28 29 #Your code and answer goes here -1 6 7 8- ## This dataset contains...
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28 29 #Your code and answer goes here -1 6 7 8- ## This dataset contains estimates of the percentage of body fat determined by underwater weighing and various body circumference measurements for 252 men. 9 10-{r} 11 #adapt this code to your working directory 12 setwd("C:/Users/jllsj/Dropbox/Teaching/DATA 1501") 13 bf <- bf <- read.csv("BodyFat.csv", header = TRUE, sep = ",") 14 head(bf) 15- 16 17 - ### Problem 1. Consider the variables Weight, Chest, Abdomen, Thigh, Hip, and Biceps. Which of these variables shows the strongest correlation to body fat? Which one shows the weakest correlation to body fat? Hint: Compute the Pearson correlation coefficient between BodyFat and each of these variables. (That is a total of six pairs.) 18 19 - ```{r} 20 21 #Your code and answer goes here 22 23 24 25 ### Problem 2. Make a scatterplot of BodyFat versus each of the two variables that were the answer to problem 1. (There should be two scatterplots here.) 26 27-````{r} 30 31- 32 33- ### Problem 3. Is the variable Wrist normally distributed? EXPLAIN your conclusion by using at least two methods (to double-check your answer). EXPLAIN THE RESULTS IN EACH METHOD. If you do all three methods: +1 Bonus point. 34 35- 36 #Your code goes here 37 38- 39 10- ### Problem 4. Create a linear regression model of BodyFat versus Height. Print the summary to the screen and report the R-squared of this model. From your understanding of what R-squared means, what percentage of the variance of BodyFat can be explained by the variable Height? Consequently, would you say that Height is a good predictor of BodyFat? B - 9 2-```{r} 3 4 ### Your code goes here 5 6- 7 Gole #HW 6 = R. Markdown 28 29 #Your code and answer goes here -1 6 7 8- ## This dataset contains estimates of the percentage of body fat determined by underwater weighing and various body circumference measurements for 252 men. 9 10-{r} 11 #adapt this code to your working directory 12 setwd("C:/Users/jllsj/Dropbox/Teaching/DATA 1501") 13 bf <- bf <- read.csv("BodyFat.csv", header = TRUE, sep = ",") 14 head(bf) 15- 16 17 - ### Problem 1. Consider the variables Weight, Chest, Abdomen, Thigh, Hip, and Biceps. Which of these variables shows the strongest correlation to body fat? Which one shows the weakest correlation to body fat? Hint: Compute the Pearson correlation coefficient between BodyFat and each of these variables. (That is a total of six pairs.) 18 19 - ```{r} 20 21 #Your code and answer goes here 22 23 24 25 ### Problem 2. Make a scatterplot of BodyFat versus each of the two variables that were the answer to problem 1. (There should be two scatterplots here.) 26 27-````{r} 30 31- 32 33- ### Problem 3. Is the variable Wrist normally distributed? EXPLAIN your conclusion by using at least two methods (to double-check your answer). EXPLAIN THE RESULTS IN EACH METHOD. If you do all three methods: +1 Bonus point. 34 35- 36 #Your code goes here 37 38- 39 10- ### Problem 4. Create a linear regression model of BodyFat versus Height. Print the summary to the screen and report the R-squared of this model. From your understanding of what R-squared means, what percentage of the variance of BodyFat can be explained by the variable Height? Consequently, would you say that Height is a good predictor of BodyFat? B - 9 2-```{r} 3 4 ### Your code goes here 5 6- 7 Gole #HW 6 = R. Markdown
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Related Book For
Income Tax Fundamentals 2013
ISBN: 9781285586618
31st Edition
Authors: Gerald E. Whittenburg, Martha Altus Buller, Steven L Gill
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