Statistical Concepts Research Articles

Early Childhood Cognitive Development in Integrated Early Childhood Education Citra Bakti

The ability to restate the concepts or principles that have been learned that is related to the ability to think. The ability to think is one of the developments of the cognitive shutter. The term cognitive comes from the word cognition which has the equivalent of knowing (knowing). Cognitive development is the stages of growth and change that occur occurs in the span of human life to understand, process information, solve problems and know something. Developmental theory is a theory that focuses on changes and developments in physical structure, behavior and mental function in various stages of life, starting from conception to near death. In the process of child development, there are six aspects where the development is very supportive of cognitive aspects in early childhood. The purpose of this study is to increase cognitive intelligence. in group B children, understand the characteristics of children in learning and can reflect on a teacher's self after doing learning. The approach of this method is to use a qualitative approach and use a quantitative approach in which it prioritizes analytical descriptive to solve concepts and also uses statistical numerical concepts.

Arithmetic statistically convergent on neutrosophic normed spaces

This work is concerned with several important different types of convergence that will be described on neutrosophic normed spaces. In the study, arithmetic convergence was combined with different types of statistical convergence and then integrated into the structure of neutrophic spaces established through the membership function. For this purpose, in the neutrophic normed space, firstly, some important definitions that can be established with lacunary sequence, ideal structures and arithmetic convergent, arithmetic statistical convergence concepts are given, and then some relationships between convergent sequences in this sense are examined. Then, new convergence definitions were established by evaluating lambda sequences together with arithmetic convergence and statistical convergence with the help of Neutrophic normed space structure properties. Finally, with the help of the definitions of degree of convergence, the inclusion relationship between the two set is given.

Bayesian Mixture Statistical Modeling Perspective in the Series of Diabetes Mellitus Disaster Mitigation in Malang Regions

Statistical modeling is one of the most important activities in Statistics in order to simplify complex problems in society, to make it easy, simple, and useful. The perspective of statistical modeling is very useful for society in various fields. Probabilistic-based statistical modeling concept is strongly influenced by the shape of the data distribution, data validity, and data availability. Bayesian concept approach in the statistical modeling has advantages compared to the non-Bayesian approach, which is any sample and any distribution of the data and in this case it often occurs in data in the community. In particular, the Bayesian mixture concept discusses the Bayesian approach with data specifications having a mixture (multimodal) distribution. Diabetes Mellitus (DM) is a disease that is not contagious but the side effects are very dangerous for humans and require large costs to handle. Indonesia ranks seventh in the world for the number of DM sufferers and it is estimated that in 2045, the number of DM sufferers in Indonesia will reach approximately 16.7 million people. Mitigation of DM disease in various regions in Indonesia continues to be pursued, including Malang regions. One of the efforts made is through the statistical modeling perspective of the Bayesian approach which can be used for efforts to control, prevent, treat, and overcome DM. The purpose of the study was to build a suitable Bayesian model for DM cases in Malang regions in order to map the DM case areas in Malang. The results showed that in each district area in the city of Malang it was divided into three groups based on the severity of DM sufferers. The three groups are DM sufferers in the categories of not yet severe, moderate, and severe with the model validation indicator using the smallest Kolmogorov-Smirnov value. Sukun District and Klojen District in the Malang region are two districts that need serious attention from the local government of Malang City in dealing with DM cases. Through the perspective of Bayesian statistical modeling, DM cases in five districts in the Malang area showed a mixture distribution with a different number of mixture components as the basis for regional mapping.

Long-term health monitoring of concrete and steel bridges under large and missing data by unsupervised meta learning

Long-term monitoring brings an important benefit for health monitoring of civil structures due to covering all possible unpredictable variations in measured vibration data and providing relatively adequate training samples for unsupervised learning algorithms. Despite such merits, this process may encounter large data with missing values and also yield erroneous results caused by severe environmental changes, particularly those emerge as sharp increases in modal frequencies during freezing weather. To address these challenges, this article proposes a novel unsupervised meta-learning method that entails four steps of an initial data analysis, data segmentation, subspace searching by a novel approach called nearest cluster selection, and anomaly detection. The first step intends to initially analyze measured data/features for cleaning missing samples. Next, the second step exploits spectral clustering to divide clean data into some segments. In the third step, the proposed nearest cluster selection is utilized to measure dissimilarities between the segments by a distance metric and select a cluster with the minimum distance as the representative of the main segment. Finally, a locally robust Mahalanobis-squared distance is applied by merging the concepts of robust statistics and local metric learning for online anomaly detection. The key innovations of this research contain developing a new unsupervised learning strategy alongside a locally robust distance and proposing the idea of nearest cluster selection. Long-term modal frequencies of full-scale concrete and steel bridges are used to verify the proposed method. Results demonstrate that this method succeeds in mitigating severe environmental effects and accurately detecting damage.

