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A,D,F,G

A,D,F,G

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 8 women are shown in the table below. Time 31 39 32 74 57 11 41 22 Pounds 106 108 104 148 120 91 137 110 a. Find the correlation coefficient: Round to 2 decimal places. b. The null and alternative hypotheses for correlation are: Ho: P = 0 H1: P + 0 o o 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. OThere 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. OThere 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. OThere 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. of d. = (Round to two decimal places) e. Interpret ² : OGiven any group of women who all weight the same amount, 72% of all of these women will weigh the predicted amount. O There is a large variation in women's weight, but if you only look at women with a fixed time on the phone , this variation on average is reduced by 72%. OThere is a 72% chance that the regression line will be a good predictor for women's weight based on their time spent on the phone. 072% of all women will have the average weight. f. The equation of the linear regression line is: (Please show your answers to two decimal places) g. Use the model to predict the weight of a woman who spends 50 minutes on the phone. Weight = whole number.) (Please round your answer to the nearest h. Interpret the slope of the regression line in the context of the question: O The slope has no practical meaning since you cannot predict a women's weight. O For every additional minute women spend on the phone, they tend to weigh on averge 0.80 additional pounds. OAs x goes up, y goes up. i. Interpret the y-intercept in the context of the question: OThe 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 85 pounds. O The best prediction for the weight of a woman who does not spend any time talking on the phone is 85 pounds. OThe average woman's weight is predicted to be 85.
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 8 women are shown in the table below. Time 31 39 32 74 57 11 41 22 Pounds 106 108 104 148 120 91 137 110 a. Find the correlation coefficient: Round to 2 decimal places. b. The null and alternative hypotheses for correlation are: Ho: P = 0 H1: P + 0 o o 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. OThere 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. OThere 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. OThere 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. of d. = (Round to two decimal places) e. Interpret ² : OGiven any group of women who all weight the same amount, 72% of all of these women will weigh the predicted amount. O There is a large variation in women's weight, but if you only look at women with a fixed time on the phone , this variation on average is reduced by 72%. OThere is a 72% chance that the regression line will be a good predictor for women's weight based on their time spent on the phone. 072% of all women will have the average weight. f. The equation of the linear regression line is: (Please show your answers to two decimal places) g. Use the model to predict the weight of a woman who spends 50 minutes on the phone. Weight = whole number.) (Please round your answer to the nearest h. Interpret the slope of the regression line in the context of the question: O The slope has no practical meaning since you cannot predict a women's weight. O For every additional minute women spend on the phone, they tend to weigh on averge 0.80 additional pounds. OAs x goes up, y goes up. i. Interpret the y-intercept in the context of the question: OThe 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 85 pounds. O The best prediction for the weight of a woman who does not spend any time talking on the phone is 85 pounds. OThe average woman's weight is predicted to be 85.
Expert Solution
Step 1

a.

The correlation value is obtained using EXCEL. The software procedure is given below:

  • Enter the data.
  • Select Data > Data Analysis >Correlation> OK.
  • Enter Input Range as A1:B9 for variables Time and Pounds.
  • Mark Labels in First Row.
  • Click OK.

The output using EXCEL is as follows:

Statistics homework question answer, step 1, image 1

From the output, the correlation coefficient r is 0.85.

Thus, the correlation coefficient (r) is 0.85.

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