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Proposes a graphical method for assessing parametric transformation of the response in a linear regression model. Examples of response formations using data from an experiment on the behavior of worsted yarns; Details of the proposed method; Application of the linearity condition to the marginal distribution of the predictors.

This paper presents theory and applications of a simple graphical method, called hazard plotting, for the analysis of multiply censored life data consisting of failure times of failed units intermixed with running times on unfailed units. Applications of the method are given for multiply censored data on service life of equipment, for strength data on an item with different failure modes, and for biological data multiply censored on both sides from paired comparisons. Theory for the hazard plotting method, which is based on the hazard function of a distribution, is developed from the properties of order statistics from Type II multiply censored samples. [ABSTRACT FROM AUTHOR]

A graphical method is proposed to display a number of point estimates while allowing for their differing standard errors. More generally, it can be viewed as a representation of interval estimates by points on a bivariate plot. The method exploits a familiar connection between standardized estimates and regression through the origin and has several advantages over some alternative plots used in the literature. It is particularly useful when there may be a mixture of parameters, as illustrated by the problem of "mixed ages" in fission track dating. [ABSTRACT FROM AUTHOR]

This paper presents theory and applications of a simple graphical method, called hazard plotting, for the analysis of multiply censored life data consisting of failure times of failed units intermixed with running times on unfailed units. Applications of the method are given for multiply censored data on service life of equipment, for strength data on an item with different failure modes, and for biological data multiply censored on both sides from paired comparisons. Theory for the hazard plotting method, which is based on the hazard function of a distribution, is developed from the properties of order statistics from Type II multiply censored samples. [ABSTRACT FROM AUTHOR]