here, we’re going load some data
library(datasets)
data(airquality)
summary(airquality)
## Ozone Solar.R Wind Temp
## Min. : 1.00 Min. : 7.0 Min. : 1.700 Min. :56.00
## 1st Qu.: 18.00 1st Qu.:115.8 1st Qu.: 7.400 1st Qu.:72.00
## Median : 31.50 Median :205.0 Median : 9.700 Median :79.00
## Mean : 42.13 Mean :185.9 Mean : 9.958 Mean :77.88
## 3rd Qu.: 63.25 3rd Qu.:258.8 3rd Qu.:11.500 3rd Qu.:85.00
## Max. :168.00 Max. :334.0 Max. :20.700 Max. :97.00
## NA's :37 NA's :7
## Month Day
## Min. :5.000 Min. : 1.0
## 1st Qu.:6.000 1st Qu.: 8.0
## Median :7.000 Median :16.0
## Mean :6.993 Mean :15.8
## 3rd Qu.:8.000 3rd Qu.:23.0
## Max. :9.000 Max. :31.0
##
Let’s first make a pairs plot of the data
pairs(airquality)
set.seed(1)
x<-rnorm(100)
mean(x)
## [1] 0.1088874
The current time is 周一 10月 10 2:06:23 2016, My favorite random number is -0.6203667.
x<- rnorm(100);y<-x+rnorm(100,sd = 0.5)
-Regression coefficients
## Warning: package 'xtable' was built under R version 3.2.5
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | -64.3421 | 23.0547 | -2.79 | 0.0062 |
| Wind | -3.3336 | 0.6544 | -5.09 | 0.0000 |
| Temp | 1.6521 | 0.2535 | 6.52 | 0.0000 |
| Solar.R | 0.0598 | 0.0232 | 2.58 | 0.0112 |