First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Economics and Political Science
ENTREPRENEURSHIP AND INNOVATION
Course unit
STATISTICS FOR MANAGEMENT
EPP6077104, A.A. 2018/19

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

Information on the course unit
Degree course Second cycle degree in
ENTREPRENEURSHIP AND INNOVATION
EP2372, Degree course structure A.Y. 2017/18, A.Y. 2018/19
N0
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Number of ECTS credits allocated 8.0
Type of assessment Mark
Course unit English denomination STATISTICS FOR MANAGEMENT
Website of the academic structure http://www.economia.unipd.it
Department of reference Department of Economics and Management
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit CANNOT 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 ANNA GIRALDO SECS-S/03

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses SECS-S/03 Statistics for Economics 8.0

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

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 8.0 56 144.0 No turn

Calendar
Start of activities 25/02/2019
End of activities 14/06/2019

Syllabus
Prerequisites: Basic statistics: descriptive statistics and probability. Inferential statistics: estimation, confidence intervals and hypothesis testing.
Target skills and knowledge: Knowledge of the main statistical techniques for the analysis of economic and business data for forecasting purposes. Skills to be acquired: ability to apply the techniques explained in class to real data, producing forecasts of economic and business aggregates.
Examination methods: Written exam aimed at verifying the acquisition of knowledge of the introduced statistical techniques and the ability to apply them to real cases.
Assessment criteria: Verifying of the knowledge of some techniques to analyse economic data and ability to apply them to real cases.
Course unit contents: The linear regression model: definition, inference, goodness of fit
Regression for binary dependent variables
Forecasting
Introduction to time series analysis
Forecasting with time series: Moving Averages and Smoothing Methods
Introduction to market segmentation (cluster analysis)

Analysis of real cases with the statistical software R
Planned learning activities and teaching methods: Lectures and practical lectures in the computer lab.
Additional notes about suggested reading: Study materials will be uploaded on the moodle.
Textbooks (and optional supplementary readings)
  • James G., Witten D., Hastie T. and Tibshirani R., An Introduction to Statistical Learning with Applications in R. --: Springer, 2013. Cerca nel catalogo
  • Hyndman R.J., Athanasopoulos G., Forecasting: Principles and Practice. --: O texts, 2018. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Case study
  • Interactive lecturing
  • Working in group
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • software R

Sustainable Development Goals (SDGs)
Quality Education Industry, Innovation and Infrastructure