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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 14 Dec 2014 16:20:29 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/14/t1418574064yh2wx7ubltppzfb.htm/, Retrieved Sun, 19 May 2024 13:04:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267726, Retrieved Sun, 19 May 2024 13:04:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact87

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-14 16:20:29] [04df4205f362f56e0d1a9032a00a5d3d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
139	70	31	39	6,0
158	114	46	62	1,0
128	69	31	33	1,0
224	176	67	52	5,5
105	121	52	77	6,5
159	110	77	76	4,5
167	158	37	41	2,0
165	116	32	48	5,0
159	181	36	63	0,5
119	77	38	30	5,0
163	152	54	66	5,5
137	99	42	42	3,0
153	68	34	21	0,5
148	101	112	25	6,5
188	88	47	69	7,5
149	112	47	54	5,5
244	171	37	74	4,0
148	137	109	80	7,5
150	66	20	61	4,0
146	89	7	63	0,5
132	102	30	34	3,5
161	161	92	51	2,5
105	120	43	42	4,5
97	127	55	31	4,5
131	108	49	20	6,0
166	85	71	49	2,5
111	48	29	31	0,0
145	152	56	39	5,0
162	75	46	54	6,5
163	107	19	49	5,0
59	62	23	34	6,0
109	124	30	55	5,5
90	72	61	42	1,0
83	58	38	13	6,0
116	97	32	37	5,0
42	88	16	25	1,0
148	126	19	30	5,0
155	104	22	28	6,5
125	148	48	45	7,0
116	146	23	35	4,5
138	97	33	41	8,5
96	99	24	45	7,5
164	118	34	73	3,5
202	139	43	64	9,0
66	60	28	25	3,5
214	142	26	100	6,5
188	94	67	28	7,5
99	75	59	59	7,5
157	96	58	52	1,0
78	41	47	3	NA
108	69	38	27	6,5
110	85	32	51	1,5
97	34	12	25	0,0
106	95	37	64	5,5
80	57	47	32	5,0
74	54	13	31	7,0
114	64	14	26	0,0
140	76	-2	58	4,5
98	88	24	21	1,5
126	102	23	33	2,5
98	61	24	16	5,5
95	80	14	20	8,0
110	49	52	37	1,0
70	78	15	35	5,0
86	45	19	27	3,0
130	55	35	41	3,0
96	96	24	40	8,0
99	38	19	17	5,5
48	41	23	12	0,5
50	146	16	45	7,5
150	182	33	37	9
154	192	32	37	9,5
68	35	14	10	7
194	439	52	68	8
159	341	72	143	7
39	85	13	17	9,5
100	200	40	37	4
111	158	19	27	6
138	199	24	37	8
101	297	121	58	5,5
131	227	93	66	9,5
101	108	36	21	7,5
114	86	23	19	7
114	148	41	35	8
111	178	46	48	7
75	120	18	27	7
82	207	35	43	6
121	157	17	30	10
32	128	4	25	2,5
117	323	44	72	8
71	79	10	23	6
165	70	38	13	8,5
154	146	57	61	6
126	246	23	43	9
120	127	40	36	5,5
172	228	38	34	9
114	180	37	72	8,5
156	212	37	39	9
68	130	15	25	9
89	179	2	56	7,5
167	243	43	80	10
87	305	4	42	8,5
2	96	-4	23	10
49	52	61	16	6,5
96	40	31	9	8,5
100	214	31	48	8
141	119	42	53	7
165	222	21	29	7,5
165	222	21	29	7,5
110	159	25	55	9,5
118	165	32	54	6
146	125	28	51	7
155	274	33	61	10
147	84	13	30	3,5
61	139	13	38	6,5
60	95	23	27	6,5
109	130	10	56	8,5
68	72	5	25	4
73	206	32	43	8,5
220	255	67	54	10
65	111	15	28	8
122	92	33	16	5
44	171	24	40	4,5
52	83	14	24	8,5
62	119	23	29	7
101	186	38	57	8,5
42	50	12	37	7,5
152	117	28	27	7,5
103	148	33	34	5,5
96	137	34	44	8,5
175	181	21	34	9,5
57	98	20	39	7
110	85	29	13	6,5
131	66	11	12	6,5
86	122	60	30	10
121	218	13	81	10
88	94	25	27	7,5
168	113	39	25	4,5
94	84	9	22	4,5
51	86	19	19	0,5
145	222	60	45	4,5
66	167	19	45	5,5
85	82	34	28	5
102	83	45	31	8
128	85	24	24	8,5
86	89	48	19	6,5
114	225	29	51	8
119	102	51	24	5,5
132	128	24	23	5
142	91	40	14	3,5
94	204	19	51	9
166	138	20	36	5
64	44	27	24	3
105	79	39	39	0,5
49	52	61	16	6,5
95	116	67	31	4,5
102	83	45	31	8
63	153	8	39	7,5
117	106	22	38	9,5
57	58	17	31	6,5
73	74	34	22	6
105	131	25	51	8
31	78	13	16	5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267726&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267726&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267726&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Ex[t] = + 4.87255 -0.00587081LFM[t] + 0.0196891B[t] -0.00839924PRH[t] -0.0117285CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ex[t] =  +  4.87255 -0.00587081LFM[t] +  0.0196891B[t] -0.00839924PRH[t] -0.0117285CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267726&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ex[t] =  +  4.87255 -0.00587081LFM[t] +  0.0196891B[t] -0.00839924PRH[t] -0.0117285CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267726&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267726&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
Ex[t] = + 4.87255 -0.00587081LFM[t] + 0.0196891B[t] -0.00839924PRH[t] -0.0117285CH[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.872550.5827768.3613.14328e-141.57164e-14
LFM-0.005870810.00512989-1.1440.2541870.127093
B0.01968910.003396245.7973.58972e-081.79486e-08
PRH-0.008399240.00984971-0.85270.3951030.197551
CH-0.01172850.0124124-0.94490.3461630.173081

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 4.87255 & 0.582776 & 8.361 & 3.14328e-14 & 1.57164e-14 \tabularnewline
LFM & -0.00587081 & 0.00512989 & -1.144 & 0.254187 & 0.127093 \tabularnewline
B & 0.0196891 & 0.00339624 & 5.797 & 3.58972e-08 & 1.79486e-08 \tabularnewline
PRH & -0.00839924 & 0.00984971 & -0.8527 & 0.395103 & 0.197551 \tabularnewline
CH & -0.0117285 & 0.0124124 & -0.9449 & 0.346163 & 0.173081 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267726&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]4.87255[/C][C]0.582776[/C][C]8.361[/C][C]3.14328e-14[/C][C]1.57164e-14[/C][/ROW]
[ROW][C]LFM[/C][C]-0.00587081[/C][C]0.00512989[/C][C]-1.144[/C][C]0.254187[/C][C]0.127093[/C][/ROW]
[ROW][C]B[/C][C]0.0196891[/C][C]0.00339624[/C][C]5.797[/C][C]3.58972e-08[/C][C]1.79486e-08[/C][/ROW]
[ROW][C]PRH[/C][C]-0.00839924[/C][C]0.00984971[/C][C]-0.8527[/C][C]0.395103[/C][C]0.197551[/C][/ROW]
[ROW][C]CH[/C][C]-0.0117285[/C][C]0.0124124[/C][C]-0.9449[/C][C]0.346163[/C][C]0.173081[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267726&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267726&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.872550.5827768.3613.14328e-141.57164e-14
LFM-0.005870810.00512989-1.1440.2541870.127093
B0.01968910.003396245.7973.58972e-081.79486e-08
PRH-0.008399240.00984971-0.85270.3951030.197551
CH-0.01172850.0124124-0.94490.3461630.173081






Multiple Linear Regression - Regression Statistics
Multiple R0.43808
R-squared0.191914
Adjusted R-squared0.171325
F-TEST (value)9.32154
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value8.73618e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.35421
Sum Squared Residuals870.139

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.43808 \tabularnewline
R-squared & 0.191914 \tabularnewline
Adjusted R-squared & 0.171325 \tabularnewline
F-TEST (value) & 9.32154 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 8.73618e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.35421 \tabularnewline
Sum Squared Residuals & 870.139 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267726&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.43808[/C][/ROW]
[ROW][C]R-squared[/C][C]0.191914[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.171325[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.32154[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/C][/ROW]
[ROW][C]p-value[/C][C]8.73618e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.35421[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]870.139[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267726&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267726&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.43808
R-squared0.191914
Adjusted R-squared0.171325
F-TEST (value)9.32154
