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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 11:11:17 +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/t1418641906adakl9b6lvt06js.htm/, Retrieved Thu, 16 May 2024 09:03:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268109, Retrieved Thu, 16 May 2024 09:03:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact69

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2013-11-04 07:18:26] [0307e7a6407eb638caabc417e3a6b260]
- RMPD  [Multiple Regression] [] [2014-11-13 18:59:36] [95c11abf048d3a1e472aeccb09199113]
-    D    [Multiple Regression] [] [2014-11-13 19:46:46] [95c11abf048d3a1e472aeccb09199113]
-    D      [Multiple Regression] [] [2014-12-15 10:41:33] [2fea329c6e322b1612c5dc504f90c0ef]
- R PD          [Multiple Regression] [] [2014-12-15 11:11:17] [4bf1efda48b6e8e35beb7b429a900cbb] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4.35	48	41	23	12	34
12.7	50	146	16	45	61
18.1	150	182	33	37	70
17.85	154	192	32	37	69
16.6	109	263	37	108	145
12.6	68	35	14	10	23
17.1	194	439	52	68	120
19.1	158	214	75	72	147
16.1	159	341	72	143	215
13.35	67	58	15	9	24
18.4	147	292	29	55	84
14.7	39	85	13	17	30
10.6	100	200	40	37	77
12.6	111	158	19	27	46
16.2	138	199	24	37	61
13.6	101	297	121	58	178
18.9	131	227	93	66	160
14.1	101	108	36	21	57
14.5	114	86	23	19	42
16.15	165	302	85	78	163
14.75	114	148	41	35	75
14.8	111	178	46	48	94
12.45	75	120	18	27	45
12.65	82	207	35	43	78
17.35	121	157	17	30	47
8.6	32	128	4	25	29
18.4	150	296	28	69	97
16.1	117	323	44	72	116
11.6	71	79	10	23	32
17.75	165	70	38	13	50
15.25	154	146	57	61	118
17.65	126	246	23	43	66
15.6	138	145	26	22	48
16.35	149	196	36	51	86
17.65	145	199	22	67	89
13.6	120	127	40	36	76
11.7	138	91	18	21	39
14.35	109	153	31	44	75
14.75	132	299	11	45	57
18.25	172	228	38	34	72
9.9	169	190	24	36	60
16	114	180	37	72	109
18.25	156	212	37	39	76
16.85	172	269	22	43	65
14.6	68	130	15	25	40
13.85	89	179	2	56	58
18.95	167	243	43	80	123
15.6	113	190	31	40	71
14.85	115	299	29	73	102
11.75	78	121	45	34	80
18.45	118	137	25	72	97
15.9	87	305	4	42	46
17.1	173	157	31	61	93
16.1	2	96	-4	23	19
19.9	162	183	66	74	140
10.95	49	52	61	16	78
18.45	122	238	32	66	98
15.1	96	40	31	9	40
15	100	226	39	41	80
11.35	82	190	19	57	76
15.95	100	214	31	48	79
18.1	115	145	36	51	87
14.6	141	119	42	53	95
15.4	165	222	21	29	49
15.4	165	222	21	29	49
17.6	110	159	25	55	80
13.35	118	165	32	54	86
19.1	158	249	26	43	69
15.35	146	125	28	51	79
7.6	49	122	32	20	52
13.4	90	186	41	79	120
13.9	121	148	29	39	69
19.1	155	274	33	61	94
15.25	104	172	17	55	72
12.9	147	84	13	30	43
16.1	110	168	32	55	87
17.35	108	102	30	22	52
13.15	113	106	34	37	71
12.15	115	2	59	2	61
12.6	61	139	13	38	51
10.35	60	95	23	27	50
15.4	109	130	10	56	67
9.6	68	72	5	25	30
18.2	111	141	31	39	70
13.6	77	113	19	33	52
14.85	73	206	32	43	75
14.75	151	268	30	57	87
14.1	89	175	25	43	69
14.9	78	77	48	23	72
16.25	110	125	35	44	79
19.25	220	255	67	54	121
13.6	65	111	15	28	43
13.6	141	132	22	36	58
15.65	117	211	18	39	57
12.75	122	92	33	16	50
14.6	63	76	46	23	69
9.85	44	171	24	40	64
12.65	52	83	14	24	38
11.9	62	119	23	29	53
19.2	131	266	12	78	90
16.6	101	186	38	57	96
11.2	42	50	12	37	49
15.25	152	117	28	27	56
11.9	107	219	41	61	102
13.2	77	246	12	27	40
16.35	154	279	31	69	100
12.4	103	148	33	34	67
15.85	96	137	34	44	78
14.35	154	130	41	21	62
18.15	175	181	21	34	55
11.15	57	98	20	39	59
15.65	112	226	44	51	96
17.75	143	234	52	34	86
7.65	49	138	7	31	38
12.35	110	85	29	13	43
15.6	131	66	11	12	23
19.3	167	236	26	51	77
15.2	56	106	24	24	48
17.1	137	135	7	19	26
15.6	86	122	60	30	91
18.4	121	218	13	81	94
19.05	149	199	20	42	62
18.55	168	112	52	22	74
19.1	140	278	28	85	114
13.1	88	94	25	27	52
12.85	168	113	39	25	64
9.5	94	84	9	22	31
4.5	51	86	19	19	38
11.85	48	62	13	14	27
13.6	145	222	60	45	105
11.7	66	167	19	45	64
12.4	85	82	34	28	62
13.35	109	207	14	51	65
11.4	63	184	17	41	58
14.9	102	83	45	31	76
19.9	162	183	66	74	140
17.75	128	85	24	24	48
11.2	86	89	48	19	68
14.6	114	225	29	51	80
17.6	164	237	-2	73	71
14.05	119	102	51	24	76
16.1	126	221	2	61	63
13.35	132	128	24	23	46
11.85	142	91	40	14	53
11.95	83	198	20	54	74
14.75	94	204	19	51	70
15.15	81	158	16	62	78
13.2	166	138	20	36	56
16.85	110	226	40	59	100
7.85	64	44	27	24	51
7.7	93	196	25	26	52
12.6	104	83	49	54	102
7.85	105	79	39	39	78
10.95	49	52	61	16	78
12.35	88	105	19	36	55
9.95	95	116	67	31	98
14.9	102	83	45	31	76
16.65	99	196	30	42	73
13.4	63	153	8	39	47
13.95	76	157	19	25	45
15.7	109	75	52	31	83
16.85	117	106	22	38	60
10.95	57	58	17	31	48
15.35	120	75	33	17	50
12.2	73	74	34	22	56
15.1	91	185	22	55	77
17.75	108	265	30	62	91
15.2	105	131	25	51	76
14.6	117	139	38	30	68
16.65	119	196	26	49	74
8.1	31	78	13	16	29

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=268109&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=268109&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268109&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
TOT[t] = + 8.20036 + 0.0421704LFM[t] + 0.00299256B[t] -0.396629PRH[t] -0.353426CH[t] + 0.388256H[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  8.20036 +  0.0421704LFM[t] +  0.00299256B[t] -0.396629PRH[t] -0.353426CH[t] +  0.388256H[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268109&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  8.20036 +  0.0421704LFM[t] +  0.00299256B[t] -0.396629PRH[t] -0.353426CH[t] +  0.388256H[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268109&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268109&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
TOT[t] = + 8.20036 + 0.0421704LFM[t] + 0.00299256B[t] -0.396629PRH[t] -0.353426CH[t] + 0.388256H[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.200360.56234914.581.7715e-318.85749e-32
LFM0.04217040.005343157.8923.90388e-131.95194e-13
B0.002992560.003630740.82420.4110.2055
PRH-0.3966290.364212-1.0890.2777390.13887
CH-0.3534260.36315-0.97320.3318660.165933
H0.3882560.3630431.0690.2864290.143215

\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) & 8.20036 & 0.562349 & 14.58 & 1.7715e-31 & 8.85749e-32 \tabularnewline
LFM & 0.0421704 & 0.00534315 & 7.892 & 3.90388e-13 & 1.95194e-13 \tabularnewline
B & 0.00299256 & 0.00363074 & 0.8242 & 0.411 & 0.2055 \tabularnewline
PRH & -0.396629 & 0.364212 & -1.089 & 0.277739 & 0.13887 \tabularnewline
CH & -0.353426 & 0.36315 & -0.9732 & 0.331866 & 0.165933 \tabularnewline
H & 0.388256 & 0.363043 & 1.069 & 0.286429 & 0.143215 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268109&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]8.20036[/C][C]0.562349[/C][C]14.58[/C][C]1.7715e-31[/C][C]8.85749e-32[/C][/ROW]
[ROW][C]LFM[/C][C]0.0421704[/C][C]0.00534315[/C][C]7.892[/C][C]3.90388e-13[/C][C]1.95194e-13[/C][/ROW]
[ROW][C]B[/C][C]0.00299256[/C][C]0.00363074[/C][C]0.8242[/C][C]0.411[/C][C]0.2055[/C][/ROW]
[ROW][C]PRH[/C][C]-0.396629[/C][C]0.364212[/C][C]-1.089[/C][C]0.277739[/C][C]0.13887[/C][/ROW]
[ROW][C]CH[/C][C]-0.353426[/C][C]0.36315[/C][C]-0.9732[/C][C]0.331866[/C][C]0.165933[/C][/ROW]
[ROW][C]H[/C][C]0.388256[/C][C]0.363043[/C][C]1.069[/C][C]0.286429[/C][C]0.143215[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268109&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268109&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)8.200360.56234914.581.7715e-318.85749e-32
LFM0.04217040.005343157.8923.90388e-131.95194e-13
B0.002992560.003630740.82420.4110.2055
PRH-0.3966290.364212-1.0890.2777390.13887
CH-0.3534260.36315-0.97320.3318660.165933
H0.3882560.3630431.0690.2864290.143215






Multiple Linear Regression - Regression Statistics
Multiple R0.691826
R-squared0.478623
Adjusted R-squared0.462824
F-TEST (value)30.294
F-TEST (DF numerator)5
F-TEST (DF denominator)165
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.21972
Sum Squared Residuals812.98

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.691826 \tabularnewline
R-squared & 0.478623 \tabularnewline
Adjusted R-squared & 0.462824 \tabularnewline
