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

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Presentation transcript:

Syllabus

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 Interaction (Non-Linear) Structural Equation Modeling Moderation Mediation Advanced Lasso Ridge Regularized

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

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

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

Bank Loan

Decision Trees

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

People Variables Cluster Analysis Customers are grouped by common characteristics

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

Tom Brady Not Tom Brady Machine Learning

Same Data, Different Algorithms

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

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

Text Mining / Sentiment Analysis

Social Network Analysis

Conditional Probability & Bayesian Networks