• Journal of Ocean Engineering and Technology
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• v.24 no.6
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• pp.97-102
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• 2010

This paper deals with the variability of short term creep rupture time based on previous creep rupture tests and the statistical methodology of the creep life prediction. The results of creep tests performed using constant uniaxial stresses at 600, 650, and $700^{\circ}C$ elevated temperatures were used for a statistical analysis of the inter-specimen variability of the short term creep rupture time. Even under carefully controlled identical testing conditions, the observed short-term creep rupture time showed obvious inter-specimen variability. The statistical aspect of the short term creep rupture time was analyzed using a Weibull statistical analysis. The effect of creep stress on the variability of the creep rupture time was decreased with an increase in the stress level. The effect of the temperature on the variability also decreased with increasing temperature. A long term creep life prediction method that considers this statistical variability is presented. The presented method is in good agreement with the Lason-Miller Parameter (LMP) life prediction method.

• School Mathematics
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• v.14 no.4
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• pp.495-516
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• 2012

This study distinguished thinking related to statistical variability into six components - the noticing of variability, the explanation of variability, the control of variability, the modeling of variability, the understanding of samples, and the understanding of sampling distribution and investigated the relationships among the thinking components. This study found that this distinction of thinking components related to statistical variability is reasonable. The results showed that each correlation coefficient of the modeling of variability, the understanding of samples, and the understanding of sampling distribution with regard to the noticing of variability, the explanation of variability, and the control of variability is similar. Based on this results, new variable, the understanding of sampling, has been drawn. The results also showed that while the noticing of variability and the control of variability influence the understanding of sampling, the explanation of variability does not influence it.

• Journal of Educational Research in Mathematics
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• v.20 no.4
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• pp.493-509
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• 2010

Researchers have suggested that educators have to focus their attention on variability and reasoning about variation as means of developing students' statistical thinking in school mathematics. This paper investigated knowledge for the teaching of variability and reasoning about variation; what are sources of variability, how to cope with variability, what are types of variability, how to recognize variability, and the relationship between statistical problem solving and variability. The results involve: discussion on the sources of variability and how to cope with variability promotes students' awareness of different types of variability and students' motivation in the following steps in the statistical activity; emphasis on reasoning about variation in teaching representation of data accords with objectives of statistics education; reexamination of curriculum for statistics education is needed, which has a content-oriented arrangement.

• Journal of Educational Research in Mathematics
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• v.20 no.3
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• pp.339-356
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• 2010

It is important for children to develop statistical reasoning as they think through data. In particular, it is imperative to provide children instructional situations in which they are encouraged to consider variability in data because the ability to reason about variability is fundamental to the development of statistical reasoning. Many researchers argue that even highperforming mathematics students show low levels of statistical reasoning; interventions attending to pedagogical concerns about child ren's statistical reasoning are, thus, necessary. The purpose of this study was to investigate 15 gifted elementary students' various ways of understanding important statistical concepts, with particular attention given to 3 students' reasoning about data that emerged as they engaged in the process of generating and graphing data. Analysis revealed that in recognizing variability in a context involving data, mathematically gifted students did not show any difference from previous results with general students. The authors suggest that our current statistics education may not help elementary students understand variability in their development of statistical reasoning.

• Journal of Educational Research in Mathematics
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• v.21 no.2
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• pp.201-220
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• 2011

This study investigates levels of thinking of elementary and middle school students doing their tasks of explaining and dealing with variability. According to results, on the task of explaining variability in the measurement settings five levels of thinking were identified: a lack of understanding of explanation of the causes, an insufficient understanding of the causes, an offer of physical causes, consideration of unexplained causes as the source of variability, and consideration of unexplained causes as quasi-chance variability. Also, in the chance settings five levels of thinking were identified: a lack of understanding of explanation of the causes, an insufficient understanding of the causes, an offer of physical causes, recognition of chance variability, and consideration of causes of distribution. On the task of dealing with variability in both the measurement and chance settings five levels of thinking were identified: a lack of understanding of dealing with variability, no physical control and improper statistical control, no physical control and proper statistical control, physical control and improper statistical control, and physical control and proper statistical control.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.317-323
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• 2010

Measures are very useful tools for comparing the shape variability in statistical shape analysis. For examples, the Procrustes statistic(PS) is isolated measure, and the mean Procrustes statistic(MPS) and the root mean square measure(RMS) are overall measures. But these measures are very subjective, complicated and moreover these measures are not statistical for comparing the shape variability. Therefore we need to study some tests. It is well known that the Hotelling's $T^2$ test is used for testing shape variability of two independent samples. And for testing shape variabilities of several independent samples, instead of the Hotelling's $T^2$ test, one way analysis of variance(ANOVA) can be applied. In fact, this one way ANOVA is based on the balanced samples of equal size which is called as BANOVA. However, If we have unbalanced samples with unequal size, we can not use BANOVA. Therefore we propose the unbalanced analysis of variance(UNBANOVA) for testing shape variabilities of several independent samples of unequal size.

