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Viewing: ST 430 : Introduction to Regression Analysis

Last approved: Mon, 26 Oct 2015 23:15:44 GMT

Last edit: Mon, 26 Oct 2015 23:15:39 GMT

Change Type
ST (Statistics)
430
020225
Dual-Level Course
No
Cross-listed Course
No
Introduction to Regression Analysis
Intro Regress Anly
College of Sciences
Statistics (17ST)
Term Offering
Fall and Summer
Offered Every Year
Fall 2014
Previously taught as Special Topics?
No
 
Course Delivery
Face-to-Face (On Campus)

Grading Method
Graded with S/U option
3
16
Contact Hours
(Per Week)
Component TypeContact Hours
Lecture3.0
Course Attribute(s)


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

Course Is Repeatable for Credit
No
 
 
Herle McGowan
Teaching Associate Professor

Open when course_delivery = campus OR course_delivery = blended OR course_delivery = flip
Enrollment ComponentPer SemesterPer SectionMultiple Sections?Comments
Lecture6565NoExpected enrollment in the summer term will likely be less.
Open when course_delivery = distance OR course_delivery = online OR course_delivery = remote
Prerequisites: (ST 305 or ST 312 or ST 372) and ST 307 and (MA 305 or MA 405)
Is the course required or an elective for a Curriculum?
Yes
SIS Program CodeProgram TitleRequired or Elective?
17STBSStatisticsRequired
Regression analysis as a flexible statistical problem solving methodology. Matrix review; variable selection; prediction; multicolinearity; model diagnostics; dummy variables; logistic and non-linear regression. Emphasizes use of computer.

The pre-requisites have been revised to reflect changes in the Statistics major and minor curricula. Dropping ST 302 as a prerequisite (which is no longer taught), adding ST 312 and ST 372 (in addition to ST 305) as alternative prerequisites. ST 307 (new course) is being added as a prerequisite.


No

Is this a GEP Course?
No
GEP Categories

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&
 

 
 

 
 

 
 

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.
 

No new resources are needed for this course. Course will be taught as part of faculty's regular teaching load, with the help of a 20-hour/week Teaching Assistant.

Student Learning Outcomes

Students will learn to:



  • Apply the technique of least squares regression to fit models, both linear and non-linear, to continuous outcome variables

  • Apply the technique of logistic regression to fit models to binary outcome variables

  • Assess how well a particular model fits a set of data


Evaluation MethodWeighting/Points for EachDetails
Homework100Weekly homework assignments will be posted to the course website and will be due at the beginning of class one week after it is assigned.
Multiple exams200There will be one midterm exam and one cumulative final exam for this course.

gmneugeb (Wed, 07 Oct 2015 18:20:59 GMT): UCCC has approved this action pending submission of a Fall or Spring semester syllabus. Once attached, please click approve, and the action will be routed onto OUCC review and UCCC Chair approval. Please contact Gina Neugebauer (gmneugeb@ncsu.edu) with any questions.
Key: 5087