Statistics for Management and Economics 10th Edition by Gerald Keller 1285869648 9781285869643

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ISBN 10: 1285869648

ISBN 13: 9781285869643

Author: Gerald Keller

Emphasizing applications over calculation, worldwide-bestseller STATISTICS FOR MANAGEMENT AND ECONOMICS, ABBREVIATED 10e vividly illustrates how vital statistical methods and tools are for today’s managers‚àíand teaches you how to apply them to business problems. Using a proven three-step “ICI” approach to problem solving, the text demonstrates how to IDENTIFY the correct statistical technique by focusing on the problem objective and data type; how to COMPUTE the statistics by hand, using Excel¬Æ 2013, or using MINITAB 16; and how to INTERPRET results in the context of the problem. Incorporating various functional areas of business, data-driven examples, exercises, and cases give you hands-on practice applying statistical applications used by marketing managers, financial analysts, accountants, economists, and others. Completely current, the ABBREVIATED 10e also includes updated figure and data tables, MindTap Reader, and access to Data Analysis Plus via the textbook companion website.

Statistics for Management and Economics 10th Table of contents:

Chapter 1. What Is Statistics?

1-1. Key Statistical Concepts

1-1a. Population

1-1b. Sample

1-1c. Statistical Inference

1-2. Statistical Applications in Business

1-2a. Application Sections and Subsections

1-2b. Application Boxes

1-3. Large Real Data Sets

1-3a. General Social Survey

1-3b. American National Election Survey

1-4. Statistics and the Computer

1-4a. Excel Spreadsheets

Chapter Summary

Chapter Exercises

Appendix 1. Instructions for Keller’s website

Chapter 2. Graphical Descriptive Techniques I

2-1. Types of Data and Information

2-1a. Calculations for Types of Data

2-1b. Hierarchy of Data

2-1c. Interval, Ordinal, and Nominal Variables

2-1d. Problem Objectives and Information

2-2. Describing a Set of Nominal Data

2-2a. Other Applications of Pie Charts and Bar Charts

2-2b. Describing Ordinal Data

2-3. Describing the Relationship between Two Nominal Variables and Comparing Two or More Nominal Data Sets

2-3a. Tabular Method of Describing the Relationship between Two Nominal Variables

2-3b. Comparing Two or More Sets of Nominal Data

2-3c. Data Formats

Chapter Summary

Chapter Exercises

Chapter 3. Graphical Descriptive Techniques II

3-1. Graphical Techniques to Describe a Set of Interval Data

3-1a. Determining the Number of Class Intervals

3-1b. Shapes of Histograms

3-1c. Stem-and-Leaf Display

3-1d. Ogive

3-2. Describing Time-Series Data

3-2a. Line Chart

3-3. Describing the Relationship between Two Interval Variables

3-3a. Patterns of Scatter Diagrams

3-3b. Linearity

3-3c. Direction

3-3d. Interpreting a Strong Linear Relationship

3-4. Art and Science of Graphical Presentations

3-4a. Graphical Excellence

3-4b. Graphical Deception

Chapter Summary

Chapter Exercises

Chapter 4. Numerical Descriptive Techniques

4-1. Measures of Central Location

4-1a. Arithmetic Mean

4-1b. Median

4-1c. Mode

4-1d. Mean, Median, Mode: Which Is Best?

