SQM 104 Lesson Plan

Session Plan

Faculty

Subject Code

Subject Name

Total Marks

Number of Students Enrolled`

Course Description /Syllabus

Books prescribed by Pune University

Paper Type:(Internal/External)

Suggested Study Material:Journals/Magazines/Business Reviews

(To be filled in as and when suggestions are made)

Session Plan

Mr Krishna Ramisetty

104

Statistical & Quantitative Methods

External

100

1. Arranging data to convey meaning - Tables, Graphs and Frequency Distribution 2. Measures of Central Tendency – Arithmetic Mean, Median, Mode. Measures of Dispersion – Range, Quartile, Mean Deviation, Standard Deviation, Coefficient of Variation. 3. Correlation – Karl Pearson coefficient & Rank correlation – Partial & Multiple correlation. Simple and Multiple Regression (Linear) – Equation and prediction 4. Association of Attributes: Yule’s coefficient & Coefficient of colligation. 5. Probability – Concept, Bayes’ theorem. Probability Distributions - Binomial, Poisson and Normal. 6. Linear Programming – ormulation.Graphical solution, Transportation & Assignment Problems – all methods 7. Queuing Theory - Single Server ( M/M/I , Infinite, FIFO) and Multi Server (M/M/C , Infinite, FIFO) 8. Markov Chains & simulation techniques. Monte Carlo Simulation 9. Games Theory - 2x2 zero sum game with dominance - Pure Strategy and Mixed Strategy 10. Decision Theory - Decision making under risk (EMV criteria) and Decision making under uncertainty

1. Statistical and Quantitative Methods – By Ranjit Chitale, 2. Statistical Methods - S.P.Gupta, 3. Statistics for Management - Levin and Rubin, 4. Quantitative Techniques Vol. 1 and 2 - L.C.Jhamb, 5. Statistics and Quantitative Techniques - M.G.Dhaygude, 6. Quantitative Techniques - N.D.Vohra

Week Number 1Date Range

Learning Objectives/Outcomes for the week:to provide the methodology and scope of various modes of presentation of Data

intended to help in making effective presentation of data and conclusions relating to analysis and evaluation for an assignment or a project

Objective(s)1234

Session Number

Date(DD-MMM-YYYY)

To describe various charts and graphs and thus help in using the same for any

live data in the work environment

Learning Objectives/Outcomes for the week:to provide the methodology and scope of various modes of presentation of Data


Arranging Data - Tables Graphs Frequency DistributionFrequency Distribution

Topic / Sub-topic coverage planned

Pre-reading material suggested to students

Teaching Methodology / Classroom Activity

Statistics for Management - T N

Srivastsava, Shailaja Rego, Chapter 3 -

"Presenatation of Data"Lecture / Demonstaration

on MS - Excel, Exercises

Learning Objectives/Outcomes for the week: Summary / Notes (if any)to provide the methodology and scope of various modes of presentation of Data


Completion Percentage(Approximate)



Learning Objectives/Outcomes for the week:Various Measures of Location such as Mean, Median, Mode. Also their relative advantages and limitations

Measures of variation such as range, standard deviation, mean deviation. Their relative advantages and limitations

Objective(s)5

67

8

Session Number

Date(DD-MMM-YYYY)

To use summary/descriptive statistics to describe collections of data and how data bunch up.

To describe how data spreads out using Dispersion measures

Learning Objectives/Outcomes for the week:Various Measures of Location such as Mean, Median, Mode. Also their relative advantages and limitations



Merits and Demerits


Teaching Methodology / Classroom ActivityMeasures of Central Tendency - Mean , Mode,

Median

Statistics for Management - T N Srivastsava, Shailaja

Rego, Chapter 4 - "Measures of Central Tendency and

Dispersion"

Solving Cases for better understanding the

implementation of various measures to various

situations

Measures of Dispersion - Range, Quartile, Mean Deviation,

Standard Deviation, Variance, Coefficient of Variation

Learning Objectives/Outcomes for the week: Summary / Notes (if any)Various Measures of Location such as Mean, Median, Mode. Also their relative advantages and limitations





Learning Objectives/Outcomes for the week:understand the relevance and application of relationship between two variables

Objective(s) Topic / Sub-topic coverage planned9 Karl Pearson Correlation Coefficient

10 Spearman's Rank Correlation11 Partial Correlation12 Multiple Correlation

Session Number

Date(DD-MMM-YYYY)

Determine the nature of mathematical

relationship, if it exists


Practise excercises and cases





Srivastsava, Shailaja Rego, Chapter 8,9

Summary / Notes (if any)




Objective(s)13 Simple Regression Analysis14 Multiple Regression Analysis15 Yule's Coefficient16 Coefficient of Colligation

Session Number

Date(DD-MMM-YYYY)


Determine the nature of

mathematical relationship, if it

exists





Completion Percentage(Approximate)Statistics for

Management - TN Srivastava, Shailaja Rego

- Chapter 8,9

Statistical Methods - SP Gupta




Learning Objectives/Outcomes for the week:Concepts and application of Probability

