Body fat percentage (BFP) is a measure of fitness level in humans, and is consid
ID: 3268185 • Letter: B
Question
Body fat percentage (BFP) is a measure of fitness level in humans, and is considered to be more
accurate than other popular measures, such as the body mass index. However, BFP is difficult to
measure directly: one method involves weighing a subject while they are submerged in water,
which is a time-consuming procedure. We wish to see if we can quickly predict a subject's BFP
simply by weighing them on a traditional set of scales. A sample of 251 men had their BFPs
measured, along with their overall weight.
The variables in EXCEL are:
bodyfat the subject’s body fat percentage
weight the subject’s weight (in pounds)
A lot of the analysis for this question is already filled in for you. There are several parts and
additional questions for you to address:
==By using Rstudio==
1. Comment on the plot of the data.
2. Create a scatter plot with the fitted line from the fitted model superimposed over it.
3. Look at the prediction output given and the fitted model. Discuss how useful this model is for
prediction.
4. A doctor has a male patient who weighs 90kg and wishes to use this model to predict his BFP.
Use the output to predict the patients weight. Some sources state that, generally speaking, a
BFP less than 32% is 'healthy' and that a BFP above 32% is 'unhealthy'. Is it plausible that this
patient falls into the 'healthy' category? How about the 'unhealthy' category?
5. Create a new variable, weightKg, which gives the weight in kg instead of pounds. (Note: 1
kg = 2.20462 pounds.) Repeat the R analysis and scatter plot with fitted line using this
explanatory variable instead. (You do not need to repeat the model checking.)
6. Write an appropriate Executive Summary.
Explanation / Answer
Given data is
(a)
The Scatter plot for the above data through Excel is given by
(b)
From the above graph we observe that all sample values are in increasing,then there is a positive relationship between the age and weight variables with the corresponding Body fat.
(C)
The prediction output by using Regression Analysis in Excel sheet we have
(d)
Given that a doctor has a male patient who weighs 90kg and wishes to use this model to predict his BFP is does not exist because weight is more than 120 kg we can predict his BFP.So 90kg means there is no BFP.
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body fat age weight 12.6 23 154.25 6.9 22 173.25 24.6 22 154 10.9 26 184.75 27.8 24 184.25 20.6 24 210.25 19 26 181 12.8 25 176 5.1 25 191 12 23 198.25 7.5 26 186.25 8.5 27 216 20.5 32 180.5 20.8 30 205.25 21.7 35 187.75 20.5 35 162.75 28.1 34 195.75 22.4 32 209.25 16.1 28 183.75 16.5 33 211.75 19 28 179 15.3 28 200.5 15.7 31 140.25 17.6 32 148.75 14.2 28 151.25 4.6 27 159.25 8.5 34 131.5 22.4 31 148 4.7 27 133.25 9.4 29 160.75 12.3 32 182 6.5 29 160.25 13.4 27 168 20.9 41 218.5 31.1 41 247.25 38.2 49 191.75 23.6 40 202.25 27.5 50 196.75