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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, Kruskal-Wallis test • Two-way ANOVA, Block designs • Categorical data, Pearson’s Chi-Squared test, Goodness-of-fit tests • Simple Linear Regression • Multiple Regression • Multiple Regression Continued, with applications in R • Other Regression Topics - Residuals, Diagnostic tests etc.

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Conditions of Participation

While knowledge of basic probability and statistics and linear algebra, such as knowledge of the probability distributions and elementry hypothesis testing. Knowledge of basic mathematics would normally suffice as the course is taught in a manner so that the material is accessible to all. No pre-requisites.

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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, provision of recommended resource materials and links to resources on specific websites.

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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

Examinations are delivered online via the moodle (myelearning) platform. 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. Assignments are take home. A typical breakdown of the assessment is normally: • Final Examination (50%) • Coursework Exams (40%) • Assignments (10%)

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Conditions for allocation of credit points

Pass both coursework and final examination.

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Other information

This course is an introductory course in Probability and Statistics. It is a mandatory course in the MSc in Data Science at UWI.