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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 computationThu, 18 Dec 2014 19:13:12 +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/18/t1418930018ae1i4ttljeqrel1.htm/, Retrieved Fri, 17 May 2024 01:06:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271215, Retrieved Fri, 17 May 2024 01:06:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact66

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-18 19:13:12] [a3de03a8fa2b95b1b988206b9ba33408] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
48	41	23	12	34	4,35
50	146	16	45	61	12,7
150	182	33	37	70	18,1
154	192	32	37	69	17,85
109	263	37	108	145	16,6
68	35	14	10	23	12,6
194	439	52	68	120	17,1
158	214	75	72	147	19,1
159	341	72	143	215	16,1
67	58	15	9	24	13,35
147	292	29	55	84	18,4
39	85	13	17	30	14,7
100	200	40	37	77	10,6
111	158	19	27	46	12,6
138	199	24	37	61	16,2
101	297	121	58	178	13,6
131	227	93	66	160	18,9
101	108	36	21	57	14,1
114	86	23	19	42	14,5
165	302	85	78	163	16,15
114	148	41	35	75	14,75
111	178	46	48	94	14,8
75	120	18	27	45	12,45
82	207	35	43	78	12,65
121	157	17	30	47	17,35
32	128	4	25	29	8,6
150	296	28	69	97	18,4
117	323	44	72	116	16,1
71	79	10	23	32	11,6
165	70	38	13	50	17,75
154	146	57	61	118	15,25
126	246	23	43	66	17,65
149	196	36	51	86	16,35
145	199	22	67	89	17,65
120	127	40	36	76	13,6
109	153	31	44	75	14,35
132	299	11	45	57	14,75
172	228	38	34	72	18,25
169	190	24	36	60	9,9
114	180	37	72	109	16
156	212	37	39	76	18,25
172	269	22	43	65	16,85
68	130	15	25	40	14,6
89	179	2	56	58	13,85
167	243	43	80	123	18,95
113	190	31	40	71	15,6
115	299	29	73	102	14,85
78	121	45	34	80	11,75
118	137	25	72	97	18,45
87	305	4	42	46	15,9
173	157	31	61	93	17,1
2	96	-4	23	19	16,1
162	183	66	74	140	19,9
49	52	61	16	78	10,95
122	238	32	66	98	18,45
96	40	31	9	40	15,1
100	226	39	41	80	15
82	190	19	57	76	11,35
100	214	31	48	79	15,95
115	145	36	51	87	18,1
141	119	42	53	95	14,6
165	222	21	29	49	15,4
165	222	21	29	49	15,4
110	159	25	55	80	17,6
118	165	32	54	86	13,35
158	249	26	43	69	19,1
146	125	28	51	79	15,35
49	122	32	20	52	7,6
90	186	41	79	120	13,4
121	148	29	39	69	13,9
155	274	33	61	94	19,1
104	172	17	55	72	15,25
147	84	13	30	43	12,9
110	168	32	55	87	16,1
108	102	30	22	52	17,35
113	106	34	37	71	13,15
115	2	59	2	61	12,15
61	139	13	38	51	12,6
60	95	23	27	50	10,35
109	130	10	56	67	15,4
68	72	5	25	30	9,6
111	141	31	39	70	18,2
77	113	19	33	52	13,6
73	206	32	43	75	14,85
151	268	30	57	87	14,75
89	175	25	43	69	14,1
78	77	48	23	72	14,9
110	125	35	44	79	16,25
220	255	67	54	121	19,25
65	111	15	28	43	13,6
141	132	22	36	58	13,6
117	211	18	39	57	15,65
122	92	33	16	50	12,75
63	76	46	23	69	14,6
44	171	24	40	64	9,85
52	83	14	24	38	12,65
131	266	12	78	90	19,2
101	186	38	57	96	16,6
42	50	12	37	49	11,2
152	117	28	27	56	15,25
107	219	41	61	102	11,9
77	246	12	27	40	13,2
154	279	31	69	100	16,35
103	148	33	34	67	12,4
96	137	34	44	78	15,85
175	181	21	34	55	18,15
57	98	20	39	59	11,15
112	226	44	51	96	15,65
143	234	52	34	86	17,75
49	138	7	31	38	7,65
110	85	29	13	43	12,35
131	66	11	12	23	15,6
167	236	26	51	77	19,3
56	106	24	24	48	15,2
137	135	7	19	26	17,1
86	122	60	30	91	15,6
121	218	13	81	94	18,4
149	199	20	42	62	19,05
168	112	52	22	74	18,55
140	278	28	85	114	19,1
88	94	25	27	52	13,1
168	113	39	25	64	12,85
