Recordings of the levels of pollutants and various meteorological conditions are made hourly at several stations by the Los Angeles Pollution Control District. This agency attempts to construct mathematical/statistical models to predict pollution levels and to gain a better understanding of the complexities of air pollution. Obviously, very large quantities of data are collected and analyzed, but only a small set of data will be considered in this problem. The file airpollution contains the maximum level of an oxidant (a photochemical pollutant) and the morning averages of four meteorological variables: wind speed, temperature, humidity, and insolation (a measure of the amount of sunlight). The data cover 30 days during one summer.
a. Examine the relationship of oxidant level to each of the four meteorological variables and the relationships of the meteorological variables to each other. How well can the maximum level of oxidant be predicted from some or all of the meteorological variables? Which appear to be most important?
b. The standard statistical model used in this chapter assumes that the errors are random and independent of one another. In data that are collected over time, the error at any given time may well be correlated with the error from the preceding time. This phenomenon is called serial correlation, and in its presence the estimated standard errors of the coefficients developed in this chapter may be incorrect. The parameter estimates are still unbiased, however. (Why?) Can you detect serial correlation in the errors from your fits?
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