5 classes matched your search criteria.

Spring 2020  |  PA 5032 Section 001: Applied Regression (57499)

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
Enrollment Status:
Open (0 of 48 seats filled)
Also Offered:
Course Catalog Description:
Bivariate/multivariate models of regression analysis, assumptions behind them. Problems using these models when such assumptions are not met. prereq: [5031 or equiv] or instr consent
Class Notes:
http://classinfo.umn.edu/?kudrle+PA5032+Spring2019
Class Description:
This course is designed to help you read, understand, interpret, use and evaluate empirical work. To advance that goal, attention is concentrated on one of the main techniques used by social scientists and public policy researchers: regression analysis. You will learn the assumptions that underlie both bivariate and multivariate regression.
Learning Objectives:
You will learn how to perform regressions using STATA, perhaps the most widely used computer program in advanced social science research. Most important of all, you will learn to spot violations of the assumptions that give regression results desirable qualities and how to take the corrective measures necessary to improve your ability to make valid inferences
Grading:
The course requirements include three problem sets (45 % of the course grade), a final exam (40%) and oral presentations in teams and class participation (15% together). The examination will be closed book.
Exam Format:
closed book
Class Format:
closed book
Textbooks:
https://bookstores.umn.edu/course-lookup/57499/1203
Past Syllabi:
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2019.docx (Spring 2019)
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2018.pdf (Spring 2018)
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2016.pdf (Spring 2016)
Instructor Supplied Information Last Updated:
6 November 2017

Spring 2020  |  PA 5032 Section 002: Applied Regression (57500)

Instructor(s)
Class Component:
Laboratory
Times and Locations:
First Half of Term
 
01/21/2020 - 03/16/2020
Fri 01:50PM - 02:40PM
UMTC, West Bank
Auto Enrolls With:
Section 001
Enrollment Status:
Open (0 of 24 seats filled)
Course Catalog Description:
Bivariate/multivariate models of regression analysis, assumptions behind them. Problems using these models when such assumptions are not met. prereq: [5031 or equiv] or instr consent
Class Notes:
http://classinfo.umn.edu/?kudrle+PA5032+Spring2019
Class Description:
This course is designed to help you read, understand, interpret, use and evaluate empirical work. To advance that goal, attention is concentrated on one of the main techniques used by social scientists and public policy researchers: regression analysis. You will learn the assumptions that underlie both bivariate and multivariate regression.
Learning Objectives:
You will learn how to perform regressions using STATA, perhaps the most widely used computer program in advanced social science research. Most important of all, you will learn to spot violations of the assumptions that give regression results desirable qualities and how to take the corrective measures necessary to improve your ability to make valid inferences
Grading:
The course requirements include three problem sets (45 % of the course grade), a final exam (40%) and oral presentations in teams and class participation (15% together). The examination will be closed book.
Exam Format:
closed book
Class Format:
closed book
Textbooks:
https://bookstores.umn.edu/course-lookup/57500/1203
Past Syllabi:
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2019.docx (Spring 2019)
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2018.pdf (Spring 2018)
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2016.pdf (Spring 2016)
Instructor Supplied Information Last Updated:
6 November 2017

Spring 2020  |  PA 5032 Section 003: Applied Regression (57521)

Instructor(s)
Class Component:
Laboratory
Times and Locations:
First Half of Term
 
