What is the relationship between the number of minutes per day a woman spends talking on the phone and the woman's weight? The time on the phone and weight for 7 women are shown in the table below. Time 41 32 90 32 31 33 82 Pounds 131 112 144 104 113 120 142 a. Find the correlation coefficient: r = b. The null and alternative hypotheses for correlation are: Ho: ? = 0 H₁: 20 The p-value is: Round to 2 decimal places. (Round to four decimal places) c. Use a level of significance of a = 0.05 to state the conclusion of the hypothesis test in the context of the study. There is statistically insignificant evidence to conclude that there is a correlation between the time women spend on the phone and their weight. Thus, the use of the regression line is not appropriate. O There is statistically insignificant evidence to conclude that a woman who spends more time on the phone will weigh more than a woman who spends less time on the phone. O There is statistically significant evidence to conclude that there is a correlation between the time women spend on the phone and their weight. Thus, the regression line is useful. O There is statistically significant evidence to conclude that a woman who spends more time on the phone will weigh more than a woman who spends less time on the phone.
What is the relationship between the number of minutes per day a woman spends talking on the phone and the woman's weight? The time on the phone and weight for 7 women are shown in the table below. Time 41 32 90 32 31 33 82 Pounds 131 112 144 104 113 120 142 a. Find the correlation coefficient: r = b. The null and alternative hypotheses for correlation are: Ho: ? = 0 H₁: 20 The p-value is: Round to 2 decimal places. (Round to four decimal places) c. Use a level of significance of a = 0.05 to state the conclusion of the hypothesis test in the context of the study. There is statistically insignificant evidence to conclude that there is a correlation between the time women spend on the phone and their weight. Thus, the use of the regression line is not appropriate. O There is statistically insignificant evidence to conclude that a woman who spends more time on the phone will weigh more than a woman who spends less time on the phone. O There is statistically significant evidence to conclude that there is a correlation between the time women spend on the phone and their weight. Thus, the regression line is useful. O There is statistically significant evidence to conclude that a woman who spends more time on the phone will weigh more than a woman who spends less time on the phone.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
Transcribed Image Text:**Transcription for Educational Website:**
f. The equation of the linear regression line is:
\(\hat{y} = \_\_\_\_ + \_\_\_\_x\) (Please show your answers to two decimal places)
g. Use the model to predict the weight of a woman who spends 41 minutes on the phone.
Weight = \_\_\_\_ (Please round your answer to the nearest whole number.)
h. Interpret the slope of the regression line in the context of the question:
- ○ For every additional minute women spend on the phone, they tend to weigh on average 0.54 additional pounds.
- ○ As x goes up, y goes up.
- ○ The slope has no practical meaning since you cannot predict a woman’s weight.
i. Interpret the y-intercept in the context of the question:
- ○ If a woman does not spend any time talking on the phone, then that woman will weigh 97 pounds.
- ○ The average woman's weight is predicted to be 97.
- ○ The y-intercept has no practical meaning for this study.
- ○ The best prediction for the weight of a woman who does not spend any time talking on the phone is 97 pounds.
![**Title: Analyzing the Relationship Between Phone Usage and Weight in Women**
**Research Question:**
What is the relationship between the number of minutes per day a woman spends talking on the phone and the woman’s weight? The time on the phone and weight for 7 women are shown in the table below.
| Time (min) | 41 | 32 | 90 | 32 | 31 | 33 | 82 |
|------------|----|----|----|----|----|----|----|
| Pounds | 131| 112| 144| 104| 113| 120| 142|
**Study Tasks:**
a. **Correlation Coefficient Calculation:**
- Find the correlation coefficient \( r = \) [Blank]
- (Round to 2 decimal places)
b. **Hypothesis Testing:**
- Null and alternative hypotheses for correlation are:
- \( H_0: \rho = 0 \)
- \( H_1: \rho \neq 0 \)
- The p-value is: [Blank]
- (Round to four decimal places)
c. **Statistical Conclusion:**
- Use a level of significance of \( \alpha = 0.05 \) to state the conclusion of the hypothesis test in the context of the study:
- [Selected Option] There is statistically insignificant evidence to conclude that there is a correlation between the time women spend on the phone and their weight. Thus, the use of the regression line is not appropriate.
- There is statistically insignificant evidence to conclude that a woman who spends more time on the phone will weigh more than a woman who spends less time on the phone.
- There is statistically significant evidence to conclude that there is a correlation between the time women spend on the phone and their weight. Thus, the regression line is useful.
- There is statistically significant evidence to conclude that a woman who spends more time on the phone will weigh more than a woman who spends less time on the phone.
d. **Coefficient of Determination (\( r^2 \)):**
- \( r^2 = \) [Blank]
- (Round to two decimal places)
e. **Interpretation of \( r^2 \):**
- There is a 81% chance that the regression line will be a good predictor for women’s weight based on their time spent on the](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F7ff36ea4-4b9a-42ce-ad1e-51a4d88fb5fb%2F9a110dc9-7921-496b-9d8b-688209c3fea9%2Ffnvlnia_processed.jpeg&w=3840&q=75)
Transcribed Image Text:**Title: Analyzing the Relationship Between Phone Usage and Weight in Women**
**Research Question:**
What is the relationship between the number of minutes per day a woman spends talking on the phone and the woman’s weight? The time on the phone and weight for 7 women are shown in the table below.
| Time (min) | 41 | 32 | 90 | 32 | 31 | 33 | 82 |
|------------|----|----|----|----|----|----|----|
| Pounds | 131| 112| 144| 104| 113| 120| 142|
**Study Tasks:**
a. **Correlation Coefficient Calculation:**
- Find the correlation coefficient \( r = \) [Blank]
- (Round to 2 decimal places)
b. **Hypothesis Testing:**
- Null and alternative hypotheses for correlation are:
- \( H_0: \rho = 0 \)
- \( H_1: \rho \neq 0 \)
- The p-value is: [Blank]
- (Round to four decimal places)
c. **Statistical Conclusion:**
- Use a level of significance of \( \alpha = 0.05 \) to state the conclusion of the hypothesis test in the context of the study:
- [Selected Option] There is statistically insignificant evidence to conclude that there is a correlation between the time women spend on the phone and their weight. Thus, the use of the regression line is not appropriate.
- There is statistically insignificant evidence to conclude that a woman who spends more time on the phone will weigh more than a woman who spends less time on the phone.
- There is statistically significant evidence to conclude that there is a correlation between the time women spend on the phone and their weight. Thus, the regression line is useful.
- There is statistically significant evidence to conclude that a woman who spends more time on the phone will weigh more than a woman who spends less time on the phone.
d. **Coefficient of Determination (\( r^2 \)):**
- \( r^2 = \) [Blank]
- (Round to two decimal places)
e. **Interpretation of \( r^2 \):**
- There is a 81% chance that the regression line will be a good predictor for women’s weight based on their time spent on the
Expert Solution
This question has been solved!
Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.
Step by step
Solved in 6 steps with 2 images
Recommended textbooks for you
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
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
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
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
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
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
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman