their knowledge of the subject through dedicated mentoring. This always on availability makes Statistics Help Desk a very important tool for students and researchers who urgently need the right and competent assistance in statistics.

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Statistics is the study of mathematics that deals with the gathering, processing, interpretation, presentation, and organization of data. It uses mathematical concepts and formulas to interpret the numerical data obtained from different sources. The main reason for the statistics is its role in the provision of a scientific foundation for the decision-making that is the basis for quantification of the uncertainties and the evaluation of the relationship among the variables. Statistics are not only relevant to the students but also to the whole of the fields that are dealing with the quantitative research. For PhD students, no matter what their field is, statistics is the main tool in their research. Statistics is essential for rigorous experimental design, empirical validation of theories, deeper understanding of variability, and the formulation of robust and justifiable conclusions.

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Statistics is a multifaceted branch of mathematics that is integral to understanding and interpreting data in a world inundated with information. It encompasses the methods and practices of data collection, analysis, interpretation, and presentation, forming the backbone of numerous fields such as economics, social sciences, business, healthcare, engineering, and environmental studies. There are two main branches of statistics:

This branch is fundamental in summarizing and describing the basic features of a dataset, providing a simple overview of the sample and the measures. It includes calculating measures of central tendency like the mean (average), median (middle value), and mode (most frequent value). Descriptive statistics also involve measures of variability or spread, such as range (difference between the highest and lowest values), variance (measure of how data points differ from the mean), and standard deviation (average distance of each data point from the mean).

This branch focuses on making predictions or drawing conclusions about a larger population based on a sample of data taken from that population. It is about inferring population properties from sample properties. Inferential statistics includes hypothesis testing (determining the likelihood that a given hypothesis is true for the population), confidence intervals (estimating the range within which a population parameter lies), and regression analysis (examining the relationships between variables).

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We take immense pride in offering a diverse array of online statistics assignment help services meticulously tailored to your unique academic needs. Our dedicated team of statistics assignment tutors is here to provide you with nothing short of top-notch support. Our mission is to ensure your academic success by delivering precise and insightful solutions that meet the highest standards of excellence. We invite you to explore our wide range of services and embark on a journey to experience the profound difference in statistical excellence that our platform offers. Your academic success is our commitment, and we are here to empower you every step of the way! We offer help with a wide range of statistics-related assignments, including:

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We pride ourselves on offering statistics assignment help that encompasses a wide range of statistical software. No matter which statistical software your assignment or project involves, our team has the expertise to ensure accurate and insightful results. We are committed to helping you navigate the intricacies of these software packages, making your statistical assignments more manageable and your results more robust. Our team of experts is well-versed in the following popular statistical software:

R: R is a powerful open-source statistical software known for its flexibility and vast community support. Our experts can help you with R-based assignments , data analysis, and visualizations.
SAS: SAS is widely used in the field of statistics for data management, analysis, and reporting. Our team is proficient in SAS programming and can assist you with SAS-related tasks.
SPSS: SPSS is a user-friendly statistical software commonly used in social sciences and business research. We offer support for SPSS assignments and data analysis.
STATA: STATA is popular in economics, sociology, and political science for statistical analysis. Our experts can offer help with STATA assignments and data manipulation.
Excel: Microsoft Excel is a versatile tool for basic statistical analysis and data management. We can help you with Excel-based assignments, including data visualization and analysis.
Python: Python is a versatile programming language with libraries like NumPy, SciPy, and Pandas for statistical analysis. Our team can assist with Python-based assignments and data processing.
MATLAB: MATLAB is commonly used for numerical analysis and data visualization. We offer support for MATLAB assignments, especially those related to statistics and data analysis.
JMP: JMP is a statistical software designed for data exploration and visualization. We can assist you with JMP assignments and data analysis projects.
Minitab: Minitab is user-friendly software for statistical analysis and data visualization. Our experts are skilled in Minitab and can help you with assignments and analysis.
GraphPad Prism: GraphPad Prism is a tool for scientific graphing and statistical analysis. We can provide assistance with GraphPad Prism assignments and data plotting.

