D. C. Montgomery. Design and Analysis of Experiments. John Wiley & Sons, sixth edition, 2005.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....the e ort involved in preparing subject versions (in retrospect, over 80 hours per version after establishment of the initial infrastructure) we wanted to detect meaningful e ects with a minimal number of invested resources. There are several statistical approaches for determining sample size [19], they di er in terms of the information they require as input for sample size calculation. We decided to determine sample size using an approximation of the di erence that is worth detecting in the dependent variables (also known as D ) to distinguish practical dif Treat. Treat. Treat. Treat. ....

....the degrees of freedom of the error term over the operating characteristic curve, we estimated that ve observations per cell would be sucient to achieve a power greater than .80 (probability of rejecting a false null hypothesis) for a two block factorial design with two treatments, and alpha=0. 05 [19]. Hence, each cell in Table 4 has ve observations, corresponding to ve versions from each program under each treatment combination. These versions constitute random e ects that we do not control, and we consider them samples from a population of versions. 3.4 Threats to Validity In this section ....

D. C. Montgomery. Design and Analysis of Experiments. John Wiley and Sons, New York, fourth edition, 1997.

....so that the experimenter can observe and identify the reasons for changes in the output response. If the experiment is correctly conducted, valid and objective conclusions are obtained for the system. The most complete strategy when dealing with several factors is a full factorial experiment [5]. The effect of a factor is defined as the change in response produced by the change in the level of the factor. For the multiple ground bump analysis, the two input variables are number of bumps at four levels (1, 2, 3 and 4) and CPW transmission launch configuration at two levels (radial and ....

....The output variable is chosen to be S 11 at 20 GHz, and the results of the 8 simulations performed under identical conditions, except the input variable variation, are presented in Table 1. The last column shows the value of the output. These have been statistically analysed and the F statistic [5] calculated. The results of the statistical analysis are presented in Table 2, with the value of the F statistic on the last column. Variables with higher F are more statistically significant. The threshold value for statistical significance has been calculated to be 3.2 for this application [5] ....

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Douglas C. Montgomery, "Design and analysis of experiments", J. Wiley & Sons, 1996.

....by taking vibration recordings during a predetermined set of flight conditions; these constituted fourteen maneuvers. The experiments are based on a latin square design which counter balances the flight conditions to assure that gross weight and ambient temperature changes do not bias the results [4, 10]. The use of a carefully designed experiment allows for various sources of variation and their interactions to be investigated and quantified in a systematic fashion. In this experimental design, two pilots fly fourteen maneuvers each, and repeat each maneuver three times, in two different sets. ....

D.C. Montgomery. Design and Analysis of Experiments. John Wiley and Sons, New York, 1991.

....formulated orthogonal table that yields the most information based on a predetermined model with the least number of experiments simulations. Response surface coefficients and response surface equations (RSEs) are then created based on a multivariate regression analysis for each desired response [5,6,7]. A graphical representation of the RSEs are the prediction profilers. A prediction profiler shows the relative impact of the independent design variables on a given response. The prediction profiler (Figure 2) for this study helped visualize the effects of each design variable on the maximum ....

Montgomery, D.C., Design and Analysis of Experiments. 3rd Edition., John Wiley & Sons, New York, 1991.

....of N 1 simulations. They provide the logically minimal number of simulations required to estimate the effect of each of the N parameters. The Plackett Burman (PB) design [Plackett46] is a well established approach of this type. An improvement on the basic PB design is the foldover PB design [Montgomery91]. This requires 2(N 1) runs. With this experimental design, the user can determine the effect of all of the main parameters and selected interactions. PB designs exist only in sizes that are multiples of 4. Thus a foldover PB design requires 2X simulations, where X is the next multiple of four ....

D. C. Montgomery; "Design and Analysis of Experiments" (Third Edition), Wiley 1991.

....designs, such as the PB design, are recipes that vary all N parameters simultaneously over N 1 simulations. They provide the logically minimal number of simulations required to estimate the effect of each of the N parameters. An improvement on the basic PB design is the foldover PB design [19]. This requires approximately 2N simulations. With this experimental design, the user can determine the effects of all of the main parameters and selected interactions. Since PB designs exist only in sizes that are multiples of 4, the base PB design requires X simulations, where X is the next ....

