Course Program
DESCRIPTIVE STATISTICS
Introduction and data collection.
Basic concepts: population, sample, statistical unit, and statistical variable.
Classification of statistical variables.
Data presentation: graphs (bar chart, pie chart, histogram) and tables (Frequency distributions: absolute, relative, percentage, cumulative frequencies).
Measures of central tendency: mode, median, and arithmetic mean.
Measures of variability: dispersion, concentration.
Measures of shape: skewness and kurtosis.
Percentiles and box plot.
Statistical relationships between two variables: correlation.
INFERENTIAL STATISTICS
Foundations of probability calculus: definition of experiment, event, probability, sample space.
Operations between events (negation, union, and intersection), axioms of probability, elementary and compound events, relationships between events (inclusion, independence).
Bayes’ Theorem.
Random variables: probability distribution, density function, and distribution function.
Distributions for discrete random variables (Bernoulli, Binomial, and Poisson) and continuous (Uniform and Normal).
Central Limit Theorem.
Principles of statistical inference: random sample and sampling distribution.
Notable sampling distributions (mean, proportion).
Confidence intervals (for mean, proportion).
Hypothesis testing: characteristics and logic of a statistical test, operational procedure for a hypothesis test, tests on: mean, proportion, variance, difference between means, ratio of variances.
Chi-squared test.
Simple linear regression model: assumptions underlying the classical model, estimation of model parameters.
