4. Mood, A.M., Graybill, F.A. and Boss, D.C. (1997). “Introduction to the
Theory of Statistics”. McGraw Hill, New York.
5. Rao, C.R., (2009). “Linear Statistical Inference and its Applications”,
6. Rohatgi, V. K. (1984) Statistical Inference. Courier Dover
7. Stuart, A. and Ord, J.K. (2009). Kendall’s’ “Advanced Theory of
Statistics” Vol. II. Charles Griffin, London.
8. Zacks, S. (1973), “Parametric Statistical Inference”, John Wiley, New
Introduction to Multivariate data analysis, Basics of matrix and vector
covariance matrix, linear combination of variables, Generalized variance,
Multivariate Normal Distribution: Multivariate Normal density and its
matrix including their large sample behavior. Assessing normality,
Principal components analysis, Factor Analysis, Discrimination and
1. Afifi, A. A. and Clark Virginia (1984). Computer Aided Multivariate
2. Anderson, T.W. (2003). An Introduction to Multivariate Statistical
3. Chatfield, C. and Collins, A.J. (1980). Introduction to Multivariate
4. Everett, B.J. (1974). Cluster Analysis, McGraw-Hill, New York.
5. Flurry B. (1997) A First Course in Multivariate Statistics, Springer
6. Hair, J.F., Anderson R.E., Jatham, R.L.and Black W.C., (1998).
Multivariate Data Analysis, 5th ed. Pearson Education, Re print 2005,
7. Johnson, R.A. and Wichern, D.W. Applied Multivariate Statistical
Analysis (6th ed.). Prentice Hall. London.
8. Joseph F. Hair Jr, William C. Black , Barry J. Babin ,Rolph E.
Anderson (2009). Multivariate Data Analysis (7th Edition), Pearson
education Asia Edition.
9. Manly, B.F.J. (1994). Multivariate Statistical Methods, A Primer 2nd
Edition, Chapman and Hall, London.
10. Mardia, K.V., Kent, J.T. and Bibby, J.M. (1979). Multivariate
Analysis, Academic Press, London.
11. Morrison. F. (1990). Multivariate Statistical Methods, McGraw-Hill,
New York.
12. Raykov, T. and Marcoulides, G. A. (2008) Introduction to Applied
Multivariate Analysis. Tylor & Francis.
13. Rechner, A. C. (2002) Methods of Multivariate Analysis. Wiley.
14. Sharma, S. (1996), Applied Multivariate Techniques, John Wiley and
Sons, New York.
15. Tabachnick, B.G and Fidell, L.S. (1996), Using Multivariate
Statistics, 3rd ed. Harper Collins College Publishers.
STAT- 402: Statistical Inference-II
Interval Estimation: Pivotal and other methods of finding confidence
interval, confidence interval in large samples, shortest confidence interval,
optimum confidence interval. Bayes’ Interval estimation
Tests of Hypotheses: Simple and composite hypotheses, critical regions.
Neyman-Pearson Lemma, power functions, uniformly most powerful tests.
Deriving tests of Hypothesis concerning parameters in normal, exponential,
gamma and uniform distributions, Randomized Tests, Unbiased tests,
Likelihood ratio tests and their asymptotic properties. Sequential Tests:
SPRT and its properties, A.S.N. and O.C. functions.
Pre-Requisite: STAT-401
Recommended Books:
1. Hogg, R.V. and Craig, A.T. (1996). “Introduction to Mathematical
Statistics”. Prentice Hall, New Jersey.
2. Hirai, A. S. (2012) Estimation of Parameters. Ilmi Kitab Khana
Lahore.
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3. Lehman, E.L. (2008). “Testing Statistical Hypotheses”. Springler -
Volga, New York.
4. Lindgren, B.W. (1998). “Statistical Theory”. Chapman and Hall, New
York.
5. Mood, A.M. Gray Bill, F.A. and Boss, D.C. (1997). “Introduction to
the Theory of Statistics”. McGraw Hill, New York.
6. Rao, C.R., (2009). “Linear Statistical Inference and its Applications”,
John Wiley, New York.
