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Ma 444: Regression & Time Series

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Linear regression, linear time series analysis, development and evaluation of regression and time series models, and forecasting. Exposure to a common analysis software package. Prerequisite(s): Ma 299, Ma 441. 3 Credits.

Spring 2023 Course Information

Additional Resources:

Discussion of dividing by n-1 for SD and stationarity of time series

Fully developed Linear Regression example using R

Chapters 3 - 6 Lecture Order

Text-Related Resources:

Author's Textbook Site - text errata is available here

Free Text on Regression and ANOVA in R

Forecasting:  Principles and Practices - free text using R

Little Book of R for Time Series

Matrix Differentiation - see Ch 5 (I think you will need to be on the campus network to access this) ... thanks to Stephen Sidwell for the resource

Inverse of Matrix of Matrices ... thanks to Dr. Brown for this resource

Proving :  see pgs 8-10 of the D. Zeng's document and especially slides 31, 35-36, 44, 47-48, and 52-53 of  K. Imai's slides.

Advanced Probability and Statistical Inference I - Donglin Zeng

 Linear Regression - Kosuke Imai

Additional Text:

An Introduction to Statistical Learning with Applications in R (see the SOA Exam SRM syllabus for a link to the full text)

Exam SRM: Statistics for Risk Modeling
Exam PA: Predictive Analytics

Assignments/Projects:

Recommended Homework

Project Requirements

Potential Data Sets

The projects give students the opportunity to develop the following professional competencies:

                   

Resources for Using R/RStudio/RMarkdown

Here are some of the resources that I think might be most immediately helpful:

R Markdown Reference Guide

R Markdown Cheat Sheet

Base R for Exam PA Cheat Sheet - basic math/data stuff used in analytics

Advanced R Cheat Sheet - writing functions/using subsets 

Data Visualization (ggplot2) Cheat Sheet - basic formatting of 2D and 3D plots

Plotly Cheat Sheet - dynamic graphing

Time Series Cheat Sheet

 Here's a link to additional R resources:

https://rstudio.com/resources/cheatsheets/

And don't forget the R Code that Frees has posted on the textbook website.  You can see how he generated all of the output/plots that he used in the book (under the Statistical Software link).