55 classes matched your search criteria.

Spring 2024  |  PA 5032 Section 001: Applied Regression (57167)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
Partially Online
Enrollment Requirements:
PA: major or minor in Public Policy or Science/Technology/Environmental Policy or PA PhD or Human Rights major or Development Practice major
Times and Locations:
First Half of Term
 
01/16/2024 - 03/11/2024
Mon, Wed 09:45AM - 11:00AM
UMTC, West Bank
 
01/16/2024 - 03/11/2024
UMTC, West Bank
UMN ONLINE-HYB
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.
Class Notes:
Lecture 001 will be HyFlex and students may participate either In Person or Remotely (synchronously online).. http://classinfo.umn.edu/?arfertig+PA5032+Spring2024
Class Description:
Student may contact the instructor or department for information.
Textbooks:
https://bookstores.umn.edu/course-lookup/57167/1243

Spring 2024  |  PA 5032 Section 002: Applied Regression (57181)

Instructor(s)
Class Component:
Laboratory
Times and Locations:
First Half of Term
 
01/16/2024 - 03/11/2024
Fri 09:45AM - 11:00AM
UMTC, West Bank
Hubert H Humphrey Center 85
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.
Class Notes:
Lecture 001 will be HyFlex and students may participate either In Person or Remotely (synchronously online). http://classinfo.umn.edu/?arfertig+PA5032+Spring2024
Class Description:
Student may contact the instructor or department for information.
Textbooks:
https://bookstores.umn.edu/course-lookup/57181/1243

Spring 2024  |  PA 5032 Section 003: Applied Regression (65243)

Instructor(s)
Class Component:
Laboratory
Times and Locations:
First Half of Term
 
01/16/2024 - 03/11/2024
Fri 11:15AM - 12:30PM
UMTC, West Bank
Hubert H Humphrey Center 85
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.
Class Notes:
Lecture 001 will be HyFlex and students may participate either In Person or Remotely (synchronously online). http://classinfo.umn.edu/?arfertig+PA5032+Spring2024
Class Description:
Student may contact the instructor or department for information.
Textbooks:
https://bookstores.umn.edu/course-lookup/65243/1243

Spring 2023  |  PA 5032 Section 001: Applied Regression (57534)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
Partially Online
Enrollment Requirements:
PA: major or minor in Public Policy or Science/Technology/Environmental Policy or PA PhD or Human Rights major or Development Practice major
Times and Locations:
First Half of Term
 
01/17/2023 - 03/13/2023
Mon, Wed 09:45AM - 11:00AM
UMTC, West Bank
Hubert H Humphrey Center 25
 
01/17/2023 - 03/13/2023
Mon, Wed 09:45AM - 11:00AM
UMTC, West Bank
UMN ONLINE-HYB
Enrollment Status:
Open (41 of 60 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.
Class Notes:
Lecture 001 will be HyFlex and students may participate either In Person or Remotely (synchronously online). Lab 002 will be REMOTE and Lab 003 will be In Person. http://classinfo.umn.edu/?arfertig+PA5032+Spring2023
Class Description:
Student may contact the instructor or department for information.
Textbooks:
https://bookstores.umn.edu/course-lookup/57534/1233

Spring 2023  |  PA 5032 Section 002: Applied Regression (57535)

Instructor(s)
Class Component:
Laboratory
Times and Locations:
First Half of Term
 
01/17/2023 - 03/13/2023
Fri 02:15PM - 03:30PM
Off Campus
UMN REMOTE
Auto Enrolls With:
Section 001
Enrollment Status:
Open (18 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.
Class Notes:
Lecture 001 will be HyFlex and students may participate either In Person or Remotely (synchronously online). Lab 002 will be REMOTE and Lab 003 will be In Person. http://classinfo.umn.edu/?arfertig+PA5032+Spring2023
Class Description:
Student may contact the instructor or department for information.
Textbooks:
https://bookstores.umn.edu/course-lookup/57535/1233

Spring 2023  |  PA 5032 Section 003: Applied Regression (57549)

Instructor(s)
Class Component:
Laboratory
Times and Locations:
First Half of Term
 
01/17/2023 - 03/13/2023
Fri 12:45PM - 02:00PM
UMTC, West Bank
Hubert H Humphrey Center 85
Auto Enrolls With:
Section 001
Enrollment Status:
Open (23 of 25 seats filled)
Course Catalog Description:
Bivariate/multivariate models of regression analysis, assumptions behind them. Problems using these models when such assumptions are not met.
Class Notes:
Lecture 001 will be HyFlex and students may participate either In Person or Remotely (synchronously online). Lab 002 will be REMOTE and Lab 003 will be In Person. http://classinfo.umn.edu/?arfertig+PA5032+Spring2023
Class Description:
Student may contact the instructor or department for information.
Textbooks:
https://bookstores.umn.edu/course-lookup/57549/1233

