INTRODUCTORY STATISTICS BOOK

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This text covers all the necessary points in an introductory Statistics glass with a well . When comparing numerous statistical textbooks to this book, the level of. This Introductory Statistics book covers all the introductory areas/concepts very thoroughly with the exception of Counting methods such as permutations and. Study statistics online free by downloading OpenStax's Introductory Statistics book and using our accompanying online resources.


Introductory Statistics Book

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kaz-news.info: Introduction to Statistics: Fundamental Concepts and Procedures of Data Analysis (): Howard M. Reid: Books. People who have taken intro statistics courses might recognize terms like The books are based on the concept of “statistical learning,” a. One of the best introductory statistic books to help you get started with your knowledge at undergraduate level. The authors give you well organized chapters that.

But I am still looking for a book that covers the basic curriculum: The department requires me to use SPSS in class, but I like the idea of building the analysis in a spreadsheet such as excel.

I find using these computational formulas - rather than the more intuitive and computationally intensive formula that is consistent with the rational and basic algorithm- unintuitive, unnecessary and confusing. I have used it as an intro stats text for undergraduates, have borrowed some of its ideas when teaching graduate stats courses, and have given away many copies to colleagues and clients.

There are many reasons for its popularity:. Its narrative and its problems are driven by real case studies and actual data of obvious importance, rather than the made-up drivel found in so many texts. These are truly interesting and memorable, including the Salk polio vaccine trials, the Literary Digest poll debacle, the Berkeley graduate student discrimination lawsuit hinging on Simpson's Paradox , Fisher's criticism of Mendel's pea results, and much more.

It has extensive problems at three levels: These problems require minimal or no mathematics: It uses almost no mathematical formulas. Quantitative relationships are usually expressed graphically and in words. They are so clearly conveyed that when I first read this book, as a math graduate student entirely ignorant of statistics, I was able to reproduce all the underlying mathematical theory with no trouble.

It covers most of the traditional material, including the Binomial and Normal distributions, confidence intervals, z tests, t tests, chi squared tests, regression, and the minimum amount of probability and combinatorics needed to understand these. I believe the latter two are not critical: Whether the omission of Bayesian statistics is important will depend on the instructor's tastes and aims. Finally, I should note that although the mathematical demands are as small as one could possibly imagine, my pre- and post-testing of students indicates that people who come to the book with a disposition and habit of thinking quantitatively still get much more out of it than those who do not.

The pre and post tests both included the full item CAOS test of fundamental concepts any introductory college-level stats course ought to include. The students in this class have consistently exhibited twice as much improvement as that reported in the CAOS literature; the students with poor cognitive reflection scores improved only an average amount or failed to complete the course.

I haven't the data to assign causes to this extra improvement, but suspect the textbook deserves at least some of the credit. Statistics Unplugged is a great book for introductory statistics. The author first introduces the logic of the statistical test and later gives the mathematical formula. This approach helps in digesting the new concepts. There are several examples throughout the book which are presented in the form of a problem required to be solved rather than a hypothetical statement and mathematical steps.

I read Freedman almost the entire book and OpenIntro Statistics more than a third. Both of these books are quite good. I eventually found the book that came close to what I was looking for: Learning Statistics with R: A tutorial for psychology students and other beginners by Daniel Navarro. R implementations embedded in text as topics are introduced. R has built-in functions for most of the methods explained in the book.

Where R doesn't have a built-in, the author has written his own function for it and made it available on CRAN under his lsr library, so your learning is quite complete.

I personally found this to be the biggest plus point of this book. The book is more comprehensive than Freedman and OpenIntro. Along with the basics, it covers topics like Shapiro-Wilk test, Wilcoxon test, Spearman correlation, trimmed means and a chapter on Bayesian statistics, to name a few.

The motivation behind each topic is explained clearly. There is also a good amount of history behind the topics, so you get to appreciate how a method was arrived at.

The book was written iteratively with feedback from readers and I believe the author is still improving upon the book. I am suspicious of the books that are in their 7th edition.

In my teaching experience, it means that the sections and problems were reshuffled so that the students would have to download the latest edition to generate the cash flow for the publisher and royalties for the authors keep up with the course. Few serious, research level monographs have undergone a second edition by their authors, and any higher number is obviously an outlier. Kendall's Library of Statistics is a notable exception, but I cannot really think of any other book that I know that would be in its third edition.

In my very strong opinion, Excel is a good tool for statistical analysis only when used by a Ph. Teaching undergraduate statistics with it will likely have disastrous consequences, and teaches little statistics as compared to using a modern package like R or Stata.

