Director School of Mathematics Prof Weke with some of the graduands during the 57th graduation ceremony

Carolyne A. Ogutu successfully defends her PhD Licentiate Thesis

“All our dreams can come true- if we have the courage to pursue them.”

Mr Erastus Kimani Ndekele presenting his PhD Proposal to the school academic Board

Davis Bundi defended his thesis on: Social Network Analysis for Credit Risk Modelling

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Degree Code: | I63 |

Degree Name: | BACHELOR OF SCIENCE IN STATISTICS |

Degree Type: | BACHELOR |

Degree Duration: | 4 |

Degree Description: | Click to View |

BACHELOR OF SCIENCE STATISTICS | |

Degree Courses: |
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**DEGREE REGULATIONS**

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- STA101 Introduction to Probability and Statistics
- STA102 Exploratory Data Analysis
- STA103 Principles of Sample Surveys
- STA121 Programming Methodology
- STA122 Computational Methods and Data Analysis I
- SMA101 Basic Mathematics
- SMA103 Calculus I
- SMA104 Calculus II
- SMA106 Calculus III
Frequency distributions, relative and cumulative distributions, various frequency curves, mean, mode, median, quartiles and percentiles, standard deviation, symmetrical and skewed distributions. Probability: sample space and events; definition of probability, properties of probability; random variables; probability distributions; expected values of random variables. Elements of Markov chains.
Data, grouping data, graphs and charts, standard leaf diagrams, Box and Whisker diagrams. Variables and their frequency distributions, summary measures. The comparison problem- an exploratory view. An exploratory look at association. The staircase and the short cut to inference. Distributions and their troubles. Kinds of nonnormality and robustness. The role of vague concepts. Indication, determination or inference. Indication and indicators. Data analysis and computer graphics. Graphs on determination and inference. Methods of assessing real uncertainty, Bayesian ideas, empirical priors, Bayes theorem. Prior information for normal data; binomial data and beta priors; Poisson data and gamma priors. Binomial probability paper; the normal probability paper.
Uses, scope and advantages of sample survey; types of survey; survey organisation; sample survey design. Purposive, probability and quota sampling. Simple random sampling; stratified sampling; systematic sampling; multistage sampling and pps selections. Estimation of means, totals and proportions; variance calculations. Sampling error. Sources of error, nonresponse. Management of surveys.
Principles of computer organisation. Information storage. Bits, bytes, words, ordinary and floating point representation of numbers. Character codes. Structured programming using high level language e.g. Pascal. FORTRAN, C; programme structure. Abstract data types. Mathematical expressions and operations. Logical expressions and operations. Control structures. Functions. Procedures. Report and display design, library procedures. Types of problems computers can solve. General structure of installation; mainframe versus stand alone micro computers: networking; operating systems, compiler systems and utilities.
Computer graphics. Statistical packages and libraries. Role of computers in data bases. Survey applications. Number systems; errors and accuracy; interpolation; finite differences; difference equations; successive approximation or iterative techniques. Numerical solution of non-linear equations. Writing programs to implement numerical algorithms. Application of numerical analysis, software packages such as NAG.
- STA201 Probability and Statistics I
- STA202 Principles of Statistical Inference
- STA221 Economic and Social Statistics
- STA222 Introduction to Time Series Analysis
- STA223 Operations Research I
- STA224 Computational Methods and Data Analysis II
- SMA201 Advanced Calculus
- SMA203 Linear Algebra I
- SMA204 Linear Algebra II
- SMA205 Introduction to Algebra
- SMA208 Ordinary Differential Equations
Particular distributions: Bernoulli, binomial, Poisson, geometric, hypergeometric, uniform, exponential and normal random variables and their distributions. Bivariate frequency distributions. Joint probability tables and marginal probabilities. Moments and moment generating function. Markov and Chebychev inequalities. Special univariate distributions. Bivariate probability distributions; joint marginal and conditional distributions; Independence ; Bivariate expectation; Regression and Correlation; Calculation of regression and correlation coefficients for bivariate data.
Meaning of statistics, objectives of statistical investigation. Statistical decision problems, basic concepts of inference. Role of normal distribution in statistics. Random samples, use of random number tables. Inference about population means: point and interval estimates, simple one sample and two sample tests. Linear regression and correlation analysis. Analysis of variance. Analysis of frequency data. Simple nonparametric tests.
Economic Statistics: Gross domestic product, index numbers, retail price index, consumer price index, product index; balance of payments and trade statistics. Demography: Scope, uses and sources of demographic and socio-economic data; methods of enumeration; demographic concepts and measures; current and cohort methods of description and analysis; rates and ratios; standardisation; construction of life tables. Measurement of fertility, mortality and nuptiality. Determinants of age structure and the intrinsic growth rate. Survey data; interpretation of demographic statistics, tests of consistency and reliability. Social Statistics: Nature of social statistics; sources of social statistics; conceptual problems; validity and reliability concepts; definition and classification. Measurement problems in social surveys; socio-economic indicators. Studies in the integration of social statistics. History of Statistics development in Kenya . Sources and methods in official statistics in Kenya .
