Category Archives: Statistica
Ms Jennifer Bowler Lecturer in Industrial Psychology and Human Resources at Nelson Mandela Metropolitan University, testifies on her Statistica programme provided By Statsoft Southern Africa’s trainer Merle Weberlof and how it has benefited her.
” Personally, the two days were personally extremely beneficial. I had expected a “how to work with Statistica” and what I got was how to understand the relationship between research design, analytical tools and then how to do that in Statistica. I felt very sad last night that I had not been exposed to someone like Merle much earlier on in my research career- it would have saved me many, many hours of confusion and frustration. I am however, pleased to say that my nervousness regarding Statistica has been put to rest and I have clarified many issues regarding analysis and design.
I know that Merle was worried that she lost people at certain times and I would not presume to comment for the others but each one of the group was at different stages of personal development as far as research is concerned plus we had different disciplines represented- my sense was that each person took away something of value and application even if they could not understand and/or utilise all that was offered.
Thanks very much for accommodating us for the two days.
If there is anything else that you want feedback on – please let me know.
I hope that you had a good trip to CT ”
Data comes in many formats. For use in STATISTICA, these data may need imported and possibly prepared for analysis as well. STATISTICA imports data from a variety of sources including Microsoft Excel, text files and statistical software data files. Additionally, data can be queried from a database such as Access, Oracle, SQL and more. This video shows an example import of an Excel data set that then must be rearranged to follow the structural requirements of STATISTICA.
Part 2 of this video gives a step by step example using the query tool to bring in data from a database.
Companies in the Food industry utilize STATISTICA throughout the product development, manufacturing and sensory testing processes.
Research and Development
STATISTICA provides the integrated platform for analytics empowering research and new product development within organization in the Food industry. Improvements in the time-consuming and expensive process of research and development translate directly to the organization’s bottom line. Research organizations have experienced the positive impact of the deployment of the STATISTICA Enterprise platform. STATISTICA is the multi-user, server-based analytics platform to empower scientists with analytical tools that are easy to use, relevant, and integrated with their data sources.
The STATISTICA platform results in hard and soft Return on Investment (ROI) by:
- Empowering scientists with the analytic and exploratory tools to make more sound decisions and gain greater insights from the precious data that they collect
- Saving the scientists’ time by integrating analytics in their core processes
- Saving the statisticians’ time to focus on the delivery and packaging of effective analytic tools within the STATISTICA framework
- Increasing the level of collaboration across the R&D organization by sharing study results, findings, and reports
STATISTICA provides a broad base of integrated statistical and graphical tools including:
- Tools for basic research such as Exploratory Graphical Analysis, Descriptive Statistics, t-tests, Analysis of Variance, General Linear Models, and Nonlinear Curve Fitting
- Design of Experiments (DOE), including mixture designs and response optimization
- Tools for more advanced analyses such as a variety of clustering, predictive modeling, classification and machine learning approaches including Principal Components Analysis
The STATISTICA platform meets the needs of both scientists and statisticians in your R&D organization.
STATISTICA provides a comprehensive set of tools for sensory testing. STATISTICA allows the Sensory Testing team to “break down” participant responses by group. The software allows them to perform comparisons across the responses of multiple groups. Integrated graphical analyses provide intuitive summaries of the observed differences for communication to a wider audience. STATISTICA Reports provide an effective way to summarize the data and findings from a sensory study, outputted in PDF, HTML or a Word Processor-compatible (e.g., MS Word) format.
Manufacturing / Six Sigma
STATISTICA is an integral part of the quality control and Six Sigma programs at food manufacturing organizations. STATISTICA performs real-time and offline analyses of product defects, package weights, nutritional components, and many other product attributes critical to quality.
One of the most complex as well as expensive automated manufacturing environments is that required for the manufacture of semiconductors. The typical process involves the nearly fully automated application of hundreds of processing steps to lots (“stacks”) of silicon wafers, each containing a large number of microchips. Creating a high-yield process, where most (e.g., 90% or more) of all chips pass final acceptance testing is extremely difficult and time consuming. At the same time, the cost of failure in this environment is significant, as each wafer can be many times more valuable than even the most precious metal by weight. Moreover, unexpectedly lengthy ramp-up times (to create a reliable production process) may significantly undercut the commercial value of the final product, hence jeopardizing the huge investment in the semiconductor Fab, which may well reach $2 Billion dollars or more!