7. Stuart, A and Ord, J.K. (2009). Kendall’s’ “Advanced Theory of
Statistics” Vol. II. Charles Griffin, London.
8. Welish, A. H. (2011) Aspects of Statistical Inference. Wiley.
9. Zacks, S. (1973), “Parametric Statistical Inference”, John Wiley, New
York.
STAT- 422: RESEARCH PROJECT / INTERNSHIP
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ELECTIVE COURSES
STAT- 405: Research Methodology
Definition of Research, Types of Research: Selection of Problem, Search
of References, Formation of Hypothesis and Procedure for its Testing,
Research Design, Planning of Experiments to Test Hypothesis Objectivity,
Principals of Experimental Design, Steps in Experimentation, Designing
Questionnaire, Collection of Data, Data Analysis to Determine, Functional
Relationship Between Variables, Levels of Significance, Interpretation of
Results, Components of Scientific Reports and Various Methods of Data,
Presentation, Preparation of Scientific Reports, Publication Procedures.
Qualitative Research: content analysis.
PRACTICAL: Survey of Literature on a Given Topic, Collection of
References from Various Sources. Collection of Primary and Secondary
Data, Arrangement of Primary and Secondary Data, Preparation of
Scientific Report for Publication, if Possible
Pre-Requisite: STAT-304
Recommended Books:
1. Gimbaled, J. and W.S. Acuter (1988) “MLA handbook for Writers of
Research Papers”, McGraw the Modern Language Association of
America.
2. Hashmi, N. (1989) “Style Manual of Technical Writings”,
USAID/NARC, Islamabad.
STAT-406: Operations Research
History and definition of Operation Research, Introduction to linear
programming, Formulation of LP model, Graphical solution of two
variables, Standard Form, Simplex method, Duality theory; Sensitivity
Analysis, Primal and dual form, Transportation Problem, Assignment
problem. Network Analysis, PERT/CPM techniques, Queuing Models,
Inventory models, Dynamic programming and simulation models
Recommended Books:
1. Bazarra, N.M., Jarvis J.J. and Sherali, H.D. (1990) “Linear
Programming and Network Flows”, John Wiley & Sons, 2nd ed.
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2. Bronson, R. (1983). “Operations Research – Schaums’ Outline
Series” – McGraw-Hill.
3. Gupta, P.K. & Hira, D.S. (2008). “Operations Research”. (7th ed.) S.
Chand & Co., New Delhi.
4. Hillier, F.S. and Lieberman G. J. (2005). “Introduction to Operations
Research”, (8th ed.)Holden Day.
5. Ravindran, A., Philips, D.J and Silberg, J.J. (2007). “Operations
Research: Principles and Practice” (2nd ed.) John Wiley.
6. Taha, H.A. (2002) Operations Research. Macmillan. London
STAT- 407: Stochastic Processes
Introduction, Generating Functions, Laplace Transforms, Difference
Equations, Differential-Difference Equations, Introduction to Stochastic
Processes. The Random Walk in one and two Dimensions, The Classical
Gambler’s Ruin Problem, Expected Duration of the Game
Markov Chains: Definition. Higher Transition Probabilities, Classification of
States and Chains, Markov processes with Discrete State Space, Poisson
Process and its Generalization, Pure Birth and Death Processes, Markov
Processes with Discrete State Space (Continuous Time Markov Chains),
Markov Processes with Continuous State Space. Introduction to Brownian
motion
Recommended Books
1. Cox, D.R.and Miller H.D. (1984). “The Theory of Stochastic
Processes”, Chapman and Hall, London.
2. Grimmet G. and Stirzaker D. (2001): Probability and Random
Processes, Oxford University Press.
3. Hole, P.G., Port, S. and Stone, C.L. (1984). “An Introduction to
Stochastic Process”, John Wiley, New York.
4. Karlin, S.A. and Taylor H.M. (1984). “A first course in Stochastic
Process”, Academic Press London.
