This is a class in applied single equation ordinary least squares (OLS) regression analysis. It will be assumed that students have mastered the topics of basic statistics. The first topic is a review of the single equation OLS regression model in bivariate and multivariate applications. The emphasis would be developing an understanding of the logic of OLS regression, understanding of the parameter estimates they generate, and understanding the use of statistical tests to assess such models. The second topic will be a review of the assumptions underlying the OLS regression model. The emphasis throughout will be on the substantive interpretation of regression results.The third topic will be an introduction to the logic and use of factor analysis with an emphasis on understanding and interpreting its results.
Students will have acquired knowledge of the logic of OLS regression and the assumptions underlying the OLS regression model.
Students will be able to interpret and report the results of OLS regression.
Students will be able to apply OLS regression to public administration research problems in tutorial assignments and a final group paper.
Students will be able to communicate the results of OLS regression in written form using the conventions used by practitioners and social scientists for reporting these results.
On the Public Administration front page of the E-guide you will find links to the website and timetables, uSis and Blackboard.
Mode of instruction
Lectures (14 hours)
Workgroups (8 hours)
Final exam (5 hours)
Self-study (113 hours)
Method of assessment
The following assessments will be used:
• Take-home midterm exam (20%) Enrollment on the course Blackboard site is required to receive this examination. • Final group paper (30%)
• Multiple-choice final exam (50%)
Students must receive a sufficient grade (5.5 or higher) on all three graded components to pass the course.
The following resit provisions apply:
One additional take-home midterm exam will be provided in January for students who do not pass the initial take-home midterm exam.
Students receive a maximum grade of 7 on this resit.
If the group assignment receives an insufficient grade, the group may resubmit this assignment once. If the group does not submit the initial group assignment before the deadline, they may submit it after the deadline but will not have the opportunity to retake this assignment if they receive an insufficient grade. Students receive a maximum grade of 7 on this resit.
A resit for the final exam will be given in January.
You can find more information about assessments and the timetable exams on the website.
Details for submitting papers (deadlines) are posted on Blackboard.
On the Public Administration front page of the E-guide you will find links to the website, uSis and Blackboard.
Students will be permitted to resit an examination if they have taken the first sit and earned a mark between 3 and 5.5 or with permission of the Board of Examiners.
Resit written exam
Students that want to take part in a resit for a written exam, are required to register via uSis. Use the activity number that can be found on the ‘timetable exams’.
The Blackboard site will be available for students at least one month before the start of the class so that students can enroll in the site and receive updates when new content is posted. A course outline and other information will be posted on Blackboard one week before the start of the course.
‘Multiple Regression: A Primer’ by Paul D. Allison, SAGE Publications.
Use both uSis and Blackboard to register for every course.
Register for every course and workgroup via uSis. Some courses and workgroups have a limited number of participants, so register on time (before the course starts). In uSis you can access your personal schedule and view your results. Registration in uSis is possible from four weeks before the start of the course.
Also register for every course in Blackboard. Important information about the course is posted here.
Dr. Brendan J. Carroll (Wijnhaven, 4.92), office hours by appointment, e-mail: