Welcome to the introduction to statistical forecasting module.

During this section of the course, we will explore fundamental statistical methods

which are useful in using data to develop forward expectations or forecasts.

Course participants are assumed to have had some previous exposure to statistics.

Though we will provide reference materials to concepts presented in this module.

We will explain the statistical concepts employed in the following analyses,

that encourage participants to further their study independently.

We will spend a bit longer introducing concepts in this module than we have in

the others.

Given the statistical nature of these concepts, it's important that participants

spend time to understand the statistical methods employed in this module,

as they are powerful, but nuanced.

In order to use data to produce statistical forecasts,

we need to understand regression analysis.

Regression analysis is one of the most commonly used statistical methods to

produce data driven forecasts.

Simple linear regression analysis uses one variable,

the independent variable to explain another variable, the dependent variable.

For example, you might use a person's height to explain and

predict a person's weight.

A thorough exploration of linear regression is beyond the scope of

this module, but we encourage course participants to study this powerful

statistical method, or to take one of the many related courses on Coursera.

There are a few concepts that you should understand, at least at a high level

before we proceed to their application in our Excel problem sets.

We encourage you to spend time studying these concepts independently

if you are unfamiliar.

Standard deviation is a measurement of the average dispersion of values in

a data set around their average value.

That is how spread out the data are from their average value.

This is related to variance but

it is more frequently used to describe the average dispersion of a data set.

The implication follows that a variance in the highest standard deviation should

imply lower confidence in the outputs of a statistical forecast using the data.