
Course unit
STATISTICS
SCN1028509, A.A. 2018/19
Information concerning the students who enrolled in A.Y. 2018/19
ECTS: details
Type 
ScientificDisciplinary 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 
Hours of Individual study 
Shifts 
Practice 
2.0 
32 
18.0 
No turn 
Lecture 
4.0 
32 
68.0 
No turn 
Examination board
Board 
From 
To 
Members of the board 
7 STATISTICA 20182019 
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.
Estimates
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 highsize 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 chisquare test. The chisquare 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.

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)

