Significance of Correlation coefficient
The correlation coefficient is a statistical measure widely used across various fields like Ayurveda, Science, Psychiatry, Health Sciences, Environmental Sciences, and Religion. It quantifies the strength and direction of a linear relationship between two variables, ranging from -1 to +1. Researchers use it to validate analytical methods, explore relationships between variables, assess linearity, identify redundancy, and understand the impact of different factors in their respective studies.
Synonyms: Correlation, Correlation index, Pearson's r, Statistical relationship, Pearson coefficient, Linear correlation coefficient, Pearson correlation, Correlation factor
The below excerpts are indicatory and do represent direct quotations or translations. It is your responsibility to fact check each reference.
Hindu concept of 'Correlation coefficient'
In Hinduism, the correlation coefficient is a statistical method used to analyze data and determine the relationship between studied factors, assessing the strength and direction of variable relationships.
From: Journal of Ayurveda and Integrated Medical Sciences
(1) It was used to assess the magnitude and direction of correlation between BMI with GP by using the Pearson correlation statistical method.[1] (2) Correlation coefficient, denoted as "r", is used to quantify the strength and direction of a linear relationship between two variables, and ranges from -1 to +1.[2] (3) The correlation coefficient is a statistical method used to analyze the data collected in this research, and it helps in determining the relationship between the various factors studied.[3] (4) This is a statistical measure that assesses the strength and direction of a relationship between two variables, and in this study, it was almost non-existent.[4]
The concept of Correlation coefficient in scientific sources
The correlation coefficient is a statistical measure assessing the strength and direction of the linear relationship between two variables. Values range from -1 to +1, with many examples showing values above 0.99, indicating strong correlations in analytical and experimental contexts.
From: Sustainability Journal (MDPI)
(1) These were less than 0.6, which preliminarily indicated the absence of multicollinearity between variables.[5] (2) These are presented between the SDSN’s goal scores and those of other reports under various adjustment scenarios for comparison.[6] (3) The correlation coefficient measures the statistical relationship between different variables, indicating the strength and direction of their linear association in the dataset.[7] (4) The data pretreatment approaches enhanced the data quality by improving the correlation coefficients between ETo and WSmax, Tmin, and P.[8] (5) Correlation coefficient measures the strength and direction of a linear relationship between two variables, and the correlation coefficient between SPI 3 and teleconnection index in summer was analyzed.[9]
From: International Journal of Environmental Research and Public Health (MDPI)
(1) Describes the measure of the strength and direction of a linear relationship between two variables.[10] (2) It is a statistical measure of the degree to which two variables are linearly related, influencing how risk factors are considered.[11] (3) It is between the HPWS-K and TPEI, ranging from 0.49 to 0.53, demonstrate statistically significant correlations between the HPWS-K and all four factors of the TPEI.[12] (4) These are statistical measures that indicate the strength and direction of a relationship between two variables, and these were calculated to understand how the current work context is affecting performance, work organization, sleep and periods of exposure to screens.[13] (5) These were used in the accounting of ANSP and were mainly from documents such as handbooks and emission standards, according to the provided text.[14]
From: Asian Journal of Pharmaceutics
(1) A parameter calculated from in vitro dissolution kinetic data.[15] (2) A statistical measure of the strength and direction of a linear relationship between two variables.[16] (3) A value of 0.9997 indicates the goodness-of-fit for the linear relationship between concentration and peak area.[17] (4) Good correlations were observed between in vitro and in vivo drug release, with a value of 0.996, indicating a strong relationship.[18] (5) For metformin was found to be 1812 x + 5688.9 and R 2 = 0.999, whereas for fenofibrate was fond to be 4325.2 x + 1621.6 and R 2 = 0.999.[19]
From: The Malaysian Journal of Medical Sciences
(1) Correlation coefficient is a statistical measure that determines the degree to which two variables are linearly associated, and it was used to evaluate the relationship between PTH assays and bone markers.[20] (2) Correlation coefficients, such as Kendall's-tau-B, are used to measure the strength of relatedness between demographic factors and study variables like pain and anxiety.[21] (3) This is a value that measures the strength and direction of a linear relationship.[22] (4) This is a statistical measure that indicates the strength and direction of the linear relationship between TSB and TcB measurements.[23] (5) Correlation coefficient is a statistical measure used to identify the relationship between stressors and coping strategies, indicating the degree to which they are associated.[24]
From: International Journal of Pharmacology
(1) The Cosinorwin computer software program was used to analyse circadian characteristics such as mesor, circadian amplitude, the circadian acrophase and r-value (correlation coefficient).[25] (2) The calibration curves had a coefficient larger than 0.99 in rat tissue homogenates.[26] (3) These (R 2) were used to judge the fitting of model and agreeing between theoretical values and experimental data.[27] (4) This analysis was applied to study the relationships between the studied variables and the experimental durations.[28] (5) This is a statistical measure, with a value of 0.84, that indicates the strength and direction of a linear relationship between two variables.[29]
From: South African Journal of Physiotherapy
