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.