- 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
- 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)
- 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
- 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
- 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)
- 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
- 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
- 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)
- 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
- 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)
- 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
- Multiple Regression and Model Building