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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 09:53:54 +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/t1418637282l83g0o4qmo5yil4.htm/, Retrieved Thu, 16 May 2024 09:57:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267993, Retrieved Thu, 16 May 2024 09:57:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsMultiple Regression Permanente evaluatie positieve waarden
Estimated Impact75

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 09:53:54] [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'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=267993&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=267993&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267993&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
Pe[t] = + 0.953325 + 0.00391315LFM[t] + 0.00300688B[t] + 0.121682PRH[t] + 0.128641CH[t] -0.121514H[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Pe[t] =  +  0.953325 +  0.00391315LFM[t] +  0.00300688B[t] +  0.121682PRH[t] +  0.128641CH[t] -0.121514H[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267993&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Pe[t] =  +  0.953325 +  0.00391315LFM[t] +  0.00300688B[t] +  0.121682PRH[t] +  0.128641CH[t] -0.121514H[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267993&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267993&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] = + 0.953325 + 0.00391315LFM[t] + 0.00300688B[t] + 0.121682PRH[t] + 0.128641CH[t] -0.121514H[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.9533250.1544296.1735.01316e-092.50658e-09
LFM0.003913150.001448412.7020.007619580.00380979
B0.003006880.0009939383.0250.002882620.00144131
PRH0.1216820.05713162.130.03466640.0173332
CH0.1286410.05657762.2740.02427190.0121359
H-0.1215140.0566083-2.1470.03328780.0166439

\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) & 0.953325 & 0.154429 & 6.173 & 5.01316e-09 & 2.50658e-09 \tabularnewline
LFM & 0.00391315 & 0.00144841 & 2.702 & 0.00761958 & 0.00380979 \tabularnewline
B & 0.00300688 & 0.000993938 & 3.025 & 0.00288262 & 0.00144131 \tabularnewline
PRH & 0.121682 & 0.0571316 & 2.13 & 0.0346664 & 0.0173332 \tabularnewline
CH & 0.128641 & 0.0565776 & 2.274 & 0.0242719 & 0.0121359 \tabularnewline
H & -0.121514 & 0.0566083 & -2.147 & 0.0332878 & 0.0166439 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267993&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]0.953325[/C][C]0.154429[/C][C]6.173[/C][C]5.01316e-09[/C][C]2.50658e-09[/C][/ROW]
[ROW][C]LFM[/C][C]0.00391315[/C][C]0.00144841[/C][C]2.702[/C][C]0.00761958[/C][C]0.00380979[/C][/ROW]
[ROW][C]B[/C][C]0.00300688[/C][C]0.000993938[/C][C]3.025[/C][C]0.00288262[/C][C]0.00144131[/C][/ROW]
[ROW][C]PRH[/C][C]0.121682[/C][C]0.0571316[/C][C]2.13[/C][C]0.0346664[/C][C]0.0173332[/C][/ROW]
[ROW][C]CH[/C][C]0.128641[/C][C]0.0565776[/C][C]2.274[/C][C]0.0242719[/C][C]0.0121359[/C][/ROW]
[ROW][C]H[/C][C]-0.121514[/C][C]0.0566083[/C][C]-2.147[/C][C]0.0332878[/C][C]0.0166439[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267993&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267993&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)0.9533250.1544296.1735.01316e-092.50658e-09
LFM0.003913150.001448412.7020.007619580.00380979
B0.003006880.0009939383.0250.002882620.00144131
PRH0.1216820.05713162.130.03466640.0173332
CH0.1286410.05657762.2740.02427190.0121359
H-0.1215140.0566083-2.1470.03328780.0166439






Multiple Linear Regression - Regression Statistics
Multiple R0.593608
R-squared0.352371
Adjusted R-squared0.332746
F-TEST (value)17.9551
F-TEST (DF numerator)5
F-TEST (DF denominator)165
p-value3.38618e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.608413
Sum Squared Residuals61.0774

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.593608 \tabularnewline
R-squared & 0.352371 \tabularnewline
Adjusted R-squared & 0.332746 \tabularnewline
F-TEST (value) & 17.9551 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 165 \tabularnewline
p-value & 3.38618e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.608413 \tabularnewline
Sum Squared Residuals & 61.0774 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267993&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.593608[/C][/ROW]
[ROW][C]R-squared[/C][C]0.352371[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.332746[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.9551[/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]3.38618e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.608413[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]61.0774[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267993&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267993&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 R0.593608
R-squared0.352371
Adjusted R-squared0.332746
F-TEST (value)17.9551
F-TEST (DF numerator)5
F-TEST (DF denominator)165
p-value3.38618e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.608413
Sum Squared Residuals61.0774






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
10.751.47534-0.725341
21.51.9114-0.411403
332.35680.6432
42.252.40235-0.152354
532.946630.0533685
61.51.5198-0.0197973
733.52588-0.525883
832.740830.259168
933.63216-0.632157
100.751.45657-0.706569
1132.803440.196562
122.251.484870.765131
131.52.21644-0.716441
141.52.0584-0.558399
152.252.35945-0.109449
1632.796810.203195
1732.513010.486991
181.51.82901-0.329013
192.251.79730.452704
202.253.07727-0.827272
211.52.22229-0.722295
222.252.27274-0.0227425
231.51.8031-0.303096
242.252.208980.0410223
252.252.115570.13443
2631.642281.35772
2732.926820.0731798
2832.902940.0970604
291.51.75582-0.255824
3032.030020.969976
3132.439290.56071
322.252.49641-0.24641
331.52.0905-0.590503
342.252.61678-0.366784
352.252.60033-0.350326
3632.068070.931925
370.751.91966-1.16966
382.252.158720.0912828
3932.569980.43002
4032.560660.439337
411.52.44657-0.946567
422.252.46004-0.210042
4332.485410.514589
442.252.74541-0.495406
451.51.79101-0.291015
462.252.239290.0107061
472.252.9149-0.664898
481.52.25711-0.757115
492.252.82756-0.577558
501.51.75075-0.250752
512.252.34438-0.0943843
5232.510890.489113
5332.420840.579163
5432.386530.613475
5532.676020.323984
561.51.304190.195811
572.252.62214-0.372142
581.51.51861-0.0186117
592.252.32296-0.0729617
602.252.25496-0.00495819
612.252.33543-0.0854279
6232.208870.791128
631.52.2477-0.747697
642.252.59825-0.348254
652.252.59825-0.348254
6632.258070.741935
672.252.30146-0.0514595
6832.631160.368845
692.252.2687-0.0186991
701.51.65983-0.159826
7132.434730.56527
721.52.03315-0.533154
7332.824060.175945
7432.272330.727667
7531.997141.00286
7632.28630.713698
772.251.844480.405516
782.251.983660.266341
790.751.43351-0.683508
8031.8831.117
810.751.67007-0.920066
821.52.05005-0.550048
831.51.61494-0.114937
8432.094820.905176
851.51.83281-0.332806
862.252.170250.0797511
8732.761350.238652
8832.016960.983043
891.51.54055-0.0405516
9032.079110.920891
9132.977090.0229058
921.51.74353-0.243527
931.52.16226-0.662264
942.252.3266-0.0766027
951.51.70542-0.205424
961.51.60003-0.100026
972.251.92880.321198
981.51.57979-0.0797859
992.251.64280.607202
10032.823720.176275
10132.198960.801045
1020.751.53375-0.783747
1031.51.97555-0.475552
1041.52.47219-0.972188
1052.252.067270.182732
1062.252.89186-0.641859
1071.52.04927-0.549266
1082.252.060240.18976