94	84	9	22	31	9,5
51	86	19	19	38	4,5
48	62	13	14	27	11,85
145	222	60	45	105	13,6
66	167	19	45	64	11,7
85	82	34	28	62	12,4
109	207	14	51	65	13,35
63	184	17	41	58	11,4
102	83	45	31	76	14,9
162	183	66	74	140	19,9
86	89	48	19	68	11,2
114	225	29	51	80	14,6
164	237	-2	73	71	17,6
119	102	51	24	76	14,05
126	221	2	61	63	16,1
132	128	24	23	46	13,35
142	91	40	14	53	11,85
83	198	20	54	74	11,95
94	204	19	51	70	14,75
81	158	16	62	78	15,15
166	138	20	36	56	13,2
110	226	40	59	100	16,85
64	44	27	24	51	7,85
93	196	25	26	52	7,7
104	83	49	54	102	12,6
105	79	39	39	78	7,85
49	52	61	16	78	10,95
88	105	19	36	55	12,35
95	116	67	31	98	9,95
102	83	45	31	76	14,9
99	196	30	42	73	16,65
63	153	8	39	47	13,4
76	157	19	25	45	13,95
109	75	52	31	83	15,7
117	106	22	38	60	16,85
57	58	17	31	48	10,95
120	75	33	17	50	15,35
73	74	34	22	56	12,2
91	185	22	55	77	15,1
108	265	30	62	91	17,75
105	131	25	51	76	15,2
117	139	38	30	68	14,6
119	196	26	49	74	16,65
31	78	13	16	29	8,1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 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 & 7 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271215&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]7 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=271215&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271215&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 time7 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 8.19884 + 0.0424907LFM[t] + 0.0030227B[t] -0.41126PRH[t] -0.368461CH[t] + 0.402652H[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  8.19884 +  0.0424907LFM[t] +  0.0030227B[t] -0.41126PRH[t] -0.368461CH[t] +  0.402652H[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271215&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  8.19884 +  0.0424907LFM[t] +  0.0030227B[t] -0.41126PRH[t] -0.368461CH[t] +  0.402652H[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271215&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271215&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 8.19884 + 0.0424907LFM[t] + 0.0030227B[t] -0.41126PRH[t] -0.368461CH[t] + 0.402652H[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.198840.56638714.486.86329e-313.43164e-31
LFM0.04249070.005477417.7579.58819e-134.7941e-13
B0.00302270.00365620.82670.409620.20481
PRH-0.411260.368572-1.1160.2661730.133086
CH-0.3684610.367531-1.0030.3176010.158801
H0.4026520.3673841.0960.2747270.137364

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 8.19884 & 0.566387 & 14.48 & 6.86329e-31 & 3.43164e-31 \tabularnewline
LFM & 0.0424907 & 0.00547741 & 7.757 & 9.58819e-13 & 4.7941e-13 \tabularnewline
B & 0.0030227 & 0.0036562 & 0.8267 & 0.40962 & 0.20481 \tabularnewline
PRH & -0.41126 & 0.368572 & -1.116 & 0.266173 & 0.133086 \tabularnewline
CH & -0.368461 & 0.367531 & -1.003 & 0.317601 & 0.158801 \tabularnewline
H & 0.402652 & 0.367384 & 1.096 & 0.274727 & 0.137364 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271215&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]8.19884[/C][C]0.566387[/C][C]14.48[/C][C]6.86329e-31[/C][C]3.43164e-31[/C][/ROW]
[ROW][C]LFM[/C][C]0.0424907[/C][C]0.00547741[/C][C]7.757[/C][C]9.58819e-13[/C][C]4.7941e-13[/C][/ROW]
[ROW][C]B[/C][C]0.0030227[/C][C]0.0036562[/C][C]0.8267[/C][C]0.40962[/C][C]0.20481[/C][/ROW]
