Model Selection in Statistics

This lecture by the instructor covers the topic of model selection in statistics, focusing on principles like the principle of parsimony and the principle of entropy. It discusses how to choose the right probabilistic model and why the assumed distribution is a good fit for the phenomenon. The lecture also explores methods to evaluate important characteristics when selecting a model, such as position, dispersion, and behavior of tails. It further delves into numerical and graphical summaries, including mean, median, variance, and absolute deviation. The lecture concludes with a detailed explanation of histograms, density plots, and boxplots for data visualization and analysis.