Final Exam will cover ONLY modules 10 - 14! Below is a study guide for things you should know / be able to answer for the test. What does Analysis of Variance compare? Error Sum of Squares (SSE) Treatment Sum of Squares (SST) Interpreting R-squared values Residuals (Mean of) Correlation vs. Causation Least Squares Regression What is Back propagation? Features of Forecasting (Are forecasts perfect? Does the causal system have to remain the same? Forecasting is better in groups? Are short or Long range forecasts more accurate?) Is Gradient Descent an optimization problem? What is a Decision Tree? What task do they solve (e.g. Classification, Regression, Clustering)? What are some definitions for Text Mining? What does Tokenization mean? What types of Text Mining Tasks exist? (categorization, clustering, relationship modeling, etc.) What is Stemming in Text mining? When do we choose to use a z-test over a t-test? Interpreting the p-value (stronger evidence when p-value is larger or smaller?) Understanding Type I error vs. Type II error