Just try to produce a standardized residual vs. Non-major undergrads need to get the feel for data analysis, and Excel obscures it, at best. I think this book gets at some important points without either a Too much math or b dumbing things down. I would suggest that an intro stats course for psychology and other social science types should emphasize how not to go wrong too much.

A survey of methods would also be a good thing for undergrads to get. Check out the introductory statistics book, Making Sense of Data through Statistics: An Introduction by Dorit Nevo. It is written in an extremely accessible manner and is meant for undergraduate or graduate students in business and in the social sciences. The book is sold in digital format only.

Educators may register for free access to the book and teaching materials by signing up at the Legerity Digital Press Educator Preview portal. Here is a list of books. Real life examples help too. We're looking for long answers that provide some explanation and context. Don't just give a one-line answer; explain why your answer is right, ideally with citations.

Answers that don't include explanations may be removed. I have been a TA, observer, or student in a lot of courses involving quantitative methods for psychology, with SPSS as the main program. This textbook is written in an extremely simplistic manner. The jargon and terminology that is used is explained thoroughly especially as it pertains to definitions and concepts. All terminology is thoroughly explained with extensive narrative and in many cases figures, illustrations, and formulas are use as supplemental references to assist with making topics clear.

When comparing this book with other introductory statistics textbooks, the manner in which content is presented and the reading level utilized within each chapter is very comparable and in some cases even more simplistic than other textbooks.

This textbook is extremely consistent internally.

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Each chapter is arranged in a very similar manner where the learning objectives are clearly stated followed by definitions, theories, or formulas and then a robust narrative. In each chapter, there are use of figures, illustrations, and numerous example problems that walk the reader through step-by-step problem solving strategies. Additionally, the chapters have a list of robust practice problems.

Overall, all material presented in each chapter is consistent from chapter to chapter. The modularity of the textbook is one of the best features that distinguish this book from others in the subject content area. The content for each learning objective is broken up into smaller pieces allowing for easy adoption within a course. Since the topics are broken up on a more granular level, it would be very easy to rearrange subunits without confusing the reader or creating a disconnect in the topics that are being covered.

Often times I like to teach a few topics out of order or merge topics within various chapters together in order for me to explain material better. This book allows for me to have this flexibility since there many sub-sections or units. I also appreciate the fact that there are not many run on chapters where numerous topics are all introduced within a given section.

The topics are presented in logical order as necessary for an introductory statistics course. The book begins with descriptive statistics and spread of data and moves into population sampling and introduction to basic probability, followed by inferential statistical testing.

This is commonly the flow of many comparable textbooks currently being used in the field. There are no major navigation issues when I went through this textbook. I appreciate the face that you can download the book in a PDF format. At times it was a bit difficult to read the formulas that were presented in the boxes within some of the chapters, especially when symbols were used. While it was slightly to read at times, it is still manageable and not a major concern.

There were no grammatical errors that were observed when reviewing this textbook. Additionally, there were no major issues found with the example problems or the solutions to the questions within the various chapters. This was an excellent textbook and a good alternative to books that need to be downloadd at a high cost in student stores.

I would recommend this book to be adopted as a cheaper alternative for introductory statistics courses. The text covers some of the areas needed for an Introduction to Statistics or Elementary Statistics. For example, experimental design was not well covered in chapter 1 which introduction to Statistics.

Both the table of content and index was Both the table of content and index was missing in this text, which makes it hard to know exactly what page you have to go and read the topic you want to. Lastly there was no set of instruction teaching students how to use technology to perform some of these computations.

I found the contents in the book to accurate and unbiased. Because of the well-structured contents in the textbook, it will be very easy to update it or make changes at any point in time. The content in textbook is up to date. The clarity in the book was very good for an intro to statistics course.

The textbook is easily readable and the graphics are not bad at all. 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.

The contents in the book is very consistent from beginning to the end. The contents in the book are well structured and well organized for each unit.

With Exercises, Solutions and Applications in R

The text is well sectioned into parts for students to read and understand. This textbook is highly modular, such that instructors combine or use different sections to teach the class and the students will still understand the material at a higher level. The interface of the book is very good, however the graphics could have been improved by adding some good images and diagrams.

There was no table of contents or index in the pdf version of the book. You can only see the table of contents through online, which would be a very hard to navigate to the appropriate chapters and sections in the book.