An introduction to time series in time domain and spectral domain. Estimation of trends and seasonal effects, autoregressive moving average models, forecasting, indicators, harmonic analysis, spectra.
Linear programming: Formulation of LP problems. The simplex algorithms; duality theory and economic interpretations. Post optimality (sensitivity) analysis. Introduction to transportation and assignment problems. Survey of continuous optimisation problems. Unconstrained optimisation problems and methods of solution. Introduction to constrained optimisation. Integer programming; methods of integer programming.
Numerical solution of linear systems; numerical evaluation of eigenvalues and eigenvectors. Numerical integration and differentiation. Data structures, arrays and their implementation, strings; application and implementation of stacks, queues, linked lists, trees and graphs: Survey application, questionnaire design; data processing, data editing and correction; editing and imputation principles; writing of edit specification, use of an edit specification, use of an edit package. Tabulation, table design, writing of a table specification; use of a tabulation package. Writing programs to implement numerical algorithms. Application of numerical analysis software package such as NAG. Simulation: random and pseudo random numbers; generation of uniform variates; outline of tests, mention of physical devices for uniform generators; generation of variates from standard distributions e.g. normal, exponential etc.
- STA301 Probability and Statistics II
- STA302 Linear Modelling I
- STA303 Theory of Estimation
- STA304 Testing Hypotheses
- STA305 Probability Modelling
- STA306 Applied Time Series Analysis I
- STA307 Analysis of Experimental Designs I
- STA308 Sample Survey Theory and Methods I
- STA321 Operations Research II
- STA322 Computational Methods and Data Analysis III
STA318 Statistical Inference I
Distribution functions of random variables; bivariate normal distribution. Derived distributions such as chi-square, t and F. Statistical independence. Random samples. Multinomial distributions. Functions of several random variables. The independence of and S 2 in normal samples. Order statistics. Convergence and limit theorems.
Linear models: linear regression analysis, analysis of variance and covariance, diagnostics of residuals, transformations. Non-linear regression: use of transformations, polynomial regression. Large sample theory for non-normal linear models. Collinearity. Outliers. Random effects models; estimation of variance components in one-way and two-way models. GLIM package.
Properties of point estimators. Sufficient statistics, the factorisation criterion; complete statistics. Minimum variance unbiased estimators; Cramer-Rao inequality; Fisher information; efficient estimators. Maximum likelihood estimators and their properties. Interval estimation. Least squares estimation in linear models: simple linear model, the general linear model; weighted least squares; interval estimation in linear models.
Concepts of statistical hypothesis and statistical test; optimal tests, Neyman Pearson lemma; properties of tests; unbiasedness, consistency; confidence sets and tests; generalised likelihood ratio tests; tests for correlation and regression, general linear hypotheses.
Stochastic processes, definition and examples. Bernoulli process: probability model, waiting times. Markov chains: discrete time Markov chains, holding times, stationary distributions, classification of states. Birth and death processes, stationary distributions. Queuing models: deterministic approximations, examples of queuing systems, application to arrival and departure processes, heavy traffic etc.
Stationary time series, removal of trend and seasonal differences, moments and autocorrelation. Simple autoregressive and moving average models, moments and autocorrelations, the conditions of stationarity; invertibility. Mixed (ARMA) models and the AR representation of MA and ARMA models. Fitting and testing time series models. Forecasting, methods of forecasting, scientific forecasting, basic forecasting models, forecasting criteria. Model building and identification. Series used as examples: simulated series, stock market prices etc.
General principles: randomisation, replication, blocking, covariates, orthogonality, balance, logical control or error, sequential design. Estimation of treatment contrasts and their precision, treatment structure; comparison with a control. Some common designs: completely randomised design, randomised complete block design, rationale for blocking; latin squares, rationale, randomisation, analysis; relative merits of designs. Introduction to factorial experiments: 2 2 and 2 3 designs; calculation and interpretation of effects and interactions. Incomplete block design, optimality criteria. Crossed and nested block structures.
Review of general principles of survey design. Populations and sampling frames. Simple random sampling; properties of estimates, determination of sample size. Ratio and regression estimation. Stratification, optimality considerations. One-stage and two-stage cluster sampling. Systematic sampling. Multistage designs. Criteria for choosing sampling designs.
Properties of point estimators: unbiasedness, sufficiency, minimal sufficiency, consistency, relative efficiency, minimum variance unbiased estimators; evaluating the goodness of a point estimator. Method of moments, maximum likelihood estimators and their properties. Confidence intervals, large and small sample intervals. Concepts of statistical hypothesis and statistical test; optimal tests, calculation of size and power of a test; finding the sample size. Neyman Pearson lemma; properties of tests- unbiasedness, consistency. Likelihood ratio tests-common large sample tests. Bayesian estimation; Bayesian tests and confidence sets. Fundamental aspects of nonparametric inference such as rank, permutation and goodness of fit tests; nonparametric estimation of quantiles and density functions; robust estimation of location and scale parameters.
