Step 1. Write Section 1 of the DAA. Provide a  context of the u07a1data.sav data set. Specifically, imagine that you  are a health researcher studying how well a measure of anxiety ( X1) and weight ( X2) predict systolic blood pressure ( YIn  Section 1 of the DAA, articulate your predictor variables, the outcome  variable, and the scales of measurement for each variable. Specify the  sample size of the data set.

Step 2. Write Section 2 of the DAA. Test the four  assumptions of multiple regression. Begin with SPSS output of the three  histograms on X1X 2, and and  provide visual interpretations of normality. Next, paste the SPSS  output of the scatter plot matrix and interpret it in terms of linearity  and bivariate outliers. Next, paste SPSS output of the zero-order  correlations (Pearson r) and interpret it to check the  multicollinearity assumption. Note: to test this assumption in SPSS, use  Analyze… Correlate… Bivariate Correlations to generate a two-tailed  test; do not use the default one-tailed test output from the Linear  Regression procedure. Finally, paste the SPSS plot of standardized  residuals (ZPRED = x-axis; ZRESID = y-axis) and interpret it to check the homoscedasticity assumption.

Step 3. Write Section 3 of the DAA. Specify a  research question for the overall regression model. Articulate a null  hypothesis and alternative hypothesis for the overall regression model.  Specify a research question for each predictor. Articulate the null  hypothesis and alternative hypothesis for each predictor. Specify the  alpha level.

Step 4. Write Section 4 of the DAA. Begin with a  brief statement reviewing assumptions. Next, paste the SPSS output for  the Model Summary. Report and R2; interpret R2 effect size. Next, paste the SPSS ANOVA output. Report the F test for R and interpret it against the null hypothesis. Next, paste the SPSS Coefficients output. For each predictor, report the b coefficient, the t  test results, including interpretation against the null hypothesis, the  semipartial squared correlation effect size, and the interpretation of  effect size. In your Interpretation section, following Table 11.1 on  page 460 of your Warner text, generate a table of Results for the  u07a1data.sav file that summarizes:

  • The means and standard deviations of each variable in the regression equation.
  • The zero-order (Pearson r) correlations among variables.
  • The y-intercept.
  • The coefficients of each predictor with notation of calculated p-values for rejecting the null hypothesis.
  • The β coefficients of each predictor.
  • The squared semipartial correlations of each predictor.
  • The values of RR2, and adjusted R2 with notation of p-values for rejecting the null hypothesis.

Step 5. Write Section 5 of the DAA. Discuss your  conclusions of the multiple regression as it relates to your stated  research questions for the overall regression model and the individual  predictors. Conclude with an analysis of the strengths and limitations  of multiple regression.