model you should mainly consider the adjusted R squared which is a penalized R2

Model you should mainly consider the adjusted r

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model, you should mainly consider the adjusted R-squared, which is a penalized R2 for a higher number of predictors. Adjusted R-Squared is formulated such that it penalizes the number of terms (read predictors) in your model Both R 2 and the adjusted R 2 give you an idea of how many data points fall within the line of the regression equation. However, there is one main difference between R 2 and the adjusted R 2 : R 2 assumes that every single variable explains the variation in the dependent variable . The adjusted R 2 tells you the percentage of variation explained by only the independent variables that actually affect the dependent variable .
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The test focuses on the slope of the regression line Y = Β 0 + Β 1 X where Β 0 is a constant, Β 1 is the slope (also called the regression coefficient), X is the value of the independent variable, and Y is the value of the dependent variable.
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If there is a significant linear relationship between the independent variable X and the dependent variable Y , the slope will not equal zero.
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