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We found a significant relationship between the fitted values of I/b in Waage's model and the value of Cox's model covariate (R2 = 0.27; regression equation beta = 2.15-0.066I/b; F. test = 17.57; df = 1 64, P-value <0.001).
It is required to minimize the difference between the fitted values and the actual values.
Homoscedasticity of the measurement error was verified by computing the correlation between the fitted values and the squared residuals.
Strong correlations between the fitted values for different experiments as well as between the free-energy parameters and their counterparts in aqueous solution indicate that the model captures a significant part of the underlying physical chemistry.
In order to measure the relative deprivation gap, rather than just comparing the most and least deprived tenths that would only partially use the data, we treated deprivation tenth as a linear term and then the mortality rate ratio (RR) between the fitted values for the most deprived and least deprived tenths was calculated.
After removal of two influential points, as well as the observations with incomplete covariate information, the estimated Pearson's correlation coefficient between the fitted values and squared residuals was low (r = 0.16), indicating that the assumption of homoscedasticity of the measurement error model is reasonable here.
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The difference between the observed and the fitted values (i.e. the residuals) were considered the normalized expression level, and all pair-wise Pearson correlation coefficients were calculated.
A comparison between the experimental data and the fitted values, with an extrapolation to 175°C, are shown in Figure 8.
The assumptions of a normal distribution (normality) and equal variance among the groups (homoscedasticity) were checked by looking at the distribution of the residuals (difference between observed and fitted values) and the fitted values from the model.
The residuals are the differences between the observed and fitted values, where the fitted values are the treatment means.
In stage one, SPSS linear regression will be run to generate an unstandardised residual for each subject (the difference between the observed value and the fitted value from the model for each individual participant).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com