(b) Calculate the equation of the estimated regression line. (Round all numerical values to two decimal places.) y =        (c) What percentage of observed variation in steel weight loss can be attributed to the model relationship in combination with variation in deposition rate? (Round your answer to one decimal place.)  % (d) Because the largest x value in the sample greatly exceeds the others, this observation may have been very influential in determining the equation of the line. Delete this observation and recalculate the equation. (Round all numerical values to two decimal places.) y* =        Does the new equation appear to differ substantially from the original one (you might consider predicted values)? Yes, there are significant differences.No, there are not significant differences.

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
icon
Related questions
Question
100%

The accompanying data was read from a graph. The independent variable is SO2 deposition rate (mg/m2/d) and the dependent variable is steel weight loss (g/m2).

x 15 18 40 43 45 113
y 280 350 460 500 560 1140
(a) Construct a scatter plot.
   
   

Does the simple linear regression model appear to be reasonable in this situation?
Yes, the scatter plot shows a reasonable linear relationship.No, the scatter plot does not show a reasonable linear relationship.   

(b) Calculate the equation of the estimated regression line. (Round all numerical values to two decimal places.)
y = 
 
 
 


(c) What percentage of observed variation in steel weight loss can be attributed to the model relationship in combination with variation in deposition rate? (Round your answer to one decimal place.)
 %

(d) Because the largest x value in the sample greatly exceeds the others, this observation may have been very influential in determining the equation of the line. Delete this observation and recalculate the equation. (Round all numerical values to two decimal places.)
y* = 
 
 
 


Does the new equation appear to differ substantially from the original one (you might consider predicted values)?
Yes, there are significant differences.No, there are not significant differences.  
 
I just need help with b-d, thanks
The accompanying data was read from a graph. The independent variable is So, deposition rate (mg/m2/d) and the dependent variable is steel weight loss (g/m2).
15
18
40
43
45
113
y | 280 350 460 500 560 1140
(a) Construct a scatter plot.
y
y
1200
1200
1000
1000
800아
800아
600
600
400
400
200
200아
X
20
40
60
80
100
120
20
40
60
80
100
120
y
y
1200
1200
1000
1000
800
800
600
600
400
400
200
200
20
40
60
80
100
120
20
40
60
80
100
120
Transcribed Image Text:The accompanying data was read from a graph. The independent variable is So, deposition rate (mg/m2/d) and the dependent variable is steel weight loss (g/m2). 15 18 40 43 45 113 y | 280 350 460 500 560 1140 (a) Construct a scatter plot. y y 1200 1200 1000 1000 800아 800아 600 600 400 400 200 200아 X 20 40 60 80 100 120 20 40 60 80 100 120 y y 1200 1200 1000 1000 800 800 600 600 400 400 200 200 20 40 60 80 100 120 20 40 60 80 100 120
Does the simple linear regression model appear to be reasonable in this situation?
O Yes, the scatter plot shows a reasonable linear relationship.
O No, the scatter plot does not show a reasonable linear relationship.
(b) Calculate the equation of the estimated regression line. (Round all numerical values to two decimal places.)
y =
(c) What percentage of observed variation in steel weight loss can be attributed to the model relationship in combination with variation in deposition rate? (Round your answer to one decimal place.)
%
(d) Because the largest x value in the sample greatly exceeds the others, this observation may have been very influential in determining the equation of the line. Delete this observation and recalculate the
equation. (Round all numerical values to two decimal places.)
y* =
Does the new equation appear to differ substantially from the original one (you might consider predicted values)?
O Yes, there are significant differences.
O No, there are not significant differences.
Transcribed Image Text:Does the simple linear regression model appear to be reasonable in this situation? O Yes, the scatter plot shows a reasonable linear relationship. O No, the scatter plot does not show a reasonable linear relationship. (b) Calculate the equation of the estimated regression line. (Round all numerical values to two decimal places.) y = (c) What percentage of observed variation in steel weight loss can be attributed to the model relationship in combination with variation in deposition rate? (Round your answer to one decimal place.) % (d) Because the largest x value in the sample greatly exceeds the others, this observation may have been very influential in determining the equation of the line. Delete this observation and recalculate the equation. (Round all numerical values to two decimal places.) y* = Does the new equation appear to differ substantially from the original one (you might consider predicted values)? O Yes, there are significant differences. O No, there are not significant differences.
Expert Solution
trending now

Trending now

This is a popular solution!

steps

Step by step

Solved in 3 steps with 3 images

Blurred answer
Similar questions
  • SEE MORE QUESTIONS
Recommended textbooks for you
MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
Elementary Statistics: Picturing the World (7th E…
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
The Basic Practice of Statistics
The Basic Practice of Statistics
Statistics
ISBN:
9781319042578
Author:
David S. Moore, William I. Notz, Michael A. Fligner
Publisher:
W. H. Freeman
Introduction to the Practice of Statistics
Introduction to the Practice of Statistics
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman