G. E. P. Box, W. G. Hunter, and J. S. Hunter. Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. Series in Probability and Mathematical Statistics. Wiley, New York, NY, USA, edition, 1978.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....2 as organized by the skeptic, after repeating 32 additional experiments, one for each equivalence class instance. The choice of 32 is not arbitrary; it is well known that the di#erence between a t distribution and a normal distribution becomes negligible when the number of samples exceeds 30 [29]. We report solutions for the class PC in terms of two cost functions (costIDs) time to solve and number of implications. As we show later, these costs are highly correlated for all solvers we tested. For each cost, we report results in five columns: initV, minV, meanV, maxV, max min. The ....

....number of backtracks, etc. results listed in the stat file currently include: initial value (associated with the reference formula) median value, mean value, standard deviation, minimum value, maximum value, the max min ratio, 95 confidence interval of the mean (based on t statistics [29]) and the coe#cient of variation. The computation of max min ratio always includes not only the minimum and maximum values generated from class instances but also the initial value associated with the reference formula. solverSum, a program that reads, from a designated location in the ....

G. E. P. Box, W. G. Hunter, and J. S. Hunter. Statistics for experimenters: An Introduction to Design, Data Analysis, and Model Building. John Wiley & Sons, 1978.

....use memory. 5. MRS: same as MRB, but uses only semantic features. 6. MRP: same as MRB, but uses only pictorial features. Six first time PicHunter users, naive as to the experimental purposes, participated in this study. They ran the ex periment in a 6 users x 6 versions Latin square design [33]. Each user went through 15 target searches, terminating the search under the target testing paradigm; all searches terminated successfully. The results of these experiments are shown in Table I. The first row has the average number V of 9 image displays visited before convergence to the target; ....

....V D. All 7 versions were run with the same set of 15 target images, which was different from the set of 15 images of the experiments of section V D. 7 users, who were naive as to the purposes of the experiment and had never used PicHunter before, participated in the 7 x 7 Latin square design [33]. The re sults are shown in Table V, which uses the same notation as that of Tables I and II. The same two experienced users who participated in all the previous experiments also ran a subset of the experiments. The user model in the new version of PicHunter (the results of which are shown in ....

G. E. P. Box, W. G. Hunter, and J. S. Hunter, Statistics for Experimenters: An Introduction to Design, Analysis, and Model Building, J. Wiley and Sons, New York, N.Y., 1978.

....randomized to negate any uncontrollable bias that could confound further our estimates of effects. The following section shows the results of the DOE but does not go deep into the computation of the effects. Further information about design of experiments can be found in books by Wu [2] and Box [3]. Test No. A B C D E Y 1 Y 2 Y AVE 1 1 1 1 1 1 y1 1 y1 2 y1 av 2 1 1 1 1 1 y2 1 y2 2 y2 av 3 1 1 1 1 1 y3 1 y3 2 y3 av 4 1 1 1 1 1 y4 1 y4 2 y4 av 5 1 1 1 1 1 y5 1 y5 2 y5 av 6 1 1 1 1 1 y6 1 y6 2 y6 av 7 1 1 1 1 1 y7 1 y7 2 y7 av 8 1 1 1 1 1 y8 1 y8 2 y8 av 9 1 1 1 1 1 y9 1 y9 2 y9 av ....

Box, G., Hunter, W., Hunter, J., Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building, John Wiley & Sons, Inc., 1978.

....to zero. Techniques from design of experiments can assist us to achieve this goal. 3. 3 Design of experiments The theory of Design Of Experiments (DOE) provides a mathematical basis to select an optimal sample set that allows an uncorrelated estimation of the fit parameters, given a fit template [30]. The number of sampling schemes described in literature is vast: starting from full and fractional factorial design, over Placket Burman and Taguchi schemes, to Latin hypercube and even random design. A sampling scheme that allows an uncorrelated estimation of the coefficients of (11) are ....

