Syllabus

We covered Regression in Applied Stats.

We will review Regression and cover Time Series and Principle Components Analysis.

Reference Book

Reference Book

Probabilities

1 2 3 4 5 6

1 2 3 4 5 6 7

2 3 4 5 6 7 8

3 4 5 6 7 8 9

4 5 6 7 8 9 10

5 6 7 8 9 10 11

6 7 8 9 10 11 12

Probability Distribution

Conditional Probability & Bayesian Networks

Linear Regression

More Regression

• Interaction (Non-Linear)

• Structural Equation Modeling• Moderation• Mediation

• Advanced• Lasso• Ridge• Regularized

No

YesNo

Yes

Longitudinal & Time Series

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onal

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Pan

el D

ata

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.

Peop

le

Variables

Cluster Analysis

Customers are grouped by common characteristics

Peop

le

Variables Variable/Dimension Reduction

Principle Components Analysis & Factor Analysis

Tom BradyNot 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