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Predicting Traffic Accident Rates: Human Values Add Predictive Power to Age and Gender
Published in Mark Sullman, Lisa Dorn, Advances in Traffic Psychology, 2019
Ivars Austers, Viesturs Renge, Inese Muzikante
A hierarchical regression analysis was conducted to determine the incremental validity of values over and above age and gender in predicting Violations. Table 16.2 shows the results of the regression analysis. As can be seen, values were significant predictors of Violations over and above age and gender. Adding values in Step 2 increased the explained variation in Violations by 9 per cent over that explained by age and gender, while together they explained 22 per cent of the variance in Violations. As we hypothesized, 'self-Enhancement' values positively predicted Violations and 'Conservation' values negatively predicted Violations. 'self-Transcendence' values along with 'Openness to Change' values did not predict Violations.
Numerical and statistical analysis using Microsoft Excel
Published in Tariq Muneer, Jorge Kubie, Thomas Grassie, Heat Transfer, 2012
Tariq Muneer, Jorge Kubie, Thomas Grassie
The ratio of explained variation [Σ (Yc – Ym)2] to the total variation [Σ (Yo – Ym)2] is called the coefficient of determination. Ym is the mean of the observed Y values. The ratio lies between zero and one. A high value of r2 is desirable as this shows a lower unexplained variation.
Tandard Data
Published in Stephan Konz, Steven Johnson, Work Design, 2018
The equation can explain anywhere from 0 to 100% of the variability of the data. If it explains 100%, all the points will fall on the line. The ratio of explained variation/total variation, r2, is called the coefficient of determination; r is the coefficient of correlation. An r2 value of .60 means that the equation explained 60% of the total variability.
Experimental investigation of tool wear and machining rate in rotary ultrasonic machining of nickel alloy
Published in Machining Science and Technology, 2018
These results also show that A and B have a stronger contribution to maintaining the model of MR. Higher than 0.1 value of Prob>F reveal the insignificance of the model term. The lack-of-fit F value 0.93 (Table 5) involves that it is insignificant relative to pure error. There is only 58.81% possibility that a lack-of-fit F value may occur due to noise. Therefore, the built up model can be established. To find determination coefficient (R2) is computed to check further whether the fitted model actually describes the experimental data. R2 is a measure of the degree of fit. The R2 defines the proportion of explained variation to the total variation. When determination coefficient's value move toward 1, it means that there is less difference between the experimental data and predicted data. From Table 6, it is revealed that the R2 value for the MR is found to be 0.9976. This high value of the R2, may be proposed by the adequate model in the process presentation. As far as other R2 statistics is concerned, it is better to have a good agreement with closer value of Pred R2 (0.9956) and Adj R2 (0.9968). In Table 6, the lesser value (0.99) of CV % shows reliability and precision of the carried out experiments. The CV value defines the relationship between mean and the standard deviation. Furthermore, the Adeq Precision measures the signal-to-noise ratio. The Adeq Precision found for the model is 159.904. Usually, a value greater than 4 is desirable (Montgomery, 2008). Thus, the model can be utilized to find the predicted the values of the MR. From the obtained model for the MR, the values of R2 (0.9976) and Adeq Precision (159.904) show significance for fitting and predicting the experimental results. Equations (4) and (5) define the regression models for the MR for profile 1 and profile 2, respectively: