Summary  
This chapter explains how to implement code to compute population variance by calculating the mean of a dataset, summing the squared differences between each value and the mean, and dividing by the total number of elements.  

General domain of usage  
Statistical data analysis

**Variance** measures how much the values in a dataset deviate from the mean. It provides insight into the spread or dispersion of the data.

Definition

 To better understand this concept, examine the distribution of salaries in the dataset. 

The formula for calculating variance differs depending on whether you're working with a sample or an entire population. In this chapter, the focus is on calculating **population variance**.

$$
\text{variance} = \frac{\sum^N_{i=1}(x_i-\mu)^2}{N}\\
$$

Where:
- $$N$$ - population size;
- $$i$$ - index of each element;
- $$x_i$$ - each element;
- $$\mu$$ - population mean value.

## Explanation
Population variance is calculated by summing the squares of the differences between each data point and the population mean, and then dividing by the number of elements in the population.

Build a strong foundation in statistics using Python. Learn essential statistical concepts and apply them through NumPy and pandas. Move from basic measures like mean and variance to hypothesis testing, confidence intervals, and data-driven insights with hands-on practice.

Discover core statistical principles, including data types, measures of central tendency, and key differences between samples and populations.

Learn to calculate and interpret mean, median, and mode using Python. Practice these operations with pandas to analyze real datasets.

Understand how variance and standard deviation measure data spread. Learn to compute both manually and using Python tools.

Explore how covariance and correlation describe relationships between variables. Practice calculating and comparing both metrics in Python.

Master confidence intervals to estimate population parameters. Use NumPy, pandas, and visualization libraries to calculate and interpret intervals with real data.

Learn the fundamentals of hypothesis testing and the t-test. Understand how to design, perform, and interpret tests using Python to support data-driven decisions.

Population Variance

Explanation