Contents
What regression analysis can you do if the response variable is binary?
Logistic regression is the statistical technique used to predict the relationship between predictors (our independent variables) and a predicted variable (the dependent variable) where the dependent variable is binary (e.g., sex , response , score , etc…).
Can you do logistic regression on continuous variables?
In logistic regression, as with any flavour of regression, it is fine, indeed usually better, to have continuous predictors. Given a choice between a continuous variable as a predictor and categorising a continuous variable for predictors, the first is usually to be preferred.
How to interpret binary independent variables in linear regression?
For linear regression, you would code the variables as dummy variables (1/0 for presence/absence) and interpret the predictors as “the presence of this variable increases your predicted outcome by its beta”. Your “Reality” variable with a beta of 2422.87 is suspect, despite a statistically significant p-value.
Why are binary categorical categories used in linear regression?
So, if we were to enter the variable sex into a linear regression model, the coded values of the two gender categories would be interpreted as the numerical values of each category. This would provide us with results that would not make sense, because for example, the sex Female does not have a value of 2.
How to calculate regression with categorical independent variables?
The regression equation takes the form of Y = bX + a, where b is the slope and gives the weight empirically assigned to an explanator, X is the explanatory variable, and a is the Y-intercept, and these values take on different meanings based on the coding system used. The choice of coding system does not affect the F or R2 statistics.
Can you use continuous variables in regression analysis?
In the previous two chapters, we have focused on regression analyses using continuous variables. However, it is possible to include categorical predictors in a regression analysis, but it requires some extra work in performing the analysis and extra work in properly interpreting the results.