G. E. P. Box, W. G. Hunter, and J. S. Hunter, Statistics for experimenters: an introduction to design, analysis and model building.New York: John Wiley & Sons, 1978.

....is identical (see Fig. 1) we will briefly treat it in advance. Fig. 3 illustrates how the set of performance samples is composed. The input samples X k are generated using techniques from Design of Experiments in order to optimize the effectiveness of the sample set in the fitting process [7]. This input vector controls the values of (operating point) device currents and voltages. The Operating Point Driven Solver transforms these values into the corresponding transistor geometries and bias currents and voltages. Together with the analysis cards needed for the performance extraction, ....

George E. P. Box, W. G. Hunter, and J. S. Hunter, Statistics for experimenters: an introduction to design, analysis and model building, John Wiley & Sons, New York, 1978.

....results. 2.2 Basic Terminology Before discussing how experiments are designed, it is necessary to introduce some DOE vocabulary. For a system model, there are model parameters that can be varied. These model parameters are called factors. The values assigned to factors are called levels [7]. For example, in a computer system, the factor cache size can be assigned levels of 32 Mbytes or 64 Mbytes. Here, cache size is assigned numerical values and hence is called a numerical (or quantitative) factor. The factor cache replacement policy can be assigned levels of random or ....

....experimenter has excellent knowledge of the system under study. Often, the system is complex and its behavior is difficult to predict. The best guess method may require many experiments before an acceptable result is achieved. It is also difficult to determine if the acceptable result is optimal [7]. Another popular experimentation strategy is the one factor at a time approach, in which one factor is varied while all other factors remain fixed at a nominal level. The nominal levels can be any values that the experimenter wishes to use as a baseline for comparisons. After each factor has ....

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G. E. P. Box, W. G. Hunter, and J. S. Hunter, Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. New York, New York: John Wiley & Sons, 1978.

....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 ....

G. E. P. BOX, W. G. HUNTER, and S. HUNTER, Statistics for Experimenters: An Introduction to Design, Data Analysis and Model Building, Wiley (1978).

....and other experimental design projects are open to registered participants from anywhere. Keywords: collaborative computing, Internet, experimen tal de sign, algorithms. 1 INTRODUCTION Experimental design is a well defined discipline in agricul ture, biomedical research, and manufacturing [1]. This is F. Brglez, H. Lavana, D. Ghosh, and R. Kurve have been supported by contracts from the DARPA ARO (P 3316 EL DAAH04 94 G2080 and DAAG55 97 1 0345) and a grant from Semiconductor Re search Corporation. Permission to make digital hard copy of all or part of this work for personal or ....

.... arrangements of the 2 layer graph is 522, according to the formula given in [17] Indeed, evaluating the 64 instances in the class returns a nearly normal distribution of crossing numbers with a sample average of 531.8 and a standard deviation of 45.6, giving rise to 95 confidence interval [1] for the true mean: 520.4, 543.1] The expected crossing number of 522, predicted by the formula, is clearly in this interval. The behavior of the two algorithms, denoted as Treatment 12 (implementing the algorithm dot as described in [18] and Treatment 17 (implementing the algorithm GBFS BC ....

G.E.P. Box, W. G. Hunter, and J. S. Hunter. Statistics for experimenters: An Introduction to Design, Data Analysis, and Model Building. John Wiley Sons, 1978.

....output variable to the levels of a number of predictors, or input variables, that affect it. 16] RSM has been a successful technique for efficiently building and optimizing empirical models of continuous functions since the 1950 s in chemical and mechanical engineering, chemistry, and agriculture [17]. The use of RSM provides significant insight to a previously unknown or complicated response behavior in an efficient manner. RSM approximates the dependency of output metrics to input parameters with an empirical polynomial relationship. In general, the approximation is a second order Taylor ....

Box, G.E.P., Hunter, W.G., Hunter, J.S., Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building, Wiley & Sons, New York, 1978.