[ROW][C]PRH[/C][C]-0.41126[/C][C]0.368572[/C][C]-1.116[/C][C]0.266173[/C][C]0.133086[/C][/ROW]
[ROW][C]CH[/C][C]-0.368461[/C][C]0.367531[/C][C]-1.003[/C][C]0.317601[/C][C]0.158801[/C][/ROW]
[ROW][C]H[/C][C]0.402652[/C][C]0.367384[/C][C]1.096[/C][C]0.274727[/C][C]0.137364[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271215&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271215&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.198840.56638714.486.86329e-313.43164e-31
LFM0.04249070.005477417.7579.58819e-134.7941e-13
B0.00302270.00365620.82670.409620.20481
PRH-0.411260.368572-1.1160.2661730.133086
CH-0.3684610.367531-1.0030.3176010.158801
H0.4026520.3673841.0960.2747270.137364






Multiple Linear Regression - Regression Statistics
Multiple R0.69597
R-squared0.484374
Adjusted R-squared0.468261
F-TEST (value)30.0605
F-TEST (DF numerator)5
F-TEST (DF denominator)160
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.22259
Sum Squared Residuals790.384

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.69597 \tabularnewline
R-squared & 0.484374 \tabularnewline
Adjusted R-squared & 0.468261 \tabularnewline
F-TEST (value) & 30.0605 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 160 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.22259 \tabularnewline
Sum Squared Residuals & 790.384 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271215&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.69597[/C][/ROW]
[ROW][C]R-squared[/C][C]0.484374[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.468261[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]30.0605[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]160[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.22259[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]790.384[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271215&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271215&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 R0.69597
R-squared0.484374
Adjusted R-squared0.468261
F-TEST (value)30.0605
F-TEST (DF numerator)5
F-TEST (DF denominator)160
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.22259
Sum Squared Residuals790.384






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.3510.172-5.82196
212.712.16550.534479
318.116.10351.99646
417.8516.31231.53766
516.616.9993-0.399349
612.611.01271.58727
717.119.6463-2.54631
819.117.37531.7247
916.120.255-4.15502
1013.3511.39961.95038
1118.416.95841.44158
1214.710.58224.11777
1310.613.9732-3.37315
1412.614.1525-1.55247
1516.215.72250.477489
1613.613.9269-0.326913
1718.916.30992.59009
1814.113.22490.875057
1914.513.75440.74565
2016.1518.0578-1.90779
2114.7513.93120.818798
2214.814.69850.101503
2312.4512.5166-0.0665522
2412.6513.4777-0.827666
2517.3514.69412.65586
268.610.7658-2.16577
2718.417.58530.814738
2816.116.2295-0.12952
2911.611.7521-0.152113
3017.7515.13612.61391
3115.2516.7787-1.52865
3217.6515.56842.08156
3316.3516.15360.196446
3417.6517.06290.587123
3513.614.5681-0.96812
3614.3514.5303-0.180312
3714.7516.5579-1.80792
3818.2517.03181.21824
399.916.9783-7.07833
401615.73010.269939
4118.2516.48311.76689
4216.8517.6011-0.751142
4314.612.20682.39321
4413.8514.419-0.569019
