This exercise relates to the dataset "Hypertension-risk-model-main.csv" The dataset has a few columns. 1. male 1 / female 0 2. Age 3. Current Smoker yes 1 / no 0 4. Cigarettes per day 5. Takes Blood Pressure Pills yes 1 / no 0 6. Has diabetes yes 1 / no 0 7. Total Cholesterol 8. Systolic Blood Pressure Reading 9. Diastolic Blood Pressure Reading 10. Body Mass Index (BMI) 11. Resting Heart Rate 12. Glucose 13. Risk (What you are trying to predict based on the above information) A. Read the data into R. Make sure that you have the directory set to the correct location for the data or use file.choose(). B. Need to read a little about what are good values for blood pressure, BMI, heart rate, glucose. All available from either Wikipedia, WebMD, etc. C. Produce a descriptive statistical summary of quantitative attributes in the data set. D. Scatter Plot, Box Plot, correlate, and linear and multiple linear regression plot all the quantitative attributes in the data set. E. This dataset is easier than the Cybercost example above to find attributes that will predict risk. Clearly you will find many attributes that will predict risk. The idea here is if you could choose three attributes which three attributes would you choose that give you the best prediction of risk and in what order. Example: (male, age, smoker). Why did you choose those three (i.e. justify your answer tables, graphs) F. Make up two hypotheses about the above data. Example: If a person smokes, he is at risk. (don't u