Multiple Linear Regression - Estimated Regression Equation

broodprijs[t] = + 1.43142857142857 -0.118842449260405dummy1[t] + 0.0485714285714295`dummy2+`[t] + 0.0816666666666667dummy3[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	0.004735	302.3176	0	0
dummy1	-0.118842449260405	0.029899	-3.9748	0.000207	0.000104
`dummy2+`	0.0485714285714295	0.005275	9.2071	0	0
dummy3	0.0816666666666667	0.005618	14.5355	0	0
trend	0.00359133126934983	0.000569	6.3103	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.9869510816482
R-squared	0.974072437566554
Adjusted R-squared	0.972186796662304
F-TEST (value)	516.57366753702
F-TEST (DF numerator)	4
F-TEST (DF denominator)	55
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0125272374677159
Sum Squared Residuals	0.00863124232149003

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1.43	1.43142857142858	-0.00142857142857819
2	1.43	1.43142857142857	-0.00142857142857030
3	1.43	1.43142857142857	-0.00142857142857006
4	1.43	1.43142857142857	-0.00142857142857031
5	1.43	1.43142857142857	-0.00142857142857041
6	1.43	1.43142857142857	-0.00142857142857037
7	1.44	1.43142857142857	0.00857142857142963
8	1.48	1.48	2.16840434497101e-19
9	1.48	1.48	2.16840434497101e-19
10	1.48	1.48	2.16840434497101e-19
11	1.48	1.48	2.16840434497101e-19
12	1.48	1.48	2.16840434497101e-19
13	1.48	1.48	2.16840434497101e-19
14	1.48	1.48	2.16840434497101e-19
15	1.48	1.48	2.16840434497101e-19
16	1.48	1.48	2.16840434497101e-19
17	1.48	1.48	2.16840434497101e-19
18	1.48	1.48	2.16840434497101e-19
19	1.48	1.48	2.16840434497101e-19
20	1.48	1.48	2.16840434497101e-19
21	1.48	1.48	2.16840434497101e-19
22	1.48	1.48	2.16840434497101e-19
23	1.48	1.48	2.16840434497101e-19
24	1.48	1.48	2.16840434497101e-19
25	1.48	1.48	2.16840434497101e-19
26	1.48	1.48	2.16840434497101e-19
27	1.48	1.48	2.16840434497101e-19
28	1.48	1.48	2.16840434497101e-19
29	1.48	1.48	2.16840434497101e-19
30	1.48	1.48	2.16840434497101e-19
31	1.48	1.48	2.16840434497101e-19
32	1.48	1.48	2.16840434497101e-19
33	1.48	1.48	2.16840434497101e-19
34	1.48	1.48	2.16840434497101e-19
35	1.48	1.48	2.16840434497101e-19
36	1.48	1.48	2.16840434497101e-19
37	1.48	1.56166666666667	-0.0816666666666667
38	1.57	1.56166666666667	0.00833333333333334
39	1.58	1.56166666666667	0.0183333333333334
40	1.58	1.56166666666667	0.0183333333333334
41	1.58	1.56166666666667	0.0183333333333334
42	1.58	1.56166666666667	0.0183333333333334
43	1.59	1.59725146198830	-0.00725146198830411
44	1.6	1.60084279325765	-0.000842793257653935
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	-0.00112143102855175
59	1.65	1.65471276229790	-0.00471276229790158
60	1.65	1.65830409356725	-0.0083040935672514