Level – 1
Course units effective from academic year 2016/2017 to date
Course Code  STA101G3 
Course Title  Probability Theory 
Academic Credits  03 (45 hours of lectures and tutorials) 
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sampling distribution of the sample means.


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Course Code  STA102G2 
Course Title  Introduction to Statistics 
Academic Credits  02 (30 hours of lectures and tutorials) 
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Course Code  STA103G3 
Course Title  Basic Statistical Inference 
Academic Credits  03 (45 hours of lectures and tutorials) 
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Course Code  STA104G2 
Course Title  Applied Statistics I 
Academic Credits  02 (30 hours of lectures and tutorials) 
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Level – 2
Course units effective from academic year 2016/2017 to date
Course Code  STA201G3  
Course Title  Statistical Theory  
Credit Value  03  
Hourly Breakdown 
Theory  Practical  Independent Learning 
45  —  105  
Objectives:
Provide a sound knowledge in general theory of statistical distributions and its applications 

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Course Contents:  
Fundamentals of Statistics: Random variable, Probability function, Cumulative distribution function, Mathematical expectation and variance.
Probability Distributions: Discrete Distributions: Uniform, Bernoulli and Binomial, Poisson, Poisson approximation to Binomial, Geometric, Negative binomial, Poisson approximation to Negative binomial, Hypergeometric, Binomial approximation to Hypergeometric, Multinomial distribution. Continuous Distributions: Uniform, Exponential, Normal, Gamma , Beta , ChiSquare, Weibull, Lognormal, Studentt, F distribution, Cauchy. Functions of Random variables: Probability generating function, Moment generating function, Cumulant generating function, Characteristic function , Convolution, Distributions of functions of random variables Joint Distributions: Joint Distribution, Marginal Distribution, Conditional Distribution, Conditional Expectation and Variance, Independence, Correlation, Bivariate Normal distribution, Transformations of random variables, Order statistics. 

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Lecture demonstration and Tutorial discussions  
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Course Code  STA202G2  
Course Title  Sampling Techniques  
Credit Value  02  
Prerequisite: STA102G2  
Hourly Breakdown 
Theory  Practical  Independent Learning  
30  —  70  
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Course Contents:  
Preliminaries: simple random sampling: with and without replacement
Stratified Sampling: stratification, advantages and disadvantages, stratified random sampling with associated mathematical background, estimation of mean, total and proportions and its variance in samples from finite population, confidence interval of estimators, models for cost function, allocation of sample size: proportional allocation, optimum allocation, Neyman allocation Ratio and Regression methods: estimation of population ratio in simple random sampling and stratified random sampling, ratio and regression estimators of population mean and total in simple random sampling and stratified random sampling, variance, bias and mean square error of estimators, confidence interval of estimators, efficiency of estimators Systematic sampling: design of systematic sampling, estimation of mean, total and its variance in sample from finite population, inter class correlation coefficient Cluster sampling: Introduction of cluster sampling, estimation of mean, total and its variance in sample from finite population 

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Course Code  STA203G3  
Course Title  Design and Analysis of Experiments  
Credit Value  03  
Hourly Breakdown 
Theory  Practical  Independent Learning 
40  10  100  
Objectives:  
Provide an introduction to the design and analysis of statistical experiments  
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Course Contents:  
Analysis of Variance: Observational and Experimental studies, Factor, Levels, Treatment, Experimental unit, Randomization, Replication. Models and assumptions, fixed and random effect models, Decomposing the variance, Oneway classification, Degrees of freedom, Ftest, ANOVA table, Model adequacy checking, Further analysis: LSD method, Tuckey’s method, Duncan’s multiple range method.
Factorial Experiments: Twoway classification, Interaction, Diagrammatic explanation of interaction, Threeway classification, fixed, random and mixed models, Completely Randomized Design(CRD), Randomized Complete Block Design(RCBD), Latin Square Design(LSD), Nested Design, Nested and Crossed Design, Split Plot Design. 

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Lecture demonstration, Tutorial discussions and laboratory practical.  
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Course Code  STA204G2  
Course Title  Statistical Inference  
Credit Value  02  
Prerequisite: STA103G3 and STA201G3  
Hourly Breakdown 
Theory  Practical  Independent Learning 
30  —  70  
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Level – 3
Course units effective from academic year 2016/2017 to date
Course Code  STA301G3  
Course Title  Regression Analysis  
Credit Value  03  
Prerequisite  STA103G3  
Hourly Breakdown  Theory  Practical  Independent Learning 
40  10  100  
Objective:
Provide knowledge and techniques in fitting regression models to real world data 

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Lecture demonstration, Tutorial discussions, and Laboratory practical  
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Course Code  STA302G3  
Course Title  Stochastic Processes  
Credit Value  03  
Prerequisite  STA101G3 and STA201G3  
Hourly Breakdown  Theory  Practical  Independent Learning 
45 Hours  _  105 Hours  
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Course Code  Quality Control  
Course Title  STA303G2  
Credit Value  02  
Prerequisite  STA101G3 and STA102G2  
Hourly Breakdown  Theory  Practical  Independent Learning 
25 Hours  10 Hours  65 Hours  
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Lecture demonstration, Tutorial discussions, and laboratory practical  
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Course Code  STA304G2  
Course Title  Applied Statistics II  
Credit Value  02  
Prerequisite  STA101G3, STA103G3 and STA201G3  
Hourly Breakdown  Theory  Practical  Independent Learning 
25 Hours  10 Hours  65 Hours  
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Course Title  Statistical Computing  
Course Code  STA305G2  
Credit Value  02  
Hourly Breakdown  Theory  Practical  Independent Learning 
–  60 Hours  40 Hours  
Objective:  
Provide fundamental knowledge and skills in statistical computing using statistical software  
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Laboratory practical, group assignments and eresources  
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