D. C. Montgomery, "Design and Analysis of Experiments", Third Edition, Wiley 1991.

....method time. The accuracy of time is in milliseconds, and the time (T ) values for running each run are several minutes, giving a relative error # T 600 , which provides su#cient accuracy. 4 Choice of Experimental Design Selected experimental design is a factorial design with mixed levels [1]. The design have one 3 level factor and three 2 level factors, as shown in figure 2. The experiment is complete factorial (full resolution) There is no need to create this design with blocks. Since the simulation is stochastic (actions selected by killers and monsters) there is a need for ....

Douglas C. Montgomery. Design and Analysis of Experiments, chapter 10, pages 461--466. John Wiley & Sons, Inc., 4th edition, 1997.

....Experiment and Analysis of Variance In Module 4: Design of a Single Factor Experiment and Analysis of Variance, students are taught the skills needed to properly design a complex experiment. The dry lab exercise steps them through the process of designing a robust experiment, see Table III [6]. Analysis of variance (ANOVA) is taught so the students have the statistical tools to compare multiple levels of a variable and determine if changing the variable has a statistical impact on the outcome. In the dry lab experiment, the students apply the ANOVA when analyzing the results of ....

....process and asked to determine if a chosen variable has an impact on the outcome. Students show final mastery of the concept by utilizing ANOVA in their final report to prove whether their variable was statistically significant. TABLE III STEPS USED TOPROPERLY DESIGN ANEXPERIMENT [6] # DOE Steps 1 Develop a problem statement. 2 Determine all the variables that impact the process. Determine which are controllable. 3 Choose a factor to investigate (one that may impact the problem in step 1) Choose levels of the factor that are significant (theorized to be measurably ....

Montgomery, D.C., Design and Analysis of Experiments, John Wiley & Sons, New York, NY, 1997.

....in practice. The remainder of this section will present the details of the types of factorial designs available in Mbius. 2.4. 1 Full factorial Full factorial designs are the most complete type of factorial design, because the full range of combinations of all levels of all factors is tested [8]. To illustrate the full factorial design, consider a study to design a workstation that yields the highest performance. The workstation has three parameters that will be varied: amount of RAM, number of processors, 13 and hard drive size. Table 2.1 shows the possible levels to assign each ....

....of experiments necessary to perform a screening experimental design makes it ideal for an initial study designed to justify a more detailed examination of a factor s effect on the response. A few screening experiments performed early in a study can save valuable experimentation time later on [8]. After the workstation example has been reduced to a 2 design, it is easy to construct the design matrix in Table 2.3. A design matrix lists the factor levels for each experiment. Because each factor has only two levels, the notation and is used to represent the high and low levels, ....

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D. C. Montgomery, Design and Analysis of Experiments. Fifth Edition, New York, New York: John Wiley & Sons, 2000.

....considered and the objective is to identify those factors that have significant effects on the response variables. Screening experiments are usually performed in the early stages of a project when it is likely that many of the factors initially considered have little or no effect on the response [3]. The factors i.e. the key process variables that influence solder paste depositions considered during this experiment are print speed, print pressure, snap off distance and separation speed. The test vehicle considered for the experiment consisted of a Plastic Ball Grid Array (PBGA) Ceramic Ball ....

Montgomery, D., 1991, Design and Analysis of Experiments, McGraw Hill, New York.

....associated for those in elastically scattered molecules (photons) These 785 nm diode laser supplies light through the excitation fiber of the fiber optic probe. The scattered light from the pad under measure is transmitted to the spectrometer via the collection fibers in the fiber optic probe [4]. The spectrometer measures the amount of light that each wave number or the pixel in the sampled spectrum. Pixel means a picture element and is the small part of the image. The charge coupled device inside the spectrometer is the semiconductor material device, which detects the image (lot of ....

