 [50 lectures: 3 Credit]

• Business mathematic is important as managers need to know “How much” profit they would earn, whether it is cumulative or no.

• It also helps the managers to understand the co-relation of different variables of his business.
 Unit Title No. of Lectures Unit I INTRODUCTION TO STATISTICS 12 Functions/Scope Importance Limitations DATA: Relevance of Data(Current Scenario) Type of data(Primary & Secondary) Primary(Census vs Samples, Method of Collection (In Brief) Secondary(Merits, Limitations, Sources) (In Brief)

PRESENTATION OF DATA:

• Classification – Frequency Distribution – Discrete & Continuous

• Tabulation
• Graph(Frequency, Bar Diagram, Pie Chart, Histogram, Ogives)

MEASURES OF CENTRAL TENDENCY:

• Mean(A.M, Weighted, Combined)
• Median(Calculation and graphical using Ogives)

• Mode(Calculation and Graphical using Histogram)
• Comparative analysis of all measures of Central Tendency

 Unit II MEASURES OF DISPERSION: 12 Range with C.R(Co-Efficient Of Range) Quartiles & Quartite deviation with CQ (Co-Efficient Of Quartile) Mean Deviation from mean with CMD (Co-Efficient Of Mean

Deviation)

• Standard deviation with CV(Co-Efficient Of Variance)
• Skewness & Kurtosis (Only concept)

CO-RELATION:

• Karl Pearson
• Rank Co-Relation

LINEAR REGRESSION:

• Least Square Method

 Unit IIITIME SERIES: 12

• Least Square Method
• Moving Average Method
• Determination of Season

INDEX NUMBER:

• Simple(unweighted) Aggregate Method
• Weighted Aggregate Method

• Simple Average of Price Relatives
• Weighted Average of Price Relatives
• Chain Base Index Numbers
• Base Shifting, Splicing and Deflating
• Cost of Living Index Number

 Unit IV PROBABILITY: 14

• Concept of Sample space
• Concept of Event
• Definition of Probability
• Addition & Multiplication laws of Probability
• Conditional Probability

• Bayes’ Theorem(Concept only)
• Expectation & Variance
• Concept of Probability Distribution(Only Concept)

DECISION THEORY:

• Acts
• State of Nature Events

• Pay offs
• Opportunity loss
• Decision Making under Certainty
• Decision Making under Uncertainty

• Non-Probability: Maximax, Maximin, Minimax, Regret, Laplace &Hurwicz)

• Probabilitistics(Decision Making under risk)

–          EMV

–          EOL

–          EVPI

• Decision Tree

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