insure<-read.csv("F:\\R Course\\Research Presentation\\Datasets\\Insure_auto.csv")
insure_Analyze <- insure[,-1]
n <- nrow(insure_Analyze)
fit <- lm(Operating_Cost ~ . , data=insure_Analyze)
sfit <- summary(fit)
b <- coefficients(fit)Regression output of Insurance data with 10 records is given by:
library(xtable)
options(xtable.comment=FALSE)
print(xtable(sfit), type="html",html.table.attributes="border=1")| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | -10084.2131 | 3504.1812 | -2.88 | 0.0237 |
| Home | 167.3267 | 5.8955 | 28.38 | 0.0000 |
| Automobile | 54.1053 | 3.6559 | 14.80 | 0.0000 |
It’s R2 = 0.9967184.