Statistics Symbols

This page is specifically for people in EGR 53 and represents a concordance of sorts among the lectures and the two textbooks with respect to different symbols for statistical quantities.

Symbols

The entries in the "Palm" column are taken from William J. Palm III's Introduction to Matlab 7 for Engineers, 2/e book, while those in the "Chapra" column are taken from Steven C. Chapra's Applied Numerical Methods with MATLAB for Engineers and Scientists, 2/e book. Entries in the "EGR 53" column, when not taken from Chapra or Palm, have been developed over the course of several years' of EGR 53 lectures. \( \begin{array}{|c|c|c|c|}\hline \mbox{Quantity} & \mbox{Palm} & \mbox{Chapra} & \mbox{EGR 53}\\ \hline \mbox{Independent Data} & x & x & x \\ \hline \mbox{Dependent Data} & y & y & y \\ \hline \mbox{Individual Elements} & y_i & y_i & y_i \\ \hline \mbox{Mean Value} & \bar{y}=\frac{1}{n}\sum_{i=1}^ny_i & \bar{y}=\frac{\sum y_i}{n} & \bar{y}=\frac{\sum y_i}{n} \\ \hline \mbox{Sum of Squares of Data Residuals} & S=\sum_{i=1}^m\left(y_i-\bar{y}\right)^2 & S_t=\sum\left(y_i-\bar{y}\right)^2 & S_t = \sum\left(y_i-\bar{y}\right)^2 \\ \hline \mbox{(Sample) Standard Deviation} & \sigma=\sqrt{\frac{\sum_{i=1}^n(y_i-\bar{y})^2}{n-1}} & s_y=\sqrt{\frac{S_t}{n-1}} & s_y=\sqrt{\frac{S_t}{n-1}} \\ \hline \mbox{Coefficient of Variation} & \mbox{Not Used} & \mbox{c.v.}=\frac{s_y}{\bar{y}}*100\% & \mbox{c.v.}=\frac{s_y}{\bar{y}}*100\% \\ \hline \mbox{Estimates (Linear)} & f(x_i) & a_0+a_1x_i& \hat{y}_i=P(1)x_i+P(2) \\ \hline \mbox{Estimates (General)} & f(x_i) & \hat{y}_i=\sum_{j=0}^ma_jz_{ji} & \hat{y}_i=\sum_{k=1}^Na_k\phi_k(x_i) \\ \hline \mbox{Sum of Squares of Estimate Residuals (linear fit)} & J=\sum_{i=1}^m\left[f(x_i)-y_i\right]^2 & S_r=\sum\left(y_i-a_0-a_1x_i\right)^2 & S_r=\sum\left(y_i-\hat{y}_i\right)^2 \\ \hline \mbox{Standard Error of the Estimate (linear fit)} & \mbox{Not Used} & s_{y/x} = \sqrt{\frac{S_r}{n-2}}& s_{y/x} = \sqrt{\frac{S_r}{n-2}} \\ \hline \mbox{Sum of Squares of Estimate Residuals (general fit)} & \mbox{Not Used} & S_r=\sum_{i=1}^{n} \left(y_i-\hat{y}\right)^2 & S_r=\sum\left(y_i-\hat{y}_i\right)^2 \\ \hline \mbox{Standard Error of the Estimate (general fit)} & \mbox{Not Used} & s_{y/x} = \sqrt{\frac{S_r}{n-(m+1)}}& s_{y/x} = \sqrt{\frac{S_r}{n-N}} \\ \hline \mbox{Coefficient of Determination} & r^2=1-\frac{J}{S} & r^2=\frac{S_t-S_r}{S_t} & r^2=\frac{S_t-S_r}{S_t} \\ \hline \end{array} \)

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