Course Syllabus
Please see "Course Information and Syllabus" in the folder "Course Information and Project" under Files for more details.
Time and Place
Tuesday and Thursdays from 12:40 to 2:10
R2230, Ross Business School
Instructor
Prof. Peter Lenk
Office: R5354 Ross Business School
Office phone: 734-936-2619
Home phone: 734-944-5074
Email: plenk@umich.edu
Office Hours
Location: R5354
Tuesday and Thursdays: 2:30 to 3:30
By appointment, email, or drop in
Goals
The breathtaking reduction in the costs of communication, transportation, and computing continues to increase the complexity of the business environment and to alter the DNA of firms. Competing in this whirlwind of change requires advanced business intelligence and analytics. Fortunately, information technology provides abundant data that firms can exploit to compete more effectively by better meeting consumer demands, reducing costs, increasing productivity, and furthering the value proposition for consumers, employees, share holds, and society.
Students in Applied Business Regression will develop skills in converting the tsunami of data into managerially useful information with regression models. Effectively using regression methods requires considerable field-dependent knowledge (marketing, finance, operations, etc.) along with mastery of statistical methodology. Students will develop and interpret analytical models, which improve managerial decision making.
Syllabus (Subject to Change)
|
Lecture |
Date |
Topic |
Text |
|
1 |
6-Sep |
Introduction |
Chapters 1, 2 & 3 |
|
2 |
8-Sep |
Exploratory Analysis in JMP |
Chapter 3 |
|
3 |
13-Sep |
Least Squares and Stepwise |
Chapter 6 |
|
4 |
15-Sep |
Interactions and Lasso |
Chapter 6 |
|
5 |
20-Sep |
Box-Cox Transform and Heteroscedasticity |
Chapter 6 |
|
|
|
One page description of project |
|
|
6 |
22-Sep |
General Linear Model |
Chapter 6 |
|
7 |
27-Sep |
Endogeneity & 2 Stage Least Squares |
|
|
8 |
29-Sep |
Binomial Logistic Regression |
Chapter 10 |
|
9 |
4-Oct |
Multinomial Logistic Regression |
Chapter 10 |
|
10 |
6-Oct |
Discriminate Analysis |
Chapter 12 |
|
11 |
11-Oct |
Discriminate Analysis |
Chapter 12 |
|
12 |
13-Oct |
Review |
|
|
Exam |
TBA |
Time determined by Ross |
|
|
Project |
21-Oct |
Submit to CTOOLS, Assignments |
|
Course Materials
Text (Optional)
Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro®
Galit Shmueli, Peter C. Bruce, Mia L. Stephens, and Nitin R. Patel. Wiley
- Online text through https://www.vitalsource.com/ at a reduced price
- Hard copies through the normal retail channels
JMP Pro 12 Software
- JMP gives a nice balance of powerful procedures (“platforms” in JMP), ease of use, and intelligent design.
- JMP is installed on UM and Ross’ computer networks:
- Ross’ virtual computer lab: http://www2.bus.umich.edu/myimpact/technology/instructional-technology/virtual-computer-lab
- UM’s virtual sites: https://virtualsites.umich.edu/
- Students can purchase a license of JMP Pro 12 from Computer Showcase (http://computershowcase.umich.edu/catalog.php Navigate to Software). Former students have told me that they purchased the license and downloaded the software without visiting Computer Showcase’s physical store, but they had to call Computer Showcase because the website is deficient.
Grades
Ross Elective Grading
No more than 35% EX (Outside of Ross EX = A+, but check with your school)
No more than 75% EX and GD (Outside of Ross GD = A, but check with your school)
At least 25% PS or lower (Outside of Ross PS = B+, but check with your school)
Composition of Course Grade
- Option 1 if your goal is a PS
- 50% Team Project
- 40% Assignments
- 10% Class Participation
- Option 2 if your goal is a GD or EX
- 40% Exam
- 30% Team Project
- 20% Assignments
- 10% Class participation
Option 2 is required if you want to be considered for a GD or EX; however, taking the final exam does not guarantee a GD or EX.
Please see "Course Information and Syllabus" and "Team Project" in the folder "Course Information and Project" under Files for more details.
Course Summary:
| Date | Details | Due |
|---|---|---|