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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 `sqfeet` and `beds` again. Does the correlation value make sense?