Thermatrix - Case Study

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Introduction In-Process Technology was set up in 1985 to produce and sell a thermal oxidation process that could be used to reduce industrial pollution. For years, however, the company performed dismally. In 1992, the current CEO was hired to turn things around, and the company was reorganized and renamed Thermatrix. Eventually, Thermatrix was able to attract good employees using stock options and other competitive compensation and currently has 60 employees and annual sales of approximately $15 million. Having grown and flourished because of its good customer relationships, which include partnering, delivering a quality product on time and listening to the customer’s needs, Thermatrix management recognizes that one of the main keys to its…show more content…
For the first question, then, p = 63/115 = 0.5478. This means that roughly 55% of surveyed customers agree that deliveries were on time. Whether we can use this inference as a reasonable approximation for the true population value, however, depends on the distribution of p. To do this, we look to see whether the sample size is sufficiently large enough such that np ≥ 5 and n(1-p) ≥ 5. Still considering the first question, both 115(.55) = 63.25 ≥ 5 and 115(.45) = 51.75 ≥ 5, so we can safely conclude that the sampling distribution may be approximated by a normal distribution centered at p. It may seem that we should just report our point estimate, p, as our population proportion. However, only 115 customers are accounted for out of what we can assume to be a much larger customer base. Accordingly, we must acknowledge that we should expect sampling error, which means that the 115 customers surveyed do not adequately represent the customer satisfaction of the greater population of customers. Although we cannot eliminate this potential error, we deal with it by communicating the reliability of our estimate by calculating an interval estimate known as a confidence interval. Our confidence interval will afford management with a sense of how reliable this data is and how confident they can be that the range provided contains the population proportion. The critical value

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