Note: STA318 must not be taken together with STA303 and/or STA304
Aims and scope of stochastic modelling. Decisions under risk, decision trees, decisions under uncertainty. Markov decision processes, dynamic programming models; linear programming solution of the Markovian decision problem. Queuing models, types of queues; roles of Poisson and exponential probability models; queues with combined arrivals and departures; queues with priorities of service. Traffic flow models. Inventory models, practical stock systems; types of inventory; scheduling policies; storage models. Simulation models, roles of random numbers; simulation experiments; Monte Carlo calculus and variance reduction techniques, simulation as estimation, control variates, antithetic variates, stratified and importance sampling; choice of sampling size. Analogue simulation systems e.g. queues, inventories, traffic networks, storage systems.
Application of statistical packages (e.g. GLIM, SPSS, SPLUS, GENSTAT, etc) in statistical data analysis. Simulation of simple deterministic and stochastic systems; simulation of inventory and stock control systems, queuing systems, traffic networks etc.. Polynomial interpolation, spline approximation, solution of ordinary differential equations. Stability and efficiency concepts. Monte Carlo methods. Management information systems; management of information systems. File systems and database systems; database design. Project management and implementation; use of computer development tools, Case studies. Report writing, presentations. Data communication and networks, applications; case studies.
- STA401 Measure, Probability and Integration
- STA402 Bayesian Inference and Decision Theory
- STA403 Nonparametric Methods
- STA404 Applied Multivariate Methods
- STA420 Project in Statistics
- STA405 Linear Modelling II
- STA406 Applied Stochastic Processes
- STA407 Analysis of Experimental Designs II
- STA408 Robust Methods and Nonparametrics
- STA409 Applied Time Series Analysis II
- STA410 Sample Survey Theory and Methods II
- STA419 Statistical Inference II
- STA421 Operations Research III
- STA422 Stochastic Models for Social Processes
- STA423 Stochastic Models for Biological Processes
- STA424 Statistical Methods for Industrial Processes
- STA425 Statistical Demography
- STA426 Applied Population Analysis
- STA428 Applied Demography
- STA429 Econometric Models I
- STA430 Econometric Models II
- STA432 Applied Econometrics
- STA434 Survey Research Methods
- STA435 Biometrics Methods I
- STA436 Biometrics Methods II
- STA437 Survival Analysis
Measure and integration: Measurable functions, measures, measure space; integration, monotone convergence theorem, Fatous lemma; convergence theorems; Radon Nikodym theorem; Lebesgue decomposition. Probability Theory: Probability as a measure; probability space; random variables; distribution functions and characteristic functions. Sums of random variables, independence. Modes of convergence of sequences of random variables. Borel-Canteli lemmas and the zero-one laws, laws of large numbers and central limit theorem.
Elements of decision theory: Statistical games; the no data problem. Loss and regret, mixed actions, the minimax principle, Bayes actions; decision with sample data; decision rules, risk function, Bayes decision rules. Bayesian inference: Problems associated with classical approach; Bayes approach: prior and posteri distributions; specification of prior distribution; Bayesian estimation, properties of Bayes estimators; Bayesian tests and confidence sets; examples of situations where Bayesian and classical approaches give equivalent or nearly equivalent results. One-parameter and multiparameter models, predictive checking and sensitivity analysis. Simulation of probability distributions. Sequential methods: Sequential probability ratio test; Stein fixed width confidence intervals. Current methodological issues in Statistics.
Nonparametric inference, simple one-sample tests; order statistics, empirical distribution function, ranks and runs; general nature of nonparametric tests, allocation of scores, confidence intervals; efficiency and robustness considerations; dealing with tied observations. Goodness of fit tests. General two-sample and c-sample problems; linear rank tests; Wilcoxons rank sum test; use of rank sum procedures for assessing symmetry and in analysis of variance; Friedman test, two-sample tests of dispersion. Measures and tests for association; analysis of contingency tables; Kendalls t, Spearmans rank correlation; coefficient of concordance. Efficiency of nonparametric procedures.
Examples of multivariate data; summarising multivariate data, mean vectors and covariance matrices, correlation matrix. The multinormal distribution. Sampling from the multinormal, MLEs and tests for the mean vector; simultaneous confidence intervals; tests of structural relationship. Testing equality of two population means. MLEs of partial and multiple correlations and tests; testing for complete independence; canonical correlations and variates, test of canonical correlation and reduction in dimensionality. Classification into one of two populations. Calculation and interpretation of principal components. Elements of multivariate analysis of variance, one-way grouping and two-way grouping without interaction.
Analysis of the general linear model: model building, model selection and validation, variable selection; stepwise and best subset regression. Introduction to response surface methodology. Modelling under prior and additional information, ridge regression. Modelling of nonnormal data. Treatment of outliers in regression models. Robustness, graphical techniques. Generalised linear models, measurement of association in two-way tables; log-linear and other models for contingency tables; logit, probit, categorical data, score tests, case studies.
