Big D Incorporated: Analyzing Box Office Success

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Table of Contents

A Film Company Summary
Applications and Purpose of Factor Analysis
Applications and Purpose of Multidimensional Scaling
Applications and Purpose of Cluster Analysis
Preferred Method
Lessons for Board of Directors
References

A Film Company Summary

A film studio wishes to draw broad inferences from the box office success of its multiple films. With this in mind, they uncovered a wide range of factors that influence a film’s financial success. The following factors are considered: film duration, genre, audience rating, critic rating, budget, and worldwide and domestic box office receipts. They wished to avoid taming the data’s untamed nature while focusing on the aspects that substantially impact revenue. It is accomplished through the use of factor analysis. The same production firm thought about how the film’s rating on a scale of one to 10 affected its box office haul. Multivariate scaling compares responses across multiple dimensions to examine disparities in viewpoints.

Applications and Purpose of Factor Analysis

A factor analysis can be utilized to reduce the total number of variables that influence the reaction of Big D Incorporated. As a result, a factor analysis needs to be carried out so that the data may be reduced while maintaining the greatest amount of information (Garson, 2022). Additionally, it explains relationships between various outcomes of Big D Incorporated that were caused by various underlying circumstances (Garson, 2022). This purpose is to discover several previously unexplained elements that influence the degree to which the variation between several observations in the data varies (Garson, 2022). By eliminating the basic set of linked components, it identifies a relatively smaller set of variables with no multicollinearity, which is helpful in future analyses such as multivariate regression.

Applications and Purpose of Multidimensional Scaling

It uncovers previously unknown linkages within Big D Incorporated. However, in addition to that, using multidimensional scaling can also assist in visually depicting the nature of the associations between the various data (Knezek et al., 2022). Therefore, one of the most important applications of multidimensional scaling for a data set about Big D Incorporated is to display the multicollinearity between the factors and the other observations (Knezek et al., 2022). When applied to these data, scaling serves several important objectives, one of which is to graph the relative location of the observations and visualize how they differ from each other and the whole.

Applications and Purpose of Cluster Analysis

In this context, the processing of the Big D Incorporated data consists of grouping its observations into several different clusters or ordered groups. It does this by testing the hypothesis on several connected factors and then categorizing those elements into various groups to make identifying them easier (Ventocilla & Riveiro, 2020). It makes the huge data incorporated easy to read, and each group forms an excellent data visualization. Additionally, it helps in the process of pattern recognition for the entire set of data (Ventocilla & Riveiro, 2020). Finding groups that share a wide range of traits is one of the major purposes of cluster analysis, and one of its primary purposes is to do just that.

Preferred Method

Factor analysis is useful in reducing the amount of data; however, it could be more effective in visualizing it. Again, the multidimensional scaling method helps see the data and minimizes the amount of data. In conclusion, cluster analysis does not necessarily minimize the amount of data but classifies it or clusters that share similarities. In addition to that, it makes data visualization much simpler. As a consequence, cluster analysis is the most recommended approach out of the three.

Lessons for Board of Directors

To gain a good visualization and an interpretation of the data about Big D Incorporated, the Board of Directors will learn how to partition the data into separate related groups or clusters and then execute a cluster analysis on the data. Based on the groups, it is possible to study the correlation between and within the groups, and as a result, only the groups that are not correlated between and within are taken into consideration, while all the other groups are discarded. This will make the calculations easier, minimize the amount of data, and also provide a decent notion regarding the elements that are related to the data.

References

Garson, G. D. (2022). Factor analysis and dimension reduction in R: A social scientist’s toolkit. Taylor & Francis.

Knezek, G., Gibson, D., Christensen, R., Trevisan, O., & Carter, M. (2022). Assessing approaches to learning with nonparametric multidimensional scaling. British Journal of Educational Technology. Web.

Ventocilla, E., & Riveiro, M. (2020). A comparative user study of visualization techniques for cluster analysis of Multidimensional Data Sets. Information Visualization, 19(4), 318–338. Web.

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