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primi sui motori con e-max

APPENDIX E

REGRESSION MODELS FOR NON-MOTION SIMULATORS

Regression Model Analysis

The initial scatterplot for the number of non-motion simulators and the number of corresponding helicopters showed that cubic and quadratic equations would describe the regression model with greater precision than the rest.

SPSS regression model
Figure 10. Scatterplot for number of non-motion simulators and number of helicopters, including U.S. Army Black Hawks.

The high number of the U.S. Army Black Hawks and the relatively low number of corresponding non-motion devices, though, proved to be misleading. The models did not have a logical practical explanation. For example, the quadratic and cubic models predicted that 1700 helicopters do not need any simulators at all. Thus, this extreme value had been excluded and the scatterplot was run again.

SPSS regression model for non-motion simulators
Figure 11. Scatterplot for number of non-motion simulators and number of helicopters, without U.S. Army Black Hawks.

This time the scatterplot showed that linear, cubic and quadratic equations would describe the regression model with greater precision than the rest. Also, these models seemed very close to each other.

Linear

The SPSS “curve estimation” analysis for the linear equation gave the results shown on tables 11, 12, and 13.

SPSS Tables for the Linear Model Summary and Analysis of Variance for Non-Motion Simulators
Linear Model Summary and Analysis of Variance for Non-Motion Simulators
SPSS Table with the coefficients for Non-Motion Simulators Linear Model
Coefficients for Non-Motion Simulators Linear Model

Quadratic

The SPSS “curve estimation” analysis for the quadratic equation gave the results shown on tables 14, 15, and 16.

SPSS Table with the Quadratic Model Summary and Analysis of Variance for Non-Motion Simulators Quadratic Model
Quadratic Model Summary and Analysis of Variance for Non-Motion Simulators Quadratic Model
SPSS table with Coefficients for Non-Motion Simulators Quadratic Model
Coefficients for Non-Motion Simulators Quadratic Model

Cubic

The SPSS “curve estimation” analysis for the quadratic equation gave the results shown on tables 17, 18, and 19.

SPSS Table with the Cubic Model Summary and the Analysis of Variance for Non-Motion Simulators
Cubic Model Summary and the Analysis of Variance for Non-Motion Simulators
SPSS table with the Coefficients for Non-Motion Simulators Cubic Model
Coefficients for Non-Motion Simulators Cubic Model

Chosen Model

All three models indicated that there is a strong correlation between the number of helicopters and the corresponding non-simulators (R2=.851, and R2=.852). Figure E2 presents the curve fit of the three equations. At the scatterplot it is evident that all three equations would give similar predictions for the numbers of non-motion simulators. The linear model, though, was chosen because it was the simplest one. The equation of the model is Y=0+(.034*X).

Figure 12. Curve fit analysis for number of non-motion simulators and number of helicopters.
Figure 12. Curve fit analysis for number of non-motion simulators and number of helicopters.

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