M.Sc. Statistics

Master of Science in Statistics

1. List of Academic Staff

Name	Status and Qualifications	Research Interests
G. M. Oyeyemi	Professor & Head of Department B.Sc., M.Sc., Ph.D. (Ilorin)	Multivariate Analysis
B. L. Adeleke	Professor B.Sc. (Ilorin); Dip. Agric Stat. (Washington); M.Sc., Ph.D. (Ilorin)	Design and Analysis of Experiment
A. A. Adewara	Professor B.Sc., M.Sc., Ph.D., PGDE (Ilorin)	Sample Survey Methods and Applications
A. O. Adejumo	Professor B.Sc., M.Sc. (Ilorin); Ph.D. (Munich).	Modelling, Biostatistics, Time Series & Categorical Data Analysis
W. B. Yahya	Professor N.C.E.(Ila-Orangun); B.Sc., M.Sc. (Ilorin); PGDFM, MBA (Ado-Ekiti); Ph.D. (Munich)	Microarray Analysis, Modelling, Data Mining, Categorical Data Analysis, Bayesian Inference & Biostatistics
A. A. Abiodun	Reader B.Sc., M.Sc., Ph.D. (Ilorin)	Survival Analysis & Statistical Modelling
A. O. Abidoye	Reader B.Sc.(Ilorin); M.Sc. (Ibadan); Ph.D. (Ilorin)	Biostatistics and Hypothesis Testing
O. Job	Senior Lecturer NCE (Ilorin); B.Sc., M.Sc., Ph.D. (Ilorin)	Econometrics
M. K. Garba	Senior Lecturer B.Sc , M.Sc., Ph.D.(Ilorin)	Econometrics, Time Series and Statistical Modeling
I. Oloyede	Senior Lecturer N.C.E.(Ila-Orangun); PGDS (UNAAB, Abeokuta); B.Sc.( OAU. Ile-Ife ); M.Sc.(Ago- Iwoye); Ph.D. (Ilorin)	Bayesian Inference, Econometrics and Statistical Learning
N. A. Ikoba	Senior Lecturer B.Sc.; M.Sc. (OAU. Ile-Ife); Ph.D. (Ilorin)	Stochastic Processes and Applications, Distribution Theory, Demography
A. W. Banjoko	Senior Lecturer B.Sc , M.Sc., Ph.D.(Ilorin)	Microarray Data Analysis, Biostatistics and Statistical Quality Control
R. B. Afolayan	Lecturer I B.Sc , M.Sc., Ph.D.(Ilorin)	Design and Analysis of Experiments, Regression

		Analysis, Biometry
Olakiitan I. Adeniyi	Lecturer I B.Sc , M.Sc., Ph.D.(Ilorin)	Survival Analysis
Mariam O. Adeleke	Lecturer I B.Sc., M.Sc. (Ilorin); M.Sc. (London); PhD. (London)	Medical Statistics
O. R Olaniran	Lecturer I B.Sc., M.Sc. (Ilorin); Ph.D. (UTM)	Data Mining, Bayesian Inference and Biostatistics
L. B. Amusa	Lecturer I B. Sc., M.Sc. (Ilorin); Ph.D. (KwaZulu-Natal)	Data Mining, Statistical Modelling, Biostatistics
Jumoke Popoola	Lecturer I B.Sc , M.Sc., Ph.D.(Ilorin)	Operations Research, Stochastic Processes and Mathematical Statistics
Ifeyinwa V. Omekam	Lecturer II B.Sc. (UNN); M.Sc. , Ph.D. (Ilorin)	Distribution Theory

Introduction

The programme is to offer expert teaching and supervision in various aspects of theory and applications of statistics as follows: Analysis of Variance and its Applications, Categorical Data Analysis, Design and Analysis of Experiments, Econometrics, Modeling, Multivariate Analysis, Biostatistics, Repeated Measurements and Analysis, Sample Survey and Sampling techniques, Statistical Quality Control and its Application, Survival Analysis, Stochastic Processes, and Time Series Analysis. The programme will equip students with the skills needed to begin a career as a professional statistician. Graduates from the programme are expected to have a varied skill set including core professional skills, and a portfolio of substantive applied and practical work.

C.                 Philosophy

The philosophy of the programme is anchored on the unbiased and systematic observations, accurate documentation and interpretation of facts and phenomena with view to generate a body of knowledge.

D.                 Aim and Objectives

The primary objective of the M. Sc. in Statistics is to enable graduates of:

1. Bachelor of Science (B. Sc.) Degree in Statistics with a CGPA between 2.40 and 5.00 or weighted score average of at least 50.00% to upgrade their knowledge in order to pursue higher degree programme in Statistics; and
2. Postgraduate Diploma in Statistics from University of Ilorin or from any recognized University and have minimum CGPA of 3.5 or weighted score of at least 60% to upgrade their knowledge in order to pursue higher degree programme in Statistics.

