The within model

R-squared values range from 0.2874 to 0.5612.

For Building 1, which has three different operating schemes, the

R-squared value for the proposed Model 4 is 0.985 as compared to the

R-squared value of 0.075 for Model 1, and the

R-squared value for the proposed Model 5 is 0.988 as compared to the

R-squared value of 0.115 for Model 3.

The

R-squared values are calculated and presented in Table 2.

R-squared values range from 0 to 1, and a value close to 1 indicates a better fit.

The adjusted

R-squared values (see Table 3) show that the predictive value of the response equations for the part properties of horizontally built samples is high.

Finally, we calculated the

R-squared value of the retrieval results for comparison and analysis.

This observation is supported with an overall

R-squared value of 0.989.

An

R-squared value is the square of the correlation coefficient, which indicates the percentage of the movements in the independent variable that can be explained by the dependent variable.

Of the two diagnostic models formulated, the expanded CBC-SCHEM model composed of the seven selected blood variables produced the highest

R-squared value for estimating radiation injury (93%).

Although the time series modeler offers a number of different goodness of fit statistics, here stationary

R-squared value was used.

The

R-squared value is 42 percent indicating the highest fit model, A Durbin-Watson statistics of 2.3 indicating a positive autocorrelation of the model.

Table 4 summarizes the full model, which resulted in a Chi-Square value of 43.446 and a Nagelkerke

R-Squared value of .245.

The errors between the load-displacement curves from the experiment and simulations are quantified by using the

R-squared value for the load-displacement curves, with

R-squared value being 1 when two curves are perfectly matched.

FCOJ returns and the temperature (aligned in time) show a very low

R-squared value (0.000025) indicating that the temperature alone does not explain much of the volatility in the return even when the temperatures used are actual temperatures recorded (and not the predicted ones), Durban-Watson statistics was about 1.75, as presented in Table 3.

The

R-squared value is not very high for the model (55%) due to sample characteristics; however the regression model is highly significant (probability [approximately equal to] 0)