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SOFA Statistics is an open-source statistical package.The name stands for Statistics Open For All.It has a graphical user interface and can connect directly to MySQL, PostgreSQL, SQLite, MS Access (map), and Microsoft SQL Server.
"A Short Preview of Free Statistical Software Packages for Teaching Statistics to Industrial Technology Majors" (PDF). Journal of Industrial Technology. 21 (2). Archived from the original (PDF) on October 25, 2005.
The free software packages also gave the same regression results as did excel. One of the main differences among the packages was how they handled missing data . With the example data sets used in the review, and for the package versions available in November 2006 when this review was conducted, two packages, MicrOsiris and Epi Info, could read ...
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.
This relationship is immediately obvious from the requirement that the score function satisfy the equation: [ ... [11]), SPSS (the gee procedure [12]), ...
Kernel regression estimates the continuous dependent variable from a limited set of data points by convolving the data points' locations with a kernel function—approximately speaking, the kernel function specifies how to "blur" the influence of the data points so that their values can be used to predict the value for nearby locations.
Here two sets of prediction equations are combined into a single estimation scheme and a single set of normal equations. One set is the set of forward-prediction equations and the other is a corresponding set of backward prediction equations, relating to the backward representation of the AR model:
The basic form of a linear predictor function () for data point i (consisting of p explanatory variables), for i = 1, ..., n, is = + + +,where , for k = 1, ..., p, is the value of the k-th explanatory variable for data point i, and , …, are the coefficients (regression coefficients, weights, etc.) indicating the relative effect of a particular explanatory variable on the outcome.