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value8.73618e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.35421
Sum Squared Residuals870.139






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
164.716961.28304
215.07599-4.07599
314.83222-3.83222
45.55.85014-0.350144
56.55.298651.20135
64.54.56679-0.0667887
726.21137-4.21137
855.35606-0.356061
90.56.46156-5.96156
1055.01896-0.018959
115.55.68072-0.180716
1235.17211-2.17211
130.54.7813-4.2813
146.54.758341.74166
157.54.297453.20255
165.55.174880.325122
1745.62823-1.62823
187.54.847282.65272
1944.40799-0.407988
200.54.97005-4.47005
213.55.45515-1.95515
222.55.72642-3.22642
234.55.76505-1.26505
244.55.97806-1.47806
2565.583770.416235
262.54.40053-1.90053
2704.55881-4.55881
2856.08626-1.08626
296.54.378462.12154
3055.28806-0.288062
3165.154950.845055
325.55.77704-0.277039
3314.75684-3.75684
3465.05560.944402
3555.39865-0.39865
3615.93102-4.93102
3755.97306-0.973059
386.55.497061.00294
3976.121740.878257
404.56.46247-1.96247
418.55.214183.28582
427.55.528811.97119
433.55.0913-1.5913
4495.311643.68836
453.55.13803-1.63803
466.55.020821.47918
477.54.728472.77153
487.54.580492.91951
4914.74395-3.74395
50NANA1.53879
516.510.0334-3.53341
521.56.07851-4.57851
530-0.4406840.440684
545.55.255090.244912
5553.028551.97145
56712.0408-5.04085
5700.383556-0.383556
584.58.58197-4.08197
591.54.56089-3.06089
602.52.109010.390992
615.53.037792.46221
62811.3208-3.32081
6311.46086-0.460858
6456.77741-1.77741
6534.4174-1.4174
6630.5283862.47161
6787.180550.819445
685.510.0641-4.56408
690.5-0.2085490.708549
707.55.364222.13578
7196.546032.45397
729.57.427582.07242
73710.1428-3.14284
7489.37117-1.37117
7573.508593.49141
769.512.9534-3.45337
7744.85552-0.855516
7865.344980.655022
79810.9307-2.93071
805.53.01772.4823
819.57.857351.64265
827.55.980521.51948
8375.36241.6376
8487.776220.223778
8576.327080.672923
8678.66849-1.66849
8762.758733.24127
881014.3781-4.37808
892.53.83124-1.33124
9087.657410.342586
9162.310463.68954
928.58.148860.351137
9365.278850.721155
9499.41038-0.410376
955.54.133951.36605
9697.09211.9079
978.56.862621.63738
9896.613722.38628
9998.700810.299193
1007.54.877142.62286
1011011.3408-1.34078
1028.55.01483.4852
103108.40871.5913
1046.52.730583.76942
1058.58.17560.324402
10686.413391.58661
10777.25834-0.258342
1087.57.75834-0.258342
1097.54.502282.99772
1109.510.0264-0.526386
11164.643221.35678
11275.364781.63522
1131011.7024-1.70238
1143.53.69635-0.196345
1156.55.880910.619085
1166.54.051432.44857
1178.510.0557-1.55574
11843.226840.77316
1198.55.905612.59439
120108.222051.77795
12188.50288-0.502878
12257.81035-2.81035
1234.51.802392.69761
1248.57.818260.681744
12575.454081.54592
1268.56.075692.42431
1277.55.731971.76803
1287.58.5059-1.0059
1295.53.204732.29527
1308.55.833742.66626
1319.58.342051.15795
13276.004290.995714
1336.55.169821.33018
1346.52.413924.08608
135107.395212.60479
136108.180041.81996
1377.58.49034-0.990341
1384.55.64096-1.14096
1394.59.88397-5.38397
1400.53.36053-2.86053
1414.56.08579-1.58579
1425.55.87407-0.374066
14352.166372.83363
14484.811593.18841
1458.57.493991.00601
1466.56.29160.2084
14787.972370.027632
1485.56.64647-1.14647
14956.83044-1.83044
1503.52.079541.42046
151910.0249-1.02488
15256.85487-1.85487
15337.52657-4.52657
1540.5-1.09131.5913
1556.57.67243-1.17243
1564.51.666372.83363
15787.490520.509481
1587.53.642253.85775
1599.58.173511.32649
1606.55.857370.642627
16164.027261.97274
16288.92946-0.929458
1635NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6 & 4.71696 & 1.28304 \tabularnewline
2 & 1 & 5.07599 & -4.07599 \tabularnewline
3 & 1 & 4.83222 & -3.83222 \tabularnewline
4 & 5.5 & 5.85014 & -0.350144 \tabularnewline
5 & 6.5 & 5.29865 & 1.20135 \tabularnewline
6 & 4.5 & 4.56679 & -0.0667887 \tabularnewline
7 & 2 & 6.21137 & -4.21137 \tabularnewline
8 & 5 & 5.35606 & -0.356061 \tabularnewline
9 & 0.5 & 6.46156 & -5.96156 \tabularnewline
10 & 5 & 5.01896 & -0.018959 \tabularnewline
11 & 5.5 & 5.68072 & -0.180716 \tabularnewline
12 & 3 & 5.17211 & -2.17211 \tabularnewline
13 & 0.5 & 4.7813 & -4.2813 \tabularnewline
14 & 6.5 & 4.75834 & 1.74166 \tabularnewline
15 & 7.5 & 4.29745 & 3.20255 \tabularnewline
16 & 5.5 & 5.17488 & 0.325122 \tabularnewline
17 & 4 & 5.62823 & -1.62823 \tabularnewline
18 & 7.5 & 4.84728 & 2.65272 \tabularnewline
19 & 4 & 4.40799 & -0.407988 \tabularnewline
20 & 0.5 & 4.97005 & -4.47005 \tabularnewline
21 & 3.5 & 5.45515 & -1.95515 \tabularnewline
22 & 2.5 & 5.72642 & -3.22642 \tabularnewline
23 & 4.5 & 5.76505 & -1.26505 \tabularnewline
24 & 4.5 & 5.97806 & -1.47806 \tabularnewline
25 & 6 & 5.58377 & 0.416235 \tabularnewline
26 & 2.5 & 4.40053 & -1.90053 \tabularnewline
27 & 0 & 4.55881 & -4.55881 \tabularnewline
28 & 5 & 6.08626 & -1.08626 \tabularnewline
29 & 6.5 & 4.37846 & 2.12154 \tabularnewline
30 & 5 & 5.28806 & -0.288062 \tabularnewline
31 & 6 & 5.15495 & 0.845055 \tabularnewline
32 & 5.5 & 5.77704 & -0.277039 \tabularnewline
33 & 1 & 4.75684 & -3.75684 \tabularnewline
34 & 6 & 5.0556 & 0.944402 \tabularnewline
35 & 5 & 5.39865 & -0.39865 \tabularnewline
36 & 1 & 5.93102 & -4.93102 \tabularnewline
37 & 5 & 5.97306 & -0.973059 \tabularnewline
38 & 6.5 & 5.49706 & 1.00294 \tabularnewline
39 & 7 & 6.12174 & 0.878257 \tabularnewline
40 & 4.5 & 6.46247 & -1.96247 \tabularnewline
41 & 8.5 & 5.21418 & 3.28582 \tabularnewline
42 & 7.5 & 5.52881 & 1.97119 \tabularnewline
43 & 3.5 & 5.0913 & -1.5913 \tabularnewline
44 & 9 & 5.31164 & 3.68836 \tabularnewline
45 & 3.5 & 5.13803 & -1.63803 \tabularnewline
46 & 6.5 & 5.02082 & 1.47918 \tabularnewline
47 & 7.5 & 4.72847 & 2.77153 \tabularnewline
48 & 7.5 & 4.58049 & 2.91951 \tabularnewline
49 & 1 & 4.74395 & -3.74395 \tabularnewline
50 & NA & NA & 1.53879 \tabularnewline
51 & 6.5 & 10.0334 & -3.53341 \tabularnewline
52 & 1.5 & 6.07851 & -4.57851 \tabularnewline
53 & 0 & -0.440684 & 0.440684 \tabularnewline
54 & 5.5 & 5.25509 & 0.244912 \tabularnewline
55 & 5 & 3.02855 & 1.97145 \tabularnewline
56 & 7 & 12.0408 & -5.04085 \tabularnewline
57 & 0 & 0.383556 & -0.383556 \tabularnewline
58 & 4.5 & 8.58197 & -4.08197 \tabularnewline
59 & 1.5 & 4.56089 & -3.06089 \tabularnewline
60 & 2.5 & 2.10901 & 0.390992 \tabularnewline
61 & 5.5 & 3.03779 & 2.46221 \tabularnewline
62 & 8 & 11.3208 & -3.32081 \tabularnewline
63 & 1 & 1.46086 & -0.460858 \tabularnewline
64 & 5 & 6.77741 & -1.77741 \tabularnewline
65 & 3 & 4.4174 & -1.4174 \tabularnewline
66 & 3 & 0.528386 & 2.47161 \tabularnewline
67 & 8 & 7.18055 & 0.819445 \tabularnewline
68 & 5.5 & 10.0641 & -4.56408 \tabularnewline
69 & 0.5 & -0.208549 & 0.708549 \tabularnewline
70 & 7.5 & 5.36422 & 2.13578 \tabularnewline
71 & 9 & 6.54603 & 2.45397 \tabularnewline
72 & 9.5 & 7.42758 & 2.07242 \tabularnewline
73 & 7 & 10.1428 & -3.14284 \tabularnewline
74 & 8 & 9.37117 & -1.37117 \tabularnewline
75 & 7 & 3.50859 & 3.49141 \tabularnewline
76 & 9.5 & 12.9534 & -3.45337 \tabularnewline
77 & 4 & 4.85552 & -0.855516 \tabularnewline
78 & 6 & 5.34498 & 0.655022 \tabularnewline
79 & 8 & 10.9307 & -2.93071 \tabularnewline
80 & 5.5 & 3.0177 & 2.4823 \tabularnewline
81 & 9.5 & 7.85735 & 1.64265 \tabularnewline
82 & 7.5 & 5.98052 & 1.51948 \tabularnewline
83 & 7 & 5.3624 & 1.6376 \tabularnewline
84 & 8 & 7.77622 & 0.223778 \tabularnewline
85 & 7 & 6.32708 & 0.672923 \tabularnewline
86 & 7 & 8.66849 & -1.66849 \tabularnewline
87 & 6 & 2.75873 & 3.24127 \tabularnewline
