Fitting and interpreting regression models: Logistic regression with categorical predictors

Learn how to fit a logistic regression model with a categorical predictor variable using factor-variable notation. It also shows how to test hypotheses about the parameters, estimate marginal predictions from the model, and graph those margins.
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[@papasam1062]

Thanks Chuck for this great video

[@fatenkashour8715]

very helpful, thanks

[@domillima]

Awesome video, very helpful. How would this change if you are running a multivariable logistic regression. When you do the marginal analysis, is that keeping the multivariate OR value of the covariate of interest or is it treating it as a univariate

[@statacorp]

When you include multiple covariates, the predictions are calculated with the other variables at their observed values. For example, if the model were instead *logistic highbp i.hlthstat age* , then the average predicted probabilities obtained with *margins hlthstat* would be computed with the observed values of age. For further questions, please email us at [e-mail].

[@yasamansoofian1775]

Thank you, could you please tell me how I can add standard deviation of my variables as error bars on my "scatter plot with my best fit line? I prepared my scatter plot showing the means of each variable, but I want to add the standard deviation for each variable as well

[@statacorp]

You can overlay a bar graph or range plot on top of your scatterplot with fitted values. Please type *help twoway* to learn more about overlaying plots, and *help graph bar* or *help twoway rcap* to learn more about bar graphs and range plots. Please email us at [e-mail] if you have any other questions.

[@kissakisasaki]

is the margins interpretation in percentage, or percentage points?

[@seniorpeepers]

probability so .3 = 30%

[@belachewbikoro]

Thank u