All scientific investigation deals with building models for reality. The models will necessarily be simplifications of reality, and so the actual observations we do, i.e., the data we have, will contain variability not captured in the model. When our data are numbers, like counts or measurements, statistics is an indispensable tool for dealing with the variability in a scientific way, so that conclusions about the simplified models can be drawn in spite of the extra variability in the data. When the data is the result of an experiment, the design of the experiment, i.e., how it is performed, determines how firm conclusions can be drawn about the models, in spite of the variability in the data. Good experimental design is essential for a good scientific experiment; without it, a lot of work can be wasted.

The course is given for masters students from a number of different fields within the natural sciences, and we aim to reflect some of their different subject matter in the course. However, the principles of statistical analysis and experimental design are generally the same. We will focus on ideas and methods that should be useful in many masters-projects, as well as in later scientific investigations. The students' differeing interests and backgrounds in statistics will be reflected in individual or group-work on a miniproject.

The course gives 7.5 hp.

Latest news

An overview of the results from the course evaluation is available here.

The exam has now been corrected, and a suggested solution has been written. Of the 34 students taking the exam, 29 passed. Of these, 19 got the grade G and 10 got the grade VG.

The R commands used during the two last lectures are available from the schedule below.

A list of errors in the textbook has been started below. Please mail in additional errors that you find!

Some scans of old exams have been added; see the bottom of this homepage.

The lecture plan has been updated, with an adjustment of the course content.

The overview of the course literature has been updated with a list detailing the contents of the course.

The list of exercises has been updated, see the schedule below. See also the link to an exercise not in the textbook, for Friday 16 November.

A link to the introductory lecture to R has been added in the schedule below.

HOW TO FIND LECTURE ROOMS: Follow this link, and choose Pascal, VV33, or MVF31. Pascal is on the first floor of the tall building of the Math department of Chalmer's Johanneberg campus.

Examiner and lecturer

Petter Mostad 

Exercises: Malin Östensson

Course literature

Main textbook

Box, Hunter, and Hunter: Statistics for Expermenters, SECOND edition. The book will be available at the Cremona bookstore.

The parts of this book included in the course:

Chapter 1: All

Chapter 2: All

Chapter 3: All

Chapter 4: 4.1, 4.2, except ”graphical ANOVA”

Chapter 5: 5.1 – 5.9

Chapter 6: 6.1 – 6.4

Chapter 10: 10.1

In addition: Some supplementary material about sample size computations.

ERRATA FOR THE TEXTBOOK (version printed 2005):

• Page 50, Exercise 2.13: Using the correct Hypergeometric distribution, the answer is 0.042. Using the Binomial distribution as an approximation, the answer is 0.056. Using a normal approximation to the Binomial approximation, the answer is 0.054.
• Page 102: In the equation at the bottom of the page, a tilde (~) should be inserted between the fraction on the right and the chi-square symbol, indicationg that the fraction has a chi-square distribution.
• Page 106: In the sentence above Exercise 3.18: Table F does not exist!

Reference literature

Consult the ”Elementary Concepts” and ”Basic Statistics” section of this online reference text.

