| Multiple Linear Regression - Estimated Regression Equation |
| a[t] = + 0.119617 + 0.154389b[t] + 0.00718178c[t] + 0.0353615d[t] -0.16396e[t] + 0.00425004f[t] -0.00159554g[t] -0.132678h[t] + 0.0239669i[t] + 0.424766j[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) | +0.1196 | 1.544 | +7.7470e-02 | 0.9389 | 0.4695 |
| b | +0.1544 | 0.1986 | +7.7740e-01 | 0.4448 | 0.2224 |
| c | +0.007182 | 0.05854 | +1.2270e-01 | 0.9034 | 0.4517 |
| d | +0.03536 | 0.2518 | +1.4050e-01 | 0.8895 | 0.4448 |
| e | -0.164 | 0.2319 | -7.0700e-01 | 0.4867 | 0.2433 |
| f | +0.00425 | 0.06608 | +6.4320e-02 | 0.9493 | 0.4746 |
| g | -0.001595 | 0.00889 | -1.7950e-01 | 0.8591 | 0.4296 |
| h | -0.1327 | 0.2113 | -6.2780e-01 | 0.5363 | 0.2682 |
| i | +0.02397 | 0.2774 | +8.6400e-02 | 0.9319 | 0.4659 |
| j | +0.4248 | 0.2034 | +2.0880e+00 | 0.04803 | 0.02401 |
| Multiple Linear Regression - Regression Statistics | |
| Multiple R | 0.4909 |
| R-squared | 0.241 |
| Adjusted R-squared | -0.05606 |
| F-TEST (value) | 0.8113 |
| F-TEST (DF numerator) | 9 |
| F-TEST (DF denominator) | 23 |
| p-value | 0.6111 |
| Multiple Linear Regression - Residual Statistics | |
| Residual Standard Deviation | 0.4919 |
| Sum Squared Residuals | 5.566 |
| 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 | 1 | 0.5709 | 0.4291 |
| 2 | 0 | 0.2404 | -0.2404 |
| 3 | 1 | 0.3144 | 0.6856 |
| 4 | 0 | 0.2874 | -0.2874 |
| 5 | 1 | 0.6972 | 0.3028 |
| 6 | 0 | 0.1488 | -0.1488 |
| 7 | 0 | 0.1347 | -0.1347 |
| 8 | 1 | 0.8356 | 0.1644 |
| 9 | 0 | 0.2852 | -0.2852 |
| 10 | 0 | 0.112 | -0.112 |
| 11 | 0 | 0.3151 | -0.3151 |
| 12 | 1 | 0.2752 | 0.7248 |
| 13 | 1 | 0.4132 | 0.5868 |
| 14 | 1 | 0.7176 | 0.2824 |
| 15 | 1 | 0.6022 | 0.3978 |
| 16 | 0 | 0.455 | -0.455 |
| 17 | 0 | 0.703 | -0.703 |
| 18 | 1 | 0.07968 | 0.9203 |
| 19 | 1 | 0.3338 | 0.6662 |
| 20 | 1 | 0.5938 | 0.4062 |
| 21 | 0 | 0.007153 | -0.007153 |
| 22 | 0 | 0.08508 | -0.08508 |
| 23 | 0 | 0.5977 | -0.5977 |
| 24 | 0 | 0.127 | -0.127 |
| 25 | 0 | 0.2726 | -0.2726 |
| 26 | 0 | 0.1517 | -0.1517 |
| 27 | 0 | 0.1222 | -0.1222 |
| 28 | 0 | 0.149 | -0.149 |
| 29 | 0 | 0.2572 | -0.2572 |
| 30 | 0 | 0.682 | -0.682 |
| 31 | 0 | -0.01075 | 0.01075 |
| 32 | 0 | 0.2927 | -0.2927 |
| 33 | 0 | 0.1511 | -0.1511 |
| Goldfeld-Quandt test for Heteroskedasticity | |||
| p-values | Alternative Hypothesis | ||
| breakpoint index | greater | 2-sided | less |
| 13 | 0.4537 | 0.9075 | 0.5463 |
| 14 | 0.8286 | 0.3428 | 0.1714 |
| 15 | 0.8612 | 0.2777 | 0.1388 |
| 16 | 0.9547 | 0.09051 | 0.04526 |
| 17 | 0.9221 | 0.1559 | 0.07795 |
| 18 | 0.9931 | 0.01382 | 0.00691 |
| 19 | 0.9991 | 0.001779 | 0.0008897 |
| 20 | 1 | 0 | 0 |
| Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity | |||
| Description | # significant tests | % significant tests | OK/NOK |
| 1% type I error level | 2 | 0.25 | NOK |
| 5% type I error level | 3 | 0.375 | NOK |
| 10% type I error level | 4 | 0.5 | NOK |
| Ramsey RESET F-Test for powers (2 and 3) of fitted values |
> reset_test_fitted RESET test data: mylm RESET = 0.036607, df1 = 2, df2 = 21, p-value = 0.9641 |
| Ramsey RESET F-Test for powers (2 and 3) of regressors |
> reset_test_regressors RESET test data: mylm RESET = 0.26627, df1 = 18, df2 = 5, p-value = 0.9833 |
| Ramsey RESET F-Test for powers (2 and 3) of principal components |
> reset_test_principal_components RESET test data: mylm RESET = 2.2845, df1 = 2, df2 = 21, p-value = 0.1266 |
| Variance Inflation Factors (Multicollinearity) |
> vif
b c d e f g h i
1.333335 1.568007 1.111107 1.548783 1.250825 1.248824 1.208006 1.337928
j
1.118857
|
