Regression analysis equation

Examples for compounds are given in Figure 8.1, and the regression analysis equation is provided below for the QSAR of triazine derivatives in photosynthesis (Draber, 1992). The inhibitory potency expressed as a pl50 value is equal to a lipophilicity parameter tt (log of the partition coefficient P), an electronic substitution parameter a (the Hammett constant) and to a lesser degree to a steric component Es (the Taft constant). 

Equation 4 also can be rewritten in a form suitable for regression analysis, Equation 5. 

The activity of 1,1jl-trifluoro 3-mercapto substituted phenyl propan-2-ones was not simply correlated with MR alone, therefore, a combination of Hammett a constant, Taft steric parameter (Eg) and Hansch hydrophobic constant (x) was included in the regression analysis. "Equation 36" was found to be of the best fit for compounds substituted in the meta and para positions. 

Although equations 5.13 and 5.14 appear formidable, it is only necessary to evaluate four summation terms. In addition, many calculators, spreadsheets, and other computer software packages are capable of performing a linear regression analysis based on this model. To save time and to avoid tedious calculations, learn how to use one of these tools. For illustrative purposes, the necessary calculations are shown in detail in the following example. 

There is an obvious similarity between equation 5.15 and the standard deviation introduced in Chapter 4, except that the sum of squares term for Sr is determined relative toy instead of y, and the denominator is - 2 instead of - 1 - 2 indicates that the linear regression analysis has only - 2 degrees of freedom since two parameters, the slope and the intercept, are used to calculate the values ofy . 

Standardizations using a single standard are common, but also are subject to greater uncertainty. Whenever possible, a multiple-point standardization is preferred. The results of a multiple-point standardization are graphed as a calibration curve. A linear regression analysis can provide an equation for the standardization. 

In a curve-fitting method the concentration of a reactant or product is monitored continuously as a function of time, and a regression analysis is used to fit an appropriate differential or integral rate equation to the data. Eor example, the initial concentration of analyte for a pseudo-first-order reaction, in which the concentration of a product is followed as a function of time, can be determined by fitting a rearranged form of equation 13.12... 

The reaction of H2O2 and H2SO4 generates a reddish brown solution whose absorbance is measured at a wavelength of 450 nm. A regression analysis on their data yielded the following uncoded equation for the response (Absorbance X 1000). 

Fuller-Schettler-Giddings The parameters and constants for this correlation were determined by regression analysis of 340 experimental diffusion coefficient values of 153 binary systems. Values of X Vj used in this equation are in Table 5-16. 

The Hesketh equation is empirical and is based upon a regression analysis of data from a number of industrial venturi scrubbers ... 

Equations la and lb are for a simple two-phase system such as the air-bulk solid interface. Real materials aren t so simple. They have natural oxides and surface roughness, and consist of deposited or grown multilayered structures in many cases. In these cases each layer and interface can be represented by a 2 x 2 matrix (for isotropic materials), and the overall reflection properties can be calculated by matrix multiplication. The resulting algebraic equations are too complex to invert, and a major consequence is that regression analysis must be used to determine the system s physical parameters. ... 

In a regression analysis P/ and A/ are calculated from an assumed model for the structure using the Fresnel equations, where P and A in Equation 2 are now indexed by c, to indicate that they are calculated, and by /, for each combination of wavelength and angle of incidence. 

In this equation, the substituent parameters and reflect the incremental resonance interaction with electron-demanding and electron-releasing reaction centers, respectively. The variables and r are established for a reaction series by regression analysis and are measures of the extent of the extra resonance contribution. The larger the value of r, the greater is the extra resonance contribution. Because both donor and acceptor capacity will not contribute in a single reaction process, either or r would be expected to be zero. 

The following expressions can be used to estimate the temperature and enthalpy of steam. The expressions are based upon multiple regression analysis. The equation for temperature is accurate to within 1.5 % at 1,000 psia. The expression for latent heat is accurate to within + 3 % at 1,000 psia. Input data required to use these equations is the steam pressure in psia. The parameters in the equations are defined as t for temperature in F, for latent heat in Btu/lb, and P for pressure in psia. 

A eomputer program, PROG2, was developed to fit the data by least squares of a polynomial regression analysis. The data of temperature (independent variable) versus heat eapaeity (dependent variable) were inputted in the program for an equation to an nth degree... 

A reading of Section 2.2 shows that all of the methods for determining reaction order can lead also to estimates of the rate constant, and very commonly the order and rate constant are determined concurrently. However, the integrated rate equations are the most widely used means for rate constant determination. These equations can be solved analytically, graphically, or by least-squares regression analysis. 

In the above paragraphs we saw that multiple linear regression analysis on equations of the form... 

On the basis of regression analysis of the initial rate data obtained for both isolated reactions (Vila) and (Vllb) and for each of the three reactions proceeding in the coupled system [reactions (Vlla)-(VIIc)], of the set of twenty-five equations, the best equation was always found to be of the... 

The various independent variables can be the actual experimental variables or transformations of them. Dilferent transformations can be used for different variables. The independent variables need not be actually independent. For example, linear regression analysis can be used to fit a cubic equation by setting X, X and Z as the independent variables. 

However, it is not proper to apply the regression analysis in the coordinates AH versus AS or AS versus AG , nor to draw lines in these coordinates. The reasons are the same as in Sec. IV.B., and the problem can likewise be treated as a coordinate transformation. Let us denote rcH as the correlation coefficient in the original (statistically correct) coordinates AH versus AG , in which sq and sh are the standard deviations of the two variables from their averages. After transformation to the coordinates TAS versus AG or AH versus TAS , the new correlation coefficients ros and rsH. respectively, are given by the following equations. (The constant T is without effect on the correlation coefficient.)... 

Overdetermination of the system of equations is at the heart of regression analysis, that is one determines more than the absolute minimum of two coordinate pairs (xj/yi) and xzjyz) necessary to calculate a and b by classical algebra. The unknown coefficients are then estimated by invoking a further model. Just as with the univariate data treated in Chapter 1, the least-squares model is chosen, which yields an unbiased best-fit line subject to the restriction ... 

The partial differential equations describing the catalyst particle are discretized with central finite difference formulae with respect to the spatial coordinate [50]. Typically, around 10-20 discretization points are enough for the particle. The ordinary differential equations (ODEs) created are solved with respect to time together with the ODEs of the bulk phase. Since the system is stiff, the computer code of Hindmarsh [51] is used as the ODE solver. In general, the simulations progressed without numerical problems. The final values of the rate constants, along with their temperature dependencies, can be obtained with nonlinear regression analysis. The differential equations were solved in situ with the backward... 

The final values of the rate constants along with their temperature dependencies were obtained with nonlinear regression analysis, which was applied to the differential equations. The model fits the experimental results well, having an explanation factor of 98%. Examples of the model fit are provided by Figures 8.3 and 8.4. An analogous treatment can be applied to other hemicelluloses. 

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