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)