F-TEST (value) & 30.294 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 165 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.21972 \tabularnewline
Sum Squared Residuals & 812.98 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268109&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.691826[/C][/ROW]
[ROW][C]R-squared[/C][C]0.478623[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.462824[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]30.294[/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]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.21972[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]812.98[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268109&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268109&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 R0.691826
R-squared0.478623
Adjusted R-squared0.462824
F-TEST (value)30.294
F-TEST (DF numerator)5
F-TEST (DF denominator)165
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.21972
Sum Squared Residuals812.98






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.3510.1843-5.83435
212.712.17910.520857
318.116.08292.01706
417.8516.28991.56008
516.617.0357-0.435736
612.611.01551.5845
717.119.6281-2.52814
819.117.38341.71658
916.120.3036-4.20364
1013.3511.38721.96279
1118.416.9461.45397
1214.710.58264.11738
1310.613.9697-3.36967
1412.614.1354-1.5354
1516.215.70310.496876
1613.613.9671-0.367061
1718.916.31232.58771
1814.113.21280.887249
1914.513.73430.765678
2016.1518.0672-1.9172
2114.7513.93820.811842
2214.814.70060.0994056
2312.4512.5119-0.0619229
2412.6513.4824-0.832395
2517.3514.67532.67465
268.610.7701-2.1701
2718.417.58050.819508
2816.116.2402-0.140187
2911.611.76-0.159958
3017.7515.11432.6357
3115.2516.7788-1.52884
3217.6515.55512.09492
3315.615.00230.597659
3416.3516.15690.193102
3517.6517.05990.590058
3613.614.5598-0.959794
3711.714.8729-3.1729
3814.3514.5277-0.177719
3914.7516.5251-1.7751
4018.2517.0021.24801
419.916.9486-7.04864
421615.74440.255649
4318.2516.46191.7881
4416.8517.5721-0.722119
4514.612.20212.39788
4613.8514.4229-0.572888
4718.9518.39630.553691
4815.614.66780.932192
4914.8516.2444-1.39445
5011.7513.0474-1.29741
5118.4515.88482.56517
5215.914.21131.68875
5317.118.2189-1.11895
5416.19.406556.69345
5519.917.60432.29565
5610.9510.85710.0929099
5718.4516.08822.36183
5815.112.42232.67768
591514.19520.804831
6011.3514.0531-2.7031
6115.9514.471.47995
6218.114.95873.14126
6314.615.9968-1.39678
6415.416.2688-0.868783
6515.416.2688-0.868783
6617.615.02122.5788
6713.3515.2831-1.93308
6819.116.88842.21161
6915.3516.2732-0.923156
707.611.0604-3.46045
7113.414.9605-1.56053
7213.915.2497-1.34966
7319.117.4051.69499
7415.2514.87410.37593
7512.915.5868-2.68681
7616.114.98951.11047
7717.3513.57513.77494
7813.1514.2868-1.13683
7912.1512.6316-0.481589
8012.612.40340.196616
8110.3511.7627-1.41269
8215.415.4409-0.0409315
839.612.1123-2.51228
8418.214.4023.798
8513.612.77590.824082
8614.8513.1251.72501
8714.7517.1042-2.35417
8814.114.1538-0.0538119
8914.912.50752.3925
9016.2514.45261.79739
9119.2519.5607-0.310735
9213.612.12321.47677
9313.615.6111-2.01105
9415.6514.97340.676646
9512.7514.2897-1.53968
9614.611.50043.09956
979.8511.7598-1.9098
9812.6511.36031.28972
9911.912.3768-0.476764
10019.217.13692.06309
10116.615.07151.52847
10211.211.3094-0.109352
10315.2516.0546-0.804591
10411.915.1493-3.24925
10513.213.4118-0.21182
10616.3517.6732-1.32318
10712.413.8947-1.49469
10815.8513.90651.9435
10914.3515.4718-1.12175
11018.1517.13021.01981
11111.1512.0882-0.938225
11215.6515.39590.254103
11317.7515.67982.07022
1147.6511.7008-4.05078
11512.3513.6917-1.34169
11615.614.2481.35196
11719.317.50771.79235
11815.211.51413.68594
11917.114.98482.11515
12015.613.12292.47714
12118.416.66771.73232
12219.0516.37462.67536
12318.5515.9512.599
12419.118.05041.04955
12513.112.92370.17628
12612.8516.1673-3.31733
1279.513.1066-3.60664
1284.511.1111-6.61108
12911.8510.78881.06116
13013.616.0443-2.44435
13111.712.8916-1.19159
13212.412.7208-0.32077
13313.3515.0755-1.72546
13411.412.6934-1.29338
13514.913.4531.44696
13619.917.60432.29565
13717.7514.48753.26251
13811.212.7415-1.54146
13914.615.2146-0.614586
14017.618.3848-0.784826
14114.0514.321-0.271011
14216.116.283-0.183026
14313.3514.3618-1.01176
14411.8514.2253-2.37531
14511.9514.0064-2.05635
14614.7514.39210.357935
14715.1514.11441.03557
14813.216.7-3.50001
14916.8515.62371.22632
1507.8511.6408-3.79077
1517.713.7932-6.09324
15212.613.9167-1.31671
1537.8513.8965-6.04646
15410.9510.85710.0929099
15512.3513.3203-0.97034
1569.9513.0724-3.1224
15714.913.4531.44696
15816.6514.56172.08834
15913.412.60630.793685
16013.9512.9750.974958
16115.713.66572.03432
16216.8514.59082.25919
16310.9511.715-0.765007
16415.3513.8011.54896
16512.211.98180.218191
16615.114.32290.777107
16717.7515.06782.68224
16815.214.58720.612756
16914.614.2770.323036
17016.6514.90591.74414
1718.110.1895-2.08948

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.35 & 10.1843 & -5.83435 \tabularnewline
2 & 12.7 & 12.1791 & 0.520857 \tabularnewline
3 & 18.1 & 16.0829 & 2.01706 \tabularnewline
4 & 17.85 & 16.2899 & 1.56008 \tabularnewline
5 & 16.6 & 17.0357 & -0.435736 \tabularnewline
6 & 12.6 & 11.0155 & 1.5845 \tabularnewline
7 & 17.1 & 19.6281 & -2.52814 \tabularnewline
8 & 19.1 & 17.3834 & 1.71658 \tabularnewline
9 & 16.1 & 20.3036 & -4.20364 \tabularnewline
10 & 13.35 & 11.3872 & 1.96279 \tabularnewline
11 & 18.4 & 16.946 & 1.45397 \tabularnewline
12 & 14.7 & 10.5826 & 4.11738 \tabularnewline
13 & 10.6 & 13.9697 & -3.36967 \tabularnewline
14 & 12.6 & 14.1354 & -1.5354 \tabularnewline
15 & 16.2 & 15.7031 & 0.496876 \tabularnewline
16 & 13.6 & 13.9671 & -0.367061 \tabularnewline
17 & 18.9 & 16.3123 & 2.58771 \tabularnewline
18 & 14.1 & 13.2128 & 0.887249 \tabularnewline
19 & 14.5 & 13.7343 & 0.765678 \tabularnewline
20 & 16.15 & 18.0672 & -1.9172 \tabularnewline
21 & 14.75 & 13.9382 & 0.811842 \tabularnewline
22 & 14.8 & 14.7006 & 0.0994056 \tabularnewline
23 & 12.45 & 12.5119 & -0.0619229 \tabularnewline
24 & 12.65 & 13.4824 & -0.832395 \tabularnewline
25 & 17.35 & 14.6753 & 2.67465 \tabularnewline
26 & 8.6 & 10.7701 & -2.1701 \tabularnewline
27 & 18.4 & 17.5805 & 0.819508 \tabularnewline
28 & 16.1 & 16.2402 & -0.140187 \tabularnewline
29 & 11.6 & 11.76 & -0.159958 \tabularnewline
30 & 17.75 & 15.1143 & 2.6357 \tabularnewline
31 & 15.25 & 16.7788 & -1.52884 \tabularnewline
32 & 17.65 & 15.5551 & 2.09492 \tabularnewline
33 & 15.6 & 15.0023 & 0.597659 \tabularnewline
34 & 16.35 & 16.1569 & 0.193102 \tabularnewline
35 & 17.65 & 17.0599 & 0.590058 \tabularnewline
36 & 13.6 & 14.5598 & -0.959794 \tabularnewline
37 & 11.7 & 14.8729 & -3.1729 \tabularnewline
38 & 14.35 & 14.5277 & -0.177719 \tabularnewline
39 & 14.75 & 16.5251 & -1.7751 \tabularnewline
40 & 18.25 & 17.002 & 1.24801 \tabularnewline
41 & 9.9 & 16.9486 & -7.04864 \tabularnewline
42 & 16 & 15.7444 & 0.255649 \tabularnewline
43 & 18.25 & 16.4619 & 1.7881 \tabularnewline
44 & 16.85 & 17.5721 & -0.722119 \tabularnewline
45 & 14.6 & 12.2021 & 2.39788 \tabularnewline
46 & 13.85 & 14.4229 & -0.572888 \tabularnewline
47 & 18.95 & 18.3963 & 0.553691 \tabularnewline
48 & 15.6 & 14.6678 & 0.932192 \tabularnewline
49 & 14.85 & 16.2444 & -1.39445 \tabularnewline
50 & 11.75 & 13.0474 & -1.29741 \tabularnewline
51 & 18.45 & 15.8848 & 2.56517 \tabularnewline
52 & 15.9 & 14.2113 & 1.68875 \tabularnewline
53 & 17.1 & 18.2189 & -1.11895 \tabularnewline
54 & 16.1 & 9.40655 & 6.69345 \tabularnewline
55 & 19.9 & 17.6043 & 2.29565 \tabularnewline
56 & 10.95 & 10.8571 & 0.0929099 \tabularnewline
57 & 18.45 & 16.0882 & 2.36183 \tabularnewline
58 & 15.1 & 12.4223 & 2.67768 \tabularnewline
59 & 15 & 14.1952 & 0.804831 \tabularnewline
60 & 11.35 & 14.0531 & -2.7031 \tabularnewline
61 & 15.95 & 14.47 & 1.47995 \tabularnewline
62 & 18.1 & 14.9587 & 3.14126 \tabularnewline
63 & 14.6 & 15.9968 & -1.39678 \tabularnewline
64 & 15.4 & 16.2688 & -0.868783 \tabularnewline
