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Content

Content The tentative topics for this course include: • Conditional Probability and Bayes’ Theorem • Probability distributions, including distributions of two or more random variables • Sampling distributions and the Central Limit Theorem • Confidence intervals and one- and two-sample hypothesis testing • ANOVA, Multiple comparisons and Kruskal-Wallis test • Two-way ANOVA and Block designs • Categorical data, Pearson’s Chi-Squared test and Goodness-of-fit tests • Simple Linear Regression • Multiple Regression • Multiple Regression with applications in R • Other Regression Topics - Residuals, Diagnostic tests, etc.

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Conditions of Participation

Conditions of Participation There are no prerequisites to register for this course. Knowledge of basic mathematics would suffice as the course is taught in such a manner that the material is understood by all. Knowledge of basic probability and statistics, and linear algebra, such as probability distributions and elementary hypothesis testing, may be an asset.

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Teaching Methods

• Lectures will provide valuable synthesis and evaluation of the growing body of available information, update current issues and events, and prioritise content relevant to course assessment. • Every week practical sessions will provide hands-on experience for students to gain skills required for solving basic statistical problems. Once basic competency is mastered, they will be introduced to more automated methods using statistical software. • The online teaching tool, myeLearning, will be used during this course for communication among students and staff for official posting of important notices and provision of recommended resource materials and links to resources on specific websites.

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Learning outcomes / Competences

Learning outcomes / Competences At the end of this course, students should be able to: ● Utilize data collection and interpretation methods. ● Use tabular and graphical formats to display univariate/ bivariate data sets. ● Explain and use the concepts of probability, random variables and their distributions, in particular the discrete and continuous distributions. ● Differentiate between the Parametric and Non-parametric approaches to analyses. ● Carry out correlation, regression ANOVA and randomized block design using statistical software.

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Forms of examination

Forms of examination Examinations are delivered online via the moodle platform (myelearning). Final examinations are asynchronous in nature and students are normally given 3 days to complete it. Coursework examinations are asynchronous in nature and normally completed within 2 days. Take-home assignments are also given. A typical breakdown of the assessment is: • Final Examination (50%) • Coursework Exams (40%) • Assignments (10%)

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Conditions for allocation of credit points

Students must pass both the coursework and the final examination.

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Other information

This is an introductory course in Probability and Statistics. It is a mandatory course in the Master of Science in Data Science at the UWI. Literature Resources: Statistical Software (R programming language) which is freely available. Reading Material: Statistics; Informed Decisions Using Data plus MyStatLab with Pearson eText (5th ed) by Michael Sullivan Probability and Statistics 4th edition by Morris de Groot and Schervish; Addison-Wesley Mathematical Statistics with Applications, 7th Edition, Miller and Miller, Pearson