4-1e. Measures of Central Location for Ordinal and Nominal Data

4-2. Measures of Variability

4-2a. Range

4-2b. Variance

4-2c. Interpreting the Variance

4-2d. Standard Deviation

4-2e. Coefficient of Variation

4-2f. Measures of Variability for Ordinal and Nominal Data

4-2g. Approximating the Mean and Variance from Grouped Data

4-3. Measures of Relative Standing and Box Plots

4-3a. Locating Percentiles

4-3b. Interquartile Range

4-3c. Box Plots

4-3d. Measures of Relative Standing and Variability for Ordinal Data

4-4. Measures of Linear Relationship

4-4a. Covariance

4-4b. Coefficient of Correlation

4-4c. Comparing the Scatter Diagram, Covariance, and Coefficient of Correlation

4-4d. Least Squares Method

4-4e. Coefficient of Determination

4-4f. Interpreting Correlation

4-5. (Optional) Applications in Professional Sports: Baseball

4-5a. Drafting Players

4-5b. Free Agency

4-5c. Trading

4-5d. The Oakland Athletics (and a Statistics) Success Story

4-6. (Optional) Applications in Finance: Market Model

4-6a. Systematic and Firm-Specific Risk

4-7. Comparing Graphical and Numerical Techniques

4-8. General Guidelines for Exploring Data

Chapter Summary

Chapter Exercises

Appendix 4. Review of Descriptive Techniques

Chapter 5. Data Collection and Sampling

5-1. Methods of Collecting Data

5-1a. Direct Observation

5-1b. Experiments

5-1c. Surveys

5-2. Sampling

5-3. Sampling Plans

5-3a. Simple Random Sampling

5-3b. Stratified Random Sampling

5-3c. Cluster Sampling

5-3d. Sample Size

5-4. Sampling and Nonsampling Errors

5-4a. Sampling Error

5-4b. Nonsampling Error

Chapter Summary

Chapter 6. Probability

6-1. Assigning Probability to Events

6-1a. Three Approaches to Assigning Probabilities

6-1b. Defining Events

6-1c. Probability of Events

6-1d. Interpreting Probability

6-2. Joint, Marginal, and Conditional Probability

6-2a. Intersection

6-2b. Marginal Probability

6-2c. Conditional Probability

6-2d. Independence

6-2e. Union

6-3. Probability Rules and Trees

6-3a. Complement Rule

6-3b. Multiplication Rule

6-3c. Addition Rule

6-3d. Probability Trees

6-4. Bayes’s Law

6-4a. Bayes’s Law Formula (Optional)

6-4b. Applications in Medicine and Medical Insurance (Optional)

6-4c. Developing an Understanding of Probability Concepts

6-5. Identifying the Correct Method

6-5a. Joint Probabilities Are Given

6-5b. Joint Probabilities Are Required

Chapter Summary

Chapter Exercises

Chapter 7. Random Variables and Discrete Probability Distributions

7-1. Random Variables and Probability Distributions

7-1a. Discrete Probability Distributions

7-1b. Probability Distributions and Populations

7-1c. Describing the Population/Probability Distribution

7-1d. Laws of Expected Value and Variance

7-2. Bivariate Distributions

7-2a. Marginal Probabilities

7-2b. Describing the Bivariate Distribution

7-2c. Sum of Two Variables

7-3. (Optional) Applications in Finance: Portfolio Diversification and Asset Allocation

7-3a. Portfolio Diversification in Practice

7-3b. Portfolios with More Than Two Stocks

7-4. Binomial Distribution

7-4a. Binomial Random Variable

7-4b. Cumulative Probability

7-4c. Binomial Table

7-4d. Mean and Variance of a Binomial Distribution

7-5. Poisson Distribution

7-5a. Poisson Table

Chapter Summary

Chapter Exercises

Chapter 8. Continuous Probability Distributions

8-1. Probability Density Functions

8-1a. Uniform Distribution

8-1b. Using a Continuous Distribution to Approximate a Discrete Distribution

8-2. Normal Distribution

8-2a. Calculating Normal Probabilities

8-2b. Finding Values of Z

8-2c. Z A and Percentiles

8-3. (Optional) Exponential Distribution

8-4. Other Continuous Distributions

8-4a. Student t Distribution

8-4b. F Distribution

Chapter Summary

Chapter 9. Sampling Distributions

9-1. Sampling Distribution of the Mean

9-1a. Sampling Distribution of the Mean of Two Dice

9-1b. Creating the Sampling Distribution Empirically

9-1c. Using the Sampling Distribution for Inference

9-2. Sampling Distribution of a Proportion

9-2a. (Optional) Normal Approximation to the Binomial Distribution

9-2b. Omitting the Correction Factor for Continuity

9-2c. Approximate Sampling Distribution of a Sample Proportion

9-3. Sampling Distribution of the Difference between Two Means

9-4. From Here to Inference

Chapter Summary

Chapter 10. Introduction to Estimation

10-1. Concepts of Estimation

10-1a. Point and Interval Estimators

10-1b. Developing an Understanding of Statistical Concepts

10-2. Estimating the Population Mean When the Population Standard Deviation Is Known

10-2a. Interpreting the Confidence Interval Estimate

10-2b. Information and the Width of the Interval

10-2c. (Optional) Estimating the Population Mean Using the Sample Median

10-3. Selecting the Sample Size

10-3a. Error of Estimation

10-3b. Determining the Sample Size

Chapter Summary

Chapter 11. Introduction to Hypothesis Testing

11-1. Concepts of Hypothesis Testing

11-2. Testing the Population Mean When the Population Standard Deviation Is Known

11-2a. Rejection Region

11-2b. Standardized Test Statistic

11-2c. p-Value

11-2d. Interpreting the p-Value

11-2e. Describing the p-Value

11-2f. The p-Value and Rejection Region Methods

11-2g. Solving Manually, Using Excel, and Using Minitab

11-2h. Interpreting the Results of a Test

11-2i. One- and Two-Tail Tests

11-2j. When Do We Conduct One- and Two-Tail Tests?

