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  • Least Squares Approach

Residual Sum of Squares (RSS)

RSS is the aggregated squared residuals of all of the data. This is shown with the formula:

RSS=e12+e22+...+en2RSS = e_1^2 + e_2^2 + ... + e_n^2 or alternatively, RSS=∑i=1n(yi−β^0−β^1xi)2RSS=\sum_{i=1}^n (y_i-\hat{\beta}_0-\hat{\beta}_1x_i)^2, where β^0,β^1\hat{\beta}_0, \hat{\beta}_1 are the model parameters.

The RSS should be a smaller or lower value in any regression model. The smaller the RSS, the better the model fits the data

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5 years ago

Contributors are:

Victoria Tran
Victoria Tran
🏆 3
Derek Chan
Derek Chan
✔️ 1.99
Max Chalfin
Max Chalfin
✔️ 1.99
Iman YeckehZaare
Iman YeckehZaare
✔️ 1

Who are from:

University of Michigan - Ann Arbor
University of Michigan - Ann Arbor
🏆 7.98

References

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Data Science

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Learn After

  • Residual Standard Error

  • Mean Square Error (MSE) - Evaluation Model

  • Least Squares Coefficient Estimates

  • R-Squared (Coefficient of Determination)