March 7, 2006Lecture 8bSlide #1 Class Analysis Review Predict expected temperature change (c4_34_tc), using the following independent variables: –Age (c5_3_age)

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Presentation transcript:

March 7, 2006Lecture 8bSlide #1 Class Analysis Review Predict expected temperature change (c4_34_tc), using the following independent variables: –Age (c5_3_age) –Gender (c5_4_gen) –Ideology (c4_1_ide)) –Fragile nature (c4_2_nat) –US or EU (recoded: usa_c) Run the model Evaluate the Output Draw Initial Conclusions

March 7, 2006Lecture 8bSlide #2 Model Fit K degrees of freedom Multiple R

March 7, 2006Lecture 8bSlide #3 Estimated Coefficients Standardized coefficients indicate relative magnitudes of effect

March 7, 2006Lecture 8bSlide #4 Residual Distribution

March 7, 2006Lecture 8bSlide #5 Residuals by Predicted Values

March 7, 2006Lecture 8bSlide #6 Linearity Assumption Differential effects: Age is more likely to have a “cohort” effect than a maturation effect –Possibly non-linear? Depression; Boomers; Xers… –Polynomial expression: Use X and X 2 (as in any quadratic formula) –Hypothesis test Recode Re-run analysis

March 7, 2006Lecture 8bSlide #7 Non-linear Age Hypotheses

March 7, 2006Lecture 8bSlide #8 Heteroscedasticity

March 7, 2006Lecture 8bSlide #9 Outlier Analysis

March 7, 2006Lecture 8bSlide #10 Conclusions? Model fit? –Better than using mean? –Percent variance explained? Hypothesis tests? Magnitude of estimated coefficients? Residual analysis –Linearity? –Homoscedasticity? –Outliers?