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Simple Linear regression - ANSWER>(aka Bivariate regression) generates a linear equation that best fits the
observed data to a single independent or predictor variable.
Multiple linear regression - ANSWER>generates a linear equation that best fits the observed data to multiple
independent or predictor variables.
Correlation coefficient (R) - ANSWER>R--> [-1,1}
Correlation represents a measure of the strength of the relationship between the independent and the dependent
variable(s). Correlation is measured by the correlation coefficient.
Interpretation: The correlation coefficient, r, measures the strength AND direction of a linear relationship
Coefficient of determination (R^2) - ANSWER>The coefficient of determination (denoted R^2) represents a
measurement of the variability of the data around the
regression line.
R^2 is a standard way to measure the "fit" of the regression model.
, Interpretation: The coefficient of determination, R^2, measures the proportion of the total variation in y that is
explained by the regression line, i.e., R2 measures the percentage of variation in the dependent variable y resulting
from changes in the independent variable x.
Data Analysis add-in - ANSWER>can be used to develop and evaluate a simple linear regression model
Intercept - ANSWER>Excel function that can be used to estimate a simple regression line
Slope - ANSWER>Excel function that can be used to estimate a simple regression line
Correl (R) - ANSWER>Excel function can be used to evaluate a simple linear regression model
RSQ (R^2) - ANSWER>Excel function can be used to evaluate a simple linear regression model
Adjusted R^2 - ANSWER>Adjusted R2 is a measure similar to R2. The Adjusted R2 adjusts the R2 down, adding a
penalty for including more independent variables. We need the Adjusted R2 because adding more predictor
variables will always make R2 increase (or at least stay the same!)