Question

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 was the independent variable(s)? Further, how would you use this study to highlight the difference between correlations and causation?

Please provide APA references

Thank You

Answer 1

Dear your satisfaction is more important, I provided answers with detailed explanation. If you are satisfied with my explanations please THUMBS UP.. Thanks inadvance for your support dear....

• One of the independent variable is Height of the person. Since Height plays a great impact on BMI
• Time spent for Exercise can be taken as another independent variable. Quantity of food taken, Particular Health issues etc. can be included as independent variables in the model
• Correlation will help us to understand the linear relationship between BMI and independent variables. Square of Correlation gives you coefficient of determination, which tell us how good is our model. Regression coefficients of Independent variables impact on the prediction of BMI

An example on medical field.

Chan et al. (2006).

They conducted a Regression analysis inorder to explore standard liver weight for assessing adequacies of graft size in live donor liver transplantation and remnant liver in major hepatectomy for cancer.

Independent variable is Standard Liver Weight (SLW) in grams.

Dependent variables are Body weight (BW) in kilograms, Gender [G] (male=1, female=0).

Fitted Multiple Linear Regression Model is:

SLW=218+12.3×BW+51×G

Coefficient of determination ( R​​​​​2 ) =0.48.

Correlation = ✓0.48 = 0.69282

In Fitted model regression coefficients gives you how much effect that independent variables have on SLW. I.e, a unit change in BW has 12.3 effect on SLW and a unit change in G has 51.

Correlation helps us to understand the linear relationship between dependent and Independent variables.. Here 69.282% linrar relationship is hold.

Coefficient of determination gives that only 48% of total variation in SLW can explained by BW and G.

You can highlight the difference between causation and correlation in the above example as follows:

Fitted SLW has a causation on BW and G.. Since SLW value dependent on BW and G.. That is the cause done by BW and G effect the SLW. But C Correlation tell us that how linear relationship that has. Other relationship can't be detected using Correlation.