65 & 15.4 & 16.2688 & -0.868783 \tabularnewline
66 & 17.6 & 15.0212 & 2.5788 \tabularnewline
67 & 13.35 & 15.2831 & -1.93308 \tabularnewline
68 & 19.1 & 16.8884 & 2.21161 \tabularnewline
69 & 15.35 & 16.2732 & -0.923156 \tabularnewline
70 & 7.6 & 11.0604 & -3.46045 \tabularnewline
71 & 13.4 & 14.9605 & -1.56053 \tabularnewline
72 & 13.9 & 15.2497 & -1.34966 \tabularnewline
73 & 19.1 & 17.405 & 1.69499 \tabularnewline
74 & 15.25 & 14.8741 & 0.37593 \tabularnewline
75 & 12.9 & 15.5868 & -2.68681 \tabularnewline
76 & 16.1 & 14.9895 & 1.11047 \tabularnewline
77 & 17.35 & 13.5751 & 3.77494 \tabularnewline
78 & 13.15 & 14.2868 & -1.13683 \tabularnewline
79 & 12.15 & 12.6316 & -0.481589 \tabularnewline
80 & 12.6 & 12.4034 & 0.196616 \tabularnewline
81 & 10.35 & 11.7627 & -1.41269 \tabularnewline
82 & 15.4 & 15.4409 & -0.0409315 \tabularnewline
83 & 9.6 & 12.1123 & -2.51228 \tabularnewline
84 & 18.2 & 14.402 & 3.798 \tabularnewline
85 & 13.6 & 12.7759 & 0.824082 \tabularnewline
86 & 14.85 & 13.125 & 1.72501 \tabularnewline
87 & 14.75 & 17.1042 & -2.35417 \tabularnewline
88 & 14.1 & 14.1538 & -0.0538119 \tabularnewline
89 & 14.9 & 12.5075 & 2.3925 \tabularnewline
90 & 16.25 & 14.4526 & 1.79739 \tabularnewline
91 & 19.25 & 19.5607 & -0.310735 \tabularnewline
92 & 13.6 & 12.1232 & 1.47677 \tabularnewline
93 & 13.6 & 15.6111 & -2.01105 \tabularnewline
94 & 15.65 & 14.9734 & 0.676646 \tabularnewline
95 & 12.75 & 14.2897 & -1.53968 \tabularnewline
96 & 14.6 & 11.5004 & 3.09956 \tabularnewline
97 & 9.85 & 11.7598 & -1.9098 \tabularnewline
98 & 12.65 & 11.3603 & 1.28972 \tabularnewline
99 & 11.9 & 12.3768 & -0.476764 \tabularnewline
100 & 19.2 & 17.1369 & 2.06309 \tabularnewline
101 & 16.6 & 15.0715 & 1.52847 \tabularnewline
102 & 11.2 & 11.3094 & -0.109352 \tabularnewline
103 & 15.25 & 16.0546 & -0.804591 \tabularnewline
104 & 11.9 & 15.1493 & -3.24925 \tabularnewline
105 & 13.2 & 13.4118 & -0.21182 \tabularnewline
106 & 16.35 & 17.6732 & -1.32318 \tabularnewline
107 & 12.4 & 13.8947 & -1.49469 \tabularnewline
108 & 15.85 & 13.9065 & 1.9435 \tabularnewline
109 & 14.35 & 15.4718 & -1.12175 \tabularnewline
110 & 18.15 & 17.1302 & 1.01981 \tabularnewline
111 & 11.15 & 12.0882 & -0.938225 \tabularnewline
112 & 15.65 & 15.3959 & 0.254103 \tabularnewline
113 & 17.75 & 15.6798 & 2.07022 \tabularnewline
114 & 7.65 & 11.7008 & -4.05078 \tabularnewline
115 & 12.35 & 13.6917 & -1.34169 \tabularnewline
116 & 15.6 & 14.248 & 1.35196 \tabularnewline
117 & 19.3 & 17.5077 & 1.79235 \tabularnewline
118 & 15.2 & 11.5141 & 3.68594 \tabularnewline
119 & 17.1 & 14.9848 & 2.11515 \tabularnewline
120 & 15.6 & 13.1229 & 2.47714 \tabularnewline
121 & 18.4 & 16.6677 & 1.73232 \tabularnewline
122 & 19.05 & 16.3746 & 2.67536 \tabularnewline
123 & 18.55 & 15.951 & 2.599 \tabularnewline
124 & 19.1 & 18.0504 & 1.04955 \tabularnewline
125 & 13.1 & 12.9237 & 0.17628 \tabularnewline
126 & 12.85 & 16.1673 & -3.31733 \tabularnewline
127 & 9.5 & 13.1066 & -3.60664 \tabularnewline
128 & 4.5 & 11.1111 & -6.61108 \tabularnewline
129 & 11.85 & 10.7888 & 1.06116 \tabularnewline
130 & 13.6 & 16.0443 & -2.44435 \tabularnewline
131 & 11.7 & 12.8916 & -1.19159 \tabularnewline
132 & 12.4 & 12.7208 & -0.32077 \tabularnewline
133 & 13.35 & 15.0755 & -1.72546 \tabularnewline
134 & 11.4 & 12.6934 & -1.29338 \tabularnewline
135 & 14.9 & 13.453 & 1.44696 \tabularnewline
136 & 19.9 & 17.6043 & 2.29565 \tabularnewline
137 & 17.75 & 14.4875 & 3.26251 \tabularnewline
138 & 11.2 & 12.7415 & -1.54146 \tabularnewline
139 & 14.6 & 15.2146 & -0.614586 \tabularnewline
140 & 17.6 & 18.3848 & -0.784826 \tabularnewline
141 & 14.05 & 14.321 & -0.271011 \tabularnewline
142 & 16.1 & 16.283 & -0.183026 \tabularnewline
143 & 13.35 & 14.3618 & -1.01176 \tabularnewline
144 & 11.85 & 14.2253 & -2.37531 \tabularnewline
145 & 11.95 & 14.0064 & -2.05635 \tabularnewline
146 & 14.75 & 14.3921 & 0.357935 \tabularnewline
147 & 15.15 & 14.1144 & 1.03557 \tabularnewline
148 & 13.2 & 16.7 & -3.50001 \tabularnewline
149 & 16.85 & 15.6237 & 1.22632 \tabularnewline
150 & 7.85 & 11.6408 & -3.79077 \tabularnewline
151 & 7.7 & 13.7932 & -6.09324 \tabularnewline
152 & 12.6 & 13.9167 & -1.31671 \tabularnewline
153 & 7.85 & 13.8965 & -6.04646 \tabularnewline
154 & 10.95 & 10.8571 & 0.0929099 \tabularnewline
155 & 12.35 & 13.3203 & -0.97034 \tabularnewline
156 & 9.95 & 13.0724 & -3.1224 \tabularnewline
157 & 14.9 & 13.453 & 1.44696 \tabularnewline
158 & 16.65 & 14.5617 & 2.08834 \tabularnewline
159 & 13.4 & 12.6063 & 0.793685 \tabularnewline
160 & 13.95 & 12.975 & 0.974958 \tabularnewline
161 & 15.7 & 13.6657 & 2.03432 \tabularnewline
162 & 16.85 & 14.5908 & 2.25919 \tabularnewline
163 & 10.95 & 11.715 & -0.765007 \tabularnewline
164 & 15.35 & 13.801 & 1.54896 \tabularnewline
165 & 12.2 & 11.9818 & 0.218191 \tabularnewline
166 & 15.1 & 14.3229 & 0.777107 \tabularnewline
167 & 17.75 & 15.0678 & 2.68224 \tabularnewline
168 & 15.2 & 14.5872 & 0.612756 \tabularnewline
169 & 14.6 & 14.277 & 0.323036 \tabularnewline
170 & 16.65 & 14.9059 & 1.74414 \tabularnewline
171 & 8.1 & 10.1895 & -2.08948 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268109&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]4.35[/C][C]10.1843[/C][C]-5.83435[/C][/ROW]
[ROW][C]2[/C][C]12.7[/C][C]12.1791[/C][C]0.520857[/C][/ROW]
[ROW][C]3[/C][C]18.1[/C][C]16.0829[/C][C]2.01706[/C][/ROW]
[ROW][C]4[/C][C]17.85[/C][C]16.2899[/C][C]1.56008[/C][/ROW]
[ROW][C]5[/C][C]16.6[/C][C]17.0357[/C][C]-0.435736[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.0155[/C][C]1.5845[/C][/ROW]
[ROW][C]7[/C][C]17.1[/C][C]19.6281[/C][C]-2.52814[/C][/ROW]
[ROW][C]8[/C][C]19.1[/C][C]17.3834[/C][C]1.71658[/C][/ROW]
[ROW][C]9[/C][C]16.1[/C][C]20.3036[/C][C]-4.20364[/C][/ROW]
[ROW][C]10[/C][C]13.35[/C][C]11.3872[/C][C]1.96279[/C][/ROW]
[ROW][C]11[/C][C]18.4[/C][C]16.946[/C][C]1.45397[/C][/ROW]
[ROW][C]12[/C][C]14.7[/C][C]10.5826[/C][C]4.11738[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]13.9697[/C][C]-3.36967[/C][/ROW]
[ROW][C]14[/C][C]12.6[/C][C]14.1354[/C][C]-1.5354[/C][/ROW]
[ROW][C]15[/C][C]16.2[/C][C]15.7031[/C][C]0.496876[/C][/ROW]
[ROW][C]16[/C][C]13.6[/C][C]13.9671[/C][C]-0.367061[/C][/ROW]
[ROW][C]17[/C][C]18.9[/C][C]16.3123[/C][C]2.58771[/C][/ROW]
[ROW][C]18[/C][C]14.1[/C][C]13.2128[/C][C]0.887249[/C][/ROW]
[ROW][C]19[/C][C]14.5[/C][C]13.7343[/C][C]0.765678[/C][/ROW]
[ROW][C]20[/C][C]16.15[/C][C]18.0672[/C][C]-1.9172[/C][/ROW]
[ROW][C]21[/C][C]14.75[/C][C]13.9382[/C][C]0.811842[/C][/ROW]
[ROW][C]22[/C][C]14.8[/C][C]14.7006[/C][C]0.0994056[/C][/ROW]
[ROW][C]23[/C][C]12.45[/C][C]12.5119[/C][C]-0.0619229[/C][/ROW]
[ROW][C]24[/C][C]12.65[/C][C]13.4824[/C][C]-0.832395[/C][/ROW]
[ROW][C]25[/C][C]17.35[/C][C]14.6753[/C][C]2.67465[/C][/ROW]
[ROW][C]26[/C][C]8.6[/C][C]10.7701[/C][C]-2.1701[/C][/ROW]
[ROW][C]27[/C][C]18.4[/C][C]17.5805[/C][C]0.819508[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]16.2402[/C][C]-0.140187[/C][/ROW]
[ROW][C]29[/C][C]11.6[/C][C]11.76[/C][C]-0.159958[/C][/ROW]
[ROW][C]30[/C][C]17.75[/C][C]15.1143[/C][C]2.6357[/C][/ROW]
[ROW][C]31[/C][C]15.25[/C][C]16.7788[/C][C]-1.52884[/C][/ROW]
[ROW][C]32[/C][C]17.65[/C][C]15.5551[/C][C]2.09492[/C][/ROW]
[ROW][C]33[/C][C]15.6[/C][C]15.0023[/C][C]0.597659[/C][/ROW]
[ROW][C]34[/C][C]16.35[/C][C]16.1569[/C][C]0.193102[/C][/ROW]
[ROW][C]35[/C][C]17.65[/C][C]17.0599[/C][C]0.590058[/C][/ROW]
[ROW][C]36[/C][C]13.6[/C][C]14.5598[/C][C]-0.959794[/C][/ROW]
[ROW][C]37[/C][C]11.7[/C][C]14.8729[/C][C]-3.1729[/C][/ROW]
[ROW][C]38[/C][C]14.35[/C][C]14.5277[/C][C]-0.177719[/C][/ROW]
[ROW][C]39[/C][C]14.75[/C][C]16.5251[/C][C]-1.7751[/C][/ROW]
[ROW][C]40[/C][C]18.25[/C][C]17.002[/C][C]1.24801[/C][/ROW]
[ROW][C]41[/C][C]9.9[/C][C]16.9486[/C][C]-7.04864[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]15.7444[/C][C]0.255649[/C][/ROW]
[ROW][C]43[/C][C]18.25[/C][C]16.4619[/C][C]1.7881[/C][/ROW]
[ROW][C]44[/C][C]16.85[/C][C]17.5721[/C][C]-0.722119[/C][/ROW]
[ROW][C]45[/C][C]14.6[/C][C]12.2021[/C][C]2.39788[/C][/ROW]
