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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 10:18:12 +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/t1418638752qcsaqrf3gz4ur9n.htm/, Retrieved Thu, 16 May 2024 14:54:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268027, Retrieved Thu, 16 May 2024 14:54:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsMultiple Regression Paper score positieve waarden
Estimated Impact70

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 10:18:12] [99d5c1073827aabbadf7ab1e7da1d584] [Current]
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Dataset

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

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'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268027&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268027&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268027&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'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Pa[t] = + 2.04955 + 0.000621653LFM[t] -0.000505603B[t] -0.0109593PRH[t] -0.0079361CH[t] + 0.011406H[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Pa[t] =  +  2.04955 +  0.000621653LFM[t] -0.000505603B[t] -0.0109593PRH[t] -0.0079361CH[t] +  0.011406H[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268027&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Pa[t] =  +  2.04955 +  0.000621653LFM[t] -0.000505603B[t] -0.0109593PRH[t] -0.0079361CH[t] +  0.011406H[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268027&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268027&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
Pa[t] = + 2.04955 + 0.000621653LFM[t] -0.000505603B[t] -0.0109593PRH[t] -0.0079361CH[t] + 0.011406H[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.049550.054646937.511.14679e-825.73393e-83
LFM0.0006216530.0005125381.2130.2269050.113452
B-0.0005056030.000351719-1.4380.1524630.0762316
PRH-0.01095930.0202168-0.54210.588490.294245
CH-0.00793610.0200207-0.39640.6923270.346163
H0.0114060.02003160.56940.5698590.284929

\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) & 2.04955 & 0.0546469 & 37.51 & 1.14679e-82 & 5.73393e-83 \tabularnewline
LFM & 0.000621653 & 0.000512538 & 1.213 & 0.226905 & 0.113452 \tabularnewline
B & -0.000505603 & 0.000351719 & -1.438 & 0.152463 & 0.0762316 \tabularnewline
PRH & -0.0109593 & 0.0202168 & -0.5421 & 0.58849 & 0.294245 \tabularnewline
CH & -0.0079361 & 0.0200207 & -0.3964 & 0.692327 & 0.346163 \tabularnewline
H & 0.011406 & 0.0200316 & 0.5694 & 0.569859 & 0.284929 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268027&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]2.04955[/C][C]0.0546469[/C][C]37.51[/C][C]1.14679e-82[/C][C]5.73393e-83[/C][/ROW]
[ROW][C]LFM[/C][C]0.000621653[/C][C]0.000512538[/C][C]1.213[/C][C]0.226905[/C][C]0.113452[/C][/ROW]
[ROW][C]B[/C][C]-0.000505603[/C][C]0.000351719[/C][C]-1.438[/C][C]0.152463[/C][C]0.0762316[/C][/ROW]
[ROW][C]PRH[/C][C]-0.0109593[/C][C]0.0202168[/C][C]-0.5421[/C][C]0.58849[/C][C]0.294245[/C][/ROW]
[ROW][C]CH[/C][C]-0.0079361[/C][C]0.0200207[/C][C]-0.3964[/C][C]0.692327[/C][C]0.346163[/C][/ROW]
[ROW][C]H[/C][C]0.011406[/C][C]0.0200316[/C][C]0.5694[/C][C]0.569859[/C][C]0.284929[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268027&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268027&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)2.049550.054646937.511.14679e-825.73393e-83
LFM0.0006216530.0005125381.2130.2269050.113452
B-0.0005056030.000351719-1.4380.1524630.0762316
PRH-0.01095930.0202168-0.54210.588490.294245
CH-0.00793610.0200207-0.39640.6923270.346163
H0.0114060.02003160.56940.5698590.284929






Multiple Linear Regression - Regression Statistics
Multiple R0.286421
R-squared0.0820372
Adjusted R-squared0.0542201
F-TEST (value)2.94917
F-TEST (DF numerator)5
F-TEST (DF denominator)165
p-value0.0140926
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.215295
Sum Squared Residuals7.64808

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.286421 \tabularnewline
R-squared & 0.0820372 \tabularnewline
Adjusted R-squared & 0.0542201 \tabularnewline
F-TEST (value) & 2.94917 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 165 \tabularnewline
p-value & 0.0140926 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.215295 \tabularnewline
Sum Squared Residuals & 7.64808 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268027&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.286421[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0820372[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0542201[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.94917[/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]0.0140926[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.215295[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7.64808[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268027&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268027&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 R0.286421
R-squared0.0820372
Adjusted R-squared0.0542201
F-TEST (value)2.94917
F-TEST (DF numerator)5
F-TEST (DF denominator)165
p-value0.0140926
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.215295
Sum Squared Residuals7.64808






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12.12.099160.000835892
22.72.17010.529895
32.12.1939-0.0939039
42.12.19089-0.0908877
52.12.37561-0.275613
62.12.10367-0.0036702
72.12.20737-0.107372
82.12.32291-0.222908
92.12.50434-0.404341
102.12.09980.000197477
112.42.197090.202906
121.952.09561-0.145611
132.12.15685-0.0568477
142.12.14084-0.0408403
151.952.17383-0.223828
162.12.20607-0.106075
172.42.298180.101821
182.12.14668-0.0466793
192.252.153140.0968636
202.42.308050.0919463
212.252.173940.0760569
222.552.215660.334341
231.952.13723-0.187227
242.42.16070.239296
252.12.15708-0.0570786
262.12.093260.00674334
272.42.245070.154932
282.12.22846-0.12846
292.12.12661-0.0266104
302.252.167410.0825937
312.252.30859-0.0585921
322.42.162980.237022
332.12.14998-0.0499755
342.12.22472-0.124717
352.42.281380.118617
362.12.20272-0.10272
371.952.17023-0.220234
382.12.20647-0.106474
392.252.15290.0971047
402.252.176150.0738534
412.42.194180.20582
422.252.29577-0.0457694
432.252.191190.0588074
442.12.1795-0.079498
452.12.11954-0.0195393
462.12.20958-0.109579
472.72.32730.372696
482.12.17637-0.0763741
492.12.23612-0.13612
502.252.186340.063656
512.72.314640.385364
522.42.096940.303056
532.12.31463-0.214632
542.11.99260.107402
552.42.343990.0560124
561.952.14789-0.197892
572.72.248360.451636
582.12.13408-0.0340794
592.252.157130.0928652
602.12.21073-0.11073
612.72.183920.516083
622.12.24077-0.140772
632.12.2797-0.179701
641.652.13848-0.488478
651.652.13848-0.488478
662.12.23955-0.139551
672.12.24115-0.141148
682.12.18269-0.0826939
692.12.26658-0.166582
702.12.10202-0.00201794
712.42.303890.0961075
722.42.209630.190375
732.12.23377-0.133774
742.252.225670.0243258
752.42.208360.191636
762.12.23813-0.138128
772.12.15485-0.0548539
782.42.209780.190225
792.42.153320.246677
802.12.15485-0.0548526
812.12.14278-0.042776
822.42.261770.138234
832.12.1444-0.0443967
842.72.196440.503565
852.12.16328-0.0632757
862.12.15427-0.0542748
872.252.21910.0308984
882.12.18817-0.0881737
892.42.171760.228238
902.252.223040.02696
912.252.27469-0.0246886
922.12.13769-0.0376905
932.12.20521-0.105205
942.42.158970.241035
952.252.160540.0894601
962.12.15064-0.0506431
972.12.13996-0.0399592
981.652.12944-0.47944
991.652.15023-0.500231
1002.72.272510.427494
1012.12.24446-0.144458
1021.952.18412-0.234123
1032.252.202480.0475154
1042.42.235320.164683
1051.952.08349-0.13349
1062.12.25749-0.157491
1072.42.171470.228533
1082.12.20782-0.107823
1092.12.17074-0.0707374
1102.42.194180.20582
1112.42.179690.220307
1122.42.212930.187066
1132.252.161340.0886605
1142.42.120930.279072
1152.12.14442-0.0444225
1162.12.14417-0.0441662
1171.82.22262-0.422621
1182.72.124760.575235
1192.12.13551-0.0355119
1202.12.18363-0.0836333
1212.42.301420.0985849
1222.552.196230.353771
1232.552.196930.353074
1242.12.31488-0.214877
1252.12.16158-0.0615814
1262.12.20102-0.101022
1272.252.145870.10413
1282.252.112190.137815
1292.12.10242-0.00242473
1302.12.21039-0.110393
1311.952.17077-0.220774
1322.42.173270.226726
1332.12.19587-0.0958664
1342.42.145540.25446
1352.42.198660.201339
1362.42.343990.0560124
1372.252.180140.0698585
1381.952.15679-0.206788
1392.12.19658-0.0965751
1402.12.24024-0.140242
1412.552.189420.36058
1422.12.22869-0.128694
1432.12.14601-0.0460112
1442.12.14685-0.046854
1451.952.19734-0.247344
1462.252.190290.0597078
1472.42.24230.157703
1481.952.21682-0.266819
1492.12.23766-0.137662
1502.12.16243-0.0624257
1511.952.12105-0.171054
1522.12.27009-0.170093
1532.12.22763-0.127627
1541.952.14789-0.197892
1552.12.18457-0.0845684
1561.952.18745-0.237453
1572.42.198660.201339
1582.42.182540.217464
1592.42.150250.249746
1601.952.12405-0.174054
1612.72.210180.489815
1622.12.21037-0.110371
1631.952.17082-0.220817
1642.12.15996-0.0599557
1651.952.14904-0.19904
1662.12.21325-0.113254
1672.252.199830.0501692
1682.72.236720.46328
1692.12.17307-0.0730747
1702.42.194660.20534
1711.352.09071-0.740707

