Student-generated Data in Elementary Statistics

1990 ◽  
Vol 83 (4) ◽  
pp. 322-325
Author(s):  
Ken Kundert
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Mathematics Curriculum ◽  
Box Plots ◽  
Curve Smoothing ◽  
Statistics Course ◽  
School Classroom ◽  
Exploratory Data ◽  
High School Classroom ◽  
Elementary Statistics

The recent increased emphasis on the teaching of statistics in the high school classroom has focused primarily on the techniques of exploratory data analysis. Topics include stem-and-leaf plots, box plots, median-fit lines, and curve smoothing. A number ofhigh schools, however, still teach a course in statistics for the college-bound student. Included in this course are many of the classical topics of statistics generally found in an elementary statistics course taught to college students, with only intermediate algebra as a prerequisite. Although this article highlights selected topics in such a course and describes how student-generated data can be used to illustrate these topics, the basic idea can profitably be used throughout the mathematics curriculum.

Professional Development: Preparing Teachers to Present Techniques of Exploratory Data Analysis

1994 ◽  
Vol 1 (2) ◽  
pp. 166-172
Author(s):  
Christine A. Browning ◽  
Dwayne E. Channell ◽  
Ruth A. Meyer
Keyword(s):  
Professional Development ◽  
Elementary School ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Real Data ◽  
National Council ◽  
Evaluation Standards ◽  
School Classroom ◽  
Exploratory Data ◽  
Mathematics Curricula

Why Study Statistics? We are bombarded every day with an overwhelming amount of information presented in various forms. If we are to interpret and understand the information, we must be familiar with the methods and tools of statistics. Developing an understanding and an appreciation of statistics should begin in the elementary school classroom. The National Council of Teachers of Mathematics's document Curriculum and Evaluation Standards for School Mathematics (NCTM 1989) states that the mathematics curricula for grades K-4 and 5-8 should include experiences with data analysis that involve students in collecting, organizing, describing, and interpreting data. Burrill (1990) suggests that such experiences should use real data whenever possible, progress from the concrete to the pictorial to the abstract, and use calculators and computers whenever appropriate.

scholarly journals Range-Box Plotting Relating to Discrete Distribution

MATEMATIKA ◽  
2018 ◽  
Vol 34 (2) ◽  
pp. 187-204 ◽  
Author(s):  
Mohd Bakri Adam ◽  
Babaginda Ibrahim Babura ◽  
Kathiresan Gopal
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Discrete Distribution ◽  
Box Plots ◽  
Long Time ◽  
Exploratory Data ◽  
Asymmetrical Shape ◽  
Box Plot ◽  
The Creation

The box plot has been used for a very long time since 70s in checking the existence of outliers and the asymmetrical shape of data. The existing box plot is constructed using ve values of statistics calculated from either the discrete or continous data. Many improvement of box plots have deviated from the elegant and simplier approach of exploratory data analysis by incorporating many other statistic values resulting the turning back of the noble philosophy behind the creation of box plot.The modication using range value with the minimum and maximum values are being incorporated to suit the need of selected discrete distribution when outliers is not an important criteria anymore. The new modication of box plot is not based on the asymmetrical shape of distribution but more on the spread of the data and partitioning data into range measure. The new propose name for the box plot with only three values of statistics is called range-box plot.

scholarly journals An Approach Based on the Exploratory Data Analysis to Relate the Wear Behavior with the Microstructure of Ductile Cast Irons

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Jesus A. Basurto-Hurtado ◽  
L. A. Morales-Hernández ◽  
Roque A. Osornio-Rios ◽  
Aurelio Dominguez-Gonzalez
Keyword(s):  
Image Processing ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Wear Behavior ◽  
Cast Irons ◽  
Morphological Image Processing ◽  
Box Plots ◽  
Exploratory Data ◽  
Morphological Image ◽  
Microstructure Of The Material

The aim of this work is to propose a new methodology to relate Ductile Cast Irons (DCIs) wear behavior with the separation distances and sizes of the graphite nodules through an Exploratory Data Analysis (EDA). This methodology consists of morphological image processing tools (compacity and size distribution curves), an EDA performed by the use of box plots and an EDA-based section classifying algorithm. This algorithm classifies the microstructure of DCIs into classes and levels grouping different behaviors of the separation distances and sizes of graphite nodules. Finally, it was found, through a number of tribological tests, that the obtained classes and levels have a different wear behavior. The results achieved by this methodology were compared with those of traditional techniques used to characterize the microstructure of the material.

