Statistics for Business and Economics, 13e (e-Book VS 12m)

For courses in Introductory Business Statistics.

Real Data. Real Decisions. Real Business.

Now in its 13th Edition, Statistics for Business and Economics introduces statistics in the context of contemporary business. Emphasizing statistical literacy in thinking, the text applies its concepts with real data and uses technology to develop a deeper conceptual understanding. Examples, activities, and case studies foster active learning in the classroom while emphasizing intuitive concepts of probability and teaching students to make informed business decisions. The 13th Edition continues to highlight the importance of ethical behavior in collecting, interpreting, and reporting on data, while also providing a wealth of new and updated exercises and case studies.

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 (Optional)

   1.5. Types of Data

   1.6. Collecting Data: Sampling and Related Issues

   1.7. Business Analytics: Critical Thinking with Statistics

   Statistics in Action: A 20/20 View of Surveys: Fact or Fiction?

   Activity 1.1: Keep the Change: Collecting Data

   Activity 1.2: Identifying Misleading Statistics

   Using Technology: Accessing and Listing Data; Random Sampling

    

2. Methods for Describing Sets of Data

    

   2.1. Describing Qualitative Data

   2.2. Graphical Methods for Describing Quantitative Data

   2.3. Numerical Measures of Central Tendency

   2.4. Numerical Measures of Variability

   2.5. Using the Mean and Standard Deviation to Describe Data

   2.6. Numerical Measures of Relative Standing

   2.7. Methods for Detecting Outliers: Box Plots and z-Scores

   2.8. Graphing Bivariate Relationships (Optional)

   2.9. The Time Series Plot (Optional)

   2.10. Distorting the Truth with Descriptive Techniques

   Statistics in Action: Can Money Buy Love?

   Activity 2.1: Real Estate Sales

   Activity 2.2: Keep the Change: Measures of Central Tendency and Variability

   Using Technology: Describing Data

   Making Business Decisions: The Kentucky Milk Case—Part I (Covers Chapters 1 and 2)

    

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. Bayes’s Rule

   Statistics in Action: Lotto Buster!

   Activity 3.1: Exit Polls: Conditional Probability

   Activity 3.2: Keep the Change: Independent Events

   Using Technology: Combinations and Permutations

    

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 Distribution

   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. Other Continuous Distributions: Uniform and Exponential

   Statistics in Action: Probability in a Reverse Cocaine Sting: Was Cocaine Really Sold?

   Activity 4.1: Warehouse Club Memberships: Exploring a Binomial Random Variable

   Activity 4.2: Identifying the Type of Probability Distribution

   Using Technology: Discrete Probabilities, Continuous Probabilities, and Normal Probability Plots

    

5. Sampling Distributions

    

   5.1. The Concept of a Sampling Distribution

   5.2. Properties of Sampling Distributions: Unbiasedness and Minimum Variance

   5.3. The Sampling Distribution of the Sample Mean and the Central Limit Theorem

   5.4. The Sampling Distribution of the Sample Proportion

   Statistics in Action: The Insomnia Pill: Is It Effective?

   Activity 5.1: Simulating a Sampling Distribution—Cell Phone Usage

   Using Technology: Simulating a Sampling Distribution

   Making Business Decisions: The Furniture Fire Case (Covers Chapters 3–5)

    

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

    

   6.1. Identifying and Estimating the Target Parameter

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

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

   6.4. Large-Sample Confidence Interval for a Population Proportion

   6.5. Determining the Sample Size

   6.6. Finite Population Correction for Simple Random Sampling (Optional)

   6.7. Confidence Interval for a Population Variance (Optional)

   Statistics in Action: Medicare Fraud Investigations

   Activity 6.1: Conducting a Pilot Study

   Using Technology: Confidence Intervals

    

7. Inferences Based on a Single Sample: Tests of Hypotheses

    

   7.1. The Elements of a Test of Hypothesis

   7.2. Formulating Hypotheses and Setting Up the Rejection Region

   7.3. Observed Significance Levels: p-Values

   7.4. Test of Hypothesis About a Population Mean: Normal (z) Statistic

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

   7.6. Large-Sample Test of Hypothesis About a Population Proportion

   7.7. Test of Hypothesis About a Population Variance

   7.8. Calculating Type II Error Probabilities: More About b (Optional)

   Statistics in Action: Diary of a Kleenex® User—How Many Tissues in a Box?