5. Medhi, J. (1982), “Stochastic Processes”, Wiley Eastern Ltd.
6. Ross, S. M. (2006). “Stochastic Process”, John Wiley, New York.
7. Srinivasin, S.K. and Mehta, K.M. (1988). “Stochastic Processes”.
Tata McGraw-Hill.
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STAT- 408: Reliability Theory
Basic concepts of reliability, Structural reliability, Life time distributions
(Failure models): Hazard rate; Gamma, Weibull, Gumball, Log-Normal and
Inverse Gaussian Distribution. Stochastic fatigue-rate models, Point and
interval estimation, Fatigue-life model
Testing reliability hypothesis, Monte-Carlo simulations, distribution-free and
Bayes’ methods in reliability, System reliability; series and parallel
systems, Failure models, (k-out-of-m) New-better-than used models.
Inferences for these models, Accelerated life testing
Recommended Books:
1. Achintya Haldar, Sankaran Mahadevan (2000). Reliability
Assessment Using Stochastic Finite Element Analysis”.
2. Crowder, M.J. (1994). “Statistical Analysis of Reliability Data”.
3. Gertsbakh, I.B. (1989). “Statistical Reliability Theory”. Marcel
Decker. New York.
4. Gertsbakh, I. Reliability theory : with applications to preventive
maintenance Publisher: New Delhi : Springer, 2009
5. Lawless, J.F. “Statistical Model and Methods for Lifetime Data”.(2nd
ed.)
6. Lee, J. Bain, Bain Bain, (1991). “Statistical Analysis of Reliability and
Life-Testing Models”.
7. Mann, N.R., Scheefer, R.E. and Singapoor walla, N.D. (1974).
Methods for Statistical Analysis of Reliability , John Wiley & Sons.
STAT- 409: Time Series Analysis
Time series analysis: concepts, Stochastic Process, Stationary Time-
Series, Exponential smoothing techniques, auto-correlation and auto-
covariance, estimation of auto-correlation function (ACF) and Partial
autocorrelation function (PACF) and standard errors, Periodogram,
spectral density functions, comparison with ACF, Linear stationary models:
Auto Regressive Moving Average (ARMA) and mixed models, Non-
stationary models, general ARIMA notation and models, minimum mean
square forecasting. ARIMA Seasonal Models
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Recommended Books:
1. Andy, P, West M. and Harrison, P. J. (1994). Applied Bayesian
Forecasting and Time Series Analysis, Chapman & Hall New York.
2. Bovas, A. and Johannes, L. (1983), Statistical Methods for
Forecasting, John Wiley. New York
3. Box, G.E.P. and Jenkins, G.M., and Reinsel G. C. (2008) Time
Series Analysis: Forecasting and Control, San Francisco.
4. Brock well P.J. and Davis R.A. (1991). Time Series Theory and
Methods, Springer Verlag New York.
5. Chatfield, C. (1996). The Analysis of Time Series: An Introduction,
Chapman and Hall, London.
6. Chatfield C. (2003): The Analysis of Time Series: An Introduction,
Taylor & Francis, NY, USA.
7. Cox, D. R., Hinckley D.V. and Nielsen O.E.B. (1996). Time Series
Models - In Econometrics, finances and other fields; Chapman &
Hall, London.
8. Diggle, P.J. (1990), Time Series: A Biostatistical Introduction,
Clarendon Press, Oxford.
9. Jonathan D. C. and Kung-Sik C. (2008): Time Series Analysis with
Applications in R, Springer, USA.
10. Hamilton J. D. (1994): Time Series Analysis, Princeton University
Press, UK.
11. Harvey, A.C. (1990). Forecasting Structural Time Series Models and
the Calamander, Cambridge University Press, Cambridge.
12. Peter J. B and Richard A. D (2002): Introduction to Time Series and
Forecasting, Second Edition, Springer, USA.
13. Priestley, M.B. (1981) Spectral Analysis and Time Series, Academic
Press, London.
