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Locally weighted regression is the method of regression analysis in which the polynomials of degree one (linear) or two (quadratic) are used to approximate regression function in particular 'neighbourhoods' of the space of explanatory variables. It is many times useful for smoothing scatter diagrams to allow any structure to be seen more clearly and for identifying the possible non-linear relationships between the response and the explanatory variables. A robust estimation procedure (which is usually known as loess) is taken in use to guard against deviant points distorting the smoothed points. Essentially the procedure involves an adaptation of the iteratively reweighted least squares. The example shown in the figure illustrates the situation in which the locally weighted regression differs considerably from the linear failure of y on x as fitted by least squares estimation.
Compliance : The extent to which the participants in a clinical trial follow trial protocol, for instance, following both the intervention regimen and trial procedures (clinical vi
Case-control study : The traditional case-control study is the common research design in the epidemiology where the exposures to risk factors for cases (individuals getting the dis
Growth curve analysis is t he general term for methods dealing with development of the individuals over time. A classic instance includes recordings made on a group of children, sa
Multilevel models are the regression models for the multilevel or clustered data where units i are nested in the clusters j, for example a cross-sectional study where students are
Raking adjustments is an alternative to the post stratification adjustments in the complex surveys which ensures that the adjusted weights of the respondents conform to each of th
regression line drawn as Y=C+1075x, when x was 2, and y was 239, given that y intercept was 11. calculate the residual
Cartogram : It is the diagram in which descriptive statistical information is displayed on the geographical map by the means of shading, different symbols or in some other possibly
Quantile regression is an extension of the classical least squares from estimation of the conditional mean models to the estimation of the variety of models for many conditional q
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Kalman filter : A recursive procedure which gives an estimate of the signal when only the 'noisy signal' can be observed. The estimate is efficiently constructed by putting the exp
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