# Variance And Standard Deviation

## Preliminary

```
# Import data
import math
```

## Create Data

```
# Create list of values
data = [3,2,3,4,2,3,5,2,2,33,3,5,2,2,5,6,62,2,2,3,6,6,2,23,3,2,3]
```

## Calculate Population Variance

Variance is a measurement of the spread of a data’s distribution. The higher the variance, the more “spread out” the data points are. Variance, commonly denoted as $S^{2}$, is calculated like this:

$$ \text{Population Variance} = S_n^{2} = \frac{1}{n}\sum_{i=1}^{n}(x_i-\bar{x})^{2}$$

$$ \text{Sample Variance} = S_{n-1}^{2} = \frac{1}{n-1}\sum_{i=1}^{n}(x_i-\bar{x})^{2}$$

Where $n$ is the number of observations, $\bar{x}$ is the mean of the observations, and $x_i-\bar{x}$ is an individual observation’s from the mean of the data. Note that if we were estimating the variance of a population based on a sample from that population, we should use the second equation, replacing $n$ with $n-1$.

```
# Calculate n
n = len(data)
# Calculate the mean
mean = sum(data)/len(data)
# Create a list of all deviations from the mean
all_deviations_from_mean_squared = []
# For each observation in the data
for observation in data:
# Calculate the deviation from the mean
deviation_from_mean = (observation - mean)
# Square it
deviation_from_mean_squared = deviation_from_mean**2
# Add the result to our list
all_deviations_from_mean_squared.append(deviation_from_mean_squared)
# Sum all the squared deviations in our list
sum_of_deviations_from_mean_squared = sum(all_deviations_from_mean_squared)
# Divide by n
population_variance = sum_of_deviations_from_mean_squared/n
# Show variance
population_variance
```

```
160.78463648834017
```

## Calculate Population Standard Deviation

Standard deviation is just the square root of the variance.

```
# Find the square root of the population variance
population_standard_deviation = math.sqrt(population_variance)
# Print the populaton standard deviation
population_standard_deviation
```

```
12.68008818929664
```