Statistics II
Objectives
This course offers a practical introduction to data analysis such that, as future researchers, the students can independently develop their own sample to population statistical analysis and/or interpret indicators generated from a statistical software package. Not only they will be able to decide upon a single random variable characteristics as 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.
General characterization
Code
2434
Credits
3.5
Responsible teacher
Ana Amaro
Hours
Weekly - Available soon
Total - Available soon
Teaching language
English
Prerequisites
n/a
Bibliography
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).
http://wps.prenhall.com/bp_newbold_statbuse_6/53/13699/3507189.cw/index.html we will cover part of Chapters 7, 8, 10 , 12, 13, 16 and 17.
Teaching method
Statistics II is a blended learning course with face to face - f2f - sessions and asynchronous remote sessions. These asynchronous remote sessions are mandatory: the students need to accomplish them, UNDERSTANDING (not just learning how to compute), before the following f2f session at the Campus.
Students are welcome to bring their own devices (e.g IPhones, Smartphones, IPads, Notebooks) to all the f2f sessions as these are BYOD
sessions: they will need to be connected during classes (to moodle and adequate sources of information in real time).
The course will be driven through practical exercises. Students will learn the several techniques because they will need to apply them to solve
exercises.
Typically the f2f sessions will follow asynchronous sessions in a flipped classroom mode. Students will be graded in every class for their
learning effort, by solving individual quizzes.
Understanding and being able to decide under uncertainty is the goal of this learning project. No relevant credits are given to a student that
just know who to compute statistical indicators .
Evaluation method
Along with the teaching/learning Formative (10%) and Summative (20%) quizzes will be asked to be solved
A Final Exam (70%) will be solved after the course at NovaSbe: it will be an open book exam and solved online (students will be asked to bring their own computers) and the minimum grade (rounded) is 8 (out of 20) to pass. This Final Exam is likely to be inspired in Statistics III students' Case Studies.
The Final grade = Max (Final Exam; Weighted Grade)
Subject matter
Main topics will include: one variable inferential statistics and distributions, contingency analysis, analysis of variance, simple and multiple linear regression. Excel, Gretl (freeware) and/or Jamovi might be used to conduct the statistical analyses. Research papers will also be used as sources of indicators to be interpreted.
Programs
Programs where the course is taught: