An Introduction to Statistical Methods and Data Analysis (6th ed., international ed.)

Citation

Christensen, P. (2011), "An Introduction to Statistical Methods and Data Analysis (6th ed., international ed.)", Journal of Property Investment & Finance, Vol. 29 No. 2, pp. 227-228. https://doi.org/10.1108/jpif.2011.29.2.227.1

In the sixth edition of An Introduction to Statistical Methods and Data Analysis Ott and Longnecker further expand the depth of material found in the previous editions with several enhancements and additions. The target audience continues to be advanced undergraduate or graduate students, and the book does an excellent job offering a range of examples to engage students from a variety of disciplines. If the book has a shortcoming, it is that it can be difficult for students to read and absorb the, at times, lengthy and information‐dense sentences.

However, the authors' Four‐Step Learning from Data Process is reiterated throughout the book through a broad range of case studies and examples that help students understand the fundamentals of statistics, as well as its broad applicability in real world problems. The repetition of information from a variety of sources gives students from different backgrounds an opportunity to engage with statistics and see its applicability in their disciplines.

New to the sixth edition, each chapter begins with a case study that demonstrates the same four‐step process used in the text to assist in making connections to the chapter material and further reinforce learning the process:

1. 1.

   designing the problem;

2. 2.

   gathering data;

3. 3.

   summarizing data; and

4. 4.

   analyzing data, interpreting the analysis and communicating the results of data analyses.

Through the case studies the authors teach students to solve problems encountered in research projects and illustrate not only the four‐step process, but also emphasize the importance of understanding how to interpret the results and draw conclusions from them by highlighting the sample size determination, appropriate selection of graphical data display, and most appropriate manner of interpreting and summarizing the statistical results for a complete report.

The sixth edition has also expanded the selection of examples from journal articles, newspapers and the authors' own professional experiences which are integrated into the text and further reiterate the four‐step process. The combined effect of the research studies and examples is an emphasis on the “practical usages of statistics in solving problems that are relevant to [their] everyday life.” Ultimately, Ott and Longnecker assist students in analyzing and evaluating statistical analyses in published research papers and news reports so that they become better critical readers and can more prudently assess provided information.

The first 11 chapters present material typically covered in a one‐semester introductory statistics course, and the remaining chapters cover regression modeling and design of experiments that could be taught in the second‐semester advanced statistics course. The sixth edition offers expanded and updated exercises at the end of every chapter that investigate real‐life scenarios drawn from a variety of disciplines, including agriculture, business, economics, education, engineering, medicine, law, political science, psychology, environmental studies, and sociology. There is an expanded discussion of the proper methods to design experiments in Chapter 2, while chapter 12 offers an expanded discussion of logistic regression. Chapters 5, 6 and 14 offer techniques for calculating sample sizes and the probability of Type II errors for both the t‐test and F test.

In addition, more computer resources and output are provided than in previous versions, without relying too heavily on a particular statistical package, and can be accessed on the companion web site. Some of the statistical packages utilized include Minitab, JMP, STATA, Excel and SPSS. In addition, a free study tools app is available for download from iTunes that includes flash cards, definitions, quizzes, and more.

Overall, despite a slight tendency toward overly dense sentences, the text is well written. The authors' emphasis on interpretation rather than computation teaches students the importance of:

• •

  summarizing the research goals into a statement about population parameters;

• •

  selecting the most appropriate sample size computation and test statistic;

• •

  considering both Type I and Type II error rates when discussing results;

• •

  considering both the statistical and practical significance of results; and

• •

  stating results in non‐statistical jargon to better communicate the results.

As such, this book would be an apposite textbook for either an introductory statistics class or for a more advanced regression modeling course.