Introduces the principles of microeconomic analysis, with emphasis on how prices and quantities are determined under different market structures. Topics include supply and demand, consumer and firm behavior, production and costs, market competition, pricing and distribution, and the effects of government intervention such as taxes, subsidies, price controls, and regulation. The course also considers the role of markets in allocating resources and compares free-market systems with alternative economic arrangements.
Descriptive statistics including measures of central tendency, dispersion, and skewness. Probability and theoretical probability distributions including the binomial function and the normal curve. Sampling, estimation, and test of hypothesis. Simple correlation and regression analysis.
Computational statistics is a fundamental part of modern data analysis. This course provides an understanding of the principles and concepts of using modern statistical programing languages for data analysis. Students will learn a programming language, such as R, to handle the manipulation of large data sets, as well as to simulate data, and to import and export data. As an introductory course in statistically oriented programming, no extensive programming background is assumed. Students will gain experience in analyzing both quantitative and qualitative data. They will learn important ideas of programming data structures, functions, iteration, input and output, debugging, logical design, and abstraction. They will learn how to fit basic statistical models and to assess and present the results. Students will also learn how to comment and organize code.
Computational statistics is a fundamental part of modern data analysis. This course provides an understanding of the principles and concepts of using modern statistical programing languages for data analysis. Students will learn a programming language, such as R, to handle the manipulation of large data sets, as well as to simulate data, and to import and export data. As an introductory course in statistically oriented programming, no extensive programming background is assumed. Students will gain experience in analyzing both quantitative and qualitative data. They will learn important ideas of programming data structures, functions, iteration, input and output, debugging, logical design, and abstraction. They will learn how to fit basic statistical models and to assess and present the results. Students will also learn how to comment and organize code.