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Standard deviation is a number that describes how spread out the observations are
Standard deviation is a number that describes how spread out the observations are.
A mathematical function will have difficulties in predicting precise values, if the observations are "spread". Standard deviation is a measure of uncertainty.
A low standard deviation means that most of the numbers are close to the mean (average) value.
A high standard deviation means that the values are spread out over a wider range.
Tip: Standard Deviation is often represented by the symbol Sigma: σ
We can use the std()
function from Numpy to find the standard deviation of a variable:
import numpy as np
std = np.std(full_health_data)
print(std)
The output:
What does these numbers mean?
The coefficient of variation is used to get an idea of how large the standard deviation is.
Mathematically, the coefficient of variation is defined as:
We can do this in Python if we proceed with the following code:
import numpy as np
cv = np.std(full_health_data) / np.mean(full_health_data)
print(cv)
The output:
We see that the variables Duration, Calorie_Burnage and Hours_Work has a high Standard Deviation compared to Max_Pulse, Average_Pulse and Hours_Sleep.