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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 computationMon, 15 Dec 2014 09:34:22 +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/15/t14186361687zup7pwrln10m0d.htm/, Retrieved Thu, 16 May 2024 16:09:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267983, Retrieved Thu, 16 May 2024 16:09:49 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsMtultiple Regression Permanente evaluatie
Estimated Impact102

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Paper data] [2014-12-15 09:34:22] [99d5c1073827aabbadf7ab1e7da1d584] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
48	41	23	12	34	0,75
50	146	16	45	61	1,5
150	182	33	37	70	3
154	192	32	37	69	2,25
109	263	37	108	145	3
68	35	14	10	23	1,5
194	439	52	68	120	3
158	214	75	72	147	3
159	341	72	143	215	3
67	58	15	9	24	0,75
147	292	29	55	84	3
39	85	13	17	30	2,25
100	200	40	37	77	1,5
111	158	19	27	46	1,5
138	199	24	37	61	2,25
101	297	121	58	178	3
131	227	93	66	160	3
101	108	36	21	57	1,5
114	86	23	19	42	2,25
165	302	85	78	163	2,25
114	148	41	35	75	1,5
111	178	46	48	94	2,25
75	120	18	27	45	1,5
82	207	35	43	78	2,25
121	157	17	30	47	2,25
32	128	4	25	29	3
150	296	28	69	97	3
117	323	44	72	116	3
71	79	10	23	32	1,5
165	70	38	13	50	3
154	146	57	61	118	3
126	246	23	43	66	2,25
138	145	26	22	48	1,5
149	196	36	51	86	2,25
145	199	22	67	89	2,25
120	127	40	36	76	3
138	91	18	21	39	0,75
109	153	31	44	75	2,25
132	299	11	45	57	3
172	228	38	34	72	3
169	190	24	36	60	1,5
114	180	37	72	109	2,25
156	212	37	39	76	3
172	269	22	43	65	2,25
68	130	15	25	40	1,5
89	179	2	56	58	2,25
167	243	43	80	123	2,25
113	190	31	40	71	1,5
115	299	29	73	102	2,25
78	121	45	34	80	1,5
118	137	25	72	97	2,25
87	305	4	42	46	3
173	157	31	61	93	3
2	96	-4	23	19	3
162	183	66	74	140	3
49	52	61	16	78	1,5
122	238	32	66	98	2,25
96	40	31	9	40	1,5
100	226	39	41	80	2,25
82	190	19	57	76	2,25
100	214	31	48	79	2,25
115	145	36	51	87	3
141	119	42	53	95	1,5
165	222	21	29	49	2,25
165	222	21	29	49	2,25
110	159	25	55	80	3
118	165	32	54	86	2,25
158	249	26	43	69	3
146	125	28	51	79	2,25
49	122	32	20	52	1,5
90	186	41	79	120	3
121	148	29	39	69	1,5
155	274	33	61	94	3
104	172	17	55	72	3
147	84	13	30	43	3
110	168	32	55	87	3
108	102	30	22	52	2,25
113	106	34	37	71	2,25
115	2	59	2	61	0,75
61	139	13	38	51	3
60	95	23	27	50	0,75
109	130	10	56	67	1,5
68	72	5	25	30	1,5
111	141	31	39	70	3
77	113	19	33	52	1,5
73	206	32	43	75	2,25
151	268	30	57	87	3
89	175	25	43	69	3
78	77	48	23	72	1,5
110	125	35	44	79	3
220	255	67	54	121	3
65	111	15	28	43	1,5
141	132	22	36	58	1,5
117	211	18	39	57	2,25
122	92	33	16	50	1,5
63	76	46	23	69	1,5
44	171	24	40	64	2,25
52	83	14	24	38	1,5
62	119	23	29	53	2,25
131	266	12	78	90	3
101	186	38	57	96	3
42	50	12	37	49	0,75
152	117	28	27	56	1,5
107	219	41	61	102	1,5
77	246	12	27	40	2,25
154	279	31	69	100	2,25
103	148	33	34	67	1,5
96	137	34	44	78	2,25
154	130	41	21	62	0,75
175	181	21	34	55	2,25
57	98	20	39	59	0,75
112	226	44	51	96	2,25
143	234	52	34	86	3
49	138	7	31	38	0,75
110	85	29	13	43	0,75
131	66	11	12	23	3
167	236	26	51	77	3
56	106	24	24	48	3
137	135	7	19	26	3
86	122	60	30	91	1,5
121	218	13	81	94	3
149	199	20	42	62	3
168	112	52	22	74	3
140	278	28	85	114	3
88	94	25	27	52	1,5
168	113	39	25	64	2,25
94	84	9	22	31	0,75
51	86	19	19	38	0,75
48	62	13	14	27	2,25
145	222	60	45	105	3
66	167	19	45	64	2,25
85	82	34	28	62	3
109	207	14	51	65	2,25
63	184	17	41	58	3
102	83	45	31	76	1,5
162	183	66	74	140	3
128	85	24	24	48	3
86	89	48	19	68	0,75
114	225	29	51	80	1,5
164	237	-2	73	71	3
119	102	51	24	76	3
126	221	2	61	63	3
132	128	24	23	46	2,25
142	91	40	14	53	2,25
83	198	20	54	74	3
94	204	19	51	70	1,5
81	158	16	62	78	2,25
166	138	20	36	56	2,25
110	226	40	59	100	2,25
64	44	27	24	51	0,75
93	196	25	26	52	2,25
104	83	49	54	102	1,5
105	79	39	39	78	2,25
49	52	61	16	78	1,5
88	105	19	36	55	0,75
95	116	67	31	98	1,5
102	83	45	31	76	1,5
99	196	30	42	73	2,25
63	153	8	39	47	1,5
76	157	19	25	45	1,5
109	75	52	31	83	3
117	106	22	38	60	2,25
57	58	17	31	48	1,5
120	75	33	17	50	0,75
73	74	34	22	56	2,25
91	185	22	55	77	3
108	265	30	62	91	3
105	131	25	51	76	1,5
117	139	38	30	68	1,5
119	196	26	49	74	2,25
31	78	13	16	29	0,75

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'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267983&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267983&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267983&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'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
Pe[t] = + 0.989677 + 0.00385616LFM[t] + 0.00297181B[t] + 0.0253899PRH[t] + 0.0338531CH[t] -0.0263919H[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Pe[t] =  +  0.989677 +  0.00385616LFM[t] +  0.00297181B[t] +  0.0253899PRH[t] +  0.0338531CH[t] -0.0263919H[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267983&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Pe[t] =  +  0.989677 +  0.00385616LFM[t] +  0.00297181B[t] +  0.0253899PRH[t] +  0.0338531CH[t] -0.0263919H[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267983&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267983&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
Pe[t] = + 0.989677 + 0.00385616LFM[t] + 0.00297181B[t] + 0.0253899PRH[t] + 0.0338531CH[t] -0.0263919H[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.9896770.1562116.3352.16377e-091.08188e-09
LFM0.003856160.001484242.5980.01022310.00511155
B0.002971810.001008562.9470.003678370.00183919
PRH0.02538990.1011720.2510.8021590.40108
CH0.03385310.1008770.33560.7376080.368804
H-0.02639190.100847-0.26170.7938780.396939

\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) & 0.989677 & 0.156211 & 6.335 & 2.16377e-09 & 1.08188e-09 \tabularnewline
LFM & 0.00385616 & 0.00148424 & 2.598 & 0.0102231 & 0.00511155 \tabularnewline
B & 0.00297181 & 0.00100856 & 2.947 & 0.00367837 & 0.00183919 \tabularnewline
PRH & 0.0253899 & 0.101172 & 0.251 & 0.802159 & 0.40108 \tabularnewline
CH & 0.0338531 & 0.100877 & 0.3356 & 0.737608 & 0.368804 \tabularnewline
H & -0.0263919 & 0.100847 & -0.2617 & 0.793878 & 0.396939 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267983&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]0.989677[/C][C]0.156211[/C][C]6.335[/C][C]2.16377e-09[/C][C]1.08188e-09[/C][/ROW]
[ROW][C]LFM[/C][C]0.00385616[/C][C]0.00148424[/C][C]2.598[/C][C]0.0102231[/C][C]0.00511155[/C][/ROW]
[ROW][C]B[/C][C]0.00297181[/C][C]0.00100856[/C][C]2.947[/C][C]0.00367837[/C][C]0.00183919[/C][/ROW]
[ROW][C]PRH[/C][C]0.0253899[/C][C]0.101172[/C][C]0.251[/C][C]0.802159[/C][C]0.40108[/C][/ROW]
[ROW][C]CH[/C][C]0.0338531[/C][C]0.100877[/C][C]0.3356[/C][C]0.737608[/C][C]0.368804[/C][/ROW]
[ROW][C]H[/C][C]-0.0263919[/C][C]0.100847[/C][C]-0.2617[/C][C]0.793878[/C][C]0.396939[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267983&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267983&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)0.9896770.1562116.3352.16377e-091.08188e-09
LFM0.003856160.001484242.5980.01022310.00511155
B0.002971810.001008562.9470.003678370.00183919
PRH0.02538990.1011720.2510.8021590.40108
CH0.03385310.1008770.33560.7376080.368804
H-0.02639190.100847-0.26170.7938780.396939






Multiple Linear Regression - Regression Statistics
Multiple R0.578636
R-squared0.33482
Adjusted R-squared0.314663
F-TEST (value)16.6106
F-TEST (DF numerator)5
F-TEST (DF denominator)165
p-value2.85771e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.616602
Sum Squared Residuals62.7326

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.578636 \tabularnewline
R-squared & 0.33482 \tabularnewline
Adjusted R-squared & 0.314663 \tabularnewline
F-TEST (value) & 16.6106 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 165 \tabularnewline
p-value & 2.85771e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.616602 \tabularnewline
