本文是实验设计与分析（第6版，Montgomery著，傅珏生译) 第10章拟合回归模型10.9节思考题10.1 R语言解题。主要涉及线性回归、回归的显著性、回归系数的置信区间。
vial <- seq(1, 10, 1)
Viscosity <- c(160,171,175,182,184,181,188,193,195,200)
Temperature <- c(10,15,15,20,20,20,25,25,28,30)
visc <- data.frame(vial, Viscosity, Temperature)
visc
lm.fit <- lm(Viscosity ~ Temperature, data=visc)
summary (lm.fit)
summary (aov(lm.fit))
> summary (lm.fit)
Call:
lm.default(formula = Viscosity ~ Temperature, data = visc)
Residuals:
Min 1Q Median 3Q Max
-2.7903 -1.3206 -0.2903 1.8080 2.9961
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 143.8244 2.5215 57.04 9.91e-12 ***
Temperature 1.8786 0.1165 16.12 2.20e-07 ***
---
Signif. codes:
0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.203 on 8 degrees of freedom
Multiple R-squared: 0.9701, Adjusted R-squared: 0.9664
F-statistic: 260 on 1 and 8 DF, p-value: 2.197e-07
op <- par(mfrow=c(2,2), las=1)
plot(lm.fit)
par(op)
library(car)
carPlots(lm.fit)
