Regression analysis relationships

The dry and wet bulb temperature on measure point 2 which is the outlet of the air duct and point 3 which is the air return side of the air flow on the working face were measured. According to the dating results, the regression analysis relationships were got as Figure 3 and Figure 4. 

Furthermore, QSPR models for the prediction of free-energy based properties that are based on multilinear regression analysis are often referred to as LFER models, especially, in the wide field of quantitative structure-activity relationships (QSAR). 

Multiple linear regression analysis is a widely used method, in this case assuming that a linear relationship exists between solubility and the 18 input variables. The multilinear regression analy.si.s was performed by the SPSS program [30]. The training set was used to build a model, and the test set was used for the prediction of solubility. The MLRA model provided, for the training set, a correlation coefficient r = 0.92 and a standard deviation of, s = 0,78, and for the test set, r = 0.94 and s = 0.68. 

The report is concentrated at a few procedures of data treatment that allow overcoming some drawbacks of standard statistical procedures. The main attention is paid to the problems of the regression analysis, especially to the Quantitative Stmcture-Activity Relationships (QSAR). 

If the rate law depends on the concentration of more than one component, and it is not possible to use the method of one component being in excess, a linearized least squares method can be used. The purpose of regression analysis is to determine a functional relationship between the dependent variable (e.g., the reaction rate) and the various independent variables (e.g., the concentrations). 

Figure 3-24 shows the relationship between 1/C as a function of time t. The graph is a straight line, therefore, the assumed order of the reaction is correct. The slope of the line from the regression analysis is the rate constant k. 

Table 6-2 lists the results of the analysis. [To calculate we use the relationship Oa = see Eq. 6-7).) The regression analysis was also carried out without... 

The natural and correct form of the isokinetic relationship is eq. (13) or (13a). The plot, AH versus AG , has slope Pf(P - T), from which j3 is easily obtained. If a statistical treatment is needed, the common regression analysis can usually be recommended, with AG (or logK) as the independent and AH as the dependent variable, since errors in the former can be neglected. Then the overall fit is estimated by means of the correlation coefficient, and the standard deviation from the regression line reveals whether the correlation is fulfilled within the experimental errors. 

The relationship can be seen to be acceptably linear. Linear regression analysis gives the relation ... 

Regression analysis comparing the number of days from spraying to analysis and the parathion spray residue show no significant relationship. The difference in parathion residue due to the 1/r, W, and y4-pound applications was not significant. All residues were only a fraction of 1 p.p.m. 

Once experimental data have been collected and relationships generated by regression analysis (or even derived from first principles), the formulator has many... 

The relationship between average ADFR and application rate (AR) was tested using linear regression analysis and the model ADFR = a + b AR. All data concerning high-volume applications (n = 8) and low-volume applications (n = 4) were used, separately or together. The results of these regression analyses are presented in Table 2. 

The above study was replicated later with 75 asymptomatic black children, 3-7 years old, of uniformly low socioeconomic status (Hawk et al. 1986 Schroeder and Hawk 1987). Backward stepwise multivariate regression analysis revealed a highly significant negative linear relationship between Stanford-Binet IQ scores and contemporary PbB levels over the entire range of 6-47 pg/dL (mean,... 

Statistical method to model a mathematical equation that describes the relationship between random variables (usually x and y). The goal of regression analysis is both modelling and predicting. 

Multiple linear regression (MLR) is a classic mathematical multivariate regression analysis technique [39] that has been applied to quantitative structure-property relationship (QSPR) modeling. However, when using MLR there are some aspects, with respect to statistical issues, that the researcher must be aware of ... 

The quantitative relationship of flammability of a polymer with respect to the concentration of flame retardant is usually not linear, and there is no logical reason to expect combinations of different flame retardants to show a linearly additive result either (43). The actual result is often found to be "synergistic" or "antagonistic", or in regression analysis terminology, the interaction term is often found to be statistically significant. 

Careful attention to quantitative activity vs. concentration relationships, to the effect of interaction terms in combinations (using computerized regression analysis and experimental design), and careful observation of the manner in which one mode of action supports and reinforces another, seems likely to lead us to the next generation of highly efficient flame retardant systems. 

Animal to Human Dosimetric Adjustment None applied, insufficient data Time Scaling CM=k, where n=l and k=163.8 ppm-min. A regression analysis of data from squirrel monkeys and dogs (Haun et al. 1970) for 15, 30, and 60-min indicated a near-linear relationship (n=0.97 and 0.99, respectively, for the monkey and dog data). It was the consensus of the National Advisory Committee to assume linearity (n=l). 

Best fit plot of data from Table 2.7 obtained by least squares regression analysis. (Important note This graph implies a straight line relationship down to zero concentration. It is, however, unsafe to use the extrapolated portion as there are no experimental data for this part of the curve). 

To form the process model, regression analysis was carried out. The alkylate yield x4 was a function of the olefin feed xx and the external isobutane-to-olefin ratio jc8. The relationship determined by nonlinear regression holding the reactor temperatures between 80-90°F and the reactor acid strength by weight percent at 85-93 was... 

Rat and mouse lethality data from the well-conducted study of Zwart et al. (1990) also suggest that Haber s law is valid for phosgene. The study by ten Berge et al. (1986) has shown that the concentration-exposure-time relationship for many irritant and systemically acting vapors and gasses can be described by the relationship Cnxt=k. When the 10- to 60-min rat LC50 data are utilized in a linear regression analysis a value of the exponent, n, of 0.93 is obtained. The mouse 10- to 60-min lethality data yield a value of 1.3 for n. 

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