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Pounds 127 120 109 118 143 126 121 128 153 a. Find the correlation coefficient: r = Round to 2 decimal places. b. The null and alternative hypotheses for correlation are: Ho: ? V = 0 H1: ? V + 0 The p-value is: (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. 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. 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 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. 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.

Pounds 127 120 109 118 143 126 121 128 153 a. Find the correlation coefficient: r = Round to 2 decimal places. b. The null and alternative hypotheses for correlation are: Ho: ? V = 0 H1: ? V + 0 The p-value is: (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. 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. 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 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. 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.

MATLAB: An Introduction with Applications
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 9 women are shown in the table below. Time 50 28 27 43 55 37 53 58 73 Pounds 127 120 109 118 143 126 121 128 153 a. Find the correlation coefficient: r = Round to 2 decimal places. b. The null and alternative hypotheses for correlation are: Ho: ? V = 0 H1: ? V + 0 The p-value is: (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. 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 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 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. 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. d. r? = (Round to two decimal places) e. Interpret r2 : O Given any group of women who all weight the same amount, 68% of all of these women will weigh the predicted amount. There is a 68% chance that the regression line will be a good predictor for women's weight based on their time spent on the phone. O 68% of all women will have the average weight. O 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 68%. f. The equation of the linear regression line is: ŷ = * (Please show your answers to two decimal places) g. Use the model to predict the weight of a woman who spends 54 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: O For every additional minute women spend on the phone, they tend to weigh on averge 0.74 additional pounds. O The slope has no practical meaning since you cannot predict a women's weight. O As x goes up, y goes up. i. Interpret the y-intercept in the context of the question: O The average woman's weight is predicted to be 92. O The best prediction for the weight of a woman who does not spend any time talking on the phone is 92 pounds. O The y-intercept has no practical meaning for this study. O If a woman does not spend any time talking on the phone, then that woman will weigh 92 pounds.
Transcribed Image Text: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 9 women are shown in the table below. Time 50 28 27 43 55 37 53 58 73 Pounds 127 120 109 118 143 126 121 128 153 a. Find the correlation coefficient: r = Round to 2 decimal places. b. The null and alternative hypotheses for correlation are: Ho: ? V = 0 H1: ? V + 0 The p-value is: (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. 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 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 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. 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. d. r? = (Round to two decimal places) e. Interpret r2 : O Given any group of women who all weight the same amount, 68% of all of these women will weigh the predicted amount. There is a 68% chance that the regression line will be a good predictor for women's weight based on their time spent on the phone. O 68% of all women will have the average weight. O 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 68%. f. The equation of the linear regression line is: ŷ = * (Please show your answers to two decimal places) g. Use the model to predict the weight of a woman who spends 54 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: O For every additional minute women spend on the phone, they tend to weigh on averge 0.74 additional pounds. O The slope has no practical meaning since you cannot predict a women's weight. O As x goes up, y goes up. i. Interpret the y-intercept in the context of the question: O The average woman's weight is predicted to be 92. O The best prediction for the weight of a woman who does not spend any time talking on the phone is 92 pounds. O The y-intercept has no practical meaning for this study. O If a woman does not spend any time talking on the phone, then that woman will weigh 92 pounds.
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