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Suppose you are interested in the role of social support in immune function among retired men who live alone. You ask 50 patients to record the number of days they do not see or interact with a friend or family member over a period of 1 month to see whether the number of nonsocial days in a l month correlates with the number of new illnesses they experience per year. pical fou decide to use the computational formula to calculate the Pearson correlation between the number of nonsocial days in a month and the number of illnesses per year. To do so, you call the number of nonsocial days in a month X and the number of illnesses per year Y. Then, you add up your data ralues (EX and En. add up the squares of your data values (EX and EY®L,and add up the products of your data values (EXY). The following able summarizes your results Σ ΣΥ 590 380 4,887 10,456 4.258 Find the following values: Suppose you also want to predict the number of illnesses per year from the number of nonsocial days a month among elderly men who live alone. The coefficient of determination is r2 = a) 0.03 b) 0.07 c) 0.18 d) 0.82 , indicating that A) 3 b) 0.03 c) 18 d) 97 % of the variability in the number of illnesses per year can be explained by the relationship between t number of illnesses per year and the number of nonsocial days in a month. When doing your analysis, suppose that, in addition to having data for the number of nonsocial days in a month for these elderly men who live alone, you have data for the number of face-to-face interactions in a month. You'd expe the correlation between the number of face-to-face interactions in a month and the number of nonsocial days in a month to be a) negative b) positive and the correlation between the number of face-to-face interactions in a month and the number of illnesses per year to be a) negative b) positive

Suppose you are interested in the role of social support in immune function among retired men who live alone. You ask 50 patients to record the number of days they do not see or interact with a friend or family member over a period of 1 month to see whether the number of nonsocial days in a l month correlates with the number of new illnesses they experience per year. pical fou decide to use the computational formula to calculate the Pearson correlation between the number of nonsocial days in a month and the number of illnesses per year. To do so, you call the number of nonsocial days in a month X and the number of illnesses per year Y. Then, you add up your data ralues (EX and En. add up the squares of your data values (EX and EY®L,and add up the products of your data values (EXY). The following able summarizes your results Σ ΣΥ 590 380 4,887 10,456 4.258 Find the following values: Suppose you also want to predict the number of illnesses per year from the number of nonsocial days a month among elderly men who live alone. The coefficient of determination is r2 = a) 0.03 b) 0.07 c) 0.18 d) 0.82 , indicating that A) 3 b) 0.03 c) 18 d) 97 % of the variability in the number of illnesses per year can be explained by the relationship between t number of illnesses per year and the number of nonsocial days in a month. When doing your analysis, suppose that, in addition to having data for the number of nonsocial days in a month for these elderly men who live alone, you have data for the number of face-to-face interactions in a month. You'd expe the correlation between the number of face-to-face interactions in a month and the number of nonsocial days in a month to be a) negative b) positive and the correlation between the number of face-to-face interactions in a month and the number of illnesses per year to be a) negative b) positive

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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Suppose you are interested in the role of social support in immune function among retired men who live alone. You ask 50 patients to record the number of days they do not see or interact with a friend or family member over a period of 1 month to see whether the number of nonsocial days in a typical month correlates with the number of new illnesses they experience per year. You decide to use the computational formula to calculate the Pearson correlation between the number of nonsocial days in a month and the number of illnesses per year. To do so, you call the number of nonsocial days in a month X and the number of illnesses per year Y. Then, you add up your data values EX and EY. add up the squares of your data values (EX* and EY³,, and add up the products of your data values (EXY). The following table summarizes your results: ΣΧ ΣΥ ΣΧΥ 590 380 4,887 10,456 4,258 Find the following values: Suppose you also want to predict the number of illnesses per year from the number of nonsocial days in| a month among elderly men who live alone. The coefficient of determination is r2 = a) 0.03 b) 0.07 c) 0.18 d) 0.82 indicating that A) 3 b) 0.03 c) 18 d) 97 % of the variability in the number of illnesses per year can be explained by the relationship between the number of illnesses per year and the number of nonsocial days in a month. When doing your analysis, suppose that, in addition to having data for the number of nonsocial days in a month for these elderly men who live alone, you have data for the number of face-to-face interactions in a month. You'd expect the correlation between the number of face-to-face interactions in a month and the number of nonsocial days in a month to be a) negative b) positive and the correlation between the number of face-to-face interactions in a month and the number of illnesses per year to be a) negative b) positive
Transcribed Image Text:Suppose you are interested in the role of social support in immune function among retired men who live alone. You ask 50 patients to record the number of days they do not see or interact with a friend or family member over a period of 1 month to see whether the number of nonsocial days in a typical month correlates with the number of new illnesses they experience per year. You decide to use the computational formula to calculate the Pearson correlation between the number of nonsocial days in a month and the number of illnesses per year. To do so, you call the number of nonsocial days in a month X and the number of illnesses per year Y. Then, you add up your data values EX and EY. add up the squares of your data values (EX* and EY³,, and add up the products of your data values (EXY). The following table summarizes your results: ΣΧ ΣΥ ΣΧΥ 590 380 4,887 10,456 4,258 Find the following values: Suppose you also want to predict the number of illnesses per year from the number of nonsocial days in| a month among elderly men who live alone. The coefficient of determination is r2 = a) 0.03 b) 0.07 c) 0.18 d) 0.82 indicating that A) 3 b) 0.03 c) 18 d) 97 % of the variability in the number of illnesses per year can be explained by the relationship between the number of illnesses per year and the number of nonsocial days in a month. When doing your analysis, suppose that, in addition to having data for the number of nonsocial days in a month for these elderly men who live alone, you have data for the number of face-to-face interactions in a month. You'd expect the correlation between the number of face-to-face interactions in a month and the number of nonsocial days in a month to be a) negative b) positive and the correlation between the number of face-to-face interactions in a month and the number of illnesses per year to be a) negative b) positive
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