## Statistics M A MSC Syllabi final_1 Syllabus Mumbai University by munotes

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M.A./M.Sc. Part I Syllabus

M.A./ M.Sc. Part - I

Revised for Credit Based and Choice System

Implemented from the Academic year 2018 -19.

Table showing the proposed twelve papers to be covered in the first year in two

semesters.

SEMESTER

I COURSE PSST

101 COURSE

PSST 102 COURSE

PSST 103 COURSE

PSST 104 PSST P1A &

PSST P1B

PROBABILITY

THEORY LINEAR

MODELS THEORY OF

ESTIMATION SAMPLING

TECHNIQUES STATISTICS

PRACTICAL

– I

STATISTICS

PRACTICAL

- II

SEMESTER

II COURSE PSST

201 COURSE

PSST 202 COURSE

PSST 203 COURSE

PSST 204 PSST P2A &

PSST P2B

DISTRIBUTION

THEORY REGRESSION

ANALYSIS PLANNING

AND

ANALYSIS OF

EXPERIMENTS

- I MULTIVARIATE

ANALYSIS – I STATISTICS

PRACTICAL -

III

STATISTICS

PRACTICAL

-IV

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SEMESTER -I

Total No. of Classroom Teaching 60 hours +60 notional Hours =120 hours= 4 credits

Course

Code UNIT

PROBABILITY THEORY Number of

Lectures

PSST101 I Mathematical Analysis ( Proof is not expected)

Sequence and series, limit, limit inferior, limit superior,

monotone sequence, convergence of sequence, infinite series,

Power series.

Function, limit of a function, left and right hand limit,

continuity, uniform continuity, derivati ve, mean values

theorems,

Taylor series expansion, intermediate forms, partial

derivatives, extreme values, implicit, explicit function.

Introduction to Riemann integration, integrable functions,

integration under differentiation, fundamental theorem on

calculus, mean value theorems of integral calculus, integration

by parts. Change of limits of integration.

Improper integrals: limit of integration, convergence, absolute

convergence, uniform convergence, 15

II Sets, classes of sets, algebra of sets, li mits of sequence of sets,

field, sigma -field, Borel field, minimal field, definitions:

random experiment, sample space, event. Measure,

measureable sets, non -measurable sets, Probability space,

probability definitions, Bonferroni’s inequality, Booles’

inequality, continuity theorem. 15

III Conditional probability, independence, Borel zero -one law,

Borel -Cantelli lemma, Kolmogorov zero -one law.

Random variable, Expectation and mo ments, some moment

inequalities, Convolution.

Characteristic function, contin uity theorem of characteristic

function. 15

IV Convergence of sequence of random variables, various types

of convergence and their interrelations, Monotone

convergence theorem, dominated convergence theorem. Law

of large numbers: weak, strong. Central limit theorem :

Lindberg’s central limit theorem, Liapounov’s central limit

theorem. 15

References Books :

01 Apostol, T. M. (1974): Mathematical Analysis. 2nd edition, Narosa Publishing house.

02 Bartle G. and Sherbet, D. R. (2000): Introduction to Real Analysis. 3rd edition. Wiley.

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03 Bhat B.R. (1999): Modern Probability Theory: An Introductory test book. 3rd edition. New

Age International.

04 Chandra, T. and Gangopadhyay, S. (2017): Fundamentals of Probability Theory. Narosa

Publishing House.

05 Gut, A. (2005): Probability: A Graduate Course. Springer.

06 Kumar, A and Kumaresan S. (2015): A Basic course in Real analysis. CRC Press.

07 Malik, S. C. and Arora, S. (2017): Mathematical Analysis. 5th edition. New age

International Publishers.

08 Rohatgi V.K. & Saleh A.K. Md. Ehasanes (2001) - An Introduction to Probability and

Statistics. Wiley.

