This article addresses the relationship between religious affiliation and a very specific form of firearms ownership, that is, handgun ownership. Literature is reviewed relative to explanations of gun ownership. A test of the hypothesized relationship between religious affiliation and handgun ownership demonstrated a statistically significant (p < 0.03) association. Protestants were found to have a disproportionately high level of handgun ownership compared to other religious groups. Speculation for this finding is reviewed relative to other recent research on this topic.

Much basic fatigue data may be categorized for analytical purposes as small sample quantal response data. For example, both small sample up-and-down test outcomes and most S-N data fall into this category. But reliable median fatigue-limit estimates for small samples are not directly available using large sample statistical formulas. Rather, small sample estimates must be examined carefully regarding both their variability under repeated sampling and their “sensitivity” relative to various analytical methods and assumptions. The variability of small sample response estimates has been studied by Dixon and others. This paper considers the sensitivity of these estimates to such key assumption alternatives as, for example, minimum chi square analysis versus maximum likelihood analysis, and an underlying extreme value (smallest) response distribution versus a normal response distribution. Engineering assessment of the “accuracy” of the estimated median fatigue limit requires careful consideration of both its statistical variability and its analytical sensitivity as established herein.

ABSIXACT: This paper describes an experimental study on the tensile creep behavior of a random continuous fiber mat reinforced polypropylene composite, commercially known as Azdel. A special experimental setup was designed and used to conduct these creep tests at 75 and 100°C with stress levels ranging from 30 to 60 percent of the tensile strength of Azdel. The test duration was 3,000 hours or the time to creep rupture response, whichever occurred first. A straight line on a semi-log plot adequately described the relationship between the imposed stress and the creep rupture response time for the range of stress levels studied. Moreover, the estimated secondary creep mte was inversely related to the observed creep rupture response time. INTRODUCI'IONELEcriNG COMPOSITE MATERIALS for long term applications at elevated S temperatures requires knowledge of their creep behavior. In creep tests, a constant stress is applied on the specimen and the strain is measured as a function of time. The increase in strain with time is known as creep. Depending on the stress level and ambient temperature, the specimen can also fail, thereby terminating the creep test. This fidure phenomenon is known as creep rupture and occurs at stresses much lower than the static tensile strength of the material.Creep behavior of composite materials has been studied only to a limited extent, mainly due to the fact that continuous fiber composites in thermosetting matrices do not exhibit large creep strains at their use temperature 111. However, creep deformation may be relatively large if the fiber orientation is random and/or the matrix is a thermoplastic resin. There are a few reported studies on the creep characteristics of random fiber sheet molding compound (SMC-R) composites [2-3]. In these studies, the matrix was a thermosetting resin, such as Journal of COMPOSITE MATERIALS,

No abstract

Models for mean considered in this study. 4.3. Models for scatter considered in this study. 4.4. Multiple tests of the models on the aluminum 2024-T4 data set .. 4.5. Multiple tests of the models on the Inconel 718 data set. 4.6. Multiple tests of the models on the AAW data set. .. 4.7. Multiple tests of the models on the Ti64 data set .... 4.8. Multiple tests of the models on the Ti64-300 data set. 4.9. Multiple tests of the models on the wire data set .... 4.10. 95 percentile of the chi-square variate X; ........ 4.11. Comparison of the models for the mean among survivors of the LRT 4.12. Comparion of the models for the scatter among survivors of the LRT 4.13.