## UG 121 1 Syllabus Mumbai University by munotes

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UNIVERSITY OF MUMBAI

Syllabus for the Semester I and

Semester II

Program: Post Graduate Diploma in

Applied Statistics with Software.

Course : STATISTICS

(With effect from the academic year 2019 –2020)

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Post Graduate Diploma in Applied Statistics with Software

(Semester I and Semester II) Syllabus

To be implemented from the Academic year 2019 -2020

Structure of the syllabus:

The program will have two semesters, semester I and semester II. In each of the semesters, there

are four papers .

Following is the table showing the proposed courses to be covered in semester I and semester I I.

Course Title of the course

Semester I

I Basic Statistics

II Statistics in market research

III Applied regression analysis and analysis of variance

IV Applied multivariate techniques

Semester II

I Six-Sigma and total quality management

II Statistics in healthcare and clinical research

III Business analytics

IV Communication skills, soft skills and Statistical project

DETAILED SYLLABUS:

SEMESTER I

BASIC STATISTICS :

Introduction to Statistics, need of Statistics, types of scale , variable and constant,

notion of univariate, bivariate, multivariate data.

Univariate Data presentation: simple and multiple bar diagrams, pie diagram,

histogram, frequency curve, stem -leaf display .

Summary statistics: mean, median, mode, harmonic mean, geometric mean,

variance, coefficient of variation, mean deviation about median , mean deviation

about mean, absolute mean, range, Box plot.

Raw and central Moments upto fourth order, symmetric frequency curves ,

asymetric frequency curves, skewness, measures of skewness, kurtosis, measures of

kurtosis

Random experiment, sample space, concept of probability, examples, conditional

probability, Bayes’ theorem, random variable, probability function, distribution

function, independence, expectations, examples on expectations, standard discrete

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and continuous distributions: Bernoulli, binomial, Poisson, negative binomial,

exponential, normal, chi -square, students t, F, applications of central limit theorem .

Estimation and testing of hypothesis: need of estimation, notion of statistic, random

sample, likelihoo d function, introduction to methods of estimation: maximum

likelihood estimation, method of moments, properties of estimators .

Confidence interval for mean, variance.

REFERENCE BOOKS:

1. Anderson , D. R., Sweeny , D. J. and Williams -Rochester, T. A. (2002): Statistics

for business and economics. Thomson Press.

2. Hanagal, D. D. (2017): Introduction to Applied Statistics: Non -Calculus Based

Approach. Narosa Publishing House.

3. Hogg, R., Craig , A. T. and McKean, J. W. (1995): Introduction to Mathematical

Statistics . Pearson. 6th Edition.

4. Levin , R. I. and Rubin , D. S. (1998): Statistics for management. Pearson. 6th

Edition.

5. Mood, A. M., Graybill, F. A. and Boes, D. C. (1973): Introduction to the theory

of Statistics. McGraw –Hill. 3rd Edition.

6. Wacke rly, D., Mendenhall , W. and Sche affer, R. L. (2008): Mathematical

Statistics with applications . Thomson. 7th Edition.

STATISTICS IN MARKET RESEARCH :

1. Definition of marketing research and market research, need for marketing research,

requirement of good m arketing research, manager researcher relationship,

competitive and complex nature of Indian markets, role of research in new product

development, packaging, branding, positioning, distribution and pricing, ethics in

Business Research.

2. Planning the research Process - Steps in marketing Research.

3 Techniques for identifying management problem and research problem.

4. Meaning & types of research designs -exploratory, descriptive and casual.

5. Exploratory research designs, Sampling & data collection methods

6. Causal research designs: Data collection methods

7. Descriptive research design: Sampling methods, Types of scales, questionnaire

design

8. Preparations research proposal

13. Applications of Marketing Research - Introduction, Consumer Market Research,

Business -to-Business Market Research, Product Research, Pricing Research,

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Motivational Research, Distribution Research, Advertising Research, Media

research, Sales Analysis and Fo recasting, Data Mining

14. Recent Trends in Marketing research - Introduction, Marketing Information

System and Research, Online Marketing Research, Research in Lifestyle Retail,

Marketing Research and Social Marketing, Rural Marketing Research, Trends in

Services Marketing Research, Brand Equity Research, International Marketing and

Branding Research

15. Consumer segmentation techniques: Chi -square test of independence, Cluster

analysis

16. Customer discriminating technique: Discriminant analysis

17. Product positioning techniques: Snake chart, Benefit structure analysis, Multi -

dimensional scaling technique, Factor analysis

18. CHi-squared Automatic Interaction Detector (CHAID)

19. New product development technique: Conjoint analysis

20. Report writing

REFERENCE BOOKS :

Aaker, D. A., Kumar, V., Leone, R. and Day, G. S. (2012) Marketing Research. John

Wiley. 11th Edition.