09 Rudin, W. (1976): Principles of Mathematical Analysis. 3rd edition. McGraw -Hill.

Course

Code UNIT

LINEAR MODELS I

Number of

Lectures

PSST 10 2 I Basic operations ,Vector Spaces, Linear dependence and

independence, Determinants of Matrices: Definition ,

Properties and applications of determinants for 3rd and

Higher order, Inverse of matrix ,Trace of matrix, Partition

of matrix, Rank of matrix, ec helon forms, canonical form,

generalized inverse, Solving linear equations,

Characteristic roots and characteristic vectors, properties

of characteristics roots , Idempotent matrix, Quadratic

forms, positive and Positive semi definite matrix, 15

II Linear par ametric function and its estimability, Gauss

markoff theorem, Interval estimates and test of hypothesis,

fundamental theorems on conditional error ss, Test of

β=d, generalized least squares 15

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III

Analysis of variance, fixed effect models :

i. One –way classification model

ii. Checking assumptions of ANOVA Model.

iii. Simultaneous Confidence Intervals:

Scheffe’s, Bonferroni and Turkey’s interval,

iv . Two – way classification model with and without

interaction effect, one observation per cell a nd r

observations per cell. Tukey’s test for non additivity.

v . Two – way classification model with and without

interaction effect with unequal number of

observations per cell.

15

IV i) Analysis of variance with random and Mixed

effect models: Estimation and testing of

variance components in one -way, two -way and

multiway classification models. ANOVA

method.

ii) Analysis of Covariance: Model, BLUE,

ANOCOVA table, testing of hypothesis, use of

ANOCOVA for missing observation.

15

References Books:

References Books : Linear Models

1. Hohn Franz E : Elementary Matrix Algebra

2. Searle S.R. : Matrix Algebra useful for Statistics

3. Kshirsagar A.M. : A course in Linear Models

4. Wang S. GUI and Chow S.C. : Advanced Linear Models.

5. Healy M. J. R. : Matrices for Statistics

6. Shantinarayan : Textbook of Matrices

7. Bishop: discrete data analysis.

8. Finney D, J : - Statistical methods in biological assays.

9. Graybill F.A : - An introduction to linear statis tical models Vol. I.

10. Rao C.R : - Linear statistical inference and its applications.

11. Searle S.R : - Linear models.

12. Sen A & Srivastava M. : - Regression analysis. Springer.

13. Scheffe H : - Analysis of variance.

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Course

Code UNIT

ESTIMATION THEORY Number of

Lectures

PSST103 I Problem of point Estimation, sufficiency, Neyman n

factorization theorem, minimal sufficiency, completeness ,

Ancillarity .

Unbiasedness, Uniformly minimum Variance Unbiased

Estimator, Rao-Blackwell theorem, Lehmann -Scheffe

theorem 15

II Methods of estimation: Method of moments , method of

maximum Likelihood estimation (M.L.E.), properties of

M.L.E , Scoring method, Large sample properties of MLE.

15

III Bounds for the variance: Cramer -Rao l ower bound ,

Bhattacharya bound, Chapman -Robb ins-Keifer bound for the

variance of an Estimator .

Consistency, properties of consistent estimators. 15

IV Bayes estimator, Loss function, risk functions , Minimaxity

and Admissibility, Non -parametric Estimation, Jacknife and

Bootstrap Estimator. 15

References Books

01 Casella, G. and Berger, R. L. (2002): Statistical Inference. Duxbury.

02 Cox, D. R. and Hinkley, D. V. (1996): Theoretical Statistics. Chapman and Hall.

03 Dixit, U. J. (2016): Examples in Parametric Inference with R. Springer.

04 Jun Shao (2005): Mathematical Statistics. Springer.

05 Kale, B. K. (2005): A First Course on Parametric Inference. Narosa Publishing.

06 Lehmann, E.L.and George Casella(1998) : - Theory of point estimation. Springer.

07 Rohatgi V.K. & Saleh A.K. Md. Eh asanes (2001) - An Introduction to Probability and

Statistics. Wiley.

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Course

Code UNIT

SAMPLING TECHNIQUES Number of

Lectures

PSST10 4 I Complete enumeration, need of sampling, types of sampling:

probability sampling and non probability samp ling.

Some concepts: unit, population, population parameter,

sampling unit, sampling frame, sample.

Simple random sampling, stratified random sampling, need

for stratification, allocation requiring more than 100%

sampling, effects of deviations from opti mum allocation,

Post stratification, method of collapsed strata, allocation of

more than one unit.

Determination of sample size

Ratio estimator, Unbiased type ratio estimator. Ratio method

for stratified random sampling, combined and separate ratio,

regre ssion estimators.

