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May 20, 2019

Data Management

The teaching strategy will be in form of lectures and group work with hands on learning with technology. Practical examples will be used in all classes with real datasets. Since the APID would like the learners to absorb useful and applicable information, we highly recommend that you attend all sessions. Participation in meetings will enhance the learning process and make it valuable and useful to the learners.
It is expected learners to arrive on time and be fully engaged to get the best experience. All materials provided should be read before coming to class. Since each topic builds on the earlier, one should be comfortable with the earlier materials. Each class will have hands on exercises and all learners are expected to do those. An electronic device is required for the class.

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May 17, 2019

Categorical and Bayesian Statistics

On completion of this module, students should be able to:
• To be familiar with a range of statistical analysis methodology available for categorical data.
• To detect the nature of categorical data;
• To measure dependence between categorical variables based of the type of study and type of variables (nominal or ordinal).
• Students will demonstrate how to model a binary outcome variable using continuous or categorical variables.

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May 17, 2019

Spatial Statistics and Modeling

Upon completion of this course, students should be able to:
• Apply a structured, step-based process to data modeling with Geostatistics
• Distinguish different types of spatial data (geostatistical, areal, point process) and understand how spatial autocorrelation plays a role in statistical modeling.
• Determine the best statistical model for spatial dependence, based on cross-validation and external evidence

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