Review of Random phenomena in time and space, mention of point processes, distributions, stationarity. Theory of recurrent events. Martingales, stopping times. Markov processes. Brownian motion. Renewal theory, the key renewal theorem; the renewal equation. Stochastic differential equations.
Review of experimental and statistical objectives. General 2 n design, confounding of one or more effects, partial confounding: fractional replication; block compositions. Factors at 3 levels; 3 2 and 3 3 experiments with and without confounding, estimation of effects. Split plot designs. Incomplete block designs: nature and need for incomplete blocks; types of designs, balanced designs, balanced incomplete block design, intra and inter block analysis; partially balanced incomplete blocks-two associate classes only. Youden squares; lattice designs; relative merits of designs. Planning of experiments; choice of design, economic considerations, treatment design; experimental design. Determination of optimum plot/block size and shape.
Distribution-free methods; permutation theory. Robust estimation; influence functions. Semi-empirical inference, Monte-Carlo methods, simulation models; Jacknife and bootstrapping. Robust regression.
Probability models for time series, stationary processes, the autocorrelation function; pure random process, MA and AR processes; mixed models, integrated models; the general linear process, continuous processes. Model identification and estimation, estimating the autocovariance and autocorrelation functions; fitting AR and MA processes; estimating the parameters of mixed and integrated models; the Box-Jenkins seasonal model; residual analysis. Forecasting, univariate and multivariate procedure; prediction theory. Spectral theory, the spectral density function; Fourier analysis and harmonic decompositions; periodogram analysis; spectral analysis, effects of linear filters; estimation of spectra; confidence intervals for the spectrum.
The history of survey sampling. Techniques of sample design: multiphase designs; selection with probability proportional to size (PPS); general aspects of replicated and successive sampling; panel design; model based sampling. Bias and nonresponse: sources of survey errors, non-coverage, nonresponse. African household survey capability program: scope, types of surveys undertaken, sampling techniques used, issues and problems. Use of appropriate software to calculate standard errors.
Elements of Statistical decision theory. Bayesian methods: prior and posterior distribution. Bayesian estimation , properties of Bayes estimators, one-parameter and multiparameter models, hierarchical models, predictive checking and sensitivity analysis. Simulation of probability distributions. Nonparametric inference: Order statistics, empirical distribution functions, ranks and runs; confidence intervals. Linear rank tests. Measures and tests of association; analysis of contingency tables. Efficiency of nonparametric procedures.
The project is undertaken during the second semester in the fourth year of study and is equivalent to one course unit. A satisfactory report must be completed, marked by both the students supervisor(s) and the external examiner, and presented in a final oral examination. The project shall be graded independently out of a maximum of 100 marks distributed as follows: 70% for project report and 30% for oral presentation.
Dynamic programming and heuristics. Project scheduling; probability and cost considerations in project scheduling; project control. Critical path analysis. Reliability problems; replacement and maintenance costs; discounting; group replacement, renewal process formulation, application of dynamic programming. Queuing theory in practice: obstacles in modelling queuing systems, data gathering and testing, queuing decision models, case studies. Game theory, matrix games; minimax strategies, saddle points, mixed strategies, solution of a game. Behavioural decision theory, descriptive models of human decision making; the use of decision analysis in practice.
The statistical analysis for labour turnover; Markov chains and renewal models for manpower forecasting and control; career prospects, demand forecasting. Models for size and duration, open and closed Markov models for social and occupational mobility, models for the diffusion of news and rumours and competition for social groups. Criteria for establishing priorities in planning in advanced and developing countries. Methods for forecasting the demand for education and the demand and supply of teachers. Methods of forecasting manpower requirement. Computable models for the education system.
Basic laws of genetics; mutation, inherited defects in man, their persistence and geographical variation; genetic counselling; measures of relationship and identity; effects of inbreeding; genetic linkage; Markov models in population genetics. Recovery, relapse, and death due to disease; cell survival after irradiation; compartmental analysis. Epidemic models, deterministic and stochastic versions; models for the control of infectious diseases; models for the management of insect pest populations. Leslie matrix models, application in management of natural resources.
Tolerance limits; process control, , R, p and c charts, their construction and use; cusum charts, V-mask and decision interval procedures and their properties. Acceptance sampling for attributes and variables; operating characteristic curve and average run length; single, double, sequential plans; choice in light of producers and consumers risks and by decision theory approach. Models of systems running in time. Stock control models. Reliability problems.
