Like covariance, *Pearson Correlation* (often referred to simply as “correlation”) is a scaled form of covariance. It also measures the strength of a linear relationship, but ranges from -1 to +1, making it more interpretable.

Highly associated variables with a positive linear relationship will have a correlation close to 1. Highly associated variables with a negative linear relationship will have a correlation close to -1. Variables that do not have a linear association (or a linear association with a slope of zero) will have correlations close to 0.

The `pearsonr()`

function from `scipy.stats`

can be used to calculate correlation as follows:

from scipy.stats import pearsonr corr_price_sqfeet, p = pearsonr(housing.price, housing.sqfeet) print(corr_price_sqfeet) #output: 0.507

Generally, a correlation larger than about .3 indicates a linear association. A correlation greater than about .6 suggestions a strong linear association.

### Instructions

**1.**

Use the `pearsonr`

function from scipy.stats to calculate the correlation between `sqfeet`

and `beds`

. Store the result in a variable named `corr_sqfeet_beds`

and print out the result. How strong is the linear association between these variables?

**2.**

Generate a scatter plot of `beds`

and `sqfeet`

again. Does the correlation value make sense?