# Find the regression​ equation,

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,

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 0.59 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,

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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