Burns, A. C. and Bush, R. F. (2005): Marketing Research with SPSS 13.0. Prentice. 5th

Edition.

Gibbons, J. D. and Chakrabort i, S. (2010): Nonparametric Statistical Inference. CRC

Press. 5th Edition.

Hogg, R., Craig, A. T. and McKean, J. W. (1995): Introduction to Mathematical

Statistics. Pearson. 6th Edition.

Hanagal , D. D. (2017): Introduction to Applied Statistics: Non -Calculus Based Approach.

Narosa Publishing House.

Harper, W. B., Westfall, R. and Stasch, S. F. (1989): Marketing Research: Text and

Cases. Richard d. Irwin. 7th Edition.

Kinnear, T. C. and Taylor, J . R. (1995): Marketing Research: An applied Approach.

McGraw Hill.

Kulkarni, M. B., Ghatpande, S. B. and Gore , S. D.(1999): Common Statistical Tests.

Satyajeet Prakashan.

Malhotra, N. K. and Das, S. (2019): Marketing Research: An applied orientation revi sed

Edition. Pearson. 7th Edition .

Mood, A. M., Graybill, F. A. and Boes, D. C. (1973): Introduction to the theory of

Statistics. McGraw –Hill. 3rd Edition.

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Nargundkar, R. (2003), Marketing Research Text & Cases. Tata McGraw Hills.

Paul, E., Tull , D. S. and Albaum , G. G. (2009 ): Research for Marketing Decision. PHI.

5th Edition.

APPLIED REGRESSION ANALYSIS AND ANALYSIS OF VARIANCE

Simple linear regression, interpretation of regression coefficients, estimation of

regression coefficients, test of significance of regression coefficients.

Multiple linear regression, transformation of variables, qualitative variables as predictors,

Estimation, testing of significance, Regression diagnostics, selection of variables.

Analysis of collinear data.

Logistic regression, Poisson regression

One-way analysis of variance, two -way analysis of variance with and without interaction,

multi -way anal ysis of variance, nested models, analysis of covariance, random effect

models.

REFERENCE BOOKS:

Chatterjee , S. and Hadi , A. S. (2012) : Regression Analysis by Example . John Wiley. 5th

Edition .

Chatterjee, S., Handcock, M. S. and Simonoff, J. F. (1995) A Casebook for a first course

in Statistics and data Analysis. John Wiley.

Dielman, T. E. (2004): Applied Regression analysis: A second course on Business and

Economics Statistics. Brooks/Cole. 4th Edition.

Draper , N. R. and Smith , H. (1998 ): Applied Regre ssion Analysis . John Wiley. 3rd

Edition .

Montgomery, D. C., Peck, E. A. and Vinning, G. G. (2012): Introduction to linear

regression analysis. John Wiley. 5th Edition. Onyiah, L. C. (2008): Design and analysis of

experiments: Classical and regression appr oach with SAS. CRC Press.

Seber , George A. F. (2003) Linear Regression Analysis . John Wiley. 2nd Edition.

APPLIED MULTIVARIATE TECHNIQUES

The organization of data, data display and pictorial representation , detecting outliers and

data cleaning .

Assessing the assumption o f multivariate n ormality , transformations to near multivariate

normality .

Hotelling’s T2 statistic and its applications to testing of hypotheses.

One-way, two -way multivariate analysis of variance.

Confidence Regions and simu ltaneous Comparisons of Component Means.

Large Sample Inferences about a Population Mean Vector

Multivariate Regression Model.

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Principal Component analysis

Factor Analysis

Cluster Analysis

Discrimination and Classification

Multi Dimensional Scaling

REFERENCE BOOKS:

Bishop, Y. M., Fienbeng, S. E. and Holland, P. W. (2007): Discrete Multivariate

Analysis: Theory and Practice. Springer.

Bryan, F. J. M. and Jorge A. (2017): Multivariate Statistical Methods: A primer. CRC

Press. 4th Edition.

Johnson, R. A. and Wichern, D. W. (2015): Applied Multivariate Statistical Analysis. 6th

Edition. PHI Learning Private Limited.

Husson, F., Sebastien L. and Pages, J. (2017): Exploratory Multivariate analysis by

examples using R. CRC Press.

Srivastava, M. S. (2002): Methods of Multivariate Statistics. John Wiley.

Wolfgang, K. Hardle and Leopold Simar (2015): Applied Multivariate Statistical

analysis. Springer. 4th Edition.