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2.1 & 2.09916 & 0.000835892 \tabularnewline
2 & 2.7 & 2.1701 & 0.529895 \tabularnewline
3 & 2.1 & 2.1939 & -0.0939039 \tabularnewline
4 & 2.1 & 2.19089 & -0.0908877 \tabularnewline
5 & 2.1 & 2.37561 & -0.275613 \tabularnewline
6 & 2.1 & 2.10367 & -0.0036702 \tabularnewline
7 & 2.1 & 2.20737 & -0.107372 \tabularnewline
8 & 2.1 & 2.32291 & -0.222908 \tabularnewline
9 & 2.1 & 2.50434 & -0.404341 \tabularnewline
10 & 2.1 & 2.0998 & 0.000197477 \tabularnewline
11 & 2.4 & 2.19709 & 0.202906 \tabularnewline
12 & 1.95 & 2.09561 & -0.145611 \tabularnewline
13 & 2.1 & 2.15685 & -0.0568477 \tabularnewline
14 & 2.1 & 2.14084 & -0.0408403 \tabularnewline
15 & 1.95 & 2.17383 & -0.223828 \tabularnewline
16 & 2.1 & 2.20607 & -0.106075 \tabularnewline
17 & 2.4 & 2.29818 & 0.101821 \tabularnewline
18 & 2.1 & 2.14668 & -0.0466793 \tabularnewline
19 & 2.25 & 2.15314 & 0.0968636 \tabularnewline
20 & 2.4 & 2.30805 & 0.0919463 \tabularnewline
21 & 2.25 & 2.17394 & 0.0760569 \tabularnewline
22 & 2.55 & 2.21566 & 0.334341 \tabularnewline
23 & 1.95 & 2.13723 & -0.187227 \tabularnewline
24 & 2.4 & 2.1607 & 0.239296 \tabularnewline
25 & 2.1 & 2.15708 & -0.0570786 \tabularnewline
26 & 2.1 & 2.09326 & 0.00674334 \tabularnewline
27 & 2.4 & 2.24507 & 0.154932 \tabularnewline
28 & 2.1 & 2.22846 & -0.12846 \tabularnewline
29 & 2.1 & 2.12661 & -0.0266104 \tabularnewline
30 & 2.25 & 2.16741 & 0.0825937 \tabularnewline
31 & 2.25 & 2.30859 & -0.0585921 \tabularnewline
32 & 2.4 & 2.16298 & 0.237022 \tabularnewline
33 & 2.1 & 2.14998 & -0.0499755 \tabularnewline
34 & 2.1 & 2.22472 & -0.124717 \tabularnewline
35 & 2.4 & 2.28138 & 0.118617 \tabularnewline
36 & 2.1 & 2.20272 & -0.10272 \tabularnewline
37 & 1.95 & 2.17023 & -0.220234 \tabularnewline
38 & 2.1 & 2.20647 & -0.106474 \tabularnewline
39 & 2.25 & 2.1529 & 0.0971047 \tabularnewline
40 & 2.25 & 2.17615 & 0.0738534 \tabularnewline
41 & 2.4 & 2.19418 & 0.20582 \tabularnewline
42 & 2.25 & 2.29577 & -0.0457694 \tabularnewline
43 & 2.25 & 2.19119 & 0.0588074 \tabularnewline
44 & 2.1 & 2.1795 & -0.079498 \tabularnewline
45 & 2.1 & 2.11954 & -0.0195393 \tabularnewline
46 & 2.1 & 2.20958 & -0.109579 \tabularnewline
47 & 2.7 & 2.3273 & 0.372696 \tabularnewline
48 & 2.1 & 2.17637 & -0.0763741 \tabularnewline
49 & 2.1 & 2.23612 & -0.13612 \tabularnewline
50 & 2.25 & 2.18634 & 0.063656 \tabularnewline
51 & 2.7 & 2.31464 & 0.385364 \tabularnewline
52 & 2.4 & 2.09694 & 0.303056 \tabularnewline
53 & 2.1 & 2.31463 & -0.214632 \tabularnewline
54 & 2.1 & 1.9926 & 0.107402 \tabularnewline
55 & 2.4 & 2.34399 & 0.0560124 \tabularnewline
56 & 1.95 & 2.14789 & -0.197892 \tabularnewline
57 & 2.7 & 2.24836 & 0.451636 \tabularnewline
58 & 2.1 & 2.13408 & -0.0340794 \tabularnewline
59 & 2.25 & 2.15713 & 0.0928652 \tabularnewline
60 & 2.1 & 2.21073 & -0.11073 \tabularnewline
61 & 2.7 & 2.18392 & 0.516083 \tabularnewline
62 & 2.1 & 2.24077 & -0.140772 \tabularnewline
63 & 2.1 & 2.2797 & -0.179701 \tabularnewline
64 & 1.65 & 2.13848 & -0.488478 \tabularnewline
65 & 1.65 & 2.13848 & -0.488478 \tabularnewline
66 & 2.1 & 2.23955 & -0.139551 \tabularnewline
67 & 2.1 & 2.24115 & -0.141148 \tabularnewline
68 & 2.1 & 2.18269 & -0.0826939 \tabularnewline
69 & 2.1 & 2.26658 & -0.166582 \tabularnewline
70 & 2.1 & 2.10202 & -0.00201794 \tabularnewline
71 & 2.4 & 2.30389 & 0.0961075 \tabularnewline
72 & 2.4 & 2.20963 & 0.190375 \tabularnewline
73 & 2.1 & 2.23377 & -0.133774 \tabularnewline
74 & 2.25 & 2.22567 & 0.0243258 \tabularnewline
75 & 2.4 & 2.20836 & 0.191636 \tabularnewline
76 & 2.1 & 2.23813 & -0.138128 \tabularnewline
77 & 2.1 & 2.15485 & -0.0548539 \tabularnewline
78 & 2.4 & 2.20978 & 0.190225 \tabularnewline
79 & 2.4 & 2.15332 & 0.246677 \tabularnewline
80 & 2.1 & 2.15485 & -0.0548526 \tabularnewline
81 & 2.1 & 2.14278 & -0.042776 \tabularnewline
82 & 2.4 & 2.26177 & 0.138234 \tabularnewline
83 & 2.1 & 2.1444 & -0.0443967 \tabularnewline
84 & 2.7 & 2.19644 & 0.503565 \tabularnewline
85 & 2.1 & 2.16328 & -0.0632757 \tabularnewline
86 & 2.1 & 2.15427 & -0.0542748 \tabularnewline
87 & 2.25 & 2.2191 & 0.0308984 \tabularnewline
88 & 2.1 & 2.18817 & -0.0881737 \tabularnewline
89 & 2.4 & 2.17176 & 0.228238 \tabularnewline
90 & 2.25 & 2.22304 & 0.02696 \tabularnewline
91 & 2.25 & 2.27469 & -0.0246886 \tabularnewline
92 & 2.1 & 2.13769 & -0.0376905 \tabularnewline
93 & 2.1 & 2.20521 & -0.105205 \tabularnewline
94 & 2.4 & 2.15897 & 0.241035 \tabularnewline
95 & 2.25 & 2.16054 & 0.0894601 \tabularnewline
96 & 2.1 & 2.15064 & -0.0506431 \tabularnewline
97 & 2.1 & 2.13996 & -0.0399592 \tabularnewline
98 & 1.65 & 2.12944 & -0.47944 \tabularnewline
99 & 1.65 & 2.15023 & -0.500231 \tabularnewline
100 & 2.7 & 2.27251 & 0.427494 \tabularnewline
101 & 2.1 & 2.24446 & -0.144458 \tabularnewline
102 & 1.95 & 2.18412 & -0.234123 \tabularnewline
103 & 2.25 & 2.20248 & 0.0475154 \tabularnewline
104 & 2.4 & 2.23532 & 0.164683 \tabularnewline
105 & 1.95 & 2.08349 & -0.13349 \tabularnewline
106 & 2.1 & 2.25749 & -0.157491 \tabularnewline
107 & 2.4 & 2.17147 & 0.228533 \tabularnewline
108 & 2.1 & 2.20782 & -0.107823 \tabularnewline
109 & 2.1 & 2.17074 & -0.0707374 \tabularnewline
110 & 2.4 & 2.19418 & 0.20582 \tabularnewline
111 & 2.4 & 2.17969 & 0.220307 \tabularnewline
112 & 2.4 & 2.21293 & 0.187066 \tabularnewline
113 & 2.25 & 2.16134 & 0.0886605 \tabularnewline
114 & 2.4 & 2.12093 & 0.279072 \tabularnewline
115 & 2.1 & 2.14442 & -0.0444225 \tabularnewline
116 & 2.1 & 2.14417 & -0.0441662 \tabularnewline
117 & 1.8 & 2.22262 & -0.422621 \tabularnewline
118 & 2.7 & 2.12476 & 0.575235 \tabularnewline
119 & 2.1 & 2.13551 & -0.0355119 \tabularnewline
120 & 2.1 & 2.18363 & -0.0836333 \tabularnewline
121 & 2.4 & 2.30142 & 0.0985849 \tabularnewline
122 & 2.55 & 2.19623 & 0.353771 \tabularnewline
123 & 2.55 & 2.19693 & 0.353074 \tabularnewline
124 & 2.1 & 2.31488 & -0.214877 \tabularnewline
125 & 2.1 & 2.16158 & -0.0615814 \tabularnewline
126 & 2.1 & 2.20102 & -0.101022 \tabularnewline
127 & 2.25 & 2.14587 & 0.10413 \tabularnewline
128 & 2.25 & 2.11219 & 0.137815 \tabularnewline
129 & 2.1 & 2.10242 & -0.00242473 \tabularnewline
130 & 2.1 & 2.21039 & -0.110393 \tabularnewline
131 & 1.95 & 2.17077 & -0.220774 \tabularnewline
132 & 2.4 & 2.17327 & 0.226726 \tabularnewline
133 & 2.1 & 2.19587 & -0.0958664 \tabularnewline
134 & 2.4 & 2.14554 & 0.25446 \tabularnewline
135 & 2.4 & 2.19866 & 0.201339 \tabularnewline
136 & 2.4 & 2.34399 & 0.0560124 \tabularnewline
137 & 2.25 & 2.18014 & 0.0698585 \tabularnewline
138 & 1.95 & 2.15679 & -0.206788 \tabularnewline
139 & 2.1 & 2.19658 & -0.0965751 \tabularnewline
140 & 2.1 & 2.24024 & -0.140242 \tabularnewline
141 & 2.55 & 2.18942 & 0.36058 \tabularnewline
142 & 2.1 & 2.22869 & -0.128694 \tabularnewline
143 & 2.1 & 2.14601 & -0.0460112 \tabularnewline
144 & 2.1 & 2.14685 & -0.046854 \tabularnewline
145 & 1.95 & 2.19734 & -0.247344 \tabularnewline
146 & 2.25 & 2.19029 & 0.0597078 \tabularnewline
147 & 2.4 & 2.2423 & 0.157703 \tabularnewline
148 & 1.95 & 2.21682 & -0.266819 \tabularnewline
149 & 2.1 & 2.23766 & -0.137662 \tabularnewline
150 & 2.1 & 2.16243 & -0.0624257 \tabularnewline
151 & 1.95 & 2.12105 & -0.171054 \tabularnewline
152 & 2.1 & 2.27009 & -0.170093 \tabularnewline
153 & 2.1 & 2.22763 & -0.127627 \tabularnewline
154 & 1.95 & 2.14789 & -0.197892 \tabularnewline
155 & 2.1 & 2.18457 & -0.0845684 \tabularnewline
156 & 1.95 & 2.18745 & -0.237453 \tabularnewline
157 & 2.4 & 2.19866 & 0.201339 \tabularnewline
158 & 2.4 & 2.18254 & 0.217464 \tabularnewline
159 & 2.4 & 2.15025 & 0.249746 \tabularnewline
