The linear regression model is the mathematical representation of the relationship between two variables.
This is represented by the equation \[ \hat{Y} = b_0 + b_1X_1 \]
2025-10-28
1
The linear regression model is the mathematical representation of the relationship between two variables.
This is represented by the equation \[ \hat{Y} = b_0 + b_1X_1 \]
## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 ## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 ## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 ## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 ## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 ## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
## speed dist ## Min. : 4.0 Min. : 2.00 ## 1st Qu.:12.0 1st Qu.: 26.00 ## Median :15.0 Median : 36.00 ## Mean :15.4 Mean : 42.98 ## 3rd Qu.:19.0 3rd Qu.: 56.00 ## Max. :25.0 Max. :120.00
regres <- lm(mpg ~ hp, data = mtcars) summary(regres)
## ## Call: ## lm(formula = mpg ~ hp, data = mtcars) ## ## Residuals: ## Min 1Q Median 3Q Max ## -5.7121 -2.1122 -0.8854 1.5819 8.2360 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 30.09886 1.63392 18.421 < 2e-16 *** ## hp -0.06823 0.01012 -6.742 1.79e-07 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 3.863 on 30 degrees of freedom ## Multiple R-squared: 0.6024, Adjusted R-squared: 0.5892 ## F-statistic: 45.46 on 1 and 30 DF, p-value: 1.788e-07
## `geom_smooth()` using formula = 'y ~ x'
plotly_mpg_cyl <- plot_ly(
data = mtcars,
x = ~factor(cyl),
y = ~mpg,
type = "box",
boxpoints = "all",
jitter = 0.3,
marker = list(color = 'rgba(7, 164, 181, 0.7)'),
line = list(color = 'rgba(7, 164, 181, 1)')
) %>%
layout(
title = "MPG vs. Number of Cylinders ",
xaxis = list(title = "Number of Cylinders"),
yaxis = list(title = "Miles per Gallon (MPG)")
)