Why Students Pay Professionals to Complete their Statistics Assignments

We recognize the numerous challenges students face with their assignments. Whether you're grappling with complex statistical concepts, struggling with data analysis, or simply seeking to enhance your academic performance, you can pay someone to do your stat assignment right here. We believe that your success is our success, and our commitment to your academic goals is unwavering. With our support and guidance, you can confidently navigate the world of statistics and emerge as a proficient and successful student in your academic pursuits. Feel free to contact us if you are facing any of these challenges:

Complex Concepts: Statistics involves intricate mathematical and data analysis concepts that can be challenging to grasp. Students may struggle to understand the fundamentals of statistical methods and their applications. From inferential statistics to differential statistics.
Statistical Software: Many assignments require the use of statistical software like R, SAS, SPSS, or Excel. Learning to navigate these tools can be time-consuming and complex for students who are unfamiliar with them.
Time Constraints: Statistics assignments often involve extensive data collection, analysis, and reporting. Balancing these tasks with other coursework and commitments can be challenging, leading to time constraints.
Fear of Making Errors: The fear of making mistakes in statistics assignments can be paralyzing. Students may worry about incorrect calculations or misinterpretation of data, which can hinder their progress.
Math Anxiety: Some students have math anxiety, which can be exacerbated when dealing with statistics. This anxiety can impact their confidence and performance in statistics assignments.
Limited Resources: Access to resources like textbooks, online tutorials, or reliable data sources may be limited for some students, making it challenging to research and complete assignments effectively.
High Expectations: There can be high expectations for accuracy and precision in statistics assignments, leading to added pressure and stress for students.
Desire for Excellence: Many students seek to excel academically and achieve top grades. To meet their own high standards, they may seek assistance to ensure the quality of their assignments.

Common Statistics Courses We Provide Help With

At StatisticsAssignmentHelp.com, we specialize in providing comprehensive assistance to college students with assignments in a wide array of topics. Our team is equipped with the expertise and experience to tackle complex assignments across different courses and universities. Whether it's delving into the intricacies of statistical data analysis or integrating statistical principles with other disciplines, we offer tailored support to ensure students not only complete their assignments effectively but also gain a deeper understanding of the subject matter:

STA 641: Statistical Methods (Duke University): It focuses on advanced statistical methods, encompassing topics like regression analysis, ANOVA, and time series analysis. This course is designed to equip students in universities like Duke University with the skills to apply statistical techniques in various research and professional settings. At StatisticsAssignmentHelp.com, our experts specialize in these advanced methods, offering students comprehensive assistance with their assignments and projects, ensuring a deep understanding and practical application of the course material.
STATS 290: Statistical Inference: At the University of Auckland, STATS 290 offers an in-depth exploration of statistical inference, including hypothesis testing, probability theory, and estimation methods. The course is tailored for students looking to deepen their understanding of inferential statistics. Our team at StatisticsAssignmentHelp.com is adept in these areas, providing targeted support to students for their complex assignments and helping them master the nuances of statistical inference.
STAT 280: Statistical Methods: This course covers a range of statistical methods aimed at analyzing and interpreting data effectively. Topics such as probability distributions, estimations, and hypothesis testing are integral parts of the curriculum at Rice University. Our professionals at StatisticsAssignmentHelp.com are well-versed in these subjects, offering students personalized assistance in understanding and applying these methods in their coursework and projects.

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Tips & Insights to Boost Your Knowledge of Statistics

In our blog section we offer a treasure trove of valuable resources related to statistics. Our informative blog posts cover a wide range of topics, from fundamental statistical concepts to advanced data analysis techniques. Whether you're seeking guidance on how to approach statistics assignments, looking for explanations of statistical theories, or exploring practical tips for using statistical software, our blog has you covered.

