This R programming assignment focuses on the analysis of a crime dataset from the UCI machine learning repository. The student begins with exploratory data analysis (EDA), selecting key predictors and generating scatter plots. A linear model is then fitted using the chosen predictors, and the output is interpreted. Model selection is performed using automated methods like fastbw() and stepAIC(), followed by diagnostic checks to assess model assumptions. The assignment also includes investigation of outliers and influential observations, and application of transformations if needed. Finally, the student reports inferences, makes predictions using the final model, and reports parameter estimates, confidence intervals, and prediction intervals. The assignment includes R code, output, and explanations for each step, culminating in a comprehensive analysis of the crime data.