1090.752.1034-1.3534
1102.252.42823-0.178226
1110.751.75237-1.00237
1122.252.32052-0.0705181
11332.467570.532427
1140.751.78214-1.03214
1150.751.61536-0.865365
11631.751781.24822
11732.68430.315697
11831.666281.33372
11932.031950.96805
1201.51.75907-0.259072
12132.661810.338187
12232.437460.562541
12332.113030.886966
12432.826090.173912
1251.51.77696-0.276963
1262.252.135240.114759
1270.751.73205-0.982051
1280.751.5501-0.800096
1292.251.429550.820453
13032.519060.48094
1312.252.037660.212339
13231.737781.26222
1332.252.36813-0.118127
13432.048190.951806
1351.51.83054-0.330538
13632.676020.323984
13731.884881.11512
1380.751.57943-0.82943
1391.52.44433-0.944334
14033.3144-0.3144
14131.783791.21621
14232.546010.453994
1432.252.144210.105787
1442.252.010630.239371
14532.261710.738288
1461.52.30125-0.801248
1472.252.189960.0600418
1482.252.2778-0.0277984
1492.252.36904-0.119039
1500.751.51166-0.761657
1512.251.974590.275411
1521.52.12448-0.624479
1532.251.886260.363738
1541.51.304190.195811
1550.751.87318-1.12318
1561.51.90607-0.406065
1571.51.83054-0.330538
1582.252.112940.137055
1591.51.93922-0.439215
1601.51.78266-0.282663
16131.835051.16495
1622.252.004420.245576
1631.51.57457-0.0745747
1640.751.77512-1.02512
1652.251.6240.625998
16632.261390.738609
16732.741210.258787
1681.52.1258-0.625798
1691.52.04932-0.549318
1702.252.48346-0.233456
1710.751.42539-0.675389

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.75 & 1.47534 & -0.725341 \tabularnewline
2 & 1.5 & 1.9114 & -0.411403 \tabularnewline
3 & 3 & 2.3568 & 0.6432 \tabularnewline
4 & 2.25 & 2.40235 & -0.152354 \tabularnewline
5 & 3 & 2.94663 & 0.0533685 \tabularnewline
6 & 1.5 & 1.5198 & -0.0197973 \tabularnewline
7 & 3 & 3.52588 & -0.525883 \tabularnewline
8 & 3 & 2.74083 & 0.259168 \tabularnewline
9 & 3 & 3.63216 & -0.632157 \tabularnewline
10 & 0.75 & 1.45657 & -0.706569 \tabularnewline
11 & 3 & 2.80344 & 0.196562 \tabularnewline
12 & 2.25 & 1.48487 & 0.765131 \tabularnewline
13 & 1.5 & 2.21644 & -0.716441 \tabularnewline
14 & 1.5 & 2.0584 & -0.558399 \tabularnewline
15 & 2.25 & 2.35945 & -0.109449 \tabularnewline
16 & 3 & 2.79681 & 0.203195 \tabularnewline
17 & 3 & 2.51301 & 0.486991 \tabularnewline
18 & 1.5 & 1.82901 & -0.329013 \tabularnewline
19 & 2.25 & 1.7973 & 0.452704 \tabularnewline
20 & 2.25 & 3.07727 & -0.827272 \tabularnewline
21 & 1.5 & 2.22229 & -0.722295 \tabularnewline
22 & 2.25 & 2.27274 & -0.0227425 \tabularnewline
23 & 1.5 & 1.8031 & -0.303096 \tabularnewline
24 & 2.25 & 2.20898 & 0.0410223 \tabularnewline
25 & 2.25 & 2.11557 & 0.13443 \tabularnewline
26 & 3 & 1.64228 & 1.35772 \tabularnewline
27 & 3 & 2.92682 & 0.0731798 \tabularnewline
28 & 3 & 2.90294 & 0.0970604 \tabularnewline
29 & 1.5 & 1.75582 & -0.255824 \tabularnewline
30 & 3 & 2.03002 & 0.969976 \tabularnewline
31 & 3 & 2.43929 & 0.56071 \tabularnewline
32 & 2.25 & 2.49641 & -0.24641 \tabularnewline
33 & 1.5 & 2.0905 & -0.590503 \tabularnewline
34 & 2.25 & 2.61678 & -0.366784 \tabularnewline
35 & 2.25 & 2.60033 & -0.350326 \tabularnewline
36 & 3 & 2.06807 & 0.931925 \tabularnewline
37 & 0.75 & 1.91966 & -1.16966 \tabularnewline
38 & 2.25 & 2.15872 & 0.0912828 \tabularnewline
39 & 3 & 2.56998 & 0.43002 \tabularnewline
40 & 3 & 2.56066 & 0.439337 \tabularnewline
41 & 1.5 & 2.44657 & -0.946567 \tabularnewline
42 & 2.25 & 2.46004 & -0.210042 \tabularnewline
43 & 3 & 2.48541 & 0.514589 \tabularnewline
44 & 2.25 & 2.74541 & -0.495406 \tabularnewline
45 & 1.5 & 1.79101 & -0.291015 \tabularnewline
46 & 2.25 & 2.23929 & 0.0107061 \tabularnewline
47 & 2.25 & 2.9149 & -0.664898 \tabularnewline
48 & 1.5 & 2.25711 & -0.757115 \tabularnewline
49 & 2.25 & 2.82756 & -0.577558 \tabularnewline
50 & 1.5 & 1.75075 & -0.250752 \tabularnewline
51 & 2.25 & 2.34438 & -0.0943843 \tabularnewline
52 & 3 & 2.51089 & 0.489113 \tabularnewline
53 & 3 & 2.42084 & 0.579163 \tabularnewline
54 & 3 & 2.38653 & 0.613475 \tabularnewline
55 & 3 & 2.67602 & 0.323984 \tabularnewline
56 & 1.5 & 1.30419 & 0.195811 \tabularnewline
57 & 2.25 & 2.62214 & -0.372142 \tabularnewline
58 & 1.5 & 1.51861 & -0.0186117 \tabularnewline
59 & 2.25 & 2.32296 & -0.0729617 \tabularnewline
60 & 2.25 & 2.25496 & -0.00495819 \tabularnewline
61 & 2.25 & 2.33543 & -0.0854279 \tabularnewline
62 & 3 & 2.20887 & 0.791128 \tabularnewline
63 & 1.5 & 2.2477 & -0.747697 \tabularnewline
64 & 2.25 & 2.59825 & -0.348254 \tabularnewline
65 & 2.25 & 2.59825 & -0.348254 \tabularnewline
66 & 3 & 2.25807 & 0.741935 \tabularnewline
67 & 2.25 & 2.30146 & -0.0514595 \tabularnewline
68 & 3 & 2.63116 & 0.368845 \tabularnewline
69 & 2.25 & 2.2687 & -0.0186991 \tabularnewline
70 & 1.5 & 1.65983 & -0.159826 \tabularnewline
71 & 3 & 2.43473 & 0.56527 \tabularnewline
72 & 1.5 & 2.03315 & -0.533154 \tabularnewline
73 & 3 & 2.82406 & 0.175945 \tabularnewline
74 & 3 & 2.27233 & 0.727667 \tabularnewline
75 & 3 & 1.99714 & 1.00286 \tabularnewline
76 & 3 & 2.2863 & 0.713698 \tabularnewline
77 & 2.25 & 1.84448 & 0.405516 \tabularnewline
78 & 2.25 & 1.98366 & 0.266341 \tabularnewline
79 & 0.75 & 1.43351 & -0.683508 \tabularnewline
80 & 3 & 1.883 & 1.117 \tabularnewline
81 & 0.75 & 1.67007 & -0.920066 \tabularnewline
82 & 1.5 & 2.05005 & -0.550048 \tabularnewline
83 & 1.5 & 1.61494 & -0.114937 \tabularnewline
84 & 3 & 2.09482 & 0.905176 \tabularnewline
85 & 1.5 & 1.83281 & -0.332806 \tabularnewline
86 & 2.25 & 2.17025 & 0.0797511 \tabularnewline
87 & 3 & 2.76135 & 0.238652 \tabularnewline
88 & 3 & 2.01696 & 0.983043 \tabularnewline
89 & 1.5 & 1.54055 & -0.0405516 \tabularnewline
90 & 3 & 2.07911 & 0.920891 \tabularnewline
91 & 3 & 2.97709 & 0.0229058 \tabularnewline
92 & 1.5 & 1.74353 & -0.243527 \tabularnewline
93 & 1.5 & 2.16226 & -0.662264 \tabularnewline
94 & 2.25 & 2.3266 & -0.0766027 \tabularnewline
95 & 1.5 & 1.70542 & -0.205424 \tabularnewline
96 & 1.5 & 1.60003 & -0.100026 \tabularnewline
97 & 2.25 & 1.9288 & 0.321198 \tabularnewline
98 & 1.5 & 1.57979 & -0.0797859 \tabularnewline
99 & 2.25 & 1.6428 & 0.607202 \tabularnewline
100 & 3 & 2.82372 & 0.176275 \tabularnewline
101 & 3 & 2.19896 & 0.801045 \tabularnewline
102 & 0.75 & 1.53375 & -0.783747 \tabularnewline
103 & 1.5 & 1.97555 & -0.475552 \tabularnewline
104 & 1.5 & 2.47219 & -0.972188 \tabularnewline
105 & 2.25 & 2.06727 & 0.182732 \tabularnewline
106 & 2.25 & 2.89186 & -0.641859 \tabularnewline
107 & 1.5 & 2.04927 & -0.549266 \tabularnewline
108 & 2.25 & 2.06024 & 0.18976 \tabularnewline
109 & 0.75 & 2.1034 & -1.3534 \tabularnewline
110 & 2.25 & 2.42823 & -0.178226 \tabularnewline
111 & 0.75 & 1.75237 & -1.00237 \tabularnewline
112 & 2.25 & 2.32052 & -0.0705181 \tabularnewline
113 & 3 & 2.46757 & 0.532427 \tabularnewline
114 & 0.75 & 1.78214 & -1.03214 \tabularnewline
115 & 0.75 & 1.61536 & -0.865365 \tabularnewline
116 & 3 & 1.75178 & 1.24822 \tabularnewline
117 & 3 & 2.6843 & 0.315697 \tabularnewline
118 & 3 & 1.66628 & 1.33372 \tabularnewline
119 & 3 & 2.03195 & 0.96805 \tabularnewline
120 & 1.5 & 1.75907 & -0.259072 \tabularnewline
121 & 3 & 2.66181 & 0.338187 \tabularnewline
122 & 3 & 2.43746 & 0.562541 \tabularnewline
123 & 3 & 2.11303 & 0.886966 \tabularnewline
124 & 3 & 2.82609 & 0.173912 \tabularnewline
125 & 1.5 & 1.77696 & -0.276963 \tabularnewline
126 & 2.25 & 2.13524 & 0.114759 \tabularnewline
127 & 0.75 & 1.73205 & -0.982051 \tabularnewline
128 & 0.75 & 1.5501 & -0.800096 \tabularnewline
129 & 2.25 & 1.42955 & 0.820453 \tabularnewline