[ROW][C]46[/C][C]13.85[/C][C]14.4229[/C][C]-0.572888[/C][/ROW]
[ROW][C]47[/C][C]18.95[/C][C]18.3963[/C][C]0.553691[/C][/ROW]
[ROW][C]48[/C][C]15.6[/C][C]14.6678[/C][C]0.932192[/C][/ROW]
[ROW][C]49[/C][C]14.85[/C][C]16.2444[/C][C]-1.39445[/C][/ROW]
[ROW][C]50[/C][C]11.75[/C][C]13.0474[/C][C]-1.29741[/C][/ROW]
[ROW][C]51[/C][C]18.45[/C][C]15.8848[/C][C]2.56517[/C][/ROW]
[ROW][C]52[/C][C]15.9[/C][C]14.2113[/C][C]1.68875[/C][/ROW]
[ROW][C]53[/C][C]17.1[/C][C]18.2189[/C][C]-1.11895[/C][/ROW]
[ROW][C]54[/C][C]16.1[/C][C]9.40655[/C][C]6.69345[/C][/ROW]
[ROW][C]55[/C][C]19.9[/C][C]17.6043[/C][C]2.29565[/C][/ROW]
[ROW][C]56[/C][C]10.95[/C][C]10.8571[/C][C]0.0929099[/C][/ROW]
[ROW][C]57[/C][C]18.45[/C][C]16.0882[/C][C]2.36183[/C][/ROW]
[ROW][C]58[/C][C]15.1[/C][C]12.4223[/C][C]2.67768[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]14.1952[/C][C]0.804831[/C][/ROW]
[ROW][C]60[/C][C]11.35[/C][C]14.0531[/C][C]-2.7031[/C][/ROW]
[ROW][C]61[/C][C]15.95[/C][C]14.47[/C][C]1.47995[/C][/ROW]
[ROW][C]62[/C][C]18.1[/C][C]14.9587[/C][C]3.14126[/C][/ROW]
[ROW][C]63[/C][C]14.6[/C][C]15.9968[/C][C]-1.39678[/C][/ROW]
[ROW][C]64[/C][C]15.4[/C][C]16.2688[/C][C]-0.868783[/C][/ROW]
[ROW][C]65[/C][C]15.4[/C][C]16.2688[/C][C]-0.868783[/C][/ROW]
[ROW][C]66[/C][C]17.6[/C][C]15.0212[/C][C]2.5788[/C][/ROW]
[ROW][C]67[/C][C]13.35[/C][C]15.2831[/C][C]-1.93308[/C][/ROW]
[ROW][C]68[/C][C]19.1[/C][C]16.8884[/C][C]2.21161[/C][/ROW]
[ROW][C]69[/C][C]15.35[/C][C]16.2732[/C][C]-0.923156[/C][/ROW]
[ROW][C]70[/C][C]7.6[/C][C]11.0604[/C][C]-3.46045[/C][/ROW]
[ROW][C]71[/C][C]13.4[/C][C]14.9605[/C][C]-1.56053[/C][/ROW]
[ROW][C]72[/C][C]13.9[/C][C]15.2497[/C][C]-1.34966[/C][/ROW]
[ROW][C]73[/C][C]19.1[/C][C]17.405[/C][C]1.69499[/C][/ROW]
[ROW][C]74[/C][C]15.25[/C][C]14.8741[/C][C]0.37593[/C][/ROW]
[ROW][C]75[/C][C]12.9[/C][C]15.5868[/C][C]-2.68681[/C][/ROW]
[ROW][C]76[/C][C]16.1[/C][C]14.9895[/C][C]1.11047[/C][/ROW]
[ROW][C]77[/C][C]17.35[/C][C]13.5751[/C][C]3.77494[/C][/ROW]
[ROW][C]78[/C][C]13.15[/C][C]14.2868[/C][C]-1.13683[/C][/ROW]
[ROW][C]79[/C][C]12.15[/C][C]12.6316[/C][C]-0.481589[/C][/ROW]
[ROW][C]80[/C][C]12.6[/C][C]12.4034[/C][C]0.196616[/C][/ROW]
[ROW][C]81[/C][C]10.35[/C][C]11.7627[/C][C]-1.41269[/C][/ROW]
[ROW][C]82[/C][C]15.4[/C][C]15.4409[/C][C]-0.0409315[/C][/ROW]
[ROW][C]83[/C][C]9.6[/C][C]12.1123[/C][C]-2.51228[/C][/ROW]
[ROW][C]84[/C][C]18.2[/C][C]14.402[/C][C]3.798[/C][/ROW]
[ROW][C]85[/C][C]13.6[/C][C]12.7759[/C][C]0.824082[/C][/ROW]
[ROW][C]86[/C][C]14.85[/C][C]13.125[/C][C]1.72501[/C][/ROW]
[ROW][C]87[/C][C]14.75[/C][C]17.1042[/C][C]-2.35417[/C][/ROW]
[ROW][C]88[/C][C]14.1[/C][C]14.1538[/C][C]-0.0538119[/C][/ROW]
[ROW][C]89[/C][C]14.9[/C][C]12.5075[/C][C]2.3925[/C][/ROW]
[ROW][C]90[/C][C]16.25[/C][C]14.4526[/C][C]1.79739[/C][/ROW]
[ROW][C]91[/C][C]19.25[/C][C]19.5607[/C][C]-0.310735[/C][/ROW]
[ROW][C]92[/C][C]13.6[/C][C]12.1232[/C][C]1.47677[/C][/ROW]
[ROW][C]93[/C][C]13.6[/C][C]15.6111[/C][C]-2.01105[/C][/ROW]
[ROW][C]94[/C][C]15.65[/C][C]14.9734[/C][C]0.676646[/C][/ROW]
[ROW][C]95[/C][C]12.75[/C][C]14.2897[/C][C]-1.53968[/C][/ROW]
[ROW][C]96[/C][C]14.6[/C][C]11.5004[/C][C]3.09956[/C][/ROW]
[ROW][C]97[/C][C]9.85[/C][C]11.7598[/C][C]-1.9098[/C][/ROW]
[ROW][C]98[/C][C]12.65[/C][C]11.3603[/C][C]1.28972[/C][/ROW]
[ROW][C]99[/C][C]11.9[/C][C]12.3768[/C][C]-0.476764[/C][/ROW]
[ROW][C]100[/C][C]19.2[/C][C]17.1369[/C][C]2.06309[/C][/ROW]
[ROW][C]101[/C][C]16.6[/C][C]15.0715[/C][C]1.52847[/C][/ROW]
[ROW][C]102[/C][C]11.2[/C][C]11.3094[/C][C]-0.109352[/C][/ROW]
[ROW][C]103[/C][C]15.25[/C][C]16.0546[/C][C]-0.804591[/C][/ROW]
[ROW][C]104[/C][C]11.9[/C][C]15.1493[/C][C]-3.24925[/C][/ROW]
[ROW][C]105[/C][C]13.2[/C][C]13.4118[/C][C]-0.21182[/C][/ROW]
[ROW][C]106[/C][C]16.35[/C][C]17.6732[/C][C]-1.32318[/C][/ROW]
[ROW][C]107[/C][C]12.4[/C][C]13.8947[/C][C]-1.49469[/C][/ROW]
[ROW][C]108[/C][C]15.85[/C][C]13.9065[/C][C]1.9435[/C][/ROW]
[ROW][C]109[/C][C]14.35[/C][C]15.4718[/C][C]-1.12175[/C][/ROW]
[ROW][C]110[/C][C]18.15[/C][C]17.1302[/C][C]1.01981[/C][/ROW]
[ROW][C]111[/C][C]11.15[/C][C]12.0882[/C][C]-0.938225[/C][/ROW]
[ROW][C]112[/C][C]15.65[/C][C]15.3959[/C][C]0.254103[/C][/ROW]
[ROW][C]113[/C][C]17.75[/C][C]15.6798[/C][C]2.07022[/C][/ROW]
[ROW][C]114[/C][C]7.65[/C][C]11.7008[/C][C]-4.05078[/C][/ROW]
[ROW][C]115[/C][C]12.35[/C][C]13.6917[/C][C]-1.34169[/C][/ROW]
[ROW][C]116[/C][C]15.6[/C][C]14.248[/C][C]1.35196[/C][/ROW]
[ROW][C]117[/C][C]19.3[/C][C]17.5077[/C][C]1.79235[/C][/ROW]
[ROW][C]118[/C][C]15.2[/C][C]11.5141[/C][C]3.68594[/C][/ROW]
[ROW][C]119[/C][C]17.1[/C][C]14.9848[/C][C]2.11515[/C][/ROW]
[ROW][C]120[/C][C]15.6[/C][C]13.1229[/C][C]2.47714[/C][/ROW]
[ROW][C]121[/C][C]18.4[/C][C]16.6677[/C][C]1.73232[/C][/ROW]
[ROW][C]122[/C][C]19.05[/C][C]16.3746[/C][C]2.67536[/C][/ROW]
[ROW][C]123[/C][C]18.55[/C][C]15.951[/C][C]2.599[/C][/ROW]
[ROW][C]124[/C][C]19.1[/C][C]18.0504[/C][C]1.04955[/C][/ROW]
[ROW][C]125[/C][C]13.1[/C][C]12.9237[/C][C]0.17628[/C][/ROW]
[ROW][C]126[/C][C]12.85[/C][C]16.1673[/C][C]-3.31733[/C][/ROW]
[ROW][C]127[/C][C]9.5[/C][C]13.1066[/C][C]-3.60664[/C][/ROW]
[ROW][C]128[/C][C]4.5[/C][C]11.1111[/C][C]-6.61108[/C][/ROW]
[ROW][C]129[/C][C]11.85[/C][C]10.7888[/C][C]1.06116[/C][/ROW]
[ROW][C]130[/C][C]13.6[/C][C]16.0443[/C][C]-2.44435[/C][/ROW]
[ROW][C]131[/C][C]11.7[/C][C]12.8916[/C][C]-1.19159[/C][/ROW]
[ROW][C]132[/C][C]12.4[/C][C]12.7208[/C][C]-0.32077[/C][/ROW]
[ROW][C]133[/C][C]13.35[/C][C]15.0755[/C][C]-1.72546[/C][/ROW]
[ROW][C]134[/C][C]11.4[/C][C]12.6934[/C][C]-1.29338[/C][/ROW]
[ROW][C]135[/C][C]14.9[/C][C]13.453[/C][C]1.44696[/C][/ROW]
[ROW][C]136[/C][C]19.9[/C][C]17.6043[/C][C]2.29565[/C][/ROW]
[ROW][C]137[/C][C]17.75[/C][C]14.4875[/C][C]3.26251[/C][/ROW]
[ROW][C]138[/C][C]11.2[/C][C]12.7415[/C][C]-1.54146[/C][/ROW]
[ROW][C]139[/C][C]14.6[/C][C]15.2146[/C][C]-0.614586[/C][/ROW]
[ROW][C]140[/C][C]17.6[/C][C]18.3848[/C][C]-0.784826[/C][/ROW]
[ROW][C]141[/C][C]14.05[/C][C]14.321[/C][C]-0.271011[/C][/ROW]
[ROW][C]142[/C][C]16.1[/C][C]16.283[/C][C]-0.183026[/C][/ROW]
[ROW][C]143[/C][C]13.35[/C][C]14.3618[/C][C]-1.01176[/C][/ROW]
[ROW][C]144[/C][C]11.85[/C][C]14.2253[/C][C]-2.37531[/C][/ROW]
[ROW][C]145[/C][C]11.95[/C][C]14.0064[/C][C]-2.05635[/C][/ROW]
[ROW][C]146[/C][C]14.75[/C][C]14.3921[/C][C]0.357935[/C][/ROW]
[ROW][C]147[/C][C]15.15[/C][C]14.1144[/C][C]1.03557[/C][/ROW]
[ROW][C]148[/C][C]13.2[/C][C]16.7[/C][C]-3.50001[/C][/ROW]
[ROW][C]149[/C][C]16.85[/C][C]15.6237[/C][C]1.22632[/C][/ROW]
[ROW][C]150[/C][C]7.85[/C][C]11.6408[/C][C]-3.79077[/C][/ROW]
[ROW][C]151[/C][C]7.7[/C][C]13.7932[/C][C]-6.09324[/C][/ROW]
[ROW][C]152[/C][C]12.6[/C][C]13.9167[/C][C]-1.31671[/C][/ROW]
[ROW][C]153[/C][C]7.85[/C][C]13.8965[/C][C]-6.04646[/C][/ROW]
[ROW][C]154[/C][C]10.95[/C][C]10.8571[/C][C]0.0929099[/C][/ROW]
[ROW][C]155[/C][C]12.35[/C][C]13.3203[/C][C]-0.97034[/C][/ROW]
[ROW][C]156[/C][C]9.95[/C][C]13.0724[/C][C]-3.1224[/C][/ROW]
[ROW][C]157[/C][C]14.9[/C][C]13.453[/C][C]1.44696[/C][/ROW]
[ROW][C]158[/C][C]16.65[/C][C]14.5617[/C][C]2.08834[/C][/ROW]
[ROW][C]159[/C][C]13.4[/C][C]12.6063[/C][C]0.793685[/C][/ROW]
[ROW][C]160[/C][C]13.95[/C][C]12.975[/C][C]0.974958[/C][/ROW]
[ROW][C]161[/C][C]15.7[/C][C]13.6657[/C][C]2.03432[/C][/ROW]
[ROW][C]162[/C][C]16.85[/C][C]14.5908[/C][C]2.25919[/C][/ROW]
[ROW][C]163[/C][C]10.95[/C][C]11.715[/C][C]-0.765007[/C][/ROW]
[ROW][C]164[/C][C]15.35[/C][C]13.801[/C][C]1.54896[/C][/ROW]
[ROW][C]165[/C][C]12.2[/C][C]11.9818[/C][C]0.218191[/C][/ROW]
[ROW][C]166[/C][C]15.1[/C][C]14.3229[/C][C]0.777107[/C][/ROW]
[ROW][C]167[/C][C]17.75[/C][C]15.0678[/C][C]2.68224[/C][/ROW]
[ROW][C]168[/C][C]15.2[/C][C]14.5872[/C][C]0.612756[/C][/ROW]
[ROW][C]169[/C][C]14.6[/C][C]14.277[/C][C]0.323036[/C][/ROW]
[ROW][C]170[/C][C]16.65[/C][C]14.9059[/C][C]1.74414[/C][/ROW]
[ROW][C]171[/C][C]8.1[/C][C]10.1895[/C][C]-2.08948[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268109&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268109&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