SEMESTER II

SIX-SIGMA AND TOTAL QUALITY MANAGEMENT

Introduction to Lean and six – sigma : Definition of Lean , 5 S in Lean , 7 wastes in lean

5 principles of lean. Definition of six – sigma and definition of Lean six – sigma

DMAIC over view , Define phase : VOC,VOB,VOP,CTQ,COPQ ,Project charter ,DPU ,

DPM O ,Yield , Brain Storming , SIPOC , Cause and Effect diagram

Measure phase : Process definition , Process Mapping , Value Stream Mapping ,MSA,

Process Capability Analysis , statistical techniques : Averages ,Dispersion

Analyse Phase: Correlation and Regression , Probability distributions , Determination of

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sample size ,Testing of Hypothesis

Improve Phase : Multi voting , Delphi Technique , Nominal group technique , Kaizen

Control Phase : Control plans, Poka Yoke , SPC :Control plans ,IMR chart , X – bar , R –

charts, P – chart , C and U charts

Taguchi Techniques

ISO 9000

REFERENCE BOOKS:

Harry, M. and Schroeder, R. (2006): Six Sigma: The Breakthrough Management strategy

revolutionizing the world’s top corporations. Crown Business.

Ishikawa, K. (1991): Guide To Quality Control. Asian Productivity Organization.

Montgomery , D. C. (20 12): Introduction to statistical quality control . John Wiley. 7th

Edition.

Pande , P. S., Neuman , R. P. and Cavanagh , R. R. (2002): The Six Sigma Way Team

Fieldbook: An Implementation Guide for Process Improvement teams . McGraw Hill.

Phadke, M. S. (1989): Quality Engineering Using Robust Design. Prentice Hall.

Taguchi, G. (1986): Introduction to Quality Engineering: Designing Quality into Products

and Processes. Quality Resources.

STATISTICS IN HEALTH CARE AND CLINICAL RESEARCH :

Introduction to biostatistics.

Clinical trial study designs: parallel and crossover designs. Drug

development: phases of clinical trials. Randomization and blinding.

Statistics in epidemiology .

Sampling in research: probability and non -probability sampling, Simple

random Sampling, convenience sampling, systematic sampling, stratified

random sampling, cluster sampling, bootstrap sampling, sample size

calculation .

Statis tical analysis plan (SAP) in clinical trials

Analysis of datasets : intent -to-treat, per-protocol

Data analysis in bioavailability (BA) and bioequivalence (BE) studies -

PK/PD studies: data transformation, AUC, Cmax, Tmax, softwares (SAS,

Stata, Win -Nonlin)

Early stopping of clinical trials, placebo, c ausality assessment

Multiplicity and interim analysis

Survival analysis .

Correlation and regression.

Non-parametric tests for hypothesis testing: Fisher’s exact test, Wilcoxon

signed r ank test, Wilcoxon rank sum test, Mann -Whitney ‘U’ test,

Kruskal -Wallis test, Friedman test .

Parametric tests for hypothesis testing: Analysis of variance (ANOVA), t -

test, repeat measures ANOVA

Binary response data, odds ratio, relative risk, categorical data analysis

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Meta -Analysis (Systematic review)

Method comparison and evaluation, diagnostic tools: ROC curve analysis,

Bland -Altman plot, sensitivity, specificity, negative predictive value,

positive predictive value

Agreement: intraclass correlation coefficient, Kappa’s inter -rater

agreement, Cronbach’s alpha .

REFERENCE BOOKS:

Bernard, R. (2016): Fundamentals of Biostatistics. Cengage Learning. 8th Edition.

Chap, T. L. (2003): Introductory Biostatistics. John Wiley.

Chernick, M. R. an d Friis, R. H. (2003): Introductory Biostatistics for the Health

Sciences: Modern Applications Including Bootstrap. John Wiley.

Davis, C. S. (2002): Statistical Methods for the Analysis of Repeated Measurements.

Fleiss, J. L., Bruce, L. and Paik. M. C. (20 03): Statistical Methods for Rates and

Proportions.

Petrie, A. (2005): Medical Statistics at a Glance. Blackwell Publishing. 2nd Edition.

Shoukri , M. M. and Pause , C. A. (1999): Statistical Methods for Health Sciences .

Second Edition.

Tal, J. (2011): Str ategy and Statistics in Clinical Trials (A Non -Statistician’s Guide to

Thinking, Designing, and Executing). Elsevier

BUSINESS ANALYTICS:

Introduction to Business Analytics, b asic concepts of forecasting and

decision making and data analytics

Quantitative techniques of decision making: decision tree, break-even

analysis, investment appraisal, critical path analysis.

Qualitative techniques of decision making: SWOT analysis, PESTEL

analysis, Six thinking hats technique, human mindset affecting

implementation of decision.

