Computerised analyses of estimating inaccuracy and tender variability
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Computerised analyses of estimating inaccuracy and tender variability causes, evolution and consequences by Refaat Hassan Abdel-Razek

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Published .
Written in English


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Edition Notes

Thesis(Ph.D.) - Loughborough University of Technology, 1987.

Statementby Refaat Hassan Abdel-Razek.
ID Numbers
Open LibraryOL19652636M

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Start studying Chapter 14 Time Series Forecasting Book Notes. Learn vocabulary, terms, and more with flashcards, games, and other study tools.   In a new study, MIT researchers have developed a novel approach to analyzing time series data sets using a new algorithm, termed state-space multitaper time-frequency analysis (SS-MT).SS-MT provides a framework to analyze time series data in real-time, enabling researchers to work in a more informed way with large sets of data that are nonstationary, i.e. when their characteristics . parameters of the time series regression model. i. But since we don’t a problem arises. D. The consequences of autocorrelation. 1. Recall that an estimator is unbiased if its expected value equals the population parameter it is estimating. i. Example: the mean is an unbiased estimator of the population mean because 2. 1. Introduction. Presently there are about systems for automated image analysis. Since the late ’s the scientific community of automated mineralogy has expressed the need of performing round robin testing that would help to check the variability that may arise from the use of different by: 5.