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Syllabus

Syllabus. Reference Book. We covered Regression in Applied Stats. We will review Regression and cover Time Series and Principle Components Analysis. . Reference Book. Probabilities. Probability Distribution. Conditional Probability & Bayesian Networks. Linear Regression. More Regression.

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Syllabus

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  1. Syllabus

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

  3. Reference Book

  4. Probabilities Probability Distribution

  5. Conditional Probability & Bayesian Networks

  6. Linear Regression

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

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

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

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

  11. Bank Loan

  12. Decision Trees

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

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

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

  16. Machine Learning Tom Brady Not Tom Brady

  17. Same Data, Different Algorithms

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

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

  20. Text Mining / Sentiment Analysis

  21. Social Network Analysis

  22. Conditional Probability & Bayesian Networks

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