TESTING 'New Mexico' ED DATA FOR AUTOCORRELATION
ITS MODEL: Using ALL Trend-Cycle Component data points with PRESLOPE, INTERVENTION, & POSTSLOPE as predictors

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.
    **PFITPLOT: (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 lag indicate this value as a possible choice for the order of an autoregressive model.
Variable Name=H1

Dependent Variable TCC
  Trend-Cycle Component



TESTING 'New Mexico' ED DATA FOR AUTOCORRELATION
ITS MODEL: Using ALL Trend-Cycle Component data points with PRESLOPE, INTERVENTION, & POSTSLOPE as predictors
Durbin-Watson Test and Graphical Diagnostics

The AUTOREG Procedure

Variable Name=H1

Ordinary Least Squares Estimates
SSE 19944.0258 DFE 68
MSE 293.29450 Root MSE 17.12584
SBC 626.363167 AIC 617.256502
MAE 13.509841 AICC 617.853517
MAPE 3.07764963 HQC 620.881892
    Regress R-Square 0.5290
    Total R-Square 0.5290

Durbin-Watson Statistics
Order DW Pr < DW Pr > DW
1 0.0941 <.0001 1.0000
2 0.2980 <.0001 1.0000
3 0.5903 <.0001 1.0000
4 0.9378 <.0001 1.0000
5 1.2945 0.0029 0.9971
6 1.6415 0.1448 0.8552
7 1.9690 0.6788 0.3212
8 2.2558 0.9638 0.0362
9 2.4956 0.9983 0.0017
10 2.6284 0.9999 0.0001
11 2.6845 1.0000 <.0001
12 2.6566 1.0000 <.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 1 449.2448 4.9986 89.87 <.0001
PRESLOPE 1 -0.2651 0.1187 -2.23 0.0287
INTERVENTION 1 -20.9603 13.1522 -1.59 0.1156
POSTSLOPE 1 -4.2111 2.2109 -1.90 0.0611



TESTING 'New Mexico' ED DATA FOR AUTOCORRELATION
ITS MODEL: Using ALL Trend-Cycle Component data points with PRESLOPE, INTERVENTION, & POSTSLOPE as predictors
Durbin-Watson Test and Graphical Diagnostics

The AUTOREG Procedure

Variable Name=H1

Fit Diagnostics for TCC



TESTING 'New Mexico' ED DATA FOR AUTOCORRELATION
ITS MODEL: Using ALL Trend-Cycle Component data points with PRESLOPE, INTERVENTION, & POSTSLOPE as predictors
Durbin-Watson Test and Graphical Diagnostics

The AUTOREG Procedure

Variable Name=H2

Dependent Variable TCC
  Trend-Cycle Component



TESTING 'New Mexico' ED DATA FOR AUTOCORRELATION
ITS MODEL: Using ALL Trend-Cycle Component data points with PRESLOPE, INTERVENTION, & POSTSLOPE as predictors
Durbin-Watson Test and Graphical Diagnostics

The AUTOREG Procedure

Variable Name=H2

Ordinary Least Squares Estimates
SSE 441.032389 DFE 68
MSE 6.48577 Root MSE 2.54672
SBC 351.930371 AIC 342.823707
MAE 1.90082369 AICC 343.420722
MAPE 4.15834553 HQC 346.449097
    Regress R-Square 0.8232
    Total R-Square 0.8232

Durbin-Watson Statistics
Order DW Pr < DW Pr > DW
1 0.3567 <.0001 1.0000
2 0.7141 <.0001 1.0000
3 0.9529 <.0001 1.0000
4 1.1348 0.0001 0.9999
5 1.2944 0.0029 0.9971
6 1.3674 0.0116 0.9884
7 1.4636 0.0440 0.9560
8 1.6726 0.2471 0.7529
9 1.8656 0.5897 0.4103
10 1.7537 0.4517 0.5483
11 1.6480 0.3273 0.6727
12 1.5794 0.2688 0.7312

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 43.7566 0.7433 58.87 <.0001
PRESLOPE 1 0.0129 0.0176 0.73 0.4685
INTERVENTION 1 11.4670 1.9558 5.86 <.0001
POSTSLOPE 1 0.8222 0.3288 2.50 0.0148



TESTING 'New Mexico' ED DATA FOR AUTOCORRELATION
ITS MODEL: Using ALL Trend-Cycle Component data points with PRESLOPE, INTERVENTION, & POSTSLOPE as predictors
Durbin-Watson Test and Graphical Diagnostics

The AUTOREG Procedure

Variable Name=H2

Fit Diagnostics for TCC



TESTING 'New Mexico' ED DATA FOR AUTOCORRELATION
ITS MODEL: Using ALL Trend-Cycle Component data points with PRESLOPE, INTERVENTION, & POSTSLOPE as predictors
Durbin-Watson Test and Graphical Diagnostics

The AUTOREG Procedure

Variable Name=H3

Dependent Variable TCC
  Trend-Cycle Component



TESTING 'New Mexico' ED DATA FOR AUTOCORRELATION
ITS MODEL: Using ALL Trend-Cycle Component data points with PRESLOPE, INTERVENTION, & POSTSLOPE as predictors
Durbin-Watson Test and Graphical Diagnostics

The AUTOREG Procedure

Variable Name=H3

Ordinary Least Squares Estimates
SSE 187.946525 DFE 68
MSE 2.76392 Root MSE 1.66250
SBC 290.517191 AIC 281.410527
MAE 1.33571013 AICC 282.007542
MAPE 3.36027311 HQC 285.035917
    Regress R-Square 0.9335
    Total R-Square 0.9335

Durbin-Watson Statistics
Order DW Pr < DW Pr > DW
1 0.2414 <.0001 1.0000
2 0.5540 <.0001 1.0000
3 0.7952 <.0001 1.0000
4 1.0382 <.0001 1.0000
5 1.2819 0.0024 0.9976
6 1.4766 0.0376 0.9624
7 1.6434 0.1789 0.8211
8 1.7901 0.4280 0.5720
9 1.8364 0.5409 0.4591
10 1.9036 0.6969 0.3031
11 2.0356 0.8859 0.1141
12 2.0925 0.9428 0.0572

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 30.2054 0.4852 62.25 <.0001
PRESLOPE 1 0.2581 0.0115 22.41 <.0001
INTERVENTION 1 5.3305 1.2768 4.18 <.0001
POSTSLOPE 1 -0.1479 0.2146 -0.69 0.4931



TESTING 'New Mexico' ED DATA FOR AUTOCORRELATION
ITS MODEL: Using ALL Trend-Cycle Component data points with PRESLOPE, INTERVENTION, & POSTSLOPE as predictors
Durbin-Watson Test and Graphical Diagnostics

The AUTOREG Procedure

Variable Name=H3

Fit Diagnostics for TCC