01/21/2020 - 03/16/2020
Fri 12:45PM - 01:35PM
UMTC, West Bank
Auto Enrolls With:
Section 001
Enrollment Status:
Open (0 of 24 seats filled)
Course Catalog Description:
Bivariate/multivariate models of regression analysis, assumptions behind them. Problems using these models when such assumptions are not met. prereq: [5031 or equiv] or instr consent
Class Notes:
http://classinfo.umn.edu/?kudrle+PA5032+Spring2019
Class Description:
This course is designed to help you read, understand, interpret, use and evaluate empirical work. To advance that goal, attention is concentrated on one of the main techniques used by social scientists and public policy researchers: regression analysis. You will learn the assumptions that underlie both bivariate and multivariate regression.
Learning Objectives:
You will learn how to perform regressions using STATA, perhaps the most widely used computer program in advanced social science research. Most important of all, you will learn to spot violations of the assumptions that give regression results desirable qualities and how to take the corrective measures necessary to improve your ability to make valid inferences
Grading:
The course requirements include three problem sets (45 % of the course grade), a final exam (40%) and oral presentations in teams and class participation (15% together). The examination will be closed book.
Exam Format:
closed book
Class Format:
closed book
Textbooks:
https://bookstores.umn.edu/course-lookup/57521/1203
Past Syllabi:
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2019.docx (Spring 2019)
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2018.pdf (Spring 2018)
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2016.pdf (Spring 2016)
Instructor Supplied Information Last Updated:
6 November 2017

Spring 2020  |  PA 5032 Section 004: Applied Regression (57513)

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 05:45PM - 07:00PM
UMTC, West Bank
Enrollment Status:
Open (0 of 48 seats filled)
Also Offered:
Course Catalog Description:
Bivariate/multivariate models of regression analysis, assumptions behind them. Problems using these models when such assumptions are not met. prereq: [5031 or equiv] or instr consent
Class Notes:
http://classinfo.umn.edu/?kudrle+PA5032+Spring2019
Class Description:
This course is designed to help you read, understand, interpret, use and evaluate empirical work. To advance that goal, attention is concentrated on one of the main techniques used by social scientists and public policy researchers: regression analysis. You will learn the assumptions that underlie both bivariate and multivariate regression.
Learning Objectives:
You will learn how to perform regressions using STATA, perhaps the most widely used computer program in advanced social science research. Most important of all, you will learn to spot violations of the assumptions that give regression results desirable qualities and how to take the corrective measures necessary to improve your ability to make valid inferences
Grading:
The course requirements include three problem sets (45 % of the course grade), a final exam (40%) and oral presentations in teams and class participation (15% together). The examination will be closed book.
Exam Format:
closed book
Class Format:
closed book
Textbooks:
https://bookstores.umn.edu/course-lookup/57513/1203
Past Syllabi:
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2019.docx (Spring 2019)
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2018.pdf (Spring 2018)
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2016.pdf (Spring 2016)
Instructor Supplied Information Last Updated:
6 November 2017

Spring 2020  |  PA 5032 Section 005: Applied Regression (57520)

Instructor(s)
Class Component:
Laboratory
Times and Locations:
First Half of Term
 
01/21/2020 - 03/16/2020
Wed 07:15PM - 08:05PM
UMTC, West Bank
Auto Enrolls With:
Section 004
Enrollment Status:
Open (0 of 24 seats filled)
Course Catalog Description:
Bivariate/multivariate models of regression analysis, assumptions behind them. Problems using these models when such assumptions are not met. prereq: [5031 or equiv] or instr consent
Class Notes:
http://classinfo.umn.edu/?kudrle+PA5032+Spring2019
Class Description:
This course is designed to help you read, understand, interpret, use and evaluate empirical work. To advance that goal, attention is concentrated on one of the main techniques used by social scientists and public policy researchers: regression analysis. You will learn the assumptions that underlie both bivariate and multivariate regression.
Learning Objectives:
You will learn how to perform regressions using STATA, perhaps the most widely used computer program in advanced social science research. Most important of all, you will learn to spot violations of the assumptions that give regression results desirable qualities and how to take the corrective measures necessary to improve your ability to make valid inferences
Grading:
The course requirements include three problem sets (45 % of the course grade), a final exam (40%) and oral presentations in teams and class participation (15% together). The examination will be closed book.
Exam Format:
closed book
Class Format:
closed book
Textbooks:
https://bookstores.umn.edu/course-lookup/57520/1203
Past Syllabi:
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2019.docx (Spring 2019)
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2018.pdf (Spring 2018)
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2016.pdf (Spring 2016)
Instructor Supplied Information Last Updated:
6 November 2017

ClassInfo Links - Spring 2020 5000 Level Public Affairs Classes Taught by Robert Kudrle

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