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Applied Regression Analysis (Last revised: September 2006)

Applied Regression Analysis (Last revised: September 2006). 1. Linear Prediction and Least Squares 1.1 An example: Housing Data 1.2 Prediction in Minitab 1.3 Another Example 1.4 Fitted Values and Residuals 1.5 The Least Squares Criterion 1.6 Business Stats Review:

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Applied Regression Analysis (Last revised: September 2006)

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  1. Applied Regression Analysis (Last revised: September 2006)

  2. 1. Linear Prediction and Least Squares 1.1 An example: Housing Data 1.2 Prediction in Minitab 1.3 Another Example 1.4 Fitted Values and Residuals 1.5 The Least Squares Criterion 1.6 Business Stats Review: Descriptive Statistics 1.7 Intuition for the Least Squares Formulae 1.8 Properties of Fitted Values and Residuals 1.9 R-squared and the Anova Table

  3. 2. The Simple Linear Regression Model 2.1 How Good is the Prediction? 2.2 The “True” Line 2.3 The Simple Linear Regression (SLR) model 2.4 Understanding the SLR Model 2.5 Predictive Intervals Using the SLR Model 2.6 Other Types of Predictions

  4. 3. Estimating the Model Parameters 3.1 Review: the SLR Model 3.2 The Intercept and Slope Estimates 3.3 Estimating σ 3.4 Idea of a Sampling Distribution 3.5 The Sampling Distributions

  5. 4. Confidence Intervals, Hypothesis Tests, and Forecasting 4.1 Standard Errors and t Distributions 4.2 Confidence Intervals 4.3 Hypothesis Testing 4.4 p-Values 4.5 Statistical vs. Practical Significance 4.6 One-Sided Tests 4.7 Forecasting

  6. 5. Residual Diagnostics 5.1 “When you assume, you make an …” 5.2 An Important Initial Step: Plot your Data 5.3 A Tool for Ex-Post Checks: Standardized Residuals 5.4 Non-Linearity 5.5 Non-Constant Variance (Heteroskedasticity) 5.6 Non-Normality 5.7 Outliers 5.8 Time Dependence 5.9 Summary 5.A Minitab Appendix

  7. 6. Multiple Regression 6.1 Motivation 6.2 The Multiple Regression Model 6.3 Least-Squares Estimation 6.4 Explaining Multiple Regression 6.5 Residuals, Fitted Values, R2 6.6 Extending the SLR Results 6.7 The F-Test 6.8 Partial F-Tests 6.9 Other Tests

  8. 7. Topics in Multiple Regression 7.1 Standard Errors 7.2 Multicollinearity 7.3 Dummy Variables 7.4 Variable Interaction 7.5 Polynomials and Linear Splines 7.6 Transformations 7.7 Some Observations on Model Selection

  9. 8. Time Series 8.1 Time-Series Data and Dependence 8.2 Checking for Dependence 8.3 The Autocorrelation Function 8.4 The AR(1) Model 8.5 More on the AR(1) Model 8.6 Stock Prices 8.7 Other Time-Series Models

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