How to calculate r-squared in rstudio

R-squared (R²) is a measure of goodness-of-fit that quantifies the proportion of variance in the dependent variable explained by the independent variables in a regression model.

While commonly used in linear regression, R-squared can also be calculated for generalized linear models (GLMs), which encompass various types of regression models beyond the linear framework. In this guide, we'll walk through the process of calculating R-squared for GLM in R Programming Language.

Understanding R-Squared in GLM

In GLM, R-squared represents the proportion of the deviance explained by the model.

Deviance is a measure of model fit analogous to the residual sum of squares in linear regression.

What is a good r-squared value

R-squared ranges from 0 to 1, where a higher value indicates a better fit of the model to the data.

Calculating R-Squared for GLM

To calculate R-squared for GLMs in R, you first need to fit the GLM using the function.

Once the model is fitted, you can extract the deviance and null deviance from the model summary.

Now we will discuss step by step for How to C

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