STAT- 410: Decision Theory
The nature and concept of loss functions, parameters, decisions and
sample spaces, Risk and average loss, Admissibility and the class of
admissible decisions, Minimax principle and its application to simple
decision problems, linear and quadratic losses and their uses in problems
of estimation and testing hypotheses. Asymptotically minimax procedure,
Prior distributions and conjugate priors, Bayes’ decision procedure.
Admissibility of Bayes’ and minimax procedures. Game theory
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Recommended Books:
1. Berger, J. O. (1985). “Statistical Decision Theory & Bayesian
Analysis”, Springer Verlag.
2. Blackwell, D. and Graphic, M.A. (1966). “Theory of Games and
Statistical Decision”, John Wiley, New York.
STAT- 411: Robust Methods
Introduction to Robustness, Objective function, M-estimator of location, E-
estimator, R-estimator and W-estimator, Redescending M-estimator’s The
Breakdown point of Robust estimator Influence function. M-estimator for
scale, Outliers and influential observations, Outliers in Regression analysis
Recommended Books:
1. Hamper, T.R. Brochette, E.M. Rousseau, P.J. and Satchel, W.A.
(1986). “Robust Statistics”, “The approach Based on Influence
functions”, John Wiley New York.
2. Hosmer D. W. and Lemeshow S. (2008): Applied Survival Analysis,
Wiley Interscience, USA.
3. Huber, P.J. (1981). “Robust Statistics”, John Wiley, New York.
4. Olive D. J. (2007): Applied Robust Statistics, Southern Illinois
University Department of Mathematics.
5. Rousseau, P.J. and Leroy, A.M. (1987). “Robust Regression and
outlier detection”, John Wiley. New York.
STAT- 412: Official Statistics
Official Statistics, Statistical system and international standards, set up of
national statistical organization in Pakistan, its role in development of
Statistics, working and publications.
Sources of official Statistics, National Database Registration Authority
(NADRA) and its role, Economic Statistics producers, International
classification and standards
Use of Statistics in administration and planning Concepts and evaluation of
GDP, GNP, NNP, Balance of Trade and payments, Measurement of
Income Distribution, Prices and price mechanisms. Deflation and Inflation
of series, Industrial quantum index, National sample surveys and censuses
conducted in Pakistan.
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Note: Visit of major Statistical Organizations should be a part of the
course. Alternatively, the department may invite experts from various
statistical organizations.
Recommended Books:
1. Hansen M.H. (1980). “Progress and Problems in Survey Methods
and Theory”. IIIustrated by the work of U.S. Bureau of the Census,
U.S. Department of Commerce; A Monograph.
2. NIPA (1962) “Administrative uses of Statistics” , NIPA Res. Sr.No.2
Karachi.
3. Statistical Institute for Asia & Pacific SIAP (1984). “ Training of
Trainers in Statistical Operations and Procedures” Part-I, II UNDP,
Tokyo.
4. Statistics Division (1979). “Retrospect, Perspective and Prospect”,
Islamabad.
5. Statistics Division, “Activity Report” (1988-89).Government of
Pakistan, Islamabad.
6. Various Publications of PBS, State Bank of Pakistan, Ministry of
Finance, etc.
7. Zarkovich S.S. (1966) “Quality of Statistical Data, Food and
Agricultural Organization”, The U.N. Rome.
8. Statistics Reorganization Act 2011
STAT- 413: Survival Analysis
Special features of Survival data: Patient time and study time, Survival
function and hazard function, Time dependent and censored survival data.
Nonparametric procedures: Estimation of Survival function, hazard
function, median and percentiles of Survival times. Confidence interval and
comparison of group; stratified and log-rank tests for trend, Modeling of
Survival data; Hazard function modeling; its tests and confidence interval,
The Weibull model for survival data, Exploratory data analysis and other
models, Sample size requirement for survival study, Use of software for
Survival analysis
Recommended Books:
1. Burkett, M. (1995). “Analyzing Survival Data from Clinical Trials and
Observational Studies”; John Wiley New York.
2. Collett, D. (1994). “Modeling Survival Data in Medical Research”.
Chapman & Hall, London.
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3. Cox, DR. and Oakes, D. (1984). “Analysis of Survival Data” ;
Chapman & Hall London.
