Let t = 1 to refer to the observation in hour 1 on July 15; t = 2 to refer to th
ID: 3124682 • Letter: L
Question
Let t = 1 to refer to the observation in hour 1 on July 15; t = 2 to refer to the observation in hour 2 of July 15; …; and t = 36 to refer to the observation in hour 12 of July 17. Using the dummy variables defined in part (b) and t, develop an equation to account for seasonal effects and any linear trend in the time series. Based upon the seasonal effects in the data and linear trend, compute estimates of the levels of nitrogen dioxide for July 18.
I NEED THE DATA CHART CREATED SO I CAN GET THE REGRESSION ANALYSIS. I NEED HELP SETTING UP THE CHART FOR THIS PROBLEM. HERE IS WHAT I HAVE FOR QUESTION B. (COMPLETED ALREADY.)
Hour1= 1 if the reading was made between 6:00 A.M. and 7:00 A.M.; 0 otherwise
Hour2= 1 if the reading was made between 7:00 A.M. and 8:00 A.M.; 0 otherwise
.
.
.
Hour11= 1 if the reading was made between 4:00 P.M. and 5:00 P.M.; 0 otherwise
Note that when the values of the 11 dummy variables are equal to 0, the observation corresponds to the 5:00 P.M. to 6:00 P.M. hour
Explanation / Answer
Regression Analysis
R²
0.954
Adjusted R²
0.931
n
36
R
0.977
k
12
Std. Error
4.245
Dep. Var.
score
ANOVA table
Source
SS
df
MS
F
p-value
Regression
8,663.7222
12
721.9769
40.06
1.62E-12
Residual
414.5000
23
18.0217
Total
9,078.2222
35
Regression output
confidence interval
variables
coefficients
std. error
t (df=23)
p-value
95% lower
95% upper
Intercept
11.1667
3.0018
3.720
.0011
4.9569
17.3764
t1
12.4792
3.5560
3.509
.0019
5.1229
19.8354
t2
16.0417
3.5406
4.531
.0001
8.7173
23.3660
t3
20.6042
3.5266
5.843
5.92E-06
13.3088
27.8995
t4
37.8333
3.5140
10.766
1.86E-10
30.5641
45.1026
t5
45.3958
3.5029
12.960
4.69E-12
38.1496
52.6420
t6
47.6250
3.4932
13.634
1.66E-12
40.3988
54.8512
t7
30.5208
3.4849
8.758
8.77E-09
23.3117
37.7300
t8
20.0833
3.4782
5.774
6.99E-06
12.8881
27.2786
t9
14.6458
3.4730
4.217
.0003
7.4615
21.8302
t10
4.2083
3.4692
1.213
.2374
-2.9683
11.3849
t11
2.1042
3.4669
0.607
.5498
-5.0678
9.2761
time
0.4375
0.0722
6.059
3.53E-06
0.2881
0.5869
Predictions
Predicted values for: score
95% Confidence Intervals
95% Prediction Intervals
t1
t2
t3
t4
t5
t6
t7
t8
t9
t10
t11
time
Predicted
lower
upper
lower
upper
1
0
0
0
0
0
0
0
0
0
0
37
39.833
33.624
46.043
29.078
50.589
0
1
0
0
0
0
0
0
0
0
0
38
43.833
37.624
50.043
33.078
54.589
0
0
1
0
0
0
0
0
0
0
0
39
48.833
42.624
55.043
38.078
59.589
0
0
0
1
0
0
0
0
0
0
0
40
66.500
60.290
72.710
55.744
77.256
0
0
0
0
1
0
0
0
0
0
0
41
74.500
68.290
80.710
63.744
85.256
0
0
0
0
0
1
0
0
0
0
0
42
77.167
70.957
83.376
66.411
87.922
0
0
0
0
0
0
1
0
0
0
0
43
60.500
54.290
66.710
49.744
71.256
0
0
0
0
0
0
0
1
0
0
0
44
50.500
44.290
56.710
39.744
61.256
0
0
0
0
0
0
0
0
1
0
0
45
45.500
39.290
51.710
34.744
56.256
0
0
0
0
0
0
0
0
0
1
0
46
35.500
29.290
41.710
24.744
46.256
0
0
0
0
0
0
0
0
0
0
1
47
33.833
27.624
40.043
23.078
44.589
0
0
0
0
0
0
0
0
0
0
0
48
32.167
25.957
38.376
21.411
42.922
data chart
Regression Analysis
R²
0.954
Adjusted R²
0.931
n
36
R
0.977
k
12
Std. Error
4.245
Dep. Var.
score
ANOVA table
Source
SS
df
MS
F
p-value
Regression
8,663.7222
12
721.9769
40.06
1.62E-12
Residual
414.5000
23
18.0217
Total
9,078.2222
35
Regression output
confidence interval
variables
coefficients
std. error
t (df=23)
p-value
95% lower
95% upper
Intercept
11.1667
3.0018
3.720
.0011
4.9569
17.3764
t1
12.4792
3.5560
3.509
.0019
5.1229
19.8354
t2
16.0417
3.5406
4.531
.0001
8.7173
23.3660
t3
20.6042
3.5266
5.843
5.92E-06
13.3088
27.8995
t4
37.8333
3.5140
10.766
1.86E-10
30.5641
45.1026
t5
45.3958
3.5029
12.960
4.69E-12
38.1496
52.6420
t6
47.6250
3.4932
13.634
1.66E-12
40.3988
54.8512
t7
30.5208
3.4849
8.758
8.77E-09
23.3117
37.7300
t8
20.0833
3.4782
5.774
6.99E-06
12.8881
27.2786
t9
14.6458
3.4730
4.217
.0003
7.4615
21.8302
t10
4.2083
3.4692
1.213
.2374
-2.9683
11.3849
t11
2.1042
3.4669
0.607
.5498
-5.0678
9.2761
time
0.4375
0.0722
6.059
3.53E-06
0.2881
0.5869
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