Statistics M A MSC Syllabi final_1 Syllabus Mumbai University by munotes
Page 2
1
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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2
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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3
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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4
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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5
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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6
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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8
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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9
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
Page 12
11
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
Page 14
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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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14
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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15
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.
Page 17
16
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 .
Page 18
17
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
Page 19
18
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
Page 20
19
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
20
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
__________
24 credits