Ichinkhorloo

library(ISLR)
library(tidymodels)

## ── Attaching packages ────────────────────────────────────── tidymodels 1.1.1 ──

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## ✔ ggplot2      3.5.0      ✔ tidyr        1.3.1 
## ✔ infer        1.0.6      ✔ tune         1.1.2 
## ✔ modeldata    1.3.0      ✔ workflows    1.1.4 
## ✔ parsnip      1.2.0      ✔ workflowsets 1.0.1 
## ✔ purrr        1.0.2      ✔ yardstick    1.3.0

## ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
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## ✖ dplyr::filter()  masks stats::filter()
## ✖ dplyr::lag()     masks stats::lag()
## ✖ recipes::step()  masks stats::step()
## • Search for functions across packages at https://www.tidymodels.org/find/

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats   1.0.0     ✔ readr     2.1.5
## ✔ lubridate 1.9.3     ✔ stringr   1.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

data("Auto")
str(Auto)

## 'data.frame':    392 obs. of  9 variables:
##  $ mpg         : num  18 15 18 16 17 15 14 14 14 15 ...
##  $ cylinders   : num  8 8 8 8 8 8 8 8 8 8 ...
##  $ displacement: num  307 350 318 304 302 429 454 440 455 390 ...
##  $ horsepower  : num  130 165 150 150 140 198 220 215 225 190 ...
##  $ weight      : num  3504 3693 3436 3433 3449 ...
##  $ acceleration: num  12 11.5 11 12 10.5 10 9 8.5 10 8.5 ...
##  $ year        : num  70 70 70 70 70 70 70 70 70 70 ...
##  $ origin      : num  1 1 1 1 1 1 1 1 1 1 ...
##  $ name        : Factor w/ 304 levels "amc ambassador brougham",..: 49 36 231 14 161 141 54 223 241 2 ...

model_lm <- lm(mpg ~ horsepower, data = Auto)

summary(model_lm)

## 
## Call:
## lm(formula = mpg ~ horsepower, data = Auto)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -13.5710  -3.2592  -0.3435   2.7630  16.9240 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 39.935861   0.717499   55.66   <2e-16 ***
## horsepower  -0.157845   0.006446  -24.49   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.906 on 390 degrees of freedom
## Multiple R-squared:  0.6059, Adjusted R-squared:  0.6049 
## F-statistic: 599.7 on 1 and 390 DF,  p-value: < 2.2e-16

plot(Auto$horsepower, Auto$mpg, xlab = "Horsepower", ylab = "MPG", main = "Simple Linear Regression")
abline(model_lm, col = "red")  

par(mfrow = c(2, 2))  
plot(model_lm)

model_tidymodels <- linear_reg() %>% 
  set_engine("lm") %>% 
  fit(mpg ~ horsepower, data = Auto)

tidy_results <- tidy(model_tidymodels)
glance_results <- glance(model_tidymodels)

print(tidy_results)

## # A tibble: 2 × 5
##   term        estimate std.error statistic   p.value
##   <chr>          <dbl>     <dbl>     <dbl>     <dbl>
## 1 (Intercept)   39.9     0.717        55.7 1.22e-187
## 2 horsepower    -0.158   0.00645     -24.5 7.03e- 81

print(glance_results)

## # A tibble: 1 × 12
##   r.squared adj.r.squared sigma statistic  p.value    df logLik   AIC   BIC
##       <dbl>         <dbl> <dbl>     <dbl>    <dbl> <dbl>  <dbl> <dbl> <dbl>
## 1     0.606         0.605  4.91      600. 7.03e-81     1 -1179. 2363. 2375.
## # ℹ 3 more variables: deviance <dbl>, df.residual <int>, nobs <int>