Sum Squared Residuals & 62.7326 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267983&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.578636[/C][/ROW]
[ROW][C]R-squared[/C][C]0.33482[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.314663[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]16.6106[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]165[/C][/ROW]
[ROW][C]p-value[/C][C]2.85771e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.616602[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]62.7326[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267983&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267983&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 R0.578636
R-squared0.33482
Adjusted R-squared0.314663
F-TEST (value)16.6106
F-TEST (DF numerator)5
F-TEST (DF denominator)165
p-value2.85771e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.616602
Sum Squared Residuals62.7326






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
10.751.3895-0.639497
21.51.93609-0.436092
332.351970.648031
42.252.39811-0.148113
532.960320.0396788
61.51.442890.057115
733.49765-0.497654
832.696970.303026
933.611-0.611003
100.751.47253-0.722525
1132.805610.194391
122.251.506490.743515
131.52.20564-0.705639
141.52.06967-0.569671
152.252.36523-0.115234
1632.599680.400325
1732.54230.457704
181.51.82072-0.320717
192.251.803570.446428
202.253.02023-0.770233
211.52.11556-0.615559
222.252.25874-0.00873847
231.51.81892-0.318923
242.252.206810.0431918
252.252.129650.120351
2631.675991.32401
2732.934520.0654761
2832.893860.106139
291.51.68622-0.186217
3031.919281.08072
3132.415430.584571
322.252.5044-0.254404
331.52.09083-0.590834
342.252.51756-0.267561
352.252.61807-0.368067
3632.058360.941641
370.751.93091-1.18091
382.252.161920.0880837
3932.68560.314399
4032.546110.453886
411.52.45057-0.950567
422.252.46434-0.214338
4332.475170.524826
442.252.75114-0.501141
451.51.80973-0.309731
462.252.28065-0.0306531
472.252.90962-0.659616
481.52.25745-0.757453
492.252.83732-0.587317
501.51.83225-0.332245
512.252.364-0.113999
5232.540930.459073
5332.521050.478953
5431.45831.5417
5532.644210.355787
561.51.365030.134972
572.252.62779-0.377795
581.51.51483-0.0148292
592.252.31375-0.0637529
602.252.27678-0.0267768
612.252.33834-0.0883359
6232.20850.791503
631.52.2404-0.740401
642.252.50741-0.25741
652.252.50741-0.25741
6632.271690.728311
672.252.30589-0.0558936
6832.633710.36629
692.252.27662-0.0266183
701.51.65835-0.158349
7132.437840.562159
721.52.13164-0.631637
7332.823730.176275
7432.29520.704799
7532.016970.983025
7632.291420.708579
772.251.843350.406647
782.251.982430.267569
790.751.39488-0.644883
8031.908481.09152
810.751.68177-0.931775
821.52.17775-0.67775
831.51.64739-0.147386
8432.096660.903339
851.51.8496-0.349598
862.252.172140.077863
8732.763630.236369
8832.122330.877668
891.51.61641-0.116406
9032.078550.921446
9132.931610.0683857
921.51.76408-0.264082
931.52.17223-0.672234
942.252.34085-0.0908504
951.51.79346-0.293456
961.51.58399-0.0839879
972.251.941930.308073
981.51.6019-0.101898
992.251.749340.500659
10032.855290.144714
10132.292730.707273
1020.751.56427-0.814267
1031.52.07052-0.57052
1041.52.46716-0.967164
1052.252.18070.0692973
1062.252.89642-0.646422
1071.52.0473-0.547304
1082.252.061230.188769
1090.752.08546-1.33546
1102.252.43504-0.185041
1110.751.77166-1.02166
1122.252.40324-0.153237
11332.438090.561912
1140.751.81302-1.06302
1150.751.708-0.958004
11631.769491.23051
11732.689470.310528
11831.675651.32435
11932.053910.946086
1201.51.82119-0.321191
12132.695460.304541
12232.448970.551034
12332.08240.917603
12432.935460.0645426
1251.51.78477-0.284772
1262.252.120780.129222
1270.751.75692-1.00692
1280.751.56464-0.814641
1292.251.450460.799544
13032.48420.515804
1312.252.057190.192808
13231.735981.26402
1332.252.39166-0.141657
13432.06830.931697
1351.51.81587-0.315873
13632.644210.355787
13731.890891.10911
1380.751.65307-0.903072
1391.52.4494-0.9494
14032.873080.126921
14131.853261.14674
14232.585450.414547
1432.252.053030.196967
1442.251.898450.351545
14532.281020.718978
1461.52.31989-0.819889
1472.252.218130.031865
1482.252.28847-0.0384728
1492.252.45922-0.209223
1500.751.51925-0.769246
1512.252.073320.176676
1521.52.01758-0.517577
1532.251.881260.368744
1541.51.365030.134972
1550.751.89062-1.14062
1561.51.8649-0.364905
1571.51.81587-0.315873
1582.252.210830.03917
1591.51.97027-0.470273
1601.51.89042-0.390419
16131.812081.18792
1622.252.017340.232659
1631.51.59611-0.0961065
1640.751.76908-1.01908
1652.251.621170.628831
16632.278690.721305
16732.65260.347402
1681.52.13935-0.639353
1691.52.03969-0.539689
1702.252.39697-0.146974
1710.751.44737-0.697372

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.75 & 1.3895 & -0.639497 \tabularnewline
2 & 1.5 & 1.93609 & -0.436092 \tabularnewline
3 & 3 & 2.35197 & 0.648031 \tabularnewline
4 & 2.25 & 2.39811 & -0.148113 \tabularnewline
5 & 3 & 2.96032 & 0.0396788 \tabularnewline
6 & 1.5 & 1.44289 & 0.057115 \tabularnewline
7 & 3 & 3.49765 & -0.497654 \tabularnewline
8 & 3 & 2.69697 & 0.303026 \tabularnewline
9 & 3 & 3.611 & -0.611003 \tabularnewline
10 & 0.75 & 1.47253 & -0.722525 \tabularnewline
11 & 3 & 2.80561 & 0.194391 \tabularnewline
12 & 2.25 & 1.50649 & 0.743515 \tabularnewline
13 & 1.5 & 2.20564 & -0.705639 \tabularnewline
14 & 1.5 & 2.06967 & -0.569671 \tabularnewline
15 & 2.25 & 2.36523 & -0.115234 \tabularnewline
16 & 3 & 2.59968 & 0.400325 \tabularnewline
17 & 3 & 2.5423 & 0.457704 \tabularnewline
18 & 1.5 & 1.82072 & -0.320717 \tabularnewline
19 & 2.25 & 1.80357 & 0.446428 \tabularnewline
20 & 2.25 & 3.02023 & -0.770233 \tabularnewline
21 & 1.5 & 2.11556 & -0.615559 \tabularnewline
22 & 2.25 & 2.25874 & -0.00873847 \tabularnewline
23 & 1.5 & 1.81892 & -0.318923 \tabularnewline
24 & 2.25 & 2.20681 & 0.0431918 \tabularnewline
25 & 2.25 & 2.12965 & 0.120351 \tabularnewline
26 & 3 & 1.67599 & 1.32401 \tabularnewline
27 & 3 & 2.93452 & 0.0654761 \tabularnewline
28 & 3 & 2.89386 & 0.106139 \tabularnewline
29 & 1.5 & 1.68622 & -0.186217 \tabularnewline
30 & 3 & 1.91928 & 1.08072 \tabularnewline
31 & 3 & 2.41543 & 0.584571 \tabularnewline
32 & 2.25 & 2.5044 & -0.254404 \tabularnewline
33 & 1.5 & 2.09083 & -0.590834 \tabularnewline
34 & 2.25 & 2.51756 & -0.267561 \tabularnewline
35 & 2.25 & 2.61807 & -0.368067 \tabularnewline
36 & 3 & 2.05836 & 0.941641 \tabularnewline
37 & 0.75 & 1.93091 & -1.18091 \tabularnewline
38 & 2.25 & 2.16192 & 0.0880837 \tabularnewline
39 & 3 & 2.6856 & 0.314399 \tabularnewline
40 & 3 & 2.54611 & 0.453886 \tabularnewline
41 & 1.5 & 2.45057 & -0.950567 \tabularnewline
42 & 2.25 & 2.46434 & -0.214338 \tabularnewline
43 & 3 & 2.47517 & 0.524826 \tabularnewline
44 & 2.25 & 2.75114 & -0.501141 \tabularnewline
45 & 1.5 & 1.80973 & -0.309731 \tabularnewline
46 & 2.25 & 2.28065 & -0.0306531 \tabularnewline
47 & 2.25 & 2.90962 & -0.659616 \tabularnewline
48 & 1.5 & 2.25745 & -0.757453 \tabularnewline
49 & 2.25 & 2.83732 & -0.587317 \tabularnewline
50 & 1.5 & 1.83225 & -0.332245 \tabularnewline
51 & 2.25 & 2.364 & -0.113999 \tabularnewline
52 & 3 & 2.54093 & 0.459073 \tabularnewline
53 & 3 & 2.52105 & 0.478953 \tabularnewline
54 & 3 & 1.4583 & 1.5417 \tabularnewline
55 & 3 & 2.64421 & 0.355787 \tabularnewline
56 & 1.5 & 1.36503 & 0.134972 \tabularnewline
57 & 2.25 & 2.62779 & -0.377795 \tabularnewline
58 & 1.5 & 1.51483 & -0.0148292 \tabularnewline
59 & 2.25 & 2.31375 & -0.0637529 \tabularnewline
60 & 2.25 & 2.27678 & -0.0267768 \tabularnewline
61 & 2.25 & 2.33834 & -0.0883359 \tabularnewline
62 & 3 & 2.2085 & 0.791503 \tabularnewline
63 & 1.5 & 2.2404 & -0.740401 \tabularnewline
64 & 2.25 & 2.50741 & -0.25741 \tabularnewline
65 & 2.25 & 2.50741 & -0.25741 \tabularnewline
66 & 3 & 2.27169 & 0.728311 \tabularnewline
67 & 2.25 & 2.30589 & -0.0558936 \tabularnewline
68 & 3 & 2.63371 & 0.36629 \tabularnewline
69 & 2.25 & 2.27662 & -0.0266183 \tabularnewline
