Statistics III


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 good background in calculus, and a basic background in descriptive and inference statistics at the undergraduate level.


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: 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 Case Studies (CS) that will create the need of using the several statistical techniques. Students will re-call (and, if fact learn) the several techniques because they will need to apply them in order to solve problems. Every class will be a challenge to all.

Extremely important: besides Mock exams, no ‘exercises to solve’ will be provided! One of the goals of this course is to improve the students’ research attitude and abilities; this will be done generating scenarios such that analysis and discussion out from an usual, controlled, comfort zone will occur improving the students’ self-confidence and esteem abilities in order to allow a higher level of performance (during the course… and life).

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.

Subject matter

Main topics will include (the assumption is that these topics should be already familiar to the students, analysed before e.g. during their undergraduate level studies): 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: