Se usó la data "mtcars" de R, para evaluar y corregir valores influenciales usando varios métodos para hallar posibles observaciones que estarían afectando nuestras estimaciones.

I delved deep into the world of Linear Models (Regression), where I explored various techniques to model relationships between variables. This journey began with simple linear regression on the well-known mtcars dataset. I started by fitting a model to understand how weight influences miles per gallon (mpg). I visualized the data and added regression lines, which helped me grasp the core concepts of building and interpreting linear models. Next, I explored the predict function, which allowed me to make predictions using my regression model. I enjoyed the hands-on experience of testing the model with new data and observing how well it performed. This practical aspect deepened my understanding of how predictions work and how crucial it is to use correctly formatted data frames. I also tackled the concept of weighting in regression. I found it fascinating to learn how analytic weights could enhance model precision by giving more importance to certain observations. Similarly, using sampling weights introduced me to handling data that may have sampling biases or missing values. It was intriguing to see how different weights impacted the model’s interpretation. As I progressed, I encountered nonlinearity and learned how to check for it using polynomial regression. This section was particularly enlightening as it showed me how relationships between variables might not always be linear. By fitting quadratic models, I could better capture the nuances in the data and improve model fit. In the plotting section, I had the opportunity to visualize regression results. I focused on creating publication-ready plots, which included regression lines, equations, and R-squared values. This not only enhanced my technical skills but also gave me a sense of accomplishment in presenting my findings clearly and effectively. Finally, I reflected on quality assessment of regression models. I understood the importance of diagnostic plots in checking model assumptions. By examining residuals and Q-Q plots, I could ensure my model was appropriately capturing the data's essence and meeting key assumptions like linearity and normality.

ROC Curve