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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`.