Plan for lectures and classes

Time	Place	Contents	Teacher
Tuesd Nov 6, 9:00 – 12:00	Pascal	Introduction. Scientific investigation. Chapter 1. Introduction to miniprojects.	Petter Mostad
Thurs Nov 8, 10:00 – 12:00	Pascal	Lecture 1: Sections 2.1 – 2.5	Petter Mostad
Thurs Nov 8, 13:15 – 15:00	VV33	More about miniprojects. Assignments of projects.	Petter Mostad Malin Östensson
Fri Nov 9, 10:00 – 12:00	Pascal	Lecture 2: Sections 2.6 – 2.12 and Section 3.1	Petter Mostad
Fri Nov 9, 13:15 – 15:00	MVF31	Problems from Chapter 2: 3,4,5,8,10	Malin Östensson
Mon Nov 12, 10:00 – 12:00	Pascal	Lecture 3: Sections 3.2 – 3.4	Petter Mostad
Mon Nov 12, 13:15 – 15:00	Note change: MVH12	Problems from Chapter 3: 1,4,5,7,9,12,15	Malin Östensson
Wed Nov 14, 10:00 – 12:00	Pascal	Lecture 4: Section 3.5 + Section 4.1	Petter Mostad
Wed Nov 14, 13:15 – 15:00	MVF31	Exercises from Chapter 4: 1,4	Malin Östensson
Thu Nov 15, 10:00 – 12:00	Note change: MVH12	Introduction to R. (R is an open-source program for statistical computations. R is not compulsory in this course, but may be useful in projects etc.)	Petter Mostad
Fri Nov 16, 10:00 – 12:00	Pascal	Lecture 5: Section 4.2 and start of Chapter 5	Petter Mostad
Fri Nov 16, 13:15 – 15:00	Note change: MVH12	Problems from Chapter 4: 2,3, plus extra exercise	Malin Östensson
Mon Nov 19, 10:00 – 12:00	Pascal	Lecture 6: Sections 5.1 – 5.9	Petter Mostad
Mon Nov 19, 13:15 – 15:00	Note change: MVH12	Problems from Chapter 5: 2, 3, 6a, 8, 10, 15, 18 (as much as there is time for)	Malin Östensson
Wed Nov 21, 10:00 – 12:00	Pascal	Lecture 7: Sections 6.1 – 6.4	Petter Mostad
Wed Nov 21, 13:15 – 15:00	MVF31	Problems from Chapter 6: 1,2,3.	Malin Östensson
Thu Nov 22, 10:00 – 12:00	Note change: MVH12	Help with miniprojects.	Petter Mostad Malin Östensson
Fri Nov 23, 10:00 – 12:00	Pascal	Lecture 8: Sections 2.13 – 2.14 and review of Chapter 2. R commands used during the lexture	Petter Mostad
Fri Nov 23, 13:15 – 15:00	MVF31	Problem 9 from Chapter 6, Problem 4 from Chapter 5, and Exercises 2.13 and 2.14 from Chapter 2.	Malin Östensson
Mon Nov 26, 10:00 – 12:00	Pascal	Lecture 9: Sections 3.6 – 3.8. R commands used during the lexture	Petter Mostad
Mon Nov 26, 13:15 – 15:00	Note change: MVH12	Exercises from Chapter 3: 2,3,4,7,8,12,14	Malin Östensson
Wed Nov 28, 10:00 – 12:00	Pascal	Deadline for reports on miniprojects at 10:00! Lecture 10: Section 10.1. Lecture notes. R commands used during the lexture	Petter Mostad
Wed Nov 28, 13:15 – 15:00	MVF31	Exercises: From Chapter 10: 1,2,3,4,5,9,11.	Malin Östensson
Thu Nov 29, 10:00 – 12:00	Note change: MVH12	Exercises: All chapters, and questions from old exams.	Malin Östensson
Fri Nov 30, 10:00 – 12:00	Pascal	Lecture 11: Sample size computations (supplementary material)	Petter Mostad
Fri Nov 30, 13:15 – 15:00	MVF31	Review and feedback on miniprojects	Petter Mostad
Mon Dec 3, 10:00 – 12:00	Pascal	Review of course. Exam tips.	Petter Mostad
Mon Dec 3, 13:15 – 15:00	NOTE CHANGE EULER	First hour: Exercises about sample size computations, other exercises. Second hour: Course evaluation.	Malin Östensson, Petter Mostad
Wed Dec 5, 8:00 – 13:00 NOTE THE TIME!!!	V (Väg och Vatten; the exact room will be posted at the building)	Written exam

Use of computers/calculators

To follow the course, you may need a calculator. Those who are interested, and who would like to use computer programs for computations in their miniprojects and maybe later in their thesis work, will be given help with how to use the R program.

Examination

To pass this course you must pass the written exam and complete the miniproject.

Written examination

The written exam will consist of questions similar to the exercises in the textbook. Understanding statistical ideas will be more important than memorizing formulas.

The number of points on the exam, and the number of points needed for different grades will be announced later.

The exam takes place at: Time and place will be announced later.
During the exam the following aids are permitted: An optional calculator, and one page of your own written notes.
Bring ID and receipt for your student union fee.

Solutions to the exam will be published on this homepage after the exam.
You will be notified the result of your exam by email from LADOK (This is done automatically as soon as the exams have been marked an the results are registered.)
The exams will then be kept at the students' office in the Mathematical Sciences building.
Check that the number of points and your grade given on the exam and registered in LADOK coincide.
Complaints of the marking should be written and handed in at the office. There is a form you can use, ask the person in the office.

The following link will tell you all about the examination room rules at Chalmers: Examination room instructions

Miniproject

The students should work in groups of 2-3 people. In the projects, the students should apply methods for experimental planning and/or data-analysis to a problem within their field. More specific instructions will be handed out later. The themes and contents of the miniprojects will reflect the differing backgrounds and subject area interests of the students.

Old exams

The exam will be somewhat similar to old exams in the course MSN460:

• MSN460, March 2006, with solutions.
• MSN460, Septermber 2006, with solutions.

The quality of the scans are unfortunately quite bad, but they are mostly legible when printed out. The ”last two pages” mentioned in the headings of the exams consist of one page of ”Theory” and a copy from the textbook of the table for the t-distribution. In your exam, you will have to bring your own page of notes for the ”theory” (see above), but you will get copies of the statistical tables in the textbook.