Points That Can Elevate Your Statistics Assignment's Grades A decent grade in your statistics assignment reflects both hard work and a thorough understanding of the concepts taught in class. It would help if you had the best grades to keep a scholarship, graduate, and land that dream career. Statis... Read More

Top 13 errors Students make when writing their Undergraduate Assignments Instructors and professors consider many factors when awarding marks for statistics assignment solutions. Your instructor may mark or not mark errors in your solution. If the instructor is more concerned about the grammar of y... Read More

How to Write a Statistics Paper Statistics is a science that involves the collection, analysis, and presentation of data. Statistical analysis, therefore, is the analysis of the data samples collected and using them to represent the whole population. Statistics papers mainly have 4 basic components... Read More

What are the components of a good statistics assignment? It is the desire of every professor or instructor to see students score top grades in their statistics assignments. However, all students can't score top grades because students have different learning abilities. However, instructors can deve... Read More

6 Ways to Save Your Statistics Assignment from Getting Plagiarized Plagiarism is the enemy of success, and we help you evade it today. These six ideas will keep plagiarism at bay for you all the time throughout your coursework. Plagiarism is the intentional or unintentional practice of submitt... Read More

How To Use Feedback To Perform Better In Statistics Assignments Giving and receiving feedback is an important task in assignments. Feedbacks help to understand where and what a person went wrong when doing an assignment. Good feedback outlines the negative and the positive information in work. Som... Read More

Welcome to our blog on statistics assignment help! If you are a student of statistics, you know how challenging it can be to complete assignments and projects on time, especially when you have other coursework, part-time jobs, or personal commitments to manage. Whether you are struggling with survey... Read More

Assignments in statistics can be difficult and complex, necessitating a solid grasp of mathematical principles and analytical abilities. To ensure they can submit excellent assignments and perform well in their coursework, many students turn to online statistics assignment help services. Howev... Read More

Statistics is a fascinating and important field that has an impact on a variety of fields, including the social sciences, business, and healthcare. As a student of statistics, you will have the opportunity to investigate a wide variety of subfields and applications that fall under ... Read More

In the realm of computer science and artificial intelligence, Constraint Satisfaction Problems (CSPs) play a pivotal role in modeling and solving a wide range of real-world problems. CSPs involve finding solutions that satisfy a set of constraints. A variant of CSPs frequently encountered by student... Read More

When it comes to statistics, the normal distribution is a fundamental concept that underpins many statistical techniques and methods. It is a symmetric, bell-shaped probability distribution that is characterized by its mean (μ) and standard deviation (σ). The normal distribution is used in a wi... Read More

Mortality measures serve as indispensable tools for comprehending the intricate patterns of deaths within populations, furnishing invaluable insights for a spectrum of professionals, including policymakers, public health experts, and researchers. This blog aims to unravel the compl... Read More

In the realm of economics and statistics, index numbers are indispensable tools for assessing changes in various economic indicators over time. These indicators can encompass everything from inflation rates to the price levels of specific goods and services. Index numbers are widely used in bo... Read More

Population studies play a pivotal role in understanding the dynamics of human societies. One of the fundamental aspects of demographic analysis is the computation of measures of fertility and population growth. These measures provide essential insights into the growth and reproductive patterns... Read More

In the complex landscape of industrial and manufacturing processes, the assurance of product quality stands as an indispensable priority. The discipline of Statistical Quality Control (SQC) emerges as a cornerstone, utilizing statistical methods to meticulously monitor and control processes. Fo... Read More

In the realm of statistics and data analysis, understanding how to handle hierarchical data is crucial for drawing meaningful insights. One powerful tool in this regard is Applied Multilevel Modeling. This blog aims to provide students with a comprehensive guide to the techniques involved in Hi... Read More

In the ever-evolving landscape of technology, the significance of harnessing unstructured data has become increasingly evident. With the proliferation of information in the digital age, students find themselves grappling with assignments that are not only extensive but also brimming with unor... Read More

