By Jose G. Ramirez Ph.D., Brenda S. Ramirez M.S.
In response to real-world functions, interpreting and reading non-stop information utilizing JMP: A step by step consultant, through Jose Ramirez, Ph.D., and Brenda S. Ramirez, M.S., combines statistical directions with a robust and renowned software program platform to resolve universal difficulties in engineering and technological know-how. within the many case reviews supplied, the authors sincerely organize the matter, clarify how the knowledge have been gathered, express the research utilizing JMP, interpret the output in a straightforward approach, after which draw conclusions and make innovations. This step by step layout permits clients new to stats or JMP to profit as they move, however the e-book can also be invaluable to these with a few familiarity with facts and JMP. The publication features a foreword written via Professor Douglas C. Montgomery.
Read or Download Analyzing and Interpreting Continuous Data Using JMP:: A Step-by-Step Guide PDF
Similar mathematical & statistical books
Beginners to the realm of chance face numerous power hindrances. they generally fight with key concepts-sample area, random variable, distribution, and expectation; they need to frequently confront integration, now and again mastered in calculus periods; and so they needs to exertions over long, bulky calculations.
“We reside within the age of information. within the previous couple of years, the technique of extracting insights from facts or "data technology" has emerged as a self-discipline in its personal correct. The R programming language has turn into one-stop answer for all sorts of knowledge research. The turning out to be acclaim for R is due its statistical roots and an enormous open resource package deal library.
This booklet presents accomplished assurance of the sector of outlier research from a working laptop or computer technological know-how standpoint. It integrates tools from information mining, desktop studying, and facts in the computational framework and hence appeals to a number of groups. The chapters of this booklet should be geared up into 3 categories:Basic algorithms: Chapters 1 via 7 speak about the elemental algorithms for outlier research, together with probabilistic and statistical equipment, linear tools, proximity-based tools, high-dimensional (subspace) tools, ensemble tools, and supervised equipment.
- Excel 2010 for Business Statistics: A Guide to Solving Practical Business Problems
- Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
- SAS macro programming made easy
- Modern Multidimensional Scaling: Theory and Applications
- R for Data Science
Extra resources for Analyzing and Interpreting Continuous Data Using JMP:: A Step-by-Step Guide
1 Why Statistics? 2 Which Scale? 4 What Does Confidence Level Mean? 1 Why Statistics? The twentieth century witnessed a revolution in science and engineering due to the widespread use of “statistical models of reality” (D. Salsburg, 2003) to predict and quantify the uncertainty in random events, and due to the recognition of statistical thinking as a philosophy of learning and action. At the core of statistical thinking is the fact that variation exists in all processes, and that understanding and reducing this variation is crucial, not only in science and engineering, but in other disciplines as well.
This was done to draw your attention to key concepts that are important when applying statistics to engineering and science problems. The first statistical convention is the use of statistics notes. These are numbered according to the chapter they are in, and their order of appearance. Statistics notes appear throughout a chapter to emphasize main points and things worth remembering. 2) from Chapter 4, “Comparing the Measured Performance of a Material, Process, or Product to a Standard,” summarizes the three main assumptions that are required in many statistical analyses.
Something as simple as a lot number in a production line, which is “obviously” nominal, is normally assigned sequentially and can, implicitly or explicitly Chapter 2: Overview of Statistical Concepts and Ideas 31 depending on what is recorded, contain a time sequence for analysis not deemed appropriate for a nominal scale. Or consider the common practice of using the average of numbers in an ordinal scale; although this is “not permitted” by the classification it nevertheless can lead to meaningful results, and useful decisions.