Statistics for Engineers and Scientists
4th Edition
ISBN: 9780073401331
Author: William Navidi Prof.
Publisher: McGraw-Hill Education
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Textbook Question
Chapter 7.4, Problem 1E
The following output (from MINITAB) is for the least-squares fit of the model ln y = β0 + β1 ln x + ε, where y represents the monthly production of a gas well and x represents the volume of fracture fluid pumped in. (A
- a. What is the equation of the least-squares line for predicting ln y from ln x?
- b. Predict the production of a well into which 2500 gal/ft of fluid have been pumped.
- c. Predict the production of a well into which 1600 gal/ft of fluid have been pumped.
- d. Find a 95% prediction interval for the production of a well into which 1600 gal/ft of fluid have been pumped. (Note: In 1600 = 7.3778.)
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Students have asked these similar questions
5. The relationship between the number of beers consumed (x) and the blood alcohol
content (v) was studied in 20 male students using the least squares regression. The
following regression model (equation), was obtained from the study:
y = 0.0127 +0.0180x. This equation implies that:
a. Each beer consumed increases blood alcohol by 1.27%
b. On average it takes 1.8 beers to increase blood alcohol content by 1%
c. Each beer consumed increases blood alcohol level by 0.018
d. Each beer consumed increases blood alcohol by an average amount of 1.8%
The following estimated regression equation was developed for a model involving two independent variables.
ý = 40.7 + 8.63x, + 2.71.x,
After x 2 was dropped from the model, the least squares method was used to obtain an estimated regression equation involving only
X 1 as an independent variable.
ŷ = 42.0 + 9.01x
a. In the two independent variable case, the coefficient x 1 represents the expected change in Select v corresponding to a one unit
increase in Select v when Select v is held constant.
In the single independent variable case, the coefficient x 1 represents the expected change in Select v corresponding to a one
unit increase in Select v
b. Could multicollinearity explain why the coefficient of x 1 differs in the two models? Assume that x1 and x2 are correlated.
Select
The relationship between number of beers consumed (x) and blood alcohol content (y) was studied in 16 male college students by using least squares regression. The following regression equation was obtained from this study: y-hat = -0.0127 + 0.0180x The above equation implies that:
each beer consumed increases blood alcohol by .0127
on average it takes 1.8 beers to increase blood alcohol content by .01
After consuming 1 beer, blood alcohol equals .0180.
each beer consumed increases blood alcohol by 0.018
Chapter 7 Solutions
Statistics for Engineers and Scientists
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