Bilder sagen mehr als Zahlenreihen: Wie Sie Ihre Excel Daten mit JMP graphisch analysieren können.
A picture says more than a speadsheet. See how you can visually analyze your excel data.
The Straight Way to a Final Result: Mixture Design of ExperimentsJMP software from SAS
Running experiments is an essential part of all development, improvement, upscaling and research. Very often, experiments are run following traditional legacy designs. Only one factor gets changed over a series of experiments. Single-factor experiments are not possible with mixture designs as all the components have to add up to the total.
This presentation introduces Statistical Discovery, a process that allows you to work with data to discover new, useful, insights that drive cycles of learning. After a brief overview to introduce the concept, an example involving property prices in the US will be used to demonstrate the how the process works in practice. Through this example we also exemplify the skills and aptitudes required to exercise the process successfully.
Would you like greater confidence that the models you build are genuinely useful and can drive rational decisions? This slideshow will show how to build the most useful models that fully exploit all the information in your data, simply and easily.
Join us for an upcoming live webcast to learn more about using JMP: http://www.jmp.com/uk/about/events/webcasts/
And if you'd like to try JMP, here's how: http://www.jmp.com/uk/software/try-jmp.shtml?product=jmp&ref=top
The document discusses how JMP statistical software can help ethanol producers improve quality, increase yield, and optimize experimentation. It provides examples of how JMP was used to identify a contamination source, screen for factors impacting yield, and design an efficient experiment. JMP allows users to quickly visualize, analyze, model, and report on data to speed up the time to discovery. A free trial of JMP is available for ethanol producers to learn more.
Exploring Best Practises in Design of Experiments: A Data Driven Approach to ...JMP software from SAS
Learn about best practises in the
design of experiments and a data-driven approach to DOE that increases robustness, efficiency and effectiveness. This was presented at a JMP seminar in the UK.
The Straight Way to a Final Result: Mixture Design of ExperimentsJMP software from SAS
Running experiments is an essential part of all development, improvement, upscaling and research. Very often, experiments are run following traditional legacy designs. Only one factor gets changed over a series of experiments. Single-factor experiments are not possible with mixture designs as all the components have to add up to the total.
This presentation introduces Statistical Discovery, a process that allows you to work with data to discover new, useful, insights that drive cycles of learning. After a brief overview to introduce the concept, an example involving property prices in the US will be used to demonstrate the how the process works in practice. Through this example we also exemplify the skills and aptitudes required to exercise the process successfully.
Would you like greater confidence that the models you build are genuinely useful and can drive rational decisions? This slideshow will show how to build the most useful models that fully exploit all the information in your data, simply and easily.
Join us for an upcoming live webcast to learn more about using JMP: http://www.jmp.com/uk/about/events/webcasts/
And if you'd like to try JMP, here's how: http://www.jmp.com/uk/software/try-jmp.shtml?product=jmp&ref=top
The document discusses how JMP statistical software can help ethanol producers improve quality, increase yield, and optimize experimentation. It provides examples of how JMP was used to identify a contamination source, screen for factors impacting yield, and design an efficient experiment. JMP allows users to quickly visualize, analyze, model, and report on data to speed up the time to discovery. A free trial of JMP is available for ethanol producers to learn more.
Exploring Best Practises in Design of Experiments: A Data Driven Approach to ...JMP software from SAS
Learn about best practises in the
design of experiments and a data-driven approach to DOE that increases robustness, efficiency and effectiveness. This was presented at a JMP seminar in the UK.
See how you can use statistical analysis to conduct useful and effective consumer and marketing research. These slides were used in a seminar held in the UK at The Shard. To see upcoming seminars, visit http://www.jmp.com/uk/about/events/conferences/
This document discusses best practices in design of experiments (DOE). It covers the history and principles of DOE developed by Ronald Fisher. Case studies demonstrate how definitive screening designs can identify important factors in one step when three or fewer are important, or can be augmented when more factors are important. Optimal designs allow investigation of constrained factor spaces. A holistic approach considers customer preferences in addition to technical factors.
