Types of regression
- Simple Linear Regression
y=b_0+b_1x
y=b_0+b_1x^2
- Multiple Linear Regression
y=b_0+b_1x_1+b_2x_2+b_3x_3+.........+b_nx_n
y=b_0+b_1x_1+b_2x_2^2+b_3x_3^3+.........+b_nx_n^n
- Simple Non-Linear Regression
y=b_0+b_1^2x
y=b_0+b_1^2x^2
- Multiple Non-Linear Regression
y=b_0+b_1x_1+b_2^2x_2+b_3^3x_3+.........+b_n^nx_n
y=b_0+b_1x_1+b_2^2x_2^2+b_3^3x_3^3+.........+b_n^nx_n^n
b_0, b_1, b_2, b_3, ........., b_n=Parameters/Regression\ Coefficients
x_1, x_2, x_3, .........,x_n=Independent\ Variable
y=Dependent\ Variable
In statistics, regression analysis includes any techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps us understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables — that is, the average value of the dependent variable when the independent variables are held fixed. Less commonly, the focus is on a quantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables.
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