88 & 10 & 14.3781 & -4.37808 \tabularnewline
89 & 2.5 & 3.83124 & -1.33124 \tabularnewline
90 & 8 & 7.65741 & 0.342586 \tabularnewline
91 & 6 & 2.31046 & 3.68954 \tabularnewline
92 & 8.5 & 8.14886 & 0.351137 \tabularnewline
93 & 6 & 5.27885 & 0.721155 \tabularnewline
94 & 9 & 9.41038 & -0.410376 \tabularnewline
95 & 5.5 & 4.13395 & 1.36605 \tabularnewline
96 & 9 & 7.0921 & 1.9079 \tabularnewline
97 & 8.5 & 6.86262 & 1.63738 \tabularnewline
98 & 9 & 6.61372 & 2.38628 \tabularnewline
99 & 9 & 8.70081 & 0.299193 \tabularnewline
100 & 7.5 & 4.87714 & 2.62286 \tabularnewline
101 & 10 & 11.3408 & -1.34078 \tabularnewline
102 & 8.5 & 5.0148 & 3.4852 \tabularnewline
103 & 10 & 8.4087 & 1.5913 \tabularnewline
104 & 6.5 & 2.73058 & 3.76942 \tabularnewline
105 & 8.5 & 8.1756 & 0.324402 \tabularnewline
106 & 8 & 6.41339 & 1.58661 \tabularnewline
107 & 7 & 7.25834 & -0.258342 \tabularnewline
108 & 7.5 & 7.75834 & -0.258342 \tabularnewline
109 & 7.5 & 4.50228 & 2.99772 \tabularnewline
110 & 9.5 & 10.0264 & -0.526386 \tabularnewline
111 & 6 & 4.64322 & 1.35678 \tabularnewline
112 & 7 & 5.36478 & 1.63522 \tabularnewline
113 & 10 & 11.7024 & -1.70238 \tabularnewline
114 & 3.5 & 3.69635 & -0.196345 \tabularnewline
115 & 6.5 & 5.88091 & 0.619085 \tabularnewline
116 & 6.5 & 4.05143 & 2.44857 \tabularnewline
117 & 8.5 & 10.0557 & -1.55574 \tabularnewline
118 & 4 & 3.22684 & 0.77316 \tabularnewline
119 & 8.5 & 5.90561 & 2.59439 \tabularnewline
120 & 10 & 8.22205 & 1.77795 \tabularnewline
121 & 8 & 8.50288 & -0.502878 \tabularnewline
122 & 5 & 7.81035 & -2.81035 \tabularnewline
123 & 4.5 & 1.80239 & 2.69761 \tabularnewline
124 & 8.5 & 7.81826 & 0.681744 \tabularnewline
125 & 7 & 5.45408 & 1.54592 \tabularnewline
126 & 8.5 & 6.07569 & 2.42431 \tabularnewline
127 & 7.5 & 5.73197 & 1.76803 \tabularnewline
128 & 7.5 & 8.5059 & -1.0059 \tabularnewline
129 & 5.5 & 3.20473 & 2.29527 \tabularnewline
130 & 8.5 & 5.83374 & 2.66626 \tabularnewline
131 & 9.5 & 8.34205 & 1.15795 \tabularnewline
132 & 7 & 6.00429 & 0.995714 \tabularnewline
133 & 6.5 & 5.16982 & 1.33018 \tabularnewline
134 & 6.5 & 2.41392 & 4.08608 \tabularnewline
135 & 10 & 7.39521 & 2.60479 \tabularnewline
136 & 10 & 8.18004 & 1.81996 \tabularnewline
137 & 7.5 & 8.49034 & -0.990341 \tabularnewline
138 & 4.5 & 5.64096 & -1.14096 \tabularnewline
139 & 4.5 & 9.88397 & -5.38397 \tabularnewline
140 & 0.5 & 3.36053 & -2.86053 \tabularnewline
141 & 4.5 & 6.08579 & -1.58579 \tabularnewline
142 & 5.5 & 5.87407 & -0.374066 \tabularnewline
143 & 5 & 2.16637 & 2.83363 \tabularnewline
144 & 8 & 4.81159 & 3.18841 \tabularnewline
145 & 8.5 & 7.49399 & 1.00601 \tabularnewline
146 & 6.5 & 6.2916 & 0.2084 \tabularnewline
147 & 8 & 7.97237 & 0.027632 \tabularnewline
148 & 5.5 & 6.64647 & -1.14647 \tabularnewline
149 & 5 & 6.83044 & -1.83044 \tabularnewline
150 & 3.5 & 2.07954 & 1.42046 \tabularnewline
151 & 9 & 10.0249 & -1.02488 \tabularnewline
152 & 5 & 6.85487 & -1.85487 \tabularnewline
153 & 3 & 7.52657 & -4.52657 \tabularnewline
154 & 0.5 & -1.0913 & 1.5913 \tabularnewline
155 & 6.5 & 7.67243 & -1.17243 \tabularnewline
156 & 4.5 & 1.66637 & 2.83363 \tabularnewline
157 & 8 & 7.49052 & 0.509481 \tabularnewline
158 & 7.5 & 3.64225 & 3.85775 \tabularnewline
159 & 9.5 & 8.17351 & 1.32649 \tabularnewline
160 & 6.5 & 5.85737 & 0.642627 \tabularnewline
161 & 6 & 4.02726 & 1.97274 \tabularnewline
162 & 8 & 8.92946 & -0.929458 \tabularnewline
163 & 5 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267726&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]6[/C][C]4.71696[/C][C]1.28304[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]5.07599[/C][C]-4.07599[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]4.83222[/C][C]-3.83222[/C][/ROW]
[ROW][C]4[/C][C]5.5[/C][C]5.85014[/C][C]-0.350144[/C][/ROW]
[ROW][C]5[/C][C]6.5[/C][C]5.29865[/C][C]1.20135[/C][/ROW]
[ROW][C]6[/C][C]4.5[/C][C]4.56679[/C][C]-0.0667887[/C][/ROW]
[ROW][C]7[/C][C]2[/C][C]6.21137[/C][C]-4.21137[/C][/ROW]
[ROW][C]8[/C][C]5[/C][C]5.35606[/C][C]-0.356061[/C][/ROW]
[ROW][C]9[/C][C]0.5[/C][C]6.46156[/C][C]-5.96156[/C][/ROW]
[ROW][C]10[/C][C]5[/C][C]5.01896[/C][C]-0.018959[/C][/ROW]
[ROW][C]11[/C][C]5.5[/C][C]5.68072[/C][C]-0.180716[/C][/ROW]
[ROW][C]12[/C][C]3[/C][C]5.17211[/C][C]-2.17211[/C][/ROW]
[ROW][C]13[/C][C]0.5[/C][C]4.7813[/C][C]-4.2813[/C][/ROW]
[ROW][C]14[/C][C]6.5[/C][C]4.75834[/C][C]1.74166[/C][/ROW]
[ROW][C]15[/C][C]7.5[/C][C]4.29745[/C][C]3.20255[/C][/ROW]
[ROW][C]16[/C][C]5.5[/C][C]5.17488[/C][C]0.325122[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]5.62823[/C][C]-1.62823[/C][/ROW]
[ROW][C]18[/C][C]7.5[/C][C]4.84728[/C][C]2.65272[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]4.40799[/C][C]-0.407988[/C][/ROW]
[ROW][C]20[/C][C]0.5[/C][C]4.97005[/C][C]-4.47005[/C][/ROW]
[ROW][C]21[/C][C]3.5[/C][C]5.45515[/C][C]-1.95515[/C][/ROW]
[ROW][C]22[/C][C]2.5[/C][C]5.72642[/C][C]-3.22642[/C][/ROW]
[ROW][C]23[/C][C]4.5[/C][C]5.76505[/C][C]-1.26505[/C][/ROW]
[ROW][C]24[/C][C]4.5[/C][C]5.97806[/C][C]-1.47806[/C][/ROW]
[ROW][C]25[/C][C]6[/C][C]5.58377[/C][C]0.416235[/C][/ROW]
[ROW][C]26[/C][C]2.5[/C][C]4.40053[/C][C]-1.90053[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]4.55881[/C][C]-4.55881[/C][/ROW]
[ROW][C]28[/C][C]5[/C][C]6.08626[/C][C]-1.08626[/C][/ROW]
[ROW][C]29[/C][C]6.5[/C][C]4.37846[/C][C]2.12154[/C][/ROW]
[ROW][C]30[/C][C]5[/C][C]5.28806[/C][C]-0.288062[/C][/ROW]
[ROW][C]31[/C][C]6[/C][C]5.15495[/C][C]0.845055[/C][/ROW]
[ROW][C]32[/C][C]5.5[/C][C]5.77704[/C][C]-0.277039[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]4.75684[/C][C]-3.75684[/C][/ROW]
[ROW][C]34[/C][C]6[/C][C]5.0556[/C][C]0.944402[/C][/ROW]
[ROW][C]35[/C][C]5[/C][C]5.39865[/C][C]-0.39865[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]5.93102[/C][C]-4.93102[/C][/ROW]
[ROW][C]37[/C][C]5[/C][C]5.97306[/C][C]-0.973059[/C][/ROW]
[ROW][C]38[/C][C]6.5[/C][C]5.49706[/C][C]1.00294[/C][/ROW]
[ROW][C]39[/C][C]7[/C][C]6.12174[/C][C]0.878257[/C][/ROW]
[ROW][C]40[/C][C]4.5[/C][C]6.46247[/C][C]-1.96247[/C][/ROW]
[ROW][C]41[/C][C]8.5[/C][C]5.21418[/C][C]3.28582[/C][/ROW]
[ROW][C]42[/C][C]7.5[/C][C]5.52881[/C][C]1.97119[/C][/ROW]
[ROW][C]43[/C][C]3.5[/C][C]5.0913[/C][C]-1.5913[/C][/ROW]
[ROW][C]44[/C][C]9[/C][C]5.31164[/C][C]3.68836[/C][/ROW]
[ROW][C]45[/C][C]3.5[/C][C]5.13803[/C][C]-1.63803[/C][/ROW]
[ROW][C]46[/C][C]6.5[/C][C]5.02082[/C][C]1.47918[/C][/ROW]
[ROW][C]47[/C][C]7.5[/C][C]4.72847[/C][C]2.77153[/C][/ROW]
[ROW][C]48[/C][C]7.5[/C][C]4.58049[/C][C]2.91951[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]4.74395[/C][C]-3.74395[/C][/ROW]
[ROW][C]50[/C][C]NA[/C][C]NA[/C][C]1.53879[/C][/ROW]
[ROW][C]51[/C][C]6.5[/C][C]10.0334[/C][C]-3.53341[/C][/ROW]
[ROW][C]52[/C][C]1.5[/C][C]6.07851[/C][C]-4.57851[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]-0.440684[/C][C]0.440684[/C][/ROW]
[ROW][C]54[/C][C]5.5[/C][C]5.25509[/C][C]0.244912[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]3.02855[/C][C]1.97145[/C][/ROW]
[ROW][C]56[/C][C]7[/C][C]12.0408[/C][C]-5.04085[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.383556[/C][C]-0.383556[/C][/ROW]
[ROW][C]58[/C][C]4.5[/C][C]8.58197[/C][C]-4.08197[/C][/ROW]
[ROW][C]59[/C][C]1.5[/C][C]4.56089[/C][C]-3.06089[/C][/ROW]
[ROW][C]60[/C][C]2.5[/C][C]2.10901[/C][C]0.390992[/C][/ROW]
[ROW][C]61[/C][C]5.5[/C][C]3.03779[/C][C]2.46221[/C][/ROW]
