x<-scan()
1400
1600
1700
1875
1100
1550
2350
2450
1425
1700


y<-scan()
245
312
279
308
199
219
405
324
319
255



yx.lm<-lm(y~x)

yx.lm

summary(yx.lm)

yx.lm$coe

yx.lm$re

plot(yx.lm)

predict(yx.lm, se.fit=T)


pointwise(predict(yx.lm, se.fit=T),coverage = 0.99)

confint.lm<-
function(object, alpha = 0.05, plot.it = T, ...)
{
f <- predict(object, se.fit = T)
p <- length(coef(object))
fit <- f$fit
adjust <- (p * qf(1 - alpha, p, length(fit) -
p))^0.5 * f$se.fit
lower <- fit - adjust
upper <- fit + adjust
if(plot.it) {
y <- fit + resid(object)
plot(fit, y)
abline(0, 1, lty = 2)
ord <- order(fit)
lines(fit[ord], lower[ord])
lines(fit[ord], upper[ord])
invisible(list(lower = lower, upper =
upper))
}
else list(lower = lower, upper = upper)
}

confint.lm(yx.lm,a=0.05)

x<-c(30,20,60,80,40,50,60,30,70,60)
y<-c(73,50,128,170,87,108,135,69,148,132)

yx.lm<-lm(y~x)

yx.lm

summary(yx.lm)

yx.lm$coe

yx.lm$re

plot(yx.lm)

predict(yx.lm, se.fit=T)

pointwise(predict(yx.lm, se.fit=T),coverage = 0.99)

confint.lm(yx.lm,a=0.05)

ks.gof(yx.lm$re,distribution="normal")


x<-scan()
294
247
267
358
423
311
450
534
438
697
688
630
709
627
615
999
1022
1015
700
850
980
1025
1021
1200
1250
1500
1650



y<-scan()
30
32
37
44
47
49
56
62
68
78
80
84
88
97
100
109
114
117
106
128
130
160
97
180
112
210
135

yx.lm<-lm(y~x)

yx.lm

summary(yx.lm)

yx.lm$coe

yx.lm$re

plot(yx.lm)

predict(yx.lm, se.fit=T)

pointwise(predict(yx.lm, se.fit=T),coverage = 0.99)

confint.lm(yx.lm,a=0.05)

ks.gof(yx.lm$re,distribution="normal")

x<-1/x

yx.lm<-lm(y~x)

yx.lm

summary(yx.lm)

yx.lm$coe

yx.lm$re

plot(yx.lm)

predict(yx.lm, se.fit=T)

pointwise(predict(yx.lm, se.fit=T),coverage = 0.99)

confint.lm(yx.lm,a=0.05)

ks.gof(yx.lm$re,distribution="normal")

cor.test(x,y,method="pearson")
cor.test(x,y,method="kendall")
 cor.test(x,y,method="spearman")