4518.9518.39440.555631
4615.614.67540.924647
4714.8516.2353-1.38531
4811.7513.0566-1.3066
4918.4515.87332.57665
5015.914.2191.68099
5117.118.2457-1.14569
5216.19.394816.70519
5319.917.59742.30258
5410.9510.86260.087354
5518.4516.08322.3668
5615.112.43972.6603
571514.19710.802885
5811.3514.0427-2.69268
5915.9514.4691.48096
6018.114.95743.14263
6114.616.0003-1.40026
6215.416.2889-0.888931
6315.416.2889-0.888931
6417.615.01872.58132
6513.3515.2823-1.9323
6619.116.91142.18861
6715.3516.283-0.93299
687.611.058-3.45799
6913.414.9333-1.53332
7013.915.274-1.374
7119.117.41471.68535
7215.2514.87190.378097
7312.915.6127-2.71268
7416.114.98561.11437
7517.3513.59013.75992
7613.1514.293-1.14305
7712.1512.6518-0.501793
7812.612.39830.201749
7910.3511.7606-1.41058
8015.415.4545-0.0545041
819.612.1176-2.51756
8218.214.40813.79193
8313.612.77690.823091
8414.8513.11811.73195
8514.7517.1156-2.36561
8614.114.1671-0.06711
8714.912.52172.37831
8816.2514.45371.79627
8919.2519.5871-0.337093
9013.612.12451.47554
9113.615.6305-2.03049
9215.6514.98650.663489
9312.7514.3264-1.57641
9414.611.49593.10413
959.8511.7463-1.89632
9612.6511.35931.29071
9719.217.13272.06729
9816.615.0771.523
9911.211.2963-0.0963266
10015.2516.0958-0.845832
10111.915.14-3.23998
10213.213.4367-0.236696
10316.3517.678-1.32801
10412.413.9011-1.50113
10515.8513.90371.94625
10618.1517.16350.986489
10711.1512.0783-0.92829
10815.6515.40850.241483
10917.7515.69722.05285
1107.6511.6977-4.04766
11112.3513.7272-1.37722
11215.614.28021.31979
11319.317.5281.77197
11415.211.51273.68731
11517.115.01752.08252
11615.613.13372.46634
11718.416.65671.74333
11819.0516.39532.65471
11918.5515.98042.56964
12019.118.05561.04435
12113.112.93010.169918
12212.8516.1979-3.34787
1239.513.1216-3.62158
1244.511.1119-6.61187
12511.8510.79261.05745
12613.616.0531-2.45309
12711.712.883-1.18302
12812.412.7231-0.323052
12913.3515.0792-1.72921
13011.412.6874-1.28739
13114.913.45631.44371
13219.917.59742.30258
13311.212.7611-1.56112
13414.615.217-0.61695
13517.618.3968-0.796807
13614.0514.3477-0.297735
13716.116.2891-0.189079
13813.3514.3716-1.02164
13911.8514.2393-2.38926
14011.9513.9982-2.04817
14114.7514.38970.360256
14215.1514.10021.04976
14313.216.7281-3.52811
14416.8515.63151.21851
1457.8511.6394-3.78938
1467.713.8193-6.11931
14712.613.8906-1.29057
1487.8513.8969-6.04685
14910.9510.86260.087354
15012.3513.3227-0.972696
1519.9513.0692-3.11922
15214.913.45631.44371
15316.6514.57832.07174
15413.412.60280.797217
15513.9512.99650.953455
15615.713.66932.03071
15716.8514.60052.2495
15810.9511.7097-0.759683
15915.3513.82161.52842
16012.211.98380.21616
16115.114.31580.784222
16217.7515.04772.70225
16315.214.58480.615166
16414.614.2890.311006
16516.6514.89651.75346
1668.110.187-2.08696

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.35 & 10.172 & -5.82196 \tabularnewline
2 & 12.7 & 12.1655 & 0.534479 \tabularnewline
3 & 18.1 & 16.1035 & 1.99646 \tabularnewline
4 & 17.85 & 16.3123 & 1.53766 \tabularnewline
5 & 16.6 & 16.9993 & -0.399349 \tabularnewline
6 & 12.6 & 11.0127 & 1.58727 \tabularnewline
7 & 17.1 & 19.6463 & -2.54631 \tabularnewline
8 & 19.1 & 17.3753 & 1.7247 \tabularnewline
9 & 16.1 & 20.255 & -4.15502 \tabularnewline
10 & 13.35 & 11.3996 & 1.95038 \tabularnewline
