Hytex company is a direct marketer of electronic equipment. The data in Hytex.xlsx was obtained from the database of 1,000 mail order customers. Use Excel PivotTable function to obtain a frequency distribution table (Pivot table) with the variable “Close” (Rows) and the variable “History” (Columns). Based on the pivot table, answer the following question. Approximately Answer Question 9[input]% of the customers, who had a low-spending last year, lived not close to stores with similar merchandise (please round your answer to 1 decimal place).

Question

BlackTom AI · Accepted Answer

The task is to use a PivotTable to derive a frequency distribution for two variables: Close (as Rows) and History (as Columns), based on data from 1,000 mail-order customers, and then answer a specific proportion question.

First, identify the missing pieces in the provided data: there are no answer options given for this numerical question, and the actual PivotTable results from Hytex.xlsx are not included here. Because of that, I cannot compute the exact percentage from the dataset. Instead, I will outline the precise steps you would take to obtain the requested value and where to extract the final proportion.

Step-by-step approach to compute the target percentage:
1) Build the PivotTable:
   - Source data: Hytex.xlsx with 1,000 customers.
   - Rows: Close (likely a categorical variable indicating proximity to stores, e.g., Near/Not Near or specific distance bands).
   - Columns: History (likely a categorical variable indicating last year spending history or tier).
   - Values: Use Count of Customers (or simply Count) to obtain frequencies for each combination of Close and History.

2) Identify the subset “low-spending last year”:
   - Determine which History category corresponds to low spending (e.g., a specific column label or a combined condition that designates low spend within History).
   - If History itself encodes levels of spending, note which column(s) represent low spend; if not, you may need to create a helper column in the data (e.g., History_Low) before building the PivotTable.

3) Determine the target group:
   - You want customers who had low spending last year (as defined in step 2) and, among those, the proportion who lived not close to stores with similar merchandise.
   - “Not close to stores with similar merchandise” corresponds to the Close category that indicates not near stores offering similar merchandise (e.g., Close = Not Close or a distance category indicating not nearby).

4) From the PivotTable (Rows = Close, Columns = History or vice versa):
   - Filter the PivotTable to include only the low-spending last-year category (depending on how History is laid out, you may need to focus on that specific History column).
   - Within that subset, sum the counts for the Close value representing not being close to stores with similar merchandise. This gives the numerator: the number of low-spend customers not close to matching-store merchandise.
   - Compute the denominator as the total number of low-spend last-year customers (the sum of all Close categories for that History level).

5) Calculate the percentage:
   - Percentage = (Numerator / Denominator) × 100.
   - Round the result to 1 decimal place, as requested.

6) Verification and potential pitfalls:
   - Ensure there are no data quality issues (missing Close or History values). Handle missing data consistently (e.g., exclude or categorize as a separate bin, depending on your analysis plan).
   - If History is not directly indicating spending level, you may need to derive a low-spending group from a numeric field (e.g., total spend last year) before pivoting.
   - If the dataset uses multiple segments for proximity (e.g., Near and Far with subcategories for merchandise similarity), confirm which category corresponds to "not close to stores with similar merchandise" and use that in your numerator.

Because the actual PivotTable output and the specific category labels are not provided here, I cannot supply the numeric result. When you run the PivotTable with the described configuration and apply the above filtering and summarization, you will extract the numerator and denominator and compute the percent, rounding to 1 decimal place as required.

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