第三十五篇 Tools and techniques to help process engineers do their jobs
本帖最后由 小编H 于 2011-9-23 16:28 编辑
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能够协助工艺工程师工作的工具和技法
by Robert L. Mason and John C. Young
作者：罗伯特 L. 马森 & 约翰 C.杨
Many engineers working in processing industries often are overwhelmed by the amount of data available to them. Until recently, most industries collected only a small amount of information on their processes. Process engineers had few observations on a small number of critical variables that helped make decisions on how the process was to be operated.
很多在加工制造业工作的工程师经常会淹没在海量的收集数据中。直到最近，大多数企业只在他们的过程收集少量的信息。过程工程师只是在少量的关键变差进行取样，这样就能帮助他们确认如何介入过程。很少关注能够帮助他们确定过程该如何操作的数量非常小的关键变量。
That situation has changed. With the development of electronic data-gathering systems, such as distributed control systems, process engineers now have many observations available on a multitude of variables. They also can store these observations electronically for study and review at any time.
现在这个状况发生了改变。随着电子化数据收集系统的发展，例如分布式控制系统，过程工程师可以对大量变量进行检测更多的关注多样的变量，并且可以通过电子化的存贮方式保留数据，并随时进行研究和回顾。
This task of gathering and maintaining observations has added an extra dimension to a process engineer’s job. It has created a new role—statistical engineer—that entails being able to transform the data observations into useful process information.
检测数据的收集和保存增加了工艺工程师的工作。这样就创造了一个新的角色-统计工程师-其职责是将检测数据转化为有用的过程信息。
Laying the foundation
奠定基础
To do this, process engineers need a certain degree of proficiency in using the appropriate statistical tools for analyzing many types of data problems. These problems can range from selecting random data samples to designing statistical experiments.
要做好这项工作，过程工程师需要有一定的熟练度能够选择合适的统计工具分析不同的数据问题。这些数据问题涉及从选择随机数据样本到涉及统计实验。
Process engineers also might need to know how to construct and apply various prediction (regression) equations. These analyses usually include the application of statistical process control procedures, covering univariate and multivariate samples. In addition, there is a need in all areas of application to be able to test a statistical hypothesis.
过程工程师也可能需要知道如何创建和运用各种的预报（回归）方程式。这些分析通常包括统计过程控制程序的运用，包括单变量及多变量因子。另外，要在各个运用领域具有统计性假设验证的能力。
In short, the increase in process data has led process engineers to seek more training in statistics.
一句话，过程数据的增加要求过程工程师进行更多的统计学方面的培训。
This result immediately generates questions: "What type of statistical background is necessary to produce statistical engineers?" and "Where can such training be obtained?" It is doubtful this level of training is offered in an undergraduate engineering degree program at a local university. Most university engineering degree curriculums are already filled with required engineering courses and include limited time for external course electives, such as in statistics.
这个结论立即提出以下问题：“成为统计工程师需要怎样的统计学背景是必须的”和“哪里能够这这样的培训”。在本土地方高校的本科工程学位教程中是不提供这种层次的训练的。大多数高校的工程学位的学分已经被必修课占满了，只有一小部分时间留给选修课，比如说统计学。
Often, the educational requirements for this new role can be met by obtaining a master’s degree in applied statistics or attending statistics-oriented seminars and quality programs, such as those involving Six Sigma.
通常，对于这个新角色的培训需求会在以下情况中得以满足：获得应用统计学的硕士学位或者参加统计相关的研讨会或参加质量相关的课程，比如6Sigma。
Statistical courses recommended for someone serving as a statistical engineer can vary. A first course (for example, applied statistics I) usually contains data presentation (frequency tables, histograms and box plots), descriptive statistics (mean, median, mode, range, variance, standard deviation, quartile and quantile), basic normal probability theory and statistical hypothesis testing. Several one-sample testing procedures—such as the binomial test, normal test and the t-test—are detailed, along with confidence intervals. The appropriate tests of hypotheses for (one-sample) variance problems are also included.