• The Mathematical Education
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• v.56 no.1
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• pp.19-39
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• 2017

Taking samples of data and using samples to make inferences about unknown populations are at the core of statistical investigations. So, an understanding of the nature of sample as statistical thinking is involved in the area of statistical literacy, since the process of a statistical investigation can turn out to be totally useless if we don't appreciate the part sampling plays. However, the conception of sampling is a scheme of interrelated ideas entailing many statistical notions such as repeatability, representativeness, randomness, variability, and distribution. This complexity makes many people, teachers as well as students, reason about statistical inference relying on their incorrect intuitions without understanding sample comprehensively. Some research investigated how the concept of a sample is understood by not only students but also teachers or preservice teachers, but we want to identify preservice secondary mathematics teachers' understanding of sample as the statistical literacy by a qualitative analysis. We designed four items which asked preservice teachers to write their understanding for sampling tasks including representativeness and variability. Then, we categorized the similar responses and compared these categories with Watson's statistical literacy hierarchy. As a result, many preservice teachers turned out to be lie in the low level of statistical literacy as they ignore contexts and critical thinking, expecially about sampling variability rather than sample representativeness. Moreover, the experience of taking statistics courses in university did not seem to make a contribution to development of their statistical literacy. These findings should be considered when design preservice teacher education program to promote statistics education.

• KCI Concrete Journal
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• v.12 no.2
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• pp.11-21
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• 2000

It is not easy to correctly predict deflections of reinforced concrete beams and one-way slabs due to the variability of parameters involved in the calculation of deflections. Monte Carlo simulation is used to assess the variability of deflections with known statistical data and probability distributions of variables. A deterministic deflection value is obtained using the layered beam model based on the finite element approach in which a finite element is divided into a number of layers over the depth. The model takes into account nonlinear effects such as cracking, creep and shrinkage. Statistical parameters were obtained from the literature. For the assessment of variability of deflections, 12 cases of one-way slabs and T-beams are designed on the basis of ultimate moment capacity. Several results of a probabilistic study are presented to indicate general trends indicated by results and demonstrate the effect of certain design parameters on the variability of deflections. From simulation results, the variability of deflections relies primarily on the ratio of applied moment to cracking moment and the corre-sponding reinforcement ratio.

• Korean Management Science Review
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• v.31 no.3
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• pp.27-39
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• 2014

We derive a generalized statistic form of Q control chart, which is especially suitable for short run productions and start-up processes, for the detection of process mean shifts. The generalization means that the derived control chart statistic concurrently uses within lot variability and between lot variability to explain the process variability. The latter variability source is noticeably prevalent in lot type production processes including semiconductor wafer fabrications. We first obtain the generalized Q control chart statistic when both the process mean and process variance are unknown, which represents the case of implementing statistical process control charting for short run productions and start-up processes. Also, we provide the corresponding generalized Q control chart statistics for the rest of three cases of previous Q control chart statistics : (1) both the process mean and process variance are known (2) only the process mean is unknown and (3) only the process variance is unknown.

• School Mathematics
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• v.9 no.1
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• pp.29-44
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• 2007

The aim of statistics education is to enhance statistical thinking. Variability is the key components of statistical thinking. The research has been reviewed preceding research about variability of data. Proceeding from what has been considered above, this research developed learning materials that investigated the concept of variability as it relates to Freudenthal's context by having students sort a particular context. The research is executed the case study evidently aimed at Junior High School 3rd Grade Student's Understanding of Variability. The study of variability in data can be an important start to reach a testing of statistical hypothesis; students reduce data and draw graphs by relating probability distribution to relative frequency and normal distribution. Thus, this study offers basic materials into developing both contents and methods of education need to consider with this sense of purpose held by students to achieve this goal.