1. Fit a regression model using SES and IQ to predict children’s kindergarten reading score. Report descriptive statistics (mean, standard deviation, and correlation), model fit, regression coefficients, regression equation. Interpret the result.
2. Use both hand calculation and SPSS to obtain the partial and semi-partial correlation coefficients for SES. Interpret the result.
3. Are there suppression effects Why and How?
4. Checking assumptions. Please check all 6 assumptions, for assumptions you cannot check, provide reasons.
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No. It’s unlikely. The suppression effect is caused by the presence of highly correlated covariates. In our case, SES and IQ are little correlation with the correlation coefficient of 0.0959.
We will never know that this simple linear multiple regression model is a correct model or not. It is always to check if the model may be wrong by checking the model fit measurements and/or significance of coefficient estimates. It is always good to try several specifications such as including quadratic and/or interaction terms to see whether models with those improve the regression fit or not statistically significantly. Some data transformation such as log-transformation may help improve the fit....
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