Simple Random Sampling with Real Data

Example of Simple Random Sampling

• Introduction to Simple Random Sampling

  • Utilizes real data for practical understanding.
  • Example: Cities in Riverside County, California.
  • Key data attributes:
  • Revenues
  • Population
  • Revenues per capita:
    • Definition: Total city revenue divided evenly among residents, indicating how much revenue each resident would receive if distributed equally.

• Objective: Conduct an analysis by sampling a portion of the data.

  • Choose four cities for simplicity.

• Methods of Random Sampling

  • Traditional Method:
  • Writing city names on slips of paper and drawing randomly from a hat.
  • Time-consuming.
  • Efficient Method Using Technology:
  • Use spreadsheet row numbers to simplify the sampling process.
  • Data occupies rows 2 to 29 in the spreadsheet (row 1 contains header information).

• Process of Random Sampling with a Calculator

  • Steps:
  1. Identify the range of data rows:
     • Start with Row 2 (Banning)
     • End with Row 29 (last city with data)
  2. Determine sample size:
     • Request a sample of 4 cities.
  3. Input this information into a calculator's sampling function.
  4. Evaluate to receive random row numbers.

• Result:

  • The randomly chosen row numbers may include:
  • Row 9: Corona
  • Row 29: Wildomar
  • Row 2: Banning
  • Row 21: Norco
  • These cities represent the chosen sample using simple random sampling.

• Conclusion:

  • Simple random sampling is an effective method to select a sample without bias, ensuring every city had an equal chance of being chosen.
  • Understanding this process allows for more accurate data analysis.