
Course unit
STATISTICS 1 (Ult. numero di matricola dispari)
SCP4063430, A.A. 2018/19
Information concerning the students who enrolled in A.Y. 2018/19
ECTS: details
Type 
ScientificDisciplinary Sector 
Credits allocated 
Basic courses 
SECSS/01 
Statistics 
6.0 
Course unit organization
Period 
Second semester 
Year 
1st Year 
Teaching method 
frontal 
Type of hours 
Credits 
Teaching hours 
Hours of Individual study 
Shifts 
Practice 
1.5 
14 
23.5 
2 
Laboratory 
0.5 
6 
6.5 
2 
Lecture 
4.0 
34 
66.0 
No turn 
Examination board
Board 
From 
To 
Members of the board 
4 Commissione a.a.2019/20 (matr.pari) 
01/10/2019 
30/09/2020 
DALLA VALLE
ALESSANDRA
(Presidente)
CANALE
ANTONIO
(Membro Effettivo)
GUOLO
ANNAMARIA
(Membro Effettivo)

3 Commissione a.a.2019/20 (matr.dispari) 
01/10/2019 
30/09/2020 
GUOLO
ANNAMARIA
(Presidente)
CANALE
ANTONIO
(Membro Effettivo)
DALLA VALLE
ALESSANDRA
(Membro Effettivo)

2 Commissione a.a.2018/19 (matr.pari) 
01/10/2018 
30/09/2019 
DALLA VALLE
ALESSANDRA
(Presidente)
CATTELAN
MANUELA
(Membro Effettivo)
VENTURA
LAURA
(Membro Effettivo)

1 Commissione a.a.2018/19 (matr.dispari) 
01/10/2018 
30/09/2019 
DALLA VALLE
ALESSANDRA
(Presidente)
CATTELAN
MANUELA
(Membro Effettivo)
VENTURA
LAURA
(Membro Effettivo)

Prerequisites:

Basic knowledge in Mathematics 
Target skills and knowledge:

The course is characterized by the following target skills and knowledge:
1) Capability of using basic statistical techniques for graphical description and construction of appropriate indicators of a real phenomenon;
2) Capability of using basic statistical techniques for studying the relationship between 2 or more phenomena from a cognitive and a predictive point of view;
3) Capability of using appropriate tools for a critical evaluation of the results;
4) Capability of performing basic analyses on real datasets using software R. 
Examination methods:

The examination is composed by two written parts.
1) The first part (30 minutes) takes place in the computer classroom and it is constituted by some questions about the analysis of a dataset to be carried out using software R. The responses are expected to be given on a precompleted sheet provided by the teacher at the beginning of the exam. The score for the first part of the examination is from 0 to 3.
2) The second part (1 hour and 45 minutes) includes questions with multiple choices and exercises on theory and analysis of some datasets. The score for the second part of the exam is from 0 to 30. During the second part of the examination a pocket calculator is allowed.
The examination is passed when the scoring for the second part is equal to or above 18/30. The final score is the sum of the scores of the two parts. 
Assessment criteria:

Students' preparation is evaluated on the basis of:
1) Completeness of the acquired knowledge;
2) Capability of describing a data set from both a graphical and an analytical point of view;
3) Appropriateness of the statistical terminology;
4) Coherence of the comments on the analyses;
5) Capability of using the functionalities available in R for graphical and modeling purposes. 
Course unit contents:

 Population; statistical units; variables; values of variables.
 Tables; absolute, relative and cumulative frequencies.
 Histograms and graphical representations.
 Measures of location: means; quartiles and quantiles. Boxplot.
 Empirical cumulative distribution function.
 Variability and mutability.
 Asymmetry and kurtosis: brief notes.
 Mean and variance of a linear transformation. Standardization.
 Decomposition of the arithmetic mean and of the variance for subpopulations.
 Contingency tables; marginal and conditional distributions; absolute and relative frequencies.
 Distributional dependence: factorization and indices.
 Dependence in mean: correlation ratio.
 Linear dependence: regression, correlation, evaluation of accuracy. 
Planned learning activities and teaching methods:

The course consists of
1) lectures (34 hours), where theory will be illustrated through slides;
2) exercise classes (14 hours) about the application of the techniques to the analysis of datasets; during the classes blackboard will be used;
3) laboratory classes (6 hours) for learning basic R functionalities for data analysis; laboratory classes will take place in computer classroom. 
Additional notes about suggested reading:

Material will be made available by the teacher through the Moodle platform: slides for the theoretical arguments, material for exercise classes, notes for laboratory classes, papers and notes from statistical literature. In order to stimulate learning, additional exercises will be made available through Moodle platform as long as the topics of the course are illustrated. The summarized solutions will be made available later through the Moodle platform. 
Textbooks (and optional supplementary readings) 

Cicchitelli, Giuseppe; Minozzo, Marco, Statistica: principi e metodi. Milano: Torino, Pearson, 2017. Testo di riferimento

Pace, Luigi; Salvan, Alessandra, Introduzione alla statistica: statistica descrittiva. Padova: CEDAM, 1996. Testo consigliato