....and Laser Testing The following analysis has been conducted for the Ultrasound testing method. Appendix 1 shows a diagrammatic representation of the ultrasonic testing methodology conducted on the CMP pad) For the analysis of the data two methods have been adopted: the t test and the F test [4]. The first method adopted for this analysis was the t test for the difference of means. The entire sector is divided into six sectors of 60 degrees each. The angle of rotation of the pad under study is 6 degrees and hence 11 lines are considered for each sector. The output reading, which is in ....

Montgomery D.C., 2001, Design and Analysis of Experiments, Fifth Edition, John Wiley & Sons INC. (COMPARISON OF MEANS MADE ALONG AN ANTI-CLOCKWISE DIRECTION ON THE PAD) Appendix 1 Diagrammatic representation of the ultrasonic testing methodology

....one number. Efforts have been directed towards understanding Taguchi s ideas and developing more statistically efficient alternatives [1, 2, 4, 6] Several authors revealed weaknesses in two aspects of Taguchi s approach, one being the experimental format and the other his data analysis strategy [5, 8]. These authors have each suggested independently the use of single array for studying the effects of both the control and noise factors. However, the methods proposed by them are more complex and less cost effective compared to the methods proposed by Taguchi. 2. Methodology To design products ....

Montgomery, D. C., 1991, Design and Analysis of Experiments, John Wiley, New York.

....1=d is the unbiased estimator for W [j] at any value point j by lemma D.1(see appendix) Normal distribution N( 0; can capture this feature on the positive horizontal axis. In order to check the assumption that W [j] at any j is normally distributed, we conduct a normal probability plot [21]. The plotted points fall along a straight line, which con rms that the hypothesis model is appropriate. The 75 percentile of N(0; is very close to the mean of the right half when is very small. Later we will show that is actually very small. Thus we approximate from the following ....

Douglas C. Montgomery. Design and Analysis of Experiments. John Wiley & Sons INC, 5th edition, 2000.

....is to correlate network or service layer symptoms; however, this process is usually impaired by the large number of a system s layers and parameters [1, 8, 22] their interactions, and the uncertainty about their state. This paper presents a preliminary study of applying statistical techniques [16, 18] known in the engineering quality control to cope with the exponential complexity that often hampers the event correlation process. The concept of orthogonal arrays [10] is used to select a feasible number of potential failure combinations. Each combination is evaluated with respect to the ....

....for small values of n, the number of configurations becomes prohibitively large. For example, in MIL STD 188 220B [7] Intranet Layer, there may be as many as n = 16 nodes yielding up to 3 120 = 1.8e57 configurations. To cope with the above problem, we will adopt the combinatorial design paradigm [4, 6, 10, 16], which has been successfully used in applications ranging from medicine and biology, to quality engineering [18] to testing network interfaces [24, 23] and software intensive systems [3, 4, 5] 4.1 Statistical Paradigm Suppose that a system is described by parameters p 1 , p k , where ....

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D. C. Montgomery. Design and Analysis of Experiments. Wiley & Sons, New York, NY, 5th ed., 2000.

....what has more impact on performance results: the system being studied, or the methodology. Subsequent sections provide a detailed analysis of some of the results. 2. 1 Experimental Design Experimental design is a useful technique to study the effect of different factors on a system s performance [11]. The typical methodology is to first identify all the factors that may influence the system, determine the typical range of values (usually called levels ) of each factor, and then design a set of experiments that will determine the relative importance of each of these factors. After identifying ....

D. C. Montgomery, Design and Analysis of Experiments. John Wiley & Sons, 5 ed., 2001.

....Models are solved after each global variable is assigned a specific value. One such assignment forms an experiment. Experiments can be grouped together to form a study. The Mobius tool supports several study editors, the most sophisticated of which is based on a Design of Experiments approach (DOE [41]) A DOE study generates a set of experiments and then analyzes the reward variable solutions to determine how the chosen global variables affect the reward variables. Sensitivity analysis can measure Note that although these variables are called performance variables, they are generic and can ....