Embarking on a research-oriented assignment is a commendable venture that demands a meticulous approach. Among the myriad aspects that require attention, statistical power and sample size calculations stand out as crucial elements. This blog aims to unravel the significance of statistical power... Read More

In the dynamic realm of data science and analytics, the significance of statistical consulting continues to grow exponentially. Students navigating through assignments in this field are consistently confronted with real-world data challenges. These challenges necessitate not only a profound c... Read More

In the ever-evolving landscape of data analysis and statistics, the ability to convey insights effectively is paramount. Students engaged in data analysis assignments often grapple with the challenge of presenting their findings in a clear and interactive manner. This is where Shiny web appli... Read More

In the contemporary landscape of extensive data and sophisticated statistical analysis, the ethical dimensions of research projects and safeguarding data privacy have gained unprecedented significance. As students endeavor to complete their Statistics assignment involving statistical analysis... Read More

In the intricate landscape of statistics, decision theory emerges as a pivotal guide for individuals and organizations navigating the complexities of decision-making. Seamlessly blending statistical principles with logical reasoning, decision theory establishes a systematic framework, enablin... Read More

In the vast landscape of statistics, students frequently grapple with intricate data sets that necessitate sophisticated analytical tools for profound interpretation. Factor Analysis, a robust statistical technique, emerges as a potent method designed to unveil concealed patterns and relation... Read More

In the ever-evolving landscape of data analysis and interpretation, statistical graphics and data visualization play a pivotal role in conveying complex information in a clear and accessible manner. As students delve into the world of assignments and projects, mastering the art of visualizing d... Read More

In contemporary academic research, survey-based studies serve as a prevalent method for data collection and insight generation. The crux of their effectiveness lies in the meticulous application of sampling techniques, a factor that fundamentally shapes the reliability and validity of research ... Read More

In the dynamic and competitive world of sports, success transcends the mere final score; it is intricately intertwined with the nuanced details of player and team performance. The relentless march of technology has ushered in an era where sports statistics stand as a beacon, guiding the underst... Read More

When it comes to statistical analysis, dealing with categorical data is a common challenge. Categorical data, representing groups or categories, is discrete in nature and demands specialized techniques for meaningful analysis. This unique type of data often arises in various fields, making it c... Read More

In the dynamic realm of academia, students frequently grapple with intricate assignments that necessitate a deep comprehension of diverse subjects. Among these subjects, statistics stands out as both challenging and indispensable. Extending far beyond the confines of conventional textbooks, sta... Read More

In the contemporary landscape dominated by climate change and growing environmental concerns, the imperative of comprehending and deciphering ecological and climate data is paramount. As students immerse themselves in the intricate domain of environmental science, they frequently confront assig... Read More

Spatial statistics is a potent analytical instrument for unraveling the complexities within geographical and environmental data. In the academic landscape of geography and environmental studies, a deep comprehension of spatial statistics is paramount for students undertaking assignments. This b... Read More

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Statistics is a branch of mathematics and it is considered as one of the most difficult subject. Dealing with the assignment of this subject is a hard nut to crack. And you need to put a lot of efforts and attention while making statistic assignment. A little mistake can ruin a whole assignment and it will leave you with no energy to finish. However there is no need to be panic about it as you can just take assignment help from Allassignmenthelp.com and resolve all your worries in a seconds.

Statistics is the study of the collection, organization, analysis, interpretation, and presentation of data. It deals with all aspects of this, including the planning of data collection in terms of the design of surveys and experiments. The word statistics, when referring to the scientific discipline, is singular, as in Statistics is an art. We have assignment experts who can put an end to all your stress and anxiety. Our assignment experts are quite responsible for their job and they can help you to get good academic grade.

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Statistics is a branch of math where you come across various difficult concepts. You have to work on business statistics assignments , applied statistics, statistics problems on mean median and mode and many more. However, we have classified some areas of statistics where you get assignments in College. The below areas are from advanced stats where you can rely on our online homework help service.

