Furthermore the model see Parameter Estimates table above shows that for every

Furthermore the model see parameter estimates table

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Furthermore, the model (see Parameter Estimates table above) shows that for every unit increase in age, there is a 0.629 unit increase in the systolic blood pressure. In a similar way, for every unit increase in BMI, there is a 1.434 units increase in the systolic blood pressure. The result for sex shows that, if every other variable is kept constant, males have about 11.372 units’ higher systolic blood pressure than females.
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4. Discuss whether or not there is interaction (effect modification) between sex and age. (5 points) Age was statistically significant in the model until I added the interaction between age and sex (age * sex) to the model. Therefore, there is effect modification. Tests of Between-Subjects Effects Dependent Variable: Systolic Blood Pressure Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 38437.904 a 32 1201.185 2.582 .000 Intercept 4145437.900 1 4145437.900 8910.790 .000 sex 12007.643 1 12007.643 25.811 .000 age 28133.051 31 907.518 1.951 .002 Error 170734.096 367 465.216 Total 7010836.000 400 Corrected Total 209172.000 399 a. R Squared = .184 (Adjusted R Squared = .113) Tests of Between-Subjects Effects Dependent Variable: Systolic Blood Pressure Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 50594.384 a 60 843.240 1.803 .001 Intercept 3619127.005 1 3619127.005 7736.805 .000 sex 7086.002 1 7086.002 15.148 .000 age 18903.009 31 609.774 1.304 .134 sex * age 12156.479 28 434.160 .928 .574 Error 158577.616 339 467.781 Total 7010836.000 400 Corrected Total 209172.000 399 a. R Squared = .242 (Adjusted R Squared = .108)
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TO ANSWER THIS QUESTION YOU NEEDED TO PERFORM AND SHOW 3 REGRESSION 1) SEX ONLY 2) AGE ONLY, AND 3 )BOTH SEX AND AGE AND SEE HOW THE EFFECTS ARE CHANGING. (-4)
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III. Multiple Logistic Regression (25 points) Use the Final Exam – Linear and Logistic (SPSS document) dataset to assess the impact of sex, age, and BMI on the risk of coronary heart disease. 1. Conduct simple logistic regression of coronary heart disease and sex. (6 points) Model Summary Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square 1 483.271 a .058 .081 a. Estimation terminated at iteration number 4 because parameter estimates changed by less than . 001. Variables in the Equation B S.E. Wald df Sig. Exp(B) 95% C.I.for EXP(B) Lower Upper Step 1 a sex (1) -1.103 .234 22.319 1 .000 .332 .210 .524 Constant -.842 .117 51.978 1 .000 .431 a. Variable(s) entered on step 1: sex.
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2. Conduct a multiple logistic regression using SPSS to address the research question: What is the association between sex and coronary heart disease after controlling for age and BMI? (9 points) Model Summary Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square 1 449.583 a .134 .187 a. Estimation terminated at iteration number 5 because parameter estimates changed by less than . 001. Variables in the Equation B S.E. Wald df Sig. Exp(B) 95% C.I.for EXP(B) Lower Upper Step 1 a sex (1) -1.296 .254 25.993 1 .000 .274 .166 .450 age .051 .014 12.500 1 .000 1.052 1.023 1.082 bmi .125 .032 15.379 1 .000 1.133 1.064 1.206 Constant -6.342 1.015 39.038 1 .000 .002 a. Variable(s) entered on step 1: sex, age, bmi.
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3. Discuss how the addition of age and BMI in the model affected the association of sex and coronary heart disease using the Odds Ratios and confidence intervals in your output. (5 points) The addition of the new variables affected the relationship between sex and coronary heart disease. For example, the odds ratio for sex before adding BMI and age to the model is 0.332 and the confidence interval is (0.210, 0.524). However, after adding age and BMI to the
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