Factor effects analysis

Pradhan, B. Lee, S. 2010. Landslide susceptibility assessment and factor effect analysis backpropaga-tion artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling and Software 25(6) 747-759. 

To understand the effect of different factors and their combinations on the overall value of resistivity, factor-effect analysis was performed for RG02-1. The results were plotted to depict the individual and interaction factor effects as detailed in following sections. 

A failure modes and effects analysis delineates components, their interaction.s ith each other, and the effects of their failures on their system. A key element of fault tree analysis is the identification of related fault events that can contribute to the top event. For a quantitative evaluation, the failure modes must be clearly defined and related to a numerical database. Component failure modes should be realistically and consistently postulated within the context of system operational requirements and environmental factors. 

An effective HE or cost-effectiveness analysis is designed to answer certain questions, such as Is the treatment effective What will it cost and How do the gains compare with the costs By combining answers to all of these questions, the technique helps decision makers weigh the factors, compare alternative treatments, and decide which treatments are most appropriate for specific situations. Typically, one chooses the option with the least cost per unit of measure gained the results are represented by the ratio of cost to effectiveness (C E). With this type of analysis, called a cost-effectiveness analysis (CEA), various disease end points that are affected by therapy (risk markers, disease severity, death) can be assessed by corresponding indexes of therapeutic outcome (mmHg blood pressure reduction, hospitalizations averted, life years saved, respectively). It is beyond the scope of this chapter to elaborate further on principles of cost-effectiveness analyses. A number of references are available for this purpose [11-13]. 

Inflation can be a significant factor in analysis of profitability. High inflation rates frequently occur in many countries. In computing the rate of return or net present value, you need to obtain a measure of profitability that is independent of the inflation rate. If you inflate projections of future annual income, the computed rate of return may largely result from the effects of inflation. Most companies strive for an internal rate of return (after taxes) of 10-20 percent in the absence of inflation ... 

The two-factor interaction effects and the dummy factor effects in FF and PB designs, respectively, are often considered negligible in robustness testing. Since the estimates for those effects are then caused by method variability and thus by experimental error, they can be used in the statistical analysis of the effects. Requirement is that enough two-factor interaction or dummy factor effects (>3) can be estimated to allow a proper error estimate (see Section VII.B.2.(b)). 

Full second-order polynomial models used with central composite experimental designs are very powerful tools for approximating the true behavior of many systems. However, the interpretation of the large number of estimated parameters in multifactor systems is not always straightforward. As an example, the parameter estimates of the coded and uncoded models in the previous section are quite different, even though the two models describe essentially the same response surface (see Equations 12.63 and 12.64). It is difficult to see this similarity by simple inspection of the two equations. Fortunately, canonical analysis is a mathematical technique that can be applied to full second-order polynomial models to reveal the essential features of the response surface and allow a simpler understanding of the factor effects and their interactions. 

Table 14.4 shows a typical regression analysis output for the 2 factorial design in Table 14.3. Most of the output is self-explanatory. For the moment, however, note the regression analysis estimates for the parameters of the model given by Equation 14.5 and compare them to the estimates obtained in Equations 14.8-14.15 above. The mean is the same in both cases, but the other non-zero parameters (the factor effects and interactions) in the regression analysis are just half the values of the classical factor effects and interaction effects How can the same data set provide two different sets of values for these effects ...

Thus, in modem research using interval and ratio scales the 5x usually shouldn t be ignored. Let s add 8x, to the calculation to obtain b, as would be done with regression analysis. Because Xj went from a coded level of -1 to a coded level of -t-1, 5x, = 2. Thus, b (the factor effect in the coded factor space) = 8y,/8xJ = -i-3.6% per 2 coded units = -i-1.8% per coded unit. The fact that 8x is equal to 2 with this system of coding is why regression analysis of coded data gives results that are smaller by Vi from the results obtained from the classical approach ... 

What is the equivalent four-parameter linear model expressing y, as a function of jci and xfl Use matrix least squares (regression analysis) to fit this linear model to the data. How are the classical factor effects and the regression factor effects related. Draw the sums of squares and degrees of freedom tree. How many degrees of freedom are there for SS, 55, and SS 7... 

What clue is there in Equation 14.7 that suggests that there will be a difference between the classical calculation of factor effects and the regression analysis calculation of factor effects ... 

A cause and effect diagram (sometimes known as the Ishikawa"" or the fishbone diagram"") represents the relationships between a given effect and its potential causes. The cause and effect analysis relates the interactions among the factors affecting a process. 

Factorial design of experiments, combined with statistical methods of data analysis, offers wider and more differentiated information on the system, while conclusions are of greater usability. The results of all the eight runs in the analyzed example serve for determining the factor effects, with seven trials being independent possibilities of testing the effects and one serving for their comparison with the chosen fixed values. Three out of seven independently determined factor effects serve for... 

Since these randomized blocks are applied to single out inequality effects of a research subject from factor effects, the variance of analysis confidence is increased as experimental error is diminished. The block denotes the part of design points where experimental error is lower than in the experiment as a whole. 

It is clear from the table of analysis of variance that the factor effect is statistically highly significant. The effect of blocks is also important, which justifies the division of experimental conditions into blocks. 

Analysis of variance shows that only factor effect X3 is significant. This means that out of four different dyestuffs one should choose the most convenient one. 

In this first case, system security is associated with preventing the accidental or intentional alteration and corruption of the data to be displayed on the screen, or be used to make a decision to control the operation. To avoid accidental or intentional loss of data, the data collected must be defined, along with the procedures used to collect it, and the means to verily its integrity, accuracy, reliability, and consistency. A failure modes-and-effects analysis (FMEA) is one of many methods used to uncover and solve these factors. For example, to avoid data corruption, an ongoing verification program (Chapter 18) should be implemented. 

After having studied the method above systematically, NRCCRM established a practical analysis procedure which could control the error factors effectively at the same time. 

When the 2f design is replicated, the statistical significance of the factor effects can be evaluated formally using the analysis of variance. However, in screening experiments with four or more factors, it is relatively common to conduct only... 

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