The format is consistent throughout the textbook. The writing in this book is above average. Also, non-parametric alternatives would be nice, especially Monte Carlo/bootstrapping methods. The drawback of this book is that it does not cover how to use any computer software or even a graphing calculator to perform the calculations for inferences. This can be particularly confusing to "beginners.". Things flow together so well that the book can be used as is. Introductory statistics courses prepare students to think statistically but cover relatively few statistical methods. There are a lot of topics covered. However, there are a few instances where he/she are used to refer to a "theoretical person" rather than using they/them, Reviewed by Alice Brawley Newlin, Assistant Professor, Gettysburg College on 3/31/20, I found the book to be very comprehensive for an undergraduate introduction to statistics - I would likely skip several of the more advanced sections (a few of these I mention below in my comments on its relevance) for this level, but I was glad Notation, language, and approach are maintained throughout the chapters. I do think there are some references that may become obsolete or lost somewhat quickly; however, I think a diligent editorial team could easily update data sets and questions to stay current. More color, diagrams, etc.? 4th edition solutions and quizlet . At This text covers more advanced graphical su Understanding Statistics and Experimental Design, Empirical Research in Statistics Education, Statistics and Analysis of Scientific Data. The code and datasets are available to reproduce materials from the book. The statistical terms, definitions, and equation notations are consistent throughout the text. I value the unique organization of chapters, the format of the material, and the resources for instructors and students. The overall organization of the text is logical. Professors looking for in-depth coverage of research methods and data collection techniques will have to look elsewhere. My biggest complaint is that Unless I missed something, the following topics do not seem to be covered: stem-and-leaf plots, outlier analysis, methods for finding percentiles, quartiles, Coefficient of Variation, inclusion of calculator or other software, combinatorics, simulation methods, bootstrap intervals, or CI's for variance, critical value method for testing, and nonparametric methods. For example, types of data, data collection, probability, normal model, confidence intervals and inference for single proportions. Perhaps we don't help the situation much with the way we begin launching statistical terminology while demonstrating a few "concepts" on a white board. I did have a bit of trouble looking up topics in the index - the page numbers seemed to be off for some topics (e.g., effect size). I found the book to be very comprehensive for an undergraduate introduction to statistics - I would likely skip several of the more advanced sections (a few of these I mention below in my comments on its relevance) for this level, but I was glad to see them included. These are essential components of quantitative analysis courses in the social sciences. It should be pointed out that logistic regression is using a logistic function to model a binary dependent variable. The text needs real world data analysis examples from finance, business and economics which are more relevant to real life. In addition to the above item-specific comments: #. I do like the case studies, videos, and slides. Also, grouping confidence intervals and hypothesis testing in Ch.5 is odd, when Ch.7 covers hypothesis testing of numerical data. Extra Content. Also, non-parametric alternatives would be nice, especially Monte Carlo/bootstrapping methods. Each chapter is separated into sections and subsections. I feel that the greatest strength of this text is its clarity. The book uses relevant topics throughout that could be quickly updated. read more. The authors also make GREAT use of statistical graphics in all the chapters. Some of these will continue to be useful over time, but others may be may have a shorter shelf life. Some more modern concepts, such as various effect size measures, are not covered well or at all (for example, eta squared in ANOVA). #. This textbook did not contain much real world application data sets which can be a draw back on its relevance to today's data science trend. None. Use of the t-distribution is motivated as a way to "resolve the problem of a poorly estimated standard error", when really it is a way to properly characterize the distribution of a test statistic having a sample-based standard error in the denominator. To many texts that cover basic theory are organized as theorem/proof/example which impedes understanding of the beginner. The student-facind end, while not flashy or gamified in any way, is easy to navigate and clear. I did not find any grammatical errors or typos. I think that the first chapter has some good content about experiments vs. observational studies, and about sampling. Reviewed by Leanne Merrill, Assistant Professor, Western Oregon University on 6/14/21, This book has both the standard selection of topics from an introductory statistics course along with several in-depth case studies and some extended topics. In particular, I like that the probability chapter (which comes early in the text) is not necessary for the chapters on inference. Another example that would be easy to update and is unlikely to become non-relevant is email and amount of spam, used for numerous topics. Some of the content seems dated. These sections generally are all under ten page in total. Statistics is not a subject that becomes out of date, but in the last couple decades, more emphasis has been given to usage of computer technology and relevant data. Reviewed by Lily Huang, Adjunct Math Instructor , Bethel University on 11/13/18, The text covers all the core topics of statisticsdata, probability and statistical theories and tools. In particular, examples and datasets about county characteristics, elections, census data, etc, can become outdated fairly quickly. Each chapter is broken up into sections and each section has sub-sections using standard LaTex numbering. Reviewed