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Residual Sum of Squares are used to determine the Coefficient of Determination, Pearson's Sample Correlation Coefficient distribution-free version based on ranks is the Spearman's Correlation Coefficient, Regression Analysis makes predictions for the Response Variable, Power Transformations are used to fit curves between Response Variable, Power Transformations are used to fit curves between Predictor Variable, Pearson's Sample Correlation Coefficient converges to Population Correlation Coefficient, Bivariate Data can be used for Regression Analysis, Least Squares Line gives the best linear fit between Response Variable, Least Squares Line gives the best linear fit between Predictor Variable, Bivariate Data consists of simultaneous observations made on two Random Variables, Total Sum of Squares represents the variation in the Response Variable, Total Sum of Squares are used to determine the Coefficient of Determination, Bivariate Data can be represented using a Scatter Plot, Residual Sum of Squares represents the variation about the Least Squares Line, Polynomial Functions are used to fit curves between Response Variable, Polynomial Functions are used to fit curves between Predictor Variable, Population Correlation Coefficient represents the strength of the linear relationship between Random Variables, LOWESS Curves are used to fit curves between Response Variable, LOWESS Curves are used to fit curves between Predictor Variable, Coefficient of Determination is the square of the Pearson's Sample Correlation Coefficient, Regression Analysis uses information about the Predictor Variable, Bivariate Data is a special case of Multivariate Data, Population Correlation Coefficient for a sample of paired data is given by Pearson's Sample Correlation Coefficient, Population Correlation Coefficient is independent of the Units of Measurement, Scatter Plot can be used to estimate the Population Correlation Coefficient