14.3510.1843-5.83435
212.712.17910.520857
318.116.08292.01706
417.8516.28991.56008
516.617.0357-0.435736
612.611.01551.5845
717.119.6281-2.52814
819.117.38341.71658
916.120.3036-4.20364
1013.3511.38721.96279
1118.416.9461.45397
1214.710.58264.11738
1310.613.9697-3.36967
1412.614.1354-1.5354
1516.215.70310.496876
1613.613.9671-0.367061
1718.916.31232.58771
1814.113.21280.887249
1914.513.73430.765678
2016.1518.0672-1.9172
2114.7513.93820.811842
2214.814.70060.0994056
2312.4512.5119-0.0619229
2412.6513.4824-0.832395
2517.3514.67532.67465
268.610.7701-2.1701
2718.417.58050.819508
2816.116.2402-0.140187
2911.611.76-0.159958
3017.7515.11432.6357
3115.2516.7788-1.52884
3217.6515.55512.09492
3315.615.00230.597659
3416.3516.15690.193102
3517.6517.05990.590058
3613.614.5598-0.959794
3711.714.8729-3.1729
3814.3514.5277-0.177719
3914.7516.5251-1.7751
4018.2517.0021.24801
419.916.9486-7.04864
421615.74440.255649
4318.2516.46191.7881
4416.8517.5721-0.722119
4514.612.20212.39788
4613.8514.4229-0.572888
4718.9518.39630.553691
4815.614.66780.932192
4914.8516.2444-1.39445
5011.7513.0474-1.29741
5118.4515.88482.56517
5215.914.21131.68875
5317.118.2189-1.11895
5416.19.406556.69345
5519.917.60432.29565
5610.9510.85710.0929099
5718.4516.08822.36183
5815.112.42232.67768
591514.19520.804831
6011.3514.0531-2.7031
6115.9514.471.47995
6218.114.95873.14126
6314.615.9968-1.39678
6415.416.2688-0.868783
6515.416.2688-0.868783
6617.615.02122.5788
6713.3515.2831-1.93308
6819.116.88842.21161
6915.3516.2732-0.923156
707.611.0604-3.46045
7113.414.9605-1.56053
7213.915.2497-1.34966
7319.117.4051.69499
7415.2514.87410.37593
7512.915.5868-2.68681
7616.114.98951.11047
7717.3513.57513.77494
7813.1514.2868-1.13683
7912.1512.6316-0.481589
8012.612.40340.196616
8110.3511.7627-1.41269
8215.415.4409-0.0409315
839.612.1123-2.51228
8418.214.4023.798
8513.612.77590.824082
8614.8513.1251.72501
8714.7517.1042-2.35417
8814.114.1538-0.0538119
8914.912.50752.3925
9016.2514.45261.79739
9119.2519.5607-0.310735
9213.612.12321.47677
9313.615.6111-2.01105
9415.6514.97340.676646
9512.7514.2897-1.53968
9614.611.50043.09956
979.8511.7598-1.9098
9812.6511.36031.28972
9911.912.3768-0.476764
10019.217.13692.06309
10116.615.07151.52847
10211.211.3094-0.109352
10315.2516.0546-0.804591
10411.915.1493-3.24925
10513.213.4118-0.21182
10616.3517.6732-1.32318
10712.413.8947-1.49469
10815.8513.90651.9435
10914.3515.4718-1.12175
11018.1517.13021.01981
11111.1512.0882-0.938225
11215.6515.39590.254103
11317.7515.67982.07022
1147.6511.7008-4.05078
11512.3513.6917-1.34169
11615.614.2481.35196
11719.317.50771.79235
11815.211.51413.68594
11917.114.98482.11515
12015.613.12292.47714
12118.416.66771.73232
12219.0516.37462.67536
12318.5515.9512.599
12419.118.05041.04955
12513.112.92370.17628
12612.8516.1673-3.31733
1279.513.1066-3.60664
1284.511.1111-6.61108
12911.8510.78881.06116
13013.616.0443-2.44435
13111.712.8916-1.19159
13212.412.7208-0.32077
13313.3515.0755-1.72546
13411.412.6934-1.29338
13514.913.4531.44696
13619.917.60432.29565
13717.7514.48753.26251
13811.212.7415-1.54146
13914.615.2146-0.614586
14017.618.3848-0.784826
14114.0514.321-0.271011
14216.116.283-0.183026
14313.3514.3618-1.01176
14411.8514.2253-2.37531
14511.9514.0064-2.05635
14614.7514.39210.357935
14715.1514.11441.03557
14813.216.7-3.50001
14916.8515.62371.22632
1507.8511.6408-3.79077
1517.713.7932-6.09324
15212.613.9167-1.31671
1537.8513.8965-6.04646
15410.9510.85710.0929099
15512.3513.3203-0.97034
1569.9513.0724-3.1224
15714.913.4531.44696
15816.6514.56172.08834
15913.412.60630.793685
16013.9512.9750.974958
16115.713.66572.03432
16216.8514.59082.25919
16310.9511.715-0.765007
16415.3513.8011.54896
16512.211.98180.218191
16615.114.32290.777107
16717.7515.06782.68224
16815.214.58720.612756
16914.614.2770.323036
17016.6514.90591.74414
1718.110.1895-2.08948






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.8378020.3243950.162198
100.7452520.5094950.254748
110.6592040.6815920.340796
120.6893150.6213690.310685
130.761750.47650.23825
140.8572360.2855280.142764
150.8097480.3805040.190252
160.8929040.2141930.107096
170.8673710.2652580.132629
180.8217240.3565530.178276
190.7700030.4599950.229997
200.7397760.5204470.260224
210.7331540.5336910.266846
220.6689940.6620120.331006
230.6069120.7861750.393088
240.5432560.9134870.456744
250.5357190.9285620.464281
260.5172420.9655170.482758
270.485180.970360.51482
280.4398090.8796180.560191
290.3829020.7658050.617098
300.3512480.7024960.648752
310.3774730.7549450.622527
320.3661210.7322420.633879
330.3215850.643170.678415
340.2761450.5522890.723855
350.229920.459840.77008
360.2170570.4341130.782943
370.3665420.7330850.633458
380.314920.629840.68508
390.3191190.6382380.680881
400.2763440.5526880.723656
410.762960.474080.23704
420.7241720.5516560.275828
430.7051940.5896110.294806
440.6606740.6786520.339326
450.6615580.6768850.338442
460.6140020.7719950.385998
470.5751370.8497260.424863
480.5320480.9359050.467952
490.4941480.9882960.505852
500.484780.9695590.51522
510.5059660.9880690.494034
520.4984250.9968490.501575
530.4649710.9299420.535029
540.7781530.4436930.221847
550.7814280.4371430.218572
560.7514690.4970630.248531
570.7524150.495170.247585
580.759120.481760.24088
590.7236230.5527540.276377
600.7498340.5003320.250166
610.7248990.5502010.275101
620.7512410.4975190.248759
630.7335540.5328910.266446
640.69720.60560.3028
650.6587730.6824550.341227
660.6652110.6695780.334789
670.6613770.6772450.338623
680.6618720.6762550.338128
690.6289350.742130.371065
700.7062520.5874950.293748
710.6948880.6102230.305112
720.675330.649340.32467
730.6567780.6864430.343222
740.6146030.7707950.385397
750.6347190.7305620.365281
760.5992270.8015460.400773
770.6749790.6500410.325021
780.6475130.7049730.352487
790.6093330.7813340.390667
800.5675530.8648930.432447
810.5447940.9104130.455206
820.5005420.9989160.499458
830.5127940.9744110.487206
840.5923340.8153310.407666
850.5552450.889510.444755
860.5378920.9242150.462108
870.5441130.9117740.455887
880.4998880.9997760.500112
890.5001110.9997780.499889
900.4808110.9616210.519189
910.4428830.8857660.557117
920.4249320.8498640.575068
930.4177640.8355270.582236
940.3793210.7586410.620679
950.3598870.7197740.640113
960.4086610.8173220.591339
970.3959170.7918340.604083
980.3805980.7611960.619402
990.3423730.6847450.657627
1000.330880.661760.66912
1010.3040590.6081180.695941
1020.2699260.5398530.730074
1030.2401460.4802910.759854
1040.2909610.5819210.709039
1050.2554690.5109380.744531
1060.2448180.4896360.755182
1070.2245330.4490660.775467
1080.2157360.4314720.784264
1090.1926640.3853290.807336
1100.1666180.3332360.833382
1110.1429590.2859180.857041
1120.1182430.2364850.881757
1130.110830.2216590.88917
1140.1495320.2990640.850468
1150.1307290.2614580.869271
1160.1206070.2412130.879393
1170.1085170.2170330.891483
1180.1772310.3544620.822769
1190.1948190.3896380.805181
1200.203890.4077810.79611
1210.1811240.3622470.818876
1220.2039160.4078310.796084
1230.237310.4746210.76269
1240.203750.40750.79625
1250.1762450.3524910.823755
1260.194620.389240.80538
1270.2191740.4383480.780826
1280.4952650.990530.504735
1290.4815520.9631030.518448
1300.495240.990480.50476
1310.4500110.9000210.549989
1320.3973430.7946870.602657
1330.372060.744120.62794
1340.3300970.6601940.669903
1350.3087570.6175140.691243
1360.2801210.5602420.719879
1370.4066360.8132720.593364
1380.3596160.7192330.640384
1390.3132310.6264630.686769
1400.2718120.5436250.728188
1410.2282210.4564420.771779
1420.1864660.3729310.813534
1430.1487620.2975250.851238
1440.127570.2551390.87243
1450.1332280.2664560.866772
1460.1014390.2028770.898561
1470.07590130.1518030.924099
1480.1149570.2299140.885043
1490.08684430.1736890.913156
1500.1009650.2019310.899035
1510.6228090.7543810.377191
1520.5507520.8984970.449248
1530.9758820.04823610.0241181
1540.9829140.03417190.017086
1550.9863090.02738250.0136912
1560.9990770.00184670.000923348
1570.9974520.005095160.00254758
1580.9930920.01381540.00690769