(1) Convergent validity was determined by the RHDS items having this with a score of their own dimensions greater than 0.40.[30] (2) This statistical measure is used to test the strength of the relationship between different gait parameters and functional ambulation, with a value less than 0.05 being significant.[31] (3) This is a statistical measure that indicates the strength and direction of the relationship between two variables.[32] (4) This is a statistical measure that indicates the strength and direction of a linear relationship between two variables. The intra-tester correlation coefficient ranged between 0.91 and 0.98, and the correlation coefficient between testers was 0.96.[33] (5) This is a statistical measure used to determine the relationship between test results, and is used to evaluate the test-retest reliability.[34]
From: African Journal of Primary Health Care and Family Medicine
(1) Linearity in a concentration range from 0.03-0.1 mg/ml was obtained, giving a correlation coefficient of 0.9639, which is used in the study for the quantification of stavudine using HPLC.[35] (2) Correlation coefficients are statistical values used to measure the strength and direction of the relationship between two variables, and they were calculated to determine the associations between different body composition variables and blood pressure readings.[36] (3) Correlation coefficient is a statistical measure that quantifies the strength and direction of a linear relationship between two variables, used in the study to assess associations.[37] (4) These are used to determine the magnitude of negative or positive relationships, denoted by the letter r, among various test items.[38] (5) A 'correlation coefficient' is usually calculated between 0 (no correlation) and ± 1 (complete correlation), and this is described in the article.[39]
From: Journal of Public Health in Africa
(1) The correlation coefficient measures the strength and direction of the relationship between two variables, and the calculated coefficient values in the study indicate the relationship between exercise habits and blood sugar levels.[40] (2) This is a statistical measure that represents the strength and direction of a linear relationship between two variables, often denoted as 'r'.[41] (3) This is a statistical measure that shows the strength and direction of the relationship between two variables, which in this case is used in the study.[42] (4) Correlation coefficient is a numerical value that indicates the strength and direction of the relationship between two variables, and the study used it to understand the interrelationships between the key variables.[43] (5) This is a statistical measure used to quantify the strength and direction of the relationship between Rhodamine B administration and BAX or BCL-2 expression.[44]
From: Onderstepoort Journal of Veterinary Research
(1) The correlation coefficient is a measure of the strength and direction of a linear relationship between two variables, indicating the linearity of the calibration curve.[45] (2) Statistical values that measure the strength and direction of a linear relationship between variables, used to assess the linearity of the calibration plots.[46] (3) The significance of the correlation coefficient for the linear regression equations was tested using the formula suggested by Smillie (1966) and Varkevisser, Pathmanathan and Brownlee (1991).[47] (4) These are statistical measures used to determine the relationships between different farm parameters, helping to understand the factors influencing performance.[48] (5) This is a statistical measure used to determine the relationship between the time of harvesting and the concentration of protozoa.[49]
From: South African Family Practice
(1) Statistical measures that indicate the extent to which two or more variables fluctuate together.[50] (2) It is a statistical measure of the strength and direction of a linear relationship between two variables, and the study uses the correlation coefficient to compare the results of the HemoCue and laboratory methods, and it is to find accuracy.[51] (3) A statistical measure that indicates the strength and direction of a linear relationship between two variables, such as obesity indices and blood pressure.[52] (4) Correlation coefficient is a statistical measure used to assess the relationship between two sets of data, but the study showed that it could be misleading when comparing different blood pressure measurement protocols.[53]
From: South African Journal of HIV Medicine
(1) Correlation coefficients were reported as not strong, but the strength of the correlation coefficients are similar to what has been reported in other research studies.[54]
From: Religions Journal (MDPI)
(1) It showed up at a high level and presenting as positive, specifically ranging from 0.50 to 0.62 within the context of the presented research.[55] (2) The text notes that no correlation coefficients exceeded 0.70, suggesting that the data does not exhibit multicollinearity.[56] (3) These are statistical measures that indicate the strength and direction of the relationship between two variables.[57] (4) A statistical measure of the linear association between two variables, ranging from -1 to +1, indicating the strength and direction of the relationship.[58] (5) These are contained in Table 3, indicating the strength and direction of the relationship between all scales in the study.[59]
From: South African Journal of Psychiatry
(1) This is a statistical measure that describes the strength and direction of a linear relationship between two variables, and it was used to analyze the relationship between EDS, sleep duration and psychopathology.[60] (2) These values indicate the strength and direction of the relationships between variables, such as the correlation between parental rejection and behavioral problems.[61] (3) This is a type of statistical analysis used to explore the relationships between BRUMS scores and biographical variables, such as age.[62] (4) This is a statistical measure that indicates the strength and direction of the relationship between two variables, such as hopelessness and depression.[63]
From: International Journal of Pharmacology
(1) A high value signifies an excellent correlation between the independent variables, demonstrating a good agreement between experimental and predicted results.[64] (2) A correlation coefficient is a statistical value that indicates the strength and direction of a linear relationship between two variables, used here to link phenolic content with antioxidant and antimutagenic activities.[65] (3) A statistical measure indicating the strength and direction of a linear relationship between two variables, used to validate calibration curves.[66]