MATH 225N Statistical Reasoning For The Health Sciences (MATH225N)
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MATH 225N Statistical Reasoning For The Health Sciences (MATH225N)
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MATH 225N Statistical Reasoning For The Health Sciences (MATH225N)
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Chamberlain College Of Nursing
MATH 225N Week 8 Discussion: Correlation and Regression, Chamberlain. Initial Post Instructions If a regression analysis was to … completed on body mass index (BMI), what could be an independent variable in that analysis? Why? If we could, what other independent variables should … included in t...
math 225n statistical reasoning for the health sciences math225n
statistical reasoning for the health sciences math225n
math 225n statistical reasoni
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Chamberlain College Of Nursing
MATH 225N Statistical Reasoning For The Health Sciences (MATH225N)
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Initial Post Instructions
If a regression analysis was to be completed on body mass index (BMI), what could be an
independent variable in that analysis? Why? If we could, what other independent variables
should be included in the analysis? What statistic(s) would show the value of that regression in
understanding BMI?
Alternatively, find an article that uses regression analysis to study a medical concern. In that
study, what was the dependent variable and what were the independent variable(s)? Further, how
would you use this study to highlight the difference between correlations and causation?
According to out text, regression analysis "can provide an estimate of the magnitude of the
impact of a change in one variable or another" (Holmes, Illowsky, & Dean, 2017). If regression
analysis was to be completed on BMI, an independent variable associated in that analysis would
be the height and weight, which is how the BMI is calculated. This is because as the body ages,
the amount of body fat is typically greater than that of a younger person. Additionally, females
typically have a higher percentage of body fat as opposed males. Other independent variables
that could be included in the analysis would be physical activity, age, sex, build of body/cultures.
Another statistic that would show the value of that regression in understanding BMI would be
the amount of physical activity. The greater the amount of physical activity, the lower the BMI.
The article I researched that I found interesting regarding regression analysis discussed the
correlation between cognitive vulnerability to depression and glycemic control in patients with
type 2 diabetes. Other independent variables such as health history, comorbidities, smoking
history, age, gender, alcohol use, and etc. were explored and analyzed as potential causative
factors or correlations. The dependent variable in the study was type 2 diabetes. In the article, "in
the final multivariate regression model, 44% of the variance in HbA1c values could be explained
by the chosen variables" (Ma et al., 2018).
Holmes, A., Illowsky, B., & Dean, S. (2017). Introductory Business Statistics. Retrieved from
https://openstax.org/details/books/introductory-business-statistics (Links to an external site.)
(Links to an external site.)Ma, Y., Li, X., Zhao, D., Wu, R., Sun, H., Chen, S., … Gao, S. (2018).
Association between cognitive vulnerability to depression - dysfunctional attitudes and
glycaemic control among in-patients with type 2 diabetes in a hospital in Beijing: a multivariate
regression analysis. Psychology, Health & Medicine, 23(2), 189–197.
https://doi.org/10.1080/13548506.2017.1339894
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