Brawdy Plastics, Inc., produces plastic seat belt retainers for General Motors at their plant in Buffalo, New York. After final assembly and painting, the parts are placed on a conveyor belt that moves the parts past a final inspection station. How fast the parts move past the final inspection station depends upon the line speed of the conveyor belt (feet per minute). Although faster line speeds are desirable, management is concerned that increasing the line speed too much may not provide enough time for inspectors to identify which parts are actually defective. To test this theory, Brawdy Plastics conducted an experiment in which the same batch of parts, with a known number of defective parts, was inspected using a variety of line speeds. The following data were collected. Line Speed   Number of Defective Parts Found 20   23 20   21 30   19 30   16 40   15 40   17 50   14 50   11 Use the least squares method to develop the estimated regression equation (to 1 decimal). ŷ=_______+_______X

College Algebra
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ISBN:9781938168383
Author:Jay Abramson
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Chapter6: Exponential And Logarithmic Functions
Section6.8: Fitting Exponential Models To Data
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Brawdy Plastics, Inc., produces plastic seat belt retainers for General Motors at their plant in Buffalo, New York. After final assembly and painting, the parts are placed on a conveyor belt that moves the parts past a final inspection station. How fast the parts move past the final inspection station depends upon the line speed of the conveyor belt (feet per minute). Although faster line speeds are desirable, management is concerned that increasing the line speed too much may not provide enough time for inspectors to identify which parts are actually defective. To test this theory, Brawdy Plastics conducted an experiment in which the same batch of parts, with a known number of defective parts, was inspected using a variety of line speeds. The following data were collected.

Line
Speed
  Number of
Defective
Parts Found
20   23
20   21
30   19
30   16
40   15
40   17
50   14
50   11

Use the least squares method to develop the estimated regression equation (to 1 decimal).

ŷ=_______+_______X

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