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It is a process of selecting a sample in a random way. In SRS, each subset of k individuals has the same probability of being chosen for the sample as any other subset of k individuals. [1] Simple random sampling is a basic type of sampling and can be a component of other more complex sampling methods. [2]
The re-sampling techniques are implemented in four different categories: undersampling the majority class, oversampling the minority class, combining over and under sampling, and ensembling sampling. The Python implementation of 85 minority oversampling techniques with model selection functions are available in the smote-variants [2] package.
A visual representation of the sampling process. In statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a statistical population to estimate characteristics of the whole population. The subset is meant to reflect the whole ...
Japanese theorem for concyclic polygons (Euclidean geometry) Japanese theorem for concyclic quadrilaterals (Euclidean geometry) John ellipsoid ; Jordan curve theorem ; Jordan–Hölder theorem (group theory) Jordan–Schönflies theorem (geometric topology) Jordan–Schur theorem (group theory)
The four color theorem and optimal sphere packing were two major problems of discrete mathematics solved in the second half of the 20th century. [44] The P versus NP problem , which remains open to this day, is also important for discrete mathematics, since its solution would potentially impact a large number of computationally difficult problems.
Given a discrete uniform distribution,, …, with unknown maximum, the UMVU estimator for the maximum is given by + = + where m is the sample maximum and k is the sample size, sampling without replacement. [2] [3] This problem is commonly known as the German tank problem, due to application of maximum estimation to estimates of German tank ...
The sampling theorem states that sampling frequency would have to be greater than 200 Hz. Sampling at four times that rate requires a sampling frequency of 800 Hz. This gives the anti-aliasing filter a transition band of 300 Hz ((f s /2) − B = (800 Hz/2) − 100 Hz = 300 Hz) instead of 0 Hz if the sampling frequency was 200 Hz. Achieving an ...
The initial stage of data collection depends largely on a general subject or problem area, which is based on the analyst's general perspective of the subject area. The initial decisions are not based on a preconceived theoretical framework. [2] The researcher begins by identifying some key concepts and features which they will research about.