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
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Concept explainers
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
Question
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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