Purpose: One of the most commonly made decisions is based on comparing the means of multiple conditions. Significance tests for comparing means are critical in the decision making process. In this assignment, you will practice how to use t tests and ANOVA tests to compare the means of multiple conditions under one independent variable in different experiment design (between-group and within-group) Tasks: Please write an R script that performs the following operations in the order listed. At the beginning of each task, write a comment marking the task number. Name the file as: lab6-.R ANOVA tests 8. Import the 'iris.csv' dataset. Browse and understand the data in each column 9. Compute descriptive statistics and create boxplot for sepal length and species. 10. Select the appropriate test to determine whether there is significant difference in sepal length between the three species. Follow the steps of ANOVA analysis. Make sure you check whether the assumptions are met. If a specific assumption is not met, you need to take specific action or make the decision that ANOVA test is not suited for the data. Use the standard template to report the result if ANOVA test is appropriate. Note: If the homogeneity is not met, you need to transform the data to logarithm. Sample code: iris$Sepall.Log
Purpose: One of the most commonly made decisions is based on comparing the means of multiple conditions. Significance tests for comparing means are critical in the decision making process. In this assignment, you will practice how to use t tests and ANOVA tests to compare the means of multiple conditions under one independent variable in different experiment design (between-group and within-group)
Tasks: Please write an R script that performs the following operations in the order
listed. At the beginning of each task, write a comment marking the task number.
Name the file as: lab6-<your last name>.R
ANOVA tests
8. Import the 'iris.csv' dataset. Browse and understand the data in each column 9. Compute descriptive statistics and create boxplot for sepal length and
species.
10. Select the appropriate test to determine whether there is significant difference in sepal length between the three species. Follow the steps of ANOVA analysis. Make sure you check whether the assumptions are met. If a specific assumption is not met, you need to take specific action or make the decision that ANOVA test is not suited for the data. Use the standard template to report the result if ANOVA test is appropriate. Note: If the homogeneity is not met, you need to transform the data to
logarithm. Sample code:
iris$Sepall.Log<log(iris$Sepal.Length)
11. Import the weight.csv' dataset. This dataset contains the weight of mice measured at 3 different time points. Browse and understand the data in each column
12. Compute descriptive statistics and create boxplot to acquire general understanding of the data.
13. Select the appropriate test to determine whether there is significant difference in weight between different time points. Follow the steps of ANOVA analysis. Make sure you check whether the assumptions are met. If a specific assumption is not met, you need to take specific action or make the decision that ANOVA test is not suited for the data. Use the standard template to report the result if ANOVA test is appropriate.
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