Significance of Estimated coefficient
Estimated coefficient represents the values b1 and b2. These values quantify the estimated impact of factors X1 and X2 on a dependent variable within a statistical model. They indicate the magnitude and direction of change in the dependent variable for each unit change in the independent variables. These coefficients are crucial for understanding the relationship between predictors and outcomes in various analyses.
Synonyms: Coefficient, Parameter, Constant, Factor, Multiplier, Value, Coefficient estimate, Regression coefficient estimate, Parameter estimate
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The concept of Estimated coefficient in scientific sources
Estimated coefficients, b1 and b2, quantify the predicted impact of independent variables X1 and X2 on the dependent variable within a regional model.
From: Sustainability Journal (MDPI)
(1) The estimated coefficients, (β1, β2, β3, ..., β7) and (δ1, δ2, δ3, ..., δ7), represent the variables’ estimated coefficients in Equations (6) and (7), respectively, quantifying the impact of each variable on environmental quality.[1] (2) The estimated coefficient, represented by α, is a value determined through statistical analysis, quantifying the relationship between independent and dependent variables in the equations.[2] (3) With the same restrictions on city-fixed effects and year-fixed effects, the estimated coefficients of variation are significantly positive, indicating a strong incentive effect.[3] (4) The estimated coefficients (b) for the independent variables included in the logit model were statistically significant.[4] (5) The estimated coefficient of RHC is positive and significant at the level of 1%, indicating that RHC has a significant role in promoting the improvement of AEE.[5]
From: International Journal of Environmental Research and Public Health (MDPI)
(1) Estimated coefficients in Table 11 are statistically significant, allowing the relationships between variables to be presented in Equations (16) and (17).[6] (2) Estimated coefficients are numerical values that quantify the strength and direction of the relationship between variables in a model, such as the coefficients linking vibration exposure and concern.[7] (3) These are the values that quantify the relationship between independent and dependent variables in a model, indicating the strength and direction of the effect.[8] (4) These are values derived from the regression model, representing the logarithm of the odds ratio of a household experiencing energy poverty relative to non-energy poverty.[9] (5) Represented by β and δ, they measure the magnitude and direction of the relationship between the independent variables and environmental pollution in the dynamic spatial STIRPAT panel model.[10]
From: Asian Journal of Pharmaceutics
(1) The average results of changing one factor at a time from its low to high value.[11] (2) The b 1 , b 2 and b 3 for the factor A, B and C respectively, where Yi was the dependent variable.[12] (3) b i (b 10, b 01, b 20, and b 11) is the estimated coefficient for the corresponding factor Xi (X 10, X 01, X 20, and X 11) in the polynomial equation.[13] (4) This is the coefficient for the factor X i in the statistical model used for analysis.[14]