Spring 2022  |  PA 5032 Section 001: Applied Regression (58899)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
Partially Online
Enrollment Requirements:
PA: major or minor in Public Policy or Science/Technology/Environmental Policy or PA PhD or Human Rights major or Development Practice major
Times and Locations:
First Half of Term
 
01/18/2022 - 03/14/2022
Mon, Wed 09:45AM - 11:00AM
UMTC, West Bank
Carlson School of Management 2-215
 
01/18/2022 - 03/14/2022
Mon, Wed 09:45AM - 11:00AM
UMTC, West Bank
UMN ONLINE-HYB
Enrollment Status:
Open (56 of 61 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.
Class Notes:
Class will be offered HyFlex. Students may participate either In Person or Remotely (synchronously online). http://classinfo.umn.edu/?arfertig+PA5032+Spring2022
Class Description:
Student may contact the instructor or department for information.
Textbooks:
https://bookstores.umn.edu/course-lookup/58899/1223

Spring 2022  |  PA 5032 Section 002: Applied Regression (58900)

Instructor(s)
Class Component:
Laboratory
Times and Locations:
First Half of Term
 
01/18/2022 - 03/14/2022
Fri 02:15PM - 03:30PM
UMTC, West Bank
Hubert H Humphrey Center 85
 
01/18/2022 - 03/14/2022
Fri 02:15PM - 03:30PM
UMTC, West Bank
UMN ONLINE-HYB
Auto Enrolls With:
Section 001
Enrollment Status:
Open (19 of 20 seats filled)
Course Catalog Description:
Bivariate/multivariate models of regression analysis, assumptions behind them. Problems using these models when such assumptions are not met.
Class Notes:
Class will be offered HyFlex. Students may participate either In Person or Remotely (synchronously online). http://classinfo.umn.edu/?arfertig+PA5032+Spring2022
Class Description:
Student may contact the instructor or department for information.
Textbooks:
https://bookstores.umn.edu/course-lookup/58900/1223

Spring 2022  |  PA 5032 Section 003: Applied Regression (58915)

Instructor(s)
Class Component:
Laboratory
Times and Locations:
First Half of Term
 
01/18/2022 - 03/14/2022
Fri 12:45PM - 02:00PM
UMTC, West Bank
Hubert H Humphrey Center 85
 
01/18/2022 - 03/14/2022
Fri 12:45PM - 02:00PM
UMTC, West Bank
UMN ONLINE-HYB
Auto Enrolls With:
Section 001
Enrollment Status:
Open (18 of 20 seats filled)
Course Catalog Description:
Bivariate/multivariate models of regression analysis, assumptions behind them. Problems using these models when such assumptions are not met.
Class Notes:
Class will be offered HyFlex. Students may participate either In Person or Remotely (synchronously online). http://classinfo.umn.edu/?arfertig+PA5032+Spring2022
Class Description:
Student may contact the instructor or department for information.
Textbooks:
https://bookstores.umn.edu/course-lookup/58915/1223

Spring 2022  |  PA 5032 Section 004: Applied Regression (69672)

Instructor(s)
Class Component:
Laboratory
Times and Locations:
First Half of Term
 
01/18/2022 - 03/14/2022
Thu 01:00PM - 02:15PM
UMTC, West Bank
Hubert H Humphrey Center 85
 
01/18/2022 - 03/14/2022
UMTC, West Bank
UMN ONLINE-HYB
Auto Enrolls With:
Section 001
Enrollment Status:
Open (19 of 20 seats filled)
Course Catalog Description:
Bivariate/multivariate models of regression analysis, assumptions behind them. Problems using these models when such assumptions are not met.
Class Notes:
Class will be offered HyFlex. Students may participate either In Person or Remotely (synchronously online). http://classinfo.umn.edu/?arfertig+PA5032+Spring2022
Class Description:
Student may contact the instructor or department for information.
Textbooks:
https://bookstores.umn.edu/course-lookup/69672/1223

Spring 2021  |  PA 5032 Section 001: Applied Regression (54082)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
Primarily Online
Enrollment Requirements:
PA: major or minor in Public Policy or Science/Technology/Environmental Policy or PA PhD or Human Rights major or Development Practice major
Times and Locations:
First Half of Term
 
