深圳大学管理学院：《运筹学》课程教学资源（PPT课件讲稿）决策与对策（决策论）运筹学3类 INTRODUCTION TO DATA ANALYSIS

CHAPTER14 INTRODUCTION TO DATAANALYSIS

CHAPTER14: INTRODUCTION TO DATA ANALYSIS

14.1 INTRODUCTION There are many situations in business where data is collected and analysed The key ideas of data analysis are important in the modern business environment Summarising and understanding the main features of the variables contained within the data, and investigate the nature of any linkages between the variables that may exist

14.1 INTRODUCTION  There are many situations in business where data is collected and analysed.  The key ideas of data analysis are important in the modern business environment.  Summarising and understanding the main features of the variables contained within the data, and investigate the nature of any linkages between the variables that may exist

14.2 WHAT IS DATA ◆ Example I Population: the set of all people/objects of interest in the study being undertaken ry large Enumerated precisely Cannot be Enumerated physically Population member

14.2 WHAT IS DATA  Example 1  Population: the set of all people/objects of interest in the study being undertaken. – Very large – Enumerated precisely – Cannot be Enumerated physically Population member

The information for each member of the population Age Gender Parish Will you vote in the by-election? Will you vote for me Variables: one piece of intormation Five variables

 The information for each member of the population – Age: – Gender: – Parish: – Will you vote in the by-election?: – Will you vote for me?  Variables: one piece of information – Five variables

o To investigate the connection between the two pairs of variables Will you vote for me' and 'Age Will you vote for me' and Gender' Will you vote for me' and Parish' Population data is used the outcomes of the analysis are precise >'perfect information results

 To investigate the connection between the two pairs of variables: – 'Will you vote for me' and 'Age' – 'Will you vote for me' and 'Gender' – 'Will you vote for me' and 'Parish'  Population data is used → the outcomes of the analysis are precise → 'perfect information' results

◆ Example2 VARIABLE Customer's Age Household Income(E per annum) Estimated monthly outgoing on mortgage/rent/rates/utilities/credit card payments etc. Does the customer own their own house? Coded 0=Yes, 1=No The Region in which the customer is resident Coded South west 2 South east London Midland North The amount borrowed on credit o Population: the set of all customers

 Example 2  Population: the set of all customers

A sensible initial set of questions is Do you understand exactly what each variable is measuring/recording Do you understand the problem under investigation and are the objectives of the investigation clear

 A sensible initial set of questions is: – Do you understand exactly what each variable is measuring/recording? – Do you understand the problem under investigation and are the objectives of the investigation clear.?

14.3 DESCRIBING VARIABLES Classification of variable types Attribute variables Measured variables

14.3 DESCRIBING VARIABLES  Classification of variable types – Attribute variables – Measured variables

Attribute Variables: An attribute variable has its outcomes described in terms of its characteristics or attributes Example 1 By-Election Data Attiute arable Outcome nder or fema 「W则m

 Attribute Variables: – An attribute variable has its outcomes described in terms of its characteristics or attributes. – Example 1 'By-Election Data':

Example 2 'Credit Data Does the customer own their own house? 0=Yes 1=No The Region in which the customer is resident? South West 2--South east 3--London 4--Midland 5— North Handling attribute data is to give it a numerical codeo.. 2

– Example 2 'Credit Data' • Does the customer own their own house? – 0=Yes 1=No • The Region in which the customer is resident? – 1—South West – 2—South East – 3—London – 4—Midland – 5—North • Handling attribute data is to give it a numerical code 0, 1, 2 ,…