linear regression

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Noun1.linear regression - the relation between variables when the regression equation is linear: e.g., y = ax + b
statistics - a branch of applied mathematics concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate population parameters
regression toward the mean, simple regression, statistical regression, regression - the relation between selected values of x and observed values of y (from which the most probable value of y can be predicted for any value of x)
regression coefficient - when the regression line is linear (y = ax + b) the regression coefficient is the constant (a) that represents the rate of change of one variable (y) as a function of changes in the other (x); it is the slope of the regression line
References in periodicals archive ?
Multiple linear regression analysis was conducted to test if vitamin B12 levels, gestational age and gravidity significantly predicted fasting blood sugar levels.
In the study, multiple linear regression method was adopted in the prescreening of independent variables to identify the influencing factors of medical expenses within CKD patients, and the assignments of each variable are shown in [Table 1].
Potential variables for the multiple linear regression model were selected using the StepWise technique, which iteratively constructs a sequence of regression models through the addition and removal of variables, selecting those with highest correlation with the main variable (y) using the partial F statistics, according to Eq.
Table-II: Multiple linear regression analysis of predictors of total emotional intelligence score among medical students in King Abdulaziz University.
The multiple linear regressions (MLR) and Back ward methods (with significant at the 0.
Table 4: Testing of Multiple Linear Regression Model ANOVA
Table 2 also shows the multiple linear regression model summary and overall fit statistics.
First, the simple linear model was extended to a multiple linear regression consisting of more than one independent variable.
248 475 Table 5: Multiple Linear Regression Analysis Model Unstandardizcd Standardized t p-value R Coefficient Cuefficient Beta Beta (Constant) 0.
After setting the coefficient [beta] estimates b, multiple linear regression function suitable for the prediction of fixed estimates of independent variables of the average Y value is obtained
Additionally, covariates declared significant in the EWAS were included in a multiple linear regression model and were retained if their two-sided Rvalue was below 5% (Tzoulaki et al.
The data were analysed by multiple linear regression to assess the independent contribution of each of these 9 predictors with OLA Pa[O.

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