• Communications of Mathematical Education
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• v.36 no.4
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• pp.535-558
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• 2022

In the current society, where statistical literacy is recognized as an important ability, statistical education utilizing the statistical problem solving, a series of processes for performing statistics, is required. The result interpretation stage is especially important because many forms of statistics we encounter in our daily lives are the information from the analysis results. In this study, data on private education were provided to pre-service mathematics teachers, and a project was carried out in which they could experience a statistical problem solving process using the population mean estimation. Therefore, this study analyzed the characteristics shown by pre-service mathematics teachers during the result interpretation stage. First, many pre-service mathematics teachers interpreted results based on the data, but the inference was found to be a level of 2 which is not reasonable. Second, pre-service mathematics teachers in this study made various kinds of decisions related to public education, such as improving classes and after-school classes. In addition, the pre-service mathematics teachers in this study seem to have made decisions based on statistical analysis results, but they made general decisions that teachers could make, rather than specifically. Third, the pre-service mathematics teachers of this study were reflective about the question formulation stage, organizing & reducing data stage, and the result interpretation stage, but no one was reflective about the result interpretation stage.

• Research in Mathematical Education
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• v.10 no.4 s.28
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• pp.259-288
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• 2006

This study considers the performance of students in Grades 5 to 10 on four tasks assessing students' ability to evaluate data presented in numerical form, for example, in a list or table, or in graphical form, for example, in a frequency graph or scatter graph. The ability to tell a story from data or a graph is an important aspect of statistical literacy. The samples provide the opportunity to consider the association of two pairs of items, one from each type of interpretation, numerical and graphical. Educational implications for the outcomes and the classroom use of the items are considered.

• Molecular & Cellular Toxicology
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• v.1 no.4
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• pp.281-283
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• 2005

Extraction of biologically meaningful data and their validation are very important for toxicogenomics study because it deals with huge amount of heterogeneous data. BINGO is an annotation mining tool for biological interpretation of gene groups. Several statistical modeling approaches using Gene Ontology (GO) have been employed in many programs for that purpose. The statistical methodologies are useful in investigating the most significant GO attributes in a gene group, but the coherence of the resultant GO attributes over the entire group is rarely assessed. BINGO complements the statistical methods with graph-theoretic measures using the GO directed acyclic graph (DAG) structure. In addition, BINGO visualizes the consistency of a gene group more intuitively with a group-based GO subgraph. The input group can be any interesting list of genes or gene products regardless of its generation process if the group is built under a functional congruency hypothesis such as gene clusters from DNA microarray analysis.

• The Plant Pathology Journal
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• v.38 no.3
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• pp.175-181
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• 2022

Statistical analysis of data is an integral part of research projects in all scientific disciplines including the plant pathology. Appropriate design, application and interpretation of statistical analysis are also, therefore, at the center of publishing and properly evaluating studies in plant pathology. A survey of research works published in the Plant Pathology Journal, however, cast doubt on high standard of statistical analysis required for scientific rigor and reproducibility in the journal. Here I first describe, based on the survey of published works, what mistakes are commonly made and what components are often lacking during statistical analysis and interpretation of its results. Next, I provide possible remedies and suggestions to help guide researchers in preparing manuscript and reviewers in evaluating manuscripts submitted to the Plant Pathology Journal. This is not aiming at delineating technical and practical details of particular statistical methods or approaches.

• The Mathematical Education
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• v.55 no.3
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• pp.317-334
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• 2016

Knowledge and data interpretation on statistical estimation was important to have statistical literacy that current curriculum was said not to satisfy. The author investigated mathematics teachers' MKT on statistical estimation concerning interpretation of confidence interval by using questionnaire and interview. SMK of teachers' confidence was limited to the area of textbooks to be difficult to interpret data of real life context. Most of teachers wrongly understood SMK of interpretation of confidence interval to have influence upon PCK making correction of students' wrong concept. SMK of samples and sampling distribution that were basic concept of reliability and confidence interval cognized representation of samples rather exactly not to understand importance and value of not only variability but also size of the sample exactly, and not to cognize appropriateness and needs of each stage from sampling to confidence interval estimation to have great difficulty at proper teaching of statistical estimation. PCK that had teaching method had problem of a lot of misconception. MKT of sample and sampling distribution that interpreted confidence interval had almost no relation with teachers' experience to require opportunity for development of teacher professionalism. Therefore, teachers were asked to estimate statistic and to get confidence interval and to understand concept of the sample and think much of not only relationship of each concept but also validity of estimated values, and to have knowledge enough to interpret data of real life contexts, and to think and discuss students' concepts. So, textbooks should introduce actual concepts at real life context to make use of exact orthography and to let teachers be reeducated for development of professionalism.

• Communications for Statistical Applications and Methods
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• v.3 no.1
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• pp.11-19
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• 1996

Canonical correlation analysis is a multivariate technique for identifying and quantifying the statistical relationship between two sets of variables. Like most multivariate techniques, the main objective of canonical correlation analysis is to reduce the dimensionality of the dataset. It would be particularly useful if high dimensional data can be represented in a low dimensional space. In this study, we will construct statistical graphs for paired sets of multivariate data. Specifically, plots of the observations as well as the variables are proposed. We discuss the geometric interpretation and goodness-of-fit of the proposed plots. We also provide a numerical example.

• Journal of Preventive Medicine and Public Health
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• v.40 no.2
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• pp.108-113
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• 2007

When conducting large-scale cohort studies, numerous statistical issues arise from the range of study design, data collection, data analysis and interpretation. In genomic cohort studies, these statistical problems become more complicated, which need to be carefully dealt with. Rapid technical advances in genomic studies produce enormous amount of data to be analyzed and traditional statistical methods are no longer sufficient to handle these data. In this paper, we reviewed several important statistical issues that occur frequently in large-scale genomic cohort studies, including measurement error and its relevant correction methods, cost-efficient design strategy for main cohort and validation studies, inflated Type I error, gene-gene and gene-environment interaction and time-varying hazard ratios. It is very important to employ appropriate statistical methods in order to make the best use of valuable cohort data and produce valid and reliable study results.

• Journal of Veterinary Clinics
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• v.26 no.3
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• pp.189-199
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• 2009

With the growing importance of evidence-based medicine, clinical or biomedical research relies critically on the validity and reliability of data, and the subsequent statistical inferences for medical decision-making may lead to valid conclusion. Despite widespread use of analytical techniques in papers published in the Journal of Veterinary Clinics statistical errors particularly in design of experiments, research methodology or data analysis methods are commonly encountered. These flaws often leading to misinterpretation of the data, thereby, subjected to inappropriate conclusions. This article is the first in a series of nontechnical introduction designed not to systemic review of medical statistics but intended to provide the journal readers with an understanding of common statistical concepts, including data scale, selection of appropriate statistical methods, descriptive statistics, data transformation, confidence interval, the principles of hypothesis testing, sampling distribution, and interpretation of results.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.937-943
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• 2000

Data mining usually deal with undesigned massive data containing many variables for which their characteristics and association rules are unknown, therefore it is actually not easy to interpret the results of analysis. In this paper, it is shown that decision tree can be very useful in interpreting data mining prediction model using two real examples.

• Communications for Statistical Applications and Methods
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• v.23 no.6
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• pp.467-478
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• 2016

The accelerated failure time model or accelerated life model relates the logarithm of the failure time linearly to the covariates. The parameters in the model provides a direct interpretation. In this paper, we review some newly developed practically useful estimation and inference methods for the model in the analysis of right censored data.