D. Montgomery, Design and Analysis of Experiments, John Wiley & Sons, Inc., 5th edition, 2001.

....necessary to apply segregated population balances as well [13] which bring additional complexity to the system. Accurate models describing the be havior of recombinant microorganisms are required for the organization of both experimental and literature information, for the design of experiments [14, 15] and for the control and op timization of recombinant processes [16] There are two alternative approaches in modeling of the underlying dynamics. One alternative is to directly incorporate all the relevant mea surables in the model, and the other is to construct a simplified model that relates ....

D.C. MONTGOMERY, Design and Analysis of Experiments, 3rd Ed., Wiley (1991).

....d i , d i (i = 5 9; corresponding to goals, Eqns. 19 23) Satisfy: SFC 1.030 lbm h lbf normalized [8] Thrust 990 lbf normalized [9] Mixing pressure ratio (Phot Pcold) 0.94 [10] SFC 0.96 d 1 = 1 [12] STDSFC 0. 01 d 2 = 1 [13] THRUST 1052 d 3 = 1 [14] STDTHR 29 d 4 = 1 [15] Weight 1425 lbs [16] Length 7 ft [17] Fan diameter 38.4 in [18] Thegoals: WEIGHT 1350 d 5 = 1 [19] STDWGT 0.005 d 6 = 1 [20] LENGTH 6 d 7 = 1 [21] STDLNG 0.02 d 8 = 1 [22] FANDIA 37 d 9 = 1 [23] Bounds on the control factors (Table 3) d i . d i = 0, ....

Montgomery, D.C., Design and Analysis of Experiments, Fourth Edition, New York, John Wiley & Sons, 1997.

....that the modeler wishes to calculate. A solvable model may be parameterized on a set of global variables to produce a study which is composed of a set of experiments. Current work is focusing on inferring statistics from a constrained set of experiments using a design of experiments approach [9]. Finally, the modeler must choose a technique for solving the collection of experiments, and the particular solver to use. The Mobius tool provides a number of analytical solvers that use a variety of linear algebra techniques for solving for steady state and transient measures. Alternatively it ....

D. Montgomery, Design and Analysis of Experiments, John Wiley & Sons, Inc., 5th edition, 2001.

....unit. Models are solved after each global variable is assigned a specific value. One such assignment forms an experiment. Experiments can be grouped together to form a study.Mobius supports several study editors, the most sophisticated of which is based on a Design of Experiments approach (DOE [19]) A DOE study generates a set of experiments, and then analyzes the reward variable solutions to determine how the chosen global variables a#ect the reward variables. Sensitivity analysis can measure the e#ects of all model parameters and their interactions on each solved reward variable. In ....

D. Montgomery, Design and Analysis of Experiments, John Wiley & Sons, Inc., 5th edition, 2001.

....Evaluation of Code Review Methods through Interviews and Experimentation 9 4.2.2 Difference between the review methods In this investigation the size of the reviewed code was 3.80 KLOC in review 1, and 4.11 KLOC in review 2. In order to test if there is a significant difference, a paired t test [Montgomery91] can be used to test the null hypothesis H 0 : mean(d) 0, where d i = f i1 f i2 , and f im is the number of faults KLOC found by reviewer i (1 5, 7) with review method m (1 2) It is not possible to reject H 0 with either a t test (p=0.65) or a Wilcoxon signed rank test [Siegel88] p=0.44) ....

D. Montgomery, `Design and Analysis of Experiments', John Wiley & Sons, third edition, 1991

....set to fixed values and uncontrollable factors given random values. Results from a large number of tests provide data points, which can be used to derive functional approximations. Derivation of functional 8 approximation can be done using a number of approaches, including statistical regression [8], rough sets [5] 14] and visualization [6] 9] The functions found should then be tested to verify their ability to approximate the desired quality measures. Tests could involve either simulations or preferably physical experiments for AUV s. The AUV s evaluate functions using values for ....

Montgomery D.C.: Design and Analysis of Experiments, Wiley, New York, NY.

