Covariance | Covariance vs Correlation
Learning Statistics with Python

Course Content

Learning Statistics with Python

Learning Statistics with Python

1. Basic Concepts
2. Mean, Median and Mode with Python
3. Variance and Standard Deviation
4. Covariance vs Correlation
5. Confidence Interval
6. Statistical Testing

Covariance

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

 The value of covariance Meaning Positive Two variables move in the same direction 0 Two variables no linear relationship Negative Two variables move in opposite directions

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.

Calculating Covariance with Python:

To compute covariance in Python, you can use the `np.cov()` function from the NumPy library. It requires two parameters: the sequences of data for which you want to calculate the covariance.

The result is the value at index [0,1]. This course won't cover the other values in the output, refer to the example:

This indicates that the values move in the same direction. This makes sense because a larger store area corresponds to a greater number of items. One significant drawback of covariance is that the value can be infinite.

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Section 4. Chapter 1
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