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Assignments Applied Regression Analysis and Other Multivariable
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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.

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.

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.

1. Trend analysis for net sales and net income using 21114 as the base year.

Net Sales 131634 155:913 191329 211299

Net Income 4:431} 5:22? 6:295 6:621

Trend Percentages

Net Sales 100% 120% 139%...

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This question was answered on: Feb 21, 2020

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