1.) The following output was generated from conducting a forward multiple regression to identify which IVs (urban, birthrat, lnphone, and lnradio) predict lngdp. The data that were analyzed were from the SPSS country.sav data file.
a.) Evaluate the tolerance statistics. Is multicollinearity a problem?
b.) What variables create the model to predict lngdp? What statistics support your response?
c.) Is the model significant in predicting lngdp? Explain.
d.) What percentage of variance in lngdpis explained by the model?
e.) Write the regression equation for lngdp.
2.) This question utilizes the data set gss.sav, which can be downloaded from this Web site:
https://edhd.bgsu.edu/amm/datasets.html
You are interested in examing whether the variables shown here in brackets [years of age (age), hours worked per week (hrs1), years of education (educ), years of education for mother (maeduc), and years of education for father (paeduc)] are predictors of individual income (rincmdol). Complete the following steps to conduct this analysis.
a.) Conduct a preliminary regression to calculate Mahalanobis distance. Identify the ritical value for chi square. Conduct Explore to identify outliers. Which cases should be removed from further analysis?
b.) Create a scatterplot matrix. Can you assume linearity and normality?
c.) Conduct a preliminary regression to create a residual plot. Can you assume normality and homoscedasticity?
d.) Conduct multiple regression using the Enter method. Evaluate the tolerance statistics. Is multicollinearity a problem?
e.) Does the model significantly predict rincmdol? Explain.
f.) Which variables significantly predict rincmdol? Which variable is the best predictor of the DV?
g.) What percentage of variance in rincmdol is explained by the model?
h.) Write the regression equation for the standardized variables.
i.) Explain why the variables of mother's and father's education are not significant predictors of rincmdol?