Box Plots: Basic and Advanced

1990 ◽  
Vol 83 (2) ◽  
pp. 108-112
Author(s):  
James L. Mullenex
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Statistical Techniques ◽  
Graphical Representations ◽  
Box Plots ◽  
Exploratory Data

Box plots are used for the purpose of analyzing and displaying important features of sets of data. More specifically, box plots are used as graphical representations of five-number summaries. Box plots and five-number summaries are new statistical techniques that were developed by John W. Tukey of Bell Telephone Laboratories. They are parts of a larger set of modern statistical techniques known collectively as exploratory data analysis, or EDA.

scholarly journals MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets

2019 ◽  
Author(s):  
Urminder Singh ◽  
Manhoi Hur ◽  
Karin Dorman ◽  
Eve Wurtele
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Permutation Test ◽  
Human Cancer ◽  
Easy Access ◽  
Differential Correlation ◽  
Box Plots ◽  
Scatter Plots ◽  
Hidden Knowledge ◽  
Exploratory Data

The diverse and growing omics data in public domains provide researchers with a tremendous opportunity to extract hidden knowledge. However, the challenge of providing domain experts with easy access to these big data has resulted in the vast majority of archived data remaining unused. Here, we present MetaOmGraph (MOG), a free, open-source, standalone software for exploratory data analysis of massive datasets by scientific researchers. Using MOG, a researcher can interactively visualize and statistically analyze the data, in the context of its metadata. Researchers can interactively hone-in on groups of experiments or genes based on attributes such as expression values, statistical results, metadata terms, and ontology annotations. MOG’s statistical tools include coexpression, differential expression, and differential correlation analysis, with permutation test-based options for significance assessments. Multithreading and indexing enable efficient data analysis on a personal computer, with no need for writing code. Data can be visualized as line charts, box plots, scatter plots, and volcano plots. A researcher can create new MOG projects from any data or analyze an existing one. An R-wrapper lets a researcher select and send smaller data subsets to R for additional analyses. A researcher can save MOG projects with a history of the exploratory progress and later reopen or share them. We illustrate MOG by case studies of large curated datasets from human cancer RNA-Seq, in which we assembled a list of novel putative biomarker genes in different tumors, and microarray and metabolomics from A. thaliana.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Covid-19 Cases in India: A Visual Exploratory Data Analysis Model (Preprint)

2020 ◽  
Author(s):  
Jayesh S
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Case Fatality ◽  
Public Health Emergency ◽  
Virus Spread ◽  
Analysis Model ◽  
Case Fatality Ratio ◽  
First Case ◽  
Exploratory Data ◽  
The Government

UNSTRUCTURED Covid-19 outbreak was first reported in Wuhan, China. The deadly virus spread not just the disease, but fear around the globe. On January 2020, WHO declared COVID-19 as a Public Health Emergency of International Concern (PHEIC). First case of Covid-19 in India was reported on January 30, 2020. By the time, India was prepared in fighting against the virus. India has taken various measures to tackle the situation. In this paper, an exploratory data analysis of Covid-19 cases in India is carried out. Data namely number of cases, testing done, Case Fatality ratio, Number of deaths, change in visits stringency index and measures taken by the government is used for modelling and visual exploratory data analysis.

scholarly journals Multivariate Statistical Approach for Nephrines in Women with Obesity

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1393
Author(s):  
Ralitsa Robeva ◽  
Miroslava Nedyalkova ◽  
Georgi Kirilov ◽  
Atanaska Elenkova ◽  
Sabina Zacharieva ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Metabolic Syndrome ◽  
Data Analysis ◽  
Stress Response ◽  
Exploratory Data Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Obese Women ◽  
Exploratory Data

Catecholamines are physiological regulators of carbohydrate and lipid metabolism during stress, but their chronic influence on metabolic changes in obese patients is still not clarified. The present study aimed to establish the associations between the catecholamine metabolites and metabolic syndrome (MS) components in obese women as well as to reveal the possible hidden subgroups of patients through hierarchical cluster analysis and principal component analysis. The 24-h urine excretion of metanephrine and normetanephrine was investigated in 150 obese women (54 non diabetic without MS, 70 non-diabetic with MS and 26 with type 2 diabetes). The interrelations between carbohydrate disturbances, metabolic syndrome components and stress response hormones were studied. Exploratory data analysis was used to determine different patterns of similarities among the patients. Normetanephrine concentrations were significantly increased in postmenopausal patients and in women with morbid obesity, type 2 diabetes, and hypertension but not with prediabetes. Both metanephrine and normetanephrine levels were positively associated with glucose concentrations one hour after glucose load irrespectively of the insulin levels. The exploratory data analysis showed different risk subgroups among the investigated obese women. The development of predictive tools that include not only traditional metabolic risk factors, but also markers of stress response systems might help for specific risk estimation in obesity patients.

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