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APPENDIX F

REGRESSION MODELS FOR THE SUM OF SIMULATORS

Regression Model Analysis

Since the U.S. Army Black Hawks were excluded from the scatterplot of the non-motion simulators, they were excluded from the sum of simulators, too. The scatterplot for the sum of simulators and the number of corresponding helicopters showed that linear and cubic equations would describe the regression model with high precision.

Figure 13. Scatterplot for the sum of simulators and number of helicopters.
Figure 13. Scatterplot for the sum of simulators and number of helicopters.

The quadratic model has been excluded since its prediction for high number of helicopters were not logical.

Linear

The SPSS “curve estimation” analysis for the linear equation gave the results shown on tables 20, 21, and 22.

Linear Model Summary and Analysis of Variance for the Sum of Simulators Linear Model
Linear Model Summary and Analysis of Variance for the Sum of Simulators Linear Model
Coefficients for the Sum of Simulators Linear Model
Coefficients for the Sum of Simulators Linear Model

Cubic

The SPSS “curve estimation” analysis for the quadratic equation gave the results shown on tables 23, 24, and 25.

Cubic Model Summary and Analysis of Variance for the Sum of Simulators
Cubic Model Summary and Analysis of Variance for the Sum of Simulators
Coefficients for the Sum of Simulators Cubic Model
Coefficients for the Sum of Simulators Cubic Model

Chosen Model

Both models indicated that there is a strong correlation between the number of helicopters and the corresponding sum of simulators (R2=.857, R2=.869). Although the cubic equation had the best fit, the preferred one was the linear model, because it was the simplest one, while having a very high correlation, too. The equation of the model is Y=0+(.055*X).


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