Please provide the code and explanation for the following in R for One Way ANOVA with the penguins data set a. Load the penguins data set (from the palmerpenguins library) b. Data Summaries & Assumption Check i. Create a new data frame that only has the columns species and bill_depth_mm from the original penguins data frame. Remove NA values from the data frames using na.omit() ii. Create a single graph with 3 boxplots on the same scale, one for the bill depth for each of the penguin species. Each boxplot should be a different color. From this plot, are the means the same? iii. Create a new data frame for each species with the bill depth data for that species. How many observations are there for each species? iv. Check the normality assumption for each subset by creating histograms and qq plots. Make sure each plot has an appropriate title. Divide your plot region into 6 sections so you can see the histogram and qqplot for each species side by side par(mfrow=c(3,2)) will give you a 3 row 2 column setup to work with v. What is the sample variance for each diet? Do you think that the assumption of common variance holds? Why? How could you test this? c. Conduct a test using one way anova to test the null hypothesis that the mean bill depth is the same for all 3 species. 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

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Please provide the code and explanation for the following in R for One Way ANOVA with the penguins data set
a. Load the penguins data set (from the palmerpenguins library)
b. Data Summaries & Assumption Check
i. Create a new data frame that only has the columns species and bill_depth_mm
from the original penguins data frame. Remove NA values from the data
frames using na.omit()
ii. Create a single graph with 3 boxplots on the same scale, one for the bill depth
for each of the penguin species. Each boxplot should be a different color. From
this plot, are the means the same?
iii. Create a new data frame for each species with the bill depth data for that
species. How many observations are there for each species?
iv. Check the normality assumption for each subset by creating histograms and qq
plots. Make sure each plot has an appropriate title. Divide your plot region into 6 sections so you can see the histogram and qqplot for each species side by side par(mfrow=c(3,2)) will give you a 3 row 2 column setup to work with
v. What is the sample variance for each diet? Do you think that the assumption of
common variance holds? Why? How could you test this?
c. Conduct a test using one way anova to test the null hypothesis that the mean bill depth
is the same for all 3 species.
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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