# Correlation

In R, basic correlation tests are executed with two commands: `cor()`

and `lm()`

(where `lm`

stands for linear model).

## Calculating correlation

To calculate product moment correlation coefficient between `Maxle`

and
`Maxwi`

for bronze spears:

```
> Bronze = subset(spearhead, subset=Mat=="1")
> cor(Bronze$Maxle, Bronze$Maxwi)
[1] 0.6892216
```

To calculate Spearman's rank correlation coefficient between `Date`

and
`Weight`

for bronze spears:

```
> cor(Bronze$Date, Bronze$Weight, method="spearman")
[1] 0.1269293
```

## Plotting correlation

To draw a scatterplot for `Maxle`

and `Maxwi`

:

```
> plot(Bronze$Maxle, Bronze$Maxwi)
```

The scatterplot by itself is already interesting, but R gives us another
interesting function with the `lm()`

command (where `lm`

stands for
*linear model*).

```
> result <- lm(Bronze$Maxwi ~ Bronze$Maxle)
> result
Call:
lm(formula = Maxwi ~ Maxle)
Coefficients:
(Intercept) Maxle
1.5053 0.1277
```

- note that the order of arguments to
`lm()`

is inverse: the basic use is`lm(y ~ x)`

(with`y`

as*dependent*variable) - the result of
`lm()`

is a rect. You can see by yourself*plotting*it over the scatterplot

```
> abline(result$coefficients, col="blue")
```

Plotting the `lm()`

result by itself like

```
> plot(result)
```

gives you more informative graphs about the linear model, but their content is beyond the scope of this tutorial.