95,0% Confidence intervals for coefficient estimates
-----------------------------------------------------------------------------
Standard
Parameter Estimate Error Lower Limit Upper Limit
-----------------------------------------------------------------------------
CONSTANT 37,8089 6,4945 24,4332 51,1846
t -6,05758 2,80081 -11,826 -0,289188
t^2 1,81869 0,359059 1,07919 2,55818
t^3 -0,114605 0,0172693 -0,150172 -0,0790381
t^4 0,00203425 0,000276526 0,00146473 0,00260376
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Приложение № 3
|
t |
(t) |
|
|
(t) |
-0,3961 |
v(t) |
T(t) |
|
1 |
-3,4574 |
|
|
-3,4574 |
- |
1 |
1 |
|
2 |
5,3158 |
|
|
5,3158 |
+ |
2 |
1 |
|
3 |
-1,1748 |
|
|
-1,1748 |
- |
3 |
1 |
|
4 |
0,8364 |
|
|
0,8364 |
+ |
|
|
|
5 |
1,0661 |
|
|
1,0661 |
+ |
4 |
2 |
|
6 |
-1,1178 |
|
|
-1,1178 |
- |
5 |
1 |
|
7 |
-0,3961 |
|
|
-0,3961 |
|
|
|
|
8 |
-1,4986 |
|
|
-1,4986 |
- |
|
|
|
9 |
-3,0038 |
|
|
-3,0038 |
- |
6 |
2 |
|
10 |
2,7609 |
|
|
2,7609 |
+ |
7 |
1 |
|
11 |
-4,5806 |
|
|
-4,5806 |
- |
8 |
2 |
|
12 |
0,6467 |
|
|
0,6467 |
+ |
|
|
|
13 |
6,5690 |
|
|
6,5690 |
+ |
|
|
|
14 |
8,1635 |
|
|
8,1635 |
+ |
9 |
3 |
|
15 |
-12,7411 |
|
|
-12,7411 |
- |
|
|
|
16 |
-6,1652 |
|
|
-6,1652 |
- |
10 |
2 |
|
17 |
0,6222 |
|
|
0,6222 |
+ |
|
|
|
18 |
15,6030 |
|
|
15,6030 |
+ |
|
|
|
19 |
6,3107 |
|
|
6,3107 |
+ |
11 |
3 |
|
20 |
-7,0705 |
|
|
-7,0705 |
- |
|
|
|
21 |
-5,9049 |
|
|
-5,9049 |
- |
|
|
|
22 |
-4,2058 |
|
|
-4,2058 |
- |
|
|
|
23 |
-1,5351 |
|
|
-1,5351 |
- |
12 |
4 |
|
24 |
0,9963 |
|
|
0,9963 |
+ |
|
|
|
25 |
1,7286 |
|
|
1,7286 |
+ |
13 |
2 |
|
|
|
|
|
|
|
|
|
|
|
|
Медиана = |
-0,3961 |
Табличные = |
13 |
4 | |
|
|
|
|
|
|
Расчетные = |
8,199 |
7,91 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Приложение № 4
SINGLE-SERIES ARIMA RESULTS
Variable: RES_25
Model: (2,0,0)
Estimation: Approximate Max. Likelihood Method (McLeod & Sales)
No. of obs.: 25 Initial SS=787,82 Final SS=587,52 (74,58%) MS= 26,705
Parameters (p/Ps=Autoregressive/seasonal, q/Qs=Moving average/seasonal)
STAT. Input: RES_25 (dz1.sta)
TIME Transformations: none
SERIES Model:(2,0,0) MS Residual=25,544
|
|
|
Asympt. |
Asympt. |
|
Lower |
Upper |
|
Paramet. |
Param. |
Std.Err. |
t( 23) |
p |
95% Conf |
95% Conf |
|
p(1) |
,228372 |
,186638 |
1,22361 |
,233487 |
-,157718 |
,614462 |
|
p(2) |
-,487029* |
,186756* |
-2,60783* |
,015731* |
-,873363* |
-,100694* |
tТАБЛ(0,05;23)=2,07
STAT. Parameter Covariances (dz1.sta)
TIME Input: RES_25
SERIES Model:(2,0,0) MS Residual=25,544
|
Paramet. |
p(1) |
p(2) |
|
p(1) |
,034834 |
-,005305 |
|
p(2) |
-,005305 |
,034878 |
STAT. Parameter Correlations (dz1.sta)
TIME Input: RES_25
SERIES Model:(2,0,0) MS Residual=25,544
|
Paramet. |
p(1) |
p(2) |
|
p(1) |
1,000000 |
-,152197 |
|
p(2) |
-,152197 |
1,000000 |
SINGLE-SERIES ARIMA RESULTS
Variable: RES_25
Model: (3,0,0)
Estimation: Approximate Max. Likelihood Method (McLeod & Sales)
No. of obs.: 25 Initial SS=787,82 Final SS=455,74 (57,85%) MS= 21,702
Parameters (p/Ps=Autoregressive/seasonal, q/Qs=Moving average/seasonal)
STAT. Input: RES_25 (dz1.sta)
TIME Transformations: none
SERIES Model:(3,0,0) MS Residual=20,715
|
Paramet. |
Param. |
Asympt. Std.Err. |
Asympt. t( 22) |
p |
Lower 95% Conf |
Upper 95% Conf |
|
p(1) |
-,001033 |
,191756 |
-,00539 |
,995750 |
-,398710 |
,396644 |
