I am learning so bear with me.
Aim: I am trying to figure out if my data fit the criteria for multiple linear regression.
Context: My model has two numeric and four categoric variables.
kiva_model <- lm(lender_count ~ loan_usd+ #Numeric sector+ #Categoric 11 levels term_in_months+ #Numeric borrower_genders+ #Categoric 5 levels repayment_interval+ #Categoric 3 levels country_code, #Categoric 59 levels data=kiva_omit)
Sample size is 504,528.
I checked the correlation between the three numeric variables as follows:
loan_usd is a strong predictor or
I have made plots to assess: a) Linearity of numerical variables b) nearly normal residuals c) constant variability.
The images show the before (above) and after (below) of outlier removal using
I cannot figure out if this is acceptable enough to proceed with my analysis or if I have to further manipulate the data prior the final regression.
To summarise my main concerns:
- Conical shape in
- Slight S-shape in Normal probability plot.
- Strong line in Residuals vs. predicted.
a) Linearity of numerical variables
b) Nearly normal residuals
c) Constant variability
Residuals vs. predicted
Absolute value of residuals vs. predicted