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Notes 5: Simple Linear Regression

Notes 5: Simple Linear Regression. 1. The Simple Linear Regression Model 2. Estimates and Plug-in Prediction 3. Confidence Intervals and Hypothesis Tests 4. Fits, residuals, and R-squared. 1. The Simple Linear Regression Model. price: thousands of dollars

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Multiple Regression Analysis

Multiple Regression Analysis. y = b 0 + b 1 x 1 + b 2 x 2 + . . . b k x k + u 2. Inference. Assumptions of the Classical Linear Model (CLM). So far, we know that given the Gauss-Markov assumptions, OLS is BLUE,

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Random Variables and Probability Distributions

Random Variables and Probability Distributions. Random Variables - Random responses corresponding to subjects randomly selected from a population. Probability Distributions - A listing of the possible outcomes and their probabilities (discrete r.v. s ) or their densities (continuous r.v. s )

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Notes 5: Simple Linear Regression

Multiple Regression Analysis

Random Variables and Probability Distributions