Applied Statistics and Probability for Engineers. Third Edition. Douglas C. Montgomery. Arizona State University. George C. Runger. Arizona State University. Introduction to engineering statistics, By Montgomery and Runger appropriate for a one-semester .. The pdf is variable and shown in Fig. Applied Statistics and Probability for Engineers, Sixth Edition by Montgomery and in probability and statistics for students in engineering and applied sciences.
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We have taken the key topics for a one-semester course from that book as the basis of this text.
As a result of this condensation and revision, this book has a modest mathematical level. Our intent is to give the student an understanding of statistical method- ology and how it may be applied in the solution of engineering problems, rather than the math- ematical theory of statistics.
Margin notes help to guide the student in this interpretation and x understanding. Throughout the book, we provide guidance on how statistical methodology is a key part of the problem-solving process. Chapter 1 introduces the role of statistics and probability in engineering problem solving. Statistical thinking and the associated methods are illustrated and contrasted with other engi- neering modeling approaches within the context of the engineering problem-solving method. Highlights of the value of statistical methodologies are discussed using simple examples.
Simple summary statistics are introduced. Chapter 2 illustrates the useful information provided by simple summary and graphical displays. Computer procedures for analyzing large data sets are given. Data analysis methods such as histograms, stem-and-leaf plots, and frequency distributions are illustrated. Using these displays to obtain insight into the behavior of the data or underlying system is emphasized. Chapter 3 introduces the concepts of a random variable and the probability distribution that describes the behavior of that random variable.
We introduce a simple 3-step procedure for struc- turing a solution to probability problems. We concentrate on the normal distribution, because of its fundamental role in the statistical tools that are frequently applied in engineering.
We have tried to avoid using sophisticated mathematics and the event—sample space orientation traditionally used to present this material to engineering students. An in-depth understanding of probability is not nec- essary to understand how to use statistics for effective engineering problem solving.
Other topics in this chapter include expected values, variances, probability plotting, and the central limit theorem.
Techniques for a single sample are in Chapter 4, and two-sample inference techniques are in Chapter 5. Our presentation is distinctly applications oriented and stresses the simple comparative-experiment nature of these procedures. We want engineering students to become interested in how these methods can be used to solve real- world problems and to learn some aspects of the concepts behind them so that they can see how to apply them in other settings.
We give a logical, heuristic development of the tech- niques, rather than a mathematically rigorous one. In this edition, we have focused more extensively on the P-value approach to hypothesis testing because it is relatively easy to un- derstand and is consistent with how modern computer software presents the concepts.
Empirical model building is introduced in Chapter 6. Both simple and multiple linear re- gression models are presented, and the use of these models as approximations to mechanistic models is discussed. Chapter 7 formally introduces the design of engineering experiments, although much of Chapters 4 and 5 was the foundation for this topic.
We emphasize the factorial design and, in particular, the case in which all of the experimental factors are at two levels. Our practical ex- perience indicates that if engineers know how to set up a factorial experiment with all factors at two levels, conduct the experiment properly, and correctly analyze the resulting data, they can successfully attack most of the engineering experiments that they will encounter in the real world.
Consequently, we have written this chapter to accomplish these objectives. We also introduce fractional factorial designs and response surface methods. Statistical quality control is introduced in Chapter 8. The important topic of Shewhart control charts is emphasized.
The X and R charts are presented, along with some simple control charting techniques for individuals and attribute data. We also discuss some aspects of estimating the capability of a process. The students should be encouraged to work problems to master the subject matter. The end-of-section exercises are intended to reinforce the concepts and techniques introduced in that section.
These exercises xii PREFACE are more structured than the end-of-chapter supplemental exercises, which generally require more formulation or conceptual thinking. We use the supplemental exercises as integrating problems to reinforce mastery of concepts as opposed to analytical technique.
The team exercises challenge the student to apply chapter methods and concepts to problems requiring data collection. As noted later, the use of statistics software in problem solution should be an integral part of the course. There is a tendency in teaching these courses to spend a great deal of time on probability and random variables and, indeed, some engineers, such as industrial and electrical engineers, do need to know more about these subjects than stu- dents in other disciplines and to emphasize the mathematically oriented aspects of the subject.
