Experimental Investigation of Thermal Conductivity of Woods by Linear Regression Approach (Published)
This research tries to study the experimental investigation on thermal conductivity of Melina and wall nut woods using linear regression approach .From the thermal conductivity investigation of woods by linear regression approach , test results show that the values of thermal conductivity for Melina wood falls in the range of K between 0.002w/mk-1 to 0.007w/mk-1 which is on the low side and the wall nut between 0.0001w/mk-1 to 0.041w/mk-1 which is on the high side .It therefore depicts that Melina has a better thermal conductivity than wall nut since thermal conductivity of it (Melina) agrees with the literature that thermal conductivity is better with lower values of K.
Alternative Outliers Detection Procedures In Linear Regression Analysis : A Comparative Study (Published)
A Common problem in linear regression analysis is outliers, which produces undesirable effects on the least squares estimates. Many widely used regression diagnostics procedures have been introduced to detect these outliers. However, such diagnostics, which are based on the least squares estimates, are not efficient and cannot detect correctly swamping and masking effects. In this paper, we attempt to investigate the robustness of some well known diagnostics tools, namely, Cook’s distance, the Welsch-Kuh distance and the Hadi measure. The robust version of these diagnostics based on the Huber-M estimation have been proposed to identify the outliers. A simulation study is performed to compare the performance of the classical diagnostics with the proposed versions. The findings of this study indicate that, the proposed alternative versions seem to be reasonable well and should be considered as worthy robust alternative to the least squares method