Mann-Whitney U test: There are multiple names that are given to the Mann-Whitney U test. A few of the known names are Wilcoxon rank-sum test, MWW tests, etc. The aforementioned is a non-parametric test of the null hypothesis that two populations are the same against an alternative hypothesis, especially that a particular population tends to have larger values than others. It is a difficult method to understand, and most university students suffer to solve the statistics problems based on the Mann test. The reason for the difficulty is the intensive mathematics and calculus in particular. There are few assumptions that are made under Mann- Whitney test in which observations for both groups are considered independent.
Regression analysis in statistics: This is the process by which experts estimate the relationship between variables. The main purpose of regression is to establish a relationship between one or more independent variables. To understand regression analysis in native terms follows the definition given by our experts. Regression analysis helps one understand how the value of the dependent variable changes with the variation in the independent variable. For more detailed concepts of Regression analysis, you can see free statistics samples gave on our website or you can request area specifically free statistics samples from our chat representative.
Spearman's rank correlation coefficient: This is named after Charles Spearman and is also known as spearman's rho. It is used to estimate the statistical dependence between two variables. This technique employs a monotonous function to establish a relationship between the two variables.

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Reviews - This is a powerful statistical analysis tool used for forecasting and modeling. We have statistics helpers comfortable using Eviews. You can ask for Eviews assignment help from us.
Minitab - Statistical analysis tool developed at Pennsylvania State University in the US. We have online stats experts with a strong command on Minitab software to do you statistics assignment.
SPSS - It is used for data analysis and is commonly used to analyze gathered data for your dissertation. We can do any homework or analysis based on SPSS fo statistical analysis.
R Programming - R programming has become popular with the advent of Data mining and machine learning. With the help of a powerful statistics package in R, you can do various modeling and analysis.
Matlab - It has been an old software that is still used for descriptive and quantitative statistics assignments. It helps with basic statistics tasks and is not as powerful as Eviews and Minitab.
Excel - It is a commonly used statistics tool that everyone has been working. For basic graphical analysis and data presentation for your stats assignment, you can use Microsoft Excel.
ANOVA: This is a statistical method that is used to differentiate between group means and the procedure associated with them. ANOVA is useful in testing three or more means for statistical significance. It is present as a method under different statistics software.

There are many other online statistics software that is used for doing assignments on Statistics. We cannot list down all software, but we have comfort with almost any software since we are great with statistics concepts, not just the software.

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Descriptive and inferential statistics assignments are common at university. It requires running both types of analysis on a given data set. For example, you can do a descriptive and inferential analysis of the same data source. We cannot conclude anything from descriptive analysis as it only provides information about the data under consideration. However, Inferential statistics can be helpful in concluding something out of the pattern. To do your statistics assignment on Descriptive of Inferential statistics, we have to use the concept of hypothesis testing and a few more testing methods used to do your descriptive and Inferential stats assignment.

Mean square weighted deviation (MSWD): This method is used in geochronology(Science that is used to determine the age of rocks and fossils). This is highly mathematical, and the results obtained are analyzed on the basis of the value of MSWD.
Chi-squared test: This is a popular test, and most people have heard about this. The reason for the popularity of the chi-square test is the extensive use in the other fields as well. Here is a test in which the sampling distribution of the test statistics is chi-squared distribution when the null hypothesis is true.
Correlation in statistics: This is a broad term that determines the relationship between two variables. We have seen the Mann-Whitney U test use correlation to establish a relationship. There are many other forms of statistics that are vital and are offered as a course in the universities in Australia, the UK, and The USA. Business Statistics is another area that is covered by the team of our experts.

Statistical techniques cover a wide area and diverse fields including computational sociology, network biology, biostatistics, computational biology, social science, sociology and social research. Some fields of inquiry use applied statistics so extensively that they have specialized terminology. These disciplines include :

Actuarial science (risk assessment in the insurance and finance industries)
Data mining (applying statistics and pattern recognition to discover knowledge from data)
Applied information economics

In addition, there are particular types of statistical analysis that have also developed their own specialized terminology and methodology:

Structured data analysis (statistics)
Bootstrap & Jackknife Resampling
Statistical surveys
Multivariate statistics
Statistics in sports, particularly cricket and baseball
Structural equation modeling

There are many other areas where stats have application and we know applied statistics can be troublesome for students with limited data analysis skills. Hence, hiring an online statistics assignment helper is the best choice you can make.

