1.Business Mathematics
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Transcript of 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)