Applied Regression Analysis and Other Multivariable Methods, 5th ed, Kleinbaum et. al.
Incorporate any generated computer output. Show all work.
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Get Help Now!Chapter 11: Confounding and Interaction Due ________________
Pages: 242–256 2, 3, 4
Problem 2:
Using the appropriate regression coefficient as your measure of association, determine whether confounding exists. Explain.
Suppose that confounding was defined to require a comparison of crude versus adjusted (partial) correlation coefficients. What conclusion would you draw?
Problem 3:
Using the appropriate regression coefficient as your measure of association, determine whether confounding exists. Explain.
Suppose that confounding was defined to require a comparison of crude versus adjusted (partial) correlation coefficients. What conclusion would you draw?
What does this example illustrate about using a test of the hypothesis H0: b2 = 0 in order to assess confounding.
Problem 4:
Fill in the following ANOVA table for the model .
Test if X2 should be entered into a model already containing X1.
Test if X3 should be entered into a model already containing X1 & X2.
Test if X2 & X3 should be entered into a model already containing X1.
Based upon the previous tests, what is the most appropriate regression model?
Based upon the information provided, can you assess whether X1 is a confounder of the X2–Y relationship? Explain.
Assignments
Applied Regression Analysis and Other Multivariable Methods, 5th ed, Kleinbaum et. al.
Incorporate any generated computer output. Show all work.
Chapter 11: Confounding and Interaction Due ________________
Pages: 242–256 2, 3, 4
Problem 2:
Using the appropriate regression coefficient as your measure of association, determine whether confounding exists. Explain.
Suppose that confounding was defined to require a comparison of crude versus adjusted (partial) correlation coefficients. What conclusion would you draw?
Problem 3:
Using the appropriate regression coefficient as your measure of association, determine whether confounding exists. Explain.
Suppose that confounding was defined to require a comparison of crude versus adjusted (partial) correlation coefficients. What conclusion would you draw?
What does this example illustrate about using a test of the hypothesis H0: b2 = 0 in order to assess confounding.
Problem 4:
Fill in the following ANOVA table for the model .
Test if X2 should be entered into a model already containing X1.
Test if X3 should be entered into a model already containing X1 & X2.
Test if X2 & X3 should be entered into a model already containing X1.
Based upon the previous tests, what is the most appropriate regression model?
Based upon the information provided, can you assess whether X1 is a confounder of the X2–Y relationship? Explain.
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