On the whole, the textbook would be a very good book to use for an introduction to statistics class or elementary statistics, however I would recommend the authors adding an in-depth experimental design contents to Chapter 1. Secondly I would recommend the authors to add a table of content and an index to the textbook. The consensus introductory statistics curriculum is typically presented in three major units: This textbook covers all of these topics.

Topic 1 is chapters 1 and 2. Topic 2 is chapters 3 through 7. Topic 3 is done in chapters 8 to There are more chapters on the third topic.

Inevitably instructors might not use them all. Each chapter comes with plenty of exercises and exercise answers. There is a good index and glossary. The coverage in each topic is very competent and clear. There is, however, nothing exciting or novel in the the manner in which the topics are covered or the pedagogical approach. Recent trends in teaching introductory statistics have emphasized statistics as a part of scientific investigations.

So they have integrated the learning of statistics into the understanding of science. This text does little of that. An emerging trend is to make heavy use of computer simulation and even physical simulation techniques to aid learning. This text does none of that.

Content is very competent, accurate, error-free, and unbiased. Instructors will find the many exercises are US-centric. They may find they want exercises that are not that. The content is fairly timeless in its coverage. It is certainly arranged in ways that would make altering it -- for example, to update it or make less US-centric it -- pretty straightforward. The textbook is very clear. The writing style is quite accessible. Many of our students do not have English as a first language.

It doesn't look like the text would present issues for their understanding. On the other hand BCCampus might consider having the textbook translated into other languages as its contribution.

The use of statistics terminology is consistent through the text. The organization of material is similar in each chapter. The textbook is broken into smaller chunks. It looks like an instructor could skip or reorder sections without there being a problem.

The text looks like a professionally published textbook. There isn't color and there aren't images. But in other respects it looks good.

There aren't any navigation or user interface issues. The text is not culturally offensive in any way. The examples and exercises are often US-centric. As previously noted many examples and exercises are US-centric. There is no investigation of causal studies. This something some although not all introductory statistics cover. Most introductory statistics texts use the logical structure of descriptive statistics, probability, and inferential statistics to deliver the materials to new students.

This Introductory Statistics textbook by Shafer and Zhang is no exception This Introductory Statistics textbook by Shafer and Zhang is no exception. There is an introduction chapter chapter 1 that sets out the main definitions and conceptual foundation for the rest of the book. Descriptive statistics is covered in one chapter chapter 2. Probability and related concepts are covered across four chapters chapters Inferential statistics chapters and their applications to statistical model building and testing chapter form the remaining parts of the content.

Collectively these topics form a useful and standard foundation for learning statistics. The online version of the text contains a detailed and functioning hyperlinked Table of Contents for the Chapters and Section headings. I was unable to find a glossary or index, but maybe the same functional benefits can be obtained by clicking on the appropriate topic hyperlink and scrolling through the text.

One aspect of the content that might be useful to include is the bigger picture notion of: How is statistics used in the real world? The examples and exercises sections provide some hints to students, but contemporary issues such as population growth, climate change and sea level rise are hardly ever mentioned. Including these issues and a connection to the statistical tools that can provide solutions to these problems would help make statistics fun for multidisciplinary students who often perceive statistics as boring and irrelevant.

Another aspect of the content is the heavy reliance on the use of a calculator to perform many of the statistical calculations. Whilst this may have some value in terms of flexibility for the instructor as stated by the authors in the Preface, the reality is that once students pursue further statistics and other related courses they will be confronted with the needed to use computer software tools.

Including this explicitly would have made the book more comprehensive and relevant to the modern statistics student. It should be noted that in the Large Data Set Exercises sections of the book there are some links to digital spreadsheet data that can be articulated as computer-based data analysis practice for students. The contents are free of errors. In the Acknowledgments section the authors listed at least 16 individuals linked to higher education that have provided feedback and suggestions for improving the materials.

This adds confidence in the quality of the materials. Many of the exercises and examples use concepts SAT scores for example and data that are best understood within the context of the United States.

Using the textbook outside of that geographic context may prove to be a limitation in terms of asking students to grasp an understanding of the problem domain before attempting a statistical solution. However, there are a few examples that attempt to break the mold - Section 2.

Theory and application

The statistical core that the textbook focuses on is relatively stable and so changes would be few and far between. This statistical core is up-to-date. The examples and exercises that wrap around the statistical core could use some modifications. For example, issues climate change, population growth, etc. Making these changes to the existing online HTML files would be relatively easy and straightforward to implement.

The text is written in simple and clear prose. There are hardly any sentences more than 20 words long making the statistical messages easily digestible to students whose first language may not be English.

