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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
The graduates with the dignitaries

UB-UCC Graduate 3rd Cohort of Leadership Empowerment Academy Programme

13 Jul, 2020 By louis Mensah

Graduates of the University of Cape Coast (UCC) and University at Buffalo (UB) Leadership Empowerment Academy Programme have been charged to be innovative leaders even in the midst of crises.
    
The graduation ceremony was held for 22 postgraduate students, who enrolled on the programme in 2019 and successfully completed their studies in June, 2020. 

Opportunity

Speaking at the ceremony via zoom, the Dean of the School of Management, UB, Prof. Tesluk, noted that the challenges the world was going through as a result of COVID-19 should serve as an opportunity for the graduates to make great strides as they embark on their leadership journey. “The pandemic has not only brought about challenges but optimism, ingenuity, empathy, love and support that we need to show to others,” he stated.

Prof. Tesluk reminded the graduates that the knowledge and soft skills on leadership that they had acquired through the programme should empower them to bring out the best in them even in the midst of the current predicament. He was confident that the programme would contribute towards their development as they progressed through the stages of life. “Your response to adversity shows how important the qualities you have developed will be beneficial to others” he stated.

Touching on the relevance of the programme, Prof. Tesluk said it was one aspect of the multi-faceted collaboration between UCC and UB. He indicated that the two institutions would continue to work towards enhancing the mutual relationship existing between them.

UCC's Commitment

In her remarks, the Pro Vice-Chancellor, Prof. Dora Francisca Edu-Buandoh said UCC was committed to the Memorandum of Understanding (MoU) that introduced the innovative programme in May 2017. According to her, the MoU focused on the promotion of collaborative research; Leadership Empowerment Certificate Programme for interested postgraduate students in UCC and Library Services partnership.

Prof. Edu-Buandoh said it was heartwarming that the UB-UCC Leadership Empowerment Academy was attracting postgraduate students from the five colleges of UCC. “I am excited to share that we have received interests from students in many other universities in Ghana. I believe that in consultation with the UB leadership, we may soon expand the Leadership Empowerment Programme to include interested students from other Ghanaian universities,” she indicated.
The Pro Vice-Chancellor noted that due to COVID-19, the graduating class had virtual sessions for the experiential learning which should have taken place on UB campus in Buffalo, New York.  She announced that all sessions of the fourth cohort of the programme which would commence in August would be held online. “I believe participants of Cohort Four will have a great experience. This, indeed, is the new normal,” she stressed.

To the graduating class, Prof. Edu-Buandoh encouraged them to develop an interest in adding value to their degrees through various “add-on” programmes that have the potential of giving them a competitive advantage over their colleagues and peers.

Appreciation

Prof. Edu-Buandoh commended the UB and UCC Coordinators, and the Dean and staff of the Centre for International Education (CIE), UCC for their dedication and support to the programme. She also thanked Prof. Paul Tesluk, Dr. Dorothy Siaw-Asamoah, Miss Molly Anderson, Prof. Charles Lindsey and the entire UB team for their commitment to the May 2017 MoU.

Valedictorian

Ms. Ruth Boamah-Agyekum who gave the valedictory address on behalf of her class noted that they had been exposed to valuable lessons on leadership which would propel them to make positive changes at their work places. “It’s been a wonderful experience, and what I have learnt personally is that every individual at the workplace is unique and you can tap into their potentials by forming strong teams,” she explained. She urged postgraduate students of UCC to enroll on the programme in order to learn and develop exemplary leadership wherever they find themselves after their studies.

The UB Team participated in the ceremony online.
 

Leadership Empowerment Academy Programme

The leadership Empowerment Academy Programme is a one-year Programme run by the Center for Leadership & Organizational Effectiveness at UB. The programme is designed to empower leaders by using innovative approaches to prepare participants to demonstrate exemplary leadership practices.

The programme comprises four face-to-face sessions with three sessions at UCC and one at UB. 

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