Contenido del Curso
Learning Statistics with Python
2. Mean, Median and Mode with Python
4. Covariance vs Correlation
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 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.
¿Todo estuvo claro?
Contenido del Curso
Learning Statistics with Python
2. Mean, Median and Mode with Python
4. Covariance vs Correlation
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 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.
¿Todo estuvo claro?
