STATISTICS FOR DATA ANALYTICS - 22
Regression Analysis
What is Regression analysis ?
It is a tool to investigate how two or more variables are related.
X is a predictor value or explanatory variable. ( independent variable )
Y is a response variable ( dependent variable )
Based on the number of independent variables, regression is of 2 type.
Simple Linear Regression
Multiple Linear Regression.
Simple Linear Regression :-
There is one independent and another one dependent value.
When and why to use Simple Linear Regression ?
The relationship between two quantitative variables.How strong the relationship is between two variables.
The value of the dependent variables(x) at a certain value of the independent variable (y).
Simple linear regression explains the relationship between a dependent variable and independent variable using a straight line.
y= mx + c
M = slope
C = at which part of y the line cuts.
Slope
Slope ( + ) = there is a positive linear relationship, i.e., as one increases, the other increases.
Slope ( - ) = there is a negative linear relationship, i.e., as one increases, the other variable decreases.
Linear regression finds the line which fits best through your data, which we call Best Fit Line.
Residuals is the error between a predicted value and the observed actual value.
How to find the best fit line ?
Using Ordinary Least Squares ( OLS ) Method.
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 )
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