Applied Regression Analysis: A Second Course in Business and Economic Statistics 4th edition

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• Chapter 1: An Introduction to Regression Analysis
  • 1.1: Introduction
  
  
• Chapter 2: Review of Basic Statistical Concepts
  • 2.1: Introduction
  • 2.2: Descriptive Statistics (2)
  • 2.3: Discrete Random Variables and Probability Distributions (5)
  • 2.4: The Normal Distribution (7)
  • 2.5: Populations, Samples, and Sample Distributions (7)
  • 2.6: Estimating a Population Mean (5)
  • 2.7: Hypothesis Tests About a Population Mean (8)
  • 2.8: Estimating the Difference Between Two Population Means (5)
  • 2.9: Hypothesis Tests About the Difference Between Two Population Means (8)
  • 2: Additional Exercises (24)
  
  
• Chapter 3: Simple Regression Analysis
  • 3.1: Using Simple Regression to Describe a Linear Relationship (2)
  • 3.2: Examples of Regression as a Descriptive Technique (1)
  • 3.3: Inferences from a Simple Regression Analysis (5)
  • 3.4: Assessing the Fit of the Regression Line (6)
  • 3.5: Prediction or Forecasting with a Simple Linear Regression Equation (4)
  • 3.6: Fitting a Linear Trend to Time-Series Data (1)
  • 3.7: Some Cautions in Interpreting Regression Results (1)
  • 3: Additional Exercises (22)
  
  
• Chapter 4: Multiple Regression Analysis
  • 4.1: Using Multiple Regression to Describe a Linear Relationship
  • 4.2: Inferences from a Multiple Regression Analysis
  • 4.3: Assessing the Fit of the Regression Line (3)
  • 4.4: Comparing Two Regression Models (3)
  • 4.5: Prediction with a Multiple Regression Equation
  • 4.6: Multicollinearity: A Potential Problem in Multiple Regression
  • 4.7: Lagged Variables as Explanatory Variables in Time-Series Regression (1)
  • 4: Additional Exercises (15)
  
  
• Chapter 5: Fitting Curves to Data
  • 5.1: Introduction
  • 5.2: Fitting Curvilinear Relationships (8)
  • 5: Additional Exercises
  
  
• Chapter 6: Assessing the Assumptions of the Regression Model
  • 6.1: Introduction
  • 6.2: Assumptions of the Multiple Linear Regression Model
  • 6.3: The Regression Residuals
  • 6.4: Assessing the Assumption That the Relationship Is Linear (2)
  • 6.5: Assessing the Assumption That the Variance Around the Regression Line is Constant (1)
  • 6.6: Assessing the Assumption That the Disturbances Are Normally Distributed
  • 6.7: Influential Observations (1)
  • 6.8: Assessing the Assumption That the Disturbances Are Independent (1)
  • 6: Additional Exercises (15)
  
  
• Chapter 7: Using Indicator and Interaction Variables
  • 7.1: Using and Interpreting Indicator Variables (2)
  • 7.2: Interaction Variables (2)
  • 7.3: Seasonal Effects in Time-Series Regression (1)
  • 7: Additional Exercises (11)
  
  
• Chapter 8: Variable Selection
  • 8.1: Introduction
  • 8.2: All Possible Regressions
  • 8.3: Other Variable Selection Techniques
  • 8.4: Which Variable Selection Procedure Is Best? (5)
  • 8: Additional Exercises
  
  
• Chapter 9: An Introduction to Analysis of Variance
  • 9.1: One-Way Analysis of Variance
  • 9.2: Analysis of Variance Using a Randomized Block Design
  • 9.3: Two-Way Analysis of Variance
  • 9.4: Analysis of Covariance
  • 9: Additional Exercises
  
  
• Chapter 10: Qualitative Dependent Variables: An Introduction to Discriminant Analysis and Logistic Regression
  • 10.1: Introduction
  • 10.2: Discriminant Analysis
  • 10.3: Logistic Regression (6)
  • 10: Additional Exercises
  
  
• Chapter 11: Forecasting Methods for Time-Series Data
  • 11.1: Introduction
  • 11.2: Naïve Forecasts
  • 11.3: Measuring Forecast Accuracy
  • 11.4: Moving Averages
  • 11.5: Exponential Smoothing
  • 11.6: Decomposition
  • 11: Additional Exercises
  
  
• A: Summation Notation
• B: Statistical Tables
• C: A Brief Introduction to Microsoft® Excel®, MINITAB®, and SAS®
• D: Matrices and Their Application to Regression Analysis