74 A manufacturer wants to minimize the cost of producing a new item. This new item is similar to an old item, and the company thinks costs for both items will be minimized for the same number of items produced. The company uses polynomial regression and finds the function C(x) = 0.09x – 1.78x + 11.03 fits the-data well (with 7 = 0.96), where C is the cost per item to produce x items (in hundreds). The company concludes that producing about 1,000 items would minimize the cost per item.%3D141210-8-62-4-26810121416182022Is this a valid conclusion and why?Yes, this is a valid conclusion because the graph is concave up and clearly has a minimum.Yes, this is a valid conclusion because x = 10 is close to the other data values, the data points are all close to the curve, and the ? value is close to 1.No, this is not a valid conclusion because there are not data values close enough to x = 10.O No, this is not a valid conclusion because the correlation coefficient is too close to 1.

We have to minimise the cost for hie much production of items.

A manufacturer wants to minimize the cost of producing a new item. This new item is similar to an old item, and the company thinks costs for both items will be minimized for the same number of items produced. The company uses polynomial regression and finds the function Cx) - 0.09x - 1.78x + 11.03 fits the data well (with 2 - 0.96), where Cis the cost per item to produce x items (in hundreds). The company concludes that producing about 1,000 items would minimize the cost per item. Is this a valid conclusion and why? O Yes, this is a valid conclusion because the graph is concave up and clearly has a minimum. O Yes, this is a valid conclusion because x= 10 is close to the other data values, the data points are all close to the curve, and the value is close to 1. O No, this is not a valid conclusion because there are not data values close enough to x = 10. O No, this is not a valid conclusion because the correlation coefficient is too close to 1.