01. Design and Analysis of Experiments

TL;DR: A large proportion of people in western societies do not believe in the concept of human-induced climate change (HICC), and oil-dependent economies might be expected to have more climate contrarian...

TL;DR: In this article, the effect of four process parameters (average rubbing velocity, weld pressure, upset distance, and preheat temperature) on three response variables (weld strength, heat affected zone (HAZ) width/peak hardness, and energy usage) for the induction heating (IH) and linear friction welding (LFW) of AISI 1020 steel.

TL;DR: In this paper, the efficiency of photo-Fenton oxidation process in the removal of polycyclic aromatic hydrocarbons (PAHs) and COD from landfill leachate, and investigate its effect on 16 PAHs according to their number of aromatic rings.

TL;DR: An overview of investigating Ni-based self-fluxing alloy coatings deposited by thermal spraying by the use of designed experiments and statistical methods and concluded that investigators have used the process knowledge to analyze and interpret the results of the statistical analysis of experimental data.

TL;DR: In this paper, it is shown that Tukey's test of additivity is equivalent to the test of a regression coefficient in a model with a single covariate, and the derivation given is general for the standard experimental designs, such as complete and incomplete randomized blocks and Latin squares.

TL;DR: In this paper, the authors provide a detailed discussion of the calculation of sample sizes using the width of confidence intervals for pairwise comparisons, treatment-versus-control comparisons, trends, and difference of averages.

TL;DR: In this article, an alternate formula for computing the adjusted treatment sum of squares for Group Divisible Partially Balanced Incomplete Block Designs (GD-PBIB) was proposed.

TL;DR: The authors summarize information scattered in the technical literature on a subject too new to be included in most textbooks, but which is of interest to statisticians, and those who use statistics in science and education, at an advanced undergraduate or higher level.