3rd Term MBA-2016
QUANTITATIVE METHODS FOR MANAGEMENT
COURSE OUTLINE
1. Introduction
The main task of a manager is to make decisions. To make decisions managers need
information. Information is spread in a great variety of forms: experience of the
managers, judgments and opinions, and mainly information is stored in form of data.
The goal of this course is to learn to extract information contained in data that can
help managers to make better decisions.
The course is a half credit course with 12 sessions. But for the first one, all the
sessions are case discussions.
2. Objectives
In this course, the student is expected to get experience in business-oriented
quantitative analysis and familiarity with the elementary techniques of data analysis.
The course takes advantage of the technical notes, cases and assignments to present
a range of situations which illustrate how the quantitative analysis allows to extract
information from the data in order to describe and predict demand, customer
behaviour and other issues helpful to make decisions.
3. Learning Outcomes
i.
Understand the informational needs a company has and to use the information
companies have in order to use that information for making decisions.
ii.
Learn how to produce sales forecasts using past sales data using curve fitting
techniques
iii.
Learn regression techniques in order to relate sales to other variables external
to the company.
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IESE Business School
Quantitative Methods for Management
iv.
Learn classification techniques in order to classify groups of elements (good
clients vs. default clients)
v.
To use common sense and business experience to understand what are the
most important techniques in each situation
4. Competences
General Competences:
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Decision Making
Analytic reasoning
Judgment
Communication
Specific Competences:
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To learn the most basics statistics techniques in order to analyze the information a
company has in databases.
To learn how to apply the outcomes of the statistics techniques to make decisions.
5. Content
The main topics covered by this course are:
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Forecasting with time series data.
Forecasting with simple and multiple linear regression.
Classification of clients and products.
6. Methodology
The sessions are based on cases which show various applications of quantitative
analysis in business. Each case has a companion Excel file with the corresponding data
set. The students are expected to prepare each case before the class. To help them,
a set of several technical notes, covering the topics mentioned above, is made
available. The notes come with examples which are similar to the cases and
assignments.
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Quantitative Methods for Management
The discussion of the cases in the classroom is supported by statistical analyses
performed with Excel. The regression and correlation analyses involved in the
discussion of the cases are performed with Excel’s Analysis ToolPak.
7. Evaluation
The evaluation of the course is based on class participation, individual assignments,
some possible quizz, team assignments and the final team project.
8. Course Outline & Bibliography
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Title of Session & Material
Introduction to Quantitative Methods for Management
Technical notes:
-ADN-248-E ‘Forecasting in companies’
-ADN-251-E ‘The normal distribution’
-ADN-258 ‘Measuring uncertainties: Probability Functions’
This first session will be a lecture.
After the class you can read the technical note ADN-248-E ‘Forecasting in
companies’.
At some moment during this first week of the course, it would be useful to read
and work through the document ADN-251-E ‘The normal distribution’ (only pages
1 to 6).
To understand better some statistical concepts, read the note ADN-258
‘Measuring uncertainties: Probability Functions’.
Sales Forecasting (I)
Time Series Sales Forecasting
Case: AD-317-E ‘Forecasting at The Gap Company’
Data: AD-317-E GAP DATA 85-95.xlsx
Prepare a forecast of the sales of The GAP Co. for the whole year 1996 with an
explanation of how you got that forecast.
Individual Assignment submission (I): Follow the instructions given in the file QMM
1st Assignment 2015D.xls
Sales Forecasting (II)
Technical note: ADN-279-E Time Series Sales Forecasting
Case: AD-317-E ‘Forecasting at The Gap Company’
Data: AD-317-E GAP DATA 85-95.xlsx
Prepare a forecast of the sales of The GAP Co. for each quarter of 1996 with an
explanation of how you got that forecast.
Individual assignment submission (II): Present a forecast of each quarter of 1996
of the GAP Company following the Holt Winters Method explained in the technical
note.
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Quantitative Methods for Management
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Sales forecasting (III)
Technical note: Holt Winters Forecast Technique
Case: AD-317-E ‘Forecasting at The Gap Company’
Data: AD-317-E GAP DATA 85-95.xlsx
Prepare a forecast of the sales of each quarter of 1996 using the Holt Winters
method described in the Technical Note "Holt Winters Forecast Technique"
Simple linear regression (I)
Case: [AD-342-E] Forecasting at the Gap Company (B)
Data: AD-342-E GAP (B) data.xlsx
Technical note: [QMM-TN-02] The regression line
Data: qmm-tn-02.xls
- Is there any relationship between the sales of the GAP Company and the
growth rate of the US GDP?
- Is there any way to predict Sales from other variables?
- What level of sales would you expect for GAP for 2013 if the growth rate of
the US GDP were 3%??
Simple linear regression (II)
Case: AD-235-E Return, volatility and beta.
Data: AD-235-E Return, volatility and beta.xls
This session will be devoted to applying the linear regression concept to a
financial problem. Work the document AD-235-E.
The section “The CAPM” may help you to understand what you are doing. In
Finance courses you will learn (and are already learning) more about this.
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Calculate expected (mean) returns, standard deviations and betas for the
IGBM and each Industry.
How do the expected returns of these sectors compare with the risk-free
asset yearly return of 14%?
State the linear relationship between the return of some industry (say,
Metals, Banks and Food) and the return of the General Index.
What conclusions do you draw from the analysis about the soundness of
investing in each industry?
Hint: To get the annualized return of an investment one has to multiply the
average daily return by the number of days of the year. To get the volatility, one
has to multiply the standard deviation of the daily returns times the square root
of the number of days of the year
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Multiple linear regression (I)
Technical note: [QMM-TN-03] Multiple regression
Case: [QMM-13] Windsor housing prices
Data: qmm-tn-03.xls, qmm-13.xls
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Are the houses located in a preferred neighborhood more expensive than
the others? For how much?
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Quantitative Methods for Management
What is the relationship between price and size of different houses? What
is the interpretation of this relationship?
- What is the relationship between price and number of bedrooms? What is
the interpretation of this relationship?
- What is the relationship between price and number of bathrooms?
Individual assignment (III): Present an analysis of the Hot Dog Pricing case
Multiple linear regression (II)
Case: [QMM-02] Hot dog pricing
Data: qmm-02.xls
Answer the questions proposed at the end of the exercise
Regression with lagged variables
Technical note: [QMM-TN-06] Regression with lagged variables
Case: HBS 9-171-248 ‘Harmon Foods, Inc.’
Data: HBS 9-171-248 ‘Harmon Foods.xlsx, qmm-tn-06.xls
- Which model do you think makes more sense?
- What do you learn from the coefficients of model selected?
- What improvements would you recommend to the product manager in
terms of timing and amount of promotions?
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10 Classification (I)
Technical note: [QMM-TN-04] Classification
Case: [QMM-15] The churn model
Data: qmm-tn-04.xls, qmm-15.xls
In this case we pretend to anticipate what clients that will churn in the future
and to think about what we can offer them for them not to churn.
 Develop a profile of the potential churner, i.e. identify the characteristics of
the customers most likely to churn.
 Develop a regression model for predict churn
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Team assignment (I): Follow the instructions that will have been given in the
previous session
11 Classification (II)
Case: [QMM-15] The churn model (second round)
In this session we will continue with ‘The churn model’ case. Follow the
instructions given at the end of the previous session.
12 Final session
Case: [QMM-11] Tracking churners at movtel
Data: qmm-11.xls
Answer the questions proposed in the case
Team Project
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