Predictive Analytics
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Predictive analytics is the branch of data mining concerned with the prediction of future probabilities and trends. The central element of predictive analytics is the predictor, a variable that can be measured for an individual or other entity to predict future behavior. This lesson explains several predictive models for forecasting.
Objectives
Upon completion of this lesson, you will be able to
Upon completion of this lesson, you will be able to
- build weighted average forecasting models
- construct simple, multiple, and logistic regression models
- evaluate multiple regression models
- incorporate trend and seasonality
- compare models for reliability
- recognize bias in results
Required Readings
- Text Book
Example Data Sets & Code
- Cereal Data | IQ & Rock Music Data | Sales Data | Grades Data | Normality Testing | Slide Deck Data
- Call Center Data | Housing Data | Data Center Data | UFFI Data | Salary Data (XSLX | CSV)
- Titanic Survival Data
Supporting Software & Tools
- TBD
Suggested Readings
- Keenan, Tyler (2017). An Introduction to Predictive Analytics. B2C. February 27, 2017.
- Ray, Sunill (2015). 7 types of regression you should know. Analytics Vidhya.
- Quick, John. Multiple Linear Regression in R. R-Bloggers. December 8, 2009.
- R-Tutor: Multiple Linear Regression.
- Simple Linear Regression in R. R-Bloggers. April 23, 2010.
- R: lm() vs glm() for Regression Modeling.
- Martz, E. (2013). Enough Is Enough! Handling Multicollinearity in Regression Analysis. 16 April, 2013
- Alice, Michy. How to perform a Logistic Regression in R. R-Blogger, September 13, 2015.
- Tashian, Carl (2017). A Brief History of Random Numbers. March 10, 2017.
- Vorhies, William (2015). 7 Common Biases that Skew Big Data Results. Data Science Central. July 16, 2015.
- King, William (2015). Statistics Tutorials: Logistic Regression.
- Woolridge, Jeffrey (2015). Introductory Econometrics. Chapters 10 - 12 on Time Series Regression.