STATISTICS IN PRACTICE: DUKE ENERGY 20.1 TERMINOLOGY USED IN SAMPLE SURVEYS 20.2 TYPES OF SURVEYS AND SAMPLING METHODS 20.3 SURVEY ERRORS Nonsampling Error Sampling Error 20.4 SIMPLE RANDOM SAMPLING Population Mean Population Total Population Proportion Using Excel for Simple Random Sampling Determining theSample Size 20.5 STRATIFIED SIMPLE RANDOM SAMPLING Population Mean Using Excel: Population Mean Population Total Using Excel: Population Total Population Proportion Using Excel: Population Proportion Determining the Sample Size
20.6 CLUSTER SAMPLING Population Mean Population Total Population Proportion Using Excel for Cluster Sampling Determining the Sample Size 20.7 SYSTEMATIC SAMPLING56130_20a_ch20_p01-43.qxd 2/8/08 5:32 PM Page 20-2
STATISTICS in PRACTICE DUKE ENERGY*
CHARLOTTE, NORTH CAROLINA
Duke Energy is a diversified energy company with a portfolio of natural gas and electric businesses and an affiliated real estate company. In 2006, Duke Energy merged with Cinergy of Cincinnati, Ohio, to create one of North America’slargest energy companies, with assets totaling more than $70 billion. Today, Duke Energy serves more than 5.5 million electric and gas customers in North Carolina, South Carolina, Ohio, Kentucky, Indiana, and Ontario, Canada. To improve service to its customers, Duke Energy continually looks for emerging customers needs. In the following example, we discuss how the company used a BuildingCharacteristics Survey to learn about the energy requirements of commercial buildings in the Cincinnati, Ohio, service area. A variety of information concerning commercial buildings was sought, such as the floor space, number of employees, energy end-use, age of the building, type of building materials, and energy conservation measures. During preparations for the survey, analysts determined that approximately27,000 commercial buildings were in operation in the Cincinnati service area. Based on available funds and the precision desired in the results, a sample of 616 commercial buildings was recommended. The sample design chosen was stratified simple random sampling. Total electrical usage over the past year for each commercial building in the service area was available from company records, andbecause many of the building characteristics of interest (size, number of employees, etc.) were related to usage, it was the criterion used to divide the population of buildings into six strata. The first stratum contained the commercial buildings for the 100 largest energy users; each building in this
*The authors are indebted to Jim Riddle of Duke Energy for providing this Statistics in Practice.A sample survey of energy requirements for commercial buildings was conducted in Cincinnati, Ohio. © Getty Images/PhotoDisc.
stratum was included in the sample. Although these buildings constituted only .2% of the population, they accounted for 14.4% of the total electrical usage. For the other strata, the number of buildings sampled was determined on the basis of obtaining the greatestprecision possible per unit cost. A questionnaire was developed and pretested before the actual survey was conducted. Data were collected through personal interviews. Completed surveys totaled 526 out of the sample of 616 commercial buildings. This response rate of 85.4% was considered to be excellent. The company used the survey results to improve the forecasts of energy demand and to improve service toits commercial customers. In this chapter you will learn about the issues that statisticians consider in the design and execution of a sample survey such as the one conducted by Duke Energy. Sample surveys are often used to develop profiles of a company’s customers; they are also used by the government and other agencies to learn about various segments of the population.