SF1900 Probability Theory and Statistics 6.0 credits
Sannolikhetsteori och statistik
The overall purpose of the course is that the student should be well acquainted with basic concepts, theory, models and solution methods in probability theory and statistical inference.
Education cycle
First cycleMain field of study
Technology
Grading scale
A, B, C, D, E, FX, F
Course offerings
Autumn 19 for programme students

Periods
Autumn 19 P1 (6.0 credits)

Application code
50442
Start date
26/08/2019
End date
25/10/2019
Language of instruction
English
Campus
KTH Kista
Tutoring time
Daytime
Form of study
Normal

Number of places
No limitation
Course responsible
Camilla Johansson Landén <landen@kth.se>
Teacher
Camilla Johansson Landén <landen@kth.se>
Part of programme
Autumn 18 TCOMK3 for programme students

Periods
Autumn 18 P1 (6.0 credits)

Application code
50162
Start date
27/08/2018
End date
26/10/2018
Language of instruction
English
Campus
Campus Kista
Tutoring time
Daytime
Form of study
Normal

Number of places
No limitation
Schedule
Course responsible
Per Jörgen SäveSöderbergh <pjss@kth.se>
Teacher
Per Jörgen SäveSöderbergh <pjss@kth.se>
Target group
Only TCOMK
Part of programme
Intended learning outcomes
To pass the course, the student should be able to do the following:
 construct elementary statistical models for experiments
 describe standard models and account for the applicability of the models in given examples
 efine and calculate descriptive quantities for probability distributions and data sets, such as measures of central tendency, dispersion and dependence
 using standard methods, such as maximum likelihood estimation and the method of least squares, calculate estimates of unknown quantities and quantify the uncertainty in these estimates by means of for example error propagation and confidence intervals
 value and compare methods of estimation, for example with respect to bias and efficiency
 analyse how measuring accuracy affects conclusions and quantify risks and error probabilities in statistical hypothesis testing
To receive the highest grade, the student should in addition be able to do the following:
 Combine all the concepts and methods mentioned above in order to solve more complex problems.
Course main content
Basic concepts such as probability, conditional probability and independent events. Discrete and continuous random variables, in particular one dimensional random variables. Measures of central tendency, dispersion and dependence of random variables and data sets. Common distributions and models, such as the normal, binomial and Poisson distributions. The Central limit theorem and the Law of large numbers.
Descriptive statistics.
Point estimates and general methods of estimation, such as maximum likelihood estimation and the method of least squares. General confidence intervals and in particular confidence intervals for the mean and variance of normally distributed data. Confidence intervals for proportions and for difference in means and proportions.
Statistical hypothesis testing. Chi2tests of goodness of fit, homogeneity and independence. Linear regression.
Eligibility
Basic linear algebra, calculus in one variable, calculus in several variables.
Only for students enrolled in a bachelor programme in Information and Communication Technology (TCOMK).
Recommended prerequisites
SF1626/SF1686 Calculus in Several Variable, SF1624/SF1684 Algebra and Geometry
Literature
Blom, Gunnar. Probability and Statistics (1989), Studentlitteratur
Complemental material from the department.
Examination
 TEN1  Examination, 6.0, grading scale: A, B, C, D, E, FX, F
Requirements for final grade
Passed written examination.
Offered by
SCI/Mathematics
Contact
Per Jörgen SäveSöderbergh (pjss@kth.se)
Examiner
Per Jörgen SäveSöderbergh <pjss@kth.se>
Supplementary information
Only for students enrolled in a bachelor programme in Information and Communication Technology (TCOMK).
Version
Course syllabus valid from: Autumn 2017.
Examination information valid from: Autumn 2017.