1590.9879560.0240880.012044
1600.9861090.02778250.0138913
1610.963390.07322030.0366101
1620.9488570.1022860.0511432

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.837802 & 0.324395 & 0.162198 \tabularnewline
10 & 0.745252 & 0.509495 & 0.254748 \tabularnewline
11 & 0.659204 & 0.681592 & 0.340796 \tabularnewline
12 & 0.689315 & 0.621369 & 0.310685 \tabularnewline
13 & 0.76175 & 0.4765 & 0.23825 \tabularnewline
14 & 0.857236 & 0.285528 & 0.142764 \tabularnewline
15 & 0.809748 & 0.380504 & 0.190252 \tabularnewline
16 & 0.892904 & 0.214193 & 0.107096 \tabularnewline
17 & 0.867371 & 0.265258 & 0.132629 \tabularnewline
18 & 0.821724 & 0.356553 & 0.178276 \tabularnewline
19 & 0.770003 & 0.459995 & 0.229997 \tabularnewline
20 & 0.739776 & 0.520447 & 0.260224 \tabularnewline
21 & 0.733154 & 0.533691 & 0.266846 \tabularnewline
22 & 0.668994 & 0.662012 & 0.331006 \tabularnewline
23 & 0.606912 & 0.786175 & 0.393088 \tabularnewline
24 & 0.543256 & 0.913487 & 0.456744 \tabularnewline
25 & 0.535719 & 0.928562 & 0.464281 \tabularnewline
26 & 0.517242 & 0.965517 & 0.482758 \tabularnewline
27 & 0.48518 & 0.97036 & 0.51482 \tabularnewline
28 & 0.439809 & 0.879618 & 0.560191 \tabularnewline
29 & 0.382902 & 0.765805 & 0.617098 \tabularnewline
30 & 0.351248 & 0.702496 & 0.648752 \tabularnewline
31 & 0.377473 & 0.754945 & 0.622527 \tabularnewline
32 & 0.366121 & 0.732242 & 0.633879 \tabularnewline
33 & 0.321585 & 0.64317 & 0.678415 \tabularnewline
34 & 0.276145 & 0.552289 & 0.723855 \tabularnewline
35 & 0.22992 & 0.45984 & 0.77008 \tabularnewline
36 & 0.217057 & 0.434113 & 0.782943 \tabularnewline
37 & 0.366542 & 0.733085 & 0.633458 \tabularnewline
38 & 0.31492 & 0.62984 & 0.68508 \tabularnewline
39 & 0.319119 & 0.638238 & 0.680881 \tabularnewline
40 & 0.276344 & 0.552688 & 0.723656 \tabularnewline
41 & 0.76296 & 0.47408 & 0.23704 \tabularnewline
42 & 0.724172 & 0.551656 & 0.275828 \tabularnewline
43 & 0.705194 & 0.589611 & 0.294806 \tabularnewline
44 & 0.660674 & 0.678652 & 0.339326 \tabularnewline
45 & 0.661558 & 0.676885 & 0.338442 \tabularnewline
46 & 0.614002 & 0.771995 & 0.385998 \tabularnewline
47 & 0.575137 & 0.849726 & 0.424863 \tabularnewline
48 & 0.532048 & 0.935905 & 0.467952 \tabularnewline
49 & 0.494148 & 0.988296 & 0.505852 \tabularnewline
50 & 0.48478 & 0.969559 & 0.51522 \tabularnewline
51 & 0.505966 & 0.988069 & 0.494034 \tabularnewline
52 & 0.498425 & 0.996849 & 0.501575 \tabularnewline
53 & 0.464971 & 0.929942 & 0.535029 \tabularnewline
54 & 0.778153 & 0.443693 & 0.221847 \tabularnewline
55 & 0.781428 & 0.437143 & 0.218572 \tabularnewline
56 & 0.751469 & 0.497063 & 0.248531 \tabularnewline
57 & 0.752415 & 0.49517 & 0.247585 \tabularnewline
58 & 0.75912 & 0.48176 & 0.24088 \tabularnewline
59 & 0.723623 & 0.552754 & 0.276377 \tabularnewline
60 & 0.749834 & 0.500332 & 0.250166 \tabularnewline
61 & 0.724899 & 0.550201 & 0.275101 \tabularnewline
62 & 0.751241 & 0.497519 & 0.248759 \tabularnewline
63 & 0.733554 & 0.532891 & 0.266446 \tabularnewline
64 & 0.6972 & 0.6056 & 0.3028 \tabularnewline
65 & 0.658773 & 0.682455 & 0.341227 \tabularnewline
66 & 0.665211 & 0.669578 & 0.334789 \tabularnewline
67 & 0.661377 & 0.677245 & 0.338623 \tabularnewline
68 & 0.661872 & 0.676255 & 0.338128 \tabularnewline
69 & 0.628935 & 0.74213 & 0.371065 \tabularnewline
70 & 0.706252 & 0.587495 & 0.293748 \tabularnewline
71 & 0.694888 & 0.610223 & 0.305112 \tabularnewline
72 & 0.67533 & 0.64934 & 0.32467 \tabularnewline
73 & 0.656778 & 0.686443 & 0.343222 \tabularnewline
74 & 0.614603 & 0.770795 & 0.385397 \tabularnewline
75 & 0.634719 & 0.730562 & 0.365281 \tabularnewline
76 & 0.599227 & 0.801546 & 0.400773 \tabularnewline
77 & 0.674979 & 0.650041 & 0.325021 \tabularnewline
78 & 0.647513 & 0.704973 & 0.352487 \tabularnewline
79 & 0.609333 & 0.781334 & 0.390667 \tabularnewline
80 & 0.567553 & 0.864893 & 0.432447 \tabularnewline
81 & 0.544794 & 0.910413 & 0.455206 \tabularnewline
82 & 0.500542 & 0.998916 & 0.499458 \tabularnewline
83 & 0.512794 & 0.974411 & 0.487206 \tabularnewline
84 & 0.592334 & 0.815331 & 0.407666 \tabularnewline
85 & 0.555245 & 0.88951 & 0.444755 \tabularnewline
86 & 0.537892 & 0.924215 & 0.462108 \tabularnewline
87 & 0.544113 & 0.911774 & 0.455887 \tabularnewline
88 & 0.499888 & 0.999776 & 0.500112 \tabularnewline
89 & 0.500111 & 0.999778 & 0.499889 \tabularnewline
90 & 0.480811 & 0.961621 & 0.519189 \tabularnewline
91 & 0.442883 & 0.885766 & 0.557117 \tabularnewline
92 & 0.424932 & 0.849864 & 0.575068 \tabularnewline
93 & 0.417764 & 0.835527 & 0.582236 \tabularnewline
94 & 0.379321 & 0.758641 & 0.620679 \tabularnewline
95 & 0.359887 & 0.719774 & 0.640113 \tabularnewline
96 & 0.408661 & 0.817322 & 0.591339 \tabularnewline
97 & 0.395917 & 0.791834 & 0.604083 \tabularnewline
98 & 0.380598 & 0.761196 & 0.619402 \tabularnewline
99 & 0.342373 & 0.684745 & 0.657627 \tabularnewline
100 & 0.33088 & 0.66176 & 0.66912 \tabularnewline
101 & 0.304059 & 0.608118 & 0.695941 \tabularnewline
102 & 0.269926 & 0.539853 & 0.730074 \tabularnewline
103 & 0.240146 & 0.480291 & 0.759854 \tabularnewline
104 & 0.290961 & 0.581921 & 0.709039 \tabularnewline
105 & 0.255469 & 0.510938 & 0.744531 \tabularnewline
106 & 0.244818 & 0.489636 & 0.755182 \tabularnewline
107 & 0.224533 & 0.449066 & 0.775467 \tabularnewline
108 & 0.215736 & 0.431472 & 0.784264 \tabularnewline
109 & 0.192664 & 0.385329 & 0.807336 \tabularnewline
110 & 0.166618 & 0.333236 & 0.833382 \tabularnewline
111 & 0.142959 & 0.285918 & 0.857041 \tabularnewline
112 & 0.118243 & 0.236485 & 0.881757 \tabularnewline
113 & 0.11083 & 0.221659 & 0.88917 \tabularnewline
114 & 0.149532 & 0.299064 & 0.850468 \tabularnewline
115 & 0.130729 & 0.261458 & 0.869271 \tabularnewline
116 & 0.120607 & 0.241213 & 0.879393 \tabularnewline
117 & 0.108517 & 0.217033 & 0.891483 \tabularnewline
118 & 0.177231 & 0.354462 & 0.822769 \tabularnewline
119 & 0.194819 & 0.389638 & 0.805181 \tabularnewline
120 & 0.20389 & 0.407781 & 0.79611 \tabularnewline
121 & 0.181124 & 0.362247 & 0.818876 \tabularnewline
122 & 0.203916 & 0.407831 & 0.796084 \tabularnewline
123 & 0.23731 & 0.474621 & 0.76269 \tabularnewline
124 & 0.20375 & 0.4075 & 0.79625 \tabularnewline
125 & 0.176245 & 0.352491 & 0.823755 \tabularnewline
126 & 0.19462 & 0.38924 & 0.80538 \tabularnewline
127 & 0.219174 & 0.438348 & 0.780826 \tabularnewline
128 & 0.495265 & 0.99053 & 0.504735 \tabularnewline
129 & 0.481552 & 0.963103 & 0.518448 \tabularnewline
130 & 0.49524 & 0.99048 & 0.50476 \tabularnewline
131 & 0.450011 & 0.900021 & 0.549989 \tabularnewline
132 & 0.397343 & 0.794687 & 0.602657 \tabularnewline
133 & 0.37206 & 0.74412 & 0.62794 \tabularnewline
134 & 0.330097 & 0.660194 & 0.669903 \tabularnewline
135 & 0.308757 & 0.617514 & 0.691243 \tabularnewline
136 & 0.280121 & 0.560242 & 0.719879 \tabularnewline
137 & 0.406636 & 0.813272 & 0.593364 \tabularnewline
138 & 0.359616 & 0.719233 & 0.640384 \tabularnewline
139 & 0.313231 & 0.626463 & 0.686769 \tabularnewline
140 & 0.271812 & 0.543625 & 0.728188 \tabularnewline
141 & 0.228221 & 0.456442 & 0.771779 \tabularnewline
142 & 0.186466 & 0.372931 & 0.813534 \tabularnewline
143 & 0.148762 & 0.297525 & 0.851238 \tabularnewline
144 & 0.12757 & 0.255139 & 0.87243 \tabularnewline
145 & 0.133228 & 0.266456 & 0.866772 \tabularnewline
146 & 0.101439 & 0.202877 & 0.898561 \tabularnewline
147 & 0.0759013 & 0.151803 & 0.924099 \tabularnewline
148 & 0.114957 & 0.229914 & 0.885043 \tabularnewline
149 & 0.0868443 & 0.173689 & 0.913156 \tabularnewline
150 & 0.100965 & 0.201931 & 0.899035 \tabularnewline
151 & 0.622809 & 0.754381 & 0.377191 \tabularnewline
152 & 0.550752 & 0.898497 & 0.449248 \tabularnewline
153 & 0.975882 & 0.0482361 & 0.0241181 \tabularnewline
154 & 0.982914 & 0.0341719 & 0.017086 \tabularnewline
155 & 0.986309 & 0.0273825 & 0.0136912 \tabularnewline
156 & 0.999077 & 0.0018467 & 0.000923348 \tabularnewline
157 & 0.997452 & 0.00509516 & 0.00254758 \tabularnewline