Objective(s) Topic / Sub-topic coverage planned17

18 Conditional Rule, Bayee's rule19

20 Normal Distribution

Session Number

Date(DD-MMM-YYYY)

To determine probabilities in

various Management and Business situations

Probability - Concepts, Additional Rule, Multiplication rule,

Probability Distributions - Binomial, Poisson

Learning Objectives/Outcomes for the week:Concepts and application of Probability






Srivastsava, Shailaja Rego, Chapter 5, 7




Learning Objectives/Outcomes for the week:Optimum utilization of Resources through various statistica l models

Objective(s) Topic / Sub-topic coverage planned21 Linear Programming - Formulation22 Graphical Solution

2324 VAM

Session Number

Date(DD-MMM-YYYY)

Solving product mix problems, distribution

and wahre house allocation problems

Transportation Problems - Northwest corner rule, least cost entry method

Learning Objectives/Outcomes for the week:Optimum utilization of Resources through various statistica l models





Quantitative Techniques in Management - ND

Vohra - Chapter 2,5




Learning Objectives/Outcomes for the week:Optimum solution for Transportation Models

Queuing theory

Objective(s)

25

26

27 Queuing Theory - M/M/I

28 M/M/C

Session Number

Date(DD-MMM-YYYY)


Transportation problems - Modi Method

Assignment Problems - Hungarian Method

Effective Customer service models

Learning Objectives/Outcomes for the week:Optimum solution for Transportation Models

Queuing theory





Quantitative Techniques in Management - ND VOHRA - Chapter 5,6

Quantitative Techniques in Management - ND VOHRA - Chapter 10




Learning Objectives/Outcomes for the week:Brand Switching example

Objective(s)

29 Markov Chains

30 Markov Chains31 Simulation - Monte Carlo

32 Simulation - Monte Carlo

Session Number

Date(DD-MMM-YYYY)


Input and output markovian analysis

Use of Random Numbers in Monte Carlo simulation

Learning Objectives/Outcomes for the week:Brand Switching example





Quantitative Techniques in Management - ND VOHRA -

Chapter 14

Quantitative Techniques in Management - ND VOHRA -

Chapter 17




Learning Objectives/Outcomes for the week:Game Theory

Objective(s)33 Game Theory - 2x2 zero sum with 34 Pure Strategy 35 Mixed Strategy36 Exercises

Session Number

Date(DD-MMM-YYYY)


To know the consequences of interplay of combination

of strategies for success

Learning Objectives/Outcomes for the week:Game Theory









Learning Objectives/Outcomes for the week:to learn how to choose best course of action out of the different alternatives available

Objective(s)37 Decision Making under Risk 38 Case Solving39 under Uncertainity40 Case Solving

Session Number

Date(DD-MMM-YYYY)


to develop a pay-off matrix, regret matrix to make

decisions

Learning Objectives/Outcomes for the week:to learn how to choose best course of action out of the different alternatives available








Session Count Summary

Standard Actual Variance

Sessions 32 32

Time (Hours) 40 40

Session Count Summary

Notes, if any

Sessions No.123456789

101112131415161718192021222324252627282930313233343536

Session Plan Summary

Planned CoverageArranging Data -

Tables Graphs Frequency DistributionFrequency Distribution

Measures of Central Tendency - Mean , Mode, MedianMerits and Demerits

Measures of Dispersion - Range, Quartile, Mean Deviation, Standard Deviation, Variance, Coefficient of Variation

Karl Pearson Correlation CoefficientSpearman's Rank Correlation

Partial CorrelationMultiple Correlation

Simple Regression AnalysisMultiple Regression Analysis

Yule's CoefficientCoefficient of Colligation

Probability - Concepts, Additional Rule, Multiplication rule, Conditional Rule, Bayee's rule

Probability Distributions - Binomial, Poisson Normal Distribution

Linear Programming - FormulationGraphical Solution

Transportation Problems - Northwest corner rule, least cost entry methodVAM

Transportation problems - Modi MethodAssignment Problems - Hungarian Method

Queuing Theory - M/M/IM/M/C

Markov ChainsMarkov Chains

Simulation - Monte CarloSimulation - Monte Carlo

Game Theory - 2x2 zero sum with dominance, Pure Strategy Mixed Strategy

Exercises

Do NOT fill inanything in Col B

B3

Do NOT fill in anything in Col B


Actual Coverage Deviation details


Notes, if any

Assessment Summary

Assessment Type Session No. Case study Session No.Marks 20

(Enter the Topic / Sub topic details covered by the assessment)

Assessment Summary

Assignment Session No. Presentation Session No.20 20

(Enter the Topic / Sub topic details covered by the assessment)

Quiz Session No. Tests Session No.20 40

Mid-term Session No. End-term50 70

SQM 104 Lesson Plan

Documents

reading material

suggested

summary notes

northwest

topic coverage

topic

monte carlo

central tendency