1. Nine steel specimens were submerged in seawater at various temperatures, and the corrosion rates were measured. The results are presented in the following table: Temperature (°C) 26.6 26 27.4 21.7 14.9 11.3 15 8.7 8.2 Corrosion (mm/yr.) 1.58 1.45 1.13 0.96 0.99 1.05 0.82 0.68 0.59 a. Construct a scatterplot of corrosion (y) vs. temperature (x). Verify that a linear model is appropriate. b. What is the least-square line (trendline/regression line) for predicting corrosion from temperature? Two steel specimens whose temperatures differ by 10°C are submerged in seawater. By how much would you predict their corrosion rates differ? d. Predict the corrosion rate for steel submerged in seawater at a temperature of 20°C. Construct a table with predicted values from the original dataset. f. Compute the residuals. Which point has the residual with the largest magnitude? g. What is the correlation between temperature and corrosion rate? What is the regression value? What can be said about variability? h. Construct a residual plot. What conclusions can be drawn from this plot.

Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter3: The Derivative
Section3.3: Rates Of Change
Problem 29E: Explain the difference between the average rate of change of y as x changes a to b, and the...
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1. Nine steel specimens were submerged in seawater at various temperatures, and the corrosion
rates were measured. The results are presented in the following table:
Temperature
(°C)
26.6
26
27.4
21.7
14.9
11.3
15
8.7
8.2
Ở
Corrosion
(mm/yr.)
1.58
1.45
1.13
0.96
0.99
1.05
0.82
0.68
0.59
Construct a scatterplot of corrosion (y) vs. temperature (x). Verify that a linear model is
appropriate.
b. What is the least-square line (trendline/regression line) for predicting corrosion from
temperature?
Two steel specimens whose temperatures differ by 10°C are submerged in seawater. By how
much would you predict their corrosion rates differ?
d.
Predict the corrosion rate for steel submerged in seawater at a temperature of 20°C.
Construct a table with predicted values from the original dataset.
f.
Compute the residuals. Which point has the residual with the largest magnitude?
g. What is the correlation between temperature and corrosion rate? What is the regression value?
What can be said about variability?
h. Construct a residual plot. What conclusions can be drawn from this plot.
Transcribed Image Text:1. Nine steel specimens were submerged in seawater at various temperatures, and the corrosion rates were measured. The results are presented in the following table: Temperature (°C) 26.6 26 27.4 21.7 14.9 11.3 15 8.7 8.2 Ở Corrosion (mm/yr.) 1.58 1.45 1.13 0.96 0.99 1.05 0.82 0.68 0.59 Construct a scatterplot of corrosion (y) vs. temperature (x). Verify that a linear model is appropriate. b. What is the least-square line (trendline/regression line) for predicting corrosion from temperature? Two steel specimens whose temperatures differ by 10°C are submerged in seawater. By how much would you predict their corrosion rates differ? d. Predict the corrosion rate for steel submerged in seawater at a temperature of 20°C. Construct a table with predicted values from the original dataset. f. Compute the residuals. Which point has the residual with the largest magnitude? g. What is the correlation between temperature and corrosion rate? What is the regression value? What can be said about variability? h. Construct a residual plot. What conclusions can be drawn from this plot.
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This question examines the correlation between corrosion rates and temperature for nine steel specimens submerged in seawater. A scatterplot and linear regression line will be constructed to show the relationship between the temperature and corrosion rate.
The least-squares line will be used to predict corrosion rate from temperature and the correlation between temperature and corrosion rate will be determined. A residual plot will be constructed and the variability of the data will be discussed.

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