160 & 1.95 & 2.12405 & -0.174054 \tabularnewline
161 & 2.7 & 2.21018 & 0.489815 \tabularnewline
162 & 2.1 & 2.21037 & -0.110371 \tabularnewline
163 & 1.95 & 2.17082 & -0.220817 \tabularnewline
164 & 2.1 & 2.15996 & -0.0599557 \tabularnewline
165 & 1.95 & 2.14904 & -0.19904 \tabularnewline
166 & 2.1 & 2.21325 & -0.113254 \tabularnewline
167 & 2.25 & 2.19983 & 0.0501692 \tabularnewline
168 & 2.7 & 2.23672 & 0.46328 \tabularnewline
169 & 2.1 & 2.17307 & -0.0730747 \tabularnewline
170 & 2.4 & 2.19466 & 0.20534 \tabularnewline
171 & 1.35 & 2.09071 & -0.740707 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268027&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]2.1[/C][C]2.09916[/C][C]0.000835892[/C][/ROW]
[ROW][C]2[/C][C]2.7[/C][C]2.1701[/C][C]0.529895[/C][/ROW]
[ROW][C]3[/C][C]2.1[/C][C]2.1939[/C][C]-0.0939039[/C][/ROW]
[ROW][C]4[/C][C]2.1[/C][C]2.19089[/C][C]-0.0908877[/C][/ROW]
[ROW][C]5[/C][C]2.1[/C][C]2.37561[/C][C]-0.275613[/C][/ROW]
[ROW][C]6[/C][C]2.1[/C][C]2.10367[/C][C]-0.0036702[/C][/ROW]
[ROW][C]7[/C][C]2.1[/C][C]2.20737[/C][C]-0.107372[/C][/ROW]
[ROW][C]8[/C][C]2.1[/C][C]2.32291[/C][C]-0.222908[/C][/ROW]
[ROW][C]9[/C][C]2.1[/C][C]2.50434[/C][C]-0.404341[/C][/ROW]
[ROW][C]10[/C][C]2.1[/C][C]2.0998[/C][C]0.000197477[/C][/ROW]
[ROW][C]11[/C][C]2.4[/C][C]2.19709[/C][C]0.202906[/C][/ROW]
[ROW][C]12[/C][C]1.95[/C][C]2.09561[/C][C]-0.145611[/C][/ROW]
[ROW][C]13[/C][C]2.1[/C][C]2.15685[/C][C]-0.0568477[/C][/ROW]
[ROW][C]14[/C][C]2.1[/C][C]2.14084[/C][C]-0.0408403[/C][/ROW]
[ROW][C]15[/C][C]1.95[/C][C]2.17383[/C][C]-0.223828[/C][/ROW]
[ROW][C]16[/C][C]2.1[/C][C]2.20607[/C][C]-0.106075[/C][/ROW]
[ROW][C]17[/C][C]2.4[/C][C]2.29818[/C][C]0.101821[/C][/ROW]
[ROW][C]18[/C][C]2.1[/C][C]2.14668[/C][C]-0.0466793[/C][/ROW]
[ROW][C]19[/C][C]2.25[/C][C]2.15314[/C][C]0.0968636[/C][/ROW]
[ROW][C]20[/C][C]2.4[/C][C]2.30805[/C][C]0.0919463[/C][/ROW]
[ROW][C]21[/C][C]2.25[/C][C]2.17394[/C][C]0.0760569[/C][/ROW]
[ROW][C]22[/C][C]2.55[/C][C]2.21566[/C][C]0.334341[/C][/ROW]
[ROW][C]23[/C][C]1.95[/C][C]2.13723[/C][C]-0.187227[/C][/ROW]
[ROW][C]24[/C][C]2.4[/C][C]2.1607[/C][C]0.239296[/C][/ROW]
[ROW][C]25[/C][C]2.1[/C][C]2.15708[/C][C]-0.0570786[/C][/ROW]
[ROW][C]26[/C][C]2.1[/C][C]2.09326[/C][C]0.00674334[/C][/ROW]
[ROW][C]27[/C][C]2.4[/C][C]2.24507[/C][C]0.154932[/C][/ROW]
[ROW][C]28[/C][C]2.1[/C][C]2.22846[/C][C]-0.12846[/C][/ROW]
[ROW][C]29[/C][C]2.1[/C][C]2.12661[/C][C]-0.0266104[/C][/ROW]
[ROW][C]30[/C][C]2.25[/C][C]2.16741[/C][C]0.0825937[/C][/ROW]
[ROW][C]31[/C][C]2.25[/C][C]2.30859[/C][C]-0.0585921[/C][/ROW]
[ROW][C]32[/C][C]2.4[/C][C]2.16298[/C][C]0.237022[/C][/ROW]
[ROW][C]33[/C][C]2.1[/C][C]2.14998[/C][C]-0.0499755[/C][/ROW]
[ROW][C]34[/C][C]2.1[/C][C]2.22472[/C][C]-0.124717[/C][/ROW]
[ROW][C]35[/C][C]2.4[/C][C]2.28138[/C][C]0.118617[/C][/ROW]
[ROW][C]36[/C][C]2.1[/C][C]2.20272[/C][C]-0.10272[/C][/ROW]
[ROW][C]37[/C][C]1.95[/C][C]2.17023[/C][C]-0.220234[/C][/ROW]
[ROW][C]38[/C][C]2.1[/C][C]2.20647[/C][C]-0.106474[/C][/ROW]
[ROW][C]39[/C][C]2.25[/C][C]2.1529[/C][C]0.0971047[/C][/ROW]
[ROW][C]40[/C][C]2.25[/C][C]2.17615[/C][C]0.0738534[/C][/ROW]
[ROW][C]41[/C][C]2.4[/C][C]2.19418[/C][C]0.20582[/C][/ROW]
[ROW][C]42[/C][C]2.25[/C][C]2.29577[/C][C]-0.0457694[/C][/ROW]
[ROW][C]43[/C][C]2.25[/C][C]2.19119[/C][C]0.0588074[/C][/ROW]
[ROW][C]44[/C][C]2.1[/C][C]2.1795[/C][C]-0.079498[/C][/ROW]
[ROW][C]45[/C][C]2.1[/C][C]2.11954[/C][C]-0.0195393[/C][/ROW]
[ROW][C]46[/C][C]2.1[/C][C]2.20958[/C][C]-0.109579[/C][/ROW]
[ROW][C]47[/C][C]2.7[/C][C]2.3273[/C][C]0.372696[/C][/ROW]
[ROW][C]48[/C][C]2.1[/C][C]2.17637[/C][C]-0.0763741[/C][/ROW]
[ROW][C]49[/C][C]2.1[/C][C]2.23612[/C][C]-0.13612[/C][/ROW]
[ROW][C]50[/C][C]2.25[/C][C]2.18634[/C][C]0.063656[/C][/ROW]
[ROW][C]51[/C][C]2.7[/C][C]2.31464[/C][C]0.385364[/C][/ROW]
[ROW][C]52[/C][C]2.4[/C][C]2.09694[/C][C]0.303056[/C][/ROW]
[ROW][C]53[/C][C]2.1[/C][C]2.31463[/C][C]-0.214632[/C][/ROW]
[ROW][C]54[/C][C]2.1[/C][C]1.9926[/C][C]0.107402[/C][/ROW]
[ROW][C]55[/C][C]2.4[/C][C]2.34399[/C][C]0.0560124[/C][/ROW]
[ROW][C]56[/C][C]1.95[/C][C]2.14789[/C][C]-0.197892[/C][/ROW]
[ROW][C]57[/C][C]2.7[/C][C]2.24836[/C][C]0.451636[/C][/ROW]
[ROW][C]58[/C][C]2.1[/C][C]2.13408[/C][C]-0.0340794[/C][/ROW]
[ROW][C]59[/C][C]2.25[/C][C]2.15713[/C][C]0.0928652[/C][/ROW]
[ROW][C]60[/C][C]2.1[/C][C]2.21073[/C][C]-0.11073[/C][/ROW]
[ROW][C]61[/C][C]2.7[/C][C]2.18392[/C][C]0.516083[/C][/ROW]
[ROW][C]62[/C][C]2.1[/C][C]2.24077[/C][C]-0.140772[/C][/ROW]
[ROW][C]63[/C][C]2.1[/C][C]2.2797[/C][C]-0.179701[/C][/ROW]
[ROW][C]64[/C][C]1.65[/C][C]2.13848[/C][C]-0.488478[/C][/ROW]
[ROW][C]65[/C][C]1.65[/C][C]2.13848[/C][C]-0.488478[/C][/ROW]
[ROW][C]66[/C][C]2.1[/C][C]2.23955[/C][C]-0.139551[/C][/ROW]
[ROW][C]67[/C][C]2.1[/C][C]2.24115[/C][C]-0.141148[/C][/ROW]
[ROW][C]68[/C][C]2.1[/C][C]2.18269[/C][C]-0.0826939[/C][/ROW]
[ROW][C]69[/C][C]2.1[/C][C]2.26658[/C][C]-0.166582[/C][/ROW]
[ROW][C]70[/C][C]2.1[/C][C]2.10202[/C][C]-0.00201794[/C][/ROW]
[ROW][C]71[/C][C]2.4[/C][C]2.30389[/C][C]0.0961075[/C][/ROW]
[ROW][C]72[/C][C]2.4[/C][C]2.20963[/C][C]0.190375[/C][/ROW]
[ROW][C]73[/C][C]2.1[/C][C]2.23377[/C][C]-0.133774[/C][/ROW]
[ROW][C]74[/C][C]2.25[/C][C]2.22567[/C][C]0.0243258[/C][/ROW]
[ROW][C]75[/C][C]2.4[/C][C]2.20836[/C][C]0.191636[/C][/ROW]
[ROW][C]76[/C][C]2.1[/C][C]2.23813[/C][C]-0.138128[/C][/ROW]
[ROW][C]77[/C][C]2.1[/C][C]2.15485[/C][C]-0.0548539[/C][/ROW]
[ROW][C]78[/C][C]2.4[/C][C]2.20978[/C][C]0.190225[/C][/ROW]
[ROW][C]79[/C][C]2.4[/C][C]2.15332[/C][C]0.246677[/C][/ROW]
[ROW][C]80[/C][C]2.1[/C][C]2.15485[/C][C]-0.0548526[/C][/ROW]
[ROW][C]81[/C][C]2.1[/C][C]2.14278[/C][C]-0.042776[/C][/ROW]
[ROW][C]82[/C][C]2.4[/C][C]2.26177[/C][C]0.138234[/C][/ROW]
[ROW][C]83[/C][C]2.1[/C][C]2.1444[/C][C]-0.0443967[/C][/ROW]
[ROW][C]84[/C][C]2.7[/C][C]2.19644[/C][C]0.503565[/C][/ROW]
[ROW][C]85[/C][C]2.1[/C][C]2.16328[/C][C]-0.0632757[/C][/ROW]
[ROW][C]86[/C][C]2.1[/C][C]2.15427[/C][C]-0.0542748[/C][/ROW]
[ROW][C]87[/C][C]2.25[/C][C]2.2191[/C][C]0.0308984[/C][/ROW]
[ROW][C]88[/C][C]2.1[/C][C]2.18817[/C][C]-0.0881737[/C][/ROW]
[ROW][C]89[/C][C]2.4[/C][C]2.17176[/C][C]0.228238[/C][/ROW]
[ROW][C]90[/C][C]2.25[/C][C]2.22304[/C][C]0.02696[/C][/ROW]
[ROW][C]91[/C][C]2.25[/C][C]2.27469[/C][C]-0.0246886[/C][/ROW]
[ROW][C]92[/C][C]2.1[/C][C]2.13769[/C][C]-0.0376905[/C][/ROW]
[ROW][C]93[/C][C]2.1[/C][C]2.20521[/C][C]-0.105205[/C][/ROW]
[ROW][C]94[/C][C]2.4[/C][C]2.15897[/C][C]0.241035[/C][/ROW]
[ROW][C]95[/C][C]2.25[/C][C]2.16054[/C][C]0.0894601[/C][/ROW]
[ROW][C]96[/C][C]2.1[/C][C]2.15064[/C][C]-0.0506431[/C][/ROW]
[ROW][C]97[/C][C]2.1[/C][C]2.13996[/C][C]-0.0399592[/C][/ROW]
[ROW][C]98[/C][C]1.65[/C][C]2.12944[/C][C]-0.47944[/C][/ROW]
[ROW][C]99[/C][C]1.65[/C][C]2.15023[/C][C]-0.500231[/C][/ROW]
[ROW][C]100[/C][C]2.7[/C][C]2.27251[/C][C]0.427494[/C][/ROW]
[ROW][C]101[/C][C]2.1[/C][C]2.24446[/C][C]-0.144458[/C][/ROW]
[ROW][C]102[/C][C]1.95[/C][C]2.18412[/C][C]-0.234123[/C][/ROW]
[ROW][C]103[/C][C]2.25[/C][C]2.20248[/C][C]0.0475154[/C][/ROW]
[ROW][C]104[/C][C]2.4[/C][C]2.23532[/C][C]0.164683[/C][/ROW]
[ROW][C]105[/C][C]1.95[/C][C]2.08349[/C][C]-0.13349[/C][/ROW]
[ROW][C]106[/C][C]2.1[/C][C]2.25749[/C][C]-0.157491[/C][/ROW]
[ROW][C]107[/C][C]2.4[/C][C]2.17147[/C][C]0.228533[/C][/ROW]
[ROW][C]108[/C][C]2.1[/C][C]2.20782[/C][C]-0.107823[/C][/ROW]
[ROW][C]109[/C][C]2.1[/C][C]2.17074[/C][C]-0.0707374[/C][/ROW]
[ROW][C]110[/C][C]2.4[/C][C]2.19418[/C][C]0.20582[/C][/ROW]
[ROW][C]111[/C][C]2.4[/C][C]2.17969[/C][C]0.220307[/C][/ROW]
[ROW][C]112[/C][C]2.4[/C][C]2.21293[/C][C]0.187066[/C][/ROW]
[ROW][C]113[/C][C]2.25[/C][C]2.16134[/C][C]0.0886605[/C][/ROW]
