Sequential accuracy in parameter estimation for population correlation coefficients.

2019 ◽  
Vol 24 (4) ◽  
pp. 492-515 ◽  
Author(s):  
Ken Kelley ◽  
Francis Bilson Darku ◽  
Bhargab Chattopadhyay
Keyword(s):  
Parameter Estimation ◽  
Correlation Coefficients ◽  
Population Correlation

scholarly journals Modeling E-Behaviour, Personality and Academic Performance with Machine Learning

2021 ◽  
Vol 11 (22) ◽  
pp. 10546
Author(s):  
Serepu Bill-William Seota ◽  
Richard Klein ◽  
Terence van Zyl
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Academic Performance ◽  
Student Performance ◽  
Correlation Coefficients ◽  
Students At Risk ◽  
Intervention Process ◽  
Population Correlation ◽  
Online Behaviour ◽  
Personality Features

The analysis of student performance involves data modelling that enables the formulation of hypotheses and insights about student behaviour and personality. We extract online behaviours as proxies to Extraversion and Conscientiousness, which have been proven to correlate with academic performance. The proxies of personalities we obtain yield significant (p<0.05) population correlation coefficients for traits against grade—0.846 for Extraversion and 0.319 for Conscientiousness. Furthermore, we demonstrate that a student’s e-behaviour and personality can be used with deep learning (LSTM) to predict and forecast whether a student is at risk of failing the year. Machine learning procedures followed in this report provide a methodology to timeously identify students who are likely to become at risk of poor academic performance. Using engineered online behaviour and personality features, we obtain a classification accuracy (κ) of students at risk of 0.51. Lastly, we show that we can design an intervention process using machine learning that supplements the existing performance analysis and intervention methods. The methodology presented in this article provides metrics that measure the factors that affect student performance and complement the existing performance evaluation and intervention systems in education.

scholarly journals Correlation and Regression using VASSARSTATS

2016 ◽  
Vol 3 (2) ◽  
Author(s):  
Suma AP ◽  
KP Suresh
Keyword(s):  
Correlation Coefficient ◽  
Partial Correlation ◽  
Rank Order ◽  
Correlation Coefficients ◽  
Regression Coefficients ◽  
Multiple Correlation ◽  
Population Correlation ◽  
Coefficient Corrélation ◽  
Sample Correlation ◽  
Simple Logistic

In a bivariate or a multivariate data, to understand the association between the variables Correlation is the best tool. It gives the degree of relationship between the variables. Regression gives the exact linear relationship between the variables. This article gives details of capabilities of Vassarstats Correlation and Regression and procedure to calculate Correlation coefficient and Regression coefficients with examples. Vassarstats Correlation and Regression can perform Linear Correlation and Regression, Intercorrelations, Multiple Correlation and Regression, Partial Correlation, 0.95 and 0.99 Confidence intervals for population correlation coefficient, Estimating the Population Value of rho, Significance of value of r, Significance of difference between two correlation coefficients, Significance of difference between sample correlation coefficient and hypothetical value of population Correlation coefficient, Rank Order Correlation, Correlation coefficient for a 2*2 contingency table, Point biserial correlation coefficient, Correlation for unordered pairs, and then Simple Logistic Regression.

Rate Data and Material Model Parameter Estimation

1998 ◽  
Vol 120 (1) ◽  
pp. 7-12 ◽  
Author(s):  
A. F. Fossum
Keyword(s):  
Parameter Estimation ◽  
Correlation Coefficients ◽  
Material Model ◽  
Parameter Estimates ◽  
Viscoplastic Material ◽  
Stress Strain ◽  
Material Models ◽  
Response Models ◽  
Strain Data ◽  
Rate Form

Traditionally stress-strain data are used to estimate material parameters in viscoplastic material models. Since the models exist in rate form, they must be integrated in time for the duration of the laboratory tests to give response models for parameter estimation. In this paper the alternative is considered in which the rate form of the material models is fitted to time differentiated stress strain data. Material parameteres, their 95 percent confidence intervals, and their correlation coefficients are compared with their counterparts determined from the more traditional stress versus strain form of the data. It was found that the use of the stress rate form of the data gave well determined parameter estimates with equivalent or superior fitting statistics to the same data can be used to extract significant and distinct sensitivity coefficients to enchance the overall robustness of an optimization algorithm for a parameter estimation.

