Applied Statistics for Business and Economics: 1st Edition (Hardback) book cover

Applied Statistics for Business and Economics

1st Edition

By Robert M. Leekley

CRC Press

496 pages | 277 B/W Illus.

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pub: 2010-03-16
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Description

Designed for a one-semester course, Applied Statistics for Business and Economics offers students in business and the social sciences an effective introduction to some of the most basic and powerful techniques available for understanding their world. Numerous interesting and important examples reflect real-life situations, stimulating students to think realistically in tackling these problems. Calculations can be performed using any standard spreadsheet package. To help with the examples, the author offers both actual and hypothetical databases on his website http://iwu.edu/~bleekley

The text explores ways to describe data and the relationships found in data. It covers basic probability tools, Bayes’ theorem, sampling, estimation, and confidence intervals. The text also discusses hypothesis testing for one and two samples, contingency tables, goodness-of-fit, analysis of variance, and population variances. In addition, the author develops the concepts behind the linear relationship between two numeric variables (simple regression) as well as the potentially nonlinear relationships among more than two variables (multiple regression). The final chapter introduces classical time-series analysis and how it applies to business and economics.

This text provides a practical understanding of the value of statistics in the real world. After reading the book, students will be able to summarize data in insightful ways using charts, graphs, and summary statistics as well as make inferences from samples, especially about relationships.

Reviews

While there are numerous texts on the market with the same goal, this text takes a practical and effective approach to engaging students with a topic that, as the author notes, they are most likely not that interested in learning. The text accomplishes this goal quite well. … It is written in a very straightforward and understandable manner that fits its intended audience quite well. … Given that there are dozens of introductory statistics texts on the market. it has become exceedingly difficult to create one that can be truly differentiated from the rest. However, in this case, the author appears to have succeeded. This is accomplished in large part by taking a down-to-earth, almost intuitive, approach to the material which is both refreshing and welcome.

—Tom Page, The American Statistician, August 2011

In this excellent textbook Professor Leekley takes the reader, as if they were his students, through every detail of examples, working all steps with great patience. This generous approach is even extended to explaining how mathematical and statistical notation and symbols are read; a very rare and valuable education. … This book is highly recommended as a textbook for business statistics and it can also be used as a manual for self-study.

Journal of the Royal Statistical Society: Series A, July 2011

For the mathematician, this text does an outstanding job of integrating things on the mathematical level. … 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. … [Students] will like the clear and to the point writing.

MAA Reviews, September 2010

Table of Contents

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

Sampling

What Are Sampling Distributions and Why Are They Interesting?

The Sampling Distribution of a Proportion

The Sampling Distribution of a Mean: σXKnown

The Sampling Distribution of a Mean: σXUnknown

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

Index

About the Author

Robert M. Leekley is a professor in the Department of Economics at Illinois Wesleyan University.

Subject Categories

BISAC Subject Codes/Headings:
BUS069000
BUSINESS & ECONOMICS / Economics / General
MAT029000
MATHEMATICS / Probability & Statistics / General