11 & 18.4 & 16.9584 & 1.44158 \tabularnewline
12 & 14.7 & 10.5822 & 4.11777 \tabularnewline
13 & 10.6 & 13.9732 & -3.37315 \tabularnewline
14 & 12.6 & 14.1525 & -1.55247 \tabularnewline
15 & 16.2 & 15.7225 & 0.477489 \tabularnewline
16 & 13.6 & 13.9269 & -0.326913 \tabularnewline
17 & 18.9 & 16.3099 & 2.59009 \tabularnewline
18 & 14.1 & 13.2249 & 0.875057 \tabularnewline
19 & 14.5 & 13.7544 & 0.74565 \tabularnewline
20 & 16.15 & 18.0578 & -1.90779 \tabularnewline
21 & 14.75 & 13.9312 & 0.818798 \tabularnewline
22 & 14.8 & 14.6985 & 0.101503 \tabularnewline
23 & 12.45 & 12.5166 & -0.0665522 \tabularnewline
24 & 12.65 & 13.4777 & -0.827666 \tabularnewline
25 & 17.35 & 14.6941 & 2.65586 \tabularnewline
26 & 8.6 & 10.7658 & -2.16577 \tabularnewline
27 & 18.4 & 17.5853 & 0.814738 \tabularnewline
28 & 16.1 & 16.2295 & -0.12952 \tabularnewline
29 & 11.6 & 11.7521 & -0.152113 \tabularnewline
30 & 17.75 & 15.1361 & 2.61391 \tabularnewline
31 & 15.25 & 16.7787 & -1.52865 \tabularnewline
32 & 17.65 & 15.5684 & 2.08156 \tabularnewline
33 & 16.35 & 16.1536 & 0.196446 \tabularnewline
34 & 17.65 & 17.0629 & 0.587123 \tabularnewline
35 & 13.6 & 14.5681 & -0.96812 \tabularnewline
36 & 14.35 & 14.5303 & -0.180312 \tabularnewline
37 & 14.75 & 16.5579 & -1.80792 \tabularnewline
38 & 18.25 & 17.0318 & 1.21824 \tabularnewline
39 & 9.9 & 16.9783 & -7.07833 \tabularnewline
40 & 16 & 15.7301 & 0.269939 \tabularnewline
41 & 18.25 & 16.4831 & 1.76689 \tabularnewline
42 & 16.85 & 17.6011 & -0.751142 \tabularnewline
43 & 14.6 & 12.2068 & 2.39321 \tabularnewline
44 & 13.85 & 14.419 & -0.569019 \tabularnewline
45 & 18.95 & 18.3944 & 0.555631 \tabularnewline
46 & 15.6 & 14.6754 & 0.924647 \tabularnewline
47 & 14.85 & 16.2353 & -1.38531 \tabularnewline
48 & 11.75 & 13.0566 & -1.3066 \tabularnewline
49 & 18.45 & 15.8733 & 2.57665 \tabularnewline
50 & 15.9 & 14.219 & 1.68099 \tabularnewline
51 & 17.1 & 18.2457 & -1.14569 \tabularnewline
52 & 16.1 & 9.39481 & 6.70519 \tabularnewline
53 & 19.9 & 17.5974 & 2.30258 \tabularnewline
54 & 10.95 & 10.8626 & 0.087354 \tabularnewline
55 & 18.45 & 16.0832 & 2.3668 \tabularnewline
56 & 15.1 & 12.4397 & 2.6603 \tabularnewline
57 & 15 & 14.1971 & 0.802885 \tabularnewline
58 & 11.35 & 14.0427 & -2.69268 \tabularnewline
59 & 15.95 & 14.469 & 1.48096 \tabularnewline
60 & 18.1 & 14.9574 & 3.14263 \tabularnewline
61 & 14.6 & 16.0003 & -1.40026 \tabularnewline
62 & 15.4 & 16.2889 & -0.888931 \tabularnewline
63 & 15.4 & 16.2889 & -0.888931 \tabularnewline
64 & 17.6 & 15.0187 & 2.58132 \tabularnewline
65 & 13.35 & 15.2823 & -1.9323 \tabularnewline
66 & 19.1 & 16.9114 & 2.18861 \tabularnewline
67 & 15.35 & 16.283 & -0.93299 \tabularnewline
68 & 7.6 & 11.058 & -3.45799 \tabularnewline
69 & 13.4 & 14.9333 & -1.53332 \tabularnewline
70 & 13.9 & 15.274 & -1.374 \tabularnewline
71 & 19.1 & 17.4147 & 1.68535 \tabularnewline
72 & 15.25 & 14.8719 & 0.378097 \tabularnewline
73 & 12.9 & 15.6127 & -2.71268 \tabularnewline
74 & 16.1 & 14.9856 & 1.11437 \tabularnewline
75 & 17.35 & 13.5901 & 3.75992 \tabularnewline
76 & 13.15 & 14.293 & -1.14305 \tabularnewline
77 & 12.15 & 12.6518 & -0.501793 \tabularnewline
78 & 12.6 & 12.3983 & 0.201749 \tabularnewline
79 & 10.35 & 11.7606 & -1.41058 \tabularnewline
