Statistics 1 for Economics

Description

This course reviews and explains the basic statistical concepts and techniques in Economics and Business Economics. It will also discuss how to identify and solve potential problems in data and results. The topics addressed in this course are divided into three groups. Basic descriptive techniques: the hierarchy of data types: measures of central location, dispersion, skewness, and graphical techniques; quantifying the relationship between two quantitative variables: correlation, covariance and the linear regression equation. Basic probability rules and distributions: probability calculus: probability rules, marginal and conditional probabilities and joint probability tables; discrete probability distributions: expectation and variance and specifically the Binomial and Poisson distributions, including the application of tables; continuous probability distributions: Uniform and (standard) Normal distributions, including the application of tables; bivariate distributions (covariance and correlation) and linear combinations of random variables: expectation and variance; random sampling, independent and identically distributed variables and sample distributions of the mean and proportion. Inferential statistics: theory of estimation: estimators (unbiased, efficient and consistent), confidence intervals for the population mean and population proportion, including T-distribution; theory of testing: testing hypotheses on mean and proportion, the p-value, the power of a test, and finding the probability of a type two error; finding the desired sample size; application of the sign test for the median of ordinal scaled variables or for quantitative scaled variables. This list of topics will be extended in year 2 by the subsequent course Statistics 2 for Economics . Objectives: Upon completion of this course, students will be able to: understand the concept of probability and apply the basic rules for probability calculations; understand and use basic discrete and continuous random variables and their parameters; understand and apply the distribution of a sample mean and connected t-statistic; describe the quality of estimators, the concept of statistical hypothesis testing, and confidence intervals; list the basic statistical techniques in the field of exploratory data analysis of one or two variables and inferential statistics of one or two variables; comprehend the procedure underlying each of these techniques; describe the purpose of each technique and the conditions for its validity and recognise the circumstances in which each technique can be used; interpret and report the results of these techniques correctly; critically assess the findings in academic publications and research reports that are based on these techniques.