   Activity 7.1: Challenging a Company’s Claim: Tests of Hypotheses

   Activity 7.2: Keep the Change: Tests of Hypotheses

   Using Technology: Tests of Hypotheses

    

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

    

   8.1. Identifying the Target Parameter

   8.2. Comparing Two Population Means: Independent Sampling

   8.3. Comparing Two Population Means: Paired Difference Experiments

   8.4. Comparing Two Population Proportions: Independent Sampling

   8.5. Determining the Required Sample Size

   8.6. Comparing Two Population Variances: Independent Sampling

   Statistics in Action: ZixIt Corp. v. Visa USA Inc.—A Libel Case

   Activity 8.1: Box Office Receipts: Comparing Population Means

   Activity 8.2: Keep the Change: Inferences Based on Two Samples

   Using Technology: Two-Sample Inferences

   Making Business Decisions: The Kentucky Milk Case—Part II (Covers Chapters 6–8)

    

9. Design of Experiments and Analysis of Variance

    

   9.1. Elements of a Designed Experiment

   9.2. The Completely Randomized Design: Single Factor

   9.3. Multiple Comparisons of Means

   9.4. The Randomized Block Design

   9.5. Factorial Experiments: Two Factors

   Statistics in Action: Tax Compliance Behavior—Factors That Affect Your Level of Risk Taking When F

   Activity 9.1: Designed vs. Observational Experiments

   Using Technology: Analysis of Variance

    

10. Categorical Data Analysis

     

    10.1. Categorical Data and the Multinomial Experiment

    10.2. Testing Category Probabilities: One-Way Table

    10.3. Testing Category Probabilities: Two-Way (Contingency) Table

    10.4. A Word of Caution About Chi-Square Tests

    Statistics in Action: The Illegal Transplant Tissue Trade—Who Is Responsible for Paying Damages?

    Activity 10.1: Binomial vs. Multinomial Experiments

    Activity 10.2: Contingency Tables

    Using Technology: Chi-Square Analyses

    Making Business Decisions: Discrimination in the Workplace (Covers Chapters 9–10)

     

11. Simple Linear Regression

     

    11.1. Probabilistic Models

    11.2. Fitting the Model: The Least Squares Approach

    11.3. Model Assumptions

    11.4. Assessing the Utility of the Model: Making Inferences About the Slope b1

    11.5. The Coefficients of Correlation and Determination

    11.6. Using the Model for Estimation and Prediction

    11.7. A Complete Example

    Statistics in Action: Legal Advertising—Does It Pay?

    Activity 11.1: Applying Simple Linear Regression to Your Favorite Data

    Using Technology: Simple Linear Regression

     

12. Multiple Regression and Model Building

12.1. Multiple Regression Models

Part I: First-Order Models with Quantitative Independent Variables

12.2. Estimating and Making Inferences About the b Parameters

12.3. Evaluating Overall Model Utility

12.4. Using the Model for Estimation and Prediction

Part II: Model Building in Multiple Regression

12.5. Interaction Models

12.6. Quadratic and Other Higher-Order Models

12.7. Qualitative (Dummy) Variable Models

12.8. Models with Both Quantitative and Qualitative Variables

12.9. Comparing Nested Models

12.10. Stepwise Regression

Part III: Multiple Regression Diagnostics

12.11. Residual Analysis: Checking the Regression Assumptions

12.12. Some Pitfalls: Estimability, Multicollinearity, and Extrapolation

Statistics in Action: Bid Rigging in the Highway Construction Industry

Activity 12.1: Insurance Premiums: Collecting Data for Several Variables

Activity 12.2: Collecting Data and Fitting a Multiple Regression Model

Using Technology: Multiple Regression

Making Business Decisions: The Condo Sales Case (Covers Chapters 11–12)

Appendix A: Summation Notation

Appendix B: Basic Counting Rules

Appendix C: Calculation Formulas for Analysis of Variance

C.1. Formulas for the Calculations in the Completely Randomized Design

C.2. Formulas for the Calculations in the Randomized Block Design

C.3. Formulas for the Calculations for a Two-Factor Factorial Experiment

C.4. Tukey’s Multiple Comparisons Procedure (Equal Sample Sizes)

C.5. Bonferroni Multiple Comparisons Procedure (Pairwise Comparisons)

C.6. Scheffé’s Multiple Comparisons Procedure (Pairwise Comparisons)

Appendix D: Tables

Table I: Binomial Probabilities

Table II: Normal Curve Areas

Table III: Critical Values of t

Table IV: Critical Values of x2

Table V: Percentage Points of the F-Distribution, a = .10

Table VI: Percentage Points of the F-Distribution, a = .05

Table VII: Percentage Points of the F-Distribution, a = .025

Table VIII: Percentage Points of the F-Distribution, a = .01

Table IX: Control Chart Constants

Table X: Critical Values for the Durbin-Watson d-Statistic, a = .05

Table XI: Critical Values for the Durbin-Watson d-Statistic, a = .01

Table XII: Critical Values of TL and TU for the Wilcoxon Rank Sum Test: Independent Samples

Table XIII: Critical Values of T0 in the Wilcoxon Paired Difference Signed Rank Test

Table XIV: Critical Values of Spearman’s Rank Correlation Coefficient

Table XV: Critical Values of the Studentized Range, a = .05

Answers to Selected Exercises

Selected Formulas

Index