[ROW][C]62[/C][C]8[/C][C]11.3208[/C][C]-3.32081[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]1.46086[/C][C]-0.460858[/C][/ROW]
[ROW][C]64[/C][C]5[/C][C]6.77741[/C][C]-1.77741[/C][/ROW]
[ROW][C]65[/C][C]3[/C][C]4.4174[/C][C]-1.4174[/C][/ROW]
[ROW][C]66[/C][C]3[/C][C]0.528386[/C][C]2.47161[/C][/ROW]
[ROW][C]67[/C][C]8[/C][C]7.18055[/C][C]0.819445[/C][/ROW]
[ROW][C]68[/C][C]5.5[/C][C]10.0641[/C][C]-4.56408[/C][/ROW]
[ROW][C]69[/C][C]0.5[/C][C]-0.208549[/C][C]0.708549[/C][/ROW]
[ROW][C]70[/C][C]7.5[/C][C]5.36422[/C][C]2.13578[/C][/ROW]
[ROW][C]71[/C][C]9[/C][C]6.54603[/C][C]2.45397[/C][/ROW]
[ROW][C]72[/C][C]9.5[/C][C]7.42758[/C][C]2.07242[/C][/ROW]
[ROW][C]73[/C][C]7[/C][C]10.1428[/C][C]-3.14284[/C][/ROW]
[ROW][C]74[/C][C]8[/C][C]9.37117[/C][C]-1.37117[/C][/ROW]
[ROW][C]75[/C][C]7[/C][C]3.50859[/C][C]3.49141[/C][/ROW]
[ROW][C]76[/C][C]9.5[/C][C]12.9534[/C][C]-3.45337[/C][/ROW]
[ROW][C]77[/C][C]4[/C][C]4.85552[/C][C]-0.855516[/C][/ROW]
[ROW][C]78[/C][C]6[/C][C]5.34498[/C][C]0.655022[/C][/ROW]
[ROW][C]79[/C][C]8[/C][C]10.9307[/C][C]-2.93071[/C][/ROW]
[ROW][C]80[/C][C]5.5[/C][C]3.0177[/C][C]2.4823[/C][/ROW]
[ROW][C]81[/C][C]9.5[/C][C]7.85735[/C][C]1.64265[/C][/ROW]
[ROW][C]82[/C][C]7.5[/C][C]5.98052[/C][C]1.51948[/C][/ROW]
[ROW][C]83[/C][C]7[/C][C]5.3624[/C][C]1.6376[/C][/ROW]
[ROW][C]84[/C][C]8[/C][C]7.77622[/C][C]0.223778[/C][/ROW]
[ROW][C]85[/C][C]7[/C][C]6.32708[/C][C]0.672923[/C][/ROW]
[ROW][C]86[/C][C]7[/C][C]8.66849[/C][C]-1.66849[/C][/ROW]
[ROW][C]87[/C][C]6[/C][C]2.75873[/C][C]3.24127[/C][/ROW]
[ROW][C]88[/C][C]10[/C][C]14.3781[/C][C]-4.37808[/C][/ROW]
[ROW][C]89[/C][C]2.5[/C][C]3.83124[/C][C]-1.33124[/C][/ROW]
[ROW][C]90[/C][C]8[/C][C]7.65741[/C][C]0.342586[/C][/ROW]
[ROW][C]91[/C][C]6[/C][C]2.31046[/C][C]3.68954[/C][/ROW]
[ROW][C]92[/C][C]8.5[/C][C]8.14886[/C][C]0.351137[/C][/ROW]
[ROW][C]93[/C][C]6[/C][C]5.27885[/C][C]0.721155[/C][/ROW]
[ROW][C]94[/C][C]9[/C][C]9.41038[/C][C]-0.410376[/C][/ROW]
[ROW][C]95[/C][C]5.5[/C][C]4.13395[/C][C]1.36605[/C][/ROW]
[ROW][C]96[/C][C]9[/C][C]7.0921[/C][C]1.9079[/C][/ROW]
[ROW][C]97[/C][C]8.5[/C][C]6.86262[/C][C]1.63738[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]6.61372[/C][C]2.38628[/C][/ROW]
[ROW][C]99[/C][C]9[/C][C]8.70081[/C][C]0.299193[/C][/ROW]
[ROW][C]100[/C][C]7.5[/C][C]4.87714[/C][C]2.62286[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]11.3408[/C][C]-1.34078[/C][/ROW]
[ROW][C]102[/C][C]8.5[/C][C]5.0148[/C][C]3.4852[/C][/ROW]
[ROW][C]103[/C][C]10[/C][C]8.4087[/C][C]1.5913[/C][/ROW]
[ROW][C]104[/C][C]6.5[/C][C]2.73058[/C][C]3.76942[/C][/ROW]
[ROW][C]105[/C][C]8.5[/C][C]8.1756[/C][C]0.324402[/C][/ROW]
[ROW][C]106[/C][C]8[/C][C]6.41339[/C][C]1.58661[/C][/ROW]
[ROW][C]107[/C][C]7[/C][C]7.25834[/C][C]-0.258342[/C][/ROW]
[ROW][C]108[/C][C]7.5[/C][C]7.75834[/C][C]-0.258342[/C][/ROW]
[ROW][C]109[/C][C]7.5[/C][C]4.50228[/C][C]2.99772[/C][/ROW]
[ROW][C]110[/C][C]9.5[/C][C]10.0264[/C][C]-0.526386[/C][/ROW]
[ROW][C]111[/C][C]6[/C][C]4.64322[/C][C]1.35678[/C][/ROW]
[ROW][C]112[/C][C]7[/C][C]5.36478[/C][C]1.63522[/C][/ROW]
[ROW][C]113[/C][C]10[/C][C]11.7024[/C][C]-1.70238[/C][/ROW]
[ROW][C]114[/C][C]3.5[/C][C]3.69635[/C][C]-0.196345[/C][/ROW]
[ROW][C]115[/C][C]6.5[/C][C]5.88091[/C][C]0.619085[/C][/ROW]
[ROW][C]116[/C][C]6.5[/C][C]4.05143[/C][C]2.44857[/C][/ROW]
[ROW][C]117[/C][C]8.5[/C][C]10.0557[/C][C]-1.55574[/C][/ROW]
[ROW][C]118[/C][C]4[/C][C]3.22684[/C][C]0.77316[/C][/ROW]
[ROW][C]119[/C][C]8.5[/C][C]5.90561[/C][C]2.59439[/C][/ROW]
[ROW][C]120[/C][C]10[/C][C]8.22205[/C][C]1.77795[/C][/ROW]
[ROW][C]121[/C][C]8[/C][C]8.50288[/C][C]-0.502878[/C][/ROW]
[ROW][C]122[/C][C]5[/C][C]7.81035[/C][C]-2.81035[/C][/ROW]
[ROW][C]123[/C][C]4.5[/C][C]1.80239[/C][C]2.69761[/C][/ROW]
[ROW][C]124[/C][C]8.5[/C][C]7.81826[/C][C]0.681744[/C][/ROW]
[ROW][C]125[/C][C]7[/C][C]5.45408[/C][C]1.54592[/C][/ROW]
[ROW][C]126[/C][C]8.5[/C][C]6.07569[/C][C]2.42431[/C][/ROW]
[ROW][C]127[/C][C]7.5[/C][C]5.73197[/C][C]1.76803[/C][/ROW]
[ROW][C]128[/C][C]7.5[/C][C]8.5059[/C][C]-1.0059[/C][/ROW]
[ROW][C]129[/C][C]5.5[/C][C]3.20473[/C][C]2.29527[/C][/ROW]
[ROW][C]130[/C][C]8.5[/C][C]5.83374[/C][C]2.66626[/C][/ROW]
[ROW][C]131[/C][C]9.5[/C][C]8.34205[/C][C]1.15795[/C][/ROW]
[ROW][C]132[/C][C]7[/C][C]6.00429[/C][C]0.995714[/C][/ROW]
[ROW][C]133[/C][C]6.5[/C][C]5.16982[/C][C]1.33018[/C][/ROW]
[ROW][C]134[/C][C]6.5[/C][C]2.41392[/C][C]4.08608[/C][/ROW]
[ROW][C]135[/C][C]10[/C][C]7.39521[/C][C]2.60479[/C][/ROW]
[ROW][C]136[/C][C]10[/C][C]8.18004[/C][C]1.81996[/C][/ROW]
[ROW][C]137[/C][C]7.5[/C][C]8.49034[/C][C]-0.990341[/C][/ROW]
[ROW][C]138[/C][C]4.5[/C][C]5.64096[/C][C]-1.14096[/C][/ROW]
[ROW][C]139[/C][C]4.5[/C][C]9.88397[/C][C]-5.38397[/C][/ROW]
[ROW][C]140[/C][C]0.5[/C][C]3.36053[/C][C]-2.86053[/C][/ROW]
[ROW][C]141[/C][C]4.5[/C][C]6.08579[/C][C]-1.58579[/C][/ROW]
[ROW][C]142[/C][C]5.5[/C][C]5.87407[/C][C]-0.374066[/C][/ROW]
[ROW][C]143[/C][C]5[/C][C]2.16637[/C][C]2.83363[/C][/ROW]
[ROW][C]144[/C][C]8[/C][C]4.81159[/C][C]3.18841[/C][/ROW]
[ROW][C]145[/C][C]8.5[/C][C]7.49399[/C][C]1.00601[/C][/ROW]
[ROW][C]146[/C][C]6.5[/C][C]6.2916[/C][C]0.2084[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]7.97237[/C][C]0.027632[/C][/ROW]
[ROW][C]148[/C][C]5.5[/C][C]6.64647[/C][C]-1.14647[/C][/ROW]
[ROW][C]149[/C][C]5[/C][C]6.83044[/C][C]-1.83044[/C][/ROW]
[ROW][C]150[/C][C]3.5[/C][C]2.07954[/C][C]1.42046[/C][/ROW]
[ROW][C]151[/C][C]9[/C][C]10.0249[/C][C]-1.02488[/C][/ROW]
[ROW][C]152[/C][C]5[/C][C]6.85487[/C][C]-1.85487[/C][/ROW]
[ROW][C]153[/C][C]3[/C][C]7.52657[/C][C]-4.52657[/C][/ROW]
[ROW][C]154[/C][C]0.5[/C][C]-1.0913[/C][C]1.5913[/C][/ROW]
[ROW][C]155[/C][C]6.5[/C][C]7.67243[/C][C]-1.17243[/C][/ROW]
[ROW][C]156[/C][C]4.5[/C][C]1.66637[/C][C]2.83363[/C][/ROW]
[ROW][C]157[/C][C]8[/C][C]7.49052[/C][C]0.509481[/C][/ROW]
[ROW][C]158[/C][C]7.5[/C][C]3.64225[/C][C]3.85775[/C][/ROW]
[ROW][C]159[/C][C]9.5[/C][C]8.17351[/C][C]1.32649[/C][/ROW]
[ROW][C]160[/C][C]6.5[/C][C]5.85737[/C][C]0.642627[/C][/ROW]
[ROW][C]161[/C][C]6[/C][C]4.02726[/C][C]1.97274[/C][/ROW]
[ROW][C]162[/C][C]8[/C][C]8.92946[/C][C]-0.929458[/C][/ROW]
[ROW][C]163[/C][C]5[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267726&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267726&T=4

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
164.716961.28304
215.07599-4.07599
314.83222-3.83222
45.55.85014-0.350144
56.55.298651.20135
64.54.56679-0.0667887
726.21137-4.21137
855.35606-0.356061
90.56.46156-5.96156
1055.01896-0.018959
115.55.68072-0.180716
1235.17211-2.17211
130.54.7813-4.2813
146.54.758341.74166
157.54.297453.20255
165.55.174880.325122
1745.62823-1.62823
187.54.847282.65272
1944.40799-0.407988
200.54.97005-4.47005
213.55.45515-1.95515
222.55.72642-3.22642
234.55.76505-1.26505
244.55.97806-1.47806
2565.583770.416235
262.54.40053-1.90053
2704.55881-4.55881
2856.08626-1.08626
296.54.378462.12154
3055.28806-0.288062
3165.154950.845055
325.55.77704-0.277039
3314.75684-3.75684
3465.05560.944402
3555.39865-0.39865
3615.93102-4.93102
3755.97306-0.973059
386.55.497061.00294
3976.121740.878257
404.56.46247-1.96247
418.55.214183.28582
427.55.528811.97119
433.55.0913-1.5913
4495.311643.68836
453.55.13803-1.63803
466.55.020821.47918
477.54.728472.77153
487.54.580492.91951
4914.74395-3.74395
50NANA1.53879
516.510.0334-3.53341
521.56.07851-4.57851
530-0.4406840.440684
545.55.255090.244912