80 & 15.4 & 15.4545 & -0.0545041 \tabularnewline
81 & 9.6 & 12.1176 & -2.51756 \tabularnewline
82 & 18.2 & 14.4081 & 3.79193 \tabularnewline
83 & 13.6 & 12.7769 & 0.823091 \tabularnewline
84 & 14.85 & 13.1181 & 1.73195 \tabularnewline
85 & 14.75 & 17.1156 & -2.36561 \tabularnewline
86 & 14.1 & 14.1671 & -0.06711 \tabularnewline
87 & 14.9 & 12.5217 & 2.37831 \tabularnewline
88 & 16.25 & 14.4537 & 1.79627 \tabularnewline
89 & 19.25 & 19.5871 & -0.337093 \tabularnewline
90 & 13.6 & 12.1245 & 1.47554 \tabularnewline
91 & 13.6 & 15.6305 & -2.03049 \tabularnewline
92 & 15.65 & 14.9865 & 0.663489 \tabularnewline
93 & 12.75 & 14.3264 & -1.57641 \tabularnewline
94 & 14.6 & 11.4959 & 3.10413 \tabularnewline
95 & 9.85 & 11.7463 & -1.89632 \tabularnewline
96 & 12.65 & 11.3593 & 1.29071 \tabularnewline
97 & 19.2 & 17.1327 & 2.06729 \tabularnewline
98 & 16.6 & 15.077 & 1.523 \tabularnewline
99 & 11.2 & 11.2963 & -0.0963266 \tabularnewline
100 & 15.25 & 16.0958 & -0.845832 \tabularnewline
101 & 11.9 & 15.14 & -3.23998 \tabularnewline
102 & 13.2 & 13.4367 & -0.236696 \tabularnewline
103 & 16.35 & 17.678 & -1.32801 \tabularnewline
104 & 12.4 & 13.9011 & -1.50113 \tabularnewline
105 & 15.85 & 13.9037 & 1.94625 \tabularnewline
106 & 18.15 & 17.1635 & 0.986489 \tabularnewline
107 & 11.15 & 12.0783 & -0.92829 \tabularnewline
108 & 15.65 & 15.4085 & 0.241483 \tabularnewline
109 & 17.75 & 15.6972 & 2.05285 \tabularnewline
110 & 7.65 & 11.6977 & -4.04766 \tabularnewline
111 & 12.35 & 13.7272 & -1.37722 \tabularnewline
112 & 15.6 & 14.2802 & 1.31979 \tabularnewline
113 & 19.3 & 17.528 & 1.77197 \tabularnewline
114 & 15.2 & 11.5127 & 3.68731 \tabularnewline
115 & 17.1 & 15.0175 & 2.08252 \tabularnewline
116 & 15.6 & 13.1337 & 2.46634 \tabularnewline
117 & 18.4 & 16.6567 & 1.74333 \tabularnewline
118 & 19.05 & 16.3953 & 2.65471 \tabularnewline
119 & 18.55 & 15.9804 & 2.56964 \tabularnewline
120 & 19.1 & 18.0556 & 1.04435 \tabularnewline
121 & 13.1 & 12.9301 & 0.169918 \tabularnewline
122 & 12.85 & 16.1979 & -3.34787 \tabularnewline
123 & 9.5 & 13.1216 & -3.62158 \tabularnewline
124 & 4.5 & 11.1119 & -6.61187 \tabularnewline
125 & 11.85 & 10.7926 & 1.05745 \tabularnewline
126 & 13.6 & 16.0531 & -2.45309 \tabularnewline
127 & 11.7 & 12.883 & -1.18302 \tabularnewline
128 & 12.4 & 12.7231 & -0.323052 \tabularnewline
129 & 13.35 & 15.0792 & -1.72921 \tabularnewline
130 & 11.4 & 12.6874 & -1.28739 \tabularnewline
131 & 14.9 & 13.4563 & 1.44371 \tabularnewline
132 & 19.9 & 17.5974 & 2.30258 \tabularnewline
133 & 11.2 & 12.7611 & -1.56112 \tabularnewline
134 & 14.6 & 15.217 & -0.61695 \tabularnewline
135 & 17.6 & 18.3968 & -0.796807 \tabularnewline
136 & 14.05 & 14.3477 & -0.297735 \tabularnewline
137 & 16.1 & 16.2891 & -0.189079 \tabularnewline
138 & 13.35 & 14.3716 & -1.02164 \tabularnewline
139 & 11.85 & 14.2393 & -2.38926 \tabularnewline
140 & 11.95 & 13.9982 & -2.04817 \tabularnewline
141 & 14.75 & 14.3897 & 0.360256 \tabularnewline
142 & 15.15 & 14.1002 & 1.04976 \tabularnewline
143 & 13.2 & 16.7281 & -3.52811 \tabularnewline
144 & 16.85 & 15.6315 & 1.21851 \tabularnewline
145 & 7.85 & 11.6394 & -3.78938 \tabularnewline
146 & 7.7 & 13.8193 & -6.11931 \tabularnewline
147 & 12.6 & 13.8906 & -1.29057 \tabularnewline