Was This in Your Statistics Textbook? VI. Regression and Covariance

1989 ◽  
Vol 25 (3) ◽  
pp. 291-311 ◽  
Author(s):  
D. J. Finney
Keyword(s):  
Experimental Data ◽  
Parameter Estimation ◽  
Final Section ◽  
Correlation Coefficients ◽  
Regression Function ◽  
Covariance Analysis ◽  
Analysis Of Covariance ◽  
Regression Equations ◽  
Missing Observations ◽  
Proper Number

SUMMARYThis paper concludes the series with comments on regression, covariance analysis, and correlation. The importance of choosing a regression function, whether linear or non-linear, appropriate to problem and data is emphasized and the nature of parameter estimation is outlined. The merits of transformation of variates and the importance of introducing a proper number of parameters are briefly discussed. Common misunderstandings of the meaning of ‘the regression of y on x’ for experimental data are related to the analysis of covariance.Regression with two or more regressors increases the computations but does not alter principles. Adjustment of means by use of covariance analysis is a much under-exploited technique in its direct sense; it also offers a computationally convenient way of handling missing observations and related problems. An attempt is made to overcome the confusions of interpretation between the regression equations of y on x and of x on y. A final section warns against misuses of correlation coefficients; the opportunities for misuse are too many for brief summary, yet such coefficients can be helpful when interpreted with care.

Bootstrapping to test for nonzero population correlation coefficients using univariate sampling.

2007 ◽  
Vol 12 (4) ◽  
pp. 414-433 ◽  
Author(s):  
William Howard Beasley ◽  
Lise DeShea ◽  
Larry E. Toothaker ◽  
Jorge L. Mendoza ◽  
David E. Bard ◽  
...  
Keyword(s):  
Correlation Coefficients ◽  
Population Correlation

Reliability of the Minimum Masking Level as Outcome Variable in Tinnitus Clinical Research

2020 ◽  
Vol 29 (3) ◽  
pp. 429-435
Author(s):  
Patricia C. Mancini ◽  
Richard S. Tyler ◽  
Hyung Jin Jun ◽  
Tang-Chuan Wang ◽  
Helena Ji ◽  
...  
Keyword(s):  
Standard Deviation ◽  
High Frequency ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Outcome Variable ◽  
Reliable Estimate ◽  
Moderate Correlation ◽  
Trained Subjects ◽  
The Relationship ◽  
Minimum Intensity

Purpose The minimum masking level (MML) is the minimum intensity of a stimulus required to just totally mask the tinnitus. Treatments aimed at reducing the tinnitus itself should attempt to measure the magnitude of the tinnitus. The objective of this study was to evaluate the reliability of the MML. Method Sample consisted of 59 tinnitus patients who reported stable tinnitus. We obtained MML measures on two visits, separated by about 2–3 weeks. We used two noise types: speech-shaped noise and high-frequency emphasis noise. We also investigated the relationship between the MML and tinnitus loudness estimates and the Tinnitus Handicap Questionnaire (THQ). Results There were differences across the different noise types. The within-session standard deviation averaged across subjects varied between 1.3 and 1.8 dB. Across the two sessions, the Pearson correlation coefficients, range was r = .84. There was a weak relationship between the dB SL MML and loudness, and between the MML and the THQ. A moderate correlation ( r = .44) was found between the THQ and loudness estimates. Conclusions We conclude that the dB SL MML can be a reliable estimate of tinnitus magnitude, with expected standard deviations in trained subjects of about 1.5 dB. It appears that the dB SL MML and loudness estimates are not closely related.

Clausal Density Between Ages 4 and 9 Years for the Edmonton Narrative Norms Instrument: Reference Data and Psychometric Properties

2020 ◽  
pp. 1-15
Author(s):  
Ling-Yu Guo ◽  
Phyllis Schneider ◽  
William Harrison
Keyword(s):  
Diagnostic Accuracy ◽  
Psychometric Properties ◽  
Language Impairment ◽  
Reference Data ◽  
Reliability And Validity ◽  
Correlation Coefficients ◽  
Criterion Validity ◽  
Likelihood Ratios ◽  
Generation Task ◽  
Sensitivity Specificity

Purpose This study provided reference data and examined psychometric properties for clausal density (CD; i.e., number of clauses per utterance) in children between ages 4 and 9 years from the database of the Edmonton Narrative Norms Instrument (ENNI). Method Participants in the ENNI database included 300 children with typical language (TL) and 77 children with language impairment (LI) between the ages of 4;0 (years;months) and 9;11. Narrative samples were collected using a story generation task, in which children were asked to tell stories based on six picture sequences. CD was computed from the narrative samples. The split-half reliability, concurrent criterion validity, and diagnostic accuracy were evaluated for CD by age. Results CD scores increased significantly between ages 4 and 9 years in children with TL and those with LI. Children with TL produced higher CD scores than those with LI at each age level. In addition, the correlation coefficients for the split-half reliability and concurrent criterion validity of CD scores were all significant at each age level, with the magnitude ranging from small to large. The diagnostic accuracy of CD scores, as revealed by sensitivity, specificity, and likelihood ratios, was poor. Conclusions The finding on diagnostic accuracy did not support the use of CD for identifying children with LI between ages 4 and 9 years. However, given the attested reliability and validity for CD, reference data of CD from the ENNI database can be used for evaluating children's difficulties with complex syntax and monitoring their change over time. Supplemental Material https://doi.org/10.23641/asha.13172129

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