This assignment presents a comprehensive mini-analysis of linear models, focusing on data transformation techniques and the validation of underlying assumptions. It begins with an examination of TV data, exploring various transformations like log and inverse to address violations of linear regression assumptions. The analysis then shifts to King County house prices, employing univariate linear regression and transformations to model the relationship between price and factors like bathrooms, living room size, and lot size. The assignment further delves into building multivariate models, identifying statistically significant variables for price prediction, and evaluating model fit using metrics like R-squared. Finally, it explores the application of Poisson and negative binomial regression for predicting the number of bedrooms, highlighting the importance of variable selection and model interpretation. The assignment uses R for statistical analysis, includes code snippets, and presents visualizations to support findings. The assignment is available on Desklib, a platform offering study tools and resources for students.
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