oldoc63
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1 year ago 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. Print out the value stored in the variable cov_mat_sqfeet_beds. Look at the covariance matrix and find the covariance of sqfeet and beds.
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 negativity 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.
To calculate covariance, we can use the cov() function from NumPy, which produces a covariance matrix for two or more variables. Notice that the covariance appears twice in this matrix.