I need help interpreting the residual plot and model diagnostics. I built a model for number of ticket sales for an event. so the dependent variable is a continuous variable. Below is how the dependent variable looks like.
I ran linear regression and upon doing validation for regression assumption.
Test of normality
From test of normality, histogram of dependent variable and boxplot of dependent variable, it does seem like it's skewed to right. So my question is will log transformation of dependent variable help for handling skewness of data?
Test of linearity and heteroskedasticity
While I do understand that both are violated, but unable to interpret this further and how to proceed further.
VIF of the intercept is 86. One of the reasons can be very few observations in a dummy variable, which is the case for 19 independent variables which identifies buyer demographics and psychological buyer behavior categories. Also, some independent variables have VIF as inf, which means perfect collinearity between variables. Some variables are closely related and i can take these down, no problem.
Can anyone help me get some direction by diagnose the residual plot and point out what I should do next by using this information?