Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.1 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05.

Overhead_Width_(cm) Weight_(kg)

8.4 195

7.6 191

9.7 276

9.5 241

8.7 228

8.3 216

The regression equation is y=—+—-x. (Round to one decimal place as needed.)

The best predicted weight for an overhead width of 2.1 cm is = ___ kg.

Can the prediction be correct? What is wrong with predicting the weight in this case?

A.

The prediction cannot be correct because a negative weight does not make sense. The regression does not appear to be useful for making predictions.

B.

The prediction cannot be correct because there is not sufficient evidence of a linear correlation. The width in this case is beyond the scope of the available sample data.

C.

The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample data.

D.

The prediction can be correct. There is nothing wrong with predicting the weight in this case.

Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.1cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of

0.05.

The regression equation is

y^=_____+_____x.

overhead width (cm) | 8.2 | 7.4 | 9.6 | 7.9 | 7.6 | 8.7 |

weight (kg) | 159 | 156 | 241 | 144 | 154 | 207 |

n | α=0.05 | α = 0.01 |

4 | 0.950 | 0.990 |

5 | 0.878 | 0.959 |

6 | 0.811 | 0.917 |

7 | 0.754 | 0.875 |

8 | 0.707 | 0.834 |

9 | 0.666 | 0.798 |

10 | 0.632 | 0.765 |

11 | 0.602 | 0.735 |

12 | 0.576 | 0.708 |

13 | 0.553 | 0.684 |

14 | 0.532 | 0.661 |

15 | 0.514 | 0.641 |

16 | 0.497 | 0.623 |

17 | 0.482 | 0.606 |

18 | 0.468 | 0.590 |

19 | 0.456 | 0.575 |

20 | 0.444 | 0.561 |

25 | 0.396 | 0.505 |

30 | 0.361 | 0.463 |

35 | 0.335 | 0.430 |

40 | 0.312 | 0.402 |

45 | 0.294 | 0.378 |

50 | 0.279 | 0.361 |

60 | 0.254 | 0.330 |

70 | 0.236 | 0.305 |

80 | 0.220 | 0.286 |

90 | 0.207 | 0.269 |

100 | 0.196 | 0.256 |

n | α = 0.05 | α = 0.01 |

Find the regression equation, letting the first variable be the predictor (x) variable. Using the listed lemon/crash data, where lemon imports are in metric tons and the fatality rates are per 100,000 people, find the best predicted crash fatality rate for a year in which there are 525 metric tons of lemon imports. Is the prediction worthwhile?

1. Find the equation of the regression line.

yhat=__+___x

2. find the best predicted crash fatality rate for a year in which there are 525 metric tons of lemon imports.

Lemon Imports Crash Fatality Rate

229 15.9

268 15.7

365 15.4

487 15.4

526 14.8

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