130 & 3 & 2.51906 & 0.48094 \tabularnewline
131 & 2.25 & 2.03766 & 0.212339 \tabularnewline
132 & 3 & 1.73778 & 1.26222 \tabularnewline
133 & 2.25 & 2.36813 & -0.118127 \tabularnewline
134 & 3 & 2.04819 & 0.951806 \tabularnewline
135 & 1.5 & 1.83054 & -0.330538 \tabularnewline
136 & 3 & 2.67602 & 0.323984 \tabularnewline
137 & 3 & 1.88488 & 1.11512 \tabularnewline
138 & 0.75 & 1.57943 & -0.82943 \tabularnewline
139 & 1.5 & 2.44433 & -0.944334 \tabularnewline
140 & 3 & 3.3144 & -0.3144 \tabularnewline
141 & 3 & 1.78379 & 1.21621 \tabularnewline
142 & 3 & 2.54601 & 0.453994 \tabularnewline
143 & 2.25 & 2.14421 & 0.105787 \tabularnewline
144 & 2.25 & 2.01063 & 0.239371 \tabularnewline
145 & 3 & 2.26171 & 0.738288 \tabularnewline
146 & 1.5 & 2.30125 & -0.801248 \tabularnewline
147 & 2.25 & 2.18996 & 0.0600418 \tabularnewline
148 & 2.25 & 2.2778 & -0.0277984 \tabularnewline
149 & 2.25 & 2.36904 & -0.119039 \tabularnewline
150 & 0.75 & 1.51166 & -0.761657 \tabularnewline
151 & 2.25 & 1.97459 & 0.275411 \tabularnewline
152 & 1.5 & 2.12448 & -0.624479 \tabularnewline
153 & 2.25 & 1.88626 & 0.363738 \tabularnewline
154 & 1.5 & 1.30419 & 0.195811 \tabularnewline
155 & 0.75 & 1.87318 & -1.12318 \tabularnewline
156 & 1.5 & 1.90607 & -0.406065 \tabularnewline
157 & 1.5 & 1.83054 & -0.330538 \tabularnewline
158 & 2.25 & 2.11294 & 0.137055 \tabularnewline
159 & 1.5 & 1.93922 & -0.439215 \tabularnewline
160 & 1.5 & 1.78266 & -0.282663 \tabularnewline
161 & 3 & 1.83505 & 1.16495 \tabularnewline
162 & 2.25 & 2.00442 & 0.245576 \tabularnewline
163 & 1.5 & 1.57457 & -0.0745747 \tabularnewline
164 & 0.75 & 1.77512 & -1.02512 \tabularnewline
165 & 2.25 & 1.624 & 0.625998 \tabularnewline
166 & 3 & 2.26139 & 0.738609 \tabularnewline
167 & 3 & 2.74121 & 0.258787 \tabularnewline
168 & 1.5 & 2.1258 & -0.625798 \tabularnewline
169 & 1.5 & 2.04932 & -0.549318 \tabularnewline
170 & 2.25 & 2.48346 & -0.233456 \tabularnewline
171 & 0.75 & 1.42539 & -0.675389 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267993&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]1.47534[/C][C]-0.725341[/C][/ROW]
[ROW][C]2[/C][C]1.5[/C][C]1.9114[/C][C]-0.411403[/C][/ROW]
[ROW][C]3[/C][C]3[/C][C]2.3568[/C][C]0.6432[/C][/ROW]
[ROW][C]4[/C][C]2.25[/C][C]2.40235[/C][C]-0.152354[/C][/ROW]
[ROW][C]5[/C][C]3[/C][C]2.94663[/C][C]0.0533685[/C][/ROW]
[ROW][C]6[/C][C]1.5[/C][C]1.5198[/C][C]-0.0197973[/C][/ROW]
[ROW][C]7[/C][C]3[/C][C]3.52588[/C][C]-0.525883[/C][/ROW]
[ROW][C]8[/C][C]3[/C][C]2.74083[/C][C]0.259168[/C][/ROW]
[ROW][C]9[/C][C]3[/C][C]3.63216[/C][C]-0.632157[/C][/ROW]
[ROW][C]10[/C][C]0.75[/C][C]1.45657[/C][C]-0.706569[/C][/ROW]
[ROW][C]11[/C][C]3[/C][C]2.80344[/C][C]0.196562[/C][/ROW]
[ROW][C]12[/C][C]2.25[/C][C]1.48487[/C][C]0.765131[/C][/ROW]
[ROW][C]13[/C][C]1.5[/C][C]2.21644[/C][C]-0.716441[/C][/ROW]
[ROW][C]14[/C][C]1.5[/C][C]2.0584[/C][C]-0.558399[/C][/ROW]
[ROW][C]15[/C][C]2.25[/C][C]2.35945[/C][C]-0.109449[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]2.79681[/C][C]0.203195[/C][/ROW]
[ROW][C]17[/C][C]3[/C][C]2.51301[/C][C]0.486991[/C][/ROW]
[ROW][C]18[/C][C]1.5[/C][C]1.82901[/C][C]-0.329013[/C][/ROW]
[ROW][C]19[/C][C]2.25[/C][C]1.7973[/C][C]0.452704[/C][/ROW]
[ROW][C]20[/C][C]2.25[/C][C]3.07727[/C][C]-0.827272[/C][/ROW]
[ROW][C]21[/C][C]1.5[/C][C]2.22229[/C][C]-0.722295[/C][/ROW]
[ROW][C]22[/C][C]2.25[/C][C]2.27274[/C][C]-0.0227425[/C][/ROW]
[ROW][C]23[/C][C]1.5[/C][C]1.8031[/C][C]-0.303096[/C][/ROW]
[ROW][C]24[/C][C]2.25[/C][C]2.20898[/C][C]0.0410223[/C][/ROW]
[ROW][C]25[/C][C]2.25[/C][C]2.11557[/C][C]0.13443[/C][/ROW]
[ROW][C]26[/C][C]3[/C][C]1.64228[/C][C]1.35772[/C][/ROW]
[ROW][C]27[/C][C]3[/C][C]2.92682[/C][C]0.0731798[/C][/ROW]
[ROW][C]28[/C][C]3[/C][C]2.90294[/C][C]0.0970604[/C][/ROW]
[ROW][C]29[/C][C]1.5[/C][C]1.75582[/C][C]-0.255824[/C][/ROW]
[ROW][C]30[/C][C]3[/C][C]2.03002[/C][C]0.969976[/C][/ROW]
[ROW][C]31[/C][C]3[/C][C]2.43929[/C][C]0.56071[/C][/ROW]
[ROW][C]32[/C][C]2.25[/C][C]2.49641[/C][C]-0.24641[/C][/ROW]
[ROW][C]33[/C][C]1.5[/C][C]2.0905[/C][C]-0.590503[/C][/ROW]
[ROW][C]34[/C][C]2.25[/C][C]2.61678[/C][C]-0.366784[/C][/ROW]
[ROW][C]35[/C][C]2.25[/C][C]2.60033[/C][C]-0.350326[/C][/ROW]
[ROW][C]36[/C][C]3[/C][C]2.06807[/C][C]0.931925[/C][/ROW]
[ROW][C]37[/C][C]0.75[/C][C]1.91966[/C][C]-1.16966[/C][/ROW]
[ROW][C]38[/C][C]2.25[/C][C]2.15872[/C][C]0.0912828[/C][/ROW]
[ROW][C]39[/C][C]3[/C][C]2.56998[/C][C]0.43002[/C][/ROW]
[ROW][C]40[/C][C]3[/C][C]2.56066[/C][C]0.439337[/C][/ROW]
[ROW][C]41[/C][C]1.5[/C][C]2.44657[/C][C]-0.946567[/C][/ROW]
[ROW][C]42[/C][C]2.25[/C][C]2.46004[/C][C]-0.210042[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]2.48541[/C][C]0.514589[/C][/ROW]
[ROW][C]44[/C][C]2.25[/C][C]2.74541[/C][C]-0.495406[/C][/ROW]
[ROW][C]45[/C][C]1.5[/C][C]1.79101[/C][C]-0.291015[/C][/ROW]
[ROW][C]46[/C][C]2.25[/C][C]2.23929[/C][C]0.0107061[/C][/ROW]
[ROW][C]47[/C][C]2.25[/C][C]2.9149[/C][C]-0.664898[/C][/ROW]
[ROW][C]48[/C][C]1.5[/C][C]2.25711[/C][C]-0.757115[/C][/ROW]
[ROW][C]49[/C][C]2.25[/C][C]2.82756[/C][C]-0.577558[/C][/ROW]
[ROW][C]50[/C][C]1.5[/C][C]1.75075[/C][C]-0.250752[/C][/ROW]
[ROW][C]51[/C][C]2.25[/C][C]2.34438[/C][C]-0.0943843[/C][/ROW]
[ROW][C]52[/C][C]3[/C][C]2.51089[/C][C]0.489113[/C][/ROW]
[ROW][C]53[/C][C]3[/C][C]2.42084[/C][C]0.579163[/C][/ROW]
[ROW][C]54[/C][C]3[/C][C]2.38653[/C][C]0.613475[/C][/ROW]
[ROW][C]55[/C][C]3[/C][C]2.67602[/C][C]0.323984[/C][/ROW]
[ROW][C]56[/C][C]1.5[/C][C]1.30419[/C][C]0.195811[/C][/ROW]
[ROW][C]57[/C][C]2.25[/C][C]2.62214[/C][C]-0.372142[/C][/ROW]
[ROW][C]58[/C][C]1.5[/C][C]1.51861[/C][C]-0.0186117[/C][/ROW]
[ROW][C]59[/C][C]2.25[/C][C]2.32296[/C][C]-0.0729617[/C][/ROW]
[ROW][C]60[/C][C]2.25[/C][C]2.25496[/C][C]-0.00495819[/C][/ROW]
[ROW][C]61[/C][C]2.25[/C][C]2.33543[/C][C]-0.0854279[/C][/ROW]
[ROW][C]62[/C][C]3[/C][C]2.20887[/C][C]0.791128[/C][/ROW]
[ROW][C]63[/C][C]1.5[/C][C]2.2477[/C][C]-0.747697[/C][/ROW]
[ROW][C]64[/C][C]2.25[/C][C]2.59825[/C][C]-0.348254[/C][/ROW]
[ROW][C]65[/C][C]2.25[/C][C]2.59825[/C][C]-0.348254[/C][/ROW]
[ROW][C]66[/C][C]3[/C][C]2.25807[/C][C]0.741935[/C][/ROW]
[ROW][C]67[/C][C]2.25[/C][C]2.30146[/C][C]-0.0514595[/C][/ROW]
[ROW][C]68[/C][C]3[/C][C]2.63116[/C][C]0.368845[/C][/ROW]
[ROW][C]69[/C][C]2.25[/C][C]2.2687[/C][C]-0.0186991[/C][/ROW]
[ROW][C]70[/C][C]1.5[/C][C]1.65983[/C][C]-0.159826[/C][/ROW]
[ROW][C]71[/C][C]3[/C][C]2.43473[/C][C]0.56527[/C][/ROW]
[ROW][C]72[/C][C]1.5[/C][C]2.03315[/C][C]-0.533154[/C][/ROW]
[ROW][C]73[/C][C]3[/C][C]2.82406[/C][C]0.175945[/C][/ROW]
[ROW][C]74[/C][C]3[/C][C]2.27233[/C][C]0.727667[/C][/ROW]
[ROW][C]75[/C][C]3[/C][C]1.99714[/C][C]1.00286[/C][/ROW]
[ROW][C]76[/C][C]3[/C][C]2.2863[/C][C]0.713698[/C][/ROW]
[ROW][C]77[/C][C]2.25[/C][C]1.84448[/C][C]0.405516[/C][/ROW]
[ROW][C]78[/C][C]2.25[/C][C]1.98366[/C][C]0.266341[/C][/ROW]
[ROW][C]79[/C][C]0.75[/C][C]1.43351[/C][C]-0.683508[/C][/ROW]
[ROW][C]80[/C][C]3[/C][C]1.883[/C][C]1.117[/C][/ROW]
[ROW][C]81[/C][C]0.75[/C][C]1.67007[/C][C]-0.920066[/C][/ROW]
[ROW][C]82[/C][C]1.5[/C][C]2.05005[/C][C]-0.550048[/C][/ROW]
[ROW][C]83[/C][C]1.5[/C][C]1.61494[/C][C]-0.114937[/C][/ROW]
[ROW][C]84[/C][C]3[/C][C]2.09482[/C][C]0.905176[/C][/ROW]
[ROW][C]85[/C][C]1.5[/C][C]1.83281[/C][C]-0.332806[/C][/ROW]
[ROW][C]86[/C][C]2.25[/C][C]2.17025[/C][C]0.0797511[/C][/ROW]
[ROW][C]87[/C][C]3[/C][C]2.76135[/C][C]0.238652[/C][/ROW]
[ROW][C]88[/C][C]3[/C][C]2.01696[/C][C]0.983043[/C][/ROW]
[ROW][C]89[/C][C]1.5[/C][C]1.54055[/C][C]-0.0405516[/C][/ROW]
