Go toLink(Links to an external site.)https://rconnect.byu.edu/Stat121App/On the left panel, select "ANOVA". Selectthe "Plant Growth" dataset, then select "group" as your categorical explanatory variable and "weight" asyour quantitative response variable. Generate boxplots of your data.

1. According to the boxplots, is the normality of the population's condition met?

A.

No, at least one of the boxplots has at least one outlier.

B.

No, normality is not a condition for ANOVA.

C.

Yes, there are no outliers in the boxplots.

D.

Yes, at least one of the sample distributions appears to be normal.

2. Calculate the standard deviations of the plant weights in each group. Is the equal population standard deviation met?

A.

Yes, .79/.58 equals 1.36, which is less than 2.

B.

Yes, .44/.79 equals .55, which is less than 2.

C.

Yes, .79/.44 equals 1.80, which is less than 2.

D.

No, .58 does not equal .79 or .44.

3. Assume all conditions are met. Select "Proceed to Statistical Inference." Calculate the test statistic and p-value. Assuming that = 0.01, what is the appropriate conclusion to make?

A.

Since the p-value is less than 0.05, we reject the null hypothesis and conclude that at least one plant group's mean weight is different.

B.

Since the p-value is greater than 0.05, we fail to reject the null hypothesis and have insufficient evidence to conclude that at least one plant group's mean weight is different.

C.

Since the p-value is greater than 0.01, we reject the null hypothesis and conclude that at least one plant group's mean weight is different.

D.

Since the p-value is greater than 0.01, we fail to reject the null hypothesis and have insufficient evidence to conclude that at least one plant group's mean weight is different.