Lasso Regression

Lasso is able to achieve both of these goals by forcing the sum of the absolute value of the regression coefficients to be less than a fixed value, which forces certain coefficients to be set to zero, effectively choosing a simpler model that does not include those coefficients.

source:
en.wikipedia.org

image:
stats.stackexchange.com

Linear Regression

How to Interpret Regression Analysis Results: P ... How to Interpret Regression Analysis Results: P-values ... How Do I Interpret the Regression Coefficients for ...

source:
blog.minitab.com

image:
slideshare.net

Logistic Regression

In it, there is a chart which displays logistic regression coefficients. From courses years back and a little reading up, I understand logistic regression to be a way of describing the relationship between multiple independent variables and a binary response variable.

source:
stats.stackexchange.com

image:
stats.stackexchange.com

Polynomial Regression

Polynomial regression is one of several methods of curve fitting. With polynomial regression, the data is approximated using a polynomial function. A polynomial is a function that takes the form f( x ) = c 0 + c 1 x + c 2 x 2 ⋯ c n x n where n is the degree of the polynomial and c is a set of coefficients.

source:
polynomialregression.drque.net

image:
httprover2.blogspot.com

Ridge Regression

Ridge Regression is a remedial measure taken to alleviate multicollinearity amongst regression predictor variables in a model. Often predictor variables used in a regression are highly correlated. When they are, the regression coefficient of any one variable depend on which other predictor variables are included in the model, and which ones are left out.

source:
stats.stackexchange.com

image:
slideplayer.com

Stepwise Regression

Stepwise regression is a semi-automated process of building a model by successively adding or removing variables based solely on the t-statistics of their estimated coefficients.

source:
people.duke.edu

image:
spss-tutorials.com