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13 Terms
1
Regression
A statistical method used to model the relationship between a dependent variable and one or more independent variables.
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2
Bivariate Regression
A type of regression analysis that involves two variables: one dependent variable and one independent variable.
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3
Dependent Variable (Y)
The outcome variable that researchers are interested in explaining or predicting.
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4
Predictor Variable (X)
An independent variable used to explain variations in the dependent variable.
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5
Multiple Regression
A regression analysis involving multiple predictor variables to understand their effect on a dependent variable.
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6
Variance
A statistical measure of how much values differ from the average value; crucial in understanding variability in data.
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7
Y-intercept (A)
The value of the dependent variable when the independent variable is zero, representing the starting point of the regression line.
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8
Slope (B)
A measure of the steepness of the regression line, indicating the change in the dependent variable for every one-unit change in the independent variable.
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9
Simple Linear Model
The simplest form of regression model defined by the equation Y = A + BX, where A is the y-intercept and B is the slope.
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10
Predicted Values (Ŷ)
Values estimated by the regression model, based on the model parameters and input values of the independent variable.
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11
Causal Modeling
An approach in statistical analysis focused on understanding the causes and relationships between variables.
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12
Model Evaluation
The process of assessing how well a regression model fits the data and how accurately it predicts outcomes.
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13
Observed Values of Y
The actual measurements of the dependent variable that are collected during the study.
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