1. Review the article “Beware Spurious Correlations” posted in this module readings. According to the article, which of the examples below may lead to a spurious correlation?

   		Comparing dissimilar variables
   		Manipulating the ranges to align data
   		Plotting unrelated data sets together
   		All of the above

5 points

QUESTION 2

1. Review the article “Correlations in Finance” by Investopedia posted in this module readings. According to the article, if the stock is moving in the same direction as a benchmark index such as S&P 500, then

   		no correlation exists between the stock and the index
   		there is a positive correlation between the stock and the index
   		there is a negative correlation between the stock and the index
   		None of the above

5 points

QUESTION 3

1. What is a statistical measure that is used to analyze the strength of relationships between attributes in the dataset?

   		R-Squared
   		p-value
   		correlation
   		alpha score

5 points

QUESTION 4

1. Assume that the correlation between humidity and cement strength is -0.5959 and statistically significant. How can we interpret this result?

   		The more humidity the weaker the cement
   		The more humidity the stronger the cement
   		There is no effect of humidity on cement strength
   		Humidity and cement strength are not correlated

5 points

QUESTION 5

1. Assume that the correlation between sleep and level of oxygen is 0.8622 and statistically significant. How can we interpret this result?

   		The more sleep the higher the level of oxygen
   		The more sleep the lower the level of oxygen
   		There is no effect of sleep on the level of oxygen
   		Sleep and oxygen are not correlated

5 points

QUESTION 6

1. What is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups?

   		Itemset analysis
   		Cluster analysis
   		Network analysis
   		Social network analysis

5 points

QUESTION 7

1. Cluster analysis is

   		an unsupervised technique
   		a supervised technique
   		a semi-supervised technique
   		a reinforced learning technique

5 points

QUESTION 8

1. Which cluster solution is appropriate for nominal (categorical) data?

   		Hierarchical clustering
   		K-means clustering
   		Dendrogram
   		Complete linkage

5 points

QUESTION 9

1. In RapidMiner, which operator select the optimal number of clusters based on data?

   		K-means
   		Divisive
   		Agglomeration
   		X-means

5 points

QUESTION 10

1. When the distance between two clusters is defined as the distance between the nearest pair of objects with each object in the pair belonging to a distinct cluster, it is called

   		Nearest neighbor linkage
   		Complete linkage
   		Farthest neighbor linkage
   		Average linkage

5 points

QUESTION 11

1. Questions 11-15 are based on the “Chapter 4 Exercise” dataset. Upload Chapter04Exercise data to RapidMiner, and answer the following questions:
   
   Upload the file, run the model, and explore the results. How many attributes are in the dataset?

   		3
   		4
   		5
   		7

5 points

QUESTION 12

1. Add the operator Correlation Matrix, connect the ports, and run the model. Examine the correlation matrix.
   
   Is the following statement true: attributes Vehicle Weight (Vweight) and Engine Size (Engine_Size) are highly positively correlated?

   True

   False

5 points

QUESTION 13

1. Continue examining the correlation matrix. Is the following assumption true: the larger the engine size the greater the number of cylinders.

   True

   False

5 points

QUESTION 14

1. Continue examining the correlation matrix. Is the following assumption true: the larger the engine size the smaller the horse power.

   True

   False

5 points

QUESTION 15

1. Continue examining the correlation matrix. Is the following assumption true: the smaller the vehicle weight, the smaller MPG.

   True

   False

5 points

QUESTION 16

1. Questions 16-20 are based on the “Chapter 6 Exercise” dataset.
   
   Upload Chapter06Exercise data to RapidMiner. Use operator Select Attributes to remove categorical variables First Name
   and Last Name from the analysis. Then use operator Set Role to set the target role ‘id’ to the attribute Student_ID How many regular attributes are now in the dataset?

   		7
   		8
   		9
   		10

5 points

QUESTION 17

1. Add the operator K-Means. In the parameters, change the number of k to 3, and have ‘add as label’ checked. Run the model and check the stats. What is the size of the largest cluster?

   		266
   		365
   		621
   		468

5 points

QUESTION 18

1. Add the operator Cluster Model Visualizer. Run the results. Explore the Overview section of the ClusterModelVisualizer results. Which cluster has the lowest number of absences?

   		The largest cluster
   		The smallest cluster
   		The middle cluster
   		There is no difference between clusters

5 points

QUESTION 19

1. Replace the operator K-Means with operator K-Means(fast). Keep all other operators. In the parameters of K-Means(fast) change the number of k to 3 and have ‘add as label’ checked. Run the model and check the stats. What is the size of the largest cluster?

   		266
   		365
   		621
   		468

5 points

QUESTION 20

1. In the Design View, change the parameters of K-Means(fast). Change the ‘numerical measure’ to ‘ManhattanDistance’. Run the model and check the stats. What is the size of the largest cluster?

   		266
   		365
   		621
   		468