This research explains the methodology for deriving the confidence interval on the cost estimate of a part, when a feature-based approach is used. The components of a steam turbine are used in order to demonstrate the methodology. With a parametric approach to estimate cost, developing a confidence interval is straightforward because there is one cost-estimating relationship (CER) that incorporates a design's parameters. However, in feature-based cost estimating, there are multiple CERs that each estimate the cost of a part feature and the feature estimates are accumulated to get the total manufacturing cost. This makes deriving a confidence interval more complex, since the variance in each CER must be incorporated into determining the overall variance of the estimate.
Confidence intervals are derived for multiple CER generation techniques that utilize both regression and Artificial Neural Networks. The differences between their parametric and feature-based results are statistically tested to determine whether a difference exists. The testing shows that in 7 out 8 instances tested, the differences between the two approaches were not found to be statistically significantly different. Feature-based models are more transparent than multivariate models because exactly how each parameter affects an estimate can be easily determined. When there is no difference between the two methods than the feature-based method should be used by the analyst.
","abstract_html":"<p>This research explains the methodology for deriving the confidence interval on the cost estimate of a part, when a feature-based approach is used. The components of a steam turbine are used in order to demonstrate the methodology. With a parametric approach to estimate cost, developing a confidence interval is straightforward because there is one cost-estimating relationship (CER) that incorporates a design's parameters. However, in feature-based cost estimating, there are multiple CERs that each estimate the cost of a part feature and the feature estimates are accumulated to get the total manufacturing cost. This makes deriving a confidence interval more complex, since the variance in each CER must be incorporated into determining the overall variance of the estimate.</p><p>Confidence intervals are derived for multiple CER generation techniques that utilize both regression and Artificial Neural Networks. The differences between their parametric and feature-based results are statistically tested to determine whether a difference exists. The testing shows that in 7 out 8 instances tested, the differences between the two approaches were not found to be statistically significantly different. Feature-based models are more transparent than multivariate models because exactly how each parameter affects an estimate can be easily determined. When there is no difference between the two methods than the feature-based method should be used by the analyst.</p>","abstract_has_math":false,"creators":["Iacianci, Bryon C."],"institution":"Ohio University","degree_name":"Master of Science (MS)","degree_level":"masters","degree_discipline":"Industrial and Systems Engineering (Engineering and Technology)","degree_department":null,"school":null,"contributors":["Masel, Dale"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012","date_published":"2012","updated_at":"2026-07-24T03:35:52Z","subjects":["Engineering","Industrial Engineering","cost estimating relationship","artificial neural network","confidence interval","cost prediction","CER","ANN","composite confidence interval","feature based"],"languages":["English"],"rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1353594939","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Masel, Dale"]},{"key":"dc:creator","label":"Author","values":["Iacianci, Bryon C."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2012"]},{"key":"dc:publisher","label":"Institution","values":["Ohio University / OhioLINK"]},{"key":"dc:type","label":"Dc Type","values":["Electronic Thesis or Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial and Systems Engineering (Engineering and Technology)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Ohio University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Engineering","Industrial Engineering","cost estimating relationship","artificial neural network","confidence interval","cost prediction","CER","ANN","composite confidence interval","feature based"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:rights","label":"Dc Rights","values":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1353594939"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This research explains the methodology for deriving the confidence interval on the cost estimate of a part, when a feature-based approach is used. The components of a steam turbine are used in order to demonstrate the methodology. With a parametric approach to estimate cost, developing a confidence interval is straightforward because there is one cost-estimating relationship (CER) that incorporates a design's parameters. However, in feature-based cost estimating, there are multiple CERs that each estimate the cost of a part feature and the feature estimates are accumulated to get the total manufacturing cost. This makes deriving a confidence interval more complex, since the variance in each CER must be incorporated into determining the overall variance of the estimate.
Confidence intervals are derived for multiple CER generation techniques that utilize both regression and Artificial Neural Networks. The differences between their parametric and feature-based results are statistically tested to determine whether a difference exists. The testing shows that in 7 out 8 instances tested, the differences between the two approaches were not found to be statistically significantly different. Feature-based models are more transparent than multivariate models because exactly how each parameter affects an estimate can be easily determined. When there is no difference between the two methods than the feature-based method should be used by the analyst.
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Confidence intervals are derived for multiple CER generation techniques that utilize both regression and Artificial Neural Networks. The differences between their parametric and feature-based results are statistically tested to determine whether a difference exists. The testing shows that in 7 out 8 instances tested, the differences between the two approaches were not found to be statistically significantly different. Feature-based models are more transparent than multivariate models because exactly how each parameter affects an estimate can be easily determined. When there is no difference between the two methods than the feature-based method should be used by the analyst.
"],"dc:format":["application/pdf","p.115","1.08 MB"],"dc:identifier":["http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1353594939"],"dc:language":["English"],"dc:publisher":["Ohio University / OhioLINK"],"dc:rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."],"dc:subject":["Engineering","Industrial Engineering","cost estimating relationship","artificial neural network","confidence interval","cost prediction","CER","ANN","composite confidence interval","feature based"],"dc:title":["Confidence Intervals on Cost Estimates When Using a Feature-based Approach"],"dc:type":["Electronic Thesis or Dissertation"],"thesis:degree_discipline":["Industrial and Systems Engineering (Engineering and Technology)"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science (MS)"],"thesis:institution_name":["Ohio University"]},"updated_at":"2026-07-24T03:35:52Z"}