[ROW][C]114[/C][C]2.4[/C][C]2.12093[/C][C]0.279072[/C][/ROW]
[ROW][C]115[/C][C]2.1[/C][C]2.14442[/C][C]-0.0444225[/C][/ROW]
[ROW][C]116[/C][C]2.1[/C][C]2.14417[/C][C]-0.0441662[/C][/ROW]
[ROW][C]117[/C][C]1.8[/C][C]2.22262[/C][C]-0.422621[/C][/ROW]
[ROW][C]118[/C][C]2.7[/C][C]2.12476[/C][C]0.575235[/C][/ROW]
[ROW][C]119[/C][C]2.1[/C][C]2.13551[/C][C]-0.0355119[/C][/ROW]
[ROW][C]120[/C][C]2.1[/C][C]2.18363[/C][C]-0.0836333[/C][/ROW]
[ROW][C]121[/C][C]2.4[/C][C]2.30142[/C][C]0.0985849[/C][/ROW]
[ROW][C]122[/C][C]2.55[/C][C]2.19623[/C][C]0.353771[/C][/ROW]
[ROW][C]123[/C][C]2.55[/C][C]2.19693[/C][C]0.353074[/C][/ROW]
[ROW][C]124[/C][C]2.1[/C][C]2.31488[/C][C]-0.214877[/C][/ROW]
[ROW][C]125[/C][C]2.1[/C][C]2.16158[/C][C]-0.0615814[/C][/ROW]
[ROW][C]126[/C][C]2.1[/C][C]2.20102[/C][C]-0.101022[/C][/ROW]
[ROW][C]127[/C][C]2.25[/C][C]2.14587[/C][C]0.10413[/C][/ROW]
[ROW][C]128[/C][C]2.25[/C][C]2.11219[/C][C]0.137815[/C][/ROW]
[ROW][C]129[/C][C]2.1[/C][C]2.10242[/C][C]-0.00242473[/C][/ROW]
[ROW][C]130[/C][C]2.1[/C][C]2.21039[/C][C]-0.110393[/C][/ROW]
[ROW][C]131[/C][C]1.95[/C][C]2.17077[/C][C]-0.220774[/C][/ROW]
[ROW][C]132[/C][C]2.4[/C][C]2.17327[/C][C]0.226726[/C][/ROW]
[ROW][C]133[/C][C]2.1[/C][C]2.19587[/C][C]-0.0958664[/C][/ROW]
[ROW][C]134[/C][C]2.4[/C][C]2.14554[/C][C]0.25446[/C][/ROW]
[ROW][C]135[/C][C]2.4[/C][C]2.19866[/C][C]0.201339[/C][/ROW]
[ROW][C]136[/C][C]2.4[/C][C]2.34399[/C][C]0.0560124[/C][/ROW]
[ROW][C]137[/C][C]2.25[/C][C]2.18014[/C][C]0.0698585[/C][/ROW]
[ROW][C]138[/C][C]1.95[/C][C]2.15679[/C][C]-0.206788[/C][/ROW]
[ROW][C]139[/C][C]2.1[/C][C]2.19658[/C][C]-0.0965751[/C][/ROW]
[ROW][C]140[/C][C]2.1[/C][C]2.24024[/C][C]-0.140242[/C][/ROW]
[ROW][C]141[/C][C]2.55[/C][C]2.18942[/C][C]0.36058[/C][/ROW]
[ROW][C]142[/C][C]2.1[/C][C]2.22869[/C][C]-0.128694[/C][/ROW]
[ROW][C]143[/C][C]2.1[/C][C]2.14601[/C][C]-0.0460112[/C][/ROW]
[ROW][C]144[/C][C]2.1[/C][C]2.14685[/C][C]-0.046854[/C][/ROW]
[ROW][C]145[/C][C]1.95[/C][C]2.19734[/C][C]-0.247344[/C][/ROW]
[ROW][C]146[/C][C]2.25[/C][C]2.19029[/C][C]0.0597078[/C][/ROW]
[ROW][C]147[/C][C]2.4[/C][C]2.2423[/C][C]0.157703[/C][/ROW]
[ROW][C]148[/C][C]1.95[/C][C]2.21682[/C][C]-0.266819[/C][/ROW]
[ROW][C]149[/C][C]2.1[/C][C]2.23766[/C][C]-0.137662[/C][/ROW]
[ROW][C]150[/C][C]2.1[/C][C]2.16243[/C][C]-0.0624257[/C][/ROW]
[ROW][C]151[/C][C]1.95[/C][C]2.12105[/C][C]-0.171054[/C][/ROW]
[ROW][C]152[/C][C]2.1[/C][C]2.27009[/C][C]-0.170093[/C][/ROW]
[ROW][C]153[/C][C]2.1[/C][C]2.22763[/C][C]-0.127627[/C][/ROW]
[ROW][C]154[/C][C]1.95[/C][C]2.14789[/C][C]-0.197892[/C][/ROW]
[ROW][C]155[/C][C]2.1[/C][C]2.18457[/C][C]-0.0845684[/C][/ROW]
[ROW][C]156[/C][C]1.95[/C][C]2.18745[/C][C]-0.237453[/C][/ROW]
[ROW][C]157[/C][C]2.4[/C][C]2.19866[/C][C]0.201339[/C][/ROW]
[ROW][C]158[/C][C]2.4[/C][C]2.18254[/C][C]0.217464[/C][/ROW]
[ROW][C]159[/C][C]2.4[/C][C]2.15025[/C][C]0.249746[/C][/ROW]
[ROW][C]160[/C][C]1.95[/C][C]2.12405[/C][C]-0.174054[/C][/ROW]
[ROW][C]161[/C][C]2.7[/C][C]2.21018[/C][C]0.489815[/C][/ROW]
[ROW][C]162[/C][C]2.1[/C][C]2.21037[/C][C]-0.110371[/C][/ROW]
[ROW][C]163[/C][C]1.95[/C][C]2.17082[/C][C]-0.220817[/C][/ROW]
[ROW][C]164[/C][C]2.1[/C][C]2.15996[/C][C]-0.0599557[/C][/ROW]
[ROW][C]165[/C][C]1.95[/C][C]2.14904[/C][C]-0.19904[/C][/ROW]
[ROW][C]166[/C][C]2.1[/C][C]2.21325[/C][C]-0.113254[/C][/ROW]
[ROW][C]167[/C][C]2.25[/C][C]2.19983[/C][C]0.0501692[/C][/ROW]
[ROW][C]168[/C][C]2.7[/C][C]2.23672[/C][C]0.46328[/C][/ROW]
[ROW][C]169[/C][C]2.1[/C][C]2.17307[/C][C]-0.0730747[/C][/ROW]
[ROW][C]170[/C][C]2.4[/C][C]2.19466[/C][C]0.20534[/C][/ROW]
[ROW][C]171[/C][C]1.35[/C][C]2.09071[/C][C]-0.740707[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268027&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268027&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
12.12.099160.000835892
22.72.17010.529895
32.12.1939-0.0939039
42.12.19089-0.0908877
52.12.37561-0.275613
62.12.10367-0.0036702
72.12.20737-0.107372
82.12.32291-0.222908
92.12.50434-0.404341
102.12.09980.000197477
112.42.197090.202906
121.952.09561-0.145611
132.12.15685-0.0568477
142.12.14084-0.0408403
151.952.17383-0.223828
162.12.20607-0.106075
172.42.298180.101821
182.12.14668-0.0466793
192.252.153140.0968636
202.42.308050.0919463
212.252.173940.0760569
222.552.215660.334341
231.952.13723-0.187227
242.42.16070.239296
252.12.15708-0.0570786
262.12.093260.00674334
272.42.245070.154932
282.12.22846-0.12846
292.12.12661-0.0266104
302.252.167410.0825937
312.252.30859-0.0585921
322.42.162980.237022
332.12.14998-0.0499755
342.12.22472-0.124717
352.42.281380.118617
362.12.20272-0.10272
371.952.17023-0.220234
382.12.20647-0.106474
392.252.15290.0971047
402.252.176150.0738534
412.42.194180.20582
422.252.29577-0.0457694
432.252.191190.0588074
442.12.1795-0.079498
452.12.11954-0.0195393
462.12.20958-0.109579
472.72.32730.372696
482.12.17637-0.0763741
492.12.23612-0.13612
502.252.186340.063656
512.72.314640.385364
522.42.096940.303056
532.12.31463-0.214632
542.11.99260.107402
552.42.343990.0560124
561.952.14789-0.197892
572.72.248360.451636
582.12.13408-0.0340794
592.252.157130.0928652
602.12.21073-0.11073
612.72.183920.516083
622.12.24077-0.140772
632.12.2797-0.179701
641.652.13848-0.488478
651.652.13848-0.488478
662.12.23955-0.139551
672.12.24115-0.141148
682.12.18269-0.0826939
692.12.26658-0.166582
702.12.10202-0.00201794
712.42.303890.0961075
722.42.209630.190375
732.12.23377-0.133774
742.252.225670.0243258
752.42.208360.191636
762.12.23813-0.138128
772.12.15485-0.0548539
782.42.209780.190225
792.42.153320.246677
802.12.15485-0.0548526
812.12.14278-0.042776
822.42.261770.138234
832.12.1444-0.0443967
842.72.196440.503565
852.12.16328-0.0632757
862.12.15427-0.0542748
872.252.21910.0308984
882.12.18817-0.0881737
892.42.171760.228238
902.252.223040.02696
912.252.27469-0.0246886
922.12.13769-0.0376905
932.12.20521-0.105205
942.42.158970.241035
952.252.160540.0894601
962.12.15064-0.0506431
972.12.13996-0.0399592
981.652.12944-0.47944
991.652.15023-0.500231
1002.72.272510.427494
1012.12.24446-0.144458
1021.952.18412-0.234123
1032.252.202480.0475154
1042.42.235320.164683
1051.952.08349-0.13349
1062.12.25749-0.157491
1072.42.171470.228533
1082.12.20782-0.107823
1092.12.17074-0.0707374
1102.42.194180.20582
1112.42.179690.220307
1122.42.212930.187066
1132.252.161340.0886605
1142.42.120930.279072
1152.12.14442-0.0444225
1162.12.14417-0.0441662
1171.82.22262-0.422621
1182.72.124760.575235
1192.12.13551-0.0355119
1202.12.18363-0.0836333
1212.42.301420.0985849
1222.552.196230.353771
1232.552.196930.353074
1242.12.31488-0.214877
1252.12.16158-0.0615814
1262.12.20102-0.101022
1272.252.145870.10413
1282.252.112190.137815
1292.12.10242-0.00242473
1302.12.21039-0.110393
1311.952.17077-0.220774
1322.42.173270.226726
1332.12.19587-0.0958664
1342.42.145540.25446
1352.42.198660.201339
1362.42.343990.0560124
1372.252.180140.0698585
1381.952.15679-0.206788
1392.12.19658-0.0965751
1402.12.24024-0.140242
1412.552.189420.36058
1422.12.22869-0.128694
1432.12.14601-0.0460112
1442.12.14685-0.046854
1451.952.19734-0.247344
1462.252.190290.0597078
1472.42.24230.157703
1481.952.21682-0.266819
1492.12.23766-0.137662
1502.12.16243-0.0624257
1511.952.12105-0.171054
1522.12.27009-0.170093
1532.12.22763-0.127627
1541.952.14789-0.197892
1552.12.18457-0.0845684
1561.952.18745-0.237453
1572.42.198660.201339
1582.42.182540.217464
1592.42.150250.249746
1601.952.12405-0.174054
1612.72.210180.489815
1622.12.21037-0.110371
1631.952.17082-0.220817
1642.12.15996-0.0599557
1651.952.14904-0.19904
1662.12.21325-0.113254
1672.252.199830.0501692
1682.72.236720.46328
1692.12.17307-0.0730747
1702.42.194660.20534
1711.352.09071-0.740707






