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diggingnumbers:correlation

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:

> cor(Bronze\$Maxle, Bronze\$Maxwi)
 0.6892216

To calculate Spearman's rank correlation coefficient between Date and Weight for bronze spears:

> cor(Bronze\$Date, Bronze\$Weight, method="spearman")
 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
1. note that the order of arguments to lm() is inverse: the basic use is lm(y ~ x) (with y as dependent variable)
2. 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. 