Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making.

Key methods include the use of Log Transform to normalize the burned area data, improving the performance of linear regression models. Data analysis and visualizations like histograms and QQ plots are ...

This project focuses on predicting forest fire areas using the Fire Weather Index (FWI) dataset. Key methods include the use of Log Transform to normalize the burned area data, improving the ...

Assumptions Checking: Linear regression relies on certain assumptions, such as linearity, independence of errors, homoscedasticity, and normality of residuals. Verify these assumptions to ensure the ...

Managing the General Linear Model Assumption checking: Regularly check the assumptions of the GLM using diagnostic plots and tests.