为想成为统计工程师的人提供的统计学课程是多样的。第一节课（举例，统计学运用1）通常包括数据的描述（表次数表、直方图和箱型图），描述性统计量（均值，中值，众数，范围，变量，标准偏差，四分位数和分位数），基础正态分布概率理论和统计性假设测试验证。集中单一因子测试过程一些单因子验证程序-比如二次分布测试二项分布验证，正态分布验证测试和T-都进行详细描述，同时还有置信区间的解释。对（单一因子）变量问题的选择恰当的统计假设验证方法也包括在培训内容中。
A continuation of this course (for example, applied statistics II) will expand the coverage to two-sample tests of hypotheses for the mean (for example, two sample t-test and paired t-test) and the two-sample variance test (for example, the F-test The chi-square goodness of fit test through the one-way analysis of variance (ANOVA) procedure will also be covered.
接下来的课程（比如，统计运用2）会延伸展到均值的双因子假设验证（比如，双因子T验证和成对T验证）和双因子方差检验（比如，F验证）。课程还会涉及，通过单因子方差分析（ANOVA）所做的卡方匹配性验证。
An important topic covered in this second course is the correlation between two variables. Not only is the technical definition of correlation presented, but graphical techniques are also introduced to recognize when correlation exists between two variables.
在这个第二阶段的课程中，一个重要的课题是两变量间的相关性。这里不仅介绍定义相关性的技法，同时还通过图形技法来识别两个变量之间相关性的存在。
Figure 1 shows a scatterplot of the shoe size versus height of 85 male college students. In this plot, the height variable increases as the shoe size increases. In general, this second set of applied statistics courses emphasizes the appropriate application of these procedures.
图1为85位男性大学生与身高（相关性）的散点图。从图中，我们可以知道随着鞋的增加，身高也在变化增加。通常的，第二阶段统计学运用课程强调恰当的运用这些统计工具。
Figure 1
图表1
We cannot overemphasize the importance of these two foundational courses, which lay the groundwork for the development of other statistical procedures. For example, it is easy to develop Shewhart charting procedures (univariate statistical process control) by establishing confidence intervals on the population mean. Also, good coverage of the testing of hypotheses of equal means for the two-sample problem provides a natural lead-in to ANOVA procedures.
我们不能过分强调这两个基础课程的重要性，这些知识是为其他统计课程打基础的。举例来说，通过总体平均值建立置信区间来很容易要选择休哈特图分析流程（单变量统计过程控制）那么，对双因子等分散假设验证问题，则很自然的采用方差分析流程。
**Knowing ANOVA
了解方差分析**
ANOVA procedures constitute a second important area of statistical concentration for a statistics engineer. This topic is important not only in learning how to compute different ANOVA procedures (for example, one-way and two-way), but also to understand experimental design concepts, such as completely randomized design, randomized block design and factorial design.
对一个统计工程师来说，方差分析是统计学精华中的第二个重要部分。这个课题的重要性不仅仅是学习如何计算不同的ANOVA方差分析过程（比如，单因子和双因子），同时也要理解实验设计的概念。例如完全随机设计，随机区组设计及因子设计。
For example, in a block design, similar experimental units are grouped together to form a homogenous group called a statistical block. Units between blocks, however, are different. By combining several blocks in an experiment, you can improve the efficiency of the design because a source of variation—the blocks—has been accounted for in the experiment.
举个例子，在区组设计中，类似的实验单元汇总到一起形成一个同质组，被称为统计性区组。不同区组的单元无论如何都是不同的。通过在一个实验中划分不同的区组，你可以提高设计的效率，因为一系列的变差—区组—。
Consider the nine different circles in Figure 2. Using a block design, you arrange the circles into three blocks, with three similar circles within each block, shown in Figure 3.
区分图2中的9个不同的圆点。使用区组设计，我们可以将这些圆点归类在3个区组中，每个组曲有3个相似的圆点，见图3.
Figure 2
图2
Figure 3
图3
Notice the variation within the three blocks is minimal. The variation between the blocks, which is due to the heterogeneity of the blocks, can be included as a source of variation in the ANOVA.