Simple models of population growth; analysis of mortality using life tables; model life tables; continuous and multiple decrement formulations; statistical properties of life table estimators; proportional hazards and multistate life tables. Stable and stationary populations and their use for estimation of demographic parameters; continuous formulation of population dynamics equation; solutions of renewal equation. Discrete formulation of population projections. Parity progression ratios. Mathematical models for fertility and mortality schedules. Quantitative models of nuptiality; models of reproductivity and measurement of fecundability. Analytic and simulation approaches to reproductivity and household structure. Sources of demographic data.
This course is intended to deal with the use of demographic concepts and techniques in social, economic, and medical planning and research. Examples of topics to be dealt with include: The use of population projections at national level; studies of provision of places in higher education; the momentum of population growth in developing countries; the application of regional and local projections in planning housing, schooling etc; manpower planning; household and family structure and composition; impact of contraception and abortion on fertility; the use of survey data in assessing family planning programmes; womens participation in labour force; the impact of breast feeding on fertility and infant mortality; inputs to planning of primary health care; occupational mortality; unemployment and mortality.
Collection of demographic data: Historical development of demographic statistics; stages involved in planning a census; content of census and survey schedules; basic response errors; structure of census organisations; vital registration, types of demographic sample survey; the World Fertility Survey Programme. Demographic sampling and survey design: Applications of principles of statistical sampling to demographic surveys; types of demographic sample survey designs; questionnaire and schedule design. Evaluation of family planning programmes: Aims of programmes; methods of evaluation; evaluation of programmes demographic impact, methods of analysis; case studies. Report Writing on analysis of demographic data: Evaluation of data; analysis and checking for consistency and convergence of parameters; interpretations of findings; implications for planning and policy formulation; conclusions.
Sources of data, national accounts, price indices. Econometrics; methods and applications; aggregated and desegregated models; models of the national economy, models of sectors. The linear model: multiple regression, t- and F-tests, dummy variables, multicollinearity, general linear restrictions, dynamic models. Time series autoregressive models, seasonal adjustment, generalised least squares, serial correlation, heteroscedasticity, distributed lags, simultaneous equation systems, instrumental variables and two-stage least squares. General linear model, model specification, autocorrelation in linear models.
Structural and reduced forms, lagged endogenous variables; identifiability, global and local identifiablility, multicollinearity; estimation of simultaneous equation systems, subsystems, and single equations; types of estimators, their asymptotic properties; hypothesis testing, types of tests and their asymptotic properties, testing over-identifying constraints; testing for misspecification.
Econometric model building and testing; probit and tobit analysis; use of econometric concepts and techniques in economic and development planning and research; models for plan preparation at the enterprise and national levels; derivation and use of shadow prices in development planning and project scheduling; demand analysis; labour market behaviour; models of unemployment; econometric analysis of inflation; monetarist models; rational expectations and the natural rate hypotheses; models of consumer and investment behaviour; use of the standard computer packages in econometrics including practical exercises; case studies.
This course is intended to deal with the practical issues in the planning, design, execution and management of sample surveys.The course work will consist of several assignments of practical problems. Course Outline: Problems of measurement and scaling; attitude measurement; study design; methods of data collection; interviewing techniques; questionnaire design; response errors; structure of interviewer effects; problems of and procedures for compensation for non-response.
Biological assay: direct and indirect assays; dilution assays; dose response relationships; parallel line and slope ratio assays; multiple assays; assays based on quantal responses. Agricultural trials; crop weather modelling; plot sampling techniques; lay out of field experiments; combining analyses over sites and seasons; planning future experiments. Sampling and estimating biological populations. Longitudinal data analysis: Design considerations; exploring longitudinal data; generalised linear models for longitudinal data.
Clinical trials: protocal design; parallel studies; cross over designs; drop outs and protocal violators; repeated measurements; multi-centre trials; sequential methods. Epidemiological studies: descriptive epidemiology; investigative epidemiology; causation, case control and cohort studies; outbreak investigations; community diagnosis; sources of bias in epidemiological studies.
Survival function; hazard function; cumulative hazard function; censoring; Kaplan-Meier survival curve; parametric and nonparametric representation of the survival and hazard distributions. Two-sample and k-sample tests; proportional hazard models; accelerated failure time models; models for grouped survival data; inclusion of covariates - Coxs P.H model; applications of model checking; competing risks - extensions of Coxs model. Frailty models.
- STA432 Applied Econometrics
- STA434 Survey Research Methods
- STA435 Biometrics Methods I
- STA436 Biometrics Methods II
- STA437 Survival Analysis
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Course Duration | View Details | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

