PAF 516 Final Dashboard - Christopher Ogino

This chapter dives into the "engine room" of Data Science: Preprocessing. Students will learn that the quality of a model is determined long before it is trained, focusing on the critical steps required to turn messy, real-world data into a "model-ready" format. Core Topics covered: Why Preprocessing Matters Handling Missing Data Outlier Detection and Treatment Data Transformation Encoding Categorical Variables Feature Scaling Data Integration and Reshaping Chapter Lab Activity: Full Preprocessing Pipeline with msleep

This chapter addresses the foundational question of data science: "How do we ensure our data truly represents the world?" It explores the mechanics of selection, the math of sample size, and the power of computational resampling. Core Topics covered: Why Sampling Matters Probability Sampling Methods Non-Probability Sampling Methods Sample Size Determination Sampling Bias and Common Pitfalls Bootstrap Resampling Evaluating Sample Quality Chapter Lab Activity: Exploring Sampling with nhanes-Style Data

This chapter explores the statistical frameworks used to detect and quantify relationships between variables. It moves from testing the independence of categorical factors to measuring the strength and direction of associations in both discrete and continuous data. Core Topics covered: The Concept of Independence Chi-Square Test of Independence Fisher’s Exact Test Cramér’s V and Effect Size for Categorical Association Correlation Tests Point-Biserial and Phi Coefficients Partial Correlation Chapter Lab Activity: Exploring Independence with the titanic and mtcars Datasets

STAT 3280 Project