Application of Random Radial Point Interpolation Method to Foundations Bearing Capacity Considering Progressive Failure

In conventional analyzes of foundations failure, strengh parameters are assumed constant. However, during the failure, soil resistance exhibits maximum and residual amounts, and its strength decreases prematurely by increasing the plastic strain. In addition to change soil strengh parameters in the progressive mechanism, the non-uniform nature of the soil also causes spatial variations of these parameters. Therefore, geotechnical systems should be considered in terms of the uncertainty of soil parameters values, uncertainly using the concepts of statistics and probabilities. The purpose of this study is to investigate foundations in meshless method. In this article, radial point interpolation method (RPIM), a meshless method is proposed for simulation of soil foundation. Difficulties of methods related to mesh are solved by using this method. A code has been developed based on this method and some examples are solved for analyzing the code. In this research, a RPIM in combination with a random field was used to model the spatial variations of soil strengh properties and foundation bearing capacity analysis. For probabilistic analysis, random field is also used to determine the cohesion and the friction angle as well as the dilation angle based on their mean values and standard deviation. In order to investigate the application of the point interpolation method with randomized radial functions, a foundation with definite geometry has been analyzed deterministic and probabilistic and its safety factor has been investigated. Based on the analysis of the progressive failure modeling, it is concluded that the actual failure of the soil and the occurrence of continuous displacements occur simultaneously with the formation of a progressive mechanism of soil failure and the arrival of the slipping path to the ground. In the following, probabilistic distribution functions of the safety factor were determined by probabilistic analysis and the production of random fields, and then the statistical parameters are calculated.

Addressing confirmation bias in middle school data science education

<p style='text-indent:20px;'>More research is needed involving middle school students' engagement in the statistical problem-solving process, particularly the beginning process steps: formulate a question and make a plan to collect data/consider the data. Further, the increased availability of large-scale electronically accessible data sets is an untapped area of study. This interpretive study examined middle school students' understanding of statistical concepts involved in making a plan to collect data to answer a statistical question within a social issue context using data available on the internet. Student artifacts, researcher notes, and audio and video recordings from nine groups of 20 seventh-grade students in two gifted education pull-out classes at a suburban middle school were used to answer the study research questions. Data were analyzed using a priori codes from previously developed frameworks and by using an inductive approach to find themes.</p><p style='text-indent:20px;'>Three themes that emerged from data related to confirmation bias. Some middle school students held preconceptions about the social issues they chose to study that biased their statistical questions. This in turn influenced the sources of data students used to answer their questions. Confirmation bias is a serious issue that is exacerbated due to endless sources of data electronically available. We argue that this type of bias should be addressed early in students' educational experiences. Based on the findings from this study, we offer recommendations for future research and implications for statistics and data science education.</p>

Factors Influencing Students’ Understanding of Basic Statistical Concepts

This study focused on modelling the influence of teaching practices, students’ attitudes towards statistics, learning practices and perceived ability on students’ understanding in basic statistical concepts using Structural Equation Modelling. A total of 416 students from four Malaysian public tertiary institutions made up the sample for this study. The results showed that students’ perceived ability in statistics has a strong influence on students’ understanding of basic statistical concepts (measured by performance) and students’ attitudes toward statistics. Teaching practices also significantly affect students’ learning practices. This study found that learning practices and teaching practices do not affect students’ attitudes toward statistics and do not lead to an increase in students’ understanding of basic statistical concepts as measured through their test performance.

