Date Submitted: Tue, 20 Mar 2018 19:01:14 GMT

Viewing: ST 434 / ST 534 : Applied Time Series

Last approved: Wed, 07 Mar 2018 16:07:30 GMT

Last edit: Wed, 07 Mar 2018 16:07:27 GMT

Changes proposed by: boos
Catalog Pages referencing this course
Change Type
Major
ST (Statistics)
434
032430
Dual-Level Course
Yes
534
Cross-listed Course
No
Applied Time Series
Applied Time Series
College of Sciences
Statistics (17ST)
Term Offering
Fall Only
Offered Every Year
Fall 2015
Previously taught as Special Topics?
Yes
2
 
Course Prefix/NumberSemester/Term OfferedEnrollment
ST 495/ST 590Fall2015: 495-8, 590-22; 2016: 495-5, 590-31
Course Delivery
Face-to-Face (On Campus)
Distance Education (DELTA)
Online (Internet)

Grading Method
Graded with S/U option
3
16
Contact Hours
(Per Week)
Component TypeContact Hours
Lecture3
Course Attribute(s)


If your course includes any of the following competencies, check all that apply.
University Competencies

Course Is Repeatable for Credit
No
 
 
Soumendra Lahiri
Professor
full

Open when course_delivery = campus OR course_delivery = blended OR course_delivery = flip
Enrollment ComponentPer SemesterPer SectionMultiple Sections?Comments
Lecture1010NoThis will be a dual listed course along with ST 534, graduate version. We expect 40 total students per semester.
Open when course_delivery = distance OR course_delivery = online OR course_delivery = remote
Delivery FormatPer SemesterPer SectionMultiple Sections?Comments
LEC1010Nograduate version online
P: ST 422 and ST 430
P: ST 512 or ST 514 or ST 515 or ST 517
Is the course required or an elective for a Curriculum?
Yes
SIS Program CodeProgram TitleRequired or Elective?
17STBSStatistics BSElective
Statistical models and methods for the analysis of time series data using both time domain and frequency domain approaches. A brief review of necessary statistical concepts and R will be given at the beginning. Analyses of real data sets using the statistical software packages will be emphasized.

Applied Time Series plays an important role in equipping students from diverse disciplines within the university with the tools necessary to perform rigorous analyses of time series data sets.  This new course is aimed at Statistics Master’s students, advanced undergraduate Statistics majors, and PhD students outside of Statistics.  It had previously been taught as ST 730, but undergraduates were not allowed to take it.  The last two years we have taught this new dual-level course as a special topics course.


No

Is this a GEP Course?
No
GEP Categories

Humanities Open when gep_category = HUM
Each course in the Humanities category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

Mathematical Sciences Open when gep_category = MATH
Each course in the Mathematial Sciences category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

Natural Sciences Open when gep_category = NATSCI
Each course in the Natural Sciences category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

Social Sciences Open when gep_category = SOCSCI
Each course in the Social Sciences category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

Interdisciplinary Perspectives Open when gep_category = INTERDISC
Each course in the Interdisciplinary Perspectives category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

 
 

 
 

Visual & Performing Arts Open when gep_category = VPA
Each course in the Visual and Performing Arts category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

Health and Exercise Studies Open when gep_category = HES
Each course in the Health and Exercise Studies category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
&
 

 
 

 
 

 
 

Global Knowledge Open when gep_category = GLOBAL
Each course in the Global Knowledge category of the General Education Program will provide instruction and guidance that help students to achieve objective #1 plus at least one of objectives 2, 3, and 4:
 
 

 
 

 
Please complete at least 1 of the following student objectives.
 

 
 

 
 

 
 

 
 

 
 

US Diversity Open when gep_category = USDIV
Each course in the US Diversity category of the General Education Program will provide instruction and guidance that help students to achieve at least 2 of the following objectives:
Please complete at least 2 of the following student objectives.
 
 

 
 

 
 

 
 

 
 

 
 

 
 

 
 

Requisites and Scheduling
 
a. If seats are restricted, describe the restrictions being applied.
 

 
b. Is this restriction listed in the course catalog description for the course?
 

 
List all course pre-requisites, co-requisites, and restrictive statements (ex: Jr standing; Chemistry majors only). If none, state none.
 

 
List any discipline specific background or skills that a student is expected to have prior to taking this course. If none, state none. (ex: ability to analyze historical text; prepare a lesson plan)
 

Additional Information
Complete the following 3 questions or attach a syllabus that includes this information. If a 400-level or dual level course, a syllabus is required.
 
Title and author of any required text or publications.
 

 
Major topics to be covered and required readings including laboratory and studio topics.
 

 
List any required field trips, out of class activities, and/or guest speakers.
 

Dr. Lahiri will teach the course as part of his standard teaching obligation, thus no new resources are required.

Students will gain a basic competency in statistical analysis of time series data sets. They will be able to use SAS or R to carry out these analyses. Students will learn to interpret the output from these programs and be able to integrate those results into their research publications.


Student Learning Outcomes

By the end of the course, the students will be able to:


1] Select, carry out, and interpret appropriate statistical methods for time series data sets;


2] Use Statistical software package(s) to produce analyses of time series data and effectively communicate their findings through graphical and quantitative representations.


3] Critically evaluate their own and other researchers’ use of time series methods.


ST 534 Only: Demonstrate the ability to work with more theoretical aspects of selected topics via derivations, proofs, or other more advanced statistical techniques 


Evaluation MethodWeighting/Points for EachDetails
Homework20%Up to 6 problems every two weeks. ST 534 students will have additional HW problems that address the additional learning outcomes for graduate students.
Midterm40%In-class, closed book / closed notes. ST 534 students will have additional Exam questions that address the additional learning outcomes for graduate students.
Final Exam40%ST 534 students will have additional Exam questions that address the additional learning outcomes for graduate students.
TopicTime Devoted to Each TopicActivity
Introductory data examples 1 week
Different approaches to time series analysis 1 week
Brief introduction to software1 week
Exploratory Time Series Data Analysis techniques1 week
Basic time domain models 2 weeks
Autocovariance and related functions and their properties 1 week
Review of basic statistical inference concepts1 week
Large sample distributions and diagnostic tools1 week
Prediction in the Time Domain approach 1 week
Seasonal time series 1 week
Introduction to the Frequency Domain approach1 week
Spectral densities and their properties 1 week
Time series regression and forecasting1 week
ARCH/GARCH models1 week
Final exam1 week
ST 434 and 534 will differ in the following way. 534 will have an additional learning outcome: Demonstrate the ability to work with more theoretical aspects of selected topics via derivations, proofs, or other more advanced statistical techniques. 534 students will have additional HW and Exam questions to evaluate this learning outcome. Note that ST 534 was previously taught as ST 730, which no longer exists.

mlnosbis 1/10/2017: No overlapping courses, no consultation required. There are several 700-level ST courses related to time series data, but the focus appears different from this proposed course. 1) Has this been taught previously as a special topics course to generate and track interest? What are the previous enrollment numbers?

ABGS Reviewer Comments:
-Do we want additional detail about the additional homework and additional exam questions for the 500-level? It seems that in the past we have asked for clearer articulation.

pjharrie 1/31/2017 I think the more clearly the differences between 400/500 levels are articulated, the better. So, I feel that the ABGS Reviewer comments should be addressed.
lamarcus (Fri, 05 Feb 2016 21:42:33 GMT): Rollback: Rolled back at instructor request
allloyd (Mon, 21 Nov 2016 15:35:13 GMT): Passed college committee: 11/11/16
Key: 8733