by Denise Wilkinson, Professor of Mathematics, Virginia Wesleyan University on 4/20/21, This text book covers most topics that fit well with an introduction statistics course and in a manageable format. The coverage of probability and statistics is, for the most part, sound. The text has a thorough introduction to data exploration, probability, statistical distributions, and the foundations of inference, but less complete discussions of specific methods, including one- and two-sample inference, contingency tables, and linear and logistic regression. Covers all of the topics usually found in introductory statistics as well as some extra topics (notably: log transforming data, randomization tests, power calculation, multiple regression, logistic regression, and map data). For example, when introducing the p-value, the authors used the definition "the probability of observing data at least as favorable to the alternative hypothesis as our current data set, if the null hypothesis is true." There are a few instances referencing specific technology (such as iPods) that makes the text feel a bit dated. The chapter summaries are easy to follow and the order of the chapters begin with "Introduction to Data," which includes treatment The text offered quite a lot of examples in the medical research field and that is probably related to the background of the authors. I also particularly like that once the basics chapters are covered, the instructor can then pick and choose those topics that will best serve the course or needs of students. There are a variety of exercises that do not represent insensitivity or offensive to the reader. Any significant rearranging of those sections would be incredibly detrimental to the reader, but that is true of any statistics textbook, especially at the introductory level: Earlier concepts provide the basis for later concepts. Also, as fewer people do manual computations, interpretation of computer software output becomes increasingly important. read more. Nothing was jarring in this aspect, and the sections/chapters were consistent. It would be nice if the authors can start with the big picture of how people perform statistical analysis for a data set. read more. I see essentially no errors in this book. Most of the examples are general and not culturally related. While it would seem that the data in a statistics textbook would remain relevant forever, there are a few factors that may impact such a textbook's relevance and longevity. Parts I and II give an overview of the most common tests (t-test, ANOVA, correlations) and work out their statistical principles. The authors used a consistent method of presenting new information and the terminology used throughout the text remained consistent. HS Statistics (2nd Ed) exercise solutions Available to Verified Teachers, click here to apply for access Intro Stat w/Rand & Sim exercise solutions Available to Verified Teachers, click here to apply for access Previous Editions Click below to explore the history of each textbook that is in its 2nd or later edition. It defines terms, explains without jargon, and doesnt skip over details. read more. The book has a great logical order, with concise thoughts and sections. Reviewed by Gregg Stall, Associate Professor, Nicholls State University on 2/8/17, The text covers the foundations of data, distributions, probability, regression principles and inferential principles with a very broad net. The book used plenty of examples and included a lot of tips to understand basic concepts such as probabilities, p-values and significant levels etc. I found the content in the 4th edition is extremely up-to-date - both in terms of its examples, and in terms of keeping up with the "movements" in many disciplines to be more transparent and considered in hypothesis testing choices (e.g., all hypothesis tests are two-tailed [though the reasoning for this is explained, especially in Section 5.3.7 on one-tailed tests), they include Bayes' theorem, many less common distributions for the introductory level like Bernoulli and Poisson, and estimating statistical power/desired sample size). Choosing the population proportion rather than the population mean to be covered in the foundation for inference chapter is a good idea because it is easier for students to understand compared to the population mean. The graphs and diagrams were also clear and provided information in a way that aided in understanding concepts. 100% 100% found this document not useful, Mark this document as not useful. These blend well with the Exercises that contain the odd solutions at the end of the text. Although accurate, I believe statistics textbooks will increasingly need to incorporate non-parametric and computer-intensive methods to stay relevant to a field that is rapidly changing. Reviewed by Greg McAvoy, Professor, University of North Carolina at Greensboro on 12/5/16, The book covers the essential topics in an introductory statistics course, including hypothesis testing, difference of means-tests, bi-variate regression, and multivariate regression. Similar to most intro The writing style and context to not treat students like Phd academics (too high of a reading level), nor does it treat them like children (too low of a reading level). As in many/most statistics texts, it is a challenge to understand the authors' distinction between "standard deviation" and "standard error". read more. Probability is optional, inference is key, and we feature real data whenever . Chapters 4-6 on statistical inference are especially strong, and the discussion of outliers and leverage in the regression chapters should prove useful to students who work with small n data sets. This easily allow for small sets of reading on a class to class basis or larger sets of reading over a weekend. This book covers almost all the topics needed for an introductory statistics course from introduction to data to multiple and logistic regression models. The chapters are bookmarked along the side of the pdf file (once downloaded). And sections continue to be useful over time, but others may be may have shorter., is easy to navigate and clear i do like the case studies, videos and... 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