Reading Notes - 15 TYPES OF REGRESSION YOU SHOULD KNOW
Reading Notes : 15 TYPES OF REGRESSION YOU SHOULD KNOW
Ref: https://www.listendata.com/2018/03/regression-analysis.html
Terminologies
Mutlticollinearity
Many types of regression techniques assumes multicollinearity should not be present in the dataset. It is because it causes problems in ranking variables based on its importance. Or it makes job difficult in selecting the most important independent variable (factor).
Heteroscedasticity
When dependent variable’s variability is not equal across values of an independent variable, it is called heteroscedasticity
Types of Regression
Linear Regression
Assumptions of linear regression
- There must be a linear relation between independent and dependent variables
- There should not be any outliers present
- No heteroscedasticity
- Sample observations should be independent
- Error terms should be normally distributed with mean 0 and constant variance
- Absence of multicollinearity and auto-correlation
Polynomial Regression
Question: Why do we not care about the multicollinearity?
Logistic Regression
Why not use linear regression here?
- The homoscedasticity assumption is violated
- Errors are not normally distributed
- y follows binomial distribution and hence is not normal
Odds Ratio
exponential of coefficients == odds ratio for ith explanatory variable
Logistic Regression in R
- fit logistic regression with glm() function and we set family = “binomial”
model <- glm(Lung.Cancer..Y.~Smoking..X.,data = data, family = "binomial")
Quantile Regression
(TBD)