70 & 1.5 & 1.65835 & -0.158349 \tabularnewline
71 & 3 & 2.43784 & 0.562159 \tabularnewline
72 & 1.5 & 2.13164 & -0.631637 \tabularnewline
73 & 3 & 2.82373 & 0.176275 \tabularnewline
74 & 3 & 2.2952 & 0.704799 \tabularnewline
75 & 3 & 2.01697 & 0.983025 \tabularnewline
76 & 3 & 2.29142 & 0.708579 \tabularnewline
77 & 2.25 & 1.84335 & 0.406647 \tabularnewline
78 & 2.25 & 1.98243 & 0.267569 \tabularnewline
79 & 0.75 & 1.39488 & -0.644883 \tabularnewline
80 & 3 & 1.90848 & 1.09152 \tabularnewline
81 & 0.75 & 1.68177 & -0.931775 \tabularnewline
82 & 1.5 & 2.17775 & -0.67775 \tabularnewline
83 & 1.5 & 1.64739 & -0.147386 \tabularnewline
84 & 3 & 2.09666 & 0.903339 \tabularnewline
85 & 1.5 & 1.8496 & -0.349598 \tabularnewline
86 & 2.25 & 2.17214 & 0.077863 \tabularnewline
87 & 3 & 2.76363 & 0.236369 \tabularnewline
88 & 3 & 2.12233 & 0.877668 \tabularnewline
89 & 1.5 & 1.61641 & -0.116406 \tabularnewline
90 & 3 & 2.07855 & 0.921446 \tabularnewline
91 & 3 & 2.93161 & 0.0683857 \tabularnewline
92 & 1.5 & 1.76408 & -0.264082 \tabularnewline
93 & 1.5 & 2.17223 & -0.672234 \tabularnewline
94 & 2.25 & 2.34085 & -0.0908504 \tabularnewline
95 & 1.5 & 1.79346 & -0.293456 \tabularnewline
96 & 1.5 & 1.58399 & -0.0839879 \tabularnewline
97 & 2.25 & 1.94193 & 0.308073 \tabularnewline
98 & 1.5 & 1.6019 & -0.101898 \tabularnewline
99 & 2.25 & 1.74934 & 0.500659 \tabularnewline
100 & 3 & 2.85529 & 0.144714 \tabularnewline
101 & 3 & 2.29273 & 0.707273 \tabularnewline
102 & 0.75 & 1.56427 & -0.814267 \tabularnewline
103 & 1.5 & 2.07052 & -0.57052 \tabularnewline
104 & 1.5 & 2.46716 & -0.967164 \tabularnewline
105 & 2.25 & 2.1807 & 0.0692973 \tabularnewline
106 & 2.25 & 2.89642 & -0.646422 \tabularnewline
107 & 1.5 & 2.0473 & -0.547304 \tabularnewline
108 & 2.25 & 2.06123 & 0.188769 \tabularnewline
109 & 0.75 & 2.08546 & -1.33546 \tabularnewline
110 & 2.25 & 2.43504 & -0.185041 \tabularnewline
111 & 0.75 & 1.77166 & -1.02166 \tabularnewline
112 & 2.25 & 2.40324 & -0.153237 \tabularnewline
113 & 3 & 2.43809 & 0.561912 \tabularnewline
114 & 0.75 & 1.81302 & -1.06302 \tabularnewline
115 & 0.75 & 1.708 & -0.958004 \tabularnewline
116 & 3 & 1.76949 & 1.23051 \tabularnewline
117 & 3 & 2.68947 & 0.310528 \tabularnewline
118 & 3 & 1.67565 & 1.32435 \tabularnewline
119 & 3 & 2.05391 & 0.946086 \tabularnewline
120 & 1.5 & 1.82119 & -0.321191 \tabularnewline
121 & 3 & 2.69546 & 0.304541 \tabularnewline
122 & 3 & 2.44897 & 0.551034 \tabularnewline
123 & 3 & 2.0824 & 0.917603 \tabularnewline
124 & 3 & 2.93546 & 0.0645426 \tabularnewline
125 & 1.5 & 1.78477 & -0.284772 \tabularnewline
126 & 2.25 & 2.12078 & 0.129222 \tabularnewline
127 & 0.75 & 1.75692 & -1.00692 \tabularnewline
128 & 0.75 & 1.56464 & -0.814641 \tabularnewline
129 & 2.25 & 1.45046 & 0.799544 \tabularnewline
130 & 3 & 2.4842 & 0.515804 \tabularnewline
131 & 2.25 & 2.05719 & 0.192808 \tabularnewline
132 & 3 & 1.73598 & 1.26402 \tabularnewline
133 & 2.25 & 2.39166 & -0.141657 \tabularnewline
134 & 3 & 2.0683 & 0.931697 \tabularnewline
135 & 1.5 & 1.81587 & -0.315873 \tabularnewline
136 & 3 & 2.64421 & 0.355787 \tabularnewline
137 & 3 & 1.89089 & 1.10911 \tabularnewline
138 & 0.75 & 1.65307 & -0.903072 \tabularnewline
139 & 1.5 & 2.4494 & -0.9494 \tabularnewline
140 & 3 & 2.87308 & 0.126921 \tabularnewline
141 & 3 & 1.85326 & 1.14674 \tabularnewline
142 & 3 & 2.58545 & 0.414547 \tabularnewline
143 & 2.25 & 2.05303 & 0.196967 \tabularnewline
144 & 2.25 & 1.89845 & 0.351545 \tabularnewline
145 & 3 & 2.28102 & 0.718978 \tabularnewline
146 & 1.5 & 2.31989 & -0.819889 \tabularnewline
147 & 2.25 & 2.21813 & 0.031865 \tabularnewline
148 & 2.25 & 2.28847 & -0.0384728 \tabularnewline
149 & 2.25 & 2.45922 & -0.209223 \tabularnewline
150 & 0.75 & 1.51925 & -0.769246 \tabularnewline
151 & 2.25 & 2.07332 & 0.176676 \tabularnewline
152 & 1.5 & 2.01758 & -0.517577 \tabularnewline
153 & 2.25 & 1.88126 & 0.368744 \tabularnewline
154 & 1.5 & 1.36503 & 0.134972 \tabularnewline
155 & 0.75 & 1.89062 & -1.14062 \tabularnewline
156 & 1.5 & 1.8649 & -0.364905 \tabularnewline
157 & 1.5 & 1.81587 & -0.315873 \tabularnewline
158 & 2.25 & 2.21083 & 0.03917 \tabularnewline
159 & 1.5 & 1.97027 & -0.470273 \tabularnewline
160 & 1.5 & 1.89042 & -0.390419 \tabularnewline
161 & 3 & 1.81208 & 1.18792 \tabularnewline
162 & 2.25 & 2.01734 & 0.232659 \tabularnewline
163 & 1.5 & 1.59611 & -0.0961065 \tabularnewline
164 & 0.75 & 1.76908 & -1.01908 \tabularnewline
165 & 2.25 & 1.62117 & 0.628831 \tabularnewline
166 & 3 & 2.27869 & 0.721305 \tabularnewline
167 & 3 & 2.6526 & 0.347402 \tabularnewline
168 & 1.5 & 2.13935 & -0.639353 \tabularnewline
169 & 1.5 & 2.03969 & -0.539689 \tabularnewline
170 & 2.25 & 2.39697 & -0.146974 \tabularnewline
171 & 0.75 & 1.44737 & -0.697372 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267983&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]0.75[/C][C]1.3895[/C][C]-0.639497[/C][/ROW]
[ROW][C]2[/C][C]1.5[/C][C]1.93609[/C][C]-0.436092[/C][/ROW]
[ROW][C]3[/C][C]3[/C][C]2.35197[/C][C]0.648031[/C][/ROW]
[ROW][C]4[/C][C]2.25[/C][C]2.39811[/C][C]-0.148113[/C][/ROW]
[ROW][C]5[/C][C]3[/C][C]2.96032[/C][C]0.0396788[/C][/ROW]
[ROW][C]6[/C][C]1.5[/C][C]1.44289[/C][C]0.057115[/C][/ROW]
[ROW][C]7[/C][C]3[/C][C]3.49765[/C][C]-0.497654[/C][/ROW]
[ROW][C]8[/C][C]3[/C][C]2.69697[/C][C]0.303026[/C][/ROW]
[ROW][C]9[/C][C]3[/C][C]3.611[/C][C]-0.611003[/C][/ROW]
[ROW][C]10[/C][C]0.75[/C][C]1.47253[/C][C]-0.722525[/C][/ROW]
[ROW][C]11[/C][C]3[/C][C]2.80561[/C][C]0.194391[/C][/ROW]
[ROW][C]12[/C][C]2.25[/C][C]1.50649[/C][C]0.743515[/C][/ROW]
[ROW][C]13[/C][C]1.5[/C][C]2.20564[/C][C]-0.705639[/C][/ROW]
[ROW][C]14[/C][C]1.5[/C][C]2.06967[/C][C]-0.569671[/C][/ROW]
[ROW][C]15[/C][C]2.25[/C][C]2.36523[/C][C]-0.115234[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]2.59968[/C][C]0.400325[/C][/ROW]
[ROW][C]17[/C][C]3[/C][C]2.5423[/C][C]0.457704[/C][/ROW]
[ROW][C]18[/C][C]1.5[/C][C]1.82072[/C][C]-0.320717[/C][/ROW]
[ROW][C]19[/C][C]2.25[/C][C]1.80357[/C][C]0.446428[/C][/ROW]
[ROW][C]20[/C][C]2.25[/C][C]3.02023[/C][C]-0.770233[/C][/ROW]
[ROW][C]21[/C][C]1.5[/C][C]2.11556[/C][C]-0.615559[/C][/ROW]
[ROW][C]22[/C][C]2.25[/C][C]2.25874[/C][C]-0.00873847[/C][/ROW]
[ROW][C]23[/C][C]1.5[/C][C]1.81892[/C][C]-0.318923[/C][/ROW]
[ROW][C]24[/C][C]2.25[/C][C]2.20681[/C][C]0.0431918[/C][/ROW]
[ROW][C]25[/C][C]2.25[/C][C]2.12965[/C][C]0.120351[/C][/ROW]
[ROW][C]26[/C][C]3[/C][C]1.67599[/C][C]1.32401[/C][/ROW]
[ROW][C]27[/C][C]3[/C][C]2.93452[/C][C]0.0654761[/C][/ROW]
[ROW][C]28[/C][C]3[/C][C]2.89386[/C][C]0.106139[/C][/ROW]
[ROW][C]29[/C][C]1.5[/C][C]1.68622[/C][C]-0.186217[/C][/ROW]
[ROW][C]30[/C][C]3[/C][C]1.91928[/C][C]1.08072[/C][/ROW]
[ROW][C]31[/C][C]3[/C][C]2.41543[/C][C]0.584571[/C][/ROW]
[ROW][C]32[/C][C]2.25[/C][C]2.5044[/C][C]-0.254404[/C][/ROW]
[ROW][C]33[/C][C]1.5[/C][C]2.09083[/C][C]-0.590834[/C][/ROW]
[ROW][C]34[/C][C]2.25[/C][C]2.51756[/C][C]-0.267561[/C][/ROW]
[ROW][C]35[/C][C]2.25[/C][C]2.61807[/C][C]-0.368067[/C][/ROW]
[ROW][C]36[/C][C]3[/C][C]2.05836[/C][C]0.941641[/C][/ROW]
[ROW][C]37[/C][C]0.75[/C][C]1.93091[/C][C]-1.18091[/C][/ROW]
[ROW][C]38[/C][C]2.25[/C][C]2.16192[/C][C]0.0880837[/C][/ROW]
[ROW][C]39[/C][C]3[/C][C]2.6856[/C][C]0.314399[/C][/ROW]
[ROW][C]40[/C][C]3[/C][C]2.54611[/C][C]0.453886[/C][/ROW]
[ROW][C]41[/C][C]1.5[/C][C]2.45057[/C][C]-0.950567[/C][/ROW]
[ROW][C]42[/C][C]2.25[/C][C]2.46434[/C][C]-0.214338[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]2.47517[/C][C]0.524826[/C][/ROW]
[ROW][C]44[/C][C]2.25[/C][C]2.75114[/C][C]-0.501141[/C][/ROW]
[ROW][C]45[/C][C]1.5[/C][C]1.80973[/C][C]-0.309731[/C][/ROW]
[ROW][C]46[/C][C]2.25[/C][C]2.28065[/C][C]-0.0306531[/C][/ROW]
[ROW][C]47[/C][C]2.25[/C][C]2.90962[/C][C]-0.659616[/C][/ROW]
[ROW][C]48[/C][C]1.5[/C][C]2.25745[/C][C]-0.757453[/C][/ROW]
[ROW][C]49[/C][C]2.25[/C][C]2.83732[/C][C]-0.587317[/C][/ROW]
