Day- 2 Team :D

Team D

Today’s main topic was about the different types of scales used to interpret data in sequential form that help in making business decisions.  A variable can be treated as scale (continuous) when its values represent ordered categories with a meaningful metric, so that distance comparisons between values are appropriate. Examples of scale variables include age in years and income in thousands of dollars. The various types of scales are:

Ordinal Scale-A variable can be treated as ordinal when its values represent categories with some intrinsic ranking (for example, levels of service satisfaction from highly dissatisfied to highly satisfied). Examples of ordinal variables include attitude scores representing degree of satisfaction or confidence and preference rating scores.

Nominal Scale- A variable can be treated as nominal when its values represent categories with no intrinsic ranking (for example, the department of the company in which an employee works). Examples of nominal variables include region, zip code, and religious affiliation.

Today’s case study was about analysing data that was gathered in the retail industry indicating consumer behaviour from four different stores. Our main objective was to find out the association among different parameters in the shopping category i.e. distance from the store, gender of shoppers, frequency of shopping, service satisfaction, contact with employees and age category. As part of the analysis, we had to come to a conclusion of the findings using statistical data that took a form of Chi-square tests. This test is useful in deciding whether to reject or accept the Null hypothesis given the results in values.

We also approached the problem using some analytical tools that indicated the correlation among data sets. These include:

Cluster Analysis-   Hierarchical (<50)- includes objects like people, brands, etc

2.       K-means (>50) objects are present

Proportionate hazard Analysis- This analysis is used to determine the availability in a particular space, or to see if the particular item fits in a shelf in a retail outlet or not. This again depends on a number of other factors which invariably determines the placement of this particular product in the shelf.

3. Divisional Cluster- A single object in this particular cluster is divided into a number of objects.

After the particular cluster in a group has been identified, it has to be combined in a methodical manner so that it becomes easier for the analyst to take out the meaningful data. One of the most widely used techniques of combining the data clusters is Agglomerative Hierarchical Clustering. It marks every variable as a cluster, and after that two variables having the smallest values are combined into a single cluster. A third cluster is added when the third variable is very less in value, and the number of clusters still remains as 2. This goes on till there are 2 clusters remaining. When you have only 2 clusters remaining, the smallest distance between them is unambiguous. For example, if cluster A has variables 1 and 4, and cluster B has variables 5, 6, and 7, you need a measure of how different or similar the two clusters are.

-          By: Tumuramye Dan Rupiiha