This page includes information about aim and learning outcomes, teachers, literature, examination, examination procedures, old exams and course evaluation. The program for all teaching sessions can be found on a separate page (Course syllabus).

Aim and learning outcomes

The aim and learning outcomes of the course can be found in the course plan.

Changes compared to the last occasion

Teachers

Examiner and lecturer:

Umberto Picchini (picchini@chalmers.se)

Teaching assistants:

Juan Inda (inda@chalmers.se)

Konstantinos Konstantinou (konkons@chalmers.se)

Office hours

One hour each Mondays 16.00-17.00 either in my office H3030 at maths department or on Zoom at https://chalmers.zoom.us/my/picchini. The office hour is for you to have a private meeting with the lecturer. The office hours via zoom work as follows: if you come via zoom, you enter the "waiting room" of the zoom office, and I admit one student at a time from that waiting room, on a first-come first-served basis. Each student should limit their question time to about 10 minutes, if there is a line. I will also try to answer questions posed here in Canvas when I see them.

Student representatives (in alphabetical order)

Victor Brun (victor.w.brun@gmail.com)

Mercedes Dalman (alinned@student.chalmers.se)

Iris Gold Rodal (rodal@student.chalmers.se)

Hanna Skytt (skytth@student.chalmers.se)

Course literature

The main reference is the lecture notes by Prof. Rebecka Jörnsten. However we will not cover all the material in such notes.

The following are recommended references:

(PDF is free! Get it here) James, Witten, Hastie, Tibshirani: An Introduction to Statistical Learning with Applications in R, 2nd edition. [if you wish to own a physical copy, I recommend this link for a rather good price via Chalmers. Click the "MyCopy softcover" ]
a monography for linear models: J.O. Rawlings, S.G. Pantula, D.A. Dickey. Applied Regression Analysis. This is available as ebook.
a monography for generalised linear models: Alan Agresti. "Categorical Data Analysis", Wiley. This is available as ebook.

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Examination

The examination consists of a large project and a written exam. The written exam accounts for 50% of your final grade. The project accounts for the remaining 50%, and this 50% is considered split in two parts:

short summary of the several minianalyses (10%);
a longer analysis of completely new datasets (40%).

The exact formula for how all the above is put together to form the final grade will be discussed at lecture and also sent as an Announcement via Canvas.

Mini-Analyses are exercises which you will have a week to perform and prepare slides to present in front of everyone on FRIDAYS. You must team-up and create a group (*). A few groups will be selected at random on Friday Mini sessions. Attendance is mandatory either in person or via zoom. We discuss your findings in class (your performance in class is NOT graded). Slides should be uploaded on Canvas by a deadline to be communicated during the course. If you do not attend Friday sessions and/or you did not send me your slides you will have to write a report, which is a lot more work! Also, according to students' course evaluations, mini-presentations is one of the most appreciated activities.

The final project report should be submitted via Canvas as a group work.

If you fail the exam you can retake this component. If you failed the project (or never submitted it during the course regular running time), you can resubmit a fixed version during reexamination periods. However, project resubmission is only possible within the same academic year and in case of resubmissions for your project you can only aspire to a 3 (for the project, to be weighted with your exam score). You cannot reattempt the same project in another academic year. Every new academic year you will have to attempt a brand new project. If this year you passed the project but not the exam in all three opportunities, next year you may reattempt the exam and keep the project passing grade (but not beyond next year).

(*) I recommend teams of max two people, as in the end the project will be written by 2 persons. If you are 3 persons doing the minis, it will be awkward for the third person having to find a project partner all of a sudden.

The dates, times and places for the exams are in the student portal.

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Examination procedures

In Chalmers Student Portal you can read about when exams are given and what rules apply to exams at Chalmers. In addition to that, there is a schedule when exams are given for courses at the University of Gothenburg.

Before the exam, it is important that you sign up for the examination. You sign up through Ladok.

At the exam, you should be able to show valid identification.

After the exam has been graded, you can see your results in Ladok.

At the annual (regular) examination:
When it is practical, a separate review is arranged. The date of the review will be announced here on the course homepage. Anyone who can not participate in the review may thereafter retrieve and review their exam at the Mathematical Sciences Student office. Check that you have the right grades and score. Any complaints about the marking must be submitted in writing at the office, where there is a form to fill out.

At re-examination:
Exams are reviewed and retrieved at the Mathematical Sciences Student office. Check that you have the right grades and score. Any complaints about the marking must be submitted in writing at the office, where there is a form to fill out.

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Course evaluation

At the beginning of the course, at least two student representatives should have been appointed to carry out the course evaluation together with the teachers. The evaluation takes place through conversations between teachers and student representatives during the course and at a meeting after the end of the course when the survey result is discussed and a report is written.

Guidelines for Course evaluation in Chalmers student portal.

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Old exams

Old exams: January 2022, January 2021, Final Jan 2020, Final Jan 2019

Solutions: January 2022, January 2021, Final Jan 2020, Final Jan 2019

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Program

The program can be found on the Course syllabus.

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