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Description

This course is about the family of statistical data analysis tools called regression analysis.The course can be viewed as a logical successor to the core Managerial Statistics course,and is most frequently taken by 2ndyear M!" students wishing to solidify and e#tend theirquantitative skills.

The purpose of a regression analysis is to build a mathematical$statistical representation ofthe relationships between variables that can be used for prediction of outcomes andenhanced understanding of causes. %hen regression is used in a business conte#t theultimate managerial goal is, of course, better business decisions.

&inear regression models are widely used in the business world, as well as in many other'elds such as economics, engineering, social research and in the health and biologicalsciences. (n the business world regressions have been used in marketing analyses of

customer behavior, in 'nancial analyses of investment opportunities, in human resources totest the fairness of employment policies, in operations to identify the determinants of outputquality, and in strategic planning to create sales forecasts.

)ontemporary computing hardware and modern statistical software has made ite#traordinarily easy to produce regression models. *or e#ample, Microsoft +#cel has a quitepowerful regression tool that is very easy to use with absolutely no knowledge of theunderlying concepts. Though it has become childs play to -run a regression, it is quite achallenge to create a regression model that is really useful and reliable. /ne might arguethat regression is almost too easy to do 0 it being very easy to mechanically produce -badregressions. The goal of this course is to learn how to create good regressions, and how to

1udge if a good regression is even possible. The course is based on the premise thatsuccessful applications of regression require sound understanding of both the underlying

theory and the practical problems that are encountered when building and using models ofreallife situations with serious consequences. Therefore this course seeks to blend theoryand applications eectively, thereby avoiding the e#tremes of presenting theory in isolationand of giving elements of applications without the foundation concepts that are needed forpractical understanding.

The course will deal with three topics in an interwoven way. *irst and most basic, is anapproach to data and data analysis that is based on statistical theory, the scienti'c methodand on some very pragmatic epistemology. Second, is regression analysis itself, includinge#tensions to the basic linear model such as logistic regression and some basic multivariatemodels. Third, is forecasting of time series from historical data. The title of our te#tbook isdescriptive of our approach3 regression by e#ample. +ach concept and procedure shall bee#plained, indeed, whenever possible introduced by an e#ample. Moreover, we will

emphasi4e e#amples in which the business conte#t matters and may in5uence the analyticapproach taken.

Computing

The course will be very computationally handson from the very 'rst lecture. 6our laptopcomputer will be used e#tensively for data analysis. Most of this work can be done in +#celand we assume a basic familiarity with the data analysis tools of +#cel 0 including theregression tool. 7owever, even though most of the work can be done in +#cel, there can be

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some advantages to using a statistical software package. Moreover, several of the specialand important analytic tools stepwise regression and logistic regression 0 cannot be donein +#cel. Therefore we will supplement +#cel with the Minitab statistical analysis system.Minitab gives us professional statistical analysis capabilities while being easy to learn anduse. "ny version of Minitab that can do regression, stepwise regression and logisticregression will be adequate. Students who already are familiar with another software

package that has the aforementioned capabilities are welcome to use it. 8S9SS, S"S, :,Stata;

Conduct of the Course

)ourse 9ro1ect3 " ma1or part of the course work will be a data analysis pro1ect. The pro1ectwill be a signi'cant data analysis in a real business conte#t. ( will provide a standard pro1ect.7owever, ( strongly suggest that students who have particular interests propose their ownpro1ect. This can be a way of increasing the value you get out of the course. 9ro1ects will bedue and presented in class in the last class session.

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Textbooks and Software

The course will follow the same general outline as the te#t by )hatter1ee, 7adi and 9ricewhich is listed below. This book strikes a good balance between providing a theoreticalunderstanding and keeping a very concrete focus on applications. ( will, in general usedierent e#amples than those in the te#t. (t will thereby oer a second view on most issuesand procedures. ( recommend purchasing it. (n addition to +#cel we will use the Minitabstatistical package, the software for which comes with a helpful users manual.

Textbook:

Samprit )hatter1ee and "li 7adiRegression Analysis by Example, ?thedition 8%iley 2@=2;(S!A3 BC@EC@B@?E?

Aote3 The te#tbook is recommended, not required. (t is entirely possible to do very well inthis course without buying the te#tbook. 7owever, it is a very good resource on this sub1ect,and goes into greater depth than we will have time for in the class meetings.

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Computer Software:

Minitab" free >@day trial version may be downloaded from http3$$it.minitab.com$enus$products$minitab$freetrial.asp#

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