The material in this course covers assumptions of multivariate regression and discusses the most common econometric problems and the potential consequences and remedies. Moreover, it discusses heteroscedasticity, time series, autocorrelation, multicollinearity, outliers, logistic regression analysis, pooled cross sections, and the use of dummy variables. The purpose of this course is to provide students with a conceptual understanding of the tools of quantitative analysis used in economics and social sciences research. The course has a strong practical approach, and the emphasis is on how to model, use, and interpret data to perform research analysis. Indeed, the topics will be heavily tilted towards interpreting pre-estimated quantitative output and identifying econometrics issues with estimation or testing procedure that might affect the interpretation of the output. In addition, class examples together with TA sessions will make students familiar with the main software used to perform quantitative research: STATA. This course will enable students to comfortably attend furthermore advanced courses in Economics and Finance. Prerequisite: Statistics for Data Analysis, Elements of Statistics, or Introduction to Statistics.

Prerequisites: SA.100.501[C] OR SA.100.502[C] OR SA.630.724[C] OR SA.999.702[C]

Covers basic statistical tools for data analysis. Emphasizes facility in problem-solving in statistical inference and two-variable regression and correlation analysis. Presents descriptive statistics, probability, and probability distributions and their use in hypothesis testing. Uses computer to solve problems and to reinforce statistical concepts.

Prerequisites: Students may not register for this class if they have already received credit for SA.340.709[C]

This course is designed to furnish students with the fundamental tools of statistical analysis, including analysis of descriptive statistics, statistical inference via confidence intervals and significance tests, and correlation and simple/multiple linear regression analysis. Aim of the course is to introduce the basic statistical tools required to conduct and evaluate empirical research in economics and the social sciences. Special attention will be given to the application of these statistical tools to the analysis of real phenomena using computer to solve problems and to reinforce statistical concepts.

This course develops the basic quantitative tools that are necessary for risk analysis. It gives a review of basic mathematical concepts used in economics and risk analysis, including pre-calculus and calculus principles. It also develops tools for data management using Excel. The course therefore provides students with a ready-to-use statistical toolbox that can be used during the remainder of the program.

In order to understand and evaluate risk and uncertainty it is essential to have a strong command of basic statistical concepts and techniques. This course is designed to furnish students with the fundamental tools of statistical analysis, including analysis of descriptive statistics, probability distributions, statistical inference and related tests, correlation and conditional expectation. In addition to providing familiarity with statistical principles, the course will also include an introduction to basic statistical software packages, namely STATA and advanced tools in Excel. It is a required course for quantitative approaches to risk assessment.

This course is designed to furnish students with the fundamental tools of statistical analysis, including analysis of descriptive statistics, probability distributions, statistical inference and related tests, correlation and conditional expectation. The aim of the course is to introduce the basic statistical tools required to conduct and evaluate empirical research in economics and the social sciences. Special attention will be given to the application of these statistical tools to the analysis of real phenomena. This course is a prerequisite for more advanced courses in econometrics and fulfills a Data Analytics requirement for the MAIR degree.