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.2092040.4184080.790796
100.4738450.947690.526155
110.4577480.9154970.542252
120.7285850.5428310.271415
130.6333690.7332620.366631
140.5402920.9194160.459708
150.5133410.9733180.486659
160.4202830.8405660.579717
170.3962530.7925060.603747
180.3133210.6266430.686679
190.279890.559780.72011
200.309020.6180390.69098
210.303620.607240.69638
220.4324820.8649640.567518
230.4556360.9112720.544364
240.4312770.8625550.568723
250.3651330.7302660.634867
260.3236080.6472160.676392
270.3224750.6449510.677525
280.2914920.5829830.708508
290.2370350.4740690.762965
300.2173030.4346070.782697
310.1749570.3499140.825043
320.1798210.3596420.820179
330.1467510.2935030.853249
340.1166370.2332750.883363
350.1116190.2232380.888381
360.09126540.1825310.908735
370.09332070.1866410.906679
380.0752530.1505060.924747
390.05749990.1150.9425
400.04429060.08858120.955709
410.04673180.09346350.953268
420.03517240.07034480.964828
430.02605280.05210570.973947
440.02042210.04084420.979578
450.0150570.0301140.984943
460.0113710.02274210.988629
470.0379230.07584610.962077
480.02979710.05959430.970203
490.02490840.04981690.975092
500.01823980.03647950.98176
510.04614340.09228670.953857
520.05646310.1129260.943537
530.05798080.1159620.942019
540.04850930.09701860.951491
550.04054870.08109730.959451
560.03866420.07732830.961336
570.09127410.1825480.908726
580.07281250.1456250.927187
590.05957440.1191490.940426
600.05143190.1028640.948568
610.144830.289660.85517
620.1300760.2601520.869924
630.1221410.2442820.877859
640.2544110.5088210.745589
650.407920.815840.59208
660.383550.7670990.61645
670.3587080.7174160.641292
680.3210390.6420780.678961
690.302830.6056610.69717
700.2666410.5332830.733359
710.2341470.4682940.765853
720.2260610.4521230.773939
730.2056240.4112480.794376
740.1746210.3492420.825379
750.176160.352320.82384
760.1611360.3222720.838864
770.1360760.2721530.863924
780.1319030.2638060.868097
790.1454010.2908020.854599
800.1248590.2497180.875141
810.1051210.2102410.894879
820.09168160.1833630.908318
830.07592310.1518460.924077
840.1728330.3456660.827167
850.1489890.2979790.851011
860.1272570.2545150.872743
870.1057170.2114330.894283
880.0909720.1819440.909028
890.09072870.1814570.909271
900.07380170.1476030.926198
910.06168150.1233630.938319
920.05002950.1000590.94997
930.04181610.08363220.958184
940.04404020.08808040.95596
950.03580050.07160090.9642
960.02846510.05693020.971535
970.02256110.04512210.977439
980.05741780.1148360.942582
990.1341290.2682580.865871
1000.2119690.4239380.788031
1010.1939990.3879980.806001
1020.1986780.3973560.801322
1030.1695390.3390780.830461
1040.1569970.3139940.843003
1050.1381650.276330.861835
1060.1254880.2509760.874512
1070.1271860.2543710.872814
1080.1099150.2198290.890085
1090.09170060.1834010.908299
1100.08921280.1784260.910787
1110.08833080.1766620.911669
1120.08361360.1672270.916386
1130.0707630.1415260.929237
1140.08455520.169110.915445
1150.06814120.1362820.931859
1160.05448760.1089750.945512
1170.09761230.1952250.902388
1180.3145750.6291510.685425
1190.2743240.5486480.725676
1200.2391140.4782290.760886
1210.2069810.4139610.793019
1220.261490.5229810.73851
1230.3139750.627950.686025
1240.332270.664540.66773
1250.2888230.5776450.711177
1260.2597510.5195020.740249
1270.2332770.4665530.766723
1280.236920.4738390.76308
1290.2120410.4240820.787959
1300.1899560.3799120.810044
1310.174870.349740.82513
1320.1906230.3812470.809377
1330.1605950.3211910.839405
1340.2227940.4455890.777206
1350.2191190.4382390.780881
1360.2240270.4480540.775973
1370.1888660.3777310.811134
1380.1752420.3504840.824758
1390.1474220.2948440.852578
1400.1208030.2416060.879197
1410.1618110.3236230.838189
1420.1517720.3035440.848228
1430.1191250.2382510.880875
1440.09238070.1847610.907619
1450.09913670.1982730.900863
1460.07435250.1487050.925648
1470.05606780.1121360.943932
1480.1224220.2448440.877578
1490.1792520.3585040.820748
1500.157360.314720.84264
1510.139620.2792410.86038
1520.1601490.3202980.839851
1530.2075030.4150060.792497
1540.1589720.3179440.841028
1550.1213850.242770.878615
1560.1657270.3314550.834273
1570.115990.2319790.88401
1580.07674830.1534970.923252
1590.4760280.9520560.523972
1600.7933430.4133140.206657
1610.6801510.6396980.319849
1620.7831390.4337220.216861

