Member-only story

Statistics: Statistical Analysis Illustrated - Variance Everywhere

Ivan Cox

·6.4k Followers· Follow

Published in Statistics Statistical Analysis Illustrated: Variance Everywhere

4 min read

872 View Claps

83 Respond

Save

Listen

In everyday life, we are constantly bombarded with data. From the weather forecast to the stock market, we are surrounded by numbers that we need to make sense of. Statistics is the science of collecting, analyzing, interpreting, and presenting data. It is a powerful tool that can be used to make informed decisions, solve problems, and understand the world around us.

Statistics & Statistical Analysis Illustrated: Variance Everywhere

by Richard G. Brown

4.4 out of 5

Language	:	English
File size	:	5344 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	80 pages
Lending	:	Enabled
Screen Reader	:	Supported

One of the most important concepts in statistics is variance. Variance is a measure of how spread out a set of data is. It is calculated by taking the average of the squared differences between each data point and the mean.

Variance in Action

Variance can be used to compare the variability of different data sets. For example, let's say we have two data sets: the heights of men and the heights of women. The mean height of men is 5 feet 9 inches, and the mean height of women is 5 feet 4 inches. The variance of the heights of men is 2 inches squared, and the variance of the heights of women is 1 inch squared. This means that the heights of men are more variable than the heights of women.

Variance can also be used to make predictions about future data points. For example, let's say we know the mean and variance of the heights of men. We can use this information to predict the height of a new man that we meet. We would simply take the mean height of men and add or subtract a random number that is drawn from a normal distribution with a mean of 0 and a variance of 2 inches squared.

Variance is a powerful statistical tool that can be used to understand the world around us. It is a measure of how spread out a set of data is, and it can be used to compare the variability of different data sets and to make predictions about future data points.