These two analyses both produce Breakdown tables. If the data is well balanced, there is no difference between the two. However, when the combination of your categories is not well balanced, the Breakdown; non-factorial table analysis will produce a much denser table by eliminating the empty cells.
Yes. Select Batch (ByGroup) Analysis from the Statistics menu or from the Graphs menu to display the ByGroup Statistics Browser or ByGroup Graph Browser, respectively, which contains all of the available analyses and graphs.
A Gage Linearity and Bias Study answers the questions "How biased is my gage when compared to a master value?" and "Does the accuracy of my gage change when the size of the parts being measured changes?" These are called Bias and Linearity, respectively.
Yes. STATISTICA Data Miner Recipes (DMR) is an easy step-by-step data mining guide with a wizard-like user interface. Novice data miners can quickly clean and analyze data, while advanced users can work more efficiently and have one more option to automate routine tasks. DMR explores the data and makes default decisions for you. You can easily modify these defaults as needed and save them for repeated use.
Yes. STATISTICA Data Miner provides convenient and effective ways of saving your existing models for later deployment and use. Predictive Model Markup Language (PMML) is particularly fast to execute.
STATISTICA Sequence, Association, and Link Analysis (SAL) can be applied to any data set that contains market-basket type data. The market-basket problem assumes there are many products that can be purchased by the customer. Such products can be, for example, supermarket items, different insurance plans, etc. Customers fill their basket with only a fraction of the available items. STATISTICA SAL can use this information to predict what customers will purchase and, hence, help you to boost your sales and meet the supply and demand in your business.
Try the STATISTICA Distributions & Simulation module. It has standard (normal, half-normal, log-normal, Weibull) and specialized (Johnson, Gaussian Mixture, Generalized Pareto, Generalized Extreme Value) distributions. STATISTICA automatically ranks the quality of the fit for each selected distribution and variable.
In addition, the distributions fit to the list of selected variables and the covariance between the selected variables can be saved for deployment. The Distributions & Simulation module uses this deployment information to generate simulated data sets that not only faithfully reproduce the respective distributions, but also the covariances between variables. In short, in addition to facilitating efficient distribution fitting to large numbers of variables, this module enables users to fit general multivariate distributions and simulate from those distributions using simulation techniques such as Latin-Hypercube.
Yes. The STATISTICA General Optimization module enables you to optimize arbitrary functions of virtually any complexity, using Simplex, Genetic Algorithm, or Grid-Search methods. This module finds the best parameters that control specific processes to achieve optimal results according to user-specified criteria. The function to be optimized can be specified in a simple STATISTICA Visual Basic (SVB) function or a set of formulas. This module can repeatedly invoke other STATISTICA (or R-language) functions in an efficient manner.