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The program uses a wizard based interface which asks the user questions about the project and its data. After a test is run, the user receives a detailed report that interprets the results. If installed with SigmaPlot, SigmaStat integrated with SigmaPlot and SigmaPlot gained advanced statistical analysis capabilities from version 11. SigmaStat ...
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.
The logrank test statistic compares estimates of the hazard functions of the two groups at each observed event time. It is constructed by computing the observed and expected number of events in one of the groups at each observed event time and then adding these to obtain an overall summary across all-time points where there is an event.
A plot of the Kaplan–Meier estimator is a series of declining horizontal steps which, with a large enough sample size, approaches the true survival function for that population. The value of the survival function between successive distinct sampled observations ("clicks") is assumed to be constant.
An alternative to building a single survival tree is to build many survival trees, where each tree is constructed using a sample of the data, and average the trees to predict survival. [7] This is the method underlying the survival random forest models. Survival random forest analysis is available in the R package "randomForestSRC". [10]
Epi Info has been in development for over 20 years. The first version, Epi Info 1, was originally developed by Jeff Dean while he was in high school. [3] [4] It was an MS-DOS batch file on 5.25" floppy disks and released in 1985. [5]
The original SPSS manual (Nie, Bent & Hull, 1970) [11] has been described as one of "sociology's most influential books" for allowing ordinary researchers to do their own statistical analysis. [12] In addition to statistical analysis, data management (case selection, file reshaping and creating derived data) and data documentation (a metadata ...
This approach to survival data is called application of the Cox proportional hazards model, [2] sometimes abbreviated to Cox model or to proportional hazards model. [3] However, Cox also noted that biological interpretation of the proportional hazards assumption can be quite tricky. [4] [5]