Multiple Linear Regression - Estimated Regression Equation

y[t] = + 598.544186046512 + 18.2790697674418x[t] -20.6372093023255M1[t] -30.5116279069767M2[t] -47.3116279069768M3[t] -42.6558139534884M4[t] -33.3441860465117M5[t] -37.3441860465116M6[t] -46.3441860465116M7[t] -53.3441860465116M8[t] -60.7441860465116M9[t] -60.3441860465116M10[t] -6.74418604651162M11[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)	598.544186046512	15.245865	39.2594	0	0
x	18.2790697674418	13.995477	1.3061	0.197753	0.098877
M1	-20.6372093023255	20.377722	-1.0127	0.316265	0.158133
M2	-30.5116279069767	21.921284	-1.3919	0.170377	0.085189
M3	-47.3116279069768	21.921284	-2.1583	0.03594	0.01797
M4	-42.6558139534884	21.378444	-1.9953	0.051706	0.025853
M5	-33.3441860465117	21.378444	-1.5597	0.125398	0.062699
M6	-37.3441860465116	21.378444	-1.7468	0.087066	0.043533
M7	-46.3441860465116	21.378444	-2.1678	0.035164	0.017582
M8	-53.3441860465116	21.378444	-2.4952	0.016079	0.00804
M9	-60.7441860465116	21.378444	-2.8414	0.006574	0.003287
M10	-60.3441860465116	21.378444	-2.8227	0.006911	0.003456
M11	-6.74418604651162	21.378444	-0.3155	0.753775	0.376887

Multiple Linear Regression - Regression Statistics
Multiple R	0.568606440562468
R-squared	0.32331328424912
Adjusted R-squared	0.154141605311400
F-TEST (value)	1.91115490653815
F-TEST (DF numerator)	12
F-TEST (DF denominator)	48
p-value	0.0566763849716443
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	33.5113020388806
Sum Squared Residuals	53904.3534883721

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	523	577.906976744186	-54.9069767441859
2	519	568.032558139535	-49.0325581395349
3	509	551.232558139535	-42.2325581395348
4	512	555.888372093023	-43.8883720930234
5	519	565.2	-46.2
6	517	561.2	-44.2000000000001
7	510	552.2	-42.2
8	509	545.2	-36.2000000000000
9	501	537.8	-36.8
10	507	538.2	-31.2
11	569	591.8	-22.8
12	580	598.544186046512	-18.5441860465116
13	578	596.186046511628	-18.1860465116280
14	565	586.311627906977	-21.3116279069767
15	547	569.511627906977	-22.5116279069767
16	555	555.888372093023	-0.888372093023233
17	562	565.2	-3.20000000000000
18	561	561.2	-0.200000000000016
19	555	552.2	2.79999999999999
20	544	545.2	-1.20000000000001
21	537	537.8	-0.800000000000014
22	543	538.2	4.8
23	594	591.8	2.20000000000000
24	611	616.823255813953	-5.82325581395349
25	613	596.186046511628	16.8139534883720
26	611	586.311627906977	24.6883720930232
27	594	569.511627906977	24.4883720930233
28	595	574.167441860465	20.8325581395349
29	591	565.2	25.8
30	589	561.2	27.8
31	584	552.2	31.8
32	573	545.2	27.8
33	567	537.8	29.2
34	569	538.2	30.8
35	621	591.8	29.2
36	629	598.544186046512	30.4558139534884
37	628	577.906976744186	50.0930232558139
38	612	568.032558139535	43.9674418604651
39	595	551.232558139535	43.7674418604651
40	597	555.888372093023	41.1116279069768
41	593	565.2	27.8
42	590	561.2	28.8
43	580	552.2	27.8
44	574	545.2	28.8
45	573	537.8	35.2
46	573	538.2	34.8
47	620	591.8	28.2
48	626	598.544186046512	27.4558139534884
49	620	577.906976744186	42.0930232558139
50	588	586.311627906977	1.68837209302325
51	566	569.511627906977	-3.51162790697674
52	557	574.167441860465	-17.1674418604651
53	561	565.2	-4.2
54	549	561.2	-12.2000000000000
55	532	552.2	-20.2
56	526	545.2	-19.2
57	511	537.8	-26.8
58	499	538.2	-39.2
59	555	591.8	-36.8
60	565	598.544186046512	-33.5441860465116
61	542	577.906976744186	-35.9069767441861