Significance of Regression equation
A regression equation is a mathematical formula that represents the relationship between variables, allowing for predictions based on observed data. It is crucial in various fields, including chromatography and pharmacology, as it helps in estimating drug concentrations and responses. By deriving these equations from statistical analysis, researchers can enhance accuracy in quantitative assessments, such as determining absorbance versus concentration relationships. Overall, regression equations serve as valuable tools for understanding and analyzing data relationships in scientific contexts.
Synonyms: Predictive model, Regression model, Regression formula, Predictive equation, Linear equation
The below excerpts are indicatory and do represent direct quotations or translations. It is your responsibility to fact check each reference.
The concept of Regression equation in scientific sources
The regression equation is a mathematical formula representing relationships between dependent and independent variables, essential in statistical analysis for calibrating measurements, predicting outcomes, and correlating various parameters in analytical methods and quantitative assessments.
From: The Malaysian Journal of Medical Sciences
(1) Mathematical formulas derived from data to predict cranial measurements based on variables such as age and gender.[1] (2) A mathematical equation used to predict the relationship between two variables, in this context relating FD to associated heterophoria values.[2] (3) Mathematical formulas derived to predict pulmonary function parameters based on physical characteristics such as age and height.[3] (4) A mathematical function derived from data that describes the relationship between two or more variables; in this context, used to estimate VO2max.[4]
From: South African Journal of Physiotherapy
(1) Equations that will be obtained so that existing nomograms can be adjusted, and the data collected will be used to draw them up.[5] (2) This is a mathematical formula generated by regression analysis, which was used to determine predictive factors of the 6-minute walk test.[6] (3) These equations were derived from the study and can be used to improve the accuracy of estimating repetition maximum values, as the results suggest.[7] (4) This is a mathematical formula used to determine the relationship between variables, such as the relationship between distance and VO2peak, and is compared with COPD patients.[8]
From: Asian Journal of Pharmaceutics
(1) It was calculated for cedazuridine as y = 23,422 x + 9732 and for decitabine as y = 23,000 x + 1638.[9] (2) The amount of AC in each sample was calculated with reference to this generated from suitably constructed calibration curve of AC.[10] (3) A formula, FVC = 80.74 - 13.14 (CX), that represents the relationship between forced vital capacity and the concentration of formaldehyde.[11] (4) The regression equation, represented as y = mx + c, is used to determine the relationship between the absorbance and the concentration of the drug.[12]
From: Onderstepoort Journal of Veterinary Research
(1) Mathematical formulas that describe the relationship between variables, used to create calibration plots in high performance liquid chromatography.[13] (2) A mathematical formula used to establish the relationship between oxalate concentration and peak area, offering insight into the method's linearity.[14] (3) After correction using the regression equation for all cattle, the proportion of samples for which determined Hb concentration was higher than calculated Hb concentration dropped to 46.4 %.[15]
From: International Journal of Pharmacology
(1) Information of the regression equations, linearity ranges, LOQs and LODs was displayed in Table 3 and 4.[16] (2) A mathematical formula derived from regression analysis that describes the relationship between variables.[17] (3) The regression equations for enhydrin and uvedalin were determined, relating the peak area ratio to the plasma concentration of each compound.[18]
From: Journal of Public Health in Africa
(1) This is a mathematical formula, Y = a + bX, used to test the feasibility of the CALF model of strategic procurement of medicals.[19] (2) This is a representation of the multiple regression model, which is used to evaluate the research hypotheses, and is formulated to assess relationships between variables.[20]
From: African Journal of Primary Health Care and Family Medicine
(1) Mathematical formulas used to describe the relationship between blood pressure measurements obtained by different methods, providing insights into the study's findings.[21]
From: International Journal of Pharmacology
(1) A mathematical formula derived from data that describes the relationship between variables, used here to correlate ceftriaxone concentration with its inhibitory effect on bacterial growth.[22]
From: Religions Journal (MDPI)
(1) It includes a constant, attitudes, knowledge, age, gender, educational background, and religious affiliation to determine Islamic religious moderation intentions.[23]