Flash cards
Review the key moves
What is the main idea behind Data Science - Statistics Standard Deviation?
Lesson checks
Practice each idea before moving on
Short Mimo-style checks built from this lesson's code, terms, and sequence.
Which statement best captures the main point of this lesson?
Complete the missing token from the example code.
___ numpy as npPut the learning moves in the order that makes the concept easiest to apply.
Before charting or modeling a dataset, which move should come first?
Standard Deviation
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:
Example
import numpy as np
std = np.std(full_health_data)
print(std)The output
What does these numbers mean?
Coefficient of Variation
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:
Coefficient of Variation = Standard Deviation / MeanWe can do this in Python if we proceed with the following code:
Example
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.