# Data Analysis Using the Least-Squares Method by John Wolberg

By John Wolberg

The popular approach to information research of quantitative experiments is the strategy of least squares. frequently, even if, the entire strength of the strategy is ignored and intensely few books take care of this topic on the point that it merits. the aim of knowledge research utilizing the strategy of Least Squares is to fill this hole and contain the kind of details required to assist scientists and engineers observe the strategy to difficulties of their designated fields of curiosity. furthermore, graduate scholars in technological know-how and engineering doing paintings of experimental nature can take advantage of this ebook. quite, either linear and non-linear least squares, using experimental errors estimates for facts weighting, systems to incorporate past estimates, technique for choosing and checking out types, prediction research, and a few non-parametric tools are mentioned.

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Additional resources for Data Analysis Using the Least-Squares Method

Sample text

I discussed this with my thesis advisors and we agreed that one or both of the experiments was plagued by systematic errors that biased the results in a particular direction. We were proposing a new method which we felt was much less prone to systematic errors. One of the basic assumptions mentioned in the previous section is that the errors in the data are random about the true values. In other words, if a measurement is repeated n times, the average value would approach the true value as n approaches infinity.

Davidian and Giltinan discuss problem in the biostatistics field in which repeated data measurements are taken. For example, in clinical trials, data might be taken for many different patients over a fixed time period. For such problems we can use the term Yijj to represent the measurement at time ti for patient j. Clearly it is reasonable to assume that εijj is correlated with the error at time ti+1 for the same patient. In this book, no attempt is made to treat such problems. Many statistical textbooks include discussions of the method of least squares but use the assumption that all the σ i’s are equal.

Another area that is subject to similar problems is the modeling of insurance claims. Most of the data represents relatively small claims but there are usually a small fraction of claims that are much larger, negating the assumption of normality. In this book such problems are not considered. , uncorrelated errors) is invalid? There are areas of science and engineering where this assumption is not really reasonable and therefore the method of least squares must be modified to take error correlation into consideration [DA95].