Let's simulate some data
x <- rnorm(100)
y <- x + rnorm(100, sd = 0.5)
Here is a scatter plot of the above data
par(mar = c(5, 4, 1, 1), las = 1)
plot(x, y, main = "My Simulated Data")
library(datasets)
data(airquality)
fit <- lm(Ozone ~ Wind + Temp + Solar.R, data = airquality)
Here is table of regression coefficients
library(xtable)
xt <- xtable(summary(fit))
print(xt, type = "html")
| 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 |