Statistics for Environmental Engineers

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The pitfalls inherent with autocorrelated errors provide a strong incentive to plan experiments to include proper randomization whenever possible. If an experiment is intended to define a relationship between x and y, the experiments should not be conducted by gradually increasing (or decreasing) the x’s. Randomize over the settings of x to eliminate autocorrelation due to time effects in the experiments.


Chapter 51 discusses how to deal with serial correlation.

References


Box, G. E. P., W. G. Hunter, and J. S. Hunter (1978). Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building, New York, Wiley Interscience.


Durbin, J. and G. S. Watson (1951). “Testing for Serial Correlation in Least Squares Regression, II,” Bio-metrika, 38, 159-178.


Durbin, J. and G. S. Watson (1971). “Testing for Serial Correlation in Least Squares Regression, III,” Biometrika, 58, 1-19.


Neter, J., W. Wasserman, and M. H. Kutner (1983). Applied Regression Models, Homewood, IL, Richard D. Irwin Co.

Exercises


41.1 Blood Lead. The data below relate the lead level measured in the umbilical cord blood of infants born in a Boston hospital in 1980 and 1981 to the total amount of leaded gasoline sold in Massachusetts in the same months. Do you think autocorrelation might be a problem in this data set? Do you think the blood levels are related directly to the gasoline sales in the month of birth, or to gasoline sales in the previous several months? How would this influence your model building strategy?


Month


Year


Leaded Gasoline Sold

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