X <- c(2, 5, 7, 8)
Y <- c(1, 2, 3, 3)
linear_regression = lm(Y ~ X)
summary(linear_regression)
##
## Call:
## lm(formula = Y ~ X)
##
## Residuals:
## 1 2 3 4
## 1.804e-16 -7.143e-02 2.143e-01 -1.429e-01
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.28571 0.24571 1.163 0.3649
## X 0.35714 0.04124 8.660 0.0131 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.189 on 2 degrees of freedom
## Multiple R-squared: 0.974, Adjusted R-squared: 0.961
## F-statistic: 75 on 1 and 2 DF, p-value: 0.01307
#linear regression coef
linear_coef <- coef(linear_regression)
print(linear_coef)
## (Intercept) X
## 0.2857143 0.3571429
plot(X, Y,
col = 'red',
main = 'X vs Y Regression',
xlim = c(0,10),
ylim = c(0,10))
abline(linear_regression, col = 'blue')
legend("topleft",
legend = paste("Slope =", linear_coef[2], "Y_Intercept =", linear_regression[1]))