The complete course requires 8 semesters each of 15 weeks. Flexible registration rules allow students to control their own pace of progress through the programme. The minimum number of units a student may take in one semester is three. The total number of units required in the course is 44. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Credit Transfers & Exemptions | View Details | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

A candidate may be exempted from some course units and credit transferred from approved institutions, subject to the following conditions. (i)Request for exemption should be made in writing, on admission, addressed to the Dean of the Faculty of Science and must be accompanied by officially endorsed supporting documents including the institutions syllabuses for the relevant courses. (ii)Satisfactory performance in applicable examinations in the relevant courses. (iii)Payment of appropriate exemption fees. (iv) No candidate shall be exempted from more than one third of the total number of units required in the course. (v)A candidate may be required to sit and pass applicable University of Nairobi examinations in the relevant course units, provided they have paid the appropriate examinations fees. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Data Analysis | View Details | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

This is a distinctive feature of the training programme. It consists of a series of practical exercises for each of which students write a report and take part in a class discussion | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Entry Requirements | View Details | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

All applicants should hold one of the following minimum qualification or equivalent: (i)Mean grade C+ at KCSE plus at least grade B in Mathematics. (ii) Diploma in Statistics (iii) Diploma in Computer Studies (iv) Diploma in Education, with mathematics as a major subject. (v) A-level: 2 Principal passes in maths/physics, maths/chem., maths/geog, maths/econ. (vi) A degree in a mathematical subject from a recognised university. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Introduction | View Details | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

