1. Thomas Knotts

2. Engineers often: Regress data  Analysis  Fit to theory  Data reduction Use the regression of others  Antoine Equation  DIPPR

3. There are two classes of regressions  Linear  Non-linear “Linear” refers to the parameters, not the functional dependence of the independent variable  You can use the Mathcad function “linfit” on linear equations

4. There are two classes of regressions  Linear  Non-linear “Linear” refers to the parameters, not the functional dependence of the independent variable  You can use the Mathcad function “linfit” on linear equations 1. 2. 3. 4. 5.

5. residual (error) “X” data “Y” predicted slope Intercept

6. residual (error) sum squared error “X” data “Y” data intercept 0.92291455 slope 0.516173934 12.7490321781.4390884831.309943694 23.7199102242.3620030331.357907192 30.9259950173.284917582-2.35892257 42.6234826864.207832132-1.58434945 56.5397973425.1307466811.409050661 66.7799091776.0536612310.726247946 74.9461504016.976575781-2.03042538 89.6741780697.899490331.774687739 97.619598218.82240488-1.20280667 107.6500209969.745319429-2.09529843 1111.51410.668233980.845766021 1213.1828506811.591148531.591702152 1313.2817363512.514063080.767673275 1413.6044459213.436977630.16746829 1512.7953521814.35989218-1.56454 1617.8237477815.282806732.540941056 1714.5506837916.20572128-1.65503748 42.76608602SS E “Y” predicted

7. A useful statistic but not definitive Tells you how well the data fit the model. It does not tell you if the model is correct.

11. When you fit data to a straight line, the slope and intercept are only estimates of the true slope and intercept.

14. Linear regression can be written in matrix form. XY 21186 24214 32288 47425 50455 59539 68622 74675 62562 50453 41370 30274

15. Standard Error of b i is the square root of the i-th diagonal term of the matrix