1. 6 Points. In case of = 0.5, generate 1,000 results for the query over the original dataset, and generate 1,000 results for the query over each of three other datasets: removing a record with the most frequent Education; removing any record with the second most frequent Education; and removing any record with the least frequent Education.

2. 6 Points. In each of the above 4 groups of 1,000 results, define a measure and utilize it to validate that each of the last 3 groups of results and the original results are 0.5-indistinguishable.

3. 6 Points. Repeat all the above for = 1.0, utilize the above measure to validate that each of the last 3 groups of results and the original results are 1.0-indistinguishable.

7 Points. Define another measure and utilize it to justify that the distortion of the 4,000 results for = 1.0 is less than that of = 0.5. For each task, submit a separate source code file (can be only a few lines) and a result file, including the quantitative results and the measure (if requested in the task).