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The design and development of a product is a complex process, which requires many resources and various types of expertise. Because the process is complex, it is essential to estimate the design effort required to complete a product development project. The estimation of design effort is, in turn, a prime factor to predicting lead-time, cost, and effort requirements of a project. In this thesis, parametric models for estimating design effort are proposed. A case study involving engineering departments at Pratt & Whitney Canada (PWC) is presented. First, research is conducted on each of the design, aerodynamics, analytical and drafting departments at PWC to identify factors that could be best utilized in estimating design effort. Four factors are identified for parametric modeling. These factors are type of design, degree of change, concurrency, and experience of departmental personnel. The parametric model applied to each department uses least square regression. Furthermore, the jackknife technique is utilized to ameliorate the bias in regression equations coefficients. This research also uses a data masking technique in order to protect confidential data information of PWC. The masking technique enables to calibrate the impact of each factor considered in the parametric modeling, while not being affected by the masking. Data analysis is first utilized to establish regression based parametric models. Later, the regression equations are tested for their validation. It is found that the proposed parametric models provide good estimate of design effort when compared to the original estimates, with maximum relative errors of less than 10%. Furthermore, in each parametric model, the factors that significantly affect the design effort are identified using ANOVA table. Based on the outcomes reported in ANOVA, the number of factors is reduced and new models are developed with the reduced number of factors. Lastly, the application of the models, limitations and possible future studies are also discussed in this thesis.
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