Slides accompanying Malcolm Moore’s 2014 webcast on statistical and predictive modelling where he demonstrates JMP as an effective tool for exploratory data analysis, and JMP Pro as an expert modelling tool that scales to any number of Xs and Ys, is effective with messy data, and reduces the risk of selecting the wrong model. Watch the webcasts at http://www.jmp.com/uk/about/events/webcasts/
An overview of the basic principles of system evaluation, measurement system analysis, Gauge R&R, process monitoring and the methods for evaluating the measurement process popularized by Donald J. Wheeler. These slides accompanied Peter Bartell’s JMP webcast on Evaluating & Monitoring Your Process Using MSA & SPC. Watch the webcasts at http://www.jmp.com/mastering
Everything You Wanted to Know About Definitive Screening DesignsJMP software from SAS
An introduction to definitive screening designs (DSDs). These slides describe issues with standard screening designs and how to overcome these issues by using DSDs and orthogonally blocked DSD, first introduced by Bradley Jones of SAS and Christopher Nachtsheim of the Carlson School of Management, University of Minnesota. For information about using JMP software for design of experiments and DSDs, see http://www.jmp.com/applications/doe/
These slides provide an overview of the basics of design of experiments. They also describe and give examples of categorical and continuous factors and responses, discrete numeric and mixture variables, and blocking factors. The slides were presented live and in recorded videos as part of the Mastering JMP webcast series. Watch the webcasts at http://www.jmp.com/mastering
This presentation was given live at JMP Discovery Summit 2012 in Cary, North Carolina, USA.More information about statistical modeling is available at http://www.jmp.com/applications/statistics/
This document discusses common misconceptions about optimal experimental designs. It notes that while optimal designs are not always orthogonal, standard orthogonal textbook designs are optimal under certain models. Orthogonal designs also depend on the assumed model. The document introduces alias optimal designs as a new criterion that can reduce aliasing in optimal designs compared to traditional D-optimal designs. It provides examples of custom designs in JMP and concludes that optimal designs generally perform well across a range of models without requiring an exact pre-specified model.
Visual Analytic Approaches for the Analysis of Spontaneously Reported Adverse...JMP software from SAS
This document discusses approaches for analyzing spontaneously reported adverse events from post-market drug surveillance. It describes how clinical trials provide an incomplete safety profile and how data from post-market use can reveal rare or long-term safety issues. Statistical methods like disproportionality analysis are used to detect unexpected drug-event combinations in spontaneous reporting data by comparing the frequency of reports for a drug-event pair to what would be expected based on overall reporting rates. Stratifying the data by patient characteristics can improve the accuracy of these analyses. Signals of disproportionate reporting are defined as drug-event pairs where the confidence or credible interval for their association exceeds a threshold value.
This talk was presented live at JMP Discovery Summit 2012 in Cary, North Carolina, USA. More information about design of experiments is available at http://www.jmp.com/applications/doe/
This slide deck presents an introduction to statistical modeling by Don McCormack of JMP. Don presents at Building Better Models seminars throughout the world. Upcoming complimentary US seminars are listed here: http://jmp.com/about/events/seminars/
This presentation was given live at JMP Discovery Summit 2013 in San Antonio, Texas, USA. To sign up to attend this year's conference, visit http://jmp.com/summit
When a Linear Model Just Won't Do: Fitting Nonlinear Models in JMPJMP software from SAS
This presentation was given live at JMP Discovery Summit 2013 in San Antonio, Texas, USA. To sign up to attend this year's conference, visit http://jmp.com/summit
This document provides an overview of resampling techniques including the bootstrap, permutation tests, and parametric bootstrap. It discusses how these methods can be used to estimate variances and confidence intervals for statistics. It also covers how the bootstrap can be used for hypothesis testing and improving predictions through techniques like bagging. Examples are provided for implementing various resampling methods in JMP using JSL scripts.
This presentation was given live at JMP Discovery Summit 2013 in San Antonio, Texas, USA. To sign up to attend this year's conference, visit http://jmp.com/summit
See how you can use statistical analysis to conduct useful and effective consumer and marketing research. These slides were used in a seminar held in the UK at The Shard. To see upcoming seminars, visit http://www.jmp.com/uk/about/events/conferences/
This document discusses best practices in design of experiments (DOE). It covers the history and principles of DOE developed by Ronald Fisher. Case studies demonstrate how definitive screening designs can identify important factors in one step when three or fewer are important, or can be augmented when more factors are important. Optimal designs allow investigation of constrained factor spaces. A holistic approach considers customer preferences in addition to technical factors.