观察注意这3个区组内的变化是极其微小的。由于区组间的差异性，区组间的变产能够当做是方差分析中变差的来源。
After learning the concept of blocking, the process engineer usually understands how to control the effect of an extraneous variable on the total variation of an experiment.
通过学习过区组的概念后，过程过程工程师通常可以了解在一个实验中如何控制一个外来变化在一个实验中对总变差的影响。
For example, by holding the temperature constant for each block, the variation of a temperature component can be removed from the total process variation. The process engineer quickly realizes this concept can be extended and that more than one variable can be blocked. This may be the first time the engineer has been exposed to the concept of changing the value of more than one variable in an experiment.
举个例子来说，通过对每个区组的温度控制，温度要素的变差就可以从总变差中移除了。过程工程师能够很快的掌握意识到这个概念可以被延伸，并了解不止一个变量可以被区分。 这也许是工程师第一次得到这样的概念，在一个试验中可以改变不止一个变量的价值。
**Multivariate techniques a must
必须具备多变量技术**
A third area of study that is as important as the first two is applied multivariate techniques.
和前两部分课程同样重要的第三部分的学习是多变量技法的运用。
The first multivariate technique is multiple regression analysis. This procedure is necessary if you are going to examine many variables that are interrelated and move together as a group.
第一个多变量技法是多重回归分析。如果你想验证关联在一起的多重变量，这个是必须的过程。
Examples include the many variables associated with certain chemical processes and the numerous variables involved with the generation of electricity. In both cases, the variables form a multivariate group—that is, an interrelated group of variables that move together and are not independent of one another.1
例子包含有关某种化学反应或者电学反应的大量变量。在这两过程中，这些变量来自形成多变量的群组族群-这个多变量族群群组指一组相关联的变量组合在一起一起变化而且相互不独立存在。
There are numerous applications of multiple regression techniques in an industrial process. Through these applications, the engineer learns how to construct the best prediction equation for the variable of interest in terms of the observations taken on the other related variables.
在工业生产过程中，多重回归分析技术得到大量的运用。通过这些运用，工程师学会如何通过其他相关变量的检测结果为某一感兴趣变量的变化趋势为感兴趣的变量构建最好的方程。
For example, you might be interested in predicting the amount of fuel used to produce a certain amount of electricity. Also, through residual analysis,2 you learn how to examine the variable of interest with the effect of another variable removed. This technique also can be extended to remove a time dependency (autocorrelation) within an individual variable. This can be an extremely valuable tool when analyzing autocorrelated data.
举例来说，你可能会感兴趣预测发一定额度的点需要燃烧多少燃料。想通过多少燃料预测可以生产多少电。同样，通过残差分析，你知道如何通过另一个变量的移除的影响来检验对感兴趣变量的影响。这个技术技法同样可以运用在移除某一不随时间变化的单一变量上（自相关过程）。当分析自相关过程数据这个是非常有用的工具。
Another important use of regression analysis is the study of total variation of a system and how an individual variable contributes to the total variation.
回归分析的另外一个重要运用是一个系统的全变量研究和单一变量是如何影响总变量的对整体变动的贡献度。
An additional benefit gained from studying multiple regression analysis is that it encourages the student to think in terms of many variables—not just one. It also reinforces the concept that more than one variable can be changed at a time. This opens the door to examine applying other useful multivariate techniques.
通过多重回归分析的研究，我们能够获得另外一个好处，它鼓励学生就多变量进行思考，而不是只是单一变量。同时，它强化了这样一个概念，不止一种变量在随着时间变化一次不止一个变量可以被改变。这些为其他别的有用的多变量技术检验运用提供了基础这为检验应用其他有用的多变量技法打开了一扇门。
One of these is principal component analysis,3 and another is discriminant analysis. Discriminant analysis is a multivariate technique that allows you to determine group membership for a particular observation. Based on a credit report, for example, it can be determined whether an individual is a good credit risk. This procedure has wide application in the process industry in making a decision as to the quality of the product.