Samogitian Cultural Heritage in Numbers: Ignas Končius’ Statistics of Crosses and Chapels in 1911–1940

The article analyzes how studies of the Samogitian crosses and chapels, conducted in 1911–1940 by the physicist and ethnographer Ignas Končius (1886–1975) correlate with the global problematics of statistics and ethnography. Throughout the 19th century and in the beginning of the 20th century, the Lithuanian research on folklore and ethnography followed the canonic trends of statistics in the Russian Empire. While working for the Imperial Russian Geographic Society, the Latvian linguist and ethnographer Eduard Volter (1856–1941) developed his ethnographic-statistical studies aimed at defining the “tribal” constitution of the inhabitants, along with the research of their material culture. The Lithuanian scholar Končius assisted Volter in his fieldwork, absorbing his ideas and making statistical accounts of the Samogitian crosses and chapels in 1911–1940. He presented his discoveries and defined his concept of statistics in seven small books titled “Statistics of Crosses and Chapels under the Samogitian Sky”. The author of the article elucidates the connections between the statistical investigation of the Samogitian cultural heritage by Končius and the scholarly cannons of the Russian Empire, as well as the global ethnographic theory.By using the comparative historical analytical approach, she addresses the following issues: (1) characteristics of the Končius’ statistical investigation of 1912, (2) statistical accounts of the years 1932–1940, and (3) his academic connections. The author concludes that the works of Končius have many similarities with German statistics, Johann Gottfried Herder’s anthropology, and in their own way they reveal the history of Samogitia cultural heritage.

Feature selection based on absolute deviation factor for text classification

In text classification, it is necessary to perform feature selection to alleviate the curse of dimensionality caused by high-dimensional text data. In this paper, we utilize class term frequency (CTF) and class document frequency (CDF) to characterize the relevance between terms and categories in the level of term frequency (TF) and document frequency (DF). On the basis of relevance measurement above, three feature selection methods (ADF based on CTF (ADF-CTF), ADF based on CDF (ADF-CDF), and ADF based on both CTF and CDF (ADF-CTDF)) are proposed to identify relevant and discriminant terms by introducing absolute deviation factors (ADFs). Absolute deviation, a statistic concept, is first adopted to measure the relevance divergence characterized by CTF and CDF. In addition, ADF-CTF and ADF-CDF can be combined with existing DF-based and TF-based methods, respectively, which results in new ADF-based methods. Experimental results on six high-dimensional textual datasets using three classifiers indicate that ADF-based methods outperform original DF-based and TF-based ones in 89% cases in terms of Micro-F1 and Macro-F1, which demonstrates the role of ADF integrated in existing methods to boost the classification performance. In addition, findings also show that ADF-CTDF ranks first averagely among multiple datasets and significantly outperforms other methods in 99% cases.

Measuring landing independence and interactions using statistical physics

Benchmarking different aspects of air transport, and especially airport operations, is a research topic both important and complex, as proposed solutions are usually limited by data availability and underlying hypotheses. We here propose the use of two well-known statistical physics concepts, the Hurst exponent and the irreversibility, to measure the presence of interactions between landing flights from a macro-scale perspective. We firstly present a synthetic model of landings at an airport, showing how the two metrics are able to detect interactions arising from high volumes of traffic. Real landing data for twelve major European airports and ten Chinese ones are then analysed, showing that more interactions are present than what expected for the corresponding traffic. Results are nevertheless not homogeneous, with some airports (most notably Milano Malpensa, Madrid and Paris Charles de Gaulle) yielding statistically significant high values; and with Frankfurt airport having been especially impacted by the COVID-19 pandemics, which resulted in an increase in interactions in spite of a reduction in traffic. Some possible reasons behind these results, and their operational significance in terms of efficiency and safety, are finally discussed.

The Chair's Role in Assurance of Learning

The Chair's Role in Assurance of Learning

Lembar Kerja Berbasis Discovery-Learning untuk Pembelajaran Matematika Penyajian Data Statistik Kelas VII

Students’understanding of mathematical concepts is important to be aquired, due to the use of mathematical concepts is variety of context in everyday life. One of the mathematical material that is widely used in real life is concept of Statistics: presenting statistical data. However, based on the facts in the field and results of several previous studies, they show that many students have difficulty presenting statistical data. This is due to a lack of initial understanding. Therefore, in this study, teaching materials are developed to support learning. In this study, Discovery Learning-based worksheets are developed on the material of presenting statistical data for Junior High School students (SMP/MTs). This study is a development research by applying ADDIE model. Based on results of expert validation, which is consist of mathematics lecturers and teachers, an average rating of 87.5% was obtained and met "Valid" criteria. LKPD obtained an assessment result of 87.59% which met "Practical" criteria. The results of implementation phase obtained an average learning outcome of 81.63 and is above KKM. This shows that the use of Discovery Learning-based LKPD can help students learn concept of Statistics: presenting statistical data.

