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Return to Research Methods Using R 1e Student Resources
Chapter 13 Multiple choice questions
Quiz Content
*
not completed
.
Download the data file for the multiple-choice questions for Chapter 13,
and open it in
R.
Conduct k-means clustering with 3 clusters on the data stored in
clusterdata
. How many observations are in the largest cluster?
8
correct
incorrect
11
correct
incorrect
13
correct
incorrect
15
correct
incorrect
*
not completed
.
What percentage of the variance is explained by the three-cluster solution?
78%
correct
incorrect
64%
correct
incorrect
55%
correct
incorrect
12%
correct
incorrect
*
not completed
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Try running clustering solutions with 2, 4, 5 and 6 clusters. Which one explains the greatest proportion of the variance?
2 clusters
correct
incorrect
4 clusters
correct
incorrect
5 clusters
correct
incorrect
6 clusters
correct
incorrect
*
not completed
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Consider the following cluster solutions, with between 2 and 6 clusters. The values are residual sums of squares:
Clusters (k): 2 3 4 5 6
RSS: 138 120 99 86 85
Calculate the AIC score for each solution using the expression:
AIC <- 2*k + n*log(RSS
)
Where k is the number of clusters, and n is the sample size of 20 data points.
Which solution has the lowest AIC score?
2 clusters
correct
incorrect
3 clusters
correct
incorrect
5 clusters
correct
incorrect
6 clusters
correct
incorrect
*
not completed
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Calculate the distance matrix for the data in clusterdata. What is the largest distance between any two points?
3.01
correct
incorrect
9.09
correct
incorrect
0.17
correct
incorrect
3.94
correct
incorrect
*
not completed
.
The dataset
swiss
is part of the
datasets
package and contains demographic information for 47 Swiss provinces. Load it into the environment with
data(swiss)
. The fifth column contains information about the percentage of Catholics in each province. How many provinces had more than 50% Catholic citizens?
10
correct
incorrect
18
correct
incorrect
24
correct
incorrect
29
correct
incorrect
*
not completed
.
Calculate a distance matrix on a subset of the
swiss
data that excludes column 5. What is the smallest distance in the matrix?
117.84
correct
incorrect
35.98
correct
incorrect
4.99
correct
incorrect
3.75
correct
incorrect
*
not completed
.
Conduct metric multidimensional scaling on the distance matrix you calculated in Question 7, to reduce it to two dimensions. Plot the data, representing the data for majority Catholic provinces in a different colour from majority non-Catholic provinces. Which of the following best describes the results?
The Catholic data points cluster in the lower right corner, apart from two outliers
correct
incorrect
The Catholic data points cluster in the upper left corner, apart from two outliers
correct
incorrect
The data points are not well segregated, and there is no clear structure to the results
correct
incorrect
The Catholic data points cluster in the centre of the plot, with non-Catholic data points at more extreme locations
correct
incorrect
*
not completed
.
Now conduct k-means clustering on the subset of Swiss data excluding the religion column, to find two clusters. Plot the scaled data again, but this time colour code according to cluster. What do you notice?
There is very little correspondence between the k-means clusters and the religion categories
correct
incorrect
The clusters map exactly onto the religious groupings
correct
incorrect
The clusters reveal similar groupings to the religion data, but the correspondence is not perfect
correct
incorrect
All of the Catholic provinces fall within one cluster
correct
incorrect
*
not completed
.
Use the isoMDS function in the MASS package to conduct non-metric multidimensional scaling on the same data. Plot the results. How do they compare to the metric scaling?
The results are identical
correct
incorrect
The results are completely different
correct
incorrect
The results are substantially different, but the overall structure is similar
correct
incorrect
The results are very similar, with only slight differences in the point locations
correct
incorrect
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