5 classes matched your search criteria.
PA 5032 is also offered in Spring 2025
PA 5032 is also offered in Spring 2024
PA 5032 is also offered in Spring 2023
PA 5032 is also offered in Spring 2022
Spring 2018 | PA 5032 Section 001: Regression Analysis (54794)
- Instructor(s)
- Class Component:
- Lecture
- Instructor Consent:
- No Special Consent Required
- Instruction Mode:
- In Person Term Based
- Times and Locations:
- First Half of Term01/16/2018 - 03/05/2018Mon, Wed 09:45AM - 11:00AMUMTC, West BankHanson Hall 1-107
- Enrollment Status:
- Open (39 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+Spring2018
- 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/54794/1183
- Syllabus:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2018.pdf
- Past Syllabi:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2019.docx (Spring 2019)
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2016.pdf (Spring 2016) - Instructor Supplied Information Last Updated:
- 6 November 2017
Spring 2018 | PA 5032 Section 002: Regression Analysis (54795)
- Instructor(s)
- Class Component:
- Laboratory
- Times and Locations:
- First Half of Term01/16/2018 - 03/05/2018Fri 01:50PM - 02:40PMUMTC, West BankHubert H Humphrey Center 85
- Auto Enrolls With:
- Section 001
- Enrollment Status:
- Open (19 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+Spring2018
- 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/54795/1183
- Syllabus:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2018.pdf
- Past Syllabi:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2019.docx (Spring 2019)
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2016.pdf (Spring 2016) - Instructor Supplied Information Last Updated:
- 6 November 2017
Spring 2018 | PA 5032 Section 003: Regression Analysis (54820)
- Instructor(s)
- Class Component:
- Laboratory
- Times and Locations:
- First Half of Term01/16/2018 - 03/05/2018Fri 12:45PM - 01:35PMUMTC, West BankHubert H Humphrey Center 85
- Auto Enrolls With:
- Section 001
- Enrollment Status:
- Open (20 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+Spring2018
- 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/54820/1183
- Syllabus:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2018.pdf
- Past Syllabi:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2019.docx (Spring 2019)
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2016.pdf (Spring 2016) - Instructor Supplied Information Last Updated:
- 6 November 2017
Spring 2018 | PA 5032 Section 004: Regression Analysis (54808)
- Instructor(s)
- Class Component:
- Lecture
- Instructor Consent:
- No Special Consent Required
- Instruction Mode:
- In Person Term Based
- Times and Locations:
- First Half of Term01/16/2018 - 03/05/2018Mon, Wed 05:45PM - 07:00PMUMTC, West BankCarlson School of Management 1-123
- Enrollment Status:
- Open (29 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+Spring2018
- 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/54808/1183
- Syllabus:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2018.pdf
- Past Syllabi:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2019.docx (Spring 2019)
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2016.pdf (Spring 2016) - Instructor Supplied Information Last Updated:
- 6 November 2017
Spring 2018 | PA 5032 Section 005: Regression Analysis (54819)
- Instructor(s)
- Class Component:
- Laboratory
- Times and Locations:
- First Half of Term01/16/2018 - 03/05/2018Wed 07:15PM - 08:05PMUMTC, West BankHubert H Humphrey Center 85
- Auto Enrolls With:
- Section 004
- Enrollment Status:
- Closed (29 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+Spring2018
- 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/54819/1183
- Syllabus:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2018.pdf
- Past Syllabi:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2019.docx (Spring 2019)
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2016.pdf (Spring 2016) - Instructor Supplied Information Last Updated:
- 6 November 2017
ClassInfo Links - Spring 2018 Public Affairs Classes Taught by Robert Kudrle
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