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1.3. If the coefficient of multiple determination is as follows explain what it measures. 1.4. The following 95 % confidence intervals were then obtained for ₁ and ₂ B₁: (5≤B₁ ≤6) B₂: (-2≤B₂ ≤ -0.5) Interpret both intervals. 1.5. Is the predictor variable AGE contributing in the explaining of the variation in scrap metal waste? Show how you can answer this question if the standard deviation of AGE is 0.6223 years. 1.6. The regression is run again on the dataset but the predictor AGE is taken out. Can we use the model given by SAS but with AGE taken out? Motivate your answer.

1.3. If the coefficient of multiple determination is as follows explain what it measures. 1.4. The following 95 % confidence intervals were then obtained for ₁ and ₂ B₁: (5≤B₁ ≤6) B₂: (-2≤B₂ ≤ -0.5) Interpret both intervals. 1.5. Is the predictor variable AGE contributing in the explaining of the variation in scrap metal waste? Show how you can answer this question if the standard deviation of AGE is 0.6223 years. 1.6. The regression is run again on the dataset but the predictor AGE is taken out. Can we use the model given by SAS but with AGE taken out? Motivate your answer.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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1.3. If the coefficient of multiple determination is as follows explain what it measures. 1.4. The following 95 % confidence intervals were then obtained for ₁ and ₂ B₁: (5 ≤B₁ ≤6) B₂: (-2≤ B₂ ≤ -0.5) Interpret both intervals. 1.5. Is the predictor variable AGE contributing in the explaining of the variation in scrap metal waste? Show how you can answer this question if the standard deviation of AGE is 0.6223 years. 1.6. The regression is run again on the dataset but the predictor AGE is taken out. Can we use the model given by SAS but with AGE taken out? Motivate your answer.
Transcribed Image Text:1.3. If the coefficient of multiple determination is as follows explain what it measures. 1.4. The following 95 % confidence intervals were then obtained for ₁ and ₂ B₁: (5 ≤B₁ ≤6) B₂: (-2≤ B₂ ≤ -0.5) Interpret both intervals. 1.5. Is the predictor variable AGE contributing in the explaining of the variation in scrap metal waste? Show how you can answer this question if the standard deviation of AGE is 0.6223 years. 1.6. The regression is run again on the dataset but the predictor AGE is taken out. Can we use the model given by SAS but with AGE taken out? Motivate your answer.
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 Total 110 80 90 80 60 200 250 X₁ 1.5 1.4. Answer the following questions. 1 1 1.2 0.9 0.83 DF 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. a b 59 SAS produced the following regression function. 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
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 Total 110 80 90 80 60 200 250 X₁ 1.5 1.4. Answer the following questions. 1 1 1.2 0.9 0.83 DF 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. a b 59 SAS produced the following regression function. 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
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