4. Eland Johnson, R. C. and Johnson N. L. (1989), “Survival Models &
Data Analysis”. John Wiley N.Y.
5. Lee, E.T. (1992). “Statistical Methods for Survival Data Analysis”;
John Wiley. N.Y.
6. Lee, E.T. (1997). “Applied Survival Analysis”, John Wiley and Sons,
New York.
7. Muller, R.G. and Xian Zhou (1996). “Survival Analysis with long-term
Survivors”, John Wiley. New York.
8. Parmer M.K.B. & Macklin D. (1995). “Survival Analysis: A Practical
Approach ”; John Wiley New York.
9. Turkey, J. (1987). “Exploratory Data Analysis”, John Wiley, New
York.
STAT- 414: Biostatistics
Definition of Biostatistics, type of variables and observations in biological,
health and medical sciences, Uniqueness in terms of behaviour of
variables their domain, and units; Categorical, numerical and censored
data. Populations, Target populations and sampled Population: Role of
sampling in biostatistics, Size of samples of various types of studies,
Proportions, rates and ratios; incidence, prevalence and odds.
Distributional behaviour of biological variables (Binomial, Poisson and
Normal), Role of transformation for analysis of biological variables, Probit
and Logit transformations and their analysis, p values, its importance and
role, Confidence Interval in simple and composite hypothesis testing
Recommended Books:
1. Alfassi Z. B., Boger, Z. and Ronen, Y. (2005): Statistical Treatment
of Analytical Data, Blackwell Science, USA.
2. Altman, G. (1991). “Practical Statistics for Medical Research”.
Chapman & Hall, London.
3. Ahmad, M., Ahmad, A., and Hanif, M. (2004) Manual of Statistics for
Medical Sciences. ISOSS Publications Lahore.
4. Daniel, W.W. (2010). “Biostatistics: A Foundation for the Health
Sciences”, 6th Edition, John Wiley, New York.
5. Diggle, J. P., Liang, Kung-Yee and Zeger, S. L. (1996). “Analysis of
Longitudinal Data”, Clarendon Press, Oxford.
6. Dunn, G. and Everit, B. (1995). “Clinical Biostatistics”, Edward
Arnold, London.
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7. Hanif M., Munir A. and Aftab M. A. (2006): Biostatistics for Health
Students with Manual on Software Applications, ISOSS Publication.
8. Harris, E. K. and Albert, A. (1991). “Survivorship Analysis for Clinical
Studies”. Marcel Decker, New York.
9. Lawless, J. F. (1982). Statistical Models and Methods for Life Time
Data. John Wiley, New York.
10. Lee, E.T. (1992). “Statistical Methods for Survival Data Analysis”, 2nd
Edition, John Wiley, New York.
11. Rosner, B. (2006). “Fundamentals of Biostatistics”, Duxbury Press.
12. Shoukri, M. M. & Pause, C. A. (1999). “Statistical Methods for Health
Sciences”. 2nd Edition, CRC Press, Florida.
13. Zar, J. (2000). “Biostatistical Analysis”, 5th Edition, John Wiley and
Sons.
14. Zolman, J.F. (1993). “Biostatistics: Experimental Design and
Statistical Inference”, Oxford University Press, New York.
STAT- 415: Data Mining
Introduction to databases including simple and relational databases, data
warehouses, Review of classification methods from multivariate analysis;
classification, decision trees: classification and regression trees. Clustering
methods from both statistical and data mining viewpoints; vector
quantization. Unsupervised learning from univariate and multivariate data;
dimension reduction and feature selection. Supervised learning from
moderate to high dimensional input spaces; artificial neural networks and
extensions of regression models, regression trees. Association rules and
prediction; applications to electronic commerce
Recommended Books
1. Benson and Smith, S.J. (1997). “Data Warehousing, Data Mining’,
and OLAP. McGraw-Hill.
2. Bramer M (2007): Principles of Data Mining. Springer-Verlag London
Limited UK.
