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DATA ANALYSIS I

Introduction to Statistical Software: Eg. SPSS, Minitab, R, Matlab. Statistical data – Data from designed experiments; sample surveys; observational studies.    Data exploration - sample descriptive techniques: measures of location and spread; correlation, etc.  Diagrammatic representation of data: the histogram; stem - and leaf -; box-plot; charts, etc.  Tabulation, interpretation of summary statistics and diagrams. Regression and Correlation Analysis: Simple Linear Regression. Interpretation of coefficients, Correlation and coefficient of determination. One-way ANOVA.

Course Code: 
STA 304
No. of Credits: 
3
Level: 
Level 300
Course Semester: 
Second Semester
Pre-requisite: 
STA 201, STA 202 or HND Statistics
Select Programme(s): 
Statistics

MULTIVARIATE DISTRIBUTIONS

Vector random variables: expected values of random vectors and matrices; covariance matrices; linear transforms of random vectors; further properties of the covariance matrix; singular and non-singular distributions; quadratic functions of random vectors. Distribution concepts: distribution of a random vector; multivariate moment generating functions. Transformation of random variables: vector transformation and Jacobian; change of variables in multiple integrals; distribution of functions of random vectors; some applications – the Beta-distribution family; the Chi-square, t – and  F – distributions. Order statistics: order transformation; joint distributions of order statistics; marginal distributions; alternative methods.  Multivariate normal distribution: definition and examples; singular and non-singular distributions; properties of the multivariate normal distribution; multivariate normal density; independence of multivariate normal vectors. Conditional distribution:

Course Code: 
STA 306
No. of Credits: 
3
Level: 
Level 300
Course Semester: 
Second Semester
Pre-requisite: 
STA 301
Select Programme(s): 
Statistics

RESEARCH METHODS

Sources of information. Report writing: Structure – title, summary, introduction, results, conclusions, recommendations, methods, general discussion, references, appendices;

Content; Presentation; Style. Oral presentation: Preparation – logistical requirements, e.g., Flip charts, transparencies, overhead projector, slides, etc. Delivery – use of Power Point software;

Introduction to proposal writing.

Course Code: 
STA 399
No. of Credits: 
3
Level: 
Level 300
Course Semester: 
First Semester
Pre-requisite: 
STA 201, STA 202 or HND Statistics
Select Programme(s): 
Statistics

DESIGN AND ANALYSIS OF EXPERIMENTS

Basic concepts/terminologies – e.g., units, treatments, factors. Completely randomized designs.  Randomized block designs-efficiency, missing data.  Latin squares.  Sensitivity of randomized block and Latin square experiment.  Factorial experiments-several factors at two levels; effects and interactions; complete and partial confounding of    factorial experiments.  Split-plot experiments-efficiency; missing data; split-plot confounding.

Course Code: 
STA 305
No. of Credits: 
3
Level: 
Level 300
Course Semester: 
First Semester
Pre-requisite: 
STA 201, STA 202 or HND Statistics
Select Programme(s): 
Statistics

STATISTICAL METHODS I

Theory of hypothesis testing-likelihood ratio tests; power functions, etc; tests concerning means; differences between means; variances; proportions.  Test for associations (contingency tables) and goodness of fit tests.  Standard assumptions and their plausibility in hypothesis testing.  Linear regression analysis ― the method of least squares (derivation of normal equations); prediction and confidence intervals; regression diagnostics.  One-and two way analysis of variance.

Course Code: 
STA 303
No. of Credits: 
3
Level: 
Level 300
Course Semester: 
First Semester
Pre-requisite: 
STA 201, STA 202 or HND Statistics
Select Programme(s): 
Statistics

PROBABILITY DISTRIBUTIONS

Further distribution concepts: Application of conditional expectation and variance, a random number of a random variable; sampling distribution of a statistic; Poisson distribution and Poisson processes; multinomial experiments. Transformation of random variables: Functions of one-dimensional random variables; the convolution theorem; distribution of a function of a random variable; Jacobian transformation; function of bivariate random variable; some applications – the Beta-distribution family; the Gamma, Chi-square, t –  and  F – distributions. Generating functions: characteristic functions; moment generating function of Beta and Gamma random variables; moment generating function of a function of a random variable; probability generating functions; some applications. Limiting Distributions: Limiting distribution function of a random variable (with proofs); the central limit theorem; law of large numbers; some applications – limiting form of the Binomial distribution; approximation to the Poisson distribution. Concepts of convergence: convergence in probability; convergence in mean square; Chebyshev inequality.

Course Code: 
STA 301
No. of Credits: 
3
Level: 
Level 300
Course Semester: 
First Semester
Pre-requisite: 
STA 201/HND Statistics
Select Programme(s): 
Statistics

FURTHER STATISTICS

Types of survey, e.g., household, demographic, health, etc.  Planning of surveys-objective; target populations; questionnaire design; pilot survey. Regression and correlation analysis: methods for simple linear regression ― graphical method, method of least squares (with derivation); interpretation of coefficients; simple coefficient of determination; correlation coefficient; standard error of estimate. Rank order correlation analysis: introduction to rank correlation; Spearman’s coefficient; Kendall’s coefficient.

Course Code: 
STA 202
No. of Credits: 
3
Level: 
Level 200
Course Semester: 
Second Semester
Pre-requisite: 
MAT 102
Select Programme(s): 
Statistics

FURTHER PROBABILITY

Distribution function of a random variable; expectation and variance of a random variable; probability distributions ― Binomial, Negative Binomial, Geometric, Hypergeometric, Poisson, Normal, Exponential (Exclude Beta and Gamma Distributions). Moment generating functions: moments of a random variable (e.g., Binomial, Poisson, etc.); moment generating function of a random variable; some applications. Bivariate distributions: bivariate random variable; joint, marginal and conditional distributions; statistical independence; conditional expectations and variance; regression function.

Course Code: 
STA 203
No. of Credits: 
3
Level: 
Level 200
Course Semester: 
First Semester
Pre-requisite: 
MAT 102
Select Programme(s): 
Statistics

INTRODUCTION TO STATISTICS

A general introduction to Statistics and statistical data:        Introduction ― branches of statistics; types of statistics, e.g., Official Statistics: Health, Industry, etc.; types of data ― categorical data and their representations; Proportions. Descriptive statistics: representations of data ― diagrams and tables; measures of central tendency; types of means; measures of dispersion; measures of skewness and peakedness; diagrammatic representations.

Course Code: 
STA 102
No. of Credits: 
3
Level: 
Level 100
Course Semester: 
Second Semester
Pre-requisite: 
Elect Math
Select Programme(s): 
Statistics

INTRODUCTION TO PROBABILITY

The course is a general introduction to preliminary concepts in probability: definitions – sample space, events, etc.; permutation and combination.  Concept of probability:

probability measure ― axioms; joint, marginal and conditional probability; Independence; total probability; Bayes’ theorem. Random variable and probability distribution:

probability distribution of a random variable (discrete and continuous)

Course Code: 
STA 101
No. of Credits: 
3
Level: 
Level 100
Course Semester: 
First Semester
Select Programme(s): 
Statistics

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