11-2k. Testing Hypotheses and Confidence Interval Estimators

11-2l. Developing an Understanding of Statistical Concepts 1

11-2m. Developing an Understanding of Statistical Concepts 2

11-3. Calculating the Probability of a Type II Error

11-3a. Effect on β of Changing α

11-3b. Judging the Test

11-3c. Developing an Understanding of Statistical Concepts: Larger Sample Size Equals More Information Equals Better Decisions

11-3d. Power of a Test

11-3e. Operating Characteristic Curve

11-3f. Determining the Alternative Hypothesis to Define Type I and Type II Errors

11-4. The Road Ahead

11-4a. Problem Objectives

11-4b. Derivations

Chapter Summary

Chapter 12. Inference about a Population

12-1. Inference about a Population Mean When the Standard Deviation Is Unknown

12-1a. Checking the Required Conditions

12-1b. Estimating the Totals of Finite Populations

12-1c. Developing an Understanding of Statistical Concepts 1

12-1d. Developing an Understanding of Statistical Concepts 2

12-1e. Developing an Understanding of Statistical Concepts 3

12-2. Inference about a Population Variance

12-2a. Statistic and Sampling Distribution

12-2b. Testing and Estimating a Population Variance

12-2C. Checking the Required Condition

12-3. Inference about a Population Proportion

12-3a. Parameter

12-3b. Statistic and Sampling Distribution

12-3c. Testing and Estimating a Proportion

12-3d. Missing Data

12-3e. Estimating the Total Number of Successes in a Large Finite Population

12-3f. Selecting the Sample Size to Estimate the Proportion

12-3g. Wilson Estimators (Optional)

12-4. (Optional) Applications in Marketing: Market Segmentation

Chapter Summary

Chapter Exercises

Chapter 13. Inference about Comparing Two Populations

13-1. Inference about the Difference between Two Means: Independent Samples

13-1a. Decision Rule: Equal-Variances or Unequal-Variances t-Tests and Estimators

13-1b. Checking the Required Condition

13-1c. Violation of the Required Condition

13-1d. Data Formats

13-1e. Developing an Understanding of Statistical Concepts 1

13-1f. Developing an Understanding of Statistical Concepts 2

13-2. Observational and Experimental Data

13-3. Inference about the Difference between Two Means: Matched Pairs Experiment

13-3a. Estimating the Mean Difference

13-3b. Independent Samples or Matched Pairs: Which Experimental Design Is Better?

13-3c. Observational and Experimental Data

13-3d. Checking the Required Condition

13-3e. Violation of Required Condition

13-3f. Developing an Understanding of Statistical Concepts 1

13-3g. Developing an Understanding of Statistical Concepts 2

13-4. Inference about the Ratio of Two Variances

13-4a. Parameter

13-4b. Statistic and Sampling Distribution

13-4c. Testing and Estimating a Ratio of Two Variances

13-5. Inference about the Difference between Two Population Proportions

13-5a. Parameter

13-5b. Statistic and Sampling Distribution

13-5c. Testing and Estimating the Difference between Two Proportions

Chapter Summary

Chapter Exercises

Appendix 13. Review of Chapters 12 and 13

Chapter 14. Analysis of Variance

14-1. One-Way Analysis of Variance

14-1a. Checking the Required Conditions

14-1b. Violation of the Required Conditions

14-1c. Can We Use the t-Test of the Difference between Two Means Instead of the Analysis of Variance?

14-1d. Can We Use the Analysis of Variance Instead of the t-Test of μ 1 – μ 2 ?

