Statistics for Engineers and Scientists
4th Edition
ISBN: 9780073401331
Author: William Navidi Prof.
Publisher: McGraw-Hill Education
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Textbook Question
Chapter 7.2, Problem 11E
An agricultural scientist planted alfalfa on several plots of land, identical except for the soil pH. Following are the dry matter yields (in pounds per acre) for each plot.
- a. Construct a
scatterplot of yield (y) versus pH (x). Verify that a linear model is appropriate. - b. Compute the least-squares line for predicting yield from pH.
- c. Compute the fitted value and the residual for each point.
- d. If the pH is increased by 0.1, by how much would you predict the yield to increase or decrease?
- e. Predict the yield for a pH of 5.5.
- f. Can the least-squares line be used to predict the yield for a pH of 7? If so, predict the yield. If not, explain why not.
- g. For what pH would you predict a yield of 1500 pounds per acre?
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Students have asked these similar questions
STER.
1. Wine Consumption. The table below gives the U.S. adult wine consumption, in gallons per
person per year, for selected years from 1980 to 2005.
a) Create a scatterplot for the data. Graph the scatterplot
Year
Wine
below.
Consumption
2.6
b) Determine what type of model is appropriate for the
1980
data.
1985
2.3
c) Use the appropriate regression on your calculator to find a
Graph the regression equation in the same coordinate
plane below.
d) According to your model, in what year was wine
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e) Use your model to predict the wine consumption in
2008.
1990
2.0
1995
2.1
2000
2.5
2005
2.8
Find the least-squares equation for the following pairs of data:
x = earthquake magnitude
2.9
4.2
3.3
4.5
2.6
3.2
3.4
y = depth of earthquake (in km)
5
10
11.2
10
7.9
3.9
5.5
A. y = 2.16 + 0.221x
B. y = 0.221 + 2.16x
C. y = 2.16 + 0.312x
D. y = 0.221 + 2.82x
The U.S. Postal Service is attempting to reduce the number of complaints made by the public against its workers. To facilitate this task, a staff analyst for the service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression in SPSS. The results are shown below.
Table 7: Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.854a
.730
.695
6.6235
a. Predictors: (Constant), Hourly Wage
Table 8: ANOVA
ANOVAb
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
1918.458
1
1918.458
129.783
.000a
Residual
709.567
48
14.782
Total
2628.025
49
a. Predictors: (Constant), Hourly Wage
b. Dependent Variable: Number of Complaints
Table 9: Coefficients
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t…
Chapter 7 Solutions
Statistics for Engineers and Scientists
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