QUANTITATIVE METHODSProfessor: MARCO CASERTA

E-Mail: mcaserta@faculty.ie.edu

Ph.D. in Operations Research, University of Illinois, USAAssociate Professor of Quantitative Methods, Operations & Technology Area, IE Business

School

Marco Caserta received his Ph.D. in Industrial Engineering and Operations Research from theUniversity of Illinois (USA), after earning a MSc in Management Engineering from the Politecnico diMilano (Italy). He is currently a “junior professor” at Hamburg University as well as an associateprofessor at the IE Business School, Madrid, Spain. He teaches optimization related courses tograduate students within the International MBA program.

His main research interest is concentrated on the design and development of metaheuristic-basedalgorithms for very large scale real-world optimization problems, with a special focus on logistics,telecommunication and transportation related problems. He has published a number of papers injournals in the area of operations research/management science.

Published by IE Publishing Department.Last revised, September 2016.

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OBJECTIVES AND CONTENT“Did you ever notice that, no matter where you stand on popular issues of the day, you canalways find statistics or surveys to back up your point of view-whether to take vitamins, whetherday care harms kids, or what foods can hurt you or save you? There is an endless flow ofinformation to help you make decisions, but is this information accurate, unbiased?” BarbaraWalters gave this introduction to a March 31, 1995, segment of the popular prime-time ABCtelevision program 20/20. The story was titled "Facts or Fiction?--Exposés of So-Called Surveys."The program illustrated how the results arising from surveys and statistical analysis that mightseem not right, might be just so, wrong or misleading.

This course if focused on providing students with a robust statistical knowledge, with the aim ofaddressing and tackling different research questions based on the analysis of data. The focus ison understanding what the role of statistics in managerial decision making is. We will introduceand use descriptive and inferential statistics, illustrating how sample data can be used toestimate, make decisions, predictions, or other generalizations about larger populations.

While on one side the course will help the student to understand the theory behind somefundamental statistical techniques, on the other hand, we will see how a widely used software,i.e., Excel, can be used to perform all the computation required by the statistical analysis. Moreimportantly, we will learn how the results and the output should be interpreted.

LEARNING OBJECTIVES

PROGRAMMAC USERS

During the course, we will make extensive use of Excel and an add-in called Analysis Toolpack. Ifyou use a Windows PC, the toolpack is already installed in Excel (and, therefore, you do not needto do anything at all to prepare your machine). During the first lecture, I will show you how to loadthe toolpack.

If you are a Mac user, you will have to download a free software. The following link allows you todownload the statplus light edition, the mac equivalent of the analysis toolpack we are going to beusing during the lectures:

http://www.analystsoft.com/en/products/statplusmacle/download.phtml

Please, make sure that you download the LE edition (and do not upgrade to the professionalversion). This version contains all the features you need to use during the course.

BEFORE SESSION 1

To get you an idea of the broad use of statistics and the relevance of some of the topics we aregoing to study together during the course, I encourage you to have a look at the following readingsbefore our first lecture:

http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/

If you want to have a look at the whole story, please read:

http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=1&_r=1&hp

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SESSIONS 1 & 2

DESCRIPTIVE STATISTICS

Graphical Methods for Describing Quantitative Data (2.2)Measures of Centrality (2.4)Measures of Dispersion and Relative Standing (2.5-2.8)Box Plots and Histograms with Excel

SESSIONS 3 & 4

INTRODUCTION TO STATISTICS

Discrete and Continuous Random variables (4.1)The Normal Distribution (4.6)Random variables based on sampling distributions (4.10)Central Limit Theorem (4.11)

SESSIONS 5, 6 & 7

CONFIDENCE INTERVALS FOR POPULATION MEAN

Confidence interval when population variance is known (5.2)Confidence interval when population is not known (5.3)Confidence interval for population proportion (5.4)Confidence interval with Excel

SESSIONS 8, 9 & 10

HYPOTHESIS TESTING ABOUT A POPULATION MEAN

Hypothesis testing (type I and type II errors) (6.1)Hypothesis testing for large sample (6.3)The significance level: p-value (6.4)Hypothesis testing for small sample (6.5)Comparing two population means: Independent sampling (7.2)Comparing two population means: Paired experiments (7.3)Comparing two population proportions: Independent Sampling (7.4)Hypothesis testing with Excel

SESSIONS 11, 12 & 13

LINEAR REGRESSION

Simple linear regression (10.1, 10.4)Multivariate linear regression (11.1, 11.2)Evaluate overall model utility (11.3)Model building in multiple regression (11.5-11.8)Linear regression with Excel

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SESSION 14

WRAP UP SESSION

Review of exercises and questions in preparation for the final exam.

SESSION 15

FINAL EXAM

EVALUATION METHODGrades will be assigned as follows:

Criteria Score %Final Exam 50%

Intermediate tests 40%Class Participation 10%

Class Participation Policy

Class participation provides the opportunity to practice speaking and persuasive skills, as well asthe ability to listen. Comments that are vague, repetitive, unrelated to the current topic,disrespectful of others, or without sufficient foundation will be evaluated negatively. What mattersis the quality of one's contributions to the class discussion, not the number of times one speaks.

Outstanding Contributor: Contributions in class reflect exceptional preparation. Ideas offered arealways substantive, provide one or more major insights as well as direction for the class.Challenges are well substantiated and persuasively presented. If this person were not a memberof the class, the quality of discussion would be diminished markedly. Grade : 90-100

Good Contributor: Contributions in class reflect thorough preparation. Ideas offered are usuallysubstantive, provide good insights and sometimes direction for the class. Challenges are wellsubstantiated and often persuasive. If this person were not a member of the class, the quality ofdiscussion would be diminished. Grade : 80

Adequate Contributor: Contributions in class reflect satisfactory preparation. Ideas offered aresometimes substantive, provide generally useful insights but seldom offer a new direction for thediscussion. Challenges are sometimes presented, fairly well substantiated, and are sometimespersuasive. If this person were not a member of the class, the quality of discussion would bediminished somewhat. Grade : 60

Non-Participant: This person says little or nothing in class. Hence, there is not an adequate basisfor evaluation. If this person were not a member of the class, the quality of discussion would notbe changed. Grade : 40

Unsatisfactory Contributor: Contributions in class reflect inadequate preparation. Ideas offeredare seldom substantive, provide few if any insights and never a constructive direction for theclass. Integrative comments and effective challenges are absent. If this person were not amember of the class, valuable air-time would be saved. Grade : 20

Note: I obtained these guidelines from the Harvard Graduate School of Education. In turn, thesehave been learned from someone else. Although the original attribution for the guidelines hasbeen lost, they continue to be so useful to so many.

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BIBLIOGRAPHYWe will use the following textbook as reference (in the syllabus, all the numbers in parentesis nextto each topic refers to the book below. The format used below is (chapter.section) ):

McClave, J.T., Benson, P.G., & Sincich, T. (2011). Statistics for business and economics(11th ed). Upper Saddle River, NJ: Pearson.

I strongly encourage you to read the indicated chapters and paragraphs prior to each sessionof the course.

https://ie.on.worldcat.org/courseReserves/course/id/10003190

https://ie.on.worldcat.org/courseReserves/course/id/10003190

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