# How to explain summary file?

I did a blog post on this, a while back.

The first line, `Call`, simply states the model that was used. In this case you are regressing "Home Run Factor" on "Average Outfield Dimension" using the Capstone_Baseball data.

The next block, `Residuals` give you a rough idea of the distribution. Here `min` and `max` have a similar absolute value, as do the first and third quartile. So the distribution isn't notably skewed in one direction or another.

`Coefficients` is in two parts. `Intercept` and the independent variable. To make it easier to discuss, it actually looks more like this (see reproducible example, called a reprex) for how to post examples like these.

``````             Estimate   Std. Error t value Pr(>|t|)
(Intercept)  6.496e+06  3.554e+05  18.279  < 2e-16 ***
#
``````

The intercept is where the regression line crosses the y axis, in this case round 6.5 million. The standard error is a measure of uncertainty of that estimate, the t value is a test statistic and Pr(>|t|) is the probability that the absolute t value is greater than that. You want that number to be as low a possible. 2e-16 is the smallest number floating point arithmetic can represent.

The next coefficient allows you to calculate the slope of your regression line, but look at the p-value of 0.185, which is very high. Basically, you'd expect this result 18.5% of the time simply by chance. Actually, you can see this on the bottom line F-statistic test, telling you the same thing.

If the p-value were reasonably low, say 0.05 (which is stil a one in twenty chance of being due to randomness), the R values would tell you how much of the variation in the dependent variable is due to the independent variable. In this case, not much.