• Linear regression is a method used to model the relationship between a dependent variable and one or more independent variables.

car_data <- data.frame (
speed = c(50,55,60,65,70,75,80,85,90),
weight = c(200,205,210,215,220, 225, 230, 235, 240)
)

print(car_data)

##   speed weight
## 1    50    200
## 2    55    205
## 3    60    210
## 4    65    215
## 5    70    220
## 6    75    225
## 7    80    230
## 8    85    235
## 9    90    240

library(ggplot2) ggplot(car_data, aes(x=speed)) + geom_histogram(binwidth = 8, fill = “yellow”, color = “black”) + labs(title = “Histogram of Speed”, x = “speed(mph)”, y = “frequency”)

\(y = \beta_0 + \beta_1 x\)

Where:

• \(y\) is the dependent variable (weight)
  

• \(x\) is the independent variable (speed)
  

• \(\beta_0\) is the intercept
  

• \(\beta_1\) is the slope.

model <- lm(weight ~ speed, data=car_data)

• \(\beta_0\) = This represents the expected value of y when x is zero
  

• \(\beta_1\) = This shows what y is going to change to while x is also growing