# PART 2: Relationship between Health Factors As a medical researcher, you attempt to relate key health factors to each other. Are those with higher BMI

**This Assessment requires submission of two (2) files. The first file is completed on the provided template and includes the two parts:**

**The second file should is your Excel file where all computations are completed for the assessment.**

**PART 1: Correlation in Research**

For this part, you will be making use of the following article from the Walden Library:

References

Slavic, D., Jakovljevic, D. K., Zubnar, A., Tapavicki, B., Aleksandric, T., & Drapsin, M. (2019). Effects of different types of training on weight loss. Medicinski Pregled/Medical Review, 72(9/10), 272–279. https://doi.org/10.2298/MPNS1910272S

On page 277 in this journal, there are six scatter plots. Use these scatter plots to answer the following questions:

- Rank the six correlations provide from lowest correlation to highest correlation. Include a 2- to 3-sentence explanation for why you ranked these the way you did.
- Choose one of the six graphs and compute the r-squared value. Interpret this value as it relates to variation.
- Using the information presented on this page, explain one reason why BMI does not always indicate an individual’s health status (correlation does not imply causation).

**PART 2: Relationship between Health Factors**

As a medical researcher, you attempt to relate key health factors to each other. Are those with higher BMIs more likely to have higher or lower HDL and LDL cholesterol levels? How are factors such as age, systolic blood pressure, and BMI related to pulse rate? Looking at the correlation between variables will help assess the relationship between key health factors. Once a relationship is established, models can be built to help healthcare professionals assess risk levels for patients based on various factors. While a causal relationship cannot be assumed between factors, regression models can help healthcare practitioners predict key values for patients. To prepare for this Assessment:

Open the data set created in STAT3001, the modified body data set based on your assigned seed number **Your assigned number for this task is 141**

Perform the following tasks to complete your data set:

For this Performance Task, you will perform a comparison of BMI and how it relates to both HDL and LDL cholesterol. Multiple regression will also be used to create a model to predict the pulse of a patient based on age, weight, and systolic blood pressure reading.

You will be assessed on the Professional Skills of *Written Communication, Interpreting Data & Quantitative Fluency, and Technology*. To make certain you are addressing all aspects of the Professional Skills, please review the rubric to determine what is necessary for meeting the requirements. Visit the Professional Skills resources, as needed.

- Scatter Plots, Correlations, and the Correlation Coefficient
- BMI and LDL cholesterol levels
- Create a scatter plot for the data in the BMI and LDL cholesterol columns. Paste it in your report.
- Using Excel, calculate the linear correlation between the data in the BMI and LDL cholesterol columns. Paste your results in your Word document.
- Explain the mathematical relationship between BMI and LDL cholesterol based on the linear correlation coefficient. Be certain to include comments about the magnitude (strength) and the direction (positive or negative) of the correlation. As BMI increases, what happens to LDL cholesterol?

- BMI and HDL cholesterol levels
- Create a scatter plot for the data in the BMI and HDL cholesterol columns. Paste it in your report.
- Using Excel, calculate the linear correlation between the data in the BMI and HDL cholesterol columns. Paste your results in your Word document.
- Explain the mathematical relationship between BMI and HDL cholesterol based on the linear correlation coefficient. Be certain to include comments about the magnitude (strength) and the direction (positive or negative) of the correlation. As BMI increases, what happens to HDL cholesterol?

- BMI and LDL cholesterol levels
- Linear Regression and Prediction
- Let’s say that we wanted to be able to predict the HDL cholesterol level of a patient based on their BMI.
- Using this sample data, perform a linear regression to determine the line of best fit. Use BMI as your x (independent) variable and HDL as your y (response) variable. Use four (4) places after the decimal in your answer. Paste it in your report.
- What is the equation of the line of best fit (linear regression equation)? Present your answer in y = bo + b1x form.
- What would you predict the HDL would be for a patient with a BMI of 25? Show your calculations.
- What would you predict the HDL would be for a patient with a BMI of 40? Show your calculations.
- What effect would you predict BMI would have on HDL levels? Use your computations above to justify your reasoning.
- Calculate the coefficient of determination (R2 value) for this data. What does this tell you about this relationship?

- Let’s say that we wanted to be able to predict the HDL cholesterol level of a patient based on their BMI.
- Multiple Regression
- Let’s say that we wanted to be able to predict a patient’s pulse using age, systolic blood pressure, and BMI. Using this sample data, perform a multiple-regression line of best fit using age, systolic blood pressure, and BMI as predictor variables and pulse rate as the response variable. Paste your Excel work in your report.
- What is the equation of the line of best fit? The form of the equation is: Y = bo + b1X1 + b2X2 + b3X3 (fill in values for bo, b1, b2, and b3). Round coefficients to three (3) decimal places.
- What would you predict the pulse rate would be for a patient with who is 33 years old with a systolic blood pressure of 110 and BMI of 27?

- What is the R2 value for this regression? What does it tell you about the regression?

- Let’s say that we wanted to be able to predict a patient’s pulse using age, systolic blood pressure, and BMI. Using this sample data, perform a multiple-regression line of best fit using age, systolic blood pressure, and BMI as predictor variables and pulse rate as the response variable. Paste your Excel work in your report.