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This week’s assignment is to test a logistic regression model.
Data preparation for this assignment:
1) If your response variable is categorical with more than two categories, you will need to collapse it down to two categories, or subset your data to select observations from 2 categories.
2) If your response variable is quantitative, you will need to bin it into two categories.
Write a blog entry that summarize in a few sentences 1) what you found, making sure you discuss the results for the associations between all of your explanatory variables and your response variable. Make sure to include statistical results (odds ratios, p-values, and 95% confidence intervals for the odds ratios) in your summary. 2) Report whether or not your results supported your hypothesis for the association between your primary explanatory variable and your response variable. 3) Discuss whether or not there was evidence of confounding for the association between your primary explanatory and the 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).
What to Submit:
Write a blog entry and submit the URL for your blog. Your blog entry should include 1) the summary of your results that addresses parts 1-3 of the assignment, 2) the output from your logistic regression model.
Example of how to write logistic regression results:
After adjusting for potential confounding factors (list them), the odds of having nicotine dependence were more than two times higher for participants with major depression than for participants without major depression (OR=2.36, 95% CI = 1.44-3.81, p=.0001). Age was also significantly associated with nicotine dependence, such that older older participants were significantly less likely to have nicotine dependence (OR= 0.81, 95% CI=0.40-0.93, p=.041).
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 program and output.
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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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