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Jeff Phang
ARE 106 HW 7
#1 Use an XY scatter plot to plot TIME! on the X axis and GT4 on the vertical axis. Print
this plot and paste it into your homework. Examine the plot: what do you see?
10
8
6
4
2
0
2
4
0
50000
100000
150000
200000
250000
300000
350000
400000
GT4
TIME
There are systematic fluctuations of positive and negative trends with a spike in GT4
followed by a drop in GT4 every 100000 time units around the mean of approx 5. This is
an example of auto correlation. Most of the observations are negative.
2. Run an Ordinary Least Squares (OLS) regression relating GT4 as the dependent
variable to a constant and TIME as the independent variables, and report the results in
standard format.
a. Plot the Fitted and Actual against TIME. Briefly, does the model do a good or bad job in
fitting the data?
Model 1: OLS estimates using the 3311 observations 20015311
Dependent variable: GT4
coefficient
std. error
tratio
pvalue

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4.63101
0.0846234
54.72
0.000
***
TIME
7.84995E07
4.98672E07
1.574
0.1155
Mean of dependent variable = 4.52391
Standard deviation of dep. var. = 2.89627
Sum of squared residuals = 27744.8
Standard error of the regression = 2.89563
Unadjusted Rsquared = 0.00075
Adjusted Rsquared = 0.00045
Degrees of freedom = 3309
DurbinWatson statistic = 0.0179302
Firstorder autocorrelation coeff. = 0.991011
Loglikelihood = 8217.36
Akaike information criterion (AIC) = 16438.7
Schwarz Bayesian criterion (BIC) = 16450.9
HannanQuinn criterion (HQC) = 16443.1
10
8
6
4
2
0
2
4
0
50000
100000
150000
200000
250000
300000
350000
400000
GT4
TIME
Actual and fitted GT4 versus TIME
actual
fitted
GT4
hat
= 4.63101 + 7.84995E07 (TIME)
R
2
= 0.00075
(
54.72) (1.574)
The Model does not do a good job estimating the data. The R
2
of
0.00075 indicates that the
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This note was uploaded on 04/11/2009 for the course ARE 106 taught by Professor Havenner during the Winter '09 term at UC Davis.
 Winter '09
 Havenner

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