这些技术技法中还包括主成分分析，还有辨别分析。辨别分析是一项多变量技术技法，它允许你决定对群组成员进行个特别的观测。比如说，依据一个信用报告，比如，它能确认一个个体是否有好的信用风险。这个工具被广泛运用于在生产工业制造业，来决定上被广泛运用于对产品质量做决定的时候。
Add SPC skills
增加统计过程控制技能
If a fourth statistical competency is to be added for the statistical engineer, it should be in the area of statistical process control (SPC Many companies are involved with some form of quality control either at the univariate or multivariate level.4 Applying SPC techniques can solve many day-to-day problems, reduce cost and produce a better quality product.
对于一个统计工程师，第四个需要的技能，应该是在统计过程控制的范畴了。许多公司都会运用一些SPC（统计过程控制）技法，无论是单变量还是多变量，不管在单变量还是多变量层面上，都有一些质量控制的表格运用。SPC技术可以解决许多日常性的问题，降低费用和提高产品质量水平。
A good understanding of basic applied statistical procedures, ANOVA and experimental design techniques, multiple regression analysis and other applied multivariate techniques, and SPC will provide the best statistical background for a statistical engineer.
对基础的统计学运用良好的理解，变方差分析和实验设计技术技法，多重回归分析和其他多变量技术技法运用，以及和SPC，是一个统计工程师最好的统计学技术背景。
You might think we’re trying to make statisticians out of engineers. This is not the case. Statistical engineers are engineers first, and they continue to be primarily concerned with the job of running processing units. Statistics can provide an additional set of tools to help engineers accomplish this goal.
你可能在想我们努力成为统计学家，而不是工程师。事实并非如此。统计工程师首先是工程师，他们不停持续不断的主要工作是进行过程单元处理，的对工作中的运行单元进行关心。统计技术能够给他们提供一些额外的工具，帮助他们实现目标。
你好，我是小编H。请对以下文章有任何意见或者建议，发短信息联系小编H，以便小编登记翻译者信息以及文章最终完成时的奖惩工作。感谢支持翻译组！**
本文由jmc_ld翻译 校稿：xy_persist**
能够协助工艺工程师工作的工具和技法
by Robert L. Mason and John C. Young
作者：罗伯特 L. 马森 & 约翰 C.杨
Many engineers working in processing industries often are overwhelmed by the amount of data available to them. Until recently, most industries collected only a small amount of information on their processes. Process engineers had few observations on a small number of critical variables that helped make decisions on how the process was to be operated.
很多在加工制造业工作的工程师经常会淹没在海量的收集数据中。直到最近，大多数企业只在他们的过程收集少量的信息。过程工程师只是在少量的关键变差进行取样，这样就能帮助他们确认如何介入过程。很少关注能够帮助他们确定过程该如何操作的数量非常小的关键变量。
That situation has changed. With the development of electronic data-gathering systems, such as distributed control systems, process engineers now have many observations available on a multitude of variables. They also can store these observations electronically for study and review at any time.
现在这个状况发生了改变。随着电子化数据收集系统的发展，例如分布式控制系统，过程工程师可以对大量变量进行检测更多的关注多样的变量，并且可以通过电子化的存贮方式保留数据，并随时进行研究和回顾。
This task of gathering and maintaining observations has added an extra dimension to a process engineer’s job. It has created a new role—statistical engineer—that entails being able to transform the data observations into useful process information.
检测数据的收集和保存增加了工艺工程师的工作。这样就创造了一个新的角色-统计工程师-其职责是将检测数据转化为有用的过程信息。
Laying the foundation
奠定基础
To do this, process engineers need a certain degree of proficiency in using the appropriate statistical tools for analyzing many types of data problems. These problems can range from selecting random data samples to designing statistical experiments.
