• Simple linear regression is a statistical method to model the relationship between two variables.
• It assumes a linear relationship between the predictor variable (X) and the response variable (Y)

• Equation of simple linear regression: \[y = \beta_0 + \beta_1 x + \epsilon\]

• Equation of multiple linear regression: \[y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + ... + \beta_n x_n + \epsilon\]

• Linearity: The relationship between X and Y is linear.
• Independence: Observations are independent of each other.
• Homoscedasticity: Constant variance of residuals.
• Normality: Residuals follow a normal distribution

• Scatter plot with regression line

## `geom_smooth()` using formula = 'y ~ x'

##Slide 7:Residual Plot -Residual Plot

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

##Slide 8:3D Plot -3D Plot