Beyond visualizing relationships, we can also use summary statistics to quantify the strength of certain associations. *Covariance* is a summary statistic that describes the strength of a linear relationship. A linear relationship is one where a straight line would best describe the pattern of points in a scatter plot.

Covariance can range from negative infinity to positive infinity. A positive covariance indicates that a larger value of one variable is associated with a **larger** value of the other. A negative covariance indicates a larger value of one variable is associated with a **smaller** value of the other. A covariance of 0 indicates no linear relationship. Here are some examples:

To calculate covariance, we can use the `cov()`

function from NumPy, which produces a covariance matrix for two or more variables. A covariance matrix for two variables looks something like this:

variable 1 | variable 2 | |
---|---|---|

variable 1 | variance(variable 1) | covariance |

variable 2 | covariance | variance(variable 2) |

In python, we can calculate this matrix as follows:

cov_mat_price_sqfeet = np.cov(housing.price, housing.sqfeet) print(cov_mat_price_sqfeet) #output: [[184332.9 57336.2] [ 57336.2 122045.2]]

Notice that the covariance appears twice in this matrix and is equal to `57336.2`

.

### Instructions

**1.**

Use the `cov()`

function from NumPy to calculate the covariance matrix for the `sqfeet`

variable and the `beds`

variable. Save the covariance matrix as `cov_mat_sqfeet_beds`

**2.**

Print out the value stored in the variable `cov_mat_sqfeet_beds`

.

**3.**

Look at the covariance matrix you just printed and find the covariance of `sqfeet`

and `beds`

. Save that number as a variable named `cov_sqfeet_beds`

.