Just part (a) and (b) pls 8. Consider the following MINITAB regression output re

Question

Just part (a) and (b) pls

8. Consider the following MINITAB regression output relating the edible mass (edible) of a sample of New Zealand horse mussels to measurements on the shell dimensions, (height), (width) and (length). Regression Analysis: edible versus height, width, length The regression equation is edible =-29.0 + 0.166 height + 0.721 width + 0.0030 length Predicto Coef SE Coef Constant 28.971 3.875 7.48 0.000 height 0.16632 0.08853 0.064 0.7213 0.1563 4.62 0.000 width length 0.00304 0.04373 0.07 0.945 S= 4.79169 R-Sq= B% R-Sq (adj)=83.3% Analysis of Variance Source Regression Residual Error 78 1790.9 Total DF sS C 9341.9 3114.0 MS D 0.000 81 11132.8 Source DF Seq Ss height 1 width 1 710.3 length 10. 1 (a) Find the values of A. B. C, D. E and Fin the above output (b) What can you say about the effeet of shell width on edible mass? Is this a significant effect? (c) Use the partial F-test (from the sequential s of squares) to test the significance up

Explanation / Answer

(a) Here A = coefficient of height / std. error of height = 0.16632/0.08853 = 1.8787

B = R- square = SSR/ SST = 9341.9/ 11132.8 = 83.91%

C = DF = 3

D = MSW/ MSE = 3114.0/ E

so we need to calculate E first

E = SSE/dF(error) = 1790.9/78 = 22.96

D = MSW/ MSE = 3114.0/ 22.96 = 135.627

F = SS(error) - SS(width) - SS(length) = 1790.9 -710.3 -0.1 = 1080.5

(i am little confuse in F part , please write in comments, what is Seq SS)

(b) Here Effect of shall width on edible mass.

t = Coefficient/ standard error = 0.7213/0.1563 = 4.62

p - vlaue = 0.000 < 0.05

so we shall reject the null hypothesis at the 005 significance level so we can conclude that there is significant effect of shall width on edible mass.

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