The subject of statistical science is concerned with the application of probability and modern mathematical methods to solve complex and practical problems which involve uncertainty. These uncertainties are usually associated with scientific, technological, economic, biological or environmental problems. The methodology employed draws upon mathematics and probability to establish a theoretical foundation, computing resources for handling data, and an understanding of the scientific method for meaningful application. The B.Sc. course is designed to provide broad education in the basic theory and methods of statistics, that would enable the student to apply the knowledge acquired to a wide range of practical problems in research, industry, economic and social development, agriculture and medical research, etc. The programme provides for four main areas of specialisation: Mathematical Statistics, Economic Statistics, Demography and Social Statistics, and Biometry | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Module II Entry Requirements | View Details | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Module II Fees | View Details | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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Nature of Statistical Work | View Details | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

People trained in Statistical science work in research teams performing such tasks as: - Design of survey programmes
- Econometric modelling of development plans
- Epidemiological investigations of disease
- Clinical trials of newly developed drugs
- Field trials of new crop varieties
- Surveys of adoption of novel farming methods
- Modelling in population and quantitative genetics
To succeed, statisticians must have a definite competence in mathematics. Mathematical ability is not the sole criterion for success. Statisticians must be able to communicate effectively with investigators untrained in statistical science. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Tution | View Details | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

The teaching is organised via a combination of lectures, compulsory reading, laboratories and homework. Class attendance is required. Courses are taught in English. Examinations are held at the end of every semester |

Level : 1 | |||

Semester: 1 | |||

Course Code |
Course Name |
Course Hours | |

CCS001 | Communication Skills | 45 | View Description |

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CCS008 | Elements Of Philosophy | 45 | View Description |

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SMA101 | Basic Mathematics | 45 | View Description |

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SMA103 | Calculus I | 45 | View Description |

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STA101 | Introduction To Probability And Statistics | 45 | View Description |

Introduction To Probability And Statistics Description | |||

STA103 | Principles Of Sample Surveys | 45 | View Description |

Principles Of Sample Surveys Description | |||

STA122 | Computational Methods And Data Analysis I | 45 | View Description |

Computational Methods And Data Analysis I Description | |||

Semester: 2 | |||

Course Code |
Course Name |
Course Hours | |

CCS004 | Law In Society | 45 | View Description |

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CCS009 | Elements Of Economics | 45 | View Description |

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CCS010 | Hiv/aids | 45 | View Description |

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SMA104 | Calculus Ii | 45 | View Description |

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SMA106 | Calculus Iii | 45 | View Description |

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STA102 | Exploratory Data Analysis | 45 | View Description |

