Statistics I
Objectives
This his course(*) will improve analytical thinking and reasoning, use of IT tools, will encourage interpersonal relations and teamwork and provide learning experience in integrating knowledge across fields
Real life data analysis from a researcher point of view and needs
On successful completion of this course students should be
able to:
A. Knowledge and Understanding
Apply the practical knowledge/tools (mathematical and
statistical based) in order to be able to analyse data (in an inference
context) to produce information to help a decision maker.
B. Subject-Specific Skills
The covered topics will be
-Samples vs Population
-Parameters, Estimates, Confidence Intervals and Hypothesis
Testing
- Central Limit Theorem
-Independence, Correlation, Causation
- Contingency Analysis
-ANOVA
-Linear Regression Analysis
(*2023-2024. The updated course’s syllabus will be available to students at the beginning of each academic term)
General characterization
Code
6304
Credits
3,5
Responsible teacher
TBA
Hours
Weekly - Available soon
Total - Available soon
Teaching language
English
Prerequisites
n/a
Bibliography
Textbook: Newbold, Carlson and Thorne Statistics for Business and Economics, 8th Edition, Pearson Education, 2013
Other editions of this book (6th and 7th) may also be used (the numbering of the chapters and exercises being different).
Online resources: http://wps.prenhall.com/bp_newbold_statbuse_ 6/53/13699/3507189.cwlindex.html
Teaching method
Students are welcome to bring their own devices (/Phones,
Smartphones, /Pads, Notebooks) to class as this is a BYOD course: you will need
to be connected (at least to moodle).
The course will be driven through Case Studies (CS) that
will create the need of using the several statistical techniques. Students will
learn the several techniques because they will need to apply them in order to
solve real problems.
Classes will be divided into individual and team building knowledge. Students will be graded in every class for their learning effort, by solving individual quizzes and developing team case studies. Every team will be part of a final workshop presenting their findings to the class.
Evaluation method
The grading scale used is typically ranging from 0 to 20 (10
points is considered the threshold pass grade). A qualitative
Pass/Fail grading can also be used.
Subject matter
A researcher is often interested in using sample data to investigate relationships between variables (quantitative or categorical), with the goal of creating a model to predict a future value for some dependent variable or just to understand the type of relationship (if any) between variables. Main topics will include: inference statistics and distributions, contingency analysis, analysis of variance, simple and multiple linear regression. Excel, Gretl (freeware) and SPSS will be used to conduct statistical analyses.