In traditional models like linear regression and ANOVA, assumptions such as linearity, independence of errors, homoscedasticity, and normality of residuals are foundational.

This can be extended to more than two explanatory variables. However, in practice it is best to keep regression models as simple as possible as it is less likely to violate the assumptions. - Multiple ...

Residual plots can be used to validate assumptions about the regression model. Figure 1: Residual plots are helpful in assessments of nonlinear trends and heteroscedasticity. A formal test of lack ...

If this hypothesis is true, then our linear model is not "useful," in the sense that our explanatory variable does not help us explain the value of our response variable. ... Regression models are ...

R 2 is a statistical measure of the goodness of fit of a linear regression model (from 0.00 to 1.00), also known as the coefficient of determination. In general, the higher the R 2 , the better ...

Duration: 12h. In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial ...

Model building via linear regression models. Method of least squares, theory and practice. Checking for adequacy of a model, examination of residuals, checking outliers. Practical hand on experience ...