\(b_0\) (“b-zero”): estimated sample y-intercept in a linear regression model (more generally, estimated value of \(y\) when all the predictors equal zero)\(\beta_0\) (“beta-zero”): population y-intercept in a regression model\(b_1\) (“b-one”): estimated sample slope in a linear regression model (more generally, estimated sample change in \(y\) for a one-unit increase in the corresponding predictor, holding all other predictors constant)\(\beta_1\) (“beta-one”): population slope in a linear regression model\(e_i\) : i -th (sample) prediction error (or residual error), equal to \(y_i-\hat{y}\_i\) \(\epsilon_i\) (“epsilon-i”): i -th (population) error, equal to \(y_i-\mbox{E}(Y_i)\) \(i\) : index for the i -th obeservation or experimental unit\(n\) : sample size (total number of observations)\(p\) : number of regression coefficients in a linear regression model (including the intercept), which means there are \(p-1\) predictor terms.\(r\) : (Pearson) correlation coefficient between two quantitative variables\(r^2\) (“r-squared”): coefficient of determination in a simple linear regression model, equal to \(SSR\) /\(SSTO\) \(R^2\) (“R-squared”): coefficient of determination in a multiple linear regression model, equal to \(SSR\) /\(SSTO\) \(SSR\) : regression sum of squares (measures deviations of \(\hat{y}\) from \(\bar{y}\) )\(SSE\) : error sum of squares (measures deviations of \(y\) from \(\hat{y}\) )\(SSTO\) : total sum of squares (measures deviations of \(y\) from \(\bar{y}\) )\(MSE\) (“mean square error”): (sample) mean square prediction error (or residual error)\(S\) : regression (residual) standard error (square root of MSE)\(\sigma^2\) (“sigma-squared”): (population) common error variance in a linear regression model\(x\) : a predictor, explanatory, or independent variable in a linear regression model\(\bar{x}\) (“x-bar”): sample mean of \(x\) \(y\) : the response, outcome, or dependent variable in a linear regression model\(\bar{y}\) (“y-bar”): (univariate) sample mean of \(y\) (ignoring any predictors)\(\hat{y}\) (“y-hat”): predicted or fitted value of \(y\) in a linear regression model (i.e., accounting for the predictors)\(\mbox{E}(Y)\) or \(\mu_Y\) (“expected value of Y”): population mean of Y in a linear regression model