E.                 Admission Requirements

Admission to the M. Sc.Programme in Statistics is open to:

1. Bachelor of Science degree graduates of the University of Ilorin or any other recognized University with at least a Second Class (Upper Division) Honours or equivalent;
2. Graduates of the University of Ilorin or any other approved University who achieved a Second Class (Lower Division) of the degree of Bachelor may be admitted provided they satisfy the Board of Postgraduate School requirement by scoring minimum of 55% in a qualifying Examination administered by the University; and
3. Postgraduate Diploma in Statistics from University of Ilorin or from any recognized University with a minimum CGPA of 3.5 or weighted score of at least 60% provided they satisfy the Board of Postgraduate School requirement by scoring minimum of 55% in a qualifying Examination administered by the University.

F.                 Programme Duration

The duration of M. Sc.in Statistics shall be a minimum of twelve (12) calendar months and a maximum of twenty-four (24) months.

G.                Details of Courses in M.Sc. Statistics

SCI801                       Management and Entrepreneurship 2 Credits

Business environment, general management, financial management, entrepreneurship development, feasibility studies, marketing, and managerial problem solving. 30h(T); C

SCI802                       Scientific Research Methodology 2 Credits

Essentials of Spreadsheets, Internet technology, Statistical Packages, Precision and Accuracy of Estimates, Principles of Scientific Research, Concepts of Hypotheses Formulation and Testing, Organization of Research and Report Writing. 30h(T); C

STA801                      Statistical Inference 3 Credits

Elements of theory of statistical games and decision. Reduction of decision problems into problems of statistical inference. Admissibility and completeness. Methods of estimation. Lehman Scheffe Theorem. Invari-ance, Confidence sets. Large sample theory for confidence bounds. Construction of tests: MP, UMP, UMPU, UMPI and likelihood ratio criterion with their applications. 45h (T); C

STA802                      Sample Surveys 3 Credits

Use of auxiliary information; multivariate ratio, regression and difference estimators and their extension to double sampling procedure. Quenouille‘s technique of bias reduction. Sampling on successive occasions, non-sampling errors. Some specialized sampling techniques. 45h(T); C.

STA803                      Design and Analysis of Experiments 3 Credits

General Linear Models; generalized inverse of a Matrix. Factorial Experiments; Symmetric and Assymmetric. Balanced and Partially Balanced Incomplete Block Designs. Resolvable, Group Divisible, Connected, Lattice Designs. Row,  Column Designs; Latin Squares, Lattice, Youden, Cross-Over designs. Response Surface Methodology, Construction of designs. 45h(T); C.

STA804                      Econometric Methods I 3 Credits

OLS, Gauss – Markoff Theorem. MLE Specification and mis-specification test. Predicative and non- predictive test; Tests of hypotheses in the linear model. The likelihood ratio, the Wald and the Language multiplier tests, Multi-colinearity. Specification bias, GLS. Dummy variables and seasonal variations. Inferences about linear model based on asymptotic distribution theory. 45h(T); E

STA805                      Measure Theory and Advanced Probability 3 Credits

Measure Theory: Measure and Measurable functions. Conditional spaces and measures. Probability: Probability measure and random variable, Distribution and Characteristic functions, Strong law of large numbers. Convergence theorems in probability and probability distributions. Central limit theorems for iid and correlated random variables. Conditional probability measures and expectation. 45h(T); E

STA806                      Multivariate Analysis 3 Credits

Fundamental Theory of Matrices and their properties. Multivariate Normal Distribution and associated multiple and partial correlation and regression theory. Estimation of parameters. Hotelling‘s and Mahalanobis‘s  .  Wishart distribution.  Tests concerning mean vectors and variance covariance matrices. Test for independence of two sets of variables and associated confidence bounds. Some other multivariance distributions. 45h (T); C

STA807                      Analysis of Categorical Data 3 Credits

Probability models for 2 x 2 tables. Measures of association for 2 x 2 tables. Probability models for s x r tables. Goodness of fit tests. Square tables and their applications. Structural models for two and higher dimensions. Iterative, proportional fitting of log linear models. Complete and incomplete multiway table. Quasi symmetry and complete symmetry. 45h (T); C

STA808                      Quality Control and Its Management I   3Credits

Analysis  and control of variations in  a  production process. Operating characteristic   of Control          charts. Control chart for attributes and variables. Cumulative sum control charts. Control charts based on   Weighted average. Methods of controlling several related characteristics. Process capability analysis. Economic, Design of Control charts, Specifications and Tolerances. 45h (T); C

STA809                      Stochastic Processes With Applications 3 Credits

Classification of stochastic processes. Random walk models, discrete queuing Chain, inventory model, branching processes. Poisson, Birth, and Death processes, waiting time models. Gaussian processes. Martingales, Mean covariance and sample functions. Integration and differentiation of SPs. Estimation problems. 45h (T); E

