Applied Regression Analysis for Business and Economics.
T. E. Dielman
Abstract
T. E. Dielman
Abstract
1. AN INTRODUCTION TO REGRESSION ANALYSIS 2. REVIEW OF BASIC STATISTICAL CONCEPTS Introduction / Descriptive Statistics / Discrete Random Variables and Probability Distributions / The Normal Distribution / Populations, Samples, and Sampling Distributions / Estimating a Population Mean / Hypothesis Tests about a Population Mean / Estimating the Difference Between Two Population Means / Hypothesis Tests about the Difference Between Two Population Means / Using the Computer 3. SIMPLE REGRESSION ANALYSIS Using Regression Analysis to Describe a Linear Relationship / Examples of Regression as a Descriptive Technique / Inferences from a Simple Regression Analysis / Assessing the Fit of the Regression Line / Prediction or Forecasting with a Simple Linear Regression Equation / Fitting a Linear Trend to Time-Series Data / Some Cautions in Interpreting Regression Results / Using the Computer 4. MULTIPLE REGRESSION ANALYSIS Using Multiple Regression to Describe a Linear Relationship / Inferences from a Multiple Regression Analysis / Assessing the Fit of the Regression Line / Comparing Two Regression Models / Prediction with a Multiple Regression Equation / Lagged Variables as Explanatory Variables in Time-Series Regression / Using the Computer 5. FITTING CURVES TO DATA Introduction / Fitting a Curvilinear Relationship / Using the Computer 6. ASSESSING THE ASSUMPTIONS OF THE REGRESSION MODEL Introduction / Assumptions of the Multiple Linear Regression Model / The Regression Residuals / Assessing the Assumption That the Relationship is Linear / Assessing the Assumption That the Variance Around the Regression Line is Constant / Assessing the Assumption That the Disturbances Are Normally Distributed / Influential Observations / Assessing the Assumption That the Disturbances Are Independent / Multicollinearity / Using the Computer 7. USING INDICATOR AND INTERACTION VARIABLES Using and Interpreting Indicator Variables / Interaction Variables / Seasonal Effects in Time-Series Regression / Using the Computer 8. VARIABLE SELECTION Introduction to Variable Selection / All-Possible Regressions / Other Variable Selection Techniques / Which Variable Selection Procedure is Best? / Using the Computer 9. INTRODUCTION TO ANALYSIS OF VARIANCE One-Way Analysis of Variance / Analysis of Variance Using a Randomized Block Design / Two-Way Analysis of Variance / Analysis of Covariance / Using the Computer. (Part contents).
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1. AN INTRODUCTION TO REGRESSION ANALYSIS 2. REVIEW OF BASIC STATISTICAL CONCEPTS Introduction / Descriptive Statistics / Discrete Random Variables and Probability Distributions / The Normal Distribution / Populations, Samples, and Sampling Distributions / Estimating a Population Mean / Hypothesis Tests about a Population Mean / Estimating the Difference Between Two Population Means / Hypothesis Tests about the Difference Between Two Population Means / Using the Computer 3. SIMPLE REGRESSION ANALYSIS Using Regression Analysis to Describe a Linear Relationship / Examples of Regression as a Descriptive Technique / Inferences from a Simple Regression Analysis / Assessing the Fit of the Regression Line / Prediction or Forecasting with a Simple Linear Regression Equation / Fitting a Linear Trend to Time-Series Data / Some Cautions in Interpreting Regression Results / Using the Computer 4. MULTIPLE REGRESSION ANALYSIS Using Multiple Regression to Describe a Linear Relationship / Inferences from a Multiple Regression Analysis / Assessing the Fit of the Regression Line / Comparing Two Regression Models / Prediction with a Multiple Regression Equation / Lagged Variables as Explanatory Variables in Time-Series Regression / Using the Computer 5. FITTING CURVES TO DATA Introduction / Fitting a Curvilinear Relationship / Using the Computer 6. ASSESSING THE ASSUMPTIONS OF THE REGRESSION MODEL Introduction / Assumptions of the Multiple Linear Regression Model / The Regression Residuals / Assessing the Assumption That the Relationship is Linear / Assessing the Assumption That the Variance Around the Regression Line is Constant / Assessing the Assumption That the Disturbances Are Normally Distributed / Influential Observations / Assessing the Assumption That the Disturbances Are Independent / Multicollinearity / Using the Computer 7. USING INDICATOR AND INTERACTION VARIABLES Using and Interpreting Indicator Variables / Interaction Variables / Seasonal Effects in Time-Series Regression / Using the Computer 8. VARIABLE SELECTION Introduction to Variable Selection / All-Possible Regressions / Other Variable Selection Techniques / Which Variable Selection Procedure is Best? / Using the Computer 9. INTRODUCTION TO ANALYSIS OF VARIANCE One-Way Analysis of Variance / Analysis of Variance Using a Randomized Block Design / Two-Way Analysis of Variance / Analysis of Covariance / Using the Computer. (Part contents).
Key concepts: Regression diagnostic, Regression analysis, Statistics, Proper linear model, Linear regression, Segmented regression, Multicollinearity, Simple linear regression