Introduction to statistical methodology. Descriptive Statistics.
Introduction to probability, random variables and probability distributions.
Introduction to sampling: Statistics and sampling distributions.
Statistical Inference: Point and interval estimation, statistical hypothesis tests.
Alan Agresti, Barbara Finlay (2015). Statistical Methods for the Social Sciences (4th edition) Pearson Prentice Hall.
Learning Objectives
The course aims to teach students to read, interpret, and analyze statistical data from complete
and sample surveys.
Prerequisites
Elements of algebra and mathematics taught in secondary schools
Teaching Methods
Lectures
Type of Assessment
Written exam
Course program
Introduction. Introduction to statistical methodology;
Descriptive statistics and inferential statistics; Variables and their measurement; Introduction to probability sampling methods.
Descriptive Statistics.
Frequency distributions;
Describing data with graphs; Measures of position (mean, median and mode); Measures of variability (range, variance, standard deviation, coefficient of variation); Bivariate frequency distributions.
Introduction to probability: postulates and basic theorems; Conditional probability; Statistical indipendence; Random variables and probability distributions; The Bernoulli probability distribution and the normal probability distribution.
Introduction to sampling: Population, sample data, statistics and sampling distributions; Sampling distributions of the sample mean; Sampling distributions of the sample proportion.
Statistical Inference: Point estimation (estimators and their properties) and interval estimation; Confidence interval for a mean;
Confidence interval for a proportion (large sample size); Choice of sample size.
Statistical Inference: Hypothesis statistical tests. Null hypothesis and alternative hypothesis; Type I and type II errors; Power of a test; Neyman-Pearson approach and p-value approach to testing; Test for a mean; Test for a proportion (large sample size)