Exploratory Data Analysis Description | |||

STA121 | Programming Methodology | 45 | View Description |

Programming Methodology Description | |||

Level : 2 | |||

Semester: 1 | |||

Course Code |
Course Name |
Course Hours | |

SMA201 | Calculus Iii (advanced ) | 45 | View Description |

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SMA203 | Linear Algebra I | 45 | View Description |

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SMA205 | Introduction To Algebra | 45 | View Description |

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STA201 | Probability And Statistics I | 45 | View Description |

Probability And Statistics I Description | |||

STA221 | Economic And Social Science | 45 | View Description |

Economic And Social Science Description | |||

STA223 | Operations Research I | 45 | View Description |

Operations Research I Description | |||

STA224 | Computational Methods And Data Analysis Ii | 45 | View Description |

Computational Methods And Data Analysis Ii Description | |||

Semester: 2 | |||

Course Code |
Course Name |
Course Hours | |

SMA204 | Linear Algebra Ii | 45 | View Description |

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SMA206 | Introduction To Analysis | 45 | View Description |

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SMA208 | Ordinary Differential Equations I | 45 | View Description |

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STA202 | Principles Of Statistical Inference | 45 | View Description |

Principles Of Statistical Inference Description | |||

STA222 | Introduction To Time Series Analysis | 45 | View Description |

Introduction To Time Series Analysis Description | |||

Level : 3 | |||

Semester: 1 | |||

Course Code |
Course Name |
Course Hours | |

STA301 | Probability And Statistics Ii | 45 | View Description |

Probability And Statistics Ii Description | |||

STA303 | Theory Of Estimation | 45 | View Description |

Theory Of Estimation Description | |||

STA305 | Probability Modelling | 45 | View Description |

Probability Modelling Description | |||

STA307 | Analysis Of Experimental Designs I | 45 | View Description |

Analysis Of Experimental Designs I Description | |||

STA321 | Operations Research Ii | 45 | View Description |

Operations Research Ii Description | |||

STA322 | Computational Methods And Data Analysis Iii | 45 | View Description |

Computational Methods And Data Analysis Iii Description | |||

Semester: 2 | |||

Course Code |
Course Name |
Course Hours | |

STA302 | Linear Modelling I | 45 | View Description |

Linear Modelling I Description | |||

STA304 | Testing Hypotheses | 45 | View Description |

Testing Hypotheses Description | |||

STA306 | Applied Time Series Analysis I | 45 | View Description |

Applied Time Series Analysis I Description | |||

STA308 | Sample Survey Theory And Methods I | 45 | View Description |

Sample Survey Theory And Methods I Description | |||

Level : 4 | |||

Semester: 1 | |||

Course Code |
Course Name |
Course Hours | |

STA401 | Measure, Probabilty And Integration | 45 | View Description |

Measure, Probabilty And Integration Description | |||

STA405 | Linear Modelling Ii | 45 | View Description |

Linear Modelling Ii Description | |||

STA407 | Analysis Of Experimental Designs Ii | 45 | View Description |

Analysis Of Experimental Designs Ii Description | |||

STA409 | Applied Time Series Analysis Ii | 45 | View Description |

Applied Time Series Analysis Ii Description | |||

Semester: 2 | |||

Course Code |
Course Name |
Course Hours | |

STA402 | Bayesian Nference And Decision Theory | 45 | View Description |

Bayesian Nference And Decision Theory Description | |||

STA403 | Nonparametric Methods | 45 | View Description |

Nonparametric Methods Description | |||

STA404 | Applied Multivariate Methods | 45 | View Description |

Applied Multivariate Methods Description | |||

STA406 | Applied Stochastic Processes | 45 | View Description |

Applied Stochastic Processes Description | |||

STA410 | Sample Survey Theory And Methods Ii | 45 | View Description |

Sample Survey Theory And Methods Ii Description | |||

STA420 | Project In Statistics | 45 | View Description |

Project In Statistics Description | |||

School of Mathematics, CBPS College.

P. O. Box 30197 - 00100

Tel: 254-02-4445751.

Mobile no :0780-834766.

Email: maths@uonbi.ac.ke.

twitter:@uon_maths

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