2 Model
\[ Y_{i} = \alpha + \beta X_{i} + e_{i} \]
2.1 Variables
x = food[,2] # Income
y = food[,1] # Food Expenditure2.2 Estimation
reg <- lm(y ~ x)2.3 Results
summary(reg) ##
## Call:
## lm(formula = y ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -223.025 -50.816 -6.324 67.879 212.044
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 83.416 43.410 1.922 0.0622 .
## x 10.210 2.093 4.877 1.95e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 89.52 on 38 degrees of freedom
## Multiple R-squared: 0.385, Adjusted R-squared: 0.3688
## F-statistic: 23.79 on 1 and 38 DF, p-value: 1.946e-052.4 b1, b2
b1 <- coef(reg)[[1]]
b2 <- coef(reg)[[2]]
b1## [1] 83.416b2## [1] 10.20964