1.business mathematics

8/3/2019 1.Business Mathematics

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Business Mathematics

Prof. Devaki Nadkarni


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ApplicationsMarketing Management:

Analysis of Market Research Information

Sales Forecast Determination of Seasonal Fluctuations

Financial Management:

Financial Forecast and Budgeting Financial Investment Decisions

Credit Policies, Credit Risk Analysis


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Applications contd

Human Resource Management Labour turnover rate

Employment trends

Performance appraisal

Wage rate and incentive plans

Production / Operations Management

Evaluation of machine performance,maintenance policies

Quality control requirements


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Syllabus

Basic Statistical Concepts

Summarisation of Data

Elementary Probability Theory

Elementary Statistical Distributions

Sampling Distributions

Statistical Estimation

Test of Hypothesis

Simple/Multiple Correlation& Regression

Analysis of Variance


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Syllabus .. cont

Elements of Differentiation and Integration

Elements of Determinants

Elements of Matrix algebra

Books:Statistics for business and economics : Anderson & Sweeney

Business Statistics for contemporary decision making: Ken Black

Statistics for management : Levin & Rubin

Business Mathematics : J. K. Sharma


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Statistics

Statistics is the art and science of collecting,analyzing, presenting and interpreting data.

In business and economics, statistics is usedgo give managers and decision makers abetter understanding of the business and

economic environment and thus enable themto make more informed and better decisions.


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Statistics

Descriptive Statisticsare the tabular,graphical, andnumerical methodsused to summarize

and present data.

Inferential StatisticsThe process ofusing data obtainedfrom a sample tomake estimates andtest hypotheses

about thecharacteristics of apopulation


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Key Terms

Data: The facts and figures.

Population:The set of all elements of interest in aparticular study.

Sample: A subset of population.

Census: The set of all elements of interest in aparticular study.

Sample Survey: A survey to collect data on asample.

Variable: A characteristic of interest for elements.


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Data Sources

Primary Data

Obtained by a study specifically designed to

fulfill the data needs of the problem at hand. Original data

Secondary Data

Data which are not originally collected butrather obtained from published or unpublishedsources are known as secondary data.


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Methods of Data Collection

Primary Data

Observation

Personal Interviews

Mail Questionnaire Method

Telephonic Interview

Secondary Data

Published and unpublished sources


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Editing Primary Data

Editing of data should be done to ensure

Completeness

Consistency

Accuracy

Homogeneity


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Secondary Data

Sources of secondary data

Published sources (Govt. Agencies,

Industry Associations, Market ResearchAgencies)

Unpublished sources (within the

organization)


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Secondary Data cont

Before using secondary data, check for

Suitability

Reliability

Adequacy


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Levels of Data Measurement

(Scales of Data Measurement)

Nominal Lowest level of

measurement Ordinal

Interval

Ratio Highest level of measurement


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Nominal Level Data

Numbers are used to classify orcategorize

Example: Employment Classification 1 for Educator

2 for Construction Worker

3 for Manufacturing Worker

Example: Nationality

1 for American

2 for Canadian


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Ordinal Level Data

Numbers are used to indicate rank or order

Relative magnitude of numbers is meaningful

Differences between numbers are not comparable

Example: Ranking productivity of employees

Example: Taste test ranking of three brands of soft drink

Example: Position within an organization

1 for President

2 for Vice President 3 for Plant Manager

4 for Department Supervisor

5 for Employee


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Example of Ordinal

Measurement

fi

n

i

s

h

1

2

3

4

5

6


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Ordinal Data

Faculty and staff should receive preferentialtreatment for parking space.

1 2 3 4 5

StronglyAgree

Agree StronglyDisagree

DisagreeNeutral


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Interval Level Data

Distances between consecutive integersare equal

Relative magnitude of numbers is meaningful Differences between numbers are comparable

Location of origin, zero, is arbitrary

Vertical intercept of unit of measure transform

function is not zeroExample: Fahrenheit Temperature

Example: Calendar Time


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Ratio Level Data

Highest level of measurement

Relative magnitude of numbers is meaningful

Differences between numbers are comparable

Location of origin, zero, is absolute (natural)

Vertical intercept of unit of measure transformfunction is zero

Examples: Height, Weight, and Volume

Example: Monetary Variables, such as Profit and

Loss, Revenues, and Expenses


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Data Level, Operations,

and Statistical Methods

Data Level

Nominal

Ordinal

Interval

Ratio

Meaningful Operations

Classifying and Counting

All of the above plus Ranking

All of the above plus Addition,Subtraction, Multiplication, andDivision

All of the above

StatisticalMethods

Nonparametric

Nonparametric

Parametric

Parametric


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Examples: Identify scale

Variables:

1. Age

2. Gender

3. Class rank

4. Make of automobile

5. Annual sales

6. Method of payment (cash, check, credit card)7. T shirt size (small, medium, large)

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