3. Washington.csv: dataset below Solution Day Result 1 1 13 1 2 22 1 3 18 1 4 39 2 1 16 2 2 24 2 3 17 2 4 44 3 1 5 3 2 4 3 3 1 3 4 22 Use the datafile washing.csv, which represents the study data discussed in Question 5. Save the data file in your local folder that you use as your R working directory. Keep in mind that when you download the datafile by clicking on the link "washing.csv" above BB Learn may change its name such as "washing(1).csv". Once you download it make sure to change the file name manually to "washing.csv" so that the code below runs without problems. Analyze the data from this study (use a significance level α = 0.05) using the R code below and using the modeling approach you selected in Question 5. Based on the R output, which statement is correct? # read the data file and make sure the factors are recognized as factors by filling out the blank part in the code below wash <- read.csv("washing.csv", as.is =T) wash$ <- as.factor(wash$ ) wash$ <- as.factor(wash$ ) # run the model by filling out the blank part in the code below summary(aov( )) A.) At the significance level 0.05, we fail to reject the null hypothesis that Days have an impact on retarding bacteria growth in milk containers. B.) The degrees of freedom associated with testing the null hypothesis that different levels of the factor Solution have no impact on retarding bacteria growth in milk containers is 2. C.) The degrees of freedom associated with testing the null hypothesis that different levels of the factor Day have no impact on retarding bacteria growth in milk containers is 6. D.) The F statistic associated with testing the null hypothesis associated with the levels of Solution and their impact on retarding bacteria growth in milk containers is 42.71.
3.
Washington.csv: dataset below
|
Solution |
Day | Result |
| 1 | 1 | 13 |
| 1 | 2 | 22 |
| 1 | 3 | 18 |
| 1 | 4 | 39 |
| 2 | 1 | 16 |
| 2 | 2 | 24 |
| 2 | 3 | 17 |
| 2 | 4 | 44 |
| 3 | 1 | 5 |
| 3 | 2 | 4 |
| 3 | 3 | 1 |
| 3 | 4 | 22 |
Use the datafile washing.csv, which represents the study data discussed in Question 5. Save the data file in your local folder that you use as your R working directory. Keep in mind that when you download the datafile by clicking on the link "washing.csv" above BB Learn may change its name such as "washing(1).csv". Once you download it make sure to change the file name manually to "washing.csv" so that the code below runs without problems.
Analyze the data from this study (use a significance level α = 0.05) using the R code below and using the modeling approach you selected in Question 5. Based on the R output, which statement is correct?
# read the data file and make sure the factors are recognized as factors by filling out the blank part in the code below
wash <- read.csv("washing.csv", as.is =T)
wash$ <- as.factor(wash$ )
wash$ <- as.factor(wash$ )
# run the model by filling out the blank part in the code below
summary(aov( ))
| A.) |
At the significance level 0.05, we fail to reject the null hypothesis that Days have an impact on retarding bacteria growth in milk containers. |
|
| B.) |
The degrees of freedom associated with testing the null hypothesis that different levels of the factor Solution have no impact on retarding bacteria growth in milk containers is 2. |
|
| C.) |
The degrees of freedom associated with testing the null hypothesis that different levels of the factor Day have no impact on retarding bacteria growth in milk containers is 6. |
|
| D.) |
The F statistic associated with testing the null hypothesis associated with the levels of Solution and their impact on retarding bacteria growth in milk containers is 42.71. |
We should use a randomized block design using a two way ANOVA model without an interaction term between Solution and Day and with a blocking factor for Bacteria growth.
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