[ROW][C]50[/C][C]1.5[/C][C]1.83225[/C][C]-0.332245[/C][/ROW]
[ROW][C]51[/C][C]2.25[/C][C]2.364[/C][C]-0.113999[/C][/ROW]
[ROW][C]52[/C][C]3[/C][C]2.54093[/C][C]0.459073[/C][/ROW]
[ROW][C]53[/C][C]3[/C][C]2.52105[/C][C]0.478953[/C][/ROW]
[ROW][C]54[/C][C]3[/C][C]1.4583[/C][C]1.5417[/C][/ROW]
[ROW][C]55[/C][C]3[/C][C]2.64421[/C][C]0.355787[/C][/ROW]
[ROW][C]56[/C][C]1.5[/C][C]1.36503[/C][C]0.134972[/C][/ROW]
[ROW][C]57[/C][C]2.25[/C][C]2.62779[/C][C]-0.377795[/C][/ROW]
[ROW][C]58[/C][C]1.5[/C][C]1.51483[/C][C]-0.0148292[/C][/ROW]
[ROW][C]59[/C][C]2.25[/C][C]2.31375[/C][C]-0.0637529[/C][/ROW]
[ROW][C]60[/C][C]2.25[/C][C]2.27678[/C][C]-0.0267768[/C][/ROW]
[ROW][C]61[/C][C]2.25[/C][C]2.33834[/C][C]-0.0883359[/C][/ROW]
[ROW][C]62[/C][C]3[/C][C]2.2085[/C][C]0.791503[/C][/ROW]
[ROW][C]63[/C][C]1.5[/C][C]2.2404[/C][C]-0.740401[/C][/ROW]
[ROW][C]64[/C][C]2.25[/C][C]2.50741[/C][C]-0.25741[/C][/ROW]
[ROW][C]65[/C][C]2.25[/C][C]2.50741[/C][C]-0.25741[/C][/ROW]
[ROW][C]66[/C][C]3[/C][C]2.27169[/C][C]0.728311[/C][/ROW]
[ROW][C]67[/C][C]2.25[/C][C]2.30589[/C][C]-0.0558936[/C][/ROW]
[ROW][C]68[/C][C]3[/C][C]2.63371[/C][C]0.36629[/C][/ROW]
[ROW][C]69[/C][C]2.25[/C][C]2.27662[/C][C]-0.0266183[/C][/ROW]
[ROW][C]70[/C][C]1.5[/C][C]1.65835[/C][C]-0.158349[/C][/ROW]
[ROW][C]71[/C][C]3[/C][C]2.43784[/C][C]0.562159[/C][/ROW]
[ROW][C]72[/C][C]1.5[/C][C]2.13164[/C][C]-0.631637[/C][/ROW]
[ROW][C]73[/C][C]3[/C][C]2.82373[/C][C]0.176275[/C][/ROW]
[ROW][C]74[/C][C]3[/C][C]2.2952[/C][C]0.704799[/C][/ROW]
[ROW][C]75[/C][C]3[/C][C]2.01697[/C][C]0.983025[/C][/ROW]
[ROW][C]76[/C][C]3[/C][C]2.29142[/C][C]0.708579[/C][/ROW]
[ROW][C]77[/C][C]2.25[/C][C]1.84335[/C][C]0.406647[/C][/ROW]
[ROW][C]78[/C][C]2.25[/C][C]1.98243[/C][C]0.267569[/C][/ROW]
[ROW][C]79[/C][C]0.75[/C][C]1.39488[/C][C]-0.644883[/C][/ROW]
[ROW][C]80[/C][C]3[/C][C]1.90848[/C][C]1.09152[/C][/ROW]
[ROW][C]81[/C][C]0.75[/C][C]1.68177[/C][C]-0.931775[/C][/ROW]
[ROW][C]82[/C][C]1.5[/C][C]2.17775[/C][C]-0.67775[/C][/ROW]
[ROW][C]83[/C][C]1.5[/C][C]1.64739[/C][C]-0.147386[/C][/ROW]
[ROW][C]84[/C][C]3[/C][C]2.09666[/C][C]0.903339[/C][/ROW]
[ROW][C]85[/C][C]1.5[/C][C]1.8496[/C][C]-0.349598[/C][/ROW]
[ROW][C]86[/C][C]2.25[/C][C]2.17214[/C][C]0.077863[/C][/ROW]
[ROW][C]87[/C][C]3[/C][C]2.76363[/C][C]0.236369[/C][/ROW]
[ROW][C]88[/C][C]3[/C][C]2.12233[/C][C]0.877668[/C][/ROW]
[ROW][C]89[/C][C]1.5[/C][C]1.61641[/C][C]-0.116406[/C][/ROW]
[ROW][C]90[/C][C]3[/C][C]2.07855[/C][C]0.921446[/C][/ROW]
[ROW][C]91[/C][C]3[/C][C]2.93161[/C][C]0.0683857[/C][/ROW]
[ROW][C]92[/C][C]1.5[/C][C]1.76408[/C][C]-0.264082[/C][/ROW]
[ROW][C]93[/C][C]1.5[/C][C]2.17223[/C][C]-0.672234[/C][/ROW]
[ROW][C]94[/C][C]2.25[/C][C]2.34085[/C][C]-0.0908504[/C][/ROW]
[ROW][C]95[/C][C]1.5[/C][C]1.79346[/C][C]-0.293456[/C][/ROW]
[ROW][C]96[/C][C]1.5[/C][C]1.58399[/C][C]-0.0839879[/C][/ROW]
[ROW][C]97[/C][C]2.25[/C][C]1.94193[/C][C]0.308073[/C][/ROW]
[ROW][C]98[/C][C]1.5[/C][C]1.6019[/C][C]-0.101898[/C][/ROW]
[ROW][C]99[/C][C]2.25[/C][C]1.74934[/C][C]0.500659[/C][/ROW]
[ROW][C]100[/C][C]3[/C][C]2.85529[/C][C]0.144714[/C][/ROW]
[ROW][C]101[/C][C]3[/C][C]2.29273[/C][C]0.707273[/C][/ROW]
[ROW][C]102[/C][C]0.75[/C][C]1.56427[/C][C]-0.814267[/C][/ROW]
[ROW][C]103[/C][C]1.5[/C][C]2.07052[/C][C]-0.57052[/C][/ROW]
[ROW][C]104[/C][C]1.5[/C][C]2.46716[/C][C]-0.967164[/C][/ROW]
[ROW][C]105[/C][C]2.25[/C][C]2.1807[/C][C]0.0692973[/C][/ROW]
[ROW][C]106[/C][C]2.25[/C][C]2.89642[/C][C]-0.646422[/C][/ROW]
[ROW][C]107[/C][C]1.5[/C][C]2.0473[/C][C]-0.547304[/C][/ROW]
[ROW][C]108[/C][C]2.25[/C][C]2.06123[/C][C]0.188769[/C][/ROW]
[ROW][C]109[/C][C]0.75[/C][C]2.08546[/C][C]-1.33546[/C][/ROW]
[ROW][C]110[/C][C]2.25[/C][C]2.43504[/C][C]-0.185041[/C][/ROW]
[ROW][C]111[/C][C]0.75[/C][C]1.77166[/C][C]-1.02166[/C][/ROW]
[ROW][C]112[/C][C]2.25[/C][C]2.40324[/C][C]-0.153237[/C][/ROW]
[ROW][C]113[/C][C]3[/C][C]2.43809[/C][C]0.561912[/C][/ROW]
[ROW][C]114[/C][C]0.75[/C][C]1.81302[/C][C]-1.06302[/C][/ROW]
[ROW][C]115[/C][C]0.75[/C][C]1.708[/C][C]-0.958004[/C][/ROW]
[ROW][C]116[/C][C]3[/C][C]1.76949[/C][C]1.23051[/C][/ROW]
[ROW][C]117[/C][C]3[/C][C]2.68947[/C][C]0.310528[/C][/ROW]
[ROW][C]118[/C][C]3[/C][C]1.67565[/C][C]1.32435[/C][/ROW]
[ROW][C]119[/C][C]3[/C][C]2.05391[/C][C]0.946086[/C][/ROW]
[ROW][C]120[/C][C]1.5[/C][C]1.82119[/C][C]-0.321191[/C][/ROW]
[ROW][C]121[/C][C]3[/C][C]2.69546[/C][C]0.304541[/C][/ROW]
[ROW][C]122[/C][C]3[/C][C]2.44897[/C][C]0.551034[/C][/ROW]
[ROW][C]123[/C][C]3[/C][C]2.0824[/C][C]0.917603[/C][/ROW]
[ROW][C]124[/C][C]3[/C][C]2.93546[/C][C]0.0645426[/C][/ROW]
[ROW][C]125[/C][C]1.5[/C][C]1.78477[/C][C]-0.284772[/C][/ROW]
[ROW][C]126[/C][C]2.25[/C][C]2.12078[/C][C]0.129222[/C][/ROW]
[ROW][C]127[/C][C]0.75[/C][C]1.75692[/C][C]-1.00692[/C][/ROW]
[ROW][C]128[/C][C]0.75[/C][C]1.56464[/C][C]-0.814641[/C][/ROW]
[ROW][C]129[/C][C]2.25[/C][C]1.45046[/C][C]0.799544[/C][/ROW]
[ROW][C]130[/C][C]3[/C][C]2.4842[/C][C]0.515804[/C][/ROW]
[ROW][C]131[/C][C]2.25[/C][C]2.05719[/C][C]0.192808[/C][/ROW]
[ROW][C]132[/C][C]3[/C][C]1.73598[/C][C]1.26402[/C][/ROW]
[ROW][C]133[/C][C]2.25[/C][C]2.39166[/C][C]-0.141657[/C][/ROW]
[ROW][C]134[/C][C]3[/C][C]2.0683[/C][C]0.931697[/C][/ROW]
[ROW][C]135[/C][C]1.5[/C][C]1.81587[/C][C]-0.315873[/C][/ROW]
[ROW][C]136[/C][C]3[/C][C]2.64421[/C][C]0.355787[/C][/ROW]
[ROW][C]137[/C][C]3[/C][C]1.89089[/C][C]1.10911[/C][/ROW]
[ROW][C]138[/C][C]0.75[/C][C]1.65307[/C][C]-0.903072[/C][/ROW]
[ROW][C]139[/C][C]1.5[/C][C]2.4494[/C][C]-0.9494[/C][/ROW]
[ROW][C]140[/C][C]3[/C][C]2.87308[/C][C]0.126921[/C][/ROW]
[ROW][C]141[/C][C]3[/C][C]1.85326[/C][C]1.14674[/C][/ROW]
[ROW][C]142[/C][C]3[/C][C]2.58545[/C][C]0.414547[/C][/ROW]
[ROW][C]143[/C][C]2.25[/C][C]2.05303[/C][C]0.196967[/C][/ROW]
[ROW][C]144[/C][C]2.25[/C][C]1.89845[/C][C]0.351545[/C][/ROW]
[ROW][C]145[/C][C]3[/C][C]2.28102[/C][C]0.718978[/C][/ROW]
[ROW][C]146[/C][C]1.5[/C][C]2.31989[/C][C]-0.819889[/C][/ROW]
[ROW][C]147[/C][C]2.25[/C][C]2.21813[/C][C]0.031865[/C][/ROW]
[ROW][C]148[/C][C]2.25[/C][C]2.28847[/C][C]-0.0384728[/C][/ROW]
[ROW][C]149[/C][C]2.25[/C][C]2.45922[/C][C]-0.209223[/C][/ROW]
[ROW][C]150[/C][C]0.75[/C][C]1.51925[/C][C]-0.769246[/C][/ROW]
[ROW][C]151[/C][C]2.25[/C][C]2.07332[/C][C]0.176676[/C][/ROW]
[ROW][C]152[/C][C]1.5[/C][C]2.01758[/C][C]-0.517577[/C][/ROW]
[ROW][C]153[/C][C]2.25[/C][C]1.88126[/C][C]0.368744[/C][/ROW]
[ROW][C]154[/C][C]1.5[/C][C]1.36503[/C][C]0.134972[/C][/ROW]
[ROW][C]155[/C][C]0.75[/C][C]1.89062[/C][C]-1.14062[/C][/ROW]
[ROW][C]156[/C][C]1.5[/C][C]1.8649[/C][C]-0.364905[/C][/ROW]
[ROW][C]157[/C][C]1.5[/C][C]1.81587[/C][C]-0.315873[/C][/ROW]
[ROW][C]158[/C][C]2.25[/C][C]2.21083[/C][C]0.03917[/C][/ROW]
[ROW][C]159[/C][C]1.5[/C][C]1.97027[/C][C]-0.470273[/C][/ROW]
[ROW][C]160[/C][C]1.5[/C][C]1.89042[/C][C]-0.390419[/C][/ROW]
[ROW][C]161[/C][C]3[/C][C]1.81208[/C][C]1.18792[/C][/ROW]
[ROW][C]162[/C][C]2.25[/C][C]2.01734[/C][C]0.232659[/C][/ROW]
[ROW][C]163[/C][C]1.5[/C][C]1.59611[/C][C]-0.0961065[/C][/ROW]
[ROW][C]164[/C][C]0.75[/C][C]1.76908[/C][C]-1.01908[/C][/ROW]
[ROW][C]165[/C][C]2.25[/C][C]1.62117[/C][C]0.628831[/C][/ROW]
[ROW][C]166[/C][C]3[/C][C]2.27869[/C][C]0.721305[/C][/ROW]
[ROW][C]167[/C][C]3[/C][C]2.6526[/C][C]0.347402[/C][/ROW]
[ROW][C]168[/C][C]1.5[/C][C]2.13935[/C][C]-0.639353[/C][/ROW]
[ROW][C]169[/C][C]1.5[/C][C]2.03969[/C][C]-0.539689[/C][/ROW]
[ROW][C]170[/C][C]2.25[/C][C]2.39697[/C][C]-0.146974[/C][/ROW]
[ROW][C]171[/C][C]0.75[/C][C]1.44737[/C][C]-0.697372[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267983&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267983&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
10.751.3895-0.639497
21.51.93609-0.436092
332.351970.648031
42.252.39811-0.148113
532.960320.0396788
61.51.442890.057115
733.49765-0.497654
832.696970.303026
933.611-0.611003
100.751.47253-0.722525
1132.805610.194391
122.251.506490.743515
131.52.20564-0.705639
141.52.06967-0.569671
152.252.36523-0.115234
1632.599680.400325