148 & 7.85 & 13.8969 & -6.04685 \tabularnewline
149 & 10.95 & 10.8626 & 0.087354 \tabularnewline
150 & 12.35 & 13.3227 & -0.972696 \tabularnewline
151 & 9.95 & 13.0692 & -3.11922 \tabularnewline
152 & 14.9 & 13.4563 & 1.44371 \tabularnewline
153 & 16.65 & 14.5783 & 2.07174 \tabularnewline
154 & 13.4 & 12.6028 & 0.797217 \tabularnewline
155 & 13.95 & 12.9965 & 0.953455 \tabularnewline
156 & 15.7 & 13.6693 & 2.03071 \tabularnewline
157 & 16.85 & 14.6005 & 2.2495 \tabularnewline
158 & 10.95 & 11.7097 & -0.759683 \tabularnewline
159 & 15.35 & 13.8216 & 1.52842 \tabularnewline
160 & 12.2 & 11.9838 & 0.21616 \tabularnewline
161 & 15.1 & 14.3158 & 0.784222 \tabularnewline
162 & 17.75 & 15.0477 & 2.70225 \tabularnewline
163 & 15.2 & 14.5848 & 0.615166 \tabularnewline
164 & 14.6 & 14.289 & 0.311006 \tabularnewline
165 & 16.65 & 14.8965 & 1.75346 \tabularnewline
166 & 8.1 & 10.187 & -2.08696 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271215&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]4.35[/C][C]10.172[/C][C]-5.82196[/C][/ROW]
[ROW][C]2[/C][C]12.7[/C][C]12.1655[/C][C]0.534479[/C][/ROW]
[ROW][C]3[/C][C]18.1[/C][C]16.1035[/C][C]1.99646[/C][/ROW]
[ROW][C]4[/C][C]17.85[/C][C]16.3123[/C][C]1.53766[/C][/ROW]
[ROW][C]5[/C][C]16.6[/C][C]16.9993[/C][C]-0.399349[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.0127[/C][C]1.58727[/C][/ROW]
[ROW][C]7[/C][C]17.1[/C][C]19.6463[/C][C]-2.54631[/C][/ROW]
[ROW][C]8[/C][C]19.1[/C][C]17.3753[/C][C]1.7247[/C][/ROW]
[ROW][C]9[/C][C]16.1[/C][C]20.255[/C][C]-4.15502[/C][/ROW]
[ROW][C]10[/C][C]13.35[/C][C]11.3996[/C][C]1.95038[/C][/ROW]
[ROW][C]11[/C][C]18.4[/C][C]16.9584[/C][C]1.44158[/C][/ROW]
[ROW][C]12[/C][C]14.7[/C][C]10.5822[/C][C]4.11777[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]13.9732[/C][C]-3.37315[/C][/ROW]
[ROW][C]14[/C][C]12.6[/C][C]14.1525[/C][C]-1.55247[/C][/ROW]
[ROW][C]15[/C][C]16.2[/C][C]15.7225[/C][C]0.477489[/C][/ROW]
[ROW][C]16[/C][C]13.6[/C][C]13.9269[/C][C]-0.326913[/C][/ROW]
[ROW][C]17[/C][C]18.9[/C][C]16.3099[/C][C]2.59009[/C][/ROW]
[ROW][C]18[/C][C]14.1[/C][C]13.2249[/C][C]0.875057[/C][/ROW]
[ROW][C]19[/C][C]14.5[/C][C]13.7544[/C][C]0.74565[/C][/ROW]
[ROW][C]20[/C][C]16.15[/C][C]18.0578[/C][C]-1.90779[/C][/ROW]
[ROW][C]21[/C][C]14.75[/C][C]13.9312[/C][C]0.818798[/C][/ROW]
[ROW][C]22[/C][C]14.8[/C][C]14.6985[/C][C]0.101503[/C][/ROW]
[ROW][C]23[/C][C]12.45[/C][C]12.5166[/C][C]-0.0665522[/C][/ROW]
[ROW][C]24[/C][C]12.65[/C][C]13.4777[/C][C]-0.827666[/C][/ROW]
[ROW][C]25[/C][C]17.35[/C][C]14.6941[/C][C]2.65586[/C][/ROW]
[ROW][C]26[/C][C]8.6[/C][C]10.7658[/C][C]-2.16577[/C][/ROW]
[ROW][C]27[/C][C]18.4[/C][C]17.5853[/C][C]0.814738[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]16.2295[/C][C]-0.12952[/C][/ROW]
[ROW][C]29[/C][C]11.6[/C][C]11.7521[/C][C]-0.152113[/C][/ROW]
[ROW][C]30[/C][C]17.75[/C][C]15.1361[/C][C]2.61391[/C][/ROW]
[ROW][C]31[/C][C]15.25[/C][C]16.7787[/C][C]-1.52865[/C][/ROW]
[ROW][C]32[/C][C]17.65[/C][C]15.5684[/C][C]2.08156[/C][/ROW]
[ROW][C]33[/C][C]16.35[/C][C]16.1536[/C][C]0.196446[/C][/ROW]
[ROW][C]34[/C][C]17.65[/C][C]17.0629[/C][C]0.587123[/C][/ROW]
[ROW][C]35[/C][C]13.6[/C][C]14.5681[/C][C]-0.96812[/C][/ROW]
[ROW][C]36[/C][C]14.35[/C][C]14.5303[/C][C]-0.180312[/C][/ROW]
