Task One Business Analytics Concepts
Part A
Cluster analysis is a method that groups the same observations into several clusters based on the observed values of numerous variables for every individual. Cluster analysis encompasses the organization of items into different groups based on how such groups or clusters are closely linked to one another. Cluster analysis aims to determine the same groups of subjects in that similarity between every pair of subjects implies some universal measure of the entire set of characteristics. Significantly, clustering analysis is characteristically utilized when no supposition is made about the probable associations within the data. The analysis provides insight regarding where relationships and patterns in the data are existent but does not provide information regarding what they may be or what they mean.
A fitting example of clustering analysis is classification. This encompasses the separation of the subjects into different groups so that every subject is more similar to other subjects contained in its groups compared to subjects that are not in that group. For instance, in the context of marketing, clustering analysis may be utilized to identify different categories within the consumer data, geographical location, age groups, earning levels, and even educational attainment. Similarly, in health care, clustering analysis may be beneficial in determining whether various geographical regions are associated with significant or insignificant levels of particular sicknesses to examine conceivable local contributing factors to health issues.
Part B
Cluster analysis is a categorization method for forming homogeneous groups contained in complex data sets. This section provides a critical discussion and analysis of five published papers that have capitalized on clustering analysis.
The article by Gaylord-Harden et al. (2008) used cluster analysis to implement the classification of adolescents based on their coping patterns and ascertain whether coping groups varied on fundamental correlates and coping outcomes such as stressors and psychological signs. In this study, the researchers utilized standard cluster analytic processes for categorizing adolescents into coping groups based on their coping behavior patterns concerning the studys coping subscales. Initially, agglomerative hierarchical cluster analysis was carried out to determine the number of clusters within the sample. After that, k-means cluster analysis processes were utilized in grouping individuals. The research findings of cluster analysis pinpointed two comping groups that varied on the regularity of coping use and the preferred coping methods.
The article by Dollfus et al. (1996) sought to pinpoint subtypes of schizophrenia through cluster analyses. It is well-known that there are two subtypes of schizophrenia, including positive and negative. This study purposed to determine whether there are any possible further subtypes through the application of cluster analysis to a certain dataset. Specifically, the researchers applied Wards cluster analysis approach to scores of 138 patients based on the Positive and Negative Syndrome Scale (PANSS). Notably, research outcomes from the cluster analysis suggest four subtypes on the minimum including positive, negative, mixed, and disorganized. There was an additional subtype encompassing patients with minimal symptoms. These subtypes are deemed valid concerning the observed differences between the clusters on various clinical features and the likeness of the cluster solution attained from various instruments.
The research conducted by Ho and Hung (2008) employed cluster analysis to investigate how The authors employed Wards linkage clustering analysis. The studys sample size comprised 640 potential students chosen from undergraduate programs in 14 different universities across the city of Taiwan. Specifically, cluster analysis was utilized to comprehend the causal dimensions of students choices based on the schools selection criteria and classify students into recognizable groups. The outcomes of clustering analysis recognized five student groups for market segmentation. They comprise the Prominence cluster, the Less aware cluster, the Pragmatic cluster, the Austerity cluster, and the Fastidious cluster.
The study by Borgen and Barnett (1987) examines the application of cluster analysis in conducting counseling psychology research. Cluster analysis is a categorization method for forming homogeneous groups within complex data sets. This study determined different steps in conducting cluster analysis. The first step is measuring proximity to pinpoint objects with the most similarity. The second step is choosing the ideal clustering method, for instance, between the hierarchical and non-hierarchical approaches. The last step encompasses assessing and generalizing the clusters.
Also, the study conducted by DiStefano and Kamphaus (2006) investigated to examine subtypes of child development. Specifically, the study sought to compare the cluster analysis approach and latent class cluster analysis in the formation of typologies. These two classification methods were used to pinpoint groups of child behavioral changes underlying a sample of children within elementary level between the ages of 6 and 11 years. Information relating to students behavior was provided by teachers ranging from 14 different subscales and was utilized as input for the analyses. Subsequently, an analysis of these processes and outcomes were compared. Research outcomes determined that the latent class cluster analysis comprises three classes signifying varying levels of childrens behavioral adjustment while the cluster analysis comprised seven different child behavior categories. This implies a significant magnitude of overlap, and every process offers distinctive information for classifying child behavior.
Task Two Marketing Analytics
a. Use Excel to determine the estimated demand quadratic equation function and describe why the quadratic form is preferred
A quadratic function takes the form, y = ax2 + bx + c. The estimated demand quadratic equation function is y = -2.1444x + 1751.7. The quadratic form is the most preferred because it can be used in solving any quadratic...
…magnitude of overlap, and every process offers distinctive information for classifying child behavior.
Task Two Marketing Analytics
a. Use Excel to determine the estimated demand quadratic equation function and describe why the quadratic form is preferred
A quadratic function takes the form, y = ax2 + bx + c. The esimated demand quadratic equation function is y = -2.1444x + 1751.7. The quadratic form is the most preferred because it can be used in solving any quadratic equation, and by using the standard form, it is conceivable to plug in the numbers. Significantly, suppose it is conceivable to express an equation in the quadratic form. In that case, it becomes conceivable to solve such an equation through any of the techniques utilized in solving ordinary quadratic equations.
b. Use Excel Solver to find the optimal price which maximizes the companys profit
By creating a scatter plot, it is possible to determine the demand curve formula to be used as the basis for our model in the Excel Solver:
In this case, the demand curve formula is: d = 762.72 0.4252* p
By making use of the Excel solver, it is determined that the optimal price which maximizes the companys profit is 1,336.90
c. Determine the optimal demand
Demand is 194
d. Compute the optimal profit
The optimal profit is 88,762
e. Interpret the results
As initially indicated, the demand curve formula is: d = 762.72 0.4252* p
It was determined that p is 848
Therefore, substituting p, we obtain:
d = 762.72 0.4252* 1,336.90
demand is 194
The optimal profit is determined by computing the total revenue less the total cost.
In this case, the total revenue is: (194.27 * 1,336.90) = 259,719.56
The total cost is: (194.27 * 880) = 170,957.60
The optimal profit is
Then the optimal profit is: 88,762
f. If the supply cost of each sofa is 750 and then 1050, determine the optimal price and interpret the results appropriately
The cost is 750, and the optimal price is 1,050
In this case,
The total revenue would be: 1,050 194 = 203,700
On the other hand, the total cost would be: 750 194 = 145,500
The total profit is obtained by revenues minus costs = 203,700 - 145,500
Therefore, the total profit is 58,200
This implies that decreasing the cost of production and supply and decreasing the optimal price resultant lower optimal profit.
g. If the company has decided to set the price more than 1200 GBP, what will be the optimal price? Interpret the results
As initially indicated, the demand curve formula is: d = 762.72 0.4252* p
It was determined that p is 1,200
Therefore, substituting p, we obtain:
d = 762.72 0.4252* 1,200
demand is 252
The optimal profit is determined by computing the total revenue less the total cost.
In this case, the total revenue is: (252* 1,200)…
