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.

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.

Semiparametric models for antagonistic drug interactions

1994 ◽  
Vol 76 (5) ◽  
pp. 2224-2233 ◽  
Author(s):  
I. F. Troconiz ◽  
L. B. Sheiner ◽  
D. Verotta
Keyword(s):  
Data Analysis ◽  
Drug Interactions ◽  
Exploratory Data Analysis ◽  
Real Data ◽  
Data Set ◽  
Semiparametric Modeling ◽  
New Class ◽  
Semiparametric Approach ◽  
Exploratory Data ◽  
Model Determination

A new class of models to describe antagonistic drug interactions are presented. They are semiparametric in that they use nonparametric functions (splines) but are forced to obey certain constraints corresponding to reasonable assumptions. We propose the models primarily for exploratory data analysis, but they may also be definitive models for such purposes as predicting future responses. Certain problems that arise in semiparametric modeling, such as model selection, are addressed so that we can propose a relatively automatic and objective approach to model determination. We demonstrate the applicability of the class of models we propose to two real data set examples involving pain relief response to opioid agonists/antagonists. The results suggest that the semiparametric approach is particularly useful when unusual shapes link dose to response.

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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