Regression analysis of ames test data

1981 ◽  
Vol 3 (5) ◽  
pp. 575-586 ◽  
Author(s):  
Lawrence E. Myers ◽  
Nancy H. Sexton ◽  
Leslie I. Southerland ◽  
Thomas J. Wolff
Keyword(s):  
Regression Analysis ◽  
Test Data ◽  
Ames Test

scholarly journals The Effects of Organizational Spirituality on Organizational Commitment and Happiness in MBA Expert Managers

2018 ◽  
pp. 75-87
Author(s):  
Maryam Mahdavi ◽  
Fatemehsadat Mousavifard ◽  
Abdolhossein Ayoubi
Keyword(s):  
Regression Analysis ◽  
Organizational Commitment ◽  
Linear Regression ◽  
Test Data ◽  
Positive Relationship ◽  
Linear Regression Analysis ◽  
Business Managers ◽  
Perception Management ◽  
Organizational Spirituality ◽  
Complete Count

This study aims at evaluating the effect of organizational commitment on organizational spirituality and happiness among MBA expert managers south of Iran; it is a descriptive correlational study. This study evaluated 338 participants based on complete count method; participants were MBA expert managers south of Iran. Data were gathered applying Hall and Edwards, organizational commitment Questionnaire of Mowdy, Porter & Steers and Oxford Happiness Test. Data were analysed by multi-variable linear regression analysis by simultaneous method by SPSS22. Findings indicate that there is a positive relationship between organizational spirituality and its components with organizational commitment and its three aspects and there is a positive relationship between organizational spirituality, happiness and its dimensions. Among different Aspects of organizational spirituality, awareness, instability, perception management and magnification could predict organizational commitment, and awareness, true acceptance and helplessness could predict variable of business managers’ happiness.

Biostatistics of Ames-Test Data

1980 ◽  
pp. 312-322 ◽  
Author(s):  
K. Norpoth ◽  
A. Reisch ◽  
A. Heinecke
Keyword(s):  
Test Data ◽  
Ames Test

scholarly journals Bacterial mutagenicity test data: collection by the task force of the Japan pharmaceutical manufacturers association

2021 ◽  
Vol 43 (1) ◽  
Author(s):  
Atsushi Hakura ◽  
Takumi Awogi ◽  
Toshiyuki Shiragiku ◽  
Atsushi Ohigashi ◽  
Mika Yamamoto ◽  
...  
Keyword(s):  
Test Data ◽  
In Silico ◽  
Ames Test ◽  
Task Force ◽  
Pharmaceutical Companies ◽  
Pharmaceutical Drugs ◽  
Pharmaceutical Manufacturers ◽  
In Silico Models ◽  
Sensitivity Specificity ◽  
In Silico Analyses

Abstract Background Ames test is used worldwide for detecting the bacterial mutagenicity of chemicals. In silico analyses of bacterial mutagenicity have recently gained acceptance by regulatory agencies; however, current in silico models for prediction remain to be improved. The Japan Pharmaceutical Manufacturers Association (JPMA) organized a task force in 2017 in which eight Japanese pharmaceutical companies had participated. The purpose of this task force was to disclose a piece of pharmaceutical companies’ proprietary Ames test data. Results Ames test data for 99 chemicals of various chemical classes were collected for disclosure in this study. These chemicals are related to the manufacturing process of pharmaceutical drugs, including reagents, synthetic intermediates, and drug substances. The structure-activity (mutagenicity) relationships are discussed in relation to structural alerts for each chemical class. In addition, in silico analyses of these chemicals were conducted using a knowledge-based model of Derek Nexus (Derek) and a statistics-based model (GT1_BMUT module) of CASE Ultra. To calculate the effectiveness of these models, 89 chemicals for Derek and 54 chemicals for CASE Ultra were selected; major exclusions were the salt form of four chemicals that were tested both in the salt and free forms for both models, and 35 chemicals called “known” positives or negatives for CASE Ultra. For Derek, the sensitivity, specificity, and accuracy were 65% (15/23), 71% (47/66), and 70% (62/89), respectively. The sensitivity, specificity, and accuracy were 50% (6/12), 60% (25/42), and 57% (31/54) for CASE Ultra, respectively. The ratio of overall disagreement between the CASE Ultra “known” positives/negatives and the actual test results was 11% (4/35). In this study, 19 out of 28 mutagens (68%) were detected with TA100 and/or TA98, and 9 out of 28 mutagens (32%) were detected with either TA1535, TA1537, WP2uvrA, or their combination. Conclusion The Ames test data presented here will help avoid duplicated Ames testing in some cases, support duplicate testing in other cases, improve in silico models, and enhance our understanding of the mechanisms of mutagenesis.

scholarly journals Pelatihan Penerapan Program Statistical Product for Service Solution (SPSS) untuk Data Keolahragaan

2020 ◽  
Vol 2 (1) ◽  
pp. 12-17
Author(s):  
Miftah FP Putra ◽  
Saharuddin Ita ◽  
Ibrahim Ibrahim
Keyword(s):  
Regression Analysis ◽  
Test Data ◽  
Descriptive Analysis ◽  
T Test ◽  
And Function ◽  
Data Requirements ◽  
Sports Data

The dedication activity is expected to make participants (1) understand well about the SPSS program, and (2) can use the SPSS program to analyze the sports data (training results) that have been conducted. The results of the activities are (1) most of the trainees only know the SPSS program, (2) only a small number of participants can install the SPSS program independently, (3) most trainees understand well about the history and function of the SPSS, (4) some large participants can do descriptive analysis, test data requirements, correlation, t test (dependent and independent) independently, (5) only a small number of participants can do regression analysis and ANOVA.

Prediction of Tire Tread Wear Rate and Tread Wear Rate Differences

1979 ◽  
Vol 7 (3) ◽  
pp. 43-57
Author(s):  
E. A. Dudley ◽  
R. F. Bauer ◽  
P. M. Reilly
Keyword(s):  
Regression Analysis ◽  
Bayesian Inference ◽  
Wear Rate ◽  
Test Data ◽  
Subjective Probability ◽  
Probability Distributions ◽  
Wear Test ◽  
Suitable Model ◽  
Standard Regression

Abstract A method is presented for the analysis of tread wear test data, having the principal aim of predicting the ultimate tread life from results of a test suspended near the halfway mark. This method uses standard regression analysis of a suitable model followed by Bayesian inference to produce subjective probability distributions of predicted tread depth and their associated uncertainty.

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