Multiple Linear Regression - Estimated Regression Equation |
^CPI[t] = + 0.10473 + 0.0186338`^M`[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) | +0.1047 | 0.01982 | +5.2850e+00 | 1.617e-06 | 8.083e-07 |
`^M` | +0.01863 | 0.01366 | +1.3650e+00 | 0.1772 | 0.08858 |
Multiple Linear Regression - Regression Statistics | |
Multiple R | 0.1681 |
R-squared | 0.02827 |
Adjusted R-squared | 0.01309 |
F-TEST (value) | 1.862 |
F-TEST (DF numerator) | 1 |
F-TEST (DF denominator) | 64 |
p-value | 0.1772 |
Multiple Linear Regression - Residual Statistics | |
Residual Standard Deviation | 0.1099 |
Sum Squared Residuals | 0.7734 |
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 | 0.1 | 0.1351 | -0.0351 |
2 | 0.07 | 0.1217 | -0.05169 |
3 | 0.18 | 0.1278 | 0.05216 |
4 | 0.03 | 0.1306 | -0.1006 |
5 | -0.077 | 0.114 | -0.191 |
6 | 0.1 | 0.1187 | -0.01871 |
7 | 0.123 | 0.109 | 0.01398 |
8 | 0.047 | 0.1083 | -0.06127 |
9 | 0.063 | 0.1122 | -0.04918 |
10 | -0.043 | 0.1075 | -0.1505 |
11 | -0.07 | 0.128 | -0.198 |
12 | -0.083 | 0.1312 | -0.2142 |
13 | 0.036 | 0.1329 | -0.09687 |
14 | -0.036 | 0.1181 | -0.1541 |
15 | 0.02 | 0.1157 | -0.09572 |
16 | 0.08 | 0.1107 | -0.03069 |
17 | 0.003 | 0.1148 | -0.1118 |
18 | 0.177 | 0.1088 | 0.06817 |
19 | 0.28 | 0.1058 | 0.1742 |
20 | 0.233 | 0.1161 | 0.1169 |
21 | 0.227 | 0.1105 | 0.1165 |
22 | 0.236 | 0.1053 | 0.1307 |
23 | 0.25 | 0.1045 | 0.1455 |
24 | 0.137 | 0.09299 | 0.04401 |
25 | 0.337 | 0.101 | 0.236 |
26 | 0.193 | 0.1321 | 0.06088 |
27 | -0.017 | 0.1319 | -0.1489 |
28 | 0.03 | 0.1379 | -0.1079 |
29 | 0.05 | 0.1394 | -0.08939 |
30 | 0.05 | 0.1271 | -0.07709 |
31 | 0.15 | 0.1234 | 0.02664 |
32 | 0.177 | 0.08125 | 0.09575 |
33 | 0.027 | 0.09784 | -0.07084 |
34 | 0.176 | 0.09914 | 0.07686 |
35 | 0.017 | 0.1295 | -0.1125 |
36 | 0.19 | 0.1029 | 0.08713 |
37 | 0.06 | 0.1178 | -0.05777 |
38 | -0.01 | 0.1239 | -0.1339 |
39 | 0.117 | 0.1204 | -0.003383 |
40 | 0.043 | 0.129 | -0.08595 |
41 | 0.117 | 0.1221 | -0.00506 |
42 | 0.113 | 0.1228 | -0.009805 |
43 | 0.087 | 0.1023 | -0.01531 |
44 | 0.073 | 0.1196 | -0.04664 |
45 | 0.097 | 0.1332 | -0.03624 |
46 | 0.056 | 0.1303 | -0.07426 |
47 | 0.187 | 0.1332 | 0.05376 |
48 | 0.083 | 0.1321 | -0.04912 |
49 | 0.127 | 0.1234 | 0.003636 |
50 | 0.05 | 0.1245 | -0.07448 |
51 | 0.07 | 0.1521 | -0.08206 |
52 | 0.143 | 0.1543 | -0.0113 |
53 | 0.097 | 0.1271 | -0.03009 |
54 | 0.2 | 0.1222 | 0.07775 |
55 | 0.093 | 0.1394 | -0.04639 |
56 | 0.167 | 0.1606 | 0.006368 |
57 | 0.297 | 0.1588 | 0.1382 |
58 | 0.29 | 0.139 | 0.151 |
59 | 0.28 | 0.09467 | 0.1853 |
60 | 0.266 | 0.114 | 0.152 |