158 & 0.993092 & 0.0138154 & 0.00690769 \tabularnewline
159 & 0.987956 & 0.024088 & 0.012044 \tabularnewline
160 & 0.986109 & 0.0277825 & 0.0138913 \tabularnewline
161 & 0.96339 & 0.0732203 & 0.0366101 \tabularnewline
162 & 0.948857 & 0.102286 & 0.0511432 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268109&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]0.837802[/C][C]0.324395[/C][C]0.162198[/C][/ROW]
[ROW][C]10[/C][C]0.745252[/C][C]0.509495[/C][C]0.254748[/C][/ROW]
[ROW][C]11[/C][C]0.659204[/C][C]0.681592[/C][C]0.340796[/C][/ROW]
[ROW][C]12[/C][C]0.689315[/C][C]0.621369[/C][C]0.310685[/C][/ROW]
[ROW][C]13[/C][C]0.76175[/C][C]0.4765[/C][C]0.23825[/C][/ROW]
[ROW][C]14[/C][C]0.857236[/C][C]0.285528[/C][C]0.142764[/C][/ROW]
[ROW][C]15[/C][C]0.809748[/C][C]0.380504[/C][C]0.190252[/C][/ROW]
[ROW][C]16[/C][C]0.892904[/C][C]0.214193[/C][C]0.107096[/C][/ROW]
[ROW][C]17[/C][C]0.867371[/C][C]0.265258[/C][C]0.132629[/C][/ROW]
[ROW][C]18[/C][C]0.821724[/C][C]0.356553[/C][C]0.178276[/C][/ROW]
[ROW][C]19[/C][C]0.770003[/C][C]0.459995[/C][C]0.229997[/C][/ROW]
[ROW][C]20[/C][C]0.739776[/C][C]0.520447[/C][C]0.260224[/C][/ROW]
[ROW][C]21[/C][C]0.733154[/C][C]0.533691[/C][C]0.266846[/C][/ROW]
[ROW][C]22[/C][C]0.668994[/C][C]0.662012[/C][C]0.331006[/C][/ROW]
[ROW][C]23[/C][C]0.606912[/C][C]0.786175[/C][C]0.393088[/C][/ROW]
[ROW][C]24[/C][C]0.543256[/C][C]0.913487[/C][C]0.456744[/C][/ROW]
[ROW][C]25[/C][C]0.535719[/C][C]0.928562[/C][C]0.464281[/C][/ROW]
[ROW][C]26[/C][C]0.517242[/C][C]0.965517[/C][C]0.482758[/C][/ROW]
[ROW][C]27[/C][C]0.48518[/C][C]0.97036[/C][C]0.51482[/C][/ROW]
[ROW][C]28[/C][C]0.439809[/C][C]0.879618[/C][C]0.560191[/C][/ROW]
[ROW][C]29[/C][C]0.382902[/C][C]0.765805[/C][C]0.617098[/C][/ROW]
[ROW][C]30[/C][C]0.351248[/C][C]0.702496[/C][C]0.648752[/C][/ROW]
[ROW][C]31[/C][C]0.377473[/C][C]0.754945[/C][C]0.622527[/C][/ROW]
[ROW][C]32[/C][C]0.366121[/C][C]0.732242[/C][C]0.633879[/C][/ROW]
[ROW][C]33[/C][C]0.321585[/C][C]0.64317[/C][C]0.678415[/C][/ROW]
[ROW][C]34[/C][C]0.276145[/C][C]0.552289[/C][C]0.723855[/C][/ROW]
[ROW][C]35[/C][C]0.22992[/C][C]0.45984[/C][C]0.77008[/C][/ROW]
[ROW][C]36[/C][C]0.217057[/C][C]0.434113[/C][C]0.782943[/C][/ROW]
[ROW][C]37[/C][C]0.366542[/C][C]0.733085[/C][C]0.633458[/C][/ROW]
[ROW][C]38[/C][C]0.31492[/C][C]0.62984[/C][C]0.68508[/C][/ROW]
[ROW][C]39[/C][C]0.319119[/C][C]0.638238[/C][C]0.680881[/C][/ROW]
[ROW][C]40[/C][C]0.276344[/C][C]0.552688[/C][C]0.723656[/C][/ROW]
[ROW][C]41[/C][C]0.76296[/C][C]0.47408[/C][C]0.23704[/C][/ROW]
[ROW][C]42[/C][C]0.724172[/C][C]0.551656[/C][C]0.275828[/C][/ROW]
[ROW][C]43[/C][C]0.705194[/C][C]0.589611[/C][C]0.294806[/C][/ROW]
[ROW][C]44[/C][C]0.660674[/C][C]0.678652[/C][C]0.339326[/C][/ROW]
[ROW][C]45[/C][C]0.661558[/C][C]0.676885[/C][C]0.338442[/C][/ROW]
[ROW][C]46[/C][C]0.614002[/C][C]0.771995[/C][C]0.385998[/C][/ROW]
[ROW][C]47[/C][C]0.575137[/C][C]0.849726[/C][C]0.424863[/C][/ROW]
[ROW][C]48[/C][C]0.532048[/C][C]0.935905[/C][C]0.467952[/C][/ROW]
[ROW][C]49[/C][C]0.494148[/C][C]0.988296[/C][C]0.505852[/C][/ROW]
[ROW][C]50[/C][C]0.48478[/C][C]0.969559[/C][C]0.51522[/C][/ROW]
[ROW][C]51[/C][C]0.505966[/C][C]0.988069[/C][C]0.494034[/C][/ROW]
[ROW][C]52[/C][C]0.498425[/C][C]0.996849[/C][C]0.501575[/C][/ROW]
[ROW][C]53[/C][C]0.464971[/C][C]0.929942[/C][C]0.535029[/C][/ROW]
[ROW][C]54[/C][C]0.778153[/C][C]0.443693[/C][C]0.221847[/C][/ROW]
[ROW][C]55[/C][C]0.781428[/C][C]0.437143[/C][C]0.218572[/C][/ROW]
[ROW][C]56[/C][C]0.751469[/C][C]0.497063[/C][C]0.248531[/C][/ROW]
[ROW][C]57[/C][C]0.752415[/C][C]0.49517[/C][C]0.247585[/C][/ROW]
[ROW][C]58[/C][C]0.75912[/C][C]0.48176[/C][C]0.24088[/C][/ROW]
[ROW][C]59[/C][C]0.723623[/C][C]0.552754[/C][C]0.276377[/C][/ROW]
[ROW][C]60[/C][C]0.749834[/C][C]0.500332[/C][C]0.250166[/C][/ROW]
[ROW][C]61[/C][C]0.724899[/C][C]0.550201[/C][C]0.275101[/C][/ROW]
[ROW][C]62[/C][C]0.751241[/C][C]0.497519[/C][C]0.248759[/C][/ROW]
[ROW][C]63[/C][C]0.733554[/C][C]0.532891[/C][C]0.266446[/C][/ROW]
[ROW][C]64[/C][C]0.6972[/C][C]0.6056[/C][C]0.3028[/C][/ROW]
[ROW][C]65[/C][C]0.658773[/C][C]0.682455[/C][C]0.341227[/C][/ROW]
[ROW][C]66[/C][C]0.665211[/C][C]0.669578[/C][C]0.334789[/C][/ROW]
[ROW][C]67[/C][C]0.661377[/C][C]0.677245[/C][C]0.338623[/C][/ROW]
[ROW][C]68[/C][C]0.661872[/C][C]0.676255[/C][C]0.338128[/C][/ROW]
[ROW][C]69[/C][C]0.628935[/C][C]0.74213[/C][C]0.371065[/C][/ROW]
[ROW][C]70[/C][C]0.706252[/C][C]0.587495[/C][C]0.293748[/C][/ROW]
[ROW][C]71[/C][C]0.694888[/C][C]0.610223[/C][C]0.305112[/C][/ROW]
[ROW][C]72[/C][C]0.67533[/C][C]0.64934[/C][C]0.32467[/C][/ROW]
[ROW][C]73[/C][C]0.656778[/C][C]0.686443[/C][C]0.343222[/C][/ROW]
[ROW][C]74[/C][C]0.614603[/C][C]0.770795[/C][C]0.385397[/C][/ROW]
[ROW][C]75[/C][C]0.634719[/C][C]0.730562[/C][C]0.365281[/C][/ROW]
[ROW][C]76[/C][C]0.599227[/C][C]0.801546[/C][C]0.400773[/C][/ROW]
[ROW][C]77[/C][C]0.674979[/C][C]0.650041[/C][C]0.325021[/C][/ROW]
[ROW][C]78[/C][C]0.647513[/C][C]0.704973[/C][C]0.352487[/C][/ROW]
[ROW][C]79[/C][C]0.609333[/C][C]0.781334[/C][C]0.390667[/C][/ROW]
[ROW][C]80[/C][C]0.567553[/C][C]0.864893[/C][C]0.432447[/C][/ROW]
[ROW][C]81[/C][C]0.544794[/C][C]0.910413[/C][C]0.455206[/C][/ROW]
[ROW][C]82[/C][C]0.500542[/C][C]0.998916[/C][C]0.499458[/C][/ROW]
[ROW][C]83[/C][C]0.512794[/C][C]0.974411[/C][C]0.487206[/C][/ROW]
[ROW][C]84[/C][C]0.592334[/C][C]0.815331[/C][C]0.407666[/C][/ROW]
[ROW][C]85[/C][C]0.555245[/C][C]0.88951[/C][C]0.444755[/C][/ROW]
[ROW][C]86[/C][C]0.537892[/C][C]0.924215[/C][C]0.462108[/C][/ROW]
[ROW][C]87[/C][C]0.544113[/C][C]0.911774[/C][C]0.455887[/C][/ROW]
[ROW][C]88[/C][C]0.499888[/C][C]0.999776[/C][C]0.500112[/C][/ROW]
[ROW][C]89[/C][C]0.500111[/C][C]0.999778[/C][C]0.499889[/C][/ROW]
[ROW][C]90[/C][C]0.480811[/C][C]0.961621[/C][C]0.519189[/C][/ROW]
[ROW][C]91[/C][C]0.442883[/C][C]0.885766[/C][C]0.557117[/C][/ROW]
[ROW][C]92[/C][C]0.424932[/C][C]0.849864[/C][C]0.575068[/C][/ROW]
[ROW][C]93[/C][C]0.417764[/C][C]0.835527[/C][C]0.582236[/C][/ROW]
[ROW][C]94[/C][C]0.379321[/C][C]0.758641[/C][C]0.620679[/C][/ROW]
[ROW][C]95[/C][C]0.359887[/C][C]0.719774[/C][C]0.640113[/C][/ROW]
[ROW][C]96[/C][C]0.408661[/C][C]0.817322[/C][C]0.591339[/C][/ROW]
[ROW][C]97[/C][C]0.395917[/C][C]0.791834[/C][C]0.604083[/C][/ROW]
[ROW][C]98[/C][C]0.380598[/C][C]0.761196[/C][C]0.619402[/C][/ROW]
[ROW][C]99[/C][C]0.342373[/C][C]0.684745[/C][C]0.657627[/C][/ROW]
[ROW][C]100[/C][C]0.33088[/C][C]0.66176[/C][C]0.66912[/C][/ROW]
[ROW][C]101[/C][C]0.304059[/C][C]0.608118[/C][C]0.695941[/C][/ROW]
[ROW][C]102[/C][C]0.269926[/C][C]0.539853[/C][C]0.730074[/C][/ROW]
[ROW][C]103[/C][C]0.240146[/C][C]0.480291[/C][C]0.759854[/C][/ROW]
[ROW][C]104[/C][C]0.290961[/C][C]0.581921[/C][C]0.709039[/C][/ROW]
[ROW][C]105[/C][C]0.255469[/C][C]0.510938[/C][C]0.744531[/C][/ROW]
[ROW][C]106[/C][C]0.244818[/C][C]0.489636[/C][C]0.755182[/C][/ROW]
[ROW][C]107[/C][C]0.224533[/C][C]0.449066[/C][C]0.775467[/C][/ROW]
[ROW][C]108[/C][C]0.215736[/C][C]0.431472[/C][C]0.784264[/C][/ROW]
[ROW][C]109[/C][C]0.192664[/C][C]0.385329[/C][C]0.807336[/C][/ROW]
[ROW][C]110[/C][C]0.166618[/C][C]0.333236[/C][C]0.833382[/C][/ROW]
[ROW][C]111[/C][C]0.142959[/C][C]0.285918[/C][C]0.857041[/C][/ROW]
[ROW][C]112[/C][C]0.118243[/C][C]0.236485[/C][C]0.881757[/C][/ROW]
[ROW][C]113[/C][C]0.11083[/C][C]0.221659[/C][C]0.88917[/C][/ROW]
[ROW][C]114[/C][C]0.149532[/C][C]0.299064[/C][C]0.850468[/C][/ROW]
[ROW][C]115[/C][C]0.130729[/C][C]0.261458[/C][C]0.869271[/C][/ROW]
[ROW][C]116[/C][C]0.120607[/C][C]0.241213[/C][C]0.879393[/C][/ROW]
[ROW][C]117[/C][C]0.108517[/C][C]0.217033[/C][C]0.891483[/C][/ROW]
[ROW][C]118[/C][C]0.177231[/C][C]0.354462[/C][C]0.822769[/C][/ROW]
[ROW][C]119[/C][C]0.194819[/C][C]0.389638[/C][C]0.805181[/C][/ROW]
[ROW][C]120[/C][C]0.20389[/C][C]0.407781[/C][C]0.79611[/C][/ROW]
[ROW][C]121[/C][C]0.181124[/C][C]0.362247[/C][C]0.818876[/C][/ROW]
[ROW][C]122[/C][C]0.203916[/C][C]0.407831[/C][C]0.796084[/C][/ROW]
[ROW][C]123[/C][C]0.23731[/C][C]0.474621[/C][C]0.76269[/C][/ROW]
[ROW][C]124[/C][C]0.20375[/C][C]0.4075[/C][C]0.79625[/C][/ROW]
