20000 y = 1172.5x + 127.94 R = 0.9995 18000 16000 14000 12000 10000 8000 6000 4000 2000 4 10 12 14 16 18 Concentration (m/m%) Based on the calibration curve above, how many of the following statements are true? • The R2 value is close to 1. • The data is accurate but it is not precise. • The data is precise but it is not accurate. • The R2 value is not close to 1. • The data fits the curve well. • The data fits the curve poorly. • The data is accurate and precise. Conductivity increases with increasing concentration. • Conductivity decreases with increasing concentration. 1 O 2 O 4 5 Conductivity (uS/cm)
20000 y = 1172.5x + 127.94 R = 0.9995 18000 16000 14000 12000 10000 8000 6000 4000 2000 4 10 12 14 16 18 Concentration (m/m%) Based on the calibration curve above, how many of the following statements are true? • The R2 value is close to 1. • The data is accurate but it is not precise. • The data is precise but it is not accurate. • The R2 value is not close to 1. • The data fits the curve well. • The data fits the curve poorly. • The data is accurate and precise. Conductivity increases with increasing concentration. • Conductivity decreases with increasing concentration. 1 O 2 O 4 5 Conductivity (uS/cm)
Chemistry & Chemical Reactivity
10th Edition
ISBN:9781337399074
Author:John C. Kotz, Paul M. Treichel, John Townsend, David Treichel
Publisher:John C. Kotz, Paul M. Treichel, John Townsend, David Treichel
Chapter11: Intermolecular Forces And Liquids
Section11.6: Properties Of Liquids
Problem 1.2ACP
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Question
![### Conductivity Versus Concentration
#### Graph Description:
The graph above displays a calibration curve with the title "Conductivity Versus Concentration". It plots the conductivity (µS/cm) on the y-axis against the concentration (m/m%) on the x-axis. The data points form a line with the equation:
\[ y = 1172.5x + 127.94 \]
where:
- \( y \) is the conductivity,
- \( x \) is the concentration.
The \( R^2 \) value is 0.9995, indicating how well the line fits the data points.
#### Analyzing the Graph:
Based on the calibration curve above, you need to determine how many of the following statements are true:
- The \( R^2 \) value is close to 1.
- The data is accurate but it is not precise.
- The data is precise but it is not accurate.
- The \( R^2 \) value is not close to 1.
- The data fits the curve well.
- The data fits the curve poorly.
- The data is accurate and precise.
- Conductivity increases with increasing concentration.
- Conductivity decreases with increasing concentration.
#### Question:
Based on the calibration curve above, how many of the following statements are true?
1. The \( R^2 \) value is close to 1.
2. The data is accurate but it is not precise.
3. The data is precise but it is not accurate.
4. The \( R^2 \) value is not close to 1.
5. The data fits the curve well.
6. The data fits the curve poorly.
7. The data is accurate and precise.
8. Conductivity increases with increasing concentration.
9. Conductivity decreases with increasing concentration.
**Options:**
- ❍ 1
- ❍ 2
- ❍ 3
- ❍ 4
- ❍ 5
Use the information from the graph to select the correct number of true statements.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F7eb32d09-79f1-492f-b21e-e525fbfadae0%2Fded0b227-ccb8-471f-ae35-ea6700c75717%2Fj1k87um_processed.png&w=3840&q=75)
Transcribed Image Text:### Conductivity Versus Concentration
#### Graph Description:
The graph above displays a calibration curve with the title "Conductivity Versus Concentration". It plots the conductivity (µS/cm) on the y-axis against the concentration (m/m%) on the x-axis. The data points form a line with the equation:
\[ y = 1172.5x + 127.94 \]
where:
- \( y \) is the conductivity,
- \( x \) is the concentration.
The \( R^2 \) value is 0.9995, indicating how well the line fits the data points.
#### Analyzing the Graph:
Based on the calibration curve above, you need to determine how many of the following statements are true:
- The \( R^2 \) value is close to 1.
- The data is accurate but it is not precise.
- The data is precise but it is not accurate.
- The \( R^2 \) value is not close to 1.
- The data fits the curve well.
- The data fits the curve poorly.
- The data is accurate and precise.
- Conductivity increases with increasing concentration.
- Conductivity decreases with increasing concentration.
#### Question:
Based on the calibration curve above, how many of the following statements are true?
1. The \( R^2 \) value is close to 1.
2. The data is accurate but it is not precise.
3. The data is precise but it is not accurate.
4. The \( R^2 \) value is not close to 1.
5. The data fits the curve well.
6. The data fits the curve poorly.
7. The data is accurate and precise.
8. Conductivity increases with increasing concentration.
9. Conductivity decreases with increasing concentration.
**Options:**
- ❍ 1
- ❍ 2
- ❍ 3
- ❍ 4
- ❍ 5
Use the information from the graph to select the correct number of true statements.
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