[ROW][C]90[/C][C]3[/C][C]2.07911[/C][C]0.920891[/C][/ROW]
[ROW][C]91[/C][C]3[/C][C]2.97709[/C][C]0.0229058[/C][/ROW]
[ROW][C]92[/C][C]1.5[/C][C]1.74353[/C][C]-0.243527[/C][/ROW]
[ROW][C]93[/C][C]1.5[/C][C]2.16226[/C][C]-0.662264[/C][/ROW]
[ROW][C]94[/C][C]2.25[/C][C]2.3266[/C][C]-0.0766027[/C][/ROW]
[ROW][C]95[/C][C]1.5[/C][C]1.70542[/C][C]-0.205424[/C][/ROW]
[ROW][C]96[/C][C]1.5[/C][C]1.60003[/C][C]-0.100026[/C][/ROW]
[ROW][C]97[/C][C]2.25[/C][C]1.9288[/C][C]0.321198[/C][/ROW]
[ROW][C]98[/C][C]1.5[/C][C]1.57979[/C][C]-0.0797859[/C][/ROW]
[ROW][C]99[/C][C]2.25[/C][C]1.6428[/C][C]0.607202[/C][/ROW]
[ROW][C]100[/C][C]3[/C][C]2.82372[/C][C]0.176275[/C][/ROW]
[ROW][C]101[/C][C]3[/C][C]2.19896[/C][C]0.801045[/C][/ROW]
[ROW][C]102[/C][C]0.75[/C][C]1.53375[/C][C]-0.783747[/C][/ROW]
[ROW][C]103[/C][C]1.5[/C][C]1.97555[/C][C]-0.475552[/C][/ROW]
[ROW][C]104[/C][C]1.5[/C][C]2.47219[/C][C]-0.972188[/C][/ROW]
[ROW][C]105[/C][C]2.25[/C][C]2.06727[/C][C]0.182732[/C][/ROW]
[ROW][C]106[/C][C]2.25[/C][C]2.89186[/C][C]-0.641859[/C][/ROW]
[ROW][C]107[/C][C]1.5[/C][C]2.04927[/C][C]-0.549266[/C][/ROW]
[ROW][C]108[/C][C]2.25[/C][C]2.06024[/C][C]0.18976[/C][/ROW]
[ROW][C]109[/C][C]0.75[/C][C]2.1034[/C][C]-1.3534[/C][/ROW]
[ROW][C]110[/C][C]2.25[/C][C]2.42823[/C][C]-0.178226[/C][/ROW]
[ROW][C]111[/C][C]0.75[/C][C]1.75237[/C][C]-1.00237[/C][/ROW]
[ROW][C]112[/C][C]2.25[/C][C]2.32052[/C][C]-0.0705181[/C][/ROW]
[ROW][C]113[/C][C]3[/C][C]2.46757[/C][C]0.532427[/C][/ROW]
[ROW][C]114[/C][C]0.75[/C][C]1.78214[/C][C]-1.03214[/C][/ROW]
[ROW][C]115[/C][C]0.75[/C][C]1.61536[/C][C]-0.865365[/C][/ROW]
[ROW][C]116[/C][C]3[/C][C]1.75178[/C][C]1.24822[/C][/ROW]
[ROW][C]117[/C][C]3[/C][C]2.6843[/C][C]0.315697[/C][/ROW]
[ROW][C]118[/C][C]3[/C][C]1.66628[/C][C]1.33372[/C][/ROW]
[ROW][C]119[/C][C]3[/C][C]2.03195[/C][C]0.96805[/C][/ROW]
[ROW][C]120[/C][C]1.5[/C][C]1.75907[/C][C]-0.259072[/C][/ROW]
[ROW][C]121[/C][C]3[/C][C]2.66181[/C][C]0.338187[/C][/ROW]
[ROW][C]122[/C][C]3[/C][C]2.43746[/C][C]0.562541[/C][/ROW]
[ROW][C]123[/C][C]3[/C][C]2.11303[/C][C]0.886966[/C][/ROW]
[ROW][C]124[/C][C]3[/C][C]2.82609[/C][C]0.173912[/C][/ROW]
[ROW][C]125[/C][C]1.5[/C][C]1.77696[/C][C]-0.276963[/C][/ROW]
[ROW][C]126[/C][C]2.25[/C][C]2.13524[/C][C]0.114759[/C][/ROW]
[ROW][C]127[/C][C]0.75[/C][C]1.73205[/C][C]-0.982051[/C][/ROW]
[ROW][C]128[/C][C]0.75[/C][C]1.5501[/C][C]-0.800096[/C][/ROW]
[ROW][C]129[/C][C]2.25[/C][C]1.42955[/C][C]0.820453[/C][/ROW]
[ROW][C]130[/C][C]3[/C][C]2.51906[/C][C]0.48094[/C][/ROW]
[ROW][C]131[/C][C]2.25[/C][C]2.03766[/C][C]0.212339[/C][/ROW]
[ROW][C]132[/C][C]3[/C][C]1.73778[/C][C]1.26222[/C][/ROW]
[ROW][C]133[/C][C]2.25[/C][C]2.36813[/C][C]-0.118127[/C][/ROW]
[ROW][C]134[/C][C]3[/C][C]2.04819[/C][C]0.951806[/C][/ROW]
[ROW][C]135[/C][C]1.5[/C][C]1.83054[/C][C]-0.330538[/C][/ROW]
[ROW][C]136[/C][C]3[/C][C]2.67602[/C][C]0.323984[/C][/ROW]
[ROW][C]137[/C][C]3[/C][C]1.88488[/C][C]1.11512[/C][/ROW]
[ROW][C]138[/C][C]0.75[/C][C]1.57943[/C][C]-0.82943[/C][/ROW]
[ROW][C]139[/C][C]1.5[/C][C]2.44433[/C][C]-0.944334[/C][/ROW]
[ROW][C]140[/C][C]3[/C][C]3.3144[/C][C]-0.3144[/C][/ROW]
[ROW][C]141[/C][C]3[/C][C]1.78379[/C][C]1.21621[/C][/ROW]
[ROW][C]142[/C][C]3[/C][C]2.54601[/C][C]0.453994[/C][/ROW]
[ROW][C]143[/C][C]2.25[/C][C]2.14421[/C][C]0.105787[/C][/ROW]
[ROW][C]144[/C][C]2.25[/C][C]2.01063[/C][C]0.239371[/C][/ROW]
[ROW][C]145[/C][C]3[/C][C]2.26171[/C][C]0.738288[/C][/ROW]
[ROW][C]146[/C][C]1.5[/C][C]2.30125[/C][C]-0.801248[/C][/ROW]
[ROW][C]147[/C][C]2.25[/C][C]2.18996[/C][C]0.0600418[/C][/ROW]
[ROW][C]148[/C][C]2.25[/C][C]2.2778[/C][C]-0.0277984[/C][/ROW]
[ROW][C]149[/C][C]2.25[/C][C]2.36904[/C][C]-0.119039[/C][/ROW]
[ROW][C]150[/C][C]0.75[/C][C]1.51166[/C][C]-0.761657[/C][/ROW]
[ROW][C]151[/C][C]2.25[/C][C]1.97459[/C][C]0.275411[/C][/ROW]
[ROW][C]152[/C][C]1.5[/C][C]2.12448[/C][C]-0.624479[/C][/ROW]
[ROW][C]153[/C][C]2.25[/C][C]1.88626[/C][C]0.363738[/C][/ROW]
[ROW][C]154[/C][C]1.5[/C][C]1.30419[/C][C]0.195811[/C][/ROW]
[ROW][C]155[/C][C]0.75[/C][C]1.87318[/C][C]-1.12318[/C][/ROW]
[ROW][C]156[/C][C]1.5[/C][C]1.90607[/C][C]-0.406065[/C][/ROW]
[ROW][C]157[/C][C]1.5[/C][C]1.83054[/C][C]-0.330538[/C][/ROW]
[ROW][C]158[/C][C]2.25[/C][C]2.11294[/C][C]0.137055[/C][/ROW]
[ROW][C]159[/C][C]1.5[/C][C]1.93922[/C][C]-0.439215[/C][/ROW]
[ROW][C]160[/C][C]1.5[/C][C]1.78266[/C][C]-0.282663[/C][/ROW]
[ROW][C]161[/C][C]3[/C][C]1.83505[/C][C]1.16495[/C][/ROW]
[ROW][C]162[/C][C]2.25[/C][C]2.00442[/C][C]0.245576[/C][/ROW]
[ROW][C]163[/C][C]1.5[/C][C]1.57457[/C][C]-0.0745747[/C][/ROW]
[ROW][C]164[/C][C]0.75[/C][C]1.77512[/C][C]-1.02512[/C][/ROW]
[ROW][C]165[/C][C]2.25[/C][C]1.624[/C][C]0.625998[/C][/ROW]
[ROW][C]166[/C][C]3[/C][C]2.26139[/C][C]0.738609[/C][/ROW]
[ROW][C]167[/C][C]3[/C][C]2.74121[/C][C]0.258787[/C][/ROW]
[ROW][C]168[/C][C]1.5[/C][C]2.1258[/C][C]-0.625798[/C][/ROW]
[ROW][C]169[/C][C]1.5[/C][C]2.04932[/C][C]-0.549318[/C][/ROW]
[ROW][C]170[/C][C]2.25[/C][C]2.48346[/C][C]-0.233456[/C][/ROW]
[ROW][C]171[/C][C]0.75[/C][C]1.42539[/C][C]-0.675389[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267993&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267993&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
10.751.47534-0.725341
21.51.9114-0.411403
332.35680.6432
42.252.40235-0.152354
532.946630.0533685
61.51.5198-0.0197973
733.52588-0.525883
832.740830.259168
933.63216-0.632157
100.751.45657-0.706569
1132.803440.196562
122.251.484870.765131
131.52.21644-0.716441
141.52.0584-0.558399
152.252.35945-0.109449
1632.796810.203195
1732.513010.486991
181.51.82901-0.329013
192.251.79730.452704
202.253.07727-0.827272
211.52.22229-0.722295
222.252.27274-0.0227425
231.51.8031-0.303096
242.252.208980.0410223
252.252.115570.13443
2631.642281.35772
2732.926820.0731798
2832.902940.0970604
291.51.75582-0.255824
3032.030020.969976
3132.439290.56071
322.252.49641-0.24641
331.52.0905-0.590503
342.252.61678-0.366784
352.252.60033-0.350326
3632.068070.931925
370.751.91966-1.16966
382.252.158720.0912828
3932.569980.43002
4032.560660.439337
411.52.44657-0.946567
422.252.46004-0.210042
4332.485410.514589
442.252.74541-0.495406
451.51.79101-0.291015
462.252.239290.0107061
472.252.9149-0.664898
481.52.25711-0.757115
492.252.82756-0.577558
501.51.75075-0.250752
512.252.34438-0.0943843
5232.510890.489113
5332.420840.579163
5432.386530.613475
5532.676020.323984
561.51.304190.195811
572.252.62214-0.372142
581.51.51861-0.0186117
592.252.32296-0.0729617
602.252.25496-0.00495819
612.252.33543-0.0854279
6232.208870.791128
631.52.2477-0.747697
642.252.59825-0.348254
652.252.59825-0.348254
6632.258070.741935
672.252.30146-0.0514595
6832.631160.368845
692.252.2687-0.0186991
701.51.65983-0.159826
7132.434730.56527
721.52.03315-0.533154
7332.824060.175945
7432.272330.727667
7531.997141.00286
7632.28630.713698
772.251.844480.405516
782.251.983660.266341
790.751.43351-0.683508
8031.8831.117
810.751.67007-0.920066
821.52.05005-0.550048
831.51.61494-0.114937
8432.094820.905176
851.51.83281-0.332806
862.252.170250.0797511
8732.761350.238652
8832.016960.983043
891.51.54055-0.0405516
9032.079110.920891
9132.977090.0229058
921.51.74353-0.243527
931.52.16226-0.662264
942.252.3266-0.0766027
951.51.70542-0.205424
961.51.60003-0.100026
972.251.92880.321198
981.51.57979-0.0797859
992.251.64280.607202
10032.823720.176275
10132.198960.801045
1020.751.53375-0.783747
1031.51.97555-0.475552
1041.52.47219-0.972188
1052.252.067270.182732
1062.252.89186-0.641859
