# SEARCH

** Fitting Experimental Data to Linear Equations by Regression **

** Linearization, linearized equations **

** Multiple linear regression equations **

Multiple linear regression equations were also developed for foam capacity and stability based on pH and the data on composition of soluble and insoluble fractions in the suspensions sumarized in Figures 2 and 4 (Tables V and VI). [Pg.158]

Figure 4.7 Determination of the linear regression equation manually |

An easy way to visualize a linear equation is with one variable as in simple linear regression. An example of the one-variable linear regression equation is between the dependent variable (a bioactivity value) and an independent variable (the descriptor coefficient) and is expressed [Pg.169]

BI- AND MULTIVARIATE DATA Table 2.1. Linear Regression Equations [Pg.98]

Scheme 7.1, relevant to an amphiprotonic solvent). Using Eq. (7.2), the multiple linear regression equation for the fluorescence maximum (expressed in 103 cm-1) is [Pg.205]

QSARs were generated for each of the four data bases. A representative multi-dimensional linear regression equation is that developed for the L-aspartylaminoethylesters (see Figure 2) [Pg.24]

LUMO decreases as the number of chlorines increases. LUMO represents 90.64% of the variance in the linear regression equation. The probability of getting a correlation of -0.9521 for a sample size of 47 is less than 1%. [Pg.160]

The coefficients of equation (5) were determined by stepwise multiple regression in which the tracer element accounting for the greatest proportion of the variation of [POM] is used to find a first order, linear regression equation of the form [POM] [Pg.201]

The task is to compute the best parameter shift vector 8p that minimises the new residuals r(p+8p) in the least-squares sense. This is a linear regression equation with the explicit solution. [Pg.149]

Figure 2. The composite standard calibration for the quantification of salvinorin A by HPLC (the error bars indicate 1 standard deviation the linear regression equation for the calibration curve is y= 759,334x 44,127). |

Figure 5.2 Aqueous solubility of the (subcooled) liquid compound at 25°C as a function of the estimated molar volume (Vjx, see Box 5.1) of the molecule for various compound classes. The linear regression equations and correlation coefficients (R2) for the various sets of compounds are given in Table 5.4. Note that for practical reasons, decadic instead of natural logarithms are used. |

** Fitting Experimental Data to Linear Equations by Regression **

** Linearization, linearized equations **

** Multiple linear regression equations **

© 2019 chempedia.info