Regression estimator, Regression method for stratified

random sampling, combined and separate regression

estimators. 15

II Systematic sampling when

, estimation

of variance of estimated mean, Comparison of systematic

random sampling with simple random sampling and without

replacement and stratified random sampling.

Varying Probability Sampling:

Probability Proportional to Size sampling with replacement

(PPSWR): Methods of obtaining a sample

i. Cumulative Total Method.

ii. Lahiri’s method

Properties of the estimator

Hansen -Hurwitz estimator. Comparison of PPSWR with

simple random sampling with replacement.

Probability Proportional to Size sampling without

replacement: Sen -Midzuno method, Des Raj’s ordered

estimator, Horvitz -Thompson estimator, Yates Grundy form

of variance. 15

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III Cluster Sampling: Cluster sampling of uniform cluster size,

efficiency of cluster sampling with respect to simple random

sampling. Optimum cluster size, Cluster sampling of unequal

cluster size

Two-stage sampling: with equal first -stage units, optimum

values of n and m, with unequal first -stage units.

Two-phase sampling (Double sampling): Double sampling for

stratification, optimum allocation. 15

IV Network sampling: multiplicity estimator, Horvitz -Thompson

estimator, stratification in network sampling.

Adaptive sampling: adaptive cluster sampling, systematic and

strip adaptive cluster sampling, stratified adaptive cluster

sampling.

Non-sampling errors: response and non -response error,

meth ods of imputation. 15

References Books

01 Bansal A, (2017): survey Sampling. Narosa.

02 Chaudhari, A and Stenger, H (1992): Survey Sampling, Marcel Dekker.

03 Chaudhari, A (2014): Modern Survey Sampling, CRC Press.

04 Cochran W.G. (1999): Samplin g techniques. Wiley series.

05 Singh Daroga and Chaudhary, F. S. (1986): Theory and Analysis of Sample Survey

Designs. New Age International Publishers.

06 Mukhopadhyay, P. (2009): Theory and Methods of Survey Sampling. Eastern Economy

Edition, 2nd Edition.

07 Murthy M.N.(1967): Sampling theory and Methods. Statistical Publishing Society,

Calcutta.

08 Sukhatme,P.V.and Sukhatme B.V.(1970) : Sampling theory of Surveys with applications.

Food and Agriculture organization.

09 Thompson, S. K. (2002): S ampling. Willey. 2nd edition.

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Course

Code STATISTICS PRACTICAL S - I

PSST

P1A

Sr.

No. Title of Practical

Practicals based on Estimation Theory & Matrix theory

01 Matrix Theory -I( Determinant, Rank of Matrix , Inverse of

matrix)

02 Matrix Theory -II-( Generalized Inverse, Simultaneous

Linear Equations ,Characteristics roots & Characteristics

Vectors )

03 Methods of estimation.

03 Uniform Minimum variance unbiased estimation – II

04 Lower bounds for variance

05 Consistency

06 Bayes’ Estimation

Sr.

No. Title of Practical

Practicals based on SamplingTechniques

07 Simple random sampling and Stratified random sampling.

08 Ratio and Regression methods of Estimation.

09 Systematic random sampling and Varying Probability

Sampling

10 Cluster sampling.

11 Two-stage and Two -phase sampling.

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Course

Code

STATISTICS PRACTICAL - II

PSST

P1B

Sr.

No.

01 Introduction to R Language, SAS and SPSS

softwares. .

02 Elementary calculation

03 Data processing and Manipulation

04 Matrix operations using R language & SAS

language

Practical’s based on Linear Models.

Sr.

No.

01 Matrix Theory -I ( Determinant, Rank of Matrix ,

Inverse of matrix)

02 Matrix Theory -II( Generalized Inverse,

Simultaneous Linear Equations , Characteristics

roots & Characteristics Vectors )

03 Linear Model -I

04 Linear Model -II

05 Techniques for Checking Assumptions of ANOVA

06 One way classification model

07 Two way classification model -I

08 Two way Classification Model -II

09 Random Effect Models

10 Analysis of Covariance

Content of Statistical practical PSSTP1A and PSSTP1B to be cove red with the help software’s

like SAS , SPSS and R.

8 hours practical per week

Therefore Practicals with Software = 8 hours per week

Hence 120 Teaching hours + 120 Notional hours

= 240 hours

= 8 credits

PSSTP1A for 4 credits and P SSTP1B for 4 credits.

