Computer Technology and Statistics Knowledge
A.Y. 2020/2021
Learning objectives
Descriptive statistics knowledge. Use of position and variability indicators. Acquisition of the principles and techniques of regression and correlation among variables. Knowledge of the basics of computer skills. Learning how to manage a spreadsheet use of formula, applications for statistical tests, and generating charts. Using search engines to find information.
Expected learning outcomes
Students will acquire the skills how to describe phenomena using the main statistical indicators. Prepare sample survey plans. Analysis of Variance with one or two factors. Objectively assess the results of statistical surveys. Use different software for management, statistical processing, data storage, and graphical representation. Ability to use reference databases.
Lesson period: Second semester
Assessment methods: Giudizio di approvazione
Assessment result: superato/non superato
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Lesson period
Second semester
Due to the present covid-19 emergency and according with prescriptions provided by Decreto Rettorale 01/15/2021 and by Determina Direttore Generale 01/15/2021 lessons will be provided using Microsoft Teams and registered video/audio files. Teaching materials will be provided through the course site at ariel.unimi.it and in the dedicated folder on Microsoft Teams (link: https://teams.microsoft.com/l/channel/19%3a07112f390c6d4d4b9a03cdd065cf4e60%40thread.tacv2/General?groupId=791208cb-a8b4-448c-a733-c9ccea92f1fe&tenantId=13b55eef-7018-4674-a3d7-cc0db06d545c
Course syllabus
1 - Basics of informatics and use of search engines to find information
2 - R software. Overview and use
3 - Organization and graphic presentation of data
4 - Measures of central tendency
5 - Random variables and probability distributions
6 - Measures of dispersion and measures of variability
7 - Hypothesis testing and p-value
8 - Analysis of variance
9 - Regression
10 - Correlation
2 - R software. Overview and use
3 - Organization and graphic presentation of data
4 - Measures of central tendency
5 - Random variables and probability distributions
6 - Measures of dispersion and measures of variability
7 - Hypothesis testing and p-value
8 - Analysis of variance
9 - Regression
10 - Correlation
Prerequisites for admission
The course has no specific prerequisites
Teaching methods
1. Online lessons using Microsoft Teams platform
2. Recorded lessons
3. Scientific papers
4. Discussion of study cases
2. Recorded lessons
3. Scientific papers
4. Discussion of study cases
Teaching Resources
1. Teaching materials (presentation of the lessons, audio file and scientific papers) will be provided through the course
2. Introduzione alla Statistica, di M. K. Pelosi e T. M. Sandifer, ed. McGraw-Hill, 2009
2. Introduzione alla Statistica, di M. K. Pelosi e T. M. Sandifer, ed. McGraw-Hill, 2009
Assessment methods and Criteria
The exam consists of a written test lasting 60 minutes with 10 multiple choice questions and 3 exercises (the latters have to be solved using the R software). For each correct answer the following score will be assigned: 1.5 for multiple choice questions; from 1 to 5 for each correctly solved exercise. The sum of the scores provides the candidate's total score. Final grade: approved (grade > or = 18/30) or not approved (grade < 18/30).
- University credits: 6
Lessons: 48 hours
Professor:
Montagna Matteo