Multiple Linear Regression - Estimated Regression Equation |
SP[t] = -250.155 + 73.3043V[t] -0.171478SQ[t] + 27.6031BR[t] -78.6611PH[t] + 50.6011GR[t] -6.22991PK[t] + 8.44973AG[t] + 82.5753Re[t] + 1.15958As[t] + 137.4C[t] -25.7549TH[t] + e[t] |
Multiple Linear Regression - Ordinary Least Squares | |||||
Variable | Parameter | S.D. | T-STAT H0: parameter = 0 | 2-tail p-value | 1-tail p-value |
(Intercept) | -250.2 | 103.1 | -2.4270e+00 | 0.02597 | 0.01298 |
V | +73.3 | 49.59 | +1.4780e+00 | 0.1567 | 0.07834 |
SQ | -0.1715 | 0.09469 | -1.8110e+00 | 0.08687 | 0.04343 |
BR | +27.6 | 45.97 | +6.0050e-01 | 0.5556 | 0.2778 |
PH | -78.66 | 47.75 | -1.6470e+00 | 0.1168 | 0.05842 |
GR | +50.6 | 50.83 | +9.9540e-01 | 0.3327 | 0.1664 |
PK | -6.23 | 46.29 | -1.3460e-01 | 0.8944 | 0.4472 |
AG | +8.45 | 3.18 | +2.6570e+00 | 0.01605 | 0.008023 |
Re | +82.58 | 48.56 | +1.7010e+00 | 0.1062 | 0.05312 |
As | +1.16 | 0.1378 | +8.4140e+00 | 1.185e-07 | 5.925e-08 |
C | +137.4 | 42.22 | +3.2540e+00 | 0.004405 | 0.002202 |
TH | -25.75 | 70.41 | -3.6580e-01 | 0.7188 | 0.3594 |
Multiple Linear Regression - Regression Statistics | |
Multiple R | 0.9833 |
R-squared | 0.9669 |
Adjusted R-squared | 0.9467 |
F-TEST (value) | 47.86 |
F-TEST (DF numerator) | 11 |
F-TEST (DF denominator) | 18 |
p-value | 5.011e-11 |
Multiple Linear Regression - Residual Statistics | |
Residual Standard Deviation | 77.03 |
Sum Squared Residuals | 1.068e+05 |
Menu of Residual Diagnostics | |
Description | Link |
Histogram | Compute |
Central Tendency | Compute |
QQ Plot | Compute |
Kernel Density Plot | Compute |
Skewness/Kurtosis Test | Compute |
Skewness-Kurtosis Plot | Compute |
Harrell-Davis Plot | Compute |
Bootstrap Plot -- Central Tendency | Compute |
Blocked Bootstrap Plot -- Central Tendency | Compute |
(Partial) Autocorrelation Plot | Compute |
Spectral Analysis | Compute |
Tukey lambda PPCC Plot | Compute |
Box-Cox Normality Plot | Compute |
Summary Statistics | Compute |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
1 | 955 | 986.7 | -31.73 |
2 | 960 | 915.7 | 44.27 |
3 | 1156 | 1166 | -9.75 |
4 | 1433 | 1429 | 4.177 |
5 | 769 | 826.4 | -57.38 |
6 | 1310 | 1352 | -41.58 |
7 | 840 | 818.5 | 21.45 |
8 | 1590 | 1488 | 102.4 |
9 | 716 | 686.6 | 29.36 |
10 | 892 | 981.8 | -89.84 |
11 | 1717 | 1651 | 66.46 |
12 | 930 | 901.5 | 28.52 |
13 | 920 | 1029 | -109.3 |
14 | 838 | 796.6 | 41.39 |
15 | 1310 | 1192 | 117.6 |
16 | 1275 | 1293 | -17.88 |
17 | 1780 | 1792 | -12.03 |
18 | 1148 | 1112 | 36.01 |
19 | 900 | 924 | -23.98 |
20 | 839 | 824.5 | 14.5 |
21 | 1975 | 1986 | -10.56 |
22 | 865 | 905.4 | -40.42 |
23 | 1018 | 960.7 | 57.35 |
24 | 1550 | 1525 | 25.15 |
25 | 1190 | 1248 | -58.24 |
26 | 1182 | 1201 | -18.76 |
27 | 783 | 686.2 | 96.77 |
28 | 920 | 1070 | -149.6 |
29 | 788 | 761.2 | 26.76 |
30 | 1193 | 1234 | -41.16 |
Goldfeld-Quandt test for Heteroskedasticity | |||
p-values | Alternative Hypothesis | ||
breakpoint index | greater | 2-sided | less |
15 | 0.4679 | 0.9359 | 0.5321 |
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity | |||
Description | # significant tests | % significant tests | OK/NOK |
1% type I error level | 0 | 0 | OK |
5% type I error level | 0 | 0 | OK |
10% type I error level | 0 | 0 | OK |
Ramsey RESET F-Test for powers (2 and 3) of fitted values |
> reset_test_fitted RESET test data: mylm RESET = 7.285, df1 = 2, df2 = 16, p-value = 0.005631 |
Ramsey RESET F-Test for powers (2 and 3) of regressors |
> reset_test_regressors RESET test data: mylm RESET = -0.36952, df1 = 22, df2 = -4, p-value = NA |
Ramsey RESET F-Test for powers (2 and 3) of principal components |
> reset_test_principal_components RESET test data: mylm RESET = 0.3125, df1 = 2, df2 = 16, p-value = 0.736 |
Variance Inflation Factors (Multicollinearity) |
> vif V SQ BR PH GR PK AG Re 1.639363 17.057586 3.335171 1.601112 1.509764 2.696835 3.891731 2.400751 As C TH 10.963412 2.243383 2.896230 |