Introduction to Statistics for Business and Economics (for material in greek see here)
The purpose of the course is to give students in the field of economics, a conceptual introduction to the field of statistics and its many applications. The course is applications oriented and presented with the needs of the nonmathematician in mind, since the only mathematical prerequisite is knowledge of basic algebra. The volume of statistical information available today is too large. However, the growth of computer science techniques and processes in the last years, grew rapidly our ability to process this information quickly, accurately and cheaply. This, combined with the parallel improvement of statistical methods and techniques, resulted in an increasing use of Statistics in Economic Theory and its applications.
This course is an introduction to Statistics for Economists and aims to present the basic techniques of the statistical analysis of a problem. It starts with the basic concepts of descriptive statistics (Tabular and Graphical Presentations, Numerical Measures). When the numerical information is only a fraction of the total information of interest, then the purpose of statistical analysis is the generalization of partial information (Statistical Inference). This generalization involves uncertainty, measured by probability. Thus, the course continues with an introduction to the basic principles of the probability theory and probability distributions of random variables. Finally, we examine the fundamental concepts of sampling and sampling distributions.
Applied Statistics for Business and Economics (for material in greek see here)
This is the second course in the two-semester sequence of the first year undergraduate studies. The detailed course outlines are presented below:
- Estimation and Confidence Intervals: Some properties of point estimators, some common unbiased point estimators, evaluating the goodness of a point estimator, confidence intervals, large sample confidence interval, selecting the sample size, small-sample confidence interval.
- Properties of Point Estimators and Method of Estimation: Unbiasedness, relative efficiency, consistency, minimal sufficiency and best linear unbiased estimators (BLUE), the method of moments, the method of maximum likelihood.
- Hypothesis Testing: Elements of a statistical test, common large-sample tests, calculation of type-I error, sample size for the Z-test, different ways of reporting the result of a test, attained significance levels or p-values, some comments on the theory of hypothesis testing, two-sample tests based on t-distributions, testing hypothesis concerning variances, power of tests, the Neyman-Pearson lemma.
- Linear Models and Estimation by Least Squares: Linear regression and estimation by least squares method.
Economic Applications of Computational Software (for material in greek see here)
Software Packages' Overview: Notational Conventions and Typesetting / Palettes / Character Formatting / Syntax and Basic Commands / Calculus / Linear Algebra / Equations / Plots / Saving Files
Statistics: Presenting and Summarizing Data / Estimating Data Parameters / Parametric Tests of Hypotheses / Non-Parametric Tests of Hypotheses / Statistical Classification / Data Regression
Economics: Consumer Choice and the Lagrangian Multiplier Method / Individual and Market Demand / Pure Exchange / Intertemporal Trade / Choice under Uncertainty and Imperfect Information / Cost Minimization / Short- and Long-run Costs / Duality / Profit Maximization / Production and Trade / Dynamic Optimization and the Calculus of Variation.
