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 404, Ma 150. 3 Credits.
Spring 2023 Course Information
Author's Textbook Site - text errata is available here
Forecasting: Principles and Practices - free text using R
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.
An Introduction to Statistical Learning with Applications in R (see the SOA Exam SRM syllabus for a link to the full text)
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:
Here's a link to additional R resources:
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).