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Statistical parametric mapping (SPM) is a statistical technique for examining differences in brain activity recorded during functional neuroimaging experiments. It was created by Karl Friston . It may alternatively refer to software created by the Wellcome Department of Imaging Neuroscience at University College London to carry out such analyses.
Shock pulse method (SPM) is a technique for using signals from rotating rolling bearings as the basis for efficient condition monitoring of machines. From the innovation of the method in 1969 it has been further developed and broadened and is a worldwide accepted philosophy for condition monitoring of rolling bearings and machine maintenance.
Scanning probe lithography [1] (SPL) describes a set of nanolithographic methods to pattern material on the nanoscale using scanning probes. It is a direct-write, mask-less approach which bypasses the diffraction limit and can reach resolutions below 10 nm. [2]
A similar analysis can be carried out for any pulse shape, such as the hyperbolic secant-squared (sech 2) pulse profile generated by most ultrashort pulse lasers. If the pulse is of sufficient intensity, the spectral broadening process of SPM can balance with the temporal compression due to anomalous dispersion and reach an equilibrium state.
Some tests perform univariate analysis on a single sample with a single variable. Others compare two or more paired or unpaired samples. Unpaired samples are also called independent samples. Paired samples are also called dependent. Finally, there are some statistical tests that perform analysis of relationship between multiple variables like ...
Descriptive statistics Nonparametric statistics Quality control Survival analysis Data processing Base stat. [Note 2] Normality tests [Note 3] CTA [Note 4] Nonparametric comparison, ANOVA: Cluster analysis Discriminant analysis BDP [Note 5] Ext. [Note 6]
Control charts are graphical plots used in production control to determine whether quality and manufacturing processes are being controlled under stable conditions. (ISO 7870-1) [1] The hourly status is arranged on the graph, and the occurrence of abnormalities is judged based on the presence of data that differs from the conventional trend or deviates from the control limit line.
Functional data analysis (FDA) is a branch of statistics that analyses data providing information about curves, surfaces or anything else varying over a continuum. In its most general form, under an FDA framework, each sample element of functional data is considered to be a random function.