In: Statistics and Probability
25. An important application of regression in manufacturing is the estimation of cost of production. Based on DATA from Ajax Widgets relating cost (Y) to volume (X), what is the cost of producing 600 widgets?
Production Volume (units) |
Total Cost $ |
400 |
3430 |
450 |
4080 |
550 |
4878 |
600 |
4884 |
700 |
5913 |
750 |
6402 |
425 |
4273 |
475 |
4362 |
575 |
5089 |
625 |
5446 |
725 |
6017 |
775 |
6591 |
Solution:
Given: cost (Y) and Production volume (X)
We have to find the cost of producing 600 widgets by using given data.
Thus we need to find regression equation to predict Y = Cost from X= Production volume.
where
Thus we need to make following table:
X : Production Volume (units) | Y : Total Cost $ | X^2 | XY |
400 | 3430 | 160000 | 1372000 |
450 | 4080 | 202500 | 1836000 |
550 | 4878 | 302500 | 2682900 |
600 | 4884 | 360000 | 2930400 |
700 | 5913 | 490000 | 4139100 |
750 | 6402 | 562500 | 4801500 |
425 | 4273 | 180625 | 1816025 |
475 | 4362 | 225625 | 2071950 |
575 | 5089 | 330625 | 2926175 |
625 | 5446 | 390625 | 3403750 |
725 | 6017 | 525625 | 4362325 |
775 | 6591 | 600625 | 5108025 |
Thus
Thus
and
Thus
Thus regression equation is:
Thus the cost of producing 600 widgets is: