Open the dataset BodyFat.RData in R. Review the R code for correlation and linear regression. This problem is worth 5 points.
You are a physician working at a clinic and are concerned with the rising level of obesity amongst your patients. With the help of some colleagues, you obtain some patient data from their charts and compile it as a dataset on R (BodyFat.RData.)
While in a meeting, a debate arises over the best predictor of patient body fat percentage. Dr. Bob believes that Age (measured in years) is the best indicator of a patient’s body fat. Dr. John believes that a person’s Weight (measured in pounds) is the most appropriate variable. You however, are certain that a patient’s Neck circumference (measured in inches) would most accurately predict a person’s body fat percentage.
You whip out your laptop and open R Studio to settle this debate:
Create scatterplots with regression lines for Bodyfat predicted by: 1) Age 2) Weight 3) Neck circumference. From the scatterplots, describe each relationship as weak, moderate, or strong.
Calculate the R2 for each of the regression models. Which linear regression model has the best fit? Which Doctor is correct? Explain your answer using your R-generated data.
You are a 35 year old 5 foot 10-inch tall and 185 pound male with a neck circumference of 38 inches. Using the best fitted regression model, predict your body fat percentage. Show your regression formula in y=mx+b format.
Is the regression model you chose a good fit to explain body fat percentage? Can you think of a reason why your model may not be a good predictor for body fat?
Is Body Mass Index (BMI) a better predictor for body fat percentage? Show a scatterplot with regression line and report R2. From your thorough analysis, pick the most appropriate variable for predicting body fat percentages in your clinic
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