## Description

**For business statistics courses taught in Economics and Business Schools**

A classic text for accuracy and statistical precision.

*Statistics for Business and Economics* enables students to conduct serious analysis of applied problems rather than running simple “canned” applications. This text is also at a mathematically higher level than most business statistics texts and provides students with the knowledge they need to become stronger analysts for future managerial positions.

The seventh edition of this book has been revised and updated to provide students with improved problem contexts for learning how statistical methods can improve their analysis and understanding of business and economics.

## Table of Contents

**CHAPTER 1 Describing Data: Graphical**

1.1 Decision Making in an Uncertain Environment

1.2 Classification of Variables

1.3 Graphs to Describe Categorical Variables

1.4 Graphs to Describe Time-Series Data

1.5 Graphs to Describe Numerical Variables

1.6 Tables and Graphs to Describe Relationships Between Variables

1.7 Data Presentation Errors

**CHAPTER 2 Describing Data: Numerical**

2.1 Measures of Central Tendency

2.2 Measures of Variability

2.3 Weighted Mean and Measures of Grouped Data

2.4 Measures of Relationships Between Variables

**CHAPTER 3 Probability**

3.1 Random Experiment, Outcomes, Events

3.2 Probability and Its Postulates

3.3 Probability Rules

3.4 Bivariate Probabilities

3.5 Bayes’ Theorem

**CHAPTER 4 Discrete Random Variables and Probability**

Distributions

Distributions

4.1 Random Variables

4.2 Probability Distributions for Discrete Random Variables

4.3 Properties of Discrete Random Variables

4.4 Binomial Distribution

4.5 Hypergeometric Distribution

4.6 The Poisson Probability Distribution

4.7 Jointly Distributed Discrete Random Variables

**CHAPTER 5 Continuous Random Variables and Probability**

Distributions

Distributions

5.1 Continuous Random Variables

5.2 Expectations for Continuous Random Variables

5.3 The Normal Distribution

5.4 Normal Distribution Approximation for Binomial Distribution

5.5 The Exponential Distribution

5.6 Jointly Distributed Continuous Random Variables

**CHAPTER 6 Sampling and Sampling Distributions**

6.1 Sampling from a Population

6.2 Sampling Distributions of Sample Means

6.3 Sampling Distributions of Sample Proportions

6.4 Sampling Distributions of Sample Variances

**CHAPTER 7 Estimation: Single Population**

7.1 Properties of Point Estimators

7.2 Confidence Interval Estimation of the Mean of a Normal Distribution: Population Variance Known

7.3 Confidence Interval Estimation of the Mean of a Normal Distribution: Population Variance Unknown

7.4 Confidence Interval Estimation of Population Proportion

7.5 Confidence Interval Estimation of the Variance of a Normal Distribution

7.6 Confidence Interval Estimation: Finite Populations

CHAPTER 8 Estimation: Additional Topics

CHAPTER 8 Estimation: Additional Topics

8.1 Confidence Interval Estimation of the Difference Between Two Normal

Population Means: Dependent Samples

8.2 Confidence Interval Estimation of the Difference Between Two Normal

Population Means: Independent Samples

8.3 Confidence Interval Estimation of the Difference Between Two Population

Proportions

8.4 Sample Size Determination: Large Populations

8.5 Sample Size Determination: Finite Populations

CHAPTER 9 Hypothesis Testing: Single Population

CHAPTER 9 Hypothesis Testing: Single Population

9.1 Concepts of Hypothesis Testing

9.2 Tests of the Mean of a Normal Distribution: Population Variance

Known

9.3 Tests of the Mean of a Normal Distribution: Population Variance Unknown

9.4 Tests of the Population Proportion

9.5 Assessing the Power of a Test

9.6 Tests of the Variance of a Normal Distribution

**CHAPTER 10 Hypothesis Testing: Additional Topics**

10.1 Tests of the Difference Between Two Population Means: Dependent Samples

