Standard deviation is a value that indicates the extent to which observations deviate from the mean.
A mathematical function may struggle to predict accurate values if the observations are widely spread. Standard deviation measures this uncertainty.
A low standard deviation indicates that most values are close to the mean (average).
A high standard deviation means that the values are more spread out across a broader range.
| Tip: Standard deviation is commonly represented by the symbol Sigma (σ). |
You can use the std() function from Numpy to calculate the standard deviation of a variable.
| import numpy as np std = np.std(full_health_data) print(std) |
The output:
The coefficient of variation is used to assess the relative size of the standard deviation.
Mathematically, it is defined as:
| Coefficient of Variation = Standard Deviation / Mean |
We can calculate this in Python using the following code:
| import numpy as np cv = np.std(full_health_data) / np.mean(full_health_data) print(cv) |
The output:
We observe that the variables Duration, Calorie_Burnage, and Hours_Work have a higher standard deviation compared to Max_Pulse, Average_Pulse, and Hours_Sleep.
