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Forecasting statplus7/14/2023 ![]() ![]() Our purpose here is to present an overview of this field by discussing the way a company ought to approach a forecasting problem, describing the methods available, and explaining how to match method to problem. The availability of data and the possibility of establishing relationships between the factors depend directly on the maturity of a product, and hence the life-cycle stage is a prime determinant of the forecasting method to be used. ![]() This kind of trade-off is relatively easy to make, but others, as we shall see, require considerably more thought.įurthermore, where a company wishes to forecast with reference to a particular product, it must consider the stage of the product’s life cycle for which it is making the forecast. If the forecaster can readily apply one technique of acceptable accuracy, he or she should not try to “gold plate” by using a more advanced technique that offers potentially greater accuracy but that requires nonexistent information or information that is costly to obtain. ![]() In general, for example, the forecaster should choose a technique that makes the best use of available data. These factors must be weighed constantly, and on a variety of levels. The selection of a method depends on many factors-the context of the forecast, the relevance and availability of historical data, the degree of accuracy desirable, the time period to be forecast, the cost/benefit (or value) of the forecast to the company, and the time available for making the analysis. The manager as well as the forecaster has a role to play in technique selection and the better they understand the range of forecasting possibilities, the more likely it is that a company’s forecasting efforts will bear fruit. Each has its special use, and care must be taken to select the correct technique for a particular application. All rights reserved.To handle the increasing variety and complexity of managerial forecasting problems, many forecasting techniques have been developed in recent years. This variation did not appear to significantly influence LHIN-specific cataract surgery wait times.Ĭopyright © 2016 Canadian Ophthalmological Society. Although the number of cataract surgeries performed was positively correlated with the population aged 65 years and older (p < 0.001), there was no statistically significant association between wait times and number of cataract cases per 1000 population (p = 0.41).Īlthough Ontario appears to have a sufficient number of ophthalmologists overall, there is significant variation in the distribution of the ophthalmology workforce at the LHIN level. Median cataract surgery wait times ranged from 30 to 72 days. LHIN-specific ratios ranged from 8.87 (Toronto Central) to 1.67 (Central West), with 3 out of 14 LHINs having met the previously recommended ratio of 3.37. There are currently 3.28 ophthalmologists per 100 000 total population in Ontario. Statistical analysis was completed using Microsoft Excel using StatPlus software. ![]() Cataract surgery wait times were obtained from the Ontario Ministry of Health. The population counts for the population aged 65 years and older were generated using the Canadian Socioeconomic Information Management System (CANSIM) table 109-5425. The total population count for Ontario was obtained from the Statistics Canada census. Ophthalmologists listed in the College of Physicians and Surgeons (CPSO) database and the Canadian population.Ī list of ophthalmologists and their practice locations were obtained from the CPSO website. To determine the current distribution of ophthalmologists across Ontario's Local Health Integration Networks (LHINs) and the influence on LHIN-specific cataract surgery wait times. ![]()
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