For a one- or two-semester course in business statistics.

Classic, yet contemporary. Theoretical, yet applied. Statistics for Business and Economics, Eleventh Edition, gives you the best of both worlds. Using a rich array of applications from a variety of industries, McClave/Sincich/Benson clearly demonstrates to students how to use statistics effectively in a business environment.

The book focuses on developing statistical thinking so the reader can better assess the credibility and value of inferences made from data. As consumers and future producers of statistical inferences, students are introduced to a wide variety of data collection and analysis techniques to help them evaluate data and make informed business decisions. As with previous editions, this revision offers an abundance of applications with many new and updated exercises that draw on real business situations and recent economic events. The authors assume a background of basic algebra.


Table of Contents:

1. Statistics, Data, and Statistical Thinking

1.1 The Science of Statistics

1.2 Types of Statistical Applications in Business

1.3 Fundamental Elements of Statistics

1.4 Processes*

1.5 Types of Data

1.6 Collecting Data

1.7 The Role of Statistics in Managerial Decision-Making


2. Methods for Describing Sets of Data

2.1 Describing Qualitative Data

2.2 Graphical Methods for Describing Quantitative Data

2.3 Summation Notation

2.4 Numerical Measures of Central Tendency

2.5 Numerical Measures of Variability

2.6 Interpreting the Standard Deviation

2.7 Numerical Measures of Relative Standing

2.8 Methods for Detecting Outliers: Box Plots and z-Scores

2.9 Graphing Bivariate Relationships*

2.10 The Time Series Plot

2.11 Distorting the Truth with Descriptive Techniques


3. Probability

3.1 Events, Sample Spaces, and Probability

3.2 Unions and Intersections

3.3 Complementary Events

3.4 The Additive Rule and Mutually Exclusive Events

3.5 Conditional Probability

3.6 The Multiplicative Rule and Independent Events

3.7 Random Sampling

3.8 Bayes’ Rule


4. Random Variables and Probability Distributions

4.1 Two Types of Random Variables



PART I: Discrete Random Variables

4.2 Probability Distributions for Discrete Random Variables

4.3 The Binomial Random Variable

4.4 Other Discrete Distributions: Poisson and Hypergeometric


PART II: Continuous Random Variables

4.5 Probability Distributions for Continuous Random Variables

4.6 The Normal Distribution

4.7 Descriptive Methods for Assessing Normality

4.8 Approximating a Binomial Distribution with a Normal Distribution

4.9 Other Continuous Distributions: Uniform and Exponential

4.10 Sampling Distributions

4.11 The Sampling Distribution of a Sample Mean and the Central Limit Theorem


5. Inferences Based on a Single Sample: Estimation with Confidence Intervals

5.1 Identifying the Target Parameter

5.2 Confidence Interval for a Population Mean: Normal (z) Statistic

5.3 Confidence Interval for a Population Mean: Student’s t-Statistic

5.4 Large-Sample Confidence Interval for a Population Proportion

5.5 Determining the Sample Size

5.6 Finite Population Correction for Simple Random Sampling

5.7 Sample Survey Designs*


6. Inferences Based on a Single Sample: Tests of Hypothesis

6.1 The Elements of a Test of Hypothesis

6.2 Formulating Hypotheses and Setting Up the Rejection Region

6.3 Test of Hypothesis about a Population Mean: Normal (z) Statistic

6.4 Observed Significance Levels: p-Values

6.4 Test of Hypothesis About a Population Mean: Student’s t-Statistic

6.5 Large-Sample Test of Hypothesis About a Population Proportion

6.6 Calculating Type II Error Probabilities: More About β*

6.7 Test of Hypothesis About a Population Variance


7. Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses

7.1 Identifying the Target Parameter

7.2 Comparing Two Population Means: Independent Sampling

7.3 Comparing Two Population Means: Paired Difference Experiments

7.4 Comparing Two Population Proportions: Independent Sampling

7.5 Determining the Sample Size

7.6 Comparing Two Population Variances: Independent Sampling


8. Design of Experiments and Analysis of Variance

8.1 Elements of a Designed Experiment

8.2 The Completely Randomized Design: Single Factor

8.3 Multiple Comparisons of Means

8.4 The Randomized Block Design

8.5 Factorial Experiments


9. Categorical Data Analysis

9.1 Categorical Data and the Multinomial Experiment

9.2 Testing Category Probabilities: One-Way Table

9.3 Testing Category Probabilities: Two-Way (Contingency) Table

9.4 A Word of Caution About Chi-Square Tests


10. Simple Linear Regression

10.1 Probabilistic Models

10.2 Fitting the Model: The Least Squares Approach

10.3 Model Assumptions

10.4 Assessing the Utility of the Model: Making Inferences about the Slope β1

10.5 The Coefficients of Correlation and Determination

10.6 Using the Model for Estimation and Prediction

10.7 A Complete Example


11. Multiple Regression and Model Building

11.1 Multiple Regression Models


PART I: First-Order Models with Quantitative Independent Variables

11.2 The First-Order Model: Estimating and Making Inferences about the β-Parameters

11.3 Evaluating Overall Model Utility

11.4 Using the Model for Estimation and Prediction



PART II: Model Building in Multiple Regression

11.5 Model Building: Interaction Models

11.6 Model Building: Quadratic and other Higher-Order Models

11.7 Model Building: Qualitative (Dummy) Variable Models

11.8 Model Building: Models with both Quantitative and Qualitative Variables

11.9 Model Building: Comparing Nested Models

11.10 Model Building: Stepwise Regression



PART III: Multiple Regression Diagnostics

11.11 Residual Analysis: Checking the Regression Assumptions

11.12 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation



12. Methods for Quality Improvement: Statistical Process Control

12.1 Quality, Processes, and Systems

12.2 Statistical Control

12.3 The Logic of Control Charts

12.4 A Control Chart for Monitoring the Mean of a Process: The x-Chart

12.5 A Control Chart for Monitoring the Variation of a Process: The R-Chart

12.6 A Control Chart for Monitoring the Proportion of Defectives Generated by a Process: The p-Chart

12.7 Diagnosing the Causes of Variation

12.8 Capability Analysis



13. Time Series: Descriptive Analyses, Models, and Forecasting (Available on CD)

13.1 Descriptive Analysis: Index Numbers

13.2 Descriptive Analysis: Exponential Smoothing

13.3 Time Series Components

13.4 Forecasting: Exponential Smoothing

13.5 Forecasting Trends: The Holt’s Method

13.6 Measuring Forecast Accuracy: MAD and RMSE

13.7 Forecasting Trends: Simple Linear Regression

13.8 Seasonal Regression Models

13.9 Autocorrelation and the Durbin-Watson Test



14. Nonparametric Statistics (available on CD)

14.1 Introduction: Distribution-Free Tests

14.2 Single Population Inferences

14.3 Comparing Two Populations: Independent Samples

14.4 Comparing Two Populations: Paired Difference Experiment

14.5 Comparing Three or More Populations: Completely Randomized Design

14.6 Comparing Three or More Populations: Randomized Block Design

14.7 Rank Correlation

* Optional Topic

Appendix A Basic Counting Rules

Appendix B Tables
Appendix C Calculation Formulas for Analysis of Variance

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