Obesity is very common in American society and is a risk factor for breast cancer in postmenopausal women. One mechanism explaining why obesity is a risk factor is that it may raise estrogen levels in women. In particular, one type of estrogen, serum estradiol, is a strong risk factor for breast cancer. To better assess these relationships, researchers studied a group of 151 African-American and 60 Caucasian premenopausal women.
Adiposity was measured in two different ways: BMI = weight (kg)/height2 (m2) and waist-hip ratio (WHR) = waist circumference/hip circumference. BMI is a measure of overall adiposity, whereas WHR is a measure of abdominal adiposity. In addition, a complete hormonal profile was obtained, including serum estradiol (ES_1). Finally, other breast-cancer risk factors were also assessed among these women, including (1) ethnicity (ETHNIC = 1 if African-American, = 0 if Caucasian), (2) age (ENTAGE), (3) parity (NUMCHILD = number of children), (4) age at first birth (AGEFBO), (5) any children (ANYKIDS = 1 if yes, = 0 if no), (6) age at menarche (AGEMNRCH = age when menstrual periods began).
What are at least two regression/correlation constructs in the case scenario? What are the steps that would be taken to analyze this scenario. Please explain each step by indicating the rationale, so that I will understand
The data are provided in the attached document.
In this example, to figure out the correlation between BMI as the dependent variable and others are used as independent variables (Any variables except BMI and WHR). By running "regression" under "data analysis" in excel, we could obtain the following output:
Multiple R 0.339916
R Square 0.115543
Standard Error 5.181373
df SS MS F Significance F
Regression 7 711.9525 101.7075 3.788465 0.000691527
Residual 203 5449.865 26.84663
Total 210 6161.818
Obesity, adiposity and biostatistical calculations are examined.