....number of implications, number ofbacktracks, etc. results listed in the stat file currently include: initial value (associated with the reference formula) median value, mean value, standard deviation, minimum value, maximum value, 95 confidence interval of the mean (based on the t statistics [32]) the standard coe#cient of variation, and the max min ratio. The computation of max min ratio always includes not only the minimum and maximum values generated from class instances but also the initial value associated with the reference formula. solverSum, a program that reads, from a ....

G. E. P. Box, W. G. Hunter, and J. S. Hunter. Statistics for experimenters: An Introduction to Design, Data Analysis, and Model Building. John Wiley & Sons, 1978.

.... instantaneous signal to noise ratio, # c [m] of each coded bit is # distributed with 2 degrees of freedom, f # c (# c ) 1 e # c # c (12) the equivalent instantaneous signal to noise ratio, #[m] of d combined coded bits will also be # distributed, but with 2d degrees of freedom [3], f # (#) 1 (d 1) # d 1 e # # (13) The pair wise error probability can now be expressed as in equation (3) and is given by P 2 (d) Note that equations (7) 14) are exact expressions. Except for the obvious over bounding of the BER, made by applying the union bound in ....

G.E.P. Box, W.G. Hunter, and J.S. Hunter, Statistics for Experimenters: An Introduction to Design, Data Analysis and Model Building, John Wiley & Sons, 1978.

.... instantaneous signal to noise ratio, # c [m] of each coded bit is # distributed with 2 degrees of freedom, f # c (# c ) 1 c # c (11) the equivalent instantaneous signal to noise ratio, #[m] of d combined coded bits will also be # distributed, but with 2d degrees of freedom [3], f # (#) 1 (d 1) # d 1 e # # (12) The pair wise error probability can now be expressed as in equation (2) and is given by P 2 (d) Note that equations (6) 13) are exact expressions. Except for the obvious over bounding of the BER, made by applying the union bound in ....

G.E.P. Box, W.G. Hunter, and J.S. Hunter, Statistics for Experimenters: An Introduction to Design, Data Analysis and Model Building, John Wiley & Sons, 1978.

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G. E. P. Box, W. G. Hunter, and J. S. Hunter. Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. Series in Probability and Mathematical Statistics. Wiley, New York, NY, USA, edition, 1978.

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G. E. P. Box, W. G. Hunter, and J. S. Hunter. Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. Wiley, New York, first edition, 1978.

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G. E. P. Box, W. G. Hunter, and J. S. Hunter. Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. John Wiley & Sons, 1978.

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G. Box, W. Hunter, and S. Hunter, Statistics for Experimenters: an Introduction to Design, Data Analysis, and Model Building, 1st ed., John Wiley & Sons, New York, NY, 1978.

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G.E.P Box, W.G. Hunter, and J.S. Hunter. Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. John Wiley and Sons, New York, 1978.

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Box GEP, Hunter JS, Hunter WG. Statistics for Experimenters: An Introduction to Design, Data Analysis and Model Building. Wiley: New York, 1978.

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Box, G.E., Hunter, J.S., and Hunter, W.G., Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. (John Wiley & Sons, 1978).

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G. E. P. Box, W. G. Hunter., and J. S. Hunter, Statistics for experimenters: an introduction to design, analysis, and model building. John Wiley & Sons, New York, 1978.

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G. E. P. Box, W. G. Hunter, and J. S. Hunter. Statistics for experimenters: an introduction to design, analysis and model building. John Wiley & Sons, New York, 1978.

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Box, G E P, Hunter, W G & Hunter, J S, 1978, Statistics for Experimenters: An Introduction to Design, Data Analysis and Model Building, Wiley Series in Probability and Mathematical Statistics.

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G.E.P. Box, W.G. Hunter, and J.S. Hunter. Statistics for Experimenters: An Introduction to Design, Data Analysis and Model Building. Wiley-Interscience, 1978.

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G. E. P. Box, W. G. Hunter, and J. S. Hunter. Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. John Wiley & Sons, 1978.

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George E. P. Box, William G. Hunter, and J. Stuart Hunter. Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. John Wiley & Sons, 1978.

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