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Reference Books : Statistical Software

1. Carver R.H. & others Data analysis with SPSS.

2. Cody R.P. & Smith J.H. Applied Statistics and the SAS programming language.

3. Darren Georage and Paul Mallery SPSS for windows.

4. Spencer N.H .(2004) SAS Programming, the one day course.

5. Random A and Everitt R.S. : A handbook of statistical analysis using R

6. Nom o’ Rowke, Larry Hatcher, Edward J. Stepansk : A Step by step approach using

SAS for univariate and multivariate Statistics ( 2nd Edition)

7. A step by step Approach using SAS for unvariate and multivariate Statistics -2nd Edition

by Nom O’ Rourke, Larry Hatcher Edward J. Stepansk. SAS Institution. Inc. Wily.

8. Data. Statistics and Decision Mod els with Excel Donald L. Harmell, James F.Horrell.

9. Cornillon, P.et.al. (2015): R for statistics, CRC Press.

Data Site :

http://www.cmie.com/ - time series data (paid site)

www.mo spi.nic.in / websitensso.htm (national sample survey site)

www.mospi.nic.in /cso_test.htm (central statistical organization)

www.cenrusindia.net (cenrus of India)

www.indiastat.com (paid site on India statistics)

www.maharashtra.gov.in /index.php (Maharashtra govt.site)

www.mospi.gov.in (government of India)

Case studies :

1. A.C Rosander : Case Studies in Sample Design

2. Business research methods – Zikund

(http://website , swlearning.com)

3. C. Ralph Buncher 21 and Jia -Yeong Tsay : Statistical in the Pharmaceutical Industry

4. Contempory Ma rketing research – carl McDaniel, Roges Gates.

(McDaniel, swcollege.com)

5. Edward J Wegmes g. Smith : Statistical Methods for Cancer Studies

6. Eugene K. Harris and Adelin Albert : Survivorship Analysis for Clinical Studies

7. Marketing research – Zikmund

(http://website.swlearing.com )

8. Marketing research – Naresh Malhotra

(http://www.prenhall.com /malhotra)

9. http://des.maharashtra.gov.in ( government of maharashtra data)

10. Richard G. Cornell :Statistical Methods for Cancer Studies

11. Stanley H. Shapiro and Thomas H.Louis Clinical Trials

12. William J. Kennedy, Jr. and James E. Gentle. Statistical Completing

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13. Case Studies in Bayesion Statistics vo l. VI

Lecture notes in Bayesion Statistics number 167 (2002)

Constantine, Gatsonis Alicia, Carriquary Andrew, Gelman

14. Wardlow A.C (2005) Practical Statistical for Experimental bilogoists

(2nd Edition)

Seminar : Case Studies listed in the paper to be disc ussed and brief summary should be

prepared.

2 hours per week : (30 Teaching hours+ 30 Notional hours )

= 60 hours

= 2 credits

Total number of Credits for First Semester

Theory 16 + Practicals 8 = 24

Exam Pattern For Theory

Internal Exam 40 Marks

Semester End Exam 60 Marks of 3 hours duration

At the end of First Semester there will be a practical examination based on practical’s listed in

pract ical papers PSSTP1A and PSSTP1B using statistical software’s like R, SAS and SPSS

where necessary .

Exam Pattern For Practical

Practicals

papers Practical

examination Viva Journal Total

PSSTP1A 80 marks 10 marks 10 marks 100

PSSTP1B 80 marks 10 marks 10 marks 100

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SEMESTER II

Total No. of Classroom Teaching 60 hours +60 notional Hours =120 hours= 4 credits

Course

Code UNIT

DISTRIB UTION THEORY Number of

Lectures

PSST 201 I Distribution function, quantile function, empirical distribution

function, P roperties of distributions , Jordan decomposition

theorem, functions of random variables.

Generating functions: probability generating function,

moment generating function . 15

II Multiple random variables, joint cumulative distribution

function, joint probability function, joint moment generating

function, conditional probability distribution , conditional

expectation, functions of sever al random variables. Moments,

covariance, correlation. Truncated distributions. Mixture of

distributions. 15

III Some special statistical univariate discrete distributions:

degenerate distribution, two -point distribution, discrete

uniform distribution, h ypergeometric distribution, negative

hypergeometric distribution, negative binomial distribution.