3. Breiman, L. Friedman, J.H. Olshen, R.A. and Stone, C.J. (1984).
“Classification and Regression Trees” Wadsworth and Brooks/Cole.
4. Han, J., Kamber, J. Pei, J., and Burlington, M. A. (2012) Data
mining: concepts and techniques. Haryana, India.
5. Han, J. and Camber, M. (2000). Data Mining; “Concepts and
Techniques”. Morgan Gaufmann.
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6. Mitchell, T.M. (1997). “Machine Learning”. McGraw-Hill.
7. Rao C. R., Wegman E. J. & Solka J. L (2005): Handbook of
Statistics, Vol. 24: Data mining and data visualization. Elsevier B.V.,
North Holland.
8. Ripley, B.D. (1996). “Pattern Recognition and Neural Networks”.
Cambridge University Press.
9. Suh, S. C. (2012) Practical applications of data mining. Suh.
Publisher
10. Tan P., Steinbach M. & Kumar V. (2006): Introduction to Data
Mining. Addison Wesley, New York .
STAT- 416: Actuarial Statistics-I
Introduction to actuarial Statistics, Utility theory, insurance and utility
theory, models for individual claims and their sums, survival function,
curate future lifetime, force of mortality
Life table and its relation with survival function, examples, assumptions for
fractional ages, some analytical laws of mortality, select and ultimate
tables.
Multiple life functions, joint life and last survivor status, insurance and
annuity benefits through multiple life functions, evaluation for special
mortality laws
Multiple decrement models, deterministic and random survivorship groups,
associated single decrement tables, central rates of multiple decrement,
net single premiums and their numerical evaluations.
Distribution of aggregate claims, compound Poisson distribution and its
applications
Recommended Books:
1. Bowers, N.L. Gerber, H.U. Hickman, J.C. Jones, D.A. and Nesbitt, C.J.
(1997). “Actuarial Mathematics”, Society of Actuarial, Ithaca, Illinois,
U.S.A
2. Dkkson, M. R., Hardy, H, Wates (2009) Actuarial Mathematics for Life
Contingent Risks. Cambridge.
3. Neill, A. (1977). “Life Contingencies”, Heineman.
4. Polard, B., John, H. (1980) Analysis of Mortality and Actuarial
Statistics. Faculty & Institute of Actuarial Sciences U.K.
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5. Spurgeon, E.T. (1972), “Life Contingencies”, Cambridge University
Press.
STAT- 417: Actuarial Statistics-II
Principles of compound interest: Nominal and effective rates of interest and
discount, force of interest and discount, compound interest, accumulation
factor, continuous compounding.
Life insurance: Insurance payable at the moment of death and at the end
of the year of death-level benefit insurance, endowment insurance,
deferred insurance and varying benefit insurance, recursions, commutation
functions.
Life annuities: Single payment, continuous life annuities, discrete life
annuities, life annuities with monthly payments, commutation functions,
varying annuities, recursions, complete annuities-immediate and
apportionable annuities-due.
Net premiums: Continuous and discrete premiums, true monthly payment
premiums, apportionable premiums, commutation functions, accumulation
type benefits.
Payment premiums, apportionable premiums, commutation functions,
accumulation type benefits.
Net premium reserves : Continuous and discrete net premium reserve,
reserves on a semi-continuous basis, reserves based on true monthly
premiums, reserves on an apportionable or discounted continuous basis,
reserves at fractional durations, allocations of loss to policy years,
recursive formulas and differential equations for reserves, commutation
functions.
Some practical considerations: Premiums that include expenses-general
expenses types of expenses, per policy expenses.
Claim amount distributions, approximating the individual model, stop-loss
insurance.
Recommended Books:
1. Borowiak, D. S., Shapiro, A. F. (2003) Financial and Actuarial
Statistics: An Introduction. CRC Press.
2. Bowers, N.L. Gerber, H.U. Hickman, J.C. Jones, D.A. and Nesbitt,
C.J. (1986) “Actuarial Mathematics”, Society of Actuaries, Ithaca,
Illinois, U.S.A. Second Edition (1997).
