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.
Select the semester and course offering above to get information from the correct course syllabus and course offering.
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. Basic decision theory and Bayesian inference. Confidence intervals. Statistical hypothesis testing. Linear regression.
To pass the course, the student should be able to
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Completed course SF1625 Calculus in one variable.
SF1626 Calculus in Several Variable, SF1624 Algebra and Geometry.
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A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
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Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.
Course web SF1918Technology
First cycle
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Camilla Johansson Landén (landen@kth.se)
Only for students enrolled in the Degree Programme in Industrial Engineering and Management (CINEK).