1732.54230.457704
181.51.82072-0.320717
192.251.803570.446428
202.253.02023-0.770233
211.52.11556-0.615559
222.252.25874-0.00873847
231.51.81892-0.318923
242.252.206810.0431918
252.252.129650.120351
2631.675991.32401
2732.934520.0654761
2832.893860.106139
291.51.68622-0.186217
3031.919281.08072
3132.415430.584571
322.252.5044-0.254404
331.52.09083-0.590834
342.252.51756-0.267561
352.252.61807-0.368067
3632.058360.941641
370.751.93091-1.18091
382.252.161920.0880837
3932.68560.314399
4032.546110.453886
411.52.45057-0.950567
422.252.46434-0.214338
4332.475170.524826
442.252.75114-0.501141
451.51.80973-0.309731
462.252.28065-0.0306531
472.252.90962-0.659616
481.52.25745-0.757453
492.252.83732-0.587317
501.51.83225-0.332245
512.252.364-0.113999
5232.540930.459073
5332.521050.478953
5431.45831.5417
5532.644210.355787
561.51.365030.134972
572.252.62779-0.377795
581.51.51483-0.0148292
592.252.31375-0.0637529
602.252.27678-0.0267768
612.252.33834-0.0883359
6232.20850.791503
631.52.2404-0.740401
642.252.50741-0.25741
652.252.50741-0.25741
6632.271690.728311
672.252.30589-0.0558936
6832.633710.36629
692.252.27662-0.0266183
701.51.65835-0.158349
7132.437840.562159
721.52.13164-0.631637
7332.823730.176275
7432.29520.704799
7532.016970.983025
7632.291420.708579
772.251.843350.406647
782.251.982430.267569
790.751.39488-0.644883
8031.908481.09152
810.751.68177-0.931775
821.52.17775-0.67775
831.51.64739-0.147386
8432.096660.903339
851.51.8496-0.349598
862.252.172140.077863
8732.763630.236369
8832.122330.877668
891.51.61641-0.116406
9032.078550.921446
9132.931610.0683857
921.51.76408-0.264082
931.52.17223-0.672234
942.252.34085-0.0908504
951.51.79346-0.293456
961.51.58399-0.0839879
972.251.941930.308073
981.51.6019-0.101898
992.251.749340.500659
10032.855290.144714
10132.292730.707273
1020.751.56427-0.814267
1031.52.07052-0.57052
1041.52.46716-0.967164
1052.252.18070.0692973
1062.252.89642-0.646422
1071.52.0473-0.547304
1082.252.061230.188769
1090.752.08546-1.33546
1102.252.43504-0.185041
1110.751.77166-1.02166
1122.252.40324-0.153237
11332.438090.561912
1140.751.81302-1.06302
1150.751.708-0.958004
11631.769491.23051
11732.689470.310528
11831.675651.32435
11932.053910.946086
1201.51.82119-0.321191
12132.695460.304541
12232.448970.551034
12332.08240.917603
12432.935460.0645426
1251.51.78477-0.284772
1262.252.120780.129222
1270.751.75692-1.00692
1280.751.56464-0.814641
1292.251.450460.799544
13032.48420.515804
1312.252.057190.192808
13231.735981.26402
1332.252.39166-0.141657
13432.06830.931697
1351.51.81587-0.315873
13632.644210.355787
13731.890891.10911
1380.751.65307-0.903072
1391.52.4494-0.9494
14032.873080.126921
14131.853261.14674
14232.585450.414547
1432.252.053030.196967
1442.251.898450.351545
14532.281020.718978
1461.52.31989-0.819889
1472.252.218130.031865
1482.252.28847-0.0384728
1492.252.45922-0.209223
1500.751.51925-0.769246
1512.252.073320.176676
1521.52.01758-0.517577
1532.251.881260.368744
1541.51.365030.134972
1550.751.89062-1.14062
1561.51.8649-0.364905
1571.51.81587-0.315873
1582.252.210830.03917
1591.51.97027-0.470273
1601.51.89042-0.390419
16131.812081.18792
1622.252.017340.232659
1631.51.59611-0.0961065
1640.751.76908-1.01908
1652.251.621170.628831
16632.278690.721305
16732.65260.347402
1681.52.13935-0.639353
1691.52.03969-0.539689
1702.252.39697-0.146974
1710.751.44737-0.697372






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.3670940.7341870.632906
100.410850.8217010.58915
110.3150140.6300270.684986
120.5650310.8699370.434969
130.5166230.9667550.483377
140.5140850.9718290.485915
150.4148620.8297250.585138
160.4023950.804790.597605
170.316480.632960.68352
180.2643110.5286230.735689
190.2356620.4713250.764338
200.2985940.5971870.701406
210.2602770.5205540.739723
220.1999050.3998090.800095
230.1554790.3109580.844521
240.1181530.2363060.881847
250.09119330.1823870.908807
260.2685620.5371240.731438
270.2243130.4486270.775687
280.1773110.3546220.822689
290.1379870.2759740.862013
300.3093280.6186560.690672
310.2896080.5792150.710392
320.2423790.4847590.757621
330.2523060.5046120.747694
340.2084520.4169050.791548
350.1760060.3520130.823994
360.216490.4329790.78351
370.3684860.7369730.631514
380.3169950.6339890.683005
390.2867170.5734330.713283
400.2689620.5379230.731038
410.3188960.6377920.681104
420.2744590.5489180.725541
430.2679430.5358860.732057
440.2409360.4818720.759064
450.2121390.4242780.787861
460.1781510.3563020.821849
470.1707090.3414180.829291
480.1872670.3745330.812733
490.1724830.3449650.827517
500.1649740.3299470.835026
510.1362450.2724910.863755
520.1345920.2691830.865408
530.1288530.2577060.871147
540.2991720.5983440.700828
550.2768240.5536490.723176
560.2476720.4953450.752328
570.2227780.4455570.777222
580.1892170.3784350.810783
590.158790.3175790.84121
600.1313130.2626260.868687
610.1079510.2159020.892049
620.1246050.2492090.875395
630.1356390.2712790.864361
640.114630.229260.88537
650.09701880.1940380.902981
660.1079050.2158090.892095
670.08791310.1758260.912087
680.07931640.1586330.920684
690.06372320.1274460.936277
700.05318360.1063670.946816
710.05050050.1010010.9495
720.05375320.1075060.946247
730.04422920.08845840.955771
740.04786760.09573520.952132
750.0715820.1431640.928418
760.07542260.1508450.924577
770.06568250.1313650.934318
780.05418330.1083670.945817
790.05731980.114640.94268
800.08614740.1722950.913853
810.119010.238020.88099
820.1260570.2521140.873943
830.1063630.2127250.893637
840.1302970.2605950.869703
850.1158180.2316360.884182
860.09541290.1908260.904587
870.08017860.1603570.919821
880.09579910.1915980.904201
890.07951320.1590260.920487
900.1008740.2017470.899126
910.08536550.1707310.914635
920.07253320.1450660.927467
930.07537740.1507550.924623
940.06111490.122230.938885
950.05141750.1028350.948582
960.04098980.08197950.95901
970.03442530.06885070.965575
980.02718180.05436350.972818
990.0254410.05088190.974559
1000.01965340.03930690.980347
1010.02167060.04334110.978329
1020.0251710.05034190.974829
1030.02454890.04909770.975451
1040.03524350.0704870.964756
1050.02764340.05528670.972357
1060.02994250.05988510.970057
1070.0283990.0567980.971601
1080.02223010.04446020.97777
1090.06560380.1312080.934396
1100.05906820.1181360.940932
1110.07783130.1556630.922169
1120.06350810.1270160.936492
1130.05634010.112680.94366
1140.07940790.1588160.920592
1150.1112750.222550.888725
1160.1712620.3425240.828738
1170.1461210.2922430.853879
1180.2808860.5617710.719114
1190.3189310.6378620.681069
1200.2901710.5803420.709829
1210.2577840.5155690.742216
1220.2386180.4772370.761382
1230.2564170.5128330.743583
1240.2180680.4361360.781932
1250.1869860.3739730.813014
1260.1551770.3103530.844823
1270.1928030.3856050.807197
1280.2044750.408950.795525
1290.2453420.4906830.754658
1300.2171560.4343120.782844
1310.1868580.3737150.813142
1320.3277680.6555370.672232
1330.2816230.5632450.718377
1340.3910140.7820270.608986
1350.350370.7007390.64963
1360.3035630.6071260.696437
1370.4157960.8315930.584204
1380.4777390.9554770.522261
1390.5813090.8373830.418691
1400.5200870.9598260.479913
1410.6302170.7395660.369783
1420.6081610.7836780.391839
1430.5615420.8769160.438458
1440.5523890.8952220.447611
1450.5967480.8065040.403252
1460.6219430.7561140.378057
1470.5542820.8914360.445718
1480.4941850.9883710.505815
1490.4933370.9866740.506663
1500.4538330.9076660.546167
1510.392940.7858810.60706
1520.4691220.9382440.530878
1530.3973620.7947250.602638
1540.3186560.6373120.681344
1550.4263690.8527370.573631
1560.5873070.8253860.412693
1570.6833130.6333740.316687
1580.5862580.8274840.413742
1590.4693910.9387820.530609
1600.3651870.7303730.634813
1610.2784380.5568760.721562
1620.4348670.8697340.565133

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.367094 & 0.734187 & 0.632906 \tabularnewline
10 & 0.41085 & 0.821701 & 0.58915 \tabularnewline
11 & 0.315014 & 0.630027 & 0.684986 \tabularnewline