03/08/2021
Mon 09:45AM - 11:00AM
UMTC, East Bank
Virtual Rooms ROOM-TBA
 
01/19/2021 - 03/08/2021
Mon, Wed 09:45AM - 11:00AM
UMTC, East Bank
UMN ONLINE-HYB
Enrollment Status:
Open (46 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.
Class Notes:
Class will be Blended. Class will meet synchronously-online during Spring 2021 during the scheduled time. Students will attend one in-person session tentatively on either Fri, 3/5 or Mon, 3/8. (Students who are unable to participate in the synchronous class sessions or the in-person session on 3/5 or 3/8 will be able to access the recorded class sessions and participate online in the in-person sessions. Contact the instructor to discuss.) http://classinfo.umn.edu/?arfertig+PA5032+Spring2021
Class Description:
Student may contact the instructor or department for information.
Textbooks:
https://bookstores.umn.edu/course-lookup/54082/1213

Spring 2021  |  PA 5032 Section 002: Applied Regression (54083)

Instructor(s)
Class Component:
Laboratory
Times and Locations:
First Half of Term
 
03/05/2021
Fri 12:45PM - 02:00PM
UMTC, East Bank
Virtual Rooms ROOM-TBA
 
01/19/2021 - 03/08/2021
Fri 12:45PM - 02:00PM
UMTC, East Bank
UMN ONLINE-HYB
Auto Enrolls With:
Section 001
Enrollment Status:
Open (46 of 48 seats filled)
Course Catalog Description:
Bivariate/multivariate models of regression analysis, assumptions behind them. Problems using these models when such assumptions are not met.
Class Notes:
Class will be Blended. Class will meet synchronously-online during Spring 2021 during the scheduled time. Students will attend one in-person session tentatively on either Fri, 3/5 or Mon, 3/8. (Students who are unable to participate in the synchronous class sessions or the in-person session on 3/5 or 3/8 will be able to access the recorded class sessions and participate online in the in-person sessions. Contact the instructor to discuss.) http://classinfo.umn.edu/?arfertig+PA5032+Spring2021
Class Description:
Student may contact the instructor or department for information.
Textbooks:
https://bookstores.umn.edu/course-lookup/54083/1213

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
Blegen Hall 130
Enrollment Status:
Open (40 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.
Class Notes:
http://classinfo.umn.edu/?arfertig+PA5032+Spring2020
Class Description:
Student may contact the instructor or department for information.
Textbooks:
https://bookstores.umn.edu/course-lookup/57499/1203

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
Hubert H Humphrey Center 85
Auto Enrolls With:
Section 001
Enrollment Status:
Open (16 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.
Class Notes:
http://classinfo.umn.edu/?arfertig+PA5032+Spring2020
Class Description:
Student may contact the instructor or department for information.
Textbooks:
https://bookstores.umn.edu/course-lookup/57500/1203

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
Hubert H Humphrey Center 85
Auto Enrolls With:
Section 001
Enrollment Status:
Closed (24 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.
Class Notes:
http://classinfo.umn.edu/?arfertig+PA5032+Spring2020
Class Description:
Student may contact the instructor or department for information.
Textbooks:
https://bookstores.umn.edu/course-lookup/57521/1203

Spring 2019  |  PA 5032 Section 001: Applied Regression (58161)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Times and Locations:
First Half of Term
 
01/22/2019 - 03/11/2019
Mon, Wed 09:45AM - 11:00AM
UMTC, West Bank
Carlson School of Management L-118
Enrollment Status:
Open (25 of 46 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/58161/1193
Syllabus:
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2019.docx
Past Syllabi:
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 2019  |  PA 5032 Section 002: Applied Regression (58162)

Instructor(s)
Class Component:
Laboratory
Times and Locations:
First Half of Term
 
01/22/2019 - 03/11/2019
Fri 01:50PM - 02:40PM
UMTC, West Bank
Hubert H Humphrey Center 85
Auto Enrolls With:
Section 001
Enrollment Status:
Open (5 of 22 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/58162/1193
Syllabus:
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2019.docx
Past Syllabi:
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 2019  |  PA 5032 Section 003: Applied Regression (58183)

Instructor(s)
Class Component:
Laboratory
Times and Locations:
First Half of Term
 
01/22/2019 - 03/11/2019
Fri 12:45PM - 01:35PM
UMTC, West Bank
Hubert 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+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/58183/1193
Syllabus:
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2019.docx
Past Syllabi:
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 2019  |  PA 5032 Section 004: Applied Regression (58175)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Times and Locations:
First Half of Term
 
