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Sampling with Real Data: Identifying Optimal Sampling Land Areas for Estimating the Population

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Title: Sampling with Real Data: Identifying Optimal Sampling Land Areas for Estimating the Population
Language: English
Authors: Samet Okumus (ORCID 0000-0001-5905-196X)
Source: Digital Experiences in Mathematics Education. 2025 11(2):276-286.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 11
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Sampling, Data Analysis, Computation, Population Distribution, Maps
DOI: 10.1007/s40751-024-00159-4
ISSN: 2199-3246
2199-3254
Abstract: This snapshot illustrates my use of the Common Online Data Analysis Platform (CODAP), a web-based tool, to perform a sampling data task embedded within a real-world phenomenon. The aim is to identify the optimal sampling land areas on the map for estimating the population. I utilized a public dataset containing densely located alternative fuel stations and expansive vacant regions spread throughout a rectangular area. I sampled rectangular areas on the map and discussed the sampling results with a focus on the variability between and across the samples, primarily using mean absolute deviation as a measure of variation. The sampling task in CODAP is shared with the reader.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1488710
Database: ERIC
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: This snapshot illustrates my use of the Common Online Data Analysis Platform (CODAP), a web-based tool, to perform a sampling data task embedded within a real-world phenomenon. The aim is to identify the optimal sampling land areas on the map for estimating the population. I utilized a public dataset containing densely located alternative fuel stations and expansive vacant regions spread throughout a rectangular area. I sampled rectangular areas on the map and discussed the sampling results with a focus on the variability between and across the samples, primarily using mean absolute deviation as a measure of variation. The sampling task in CODAP is shared with the reader.
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