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Many students are enrolled in online classes to brush up on their stats skills. However, these online stats classes can become challenging in no time. As you progress from week 1 to week 2 in your online statistics class, you will see the dramatic change in the difficult level of problems. Here you probably need someone to take an online stats class for you. Allassignmenthelp.com can rescue you from the trouble created by online stats exams, online stats quizzes, and online stats coursework. We can help you with the following Statistics classes.

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Business statistics

Just like the name suggests, business statistics draws concepts and tools from elementary statistics and applies them in business. For instance, probability can be used to project the chances of a defect coming off a factory line or future sales. Business statistics tools are founded on concepts such as mean, mode, median, basic probability, bell curve, bar graphs, regression analysis, etc. This subject provides managers with the information they need to make commercial decisions under uncertainties. Business statistics requires knowledge of data collection, description, and interpretation. You need this to understand and analyze economic theories as well as identify connections between variables and how they affect productivity, price, supply, etc. Do not get upset if your business statistics assignment is giving you a hard time. Consider procuring our low-cost service.

Biostatistics

In biostatistics, we apply statistical techniques in scientific research, and health-related fields. The knowledge of biostatistics is extensively used in public health, biology, medicine, etc. It helps researchers develop new tools that support various studies in the aforementioned fields. Biostatistics has grown in popularity since the twentieth century and has now become an indispensable tool in the elimination of illnesses and improvement of human health. Biostatisticians employ the methods and theories of statistics in designing studies and analyzing data used in research problems. Clinical researchers use biostatistics to draw references from their data, design trials, and development of protocols. Other important uses of biostatistics include data analysis and reporting, design of research framework, data monitoring, etc.

Inferential statistics

Inferential statistics are used by researchers to reach conclusions that extend beyond the immediate data. For example, we might use inferential statistics to know from our sample data what the entire population might think. Most of the widely used inferential statistics are drawn from the general linear models. Some of the examples of these models include Analysis of Variance (ANOVA), regression analysis, t-tests, Analysis of Covariance (ANCOVA), and other multivariate methods like multidimensional scaling and discriminant function analysis. It is essential that any student who wants to carve a niche in social research, then you have to be familiar with the workings of inferential statistics. The use of inferential statistics greatly depends on the research design used. There are two main types of research designs, quasi-experimental and experimental analysis.

Descriptive statistics

Descriptive statistics describe the features of data used in a study. It provides measures and a summary of the sample. Descriptive statistics is the basis of virtually all quantitative analysis of data. Unlike inferential statistics that go beyond the data, in descriptive statistics, we only describe or explain what the data shows. Descriptive statistics provides researchers with quantitative descriptions in a form that can be managed. It helps researchers simplify and make sense of a mammoth of data by reducing the data to a simpler summary. Descriptive statistics can be divided into measures of spread or variability (standard deviation, kurtosis and skewness, minimum and maximum, and variance) and measures of central tendency (mean, mode and median). Put an end to your descriptive statistics assignment misery by opting for our service.

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ICE Enforcement and Removal Operations Statistics

ICE updates online data dashboard

WASHINGTON — U.S. Immigration and Customs Enforcement today updated the online statistics dashboard to include data for first quarter of Fiscal Year 2024, which spans from Oct. 1, 2023 through Dec. 31, 2023. In the first quarter of FY24, the agency increased total removals by more than 160% and removed 27% more noncitizens with criminal convictions or pending criminal charges compared to the same period in FY23. ICE Enforcement and Removal Operations continues to prioritize its resources on threats to public safety and national security.

