Statistics

This covers the basics of data analysis needed in a Social Sciences oriented University like Sciences Po. The course begins at a level that assumes no prior knowledge about statistics. The objective is for students to understand and be able to interpret linear regression analysis. The course does not rely on math and statistics, but practical learning in order to teach the main concepts. In particular, the following topics are covered: introduction to R; working with data; linear regression; standard errors; multiple regression; categorial variables.

This course provides an introduction to practical aspects of survey sampling. Topics include proposal writing, costing, non-sampling errors, rudiments of sampling theory, questionnaire design, fieldwork, basic analytic techniques, and report writing. This course is co-taught with STAT 392.

This introductory statistics course develops a sense of numeracy and presents a number of principles and tools to support appropriate quantitative analysis. There is a strong emphasis on using Excel to perform calculations, to manipulate data, and also to demonstrate principles of statistics. Topics include statistical modeling; exploratory data analysis and summary statistics; probability theory; random variables; probability mass and density functions; binomial, Poisson, exponentials, normal, and uniform distributions; sampling distributions; confidence intervals; hypothesis testing; determining sample size; simple linear regression; and measures of correlation. Assessment: classwork (30%), final exam (70%).

"Linear Regression" is a professional basic course for the undergraduates' probability and statistics in mathematics. Students are required to learn "Statistical Inference" in advance. The goal of the course is to learn how to perform simple linear regression, multiple regression analysis, regression diagnosis, goodness of fit test, data transformation, variable selection, and generalized linear model, and require correct calculation and interpretation of results, using statistical software R to obtain practical experience. At the same time, it is required to learn some basic theories in the regression.

This course introduces generalized linear models, which can be used for modeling data such as binary data and count data, where a normal distribution would not be appropriate. These developments dramatically extend the range of problems that can be studied. The methods are implemented using R.

This course explains the theory of probability and some of the statistical methods based upon it. The course begins with the basic ideas of probability theory: events, probabilities, random variables and the notion of independence. It continues with the two crucial principles: the law of large numbers and the central limit theorem. Using these, the course covers the fundamental concepts of statistical inference (estimation and hypothesis testing), and illustrates these concepts using the most important statistical models.

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. This course draws on 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 primarily focuses on developing substantive and precise understanding of the various quantitative research designs and corresponding statistical methods. Students 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 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.