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Funnel plots, introduced by Light and Pillemer in 1984 [1] and discussed in detail by Matthias Egger and colleagues, [2] [3] are useful adjuncts to meta-analyses. A funnel plot is a scatterplot of treatment effect against a measure of study precision.
Outside of such a specialized audience, the test output as shown below is rather challenging to interpret. Tukey's Range Test results for five West Coast cities rainfall data The Tukey's range test uncovered that San Francisco & Spokane did not have statistically different rainfall mean (at the alpha = 0.05 level) with a p-value of 0.08.
In applied statistics, a partial regression plot attempts to show the effect of adding another variable to a model that already has one or more independent variables. . Partial regression plots are also referred to as added variable plots, adjusted variable plots, and individual coefficient
In 1997, Egger published a paper describing a method for detecting bias in meta-analyses by analyzing funnel plots. [4] This paper has been cited more than 38,600 times on Google Scholar as of May 2022. [5] In 2005, Egger published a study comparing 110 trials of homeopathy with 110 trials of conventional medicine in the Lancet.
Previously when assessing a dataset before running a linear regression, the possibility of outliers would be assessed using histograms and scatterplots.
The generalized additive model for location, scale and shape (GAMLSS) is a semiparametric regression model in which a parametric statistical distribution is assumed for the response (target) variable but the parameters of this distribution can vary according to explanatory variables.
IBM SPSS Modeler is a data mining and text analytics software application from IBM. It is used to build predictive models and conduct other analytic tasks. It has a visual interface which allows users to leverage statistical and data mining algorithms without programming.
Suppose there are m regression equations = +, =, …,. Here i represents the equation number, r = 1, …, R is the individual observation, and we are taking the transpose of the column vector.