install.packages(“readr”) library(readr) install.packages(“ggplot2”) library(ggplot2)

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library(readr)
ad_sales <- read_csv('https://raw.githubusercontent.com/utjimmyx/regression/master/advertising.csv')
## New names:
## Rows: 200 Columns: 6
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," dbl
## (6): ...1, X1, TV, radio, newspaper, sales
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
plot(sales ~ TV, data = ad_sales)

model_tv <- lm(sales ~ TV, data = ad_sales)
summary(model_tv)
## 
## Call:
## lm(formula = sales ~ TV, data = ad_sales)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.3860 -1.9545 -0.1913  2.0671  7.2124 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.032594   0.457843   15.36   <2e-16 ***
## TV          0.047537   0.002691   17.67   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.259 on 198 degrees of freedom
## Multiple R-squared:  0.6119, Adjusted R-squared:  0.6099 
## F-statistic: 312.1 on 1 and 198 DF,  p-value: < 2.2e-16
plot(sales ~ radio, data = ad_sales)

library(ggplot2)
ggplot(ad_sales, aes(x = radio, y = sales)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "Radio Ads vs Sales", x = "Radio Advertising", y = "Sales")
## `geom_smooth()` using formula = 'y ~ x'