Question

How would I go about this question using R studio?

In a multiple regression, investigate whether the categorical variable "Type" has a statistically significant interaction effect with any of the other covariates, A, B, C and D. Of those interactions that are statistically significant (if any), determine which one has the most impact on the model and add it to your model.

Please include general 'formulas' for the commands needed!

Answer 1

Answer:

For fitting multiple regression model in R studio with response variable Y and independent predictors as categorical variable "Type" and covariates A, B, C and D, we use the below code.

model01<-lm(Y~Type+A+B+C+D, data=data)

# For getting summary of the model, we use below command

summary(model01)

For checking whether, the categorical variable "Type" has a statistically significant interaction effect with any of the other covariates, A, B, C and D, code for linear model is modified as

model02<-lm(Y~Type+A+B+C+D+(Type*A)+(Type*B)+(Type*C)+(Type*D), data=data)

summary(model02) # summary of model

anova(model02)        #   will give ANOVA results of the fitted model.

The interaction or covariate with p-value<0.05 is considered as statistically significant.

Note: When you import data, the structure of categorical variable "Type" should be factor. If not, we need to use command as.factor(Type) to make it categorical factor.