#14 please

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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#14 please

 

Questions 12-16: The article “Effect of Environmental Factors on Steal Plate Corrosion" describes an
experiment in which nine steel specimens were submerged in seawater at various temperatures (°C). Afterwards,
the corrosion rates were measure (mm/yr). The researchers decided to use a simple linear regression approach.
12. The image below displays the result from the article. Fill in the blanks to make the image complete.
(Hint: There are 12 blank)
Summary of Fit
RSquare
RSquare Adj
Root Mean Square Error
0.649448
1.024444
Mean of Response
Observations (or Sum Wgts)
Analysis of Variance
Sum of
Source
DF
Squares Mean Square
F Ratio
Model
0.61217009
Error
Prob > F
C. Total
0.88302222
0.0053*
Parameter Estimates
Term
Estimate Std Error t Ratio Prob>|t|
Intercept
0.3939595
0.171536
0.0553
Temperature 0.0355092 0.008927
0.0053*
13. What is the least squares equation for predicting corrosion from temperature?
14. Two steel specimens whose temperatures differ by 10°C are submerged in seawater. By how much
would you predict their corrosion rates to differ?
15. What is the correlation coefficient?
16. Is temperature a significant predictor of corrosion? Use a = 0.05
Transcribed Image Text:Questions 12-16: The article “Effect of Environmental Factors on Steal Plate Corrosion" describes an experiment in which nine steel specimens were submerged in seawater at various temperatures (°C). Afterwards, the corrosion rates were measure (mm/yr). The researchers decided to use a simple linear regression approach. 12. The image below displays the result from the article. Fill in the blanks to make the image complete. (Hint: There are 12 blank) Summary of Fit RSquare RSquare Adj Root Mean Square Error 0.649448 1.024444 Mean of Response Observations (or Sum Wgts) Analysis of Variance Sum of Source DF Squares Mean Square F Ratio Model 0.61217009 Error Prob > F C. Total 0.88302222 0.0053* Parameter Estimates Term Estimate Std Error t Ratio Prob>|t| Intercept 0.3939595 0.171536 0.0553 Temperature 0.0355092 0.008927 0.0053* 13. What is the least squares equation for predicting corrosion from temperature? 14. Two steel specimens whose temperatures differ by 10°C are submerged in seawater. By how much would you predict their corrosion rates to differ? 15. What is the correlation coefficient? 16. Is temperature a significant predictor of corrosion? Use a = 0.05
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