Problem 2 Part 2

Part 2: (10 points) Now input the data into R and create a scatter plot with the fitted regression line.

#Creating the given data
x_i = c(22, 52, 60, 42, 47, 65)
y_i = c(41, 49, 69, 55, 60, 62)

data = data.frame(x_i, y_i)

#Checking work

mod = lm(y_i~x_i)
summary(mod)
## 
## Call:
## lm(formula = y_i ~ x_i)
## 
## Residuals:
##      1      2      3      4      5      6 
## -1.083 -9.141  6.577  2.212  4.535 -3.100 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  30.3064     9.3605   3.238   0.0317 *
## x_i           0.5353     0.1873   2.858   0.0460 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.385 on 4 degrees of freedom
## Multiple R-squared:  0.6713, Adjusted R-squared:  0.5891 
## F-statistic: 8.168 on 1 and 4 DF,  p-value: 0.04603
plot(data)
abline(coefficients(mod)[1],coefficients(mod)[2],lty=1, col = "red")