A Flexible Ensemble Learning Method for Survival Extrapolation.

Survival extrapolation is an important statistical concept for estimating long-term survival from short-term clinical trial data. It is widely used in health technology assessment (HTA). Survival extrapolation is often performed by fitting one or two parametric models selected based on experience or selecting a model based on some goodness of fit statistics from a predefined collection of models. The main challenge in survival extrapolation is that the result is sensitive to model misspecification. In this study, we aim to propose a new approach that has a robust performance for survival extrapolation. We propose a new method called Ensemble Learning for Survival Extrapolation (ELSE). Instead of selecting one best model from a predefined collection, ELSE builds an ensemble model based on a collection of models from the model library. Under this framework, we construct a point estimate of the long-term survival with a weighted average of the estimates of all candidate models and derive confidence intervals using nonparametric bootstrap. With our extensive numerical simulation studies, the proposed ELSE method shows better performance than the traditionally used model selection procedure based on Akaike Information Criterion (AIC). With a real data application to the Therapeutically Applicable Research to Generate Effective Treatment Wilms Tumor project (TARGET-WT) data, the ELSE method produces better survival extrapolation results in point estimate accuracy and confidence interval coverage. We developed an ensemble learning method for survival extrapolation (ELSE) which is robust for the underline data model and has good real data performance.

Ruthenium Catalyzed Oxidative Cleavage of High Oleic Sunflower Oil: Considerations Regarding the Synthesis of a Fully Biobased Triacid

AbstractTricarboxylic acids are molecules of interest for the synthesis of highly cross‐linked polymers, for instance, for the curing of epoxy resins. Herein, a synthesis route to a novel high oleic sunflower oil based triacid is described by applying a ruthenium catalyzed oxidative cleavage of its double bonds. A statistical concept is devised for the prediction of the yields of mono‐, di‐, and trifunctional derivatives that can be formed from high oleic sunflower oil, depending on the overall conversion of double bonds into this functional group and the overall oleic acid content of the used oil. This concept proved to be highly useful for the explanation of seemingly moderate triacid yields, which are inherently dependent on the unsaturated fatty acid content of the used oil. All obtained sunflower oil based polyacids are fully analyzed by attenuated total reflection infrared spectroscopy (ATR‐IR), electrospray ionization mass spectrometry (ESI‐MS), 1H, 13C, and quantitative 31P nuclear magnetic resonance (NMR) spectroscopy. In addition, a more sustainable purification procedure is developed to obtain a polymerizable mixture of polyacids containing more than 2.0 carboxylic acids per molecule in average.Practical applications: Tricarboxylic acids are valuable monomers for the synthesis of cross‐linked polymers. The herein reported procedure represents a hitherto unknown synthesis route towards a new triacid and polyacid mixture directly from high oleic sunflower oil.

Sufficient statistic and Rao–Blackwell theorem in quantum probability

In the decision-theoretic foundation of Classical Statistics, the idea and concept of a sufficient statistic play a central role. Here a parallel concept of a sufficient statistic in quantum probability is proposed and as a consequence, various levels of “quantum Rao–Blackwell theorem” are proven.

ANALYSIS OF TEACHERS’ CONFIDENCE IN TEACHING MATHEMATICS AND STATISTICS

The purpose of this study is to explore the expressions of confidence by a group of South African mathematics teachers about teaching mathematics and statistics concepts from various perspectives. The participants were 75 mathematics teachers who were teaching Grades 4 to 12 in KwaZulu-Natal (KZN) schools. They then were asked to express their opinion on their level of confidence in teaching using 17 confidence items on a 5 point Likert scale, graded from very low to very high. The study drew upon factor analysis, Rasch analysis as well as regression analysis. The findings suggest that teachers’ confidence in teaching mathematics concepts is quite different from their confidence in teaching statistics concepts and those which require connections across topics. Furthermore, the study has also found differences in teachers’ confidence level by gender during the middle teaching years as well as a significant interaction between phases of teaching and whether or not teachers completed additional professional qualifications.

