1. What is Regression Analysis?
 Regression analysis is used to model the relationship between a dependent variable and one or more independent variables.

2. Linear Regression

3. Polynomial Regression
red curve fits the data better than the green curve= situations where the relation. between the dependent and independent variable seems to be non-linear we can deploy Polynomial Regression Models.

4. Quantile (percentile) Regression
generally use it when outliers, high skeweness and heteroscedasticity exist in the data.
aims to estimate either the conditional median or other quantiles of the response variable
we try to estimate the quantile of the dependent variable given the values of X’s.

5. Logistic Regression dependent variable is binary
y follows binomial distribution and hence is not normal
the error terms are not normally distributed.

6. Cox Regression (survival analysis; proportional hazards model)
investigating the effect of several variables upon the time a specified event takes to happen
time-to-event data e.g Time from first heart attack to the second
Dual targets are set for the survival model 
1. A continuous variable representing the time to event.
2. A binary variable representing the status whether event occurred or not.

7. Ordinal Regression dependent variable is ordinal-ranked values
Example of ordinal variables – Survey responses (1 to 6 scale), patient reaction to drug dose (none, mild, severe).
Ordinal regression can be performed using a generalized linear model (GLM) that fits both a coefficient vector and a set of thresholds to a dataset.

8. Poisson Regression (log-linear model)
Dependent variable is count data
The dependent variable must meet the following conditions:
1) The dependent variable has a Poisson distribution.
2) Counts cannot be negative.
3)This method is not suitable on non-whole numbers

9. Negative Binomial Regression
deals with count data
does not assume distribution of count having variance equal to its mean
Deals with overdispersion

10. Quasi Poisson Regression
alternative to negative binomial regression
 used for overdispersed count data
Both the algorithms give similar results, there are differences in estimating the effects of covariates
variance of a quasi-Poisson model is a linear function of the mean while the variance of a negative binomial model is a quadratic function of the mean.
can handle both over-dispersion and under-dispersion

11. Principal Components Regression (PCR)
 based on principal component analysis (PCA).
calculate the principal components and then use some of these components as predictors in a linear regression model fitted using the typical least squares procedure
Dimensionality Reduction & Removal of multicollinearity

12. Partial Least Squares (PLS) Regression
alternative technique of principal component regression when you have independent variables highly correlated. It is also useful when there are a large number of independent variables.
finds a linear regression model by projecting the predicted variables and the observable variables to a new space.

13. Ridge Regression
technique for analyzing multiple regression data that suffer from multicollinearity.
Regularization parameter
When multicollinearity occurs, least squares estimates are unbiased, but their variances are large so they may be far from the true value.

14. Lasso Regression Least Absolute Shrinkage and Selection Operator
performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces.
L1 regularization technique:  mi nimize the objective function by adding a penalty term to the sum of the absolute values of coefficients.

15. Elastic Net Regression
A regularized regression method that linearly combines the L1 and L2 penalties of the lasso and ridge methods.
Elastic Net regression is preferred over both ridge and lasso regression when one is dealing with highly correlated independent variables.

16. Support Vector Regression/ Machine
can solve both linear and non- linear models
Non-parametric
uses non-linear kernel functions (such as polynomial) to find the optimal solution for non-linear models