By Carroll Croarkin
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Additional resources for Engineering statistics handbook
Xk). The independent variables can be continuous. , xk) + error Output: A good model/prediction equation relating Y to the factors. 7. General Problem Categories Time Series and Multivariate ● Least Squares Fitting ● Contour Plot Techniques: ● Least Squares Fitting ● Scatter Plot ● 6-Plot TIME SERIES MULTIVARIATE Data: Data: A column of time dependent numbers, Y. In addition, time is an indpendent variable. The time variable can be either explicit or implied. If the data are not equi-spaced, the time variable should be explicitly provided.
Underlying Assumptions Assumptions Underlying a Measurement Process There are four assumptions that typically underlie all measurement processes; namely, that the data from the process at hand "behave like": 1. random drawings; 2. from a fixed distribution; 3. with the distribution having fixed location; and 4. with the distribution having fixed variation. Univariate or Single Response Variable The "fixed location" referred to in item 3 above differs for different problem types. The simplest problem type is univariate; that is, a single variable.
In addition, there is no apparent pattern (such as the first twenty-five being positive and the second twenty-five being negative). This is the abscence of a pattern we expect to see if the data are in fact random. A few lags slightly outside the 95% and 99% confidence limits do not neccessarily indicate non-randomness. For a 95% confidence interval, we might expect about one out of twenty lags to be statistically significant due to random fluctuations. There is no associative ability to infer from a current value Yi as to what the next value Yi+1 will be.
Engineering statistics handbook by Carroll Croarkin