5553.028551.97145
56712.0408-5.04085
5700.383556-0.383556
584.58.58197-4.08197
591.54.56089-3.06089
602.52.109010.390992
615.53.037792.46221
62811.3208-3.32081
6311.46086-0.460858
6456.77741-1.77741
6534.4174-1.4174
6630.5283862.47161
6787.180550.819445
685.510.0641-4.56408
690.5-0.2085490.708549
707.55.364222.13578
7196.546032.45397
729.57.427582.07242
73710.1428-3.14284
7489.37117-1.37117
7573.508593.49141
769.512.9534-3.45337
7744.85552-0.855516
7865.344980.655022
79810.9307-2.93071
805.53.01772.4823
819.57.857351.64265
827.55.980521.51948
8375.36241.6376
8487.776220.223778
8576.327080.672923
8678.66849-1.66849
8762.758733.24127
881014.3781-4.37808
892.53.83124-1.33124
9087.657410.342586
9162.310463.68954
928.58.148860.351137
9365.278850.721155
9499.41038-0.410376
955.54.133951.36605
9697.09211.9079
978.56.862621.63738
9896.613722.38628
9998.700810.299193
1007.54.877142.62286
1011011.3408-1.34078
1028.55.01483.4852
103108.40871.5913
1046.52.730583.76942
1058.58.17560.324402
10686.413391.58661
10777.25834-0.258342
1087.57.75834-0.258342
1097.54.502282.99772
1109.510.0264-0.526386
11164.643221.35678
11275.364781.63522
1131011.7024-1.70238
1143.53.69635-0.196345
1156.55.880910.619085
1166.54.051432.44857
1178.510.0557-1.55574
11843.226840.77316
1198.55.905612.59439
120108.222051.77795
12188.50288-0.502878
12257.81035-2.81035
1234.51.802392.69761
1248.57.818260.681744
12575.454081.54592
1268.56.075692.42431
1277.55.731971.76803
1287.58.5059-1.0059
1295.53.204732.29527
1308.55.833742.66626
1319.58.342051.15795
13276.004290.995714
1336.55.169821.33018
1346.52.413924.08608
135107.395212.60479
136108.180041.81996
1377.58.49034-0.990341
1384.55.64096-1.14096
1394.59.88397-5.38397
1400.53.36053-2.86053
1414.56.08579-1.58579
1425.55.87407-0.374066
14352.166372.83363
14484.811593.18841
1458.57.493991.00601
1466.56.29160.2084
14787.972370.027632
1485.56.64647-1.14647
14956.83044-1.83044
1503.52.079541.42046
151910.0249-1.02488
15256.85487-1.85487
15337.52657-4.52657
1540.5-1.09131.5913
1556.57.67243-1.17243
1564.51.666372.83363
15787.490520.509481
1587.53.642253.85775
1599.58.173511.32649
1606.55.857370.642627
16164.027261.97274
16288.92946-0.929458
1635NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.865820.268360.13418
90.8619150.276170.138085
100.783980.4320410.21602
110.7296940.5406120.270306
120.6506940.6986120.349306
130.6841080.6317840.315892
140.5937610.8124790.406239
150.5938790.8122410.406121
160.5314380.9371240.468562
170.4494340.8988680.550566
180.3766820.7533630.623318
190.3014010.6028020.698599
200.3369050.6738090.663095
210.2916570.5833150.708343
220.3323240.6646490.667676
230.3076080.6152170.692392
240.2709230.5418450.729077
250.2967810.5935620.703219
260.3371660.6743310.662834
270.4387130.8774250.561287
280.3944040.7888080.605596
290.3975930.7951870.602407
300.3877520.7755040.612248
310.3999220.7998440.600078
320.3658610.7317210.634139
330.4847790.9695570.515221
340.4883830.9767670.511617
350.4470910.8941820.552909
360.4892180.9784360.510782
370.4766350.9532710.523365
380.5073750.9852490.492625
390.5173050.9653890.482695
400.479920.959840.52008
410.6043670.7912660.395633
420.6455470.7089050.354453
430.6158590.7682820.384141
440.7113270.5773450.288673
450.6745370.6509260.325463
460.6407220.7185570.359278
470.6473920.7052150.352608
480.6624570.6750860.337543
490.7509490.4981030.249051
500.7403840.5192310.259616
510.7794170.4411660.220583
520.8524610.2950780.147539
530.8275040.3449920.172496
540.7977210.4045590.202279
550.815640.368720.18436
560.8965890.2068220.103411
570.8845580.2308830.115442
580.9114470.1771060.0885528
590.9225010.1549980.0774988
600.9134490.1731030.0865514
610.9372730.1254540.0627268
620.9613980.07720450.0386023
630.9539840.09203150.0460157
640.9530290.09394220.0469711
650.955640.08872010.0443601
660.9621160.07576770.0378839
670.956460.08708040.0435402
680.9794110.04117710.0205885
690.9772260.04554840.0227742
700.9798570.04028530.0201426
710.9824290.03514110.0175705
720.9839220.03215520.0160776
730.9825320.03493640.0174682
740.9832520.03349510.0167476
750.9919840.01603150.00801576
760.9935910.0128190.00640948
770.9916320.01673570.00836786
780.9897210.02055750.0102787
790.9899680.02006350.0100317
800.990.02000090.0100004
810.9889610.02207850.0110392
820.9871630.0256740.012837
830.9854880.02902390.0145119
840.9811710.0376570.0188285
850.9765140.04697130.0234856
860.9721510.05569740.0278487
870.9806860.03862840.0193142
880.9896360.02072820.0103641
890.9875650.02486990.0124349
900.9837440.03251140.0162557
910.9889540.0220920.011046
920.9864590.0270810.0135405
930.9830440.03391150.0169557
940.9788360.04232720.0211636
950.975210.04958030.0247901
960.9713260.05734710.0286736
970.9673310.0653380.032669
980.9708010.05839810.0291991
990.9633540.07329110.0366455
1000.9592070.08158650.0407933
1010.9479550.104090.0520449
1020.9703150.05936980.0296849
1030.9651210.06975760.0348788
1040.980090.03982020.0199101
1050.973460.05307960.0265398
1060.9667120.06657610.0332881
1070.9563270.0873460.043673
1080.9434870.1130260.0565128
1090.9425070.1149850.0574927
1100.9358740.1282510.0641257
1110.9213860.1572270.0786136
1120.9065930.1868140.0934072
1130.9139420.1721150.0860576
1140.8918650.216270.108135
1150.8676620.2646770.132338
1160.8482170.3035650.151783
1170.8361640.3276730.163836
1180.8111990.3776020.188801
1190.8030.3940010.197
1200.7897940.4204120.210206
1210.7496320.5007350.250368
1220.7535170.4929650.246483
1230.7693370.4613270.230663
1240.7311610.5376770.268839
1250.6889850.6220290.311015
1260.6604190.6791620.339581
1270.6240360.7519280.375964
1280.5780790.8438430.421921
1290.5492730.9014540.450727
1300.5708170.8583660.429183
1310.5143010.9713980.485699
1320.4782760.9565510.521724
1330.4602080.9204160.539792
1340.5886960.8226090.411304
1350.5365940.9268120.463406
1360.5141250.9717510.485875
1370.4548760.9097530.545124
1380.396230.792460.60377
1390.6402540.7194930.359746
1400.6366550.726690.363345
1410.6232260.7535480.376774
1420.5561890.8876220.443811
1430.5626890.8746220.437311
1440.6544890.6910220.345511
1450.5960610.8078780.403939
1460.5179410.9641170.482059
1470.4295410.8590830.570459
1480.3418250.683650.658175
1490.2663090.5326170.733691
1500.1891490.3782980.810851
1510.1800190.3600370.819981
1520.146440.2928790.85356
1530.8548910.2902190.145109
1540.9704830.05903310.0295166
1550.917440.165120.0825602

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.86582 & 0.26836 & 0.13418 \tabularnewline
9 & 0.861915 & 0.27617 & 0.138085 \tabularnewline
10 & 0.78398 & 0.432041 & 0.21602 \tabularnewline
11 & 0.729694 & 0.540612 & 0.270306 \tabularnewline
12 & 0.650694 & 0.698612 & 0.349306 \tabularnewline
13 & 0.684108 & 0.631784 & 0.315892 \tabularnewline
14 & 0.593761 & 0.812479 & 0.406239 \tabularnewline
15 & 0.593879 & 0.812241 & 0.406121 \tabularnewline
16 & 0.531438 & 0.937124 & 0.468562 \tabularnewline
17 & 0.449434 & 0.898868 & 0.550566 \tabularnewline
18 & 0.376682 & 0.753363 & 0.623318 \tabularnewline
19 & 0.301401 & 0.602802 & 0.698599 \tabularnewline
20 & 0.336905 & 0.673809 & 0.663095 \tabularnewline
21 & 0.291657 & 0.583315 & 0.708343 \tabularnewline
22 & 0.332324 & 0.664649 & 0.667676 \tabularnewline
23 & 0.307608 & 0.615217 & 0.692392 \tabularnewline
24 & 0.270923 & 0.541845 & 0.729077 \tabularnewline
25 & 0.296781 & 0.593562 & 0.703219 \tabularnewline