Special properties of binomial distribution , Poisson

distribution , geometric distribution. Compound distributions.

Some special statistical bivariate distrib utions: negative

binomial distribution, hypergeometric distribution ,

Multinomial distribution . 15

IV Some special statistical univariate continuous distributions :

uniform distribution, Probability integral transform, gamma

distribution, beta distributio n, Cauchy distribution, Pareto

distribution,

Order statistics . 15

References Books

01 Bhat B.R. (1999): Modern Probability Theory: An Introductory test book.

3rd edition. New Age International.

02 David, H.A and Nagaraja, H. N. (2005 ): Order Statist ics. Wiley.

03 Johnson, N. L., Kotz S. and Balakrishnan, N (200 5): Univariate Discrete

Distributions. Wiley.

04 Johnson, N. L., Kotz S. and Balakrishnan, N (2004): Continuous

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Univ ariate Distributions. Volume -I. Wiley.

05 Johnson, N. L., Kotz S. and Balakrishnan, N (2004): Continuous

Univariate Distributions. Volume -II. Wiley.

06 Rao, C. R. (2002): Linear statistical Inference and its Applications.

Wiley.

07 Rohatgi V.K. & Saleh A.K. Md. Ehasanes (2001) - An Introduction to

Probability and Statist ics. Wiley.

08 Ross, S. M. (2014): Introduction to Probability Models. 11th edition.

Elsevier.

Course

Code UNIT

Regression Analysis

Number

of

Lectures

PSST 202 I Multiple Linear regression models: Assumptions of

Linear regression model and checkin g their assumptions,

Box-Cox Power transformation, Diagnostics of

Multicollinearity, Regression on Dummy variable,

Variable Selection methods: Subset selection, Forward

selection, backward elimination and stepwise. 15

II Regression diagnostics: Analysis of residuals, definition

of ordinary and Studentized residuals, their properties

and use in regression diagnostics, Autocorrelation,

Influ ence Analysis, Cook’s distance, PRESS Statistics,

covariance ratio , Orthogonal polynomials. 15

III

Genera lized Linear regression models:

Logistic regression: Example, model, MLE of

parameters, Iterative procedure to solve likelihood

equations, multiple regressors.

Multinomial and Ordinal Logistic Regression. Poisson

Regression.

Analysis of Categorical data: Log linear models,

Contingency tables. 15

IV Ridge regression: Ill conditioned matrix, need of ridge

regression, biased estimator, Mean square error. Bias and

MSE of ridge estimator, ridge trace method.

Sensitivity Analysis: Properties of Hat matrix, Role of

variables in regression model. 15

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References Books

1. Kshirsagar A.M. : A course in Linear Models

2. Draper N.R & Smith H : Applied Regression Analysis.

3. Song GUI Wang and S.C Chow: Advanced Linear Models.

4. Agresthi: Categorical data analysis.

5. Chattterj ee and Haddi: Sensitivity Analysis

6. David W Hosmer and Stanley Lemeshow: Applied Logistic regression.

7. Healy M. J. R. : Matrices for Statistics

8. Shantinarayan : Textbook of Matrices

9. Bishop: discrete data analysis.

10. Cox, D. R. : Analysis of binary data.

11. Chater jee and Price: Regression Analysis with examples

12. Finney D, J : - Statistical methods in biological assays.

13. Graybill F.A : - An introduction to linear statistical models Vol. I.

14. Montgomery D.C. & Peck B.A. : - Introduction to linear regression analysis.

15. Rao C. R :- Linear statistical inference and its applications.

16. Searle S.R : - Linear models.

17. Seber G.A.F : - Linear regression analysis.

18. Sen A & Srivastava M. : - Regression analysis. Springer.

19. Scheffe H : - Analysis of variance.

Course

Code UNIT

PLANNING AND ANA LYSIS OF EXPERIMENTS - I Number of

Lectures

PSST 203 I Brief History of Statistical Design. Basic principles of design.

Contrast, orthogonal contrast and mutual orthogonality of

contrasts.

General block design (GBD) - an exampl e. C- matrix and its

properties.

Properties of design – Connectedness, Balance and

orthogonal.

