Statistics
A.Y. 2024/2025
Learning objectives
The objectives of the course are on the one hand knowledge and understanding of the basic techniques of univariate and bivariate statistical analysis and secondly the ability to actually apply the knowledge learned in the particular context of the political and socio-econonomics sciences, using inferential statistics.
Expected learning outcomes
At the end of the course, the student shall know the main statistical approaches to describe a statistical variable and to measure the bivariate relationships between variables. Moreover, the student will know how to test the significance of the relationships using statistical inferential tools. The student shall also acquire the ability to read and understand simple statistical package outputs, the will be able to interpret and comment with critical approach statistical outputs results
Lesson period: First trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First trimester
Course syllabus
Introduction to statistical methodology
Descriptive statistics and inferential statistics
Sampling and Measurement
Variables and their measurement
Randomization
Descriptive statistics
Describing data with tables and graph
Describing the center of the data
Describing variability of the data
Introduction to probability
Probability distributions for discrete and continuous variables
The normal probability distribution
The binomial probability distribution
Sampling distributions
Statistical inference
Point estimators
Confidence interval for a proportion
Confidence interval for a mean
Hypothesis Tests
Test for a mean
Test for a proportion
Bivariate analysis
Association between Categorical Variables
Contingency Tables
Chi-squared test of independence
Comparison of groups
Categorical data: comparing two proportions
Quantitative data: comparing two means
Comparing means with dependent samples
Comparing more than two groups
Linear Regression and Correlation
Least squares prediction equation
The linear regression model
Inference for the slope
Model assumptions and violations
Descriptive statistics and inferential statistics
Sampling and Measurement
Variables and their measurement
Randomization
Descriptive statistics
Describing data with tables and graph
Describing the center of the data
Describing variability of the data
Introduction to probability
Probability distributions for discrete and continuous variables
The normal probability distribution
The binomial probability distribution
Sampling distributions
Statistical inference
Point estimators
Confidence interval for a proportion
Confidence interval for a mean
Hypothesis Tests
Test for a mean
Test for a proportion
Bivariate analysis
Association between Categorical Variables
Contingency Tables
Chi-squared test of independence
Comparison of groups
Categorical data: comparing two proportions
Quantitative data: comparing two means
Comparing means with dependent samples
Comparing more than two groups
Linear Regression and Correlation
Least squares prediction equation
The linear regression model
Inference for the slope
Model assumptions and violations
Prerequisites for admission
The course requires no prior knowledge of statistics, but it is advisable to have knowledge of basic mathematics.
Teaching methods
Each topic of the program will be presented theorectically during the lectures. For each topic practical lectures will be devoted to solve practical exercises. Moreover, applications to real dataset using the statistical package STATA will be showed to the students during the course. Particular attention will be devoted to the reading and interpretation of the results
Teaching Resources
Alan Agresti, Statistical Methods for the Social Sciences, Pearson (Chapters 1,2,3,4,5,6,7,8)
Stock J., Watson M. (2010) Introduction to Econometrics, Pearson (Chapters 1,2,3,4,5)
Additional materials (slides, exercises, exam simulations) in the ARIEL website
Stock J., Watson M. (2010) Introduction to Econometrics, Pearson (Chapters 1,2,3,4,5)
Additional materials (slides, exercises, exam simulations) in the ARIEL website
Assessment methods and Criteria
The exam consists of a written test (1.5 hours) in which the student must solve practical exercises (16 points) and comment a statistical outputs produced using the package STATA (16 points) to real datasets. Students are not required to know how to use the STATA package which is not part of the program, but they must be able to interpret the outputs that the statistical packages produce for the techniques of univariate and bivariate statistical part of the program.
SECS-S/01 - STATISTICS - University credits: 6
Lessons: 40 hours
Professor:
Salini Silvia
Shifts:
Turno
Professor:
Salini SilviaProfessor(s)
Reception:
The student reception is in attendance, by appointment, on Tuesday from 09.30 to 11.00 and via Teams, by appointment, on Monday from 15.00 to 16.30.
DEMM, room 30, 3° floor