10.2 Tests of the Difference Between Two Normal Population Means: Independent Samples

10.3 Tests of the Difference Between Two Population Proportions

10.4 Tests of the Equality of the Variances Between Two Normally Distributed Populations

10.5 Some Comments on Hypothesis Testing

**CHAPTER 11 Simple Regression**

11.1 Overview of Linear Models

11.2 Linear Regression Model

11.3 Least Squares Coefficient Estimators

11.4 The Explanatory Power of a Linear Regression Equation

11.5 Statistical Inference: Hypothesis Tests and Confidence Intervals

11.6 Prediction

11.7 Correlation Analysis

11.8 Beta Measure of Financial Risk

11.9 Graphical Analysis

CHAPTER 12 Multiple Regression

CHAPTER 12 Multiple Regression

12.1 The Multiple Regression Model

12.2 Estimation of Coefficients

12.3 Explanatory Power of a Multiple Regression Equation

12.4 Confidence Intervals and Hypothesis Tests for Individual Regression Coefficients

12.5 Tests on Regression Coefficients

12.6 Prediction

12.7 Transformations for Nonlinear Regression Models

12.8 Dummy Variables for Regression Models

12.9 Multiple Regression Analysis Application Procedure

CHAPTER 13 Additional Topics in Regression Analysis

CHAPTER 13 Additional Topics in Regression Analysis

13.1 Model-Building Methodology

13.2 Dummy Variables and Experimental Design

13.3 Lagged Values of the Dependent Variables as Regressors

13.4 Specification Bias

13.5 Multicollinearity

13.6 Heteroscedasticity

13.7 Autocorrelated Errors

**CHAPTER 14 ANALYSIS OF CATEGORICAL DATA**

14.1 Goodness-of-Fit Tests: Specified Probabilities

14.2 Goodness-of-Fit Tests: Population Parameters Unknown

14.3 Contingency Tables

14.4 Sign Test and Confidence Interval

14.5 Wilcoxon Signed Rank Test

14.6 Mann–Whitney U Test

14.7 Wilcoxon Rank Sum Test

14.7 Spearman Rank Correlation

CHAPTER 15 Analysis of Variance

CHAPTER 15 Analysis of Variance

15.1 Comparison of Several Population Means

15.2 One-Way Analysis of Variance

15.3 The Kruskal–Wallis Test

15.4 Two-Way Analysis of Variance: One Observation per Cell, Randomized Blocks

15.5 Two-Way Analysis of Variance: More Than One Observation per Cell

CHAPTER 16 Time-Series Analysis and Forecasting

CHAPTER 16 Time-Series Analysis and Forecasting

16.1 Index Numbers

16.2 A Nonparametric Test for Randomness

16.3 Components of a Time Series

16.4 Moving Averages

16.5 Exponential Smoothing

16.6 Autoregressive Models

16.7 Autoregressive Integrated Moving Average Models

CHAPTER 17 Sampling: Additional Topics

CHAPTER 17 Sampling: Additional Topics

17.1 Stratified Sampling

17.2 Other Sampling Methods

**CHAPTER 18 Statistical Decision Theory**

18.1 Decision Making Under Uncertainty

18.2 Solutions Not Involving Specification of Probabilities

18.3 Expected Monetary Value; TreePlan

18.4 Sample Information: Bayesian Analysis and Value

18.5 Allowing for Risk: Utility Analysis

APPENDIX TABLES

1. Cumulative Distribution Function of the Standard Normal

Distribution

2. Probability Function of the Binomial Distribution

3. Cumulative Binomial Probabilities

4. Values of e –λ

5. Individual Poisson Probabilities

6. Cumulative Poisson Probabilities

7. Cutoff Points of the Chi-Square Distribution Function

8. Cutoff Points for the Student’s t Distribution

9. Cutoff Points for the F Distribution

10. Cutoff Points for the Distribution of the Wilcoxon Test Statistic

11. Cutoff Points for the Distribution of Spearman Rank Correlation

Coefficient

12. Cutoff Points for the Distribution of the Durbin–Watson Test Statistic

13 Critical Values of the Studentized Range Q

(page 964 965 Applied Statistical Methods Carlson, Thorne Prentice Hall 1997)

14. Cumulative Distribution Function of the Runs

Test Statistic

ANSWERS TO SELECTED EVEN-NUMBERED EXERCISES

INDEX I-1

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