After successful completion of the course, students are able to learn and apply statistical theory as needed to solve a real problem and gain practical experience, problem-solving skills, and the ability to apply what they learn in methodological courses to real applications. They also obtain hands-on experience on carrying out research and assessing published methods and data analyses.   

Case studies describe real world practical examples from which others can gain insight for their own application. They will be used to bridge the gap between statistical theory and practice, and to help students develop an understanding of a range of methods in statistics. Each case study centers on a scientific question and contains one or more datasets to address the question.

Several case studies will be covered. Topics can cover a wide spectrum such as:

• Data analysis arising from large-scale clinical trials

• Statistics related to sociological and political studies

• Data analysis of industrial experiments 

• Novel data analytic and fitting methods suitable for the analysis of big data

The instructor presents an overview of the methods used in the published case study and the students study them along with competing methods to solve the problem. They may also develop new statistical approaches in order to answer the research questions. The steps in each case study are:

• A paper from one of the applied statistics journals (e.g. JASA Applications and Case studies, Annals of Applied Statistics, Biostatistics, etc.) is selected and presented to the class. 

• The problem and research questions central to the case are identified and background information on the problem and a description of data collected to address the problem are provided before any relevant statistical theory is discussed. 

• The offered solution to the problem in the paper is then studied by the students. 

• The students next explore, develop and propose alternative solutions to the problem. 

This is a collaborative course with active interaction between instructor and students. The instructor's role is to select and pose problems via published data analysis studies and describe the statistical analysis methods used. The students study the paper and the methods proposed to analyze the data at the center of the study in the following stages:

1. Identify and define the research questions - The students start with establishing the focus of the study by identifying the research object and the problem surrounding it. 
2. Understand and evaluate the analysis methods used and analyze the data accordingly - The students makes use of varied methods to analyze the accompanying data. The data are categorized, tabulated and cross checked to address the initial propositions or purpose of the study. Graphic techniques can also be used. The methods in the paper are applied to the data and the results are juxtaposed with those published.  
3. Competing or new approaches are studied, explored and applied to the data.
4. Presentation of Results - The results and the findings are summarized in a report. The results are corroborated with sufficient evidence showing that all aspects of the problem have been adequately explored. The newer insights gained and the conflicting propositions that have emerged are suitably highlighted in the report and class presentation.

This approach to teaching statistics provides students with a reason to learn statistical theory because it is needed to solve a real problem and also generates an interest in learning the material because the problems have depth and merit. Students gain practical experience, problem-solving skills, and the ability to apply what they learn in methodological courses as well as in this course to real applications. 

All students will prepare reports and in class presentations of the statistical methods and data analyses they use and/or propose for each case study.   

Statistical theory as covered in the course Introduction to Statistics (105.692).