# linear regression: evaluating regression models overview assumptions for linear regression...

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LINEAR REGRESSION: Evaluating Regression Models

Post on 19-Dec-2015

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• Slide 1
• Slide 2
• LINEAR REGRESSION: Evaluating Regression Models
• Slide 3
• Overview Assumptions for Linear Regression Evaluating a Regression Model
• Slide 4
• Assumptions for Bivariate Linear Regression Quantitative data (or dichotomous) Independent observations Predict for same population that was sampled
• Slide 5
• Assumptions for Bivariate Linear Regression Linear relationship Examine scatterplot Homoscedasticity equal spread of residuals at different values of predictor Examine ZRESID vs ZPRED plot
• Slide 6
• Checking for Homoscedasticity
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• Assumptions for Bivariate Linear Regression Independent errors Durbin Watson should be close to 2 Normality of errors Examine frequency distribution of residuals
• Slide 8
• Checking for Normality of Errors
• Slide 9
• Influential Cases Influential cases have greater impact on the slope and y-intercept Select casewise diagnostics and look for cases with large residuals
• Slide 10
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• Standard Error of the Estimate Index of how far off predictions are expected to be Larger r means smaller standard error Standard deviation of y scores around predicted y scores
• Slide 12
• Sums of Squares Total SS total squared differences of Y scores from the mean of Y Model SS total squared differences of predicted Y scores from the mean of Y Residual SS total squared differences of Y scores from predicted Y scores
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• Coefficient of Determination r 2 is the proportion of variance in Y explained by X Adjusted r 2 corrects for the fact that the r 2 often overestimates the true relationship. Adjusted r 2 will be lower when there are fewer subjects.
• Slide 17
• Goodness of Fit Dividing the Model SS by the Total SS produces r 2 The ANOVA F-test determines whether the regression equation accounted for a significant proportion of variance in Y F is the Model Mean Square divided by the Residual Mean Square
• Slide 18
• Coefficients The Constant B under unstandardized is the y-intercept b 0 The B listed for the X variable is the slope b 1 The t test is the coefficient divided by its standard error The standardized slope is the same as the correlation
• Slide 19
• Example of Reporting a Regression Analysis The linear regression for predicting quiz enjoyment from level of statistics anxiety did not account of a significant portion of variance, F(1, 24) = 1.75, p =.20, r 2 =.07.
• Slide 20
• Take-Home Points The validity of a regression procedure depends on multiple assumptions. A regression model can be evaluated based on whether and how well it predicts an outcome variable.