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This week’s assignment is to test a multiple regression model.
Data preparation for this assignment:
1) If your response variable is categorical, you will need to identify a quantitative variable in the data set that you can use as a response variable for this assignment. Variables with response scales with 4-5 values that represent a change in magnitude (eg, “strongly disagree to strongly agree”, “never to often”) can be considered quantitative for the assignment.
Write a blog entry that summarize in a few sentences 1) what you found in your multiple regression analysis. Discuss the results for the associations between all of your explanatory variables and your response variable. Make sure to include statistical results (Beta coefficients and p-values) in your summary. 2) Report whether your results supported your hypothesis for the association between your primary explanatory variable and the response variable. 3) Discuss whether there was evidence of confounding for the association between your primary explanatory and response variable (Hint: adding additional explanatory variables to your model one at a time will make it easier to identify which of the variables are confounding variables); and 4) generate the following regression diagnostic plots:
a) q-q plot
b) standardized residuals for all observations
c) leverage plot
d) Write a few sentences describing what these plots tell you about your regression model in terms of the distribution of the residuals, model fit, influential observations, and outliers.
What to Submit:
Submit the URL for your blog entry. The blog entry should include 1) the summary of your results that addresses parts 1-4 of the assignment, 2) the output from your multiple regression model, and 3) the regression diagnostic plots.
Your assessment will be based on the evidence you provide that you have completed all of the steps. When relevant, gradients in the scoring will be available to reward clarity (for example, you will get one point for submitting an inaccurate or incomplete description of your results, but two points if the description is accurate and complete). In all cases, consider that the peer assessing your work is likely not an expert in the field you are analyzing. You will be assessed equally on all parts of the assignment, and whether you post your output in your blog entry.
After adjusting for potential confounding factors (list them), major depression (Beta=1.34, p=.0001) was significantly and positively associated with number of nicotine dependence symptoms. Age was also significantly associated with nicotine dependence symptoms, such that older participants reported a greater number of nicotine dependence symptoms (Beta= 0.76, p=.025).
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This course is intended for audiences of all experiences who are interested in learning about new skills in a business context; there are no prerequisite courses.
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