Fit a model to noisy data

Least squares picks the straight line that makes the squared errors smallest. For a line it has a short formula, which you will write and use on six noisy readings.

The idea. $m = \dfrac{\sum (x - \bar{x})(y - \bar{y})}{\sum (x - \bar{x})^2}$ and $c = \bar{y} - m\bar{x}$, where $\bar{x}$ and $\bar{y}$ are the means.

Your tasks.

1. In mean, return the average of a list.
2. In slope, return $m$ by the formula above.
3. Set c to the intercept, and predict to the model's value at $x = 10$.