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.209204 & 0.418408 & 0.790796 \tabularnewline
10 & 0.473845 & 0.94769 & 0.526155 \tabularnewline
11 & 0.457748 & 0.915497 & 0.542252 \tabularnewline
12 & 0.728585 & 0.542831 & 0.271415 \tabularnewline
13 & 0.633369 & 0.733262 & 0.366631 \tabularnewline
14 & 0.540292 & 0.919416 & 0.459708 \tabularnewline
15 & 0.513341 & 0.973318 & 0.486659 \tabularnewline
16 & 0.420283 & 0.840566 & 0.579717 \tabularnewline
17 & 0.396253 & 0.792506 & 0.603747 \tabularnewline
18 & 0.313321 & 0.626643 & 0.686679 \tabularnewline
19 & 0.27989 & 0.55978 & 0.72011 \tabularnewline
20 & 0.30902 & 0.618039 & 0.69098 \tabularnewline
21 & 0.30362 & 0.60724 & 0.69638 \tabularnewline
22 & 0.432482 & 0.864964 & 0.567518 \tabularnewline
23 & 0.455636 & 0.911272 & 0.544364 \tabularnewline
24 & 0.431277 & 0.862555 & 0.568723 \tabularnewline
25 & 0.365133 & 0.730266 & 0.634867 \tabularnewline
26 & 0.323608 & 0.647216 & 0.676392 \tabularnewline
27 & 0.322475 & 0.644951 & 0.677525 \tabularnewline
28 & 0.291492 & 0.582983 & 0.708508 \tabularnewline
29 & 0.237035 & 0.474069 & 0.762965 \tabularnewline
30 & 0.217303 & 0.434607 & 0.782697 \tabularnewline
31 & 0.174957 & 0.349914 & 0.825043 \tabularnewline
32 & 0.179821 & 0.359642 & 0.820179 \tabularnewline
33 & 0.146751 & 0.293503 & 0.853249 \tabularnewline
34 & 0.116637 & 0.233275 & 0.883363 \tabularnewline
35 & 0.111619 & 0.223238 & 0.888381 \tabularnewline
36 & 0.0912654 & 0.182531 & 0.908735 \tabularnewline
37 & 0.0933207 & 0.186641 & 0.906679 \tabularnewline
38 & 0.075253 & 0.150506 & 0.924747 \tabularnewline
39 & 0.0574999 & 0.115 & 0.9425 \tabularnewline
40 & 0.0442906 & 0.0885812 & 0.955709 \tabularnewline
41 & 0.0467318 & 0.0934635 & 0.953268 \tabularnewline
42 & 0.0351724 & 0.0703448 & 0.964828 \tabularnewline
43 & 0.0260528 & 0.0521057 & 0.973947 \tabularnewline
44 & 0.0204221 & 0.0408442 & 0.979578 \tabularnewline
45 & 0.015057 & 0.030114 & 0.984943 \tabularnewline
46 & 0.011371 & 0.0227421 & 0.988629 \tabularnewline
47 & 0.037923 & 0.0758461 & 0.962077 \tabularnewline
48 & 0.0297971 & 0.0595943 & 0.970203 \tabularnewline
49 & 0.0249084 & 0.0498169 & 0.975092 \tabularnewline
50 & 0.0182398 & 0.0364795 & 0.98176 \tabularnewline
51 & 0.0461434 & 0.0922867 & 0.953857 \tabularnewline
52 & 0.0564631 & 0.112926 & 0.943537 \tabularnewline
53 & 0.0579808 & 0.115962 & 0.942019 \tabularnewline
54 & 0.0485093 & 0.0970186 & 0.951491 \tabularnewline
55 & 0.0405487 & 0.0810973 & 0.959451 \tabularnewline
56 & 0.0386642 & 0.0773283 & 0.961336 \tabularnewline
57 & 0.0912741 & 0.182548 & 0.908726 \tabularnewline
58 & 0.0728125 & 0.145625 & 0.927187 \tabularnewline
59 & 0.0595744 & 0.119149 & 0.940426 \tabularnewline
60 & 0.0514319 & 0.102864 & 0.948568 \tabularnewline
61 & 0.14483 & 0.28966 & 0.85517 \tabularnewline
62 & 0.130076 & 0.260152 & 0.869924 \tabularnewline
63 & 0.122141 & 0.244282 & 0.877859 \tabularnewline
64 & 0.254411 & 0.508821 & 0.745589 \tabularnewline
65 & 0.40792 & 0.81584 & 0.59208 \tabularnewline
66 & 0.38355 & 0.767099 & 0.61645 \tabularnewline
67 & 0.358708 & 0.717416 & 0.641292 \tabularnewline
68 & 0.321039 & 0.642078 & 0.678961 \tabularnewline
69 & 0.30283 & 0.605661 & 0.69717 \tabularnewline
70 & 0.266641 & 0.533283 & 0.733359 \tabularnewline
71 & 0.234147 & 0.468294 & 0.765853 \tabularnewline
72 & 0.226061 & 0.452123 & 0.773939 \tabularnewline
73 & 0.205624 & 0.411248 & 0.794376 \tabularnewline
74 & 0.174621 & 0.349242 & 0.825379 \tabularnewline
75 & 0.17616 & 0.35232 & 0.82384 \tabularnewline
76 & 0.161136 & 0.322272 & 0.838864 \tabularnewline
77 & 0.136076 & 0.272153 & 0.863924 \tabularnewline
78 & 0.131903 & 0.263806 & 0.868097 \tabularnewline
79 & 0.145401 & 0.290802 & 0.854599 \tabularnewline
80 & 0.124859 & 0.249718 & 0.875141 \tabularnewline
81 & 0.105121 & 0.210241 & 0.894879 \tabularnewline
82 & 0.0916816 & 0.183363 & 0.908318 \tabularnewline
83 & 0.0759231 & 0.151846 & 0.924077 \tabularnewline
84 & 0.172833 & 0.345666 & 0.827167 \tabularnewline
85 & 0.148989 & 0.297979 & 0.851011 \tabularnewline
86 & 0.127257 & 0.254515 & 0.872743 \tabularnewline
87 & 0.105717 & 0.211433 & 0.894283 \tabularnewline
88 & 0.090972 & 0.181944 & 0.909028 \tabularnewline
89 & 0.0907287 & 0.181457 & 0.909271 \tabularnewline
90 & 0.0738017 & 0.147603 & 0.926198 \tabularnewline
91 & 0.0616815 & 0.123363 & 0.938319 \tabularnewline
92 & 0.0500295 & 0.100059 & 0.94997 \tabularnewline
93 & 0.0418161 & 0.0836322 & 0.958184 \tabularnewline
94 & 0.0440402 & 0.0880804 & 0.95596 \tabularnewline
95 & 0.0358005 & 0.0716009 & 0.9642 \tabularnewline
96 & 0.0284651 & 0.0569302 & 0.971535 \tabularnewline
97 & 0.0225611 & 0.0451221 & 0.977439 \tabularnewline
98 & 0.0574178 & 0.114836 & 0.942582 \tabularnewline
99 & 0.134129 & 0.268258 & 0.865871 \tabularnewline
100 & 0.211969 & 0.423938 & 0.788031 \tabularnewline
101 & 0.193999 & 0.387998 & 0.806001 \tabularnewline
102 & 0.198678 & 0.397356 & 0.801322 \tabularnewline
103 & 0.169539 & 0.339078 & 0.830461 \tabularnewline
104 & 0.156997 & 0.313994 & 0.843003 \tabularnewline
105 & 0.138165 & 0.27633 & 0.861835 \tabularnewline
106 & 0.125488 & 0.250976 & 0.874512 \tabularnewline
107 & 0.127186 & 0.254371 & 0.872814 \tabularnewline
108 & 0.109915 & 0.219829 & 0.890085 \tabularnewline
109 & 0.0917006 & 0.183401 & 0.908299 \tabularnewline
110 & 0.0892128 & 0.178426 & 0.910787 \tabularnewline
111 & 0.0883308 & 0.176662 & 0.911669 \tabularnewline
112 & 0.0836136 & 0.167227 & 0.916386 \tabularnewline
113 & 0.070763 & 0.141526 & 0.929237 \tabularnewline
114 & 0.0845552 & 0.16911 & 0.915445 \tabularnewline
115 & 0.0681412 & 0.136282 & 0.931859 \tabularnewline
116 & 0.0544876 & 0.108975 & 0.945512 \tabularnewline
117 & 0.0976123 & 0.195225 & 0.902388 \tabularnewline
118 & 0.314575 & 0.629151 & 0.685425 \tabularnewline
119 & 0.274324 & 0.548648 & 0.725676 \tabularnewline
120 & 0.239114 & 0.478229 & 0.760886 \tabularnewline
121 & 0.206981 & 0.413961 & 0.793019 \tabularnewline
122 & 0.26149 & 0.522981 & 0.73851 \tabularnewline
123 & 0.313975 & 0.62795 & 0.686025 \tabularnewline
124 & 0.33227 & 0.66454 & 0.66773 \tabularnewline
125 & 0.288823 & 0.577645 & 0.711177 \tabularnewline
126 & 0.259751 & 0.519502 & 0.740249 \tabularnewline
127 & 0.233277 & 0.466553 & 0.766723 \tabularnewline
128 & 0.23692 & 0.473839 & 0.76308 \tabularnewline
129 & 0.212041 & 0.424082 & 0.787959 \tabularnewline
130 & 0.189956 & 0.379912 & 0.810044 \tabularnewline
131 & 0.17487 & 0.34974 & 0.82513 \tabularnewline
132 & 0.190623 & 0.381247 & 0.809377 \tabularnewline
133 & 0.160595 & 0.321191 & 0.839405 \tabularnewline
134 & 0.222794 & 0.445589 & 0.777206 \tabularnewline
135 & 0.219119 & 0.438239 & 0.780881 \tabularnewline
136 & 0.224027 & 0.448054 & 0.775973 \tabularnewline
137 & 0.188866 & 0.377731 & 0.811134 \tabularnewline
138 & 0.175242 & 0.350484 & 0.824758 \tabularnewline
139 & 0.147422 & 0.294844 & 0.852578 \tabularnewline
140 & 0.120803 & 0.241606 & 0.879197 \tabularnewline
141 & 0.161811 & 0.323623 & 0.838189 \tabularnewline
142 & 0.151772 & 0.303544 & 0.848228 \tabularnewline
143 & 0.119125 & 0.238251 & 0.880875 \tabularnewline
144 & 0.0923807 & 0.184761 & 0.907619 \tabularnewline
145 & 0.0991367 & 0.198273 & 0.900863 \tabularnewline
146 & 0.0743525 & 0.148705 & 0.925648 \tabularnewline
147 & 0.0560678 & 0.112136 & 0.943932 \tabularnewline
148 & 0.122422 & 0.244844 & 0.877578 \tabularnewline
149 & 0.179252 & 0.358504 & 0.820748 \tabularnewline
150 & 0.15736 & 0.31472 & 0.84264 \tabularnewline
151 & 0.13962 & 0.279241 & 0.86038 \tabularnewline
152 & 0.160149 & 0.320298 & 0.839851 \tabularnewline
153 & 0.207503 & 0.415006 & 0.792497 \tabularnewline
154 & 0.158972 & 0.317944 & 0.841028 \tabularnewline
155 & 0.121385 & 0.24277 & 0.878615 \tabularnewline
156 & 0.165727 & 0.331455 & 0.834273 \tabularnewline
157 & 0.11599 & 0.231979 & 0.88401 \tabularnewline