26 & 0.337166 & 0.674331 & 0.662834 \tabularnewline
27 & 0.438713 & 0.877425 & 0.561287 \tabularnewline
28 & 0.394404 & 0.788808 & 0.605596 \tabularnewline
29 & 0.397593 & 0.795187 & 0.602407 \tabularnewline
30 & 0.387752 & 0.775504 & 0.612248 \tabularnewline
31 & 0.399922 & 0.799844 & 0.600078 \tabularnewline
32 & 0.365861 & 0.731721 & 0.634139 \tabularnewline
33 & 0.484779 & 0.969557 & 0.515221 \tabularnewline
34 & 0.488383 & 0.976767 & 0.511617 \tabularnewline
35 & 0.447091 & 0.894182 & 0.552909 \tabularnewline
36 & 0.489218 & 0.978436 & 0.510782 \tabularnewline
37 & 0.476635 & 0.953271 & 0.523365 \tabularnewline
38 & 0.507375 & 0.985249 & 0.492625 \tabularnewline
39 & 0.517305 & 0.965389 & 0.482695 \tabularnewline
40 & 0.47992 & 0.95984 & 0.52008 \tabularnewline
41 & 0.604367 & 0.791266 & 0.395633 \tabularnewline
42 & 0.645547 & 0.708905 & 0.354453 \tabularnewline
43 & 0.615859 & 0.768282 & 0.384141 \tabularnewline
44 & 0.711327 & 0.577345 & 0.288673 \tabularnewline
45 & 0.674537 & 0.650926 & 0.325463 \tabularnewline
46 & 0.640722 & 0.718557 & 0.359278 \tabularnewline
47 & 0.647392 & 0.705215 & 0.352608 \tabularnewline
48 & 0.662457 & 0.675086 & 0.337543 \tabularnewline
49 & 0.750949 & 0.498103 & 0.249051 \tabularnewline
50 & 0.740384 & 0.519231 & 0.259616 \tabularnewline
51 & 0.779417 & 0.441166 & 0.220583 \tabularnewline
52 & 0.852461 & 0.295078 & 0.147539 \tabularnewline
53 & 0.827504 & 0.344992 & 0.172496 \tabularnewline
54 & 0.797721 & 0.404559 & 0.202279 \tabularnewline
55 & 0.81564 & 0.36872 & 0.18436 \tabularnewline
56 & 0.896589 & 0.206822 & 0.103411 \tabularnewline
57 & 0.884558 & 0.230883 & 0.115442 \tabularnewline
58 & 0.911447 & 0.177106 & 0.0885528 \tabularnewline
59 & 0.922501 & 0.154998 & 0.0774988 \tabularnewline
60 & 0.913449 & 0.173103 & 0.0865514 \tabularnewline
61 & 0.937273 & 0.125454 & 0.0627268 \tabularnewline
62 & 0.961398 & 0.0772045 & 0.0386023 \tabularnewline
63 & 0.953984 & 0.0920315 & 0.0460157 \tabularnewline
64 & 0.953029 & 0.0939422 & 0.0469711 \tabularnewline
65 & 0.95564 & 0.0887201 & 0.0443601 \tabularnewline
66 & 0.962116 & 0.0757677 & 0.0378839 \tabularnewline
67 & 0.95646 & 0.0870804 & 0.0435402 \tabularnewline
68 & 0.979411 & 0.0411771 & 0.0205885 \tabularnewline
69 & 0.977226 & 0.0455484 & 0.0227742 \tabularnewline
70 & 0.979857 & 0.0402853 & 0.0201426 \tabularnewline
71 & 0.982429 & 0.0351411 & 0.0175705 \tabularnewline
72 & 0.983922 & 0.0321552 & 0.0160776 \tabularnewline
73 & 0.982532 & 0.0349364 & 0.0174682 \tabularnewline
74 & 0.983252 & 0.0334951 & 0.0167476 \tabularnewline
75 & 0.991984 & 0.0160315 & 0.00801576 \tabularnewline
76 & 0.993591 & 0.012819 & 0.00640948 \tabularnewline
77 & 0.991632 & 0.0167357 & 0.00836786 \tabularnewline
78 & 0.989721 & 0.0205575 & 0.0102787 \tabularnewline
79 & 0.989968 & 0.0200635 & 0.0100317 \tabularnewline
80 & 0.99 & 0.0200009 & 0.0100004 \tabularnewline
81 & 0.988961 & 0.0220785 & 0.0110392 \tabularnewline
82 & 0.987163 & 0.025674 & 0.012837 \tabularnewline
83 & 0.985488 & 0.0290239 & 0.0145119 \tabularnewline
84 & 0.981171 & 0.037657 & 0.0188285 \tabularnewline
85 & 0.976514 & 0.0469713 & 0.0234856 \tabularnewline
86 & 0.972151 & 0.0556974 & 0.0278487 \tabularnewline
87 & 0.980686 & 0.0386284 & 0.0193142 \tabularnewline
88 & 0.989636 & 0.0207282 & 0.0103641 \tabularnewline
89 & 0.987565 & 0.0248699 & 0.0124349 \tabularnewline
90 & 0.983744 & 0.0325114 & 0.0162557 \tabularnewline
91 & 0.988954 & 0.022092 & 0.011046 \tabularnewline
92 & 0.986459 & 0.027081 & 0.0135405 \tabularnewline
93 & 0.983044 & 0.0339115 & 0.0169557 \tabularnewline
94 & 0.978836 & 0.0423272 & 0.0211636 \tabularnewline
95 & 0.97521 & 0.0495803 & 0.0247901 \tabularnewline
96 & 0.971326 & 0.0573471 & 0.0286736 \tabularnewline
97 & 0.967331 & 0.065338 & 0.032669 \tabularnewline
98 & 0.970801 & 0.0583981 & 0.0291991 \tabularnewline
99 & 0.963354 & 0.0732911 & 0.0366455 \tabularnewline
100 & 0.959207 & 0.0815865 & 0.0407933 \tabularnewline
101 & 0.947955 & 0.10409 & 0.0520449 \tabularnewline
102 & 0.970315 & 0.0593698 & 0.0296849 \tabularnewline
103 & 0.965121 & 0.0697576 & 0.0348788 \tabularnewline
104 & 0.98009 & 0.0398202 & 0.0199101 \tabularnewline
105 & 0.97346 & 0.0530796 & 0.0265398 \tabularnewline
106 & 0.966712 & 0.0665761 & 0.0332881 \tabularnewline
107 & 0.956327 & 0.087346 & 0.043673 \tabularnewline
108 & 0.943487 & 0.113026 & 0.0565128 \tabularnewline
109 & 0.942507 & 0.114985 & 0.0574927 \tabularnewline
110 & 0.935874 & 0.128251 & 0.0641257 \tabularnewline
111 & 0.921386 & 0.157227 & 0.0786136 \tabularnewline
112 & 0.906593 & 0.186814 & 0.0934072 \tabularnewline
113 & 0.913942 & 0.172115 & 0.0860576 \tabularnewline
114 & 0.891865 & 0.21627 & 0.108135 \tabularnewline
115 & 0.867662 & 0.264677 & 0.132338 \tabularnewline
116 & 0.848217 & 0.303565 & 0.151783 \tabularnewline
117 & 0.836164 & 0.327673 & 0.163836 \tabularnewline
118 & 0.811199 & 0.377602 & 0.188801 \tabularnewline
119 & 0.803 & 0.394001 & 0.197 \tabularnewline
120 & 0.789794 & 0.420412 & 0.210206 \tabularnewline
121 & 0.749632 & 0.500735 & 0.250368 \tabularnewline
122 & 0.753517 & 0.492965 & 0.246483 \tabularnewline
123 & 0.769337 & 0.461327 & 0.230663 \tabularnewline
124 & 0.731161 & 0.537677 & 0.268839 \tabularnewline
125 & 0.688985 & 0.622029 & 0.311015 \tabularnewline
126 & 0.660419 & 0.679162 & 0.339581 \tabularnewline
127 & 0.624036 & 0.751928 & 0.375964 \tabularnewline
128 & 0.578079 & 0.843843 & 0.421921 \tabularnewline
129 & 0.549273 & 0.901454 & 0.450727 \tabularnewline
130 & 0.570817 & 0.858366 & 0.429183 \tabularnewline
131 & 0.514301 & 0.971398 & 0.485699 \tabularnewline
132 & 0.478276 & 0.956551 & 0.521724 \tabularnewline
133 & 0.460208 & 0.920416 & 0.539792 \tabularnewline
134 & 0.588696 & 0.822609 & 0.411304 \tabularnewline
135 & 0.536594 & 0.926812 & 0.463406 \tabularnewline
136 & 0.514125 & 0.971751 & 0.485875 \tabularnewline
137 & 0.454876 & 0.909753 & 0.545124 \tabularnewline
138 & 0.39623 & 0.79246 & 0.60377 \tabularnewline
139 & 0.640254 & 0.719493 & 0.359746 \tabularnewline
140 & 0.636655 & 0.72669 & 0.363345 \tabularnewline
141 & 0.623226 & 0.753548 & 0.376774 \tabularnewline
142 & 0.556189 & 0.887622 & 0.443811 \tabularnewline
143 & 0.562689 & 0.874622 & 0.437311 \tabularnewline
144 & 0.654489 & 0.691022 & 0.345511 \tabularnewline
145 & 0.596061 & 0.807878 & 0.403939 \tabularnewline
146 & 0.517941 & 0.964117 & 0.482059 \tabularnewline
147 & 0.429541 & 0.859083 & 0.570459 \tabularnewline
148 & 0.341825 & 0.68365 & 0.658175 \tabularnewline
149 & 0.266309 & 0.532617 & 0.733691 \tabularnewline
150 & 0.189149 & 0.378298 & 0.810851 \tabularnewline
151 & 0.180019 & 0.360037 & 0.819981 \tabularnewline
152 & 0.14644 & 0.292879 & 0.85356 \tabularnewline
153 & 0.854891 & 0.290219 & 0.145109 \tabularnewline
154 & 0.970483 & 0.0590331 & 0.0295166 \tabularnewline
155 & 0.91744 & 0.16512 & 0.0825602 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267726&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.86582[/C][C]0.26836[/C][C]0.13418[/C][/ROW]
[ROW][C]9[/C][C]0.861915[/C][C]0.27617[/C][C]0.138085[/C][/ROW]
[ROW][C]10[/C][C]0.78398[/C][C]0.432041[/C][C]0.21602[/C][/ROW]
[ROW][C]11[/C][C]0.729694[/C][C]0.540612[/C][C]0.270306[/C][/ROW]
[ROW][C]12[/C][C]0.650694[/C][C]0.698612[/C][C]0.349306[/C][/ROW]
