EES, GPH, GED, WP
Completion of compulsory Year 1 courses Statistics and Mathematical Modeling / Mathematical Reasoning.
Today's world relies much on the accumulation, presentation, and interpretation of large quantities of information. Statistics is a tool that enables us to organize our data in an efficient manner, and provides us with methods that help us understand the relationships that occur in our data and our increasingly complex world. In this course, we will draw examples from multiple disciplines, such as political science, economics, medical sciences, and biology to demonstrate how to search for and evaluate patterns in large amounts of data, as well as to interpret what these patterns tell us about the world. The material in this course covers data display, statistical inference, regression and experimental design. The course will primarily focus on developing substantive and precise understanding of the various quantitative research designs and corresponding statistical methods. Students will develop individual projects, applying their knowledge to real-world problems using elementary computer programming in the R statistical programming package.
This course is designed to be accessible for students at all levels of mathematical skill. The focus is put on developing conceptual understanding of statistics without heavy reliance on rigorous mathematical background. The knowledge obtained from this course should provide solid background for students who wish to continue their statistical education with more advanced courses as well as prepare students to perform their own statistical analyses in their coursework and beyond.
Upon completion the course aims to provide the students with the following skills:
Apply scientific research process, including theory formulation and hypothesis testing.
Critically analyze various types of data and learn to select most appropriate elementary statistical technique to answer their research question;
Use statistical programming to enter data, generate descriptive statistics and graphs, and estimate basic statistical models.
Communicate and present statistical results to a variety of audiences – academic experts and policy-makers.
Once available, timetables will be published here.
Participation, 10%, Ongoing Weeks 1-7
Group projects, 30% (5% each), weeks 2-7
Quiz, 15%, weeks 1-7
Final exam, 30%, week 8
Research reprt, 15%, Week 8
There will be a Blackboard site available for this course. Students will be enrolled at least one week before the start of classes.
Jessica Utts, Robert F. Heckard. 2012. Mind on Statistics, 4th and International Edition, Paperback. Thomson/Brooks/Cole.
Charles, Wheelan. 2014. Naked Statistics: Stripping the Dread from the Data. W.W. Norton & Company.
Additional required reading materials will be provided by the instructor.
The Chicago Guide to Writing About Numbers (2008), Miller, Jane, University of Chicago Press, 312p.
Michael J. Crawley. 2005. Statistics: An Introduction using R, 1st Edition. Wiley-Blackwell, Paperback.
Freedman, D., Pisani, R., and Purves, R. 2007. Statistics. Norton: New York, and London, 4th edition.
This course is open to LUC students and LUC exchange students. Registration is coordinated by the Curriculum Coordinator. Interested non-LUC students should contact firstname.lastname@example.org.
Dr. Lucie Zicha
Students are required to read the whole Naked Statistics book as well as familiarize themselves with QRM syllabus posted on Blackboard before entering the first session of the class.