01/22/2019 - 03/11/2019
Mon, Wed 05:45PM - 07:00PM
UMTC, West Bank
Carlson School of Management 1-136
Enrollment Status:
Open (13 of 24 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/58175/1193
Syllabus:
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2019.docx
Past Syllabi:
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 2019  |  PA 5032 Section 005: Applied Regression (58182)

Instructor(s)
Class Component:
Laboratory
Times and Locations:
First Half of Term
 
01/22/2019 - 03/11/2019
Wed 07:15PM - 08:05PM
UMTC, West Bank
Hubert H Humphrey Center 85
Auto Enrolls With:
Section 004
Enrollment Status:
Open (13 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/58182/1193
Syllabus:
http://classinfo.umn.edu/syllabi/kudrle_PA5032_Spring2019.docx
Past Syllabi:
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 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 Term
 
01/16/2018 - 03/05/2018
Mon, Wed 09:45AM - 11:00AM
UMTC, West Bank
Hanson 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 Term
 
01/16/2018 - 03/05/2018
Fri 01:50PM - 02:40PM
UMTC, West Bank
Hubert 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 Term
 
01/16/2018 - 03/05/2018
Fri 12:45PM - 01:35PM
UMTC, West Bank
Hubert 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 Term
 
01/16/2018 - 03/05/2018
Mon, Wed 05:45PM - 07:00PM
UMTC, West Bank
Carlson 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 Term
 
01/16/2018 - 03/05/2018
Wed 07:15PM - 08:05PM
UMTC, West Bank
Hubert 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

Spring 2017  |  PA 5032 Section 001: Regression Analysis (55294)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Times and Locations:
First Half of Term
 
01/17/2017 - 03/06/2017
Mon, Wed 09:45AM - 11:00AM
UMTC, West Bank
Carlson School of Management L-114
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+Spring2017
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 percent of the course grade), a final exam (40 percent) and oral presentations in teams and class participation (15 percent together). The examination will be closed book.
Exam Format:
closed book
Class Format:
closed book
Textbooks:
https://bookstores.umn.edu/course-lookup/55294/1173
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:
21 February 2017

Spring 2017  |  PA 5032 Section 002: Regression Analysis (55295)

Instructor(s)
Class Component:
Laboratory
Times and Locations:
First Half of Term
 
01/17/2017 - 03/06/2017
Fri 01:50PM - 02:40PM
UMTC, West Bank
Hubert 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] or instr consent
Class Notes:
http://classinfo.umn.edu/?kudrle+PA5032+Spring2017
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 percent of the course grade), a final exam (40 percent) and oral presentations in teams and class participation (15 percent together). The examination will be closed book.
Exam Format:
closed book
Class Format:
closed book
Textbooks:
https://bookstores.umn.edu/course-lookup/55295/1173
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:
21 February 2017

Spring 2017  |  PA 5032 Section 003: Regression Analysis (55323)

Instructor(s)
Class Component:
Laboratory
Times and Locations:
First Half of Term
 
01/17/2017 - 03/06/2017
Fri 12:45PM - 01:35PM
UMTC, West Bank
Hubert 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] or instr consent
Class Notes:
http://classinfo.umn.edu/?kudrle+PA5032+Spring2017
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 percent of the course grade), a final exam (40 percent) and oral presentations in teams and class participation (15 percent together). The examination will be closed book.
Exam Format:
closed book
Class Format:
closed book
Textbooks:
https://bookstores.umn.edu/course-lookup/55323/1173
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:
21 February 2017

Spring 2017  |  PA 5032 Section 004: Regression Analysis (55309)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Times and Locations:
First Half of Term
 
01/17/2017 - 03/06/2017
Mon, Wed 05:45PM - 07:00PM
UMTC, West Bank
Blegen Hall 435
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+Spring2017
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 percent of the course grade), a final exam (40 percent) and oral presentations in teams and class participation (15 percent together). The examination will be closed book.
Exam Format:
closed book
Class Format:
closed book
Textbooks:
https://bookstores.umn.edu/course-lookup/55309/1173
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:
21 February 2017

Spring 2017  |  PA 5032 Section 005: Regression Analysis (55321)

Instructor(s)
Class Component:
Laboratory
Times and Locations:
First Half of Term
 