61 | 0.084 | 0.1364 | -0.05241 |
62 | 0.2 | 0.1495 | 0.05055 |
63 | 0.333 | 0.1768 | 0.1562 |
64 | 0.367 | 0.1593 | 0.2077 |
65 | 0.333 | 0.1495 | 0.1835 |
66 | 0.333 | 0.1638 | 0.1692 |
Goldfeld-Quandt test for Heteroskedasticity | |||
p-values | Alternative Hypothesis | ||
breakpoint index | greater | 2-sided | less |
5 | 0.3536 | 0.7072 | 0.6464 |
6 | 0.2912 | 0.5825 | 0.7088 |
7 | 0.2978 | 0.5956 | 0.7022 |
8 | 0.1885 | 0.377 | 0.8115 |
9 | 0.1123 | 0.2246 | 0.8877 |
10 | 0.1043 | 0.2087 | 0.8957 |
11 | 0.2059 | 0.4117 | 0.7941 |
12 | 0.312 | 0.6241 | 0.688 |
13 | 0.2412 | 0.4824 | 0.7588 |
14 | 0.2335 | 0.4669 | 0.7665 |
15 | 0.1821 | 0.3643 | 0.8179 |
16 | 0.1425 | 0.285 | 0.8575 |
17 | 0.1169 | 0.2338 | 0.8831 |
18 | 0.1581 | 0.3161 | 0.8419 |
19 | 0.3678 | 0.7355 | 0.6322 |
20 | 0.4664 | 0.9327 | 0.5336 |
21 | 0.5023 | 0.9954 | 0.4977 |
22 | 0.5149 | 0.9703 | 0.4851 |
23 | 0.5347 | 0.9305 | 0.4653 |
24 | 0.4761 | 0.9522 | 0.5239 |
25 | 0.6724 | 0.6551 | 0.3276 |
26 | 0.7169 | 0.5661 | 0.2831 |
27 | 0.7277 | 0.5447 | 0.2723 |
28 | 0.7134 | 0.5732 | 0.2866 |
29 | 0.6968 | 0.6064 | 0.3032 |
30 | 0.6606 | 0.6788 | 0.3394 |
31 | 0.6137 | 0.7727 | 0.3863 |
32 | 0.6082 | 0.7836 | 0.3918 |
33 | 0.608 | 0.7841 | 0.392 |
34 | 0.5728 | 0.8543 | 0.4272 |
35 | 0.573 | 0.854 | 0.427 |
36 | 0.5567 | 0.8867 | 0.4433 |
37 | 0.5007 | 0.9986 | 0.4993 |
38 | 0.5451 | 0.9098 | 0.4549 |
39 | 0.4772 | 0.9544 | 0.5228 |
40 | 0.4647 | 0.9295 | 0.5353 |
41 | 0.4004 | 0.8007 | 0.5996 |
42 | 0.3396 | 0.6791 | 0.6604 |
43 | 0.2827 | 0.5653 | 0.7173 |
44 | 0.2431 | 0.4861 | 0.7569 |
45 | 0.2179 | 0.4358 | 0.7821 |
46 | 0.2201 | 0.4402 | 0.7799 |
47 | 0.2047 | 0.4093 | 0.7953 |
48 | 0.1945 | 0.3889 | 0.8055 |
49 | 0.1571 | 0.3141 | 0.8429 |
50 | 0.1866 | 0.3732 | 0.8134 |
51 | 0.2755 | 0.5511 | 0.7245 |
52 | 0.311 | 0.622 | 0.689 |
53 | 0.3523 | 0.7046 | 0.6477 |
54 | 0.2958 | 0.5916 | 0.7042 |
55 | 0.4493 | 0.8986 | 0.5507 |
56 | 0.5527 | 0.8946 | 0.4473 |
57 | 0.5261 | 0.9478 | 0.4739 |
58 | 0.4591 | 0.9182 | 0.5409 |
59 | 0.4594 | 0.9188 | 0.5406 |
60 | 0.6237 | 0.7525 | 0.3763 |
61 | 0.712 | 0.576 | 0.288 |
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 = 11.255, df1 = 2, df2 = 62, p-value = 6.759e-05 |
Ramsey RESET F-Test for powers (2 and 3) of regressors |
> reset_test_regressors RESET test data: mylm RESET = 11.255, df1 = 2, df2 = 62, p-value = 6.759e-05 |
Ramsey RESET F-Test for powers (2 and 3) of principal components |
> reset_test_principal_components RESET test data: mylm RESET = 11.255, df1 = 2, df2 = 62, p-value = 6.759e-05 |