Calculate the mean of the values in x$\_$train and y$\_$train, storing the resulting values in variables named mean$\_$x and

mean$\_$y$.$ You may use the sum$()$ function, as well as the variable n$\_$train that you defined earlier in the notebook.

Print the results with text output as shown below. Round the displayed value of mean$\_$x to $2$ decimal places, and round

the displayed value of mean$\_$y to $4$ decimal places. Do not round the actual values stored within the variables

themselves, just the values displayed.

Mean of X $=$ xxxx

Mean of Y $=$ xxxx

We will now work toward calculating the variance of the values of $$ in the training set as well as the variance of the values

of $$ in the training set. The variance is a measure of how dispersed, or spread out, observations of a particular variable are.

We will calculate the desired variances by first calculating an intermediate result. Specifically, we will calculate the sum of

squared deviations for $$ and $,$ which are denoted by $$ and $,$ respectively. Formulas for these quantities are

provided below. We will use these values again in later parts of this project.

$=$

$$

$=1$

$()2=(1)2+(2)2++()2$

$=$

$$

$=1$

$()2=(1)2+(2)2++()2$

Create a markdown cell with unformatted text explaining that we will now be calculating $$ and $.$

Calculate $$ and $,$ storing the results in variables named Sxx and Syy$.$ You will need to use a loop to perform this

task. For the sake of efficient memory usage, you should not create any additional lists when performing these

calculations.

Print the results with text output as shown below. Round the displayed value of Sxx to $2$ decimal places and round the

displayed value of Syy to $4$ decimal places. Do not round the actual values stored within the variables themselves, just

the values displayed.

Sxx $=$ xxxx

Syy $=$ xxxx