12 & 0.565031 & 0.869937 & 0.434969 \tabularnewline
13 & 0.516623 & 0.966755 & 0.483377 \tabularnewline
14 & 0.514085 & 0.971829 & 0.485915 \tabularnewline
15 & 0.414862 & 0.829725 & 0.585138 \tabularnewline
16 & 0.402395 & 0.80479 & 0.597605 \tabularnewline
17 & 0.31648 & 0.63296 & 0.68352 \tabularnewline
18 & 0.264311 & 0.528623 & 0.735689 \tabularnewline
19 & 0.235662 & 0.471325 & 0.764338 \tabularnewline
20 & 0.298594 & 0.597187 & 0.701406 \tabularnewline
21 & 0.260277 & 0.520554 & 0.739723 \tabularnewline
22 & 0.199905 & 0.399809 & 0.800095 \tabularnewline
23 & 0.155479 & 0.310958 & 0.844521 \tabularnewline
24 & 0.118153 & 0.236306 & 0.881847 \tabularnewline
25 & 0.0911933 & 0.182387 & 0.908807 \tabularnewline
26 & 0.268562 & 0.537124 & 0.731438 \tabularnewline
27 & 0.224313 & 0.448627 & 0.775687 \tabularnewline
28 & 0.177311 & 0.354622 & 0.822689 \tabularnewline
29 & 0.137987 & 0.275974 & 0.862013 \tabularnewline
30 & 0.309328 & 0.618656 & 0.690672 \tabularnewline
31 & 0.289608 & 0.579215 & 0.710392 \tabularnewline
32 & 0.242379 & 0.484759 & 0.757621 \tabularnewline
33 & 0.252306 & 0.504612 & 0.747694 \tabularnewline
34 & 0.208452 & 0.416905 & 0.791548 \tabularnewline
35 & 0.176006 & 0.352013 & 0.823994 \tabularnewline
36 & 0.21649 & 0.432979 & 0.78351 \tabularnewline
37 & 0.368486 & 0.736973 & 0.631514 \tabularnewline
38 & 0.316995 & 0.633989 & 0.683005 \tabularnewline
39 & 0.286717 & 0.573433 & 0.713283 \tabularnewline
40 & 0.268962 & 0.537923 & 0.731038 \tabularnewline
41 & 0.318896 & 0.637792 & 0.681104 \tabularnewline
42 & 0.274459 & 0.548918 & 0.725541 \tabularnewline
43 & 0.267943 & 0.535886 & 0.732057 \tabularnewline
44 & 0.240936 & 0.481872 & 0.759064 \tabularnewline
45 & 0.212139 & 0.424278 & 0.787861 \tabularnewline
46 & 0.178151 & 0.356302 & 0.821849 \tabularnewline
47 & 0.170709 & 0.341418 & 0.829291 \tabularnewline
48 & 0.187267 & 0.374533 & 0.812733 \tabularnewline
49 & 0.172483 & 0.344965 & 0.827517 \tabularnewline
50 & 0.164974 & 0.329947 & 0.835026 \tabularnewline
51 & 0.136245 & 0.272491 & 0.863755 \tabularnewline
52 & 0.134592 & 0.269183 & 0.865408 \tabularnewline
53 & 0.128853 & 0.257706 & 0.871147 \tabularnewline
54 & 0.299172 & 0.598344 & 0.700828 \tabularnewline
55 & 0.276824 & 0.553649 & 0.723176 \tabularnewline
56 & 0.247672 & 0.495345 & 0.752328 \tabularnewline
57 & 0.222778 & 0.445557 & 0.777222 \tabularnewline
58 & 0.189217 & 0.378435 & 0.810783 \tabularnewline
59 & 0.15879 & 0.317579 & 0.84121 \tabularnewline
60 & 0.131313 & 0.262626 & 0.868687 \tabularnewline
61 & 0.107951 & 0.215902 & 0.892049 \tabularnewline
62 & 0.124605 & 0.249209 & 0.875395 \tabularnewline
63 & 0.135639 & 0.271279 & 0.864361 \tabularnewline
64 & 0.11463 & 0.22926 & 0.88537 \tabularnewline
65 & 0.0970188 & 0.194038 & 0.902981 \tabularnewline
66 & 0.107905 & 0.215809 & 0.892095 \tabularnewline
67 & 0.0879131 & 0.175826 & 0.912087 \tabularnewline
68 & 0.0793164 & 0.158633 & 0.920684 \tabularnewline
69 & 0.0637232 & 0.127446 & 0.936277 \tabularnewline
70 & 0.0531836 & 0.106367 & 0.946816 \tabularnewline
71 & 0.0505005 & 0.101001 & 0.9495 \tabularnewline
72 & 0.0537532 & 0.107506 & 0.946247 \tabularnewline
73 & 0.0442292 & 0.0884584 & 0.955771 \tabularnewline
74 & 0.0478676 & 0.0957352 & 0.952132 \tabularnewline
75 & 0.071582 & 0.143164 & 0.928418 \tabularnewline
76 & 0.0754226 & 0.150845 & 0.924577 \tabularnewline
77 & 0.0656825 & 0.131365 & 0.934318 \tabularnewline
78 & 0.0541833 & 0.108367 & 0.945817 \tabularnewline
79 & 0.0573198 & 0.11464 & 0.94268 \tabularnewline
80 & 0.0861474 & 0.172295 & 0.913853 \tabularnewline
81 & 0.11901 & 0.23802 & 0.88099 \tabularnewline
82 & 0.126057 & 0.252114 & 0.873943 \tabularnewline
83 & 0.106363 & 0.212725 & 0.893637 \tabularnewline
84 & 0.130297 & 0.260595 & 0.869703 \tabularnewline
85 & 0.115818 & 0.231636 & 0.884182 \tabularnewline
86 & 0.0954129 & 0.190826 & 0.904587 \tabularnewline
87 & 0.0801786 & 0.160357 & 0.919821 \tabularnewline
88 & 0.0957991 & 0.191598 & 0.904201 \tabularnewline
89 & 0.0795132 & 0.159026 & 0.920487 \tabularnewline
90 & 0.100874 & 0.201747 & 0.899126 \tabularnewline
91 & 0.0853655 & 0.170731 & 0.914635 \tabularnewline
92 & 0.0725332 & 0.145066 & 0.927467 \tabularnewline
93 & 0.0753774 & 0.150755 & 0.924623 \tabularnewline
94 & 0.0611149 & 0.12223 & 0.938885 \tabularnewline
95 & 0.0514175 & 0.102835 & 0.948582 \tabularnewline
96 & 0.0409898 & 0.0819795 & 0.95901 \tabularnewline
97 & 0.0344253 & 0.0688507 & 0.965575 \tabularnewline
98 & 0.0271818 & 0.0543635 & 0.972818 \tabularnewline
99 & 0.025441 & 0.0508819 & 0.974559 \tabularnewline
100 & 0.0196534 & 0.0393069 & 0.980347 \tabularnewline
101 & 0.0216706 & 0.0433411 & 0.978329 \tabularnewline
102 & 0.025171 & 0.0503419 & 0.974829 \tabularnewline
103 & 0.0245489 & 0.0490977 & 0.975451 \tabularnewline
104 & 0.0352435 & 0.070487 & 0.964756 \tabularnewline
105 & 0.0276434 & 0.0552867 & 0.972357 \tabularnewline
106 & 0.0299425 & 0.0598851 & 0.970057 \tabularnewline
107 & 0.028399 & 0.056798 & 0.971601 \tabularnewline
108 & 0.0222301 & 0.0444602 & 0.97777 \tabularnewline
109 & 0.0656038 & 0.131208 & 0.934396 \tabularnewline
110 & 0.0590682 & 0.118136 & 0.940932 \tabularnewline
111 & 0.0778313 & 0.155663 & 0.922169 \tabularnewline
112 & 0.0635081 & 0.127016 & 0.936492 \tabularnewline
113 & 0.0563401 & 0.11268 & 0.94366 \tabularnewline
114 & 0.0794079 & 0.158816 & 0.920592 \tabularnewline
115 & 0.111275 & 0.22255 & 0.888725 \tabularnewline
116 & 0.171262 & 0.342524 & 0.828738 \tabularnewline
117 & 0.146121 & 0.292243 & 0.853879 \tabularnewline
118 & 0.280886 & 0.561771 & 0.719114 \tabularnewline
119 & 0.318931 & 0.637862 & 0.681069 \tabularnewline
120 & 0.290171 & 0.580342 & 0.709829 \tabularnewline
121 & 0.257784 & 0.515569 & 0.742216 \tabularnewline
122 & 0.238618 & 0.477237 & 0.761382 \tabularnewline
123 & 0.256417 & 0.512833 & 0.743583 \tabularnewline
124 & 0.218068 & 0.436136 & 0.781932 \tabularnewline
125 & 0.186986 & 0.373973 & 0.813014 \tabularnewline
126 & 0.155177 & 0.310353 & 0.844823 \tabularnewline
127 & 0.192803 & 0.385605 & 0.807197 \tabularnewline
128 & 0.204475 & 0.40895 & 0.795525 \tabularnewline
129 & 0.245342 & 0.490683 & 0.754658 \tabularnewline
130 & 0.217156 & 0.434312 & 0.782844 \tabularnewline
131 & 0.186858 & 0.373715 & 0.813142 \tabularnewline
132 & 0.327768 & 0.655537 & 0.672232 \tabularnewline
133 & 0.281623 & 0.563245 & 0.718377 \tabularnewline
134 & 0.391014 & 0.782027 & 0.608986 \tabularnewline
135 & 0.35037 & 0.700739 & 0.64963 \tabularnewline
136 & 0.303563 & 0.607126 & 0.696437 \tabularnewline
137 & 0.415796 & 0.831593 & 0.584204 \tabularnewline
138 & 0.477739 & 0.955477 & 0.522261 \tabularnewline
139 & 0.581309 & 0.837383 & 0.418691 \tabularnewline
140 & 0.520087 & 0.959826 & 0.479913 \tabularnewline
141 & 0.630217 & 0.739566 & 0.369783 \tabularnewline
142 & 0.608161 & 0.783678 & 0.391839 \tabularnewline
143 & 0.561542 & 0.876916 & 0.438458 \tabularnewline
144 & 0.552389 & 0.895222 & 0.447611 \tabularnewline
145 & 0.596748 & 0.806504 & 0.403252 \tabularnewline
146 & 0.621943 & 0.756114 & 0.378057 \tabularnewline
147 & 0.554282 & 0.891436 & 0.445718 \tabularnewline
148 & 0.494185 & 0.988371 & 0.505815 \tabularnewline
149 & 0.493337 & 0.986674 & 0.506663 \tabularnewline
150 & 0.453833 & 0.907666 & 0.546167 \tabularnewline
151 & 0.39294 & 0.785881 & 0.60706 \tabularnewline
152 & 0.469122 & 0.938244 & 0.530878 \tabularnewline
153 & 0.397362 & 0.794725 & 0.602638 \tabularnewline
154 & 0.318656 & 0.637312 & 0.681344 \tabularnewline
155 & 0.426369 & 0.852737 & 0.573631 \tabularnewline
156 & 0.587307 & 0.825386 & 0.412693 \tabularnewline
157 & 0.683313 & 0.633374 & 0.316687 \tabularnewline
158 & 0.586258 & 0.827484 & 0.413742 \tabularnewline
159 & 0.469391 & 0.938782 & 0.530609 \tabularnewline
160 & 0.365187 & 0.730373 & 0.634813 \tabularnewline