[ROW][C]13[/C][C]0.684108[/C][C]0.631784[/C][C]0.315892[/C][/ROW]
[ROW][C]14[/C][C]0.593761[/C][C]0.812479[/C][C]0.406239[/C][/ROW]
[ROW][C]15[/C][C]0.593879[/C][C]0.812241[/C][C]0.406121[/C][/ROW]
[ROW][C]16[/C][C]0.531438[/C][C]0.937124[/C][C]0.468562[/C][/ROW]
[ROW][C]17[/C][C]0.449434[/C][C]0.898868[/C][C]0.550566[/C][/ROW]
[ROW][C]18[/C][C]0.376682[/C][C]0.753363[/C][C]0.623318[/C][/ROW]
[ROW][C]19[/C][C]0.301401[/C][C]0.602802[/C][C]0.698599[/C][/ROW]
[ROW][C]20[/C][C]0.336905[/C][C]0.673809[/C][C]0.663095[/C][/ROW]
[ROW][C]21[/C][C]0.291657[/C][C]0.583315[/C][C]0.708343[/C][/ROW]
[ROW][C]22[/C][C]0.332324[/C][C]0.664649[/C][C]0.667676[/C][/ROW]
[ROW][C]23[/C][C]0.307608[/C][C]0.615217[/C][C]0.692392[/C][/ROW]
[ROW][C]24[/C][C]0.270923[/C][C]0.541845[/C][C]0.729077[/C][/ROW]
[ROW][C]25[/C][C]0.296781[/C][C]0.593562[/C][C]0.703219[/C][/ROW]
[ROW][C]26[/C][C]0.337166[/C][C]0.674331[/C][C]0.662834[/C][/ROW]
[ROW][C]27[/C][C]0.438713[/C][C]0.877425[/C][C]0.561287[/C][/ROW]
[ROW][C]28[/C][C]0.394404[/C][C]0.788808[/C][C]0.605596[/C][/ROW]
[ROW][C]29[/C][C]0.397593[/C][C]0.795187[/C][C]0.602407[/C][/ROW]
[ROW][C]30[/C][C]0.387752[/C][C]0.775504[/C][C]0.612248[/C][/ROW]
[ROW][C]31[/C][C]0.399922[/C][C]0.799844[/C][C]0.600078[/C][/ROW]
[ROW][C]32[/C][C]0.365861[/C][C]0.731721[/C][C]0.634139[/C][/ROW]
[ROW][C]33[/C][C]0.484779[/C][C]0.969557[/C][C]0.515221[/C][/ROW]
[ROW][C]34[/C][C]0.488383[/C][C]0.976767[/C][C]0.511617[/C][/ROW]
[ROW][C]35[/C][C]0.447091[/C][C]0.894182[/C][C]0.552909[/C][/ROW]
[ROW][C]36[/C][C]0.489218[/C][C]0.978436[/C][C]0.510782[/C][/ROW]
[ROW][C]37[/C][C]0.476635[/C][C]0.953271[/C][C]0.523365[/C][/ROW]
[ROW][C]38[/C][C]0.507375[/C][C]0.985249[/C][C]0.492625[/C][/ROW]
[ROW][C]39[/C][C]0.517305[/C][C]0.965389[/C][C]0.482695[/C][/ROW]
[ROW][C]40[/C][C]0.47992[/C][C]0.95984[/C][C]0.52008[/C][/ROW]
[ROW][C]41[/C][C]0.604367[/C][C]0.791266[/C][C]0.395633[/C][/ROW]
[ROW][C]42[/C][C]0.645547[/C][C]0.708905[/C][C]0.354453[/C][/ROW]
[ROW][C]43[/C][C]0.615859[/C][C]0.768282[/C][C]0.384141[/C][/ROW]
[ROW][C]44[/C][C]0.711327[/C][C]0.577345[/C][C]0.288673[/C][/ROW]
[ROW][C]45[/C][C]0.674537[/C][C]0.650926[/C][C]0.325463[/C][/ROW]
[ROW][C]46[/C][C]0.640722[/C][C]0.718557[/C][C]0.359278[/C][/ROW]
[ROW][C]47[/C][C]0.647392[/C][C]0.705215[/C][C]0.352608[/C][/ROW]
[ROW][C]48[/C][C]0.662457[/C][C]0.675086[/C][C]0.337543[/C][/ROW]
[ROW][C]49[/C][C]0.750949[/C][C]0.498103[/C][C]0.249051[/C][/ROW]
[ROW][C]50[/C][C]0.740384[/C][C]0.519231[/C][C]0.259616[/C][/ROW]
[ROW][C]51[/C][C]0.779417[/C][C]0.441166[/C][C]0.220583[/C][/ROW]
[ROW][C]52[/C][C]0.852461[/C][C]0.295078[/C][C]0.147539[/C][/ROW]
[ROW][C]53[/C][C]0.827504[/C][C]0.344992[/C][C]0.172496[/C][/ROW]
[ROW][C]54[/C][C]0.797721[/C][C]0.404559[/C][C]0.202279[/C][/ROW]
[ROW][C]55[/C][C]0.81564[/C][C]0.36872[/C][C]0.18436[/C][/ROW]
[ROW][C]56[/C][C]0.896589[/C][C]0.206822[/C][C]0.103411[/C][/ROW]
[ROW][C]57[/C][C]0.884558[/C][C]0.230883[/C][C]0.115442[/C][/ROW]
[ROW][C]58[/C][C]0.911447[/C][C]0.177106[/C][C]0.0885528[/C][/ROW]
[ROW][C]59[/C][C]0.922501[/C][C]0.154998[/C][C]0.0774988[/C][/ROW]
[ROW][C]60[/C][C]0.913449[/C][C]0.173103[/C][C]0.0865514[/C][/ROW]
[ROW][C]61[/C][C]0.937273[/C][C]0.125454[/C][C]0.0627268[/C][/ROW]
[ROW][C]62[/C][C]0.961398[/C][C]0.0772045[/C][C]0.0386023[/C][/ROW]
[ROW][C]63[/C][C]0.953984[/C][C]0.0920315[/C][C]0.0460157[/C][/ROW]
[ROW][C]64[/C][C]0.953029[/C][C]0.0939422[/C][C]0.0469711[/C][/ROW]
[ROW][C]65[/C][C]0.95564[/C][C]0.0887201[/C][C]0.0443601[/C][/ROW]
[ROW][C]66[/C][C]0.962116[/C][C]0.0757677[/C][C]0.0378839[/C][/ROW]
[ROW][C]67[/C][C]0.95646[/C][C]0.0870804[/C][C]0.0435402[/C][/ROW]
[ROW][C]68[/C][C]0.979411[/C][C]0.0411771[/C][C]0.0205885[/C][/ROW]
[ROW][C]69[/C][C]0.977226[/C][C]0.0455484[/C][C]0.0227742[/C][/ROW]
[ROW][C]70[/C][C]0.979857[/C][C]0.0402853[/C][C]0.0201426[/C][/ROW]
[ROW][C]71[/C][C]0.982429[/C][C]0.0351411[/C][C]0.0175705[/C][/ROW]
[ROW][C]72[/C][C]0.983922[/C][C]0.0321552[/C][C]0.0160776[/C][/ROW]
[ROW][C]73[/C][C]0.982532[/C][C]0.0349364[/C][C]0.0174682[/C][/ROW]
[ROW][C]74[/C][C]0.983252[/C][C]0.0334951[/C][C]0.0167476[/C][/ROW]
[ROW][C]75[/C][C]0.991984[/C][C]0.0160315[/C][C]0.00801576[/C][/ROW]
[ROW][C]76[/C][C]0.993591[/C][C]0.012819[/C][C]0.00640948[/C][/ROW]
[ROW][C]77[/C][C]0.991632[/C][C]0.0167357[/C][C]0.00836786[/C][/ROW]
[ROW][C]78[/C][C]0.989721[/C][C]0.0205575[/C][C]0.0102787[/C][/ROW]
[ROW][C]79[/C][C]0.989968[/C][C]0.0200635[/C][C]0.0100317[/C][/ROW]
[ROW][C]80[/C][C]0.99[/C][C]0.0200009[/C][C]0.0100004[/C][/ROW]
[ROW][C]81[/C][C]0.988961[/C][C]0.0220785[/C][C]0.0110392[/C][/ROW]
[ROW][C]82[/C][C]0.987163[/C][C]0.025674[/C][C]0.012837[/C][/ROW]
[ROW][C]83[/C][C]0.985488[/C][C]0.0290239[/C][C]0.0145119[/C][/ROW]
[ROW][C]84[/C][C]0.981171[/C][C]0.037657[/C][C]0.0188285[/C][/ROW]
[ROW][C]85[/C][C]0.976514[/C][C]0.0469713[/C][C]0.0234856[/C][/ROW]
[ROW][C]86[/C][C]0.972151[/C][C]0.0556974[/C][C]0.0278487[/C][/ROW]
[ROW][C]87[/C][C]0.980686[/C][C]0.0386284[/C][C]0.0193142[/C][/ROW]
[ROW][C]88[/C][C]0.989636[/C][C]0.0207282[/C][C]0.0103641[/C][/ROW]
[ROW][C]89[/C][C]0.987565[/C][C]0.0248699[/C][C]0.0124349[/C][/ROW]
[ROW][C]90[/C][C]0.983744[/C][C]0.0325114[/C][C]0.0162557[/C][/ROW]
[ROW][C]91[/C][C]0.988954[/C][C]0.022092[/C][C]0.011046[/C][/ROW]
[ROW][C]92[/C][C]0.986459[/C][C]0.027081[/C][C]0.0135405[/C][/ROW]
[ROW][C]93[/C][C]0.983044[/C][C]0.0339115[/C][C]0.0169557[/C][/ROW]
[ROW][C]94[/C][C]0.978836[/C][C]0.0423272[/C][C]0.0211636[/C][/ROW]
[ROW][C]95[/C][C]0.97521[/C][C]0.0495803[/C][C]0.0247901[/C][/ROW]
[ROW][C]96[/C][C]0.971326[/C][C]0.0573471[/C][C]0.0286736[/C][/ROW]
[ROW][C]97[/C][C]0.967331[/C][C]0.065338[/C][C]0.032669[/C][/ROW]
[ROW][C]98[/C][C]0.970801[/C][C]0.0583981[/C][C]0.0291991[/C][/ROW]
[ROW][C]99[/C][C]0.963354[/C][C]0.0732911[/C][C]0.0366455[/C][/ROW]
[ROW][C]100[/C][C]0.959207[/C][C]0.0815865[/C][C]0.0407933[/C][/ROW]
[ROW][C]101[/C][C]0.947955[/C][C]0.10409[/C][C]0.0520449[/C][/ROW]
[ROW][C]102[/C][C]0.970315[/C][C]0.0593698[/C][C]0.0296849[/C][/ROW]
[ROW][C]103[/C][C]0.965121[/C][C]0.0697576[/C][C]0.0348788[/C][/ROW]
[ROW][C]104[/C][C]0.98009[/C][C]0.0398202[/C][C]0.0199101[/C][/ROW]
[ROW][C]105[/C][C]0.97346[/C][C]0.0530796[/C][C]0.0265398[/C][/ROW]
[ROW][C]106[/C][C]0.966712[/C][C]0.0665761[/C][C]0.0332881[/C][/ROW]
[ROW][C]107[/C][C]0.956327[/C][C]0.087346[/C][C]0.043673[/C][/ROW]
[ROW][C]108[/C][C]0.943487[/C][C]0.113026[/C][C]0.0565128[/C][/ROW]
[ROW][C]109[/C][C]0.942507[/C][C]0.114985[/C][C]0.0574927[/C][/ROW]
[ROW][C]110[/C][C]0.935874[/C][C]0.128251[/C][C]0.0641257[/C][/ROW]
[ROW][C]111[/C][C]0.921386[/C][C]0.157227[/C][C]0.0786136[/C][/ROW]
[ROW][C]112[/C][C]0.906593[/C][C]0.186814[/C][C]0.0934072[/C][/ROW]
[ROW][C]113[/C][C]0.913942[/C][C]0.172115[/C][C]0.0860576[/C][/ROW]
[ROW][C]114[/C][C]0.891865[/C][C]0.21627[/C][C]0.108135[/C][/ROW]
[ROW][C]115[/C][C]0.867662[/C][C]0.264677[/C][C]0.132338[/C][/ROW]
[ROW][C]116[/C][C]0.848217[/C][C]0.303565[/C][C]0.151783[/C][/ROW]
[ROW][C]117[/C][C]0.836164[/C][C]0.327673[/C][C]0.163836[/C][/ROW]
[ROW][C]118[/C][C]0.811199[/C][C]0.377602[/C][C]0.188801[/C][/ROW]
[ROW][C]119[/C][C]0.803[/C][C]0.394001[/C][C]0.197[/C][/ROW]
[ROW][C]120[/C][C]0.789794[/C][C]0.420412[/C][C]0.210206[/C][/ROW]
[ROW][C]121[/C][C]0.749632[/C][C]0.500735[/C][C]0.250368[/C][/ROW]
[ROW][C]122[/C][C]0.753517[/C][C]0.492965[/C][C]0.246483[/C][/ROW]