Statistical analysis of GBD. Randomized Block Design as a

particular case of GBD. 15

II Balanced incomplete block design (BIBD). C -matrix,

properties, stati stical analysis of BIBD. Resolvable BIBD,

Affine resolvable BIBD

Optimality of block design. : A,D,E – optimality. 15

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III Factorial design – an example. Basic definitions and

principles .

The advantage of factorial designs. The 22 factorial design.

The general 2k factorial design. Fitting response curves and

response surfaces. A single replicate of 2k design.

NPP method, half NPP method, hidden replication method,

Lenth’s method and Bisgaard’s conditional inference chart

method for detecting signi ficant effects.

The addition of centre points to the design.

15

IV Blocking and confounding of a replicated 2k factorial design.

Das method, contrast method and sign method to obtain

principal block.

Total and partial confounding. Two level fractional factorial

designs . The one half fraction and one quarter fraction of the

2k design. General 2(k-p) fractional factorial design. Alias

structure. Complete defining relation. Resolution – III

designs. Resolution -IV and Resolution - V designs.

Statistical analysis of all these designs. 15

References Books

01 Chakraborti, M. C. (1962): Mathematics of Design and analysis of Experiments. Asia

Publishing House.

02 Cochran, W. G. and cox, G. M. (1959): Experimental Design. 2nd Edition, Asia Publishing

House

03 Davies, O. L. (1954): The Design and analysis of Industrial Experiments. Oliver and

Boyd.

04 Das, M. N. and Giri, N. C. (2015): Design and analysis of Experiments. 2nd edition. New

Age International Publishers.

05 Fisher, R. A. (1935): The Desig n of Experiments. Oliver and Boyd.

06 Montgomery, D. C. (2016): Design and analysis of Experiments. 8th edition, Wiley.

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Course

Code UNIT

MULTIVARIATE ANALYSIS I Books

&

Page Numbers

PSST301 I i) Multivariate data and Multivariate graphical display.

Multivariate normal distribution, Wishart distribution,

15

II

Hotelling’ s T2 and its applications.

Regression and correlation coefficients among several

variables and their testing. 15

III

Likelihood Ratio Tests, Multivariate Analysis of variance

. 15

IV Discriminant analysis, classification of the observations

into one of the two populations. Extension to more than

two populations. 15

Reference Books :

1. Johnson Richard A and Wichern D.W.(1998) : Applied Multivariate Statis tical Analysis

(4th Edition)

2. Anderson T.W.(1958 ) : An Introduction to Multivariate Statistical Analysis.John Wiley

& Sons

3. Dillon William R & Goldstein Mathew (1984) : Multivariate Analysis : Methods and

Applications.

4. Giri Narayan C. (1995) : Multivariate Statistical Analysis.

5. Kshirsagar A. M. (1979) : Multivariate Analysis ,Marcel Dekker Inc. New York.

6. Hardle Wolfgang & Hlavka : Multivarite Statistics : Exercise & Solutions

7. Parimal Mukhopadhyay: Multivariate Statistical Analysis .

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Course

Code STATISTICS PRACTICAL S - III

PSST

P2A Practica ls based on Distribution Theory and Planning & Analysis of

Experiments -I

Sr.

No. Title of Practical

01 Generating random sample from discrete distributions.

02 Generating random sample from continuous distributions .

03 Probability plotting.

04 C Matrix: Checking Connectedness, Balance and

Orthogonality.

05 NPP, Half NPP and Hidden replication method for single

replicate 2k Design.

Practicals based on Regression analysis

Sr.

No. Title of Practical

01 Multiple linear Regression: Assumption Checking,

Multicolliearity, Selection methods.

02 Regression Diagnostics

03 Binary Logistic Regression

04 Multinomial Logistic R egression

05 Ordinal Logistic Regression.

06 Poisson Regression.

07 Orthogonal Polynomials

08 Categorical Data Analysis

09 Ridge Regression

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Course

Code STATISTICS PRACTICAL S - IV

PSST

P2B Practicals based on Planning and Analys is of Experiments.

Sr.

No. Title of Practical

01 Completely Randomized design

02 General block design

03 Randomized block design

04 Balanced Incomplete block design

05 22 factorial design.

