Titlebook: Statistical Tools for Program Evaluation; Methods and Applicat Jean-Michel Josselin,Benoît Le Maux Book 2017 Springer International Publish

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Descriptive Statistics and Interval Estimatione approximated by the skewness and kurtosis coefficients (Sect. 3.5). Last, in most cases, the description of a database is done in the context of a sample survey. Any generalization to the population of interest thus involves the calculation of confidence intervals (Sect. 3.6). Several examples ill

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Estimation of Welfare Changesces techniques, where preferences are inferred from what is observed on existing markets. For instance, the hedonic pricing method values the implicit price of non-market goods, e.g., proximity of a school or air quality, from their impact on real estate market prices (Sect. .). In the same vein, th

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Cost Benefit Analysisash flows, assumptions, or the manner in which the evaluation is set up (Sects. . and .). One may also employ a mean-variance analysis to compare the performance and risk of each competing strategy (Sect. .). Applications and examples, provided on spreadsheets and R-CRAN, will enable the reader to t

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Cost Effectiveness Analysislth outcome of a medical intervention is assessed by quality-adjusted life years (Sect. 10.6). Section 10.7 debates the uncertainty surrounding decision analytic models and uses Monte Carlo simulations to address parameter uncertainty. Section 10.8 shows how to analyze simulation outputs on the diff

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Project Follow-Up by Benchmarkingwithin the production structure. A simple reorganization of the data allows it (Sect. 12.5). Last, the method can be used to motivate operations improvement or to help a decision-maker understand where the performance falls in comparison to others (Sect. 12.6).