158 & 0.0767483 & 0.153497 & 0.923252 \tabularnewline
159 & 0.476028 & 0.952056 & 0.523972 \tabularnewline
160 & 0.793343 & 0.413314 & 0.206657 \tabularnewline
161 & 0.680151 & 0.639698 & 0.319849 \tabularnewline
162 & 0.783139 & 0.433722 & 0.216861 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268027&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]0.209204[/C][C]0.418408[/C][C]0.790796[/C][/ROW]
[ROW][C]10[/C][C]0.473845[/C][C]0.94769[/C][C]0.526155[/C][/ROW]
[ROW][C]11[/C][C]0.457748[/C][C]0.915497[/C][C]0.542252[/C][/ROW]
[ROW][C]12[/C][C]0.728585[/C][C]0.542831[/C][C]0.271415[/C][/ROW]
[ROW][C]13[/C][C]0.633369[/C][C]0.733262[/C][C]0.366631[/C][/ROW]
[ROW][C]14[/C][C]0.540292[/C][C]0.919416[/C][C]0.459708[/C][/ROW]
[ROW][C]15[/C][C]0.513341[/C][C]0.973318[/C][C]0.486659[/C][/ROW]
[ROW][C]16[/C][C]0.420283[/C][C]0.840566[/C][C]0.579717[/C][/ROW]
[ROW][C]17[/C][C]0.396253[/C][C]0.792506[/C][C]0.603747[/C][/ROW]
[ROW][C]18[/C][C]0.313321[/C][C]0.626643[/C][C]0.686679[/C][/ROW]
[ROW][C]19[/C][C]0.27989[/C][C]0.55978[/C][C]0.72011[/C][/ROW]
[ROW][C]20[/C][C]0.30902[/C][C]0.618039[/C][C]0.69098[/C][/ROW]
[ROW][C]21[/C][C]0.30362[/C][C]0.60724[/C][C]0.69638[/C][/ROW]
[ROW][C]22[/C][C]0.432482[/C][C]0.864964[/C][C]0.567518[/C][/ROW]
[ROW][C]23[/C][C]0.455636[/C][C]0.911272[/C][C]0.544364[/C][/ROW]
[ROW][C]24[/C][C]0.431277[/C][C]0.862555[/C][C]0.568723[/C][/ROW]
[ROW][C]25[/C][C]0.365133[/C][C]0.730266[/C][C]0.634867[/C][/ROW]
[ROW][C]26[/C][C]0.323608[/C][C]0.647216[/C][C]0.676392[/C][/ROW]
[ROW][C]27[/C][C]0.322475[/C][C]0.644951[/C][C]0.677525[/C][/ROW]
[ROW][C]28[/C][C]0.291492[/C][C]0.582983[/C][C]0.708508[/C][/ROW]
[ROW][C]29[/C][C]0.237035[/C][C]0.474069[/C][C]0.762965[/C][/ROW]
[ROW][C]30[/C][C]0.217303[/C][C]0.434607[/C][C]0.782697[/C][/ROW]
[ROW][C]31[/C][C]0.174957[/C][C]0.349914[/C][C]0.825043[/C][/ROW]
[ROW][C]32[/C][C]0.179821[/C][C]0.359642[/C][C]0.820179[/C][/ROW]
[ROW][C]33[/C][C]0.146751[/C][C]0.293503[/C][C]0.853249[/C][/ROW]
[ROW][C]34[/C][C]0.116637[/C][C]0.233275[/C][C]0.883363[/C][/ROW]
[ROW][C]35[/C][C]0.111619[/C][C]0.223238[/C][C]0.888381[/C][/ROW]
[ROW][C]36[/C][C]0.0912654[/C][C]0.182531[/C][C]0.908735[/C][/ROW]
[ROW][C]37[/C][C]0.0933207[/C][C]0.186641[/C][C]0.906679[/C][/ROW]
[ROW][C]38[/C][C]0.075253[/C][C]0.150506[/C][C]0.924747[/C][/ROW]
[ROW][C]39[/C][C]0.0574999[/C][C]0.115[/C][C]0.9425[/C][/ROW]
[ROW][C]40[/C][C]0.0442906[/C][C]0.0885812[/C][C]0.955709[/C][/ROW]
[ROW][C]41[/C][C]0.0467318[/C][C]0.0934635[/C][C]0.953268[/C][/ROW]
[ROW][C]42[/C][C]0.0351724[/C][C]0.0703448[/C][C]0.964828[/C][/ROW]
[ROW][C]43[/C][C]0.0260528[/C][C]0.0521057[/C][C]0.973947[/C][/ROW]
[ROW][C]44[/C][C]0.0204221[/C][C]0.0408442[/C][C]0.979578[/C][/ROW]
[ROW][C]45[/C][C]0.015057[/C][C]0.030114[/C][C]0.984943[/C][/ROW]
[ROW][C]46[/C][C]0.011371[/C][C]0.0227421[/C][C]0.988629[/C][/ROW]
[ROW][C]47[/C][C]0.037923[/C][C]0.0758461[/C][C]0.962077[/C][/ROW]
[ROW][C]48[/C][C]0.0297971[/C][C]0.0595943[/C][C]0.970203[/C][/ROW]
[ROW][C]49[/C][C]0.0249084[/C][C]0.0498169[/C][C]0.975092[/C][/ROW]
[ROW][C]50[/C][C]0.0182398[/C][C]0.0364795[/C][C]0.98176[/C][/ROW]
[ROW][C]51[/C][C]0.0461434[/C][C]0.0922867[/C][C]0.953857[/C][/ROW]
[ROW][C]52[/C][C]0.0564631[/C][C]0.112926[/C][C]0.943537[/C][/ROW]
[ROW][C]53[/C][C]0.0579808[/C][C]0.115962[/C][C]0.942019[/C][/ROW]
[ROW][C]54[/C][C]0.0485093[/C][C]0.0970186[/C][C]0.951491[/C][/ROW]
[ROW][C]55[/C][C]0.0405487[/C][C]0.0810973[/C][C]0.959451[/C][/ROW]
[ROW][C]56[/C][C]0.0386642[/C][C]0.0773283[/C][C]0.961336[/C][/ROW]
[ROW][C]57[/C][C]0.0912741[/C][C]0.182548[/C][C]0.908726[/C][/ROW]
[ROW][C]58[/C][C]0.0728125[/C][C]0.145625[/C][C]0.927187[/C][/ROW]
[ROW][C]59[/C][C]0.0595744[/C][C]0.119149[/C][C]0.940426[/C][/ROW]
[ROW][C]60[/C][C]0.0514319[/C][C]0.102864[/C][C]0.948568[/C][/ROW]
[ROW][C]61[/C][C]0.14483[/C][C]0.28966[/C][C]0.85517[/C][/ROW]
[ROW][C]62[/C][C]0.130076[/C][C]0.260152[/C][C]0.869924[/C][/ROW]
[ROW][C]63[/C][C]0.122141[/C][C]0.244282[/C][C]0.877859[/C][/ROW]
[ROW][C]64[/C][C]0.254411[/C][C]0.508821[/C][C]0.745589[/C][/ROW]
[ROW][C]65[/C][C]0.40792[/C][C]0.81584[/C][C]0.59208[/C][/ROW]
[ROW][C]66[/C][C]0.38355[/C][C]0.767099[/C][C]0.61645[/C][/ROW]
[ROW][C]67[/C][C]0.358708[/C][C]0.717416[/C][C]0.641292[/C][/ROW]
[ROW][C]68[/C][C]0.321039[/C][C]0.642078[/C][C]0.678961[/C][/ROW]
[ROW][C]69[/C][C]0.30283[/C][C]0.605661[/C][C]0.69717[/C][/ROW]
[ROW][C]70[/C][C]0.266641[/C][C]0.533283[/C][C]0.733359[/C][/ROW]
[ROW][C]71[/C][C]0.234147[/C][C]0.468294[/C][C]0.765853[/C][/ROW]
[ROW][C]72[/C][C]0.226061[/C][C]0.452123[/C][C]0.773939[/C][/ROW]
[ROW][C]73[/C][C]0.205624[/C][C]0.411248[/C][C]0.794376[/C][/ROW]
[ROW][C]74[/C][C]0.174621[/C][C]0.349242[/C][C]0.825379[/C][/ROW]
[ROW][C]75[/C][C]0.17616[/C][C]0.35232[/C][C]0.82384[/C][/ROW]
[ROW][C]76[/C][C]0.161136[/C][C]0.322272[/C][C]0.838864[/C][/ROW]
[ROW][C]77[/C][C]0.136076[/C][C]0.272153[/C][C]0.863924[/C][/ROW]
[ROW][C]78[/C][C]0.131903[/C][C]0.263806[/C][C]0.868097[/C][/ROW]
[ROW][C]79[/C][C]0.145401[/C][C]0.290802[/C][C]0.854599[/C][/ROW]
[ROW][C]80[/C][C]0.124859[/C][C]0.249718[/C][C]0.875141[/C][/ROW]
[ROW][C]81[/C][C]0.105121[/C][C]0.210241[/C][C]0.894879[/C][/ROW]
[ROW][C]82[/C][C]0.0916816[/C][C]0.183363[/C][C]0.908318[/C][/ROW]
[ROW][C]83[/C][C]0.0759231[/C][C]0.151846[/C][C]0.924077[/C][/ROW]
[ROW][C]84[/C][C]0.172833[/C][C]0.345666[/C][C]0.827167[/C][/ROW]
[ROW][C]85[/C][C]0.148989[/C][C]0.297979[/C][C]0.851011[/C][/ROW]
[ROW][C]86[/C][C]0.127257[/C][C]0.254515[/C][C]0.872743[/C][/ROW]
[ROW][C]87[/C][C]0.105717[/C][C]0.211433[/C][C]0.894283[/C][/ROW]
[ROW][C]88[/C][C]0.090972[/C][C]0.181944[/C][C]0.909028[/C][/ROW]
[ROW][C]89[/C][C]0.0907287[/C][C]0.181457[/C][C]0.909271[/C][/ROW]
[ROW][C]90[/C][C]0.0738017[/C][C]0.147603[/C][C]0.926198[/C][/ROW]
[ROW][C]91[/C][C]0.0616815[/C][C]0.123363[/C][C]0.938319[/C][/ROW]
[ROW][C]92[/C][C]0.0500295[/C][C]0.100059[/C][C]0.94997[/C][/ROW]
[ROW][C]93[/C][C]0.0418161[/C][C]0.0836322[/C][C]0.958184[/C][/ROW]
[ROW][C]94[/C][C]0.0440402[/C][C]0.0880804[/C][C]0.95596[/C][/ROW]
[ROW][C]95[/C][C]0.0358005[/C][C]0.0716009[/C][C]0.9642[/C][/ROW]
[ROW][C]96[/C][C]0.0284651[/C][C]0.0569302[/C][C]0.971535[/C][/ROW]
[ROW][C]97[/C][C]0.0225611[/C][C]0.0451221[/C][C]0.977439[/C][/ROW]
[ROW][C]98[/C][C]0.0574178[/C][C]0.114836[/C][C]0.942582[/C][/ROW]
[ROW][C]99[/C][C]0.134129[/C][C]0.268258[/C][C]0.865871[/C][/ROW]
[ROW][C]100[/C][C]0.211969[/C][C]0.423938[/C][C]0.788031[/C][/ROW]
[ROW][C]101[/C][C]0.193999[/C][C]0.387998[/C][C]0.806001[/C][/ROW]
[ROW][C]102[/C][C]0.198678[/C][C]0.397356[/C][C]0.801322[/C][/ROW]
[ROW][C]103[/C][C]0.169539[/C][C]0.339078[/C][C]0.830461[/C][/ROW]
[ROW][C]104[/C][C]0.156997[/C][C]0.313994[/C][C]0.843003[/C][/ROW]
[ROW][C]105[/C][C]0.138165[/C][C]0.27633[/C][C]0.861835[/C][/ROW]
[ROW][C]106[/C][C]0.125488[/C][C]0.250976[/C][C]0.874512[/C][/ROW]
[ROW][C]107[/C][C]0.127186[/C][C]0.254371[/C][C]0.872814[/C][/ROW]
[ROW][C]108[/C][C]0.109915[/C][C]0.219829[/C][C]0.890085[/C][/ROW]
[ROW][C]109[/C][C]0.0917006[/C][C]0.183401[/C][C]0.908299[/C][/ROW]
[ROW][C]110[/C][C]0.0892128[/C][C]0.178426[/C][C]0.910787[/C][/ROW]
[ROW][C]111[/C][C]0.0883308[/C][C]0.176662[/C][C]0.911669[/C][/ROW]
[ROW][C]112[/C][C]0.0836136[/C][C]0.167227[/C][C]0.916386[/C][/ROW]
[ROW][C]113[/C][C]0.070763[/C][C]0.141526[/C][C]0.929237[/C][/ROW]
[ROW][C]114[/C][C]0.0845552[/C][C]0.16911[/C][C]0.915445[/C][/ROW]
[ROW][C]115[/C][C]0.0681412[/C][C]0.136282[/C][C]0.931859[/C][/ROW]
[ROW][C]116[/C][C]0.0544876[/C][C]0.108975[/C][C]0.945512[/C][/ROW]
[ROW][C]117[/C][C]0.0976123[/C][C]0.195225[/C][C]0.902388[/C][/ROW]
[ROW][C]118[/C][C]0.314575[/C][C]0.629151[/C][C]0.685425[/C][/ROW]
[ROW][C]119[/C][C]0.274324[/C][C]0.548648[/C][C]0.725676[/C][/ROW]
[ROW][C]120[/C][C]0.239114[/C][C]0.478229[/C][C]0.760886[/C][/ROW]
[ROW][C]121[/C][C]0.206981[/C][C]0.413961[/C][C]0.793019[/C][/ROW]
[ROW][C]122[/C][C]0.26149[/C][C]0.522981[/C][C]0.73851[/C][/ROW]
