Multiple Linear Regression - Estimated Regression Equation |
broodprijs[t] = + 1.43142857142857 -0.135175782593738dummy1[t] + 0.0485714285714295`dummy2+`[t] + 0.098`dummy3+`[t] + 0.00359133126934983trend[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) | 1.43142857142857 | 0.001277 | 1120.8608 | 0 | 0 |
dummy1 | -0.135175782593738 | 0.008088 | -16.7134 | 0 | 0 |
`dummy2+` | 0.0485714285714295 | 0.001418 | 34.247 | 0 | 0 |
`dummy3+` | 0.098 | 0.001632 | 60.0441 | 0 | 0 |
trend | 0.00359133126934983 | 0.000154 | 23.3957 | 0 | 0 |
Multiple Linear Regression - Regression Statistics | |
Multiple R | 0.999056460646552 |
R-squared | 0.998113811559615 |
Adjusted R-squared | 0.997976634218496 |
F-TEST (value) | 7276.08366963869 |
F-TEST (DF numerator) | 4 |
F-TEST (DF denominator) | 55 |
p-value | 0 |
Multiple Linear Regression - Residual Statistics | |
Residual Standard Deviation | 0.00337883516280303 |
Sum Squared Residuals | 0.000627908988156681 |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
1 | 1.43 | 1.43142857142858 | -0.00142857142857822 |
2 | 1.43 | 1.43142857142857 | -0.00142857142857024 |
3 | 1.43 | 1.43142857142857 | -0.00142857142857009 |
4 | 1.43 | 1.43142857142857 | -0.00142857142857029 |
5 | 1.43 | 1.43142857142857 | -0.00142857142857042 |
6 | 1.43 | 1.43142857142857 | -0.00142857142857038 |
7 | 1.44 | 1.43142857142857 | 0.00857142857142963 |
8 | 1.48 | 1.48 | 3.25260651745651e-19 |
9 | 1.48 | 1.48 | 3.25260651745651e-19 |
10 | 1.48 | 1.48 | 3.25260651745651e-19 |
11 | 1.48 | 1.48 | 3.25260651745651e-19 |
12 | 1.48 | 1.48 | 3.25260651745651e-19 |
13 | 1.48 | 1.48 | 3.25260651745651e-19 |
14 | 1.48 | 1.48 | 3.25260651745651e-19 |
15 | 1.48 | 1.48 | 3.25260651745651e-19 |
16 | 1.48 | 1.48 | 3.25260651745651e-19 |
17 | 1.48 | 1.48 | 3.25260651745651e-19 |
18 | 1.48 | 1.48 | 3.25260651745651e-19 |
19 | 1.48 | 1.48 | 3.25260651745651e-19 |
20 | 1.48 | 1.48 | 3.25260651745651e-19 |
21 | 1.48 | 1.48 | 3.25260651745651e-19 |
22 | 1.48 | 1.48 | 3.25260651745651e-19 |
23 | 1.48 | 1.48 | 3.25260651745651e-19 |
24 | 1.48 | 1.48 | 3.25260651745651e-19 |
25 | 1.48 | 1.48 | 3.25260651745651e-19 |
26 | 1.48 | 1.48 | 3.25260651745651e-19 |
27 | 1.48 | 1.48 | 3.25260651745651e-19 |
28 | 1.48 | 1.48 | 3.25260651745651e-19 |
29 | 1.48 | 1.48 | 3.25260651745651e-19 |
30 | 1.48 | 1.48 | 3.25260651745651e-19 |
31 | 1.48 | 1.48 | 3.25260651745651e-19 |
32 | 1.48 | 1.48 | 3.25260651745651e-19 |
33 | 1.48 | 1.48 | 3.25260651745651e-19 |
34 | 1.48 | 1.48 | 3.25260651745651e-19 |
35 | 1.48 | 1.48 | 3.25260651745651e-19 |
36 | 1.48 | 1.48 | 3.25260651745651e-19 |
37 | 1.48 | 1.48 | 3.25260651745651e-19 |
38 | 1.57 | 1.578 | -0.008 |
39 | 1.58 | 1.578 | 0.00200000000000000 |
40 | 1.58 | 1.578 | 0.00200000000000000 |
41 | 1.58 | 1.578 | 0.00200000000000000 |
42 | 1.58 | 1.578 | 0.00200000000000000 |
43 | 1.59 | 1.59725146198830 | -0.00725146198830411 |
44 | 1.6 | 1.60084279325765 | -0.000842793257653937 |
45 | 1.6 | 1.60443412452700 | -0.00443412452700377 |
46 | 1.61 | 1.60802545579635 | 0.00197454420364641 |
47 | 1.61 | 1.61161678706570 | -0.00161678706570342 |
48 | 1.61 | 1.61520811833505 | -0.00520811833505325 |
49 | 1.62 | 1.61879944960440 | 0.00120055039559693 |
50 | 1.63 | 1.62239078087375 | 0.00760921912624688 |
51 | 1.63 | 1.62598211214310 | 0.00401788785689705 |
52 | 1.64 | 1.62957344341245 | 0.0104265565875472 |
53 | 1.64 | 1.63316477468180 | 0.0068352253181974 |
54 | 1.64 | 1.63675610595115 | 0.00324389404884757 |
55 | 1.64 | 1.64034743722050 | -0.000347437220502263 |
56 | 1.64 | 1.64393876848985 | -0.00393876848985209 |
57 | 1.65 | 1.6475300997592 | 0.00246990024079809 |
58 | 1.65 | 1.65112143102855 | -0.00112143102855174 |
59 | 1.65 | 1.65471276229790 | -0.00471276229790157 |
60 | 1.65 | 1.65830409356725 | -0.0083040935672514 |