STA811                      Non-Parametric and Sequential Methods 3 Credits

Distribution of order statistics and quantities. One and two sample tests. Confidence intervals. Transformations of statistics and their asymptotic properties. OC and ASN functions of the SPRT. SPRT for composite hypotheses. Elements of sequential estimation, stein‘s two stage sampling method for point and interval estimation. 45h (T); E

STA812                      Theory of Games and Decision 2 Credits

Elements of theory of Games; Rectangular game.  Non-randomized and randomized strategies. Optimum strategies. Numerical and graphical methods for solution of games. Elements of Decision Theory:    Relationship between Games Theory, Decision Theory and    Statistical Inference. Non- randomized, randomized. Bayes, and Min-max. Unbiased and invariants decision rules. Optimal decisions. 30h (T); E

STA813                      Time Series Analysis and its Application 3 Credits

Discrete time series models. Principles of interactive model building. ARIMA Models Identification, fitting diagnostic checking of models. Application of discrete time series models illustrated by transfer function estimation, multiple forecasting and intervention function estimation. Seasonal model application for forecasting. 45h (T); E

STA814                      Methods of Operations Research 3 Credits

Linear Programming; Simplex and graphical methods of optimum solution. Application to transportation and other problems. Sensitivity testing and duality. Non-linear programming; dynamic optimization models, Stochastic Programming models, Waiting time models; element of queuing theory. Single and multiple server models. Network analysis; Shortest-route models. Generalized network problem. Multi-commodity network. Maximum-flow problem. 45h (T); E

STA815                      Econometric Methods II  2 Credits

Non-linear models. Dynamic models. Equation Dynamics; Distributed lags, partial adjustment, adaptive expectations, difference and differential equations. Estimation of dynamic models. Dynamics; ARIMA models and forecasting. Simultaneous equation systems. Discrete choice model. 30h (T); E

STA816                      Multivariate Analysis II   2 Credits

Discriminant  and Classification analysis. Cluster Analysis. Theory of Canonical correlations. Principal Component and Factor Analyses. Characteristic roots and Characteristic vectors. 30h (T); E

STA817                      Quality Control and its Management II  2 Credits

Basic concept of sampling.  Lot-by-lot Acceptance sampling for Attributes. Single, double, sequential, multiple sampling. Military standard 105D. Dodge-Roming sampling plans. Acceptance sampling by variable.  Design of variable sampling plans with specified curve. One and double specification limits. Military standard 414. Continuous sampling. Chain sampling. Life testing and reliability. 30h (T); E

STA818                      Sampling Theory  2 Credits

Foundations of Inference. Definition of some concepts in Survey Sampling. Unified theory of sampling. T-Class of linear estimators. Horvitz and Thompson estimator (HT). Admissibility of H – T estimator. Estimators better than H – T estimator. Varying Probability selection; Midzuno – Sen, Rao – Hartley – Cochran and other schemes. Ordered and unordered estimators. 45h (T); E

STA821                      Statistics and Field Experimentation 3 Credits

Initial steps in the planning of experiments. Review of principles of Randomization, replication, blocking, Basic designs; CRD, RCBD and Latin Squares. Graeco-Latin square and Cross-Over Designs, Factorial Experiments. Confounding. Fractional Replication, Balanced and Partially Balanced Incomplete Block Designs. Introduction to Analysis of Variance-Bivariate case. The use of the computer for data analysis.45h (T); (not open to statistics major students)

STA838                      Graduate Seminar 1 Credit

Oral presentation on a topic approved by the Department. 15h (T); C

STA839                      Research Dissertation 6 Credits The research project will consist of a topic in Theoretical or Applied Statistics approved by the Department. 270h (P); C

Graduation Requirements

For the award of M.Sc. certificate in Statistics, a student must pass a minimum of 32 Credits comprising 26 Credits of Compulsory courses and 6 Credits of Elective courses.

I.                   Summary of Departmental Graduation Requirement for M. Sc.

Compulsory Courses:        STA801 (3), 838 (1), 839 (6), SCI 801(2), 802(2)

STA802 (3), 803 (3), 807 (3), 808 (3)                     = 26 Credits

Elective Courses:         Minimum of two courses from the Options listed below 6 Credits There are options for specialization. Those options are:

Option 1:         Sampling theory STA818 (2)

Option 2:         Quality Control STA817 (2)

Option 3:         Multivariate Analysis STA806 (3), STA816 (3)

Option 4:         Mathematical Statistics STA805 (3), STA809 (3), STA812 (3)

Option 5:         Econometric STA804 (3), STA815 (3),

Option 6:         Time Series STA809 (3), STA813 (3)

Option 7:         Operations Research STA805 (3), STA814 (3)

Option 8:         Non Parametric STA811 (3)

Note:      Availability of the options depend on staff on ground. Minimum total Graduation Requirements = 32 Credits.

Also, candidates will be required to carry out original research in statistics and to submit a dissertation not exceeding 30,000 words in length and abstracts not more than 300 words on a topic chosen in consultation with their supervisor and approved by the Department.