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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.](https://content.bartleby.com/qna-images/question/f9f2fdbe-c6f4-4010-82ae-6e605629d3a7/1d1429ef-fd22-4a37-843f-1b91c001d06c/453a26e_thumbnail.png)
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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