Skip to main content

Ma 415: Regression & Time Series

To edit course information, hover your mouse over this help box and click the pencil icon that appears above it. After you make changes, click Save, then click the check box that appears above this box to publish the changes.

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 2019 Course Information

Text-Related Resources:

Author's Textbook Site - Text Errata

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

Homework:

1.1, 1.2, 1.5
2.1, 2.4, 2.8, 2.9, 2.10, 2.19, 2.20, 2.21
3.4, 3.5, 3.6
4.3, 4.5, 4.6
5.3, 5.4    (ch 5-6 due Wed, April 17)
6.1 (a-h), 6.2
7.3 and for Ch 8 from https://www.otexts.org/fpp/8/11:  work at least two out of #6-10, and then review the entire solutions for #6-11
(ch 7-8 hmk due with test on 4/24)

Alternate Online Resource:

An Introduction to Statistical Learning with Applications in R

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

Projects:

BJU Academic Integrity Policy

Project Management

Each project will tentatively consist of the following components (and comprises the majority of your grade for this course): 

  • Assumptions for Analysis  and Selection of Data Set - data definitions, # data points - 5 pts
    Due 1/14, 1/23, 3/27
  • Project Management Plan - detailed, with deadlines and measurement of success - 10 pts
    Due 1/30, 2/1, 3/29
  • How-to Log - a record of how one proceeds with a regression/time series analysis - 10 pts
    Due 3/11, 4/15
  • Analysis Report - lab report style, annotated, to evaluate the thoroughness/quality of your analysis - 50 pts
    Due 3/15, 4/19
  • Decision Maker Report - to evaluate the quality of your solution and presentation - 50 pts
    Due 3/18, 4/22
  • Oral Presentation - 25 pts
    Given 4/1-4/3, 4/29