STATISTICA and the Engineering Process
The STATISTICA system provides a huge set of tools for engineers, to study processes. First, the STATISTICA system will quickly and seamlessly integrate into the existing information infrastructure, querying directly the relevant databases (practically all industry standard database formats are supported). There is no need to laboriously import the data into, for example, a limited spreadsheet format for further analyses; instead STATISTICA connects directly to your data.
Next, STATISTICA interactive graphics are extremely fast and flexible, so meaningful views and graphical summaries of key processes, variables, measurements, outcomes, etc. can be created very quickly.
Complete Customizability and Programmability
Each process is unique, and the techniques for automated manufacturing are constantly evolving in this highly competitive environment. STATISTICA is fully customizable and programmable, down to all aspects of graphs, data handling, and so on. Hence, in addition to providing an extremely sophisticated and flexible off-the-shelf tool, the system also serves as a toolbox that will enable engineers to develop custom analyses and processes quickly, to support the critical ramp-up of new manufacturing processes, and the specialized analytic tools to support them.
Advanced Data Mining and Predictive Quality Control
STATISTICA Data Miner provides an extremely comprehensive set of knowledge discovery algorithms that can be applied to support the manufacturing process. In addition to commonly used advanced neural network architectures, STATISTICA Data Miner implements the most cutting edge tools in a single integrated platform. For example, the system includes algorithms such as stochastic gradient boosting, random forests, support vector machines, multivariate adaptive regression splines (MARSplines), independent components analysis, to name a few. These techniques can be used to build robust and reliable predictive models for quality or failure, even in high-dimensional environments with large numbers of variables (but few “cases” or “rows”) and significant interactions between them (e.g., interactions between tools). All of these methods are implemented in the same efficient and programmable STATISTICA platform, yielding the most advanced set of tools for tackling difficult root-cause analysis and predictive QC problems.
STATISTICA Data Miner and KLA-Tencor
STATISTICA Data Miner and other statistical analysis algorithms are used to provide the core support in KLA-Tencor’s yield analysis and management systems. Because StatSoft is the recognized leader in the application of advanced, cutting-edge data mining techniques, KLA-Tencor has chosen STATISTICA and StatSoft as the partner, to provide critical advanced data analysis and data mining support for dedicated yield management solutions for the semiconductor industries. Indeed, the complete programmability and customizability of the STATISTICA system make it the ideal toolkit for these types of custom solution systems.
The only Internet Resource about Statistics Recommended by Encyclopedia Britannica
StatSoft has freely provided the Electronic Statistics Textbook as a public service for more than 17 years now.
This Textbook offers training in the understanding and application of statistics. The material was developed at the StatSoft R&D department based on many years of teaching undergraduate and graduate statistics courses and covers a wide variety of applications, including laboratory research (biomedical, agricultural, etc.), business statistics, credit scoring, forecasting, social science statistics and survey research, data mining, engineering and quality control applications, and many others.
The Electronic Textbook begins with an overview of the relevant elementary (pivotal) concepts and continues with a more in depth exploration of specific areas of statistics, organized by “modules” and accessible by buttons, representing classes of analytic techniques. A glossary of statistical terms and a list of references for further study are included.
(Electronic Version): StatSoft, Inc. (2011). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/.
(Printed Version): Hill, T. & Lewicki, P. (2007). STATISTICS: Methods and Applications. StatSoft, Tulsa, OK.
Overview of Elementary Concepts in Statistics. In this introduction, we will briefly discuss those elementary statistical concepts that provide the necessary foundations for more specialized expertise in any area of statistical data analysis. The selected topics illustrate the basic assumptions of most statistical methods and/or have been demonstrated in research to be necessary components of one’s general understanding of the “quantitative nature” of reality (Nisbett, et al., 1987). Because of space limitations, we will focus mostly on the functional aspects of the concepts discussed and the presentation will be very short. Further information on each of those concepts can be found in the Introductory Overview and Examples sections of this manual and in statistical textbooks. Recommended introductory textbooks are: Kachigan (1986), and Runyon and Haber (1976); for a more advanced discussion of elementary theory and assumptions of statistics, see the classic books by Hays (1988), and Kendall and Stuart (1979).