Instructor: Dr. Marianna BOLLA

Contact details of lecturer:

Prerequisite: Undergraduate Calculus

Course description
Statistics teaches us how to behave in the face of uncertainties, according to the famous mathematician Abraham Wald. Roughly speaking, we will learn strategies of treating chances in everyday life. The main concept is that our inference is based on a randomly selected sample from a large population, and hence, our observations are treated as random variables. Applications are also discussed, mainly on a theoretical basis, but we make the students capable of solving numerical exercises by choosing the most convenient method for a given real-life problem.


  1. Short introduction to probability theory (sample spaces, random variables, notable distributions, Bayes rule, laws of large numbers, Central Limit Theorem).
  2. Descriptive study of data. Statistical sample, basic statistics, histograms.
  3. Basic concepts of testing hypotheses and estimation theory.
  4. Methods of point estimation, properties of the estimators, confidence intervals.
  5. Inferences about a population, theory and applications.
  6. Parametric inference: comparing two treatments (Z, t, F tests).
  7. Nonparametric inference: Wilcoxon test and chi-square test.
  8. Analyzing categorized data (contingency tables).
  9. Introduction to linear models: regression analysis (linear regression, correlation, model fitting) and analysis of variance.
  10. If time permits, we will cover the following topics too: sufficiency, efficiency, consistency, Neyman-Fisher factorization, Neyman-Pearson theorem.

Assignments, grading: homeworks, midterm test, and final exam make up 40%, 20%, and 40% of the final grade, respectively. The final grade as the function of the total (maximum 100) points is the following. Below 45: F, 46-49: D, 50-56: C+, 57-63: B-, 64-70: B, 71-77: B+, 78-84: A-, 85-91: A, 92-100: A+.