Previous studies have identified high nonnormality in the form of skewness and kurtosis in highway construction data (hot-mix asphalt, portland cement concrete pavement, and aggregate materials) on the basis of analysis of field quality assurance data. [The authors use “nonnormality,” rather than “abnormality,” and define it as a term used in any discipline that involves statistical data analysis. —Ed.] The presence of high nonnormality in lot data is a significant finding because such nonnormality violates most state transportation agencies' normality assumption for quality assurance data analysis (e.g., F-test and t-test) and quality measure calculation (e.g., percent within limits). High nonnormality can have several adverse effects, such as increased variability in lot data and decreased efficiency of statistical verification tests in finding differences between contractor's and agency's data sets. Most important, however, nonnormal lot data tend to misdirect contractor payment; such misdirection can manifest in incorrectly penalizing contractors that deliver acceptable construction and rewarding contractors that deliver poor construction. A modified Box–Cox transformation using the golden section search algorithm is proposed: it can substantially reduce pay biases due to nonnormality even when lot sample size is small. The method is efficient and ensures fair and equitable payment to state agencies and contractors.
Nonnormality in the form of skewness and kurtosis was examined in lot acceptance quality characteristics data from seven state highway agencies for their highway construction quality assurance programs. Lot skewness and kurtosis varied significantly. For most lot data sets, skewness values varied in the range of 0.0 ± 1.0, whereas most kurtosis values varied in the range of 0.0 ± 2.0. The analysis also reveals that, on average, 50% of lot test data sets were nonnormal with 15% of lot data sets having skewness greater than ±1.0 and kurtosis greater than ±2.0. This is a significant finding because most state transportation agencies' pay factor algorithms assume normally distributed lot. Further investigation showed that high skewness and kurtosis were associated with higher lot variability. This variability produced misleading results in regard to inflated Type I error and low power for the F-test. However, the t-test was found to be quite robust for distinguishing mean differences. Significant deviation was observed in lot pay factors based on percent within limits between assumed normal data and normalized data. Effects of nonnormal distribution on the lot pay factor were found to be varied on the basis of the specification limits, the distribution of defective materials on the tails in the case of two-sided limits, and the orientation of the nonnormal distribution itself.
The aim of this study is to ensure the MLC positional and leaf speed accuracy. To check the MLC positional and leaf speed accuracy picket fence and synchronized segmented stripes test pattern were performed. The relative and absolute dosimetric verification were analyzed in this study. This project was followed by Quality control for Intensity-Modulated Radiation Therapy, as in the Recommendation No.15 from SGSMP. For relative dosimetric verification test such as different dose in same depth, same dose in different depth, chair test and inhomogeneous test were performed. All the plans were followed by Gamma index. To verify absolute dose 0.3 cc SemiFlex chamber along with a PTW solid water phantom was used. In picket fence and synchronized segmented stripes test, match-lines appear at
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