Lecture notes

• Lecture 0: Probability distributions (Chapters 1-5)
• Lectures 1, 2a: Survey sampling. Stratified sampling. (Chapter 7)
• Lectures 2b, 3: Estimation of parameters and fitting of probability distributions (Chapters 8 and 6)
• Lecture 4, 5a: Testing hypotheses and assessing goodness of fit (Chapter 9)
• Lectures 5b, 6a: Summarizing data (Chapter 10)
• Lectures 6b, 7: Introduction to Bayesian inference (2nd ed Chapter 15, 3rd ed pages: 20-23, 94-96, 285-296, 329-331, 443-444)
• Lectures 8, 9a: Comparing two samples (Chapter 11)
• Lectures 9b, 10, 11a: Analysis of variance (Chapter 12)
• Lectures 11b, 12: Categorical data analysis (Chapter 13)
• Lectures 13, 14: Linear least squares. Multiple regression. (Chapter 14)

Grading system

1. A set of optional INDIVIDUAL assignments can give a student up to 3 bonus points depending on how many assignments are worked out and how well the reports are written.

Bonus assignments

Data sets

2. The final exam is closed books. However, you will be allowed to use four A4 pages of notes. These notes should be your own summary of the course material and might be either hand-written or printed out from your own file. You are allowed to use a calculator of type Casio FX82, Texas TI30 or Sharp EL531.
Max. number of units for the final exam is 30. Passing units (including eventual bonus points for the optional assignment)

CTH students: 12 for '3', 18 for '4', 24 for '5'

GU students: 12 for 'G', 20 for 'VG'

Here comes a check list of topics that should be studied before the final exam.

3. When grading exams I use the next signs

underline - a place where it starts getting wrong or a wrong answer

three dots - the answer is not complete

question sign - I do not understand here or disagree

plus - you maid a good point here