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For all questions be sure to identify the piece or piecesof output you used to a

ID: 2952461 • Letter: F

Question

For all questions be sure to identify the piece or piecesof output you used to answer each question.Failure to do sowill

affect your final score!

1. A study is carried out to investigate the relationshipbetween the average level of aflatoxin (parts per billion)in

a mini-lot sample of peanuts and the percentage ofnon-contaminated peanuts in the batch. The followingis

the output for the regression predicting the percentage ofnon-contaminated peanuts in the patch from the

level of aflatoxin in a mini-lot sample.

a. Is there enough evidence to suggest that the linearmodel is useful in predicting the percentage ofnoncontaminated

peanuts in a batch?

b. Interpret the model parameters in a meaningfulway.

c. Interpret the R2 value.

Regression Analysis:non-contaminated versus alflatoxin

The regression equation is

non-contimated = 100 - 0.00300alflatoxin

Predictor Coef SE Coef T P

Constant 99.997 0.019 5197.740.000

alflatoxin -0.0030010 0.0004275 -7.020.000

S = 0.0697556 R-Sq = 61.4% R-Sq(adj) =60.1%

Analysis of Variance

Source DF SS MS F P

Regression 1 0.23979 0.23979 49.280.000

Residual Error 31 0.150840.00487

Total 32 0.39063

Explanation / Answer

a) Since the P-value in Analysis ofVariance is 0.000, we can conclude that there isenough evidence to suggest that the linear model is useful inpredicting the percentage of noncontaminated peanuts in abatch. b) intercept = 99.997 slope = -0.003 If there is one-unit increase in aflatoxin level, thenthere is 0.003 decrease in percentage of noncontaminatedpeanuts. c) R2 = 61.4% 61.4% variation in the percentage of noncontaminatedpeanuts is explained by the least squares regression line. b) intercept = 99.997 slope = -0.003 If there is one-unit increase in aflatoxin level, thenthere is 0.003 decrease in percentage of noncontaminatedpeanuts. c) R2 = 61.4% 61.4% variation in the percentage of noncontaminatedpeanuts is explained by the least squares regression line.
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