Editing Statistics and Statistical Programming (Winter 2021)/Problem set 16
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* You may want/need to convert some of these variables to appropriate types/classes in order to fit a logistic model. I also recommend at least turning <code>year</code> into a factor and creating a "centered" version of the <code>age</code> variable. Centering a variable means setting a new baseline by subtracting some amount from every value for a variable (often the mean of the variable) so that the new "centered" variable is 0 at the mean, negative below it, and positive above it. It's can make interpreting regressions much easier. We can discuss this in class too. | * You may want/need to convert some of these variables to appropriate types/classes in order to fit a logistic model. I also recommend at least turning <code>year</code> into a factor and creating a "centered" version of the <code>age</code> variable. Centering a variable means setting a new baseline by subtracting some amount from every value for a variable (often the mean of the variable) so that the new "centered" variable is 0 at the mean, negative below it, and positive above it. It's can make interpreting regressions much easier. We can discuss this in class too. | ||
* Be sure to state the alternative and null hypotheses related to the experimental treatment under consideration. | * Be sure to state the alternative and null hypotheses related to the experimental treatment under consideration. | ||
* It's a good idea to include the following in the presentation and interpretation of logistic model results: (1) a tabular summary/report of your fitted model including any goodness of fit statistics you can extract from R; (2) a transformation of the coefficient estimating treatment effects into an "odds ratio"; (3) model-predicted probabilities for prototypical study participants. (''please note that examples for all of these are provided in | * It's a good idea to include the following in the presentation and interpretation of logistic model results: (1) a tabular summary/report of your fitted model including any goodness of fit statistics you can extract from R; (2) a transformation of the coefficient estimating treatment effects into an "odds ratio"; (3) model-predicted probabilities for prototypical study participants. (''please note that examples for all of these are provided in [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/logistic_regression_interpretation.html Mako Hill's R tutorial on interpreting the results of logistic regression]]'') | ||
* For the model-predicted probabilities, please estimate the treatment effects for the following hypothetical individuals: Β | * For the model-predicted probabilities, please estimate the treatment effects for the following hypothetical individuals: Β | ||
** a 9-year old girl in 2015. | ** a 9-year old girl in 2015. |