The Dependent variable (or variable to model) is here the "Weight". In the ribbon, select XLSTAT > Modeling data > Linear Regression This dataset is also used in the two tutorials on simple linear regression and ANCOVA. The Linear Regression method belongs to a larger family of models called GLM (Generalized Linear Models), as do the ANOVA. Here, the dependent variable is the weight, and the explanatory variables are height and age: we have two of them so we choose multiple linear regression. Using simple linear regression, we want to find out how the weight of the children varies with their height and age, and to verify if a linear model makes sense. They concern 237 children, described by their gender, age in months, height in inches (1 inch = 2.54 cm), and weight in pounds (1 pound = 0.45 kg). Introduction to Experimental Ecology, New York: Academic Press, Inc. How to run multiple linear regression in XLSTAT? Dataset for running a multiple linear regression If you want to establish the linear relationship between only two variables, do not hesitate to check our tutorial on simple linear regression. Multiple linear regression enables you to predict a variable depending on several others, on the basis of a linear relationship inferred by a supervised learning algorithm. Not sure this is the modeling feature you are looking for? Check out this guide. Linear regression is based on Ordinary Least Squares (OLS). This tutorial will help you set up and interpret a multiple linear regression in Excel using the XLSTAT software.
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