要做好这项工作，过程工程师需要有一定的熟练度能够选择合适的统计工具分析不同的数据问题。这些数据问题涉及从选择随机数据样本到涉及统计实验。
Process engineers also might need to know how to construct and apply various prediction (regression) equations. These analyses usually include the application of statistical process control procedures, covering univariate and multivariate samples. In addition, there is a need in all areas of application to be able to test a statistical hypothesis.
过程工程师也可能需要知道如何创建和运用各种的预报（回归）方程式。这些分析通常包括统计过程控制程序的运用，包括单变量及多变量因子。另外，要在各个运用领域具有统计性假设验证的能力。
In short, the increase in process data has led process engineers to seek more training in statistics.
一句话，过程数据的增加要求过程工程师进行更多的统计学方面的培训。
This result immediately generates questions: "What type of statistical background is necessary to produce statistical engineers?" and "Where can such training be obtained?" It is doubtful this level of training is offered in an undergraduate engineering degree program at a local university. Most university engineering degree curriculums are already filled with required engineering courses and include limited time for external course electives, such as in statistics.
这个结论立即提出以下问题：“成为统计工程师需要怎样的统计学背景是必须的”和“哪里能够这这样的培训”。在本土地方高校的本科工程学位教程中是不提供这种层次的训练的。大多数高校的工程学位的学分已经被必修课占满了，只有一小部分时间留给选修课，比如说统计学。
Often, the educational requirements for this new role can be met by obtaining a master’s degree in applied statistics or attending statistics-oriented seminars and quality programs, such as those involving Six Sigma.
通常，对于这个新角色的培训需求会在以下情况中得以满足：获得应用统计学的硕士学位或者参加统计相关的研讨会或参加质量相关的课程，比如6Sigma。
Statistical courses recommended for someone serving as a statistical engineer can vary. A first course (for example, applied statistics I) usually contains data presentation (frequency tables, histograms and box plots), descriptive statistics (mean, median, mode, range, variance, standard deviation, quartile and quantile), basic normal probability theory and statistical hypothesis testing. Several one-sample testing procedures—such as the binomial test, normal test and the t-test—are detailed, along with confidence intervals. The appropriate tests of hypotheses for (one-sample) variance problems are also included.
为想成为统计工程师的人提供的统计学课程是多样的。第一节课（举例，统计学运用1）通常包括数据的描述（表次数表、直方图和箱型图），描述性统计量（均值，中值，众数，范围，变量，标准偏差，四分位数和分位数），基础正态分布概率理论和统计性假设测试验证。集中单一因子测试过程一些单因子验证程序-比如二次分布测试二项分布验证，正态分布验证测试和T-都进行详细描述，同时还有置信区间的解释。对（单一因子）变量问题的选择恰当的统计假设验证方法也包括在培训内容中。
A continuation of this course (for example, applied statistics II) will expand the coverage to two-sample tests of hypotheses for the mean (for example, two sample t-test and paired t-test) and the two-sample variance test (for example, the F-test The chi-square goodness of fit test through the one-way analysis of variance (ANOVA) procedure will also be covered.
接下来的课程（比如，统计运用2）会延伸展到均值的双因子假设验证（比如，双因子T验证和成对T验证）和双因子方差检验（比如，F验证）。课程还会涉及，通过单因子方差分析（ANOVA）所做的卡方匹配性验证。
An important topic covered in this second course is the correlation between two variables. Not only is the technical definition of correlation presented, but graphical techniques are also introduced to recognize when correlation exists between two variables.
在这个第二阶段的课程中，一个重要的课题是两变量间的相关性。这里不仅介绍定义相关性的技法，同时还通过图形技法来识别两个变量之间相关性的存在。
Figure 1 shows a scatterplot of the shoe size versus height of 85 male college students. In this plot, the height variable increases as the shoe size increases. In general, this second set of applied statistics courses emphasizes the appropriate application of these procedures.
