```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Regression Report	

```{r echo=FALSE, results='hide'}	
n <- nrow(women)	
fit <- lm(weight ~ height, data=women)	
sfit <- summary(fit)	
b <- coefficients(fit)	
```	

Linear regression was used to model the relationship between
weights and height in a sample of `r n` women. The equation
     **weight = `r b[[1]]` +  `r b[[2]]` * height**	
accounted for `r round(sfit$r.squared,2)`% of the variance
in weights. The ANOVA table is given below.

```{r echo=FALSE, results='asis'}
library(xtable)	
options(xtable.comment=FALSE)	
print(xtable(sfit), type="html", html.table.attributes="border=0")	
```

The regression is plotted in the following figure.

```{r echo=FALSE, fig.width=5, fig.height=4}
library(ggplot2)
ggplot(data=women, aes(x=height, y=weight)) +
      geom_point() + geom_smooth(method="lm")
```
