# Linear Regression Commands in R

After cleaning up the data you generated in class today a bit more, I was able to run the linear regression commands in R and get sensible results. Here are two CSV files you can use to play around with R:

And here are the R commands I used to generate the plot below:

```> math216A <- read.csv("C:/Users/bruffdo/Desktop/math216A.csv")
>   View(math216A)
> height <- math216A\$Height
> shoe <- math216A\$Shoe
> plot(height,shoe)
> fit <- lm(shoe ~ height)
> abline(fit)
> cor(height,shoe)
 0.4408186
> summary(fit)

Call:
lm(formula = shoe ~ height)

Residuals:
Min      1Q  Median      3Q     Max
-3.7778 -1.1254 -0.2565  0.3380  9.4356

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.74116    5.49626  -1.408  0.16572
height       0.26220    0.07872   3.331  0.00171 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.351 on 46 degrees of freedom
Multiple R-squared: 0.1943,    Adjusted R-squared: 0.1768
F-statistic: 11.09 on 1 and 46 DF,  p-value: 0.001713```

Here's the R-generated plot of shoe size versus height: These data have a correlation coefficient of R = 0.44.