Course Content

# Learning Statistics with Python

Learning Statistics with Python

## Covariance

Covariance is a measure of the joint variability of two random variables.

 The value of covariance Meaning Positive Two variables move in one direction together 0 Two variables don't vary together Negative Two variables move in an opposite directions together

The formulas are different for the sample and population, but we will not dive deeper into them. In this chapter, we will discuss covariances of the following dataset:

 Store_ID Store_Area Items_Available Daily_Customer_Count Store_Sales 0 0 1659 1961 530 66490 1 1 1461 1752 210 39820 2 2 1340 1609 720 54010 3 3 1451 1748 620 53730 4 4 1770 2111 450 46620
• `Store_ID` - The unique id of the store.
• `Store_Area` - The area of the store.
• `Items_Available` - The number of items that are available in the store.
• `Daily_Customer_Count` - The daily number of customers in the store.
• `Store_Sales` - The number of sales in the store.

Covariance with Python:

To work with covariance in Python, we need to use the function `np.cov()`, from the NumPy library, with two parameters: the sequences of data between which we want to find covariance.

The output is the number with the index [0,1], we will not learn the other values from the output within this course; look at the example:

It means that the values are moving in one direction. It makes sense because the bigger the store area, the bigger the number of items. The significant disadvantage of the covariance is that the value can be infinite.

Everything was clear?

Section 4. Chapter 1