1071.52.04927-0.549266
1082.252.060240.18976
1090.752.1034-1.3534
1102.252.42823-0.178226
1110.751.75237-1.00237
1122.252.32052-0.0705181
11332.467570.532427
1140.751.78214-1.03214
1150.751.61536-0.865365
11631.751781.24822
11732.68430.315697
11831.666281.33372
11932.031950.96805
1201.51.75907-0.259072
12132.661810.338187
12232.437460.562541
12332.113030.886966
12432.826090.173912
1251.51.77696-0.276963
1262.252.135240.114759
1270.751.73205-0.982051
1280.751.5501-0.800096
1292.251.429550.820453
13032.519060.48094
1312.252.037660.212339
13231.737781.26222
1332.252.36813-0.118127
13432.048190.951806
1351.51.83054-0.330538
13632.676020.323984
13731.884881.11512
1380.751.57943-0.82943
1391.52.44433-0.944334
14033.3144-0.3144
14131.783791.21621
14232.546010.453994
1432.252.144210.105787
1442.252.010630.239371
14532.261710.738288
1461.52.30125-0.801248
1472.252.189960.0600418
1482.252.2778-0.0277984
1492.252.36904-0.119039
1500.751.51166-0.761657
1512.251.974590.275411
1521.52.12448-0.624479
1532.251.886260.363738
1541.51.304190.195811
1550.751.87318-1.12318
1561.51.90607-0.406065
1571.51.83054-0.330538
1582.252.112940.137055
1591.51.93922-0.439215
1601.51.78266-0.282663
16131.835051.16495
1622.252.004420.245576
1631.51.57457-0.0745747
1640.751.77512-1.02512
1652.251.6240.625998
16632.261390.738609
16732.741210.258787
1681.52.1258-0.625798
1691.52.04932-0.549318
1702.252.48346-0.233456
1710.751.42539-0.675389






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.3783220.7566430.621678
100.4250130.8500260.574987
110.3290240.6580480.670976
120.5849310.8301380.415069
130.5383910.9232180.461609
140.5373130.9253750.462687
150.4382370.8764740.561763
160.4254590.8509190.574541
170.3380770.6761540.661923
180.2849250.569850.715075
190.2559480.5118960.744052
200.3256640.6513290.674336
210.2885260.5770510.711474
220.2248150.4496290.775185
230.1770240.3540490.822976
240.136380.272760.86362
250.1066750.2133510.893325
260.3058510.6117020.694149
270.2588220.5176440.741178
280.2078060.4156130.792194
290.1644280.3288550.835572
300.3496960.6993930.650304
310.3285980.6571950.671402
320.2785810.5571620.721419
330.2902250.580450.709775
340.2446160.4892320.755384
350.2088850.417770.791115
360.2542210.5084410.745779
370.4174980.8349960.582502
380.3639270.7278540.636073
390.3341890.6683780.665811
400.315080.6301610.68492
410.3700310.7400620.629969
420.3229780.6459570.677022
430.3160180.6320360.683982
440.2871670.5743340.712833
450.2553010.5106010.744699
460.2172760.4345520.782724
470.2106120.4212240.789388
480.2307840.4615680.769216
490.2147090.4294170.785291
500.2058750.4117510.794125
510.1725110.3450220.827489
520.1718320.3436640.828168
530.1670910.3341820.832909
540.2090140.4180280.790986
550.1885320.3770640.811468
560.1585810.3171610.841419
570.1390310.2780620.860969
580.1138940.2277880.886106
590.09227120.1845420.907729
600.07393830.1478770.926062
610.05862950.1172590.941371
620.07217530.1443510.927825
630.08093020.161860.91907
640.06771860.1354370.932281
650.05642080.1128420.943579
660.06648610.1329720.933514
670.05270480.105410.947295
680.04750430.09500860.952496
690.03716550.0743310.962835
700.02951440.05902880.970486
710.02846960.05693920.97153
720.02707620.05415240.972924
730.02169210.04338420.978308
740.02500980.05001970.97499
750.04091580.08183160.959084
760.04458040.08916080.95542
770.03874270.07748550.961257
780.03142550.06285110.968574
790.03396980.06793970.96603
800.05805010.11610.94195
810.07981920.1596380.920181
820.07864330.1572870.921357
830.06414290.1282860.935857
840.0829930.1659860.917007
850.07169580.1433920.928304
860.05767140.1153430.942329
870.04747440.09494880.952526
880.06558490.131170.934415
890.05246660.1049330.947533
900.0692230.1384460.930777
910.05694990.11390.94305
920.04708570.09417150.952914
930.04922020.09844030.95078
940.03901090.07802170.960989
950.03163510.06327030.968365
960.02461990.04923980.97538
970.0205810.0411620.979419
980.01579470.03158950.984205
990.01560550.03121110.984394
1000.01187390.02374780.988126
1010.01419010.02838020.98581
1020.01649560.03299130.983504
1030.01554330.03108670.984457
1040.02304550.0460910.976954
1050.01780850.03561710.982191
1060.01932020.03864040.98068
1070.01820310.03640610.981797
1080.01403980.02807970.98596
1090.04583860.09167710.954161
1100.04074610.08149220.959254
1110.05454770.1090950.945452
1120.0437210.08744210.956279
1130.03856980.07713960.96143
1140.05467390.1093480.945326
1150.07892790.1578560.921072
1160.1282750.2565490.871725
1170.1076870.2153740.892313
1180.2288960.4577930.771104
1190.2650560.5301120.734944
1200.2384980.4769960.761502
1210.2101960.4203930.789804
1220.1941350.388270.805865
1230.2091310.4182620.790869
1240.1758630.3517270.824137
1250.1488070.2976150.851193
1260.1214910.2429820.878509
1270.1536150.3072290.846385
1280.163490.3269790.83651
1290.2020440.4040880.797956
1300.176760.3535210.82324
1310.1506540.3013070.849346
1320.2811180.5622370.718882
1330.2386050.4772110.761395
1340.3455620.6911250.654438
1350.3071490.6142970.692851
1360.2628510.5257020.737149
1370.370070.7401390.62993
1380.4205140.8410280.579486
1390.53010.93980.4699
1400.4770950.954190.522905
1410.5947790.8104420.405221
1420.5736550.852690.426345
1430.5261160.9477670.473884
1440.5162410.9675190.483759
1450.5633830.8732330.436617
1460.5896510.8206990.410349
1470.5212520.9574960.478748
1480.461430.9228610.53857
1490.4609590.9219180.539041
1500.4224790.8449580.577521
1510.3639510.7279010.636049
1520.4429590.8859180.557041
1530.3725420.7450840.627458
1540.2964430.5928860.703557
1550.4035730.8071470.596427
1560.5673110.8653780.432689
1570.6668810.6662390.333119
1580.5690750.8618510.430925
1590.4521940.9043890.547806
1600.3488510.6977030.651149
1610.2646230.5292450.735377
1620.4222190.8444370.577781

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.378322 & 0.756643 & 0.621678 \tabularnewline
10 & 0.425013 & 0.850026 & 0.574987 \tabularnewline
11 & 0.329024 & 0.658048 & 0.670976 \tabularnewline
12 & 0.584931 & 0.830138 & 0.415069 \tabularnewline
13 & 0.538391 & 0.923218 & 0.461609 \tabularnewline
14 & 0.537313 & 0.925375 & 0.462687 \tabularnewline
15 & 0.438237 & 0.876474 & 0.561763 \tabularnewline
16 & 0.425459 & 0.850919 & 0.574541 \tabularnewline
17 & 0.338077 & 0.676154 & 0.661923 \tabularnewline
18 & 0.284925 & 0.56985 & 0.715075 \tabularnewline
19 & 0.255948 & 0.511896 & 0.744052 \tabularnewline
20 & 0.325664 & 0.651329 & 0.674336 \tabularnewline
21 & 0.288526 & 0.577051 & 0.711474 \tabularnewline
22 & 0.224815 & 0.449629 & 0.775185 \tabularnewline
23 & 0.177024 & 0.354049 & 0.822976 \tabularnewline
24 & 0.13638 & 0.27276 & 0.86362 \tabularnewline
25 & 0.106675 & 0.213351 & 0.893325 \tabularnewline
26 & 0.305851 & 0.611702 & 0.694149 \tabularnewline
27 & 0.258822 & 0.517644 & 0.741178 \tabularnewline
28 & 0.207806 & 0.415613 & 0.792194 \tabularnewline
29 & 0.164428 & 0.328855 & 0.835572 \tabularnewline
30 & 0.349696 & 0.699393 & 0.650304 \tabularnewline
31 & 0.328598 & 0.657195 & 0.671402 \tabularnewline
32 & 0.278581 & 0.557162 & 0.721419 \tabularnewline
33 & 0.290225 & 0.58045 & 0.709775 \tabularnewline
34 & 0.244616 & 0.489232 & 0.755384 \tabularnewline
35 & 0.208885 & 0.41777 & 0.791115 \tabularnewline
36 & 0.254221 & 0.508441 & 0.745779 \tabularnewline
37 & 0.417498 & 0.834996 & 0.582502 \tabularnewline
38 & 0.363927 & 0.727854 & 0.636073 \tabularnewline
39 & 0.334189 & 0.668378 & 0.665811 \tabularnewline