Slides accompanying Malcolm Moore’s 2014 webcast on statistical and predictive modelling where he demonstrates JMP as an effective tool for exploratory data analysis, and JMP Pro as an expert modelling tool that scales to any number of Xs and Ys, is effective with messy data, and reduces the risk of selecting the wrong model. Watch the webcasts at http://www.jmp.com/uk/about/events/webcasts/
An overview of the basic principles of system evaluation, measurement system analysis, Gauge R&R, process monitoring and the methods for evaluating the measurement process popularized by Donald J. Wheeler. These slides accompanied Peter Bartell’s JMP webcast on Evaluating & Monitoring Your Process Using MSA & SPC. Watch the webcasts at http://www.jmp.com/mastering
Everything You Wanted to Know About Definitive Screening DesignsJMP software from SAS
An introduction to definitive screening designs (DSDs). These slides describe issues with standard screening designs and how to overcome these issues by using DSDs and orthogonally blocked DSD, first introduced by Bradley Jones of SAS and Christopher Nachtsheim of the Carlson School of Management, University of Minnesota. For information about using JMP software for design of experiments and DSDs, see http://www.jmp.com/applications/doe/
These slides provide an overview of the basics of design of experiments. They also describe and give examples of categorical and continuous factors and responses, discrete numeric and mixture variables, and blocking factors. The slides were presented live and in recorded videos as part of the Mastering JMP webcast series. Watch the webcasts at http://www.jmp.com/mastering
This presentation was given live at JMP Discovery Summit 2012 in Cary, North Carolina, USA.More information about statistical modeling is available at http://www.jmp.com/applications/statistics/
This document discusses common misconceptions about optimal experimental designs. It notes that while optimal designs are not always orthogonal, standard orthogonal textbook designs are optimal under certain models. Orthogonal designs also depend on the assumed model. The document introduces alias optimal designs as a new criterion that can reduce aliasing in optimal designs compared to traditional D-optimal designs. It provides examples of custom designs in JMP and concludes that optimal designs generally perform well across a range of models without requiring an exact pre-specified model.
Visual Analytic Approaches for the Analysis of Spontaneously Reported Adverse...JMP software from SAS
This document discusses approaches for analyzing spontaneously reported adverse events from post-market drug surveillance. It describes how clinical trials provide an incomplete safety profile and how data from post-market use can reveal rare or long-term safety issues. Statistical methods like disproportionality analysis are used to detect unexpected drug-event combinations in spontaneous reporting data by comparing the frequency of reports for a drug-event pair to what would be expected based on overall reporting rates. Stratifying the data by patient characteristics can improve the accuracy of these analyses. Signals of disproportionate reporting are defined as drug-event pairs where the confidence or credible interval for their association exceeds a threshold value.
This talk was presented live at JMP Discovery Summit 2012 in Cary, North Carolina, USA. More information about design of experiments is available at http://www.jmp.com/applications/doe/
This slide deck presents an introduction to statistical modeling by Don McCormack of JMP. Don presents at Building Better Models seminars throughout the world. Upcoming complimentary US seminars are listed here: http://jmp.com/about/events/seminars/
This presentation was given live at JMP Discovery Summit 2013 in San Antonio, Texas, USA. To sign up to attend this year's conference, visit http://jmp.com/summit
When a Linear Model Just Won't Do: Fitting Nonlinear Models in JMPJMP software from SAS
This presentation was given live at JMP Discovery Summit 2013 in San Antonio, Texas, USA. To sign up to attend this year's conference, visit http://jmp.com/summit
This document provides an overview of resampling techniques including the bootstrap, permutation tests, and parametric bootstrap. It discusses how these methods can be used to estimate variances and confidence intervals for statistics. It also covers how the bootstrap can be used for hypothesis testing and improving predictions through techniques like bagging. Examples are provided for implementing various resampling methods in JMP using JSL scripts.
This presentation was given live at JMP Discovery Summit 2013 in San Antonio, Texas, USA. To sign up to attend this year's conference, visit http://jmp.com/summit