3 classes matched your search criteria.

Spring 2021  |  PA 5033 Section 004: Multivariate Techniques (54095)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
Completely Online
Class Attributes:
Online Course
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:
Second Half of Term
 
03/09/2021 - 05/03/2021
Mon, Wed 09:45AM - 11:00AM
Off Campus
UMN REMOTE
Enrollment Status:
Open (16 of 48 seats filled)
Also Offered:
Course Catalog Description:
Use of bivariate and multivariate statistical approaches for analyzing and evaluating public affairs issues and the assumptions behind the analytical approaches. Designed to help students read, understand, interpret, use, and evaluate empirical work used in social sciences by policy analysts and policy makers. prereq: [5032 or 5044 or equiv] or instr consent. May fulfill stats requirements in other programs.
Class Notes:
Class will be offered REMOTELY. Class will meet synchronously-online during Spring 2021 during the scheduled time. http://classinfo.umn.edu/?klein002+PA5033+Spring2021
Class Description:
This class examines how statistical approaches can be used to examine public policies. This course is designed to help the student read, understand, interpret, use and evaluate empirical work used in the social sciences and by policy analysts. The course concentrates attention on several quantitative techniques used by public policy researchers and advisors to policy makers. The course covers techniques such as time series analysis, statistical cause and effect, forecasting models, limited dependent variables, combining time series and cross section data, and an introduction to big data and machine learning. A basic statistics class is a required prerequisite. Here is a link to a video: http://player.vimeo.com/external/89316179.sd.mp4?s=5148a78bbdba654e8040327fa8ae93f1
Who Should Take This Class?:

To learn quantitative techniques such as time series analysis, statistical cause and effect, forecasting models, limited dependent variables, combining time series and cross section data, and an introduction to big data and machine learning.

https://bookstores.umn.edu/course-lookup/54095/1213

Syllabus:
http://classinfo.umn.edu/syllabi/klein002_PA5033_Spring2021.doc
Past Syllabi:
http://classinfo.umn.edu/syllabi/klein002_PA5033_Spring2019.doc (Spring 2019)
Instructor Supplied Information Last Updated:
10 December 2020

Spring 2021  |  PA 5033 Section 005: Multivariate Techniques (54096)

Instructor(s)
Class Component:
Laboratory
Class Attributes:
Online Course
Times and Locations:
Second Half of Term
 
03/09/2021 - 05/03/2021
Fri 01:50PM - 02:40PM
Off Campus
UMN REMOTE
Auto Enrolls With:
Section 004
Enrollment Status:
Open (5 of 24 seats filled)
Course Catalog Description:
Use of bivariate and multivariate statistical approaches for analyzing and evaluating public affairs issues and the assumptions behind the analytical approaches. Designed to help students read, understand, interpret, use, and evaluate empirical work used in social sciences by policy analysts and policy makers. prereq: [5032 or 5044 or equiv] or instr consent. May fulfill stats requirements in other programs.
Class Notes:
Class will be offered REMOTELY. Class will meet synchronously-online during Spring 2021 during the scheduled time. http://classinfo.umn.edu/?klein002+PA5033+Spring2021
Class Description:
This class examines how statistical approaches can be used to examine public policies. This course is designed to help the student read, understand, interpret, use and evaluate empirical work used in the social sciences and by policy analysts. The course concentrates attention on several quantitative techniques used by public policy researchers and advisors to policy makers. The course covers techniques such as time series analysis, statistical cause and effect, forecasting models, limited dependent variables, combining time series and cross section data, and an introduction to big data and machine learning. A basic statistics class is a required prerequisite. Here is a link to a video: http://player.vimeo.com/external/89316179.sd.mp4?s=5148a78bbdba654e8040327fa8ae93f1
Who Should Take This Class?:

To learn quantitative techniques such as time series analysis, statistical cause and effect, forecasting models, limited dependent variables, combining time series and cross section data, and an introduction to big data and machine learning.

