Plan du site

Boute Xavier

Statistics and business analytics

Jan intake, 2016

Professors, staff, and office hours

Professor: Xavier Boute                                         

E-mail: boute@hec.fr                                           

                                                                                                                                                           

Office hours Prof. Boute (in S building)

At 7.15 am every course’s day 

Course description

The aim of this course is to prepare you to use statistical tools in order to make better managerial decisions. At a rapidly changing pace, managers can rely on larger, more detailed, more reliable, more frequently updated data sets, on all facets of business. Tools for statistical analysis, including graphics, are becoming ever more powerful, and more user-friendly. Statistical analyses can be run from multiple locations, with short delays. So, before making a decision, and before explaining a decision to your supervisor or co-workers, you will want to have a look at relevant data, or ask for statistical analyses. The goal of this course is to prepare you to do that: to communicate with specialists rather than to prepare you for a job as statistician.

Statistical techniques are applied in a variety of management activities: marketing, human resource management, finance, accounting and audit, production.  A great number of practical examples and cases will illustrate this variety.

Learning outcomes

At the end of the course, you should be able to :

  •  identify the appropriate statistical analysis for a given managerial question
  • analyse data using a variety of statistical methods using SPSS
  • translate statistical results into substantive managerial recommendations
  • manage your relationship with statisticians: express your needs, interact during data collection and analysis, identify the potential limitations of the study.

 Key topics

  • Graphs for presentation and graphs for analysis
  • Confidence intervals around a mean
  • Comparing a mean to a standard (tests), analysis of variance
  • Cross tabs and chi-square test
  • Simple and multiple regression
  • Logistic regression
  • Factor and cluster analysis

  

Course material

Textbook
For each class session, we will suggest readings from the textbook (McClave et al.), from separate handouts, or from internet sites.

McClave, J.T., Benson, P.G. & Sincich, T. (2013) Statistics for Business and Economics, Twelfth Edition, Upper Saddle River, NJ: Pearson.

Optional reading

Field, A. (2009) Discovering Statistics using SPSS, Third Edition, London: SAGE

Additional material
For each class session, you will be able to obtain an electronic copy of the slide deck after class. We will also provide you with cases and examples related to the statistical technique discussed in class.  You can begin to work on the latter, which will be discussed at a later tutorial session.

All course material will be distributed through the website (https://studies2.hec.fr/jahia/Jahia/boute )

You will need a basic calculator (for the mid-term exam, for instance).

Teaching methods 

Each statistical technique will be introduced through a realistic, albeit small-scale, managerial problem. In most cases, we shall begin by a graphical exploration of the data. We will discuss the logic and procedure of the statistical analysis, while applying it to the example. The idea is that you do not master a statistical technique unless you can apply it to a simple case.

In addition to the regular class sessions, there will be SPSS computer labs. SPSS is a statistical software package from IBM. It is very useful for all the basic statistical and graphical analyses. During these labs we will apply the statistical methods we learn in class to real-data cases. You will do a number of exercises yourself, which will help you learn the statistical material as well as learn how to work with and analyze data yourself.  At the end of each computer lab you will be asked to submit and drop a brief assignment, which will count towards your final grade.


 

Software & data sets 

You need to install SPSS on your computer, in order to be able to use it for this course (exercises, cases, exams etc.) and other courses in the MBA (e.g. in finance, marketing, or operations management). Data sets that we will use in class will be made available to you on the website.

Prerequisites 

There are no prerequisites for this course. However, there will be a “Math Camp” organized at the beginning of the term, which provides a basic coverage of elementary topics in math and stats, as well as an introduction to SPSS.

Grading & other formalities  

Grades are based on individual and team work. The following grading scale will be used:

SPSS computer labs (Individual):             25 %

Midterm (Individual):                                 37.5 %

Final Exam (Team project):           37.5 %

 

As required by HEC Paris MBA policy, grading will be based on relative rather than absolute standards. The average grade in this course will be a 3.6 (GPA) or lower.  In this course, it is expected that each student is familiar with the HEC MBA rules & policies handbook and will behave in a manner that is consistent with this document.

The midterm exam is an individual exam covering the 1st part of the class. This will be an open book exam.

The final exam will be a team project.

 Each team is required to submit a business report summarizing the teams’ approach to analyzing the business problem in the case, the model(s) developed, the recommended course of action and its justification.

 

Slides  

 1 Introduction

2 Estimation Part 1 

2 S or sigma ? and proof of the central limit theorem

3 Simple regression

4 Multiple regression

5 factor analysis

6 Cluster analysis

 

Data  

Apartment

Auto 04

Ski 14

Salary

To practice before mid term exam  

Mid term exam 2015 ; example  1of a good copy ; example 2 of a good copy

Ad campaign

Jambon beurre

Camera

SPSS Labs  

 SPSS Labs 1 Apartment (descritives statistics)

SPSS Labs 2 Rolacola (estimation)

SPSS Labs 3 Apartment (end) (simple regression)

SPSS Labs 4 Camera (multiple regression)

SPSS Labs 5 Amaryllis (factor analysis)

 

Final exam : team project   

Bagatelle : the case

Bagatelle : the data