mying asia was read from a graph. The inceperiook vanissie is 50, depaation rate 13 18 43 114 260 350 470 500 560 1220 (a) Construct a scatter plot y 1200 1000 BOO Gook 100 200 1200 1000 BOO Goo 100 200 20 40 20 40 80 100 120 80 100 120 X y 1200 1000 BOO GOO 100 200 y 1200 1000 BOO GOO 100 200 Does the simple linear regression model appear to be reasonable in this situation? Yes, the scatter plot shows a reasonable linear relationship. O No, the scatter plot does not show a reasonable linear relationship. 20 40 20 40 80 80 (b) Calculate the equation of the estimated regression line. (Round all numerical values to two decimal places.) 100 100 120 120 X (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.)
mying asia was read from a graph. The inceperiook vanissie is 50, depaation rate 13 18 43 114 260 350 470 500 560 1220 (a) Construct a scatter plot y 1200 1000 BOO Gook 100 200 1200 1000 BOO Goo 100 200 20 40 20 40 80 100 120 80 100 120 X y 1200 1000 BOO GOO 100 200 y 1200 1000 BOO GOO 100 200 Does the simple linear regression model appear to be reasonable in this situation? Yes, the scatter plot shows a reasonable linear relationship. O No, the scatter plot does not show a reasonable linear relationship. 20 40 20 40 80 80 (b) Calculate the equation of the estimated regression line. (Round all numerical values to two decimal places.) 100 100 120 120 X (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.)
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Q15 Please answer the fill-ins
![The accompanying data was read from a graph. The independent variable is SO₂ deposition rate (mg/m²/day) and the dependent variable is steel weight loss (g/m²).
| x | 13 | 18 | 38 | 43 | 45 | 114 |
|-----|-----|-----|-----|-----|-----|------|
| y | 260 | 350 | 470 | 590 | 560 | 1220 |
### (a) Construct a scatter plot.
#### Graphs
There are four scatter plots depicted, each comparing the SO₂ deposition rate (x-axis) to the steel weight loss (y-axis). They all show data points clustered in a linear fashion, indicating a possible linear relationship between the two variables:
- **Scatter Plot 1:** Shows the data points scattered from x = 0 to x = 120 with corresponding y-values from y = 0 to y = 1200.
**Question:** Does the simple linear regression model appear to be reasonable in this situation?
- Options:
- Yes, the scatter plot shows a reasonable linear relationship. (Selected)
- 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.)
\[ y^* = \_\_\_\_ \]](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fbdd9398b-9a89-4097-8d89-380633fcbf86%2F91a56fd0-f988-4f64-90e0-aacc5040ef07%2Fvf4e1pf_processed.png&w=3840&q=75)
Transcribed Image Text:The accompanying data was read from a graph. The independent variable is SO₂ deposition rate (mg/m²/day) and the dependent variable is steel weight loss (g/m²).
| x | 13 | 18 | 38 | 43 | 45 | 114 |
|-----|-----|-----|-----|-----|-----|------|
| y | 260 | 350 | 470 | 590 | 560 | 1220 |
### (a) Construct a scatter plot.
#### Graphs
There are four scatter plots depicted, each comparing the SO₂ deposition rate (x-axis) to the steel weight loss (y-axis). They all show data points clustered in a linear fashion, indicating a possible linear relationship between the two variables:
- **Scatter Plot 1:** Shows the data points scattered from x = 0 to x = 120 with corresponding y-values from y = 0 to y = 1200.
**Question:** Does the simple linear regression model appear to be reasonable in this situation?
- Options:
- Yes, the scatter plot shows a reasonable linear relationship. (Selected)
- 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.)
\[ y^* = \_\_\_\_ \]
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