06 Single replicate 2k design

07 Confounding in 2k factor ial design

08 Two level fractional factorial design

Practicals based on Multivariate analysis

Sr.

No. Title of Practical

01 Multivariate Normal Distribution.

02 Hoteling T2

03 Multivariate Regression

04 Likelihood Ratio Test

05 Multivariate A nalysis of Variance (MANOVA)

06 Discriminant Analysis

Contents of PSST P2A & PSSTP2B to be covered with the help of Statistical Software

like SAS, SP SS, MINITAB, ‘ R’ Software etc

8 hours practical per week

Therefore Practicals with Software = 8 hours per week

Hence 120 Teaching hours + 120 Notional hours

= 240 hours

= 8 credits

PSSTP2A for 4 credits and PSSTP2B for 4 credits

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Reference Books : Statistical Software

10. Carver R.H. & others Data analysis with SPSS.

11. Cody R.P. & Smith J.H. Applied Statistics and the SAS programming language.

12. Darren Georage and Paul Mallery SPSS for windows.

13. Spencer N.H.(2004) SAS Programming, the one day course.

14. Practical Statistical for experimental biologists.

15. Random A and Everitt R.S. : A handbook of statistical analysis using R

16. Nom o’ Rowke, Larry Hatcher, Edward J. Stepansk : A Step by step approach using

SAS for univariate and multivariate Statistics ( 2nd Edition)

17. A step by step Approach using SAS for unvariate and multivariate Statistics -2nd Edition

by Nom O’ Rourke, Larry Hatcher Edward J. Stepansk. SAS Institution. Inc. Wily.

18. Data. Statistics and Decision Models with Excel Donald L. Harmell, James F.Horrell.

Data Site :

http://www.cmie.com/ - time series data (paid site )

www.mospi.nic.in / websitensso.htm (national sample survey site)

www.mospi.nic.in /cso_test.htm (central statistical organization)

www.cen rusindia.net (cenrus of India)

www.indiastat.com (paid site on India statistics)

www.maharashtra.gov.in /index.php (Maharashtra govt.site)

www.mospi.gov.in (government of India)

Case studies :

1. A.C Rosander : Case Studies in Sample Design

2. Business research methods – Zikund

(http://website , swlearning.com)

3. C. Ralph Buncher 21 and Jia -Yeong Tsay : Statist ical in the Pharmaceutical Industry

4. Contempory Marketing research – carl McDaniel, Roges Gates.

(McDaniel, swcollege.com)

5. Edward J Wegmes g. Smith : Statistical Methods for Cancer Studies

6. Eugene K. Harris and Adelin Albert : Survivorship Analysis for Cl inical Studies

7. Marketing research – Zikmund

(http://website.swlearing.com )

8. Marketing research – Naresh Malhotra

(http://www.prenhall.com /malhotra)

9. http://des.maharashtra.gov.in ( government of maharashtra data)

10. Richard G. Cornell :Statistical Methods for Cancer Studies

11. Stanley H. Shapiro and Thomas H.Louis Clinical Trials

12. William J. Kennedy, Jr. and James E. Gentle. Statistical C ompleting

13. Case Studies in Bayesion Statistics vol. VI

Lecture notes in Bayesion Statistics number 167 (2002)

## Page 21

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Constantine, Gatsonis Alicia, Carriquary Andrew, Gelman

14. Wardlow A.C (2005) Practical Statistical for Experimental bilogoists

(2nd Edition)

Seminar : Case Studies listed in the paper to be discussed and brief summary should be prepared.

2 hours per week : 30 Teaching hours+ 30 Notional hours

= 60 hours

= 2 credits

Total number of Credits for Second Semester

Theory 16 + Practicals 8 = 24

Exam Pattern For Theory

Internal Exam 40 Marks

Semester End Exam 60 Marks of 3 hours duration

At the end of second Semester there will be a practical examination based on practical’s l isted in

practical papers PSSTP2A and PSSTP2 B using statistica l software where necessory .

Exam Pattern For Practical

Practicals

papers Practical

examination Viva Journal Total

PSSTP2A 80 marks 10 marks 10 marks 100

PSSTP2B 80 marks 10 marks 10 marks 100

Semester I Theory 4 x 4=16

Practicals 8

__________

24 credits

Semester II Theory 4 x 4=16

Practicals 8

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24 credits