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Introductory Statistics: Exploring the World Through Data, by Robert N. Gould, Colleen N. Ryan
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We live in a data-driven world, and this is a book about understanding and working with that data. In order to be informed citizens, authors Rob Gould and Colleen Ryan believe that learning statistics extends beyond the classroom to an essential life skill. They teach students of all math backgrounds how to think about data, how to reason using data, and how to make decisions based on data.
With a clear, unintimidating writing style and carefully chosen pedagogy, Introductory Statistics: Exploring the World through Data makes data analysis accessible to all students. Guided Exercises support students by building their confidence as they learn to solve problems. Snapshots summarize statistical procedures and concepts for convenient studying. While this text assumes the use of statistical software, formulas are presented as an aid to understanding the concepts rather than the focus of study. Check Your Tech features demonstrate how students will get the same numerical value by-hand as when using statistical software.
- Sales Rank: #17842 in Books
- Brand: Pearson
- Published on: 2012-01-06
- Ingredients: Example Ingredients
- Original language: English
- Number of items: 1
- Dimensions: 11.00" h x 1.20" w x 8.60" l, 3.75 pounds
- Binding: Hardcover
- 736 pages
- Used Book in Good Condition
About the Author
Robert L. Gould (Ph.D., University of California, San Diego) is a leader in the statistics education community. He has served as chair of the American Statistical Association’s Committee on Teacher Enhancement, has served as chair of the ASA’s Statistics Education Section, and was a co-author of the Guidelines for Assessment in Instruction on Statistics Education (GAISE) College Report. As the associate director of professional development for CAUSE (Consortium for the Advancement of Undergraduate Statistics Education), he has worked closely with the American Mathematical Association of Two-Year Colleges (AMATYC) to provide traveling workshops and summer institutes in statistics (he also presented an AMATYC summer institute in 2009). For over ten years, he has served as Vice-Chair of Undergraduate Studies at the UCLA Department of Statistics, and he is Director of the UCLA Center for the Teaching of Statistics. In 2009, Rob was elected president of the Southern California Chapter of the American Statistical Association.
Colleen N. Ryan has taught statistics, chemistry, and physics to diverse community college students for decades. She taught at Oxnard College from 1975 to 2006, where she earned the Teacher of the Year Award. Colleen currently teaches statistics part-time at California Lutheran University. She often designs her own lab activities. Her passion is to discover new ways to make statistical theory practical, easy to understand, and sometimes even fun. Colleen earned a B.A. in physics from Wellesley College, an M.A.T. in physics from Harvard University, and an M.A. in chemistry from Wellesley College. Her first exposure to statistics was with Frederick Mosteller at Harvard. In her spare time, she sings with the Oaks Chamber Singers, has been an avid skier in the past, and enjoys time with her family.
Most helpful customer reviews
16 of 18 people found the following review helpful.
A text that focuses on the important topics and critical thinking
By Anne H. Anderson
Background: For several years, I have taught a one-semester applied statistics course at a state university to non-math majors going into fields like nursing, criminal justice, exercise physiology, and psychology. In the future, they will primarily need to evaluate statistical research done by others, but will also need to do some statistical studies themselves, at least in their "methods" courses. I want the students to improve their critical thinking about statistics, and retain an understanding of the concepts beneath the statistics. The exposition in any text I choose needs to be very clear, however, using relevant examples and common sense as much as possible, since my students are often math-phobic or have weak and rusty math backgrounds.
This review is my personal opinion, and does not reflect any official opinion of my institution.
This new text by Gould and Ryan addresses most of the weaknesses I have found in other texts. It focuses on understanding concepts and thinking critically about using statistics rather than on following step-by-step procedures. The order of presentation is clear, and good choices are made about what to include and what to leave out. It is assumed that students will use technology to produce graphs and statistics and to perform statistical tests, and students are taught what decisions need to be made about when and how to use the technology. The steps needed to perform important calculations by hand are explained clearly, but in a separate section at the end of each chapter in the context of "know what your technology is doing". Step-by-step procedures are explained where appropriate, but that is not where the emphasis lies.
My first test of a text is to see how "population" is defined. The usual definition is "the set of all subjects that are being studied." This definition confuses my students, because they consider the sample to be the set of subjects that they are actually able to "study". Gould/Ryan have a meaningful, clear explanation of what a population is, with good examples.
As an example of the focus on using technology wisely, rather than on detailed procedures, there is no presentation of steps to produce a grouped frequency distribution or histogram manually. Ungrouped frequency distributions, dotplots, and finally histograms are illustrated through examples as ways to visualize variation in data sets. Most of the histogram discussion is about the effect of choosing different bin sizes, since that is what the student/researcher needs to control when using statistical software. Boundaries and class widths are explained in the context of what students see in the example histograms. Interpretation and comparison of graphs and statistics is emphasized.
Each chapter after the first ends with straightforward descriptions of how to use a TI-83/84, Minitab, Excel (with the extra-cost XLSTAT add-in), and StatCrunch to perform the operations or produce the graphs described in the chapter. StatCrunch is a statistical software package (similar to a basic Minitab) that is included in the price of the on-line homework system, MyStatLab, that can be purchased with this text. StatCrunch integrated in a way that makes it easy for students to use StatCrunch to solve homework problems (although StatCrunch is not required). My institution uses Minitab, so I want my students to learn it, but I think they will be able to move from StatCrunch to Minitab easily.
Regression analysis is introduced immediately after summary statistics for center and variation, with a strong discussion about correlation not implying causation. The probability exposition starts with empirical probabilities, and most of the examples are real-world decision-making situations. Conditional probability is introduced in the context of contingency tables. Hypothesis testing is introduced using proportions and p-values, with only indirect discussion of critical values (which aren't even in the index). The cumulative standard normal distribution is used for pedagogical reasons as having only three cases to consider. Hypothesis testing of means is presented only after the basic concepts using proportions are thoroughly covered. Percentiles are taught in the context of the normal distribution. The student is reminded often that statistics are based on mathematical models, and are valid only to the extent that the data fit the model.
As a good example of what is left out, the text has no explanation of Chebyshev's Theorem. I love teaching that theorem and how to answer various types of questions with it, but it has little use compared to many other topics that compete for inclusion in one semester's coverage. The text does include the Empirical Rule (Normal Rule/68-95-97.3 Rule).
Some important topics Gould and Ryan cover are experimental design, correlation vs causation, and how to read published statistical research critically. These are often not covered well in other texts.
A big plus to this text for me is that the problem sets at the end of each section are head-and-shoulders over those in other books. They require students to compare, to answer "what if", to explain. Unfortunately, the problems in the on-line homework system, MyStatLab, are not quite so good, but they are still a big improvement over those for other texts.
Using this text will require major reorganization of my course, and I won't know whether it will meet my objectives for my students until I try it, but I think it is worth the try.
4 of 5 people found the following review helpful.
Succinct and clear
By Staranalyst
This book takes a direct and clear approach to an often overly complex presentatIon. It breaks the topic down in clear and comprehensible segments and is an extremely useful introduction to the field of statistical analysis. Highly recommended.
1 of 1 people found the following review helpful.
Required reading
By Lauren E
This was a required book for a statistics class. It was an online course and so we needed to use the online information and tests. I thought that the online material was a great addition especially for an online course. The book at times was unclear and I needed to google things a few times. Hard to locate lesson plans or key phrases in the book.
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