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 8 women are shown in the table below. Time8224645282127789Pounds181125156142158103148161 Find the correlation coefficient: r=r= Round to 2 decimal places.The null and alternative hypotheses for correlation are:H0:H0: ? μ ρ r == 0H1:H1: ? r ρ μ ≠≠ 0 The p-value is: (Round to four decimal places)Use a level of significance of α=0.05α=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.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. r2r2 = (Round to two decimal places) Interpret r2r2 : Given any group of women who all weight the same amount, 84% of all of these women will weigh the predicted amount.84% of all women will have the average weight.There is a large variation in women's weight, but if you only look at women with a fixed weight, this variation on average is reduced by 84%.There is a 84% chance that the regression line will be a good predictor for women's weight based on their time spent on the phone.The equation of the linear regression line is: ˆyy^ = + xx (Please show your answers to two decimal places) Use the model to predict the weight of a woman who spends 59 minutes on the phone.Weight = (Please round your answer to the nearest whole number.) 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 averge 0.76 additional pounds.The slope has no practical meaning since you cannot predict a women's weight.As x goes up, y goes up.Interpret the y-intercept in the context of the question:The average woman's weight is predicted to be 101.The y-intercept has no practical meaning for this study.If a woman does not spend any time talking on the phone, then that woman will weigh 101 pounds.The best prediction for the weight of a woman who does not spend any time talking on the phone is 101 pounds.

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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 8 women are shown in the table below. Time 82 24 64 52 82 12 77 89 Pounds 181 125 156 142 158 103 148 161 Find the correlation coefficient: r=r= Round to 2 decimal places. The null and alternative hypotheses for correlation are: H0:H0: ? μ ρ r == 0 H1:H1: ? r ρ μ ≠≠ 0 The p-value is: (Round to four decimal places) Use a level of significance of α=0.05α=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. 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.

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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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 8 women are shown in the table below.

Time	82	24	64	52	82	12	77	89
Pounds	181	125	156	142	158	103	148	161

Find the correlation coefficient: r=r= Round to 2 decimal places.
The null and alternative hypotheses for correlation are:
H0:H0: ? μ ρ r  == 0
H1:H1: ? r ρ μ   ≠≠ 0
The p-value is:    (Round to four decimal places)
Use a level of significance of α=0.05α=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.
- 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.
r2r2 = (Round to two decimal places)
Interpret r2r2 :
- Given any group of women who all weight the same amount, 84% of all of these women will weigh the predicted amount.
- 84% of all women will have the average weight.
- There is a large variation in women's weight, but if you only look at women with a fixed weight, this variation on average is reduced by 84%.
- There is a 84% chance that the regression line will be a good predictor for women's weight based on their time spent on the phone.
The equation of the linear regression line is:
ˆyy^ = + xx (Please show your answers to two decimal places)
Use the model to predict the weight of a woman who spends 59 minutes on the phone.
Weight = (Please round your answer to the nearest whole number.)
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 averge 0.76 additional pounds.
- The slope has no practical meaning since you cannot predict a women's weight.
- As x goes up, y goes up.
Interpret the y-intercept in the context of the question:
- The average woman's weight is predicted to be 101.
- The y-intercept has no practical meaning for this study.
- If a woman does not spend any time talking on the phone, then that woman will weigh 101 pounds.
- The best prediction for the weight of a woman who does not spend any time talking on the phone is 101 pounds.

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