GB/T 3358.3-2009 English PDFUS$694.00 · In stock
Delivery: <= 4 days. True-PDF full-copy in English will be manually translated and delivered via email. GB/T 3358.3-2009: Statistics -- Vocabulary and symbols -- Part 3: Design of experiments Status: Valid GB/T 3358.3: Historical versions
Basic dataStandard ID: GB/T 3358.3-2009 (GB/T3358.3-2009)Description (Translated English): Statistics -- Vocabulary and symbols -- Part 3: Design of experiments Sector / Industry: National Standard (Recommended) Classification of Chinese Standard: A41 Classification of International Standard: 03.120.30 Word Count Estimation: 35,393 Date of Issue: 2009-10-15 Date of Implementation: 2010-02-01 Older Standard (superseded by this standard): GB/T 3358.3-1993 Adopted Standard: ISO 3534-3-1999, IDT Regulation (derived from): Announcement of Newly Approved National Standards No. 11 of 2009 (No. 151 overall) Issuing agency(ies): General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, Standardization Administration of the People's Republic of China Summary: This standard specifies the experimental design and drafting other standards commonly used terms. GB/T 3358.3-2009: Statistics -- Vocabulary and symbols -- Part 3: Design of experiments---This is a DRAFT version for illustration, not a final translation. Full copy of true-PDF in English version (including equations, symbols, images, flow-chart, tables, and figures etc.) will be manually/carefully translated upon your order.. Statistics Vocabulary and symbols Part 3. Design of experiments ICS 03.120.30 A41 National Standards of People's Republic of China GB/T 3358.3-2009/ISO 3534-3.1999 Replacing GB/T 3358.3-1993 Statistics Vocabulary and symbols Part 3. Design of Experiments (ISO 3534-3.1999, IDT) Posted 2009-10-15 2010-02-01 implementation Administration of Quality Supervision, Inspection and Quarantine of People's Republic of China Standardization Administration of China released Table of ContentsPreface Ⅰ Introduction Ⅱ Range 1 1 General terms 1 2 experimental arrangement terminology 7 3 Analysis 19 terms References 24 Index 25 Pinyin index 25 English equivalents index 27 GB/T 3358.3-2009/ISO 3534-3.1999ForewordGB/T 3358 "was words and symbols" is divided into the following sections. --- Part 1. General statistical terms and terms used in probability --- Part 2. Applied Statistics --- Part 3. Experimental Design This section GB/T 3358 Part 3, identical with ISO 3534-3.1999 "Statistics Vocabulary and symbols - Part 3. experimental design". GB/T 3358 and this part ISO 3534-3. compared to 1999, revised the original error correction in the original concept of quasi-express enough Indeed part of the main changes are as follows. --- 1.10 "treatment (treatment)" definition to "a combination of different factors specified level or levels of each factor"; --- The 1.27 "repeated (replication)" definition to "the implementation of a given processing more than one experiment" and delete the note; --- 1.21 "residuals (residual)" in the definition of "(response variable) predictive value" before "based on the assumption model" is, And 1.22 to define the "residual error (residualerror)" corresponds; --- "2 k factorial experiment" in 2.1.2.1 added on by "1", "2", respectively, instead of "+", "-" indicates the level of two factor Note; --- The 2.3 "block design (blockdesign)" is defined to "all the experimental units into experimental zones set up several groups Meter "and so on. And ISO 3534-3. Compared to 1999, this section was necessary editorial changes, such as. --- To be consistent with Part 1 and Part 2, in the definition of the term adds other terms (including the entry number) references; --- To ISO 3534-3.1999 references ISO 3534-1.1993 and ISO 3534-2.1993 entries, press identical with ISO 3534- 1.2006 and ISO 3534-2. GB 2006's/T 3358.1-2009 and GB/T 3358.2-2009 appropriate content, as Appropriate changes. This Part replaces GB/T 3358.3-1993 "in statistical terms the third part of the test design terms", and GB/T 3358.3- Compared to 1993, the main changes are as follows. --- Name to "statistically Vocabulary and symbols - Part 3. Experimental Design"; --- In the text with "experimental" instead of "test" as the corresponding English word "experiment" is the preferred term, but the word "test" Reserved for the "experiment" synonymous; --- Adjust the terminology entry is set; --- Adds a number of examples and notes. This part of the application by the National Standardization Technical Committee on statistical methods and focal points. This part mainly drafted by. Academy of Mathematics and Systems Science, Peking University, China Institute of Standardization, Suzhou the University. The main drafters of this section. Feng Shi Yong, Chen Min, Shi Jian, to Emin, Dingwen Xing, Wang Renguan, where in vibration. This section was first released in 1993, this is the first revision. GB/T 3358.3-2009/ISO 3534-3.1999IntroductionExperimental design that is essentially experimental planning, in order to efficiently and economically correct and relevant conclusions. Select a specific experiment Program depends on the type of issues involved, the degree of general conclusions, and the resources (experimental materials, personnel and time) available. One After proper experimental design and implementation, often resulting in a relatively simple statistical analysis and interpretation of the results. In recent years, the application of experimental design to flourish, mainly due to recognizing experimental design to improve product and service quality Very important. Although statistical quality control, management objectives (menegementresolve), inspection and other quality tools also have this feature, in fact, Experimental design represents a selection in a complex, changing environments and interactive methods. Historically, the design of experiments in the field of agriculture Get development and prosperity, the medical field has