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

JMP Pro 12 Software

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:

Course Summary
Date Details Due