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Standard Error of Estimate
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\(SEE =\sqrt{MSE}=\sqrt{\frac{SSE}{n-2}}=\\= \sqrt{\frac{\Sigma^{n}_{i=1} (Y_{i} - \hat{Y}_{i})^{2}}{n-2}} = \sqrt{\frac{\Sigma^{n}_{i=1} (Y_{i} - \hat{b}_{0} - \hat{b}_{1}\times X_{i})^{2}}{n-2}} = \sqrt{\frac{\Sigma^{n}_{i=1} (\hat{\varepsilon}_{i})^{2}}{n-2}}\)

  • \(SEE\) - standard error estimate
  • \(MSE\) - mean square error
  • \(SSE\) - sum of squares error
  • \(Y\) - dependent variable
  • \(X\) - independent variable
  • \(\hat{b}_{0}\) - estimated value of intercept
  • \(\hat{b}_{1}\) - estimated value of slope coefficient
  • \(\hat{\varepsilon}_{i}\) - expected value of error term
  • \(n\) - number of observations in the sample