6 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 2016 | PA 5032 Section 001: Regression Analysis (47855)
- Instructor(s)
- Class Component:
- Lecture
- Instructor Consent:
- No Special Consent Required
- Instruction Mode:
- In Person Term Based
- Times and Locations:
- First Half of Term01/19/2016 - 03/07/2016Mon, Wed 09:45AM - 11:00AMUMTC, West BankHubert H Humphrey Center 25
- 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], major or minor in public policy or science/technology/environmental policy or PA PhD or instr consent
- Class Notes:
- http://classinfo.umn.edu/?kudrle+PA5032+Spring2016
- 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. 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
- Textbooks:
- https://bookstores.umn.edu/course-lookup/47855/1163
- Syllabus:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2016.pdf
- Past Syllabi:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2019.docx (Spring 2019)
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2018.pdf (Spring 2018) - Instructor Supplied Information Last Updated:
- 21 April 2014
Spring 2016 | PA 5032 Section 002: Regression Analysis (47856)
- Instructor(s)
- Class Component:
- Laboratory
- Times and Locations:
- First Half of Term01/19/2016 - 03/07/2016Fri 01:50PM - 02:40PMUMTC, West BankHubert H Humphrey Center 85
- Auto Enrolls With:
- Section 001
- 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], major or minor in public policy or science/technology/environmental policy or PA PhD or instr consent
- Class Notes:
- http://classinfo.umn.edu/?kudrle+PA5032+Spring2016
- 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. 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
- Textbooks:
- https://bookstores.umn.edu/course-lookup/47856/1163
- Syllabus:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2016.pdf
- Past Syllabi:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2019.docx (Spring 2019)
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2018.pdf (Spring 2018) - Instructor Supplied Information Last Updated:
- 21 April 2014
Spring 2016 | PA 5032 Section 003: Regression Analysis (52112)
- Instructor(s)
- Class Component:
- Laboratory
- Times and Locations:
- First Half of Term01/19/2016 - 03/07/2016Fri 12:45PM - 01:35PMUMTC, West BankHubert H Humphrey Center 85
- Auto Enrolls With:
- Section 001
- 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], major or minor in public policy or science/technology/environmental policy or PA PhD or instr consent
- Class Notes:
- http://classinfo.umn.edu/?kudrle+PA5032+Spring2016
- 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. 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
- Textbooks:
- https://bookstores.umn.edu/course-lookup/52112/1163
- Syllabus:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2016.pdf
- Past Syllabi:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2019.docx (Spring 2019)
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2018.pdf (Spring 2018) - Instructor Supplied Information Last Updated:
- 21 April 2014
Spring 2016 | PA 5032 Section 004: Regression Analysis (48577)
- Instructor(s)
- Class Component:
- Lecture
- Instructor Consent:
- No Special Consent Required
- Instruction Mode:
- In Person Term Based
- Times and Locations:
- First Half of Term01/19/2016 - 03/07/2016Mon, Wed 05:45PM - 07:00PMUMTC, West BankHubert H Humphrey Center 25
- 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], major or minor in public policy or science/technology/environmental policy or PA PhD or instr consent
- Class Notes:
- http://classinfo.umn.edu/?kudrle+PA5032+Spring2016
- 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. 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
- Textbooks:
- https://bookstores.umn.edu/course-lookup/48577/1163
- Syllabus:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2016.pdf
- Past Syllabi:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2019.docx (Spring 2019)
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2018.pdf (Spring 2018) - Instructor Supplied Information Last Updated:
- 21 April 2014
Spring 2016 | PA 5032 Section 005: Regression Analysis (51189)
- Instructor(s)
- Class Component:
- Laboratory
- Times and Locations:
- First Half of Term01/19/2016 - 03/07/2016Wed 07:15PM - 08:05PMUMTC, West BankHubert H Humphrey Center 85
- Auto Enrolls With:
- Section 004
- 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], major or minor in public policy or science/technology/environmental policy or PA PhD or instr consent
- Class Notes:
- http://classinfo.umn.edu/?kudrle+PA5032+Spring2016
- 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. 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
- Textbooks:
- https://bookstores.umn.edu/course-lookup/51189/1163
- Syllabus:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2016.pdf
- Past Syllabi:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2019.docx (Spring 2019)
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2018.pdf (Spring 2018) - Instructor Supplied Information Last Updated:
- 21 April 2014
Spring 2016 | PA 5032 Section 006: Regression Analysis (48578)
- Instructor(s)
- Class Component:
- Laboratory
- Times and Locations:
- First Half of Term01/19/2016 - 03/07/2016Wed 08:15PM - 09:05PMUMTC, West BankHubert H Humphrey Center 85
- Auto Enrolls With:
- Section 004
- 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], major or minor in public policy or science/technology/environmental policy or PA PhD or instr consent
- Class Notes:
- http://classinfo.umn.edu/?kudrle+PA5032+Spring2016
- 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. 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
- Textbooks:
- https://bookstores.umn.edu/course-lookup/48578/1163
- Syllabus:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2016.pdf
- Past Syllabi:
- http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2019.docx (Spring 2019)
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2018.pdf (Spring 2018) - Instructor Supplied Information Last Updated:
- 21 April 2014
ClassInfo Links - Spring 2016 Public Affairs Classes Taught by Robert Kudrle
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If you have questions about specific courses, we strongly encourage you to contact the department where the course resides.