In R please provide the code for the following steps (B iv,v,vi C i,ii,iii,iv) a. Load the coagulation data set (it is from the faraway library) b. Data Summaries & Assumption Check i. Use the names() function to identify the column names ii. How many rows of data are there? iii. Create a single graph with 4 boxplots on the same scale, one for the coagulation for each of the diets. Each boxplot should be a different color. Note: you can use the plot function for this task plot(colNameData~colNameCategory, data = dataset, col = color#1:color#2) colNameData is the name of the column in the data set that you want to create a boxplot for colNameCategory is the name of the column that you want to use to split the data into groups (so you want one boxplot for each category/value in this column) dataset is the name of the full dataset col=2:4 will give you 3 different colors (color indices are 1-8, then they repeat) iv. Create 4 different data frames, one for the data corresponding to each of the 4 factors. How many observations are there for each diet? Reminder: we can subset a data frame and select only the rows and columns we want by using dataName[rows, columns] You can use the which() function to specify a condition on the rows you want to keep If we want to select multiple columns by name, we can specify which ones we want using the following setup c(“col1Name”, “col2Name”) or c(col1Index, col2Index) v. Check the normality assumption for each subset by creating qq plots. Make sure each plot has an appropriate title. vi. What is the sample variance for each diet? Do you think that the assumption of common variance holds? Why or why not? How could you formally test this? c. Conduct a test using one way anova to check if the mean coagulation is the same for all 4 diets. i. Define your null and alternative hypothesis ii. Use the aov() function to conduct your test iii. Use the summary() function to see the full details of the test. iv. Report the degrees of freedom, sum of squares, p-value, and conclusion for your test

In R please provide the code for the following steps (B iv,v,vi C i,ii,iii,iv) a. Load the coagulation data set (it is from the faraway library) b. Data Summaries & Assumption Check i. Use the names() function to identify the column names ii. How many rows of data are there? iii. Create a single graph with 4 boxplots on the same scale, one for the coagulation for each of the diets. Each boxplot should be a different color. Note: you can use the plot function for this task plot(colNameData~colNameCategory, data = dataset, col = color#1:color#2) colNameData is the name of the column in the data set that you want to create a boxplot for colNameCategory is the name of the column that you want to use to split the data into groups (so you want one boxplot for each category/value in this column) dataset is the name of the full dataset col=2:4 will give you 3 different colors (color indices are 1-8, then they repeat) iv. Create 4 different data frames, one for the data corresponding to each of the 4 factors. How many observations are there for each diet? Reminder: we can subset a data frame and select only the rows and columns we want by using dataName[rows, columns] You can use the which() function to specify a condition on the rows you want to keep If we want to select multiple columns by name, we can specify which ones we want using the following setup c(“col1Name”, “col2Name”) or c(col1Index, col2Index) v. Check the normality assumption for each subset by creating qq plots. Make sure each plot has an appropriate title. vi. What is the sample variance for each diet? Do you think that the assumption of common variance holds? Why or why not? How could you formally test this? c. Conduct a test using one way anova to check if the mean coagulation is the same for all 4 diets. i. Define your null and alternative hypothesis ii. Use the aov() function to conduct your test iii. Use the summary() function to see the full details of the test. iv. Report the degrees of freedom, sum of squares, p-value, and conclusion for your test

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Author:Carey

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Chapter8: Working With Advanced Functions

Section: Chapter Questions

Problem 4.4CP

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In R please provide the code for the following steps (B iv,v,vi C i,ii,iii,iv)
a. Load the coagulation data set (it is from the faraway library)
b. Data Summaries & Assumption Check
i. Use the names() function to identify the column names
ii. How many rows of data are there?
iii. Create a single graph with 4 boxplots on the same scale, one for the coagulation
for each of the diets. Each boxplot should be a different color.
Note: you can use the plot function for this task
plot(colNameData~colNameCategory, data = dataset, col = color#1:color#2)
colNameData is the name of the column in the data set that you want to create
a boxplot for
colNameCategory is the name of the column that you want to use to split the
data into groups (so you want one boxplot for each category/value in this
column)
dataset is the name of the full dataset
col=2:4 will give you 3 different colors (color indices are 1-8, then they repeat)
iv. Create 4 different data frames, one for the data corresponding to each of the 4
factors. How many observations are there for each diet?
Reminder: we can subset a data frame and select only the rows and columns we
want by using dataName[rows, columns]
You can use the which() function to specify a condition on the rows you want to
keep
If we want to select multiple columns by name, we can specify which ones we
want using the following setup c(“col1Name”, “col2Name”) or c(col1Index,
col2Index)
v. Check the normality assumption for each subset by creating qq plots. Make
sure each plot has an appropriate title.
vi. What is the sample variance for each diet? Do you think that the assumption of
common variance holds? Why or why not? How could you formally test this?
c. Conduct a test using one way anova to check if the mean coagulation is the same for all 4 diets.
i. Define your null and alternative hypothesis
ii. Use the aov() function to conduct your test
iii. Use the summary() function to see the full details of the test.
iv. Report the degrees of freedom, sum of squares, p-value, and conclusion for your test

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