Statistical method, factors, influence

PCA [12, 16] is a multivariate statistics method frequently applied for the analysis of data tables obtained from environmental monitoring studies. It starts from the hypothesis that in the group of original data, there is a set of reduced factors or dominant components (sources of variation) which influence the observed data variance in an important way, and that these factors or components cannot be directly measured (they are hidden factors), since no specific sensors exist for them or, in other words, they cannot be experimentally observed. 

What does optimization mean in an analytical chemical laboratory The analyst can optimize responses such as the result of analysis of a standard against its certified value, precision, detection limit, throughput of the analysis, consumption of reagents, time spent by personnel, and overall cost. The factors that influence these potential responses are not always easy to define, and all these factors might not be amenable to the statistical methods described here. However, for precision, the sensitivity of the calibration relation, for example (slope of the calibration curve), would be an obvious candidate, as would the number of replicate measurements needed to achieve a target confidence interval. More examples of factors that have been optimized are given later in this chapter. 

If sorption is 50% efficient but desorption is 100% efficient, the recovery measured is 50%> and it is impossible to know whether sorption or desorption was inefficient or if reduced recovery was produced by a combination of both. Therefore, method development requires either optimizing sorption while controlling desorption, or vice versa using an iterative approach [67,72], Alternatively, a statistical factorial design can be used to determine and optimize quickly variables important to SPE [110]. Using either approach, it is important to consider the major factors influencing retention, including sample pH, sample volume, and sorbent mass. 

Having discussed statistical methodology particularly appropriate to studies whose primary purpose is simply to identify factors with the largest influence on a response, we will now consider methods aimed more directly at detailed experimental quantification of the pattern of factor influence on one or more responses. As an example, we will use a sanitized account of some statistical aspects of a highly successful and economically important process improvement project. (Data presented here are not the original data, but resemble them in structure. Naturally, details of the project not central to our expository purposes and those of a proprietary nature will be suppressed.) A more complete version of this case study appears as Chapter 11 of Vardeman.6... 

ANOVA (analysis of variance) A statistical method for comparing means of data under the influence of one or more factors. The variance of the data may be apportioned among the different factors. 

Burner tests are complex, time-consuming, and expensive experiments. In the case of this category of experiments, one should always put a strong emphasis on the planning supported by statistical methods. Should the examined value be influenced by a low number of factors (< 3) and should the dependence between the values be known as linear, it is sufficient to use the so called factorial plan [29] for fhis fype of experiment, or a fractional factorial plan [29]. Nevertheless, most experiments are supposed to have a response variable without linear dependence on some of the factors, and it... 

State that we cannot know all factors and processes that influence the values we observe for a certain wetland biogeochemical property. Simple statistical methods as a continuous response (regression analysis) or as a set of responses to discrete factors (analysis of variance, ANOVA) have been used extensively in wetland research, in order to relate elements in biogeochemical processes or determine the extent to which a particular factor may influence a process. 

Many of the most difficult problems in genetics and plant breeding involve distinguishing between effects due to genetics and environment. Several statistical methods are used to separate genetic and environmental effects. However, the efficiency of selection or plant breeding can also be improved by knowledge of the manner in which environmental factors influence particular species and cultivars. 

In order to make a meaningfiil statistical evaluation of the results of accountancy verifications, the inspector s measurements must be planned in a way, which will provide independent estimates of the overall measurement uncertainties. All factors influencing these uncertainties must be considered, such as the material type and form, the sampling procedure, and the measurement method. According to theory and experience, the inspectorate detection... 

Feature selection techniques are used for finding the chief factors influencing the target. Preliminary statistical analysis indicates that the data set has inclusive structure, so we can use two methods for this feature selection work one is based on KNN method, and the other is based on SVR using Gaussian kernel function. Fortunately both methods... 

Thus it would appear that a moderate reduction of the maternal food intake does not materially influence the fetus. However, recent studies in man based on a better knowledge of nutritional requirements and evaluated by statistical methods have shed new light on the influence of maternal diet on fetal development. They have shomi that quantitative and qualitative factors of the maternal diet affect the condition of the infant at birth without causing inanition of the mother. These studies will be discussed later in a special section (p. 91). 

ANOVA(analysis of variance) is the statistical method to find the Factor which has the largest effect to errors[4]. Table 5 is ANOVA table summarized the influence to flow length. In Table 5, the magnitude of F ... 

---