Welcome to the Simple Linear-Regression Readme!

Simple Linear Regression Model is also called as Linear Regression Model or Regression or LM Model or Linear Model.

Getting Started

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.

SLR can be defined as,

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

SLR using R,

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