Titlebook: Experimental Design; With Application in Paul D. Berger,Robert E. Maurer,Giovana B. Celli Textbook 2018Latest edition Springer Internation

Systole

書目名稱Experimental Design影響因子(影響力)




書目名稱Experimental Design影響因子(影響力)學(xué)科排名




書目名稱Experimental Design網(wǎng)絡(luò)公開度




書目名稱Experimental Design網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Experimental Design被引頻次




書目名稱Experimental Design被引頻次學(xué)科排名




書目名稱Experimental Design年度引用




書目名稱Experimental Design年度引用學(xué)科排名




書目名稱Experimental Design讀者反饋




書目名稱Experimental Design讀者反饋學(xué)科排名

inflame

Hdl348

Some Further Issues in One-Factor Designs and ANOVA made about the probability distribution of the .’s in our statistical model. Next, we discuss a nonparametric test that is appropriate if the assumption of normality, one of the standard assumptions, is seriously violated. We then review hypothesis testing, a technique that was briefly discussed in

貿(mào)易

Multiple-Comparison Testing or accept ..(..?=?..?=?..?=?…?=?..); however, they do not show how the means differ, if, indeed, they do differ. In this chapter, we will discuss the logic and Type I errors in multiple-comparison testing. We then present several procedures which can be used for multiple comparison of means, such a

Capitulate

Orthogonality, Orthogonal Decomposition, and Their Role in Modern Experimental Designares (TSS), the variability in the original data, into two components – the sum of squares between columns (SSB.), attributable to the factor under study, and the sum of squares within a column (SSW), the variability not explained by the factor under study, and instead explained by “everything else.

intercede

Two-Factor Cross-Classification Designs value of some quantity of interest, the dependent variable. By way of example, we considered whether device/usage influences battery life. We expanded on this initial analysis by introducing multiple-comparison testing and orthogonal breakdowns of sums of squares.

intercede

omnibus

HALO

Two-Level Factorial Designsveral chapters, we consider designs in which all factors have two levels. Many experiments are of this type. This is because two is the minimum number of levels a factor can have and still be studied, and by having the . (2), an experiment of a certain size can include the .. After all, an experimen

Crepitus

Confounding/Blocking in 2, Designsin Chap... Imagine coming to a point in designing our experiment where we have settled on the factors and levels of each factor to be studied. Usually this will not be an exhaustive list of all the factors that might possibly influence the experimental response, but a bigger list would likely be pro