also experienced a long history of careful experimental design. At present, the industrial environment has witnessed experimental design with To substantial benefits, because the experimental design to facilitate the work (user-friendly software), improved training, to give influential advocate for the promotion, Also accumulated a lot of success stories. Factorial experiment (see 2.1) for the experimenter provided a method of mutual interest between the Institute of multifactorial. These types of experiments, Than a simple analysis of only a factor experiment more effective. Factorial experiment is particularly suitable for determining the different levels of the other factor to take There are different factors response. In general, the quality of analysis of "breakthrough" research from the interaction (see 1.17) disclosed in internal relations. If the test Number of factors into account more, factorial experiment might take up too many resources and difficult to implement. However, factorial design (see 2.1.1) provides A possible compromise. In fact, if the initial goal is to find out what factors need further study, screening designs (see 2.2) It is more feasible. Planning an experiment, it is necessary to bias due to the experimental conditions or configuration test unit processing caused by control. Randomization (See 1.29) and partition group (see 1.28) like technology that is used to minimize annoying, the influence of external factors. Concrete partition group technology Including randomized block design (see 2.3.1), Latin square design (see 2.3.2), balanced incomplete block design (see 2.3.4.1) and the like. The experimental design was a gradual process, in order to constantly improve targeting, response surface design (see 2.4) plays a pivotal role. Through the different levels of the key factors in the study, response surface design method skillfully explains the advantages of the most close-curve effect. Mixture Design (2.5) for each factor in the overall proportion of cases, such as the alloy composition. Nested design (see 2.6) is particularly suitable The experiment was carried out across multiple labs. If the experiment exactly as implementation of the program of experimental data analysis will be direct. FIG method (see 3.1) to reveal substantially conclusion Particularly effective. According to (see 1.1 and later) model parameter estimation is often used regression analysis (see 3.3). Regression analysis can also be used Handle missing data, identify outliers, and other difficult problems arising. Excellent experimental design should. a) factors and their binding at the level of choice, describing prior knowledge and experience assumptions and other aspects; b) to process the relevant information with minimal effort; c) before the experiment to ensure that the experiment is designed to achieve the objectives of the experiment and the required precision; d) reflect the continuity of the investigation; e) clear experimental treatment arrangement and order, in order to avoid misunderstandings during the experiment. GB/T 3358.3-2009/ISO 3534-3.1999 Statistics Vocabulary and symbols Part 3. Design of Experiments range GB/T 3358 provisions of this part of the other standards in the field of experimental design and drafting of commonly used terms. General terms 1 1.1 About response variable (1.2) and predictors (1.3) assuming that the relationship between its accompanying description. Note 1. The model consists of three parts. The first part is modeled response (1.2), the second part contains or deterministic prediction system variables (1.3) model Part of the third part is a random or partially random error model, which can be described in great detail. For example, the error term can be combined into a dispersion effects (1.14), so that the variation of response with the response increasing the value increases. Example 1. a part of life and its environment conditions. Example 2. A typical model. Example 3. A common model. Example 4. Another typical model. Note 2. The above description of the model applied not only to the classical linear model with additive error, the error can also applies to a variety of distribution describes generalized linear model Type, these distributions include binomial, Poisson distribution, exponential distribution, gamma distribution and normal distribution. 1.2 A variable representing the experimental results. Note 1. A synonym is "output variable." Note 2. do not recommend the term "dependent variable" is also used as a synonym, to avoid possible and "argument" confusion (see GB/T 3358.1-2009 1.11). Note 3. From each experiment unit (1.9) when recording multiple responses, a response can be variable vector. 1.3 Variables can be used to explain the experimental results. Note 1. Common synonyms are. "input variables", "description tag" and "explanatory variables." Note 2. In a designed experiment, the degree of controllability predictors showed a potential role in which it played. Predictor variables can be controlled (solid Given) can be corrected (in the short term or in the cost of expensive controllable only) or uncontrolled (random). GB/T 3358.3-2009/ISO 3534-3.1999 ......Tips & Frequently Asked Questions:Question 1: How long will the true-PDF of GB/T 3358.3-2009_English be delivered?Answer: Upon your order, we will start to translate GB/T 3358.3-2009_English as soon as possible, and keep you informed of the progress. The lead time is typically 2 ~ 4 working days. The lengthier the document the longer the lead time.Question 2: Can I share the purchased PDF of GB/T 3358.3-2009_English with my colleagues?Answer: Yes. The purchased PDF of GB/T 3358.3-2009_English will be deemed to be sold to your employer/organization who actually pays for it, including your colleagues and your employer's intranet.Question 3: Does the price include tax/VAT?Answer: Yes. 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