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Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Average 31 35.5 31 40.5 34 39 37.5 time for Total process Average 15 20 17 18 24 time for Moulding Average 4.5 7 7 5.5 time for finishing Average time for 10 11 9. 8.5 10 8 12 packing Factory manager claims that there is a positive relationship between moulding time and total time taken for the process. As the production engineer you need to test this hypothesis using the above data. If there is a relationship, provide the best fitted straight line for the above data using linear regression. You should interpret your results in a method that can be understood by a non-technical audience. 20 25

Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Average 31 35.5 31 40.5 34 39 37.5 time for Total process Average 15 20 17 18 24 time for Moulding Average 4.5 7 7 5.5 time for finishing Average time for 10 11 9. 8.5 10 8 12 packing Factory manager claims that there is a positive relationship between moulding time and total time taken for the process. As the production engineer you need to test this hypothesis using the above data. If there is a relationship, provide the best fitted straight line for the above data using linear regression. You should interpret your results in a method that can be understood by a non-technical audience. 20 25

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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A production line of a mechanical spare part includes three stages such as moulding, finishing and packing. As the production engineer, you are required to improve the efficiency of the process immediately to cater a relatively large order which needs to be served in a limited time. On the shutdown day you only have resources and time to do detailed maintenance in only one component. In order to do that you are required to identify the component(stage) which affect the most to the total time consumed. Following details were obtained in a cycle time analysis of most recent week. Using these details find the most significant component by performing a correlation analysis. Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Average 31 35.5 31 40.5 34 39 37.5 time for Total process Average 15 20 17 25 18 24 20 time for Moulding Average 4.5 7 6. 7 5.5 time for finishing Average 10 11 9. 8.5 10 12 time for packing Factory manager claims that there is a positive relationship between moulding time and total time taken for the process. As the production engineer you need to test this hypothesis using the above data. If there is a relationship, provide the best fitted straight line for the above data using linear regression. You should interpret your results in a method that can be understood by a non-technical audience. co
Transcribed Image Text:A production line of a mechanical spare part includes three stages such as moulding, finishing and packing. As the production engineer, you are required to improve the efficiency of the process immediately to cater a relatively large order which needs to be served in a limited time. On the shutdown day you only have resources and time to do detailed maintenance in only one component. In order to do that you are required to identify the component(stage) which affect the most to the total time consumed. Following details were obtained in a cycle time analysis of most recent week. Using these details find the most significant component by performing a correlation analysis. Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Average 31 35.5 31 40.5 34 39 37.5 time for Total process Average 15 20 17 25 18 24 20 time for Moulding Average 4.5 7 6. 7 5.5 time for finishing Average 10 11 9. 8.5 10 12 time for packing Factory manager claims that there is a positive relationship between moulding time and total time taken for the process. As the production engineer you need to test this hypothesis using the above data. If there is a relationship, provide the best fitted straight line for the above data using linear regression. You should interpret your results in a method that can be understood by a non-technical audience. co
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