Form for part 2 need comepleted. ( assessment template to use is in attachments)(ST3002 assessment template)
Also attached is the rubric for this assignement- along with the BODY data set in my previous assigment ST3001
Part 2 : 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.
Perform the following tasks to complete your data set:
- 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.