Statistical rules of decision making: maximin criterion, maximax

criterion, minimax regret criterion, Laplace criterion.

Bayesian approach to decision making: prior analysis, pre-posterior

analysis, posterior analysis, sequential analysis.

Quantitative time series techniques of forecasting: trend projection

models, smoothing techniques, classical decomposition m odel, Box -

Genkins model

Selection of right forecasting method.

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Qualitative methods of forecasting: Delphi Method, subjective

probabilities method, market research.

Decision making under uncertainty, role of probability theory and

statistical techniques, forecasting -based decision making.

Characteristics of decision: unstructured or non-programmable

decisions, structured or programmable decisions.

Financial analytics, operational analytics, investment analytics

Inventory management and introduction, inventory control, costs in

inventory problems, techniques of inv entory control with selective

control ( ABC analysis, usage rate and criticality)

Techniques of inv entory control with known demand and E.O.Q with

uniform demand, prod uction runs of unequal length, with finite rate of

replenishment, problem of E.O.Q with shortage

Techniques inv entory c ontrol with uncertain demand and buffer stock

computation, stochastic problems and uniform demand.

Techniques in inventory c ontrol with price discounts

break even analysis, marginal costing

REFERENCE BOOKS:

1. Mayes Timothy R. and Shack Todd. M (2006): Financial Analysis with Microsoft

Excel.

2. Martin Mindy C., Hansen Steven M. and Klingher Beth, (1996): Mastering Excel

2000 . Premium Edition.

3. Spyros G Makrindakis Steyan C. Wheelwright Rob J. Hyndman: Forecasting:

Methods & Application s

4. Hanke, John E.,Reitsch Arthur G.,Wichern Dean W.: Business Forecasting 7th Edition

Communication Skills, Soft skills and Statistical Project

Module I: Communication Skills, Soft skills

Objectives of the Course:

i. to orient learners towards the func tional aspect of language

ii. to train learners to be effective verbal and written communicators

iii. to enhance language proficiency and to encourage learners in spoken English

iv. to develop effective writing skills to enable learners to write in cle ar, concise and persuasive

manner and to make them job -ready

Units:

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A. Fundamentals of Grammar - Basic grammar and sentence construction, Concords, Articles,

Confusing words, Spotting and avoiding grammatical and semantic errors.

B. Letter writing - Parts, Structure, Layouts of formal letters, familiarizing with different

formats of formal letters, Principles of Effective Letter Writing, Writing an impressive covering

letter

C. Curriculum Vitae - Understanding different formats of writing CV, Selecting the best-suited

CV for the learner and creating it.

D. Group Discussion - Types of GD, Methods and means to handle a GD

E. Interviews - Grooming and preparation before an interview, Checklist and bio -data, how to

be winsome and effective in an interview, Follo w up

F. Presentations - Verbal and PowerPoint presentations, how to be an effective communicator,

essentials of a good PowerPoint, Presentation Skills

Reference Books:

Allen, J. G. (2004): The Complete Q & A Job Interview Book. John Wiley.

Brown, R. (2004 ): Making Business Writing Happen: A Simple and Effective Guide to

Writing: Well , Allen and Unwin.

Krantman, S . (2001): The Resume Writer’s Workbook . Delmar.

Nierenberg, A . H. (2005): Winning the Interview Game . Amacom .

Rich, J. (2000): Great Resume: Ge t Noticed, Get Hired. Learning Express.

Sinha, N . C. (2016): Fundamentals of English Language . Prabh at.

Webliography:

http://www.onestopenglish.com

www.britishcouncil.org/learning -learn -english.htm

http://www.teachingeng1ish.org.uk

http://www.usingenglish.com?

Technical writing PDF (David McMurrey)

http://www.bbc.co.uk/

http://www.pearsoned.co.uk/AboutUs/ELT/

http://www.howisay.com/

http://www.thefreedictionary.com/

Module II: Statistical PROJECT

Students should carry out the project on Statistical Application based on data

The entire course will be taught using Statistics Software such as R/SAS/SPSS/MINITAB .

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Examination pattern and standard of passing:

In each semester and for each course ( except paper VIII) there will be internal /midterm exam of

40 marks and external exam of 60 marks. Student has to secure minimum 40% marks to pass in

that paper. Student has to pass separately in internal exam as well as external exam. Thus s tudent

has to secure minimum 16 marks out of 40 marks in internal examination and minimum 24

marks out of 60 marks in external paper. If student fails in securing minimum marks in any of

the internal or external exam paper then he has to appear for that pa per in the next exam

whenever it is conducted.

Student will be declared as passed if the student passes in all papers including project.

A registration of the student will be valid only for three years for the course.

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