161 & 0.278438 & 0.556876 & 0.721562 \tabularnewline
162 & 0.434867 & 0.869734 & 0.565133 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267983&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]9[/C][C]0.367094[/C][C]0.734187[/C][C]0.632906[/C][/ROW]
[ROW][C]10[/C][C]0.41085[/C][C]0.821701[/C][C]0.58915[/C][/ROW]
[ROW][C]11[/C][C]0.315014[/C][C]0.630027[/C][C]0.684986[/C][/ROW]
[ROW][C]12[/C][C]0.565031[/C][C]0.869937[/C][C]0.434969[/C][/ROW]
[ROW][C]13[/C][C]0.516623[/C][C]0.966755[/C][C]0.483377[/C][/ROW]
[ROW][C]14[/C][C]0.514085[/C][C]0.971829[/C][C]0.485915[/C][/ROW]
[ROW][C]15[/C][C]0.414862[/C][C]0.829725[/C][C]0.585138[/C][/ROW]
[ROW][C]16[/C][C]0.402395[/C][C]0.80479[/C][C]0.597605[/C][/ROW]
[ROW][C]17[/C][C]0.31648[/C][C]0.63296[/C][C]0.68352[/C][/ROW]
[ROW][C]18[/C][C]0.264311[/C][C]0.528623[/C][C]0.735689[/C][/ROW]
[ROW][C]19[/C][C]0.235662[/C][C]0.471325[/C][C]0.764338[/C][/ROW]
[ROW][C]20[/C][C]0.298594[/C][C]0.597187[/C][C]0.701406[/C][/ROW]
[ROW][C]21[/C][C]0.260277[/C][C]0.520554[/C][C]0.739723[/C][/ROW]
[ROW][C]22[/C][C]0.199905[/C][C]0.399809[/C][C]0.800095[/C][/ROW]
[ROW][C]23[/C][C]0.155479[/C][C]0.310958[/C][C]0.844521[/C][/ROW]
[ROW][C]24[/C][C]0.118153[/C][C]0.236306[/C][C]0.881847[/C][/ROW]
[ROW][C]25[/C][C]0.0911933[/C][C]0.182387[/C][C]0.908807[/C][/ROW]
[ROW][C]26[/C][C]0.268562[/C][C]0.537124[/C][C]0.731438[/C][/ROW]
[ROW][C]27[/C][C]0.224313[/C][C]0.448627[/C][C]0.775687[/C][/ROW]
[ROW][C]28[/C][C]0.177311[/C][C]0.354622[/C][C]0.822689[/C][/ROW]
[ROW][C]29[/C][C]0.137987[/C][C]0.275974[/C][C]0.862013[/C][/ROW]
[ROW][C]30[/C][C]0.309328[/C][C]0.618656[/C][C]0.690672[/C][/ROW]
[ROW][C]31[/C][C]0.289608[/C][C]0.579215[/C][C]0.710392[/C][/ROW]
[ROW][C]32[/C][C]0.242379[/C][C]0.484759[/C][C]0.757621[/C][/ROW]
[ROW][C]33[/C][C]0.252306[/C][C]0.504612[/C][C]0.747694[/C][/ROW]
[ROW][C]34[/C][C]0.208452[/C][C]0.416905[/C][C]0.791548[/C][/ROW]
[ROW][C]35[/C][C]0.176006[/C][C]0.352013[/C][C]0.823994[/C][/ROW]
[ROW][C]36[/C][C]0.21649[/C][C]0.432979[/C][C]0.78351[/C][/ROW]
[ROW][C]37[/C][C]0.368486[/C][C]0.736973[/C][C]0.631514[/C][/ROW]
[ROW][C]38[/C][C]0.316995[/C][C]0.633989[/C][C]0.683005[/C][/ROW]
[ROW][C]39[/C][C]0.286717[/C][C]0.573433[/C][C]0.713283[/C][/ROW]
[ROW][C]40[/C][C]0.268962[/C][C]0.537923[/C][C]0.731038[/C][/ROW]
[ROW][C]41[/C][C]0.318896[/C][C]0.637792[/C][C]0.681104[/C][/ROW]
[ROW][C]42[/C][C]0.274459[/C][C]0.548918[/C][C]0.725541[/C][/ROW]
[ROW][C]43[/C][C]0.267943[/C][C]0.535886[/C][C]0.732057[/C][/ROW]
[ROW][C]44[/C][C]0.240936[/C][C]0.481872[/C][C]0.759064[/C][/ROW]
[ROW][C]45[/C][C]0.212139[/C][C]0.424278[/C][C]0.787861[/C][/ROW]
[ROW][C]46[/C][C]0.178151[/C][C]0.356302[/C][C]0.821849[/C][/ROW]
[ROW][C]47[/C][C]0.170709[/C][C]0.341418[/C][C]0.829291[/C][/ROW]
[ROW][C]48[/C][C]0.187267[/C][C]0.374533[/C][C]0.812733[/C][/ROW]
[ROW][C]49[/C][C]0.172483[/C][C]0.344965[/C][C]0.827517[/C][/ROW]
[ROW][C]50[/C][C]0.164974[/C][C]0.329947[/C][C]0.835026[/C][/ROW]
[ROW][C]51[/C][C]0.136245[/C][C]0.272491[/C][C]0.863755[/C][/ROW]
[ROW][C]52[/C][C]0.134592[/C][C]0.269183[/C][C]0.865408[/C][/ROW]
[ROW][C]53[/C][C]0.128853[/C][C]0.257706[/C][C]0.871147[/C][/ROW]
[ROW][C]54[/C][C]0.299172[/C][C]0.598344[/C][C]0.700828[/C][/ROW]
[ROW][C]55[/C][C]0.276824[/C][C]0.553649[/C][C]0.723176[/C][/ROW]
[ROW][C]56[/C][C]0.247672[/C][C]0.495345[/C][C]0.752328[/C][/ROW]
[ROW][C]57[/C][C]0.222778[/C][C]0.445557[/C][C]0.777222[/C][/ROW]
[ROW][C]58[/C][C]0.189217[/C][C]0.378435[/C][C]0.810783[/C][/ROW]
[ROW][C]59[/C][C]0.15879[/C][C]0.317579[/C][C]0.84121[/C][/ROW]
[ROW][C]60[/C][C]0.131313[/C][C]0.262626[/C][C]0.868687[/C][/ROW]
[ROW][C]61[/C][C]0.107951[/C][C]0.215902[/C][C]0.892049[/C][/ROW]
[ROW][C]62[/C][C]0.124605[/C][C]0.249209[/C][C]0.875395[/C][/ROW]
[ROW][C]63[/C][C]0.135639[/C][C]0.271279[/C][C]0.864361[/C][/ROW]
[ROW][C]64[/C][C]0.11463[/C][C]0.22926[/C][C]0.88537[/C][/ROW]
[ROW][C]65[/C][C]0.0970188[/C][C]0.194038[/C][C]0.902981[/C][/ROW]
[ROW][C]66[/C][C]0.107905[/C][C]0.215809[/C][C]0.892095[/C][/ROW]
[ROW][C]67[/C][C]0.0879131[/C][C]0.175826[/C][C]0.912087[/C][/ROW]
[ROW][C]68[/C][C]0.0793164[/C][C]0.158633[/C][C]0.920684[/C][/ROW]
[ROW][C]69[/C][C]0.0637232[/C][C]0.127446[/C][C]0.936277[/C][/ROW]
[ROW][C]70[/C][C]0.0531836[/C][C]0.106367[/C][C]0.946816[/C][/ROW]
[ROW][C]71[/C][C]0.0505005[/C][C]0.101001[/C][C]0.9495[/C][/ROW]
[ROW][C]72[/C][C]0.0537532[/C][C]0.107506[/C][C]0.946247[/C][/ROW]
[ROW][C]73[/C][C]0.0442292[/C][C]0.0884584[/C][C]0.955771[/C][/ROW]
[ROW][C]74[/C][C]0.0478676[/C][C]0.0957352[/C][C]0.952132[/C][/ROW]
[ROW][C]75[/C][C]0.071582[/C][C]0.143164[/C][C]0.928418[/C][/ROW]
[ROW][C]76[/C][C]0.0754226[/C][C]0.150845[/C][C]0.924577[/C][/ROW]
[ROW][C]77[/C][C]0.0656825[/C][C]0.131365[/C][C]0.934318[/C][/ROW]
[ROW][C]78[/C][C]0.0541833[/C][C]0.108367[/C][C]0.945817[/C][/ROW]
[ROW][C]79[/C][C]0.0573198[/C][C]0.11464[/C][C]0.94268[/C][/ROW]
[ROW][C]80[/C][C]0.0861474[/C][C]0.172295[/C][C]0.913853[/C][/ROW]
[ROW][C]81[/C][C]0.11901[/C][C]0.23802[/C][C]0.88099[/C][/ROW]
[ROW][C]82[/C][C]0.126057[/C][C]0.252114[/C][C]0.873943[/C][/ROW]
[ROW][C]83[/C][C]0.106363[/C][C]0.212725[/C][C]0.893637[/C][/ROW]
[ROW][C]84[/C][C]0.130297[/C][C]0.260595[/C][C]0.869703[/C][/ROW]
[ROW][C]85[/C][C]0.115818[/C][C]0.231636[/C][C]0.884182[/C][/ROW]
[ROW][C]86[/C][C]0.0954129[/C][C]0.190826[/C][C]0.904587[/C][/ROW]
[ROW][C]87[/C][C]0.0801786[/C][C]0.160357[/C][C]0.919821[/C][/ROW]
[ROW][C]88[/C][C]0.0957991[/C][C]0.191598[/C][C]0.904201[/C][/ROW]
[ROW][C]89[/C][C]0.0795132[/C][C]0.159026[/C][C]0.920487[/C][/ROW]
[ROW][C]90[/C][C]0.100874[/C][C]0.201747[/C][C]0.899126[/C][/ROW]
[ROW][C]91[/C][C]0.0853655[/C][C]0.170731[/C][C]0.914635[/C][/ROW]
[ROW][C]92[/C][C]0.0725332[/C][C]0.145066[/C][C]0.927467[/C][/ROW]
[ROW][C]93[/C][C]0.0753774[/C][C]0.150755[/C][C]0.924623[/C][/ROW]
[ROW][C]94[/C][C]0.0611149[/C][C]0.12223[/C][C]0.938885[/C][/ROW]
[ROW][C]95[/C][C]0.0514175[/C][C]0.102835[/C][C]0.948582[/C][/ROW]
[ROW][C]96[/C][C]0.0409898[/C][C]0.0819795[/C][C]0.95901[/C][/ROW]
[ROW][C]97[/C][C]0.0344253[/C][C]0.0688507[/C][C]0.965575[/C][/ROW]
[ROW][C]98[/C][C]0.0271818[/C][C]0.0543635[/C][C]0.972818[/C][/ROW]
[ROW][C]99[/C][C]0.025441[/C][C]0.0508819[/C][C]0.974559[/C][/ROW]
[ROW][C]100[/C][C]0.0196534[/C][C]0.0393069[/C][C]0.980347[/C][/ROW]
[ROW][C]101[/C][C]0.0216706[/C][C]0.0433411[/C][C]0.978329[/C][/ROW]
[ROW][C]102[/C][C]0.025171[/C][C]0.0503419[/C][C]0.974829[/C][/ROW]
[ROW][C]103[/C][C]0.0245489[/C][C]0.0490977[/C][C]0.975451[/C][/ROW]
[ROW][C]104[/C][C]0.0352435[/C][C]0.070487[/C][C]0.964756[/C][/ROW]
[ROW][C]105[/C][C]0.0276434[/C][C]0.0552867[/C][C]0.972357[/C][/ROW]
[ROW][C]106[/C][C]0.0299425[/C][C]0.0598851[/C][C]0.970057[/C][/ROW]
[ROW][C]107[/C][C]0.028399[/C][C]0.056798[/C][C]0.971601[/C][/ROW]
[ROW][C]108[/C][C]0.0222301[/C][C]0.0444602[/C][C]0.97777[/C][/ROW]
[ROW][C]109[/C][C]0.0656038[/C][C]0.131208[/C][C]0.934396[/C][/ROW]
[ROW][C]110[/C][C]0.0590682[/C][C]0.118136[/C][C]0.940932[/C][/ROW]
[ROW][C]111[/C][C]0.0778313[/C][C]0.155663[/C][C]0.922169[/C][/ROW]
[ROW][C]112[/C][C]0.0635081[/C][C]0.127016[/C][C]0.936492[/C][/ROW]
[ROW][C]113[/C][C]0.0563401[/C][C]0.11268[/C][C]0.94366[/C][/ROW]
[ROW][C]114[/C][C]0.0794079[/C][C]0.158816[/C][C]0.920592[/C][/ROW]
[ROW][C]115[/C][C]0.111275[/C][C]0.22255[/C][C]0.888725[/C][/ROW]
[ROW][C]116[/C][C]0.171262[/C][C]0.342524[/C][C]0.828738[/C][/ROW]
[ROW][C]117[/C][C]0.146121[/C][C]0.292243[/C][C]0.853879[/C][/ROW]
[ROW][C]118[/C][C]0.280886[/C][C]0.561771[/C][C]0.719114[/C][/ROW]
[ROW][C]119[/C][C]0.318931[/C][C]0.637862[/C][C]0.681069[/C][/ROW]
[ROW][C]120[/C][C]0.290171[/C][C]0.580342[/C][C]0.709829[/C][/ROW]
[ROW][C]121[/C][C]0.257784[/C][C]0.515569[/C][C]0.742216[/C][/ROW]
[ROW][C]122[/C][C]0.238618[/C][C]0.477237[/C][C]0.761382[/C][/ROW]
[ROW][C]123[/C][C]0.256417[/C][C]0.512833[/C][C]0.743583[/C][/ROW]
