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This week’s assignment asks you to test a basic linear regression model for the association between your primary explanatory variable and a response variable, and to create a blog entry describing your results.
Data preparation for this assignment:
1) If your explanatory 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 (next week you’ll learn how to analyze categorical explanatory variable with more than 2 categories).
2) 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 (for example, “strongly disagree to strongly agree”, “never to often”) can be considered quantitative for the assignment.
1) If you have a categorical explanatory variable, make sure one of your categories is coded “0” and generate a frequency table for this variable to check your coding. If you have a quantitative explanatory variable, center it so that the mean = 0 (or really close to 0) by subtracting the mean, and then calculate the mean to check your centering.
2) Test a linear regression model and summarize the results in a couple of sentences. Make sure to include statistical results (regression coefficients and p-values) in your summary.
WHAT TO SUBMIT: Create a blog entry where you 1) post your program and output, and 2) post a frequency table for your (recoded) categorical explanatory variable or report the mean for your centered explanatory variable. 3) Write a few sentences describing the results of your linear regression analysis.
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 incomplete or inaccurate summary of results, but two points if the summary of results is complete and accurate). 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 your description of your frequency distributions.
The results of the linear regression model indicated that major depression (Beta=1.34, p=.0001) was significantly and positively associated with number of nicotine dependence symptoms.
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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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