Data Analysis Homework: Transactional Data, Regression, and Models

This assignment involves a comprehensive analysis of transactional data using various statistical techniques. The first part focuses on descriptive statistics, including ranking domain names and reference domains by transaction volume and summarizing key variables. The second part delves into logistic regression to predict a binary outcome (DIRECTP_D) based on several independent variables. The third part explores Poisson and negative binomial regression models to analyze the frequency of transactions (TRANS_FREQ) considering various factors. The final part employs linear regression to assess the relationship between duration and pages viewed. The assignment provides detailed model outputs, including coefficients, p-values, and interpretations, enabling a thorough understanding of the relationships within the dataset. The analysis includes model summaries, ANOVA tables, and parameter estimates, offering insights into the significance of each predictor variable and the overall model performance.