Find regression letting overhead be predictor x)variable Find the best predicted

Question

Find regression letting overhead be predictor x)variable Find the best predicted weight seal overhead with measured form a photographis 19am.canthe prediction beoomoon what is wrong with predicting the weight case? Use a signincanoe level of 0.06. overhead form) 86 85 Click ioon to view the oritical values of the Pearson oorelatoncoefmoert The regression equation is y (Round to one decimalplace as needed.) The best predicted weight for an overhead width of 19 omis (Round to one decimal plaoe as needed. Can the prediction be comect? What is wrong with predoting the weightinthis case? O A. The prediction cannot be correct because a negative weight doesnot make sense. The regression does not appear to be useuformakingpredosons. B. The prediction cannel be eomeet because a negative weight does not make sense.The width in tis case is beyond scope of the avalabie sampledata. O c. The prediction cannot be correct becausethere not ufoient evidence of a lear oomelaton The width intis case is beyond the soopeofte avalablesample data. O DA The prediction can be correct. There is nothing wrong with predicting the weight in this case.

Explanation / Answer

The statistical software output for this problem is:

Simple linear regression results:
Dependent Variable: Weight
Independent Variable: Overhead Width
Weight = -283.61722 + 56.373626 Overhead Width
Sample size: 6
R (correlation coefficient) = 0.95402462
R-sq = 0.91016298
Estimate of error standard deviation: 11.946749

Parameter estimates:

Analysis of variance table for regression model:

Predicted values:

Hence,

The regression equation is:

y = -283.6 + 56.4 x

Best predicted weight is -176.5 CM.

Option B is correct.

Parameter Estimate Std. Err. Alternative DF T-Stat P-value Intercept -283.61722 77.197245 0 4 -3.6739293 0.0213 Slope 56.373626 8.8555197 0 4 6.3659309 0.0031

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