01/17/2017 - 03/06/2017
Wed 07:15PM - 08:05PM
UMTC, West Bank
Hubert 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] or instr consent
Class Notes:
http://classinfo.umn.edu/?kudrle+PA5032+Spring2017
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 percent of the course grade), a final exam (40 percent) and oral presentations in teams and class participation (15 percent together). The examination will be closed book.
Exam Format:
closed book
Class Format:
closed book
Textbooks:
https://bookstores.umn.edu/course-lookup/55321/1173
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:
21 February 2017

Spring 2017  |  PA 5032 Section 006: Regression Analysis (55310)

Instructor(s)
Class Component:
Laboratory
Times and Locations:
First Half of Term
 
01/17/2017 - 03/06/2017
Wed 08:15PM - 09:05PM
UMTC, West Bank
Hubert 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] or instr consent
Class Notes:
http://classinfo.umn.edu/?kudrle+PA5032+Spring2017
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 percent of the course grade), a final exam (40 percent) and oral presentations in teams and class participation (15 percent together). The examination will be closed book.
Exam Format:
closed book
Class Format:
closed book
Textbooks:
https://bookstores.umn.edu/course-lookup/55310/1173
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:
21 February 2017

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 Term
 
01/19/2016 - 03/07/2016
Mon, Wed 09:45AM - 11:00AM
UMTC, West Bank
Hubert 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 Term
 
01/19/2016 - 03/07/2016
Fri 01:50PM - 02:40PM
UMTC, West Bank
Hubert 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 Term
 
01/19/2016 - 03/07/2016
Fri 12:45PM - 01:35PM
UMTC, West Bank
Hubert 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 Term
 
01/19/2016 - 03/07/2016
Mon, Wed 05:45PM - 07:00PM
UMTC, West Bank
Hubert 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 Term
 
01/19/2016 - 03/07/2016
Wed 07:15PM - 08:05PM
UMTC, West Bank
Hubert 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 Term
 
01/19/2016 - 03/07/2016
Wed 08:15PM - 09:05PM
UMTC, West Bank
Hubert 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

Spring 2015  |  PA 5032 Section 001: Regression Analysis (47707)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Class Attributes:
Delivery Medium
Times and Locations:
First Half of Term
 
01/20/2015 - 03/09/2015
Mon, Wed 09:45AM - 11:00AM
UMTC, West Bank
Carlson School of Management L-114
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 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/47707/1153
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:
21 April 2014

Spring 2015  |  PA 5032 Section 002: Regression Analysis (47708)

Instructor(s)
Class Component:
Laboratory
Class Attributes:
Delivery Medium
Times and Locations:
First Half of Term
 
01/20/2015 - 03/09/2015
Fri 01:50PM - 02:40PM
UMTC, West Bank
Hubert 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 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/47708/1153
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:
21 April 2014

Spring 2015  |  PA 5032 Section 003: Regression Analysis (52320)

Instructor(s)
Class Component:
Laboratory
Class Attributes:
Delivery Medium
Times and Locations:
First Half of Term
 
01/20/2015 - 03/09/2015
Fri 12:45PM - 01:35PM
UMTC, West Bank
Hubert 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 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/52320/1153
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:
21 April 2014

Spring 2015  |  PA 5032 Section 004: Regression Analysis (48483)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Class Attributes:
Delivery Medium
Times and Locations:
First Half of Term
 
01/20/2015 - 03/09/2015
Mon, Wed 05:45PM - 07:00PM
UMTC, West Bank
Blegen Hall 435
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 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/48483/1153
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:
21 April 2014

Spring 2015  |  PA 5032 Section 005: Regression Analysis (51343)

Instructor(s)
Class Component:
Laboratory
Class Attributes:
Delivery Medium
Times and Locations:
First Half of Term
 
01/20/2015 - 03/09/2015
Wed 07:15PM - 08:05PM
UMTC, West Bank
Hubert 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 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/51343/1153
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:
21 April 2014

Spring 2015  |  PA 5032 Section 006: Regression Analysis (48484)

Instructor(s)
Class Component:
Laboratory
Class Attributes:
Delivery Medium
Times and Locations:
First Half of Term
 
01/20/2015 - 03/09/2015
Wed 08:15PM - 09:05PM
UMTC, West Bank
Hubert 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 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/48484/1153
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:
21 April 2014

Spring 2014  |  PA 5032 Section 001: Intermediate Regression Analysis (52451)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Class Attributes:
Delivery Medium
Times and Locations:
First Half of Term
 
01/21/2014 - 03/10/2014
Mon, Wed 09:45AM - 11:00AM
UMTC, West Bank
Hubert 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.
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/52451/1143
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:
21 April 2014