[ROW][C]125[/C][C]0.176245[/C][C]0.352491[/C][C]0.823755[/C][/ROW]
[ROW][C]126[/C][C]0.19462[/C][C]0.38924[/C][C]0.80538[/C][/ROW]
[ROW][C]127[/C][C]0.219174[/C][C]0.438348[/C][C]0.780826[/C][/ROW]
[ROW][C]128[/C][C]0.495265[/C][C]0.99053[/C][C]0.504735[/C][/ROW]
[ROW][C]129[/C][C]0.481552[/C][C]0.963103[/C][C]0.518448[/C][/ROW]
[ROW][C]130[/C][C]0.49524[/C][C]0.99048[/C][C]0.50476[/C][/ROW]
[ROW][C]131[/C][C]0.450011[/C][C]0.900021[/C][C]0.549989[/C][/ROW]
[ROW][C]132[/C][C]0.397343[/C][C]0.794687[/C][C]0.602657[/C][/ROW]
[ROW][C]133[/C][C]0.37206[/C][C]0.74412[/C][C]0.62794[/C][/ROW]
[ROW][C]134[/C][C]0.330097[/C][C]0.660194[/C][C]0.669903[/C][/ROW]
[ROW][C]135[/C][C]0.308757[/C][C]0.617514[/C][C]0.691243[/C][/ROW]
[ROW][C]136[/C][C]0.280121[/C][C]0.560242[/C][C]0.719879[/C][/ROW]
[ROW][C]137[/C][C]0.406636[/C][C]0.813272[/C][C]0.593364[/C][/ROW]
[ROW][C]138[/C][C]0.359616[/C][C]0.719233[/C][C]0.640384[/C][/ROW]
[ROW][C]139[/C][C]0.313231[/C][C]0.626463[/C][C]0.686769[/C][/ROW]
[ROW][C]140[/C][C]0.271812[/C][C]0.543625[/C][C]0.728188[/C][/ROW]
[ROW][C]141[/C][C]0.228221[/C][C]0.456442[/C][C]0.771779[/C][/ROW]
[ROW][C]142[/C][C]0.186466[/C][C]0.372931[/C][C]0.813534[/C][/ROW]
[ROW][C]143[/C][C]0.148762[/C][C]0.297525[/C][C]0.851238[/C][/ROW]
[ROW][C]144[/C][C]0.12757[/C][C]0.255139[/C][C]0.87243[/C][/ROW]
[ROW][C]145[/C][C]0.133228[/C][C]0.266456[/C][C]0.866772[/C][/ROW]
[ROW][C]146[/C][C]0.101439[/C][C]0.202877[/C][C]0.898561[/C][/ROW]
[ROW][C]147[/C][C]0.0759013[/C][C]0.151803[/C][C]0.924099[/C][/ROW]
[ROW][C]148[/C][C]0.114957[/C][C]0.229914[/C][C]0.885043[/C][/ROW]
[ROW][C]149[/C][C]0.0868443[/C][C]0.173689[/C][C]0.913156[/C][/ROW]
[ROW][C]150[/C][C]0.100965[/C][C]0.201931[/C][C]0.899035[/C][/ROW]
[ROW][C]151[/C][C]0.622809[/C][C]0.754381[/C][C]0.377191[/C][/ROW]
[ROW][C]152[/C][C]0.550752[/C][C]0.898497[/C][C]0.449248[/C][/ROW]
[ROW][C]153[/C][C]0.975882[/C][C]0.0482361[/C][C]0.0241181[/C][/ROW]
[ROW][C]154[/C][C]0.982914[/C][C]0.0341719[/C][C]0.017086[/C][/ROW]
[ROW][C]155[/C][C]0.986309[/C][C]0.0273825[/C][C]0.0136912[/C][/ROW]
[ROW][C]156[/C][C]0.999077[/C][C]0.0018467[/C][C]0.000923348[/C][/ROW]
[ROW][C]157[/C][C]0.997452[/C][C]0.00509516[/C][C]0.00254758[/C][/ROW]
[ROW][C]158[/C][C]0.993092[/C][C]0.0138154[/C][C]0.00690769[/C][/ROW]
[ROW][C]159[/C][C]0.987956[/C][C]0.024088[/C][C]0.012044[/C][/ROW]
[ROW][C]160[/C][C]0.986109[/C][C]0.0277825[/C][C]0.0138913[/C][/ROW]
[ROW][C]161[/C][C]0.96339[/C][C]0.0732203[/C][C]0.0366101[/C][/ROW]
[ROW][C]162[/C][C]0.948857[/C][C]0.102286[/C][C]0.0511432[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268109&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268109&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
90.8378020.3243950.162198
100.7452520.5094950.254748
110.6592040.6815920.340796
120.6893150.6213690.310685
130.761750.47650.23825
140.8572360.2855280.142764
150.8097480.3805040.190252
160.8929040.2141930.107096
170.8673710.2652580.132629
180.8217240.3565530.178276
190.7700030.4599950.229997
200.7397760.5204470.260224
210.7331540.5336910.266846
220.6689940.6620120.331006
230.6069120.7861750.393088
240.5432560.9134870.456744
250.5357190.9285620.464281
260.5172420.9655170.482758
270.485180.970360.51482
280.4398090.8796180.560191
290.3829020.7658050.617098
300.3512480.7024960.648752
310.3774730.7549450.622527
320.3661210.7322420.633879
330.3215850.643170.678415
340.2761450.5522890.723855
350.229920.459840.77008
360.2170570.4341130.782943
370.3665420.7330850.633458
380.314920.629840.68508
390.3191190.6382380.680881
400.2763440.5526880.723656
410.762960.474080.23704
420.7241720.5516560.275828
430.7051940.5896110.294806
440.6606740.6786520.339326
450.6615580.6768850.338442
460.6140020.7719950.385998
470.5751370.8497260.424863
480.5320480.9359050.467952
490.4941480.9882960.505852
500.484780.9695590.51522
510.5059660.9880690.494034
520.4984250.9968490.501575
530.4649710.9299420.535029
540.7781530.4436930.221847
550.7814280.4371430.218572
560.7514690.4970630.248531
570.7524150.495170.247585
580.759120.481760.24088
590.7236230.5527540.276377
600.7498340.5003320.250166
610.7248990.5502010.275101
620.7512410.4975190.248759
630.7335540.5328910.266446
640.69720.60560.3028
650.6587730.6824550.341227
660.6652110.6695780.334789
670.6613770.6772450.338623
680.6618720.6762550.338128
690.6289350.742130.371065
700.7062520.5874950.293748
710.6948880.6102230.305112
720.675330.649340.32467
730.6567780.6864430.343222
740.6146030.7707950.385397
750.6347190.7305620.365281
760.5992270.8015460.400773
770.6749790.6500410.325021
780.6475130.7049730.352487
790.6093330.7813340.390667
800.5675530.8648930.432447
810.5447940.9104130.455206
820.5005420.9989160.499458
830.5127940.9744110.487206
840.5923340.8153310.407666
850.5552450.889510.444755
860.5378920.9242150.462108
870.5441130.9117740.455887
880.4998880.9997760.500112
890.5001110.9997780.499889
900.4808110.9616210.519189
910.4428830.8857660.557117
920.4249320.8498640.575068
930.4177640.8355270.582236
940.3793210.7586410.620679
950.3598870.7197740.640113
960.4086610.8173220.591339
970.3959170.7918340.604083
980.3805980.7611960.619402
990.3423730.6847450.657627
1000.330880.661760.66912
1010.3040590.6081180.695941
1020.2699260.5398530.730074
1030.2401460.4802910.759854
1040.2909610.5819210.709039
1050.2554690.5109380.744531
1060.2448180.4896360.755182
1070.2245330.4490660.775467
1080.2157360.4314720.784264
1090.1926640.3853290.807336
1100.1666180.3332360.833382
1110.1429590.2859180.857041
1120.1182430.2364850.881757
1130.110830.2216590.88917
1140.1495320.2990640.850468
1150.1307290.2614580.869271
1160.1206070.2412130.879393
1170.1085170.2170330.891483
1180.1772310.3544620.822769
1190.1948190.3896380.805181
1200.203890.4077810.79611
1210.1811240.3622470.818876
1220.2039160.4078310.796084
1230.237310.4746210.76269
1240.203750.40750.79625
1250.1762450.3524910.823755
1260.194620.389240.80538
1270.2191740.4383480.780826
1280.4952650.990530.504735
1290.4815520.9631030.518448
1300.495240.990480.50476
1310.4500110.9000210.549989
1320.3973430.7946870.602657
1330.372060.744120.62794
1340.3300970.6601940.669903
1350.3087570.6175140.691243
1360.2801210.5602420.719879
1370.4066360.8132720.593364
1380.3596160.7192330.640384
1390.3132310.6264630.686769
1400.2718120.5436250.728188
1410.2282210.4564420.771779
1420.1864660.3729310.813534
1430.1487620.2975250.851238
1440.127570.2551390.87243
1450.1332280.2664560.866772
1460.1014390.2028770.898561
1470.07590130.1518030.924099
1480.1149570.2299140.885043
1490.08684430.1736890.913156
1500.1009650.2019310.899035
1510.6228090.7543810.377191
1520.5507520.8984970.449248
1530.9758820.04823610.0241181
1540.9829140.03417190.017086
1550.9863090.02738250.0136912
1560.9990770.00184670.000923348
1570.9974520.005095160.00254758
1580.9930920.01381540.00690769
1590.9879560.0240880.012044
1600.9861090.02778250.0138913
1610.963390.07322030.0366101
1620.9488570.1022860.0511432






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.012987NOK
5% type I error level80.0519481NOK
10% type I error level90.0584416OK

\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 & 2 & 0.012987 & NOK \tabularnewline
5% type I error level & 8 & 0.0519481 & NOK \tabularnewline
10% type I error level & 9 & 0.0584416 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268109&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]2[/C][C]0.012987[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.0519481[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.0584416[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268109&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268109&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 level20.012987NOK
5% type I error level80.0519481NOK
10% type I error level90.0584416OK


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):
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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')
}