[ROW][C]123[/C][C]0.769337[/C][C]0.461327[/C][C]0.230663[/C][/ROW]
[ROW][C]124[/C][C]0.731161[/C][C]0.537677[/C][C]0.268839[/C][/ROW]
[ROW][C]125[/C][C]0.688985[/C][C]0.622029[/C][C]0.311015[/C][/ROW]
[ROW][C]126[/C][C]0.660419[/C][C]0.679162[/C][C]0.339581[/C][/ROW]
[ROW][C]127[/C][C]0.624036[/C][C]0.751928[/C][C]0.375964[/C][/ROW]
[ROW][C]128[/C][C]0.578079[/C][C]0.843843[/C][C]0.421921[/C][/ROW]
[ROW][C]129[/C][C]0.549273[/C][C]0.901454[/C][C]0.450727[/C][/ROW]
[ROW][C]130[/C][C]0.570817[/C][C]0.858366[/C][C]0.429183[/C][/ROW]
[ROW][C]131[/C][C]0.514301[/C][C]0.971398[/C][C]0.485699[/C][/ROW]
[ROW][C]132[/C][C]0.478276[/C][C]0.956551[/C][C]0.521724[/C][/ROW]
[ROW][C]133[/C][C]0.460208[/C][C]0.920416[/C][C]0.539792[/C][/ROW]
[ROW][C]134[/C][C]0.588696[/C][C]0.822609[/C][C]0.411304[/C][/ROW]
[ROW][C]135[/C][C]0.536594[/C][C]0.926812[/C][C]0.463406[/C][/ROW]
[ROW][C]136[/C][C]0.514125[/C][C]0.971751[/C][C]0.485875[/C][/ROW]
[ROW][C]137[/C][C]0.454876[/C][C]0.909753[/C][C]0.545124[/C][/ROW]
[ROW][C]138[/C][C]0.39623[/C][C]0.79246[/C][C]0.60377[/C][/ROW]
[ROW][C]139[/C][C]0.640254[/C][C]0.719493[/C][C]0.359746[/C][/ROW]
[ROW][C]140[/C][C]0.636655[/C][C]0.72669[/C][C]0.363345[/C][/ROW]
[ROW][C]141[/C][C]0.623226[/C][C]0.753548[/C][C]0.376774[/C][/ROW]
[ROW][C]142[/C][C]0.556189[/C][C]0.887622[/C][C]0.443811[/C][/ROW]
[ROW][C]143[/C][C]0.562689[/C][C]0.874622[/C][C]0.437311[/C][/ROW]
[ROW][C]144[/C][C]0.654489[/C][C]0.691022[/C][C]0.345511[/C][/ROW]
[ROW][C]145[/C][C]0.596061[/C][C]0.807878[/C][C]0.403939[/C][/ROW]
[ROW][C]146[/C][C]0.517941[/C][C]0.964117[/C][C]0.482059[/C][/ROW]
[ROW][C]147[/C][C]0.429541[/C][C]0.859083[/C][C]0.570459[/C][/ROW]
[ROW][C]148[/C][C]0.341825[/C][C]0.68365[/C][C]0.658175[/C][/ROW]
[ROW][C]149[/C][C]0.266309[/C][C]0.532617[/C][C]0.733691[/C][/ROW]
[ROW][C]150[/C][C]0.189149[/C][C]0.378298[/C][C]0.810851[/C][/ROW]
[ROW][C]151[/C][C]0.180019[/C][C]0.360037[/C][C]0.819981[/C][/ROW]
[ROW][C]152[/C][C]0.14644[/C][C]0.292879[/C][C]0.85356[/C][/ROW]
[ROW][C]153[/C][C]0.854891[/C][C]0.290219[/C][C]0.145109[/C][/ROW]
[ROW][C]154[/C][C]0.970483[/C][C]0.0590331[/C][C]0.0295166[/C][/ROW]
[ROW][C]155[/C][C]0.91744[/C][C]0.16512[/C][C]0.0825602[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267726&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267726&T=5

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The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.865820.268360.13418
90.8619150.276170.138085
100.783980.4320410.21602
110.7296940.5406120.270306
120.6506940.6986120.349306
130.6841080.6317840.315892
140.5937610.8124790.406239
150.5938790.8122410.406121
160.5314380.9371240.468562
170.4494340.8988680.550566
180.3766820.7533630.623318
190.3014010.6028020.698599
200.3369050.6738090.663095
210.2916570.5833150.708343
220.3323240.6646490.667676
230.3076080.6152170.692392
240.2709230.5418450.729077
250.2967810.5935620.703219
260.3371660.6743310.662834
270.4387130.8774250.561287
280.3944040.7888080.605596
290.3975930.7951870.602407
300.3877520.7755040.612248
310.3999220.7998440.600078
320.3658610.7317210.634139
330.4847790.9695570.515221
340.4883830.9767670.511617
350.4470910.8941820.552909
360.4892180.9784360.510782
370.4766350.9532710.523365
380.5073750.9852490.492625
390.5173050.9653890.482695
400.479920.959840.52008
410.6043670.7912660.395633
420.6455470.7089050.354453
430.6158590.7682820.384141
440.7113270.5773450.288673
450.6745370.6509260.325463
460.6407220.7185570.359278
470.6473920.7052150.352608
480.6624570.6750860.337543
490.7509490.4981030.249051
500.7403840.5192310.259616
510.7794170.4411660.220583
520.8524610.2950780.147539
530.8275040.3449920.172496
540.7977210.4045590.202279
550.815640.368720.18436
560.8965890.2068220.103411
570.8845580.2308830.115442
580.9114470.1771060.0885528
590.9225010.1549980.0774988
600.9134490.1731030.0865514
610.9372730.1254540.0627268
620.9613980.07720450.0386023
630.9539840.09203150.0460157
640.9530290.09394220.0469711
650.955640.08872010.0443601
660.9621160.07576770.0378839
670.956460.08708040.0435402
680.9794110.04117710.0205885
690.9772260.04554840.0227742
700.9798570.04028530.0201426
710.9824290.03514110.0175705
720.9839220.03215520.0160776
730.9825320.03493640.0174682
740.9832520.03349510.0167476
750.9919840.01603150.00801576
760.9935910.0128190.00640948
770.9916320.01673570.00836786
780.9897210.02055750.0102787
790.9899680.02006350.0100317
800.990.02000090.0100004
810.9889610.02207850.0110392
820.9871630.0256740.012837
830.9854880.02902390.0145119
840.9811710.0376570.0188285
850.9765140.04697130.0234856
860.9721510.05569740.0278487
870.9806860.03862840.0193142
880.9896360.02072820.0103641
890.9875650.02486990.0124349
900.9837440.03251140.0162557
910.9889540.0220920.011046
920.9864590.0270810.0135405
930.9830440.03391150.0169557
940.9788360.04232720.0211636
950.975210.04958030.0247901
960.9713260.05734710.0286736
970.9673310.0653380.032669
980.9708010.05839810.0291991
990.9633540.07329110.0366455
1000.9592070.08158650.0407933
1010.9479550.104090.0520449
1020.9703150.05936980.0296849
1030.9651210.06975760.0348788
1040.980090.03982020.0199101
1050.973460.05307960.0265398
1060.9667120.06657610.0332881
1070.9563270.0873460.043673
1080.9434870.1130260.0565128
1090.9425070.1149850.0574927
1100.9358740.1282510.0641257
1110.9213860.1572270.0786136
1120.9065930.1868140.0934072
1130.9139420.1721150.0860576
1140.8918650.216270.108135
1150.8676620.2646770.132338
1160.8482170.3035650.151783
1170.8361640.3276730.163836
1180.8111990.3776020.188801
1190.8030.3940010.197
1200.7897940.4204120.210206
1210.7496320.5007350.250368
1220.7535170.4929650.246483
1230.7693370.4613270.230663
1240.7311610.5376770.268839
1250.6889850.6220290.311015
1260.6604190.6791620.339581
1270.6240360.7519280.375964
1280.5780790.8438430.421921
1290.5492730.9014540.450727
1300.5708170.8583660.429183
1310.5143010.9713980.485699
1320.4782760.9565510.521724
1330.4602080.9204160.539792
1340.5886960.8226090.411304
1350.5365940.9268120.463406
1360.5141250.9717510.485875
1370.4548760.9097530.545124
1380.396230.792460.60377
1390.6402540.7194930.359746
1400.6366550.726690.363345
1410.6232260.7535480.376774
1420.5561890.8876220.443811
1430.5626890.8746220.437311
1440.6544890.6910220.345511
1450.5960610.8078780.403939
1460.5179410.9641170.482059
1470.4295410.8590830.570459
1480.3418250.683650.658175
1490.2663090.5326170.733691
1500.1891490.3782980.810851
1510.1800190.3600370.819981
1520.146440.2928790.85356
1530.8548910.2902190.145109
1540.9704830.05903310.0295166
1550.917440.165120.0825602






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level280.189189NOK
10% type I error level460.310811NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 28 & 0.189189 & NOK \tabularnewline
10% type I error level & 46 & 0.310811 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267726&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]28[/C][C]0.189189[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]46[/C][C]0.310811[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267726&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267726&T=6

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The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level280.189189NOK
10% type I error level460.310811NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}