Evaluation of bond strength of resin cements using different general-purpose statistical software packages for two-parameter Weibull statistics

OBJECTIVES: This study evaluated and compared Weibull parameters of resin bond strength values using six different general-purpose statistical software packages for two-parameter Weibull distribution. METHODS: Two-hundred human teeth were randomly divided into 4 groups (n=50), prepared and bonded on dentin according to the manufacturers' instructions using the following resin cements: (i) Variolink (VAN, conventional resin cement), (ii) Panavia21 (PAN, conventional resin cement), (iii) RelyX Unicem (RXU, self-adhesive resin cement) and (iv) G-Cem (GCM, self-adhesive resin cement). Subsequently, all specimens were stored in water for 24h at 37°C. Shear bond strength was measured and the data were analyzed using Anderson-Darling goodness-of-fit (MINITAB 16) and two-parameter Weibull statistics with the following statistical software packages: Excel 2011, SPSS 19, MINITAB 16, R 2.12.1, SAS 9.1.3. and STATA 11.2 (p≤0.05). Additionally, the three-parameter Weibull was fitted using MNITAB 16. RESULTS: Two-parameter Weibull calculated with MINITAB and STATA can be compared using an omnibus test and using 95% CI. In SAS only 95% CI were directly obtained from the output. R provided no estimates of 95% CI. In both SAS and R the global comparison of the characteristic bond strength among groups is provided by means of the Weibull regression. EXCEL and SPSS provided no default information about 95% CI and no significance test for the comparison of Weibull parameters among the groups. In summary, conventional resin cement VAN showed the highest Weibull modulus and characteristic bond strength. SIGNIFICANCE: There are discrepancies in the Weibull statistics depending on the software package and the estimation method. The information content in the default output provided by the software packages differs to very high extent

Weibull parameter estimation and goodness-of-fit for glass strength data

Strength data from macroscopically identical glass specimens is commonly described by a two-parameter Weibull distribution, but there is lack of research on the methods used for fitting strength data to the Weibull distribution. This study investigates 4 different methods for fitting data and estimating the parameters of the Weibull distribution namely, good linear unbiased estimators, least squares regression, weighted least squares regression and maximum likelihood estimation. These methods are implemented on fracture surface strength data from 418 annealed soda-lime-silica glass specimens, grouped in 30 nominally identical series, including as-received, naturally aged and artificially aged specimens. The strength data are evaluated based on their goodness of fit. Comparison of conservativeness of strength estimates is also provided. It is found that a weighted least squares regression is the most effective fitting method for the analysis of small samples of glass strength data