3 classes matched your search criteria.

Spring 2020  |  PA 5044 Section 001: Applied Regression, Accelerated (57556)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Times and Locations:
First Half of Term
 
01/21/2020 - 03/16/2020
Mon, Wed 09:45AM - 11:00AM
UMTC, West Bank
Hubert H Humphrey Center 25
Enrollment Status:
Open (0 of 40 seats filled)
Also Offered:
Course Catalog Description:
Bivariate/multivariate models used in regression analysis, including assumptions behind them/problems that arise when assumptions are not met. Course covers similar topics as PA 5032 but uses more mathematical notation/delves deeper into theory/application of methods. prereq: [5031 or equiv} or instr consent
Class Notes:
http://classinfo.umn.edu/?jannaj+PA5044+Spring2020
Class Description:
This course is targeted towards students who intend on taking a quantitative approach to policy analysis during graduate school and in their future career. The course will cover the theory behind basic regression models, and illustrate their application in analyzing programs and policies. In order to delve deeply into the assumptions behind such models as well as to understand specific issues that can arise when these assumptions are not met, the course material will use more advanced mathematical notation and concepts, but no calculus is required. Ideally, students entering this course will have a background in economics and/or mathematics/statistics from their undergraduate education. The approach taken to understanding regression analysis in this class will prepare students for more advanced econometrics courses, either offered at the Humphrey school or at other departments in the University.

The syllabus below is from Spring 2016, but Spring 2017's course will be virtually identical. Please see the syllabus for grading and exam information. Contact the instructor with any questions.
Learning Objectives:
This course will cover bivariate and multivariate regression models, including the assumptions behind them and the problems that arise when these assumptions are not met. It covers the same topics as PA5032 (Regression Analysis) but in more depth and using more mathematical notation. Students will also become familiar with the Stata statistical package.
Grading:
45% Assignments (3)
40% Final Exam
15% Class Participation
Class Format:
I teach my lectures from PDF slides. I will try my best to post the day's slides to Moodle by midnight on the day before class. You are welcome to print out the slides and bring them to class to facilitate note-taking.
Textbooks:
https://bookstores.umn.edu/course-lookup/57556/1203
Past Syllabi:
http://classinfo.umn.edu/syllabi/jannaj_PA5044_Spring2017.pdf (Spring 2017)
http://classinfo.umn.edu/syllabi/jannaj_PA5044_Spring2016.pdf (Spring 2016)
Instructor Supplied Information Last Updated:
21 February 2017

Spring 2020  |  PA 5044 Section 002: Applied Regression, Accelerated (57557)

Instructor(s)
Class Component:
Laboratory
Times and Locations:
First Half of Term
 
01/21/2020 - 03/16/2020
Fri 09:45AM - 11:00AM
UMTC, West Bank
Hubert H Humphrey Center 85
Auto Enrolls With:
Section 001
Enrollment Status:
Open (0 of 20 seats filled)
Course Catalog Description:
Bivariate/multivariate models used in regression analysis, including assumptions behind them/problems that arise when assumptions are not met. Course covers similar topics as PA 5032 but uses more mathematical notation/delves deeper into theory/application of methods. prereq: [5031 or equiv} or instr consent
Class Notes:
http://classinfo.umn.edu/?jannaj+PA5044+Spring2020
Class Description:
This course is targeted towards students who intend on taking a quantitative approach to policy analysis during graduate school and in their future career. The course will cover the theory behind basic regression models, and illustrate their application in analyzing programs and policies. In order to delve deeply into the assumptions behind such models as well as to understand specific issues that can arise when these assumptions are not met, the course material will use more advanced mathematical notation and concepts, but no calculus is required. Ideally, students entering this course will have a background in economics and/or mathematics/statistics from their undergraduate education. The approach taken to understanding regression analysis in this class will prepare students for more advanced econometrics courses, either offered at the Humphrey school or at other departments in the University.

The syllabus below is from Spring 2016, but Spring 2017's course will be virtually identical. Please see the syllabus for grading and exam information. Contact the instructor with any questions.
Learning Objectives:
This course will cover bivariate and multivariate regression models, including the assumptions behind them and the problems that arise when these assumptions are not met. It covers the same topics as PA5032 (Regression Analysis) but in more depth and using more mathematical notation. Students will also become familiar with the Stata statistical package.
Grading:
45% Assignments (3)
40% Final Exam
15% Class Participation
Class Format:
I teach my lectures from PDF slides. I will try my best to post the day's slides to Moodle by midnight on the day before class. You are welcome to print out the slides and bring them to class to facilitate note-taking.
Textbooks:
https://bookstores.umn.edu/course-lookup/57557/1203
Past Syllabi:
http://classinfo.umn.edu/syllabi/jannaj_PA5044_Spring2017.pdf (Spring 2017)
http://classinfo.umn.edu/syllabi/jannaj_PA5044_Spring2016.pdf (Spring 2016)
Instructor Supplied Information Last Updated:
21 February 2017

Spring 2020  |  PA 5044 Section 003: Applied Regression, Accelerated (57558)

Instructor(s)
Class Component:
Laboratory
Times and Locations:
First Half of Term
 
01/21/2020 - 03/16/2020
Fri 11:15AM - 12:30PM
UMTC, West Bank
Hubert H Humphrey Center 85
Auto Enrolls With:
Section 001
Enrollment Status:
Open (0 of 20 seats filled)
Course Catalog Description:
Bivariate/multivariate models used in regression analysis, including assumptions behind them/problems that arise when assumptions are not met. Course covers similar topics as PA 5032 but uses more mathematical notation/delves deeper into theory/application of methods. prereq: [5031 or equiv} or instr consent
Class Notes:
http://classinfo.umn.edu/?jannaj+PA5044+Spring2020
Class Description:
This course is targeted towards students who intend on taking a quantitative approach to policy analysis during graduate school and in their future career. The course will cover the theory behind basic regression models, and illustrate their application in analyzing programs and policies. In order to delve deeply into the assumptions behind such models as well as to understand specific issues that can arise when these assumptions are not met, the course material will use more advanced mathematical notation and concepts, but no calculus is required. Ideally, students entering this course will have a background in economics and/or mathematics/statistics from their undergraduate education. The approach taken to understanding regression analysis in this class will prepare students for more advanced econometrics courses, either offered at the Humphrey school or at other departments in the University.

The syllabus below is from Spring 2016, but Spring 2017's course will be virtually identical. Please see the syllabus for grading and exam information. Contact the instructor with any questions.
Learning Objectives:
This course will cover bivariate and multivariate regression models, including the assumptions behind them and the problems that arise when these assumptions are not met. It covers the same topics as PA5032 (Regression Analysis) but in more depth and using more mathematical notation. Students will also become familiar with the Stata statistical package.
Grading:
45% Assignments (3)
40% Final Exam
15% Class Participation
Class Format:
I teach my lectures from PDF slides. I will try my best to post the day's slides to Moodle by midnight on the day before class. You are welcome to print out the slides and bring them to class to facilitate note-taking.
Textbooks:
https://bookstores.umn.edu/course-lookup/57558/1203
Past Syllabi:
http://classinfo.umn.edu/syllabi/jannaj_PA5044_Spring2017.pdf (Spring 2017)
http://classinfo.umn.edu/syllabi/jannaj_PA5044_Spring2016.pdf (Spring 2016)
Instructor Supplied Information Last Updated:
21 February 2017

ClassInfo Links - Spring 2020 5000 Level Public Affairs Classes Taught by Janna Johnson

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