|
p(2) |
-,378823* |
,173957* |
-2,17769* |
,040433* |
-,739587* |
-,018060* |
|
p(3) |
-,475015* |
,192644* |
-2,46576* |
,021944* |
-,874535* |
-,075495* |
tТАБЛ(0,05;22)=2,07
STAT. Parameter Covariances (dz1.sta)
TIME Input: RES_25
SERIES Model:(3,0,0) MS Residual=20,715
|
Paramet. |
p(1) |
p(2) |
p(3) |
|
p(1) |
,036770 |
-,008353 |
,017848 |
|
p(2) |
-,008353 |
,030261 |
-,008442 |
|
p(3) |
,017848 |
-,008442 |
,037112 |
STAT. Parameter Correlations (dz1.sta)
TIME Input: RES_25
SERIES Model:(3,0,0) MS Residual=20,715
|
Paramet. |
p(1) |
p(2) |
p(3) |
|
p(1) |
1,000000 |
-,250404 |
,483141 |
|
p(2) |
-,250404 |
1,000000 |
-,251923 |
|
p(3) |
,483141 |
-,251923 |
1,000000 |
Приложение № 5
Multiple Regression Analysis
-----------------------------------------------------------------------------
Dependent variable: RES_25
-----------------------------------------------------------------------------
Standard T
Parameter Estimate Error Statistic P-Value
-----------------------------------------------------------------------------
res_t2 -0,452249 0,182637 -2,47622 0,0207
-----------------------------------------------------------------------------
tтабл(0,05;24)=2,06
Analysis of Variance
-----------------------------------------------------------------------------
Source Sum of Squares Df Mean Square F-Ratio P-Value
-----------------------------------------------------------------------------
Model 160,317 1 160,317 6,13 0,0207
Residual 627,499 24 26,1458
-----------------------------------------------------------------------------
Total 787,816 25
Fтабл(0,05;1;24)=4,26
R-squared = 20,3496 percent
R-squared (adjusted for d.f.) = 20,3496 percent
Standard Error of Est. = 5,11329
Mean absolute error = 3,903
Durbin-Watson statistic = 1,94298
The StatAdvisor
---------------
The output shows the results of fitting a multiple linear
regression model to describe the relationship between RES_25 and 1
independent variables. The equation of the fitted model is
RES_25 = 0,452249*res_t2
Since the P-value in the ANOVA table is less than 0.05, there is a
statistically significant relationship between the variables at the
95% confidence level.
The R-Squared statistic indicates that the model as fitted
explains 20,3496% of the variability in RES_25. The adjusted
R-squared statistic, which is more suitable for comparing models with
different numbers of independent variables, is 20,3496%. The standard
error of the estimate shows the standard deviation of the residuals to
be 5,11329. This value can be used to construct prediction limits for
new observations by selecting the Reports option from the text menu.
The mean absolute error (MAE) of 3,903 is the average value of the
residuals. The Durbin-Watson (DW) statistic tests the residuals to
determine if there is any significant correlation based on the order
in which they occur in your data file. Since the DW value is greater
than 1.4, there is probably not any serious autocorrelation in the
residuals.
In determining whether the model can be simplified, notice that the
highest P-value on the independent variables is 0,0207, belonging to
res_t2. Since the P-value is less than 0.05, that term is
statistically significant at the 95% confidence level. Consequently,
you probably don't want to remove any variables from the model.