Significance of Linear regression equation
A linear regression equation is a mathematical formula that illustrates the relationship between two variables, commonly applied in fields like quantitative analysis. It establishes connections between substance concentrations and their measured responses, such as absorbance or area under the curve in chromatograms. This method facilitates the calibration of analytical techniques, enabling predictions and quantifications of various compounds, including drugs like puerarin, Imidapril HCl, Glecaprevir, and Pibrentasvir through their response dynamics.
Synonyms: Linear regression model, Linear model, Regression equation, Predictive model
The below excerpts are indicatory and do represent direct quotations or translations. It is your responsibility to fact check each reference.
The concept of Linear regression equation in scientific sources
The Linear regression equation symbolizes a mathematical tool that defines the connection between a substance's concentration and its response, specifically analyzing gallic acid concentration against peak areas in chromatographic studies.
From: Asian Journal of Pharmaceutics
(1) This equation of standard rutin curve is used to calculate the unentrapped rate.[1] (2) These describe the relationship between formulation factors and cumulative % drug release at various time intervals.[2] (3) Equations that were calculated at 242.13 nm and 322.56 nm for both FLM and KT in sodium acetate buffer.[3] (4) These equations show the relationship between concentration and peak area for the drugs.[4] (5) The linear regression equation of the straight line is 1/V = 2990.8/C glucose + 8.5059 with a correlation coefficient of 0.9967.[5]
From: International Journal of Pharmacology
(1) A statistical method used to analyze the inhibition concentration values from the dose response curves.[6] (2) In order to establish the relationship between initial blood alcohol concentration and vitreous humor levels and their changes after 180 days, this was calculated.[7] (3) This equation was used to determine the IC50 value of the ME anti-angiogenic activity.[8]
From: Onderstepoort Journal of Veterinary Research
(1) Trendline analyses generated linear regression equations from scatterplots, providing a mathematical model for the data.[9]
From: Sustainability Journal (MDPI)
(1) In the first stage of the instrumental variable method, ES is treated as the response variable and L.ES as the explanatory variable and substitute them into a "linear regression equation" similar to Model (1).[10] (2) is a mathematical formula that models the relationship between variables using a straight line to predict outcomes.[11] (3) A linear regression equation is a statistical model that establishes a linear relationship between a dependent variable and one or more independent variables, often used to analyze cooling capacity.[12] (4) It describes the relationship between slope runoff rate and available Phosphorus and total Phosphorus loss rate, represented as y = ax + b.[13] (5) Describes a statistical method used to model the relationship between vehicle emissions, fuel consumption, and mileage, using the least squares method.[14]
From: International Journal of Environmental Research and Public Health (MDPI)
(1) It is a mathematical formula used to correct the output values of measurement devices, improving their accuracy by relating them to a reference device.[15] (2) The first step in estimating biological profile is constructing a simple piecewise for each trait as a function of calendar age.[16] (3) The linear regression equation was generated using data from 10–20 days preceding a face covering mandate and used to predict pre-face covering mandate infection data trends.[17]