Regression Analysis

 What is Regression Analysis?

Regression Analysis is a set of statistical processes to estimate the relationship between a dependent variable and one or more independent variables. 

One of the regression analysis types is Linear regression. Linear regression simply assumes that there is a linear relationship between the dependent and the independent variable(s). The following is a general formula for Linear Regression.

Y_i = f(X_i,β) + e_i

Y = Dependent Variable

X = Independent Variable

e = Error term

β = Parameters

i = 1,2,....n

e is an important term here. e accounts for the unexplainable variations by the model. If the error term e is greater than β terms, then our model is not a good one. We need to search for other independent variables that will help better explain our dependent variable. 

R-Square value is another important thing we have to look out for. It provides percentage variability explained by the independent variables. The lower this value, the weaker our model is.

Linear regression is not the only regression that's been available to us. There is another flavor called Non-Linear Regression. Logistic regression is one of the types that can be used to model two extreme values in our data. For example, the income of different households in the United States of America. It will be used mostly for binary predictions, although more than binary values can be modeled using coding languages like Python.