1. A quality manager is analysing the scrap metal that is created in the manufacturing of bolts from factories. He took the factors that contributes to the amount of scrap metal recorded into account. The following is a partial output of 60 of a sample of factories. X2 279 646 237 200 159 499 389 Y X₁ Source of Variation Regression Error Total 110 80 90 80 60 200 250 1.5 1.4. 1 1 1.2 0.9 0.83 X₁ = SIZE - the size of the bolt in mm produced on a production line = WEIGHT - the weight of the bolt in grams X₂ X3 = AGE- of the factory in years Y = SCRAP - scrap metal in kilograms The full model was run and the following ANOVA output obtained. DF a b 59 SAS produced the following regression function. 1.2. Interpret each of the regression coefficients. 10 12 5 6 8 11 11 X3 Sum of Squares 102.9 21.9 124.8 Ŷ = 80 + 5.5X₁ - 1.4X2 +0.9X3. Mean Square 34.3 2.557 Answer the following questions. 1.1. Is there a statistical relationship between scrap and size, weight and age? The significance level is 0.05.

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1. A quality manager is analysing the scrap metal that is created in the manufacturing of bolts from
factories. He took the factors that contributes to the amount of scrap metal recorded into account.
The following is a partial output of 60 of a sample of factories.
X₂
279
646
237
200
159
499
389
Y
Source of
Variation
Regression
Error
X₁
Total
110
80
90
80
60
200
250
1.5
1.4.
1
1
1.2
0.9
0.83
X₁ = SIZE - the size of the bolt in mm produced on a production line
X₂ = WEIGHT - the weight of the bolt in grams
X3 = AGE- of the factory in years
Y = SCRAP - scrap metal in kilograms
The full model was run and the following ANOVA output obtained.
DF
10
12
5
6
8
a
b
59
1.2. Interpret each of the regression coefficients.
11
11
X3
Sum of
Squares
102.9
21.9
124.8
SAS produced the following regression function.
Ŷ =
= 80 + 5.5X₁ - 1.4X₂ +0.9X3.
Mean
Square
34.3
2.557
Answer the following questions.
1.1. Is there a statistical relationship between scrap and size, weight and age? The significance level
is 0.05.
Transcribed Image Text:1. A quality manager is analysing the scrap metal that is created in the manufacturing of bolts from factories. He took the factors that contributes to the amount of scrap metal recorded into account. The following is a partial output of 60 of a sample of factories. X₂ 279 646 237 200 159 499 389 Y Source of Variation Regression Error X₁ Total 110 80 90 80 60 200 250 1.5 1.4. 1 1 1.2 0.9 0.83 X₁ = SIZE - the size of the bolt in mm produced on a production line X₂ = WEIGHT - the weight of the bolt in grams X3 = AGE- of the factory in years Y = SCRAP - scrap metal in kilograms The full model was run and the following ANOVA output obtained. DF 10 12 5 6 8 a b 59 1.2. Interpret each of the regression coefficients. 11 11 X3 Sum of Squares 102.9 21.9 124.8 SAS produced the following regression function. Ŷ = = 80 + 5.5X₁ - 1.4X₂ +0.9X3. Mean Square 34.3 2.557 Answer the following questions. 1.1. Is there a statistical relationship between scrap and size, weight and age? The significance level is 0.05.
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