Highlighted definition boxes and key takeaway boxes provide adequate explanations of terminology and key points. The quality, layout, terminology, sections and overall value of each chapter are all internally consistent. The online Table of Contents also provide a consistent means to access these materials in an easily accessible way.

The text is highly modular. Each Chapter is broken down into smaller sections, and on the whole the materials are covered in a very efficient way making the chapters and sections relatively short. There are a few instances where there is overflow of the topics from one chapter into another where it might not be a good fit. For example, an introduction chapter Chapter 1 begins immediately to define core statistical concepts and to start familiarizing students with data presentation.

The authors chose to continue data presentation mainly histograms in chapter 2 that has been titled Descriptive Statistics. In order to avoid any confusion in the minds of students, it would have been useful to focus the Descriptive Statistics chapter on mean, median, mode concepts. The histogram material could have been merged with the data presentation materials of Chapter 1, and maybe added newer presentation forms such as maps and sparklines, to have a more comprehensive data presentation chapter.

Experience has shown that chunking materials using clearly defined boundaries help students to learn better. A particularly useful feature is the learning objective that has been given for each Section. The interface is well designed and organized to enable easy access and pleasing display of the materials. There is some color used throughout the text and this adds to improve the readability and contrast of the images and texts.

It is fair to say that figures especially graphs are used extensively to illustrate the concepts being discussed. There is no evidence of grammatical errors. However, it should be noted that the online version of the material seems to be of the highest quality - the printed version of the book of which I had access had some symbols missing Section There is no evidence that the text is culturally insensitive in any way.

I suspect that the book was designed to be used in the United States and so many of the examples are within that context.

If the book is to be used for a student population outside of that context, then some changes either by the authors or instructors in the diversity of examples will be needed. Overall, this is a useful book. It does a good job at covering the breadth and depth of the topics one would expect for an introductory course. The content is well presented and easily accessible.

The drawback is that statistical computing is not adequately emphasized and that students in Canada will find it a challenge to relate to some of the US-context questions and examples.

Some immediate updates that are needed would be: Reviewed by Erik C. The text covers some of the areas of the subject, albeit not in-depth. Whether this approach is appropriate for an introductory course, depends on the plan for the further study. Similarly to many other introductory textbooks, the text leaves Similarly to many other introductory textbooks, the text leaves open the question "why" do the particular formulas apply.

Glossary is not provided other than chapter-by-chapter. The authors have gone great lengths towards ensuring error-free and unbiased content. As always in a text of this size, some errors would still creep in despite the best efforts. In particular: Position of the mean on the illustration of a bimodal distribution page 92 is incorrect. FWHM [full width at half maximum] and the variances for both Gaussian components of the distribution are identical, but the components have different amplitudes.

As FWHM is identical, the mean should lie closer to the peak of the component with higher amplitude. Note, that if the FWHM of the left component was twice the FWHM of the right component, the position of the mean would nearly halfway between the modes.

Pages , Multiple pages: Authors use Gaussian distribution plots to illustrate Student distribution. While technically correct for large N, this gives a wrong impression about the shape of the Student distribution.

Content is marginally up-to-date. No attention is given to non-parametric methods, Bayesian estimation, multivariate distributions, to name a few areas.

The amount of included exercises is unnecessarily overwhelming, making the text appear much longer than it actually is, and difficult to locate the actual text material. Examples are easy to update, but would benefit from reduction of their count. The text will not become obsolete any faster than similar introductory statistics books.One topic missing is s a discussion of determining normality of a data set.

This book covers all the topics typically covered in an introductory level statistics course from an introduction to probability and the basics f study design through sampling distributions, confidence intervals, tests of one and two samples for means, proportions and variances, the typical Chi square tests including independence, goodness of fit and homogeneity, regression and ANOVA.

Beyond the authors' errata which is available separately on textbook's webpage, I have found the textbook to be error-free and accurate.

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The pre and post tests both included the full item CAOS test of fundamental concepts any introductory college-level stats course ought to include. The content is accurate and error free. The knowledge given in the book is one of the best ways to gain knowledge and real world experience by looking at the scenarios through the view point of an expert.

Much of the subject matter used in the examples and exercises is timeless and would not need to be revised in order to make the text feel current.

In the experimental sciences and interdisciplinary research, data analysis has become an integral part of any scientific study. Introductory Statistics is comprehensive and includes all the topics needed for an introductory course in statistics. Download Colgate's Financial Model.