1. Syllabus

2. We covered Regression in Applied Stats. We will review Regression and cover Time Series and Principle Components Analysis. Reference Book

5. Probabilities 123456 1234567 2345678 3456789 45678910 56789 11 6789101112 Probability Distribution

6. Conditional Probability & Bayesian Networks

7. Linear Regression

8. More Regression Interaction (Non-Linear) Structural Equation Modeling Moderation Mediation Advanced Lasso Ridge Regularized

9. No Yes No Yes Longitudinal & Time Series Cross-Sectional & Panel Data PEW Mobile Phone Galton Children Height Census Stock Market Historical River Levels Old Faithful Web Analytics Titanic Survivors Bank Loans

10. plot(stl(beer,s.window="periodic")) Time Series

11. Datasets: Training and Test Develop Model Using Training Dataset and Apply to Test Data

12. Bank Loan

14. Decision Trees

16. Principle Components Analysis & Factor Analysis Here 13 variables are reduced to 4.

17. People Variables Cluster Analysis Customers are grouped by common characteristics

18. People Variables Variable/Dimension Reduction Principle Components Analysis & Factor Analysis

19. Tom Brady Not Tom Brady Machine Learning

20. Same Data, Different Algorithms

22. One aspect of Predictive Modeling is comparing the performance of various models towards then choosing the one which performs best

23. “Combine predictions from multiple, complementary models… one model’s strengths compensating for the weaknesses of others.” Ensembles of People and Approaches

24. Text Mining / Sentiment Analysis

25. Social Network Analysis

26. Conditional Probability & Bayesian Networks