“With the public release of this enforcement data, U.S. Immigration and Customs Enforcement is demonstrating its commitment to openness and transparency,” said ICE Deputy Director and Senior Official Performing the Duties of the Director Patrick J. Lechleitner. “We will continue to evaluate and enhance how the agency communicates measurable data to best inform how our officers and special agents perform their law enforcement missions.”

“Every day, our officers and staff work tirelessly to preserve national security and public safety by enforcing our nation’s immigration laws,” said Enforcement and Removal Operations Executive Associate Director Daniel A. Bible. “The statistical dashboard makes key data easily available through ICE.gov.”

In the first quarter of FY 2024, total arrests of noncitizens with criminal convictions or pending criminal charges increased 7% compared to the same period in FY 2023. Criminal arrests as a share of total arrests increased from 34% to 61% over the same time period. Since its launch in December 2023, the ICE Data Dashboard has made interior civil immigration enforcement statistics available in an interactive dashboard for arrests, detention, alternatives to detention, and removals.

This update marks the first quarterly data update since the launch of the dashboard. Data from FY 2020 through the first quarter FY 2024 can be accessed through the dashboard. The data is searchable nationwide or by ERO’s 25 field offices and will be updated quarterly.

The statistical dashboard includes:

Arrests: Arrest data is available based on criminality including individuals with U.S. criminal convictions, those with pending charges, and those with no convictions or pending charges but who have violated U.S. immigration laws.
Detention: Detention data is available by country of citizenship and criminality. Most detained individuals are transferred from U.S. Customs and Border Protection (CBP) based on a recent apprehension conducted at the border. Individuals in detention also include those with final orders of removal who will be imminently removed or those who are subject to mandatory detention.
Alternatives to Detention (ATD): ATD uses technology and case management to more closely monitor the more than 7 million cases assigned to the non-detained docket. The level of supervision and technology assigned to participants is based on their current immigration status, criminal history, compliance history, community, or family ties, being a caregiver or provider or other humanitarian or medical conditions.
Removals: ICE removals include returns, such as voluntary returns of inadmissible noncitizens who are turned over to ERO for detention and removal; voluntary departures; and arriving noncitizens’ withdrawal of applications for admission. ICE removals under Title 8 authority also include noncitizens processed by CBP for Expedited Removal (ER).

Learn more online at ICE.gov/statistics

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Atlanta Braves’ Catcher Sean Murphy to Rehab with Gwinnett

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LAWRENCEVILLE, Ga. – The Gwinnett Stripers, in conjunction with the Atlanta Braves, have announced that Braves catcher Sean Murphy is scheduled to begin a rehab assignment with the Stripers on Tuesday, May 21 as the club opens a six-game series against the Jacksonville Jumbo Shrimp at Coolray Field. The 29-year-old

The 29-year-old Murphy has been on Atlanta’s 10-day Injured List since March 30 with a strained left oblique muscle, suffered during his first game of the season on March 29 at Philadelphia. He will be the first Braves player to rehab with Gwinnett this season as he spends his first stint in the minors since 2019.

Acquired by Atlanta from the Oakland Athletics in a three-team trade on December 12, 2022, Murphy has played 109 games with the Braves over the past two seasons, batting .249 with 21 doubles, 21 home runs, 65 runs, 68 RBIs, and an .838 OPS. He was named a National League All-Star for the first time in his career in 2023 and also earned the NL Player of the Week award for May 1-7, 2023.

Murphy’s six-year Major League career with the Athletics (2019-22) and Braves (2023-24) includes a Rawlings American League Gold Glove award in 2021 and three trips to the MLB Postseason (2019 and 2020 with Oakland, 2023 with Atlanta). He is a career .239 hitter with 91 doubles, two triples, 67 homers, 215 RBIs, and a .776 OPS in 439 regular-season MLB games.

Murphy and the Stripers begin a six-game homestand against the Jacksonville Jumbo Shrimp at Coolray Field on Tuesday, May 21 at 7:05 p.m. ET. Tickets are on sale now at GoStripers.com .

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