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Engineering Statistics Handbook Episode 5 Part 10

Tham khảo tài liệu 'engineering statistics handbook episode 5 part 10', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 4.4.2.3. Using Methods that Do Not Require Function Specification ENGINEERING STATISTICS HANDBOOK home TOOLS Ậ AIDS SEARCH BACK next 4. Process Modeling 4.4. Data Analysis for Process Modeling 4.4.2. How do I select a function to describe my process 4.4.2.3. Using Methods that Do Not Require Function Functional Form Not Needed but Some Input Required Although many modern regression methods like LOESS do not require the user to specify a single type of function to fit the entire data set some initial information still usually needs to be provided by the user. Because most of these types of regression methods fit a series of simple local models to the data one quantity that usually must be specified is the size of the neighborhood each simple function will describe. This type of parameter is usually called the bandwidth or smoothing parameter for the method. For some methods the form of the simple functions must also be specified while for others the functional form is a fixed property of the method. Input Parameters Control Function Shape The smoothing parameter controls how flexible the functional part of the model will be. This in turn controls how closely the function will fit the data just as the choice of a straight line or a polynomial of higher degree determines how closely a traditional regression model will track the deterministic structure in a set of data. The exact information that must be specified in order to fit the regression function to the data will vary from method to method. Some methods may require other user-specified parameters require in addition to a smoothing parameter to fit the regression function. However the purpose of the user-supplied information is similar for all methods. Starting Simple still Best As for more traditional methods of regression simple regression functions are better than complicated ones in local regression. The complexity of a regression function can be gauged by its potential to track the data. With traditional .