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Movie companies need to predict the gross receipts of individual movies once a movie has debuted. The following results, stored in Potter Movies, are the first weekend gross, the U.S. gross, and the worldwide gross (in $millions) of the eight Harry Potter movies:$

 Title  First  Weekend  ($millions)  U.S. Gross  ($millions)  Worldwide  Gross  ($millions)  Sorcerer’s Stone 90.295317.558976.458 Chamber of Secrets 88.357261.988878.988 Prisoner of Azkaban 93.687249.539795.539 Goblet of Fire 102.335290.013896.013 Order of the Phoenix 77.108292.005938.469 Half-Blood Prince 77.836301.460934.601 Deathly Hallows Part I 125.017295.001955.417 Deathly Hallows Part II 169.189381.0111,328.111\begin{array}{lccc} {\text { Title }} & \begin{array}{c} \text { First } \\ \text { Weekend } \\ \text{ (\$millions) } \end{array} & \begin{array}{c} \text { U.S. Gross } \\ \text{ (\$millions) } \end{array} & \begin{array}{c} \text { Worldwide } \\ \text { Gross } \\ \text{ (\$millions) } \end{array} \\ \hline \text { Sorcerer's Stone } & 90.295 & 317.558 & 976.458 \\ \text { Chamber of Secrets } & 88.357 & 261.988 & 878.988 \\ \text { Prisoner of Azkaban } & 93.687 & 249.539 & 795.539 \\ \text { Goblet of Fire } & 102.335 & 290.013 & 896.013 \\ \text { Order of the Phoenix } & 77.108 & 292.005 & 938.469 \\ \text { Half-Blood Prince } & 77.836 & 301.460 & 934.601 \\ \text { Deathly Hallows Part I } & 125.017 & 295.001 & 955.417 \\ \text { Deathly Hallows Part II } & 169.189 & 381.011 & 1,328.111 \end{array}

ConstructascatterplotwithfirstweekendgrossontheConstruct a scatter plot with first weekend gross on theXaxisandworldwidegrossontheaxis and worldwide gross on theY$ axis.

Movie companies need to predict the gross receipts of individual movies once a movie has debuted. The data, shown below and stored in PotterMovies, are the first weekend gross, the U.S. gross, and the worldwide gross (in $millions) of the eight Harry Potter movies:$

 Title  First  Weekend  U.S.  Gross  Worldwide  Gross  Sorcerer’s Stone 90.295317.558976.458 Chamber of Secrets 88.357261.988878.988 Prisoner of Azkaban 93.687249.539795.539 Goblet of Fire 102.335290.013896.013 Order of the Phoenix 77.108292.005938.469 Half-Blood Prince 77.836301.460934.601 Deathly Hallows Part 1 125.017295.001955.417 Deathly Hallows Part 2 169.189381.0111,328.111\begin{array}{lrcc} \text { Title } & \begin{array}{c} \text { First } \\ \text { Weekend } \end{array} & \begin{array}{c} \text { U.S. } \\ \text { Gross } \end{array} & \begin{array}{c} \text { Worldwide } \\ \text { Gross } \end{array} \\ \hline \text { Sorcerer's Stone } & 90.295 & 317.558 & 976.458 \\ \text { Chamber of Secrets } & 88.357 & 261.988 & 878.988 \\ \text { Prisoner of Azkaban } & 93.687 & 249.539 & 795.539 \\ \text { Goblet of Fire } & 102.335 & 290.013 & 896.013 \\ \text { Order of the Phoenix } & 77.108 & 292.005 & 938.469 \\ \text { Half-Blood Prince } & 77.836 & 301.460 & 934.601 \\ \text { Deathly Hallows Part 1 } & 125.017 & 295.001 & 955.417 \\ \text { Deathly Hallows Part 2 } & 169.189 & 381.011 & 1,328.111 \end{array}

$

Based on the previous parts, what conclusions can you reach about the relationship between first weekend gross, U.S. gross, and worldwide gross?

Question

Movie companies need to predict the gross receipts of individual movies once the movie has debuted. The following results, stored in PotterMovies, are the first weekend gross, the U.S. gross, and the worldwide gross (in $millions) of the Harry Potter movies.

a. Perform a cluster analysis using the complete linkage method on the Harry Potter movies based on the first weekend gross, the U.S. gross, and the worldwide gross (in$millions).

b. What conclusions can you reach about which Harry Potter movies are most similar?

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A cluster analysis is required to a perform grouping based on the given items in the data set in terms of their similarity. This similarity is based on the Euclidean distance between the individual items, and will be computed with the given equation:

dij=k=1r(XikXjk)2,\begin{equation} d_{ij}=\sqrt{\sum^r_{k=1}(X_{ik}-X_{jk})^2}, \end{equation}

where we have:

  • dijd_{ij} is the distance between objects ii and jj,
  • XikX_{ik} is the value of object ii in dimension kk,
  • XjkX_{jk} is the value of object jj in dimension kk, and
  • rr is the number of data dimensions.

This distance value is the basis for the different methods in cluster analysis. These are enumerated in Table 1:

Table 1. Clustering Methods According to Distance Values\textbf{Table 1. Clustering Methods According to Distance Values}

Measures of DistanceBasisbetween Clusters11Complete LinkageMaximum Distance11Single LinkageMinimum Distance11Average LinkageMean DistanceWard’s minimumSum of Squaresvariance methodover all variables\begin{array}{||c|c||} \hline \textbf{Measures of Distance}&\textbf{Basis}\\ \textbf{between Clusters}&\textbf{}\\ \hline \phantom{\dfrac{1}{1}}\text{Complete Linkage}&\text{Maximum Distance}\\ \hline \phantom{\dfrac{1}{1}}\text{Single Linkage}&\text{Minimum Distance}\\ \hline \phantom{\dfrac{1}{1}}\text{Average Linkage}&\text{Mean Distance}\\ \hline \text{Ward's minimum}&\text{Sum of Squares}\\ \text{variance method}&\text{over all variables}\\ \hline \end{array}

On the other hand, clustering methods are also categorized on the variables that they work with:

  • k-means clustering are used efficiently with purely numerical variables; while
  • hierarchical clustering are good with a mix of numerical and categorical variables.

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