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could you help me to solve this Conduct a hierarchical cluster analysis using th

could you help me to solve this Conduct a hierarchical cluster analysis using the four variables specified above. Note that the video example for this module applies hierarchical cluster analysis using categorical variables, but in this case, we conduct a hierarchical cluster analysis using the same four continuous variables that we used in the k-means cluster analysis.

Variables: The variables you will use for this analysis are:

  • Age: Age of employee in years
  • MonthlyIncome: How much the employee earns per month
  • PercentSalaryHike: The percentage increase in salary over two years
  • YearsAtCompany: The total number of years the employee has been with the company
  1. Scale the data (we will be using Euclidean distance which requires us to normalize the data before using it in a distance function)
  1. Use Euclidean distance
  1. Use the Ward.D method (NOT Ward.D2)

Quiz Question #11: What is NOT true about Ward’s method for merging clusters?

  1. Generate a Dendrogram
  1. Create 4 clusters using the cutree function
  1. Link cluster assignments to the original data frame
  1. Display the number of observations in each cluster

Quiz Question #12: What is the size of the largest cluster?

Quiz Question #13: What is the size of the smallest cluster?

  1. Calculate variable averages for all non-normalized observations
  1. Calculate variable averages for each cluster

Quiz Question # 14: What are the key characteristics of cluster 1?

Quiz Question #15: What are the key characteristics of cluster 3?

Quiz Question #16: Comparing the k-means and hierarchical cluster analyses, which cluster pairs are most similar?

Frequency Tables are not used for this data since it is not categorical or binary.

could you help me to answer these Questions

Which of the following is NOT true about Ward’s method for merging clusters?

Question 11 options:

Ward’s method minimizes the loss of information that happens from representing a cluster with its centroid.

Ward’s method uses both the cluster centroid and individual differences the observations in the computations.

Ward’s method is essentially the same as the group average linkage method.

Ward’s method can only be used with hierarchical clustering using continuous variables.

Question 12 (1 point)

What is the size of the largest cluster?

What is the size of the smallest cluster?

Question 13 options:

Question 14 (1 point)

Listen

What are the key characteristics of cluster 1?

Question 14 options:

Close to average age, below average monthly income, substantially higher than average salary hike, below average number of years at the company

Close to average age, average monthly income, below average salary hike, above average years at the company

Substantially higher than average age, substantially higher than average monthly income, close to average salary hike, substantially higher than average years at the company

Below average age, below average monthly income, below average salary hike, below average number of years at the company

Question 15 (1 point)

Listen

What are the key characteristics of cluster 3?

Question 15 options:

Substantially higher than average age, substantially higher than average monthly income, close to average salary hike, substantially higher than average years at the company

Close to average age, average monthly income, below average salary hike, above average years at the company

Below average age, below average monthly income, below average salary hike, below average number of years at the company

Close to average age, below average monthly income, substantially higher than average salary hike, below average number of years at the company

Question 16 (1 point)

Listen

Comparing the k-means and hierarchical cluster analyses, which of these cluster pairs are most similar?

Question 16 options:

K-means cluster with all variables below average and Hierarchical cluster 3

K-means cluster with all variables below average and Hierarchical cluster 2

K-means cluster with the highest average age and Hierarchical cluster 3

K-means cluster with the highest monthly income and Hierarchical cluster 3

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