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• Maxillofacial dysostosis Computing R 2 is only half the job of fitting the analytical model to the discrete model data womens health weight loss discount anastrozole 1 mg with visa. The precision of the fitting procedures is indicated by the size of confidence interval and the standard error of the best-fit values womens health pdf cheap 1mg anastrozole with amex. They are calculated assuming that the equation is linear but applied to nonlinear equations women's health clinic rockdale purchase anastrozole from india. There really is no distinction between the standard error and the standard deviation of a best-fit value menstruation for more than a week cheap anastrozole 1mg with amex. So the standard error of the mean is the same as the standard deviation of the mean (which is very different than the standard deviation of the data). They are used to calculate 95% confidence intervals, which are easier to interpret (Motulsky, 1999b). In most cases the confidence interval is used to get a sense of whether the best-fit values are any good. If the confidence intervals are narrow, then the best-fit values are precisely known. In the case very wide confidence intervals are very wide, then the values are not very precisely determined. When applied to nonlinear equations, such as Equation (5-44), the method of obtaining the confidence interval is an approximation. Nevertheless, if the intention is to define the curve without a huge amount of scatter, this approximation is acceptable. Therefore, the confidence intervals give a good sense of how precisely the discrete data define the parameters of the analytical model. This value comes from the t distribution and depends on the amount of confidence wanted (in many statistical books is taken as 95%) and the number of degree of freedom. For nonlinear regression this number equals the number of data points minus the number of parameters fit by nonlinear regression. For example, G value in the C-Model ranges between 0,2599 to 0,6023 while for discrete T-Model it ranges between 0,2440 to 0,3312. Therefore, the confidence of representing the spatial structure by T-Model is greater than the C-Model. Based on the correlation model (Equation (5-1)), the analytical T-Model was determined using Equation (5-25) and a distance interval equal to 100 m. For that reason a sensitivity analysis was performed for the discrete T-Model to investigate the factors that influence the difference between these models. The factors included the size of generated data and the number of class intervals. Mogheir 5- Characterizing the Spatial Variability Using the Entropy Theory Zooming of this part Figure 5. Size of generated data Different sizes of generated data were used to construct the discrete T-Model (200, 300, 400, and 500). The number of class intervals was the same for all the different sizes of generated data (the number of class intervals was 9). This indicates that the discrete T-Model is sensitive to the size of the data available for analysis, as in the case of actual groundwater data where the data is limited in time or is incomplete. Mogheir 155 Chapter 5 1,50 Discrete 1,20 0,90 0,60 0,30 0,00 0 500 1000 1500 2000 2500 3000 3500 Nats Exponential Decay Moving Average Distance (m) Figure 5. The Chloride logarithmic data from the Gaza Strip monitoring wells are used to compare the discrete and exponential decay fitting approaches in obtaining the T values. The logarithmically transformed Chloride data are used to check the fitting of the normal function by constructing the histogram and plotting the probability diagram. The chi-square test was used to assess the adjustment of the lognormal distribution to the empirical data. This indicates that the T-Model is sensitive to the type of distribution of the data, whether its normal or lognormal. It is noted that the synthetic data is correlated by distance, which means that the smaller the distance the higher the Correlation, as represented in Figure 5. The T-Model can also be used to represent the spatial variability of the synthetic data, as shown in Figure 5.

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