Regression analysis parameters

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 . 

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 first two examples show that the interaction of the model parameters and database parameters can lead to inaccurate estimates of the model parameters. Any use of the model outside the operating conditions (temperature, pressures, compositions, etc.) upon which the estimates are based will lead to errors in the extrapolation. These model parameters are effec tively no more than adjustable parameters such as those obtained in linear regression analysis. More comphcated models mav have more subtle interactions. Despite the parameter ties to theoiy, tliey embody not only the uncertainties in the plant data but also the uncertainties in the database. 

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 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 non-linear regression analysis is employed using die Solver in Microsoft Excel spreadsheet to determine die values of and in die following examples. Example 1-5 (Chapter 1) involves the enzymatic reaction in the conversion of urea to ammonia and carbon dioxide and Example 11-1 deals with the interconversion of D-glyceraldehyde 3-Phosphate and dihydroxyacetone phosphate. The Solver (EXAMPLEll-l.xls and EXAMPLEll-3.xls) uses the Michaehs-Menten (MM) formula to compute v i- The residual sums of squares between Vg(,j, and v j is then calculated. Using guessed values of and the Solver uses a search optimization technique to determine MM parameters. The values of and in Example 11-1 are ... 

Matsui75) has computed energies (Emin) which correspond to the minimal values of Evdw in Eq. 1 for cyclodextrin-alcohol systems (Table 2). Besides normal and branched alkanols, some diols, cellosolves, and haloalkanols were involved in the calculations. The Emi values obtained were adopted as a parameter representing the London dispersion force in place of Es. Regression analysis gave Eqs. 9 and 10 for a- and P-cyclodextrin systems respectively. 

Dynamic DSC scans of resole resins show two distinguishable reaction peaks, which correspond to formaldehyde addition and die formation of edier and metiiy-lene bridges characterized by different activation energies. Kinetic parameters calculated using a regression analysis show good agreement widi experimental values.75... 

Parameter Estimate from physical data Estimate from regression analysis Units... 

GP 1] [R 1] A kinetic model for the oxidation of ammonia was coupled to a hydro-dynamic description and analysis of heat evolution [98], Via regression analysis and adjustment to experimental data, reaction parameters were derived which allow a quantitative description of reaction rates and selectivity for all products trader equilibrium conditions. The predictions of the model fit experimentally derived data well. 

The unknown model parameters will be obtained by minimizing a suitable objective function. The objective function is a measure of the discrepancy or the departure of the data from the model i.e., the lack of fit (Bard, 1974 Seinfeld and Lapidus, 1974). Thus, our problem can also be viewed as an optimization problem and one can in principle employ a variety of solution methods available for such problems (Edgar and Himmelblau, 1988 Gill et al. 1981 Reklaitis, 1983 Scales, 1985). Finally it should be noted that engineers use the term parameter estimation whereas statisticians use such terms as nonlinear or linear regression analysis to describe the subject presented in this book. 

International or in-house standards in combination with fundamental parameters software, lead to the same accuracy as conventional analysis using regression analysis of standards. Provided that accurate standards are available, the main factors that determine the accuracy of XRF are the matrix absorption correction and (in the case of EDXRF) the spectrum evaluation programme, i.e. correction for spectral overlap and background. 

Development of a distributed parameter model will rely on data obtained in vivo. Time and spatial dependencies of drug concentration in a target organ are used as the basis to estimate parameters by nonlinear regression analysis. Distribu-... 

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