Data Structure - Data Structures and Algorithms

In the Hawaii 2005 Housing dataset, Property value forms one of the items, which can be explained as being a continuous variable. The rationale for branding house value as continuous lies in the explanation given by Frankfort-Nachmias, Leon-Guerrero, and Davis (2020). The authors explain that for continuous variables, they have no minimum-sized measurement unit. It's possible to subdivide these values into smaller fractions, something varying from discrete variables, which are limited by minimum measurement sizes (Frankfort-Nachmias, Leon-Guerrero & Davis, 2020). Notably, property value denotes worth real estate, considering an agreed-upon price by the respective buyers and sellers. Hence, as a continuous variable, the house value can take infinite values, an aspect that makes the price a continuous variable (Frankfort-Nachmias, Leon-Guerrero & Davis, 2020). Since property values converge or vary based on demand and supply, there is variation in recorded values in the Hawaii dataset.

Conversion to Categorical Variable and Provision of Frequency Tables 

Based on the Hawaii 2005 dataset, property value has the following categories, separating the data into price-related subsections, with each item corresponding to a value range.

Table 1 Property Values and Respective Frequencies 

The Property values vary, with the totals ranging from less than $ 10,000 to the highest property value, which is over $ 1,000,000. By arranging the values within the specific ranges from the Hawaii 2005 dictionary, one can determine how many houses lie within each category. This rearrangement helps make conclusions on which range has how many houses/properties, vital in making descriptive conclusions. With property values taking infinite values, this categorization makes understanding the items simplified for readers.

Producing Frequency Tables for the new variable with consideration of a minimum of 6 Items

As shown in Table 1 above, there are less than six items in some of the value ranges, making it necessary to group these items into single groups. Table 2 below shows the new conversion, with items ranging from less than $ 10,000 to $ 39,999 combined into forming a total of 7 items.

Table 2 New Frequency Table 

Descriptive Statistics for Original and New Variables 

In the property value, as the chosen variable in this exercise, regrouping the items from the Hawaii 2005 housing datasets within the given ranges gives useful summaries as shown below. Descriptive statistics help offer valuable descriptions about the focus sample or population (Wagner, 2020), summarizing the Hawaii dataset property value essential. Table 3 depicts the summary before the rearrangement of frequencies to fit within ranges of at least six (6) items. In table 4, the resulting summaries are provided, providing information about the housing values.

Table 3 Original Data Summary 

Table 4 New Data Summary 

References

Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications.

Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.