Generate Data
x <- rnorm(100)
y <- rexp(100,5)
plot(y~x)
Create Model
m <- lm(y~x)
summary(m)
##
## Call:
## lm(formula = y ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.2202 -0.1673 -0.0840 0.0801 1.0255
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.21245 0.02399 8.86 3.7e-14 ***
## x -0.00554 0.02294 -0.24 0.81
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.237 on 98 degrees of freedom
## Multiple R-squared: 0.000594, Adjusted R-squared: -0.0096
## F-statistic: 0.0583 on 1 and 98 DF, p-value: 0.81
Confidence intervals for coefficients
confint(m)
## 2.5 % 97.5 %
## (Intercept) 0.16484 0.26005
## x -0.05107 0.03999
ANOVA
anova(m)
## Analysis of Variance Table
##
## Response: y
## Df Sum Sq Mean Sq F value Pr(>F)
## x 1 0.0 0.0033 0.06 0.81
## Residuals 98 5.5 0.0561
