2022
DOI: 10.1016/j.sciaf.2022.e01323
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The Efficiency of Bartlett's Test using Different forms of Residuals for Testing Homogeneity of Variance in Single and Factorial Experiments-A Simulation Study

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Cited by 5 publications
(4 citation statements)
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“…For our study, the KMO was 0,860, well above what is required. Bartlett's test evaluates the null hypothesis that the correlation matrix of the variables is an identity matrix, which would indicate no relationship between the variables (Odoi et al, 2022). For our study, Barlett's test was significant (0,000).…”
Section: Reliability Resultsmentioning
confidence: 59%
“…For our study, the KMO was 0,860, well above what is required. Bartlett's test evaluates the null hypothesis that the correlation matrix of the variables is an identity matrix, which would indicate no relationship between the variables (Odoi et al, 2022). For our study, Barlett's test was significant (0,000).…”
Section: Reliability Resultsmentioning
confidence: 59%
“…Bartlett test is used to test the hypothesis that the parameter s is equal under different stress levels [17]. The intermediate results are listed, Table 8.…”
Section: Distribution Parameter Estimationmentioning
confidence: 99%
“…Bartlett test is used to test the hypothesis that the parameter σ ${{\sigma }}$ is equal under different stress levels [17]. The intermediate results are listed, Table 8. B2=2i=1klrini-1[]boldlboldn()i=1klrini-1σ^i-boldlboldn()i=1klrini-1-2i=1klrini-1trueboldlboldnσ^i $\vcenter{\openup.5em\halign{$\displaystyle{#}$\cr B^2 = 2\left( {\sum\limits_{i = 1}^k {l_{r_i n_i } } ^{ - 1} } \right) \hfill\cr \left[ {{\bf{ln}}\left( {\sum\limits_{i = 1}^k {l_{r_i n_i } } ^{ - 1} \hat \sigma _i } \right) - {\bf{ln}}\left( {\sum\limits_{i = 1}^k {l_{r_i n_i } } ^{ - 1} } \right)} \right] \hfill\cr - 2\sum\limits_{i = 1}^k {l_{r_i n_i } } ^{ - 1} \widehat {{\bf{ln}}\sigma }_i \hfill\cr}}$ C=1+16k-1i=1klrini-()i=1klrini-1-1 $\vcenter{\openup.5em\halign{$\displaystyle{#}$\cr C = 1 + {1 \over {6\left( {k - 1} \right)}}\left[ {\mathop \sum \limits_{i = 1}^k l_{r_i n_i } - \left( {\mathop \sum \limits_{i = 1}^k l_{r_i n_i } ^{ - 1} } \right)^{ - 1} } \right]\hfill\cr}}$ …”
Section: Statistical Analysis Of Gear Accelerated Life Test Datamentioning
confidence: 99%
“…This suggests that the data collected for this research showed good partial correlation. The outcome of the Bartlett's test for sphericity is 0.0001, which is considered highly significant (21) . The scale's factors were ultimately determined by the analysis's factor solution to be 7, which accounted for 70.83 percent of the variation in the data.…”
Section: Exploratory Factor Analysismentioning
confidence: 99%