14-1e. Relationship between the F-Statistic and the t-Statistic

14-1f. Developing an Understanding of Statistical Concepts

14-2. Multiple Comparisons

14-2a. Fisher’s Least Significant Difference Method

14-2b. Bonferroni Adjustment to LSD Method

14-2c. Tukey’s Multiple Comparison Method

14-2d. Which Multiple Comparison Method to Use

14-3. Analysis of Variance Experimental Designs

14-3a. Single-Factor and Multifactor Experimental Designs

14-3b. Independent Samples and Blocks

14-3c. Fixed and Random Effects

14-4. Randomized Block (Two-Way) Analysis of Variance

14-4a. Checking the Required Conditions

14-4b. Violation of the Required Conditions

14-4c. Criteria for Blocking

14-4d. Developing an Understanding of Statistical Concepts

14-5. Two-Factor Analysis of Variance

14-5a. How the Sums of Squares for Factors A and B and Interaction Are Computed

14-5b. Test for Differences in Number of Jobs between Men and Women

14-5c. Test for Differences in Number of Jobs between Education Levels

14-5d. Test for Interaction between Factors A and B

14-5e. What Is Interaction?

14-5f. Conducting the Analysis of Variance for the Complete Factorial Experiment

14-5g. Developing an Understanding of Statistical Concepts

14-6. (Optional) Applications in Operations Management: Finding and Reducing Variation

14-6a. Taguchi Loss Function

14-6b. Design of Experiments and Taguchi Methods

Chapter Summary

Chapter Exercises

Appendix 14. Review of Chapters 12, 13, 14

Chapter 15. Chi-Squared Tests

15-1. Chi-Squared Goodness-of-Fit Test

15-1a. Test Statistic

15-1b. Required Condition

15-2. Chi-Squared Test of a Contingency Table

15-2a. Graphical Technique

15-2b. Test Statistic

15-2c. Rejection Region and p-Value

15-2d. Rule of Five

15-2e. Data Formats

15-3. Summary of Tests on Nominal Data

15-3a. Developing an Understanding of Statistical Concepts

15-4. (Optional) Chi-Squared Test for Normality

15-4a. Class Intervals

15-4b. Interpreting the Results of a Chi-Squared Test for Normality

Chapter Summary

Chapter Exercises

Appendix 15. Review of Chapters 12, 13, 14, 15

Chapter 16. Simple Linear Regression and Correlation

16-1. Model

16-2. Estimating the Coefficients

16-3. Error Variable: Required Conditions

16-3a. Observational and Experimental Data

16-4. Assessing the Model

16-4a. Sum of Squares for Error

16-4b. Standard Error of Estimate

16-4c. Testing the Slope

16-4d. Estimator and Sampling Distribution

16-4e. One-Tail Tests

16-4f. Coefficient of Determination

16-4g. Other Parts of the Computer Printout

16-4h. Developing an Understanding of Statistical Concepts

16-4i. Cause-and-Effect Relationship

16-4j. Testing the Coefficient of Correlation

16-4k. Violation of the Required Condition

16-5. Using the Regression Equation

16-5a. Predicting the Particular Value of y for a Given x

16-5b. Estimating the Expected Value of y for a Given x

16-5c. Effect of the Given Value of x on the Intervals

16-6. Regression Diagnostics—I

16-6a. Residual Analysis

16-6b. Nonnormality

16-6c. Heteroscedasticity

16-6d. Nonindependence of the Error Variable

16-6e. Outliers

16-6f. Influential Observations

16-6g. Procedure for Regression Diagnostics

Chapter Summary

Chapter Exercises

Appendix 16. Review of Chapters 12, 13, 14, 15, 16

Chapter 17. Multiple Regression

17-1. Model and Required Conditions

17-2. Estimating the Coefficients and Assessing the Model

17-2a. Step 1: Select the Independent Variables That You Believe May Be Related to the Dependent Variable

17-2b. Step 2: Use a Computer to Compute All the Coefficients and Other Statistics

17-2c. Step 3: Assess the Model

17-2d. Standard Error of Estimate

17-2e. Coefficient of Determination

17-2f. Testing the Validity of the Model

17-2g. Interpreting the Coefficients

17-2h. Testing the Coefficients

17-2i. A Cautionary Note about Interpreting the Results

17-2j. t-Tests and the Analysis of Variance

17-2k. The F-Test and the t-Test in the Simple Linear Regression Model

17-2l. Using the Regression Equation

17-3. Regression Diagnostics—II

17-3a. Multicollinearity

17-4. Regression Diagnostics—III (Time Series)

17-4a. Durbin–Watson Test

17-4b. Developing an Understanding of Statistical Concepts

Chapter Summary

Chapter Exercises

Appendix 17. Review of Chapters 12, 13, 14, 15, 16, 17

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