First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
ENVIRONMENTAL AND LAND PLANNING ENGINEERING
Course unit
STATISTICAL DATA ANALYSIS
IN02100135, A.A. 2019/20

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

Information on the course unit
Degree course First cycle degree in
ENVIRONMENTAL AND LAND PLANNING ENGINEERING
IN0510, Degree course structure A.Y. 2012/13, A.Y. 2019/20
N0
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Degree course track Common track
Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination STATISTICAL DATA ANALYSIS
Department of reference Department of Civil, Environmental and Architectural Engineering
Mandatory attendance No
Language of instruction Italian
Branch PADOVA
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

Lecturers
Teacher in charge ROSA ARBORETTI GIANCRISTOFARO SECS-S/01

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines SECS-S/01 Statistics 6.0

Course unit organization
Period First semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 6.0 48 102.0 No turn

Calendar
Start of activities 30/09/2019
End of activities 18/01/2020
Show course schedule 2019/20 Reg.2012 course timetable

Syllabus
Prerequisites: None.
Target skills and knowledge: To introduce the student of the Degree in Environmental Engineering to the main statistical techniques in application contexts where it arises spontaneously making their use. The course will focus both on the theoretical foundations and in their proper application to real problems.
The aim of the course is to provide the tools necessary to:
- Know how to manage a set of data,
- Represent a set of data in an effective way,
- Being able to read a data set in a critical way,
- Make choices,
- Be able to plan and carry out simple experiments.
Examination methods: The exam consists of a written exam with multiple choice questions jointly with a personal work project on a data analysis.
Assessment criteria: The preparation of the student will be evaluated based on the degree of knowledge of the theoretical principles and applications of statistical methods presented within the teaching.
Course unit contents: Introduction to statistics: descriptive statistics and inferential statistics. The concepts of population, sample, statistical unit.
Descriptive statistics: types of variables, indicators of central tendency and variability or dispersion, the concept of frequency, graphic representations of data, correlation and contingency tables.
The main elements of probability and probability distributions. The Gaussian or normal probability distribution.
From descriptive statistics to inferential statistics: the concept of replication and simple random sampling.
The confidence intervals and hypothesis testing. One sample tests, two sample tests and tests for paired data.
The one-way and two-way analysis of variance.
The hypothesis testing for categorical data using the chi-square test.
The simple and multiple linear regression.
Introduction to Design of Experiments.
Planned learning activities and teaching methods: Lectures and exercises also through the use of slides and material previously provided to students.
Additional notes about suggested reading: Slide and material provided by the teacher and textbook.
Textbooks (and optional supplementary readings)
  • Levine, D.M., Krehbiel T.C., Berenson M.L., Statistica. Milano: Pearson, 2010. Cerca nel catalogo
  • R.E. Walpole - R.H. Myers - S.L. Myers - K.E. Ye Mil, Analisi statistica dei dati per l'ingegneria. Milano: Pearson, --.

Innovative teaching methods: Teaching and learning strategies
  • Laboratory
  • Case study
  • Action learning
  • Auto correcting quizzes or tests for periodic feedback or exams
  • Active quizzes for Concept Verification Tests and class discussions

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

Sustainable Development Goals (SDGs)
Quality Education Affordable and Clean Energy Industry, Innovation and Infrastructure Sustainable Cities and Communities Responsible Consumption and Production