[ROW][C]123[/C][C]0.313975[/C][C]0.62795[/C][C]0.686025[/C][/ROW]
[ROW][C]124[/C][C]0.33227[/C][C]0.66454[/C][C]0.66773[/C][/ROW]
[ROW][C]125[/C][C]0.288823[/C][C]0.577645[/C][C]0.711177[/C][/ROW]
[ROW][C]126[/C][C]0.259751[/C][C]0.519502[/C][C]0.740249[/C][/ROW]
[ROW][C]127[/C][C]0.233277[/C][C]0.466553[/C][C]0.766723[/C][/ROW]
[ROW][C]128[/C][C]0.23692[/C][C]0.473839[/C][C]0.76308[/C][/ROW]
[ROW][C]129[/C][C]0.212041[/C][C]0.424082[/C][C]0.787959[/C][/ROW]
[ROW][C]130[/C][C]0.189956[/C][C]0.379912[/C][C]0.810044[/C][/ROW]
[ROW][C]131[/C][C]0.17487[/C][C]0.34974[/C][C]0.82513[/C][/ROW]
[ROW][C]132[/C][C]0.190623[/C][C]0.381247[/C][C]0.809377[/C][/ROW]
[ROW][C]133[/C][C]0.160595[/C][C]0.321191[/C][C]0.839405[/C][/ROW]
[ROW][C]134[/C][C]0.222794[/C][C]0.445589[/C][C]0.777206[/C][/ROW]
[ROW][C]135[/C][C]0.219119[/C][C]0.438239[/C][C]0.780881[/C][/ROW]
[ROW][C]136[/C][C]0.224027[/C][C]0.448054[/C][C]0.775973[/C][/ROW]
[ROW][C]137[/C][C]0.188866[/C][C]0.377731[/C][C]0.811134[/C][/ROW]
[ROW][C]138[/C][C]0.175242[/C][C]0.350484[/C][C]0.824758[/C][/ROW]
[ROW][C]139[/C][C]0.147422[/C][C]0.294844[/C][C]0.852578[/C][/ROW]
[ROW][C]140[/C][C]0.120803[/C][C]0.241606[/C][C]0.879197[/C][/ROW]
[ROW][C]141[/C][C]0.161811[/C][C]0.323623[/C][C]0.838189[/C][/ROW]
[ROW][C]142[/C][C]0.151772[/C][C]0.303544[/C][C]0.848228[/C][/ROW]
[ROW][C]143[/C][C]0.119125[/C][C]0.238251[/C][C]0.880875[/C][/ROW]
[ROW][C]144[/C][C]0.0923807[/C][C]0.184761[/C][C]0.907619[/C][/ROW]
[ROW][C]145[/C][C]0.0991367[/C][C]0.198273[/C][C]0.900863[/C][/ROW]
[ROW][C]146[/C][C]0.0743525[/C][C]0.148705[/C][C]0.925648[/C][/ROW]
[ROW][C]147[/C][C]0.0560678[/C][C]0.112136[/C][C]0.943932[/C][/ROW]
[ROW][C]148[/C][C]0.122422[/C][C]0.244844[/C][C]0.877578[/C][/ROW]
[ROW][C]149[/C][C]0.179252[/C][C]0.358504[/C][C]0.820748[/C][/ROW]
[ROW][C]150[/C][C]0.15736[/C][C]0.31472[/C][C]0.84264[/C][/ROW]
[ROW][C]151[/C][C]0.13962[/C][C]0.279241[/C][C]0.86038[/C][/ROW]
[ROW][C]152[/C][C]0.160149[/C][C]0.320298[/C][C]0.839851[/C][/ROW]
[ROW][C]153[/C][C]0.207503[/C][C]0.415006[/C][C]0.792497[/C][/ROW]
[ROW][C]154[/C][C]0.158972[/C][C]0.317944[/C][C]0.841028[/C][/ROW]
[ROW][C]155[/C][C]0.121385[/C][C]0.24277[/C][C]0.878615[/C][/ROW]
[ROW][C]156[/C][C]0.165727[/C][C]0.331455[/C][C]0.834273[/C][/ROW]
[ROW][C]157[/C][C]0.11599[/C][C]0.231979[/C][C]0.88401[/C][/ROW]
[ROW][C]158[/C][C]0.0767483[/C][C]0.153497[/C][C]0.923252[/C][/ROW]
[ROW][C]159[/C][C]0.476028[/C][C]0.952056[/C][C]0.523972[/C][/ROW]
[ROW][C]160[/C][C]0.793343[/C][C]0.413314[/C][C]0.206657[/C][/ROW]
[ROW][C]161[/C][C]0.680151[/C][C]0.639698[/C][C]0.319849[/C][/ROW]
[ROW][C]162[/C][C]0.783139[/C][C]0.433722[/C][C]0.216861[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268027&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268027&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
90.2092040.4184080.790796
100.4738450.947690.526155
110.4577480.9154970.542252
120.7285850.5428310.271415
130.6333690.7332620.366631
140.5402920.9194160.459708
150.5133410.9733180.486659
160.4202830.8405660.579717
170.3962530.7925060.603747
180.3133210.6266430.686679
190.279890.559780.72011
200.309020.6180390.69098
210.303620.607240.69638
220.4324820.8649640.567518
230.4556360.9112720.544364
240.4312770.8625550.568723
250.3651330.7302660.634867
260.3236080.6472160.676392
270.3224750.6449510.677525
280.2914920.5829830.708508
290.2370350.4740690.762965
300.2173030.4346070.782697
310.1749570.3499140.825043
320.1798210.3596420.820179
330.1467510.2935030.853249
340.1166370.2332750.883363
350.1116190.2232380.888381
360.09126540.1825310.908735
370.09332070.1866410.906679
380.0752530.1505060.924747
390.05749990.1150.9425
400.04429060.08858120.955709
410.04673180.09346350.953268
420.03517240.07034480.964828
430.02605280.05210570.973947
440.02042210.04084420.979578
450.0150570.0301140.984943
460.0113710.02274210.988629
470.0379230.07584610.962077
480.02979710.05959430.970203
490.02490840.04981690.975092
500.01823980.03647950.98176
510.04614340.09228670.953857
520.05646310.1129260.943537
530.05798080.1159620.942019
540.04850930.09701860.951491
550.04054870.08109730.959451
560.03866420.07732830.961336
570.09127410.1825480.908726
580.07281250.1456250.927187
590.05957440.1191490.940426
600.05143190.1028640.948568
610.144830.289660.85517
620.1300760.2601520.869924
630.1221410.2442820.877859
640.2544110.5088210.745589
650.407920.815840.59208
660.383550.7670990.61645
670.3587080.7174160.641292
680.3210390.6420780.678961
690.302830.6056610.69717
700.2666410.5332830.733359
710.2341470.4682940.765853
720.2260610.4521230.773939
730.2056240.4112480.794376
740.1746210.3492420.825379
750.176160.352320.82384
760.1611360.3222720.838864
770.1360760.2721530.863924
780.1319030.2638060.868097
790.1454010.2908020.854599
800.1248590.2497180.875141
810.1051210.2102410.894879
820.09168160.1833630.908318
830.07592310.1518460.924077
840.1728330.3456660.827167
850.1489890.2979790.851011
860.1272570.2545150.872743
870.1057170.2114330.894283
880.0909720.1819440.909028
890.09072870.1814570.909271
900.07380170.1476030.926198
910.06168150.1233630.938319
920.05002950.1000590.94997
930.04181610.08363220.958184
940.04404020.08808040.95596
950.03580050.07160090.9642
960.02846510.05693020.971535
970.02256110.04512210.977439
980.05741780.1148360.942582
990.1341290.2682580.865871
1000.2119690.4239380.788031
1010.1939990.3879980.806001
1020.1986780.3973560.801322
1030.1695390.3390780.830461
1040.1569970.3139940.843003
1050.1381650.276330.861835
1060.1254880.2509760.874512
1070.1271860.2543710.872814
1080.1099150.2198290.890085
1090.09170060.1834010.908299
1100.08921280.1784260.910787
1110.08833080.1766620.911669
1120.08361360.1672270.916386
1130.0707630.1415260.929237
1140.08455520.169110.915445
1150.06814120.1362820.931859
1160.05448760.1089750.945512
1170.09761230.1952250.902388
1180.3145750.6291510.685425
1190.2743240.5486480.725676
1200.2391140.4782290.760886
1210.2069810.4139610.793019
1220.261490.5229810.73851
1230.3139750.627950.686025
1240.332270.664540.66773
1250.2888230.5776450.711177
1260.2597510.5195020.740249
1270.2332770.4665530.766723
1280.236920.4738390.76308
1290.2120410.4240820.787959
1300.1899560.3799120.810044
1310.174870.349740.82513
1320.1906230.3812470.809377
1330.1605950.3211910.839405
1340.2227940.4455890.777206
1350.2191190.4382390.780881
1360.2240270.4480540.775973
1370.1888660.3777310.811134
1380.1752420.3504840.824758
1390.1474220.2948440.852578
1400.1208030.2416060.879197
1410.1618110.3236230.838189
1420.1517720.3035440.848228
1430.1191250.2382510.880875
1440.09238070.1847610.907619
1450.09913670.1982730.900863
1460.07435250.1487050.925648
1470.05606780.1121360.943932
1480.1224220.2448440.877578
1490.1792520.3585040.820748
1500.157360.314720.84264
1510.139620.2792410.86038
1520.1601490.3202980.839851
1530.2075030.4150060.792497
1540.1589720.3179440.841028
1550.1213850.242770.878615
1560.1657270.3314550.834273
1570.115990.2319790.88401
1580.07674830.1534970.923252
1590.4760280.9520560.523972
1600.7933430.4133140.206657
1610.6801510.6396980.319849
1620.7831390.4337220.216861






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level60.038961OK
10% type I error level200.12987NOK

\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 & 6 & 0.038961 & OK \tabularnewline
10% type I error level & 20 & 0.12987 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268027&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]6[/C][C]0.038961[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]20[/C][C]0.12987[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268027&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268027&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 level60.038961OK
10% type I error level200.12987NOK


Figures (Output of Computation)

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

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