First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Economics and Political Science
BUSINESS ADMINISTRATION
Course unit
STATISTICS FOR MANAGEMENT
EPP7082021, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2019/20

Information on the course unit
Degree course Second cycle degree in
BUSINESS ADMINISTRATION
EP2423, Degree course structure A.Y. 2017/18, A.Y. 2019/20
N0
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Degree course track MANAGEMENT [002PD]
Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination STATISTICS FOR MANAGEMENT
Department of reference Department of Economics and Management
E-Learning website https://elearning.unipd.it/economia/course/view.php?idnumber=2019-EP2423-002PD-2019-EPP7082021-N0
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
No lecturer assigned to this course unit

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses SECS-S/03 Statistics for Economics 9.0

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

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 9.0 63 162.0 No turn

Calendar
Start of activities 02/03/2020
End of activities 12/06/2020
Show course schedule 2019/20 Reg.2017 course timetable

Examination board
Examination board not defined

Syllabus
Prerequisites: Basic statistics: descriptive statistics and probability. Inferential statistics: estimation, confidence intervals and hypothesis testing.
Target skills and knowledge: Statistical analysis of data in the economics and the management fields.
Examination methods: Written exam.
Assessment criteria: Knowledge of some techniques to analyse economic data and ability to apply them to real cases.
Course unit contents: - A brief review of inferential statistics
- The linear regression model: definition, inference, goodness of fit
- The regression for binary dependent variables
- Forecasting
- An introduction to time series analysis
- Forecasting with time series: Moving Averages and Smoothing Methods
- An introduction to market segmentation
- An introduction to the measurement of customer satisfaction
Planned learning activities and teaching methods: Lectures and practical lectures in the computer lab. Analysis of real cases with the statistical software R. Works in groups on some case studies.
Additional notes about suggested reading: Study materials will be uploaded on Moodle.
Textbooks (and optional supplementary readings)
  • Hanke, John E.; Wichern, Dean, Business ForecastingJohn E. Hanke, Dean Wichern. Harlow: Pearson, 2014.
  • James, Gareth, <<An >>introduction to statistical learningwith applications in RGareth James ... [et al.]. New York [etc.]: Springer, 2013.
  • Wedel, Michel; Kamakura, Wagner A., Market segmentationconceptual and methodological foundationsMichel Wedel, Wagner A. Kamakura. Boston: Dordrecht, London, Kluwer academic, 2000.

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

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

Sustainable Development Goals (SDGs)
Quality Education