Pandemic and Infodemic – Which One Is More Dangerous?

Abstract This article examines the relationship between social sustainability and the fake news phenomenon in the light of the COVID-19 epidemic. Using existing statistical data and relevant media-related concepts, it compares the harmful impacts of the pandemic and the accompanying infodemic. The problem can be placed among the components of hybrid warfare. Examples, statistics, and research on COVID-19-related fake news are discussed with a focus on efforts for building resilience against fake news.

Five Years of Gene Networks Modeling in Single-cell RNA-sequencing Studies: Current Approaches and Outstanding Challenges

Abstract: Single-cell RNA-sequencing (scRNA-seq) is a rapidly growing field in transcriptomics, which generates a tremendous amount of gene expression data at the single-cell level. Improved statistical approaches and tools are required to extract informative knowledge from such data. Gene network modeling and analysis is one such approach for downstream analysis of scRNA-seq data. Therefore, newer and innovative methods have been introduced in the literature. These approaches greatly vary in their utility, basic statistical concepts, models fitted to the data, etc. Therefore, we present a comprehensive overview of the available approaches for gene network modeling and analysis in single-cell studies, along with their limitations. We also classify the approaches based on different statistical principles and present a class-wise review. We discuss the limitations that are specific to each class of approaches and how they are addressed by subsequent classes of methods. We identify several biological and methodological challenges that must be addressed to enable the development of novel and innovative single-cell gene network inference approaches and tools. These new approaches will be able to analyze the singlecell data efficiently and accurately to better understand the biological systems, increasing the specificity, sensitivity, utility, and relevance of single-cell studies. Furthermore, this review will serve as a catalog and provide guidelines to genome researchers and experimental biologists for objectively choosing the better gene network modeling approach.

STUDENT PERFORMANCE IN CURRICULA CENTERED ON SIMULATION-BASED INFERENCE

Using simulation-based inference (SBI), such as randomization tests, as the primary vehicle for introducing students to the logic and scope of statistical inference has been advocated with the potential of improving student understanding of statistical inference and the statistical investigative process as a whole. Moving beyond the individual class activity, entirely revised introductory statistics curricula centering on these ideas have been developed and tested. Preliminary assessment data have been largely positive. In this paper, we discuss three years of cross-institutional tertiary-level data from the United States comparing SBI-focused curricula and non-SBI curricula (86 distinct institutions). We examined several pre/post measures of conceptual understanding in the introductory algebra-based course using multi-level modelling to incorporate student-level, instructor-level, and institutional-level covariates. We found that pre-course student characteristics (e.g., prior knowledge) were the strongest predictors of student learning, but also that textbook choice can still have a meaningful impact on student understanding of key statistical concepts. In particular, textbook choice was the strongest “modifiable” predictor of student outcomes of those examined, with simulation-based inference texts yielding the largest changes in student learning outcomes. Further research is needed to elucidate the particular aspects of SBI curricula that contribute to observed student learning gains.

Effective Think-Pair-Share Pedagogical Strategy to Improve Inferential Statistics Concept Understanding

Abstract : The Think-Pair-Share (TPS) technique is believed to encourage students who are shy to participate in classrooms. In this method, the instructor poses a problem and the students work on it individually to begin with. Next, they work in pairs and the final step is a discussion held by the entire classroom. The strategy allows students to think for a while, then talk to their partners, or group mates and share ideas. The students are encouraged and motivated to participate in the classroom. TPS is one of the cooperative learning methods that promotes group participation, discussion, critical thinking about the question being considered, presentation of personal ideas and opinions in a supportive environment. Statistics for Computer Science being a core subject and buzz word for the field of computer science engineering and data science, this strategy was implemented for the third-year computer engineering students to bring about an improvement in the conceptual understanding of inferential statistics concept. This proposed research work is an inferential statistical analysis of implemented TPS activity. Pre and post test was conducted for the inferential statistics concept on a random samples of 25 students. Parametric paired t-test is used to validate the hypothesis. Statistical results show that there is an improvement in concept understanding after TPS activity. Moreover, the outcome of the experiment along with feedback from the students related to this strategy are also excellent. Index Terms : Think-pair-share, cooperative learning, t-test, Inferential Statistics