FinalSheet

FinalSheet - Regression Stata Output (Lecture 30): - R^2=...

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Regression Stata Output (Lecture 30): - R^2= accounts for X% variation in model - Root MSE=+/-2Se (Range or interval of variability of a specific calculated variable) - If significant use model to predict specific values o Input into equation - Or bound predictions (Prediction intervals) o (output) +/- 1.96 (Root MSE) Prediction Intervals (Lecture 30): - Calculating specific predicted values is less accurate than calculating average values - Account for Epsilon (Random Noise) in Prediction intervals Multiple Regression (Lectures 30 & 31): - Described by “k” independent variables - Slope of a given variable is the change in Y given a unit change in X holding ALL OTHER VARIABLES CONSTANT - Se=sqrt(SSE/(n-k-1)) - SST= SSR + SSE - R^2= SSR/SST - Adding X variable increases R^2, decreases SSE (be careful about adding in random variables) - Accept variables with t-statistic> abs. value(1.96), P<.05 - Overall F-Test: o Hnull:B1=B2=…=Bk=0 o Ha: At least 1 B does not = 0 o F-Test Statistic= (SSR/k)/(SSE/(n-k-1))
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This note was uploaded on 04/05/2012 for the course STAT 104 taught by Professor Stanley during the Spring '08 term at Harvard.

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FinalSheet - Regression Stata Output (Lecture 30): - R^2=...

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