Correlation coefficient - confidence interval and significance testing

Abstract Background Although the shock index is known to predict mortality and other severe outcomes, deriving it requires complex calculations. Subtracting the systolic blood pressure from the heart rate may produce a simple shock index that would be a clinically useful substitute for the shock index. In this study, we investigated whether the simple shock index was equivalent to the shock index. Methods This observational cohort study was conducted at 2 tertiary care hospitals. Patients who were transported by ambulance were recruited for this study and were excluded if they were aged < 15 years, had experienced prehospital cardiopulmonary arrest, or had undergone inter-hospital transfer. Pearson’s product-moment correlation coefficient and regression equation were calculated, and two one-sided tests were performed to examine their equivalency. Results Among 5429 eligible patients, the correlation coefficient between the shock index and simple shock index was extremely high (0.917, 95% confidence interval 0.912 to 0.921, P < .001). The regression equation was estimated as sSI = 258.55 log SI. The two one-sided tests revealed a very strong equivalency between the shock index and the index estimated by the above equation using the simple shock index (mean difference was 0.004, 90% confidence interval 0.003 to 0.005). Conclusion The simple shock index strongly correlated with the shock index.

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Confidence assessment and interval prediction for multi-input model via grey system theory

Grey Systems Theory and Application ◽

10.1108/gs-07-2017-0024 ◽

2018 ◽

Vol 8 (1) ◽

pp. 69-83 ◽

Cited By ~ 2

Author(s):

Haoliang Wang ◽

Xiwang Dong ◽

Qingdong Li ◽

Zhang Ren

Keyword(s):

Confidence Interval ◽

Correlation Coefficient ◽

Calculation Method ◽

Reference Sample ◽

Prediction Method ◽

Calculation Methods ◽

Content Type ◽

Interval Prediction ◽

Confidence Value ◽

Reference Samples

Purpose By using small reference samples, the calculation method of confidence value and prediction method of confidence interval for multi-input system are investigated. The purpose of this paper is to offer effective assessing methods of confidence value and confidence interval for the simulation models used in establishing guidance and control systems. Design/methodology/approach In this paper, first, an improved cluster estimation method is proposed to guide the selection of the small reference samples. Then, based on analytic hierarchy process method, the new calculation method of the weight of each reference sample is derived. By using the grey relation analysis method, new calculation methods of the correlation coefficient and confidence value are presented. Moreover, the confidence interval of the sample awaiting assessment is defined. A new prediction method is derived to obtain the confidence interval of the sample awaiting assessment which has no reference sample. Subsequently, by using the prediction method and original small reference samples, Bootstrap resampling method is used to obtain more correlation coefficients for the sample to reduce the probability of abandoning the true. Findings The grey relational analysis is used in assessing the confidence value and interval prediction. The numerical simulations are presented to demonstrate the effectiveness of the theoretical results. Originality/value Based on the selected small reference samples, new calculation methods of the correlation coefficient and confidence value are presented to assess the confidence value of model awaiting assessment. The calculation methods of maximum confidence interval, expected confidence interval and other required confidence intervals are presented, which can be used in assessing the validities of controller and guidance system obtained from the model awaiting assessment.

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Confidence Intervals

Zeitschrift für Psychologie / Journal of Psychology ◽

10.1027/0044-3409.217.1.15 ◽

2009 ◽

Vol 217 (1) ◽

pp. 15-26 ◽

Cited By ~ 43

Author(s):

Geoff Cumming ◽

Fiona Fidler

Keyword(s):

Confidence Interval ◽

Confidence Intervals ◽

Null Hypothesis ◽

Prediction Intervals ◽

Effect Sizes ◽

Significance Testing ◽

Null Hypothesis Significance Testing ◽

P Values ◽

Standardized Effect Sizes ◽

Mainstream Science

Most questions across science call for quantitative answers, ideally, a single best estimate plus information about the precision of that estimate. A confidence interval (CI) expresses both efficiently. Early experimental psychologists sought quantitative answers, but for the last half century psychology has been dominated by the nonquantitative, dichotomous thinking of null hypothesis significance testing (NHST). The authors argue that psychology should rejoin mainstream science by asking better questions – those that demand quantitative answers – and using CIs to answer them. They explain CIs and a range of ways to think about them and use them to interpret data, especially by considering CIs as prediction intervals, which provide information about replication. They explain how to calculate CIs on means, proportions, correlations, and standardized effect sizes, and illustrate symmetric and asymmetric CIs. They also argue that information provided by CIs is more useful than that provided by p values, or by values of Killeen’s prep, the probability of replication.

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Simulation data for an estimation of the maximum theoretical value and confidence interval for the correlation coefficient

Data in Brief ◽

10.1016/j.dib.2017.07.045 ◽

2017 ◽

Vol 14 ◽

pp. 291-294 ◽

Cited By ~ 3

Author(s):

Paolo Rocco ◽

Francesco Cilurzo ◽

Paola Minghetti ◽

Giulio Vistoli ◽

Alessandro Pedretti

Keyword(s):

Confidence Interval ◽

Correlation Coefficient ◽

Simulation Data

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Fixed-width sequential confidence interval for the correlation coefficient of a bivariate normal distribution

Communication in Statistics- Theory and Methods ◽

10.1080/03610929208830791 ◽

1992 ◽

Vol 21 (2) ◽

pp. 501-506 ◽

Cited By ~ 1

Author(s):

Mohamed Tahir

Keyword(s):

Confidence Interval ◽

Normal Distribution ◽

Correlation Coefficient ◽

Bivariate Normal Distribution ◽

Bivariate Normal

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