图1为85位男性大学生与身高（相关性）的散点图。从图中，我们可以知道随着鞋的增加，身高也在变化增加。通常的，第二阶段统计学运用课程强调恰当的运用这些统计工具。
Figure 1
图表1
We cannot overemphasize the importance of these two foundational courses, which lay the groundwork for the development of other statistical procedures. For example, it is easy to develop Shewhart charting procedures (univariate statistical process control) by establishing confidence intervals on the population mean. Also, good coverage of the testing of hypotheses of equal means for the two-sample problem provides a natural lead-in to ANOVA procedures.
我们不能过分强调这两个基础课程的重要性，这些知识是为其他统计课程打基础的。举例来说，通过总体平均值建立置信区间来很容易要选择休哈特图分析流程（单变量统计过程控制）那么，对双因子等分散假设验证问题，则很自然的采用方差分析流程。
**Knowing ANOVA
了解方差分析**
ANOVA procedures constitute a second important area of statistical concentration for a statistics engineer. This topic is important not only in learning how to compute different ANOVA procedures (for example, one-way and two-way), but also to understand experimental design concepts, such as completely randomized design, randomized block design and factorial design.
对一个统计工程师来说，方差分析是统计学精华中的第二个重要部分。这个课题的重要性不仅仅是学习如何计算不同的ANOVA方差分析过程（比如，单因子和双因子），同时也要理解实验设计的概念。例如完全随机设计，随机区组设计及因子设计。
For example, in a block design, similar experimental units are grouped together to form a homogenous group called a statistical block. Units between blocks, however, are different. By combining several blocks in an experiment, you can improve the efficiency of the design because a source of variation—the blocks—has been accounted for in the experiment.
举个例子，在区组设计中，类似的实验单元汇总到一起形成一个同质组，被称为统计性区组。不同区组的单元无论如何都是不同的。通过在一个实验中划分不同的区组，你可以提高设计的效率，因为一系列的变差—区组—。
Consider the nine different circles in Figure 2. Using a block design, you arrange the circles into three blocks, with three similar circles within each block, shown in Figure 3.
区分图2中的9个不同的圆点。使用区组设计，我们可以将这些圆点归类在3个区组中，每个组曲有3个相似的圆点，见图3.
Figure 2
图2
Figure 3
图3
Notice the variation within the three blocks is minimal. The variation between the blocks, which is due to the heterogeneity of the blocks, can be included as a source of variation in the ANOVA.
观察注意这3个区组内的变化是极其微小的。由于区组间的差异性，区组间的变产能够当做是方差分析中变差的来源。
After learning the concept of blocking, the process engineer usually understands how to control the effect of an extraneous variable on the total variation of an experiment.
通过学习过区组的概念后，过程过程工程师通常可以了解在一个实验中如何控制一个外来变化在一个实验中对总变差的影响。
For example, by holding the temperature constant for each block, the variation of a temperature component can be removed from the total process variation. The process engineer quickly realizes this concept can be extended and that more than one variable can be blocked. This may be the first time the engineer has been exposed to the concept of changing the value of more than one variable in an experiment.
举个例子来说，通过对每个区组的温度控制，温度要素的变差就可以从总变差中移除了。过程工程师能够很快的掌握意识到这个概念可以被延伸，并了解不止一个变量可以被区分。 这也许是工程师第一次得到这样的概念，在一个试验中可以改变不止一个变量的价值。
**Multivariate techniques a must
必须具备多变量技术**
A third area of study that is as important as the first two is applied multivariate techniques.
和前两部分课程同样重要的第三部分的学习是多变量技法的运用。
The first multivariate technique is multiple regression analysis. This procedure is necessary if you are going to examine many variables that are interrelated and move together as a group.
第一个多变量技法是多重回归分析。如果你想验证关联在一起的多重变量，这个是必须的过程。
Examples include the many variables associated with certain chemical processes and the numerous variables involved with the generation of electricity. In both cases, the variables form a multivariate group—that is, an interrelated group of variables that move together and are not independent of one another.1
例子包含有关某种化学反应或者电学反应的大量变量。在这两过程中，这些变量来自形成多变量的群组族群-这个多变量族群群组指一组相关联的变量组合在一起一起变化而且相互不独立存在。
There are numerous applications of multiple regression techniques in an industrial process. Through these applications, the engineer learns how to construct the best prediction equation for the variable of interest in terms of the observations taken on the other related variables.