https://bookstores.umn.edu/course-lookup/54096/1213

Syllabus:
http://classinfo.umn.edu/syllabi/klein002_PA5033_Spring2021.doc
Past Syllabi:
http://classinfo.umn.edu/syllabi/klein002_PA5033_Spring2019.doc (Spring 2019)
Instructor Supplied Information Last Updated:
10 December 2020

Spring 2021  |  PA 5033 Section 006: Multivariate Techniques (54099)

Instructor(s)
Class Component:
Laboratory
Class Attributes:
Online Course
Times and Locations:
Second Half of Term
 
03/09/2021 - 05/03/2021
Fri 12:45PM - 01:35PM
Off Campus
UMN REMOTE
Auto Enrolls With:
Section 004
Enrollment Status:
Open (11 of 24 seats filled)
Course Catalog Description:
Use of bivariate and multivariate statistical approaches for analyzing and evaluating public affairs issues and the assumptions behind the analytical approaches. Designed to help students read, understand, interpret, use, and evaluate empirical work used in social sciences by policy analysts and policy makers. prereq: [5032 or 5044 or equiv] or instr consent. May fulfill stats requirements in other programs.
Class Notes:
Class will be offered REMOTELY. Class will meet synchronously-online during Spring 2021 during the scheduled time. http://classinfo.umn.edu/?klein002+PA5033+Spring2021
Class Description:
This class examines how statistical approaches can be used to examine public policies. This course is designed to help the student read, understand, interpret, use and evaluate empirical work used in the social sciences and by policy analysts. The course concentrates attention on several quantitative techniques used by public policy researchers and advisors to policy makers. The course covers techniques such as time series analysis, statistical cause and effect, forecasting models, limited dependent variables, combining time series and cross section data, and an introduction to big data and machine learning. A basic statistics class is a required prerequisite. Here is a link to a video: http://player.vimeo.com/external/89316179.sd.mp4?s=5148a78bbdba654e8040327fa8ae93f1
Who Should Take This Class?:

To learn quantitative techniques such as time series analysis, statistical cause and effect, forecasting models, limited dependent variables, combining time series and cross section data, and an introduction to big data and machine learning.

https://bookstores.umn.edu/course-lookup/54099/1213

Syllabus:
http://classinfo.umn.edu/syllabi/klein002_PA5033_Spring2021.doc
Past Syllabi:
http://classinfo.umn.edu/syllabi/klein002_PA5033_Spring2019.doc (Spring 2019)
Instructor Supplied Information Last Updated:
10 December 2020

ClassInfo Links - Spring 2021 Public Affairs Classes Taught by Morris Kleiner

To link directly to this ClassInfo page from your website or to save it as a bookmark, use:
http://classinfo.umn.edu/?subject=PA&catalog_nbr=5033&x500=klein002&term=1213
To see a URL-only list for use in the Faculty Center URL fields, use:
http://classinfo.umn.edu/?subject=PA&catalog_nbr=5033&x500=klein002&term=1213&url=1
To see this page output as XML, use:
http://classinfo.umn.edu/?subject=PA&catalog_nbr=5033&x500=klein002&term=1213&xml=1
To see this page output as JSON, use:
http://classinfo.umn.edu/?subject=PA&catalog_nbr=5033&x500=klein002&term=1213&json=1
To see this page output as CSV, use:
http://classinfo.umn.edu/?subject=PA&catalog_nbr=5033&x500=klein002&term=1213&csv=1
Schedule Viewer
8 am
9 am
10 am
11 am
12 pm
1 pm
2 pm
3 pm
4 pm
5 pm
6 pm
7 pm
8 pm
9 pm
10 pm
s
m
t
w
t
f
s
?
Class Title

ClassInfo created and maintained by the Humphrey School of Public Affairs.

If you have questions about specific courses, we strongly encourage you to contact the department where the course resides.