Introduction to statistical methodology.
Descriptive statistics: Frequency distributions, graphs, Measures of position and variability. Bivariate and conditional frequency distributions.
Introduction to probability and random variables.
Introduction to statistical inference: Statistics and sampling distributions
Estimation: Point and interval estimation.
Comparison of two groups.
Correlation and linear regression
Agresti A., Finlay B. (2009) Statistical method for the social science, 4th Edition, Pearson.
Learning Objectives - Last names A-I
The objective of the course is to allow students to read, interpreter, analyze statistical data from complete and sample survey
Prerequisites - Last names A-I
Elements of algebra and mathematics taught in secondary schools
Teaching Methods - Last names A-I
Further information - Last names A-I
materials from e-learning Moodle
Type of Assessment - Last names A-I
Course program - Last names A-I
INTRODUCTION TO STATISTICAL METHODOLOGY: Basic concepts
DESCRIPTIVE STATISTICS: Absolute and relative frequency distributions; Graphs: bar plot and histogram, Measures of position: mean, median, mode. Measures of variability: Range; variance and standard deviation, coefficient of variation; Bivariate frequency distributions.
INTRODUCTION TO PROBABILITY AND RANDOM VARIABLES: Sample space and events; Frequentistic probability; Axioms of probability; Conditional probability; Statistical independence; Discrete and continuous random variables; Bernoulli distribution; Normal distribution.
INTRODUCTION TO STATISTICAL INFERENCE: Parameters versus statistics; Sampling distributions; Sampling distribution of the sample mean; of the sample variance and of the sample proportion.
ESTIMATION: Point and interval estimation; Confidence interval for a mean; Confidence interval for a proportion; Choice of sample size.
HYPOTHESIS TESTS: Basic concepts; Neyman-Pearson approach; P-value approach; Test for a mean; Test for a proportion
COMPARISON OF TWO GROUPS: Comparing two means; Comparing two proportions; Comparing means with dependent samples.
CORRELATION AND LINEAR REGRESSION: Covariance and Pearson correlation coefficient;The linear regression model; Decomposition of the total sum of square; Coefficient of determination