This assignment uses multiple linear regression to analyze factors influencing Mario Kart auction prices. The model evaluates variables such as auction duration, item condition, stock photo usage, and number of wheels. Results highlight the number of wheels as a key driver of price, while other variables show limited significance. The analysis includes model evaluation, coefficient interpretation, and a prediction for a new scenario, demonstrating the practical applications of regression with real-world data.

This project applies classification techniques, including logistic regression, LDA, and Bayesian methods, to analyze and predict outcomes from the given data. The analysis compares model performance, interprets key metrics such as accuracy and error rates, and evaluates how different approaches handle classification problems. The assignment highlights the strengths and limitations of each method and demonstrates practical applications of statistical learning techniques in real-world scenarios.

This assignment explores different types of data using the Gapminder dataset, including variables such as life expectancy, population, and GDP per capita. The analysis focuses on distinguishing between quantitative and categorical data and understanding how data types influence interpretation and analysis.