Business Statistics

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Session 1

Catalina Stefanescu-Cuntze

Mediterranean School of Business

Business Statistics

Statistics

* Statistics include tools to:

- Present information/data

- Handle and quantify uncertainty

- Interpret and draw conclusions from information/data

* What are "managerial statistics"?

- numbers which we use in day to day analysis and decision making

* e.g. average salaries, forecasted sales, brand preferences, customer

response rates

* Which aspects of business are they relevant to:

- Almost any...

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Course Objectives

Learn how to:

 quantify and present uncertainty

 make decisions when facing uncertainty

Which courses is this most relevant to:

- Marketing

- Decision Models, Operations Management

- Corporate Finance, Investment Management, Financial Engineering

Introduction

Catalina Stefanescu-Cuntze

* BS Mathematics

University of Bucharest

* MS and PhD, Operations Research

Cornell University

* Assistant Professor of Decision Sciences at London Business School, 2002-2009

* Associate Professor and Director of Research at European School of Management and Technology

(ESMT), Berlin

* Research interests:

Statistics, Applied Probability, Finance

Revenue Management, Credit Risk

Contact information: catalina.stefanescu-cuntze@esmt.org

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Course Material and Assessment

* Lecture slides, workshop exercises, additional readings

- In your course materials

* Book (background reading):

- Quantitative Methods for Decision Makers by M.Wisniewski, (FT

Prentice-Hall, 5th edition).

* Course web page:

- Class material, data files

Course assessment:

* Group project: solve a case handed out on Saturday afternoon, present it in

class on Sunday morning, and write up a report with your solution.

Course Outline

Part I: Data description

- Mean, standard deviation

- Distributions - The normal distribution

Part II: Sampling

- How to derive knowledge on the underlying population from a sample

- Point estimates: means, proportions

- Interval estimates: confidence intervals

- Testing claims about the population using information from a sample:

Hypothesis testing

Part III: Regression

- How to explain a variable with other factors - model building.

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Data Description

* Summary statistics are methods for organizing and summarizing data.

* Data are measurements on variables of interest, which often contain some

randomness - they are called random variables.

* Random variables are not completely random

- typically the possible values might center on an average value

- there will be a certain range or spread around the average

* We use probability distributions to describe the set of possible values and

the probability of each occurring.

It is better to be roughly right than precisely wrong.

Maynard Keynes

Age

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Frequency

Frequency Table

25 MBA students were asked about their age:

Age

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30 1

31 1 1 1

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33 1 1 1 1 1

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36 1

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38 1

Frequency

5

Age

29 1

30 1

31 1 1 1

32 1 1 1 1 1 1

33 1 1 1 1 1

34 1 1 1 1

35 1 1

36