Class Schedule (from february 26th)
- Monday 10:00-12:00 – Aula Caparrelli B
- Tuesday 10:00-12:00 – Aula Caparrelli B
- Wednesday 10:00-12:00 – Aula Caparrelli B
Office hours
Friday 11.00-13.00, room 215, 2nd floor
Objectives
The aim of the course is to provide students with the main quantitative-statistical methods.
In particular learning outcomes are:
– Knowledge and understanding of descriptive statistics, basic probability theory, point and interval estimation and hypothesis testing;
– Ability to use quantitative methods for decribing economic and social phenomena;
– Ability to perform statistical analysis using R software;
– Effectively communicate statistical data outcomes
Recommended prerequisites
Knowledge of elementary mathematics. Basic notions of differential and integral calculus.
Programme
Descriptive statistics
Variables and observations. Frequency distributions and frequency distributions for continous variables. Measures of location: mode, median, arithmetic mean. Measures of variation: quartiles, variance, coefficient of variation. Pairs of variables. Covariance and correlation. Linear regression.
Probability
Events, rules for events, probability space. Conditional probabilities and independence.
Discrete and continuous random variables, expectation and variance. Vectors of random variables and independence.
Statistical Inference
Estimators and their properties. Maximum likelihood estimators. Confidence intervals. Parametric hypothesis testing. Chi-square tests for independence and homogeneity.
R software:
Introduction to R. Descriptive data analysis and basis of statistical inference with R.
Reading
Custom publishing (suggested but not mandatory)
Statistics: principles and methods. Ediz. Mylab. G. Chicchitelli, P. D’Urso, M. Minozzo, Pearson
Supplementary material
Supplementary material will be available logging on http://elearning.unisi.it/moodle/ .
Further readings will be announced during the course
Password for Moodle registration will be share at the beginning of the course
Assessment method
Learning assessment will take place through four assignments and a final exam.
1. First assignment: multiple choice and/or open choice questions where the student will have to demonstrate the acquisition of knowledge relating to univariate and descriptive descriptive statistics. The evaluation of the first assignment will weigh 18% of the total.
2. Second assignment: multiple choice and/or open choice questions where the student will have to demonstrate the acquisition of knowledge relating to association between variables in the descriptive framework. The evaluation of the second assignment will weigh 18% of the total.
3. Third assignment: multiple choice and/or open choice questions where the student will have to demonstrate the acquisition of knowledge relating to probability and random variables. The evaluation of the third assignment will weigh 18% of the total.
4. Fourth assignment: presentation of an individual and/or group project in which the student will use the R software to implement all the knowledge acquired during the course. The project needs to be presented using a video demonstrating the student contribute. The evaluation of the project and the evaluation of the video presentation will both weight 8% of the total.
5. Final exam: it will weigh 30% of the total, will be a written test including multiple choice and/or open choice questions. The aim of the final test is to verify the understanding of all the statistical techniques addressed during the course, with particular emphasis on statistical inference
REMARKS:
– NO REPETITION OF ASSIGNMENTS WILL BE ALLOWED
– If the score of the two assignments are such that you cannot achieve a sufficient score even perfoming at the best the final test, the exam will be insufficient (see examples in the table below) and you need to attend the course next year attending all the assignments again. Additional sessions in July and September are dedicated only for repeating the final exam
– 1st June session is dedicated to the collective exam
Table reporting some examples:
| 1st assignment mark | 2nd assignment mark | 3rd assignment mark | 4th assignment mark | Final exam mark | FINAL RESULT |
|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 30 | 8-INSUFFICIENT |
| 0 | 18 | 0 | 18 | 30 | 15-INSUFFICIENT |
| 20 | 0 | 18 | 18 | 18 | 15-INSUFFICIENT |
| 18 | 18 | 18 | 18 | 0 | 13-INSUFFICIENT |
| 16 | 18 | 20 | 18 | 18 | 18 |
| 30 | 25 | 20 | 0 | 18 | 18 |
| 25 | 18 | 30 | 27 | 25 | 25 |
| 28 | 25 | 30 | 30 | 30 | 29 |