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Computing the covariance

The covariance may be computed using the Numpy function np.cov(). For example, we have two sets of data x and y, np.cov(x, y) returns a 2D array where entries [0,1] and [1,0] are the covariances. Entry [0,0] is the variance of the data in x, and entry [1,1] is the variance of the data in y. This 2D output array is called the covariance matrix, since it organizes the self- and covariance.

To remind you how the I. versicolor petal length and width are related, we include the scatter plot you generated in a previous exercise.

This is a part of the course

“Statistical Thinking in Python (Part 1)”

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Exercise instructions

  • Use np.cov() to compute the covariance matrix for the petal length (versicolor_petal_length) and width (versicolor_petal_width) of I. versicolor.
  • Print the covariance matrix.
  • Extract the covariance from entry [0,1] of the covariance matrix. Note that by symmetry, entry [1,0] is the same as entry [0,1].
  • Print the covariance.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Compute the covariance matrix: covariance_matrix


# Print covariance matrix


# Extract covariance of length and width of petals: petal_cov


# Print the length/width covariance

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