Tag(s)
[BSc]
M:ID+S+WP, PS
Admission Requirements
Numeracy I, Numeracy II
Description
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 proceeds gradually from simple descriptive statistics to more complex inferential statistics, such as multivariate regression. The course will primarily focus on developing substantive and precise understanding of the statistical concepts and their application to real-world examples using elementary computer programming in the R statistical programming package.
Course Objectives
This course aims to build students’ quantitative skills from descriptive (summary) statistics to inferential statistics. 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:
Critically analyze various types of data and learn to select most appropriate statistical technique to answer their research question;
Use statistical programming to enter data, generate descriptive statistics and graphs, perform hypothesis tests and estimate regression models;
Confidently communicate and present statistical results to a variety of audiences – academic experts and policy-makers.
Mode of Instruction
The course will consist of a combination of interactive lectures and lab seminars. Interactive lectures will mainly focus on developing an understanding of the main statistical concepts. Lab seminars are designed for the students to apply these concepts in a series of assignments using R statistical software. Students must bring their laptops for lab seminar.
Assessment
Assessment for this course comprises of a cumulative examination, individual assignments, final research project and participation.
Assessment: In-class participation
Percentage: 15%
Deadline: Ongoing Weeks 1 – 7
Assessment: Weekly Individual Assignments
Percentage: 30%
Deadline: Weeks 1 – 7
Assessment: Midterm and final exam
Percentage: 30%
Deadline: Week 4
Assessment: Final research essay (2000 words)
Percentage: 25%
Deadline: Week 8
Literature
Required:
Jessica Utts, Robert F. Heckard. 2011. Mind on Statistics, International Edition, Paperback. Thomson/Brooks/Cole.
OR
Jessica Utts, Robert F. Heckard. 2012. Mind on Statistics, 4th and International Edition, Paperback. Thomson/Brooks/Cole.
Recommended:
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.
Contact Information
l.spanihelova@montesquieu-instituut.nl, lspanihel@gmail.com
Weekly Overview
Week 1: What is Statistics ?;
Week 2: From Data to Information;
Week 3: Sampling and Survey Design;
Week 4: Inferential Statistics;
Week 5: Hypothesis Testing and Confidence Intervals;
Week 6: Correlation and Regression;
Week 7: Multivariate Regression and Discrete Variables,
Week 8: Reading Week.
Preparation for first session
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