This type of course can be fun to teach and much easier on the instructor because it is almost always easier to teach theory than application, but it does not prepare the student for professional practice.
In our course taught at Arizona State University, students meet twice weekly, once in a large classroom and once in a small computer laboratory. Students are responsible for reading assignments, individual homework problems, and team projects.
In-class team activities in- clude designing experiments, generating data, and performing analyses. The supplemental problems and team exercises in this text are a good source for these activities. The intent is to provide an active learning environment with challenging problems that foster the development of skills for analysis and synthesis.
Therefore, we strongly recommend that the computer be integrated into the course. Throughout the book, we have presented output from Minitab as typical examples of what can be done with modern computer software.
In teaching, we have used Statgraphics, Minitab, Excel, and several other statistics packages or spreadsheets. We did not clutter the book with examples from many different packages because how the instructor integrates the software into the class is ultimately more important than which package is used.
All text data and the instructor manual are available in electronic form. In our large-class meeting times, we have access to computer software. In these cases, a quasi-experimental design may be used. Causal attributions[ edit ] In the pure experimental design, the independent predictor variable is manipulated by the researcher - that is - every participant of the research is chosen randomly from the population, and each participant chosen is assigned randomly to conditions of the independent variable.
Only when this is done is it possible to certify with high probability that the reason for the differences in the outcome variables are caused by the different conditions.
Therefore, researchers should choose the experimental design over other design types whenever possible. However, the nature of the independent variable does not always allow for manipulation. In those cases, researchers must be aware of not certifying about causal attribution when their design doesn't allow for it. For example, in observational designs, participants are not assigned randomly to conditions, and so if there are differences found in outcome variables between conditions, it is likely that there is something other than the differences between the conditions that causes the differences in outcomes, that is - a third variable.
The same goes for studies with correlational design. Statistical control[ edit ] It is best that a process be in reasonable statistical control prior to conducting designed experiments. When this is not possible, proper blocking, replication, and randomization allow for the careful conduct of designed experiments. Investigators should ensure that uncontrolled influences e.
A manipulation check is one example of a control check. Manipulation checks allow investigators to isolate the chief variables to strengthen support that these variables are operating as planned.
One of the most important requirements of experimental research designs is the necessity of eliminating the effects of spurious , intervening, and antecedent variables. In the most basic model, cause X leads to effect Y. But there could be a third variable Z that influences Y , and X might not be the true cause at all.
Z is said to be a spurious variable and must be controlled for. The same is true for intervening variables a variable in between the supposed cause X and the effect Y , and anteceding variables a variable prior to the supposed cause X that is the true cause.
When a third variable is involved and has not been controlled for, the relation is said to be a zero order relationship. In most practical applications of experimental research designs there are several causes X1, X2, X3. In most designs, only one of these causes is manipulated at a time.
Experimental designs after Fisher[ edit ] Some efficient designs for estimating several main effects were found independently and in near succession by Raj Chandra Bose and K. Kishen in at the Indian Statistical Institute , but remained little known until the Plackett—Burman designs were published in Biometrika in About the same time, C.
Rao introduced the concepts of orthogonal arrays as experimental designs. This concept played a central role in the development of Taguchi methods by Genichi Taguchi , which took place during his visit to Indian Statistical Institute in early s. His methods were successfully applied and adopted by Japanese and Indian industries and subsequently were also embraced by US industry albeit with some reservations. In , Gertrude Mary Cox and William Gemmell Cochran published the book Experimental Designs, which became the major reference work on the design of experiments for statisticians for years afterwards.
Developments of the theory of linear models have encompassed and surpassed the cases that concerned early writers. Today, the theory rests on advanced topics in linear algebra , algebra and combinatorics.
As with other branches of statistics, experimental design is pursued using both frequentist and Bayesian approaches: In evaluating statistical procedures like experimental designs, frequentist statistics studies the sampling distribution while Bayesian statistics updates a probability distribution on the parameter space.
Some important contributors to the field of experimental designs are C.
Peirce , R. Fisher , F. Yates , C. Rao , R. Bose , J. Srivastava , Shrikhande S. Raghavarao , W. Cochran , O. Kempthorne , W. Federer, V. Fedorov, A. Hedayat, J.