TESTING 'New Mexico' ED DATA FOR AUTOCORRELATION
FORECASTING MODEL: Using PRE-TRANSITION Trend-Cycle Component data points with TIME as predictor

INTERPRETING DIAGNOSTIC GRAPHS:
    **RES: (Residuals over time) If the data are independent, residuals should look randomly scattered about 0. If a noticeable pattern emerges (particularly one that is cyclical) then dependency is likely an issue.
    **FITPLOT: (Predicted and actual values over time) Potential patterns in the residuals can also be seen on this plot
    **ACF: (ACF versus the lag) If you see large ACF values and a non-random pattern, then likely the values are serially correlated.
    **PACF: (PACF versus the lag) The pattern will usually appear random, but large PACF values at a given non-zero lag indicate this value as a possible choice for the order of an autoregressive model.
Variable Name=H1

Dependent Variable TCC_PRE


TESTING 'New Mexico' ED DATA FOR AUTOCORRELATION
FORECASTING MODEL: Using PRE-TRANSITION Trend-Cycle Component data points with TIME as predictor
Durbin-Watson Test and Graphical Diagnostics

The AUTOREG Procedure

Variable Name=H1

Ordinary Least Squares Estimates
SSE 19840.689 DFE 61
MSE 325.25720 Root MSE 18.03489
SBC 549.470914 AIC 545.184644
MAE 15.0240922 AICC 545.384644
MAPE 3.40945714 HQC 546.870455
    Regress R-Square 0.0687
    Total R-Square 0.0687

Durbin-Watson Statistics
Order DW Pr < DW Pr > DW
1 0.0643 <.0001 1.0000
2 0.2385 <.0001 1.0000
3 0.5034 <.0001 1.0000
4 0.8349 <.0001 1.0000
5 1.1931 0.0016 0.9984
6 1.5513 0.1011 0.8989
7 1.8928 0.5957 0.4043
8 2.1830 0.9384 0.0616
9 2.4053 0.9952 0.0048
10 2.5600 0.9997 0.0003
11 2.6250 0.9999 <.0001
12 2.5978 0.9999 <.0001

NOTE: Pr<DW is the p-value for testing positive autocorrelation, and Pr>DW is the p-value for testing negative autocorrelation.


Parameter Estimates
Variable DF Estimate Standard
Error		 t Value Approx
Pr > |t|
Intercept 1 449.5099 5.3769 83.60 <.0001
TIME 1 -0.2651 0.1250 -2.12 0.0379