is a chart that portrays the relationship between two variables.
the variable that is being predicted or estimated. it is scaled on the 𝑦-axis.
the variable that provides the basis for estimation. it is the predictor variable. it is scaled on the 𝑥-axis.
it's the objective to use the data to position a line that best represents the relationship between the two variables. Since personal judgment is subjective, we prefer a more objective method that results in a simple, best regression line.
is an equation that expresses the linear relationship between two variables. the general form of the linear regression equation is given on the next slide.
a group of techniques to measure the strength of the association between two variables. interval or ratio-level data are required. the sample coefficient of correlation is identified by the lowercase letter r.
it's a measure of the strength of the linear relationship between two variables.
is the proportion of the total variation in the dependent variable, Y, that is explained, or accounted for, by the variation in the independent variable, X. it is computed by squaring the coefficient of correlation.
a high correlation does not mean cause and effect.
the coefficient of determination (r2) is the proportion of the total variation in the dependent variable (Y) that is explained or accounted for by the variation in the independent variable (X).