Write Section 1 of the DAA. Provide a context of the u07a1data.sav data set. Specifically, imagine...

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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 ( Y) . In 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 X1, X 2, and Y 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 R 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

David · Accepted Answer

Step 1: 
My dependent variable is systolic blood pressure (Y). And independent variables are 
anxiety (X1) and weight (X2). All three variables are measured on ratio scale of measurement. 
The sample size for this data set is 25.
Step 2: 
Assumptions of regression analysis 
The graph of SBP is slightly skewed to the left. This implies there are very few variables 
with low values of SBP. The histogram for the same is shown below. 
 
 
The graph of Anxiety is slightly skewed to the left. This implies there are very few 
variables with low values of Anxiety. The histogram for the same is shown below.
 
The graph of Weight is slightly skewed to the right. This implies there are very few 
variables with high values of weight. The histogram for the same is shown below. 
 
  
The scatter plot between SBP, anxiety and weight is given below.
I observe that there is very weak positive linear relationship or almost no linear 
relationship between anxiety, SBP and weak positive linear relationship between SBP and 
weight. There are no outliers in the study.

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)...

Answer To: Write Section 1 of the DAA. Provide a context of the u07a1data.sav data set. Specifically, imagine...

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