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Applied Regression Analysis

Fall 2014 MBA Course

B8831-001: Applied Regression Analysis

R - B Term, 02:15PM to 05:30PM
Location: URI 330

Instructor: Peter Kolesar


COURSE DESCRIPTION

This course studies the family of statistical methods called
regression analysis and is a logical successor to the core B6100 Managerial
Statistics course.  It is frequently
taken by 2nd-year MBA students wishing to solidify and extend their
quantitative and statistical data analysis skills.

Regression analysis is used to build statistical models of
the relationships between variables that can be used for enhanced understanding
of the causes of a phenomenon and, when it works, for prediction of future
outcomes.  In business the ultimate goal
of regression analysis is often to support better decision making.  In the contemporary world of ‘big data’,
regression provides foundational methods and ideas for many of the techniques
used in ‘data mining.’

Regressions have been used in financial analyses of
investment opportunities, in marketing analyses of customer behavior, in human
resources to test the fairness of employment policies, in operations to
identify the determinants of product quality, and in strategic planning to
create sales forecasts.  Regression
models are also widely used in many other fields in the sciences, economics and
engineering. 

Although contemporary computing hardware and statistical
software has made it extraordinarily easy to mechanically produce regression
analyses (for example, Microsoft Excel has a powerful regression tool that is
easy to use without any knowledge of the underlying concepts or theory) it is a
challenge to create a regression model that is really useful and reliable.   The explicit goal of this course is to learn
how, in a business context,  to create
reliable, valid and useful regressions, and to be able to judge the validity
and usefulness of regressions done by others. 
The course premise is that successful applications of regression require
understanding of both the practical problem situation, and the underlying
statistical theory.   The course blends
theory and applications -- avoiding the extremes of presenting unneeded theory
in isolation, or of giving application tools without the foundation needed for
practical understanding.

The course integrates three topics:   First and most basic, is an approach to data
and data analysis that is based on statistical theory, the scientific method
and on some pragmatic epistemology. 
Second, is regression analysis mechanics and theory, including
extensions of the basic linear regression model to logistic regressions,
non-linear models and multivariate methods.  
Third, is forecasting of time series from historical data.  We will seek to introduce some elements of
modern ‘big data/data mining’ as time permits. The title of our textbook is
descriptive of our approach:  Regression by Example.   Concepts and procedures shall generally be
introduced by example.  Moreover, we will
emphasize applications in which the business context matters.

Computing:     The
course will be computationally hands-on from the very first lecture.  Your laptop computer will be used for all
data analysis.  Some of the course work,
at least at the outset, can be done in Excel
and we assume a basic familiarity with its data analysis tools and
capabilities.   However, there are
advantages and conveniences to using a statistical software package.  Several important regression procedures cannot
be done in Excel, so we will
supplement it with the Minitab
statistical analysis system.  Minitab gives us professional
statistical analysis capabilities while being inexpensive and very easy to
learn and use.  An advantage of Minitab
is the ease with which it interfaces with Microsoft’s Excel , Word and
PowerPoint.  Any version of Minitab, or indeed any other software  that can do regression, stepwise regression
and logistic regression will be adequate. 
Students who already are familiar with, or have access to, another
software package that has these capabilities are welcome to use it instead. (
e.g.  STATA,
BMDP, SAS, S4, JMPIN
,R)

 

Conduct of the Course

Course Project:  
A major part of the course will be a term project consisting of a
significant regression oriented data analysis in a real business context.  I will provide a standard ‘default’
project.  However, I suggest that
students who have particular application interests propose their own project,
as this can increase greatly the value you get out of the course.   The term project can be either an individual
effort or by a team of two. 
Specifications for the final project report and timing will be provided
in class.

Workload and Grading:   It is expected that students will attend
class regularly and participate fully in class discussions.  Since many of these discussions will be based
on our analytic homework assignments (mini-cases), it is important that
assigned work be done thoroughly and on time.  
Most regular homework will be of the Business School’s “Type A” variety,
but with the group size limited to a maximum of 2 people.  You may make one submission and an identical
grade will be given for both members of the group. You have the option of doing
these exercises individually as well. 
Some homework, specified in advance, will need to be done
individually. 

There will be one short electronically administered exam.

In class I will generally expect professional comportment
appropriate to serious learning environment. 
On the other hand, I intend that we all will have fun while learning.

The overall work load should be moderate, but as in any
serious learning endeavor, you will benefit from the course in proportion to
what you put in.    The final course
grade will be composed of four components:

Exam                                                                     15%

Attendance and class participation                       15%

Written Assignments                                            35%

Term Project                                                         35%