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3. Corazza, M. and Claudio, P. (2010) Mathematical and Statistical
Methods for Actuarial Sciences and Finance. Springer.
4. Neill, A. (1977). “Life Contingencies”, Heinemann.
5. Spurgeon, E.T. (1972). “Life Contingencies”, Cambridge University
Press.
STAT- 418: Mathematical Modeling and Simulation
Monte Carlo methods: Different methods of generating random numbers,
generation of random variables, acceptance and rejection techniques from
various distributions. Comparison of algorithms to generate random
variables, generating random variables from failure rates,
Generation from multinomial distribution / Monte Carlo integration, Gibbs
sampling and other resampling techniques, Variance reduction techniques:
importance sampling for integration, control variates and antithetic
variables.
Recommended Books:
1. Bernard P Autor Zeigler, Herbert Praehofer, Tag Gon Kim (2000)
Theory of Modeling and Simulation
2. Daniel P. M, Maynard T. (2006): Mathematical Modeling and
Computer Simulation, Thomson Brooks/Cole
3. Fishman, G.S. (1996). Monte Carlo: “Concepts, Algorithms, and
Applications”, (Springer).
4. Ripley, B.D. (1987) “Stochastic Simulations” ( Wiley)
5. Ross, S.M. (2002). “Simulation” ( Third Edition) (Academic)
6. Rubinstein, R.Y. (1981). “Simulation and the Monte Carlo Method”,
(Wiley).
7. Velten, K. (2009): Mathematical modeling and simulation, Wiley
VCH, Germany
STAT- 419: Categorical Data Analysis
A brief history of categorical data analysis, Principles of likelihood-based
inference, Sampling distributions for contingency tables, Measures of
association for 2x2 tables, Testing independence in contingency tables,
Exact inference for two-way tables, Inferences for three-way tables.
Introduction to generalized linear models, Logistic regression, Model
building, Alternative link functions for binary outcome, Diagnostics, Exact
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methods and conditional logistic regression, Methods for analyzing
matched case-control data, Multinomial response models for nominal data,
Multinomial response models for ordinal data.
Poisson regression model, Poisson regression for rates, Log linear models
for contingency tables
Recommended Books:
1. Alan Agresti (2012) Categorical Data Analysis (3rd edition). Wiley.
2. Alan Agresti (2007) An Introduction to Categorical Data Analysis (2nd
edition). John Wiley & Sons.
3. Chap T. Le (1998) Applied Categorical Data Analysis. Wiley
4. Collett D. (2003) Modeling Binary Data. Champman and Hall/CRC.
5. Hosmer D. W., Lemeshow S. (2004) Applied Logistic Regression.
John Wiley & Sons.
6. Lloyd C. J. (1999) Statistical Analysis of Categorical Data. Willey
7. Powers D. A., and Yu Xie (2008) Statistical Methods for Categorical
data analysis (2nd edition). Emerald Group publishing.
8. Ronald C. (1997) Log-linear models and logistic regression (2nd
edition). Springer.
9. Simonoff J. S. (2003) Analyzing Categorical Data. Springer
STAT- 422: Bayesian Inference
Conditional Probability, Prior information, Prior distributions, Methods of
elicitation of prior distributions, Posterior distributions: The posterior
means, medians (Bayes estimators under loss functions) and variances of
univariate and bivariate posterior distributions, Non-informative priors:
Methods of elicitation of non-informative priors, Bayesian Hypotheses
Testing: Bayes factor; The highest density region; Posterior probability of
the hypothesis.
Recommended Books:
1. Berger, J.O., Statistical Decision Theory and Bayesian Analysis (2nd
Ed.), New York, Springer Verlag (1985).
2. Bernardo, J. M. & Smith, A.F.M., Bayesian Theory, John Wiley, New
York (1994).
3. Box, G.E. P & Tiao, G. C. Bayesian Inference in Statistical Analysis,
Reading Addison-Wesley (1973).
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4. Introduction to Bayesian Statistics by William M. Bolstad (2004)
5. Lee, P.M. Bayesian Statistics, an Introduction, Oxford University
Press, New York (1991).
