Statistics II
Code
2434
Academic unit
null
Department
null
Credits
3,5
Teacher in charge
Ana Amaro
Teaching language
English
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.
Prerequisites
To be able to attend this course the students must have a basic background in calculus, descriptive statistics and statistical distributions (e.g. Normal, t-Student): Statistics I (or equivalent).
Subject matter
Main topics will include: inference statistics and distributions, contingency analysis, analysis of variance, simple and multiple linear regression. Excel, Gretl (freeware) and/or SPSS will be used to conduct the statistical analysis. Research papers will also be used as sources of indicators to be interpreted.
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).
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.
All the materials including lecture notes, homework, solutions, links to videos and web resources etc. will be available on our moodle site.
Teaching method
Students are welcome to bring their own devices (e.g IPhones, Smartphones, IPads, Notebooks) to class as this is a BYOD course: they will need to be connected during classes (at least to moodle).
The course will be driven through practical exercises. Students will learn the several techniques because they will need to apply them in order to solve exercises.
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.
Evaluation method
In-class quizzes (ICQ) 10%
Team reports (TR) 20%
Final exam (minimum 8 out of 20) 70%
Final grade = Max (Final Exam; Weighted Grade)
The final exam contains a form (previously available on Moodle) and tables with statistical distributions. The only allowed and required material is a pen and a calculator. Any additional sources of information in addition to those previously mentioned are not allowed.