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Applied Statistics for Business and Economics

Robert M. Leekley
Chapman & Hall/CRC
Publication Date: 
Number of Pages: 
[Reviewed by
Robert W. Hayden
, on

This book is a curious mixture of strengths and weaknesses. Your colleagues in Business may like it. Most of the examples and exercises sound like business applications, though few involve real data or research. The communication function of graphics is presented rather than just the analytical use of graphics. Spreadsheets are integrated throughout. In most cases, the treatment is generic, but when functions are given by name they are Excel functions. The integration is sometimes subtle. Computations are laid out in a tabular format that fits a spreadsheet rather than a plugging-numbers-into-formula layout.

For the mathematician, this text does an outstanding job of integrating things on the mathematical level. The situations where one test is a special case of another (say, two-sample t of ANOVA) are mentioned, as are similarities in formulae. For example, many test statistics look like a measure of discrepancy between the data and a model for the data, divided by a measure of variability in the data. This is one of the few texts to try to make plausible the complex formula for two-sample t degrees of freedom when we do not assume the two variances are equal.

Perhaps the only people not happy will be statisticians. Consider the section on comparing two means as an example. The pattern is to present some numbers to use to show the steps of computing a hypothesis test. Then this is followed by multiple examples of carrying out these computations, in turn followed by exercises asking students to do more computations with their spreadsheet. There is not a single graph of any of the data. About the only assumption addressed is whether the two populations have the same variance, and that is addressed later in the text with the usual F-test for two variances. Unfortunately, that test is much more subject to assumption violations than is the t-test, and using it in this fashion is generally held in disrepute by statisticians. No attention is given to study design, and interpretation of the results is limited to the decision on whether to reject the null. In general, this text puts most of the emphasis on formulae and computations, with very little that matches the recommendations of the joint ASA/MAA committee on the teaching of statistics. (You can find one statement of those recommendations in Teaching Statistics.)

That leaves the students. What will they think of this book? They will like the clear and to the point writing. The book is very plain, with no color and no pictures, but then the price is far below most of the more colorful textbooks. It is reasonably compact and shorter than most business statistics textbooks. Some students will like the routine of not being asked to do more than crunch numbers, while others will miss being challenged to grapple with real data and real research issues.

After a few years in industry, Robert W. Hayden ( taught mathematics at colleges and universities for 32 years and statistics for 20 years. In 2005 he retired from full-time classroom work. He now teaches statistics online at and does summer workshops for high school teachers of Advanced Placement Statistics. He contributed the chapter on evaluating introductory statistics textbooks to the MAA's Teaching Statistics.

Introduction to Statistics
What Is Statistics Good for?
Some Further Applications of Statistics
Some Basic Statistical Ideas
On Studying Statistics

Describing Data: Tables and Graphs
Looking at a Single Variable
Looking for Relationships
Looking at Variables over Time

Describing Data: Summary Statistics
When Pictures Will Not Do
Measures of a Single Numeric Variable
Measures of a Single Categorical Variable
Measures of a Relationship

Basic Probability
Why Probability?
The Basics
Computing Probabilities
Some Tools That May Help
Revising Probabilities with Bayes’ Theorem

Probability Distributions
Discrete Random Variables
The Binomial Probability Distribution
Continuous Random Variables
The Normal Distribution: The Bell-Shaped Curve
The Normal Approximation to the Binomial

Sampling and Sampling Distributions
What Are Sampling Distributions and Why Are They Interesting?
The Sampling Distribution of a Proportion
The Sampling Distribution of a Mean: σX Known
The Sampling Distribution of a Mean: σX Unknown
Other Sampling Distributions

Estimation and Confidence Intervals
Point and Interval Estimators of Unknown Population Parameters
Estimates of the Population Proportion
Estimates of the Population Mean
A Final Word on Confidence Intervals

Tests of Hypotheses: One-Sample Tests
Testing a Claim: Type I and Type II Errors
A Two-Tailed Test for the Population Proportion
A One-Tailed Alternative for the Population Proportion
Tests for the Population Mean
A Two-Tailed Test for the Population Mean
A One-Tailed Alternative for the Population Mean
A Final Word on One-Sample Tests

Tests of Hypotheses: Two-Sample Tests
Looking for Relationships Again
A Difference in Population Proportions
A Difference in Population Means
A Difference in Means: σs Known
A Difference in Means: σs Unknown but Equal
A Difference in Means: σs Unknown and Unequal
A Difference in Means: Using Paired Data
A Final Word on Two-Sample Tests

Tests of Hypotheses: Contingency and Goodness-of-Fit
A Difference in Proportions: An Alternate Approach
Contingency Tables with Several Rows and/or Columns
A Final Word on Contingency Tables
Testing for Goodness-of-Fit
A Final Example on Testing for Goodness-of-Fit

Tests of Hypotheses: ANOVA and Tests of Variances
A Difference in Means: An Alternate Approach
ANOVA with Several Categories
A Final Word on ANOVA
A Difference in Population Variances

Simple Regression and Correlation
The Population Regression Line
The Sample Regression Line
Evaluating the Sample Regression Line
Evaluating the Sample Regression Slope
The Relationship of F and t: Here and Beyond
Predictions Using the Regression Line
Regression and Correlation
Another Example
Dummy Explanatory Variables
The Need for Multiple Regression

Multiple Regression
Extensions of Regression Analysis
The Population Regression Line
The Sample Regression Line
Evaluating the Sample Regression Line
Evaluating the Sample Regression Slopes
Predictions Using the Regression Line
Categorical Variables
Estimating Curved Lines
Additional Examples

Time-Series Analysis
Exploiting Patterns over Time
The Basic Components of a Time Series
Moving Averages
Seasonal Variation
The Long-Term Trend
The Business Cycle
Putting It All Together: Forecasting
Another Example

Appendix A
Appendix B: Answers to Odd-Numbered Exercises
Appendix C