Run the appropriate regression (linear regression or logistic regression) using only your main explanatory and response variable. You should compare the results of this regression to your findings in Project Component H.
Optional (encouraged): Construct a linear regression or logistic regression plot to accompany your model. The code for this can be found in graphing resources.
- Example of how to write results for Simple Regression: Remember use this model when your response variable is quantitative. Suppose that our research question is: Does major depression significantly relate to number of nicotine dependence symptoms? That is, my response variable is quantitative.
- Major depression (Beta=1.34, CI 1.29-1.39, p=.0001) was significantly and positively associated with number of nicotine dependence symptoms. On average, someone with major depression is expected to have 1.34 additional symptoms more than someone without major depression.
- Example of how to write results for Logistic Regression: Remember use this model when your response variable is binary categorical. Suppose that our research question is: Does major depression significantly relate to whether someone has nicotine dependence? Notice that in this version – the response variable is binary categorical – the participant either has nicotine dependence (1) or does not (0).
- Major depression (O.R. 4.0, CI 2.94-5.37) is significantly associated with the likelihood of meeting criteria for nicotine dependence. Those with major depression have an expected odds of nicotine dependence that is 4 times higher than those without major depression.