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Correlation analysis

The goal of this study is to determine the strength and direction of the age, height, and weight variables in correlation with the FVC measure in the population.

There were 31 variables measured on 800 elderly subjects. Variables include selected demographics (age, gender, race); selected anthropometrics (height, waist circumference, hip circumference, and body mass index); smoking history (status and pack-years for current or former smokers); and disease history (history of emphysema, bronchitis, asthma, pneumonia, CHD, diabetes).

A bivariate correlation analysis was conducted to test the assumption that there is a linear relationship between variables. The age and height variables were compared to the Forced Vital capacity (FVC) by viewing scatterplots. Analysis of scatterplots indicated that the data points for age verses FVC have a weak negative association. The scatterplot for height verses FVC indicated a moderate positive association.

A further measure of correlation was conducted on the age, weight, height, and FVC variables using the Pearson product-moment correlation (Pearson's r). Per the Pearson correlation, the age variable had a weak negative association with FVC (r = -.172, (df = 798), p < .001, CI =-.238, .104) and the height variable had a moderate positive association with FVC (r=.683, (df=798) p < .001, CI = .644,.718). The weight variable had a weak positive correlation to FVC (r= .331, (df = 798), p<.001, CI = .268,.391).

Using a point biserial correlation to account for the nominal variables, the race variable showed a negative correlation to FVC (r= -.156, (df=798), p< .001) and the gender variable showed a moderately strong correlation to FVC (r= .619, (df=798), p< .001).

See attached file for full problem description.

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Solution Summary

The solution gives the details correlation analysis. Step by step procedure is given with interpretation of the results obtained.