Multiple linear regression is a machine learning algorithm. With the use of this method, predictions and trends based on a variety of explanatory factors can
Multiple linear regression: what is it?
Similar to basic linear regression, multiple linear regression uses more than one explanatory variable (called the independent variable) to determine an outcome (the so-called explained variable). Explanatory variables can be continuous or categorical, while the latter is invariably continuous. Making predictions is the goal, just like with ordinary linear regression.
For example, multiple linear regression can predict the amount of sales of a product based on shopper demographics, such as age, income level, and address.
Why use multiple regressions?
Multiple regression essentially seeks to find the associations that already exist between a certain number of independent or predictive factors and an explanatory variable (known as dependent)
What situations require multiple linear regression?
A method for finding correlations between an outcome (the so-called explained variable) and a number of independent and explanatory factors is multiple linear regression. As mentioned earlier, it may be necessary to predict the sales performance of a certain product (a high-end computer, for example) based on the demographics of potential customers, such as age, income level, address, etc.
How can I use Excel to run multiple linear regression?
The XLSTAT module must first be integrated into the spreadsheet extensions in order to use Excel to run multiple linear regression. You must then choose the “modeling” option, followed by the “linear regression” function, from the newly installed tab. To build a common multiple model, the configuration mainly focuses on the “general” tab. This includes detailing the explanatory and explained factors.
alternatives, verification, prediction, missing data and exits.
All that remains is to validate after having completed the settings by pressing the “ok” button to produce the multiple linear regression.
Multiple linear regression applications:
There are several applications for multiple linear regression. Indeed, more complicated models than the basic linear systems can be developed due to the support of many variables. The following skill areas can use it:
- the expected weather;
- climate change over a more or less extensive geographical area;
- the distribution of a viral infection in a country or according to a classification of people at risk or infected;
- study of financial and stock markets;
- statistical analysis and econometrics.