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Simple Linear Regression Model is also called as Linear Regression Model or Regression or LM Model or Linear Model.
Whats is Regression?
Ans.In statistical modeling, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors').
Regression is nothing but the Prediction.
For Applying SLR,
- Data Type must be Numeric or Continuous.
- There should be One dependent variable and One independent variable
The Linear relation between two variables which is a relation between dependent and independent variable.
Note: In SLR the model can have only one dependent variable and one independent variable.
Y = α + β(x)
Where ,
Y -> Dependent Variable
α -> Constant
β -> Coefficient
x -> Independent Variable
So here,
- The variable Y is dependent on independent variable x.
- Both variable must be Numeric & Continuous
We create a relationship model using the lm() functions in R,
fit <- lm(formula=speed~dist,data=cars)
- Basically we are looking for R-squared measure just to see how close the data are to the fitted regression line. It is also known as the coefficient of determination.
It is defined as, R-squared = Explained variation / Total variation
Total variation = Explained variation + Unexplained variation
#############Applying Simple linear regression model###########
