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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