STATISTICS FOR DATA ANALYTICS - 24
MULTIPLE LINEAR REGRESSION
Multiple linear regression is needed when one variable is not sufficient to create a good model and make accurate predictions.
The value of a dependent variable at a certain value of the independent variables.
Different Assumption
There is a linear relationship between x and y
Residuals/Error terms are normally distributed with mean zero(not X,Y )
Error terms are independent of each other.
Error terms have constant variance ( homoscedasticity )
This thing can be done in Excel but for more concept learning we will learn in Machine learning.
This below topic we will explore more in machine learning part :-
Overfitting
As you keep adding variables, the model may become far too complex.
It may end up memorising the data and, consequently, fail to give accurate results on new data.
Multicollinearity
This refers to associations between independent variables, they might be interrelated.
Feature selection
Selecting an optimal set from a pool of given features, many of which might be reductant.
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