在工业生产过程中，多重回归分析技术得到大量的运用。通过这些运用，工程师学会如何通过其他相关变量的检测结果为某一感兴趣变量的变化趋势为感兴趣的变量构建最好的方程。
For example, you might be interested in predicting the amount of fuel used to produce a certain amount of electricity. Also, through residual analysis,2 you learn how to examine the variable of interest with the effect of another variable removed. This technique also can be extended to remove a time dependency (autocorrelation) within an individual variable. This can be an extremely valuable tool when analyzing autocorrelated data.
举例来说，你可能会感兴趣预测发一定额度的点需要燃烧多少燃料。想通过多少燃料预测可以生产多少电。同样，通过残差分析，你知道如何通过另一个变量的移除的影响来检验对感兴趣变量的影响。这个技术技法同样可以运用在移除某一不随时间变化的单一变量上（自相关过程）。当分析自相关过程数据这个是非常有用的工具。
Another important use of regression analysis is the study of total variation of a system and how an individual variable contributes to the total variation.
回归分析的另外一个重要运用是一个系统的全变量研究和单一变量是如何影响总变量的对整体变动的贡献度。
An additional benefit gained from studying multiple regression analysis is that it encourages the student to think in terms of many variables—not just one. It also reinforces the concept that more than one variable can be changed at a time. This opens the door to examine applying other useful multivariate techniques.
通过多重回归分析的研究，我们能够获得另外一个好处，它鼓励学生就多变量进行思考，而不是只是单一变量。同时，它强化了这样一个概念，不止一种变量在随着时间变化一次不止一个变量可以被改变。这些为其他别的有用的多变量技术检验运用提供了基础这为检验应用其他有用的多变量技法打开了一扇门。
One of these is principal component analysis,3 and another is discriminant analysis. Discriminant analysis is a multivariate technique that allows you to determine group membership for a particular observation. Based on a credit report, for example, it can be determined whether an individual is a good credit risk. This procedure has wide application in the process industry in making a decision as to the quality of the product.
这些技术技法中还包括主成分分析，还有辨别分析。辨别分析是一项多变量技术技法，它允许你决定对群组成员进行个特别的观测。比如说，依据一个信用报告，比如，它能确认一个个体是否有好的信用风险。这个工具被广泛运用于在生产工业制造业，来决定上被广泛运用于对产品质量做决定的时候。
Add SPC skills
增加统计过程控制技能
If a fourth statistical competency is to be added for the statistical engineer, it should be in the area of statistical process control (SPC Many companies are involved with some form of quality control either at the univariate or multivariate level.4 Applying SPC techniques can solve many day-to-day problems, reduce cost and produce a better quality product.
对于一个统计工程师，第四个需要的技能，应该是在统计过程控制的范畴了。许多公司都会运用一些SPC（统计过程控制）技法，无论是单变量还是多变量，不管在单变量还是多变量层面上，都有一些质量控制的表格运用。SPC技术可以解决许多日常性的问题，降低费用和提高产品质量水平。
A good understanding of basic applied statistical procedures, ANOVA and experimental design techniques, multiple regression analysis and other applied multivariate techniques, and SPC will provide the best statistical background for a statistical engineer.
对基础的统计学运用良好的理解，变方差分析和实验设计技术技法，多重回归分析和其他多变量技术技法运用，以及和SPC，是一个统计工程师最好的统计学技术背景。
You might think we’re trying to make statisticians out of engineers. This is not the case. Statistical engineers are engineers first, and they continue to be primarily concerned with the job of running processing units. Statistics can provide an additional set of tools to help engineers accomplish this goal.
你可能在想我们努力成为统计学家，而不是工程师。事实并非如此。统计工程师首先是工程师，他们不停持续不断的主要工作是进行过程单元处理，的对工作中的运行单元进行关心。统计技术能够给他们提供一些额外的工具，帮助他们实现目标。
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