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Statistical Data Analysis

Descrizione

Course Description

Statistical data analysis is a pervasive issue especially nowadays when we are experiencing the Big Data revolution. “The evidence is clear: Data-driven decisions tend to be better decisions. Leaders will either embrace this fact or be replaced by others who do” says Andrew McAfeethe co-director of the Initiative on the Digital Economyin the MIT Sloan School of Management. Knowledge on how to treat data is thus an essential requirement in many master courses and Digital Fashion Communication is not an exception to this rule. In this course, I will provide the students with an introduction to statistical thinking, to the principles of inductive reasoning and to a data driven approach to decision making. This will require understanding the logic behind statistical methods and techniques, acquiring skills on how to reproduce them in a friendly software environment and interpreting the results of the analysis

Course Objectives

  • Understanding the logic of the quantitative approach
  • Familiarize with main concepts in probability and statistics
  • Being introduced to descriptive and inferential methods

Attendance

Although not compulsory, due to the nature of the topics treated and the need of assistance during the practical laboratories, attendance is highly recommended for all students.

Learning Methods

  • Traditional lectures.
  • Laboratory guided sessions

Evaluation Procedures & Grading Criteria

The final exam will be a written online test in English

To answer the final test’s questions, it will be necessary to perform some calculation with the dedicated software introduced in the course.

Required Materials

Required materials for passing the exam:

  • Lectures’ slides
  • Video tutorial

Optional material for a better understanding of the topic:

  • Field, A. (2000) Discovering Statistics Using SPSS, Sage Publishers.

Persone

 

Arbia G.

Docente titolare del corso

Reinhold H. J.

Assistente

Informazioni aggiuntive

Semestre
Primaverile
Anno accademico
2021-2022
ECTS
3
Lingua
Inglese