In this particular case, 60 % of the variability of the Weight is explained by the Height. The closer to 1 the R² is, the better the fit.
The R² (coefficient of determination) indicates the % of variability of the dependent variable which is explained by the explanatory variables. The first table displays the goodness of fit coefficients of the model. Interpreting the results of a simple linear regression The computations begin once you have clicked on OK. The quantitative explanatory variable is the "Height".Īs the column header was selected for the variables, the Variable labels option needs to be activated. In our case the Dependent variable (or variable to model) is the "Weight". Once you've clicked on the button, the Linear Regression dialog box will appear. Setting up a simple linear regressionĪfter opening XLSTAT, select the XLSTAT / Modeling data / Regression command (see below). This dataset is also used in the two tutorials on multiple linear regression and ANCOVA, with the Height, Age and then Gender as explanatory variables. The Linear Regression method belongs to a larger family of models called GLM (Generalized Linear Models), as do the ANCOVA. Using simple linear regression, we want to find out how the weight of the children varies with their height, 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. Not sure this is the modeling feature you are looking for? Check out this guide. Simple linear regression is based on Ordinary Least Squares (OLS). This tutorial will help you set up and interpret a simple linear regression in Excel using the XLSTAT software.