[ROW][C]124[/C][C]0.218068[/C][C]0.436136[/C][C]0.781932[/C][/ROW]
[ROW][C]125[/C][C]0.186986[/C][C]0.373973[/C][C]0.813014[/C][/ROW]
[ROW][C]126[/C][C]0.155177[/C][C]0.310353[/C][C]0.844823[/C][/ROW]
[ROW][C]127[/C][C]0.192803[/C][C]0.385605[/C][C]0.807197[/C][/ROW]
[ROW][C]128[/C][C]0.204475[/C][C]0.40895[/C][C]0.795525[/C][/ROW]
[ROW][C]129[/C][C]0.245342[/C][C]0.490683[/C][C]0.754658[/C][/ROW]
[ROW][C]130[/C][C]0.217156[/C][C]0.434312[/C][C]0.782844[/C][/ROW]
[ROW][C]131[/C][C]0.186858[/C][C]0.373715[/C][C]0.813142[/C][/ROW]
[ROW][C]132[/C][C]0.327768[/C][C]0.655537[/C][C]0.672232[/C][/ROW]
[ROW][C]133[/C][C]0.281623[/C][C]0.563245[/C][C]0.718377[/C][/ROW]
[ROW][C]134[/C][C]0.391014[/C][C]0.782027[/C][C]0.608986[/C][/ROW]
[ROW][C]135[/C][C]0.35037[/C][C]0.700739[/C][C]0.64963[/C][/ROW]
[ROW][C]136[/C][C]0.303563[/C][C]0.607126[/C][C]0.696437[/C][/ROW]
[ROW][C]137[/C][C]0.415796[/C][C]0.831593[/C][C]0.584204[/C][/ROW]
[ROW][C]138[/C][C]0.477739[/C][C]0.955477[/C][C]0.522261[/C][/ROW]
[ROW][C]139[/C][C]0.581309[/C][C]0.837383[/C][C]0.418691[/C][/ROW]
[ROW][C]140[/C][C]0.520087[/C][C]0.959826[/C][C]0.479913[/C][/ROW]
[ROW][C]141[/C][C]0.630217[/C][C]0.739566[/C][C]0.369783[/C][/ROW]
[ROW][C]142[/C][C]0.608161[/C][C]0.783678[/C][C]0.391839[/C][/ROW]
[ROW][C]143[/C][C]0.561542[/C][C]0.876916[/C][C]0.438458[/C][/ROW]
[ROW][C]144[/C][C]0.552389[/C][C]0.895222[/C][C]0.447611[/C][/ROW]
[ROW][C]145[/C][C]0.596748[/C][C]0.806504[/C][C]0.403252[/C][/ROW]
[ROW][C]146[/C][C]0.621943[/C][C]0.756114[/C][C]0.378057[/C][/ROW]
[ROW][C]147[/C][C]0.554282[/C][C]0.891436[/C][C]0.445718[/C][/ROW]
[ROW][C]148[/C][C]0.494185[/C][C]0.988371[/C][C]0.505815[/C][/ROW]
[ROW][C]149[/C][C]0.493337[/C][C]0.986674[/C][C]0.506663[/C][/ROW]
[ROW][C]150[/C][C]0.453833[/C][C]0.907666[/C][C]0.546167[/C][/ROW]
[ROW][C]151[/C][C]0.39294[/C][C]0.785881[/C][C]0.60706[/C][/ROW]
[ROW][C]152[/C][C]0.469122[/C][C]0.938244[/C][C]0.530878[/C][/ROW]
[ROW][C]153[/C][C]0.397362[/C][C]0.794725[/C][C]0.602638[/C][/ROW]
[ROW][C]154[/C][C]0.318656[/C][C]0.637312[/C][C]0.681344[/C][/ROW]
[ROW][C]155[/C][C]0.426369[/C][C]0.852737[/C][C]0.573631[/C][/ROW]
[ROW][C]156[/C][C]0.587307[/C][C]0.825386[/C][C]0.412693[/C][/ROW]
[ROW][C]157[/C][C]0.683313[/C][C]0.633374[/C][C]0.316687[/C][/ROW]
[ROW][C]158[/C][C]0.586258[/C][C]0.827484[/C][C]0.413742[/C][/ROW]
[ROW][C]159[/C][C]0.469391[/C][C]0.938782[/C][C]0.530609[/C][/ROW]
[ROW][C]160[/C][C]0.365187[/C][C]0.730373[/C][C]0.634813[/C][/ROW]
[ROW][C]161[/C][C]0.278438[/C][C]0.556876[/C][C]0.721562[/C][/ROW]
[ROW][C]162[/C][C]0.434867[/C][C]0.869734[/C][C]0.565133[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267983&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267983&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.3670940.7341870.632906
100.410850.8217010.58915
110.3150140.6300270.684986
120.5650310.8699370.434969
130.5166230.9667550.483377
140.5140850.9718290.485915
150.4148620.8297250.585138
160.4023950.804790.597605
170.316480.632960.68352
180.2643110.5286230.735689
190.2356620.4713250.764338
200.2985940.5971870.701406
210.2602770.5205540.739723
220.1999050.3998090.800095
230.1554790.3109580.844521
240.1181530.2363060.881847
250.09119330.1823870.908807
260.2685620.5371240.731438
270.2243130.4486270.775687
280.1773110.3546220.822689
290.1379870.2759740.862013
300.3093280.6186560.690672
310.2896080.5792150.710392
320.2423790.4847590.757621
330.2523060.5046120.747694
340.2084520.4169050.791548
350.1760060.3520130.823994
360.216490.4329790.78351
370.3684860.7369730.631514
380.3169950.6339890.683005
390.2867170.5734330.713283
400.2689620.5379230.731038
410.3188960.6377920.681104
420.2744590.5489180.725541
430.2679430.5358860.732057
440.2409360.4818720.759064
450.2121390.4242780.787861
460.1781510.3563020.821849
470.1707090.3414180.829291
480.1872670.3745330.812733
490.1724830.3449650.827517
500.1649740.3299470.835026
510.1362450.2724910.863755
520.1345920.2691830.865408
530.1288530.2577060.871147
540.2991720.5983440.700828
550.2768240.5536490.723176
560.2476720.4953450.752328
570.2227780.4455570.777222
580.1892170.3784350.810783
590.158790.3175790.84121
600.1313130.2626260.868687
610.1079510.2159020.892049
620.1246050.2492090.875395
630.1356390.2712790.864361
640.114630.229260.88537
650.09701880.1940380.902981
660.1079050.2158090.892095
670.08791310.1758260.912087
680.07931640.1586330.920684
690.06372320.1274460.936277
700.05318360.1063670.946816
710.05050050.1010010.9495
720.05375320.1075060.946247
730.04422920.08845840.955771
740.04786760.09573520.952132
750.0715820.1431640.928418
760.07542260.1508450.924577
770.06568250.1313650.934318
780.05418330.1083670.945817
790.05731980.114640.94268
800.08614740.1722950.913853
810.119010.238020.88099
820.1260570.2521140.873943
830.1063630.2127250.893637
840.1302970.2605950.869703
850.1158180.2316360.884182
860.09541290.1908260.904587
870.08017860.1603570.919821
880.09579910.1915980.904201
890.07951320.1590260.920487
900.1008740.2017470.899126
910.08536550.1707310.914635
920.07253320.1450660.927467
930.07537740.1507550.924623
940.06111490.122230.938885
950.05141750.1028350.948582
960.04098980.08197950.95901
970.03442530.06885070.965575
980.02718180.05436350.972818
990.0254410.05088190.974559
1000.01965340.03930690.980347
1010.02167060.04334110.978329
1020.0251710.05034190.974829
1030.02454890.04909770.975451
1040.03524350.0704870.964756
1050.02764340.05528670.972357
1060.02994250.05988510.970057
1070.0283990.0567980.971601
1080.02223010.04446020.97777
1090.06560380.1312080.934396
1100.05906820.1181360.940932
1110.07783130.1556630.922169
1120.06350810.1270160.936492
1130.05634010.112680.94366
1140.07940790.1588160.920592
1150.1112750.222550.888725
1160.1712620.3425240.828738
1170.1461210.2922430.853879
1180.2808860.5617710.719114
1190.3189310.6378620.681069
1200.2901710.5803420.709829
1210.2577840.5155690.742216
1220.2386180.4772370.761382
1230.2564170.5128330.743583
1240.2180680.4361360.781932
1250.1869860.3739730.813014
1260.1551770.3103530.844823
1270.1928030.3856050.807197
1280.2044750.408950.795525
1290.2453420.4906830.754658
1300.2171560.4343120.782844
1310.1868580.3737150.813142
1320.3277680.6555370.672232
1330.2816230.5632450.718377
1340.3910140.7820270.608986
1350.350370.7007390.64963
1360.3035630.6071260.696437
1370.4157960.8315930.584204
1380.4777390.9554770.522261
1390.5813090.8373830.418691
1400.5200870.9598260.479913
1410.6302170.7395660.369783
1420.6081610.7836780.391839
1430.5615420.8769160.438458
1440.5523890.8952220.447611
1450.5967480.8065040.403252
1460.6219430.7561140.378057
1470.5542820.8914360.445718
1480.4941850.9883710.505815
1490.4933370.9866740.506663
1500.4538330.9076660.546167
1510.392940.7858810.60706
1520.4691220.9382440.530878
1530.3973620.7947250.602638
1540.3186560.6373120.681344
1550.4263690.8527370.573631
1560.5873070.8253860.412693
1570.6833130.6333740.316687
1580.5862580.8274840.413742
1590.4693910.9387820.530609
1600.3651870.7303730.634813
1610.2784380.5568760.721562
1620.4348670.8697340.565133






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level40.025974OK
10% type I error level150.0974026OK

\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 & 4 & 0.025974 & OK \tabularnewline
10% type I error level & 15 & 0.0974026 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267983&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]4[/C][C]0.025974[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]15[/C][C]0.0974026[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267983&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267983&T=6

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


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 level40.025974OK
10% type I error level150.0974026OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; 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')
}