40 & 0.31508 & 0.630161 & 0.68492 \tabularnewline
41 & 0.370031 & 0.740062 & 0.629969 \tabularnewline
42 & 0.322978 & 0.645957 & 0.677022 \tabularnewline
43 & 0.316018 & 0.632036 & 0.683982 \tabularnewline
44 & 0.287167 & 0.574334 & 0.712833 \tabularnewline
45 & 0.255301 & 0.510601 & 0.744699 \tabularnewline
46 & 0.217276 & 0.434552 & 0.782724 \tabularnewline
47 & 0.210612 & 0.421224 & 0.789388 \tabularnewline
48 & 0.230784 & 0.461568 & 0.769216 \tabularnewline
49 & 0.214709 & 0.429417 & 0.785291 \tabularnewline
50 & 0.205875 & 0.411751 & 0.794125 \tabularnewline
51 & 0.172511 & 0.345022 & 0.827489 \tabularnewline
52 & 0.171832 & 0.343664 & 0.828168 \tabularnewline
53 & 0.167091 & 0.334182 & 0.832909 \tabularnewline
54 & 0.209014 & 0.418028 & 0.790986 \tabularnewline
55 & 0.188532 & 0.377064 & 0.811468 \tabularnewline
56 & 0.158581 & 0.317161 & 0.841419 \tabularnewline
57 & 0.139031 & 0.278062 & 0.860969 \tabularnewline
58 & 0.113894 & 0.227788 & 0.886106 \tabularnewline
59 & 0.0922712 & 0.184542 & 0.907729 \tabularnewline
60 & 0.0739383 & 0.147877 & 0.926062 \tabularnewline
61 & 0.0586295 & 0.117259 & 0.941371 \tabularnewline
62 & 0.0721753 & 0.144351 & 0.927825 \tabularnewline
63 & 0.0809302 & 0.16186 & 0.91907 \tabularnewline
64 & 0.0677186 & 0.135437 & 0.932281 \tabularnewline
65 & 0.0564208 & 0.112842 & 0.943579 \tabularnewline
66 & 0.0664861 & 0.132972 & 0.933514 \tabularnewline
67 & 0.0527048 & 0.10541 & 0.947295 \tabularnewline
68 & 0.0475043 & 0.0950086 & 0.952496 \tabularnewline
69 & 0.0371655 & 0.074331 & 0.962835 \tabularnewline
70 & 0.0295144 & 0.0590288 & 0.970486 \tabularnewline
71 & 0.0284696 & 0.0569392 & 0.97153 \tabularnewline
72 & 0.0270762 & 0.0541524 & 0.972924 \tabularnewline
73 & 0.0216921 & 0.0433842 & 0.978308 \tabularnewline
74 & 0.0250098 & 0.0500197 & 0.97499 \tabularnewline
75 & 0.0409158 & 0.0818316 & 0.959084 \tabularnewline
76 & 0.0445804 & 0.0891608 & 0.95542 \tabularnewline
77 & 0.0387427 & 0.0774855 & 0.961257 \tabularnewline
78 & 0.0314255 & 0.0628511 & 0.968574 \tabularnewline
79 & 0.0339698 & 0.0679397 & 0.96603 \tabularnewline
80 & 0.0580501 & 0.1161 & 0.94195 \tabularnewline
81 & 0.0798192 & 0.159638 & 0.920181 \tabularnewline
82 & 0.0786433 & 0.157287 & 0.921357 \tabularnewline
83 & 0.0641429 & 0.128286 & 0.935857 \tabularnewline
84 & 0.082993 & 0.165986 & 0.917007 \tabularnewline
85 & 0.0716958 & 0.143392 & 0.928304 \tabularnewline
86 & 0.0576714 & 0.115343 & 0.942329 \tabularnewline
87 & 0.0474744 & 0.0949488 & 0.952526 \tabularnewline
88 & 0.0655849 & 0.13117 & 0.934415 \tabularnewline
89 & 0.0524666 & 0.104933 & 0.947533 \tabularnewline
90 & 0.069223 & 0.138446 & 0.930777 \tabularnewline
91 & 0.0569499 & 0.1139 & 0.94305 \tabularnewline
92 & 0.0470857 & 0.0941715 & 0.952914 \tabularnewline
93 & 0.0492202 & 0.0984403 & 0.95078 \tabularnewline
94 & 0.0390109 & 0.0780217 & 0.960989 \tabularnewline
95 & 0.0316351 & 0.0632703 & 0.968365 \tabularnewline
96 & 0.0246199 & 0.0492398 & 0.97538 \tabularnewline
97 & 0.020581 & 0.041162 & 0.979419 \tabularnewline
98 & 0.0157947 & 0.0315895 & 0.984205 \tabularnewline
99 & 0.0156055 & 0.0312111 & 0.984394 \tabularnewline
100 & 0.0118739 & 0.0237478 & 0.988126 \tabularnewline
101 & 0.0141901 & 0.0283802 & 0.98581 \tabularnewline
102 & 0.0164956 & 0.0329913 & 0.983504 \tabularnewline
103 & 0.0155433 & 0.0310867 & 0.984457 \tabularnewline
104 & 0.0230455 & 0.046091 & 0.976954 \tabularnewline
105 & 0.0178085 & 0.0356171 & 0.982191 \tabularnewline
106 & 0.0193202 & 0.0386404 & 0.98068 \tabularnewline
107 & 0.0182031 & 0.0364061 & 0.981797 \tabularnewline
108 & 0.0140398 & 0.0280797 & 0.98596 \tabularnewline
109 & 0.0458386 & 0.0916771 & 0.954161 \tabularnewline
110 & 0.0407461 & 0.0814922 & 0.959254 \tabularnewline
111 & 0.0545477 & 0.109095 & 0.945452 \tabularnewline
112 & 0.043721 & 0.0874421 & 0.956279 \tabularnewline
113 & 0.0385698 & 0.0771396 & 0.96143 \tabularnewline
114 & 0.0546739 & 0.109348 & 0.945326 \tabularnewline
115 & 0.0789279 & 0.157856 & 0.921072 \tabularnewline
116 & 0.128275 & 0.256549 & 0.871725 \tabularnewline
117 & 0.107687 & 0.215374 & 0.892313 \tabularnewline
118 & 0.228896 & 0.457793 & 0.771104 \tabularnewline
119 & 0.265056 & 0.530112 & 0.734944 \tabularnewline
120 & 0.238498 & 0.476996 & 0.761502 \tabularnewline
121 & 0.210196 & 0.420393 & 0.789804 \tabularnewline
122 & 0.194135 & 0.38827 & 0.805865 \tabularnewline
123 & 0.209131 & 0.418262 & 0.790869 \tabularnewline
124 & 0.175863 & 0.351727 & 0.824137 \tabularnewline
125 & 0.148807 & 0.297615 & 0.851193 \tabularnewline
126 & 0.121491 & 0.242982 & 0.878509 \tabularnewline
127 & 0.153615 & 0.307229 & 0.846385 \tabularnewline
128 & 0.16349 & 0.326979 & 0.83651 \tabularnewline
129 & 0.202044 & 0.404088 & 0.797956 \tabularnewline
130 & 0.17676 & 0.353521 & 0.82324 \tabularnewline
131 & 0.150654 & 0.301307 & 0.849346 \tabularnewline
132 & 0.281118 & 0.562237 & 0.718882 \tabularnewline
133 & 0.238605 & 0.477211 & 0.761395 \tabularnewline
134 & 0.345562 & 0.691125 & 0.654438 \tabularnewline
135 & 0.307149 & 0.614297 & 0.692851 \tabularnewline
136 & 0.262851 & 0.525702 & 0.737149 \tabularnewline
137 & 0.37007 & 0.740139 & 0.62993 \tabularnewline
138 & 0.420514 & 0.841028 & 0.579486 \tabularnewline
139 & 0.5301 & 0.9398 & 0.4699 \tabularnewline
140 & 0.477095 & 0.95419 & 0.522905 \tabularnewline
141 & 0.594779 & 0.810442 & 0.405221 \tabularnewline
142 & 0.573655 & 0.85269 & 0.426345 \tabularnewline
143 & 0.526116 & 0.947767 & 0.473884 \tabularnewline
144 & 0.516241 & 0.967519 & 0.483759 \tabularnewline
145 & 0.563383 & 0.873233 & 0.436617 \tabularnewline
146 & 0.589651 & 0.820699 & 0.410349 \tabularnewline
147 & 0.521252 & 0.957496 & 0.478748 \tabularnewline
148 & 0.46143 & 0.922861 & 0.53857 \tabularnewline
149 & 0.460959 & 0.921918 & 0.539041 \tabularnewline
150 & 0.422479 & 0.844958 & 0.577521 \tabularnewline
151 & 0.363951 & 0.727901 & 0.636049 \tabularnewline
152 & 0.442959 & 0.885918 & 0.557041 \tabularnewline
153 & 0.372542 & 0.745084 & 0.627458 \tabularnewline
154 & 0.296443 & 0.592886 & 0.703557 \tabularnewline
155 & 0.403573 & 0.807147 & 0.596427 \tabularnewline
156 & 0.567311 & 0.865378 & 0.432689 \tabularnewline
157 & 0.666881 & 0.666239 & 0.333119 \tabularnewline
158 & 0.569075 & 0.861851 & 0.430925 \tabularnewline
159 & 0.452194 & 0.904389 & 0.547806 \tabularnewline
160 & 0.348851 & 0.697703 & 0.651149 \tabularnewline
161 & 0.264623 & 0.529245 & 0.735377 \tabularnewline
162 & 0.422219 & 0.844437 & 0.577781 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267993&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]0.378322[/C][C]0.756643[/C][C]0.621678[/C][/ROW]
[ROW][C]10[/C][C]0.425013[/C][C]0.850026[/C][C]0.574987[/C][/ROW]
[ROW][C]11[/C][C]0.329024[/C][C]0.658048[/C][C]0.670976[/C][/ROW]
[ROW][C]12[/C][C]0.584931[/C][C]0.830138[/C][C]0.415069[/C][/ROW]
[ROW][C]13[/C][C]0.538391[/C][C]0.923218[/C][C]0.461609[/C][/ROW]
[ROW][C]14[/C][C]0.537313[/C][C]0.925375[/C][C]0.462687[/C][/ROW]
[ROW][C]15[/C][C]0.438237[/C][C]0.876474[/C][C]0.561763[/C][/ROW]
[ROW][C]16[/C][C]0.425459[/C][C]0.850919[/C][C]0.574541[/C][/ROW]
[ROW][C]17[/C][C]0.338077[/C][C]0.676154[/C][C]0.661923[/C][/ROW]
[ROW][C]18[/C][C]0.284925[/C][C]0.56985[/C][C]0.715075[/C][/ROW]
[ROW][C]19[/C][C]0.255948[/C][C]0.511896[/C][C]0.744052[/C][/ROW]
[ROW][C]20[/C][C]0.325664[/C][C]0.651329[/C][C]0.674336[/C][/ROW]
[ROW][C]21[/C][C]0.288526[/C][C]0.577051[/C][C]0.711474[/C][/ROW]
