MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
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An article in Air and Waste ["Update on Ozone Trends in California's South Coast Air Basin" (Vol. 43, 1993)] studied the ozone levels on the South Coast air basin of California for the years 1976–1991. The author believes that the number of days that the ozone level exceeds 0.20 parts per million depends on the seasonal meteorological index (the seasonal average 850 millibar temperature). The data follow: Year Days Index Year Days Index 1976 91 16.7 1984 81 18.0 1977 105 17.1 1985 65 17.2 1978 106 18.2 1986 61 16.9 1979 108 18.1 1987 48 17.1 1980 88 17.2 1988 61 18.2 1981 91 18.2 1989 43 17.3 1982 58 16.0 1990 33 17.5 1983 82 17.2 1991 36 16.6 1. Assuming that a simple linear regression model is appropriate, obtain the least squares fit relating number of days and seasonal meteorological index. 2. What is the estimate of o?? 3. Determine the fitted values and the residuals. 4. What change in the number of days is associated with an increase of 1.5 of the index? 5. What change in the index is associated with a decrease of 10 days? 6. Test for the significance of the intercept and the slope using t-test with a=0.05. 7. Test for significance of regression using a=0.05. What conclusions can you draw? 8. Test for the adequacy of the regression model using the three methods.
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Transcribed Image Text:An article in Air and Waste ["Update on Ozone Trends in California's South Coast Air Basin" (Vol. 43, 1993)] studied the ozone levels on the South Coast air basin of California for the years 1976–1991. The author believes that the number of days that the ozone level exceeds 0.20 parts per million depends on the seasonal meteorological index (the seasonal average 850 millibar temperature). The data follow: Year Days Index Year Days Index 1976 91 16.7 1984 81 18.0 1977 105 17.1 1985 65 17.2 1978 106 18.2 1986 61 16.9 1979 108 18.1 1987 48 17.1 1980 88 17.2 1988 61 18.2 1981 91 18.2 1989 43 17.3 1982 58 16.0 1990 33 17.5 1983 82 17.2 1991 36 16.6 1. Assuming that a simple linear regression model is appropriate, obtain the least squares fit relating number of days and seasonal meteorological index. 2. What is the estimate of o?? 3. Determine the fitted values and the residuals. 4. What change in the number of days is associated with an increase of 1.5 of the index? 5. What change in the index is associated with a decrease of 10 days? 6. Test for the significance of the intercept and the slope using t-test with a=0.05. 7. Test for significance of regression using a=0.05. What conclusions can you draw? 8. Test for the adequacy of the regression model using the three methods.
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