First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
Course unit
SCN1028509, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course First cycle degree in
SC1165, Degree course structure A.Y. 2008/09, A.Y. 2018/19
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination STATISTICS
Website of the academic structure
Department of reference Department of Biology
E-Learning website
Mandatory attendance
Language of instruction Italian
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Teacher in charge MARTINO GRASSELLI SECS-S/06
Other lecturers GIORGIA CALLEGARO MAT/06

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Basic courses INF/01 Computer Science 1.0
Basic courses MAT/06 Probability and Mathematical Statistics 5.0

Course unit organization
Period Second semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Practice 2.0 32 18.0 No turn
Lecture 4.0 32 68.0 No turn

Start of activities 25/02/2019
End of activities 14/06/2019
Show course schedule 2019/20 Reg.2008 course timetable

Examination board
Board From To Members of the board
8 STATISTICA 2019-2020 01/10/2019 27/11/2020 GRASSELLI MARTINO (Presidente)
CALLEGARO GIORGIA (Membro Effettivo)
7 STATISTICA 2018-2019 01/10/2018 30/11/2019 GRASSELLI MARTINO (Presidente)
CALLEGARO GIORGIA (Membro Effettivo)
6 STATISTICA 2017/2018 01/10/2017 25/11/2018 GRASSELLI MARTINO (Presidente)
CALLEGARO GIORGIA (Membro Effettivo)

Prerequisites: Prerequisites: basic Mathematics, such as summations, limits and differential and integral calculus in one variable.
Target skills and knowledge: The course aims to provide the tools of basic inferential statistics, such as parameter estimates and hypothesis tests, useful for a biological profession. In particular, after a first necessary part of Probability theory, we will examine the problems of parameter estimates and hypothesis testing in the context of continuous statistics, discrete statistics and the linear regression model.
Examination methods: Written exam with multiple choice questions and exercises to develop
Assessment criteria: The evaluation of knowledge aims at verifying a critical capacity in applying the definitions and theorems demonstrated in the classroom through articulated exercises.
Course unit contents: Descriptive and inferential statistics
Descriptive statistics. Average. Variability. The normal distribution. Percentiles and quantiles. Inferential statistics.

Probability calculus Elements
Sample space and probability, properties of a probability. Uniform probability. Random variables. Law and distribution function of a random variable. Conditional probability and independence. Discrete aleatory variables (of Bernoulli, binomial, of Poisson) and their properties. Expected value and variance. Continuous random variables (normal, chi square, Student) and their properties. Poisson approximation. Limit theorems, normal approximation.

Sample mean and variance. Percentiles and quantiles. Inferential statistics: estimates.

Test theory
General theory of tests: hypothesis and alternative, critical region, critical value, first and second species errors, value P. Student test. Student's t test on the difference in means. Bilateral and unilateral tests. Test on the average. Coupled tests.

Errors of first and second kinds
Second kinds error. Power of a test. What determines the power of a test: the probability of making a mistake of the first kind, the difference that one wants to measure, the size of the sample. Practical problems related to power. Calculation of power with high-size samples.

Confidence intervals
Definition and meaning of the confidence interval. Use of confidence intervals for hypothesis testing. Confidence intervals for the average.

Discrete statistics
Estimates, confidence intervals and hypothesis tests for proportions and proportions differences. Contingency table method: the chi-square test. The chi-square test for more than two groups or results. Split the contingency tables. The chi square test with a finite number of states. Test of adaptation to distributions with an infinite number of states: discrete case and continuous case.

Linear regression
The linear model. How to estimate parameters from a sample. Variability around the regression line. Standard errors, confidence intervals and hypothesis testing on the regression coefficients. Forecast around the regression line and related confidence intervals.
Planned learning activities and teaching methods: The course is divided into three weekly lectures, in which the theory is explained, and in classroom exercises, where exercises on the theory are carried out.
Additional notes about suggested reading: Material from the web page of the course (Moodle)
Textbooks (and optional supplementary readings)
  • S. Ross, Introduzione alla Statistica. --: Apogeo, 2008. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • One Note (digital ink)