Statistics II


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





Responsible teacher

Ana Amaro


Weekly - Available soon

Total - Available soon

Teaching language



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).


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). 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

All the course will be delivered online, driven by Quizzes.

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

  • Formative Quizzes - 30% (the time range for the quizzes will need to be checked by the student and depends on the class run)
    Summative Quizzes – 70%

  • EXTREMELY IMPORTANT: after the deadline, no Quizz re-open will be done.

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.


Programs where the course is taught: