Chapter 3 Multiple choice questions

. Download the data file for the multiple-choice questions for Chapter 3, and open it in R. Inspect the data objects a and b. Which of the following best describes them?

Both objects are in wide format

Both objects are in long format

Object a is in wide format, and b is in long format

Object b is in wide format, and a is in long format

. Plot a histogram of the Measure column of b. How do the data appear?

Approximately normally distributed

Positively skewed

Negatively skewed

Uniformly distributed

. Use the boxplot function to display the data stored in outlierdata. How would you describe the results?

There is a single outlier above the inner fence

There is a single outlier below the inner fence

There are multiple outliers on either side of the inner fence

There are no outliers

. What is the standard deviation of outlierdata?

-0.04

0.12

1.15

1.22

. Calculate Chauvenet's criterion using the function on page 28. Use it to identify the index and value of any outliers in outlierdata.

There are no outliers according to this measure

There is one outlier in row 72, with a value of -2.48

There are two outliers in rows 72 and 37, with values of -2.48 and 7.1

There is one outlier in row 37, with a value of 7.1

. The two columns of mvdata contain two different dependent variables. Use the mahalanobis function to calculate the squared Mahalanobis distance for each pair of points. The largest value is:

26.02

9.72

2.34

3.12

. Use the scale function to rescale the data in mvdata$DV2 by its standard deviation, but do not subtract the mean. The new mean after rescaling is:

0.99

-3.86*10^-16

-1.12*10^-15

20.04

. Conduct a Kolmogorov-Smirnov test on the data in b$Measure. What is the outcome?

The test is not significant (p>0.05), indicating the data are not normally distributed

The test is not significant (p>0.05), indicating the data are normally distributed

The test is significant (p<0.05), indicating the data are not normally distributed

The test is significant (p<0.05), indicating the data are normally distributed

. Calculate a square root transform of the data in b$Measure using the sqrt function, and repeat the Kolmogorov-Smirnov test on the transformed data. What is the outcome?

The test is not significant (p>0.05), indicating the data are not normally distributed

The test is not significant (p>0.05), indicating the data are normally distributed

The test is significant (p<0.05), indicating the data are not normally distributed

The test is significant (p<0.05), indicating the data are normally distributed

. Use the qqnorm and qqline functions to create a Q-Q plot of b$Measure. Which description is most accurate?

The points fall close to the line

Most points at the extremes are below the line

Most points at the extremes are above the line

Points at the extremes are both above and below the line

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