Lecture 18 2010

Lecture 18 2010 - IV. Multiple Regression. A. Introduction...

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1 IV. Multiple Regression . A. Introduction B. CRM C. Estimation D. Interpretation of Parameter Estimates E. Properties of Estimators. F. Estimator for σ 2 and Variances for G. Inferences in Multiple Regression H. Goodness of Fit I. Analysis of Variance ˆ s β H. Goodness of Fit–R 2 1. Multiple Coefficient of Determination–R 2 2. Calculation–exactly the same. R 2 = ESS / TSS = 1–(R SS / TSS) 3 Adjusted R 2 3. Adjusted R : 2 1 1 1 RSS n R TSS n K ⎛⎞ =− ⎜⎟ −− ⎝⎠ R 2 – used for assessing how well a model fits. Adj. R 2 – used to compare two models . Same sample – same Y values. I. Analysis of Variance–F tests. 1. Introduction: •R 2 –measures how much variation is explained by the regression. • Suppose R 2 = 0.12. The regression explains 12% of the variation in Y. •I s 12% a significant portion of the variation in Y? F tests : statistical tests that determine whether the amount of variation explained is statistically significant.
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2 2. F tests: Always compare two models.
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This note was uploaded on 12/08/2011 for the course ECON 312 taught by Professor Daniellass during the Winter '10 term at UMass (Amherst).

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Lecture 18 2010 - IV. Multiple Regression. A. Introduction...

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