[ROW][C]22[/C][C]0.224815[/C][C]0.449629[/C][C]0.775185[/C][/ROW]
[ROW][C]23[/C][C]0.177024[/C][C]0.354049[/C][C]0.822976[/C][/ROW]
[ROW][C]24[/C][C]0.13638[/C][C]0.27276[/C][C]0.86362[/C][/ROW]
[ROW][C]25[/C][C]0.106675[/C][C]0.213351[/C][C]0.893325[/C][/ROW]
[ROW][C]26[/C][C]0.305851[/C][C]0.611702[/C][C]0.694149[/C][/ROW]
[ROW][C]27[/C][C]0.258822[/C][C]0.517644[/C][C]0.741178[/C][/ROW]
[ROW][C]28[/C][C]0.207806[/C][C]0.415613[/C][C]0.792194[/C][/ROW]
[ROW][C]29[/C][C]0.164428[/C][C]0.328855[/C][C]0.835572[/C][/ROW]
[ROW][C]30[/C][C]0.349696[/C][C]0.699393[/C][C]0.650304[/C][/ROW]
[ROW][C]31[/C][C]0.328598[/C][C]0.657195[/C][C]0.671402[/C][/ROW]
[ROW][C]32[/C][C]0.278581[/C][C]0.557162[/C][C]0.721419[/C][/ROW]
[ROW][C]33[/C][C]0.290225[/C][C]0.58045[/C][C]0.709775[/C][/ROW]
[ROW][C]34[/C][C]0.244616[/C][C]0.489232[/C][C]0.755384[/C][/ROW]
[ROW][C]35[/C][C]0.208885[/C][C]0.41777[/C][C]0.791115[/C][/ROW]
[ROW][C]36[/C][C]0.254221[/C][C]0.508441[/C][C]0.745779[/C][/ROW]
[ROW][C]37[/C][C]0.417498[/C][C]0.834996[/C][C]0.582502[/C][/ROW]
[ROW][C]38[/C][C]0.363927[/C][C]0.727854[/C][C]0.636073[/C][/ROW]
[ROW][C]39[/C][C]0.334189[/C][C]0.668378[/C][C]0.665811[/C][/ROW]
[ROW][C]40[/C][C]0.31508[/C][C]0.630161[/C][C]0.68492[/C][/ROW]
[ROW][C]41[/C][C]0.370031[/C][C]0.740062[/C][C]0.629969[/C][/ROW]
[ROW][C]42[/C][C]0.322978[/C][C]0.645957[/C][C]0.677022[/C][/ROW]
[ROW][C]43[/C][C]0.316018[/C][C]0.632036[/C][C]0.683982[/C][/ROW]
[ROW][C]44[/C][C]0.287167[/C][C]0.574334[/C][C]0.712833[/C][/ROW]
[ROW][C]45[/C][C]0.255301[/C][C]0.510601[/C][C]0.744699[/C][/ROW]
[ROW][C]46[/C][C]0.217276[/C][C]0.434552[/C][C]0.782724[/C][/ROW]
[ROW][C]47[/C][C]0.210612[/C][C]0.421224[/C][C]0.789388[/C][/ROW]
[ROW][C]48[/C][C]0.230784[/C][C]0.461568[/C][C]0.769216[/C][/ROW]
[ROW][C]49[/C][C]0.214709[/C][C]0.429417[/C][C]0.785291[/C][/ROW]
[ROW][C]50[/C][C]0.205875[/C][C]0.411751[/C][C]0.794125[/C][/ROW]
[ROW][C]51[/C][C]0.172511[/C][C]0.345022[/C][C]0.827489[/C][/ROW]
[ROW][C]52[/C][C]0.171832[/C][C]0.343664[/C][C]0.828168[/C][/ROW]
[ROW][C]53[/C][C]0.167091[/C][C]0.334182[/C][C]0.832909[/C][/ROW]
[ROW][C]54[/C][C]0.209014[/C][C]0.418028[/C][C]0.790986[/C][/ROW]
[ROW][C]55[/C][C]0.188532[/C][C]0.377064[/C][C]0.811468[/C][/ROW]
[ROW][C]56[/C][C]0.158581[/C][C]0.317161[/C][C]0.841419[/C][/ROW]
[ROW][C]57[/C][C]0.139031[/C][C]0.278062[/C][C]0.860969[/C][/ROW]
[ROW][C]58[/C][C]0.113894[/C][C]0.227788[/C][C]0.886106[/C][/ROW]
[ROW][C]59[/C][C]0.0922712[/C][C]0.184542[/C][C]0.907729[/C][/ROW]
[ROW][C]60[/C][C]0.0739383[/C][C]0.147877[/C][C]0.926062[/C][/ROW]
[ROW][C]61[/C][C]0.0586295[/C][C]0.117259[/C][C]0.941371[/C][/ROW]
[ROW][C]62[/C][C]0.0721753[/C][C]0.144351[/C][C]0.927825[/C][/ROW]
[ROW][C]63[/C][C]0.0809302[/C][C]0.16186[/C][C]0.91907[/C][/ROW]
[ROW][C]64[/C][C]0.0677186[/C][C]0.135437[/C][C]0.932281[/C][/ROW]
[ROW][C]65[/C][C]0.0564208[/C][C]0.112842[/C][C]0.943579[/C][/ROW]
[ROW][C]66[/C][C]0.0664861[/C][C]0.132972[/C][C]0.933514[/C][/ROW]
[ROW][C]67[/C][C]0.0527048[/C][C]0.10541[/C][C]0.947295[/C][/ROW]
[ROW][C]68[/C][C]0.0475043[/C][C]0.0950086[/C][C]0.952496[/C][/ROW]
[ROW][C]69[/C][C]0.0371655[/C][C]0.074331[/C][C]0.962835[/C][/ROW]
[ROW][C]70[/C][C]0.0295144[/C][C]0.0590288[/C][C]0.970486[/C][/ROW]
[ROW][C]71[/C][C]0.0284696[/C][C]0.0569392[/C][C]0.97153[/C][/ROW]
[ROW][C]72[/C][C]0.0270762[/C][C]0.0541524[/C][C]0.972924[/C][/ROW]
[ROW][C]73[/C][C]0.0216921[/C][C]0.0433842[/C][C]0.978308[/C][/ROW]
[ROW][C]74[/C][C]0.0250098[/C][C]0.0500197[/C][C]0.97499[/C][/ROW]
[ROW][C]75[/C][C]0.0409158[/C][C]0.0818316[/C][C]0.959084[/C][/ROW]
[ROW][C]76[/C][C]0.0445804[/C][C]0.0891608[/C][C]0.95542[/C][/ROW]
[ROW][C]77[/C][C]0.0387427[/C][C]0.0774855[/C][C]0.961257[/C][/ROW]
[ROW][C]78[/C][C]0.0314255[/C][C]0.0628511[/C][C]0.968574[/C][/ROW]
[ROW][C]79[/C][C]0.0339698[/C][C]0.0679397[/C][C]0.96603[/C][/ROW]
[ROW][C]80[/C][C]0.0580501[/C][C]0.1161[/C][C]0.94195[/C][/ROW]
[ROW][C]81[/C][C]0.0798192[/C][C]0.159638[/C][C]0.920181[/C][/ROW]
[ROW][C]82[/C][C]0.0786433[/C][C]0.157287[/C][C]0.921357[/C][/ROW]
[ROW][C]83[/C][C]0.0641429[/C][C]0.128286[/C][C]0.935857[/C][/ROW]
[ROW][C]84[/C][C]0.082993[/C][C]0.165986[/C][C]0.917007[/C][/ROW]
[ROW][C]85[/C][C]0.0716958[/C][C]0.143392[/C][C]0.928304[/C][/ROW]
[ROW][C]86[/C][C]0.0576714[/C][C]0.115343[/C][C]0.942329[/C][/ROW]
[ROW][C]87[/C][C]0.0474744[/C][C]0.0949488[/C][C]0.952526[/C][/ROW]
[ROW][C]88[/C][C]0.0655849[/C][C]0.13117[/C][C]0.934415[/C][/ROW]
[ROW][C]89[/C][C]0.0524666[/C][C]0.104933[/C][C]0.947533[/C][/ROW]
[ROW][C]90[/C][C]0.069223[/C][C]0.138446[/C][C]0.930777[/C][/ROW]
[ROW][C]91[/C][C]0.0569499[/C][C]0.1139[/C][C]0.94305[/C][/ROW]
[ROW][C]92[/C][C]0.0470857[/C][C]0.0941715[/C][C]0.952914[/C][/ROW]
[ROW][C]93[/C][C]0.0492202[/C][C]0.0984403[/C][C]0.95078[/C][/ROW]
[ROW][C]94[/C][C]0.0390109[/C][C]0.0780217[/C][C]0.960989[/C][/ROW]
[ROW][C]95[/C][C]0.0316351[/C][C]0.0632703[/C][C]0.968365[/C][/ROW]
[ROW][C]96[/C][C]0.0246199[/C][C]0.0492398[/C][C]0.97538[/C][/ROW]
[ROW][C]97[/C][C]0.020581[/C][C]0.041162[/C][C]0.979419[/C][/ROW]
[ROW][C]98[/C][C]0.0157947[/C][C]0.0315895[/C][C]0.984205[/C][/ROW]
[ROW][C]99[/C][C]0.0156055[/C][C]0.0312111[/C][C]0.984394[/C][/ROW]
[ROW][C]100[/C][C]0.0118739[/C][C]0.0237478[/C][C]0.988126[/C][/ROW]
[ROW][C]101[/C][C]0.0141901[/C][C]0.0283802[/C][C]0.98581[/C][/ROW]
[ROW][C]102[/C][C]0.0164956[/C][C]0.0329913[/C][C]0.983504[/C][/ROW]
[ROW][C]103[/C][C]0.0155433[/C][C]0.0310867[/C][C]0.984457[/C][/ROW]
[ROW][C]104[/C][C]0.0230455[/C][C]0.046091[/C][C]0.976954[/C][/ROW]
[ROW][C]105[/C][C]0.0178085[/C][C]0.0356171[/C][C]0.982191[/C][/ROW]
[ROW][C]106[/C][C]0.0193202[/C][C]0.0386404[/C][C]0.98068[/C][/ROW]
[ROW][C]107[/C][C]0.0182031[/C][C]0.0364061[/C][C]0.981797[/C][/ROW]
[ROW][C]108[/C][C]0.0140398[/C][C]0.0280797[/C][C]0.98596[/C][/ROW]
[ROW][C]109[/C][C]0.0458386[/C][C]0.0916771[/C][C]0.954161[/C][/ROW]
[ROW][C]110[/C][C]0.0407461[/C][C]0.0814922[/C][C]0.959254[/C][/ROW]
[ROW][C]111[/C][C]0.0545477[/C][C]0.109095[/C][C]0.945452[/C][/ROW]
[ROW][C]112[/C][C]0.043721[/C][C]0.0874421[/C][C]0.956279[/C][/ROW]
[ROW][C]113[/C][C]0.0385698[/C][C]0.0771396[/C][C]0.96143[/C][/ROW]
[ROW][C]114[/C][C]0.0546739[/C][C]0.109348[/C][C]0.945326[/C][/ROW]
[ROW][C]115[/C][C]0.0789279[/C][C]0.157856[/C][C]0.921072[/C][/ROW]
[ROW][C]116[/C][C]0.128275[/C][C]0.256549[/C][C]0.871725[/C][/ROW]
[ROW][C]117[/C][C]0.107687[/C][C]0.215374[/C][C]0.892313[/C][/ROW]
[ROW][C]118[/C][C]0.228896[/C][C]0.457793[/C][C]0.771104[/C][/ROW]
[ROW][C]119[/C][C]0.265056[/C][C]0.530112[/C][C]0.734944[/C][/ROW]
[ROW][C]120[/C][C]0.238498[/C][C]0.476996[/C][C]0.761502[/C][/ROW]
[ROW][C]121[/C][C]0.210196[/C][C]0.420393[/C][C]0.789804[/C][/ROW]
[ROW][C]122[/C][C]0.194135[/C][C]0.38827[/C][C]0.805865[/C][/ROW]
[ROW][C]123[/C][C]0.209131[/C][C]0.418262[/C][C]0.790869[/C][/ROW]
[ROW][C]124[/C][C]0.175863[/C][C]0.351727[/C][C]0.824137[/C][/ROW]
[ROW][C]125[/C][C]0.148807[/C][C]0.297615[/C][C]0.851193[/C][/ROW]
[ROW][C]126[/C][C]0.121491[/C][C]0.242982[/C][C]0.878509[/C][/ROW]
[ROW][C]127[/C][C]0.153615[/C][C]0.307229[/C][C]0.846385[/C][/ROW]
[ROW][C]128[/C][C]0.16349[/C][C]0.326979[/C][C]0.83651[/C][/ROW]
[ROW][C]129[/C][C]0.202044[/C][C]0.404088[/C][C]0.797956[/C][/ROW]
[ROW][C]130[/C][C]0.17676[/C][C]0.353521[/C][C]0.82324[/C][/ROW]
[ROW][C]131[/C][C]0.150654[/C][C]0.301307[/C][C]0.849346[/C][/ROW]
[ROW][C]132[/C][C]0.281118[/C][C]0.562237[/C][C]0.718882[/C][/ROW]