6. O.Hagan A. Kendall’s Advanced Theory of Statistics (Vol.2B),
Bayesian Inference, Cambridge, The University Press (1994).
STAT- 423: Statistical Quality Control
Concept of quality control and Quality assurance, Total Quality
Management (TQM) Statistical Methods in Quality Improvement, Statistical
Process Control (SPC), Statistical Quality Control (SQC), Shewhart control
charts: philosophy, construction, advantages. CUSUM and moving
average control charts: Average Run Length (ARL); Fast Initial Response
(FIR). ARL and FIR for control charts
Process capability analysis: Process improvements using design of
experiments.
Acceptance sampling for attributes and variables, Acceptance sampling
plans: Single, double, and multiple sampling plans with their O.C. curves,
Military Standard 501 Sampling Plans. Introduction to ISO- 9000 and ISO-
14000 series
Pre-Requisite: STAT-301
Recommended Books:
1. Banks, J. (1989). “Principles of Quality Control”. John Wiley, New
York.
2. Feigenbaum, A.V. (1986). “Total Quality Control”. McGraw-Hill, New
York.
3. Juran, J.M. and Guyana, F.K. (1988). “Juan’s Quality Control
Handbook”. McGraw Hill New York.
4. Miltag H. J. and Rinne H. (1993). “Statistical Methods of Quality
Assurance” , Chapman & Hall, London.
5. Montgomery, D.C. (2013). “Introduction to Statistical Quality
Control”. McGraw Hill, New York.
6. Nelson, W. (1990). “Accelerated Testing” . John Wiley, New York.
7. Ryan, T.P. (1989). “Statistical Methods for Quality Improvement”.
John Wiley, New York.
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Recommendations
The following recommendations were made by the committee to enhance
the teaching and learning of Statistics:
1. Departments of Statistics in the universities should make efforts to
interact with national and international statistical organizations such
as PBS, industry and other users of statistics in the public and
private sector.
2. Internship should be funded by the HEC and/or other funding
agencies, and offered to the students.
3. All universities’ departments should develop and maintain an
internship / career services department to facilitate the internship
students of Statistics.
4. Most of the courses may be taught using statistical packages.
5. Since there is a shortage of highly qualified statisticians in Pakistan.
Therefore, allocating extra quota for statistics students to pursue
higher education is needed.
6. The committee strongly recommends the creation of “Department of
Biostatistics” for teaching and research guidance at all medical
colleges/universities and the posts of biostatisticians in all
hospitals/other institutions.
7. Practicum conducted during the course work should be in the form of
case studies. The data published by different organizations may be
used in such case studies.
8. A course on Statistics may be added in curriculum of FSc (Pre-
Medical & Pre-Eng.) to prepare students for their professional
education.
9. The department of Statistics in each university may establish a
statistics consultancy center to attract potential researchers. HEC
should provide technical and financial support to these research
cells.
10. Refresher courses for the faculty should be regularly arranged by the
HEC.
11. HEC should support universities for the development of computer
labs, departmental libraries, students and staff participation in
seminars, workshops, and conferences.
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12. The department websites should be updated on a regular basis so
that research interests of the faculty may become public.
13. PGD (Post Graduate Diploma) / Short courses should be offered by
the universities/department of Statistics to the non-statisticians.
14. Professional ethics should be an integral part of the training of
students at both the undergraduate and graduate level.
15. Since 4 year BS Programme is equivalent to old M.Sc. Programme
in Statistics, therefore, the relevant recruitment rules for the post of
BPS-17 may be amended by the concerned departments (FPSC,
Establishment Division) and B.S. (Four year Programme) may be
added in the eligibility criteria for the posts.
16. The department of Statistics in each university should make concrete
efforts for establishing university-industry linkages for MS level
research.
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Annexure “A”
COMPULSORY COURSES IN ENGLISH FOR BS
(4 YEAR) IN BASIC & SOCIAL SCIENCES
English I (Functional English)
Objectives: Enhance language skills and develop critical thinking.
Course Contents
Basics of Grammar