Spring 2014  |  PA 5032 Section 002: Intermediate Regression Analysis (52452)

Instructor(s)
Class Component:
Laboratory
Class Attributes:
Delivery Medium
Times and Locations:
First Half of Term
 
01/21/2014 - 03/10/2014
Fri 01:50PM - 02:40PM
UMTC, West Bank
Hubert 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.
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/52452/1143
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:
21 April 2014

Spring 2014  |  PA 5032 Section 003: Intermediate Regression Analysis (57300)

Instructor(s)
Class Component:
Laboratory
Class Attributes:
Delivery Medium
Times and Locations:
First Half of Term
 
01/21/2014 - 03/10/2014
Fri 12:45PM - 01:35PM
UMTC, West Bank
Hubert 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.
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/57300/1143
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:
21 April 2014

Spring 2014  |  PA 5032 Section 004: Intermediate Regression Analysis (53252)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Class Attributes:
Delivery Medium
Times and Locations:
First Half of Term
 
01/21/2014 - 03/10/2014
Mon, Wed 05:45PM - 07:00PM
UMTC, West Bank
Hubert 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.
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/53252/1143
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:
21 April 2014

Spring 2014  |  PA 5032 Section 005: Intermediate Regression Analysis (56277)

Instructor(s)
Class Component:
Laboratory
Class Attributes:
Delivery Medium
Times and Locations:
First Half of Term
 
01/21/2014 - 03/10/2014
Wed 07:15PM - 08:05PM
UMTC, West Bank
Hubert 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.
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/56277/1143
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:
21 April 2014

Spring 2014  |  PA 5032 Section 006: Intermediate Regression Analysis (53253)

Instructor(s)
Class Component:
Laboratory
Class Attributes:
Delivery Medium
Times and Locations:
First Half of Term
 
01/21/2014 - 03/10/2014
Wed 08:15PM - 09:05PM
UMTC, West Bank
Hubert 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.
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/53253/1143
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:
21 April 2014

Spring 2013  |  PA 5032 Section 001: Intermediate Regression Analysis (47485)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Class Attributes:
Delivery Medium
Times and Locations:
First Half of Term
 
01/22/2013 - 03/11/2013
Mon, Wed 09:45AM - 11:00AM
UMTC, West Bank
Hubert 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.
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/47485/1133
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:
22 April 2013

Spring 2013  |  PA 5032 Section 002: Intermediate Regression Analysis (47486)

Instructor(s)
Class Component:
Laboratory
Class Attributes:
Delivery Medium
Times and Locations:
First Half of Term
 
01/22/2013 - 03/11/2013
Fri 01:50PM - 02:40PM
UMTC, West Bank
Hubert 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.
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/47486/1133
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:
22 April 2013

Spring 2013  |  PA 5032 Section 003: Intermediate Regression Analysis (52518)

Instructor(s)
Class Component:
Laboratory
Class Attributes:
Delivery Medium
Times and Locations:
First Half of Term
 
01/22/2013 - 03/11/2013
Fri 12:45PM - 01:35PM
UMTC, West Bank
Hubert 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.
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/52518/1133
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:
22 April 2013

Spring 2013  |  PA 5032 Section 004: Intermediate Regression Analysis (48301)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Class Attributes:
Delivery Medium
Times and Locations:
First Half of Term
 
01/22/2013 - 03/11/2013
Mon, Wed 05:45PM - 07:00PM
UMTC, West Bank
Blegen Hall 435
Also Offered:
Course Catalog Description:
Bivariate/multivariate models of regression analysis, assumptions behind them. Problems using these models when such assumptions are not met.
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/48301/1133
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:
22 April 2013

Spring 2013  |  PA 5032 Section 005: Intermediate Regression Analysis (51460)

Instructor(s)
Class Component:
Laboratory
Class Attributes:
Delivery Medium
Times and Locations:
First Half of Term
 
01/22/2013 - 03/11/2013
Wed 07:15PM - 08:05PM
UMTC, West Bank
Hubert 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.
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/51460/1133
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:
22 April 2013

Spring 2013  |  PA 5032 Section 006: Intermediate Regression Analysis (48302)

Instructor(s)
Class Component:
Laboratory
Class Attributes:
Delivery Medium
Times and Locations:
First Half of Term
 
01/22/2013 - 03/11/2013
Wed 08:15PM - 09:05PM
UMTC, West Bank
Hubert 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.
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/48302/1133
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:
22 April 2013

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