[ROW][C]133[/C][C]0.238605[/C][C]0.477211[/C][C]0.761395[/C][/ROW]
[ROW][C]134[/C][C]0.345562[/C][C]0.691125[/C][C]0.654438[/C][/ROW]
[ROW][C]135[/C][C]0.307149[/C][C]0.614297[/C][C]0.692851[/C][/ROW]
[ROW][C]136[/C][C]0.262851[/C][C]0.525702[/C][C]0.737149[/C][/ROW]
[ROW][C]137[/C][C]0.37007[/C][C]0.740139[/C][C]0.62993[/C][/ROW]
[ROW][C]138[/C][C]0.420514[/C][C]0.841028[/C][C]0.579486[/C][/ROW]
[ROW][C]139[/C][C]0.5301[/C][C]0.9398[/C][C]0.4699[/C][/ROW]
[ROW][C]140[/C][C]0.477095[/C][C]0.95419[/C][C]0.522905[/C][/ROW]
[ROW][C]141[/C][C]0.594779[/C][C]0.810442[/C][C]0.405221[/C][/ROW]
[ROW][C]142[/C][C]0.573655[/C][C]0.85269[/C][C]0.426345[/C][/ROW]
[ROW][C]143[/C][C]0.526116[/C][C]0.947767[/C][C]0.473884[/C][/ROW]
[ROW][C]144[/C][C]0.516241[/C][C]0.967519[/C][C]0.483759[/C][/ROW]
[ROW][C]145[/C][C]0.563383[/C][C]0.873233[/C][C]0.436617[/C][/ROW]
[ROW][C]146[/C][C]0.589651[/C][C]0.820699[/C][C]0.410349[/C][/ROW]
[ROW][C]147[/C][C]0.521252[/C][C]0.957496[/C][C]0.478748[/C][/ROW]
[ROW][C]148[/C][C]0.46143[/C][C]0.922861[/C][C]0.53857[/C][/ROW]
[ROW][C]149[/C][C]0.460959[/C][C]0.921918[/C][C]0.539041[/C][/ROW]
[ROW][C]150[/C][C]0.422479[/C][C]0.844958[/C][C]0.577521[/C][/ROW]
[ROW][C]151[/C][C]0.363951[/C][C]0.727901[/C][C]0.636049[/C][/ROW]
[ROW][C]152[/C][C]0.442959[/C][C]0.885918[/C][C]0.557041[/C][/ROW]
[ROW][C]153[/C][C]0.372542[/C][C]0.745084[/C][C]0.627458[/C][/ROW]
[ROW][C]154[/C][C]0.296443[/C][C]0.592886[/C][C]0.703557[/C][/ROW]
[ROW][C]155[/C][C]0.403573[/C][C]0.807147[/C][C]0.596427[/C][/ROW]
[ROW][C]156[/C][C]0.567311[/C][C]0.865378[/C][C]0.432689[/C][/ROW]
[ROW][C]157[/C][C]0.666881[/C][C]0.666239[/C][C]0.333119[/C][/ROW]
[ROW][C]158[/C][C]0.569075[/C][C]0.861851[/C][C]0.430925[/C][/ROW]
[ROW][C]159[/C][C]0.452194[/C][C]0.904389[/C][C]0.547806[/C][/ROW]
[ROW][C]160[/C][C]0.348851[/C][C]0.697703[/C][C]0.651149[/C][/ROW]
[ROW][C]161[/C][C]0.264623[/C][C]0.529245[/C][C]0.735377[/C][/ROW]
[ROW][C]162[/C][C]0.422219[/C][C]0.844437[/C][C]0.577781[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267993&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267993&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
90.3783220.7566430.621678
100.4250130.8500260.574987
110.3290240.6580480.670976
120.5849310.8301380.415069
130.5383910.9232180.461609
140.5373130.9253750.462687
150.4382370.8764740.561763
160.4254590.8509190.574541
170.3380770.6761540.661923
180.2849250.569850.715075
190.2559480.5118960.744052
200.3256640.6513290.674336
210.2885260.5770510.711474
220.2248150.4496290.775185
230.1770240.3540490.822976
240.136380.272760.86362
250.1066750.2133510.893325
260.3058510.6117020.694149
270.2588220.5176440.741178
280.2078060.4156130.792194
290.1644280.3288550.835572
300.3496960.6993930.650304
310.3285980.6571950.671402
320.2785810.5571620.721419
330.2902250.580450.709775
340.2446160.4892320.755384
350.2088850.417770.791115
360.2542210.5084410.745779
370.4174980.8349960.582502
380.3639270.7278540.636073
390.3341890.6683780.665811
400.315080.6301610.68492
410.3700310.7400620.629969
420.3229780.6459570.677022
430.3160180.6320360.683982
440.2871670.5743340.712833
450.2553010.5106010.744699
460.2172760.4345520.782724
470.2106120.4212240.789388
480.2307840.4615680.769216
490.2147090.4294170.785291
500.2058750.4117510.794125
510.1725110.3450220.827489
520.1718320.3436640.828168
530.1670910.3341820.832909
540.2090140.4180280.790986
550.1885320.3770640.811468
560.1585810.3171610.841419
570.1390310.2780620.860969
580.1138940.2277880.886106
590.09227120.1845420.907729
600.07393830.1478770.926062
610.05862950.1172590.941371
620.07217530.1443510.927825
630.08093020.161860.91907
640.06771860.1354370.932281
650.05642080.1128420.943579
660.06648610.1329720.933514
670.05270480.105410.947295
680.04750430.09500860.952496
690.03716550.0743310.962835
700.02951440.05902880.970486
710.02846960.05693920.97153
720.02707620.05415240.972924
730.02169210.04338420.978308
740.02500980.05001970.97499
750.04091580.08183160.959084
760.04458040.08916080.95542
770.03874270.07748550.961257
780.03142550.06285110.968574
790.03396980.06793970.96603
800.05805010.11610.94195
810.07981920.1596380.920181
820.07864330.1572870.921357
830.06414290.1282860.935857
840.0829930.1659860.917007
850.07169580.1433920.928304
860.05767140.1153430.942329
870.04747440.09494880.952526
880.06558490.131170.934415
890.05246660.1049330.947533
900.0692230.1384460.930777
910.05694990.11390.94305
920.04708570.09417150.952914
930.04922020.09844030.95078
940.03901090.07802170.960989
950.03163510.06327030.968365
960.02461990.04923980.97538
970.0205810.0411620.979419
980.01579470.03158950.984205
990.01560550.03121110.984394
1000.01187390.02374780.988126
1010.01419010.02838020.98581
1020.01649560.03299130.983504
1030.01554330.03108670.984457
1040.02304550.0460910.976954
1050.01780850.03561710.982191
1060.01932020.03864040.98068
1070.01820310.03640610.981797
1080.01403980.02807970.98596
1090.04583860.09167710.954161
1100.04074610.08149220.959254
1110.05454770.1090950.945452
1120.0437210.08744210.956279
1130.03856980.07713960.96143
1140.05467390.1093480.945326
1150.07892790.1578560.921072
1160.1282750.2565490.871725
1170.1076870.2153740.892313
1180.2288960.4577930.771104
1190.2650560.5301120.734944
1200.2384980.4769960.761502
1210.2101960.4203930.789804
1220.1941350.388270.805865
1230.2091310.4182620.790869
1240.1758630.3517270.824137
1250.1488070.2976150.851193
1260.1214910.2429820.878509
1270.1536150.3072290.846385
1280.163490.3269790.83651
1290.2020440.4040880.797956
1300.176760.3535210.82324
1310.1506540.3013070.849346
1320.2811180.5622370.718882
1330.2386050.4772110.761395
1340.3455620.6911250.654438
1350.3071490.6142970.692851
1360.2628510.5257020.737149
1370.370070.7401390.62993
1380.4205140.8410280.579486
1390.53010.93980.4699
1400.4770950.954190.522905
1410.5947790.8104420.405221
1420.5736550.852690.426345
1430.5261160.9477670.473884
1440.5162410.9675190.483759
1450.5633830.8732330.436617
1460.5896510.8206990.410349
1470.5212520.9574960.478748
1480.461430.9228610.53857
1490.4609590.9219180.539041
1500.4224790.8449580.577521
1510.3639510.7279010.636049
1520.4429590.8859180.557041
1530.3725420.7450840.627458
1540.2964430.5928860.703557
1550.4035730.8071470.596427
1560.5673110.8653780.432689
1570.6668810.6662390.333119
1580.5690750.8618510.430925
1590.4521940.9043890.547806
1600.3488510.6977030.651149
1610.2646230.5292450.735377
1620.4222190.8444370.577781






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level140.0909091NOK
10% type I error level340.220779NOK

\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 & 14 & 0.0909091 & NOK \tabularnewline
10% type I error level & 34 & 0.220779 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267993&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]14[/C][C]0.0909091[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]34[/C][C]0.220779[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267993&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267993&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 level140.0909091NOK
10% type I error level340.220779NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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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):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '6'
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')
}