SF1924 Probability Theory and Statistics
KTH Royal Institute of Technology
Completed course SF1625 Calculus in one variable.
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. Chi2-tests of goodness of fit, homogeneity and independence. Linear regression.
To pass the course, the student should be able to
- solve problems that require knowledge about standard concepts and methods in probability theory
- solve problems that require knowledge about standard concepts and methods in statistics
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