....the effort involved in preparing subject versions (in retrospect, over 80 hours per version after establishment of the initial infrastructure) we wanted to detect meaningful effects with a minimal number of invested resources. There are several statistical approaches for determining sample size [19], they differ in terms of the information they require as input for sample size calculation. We decided to determine sample size using an approximation of the difference that is worth detecting in the dependent variables (also known as D ) to distinguish practical dif6 Treat. Treat. Treat. Treat. ....

....degrees of freedom of the error term over the operating characteristic curve, we estimated that five observations per cell would be sufficient to achieve a power greater than .80 (probability of rejecting a false null hypothesis) for a two block factorial design with two treatments, and alpha=0. 05 [19]. Hence, each cell in Table 4 has five observations, corresponding to five versions from each program under each treatment combination. These versions constitute random effects that we do not control, and we consider them samples from a population of versions. 3.4 Threats to Validity In this ....

D. C. Montgomery. Design and Analysis of Experiments. John Wiley and Sons, New York, fourth edition, 1997.

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D. C. Montgomery. Design and Analysis of Experiments. John Wiley & Sons, sixth edition, 2005.

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D. C. Montgomery. Design and Analysis of Experiments. John Wiley and Sons, New York, fourth edition, 1997.

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D. C. Montgomery, "Design and Analysis of Experiments". John Wiley & Sons, New York, 5. ed., 2001.

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D. C. Montgomery. Design and Analysis of Experiments. John Wiley & Sons, New York, NY, 5th edition, 2001.

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Douglas C. Montgomery. Design and Analysis of Experiments. John Wiley and Sons, 3 edition.

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D. C. Montgomery. Design and Analysis of Experiments. John Wiley and Sons Inc., New York, 2001.

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D. C. Montgomery. Design and Analysis of Experiments. John Wiley & Sons, Inc., New York, NY, 5th edition, 1997.

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D. C. Montgomery. Design and Analysis of Experiments. John Wiley & Sons, New York, NY, 5th edition, 2001.

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D.C. Montgomery. Design and Analysis of Experiments. J. Wiley & Sons, New York, fourth edition, 1997.

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D. C. Montgomery. Design and Analysis of Experiments. John Wiley & Sons, New York, NY, 5th edition, 2001.

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D. C. Montgomery, Design and Analysis of Experiments, 5th ed., Wiley, New York #2000#.

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D. Montgomery, "Design and Analysis of Experiments" (Third Edition), Wiley 1991.

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Douglas C. Montgomery. Design and Analysis of Experiments, chapter 10, pages 461--466. John Wiley & Sons, Inc., 4th edition, 1997.

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D. Montgomery (1991), Design and Analysis of Experiments. John Wiley & sons, Inc.

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D. Montgomery, "Design and Analysis of Experiments" (Third Edition), Wiley 1991.

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D. C. Montgomery. Design and Analysis of Experiments. John Wiley & Sons, New York, NY, 5th edition, 2001.

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Montgomery DC (1991) Design and Analysis of Experiments, Edition, John Wiley & Sons, Singapore.

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D. C. Montgomery, Design and analysis of experiments, 4th ed. New York, New York: John Wiley and Sons, 1997.

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D. C. Montgomery. Design and Analysis of Experiments. John Wiley & Sons, New York, NY, 5th edition, 2001.

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Montgomery, 1997. Design and Analysis of Experiments. Fourth Edition. Wiley.

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Douglas C.Montgomery, "Design and Analysis of Experiments," New york -- John Wiley , 3 Edition 1991.

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D. C. Montgomery, Design and Analysis of Experiments, John Wiley & Sons, New York, 1997.

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D. Montgomery, Design and Analysis of Experiments, Wiley, New York, 1976.

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D. C. Montgomery. Design and Analysis of Experiments. John Wiley, fifth edition, 2001.

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Montgomery D. (1997), Design and analysis of experiments, Wiley.

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Montgomery, D.C., Design and Analysis of Experiments, John Wiley& Sons, 1991.

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Montgomery, D.C., Design and Analysis of Experiments. John Wiley & Sons, Inc., New York, 1991.

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