Linear Regression
1 The model
We built a linear regression model using
We get the following output
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | -17.579095 | 6.7584402 | -2.601058 | 0.0123188 |
| speed | 3.932409 | 0.4155128 | 9.463990 | 0.0000000 |
Note that the slope of the regression is 3.9324088.
The model residulas (fit$residuals) are
| Residulas |
|---|
| 3.849460 |
| 11.849460 |
| -5.947766 |
| 12.052234 |
| 2.119825 |
| -7.812584 |
| -3.744993 |
| 4.255007 |
| 12.255007 |
| -8.677402 |
| 2.322598 |
| -15.609810 |
| -9.609810 |
| -5.609810 |
| -1.609810 |
| -7.542219 |
| 0.457781 |
| 0.457781 |
| 12.457781 |
| -11.474628 |
| -1.474628 |
| 22.525372 |
| 42.525372 |
| -21.407037 |
| -15.407037 |
| 12.592963 |
| -13.339445 |
| -5.339445 |
| -17.271854 |
| -9.271854 |
| 0.728146 |
| -11.204263 |
| 2.795737 |
| 22.795737 |
| 30.795737 |
| -21.136671 |
| -11.136672 |
| 10.863328 |
| -29.069080 |
| -13.069080 |
| -9.069080 |
| -5.069080 |
| 2.930920 |
| -2.933898 |
| -18.866307 |
| -6.798715 |
| 15.201285 |
| 16.201285 |
| 43.201285 |
| 4.268876 |
2 Some plots
