The objective of this code is to predict rainfall using simulated climate variables (temperature, pressure, humidity, wind) through various modeling approaches, ranging from linear and generalized regression to advanced models like M5P regression trees. The focus is on building and comparing predictive models, validating their performance using train/test splits and cross-validation, and quantitatively evaluating predictions with metrics such as RMSE and R². This code enables the exploration of model robustness, identification of the most influential variables, and visualization of model fit through plots comparing observed and predicted values, making the analysis both educational and applicable to real climate datasets.

The objective of this project is to identify relationships between various meteorological variables. This pursuit has two main goals: first, to explore correlations and the significance of relationships between climatic factors; second, to build and compare multiple linear regression models to determine the most relevant predictors of rainfall. This approach allows testing variable selection methods and performance criteria (adjusted R², MSE, AIC, Mallows’ Cp, etc.) in a controlled setting, providing a pedagogical exercise and a methodological foundation transferable to real-world climate data analysis.

wooldridge,stargazer,dplyr

Questo report offre un’analisi dettagliata del mercato immobiliare texano per il periodo 2010–2014, utilizzando esclusivamente i dati forniti nel file texas_data.csv. Le città analizzate sono: Beaumont, Bryan-College Station, Tyler, Wichita Falls.