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Precast segmental bridge construction in seismic zones
Published in Fabio Biondini, Dan M. Frangopol, Bridge Maintenance, Safety, Management, Resilience and Sustainability, 2012
Fabio Biondini, Dan M. Frangopol
For this purpose, the impact factors generated previously were collected for each RSC, resulting in a total of 400 impact factors for each road surface condition (10 speeds X 40 replicates). A Chi-square test was then performed on these impact factors to determine their distributions. Chi-square Test is a statistic test that can be used to test whether a set of data follows a certain type of distribution by comparing the Chi-square test value against a threshold value which can be determined by the number of intervals used in the histogram and the preset significance level for the test. The Chi-square test value can be calculated as following:
WinSTAT
Published in Paul W. Ross, The Handbook of Software for Engineers and Scientists, 2018
This command compares measured frequencies with expected frequencies for a single variable, and is best explained with an example. suppose we throw a die a number of times and record the number of occurrences for each possible outcome, 1 to 6. The expected frequencies would be equal for each possibility. The chi-square test compares the actual frequencies with these expected frequencies, and determines the significance of the differences. A high significance (low p value) indicates that we can assume the variable is behaving other than expected.
A Comprehensive Survey on the Detection of Diabetic Retinopathy
Published in IETE Journal of Research, 2022
Statistical test is carried out to select the best features for developing machine learning model. It is used also to compare many models to choose the best performing model. Analysis of Variance (ANOVA), Chi-square test (χ2) and t-test are some of the more frequently used statistical tests. Significance level and p-value decide whether null hypothesis is accepted or rejected. The significance level is the probability of evidence strength before calculating results statistically. p-Value is the probability of getting outcomes as observed. Significance level and p-value are compared to obtain the status of the hypothesis. The null hypothesis is rejected if the p-value is smaller than the significance value; else, it is accepted. The statistic is calculated by using the expression shown in Table 10. ANOVA is a method of checking the means of two or more groups separated from each other statistically. In the chi-square test, the calculated chi-value is compared with the critical value from the table for the selected significance level and degree of freedom. It checks the hypothesis for acceptance or rejection. If the calculated value is less than the critical value obtained from the table based on the degree of freedom and significance level. The research hypothesis is acceptable if strong statistical evidence is obtained against the null hypothesis with considerable possibility [95].
Applicability and improvement of soil classification methods in Delta regions based on the CPTU database
Published in Marine Georesources & Geotechnology, 2022
Hualei Feng, Xuening Liu, Guojun Cai, Wei Duan
The weighted kappa coefficient expresses the degree of consistency, but it does not directly evaluate the capability of the soil classification methods to represent the variation trend towards the soil types obtained from borehole records along the depth. Therefore, a correlation analysis was performed to assess the degree of trend similarity of the classified soil types. The chi-square test is mainly used to compare the correlation analysis of multiple variables and test the degree of difference between two or more samples at a certain significance level. Given that the soil types in this study are re-scaled to integer indices for both CPTU and borehole records, regarded as ordinal categorical variables, the Mantel–Haenszel chi-square test was conducted to verify the presence of a linear association between the two sets of ordinal categorical data (Norman and Streiner 2008): where χ2 is the chi-square value, and oi and ei are the observed number (or frequency) and expected number for interval i, respectively.
Fuzzy-Based Relaying Scheme for Transmission Line Based on Unsynchronized Voltage Measurement
Published in IETE Journal of Research, 2022
Biswapriya Chatterjee, Sudipta Debnath
The previous sections describe the efficacy of the FIS model to estimate fault location in a transmission line fed at both ends. The percentage error obtained has a wide range, which spreads from 0 to maximum 5.0%.Figure 9 represents that the distribution of percentage error of different error ranges for all types of faults. Since the errors computed are totally random in nature with respect to the time of occurrence and location, it is necessary to prove its efficiency in view of its use. In this research, the chi square (χ2) test has been used for error analysis. The chi square test is a statistical method, which shows the relation between two variables, which are categorized as countable variable and categorical variable. The result of this test indicates the degree of differences between the observed count of countable variable and expected count of categorical variable. The low value of χ2 signifies the high degree of association between two sets of variables. In order to accept the null hypothesis, the computed value of χ2 must be less than a threshold, which is determined by significance level of this test.