[ROW][C]37[/C][C]14.75[/C][C]16.5579[/C][C]-1.80792[/C][/ROW]
[ROW][C]38[/C][C]18.25[/C][C]17.0318[/C][C]1.21824[/C][/ROW]
[ROW][C]39[/C][C]9.9[/C][C]16.9783[/C][C]-7.07833[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.7301[/C][C]0.269939[/C][/ROW]
[ROW][C]41[/C][C]18.25[/C][C]16.4831[/C][C]1.76689[/C][/ROW]
[ROW][C]42[/C][C]16.85[/C][C]17.6011[/C][C]-0.751142[/C][/ROW]
[ROW][C]43[/C][C]14.6[/C][C]12.2068[/C][C]2.39321[/C][/ROW]
[ROW][C]44[/C][C]13.85[/C][C]14.419[/C][C]-0.569019[/C][/ROW]
[ROW][C]45[/C][C]18.95[/C][C]18.3944[/C][C]0.555631[/C][/ROW]
[ROW][C]46[/C][C]15.6[/C][C]14.6754[/C][C]0.924647[/C][/ROW]
[ROW][C]47[/C][C]14.85[/C][C]16.2353[/C][C]-1.38531[/C][/ROW]
[ROW][C]48[/C][C]11.75[/C][C]13.0566[/C][C]-1.3066[/C][/ROW]
[ROW][C]49[/C][C]18.45[/C][C]15.8733[/C][C]2.57665[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]14.219[/C][C]1.68099[/C][/ROW]
[ROW][C]51[/C][C]17.1[/C][C]18.2457[/C][C]-1.14569[/C][/ROW]
[ROW][C]52[/C][C]16.1[/C][C]9.39481[/C][C]6.70519[/C][/ROW]
[ROW][C]53[/C][C]19.9[/C][C]17.5974[/C][C]2.30258[/C][/ROW]
[ROW][C]54[/C][C]10.95[/C][C]10.8626[/C][C]0.087354[/C][/ROW]
[ROW][C]55[/C][C]18.45[/C][C]16.0832[/C][C]2.3668[/C][/ROW]
[ROW][C]56[/C][C]15.1[/C][C]12.4397[/C][C]2.6603[/C][/ROW]
[ROW][C]57[/C][C]15[/C][C]14.1971[/C][C]0.802885[/C][/ROW]
[ROW][C]58[/C][C]11.35[/C][C]14.0427[/C][C]-2.69268[/C][/ROW]
[ROW][C]59[/C][C]15.95[/C][C]14.469[/C][C]1.48096[/C][/ROW]
[ROW][C]60[/C][C]18.1[/C][C]14.9574[/C][C]3.14263[/C][/ROW]
[ROW][C]61[/C][C]14.6[/C][C]16.0003[/C][C]-1.40026[/C][/ROW]
[ROW][C]62[/C][C]15.4[/C][C]16.2889[/C][C]-0.888931[/C][/ROW]
[ROW][C]63[/C][C]15.4[/C][C]16.2889[/C][C]-0.888931[/C][/ROW]
[ROW][C]64[/C][C]17.6[/C][C]15.0187[/C][C]2.58132[/C][/ROW]
[ROW][C]65[/C][C]13.35[/C][C]15.2823[/C][C]-1.9323[/C][/ROW]
[ROW][C]66[/C][C]19.1[/C][C]16.9114[/C][C]2.18861[/C][/ROW]
[ROW][C]67[/C][C]15.35[/C][C]16.283[/C][C]-0.93299[/C][/ROW]
[ROW][C]68[/C][C]7.6[/C][C]11.058[/C][C]-3.45799[/C][/ROW]
[ROW][C]69[/C][C]13.4[/C][C]14.9333[/C][C]-1.53332[/C][/ROW]
[ROW][C]70[/C][C]13.9[/C][C]15.274[/C][C]-1.374[/C][/ROW]
[ROW][C]71[/C][C]19.1[/C][C]17.4147[/C][C]1.68535[/C][/ROW]
[ROW][C]72[/C][C]15.25[/C][C]14.8719[/C][C]0.378097[/C][/ROW]
[ROW][C]73[/C][C]12.9[/C][C]15.6127[/C][C]-2.71268[/C][/ROW]
[ROW][C]74[/C][C]16.1[/C][C]14.9856[/C][C]1.11437[/C][/ROW]
[ROW][C]75[/C][C]17.35[/C][C]13.5901[/C][C]3.75992[/C][/ROW]
[ROW][C]76[/C][C]13.15[/C][C]14.293[/C][C]-1.14305[/C][/ROW]
[ROW][C]77[/C][C]12.15[/C][C]12.6518[/C][C]-0.501793[/C][/ROW]
[ROW][C]78[/C][C]12.6[/C][C]12.3983[/C][C]0.201749[/C][/ROW]
[ROW][C]79[/C][C]10.35[/C][C]11.7606[/C][C]-1.41058[/C][/ROW]
[ROW][C]80[/C][C]15.4[/C][C]15.4545[/C][C]-0.0545041[/C][/ROW]
[ROW][C]81[/C][C]9.6[/C][C]12.1176[/C][C]-2.51756[/C][/ROW]
[ROW][C]82[/C][C]18.2[/C][C]14.4081[/C][C]3.79193[/C][/ROW]
[ROW][C]83[/C][C]13.6[/C][C]12.7769[/C][C]0.823091[/C][/ROW]
[ROW][C]84[/C][C]14.85[/C][C]13.1181[/C][C]1.73195[/C][/ROW]
[ROW][C]85[/C][C]14.75[/C][C]17.1156[/C][C]-2.36561[/C][/ROW]
[ROW][C]86[/C][C]14.1[/C][C]14.1671[/C][C]-0.06711[/C][/ROW]
[ROW][C]87[/C][C]14.9[/C][C]12.5217[/C][C]2.37831[/C][/ROW]
[ROW][C]88[/C][C]16.25[/C][C]14.4537[/C][C]1.79627[/C][/ROW]
[ROW][C]89[/C][C]19.25[/C][C]19.5871[/C][C]-0.337093[/C][/ROW]
[ROW][C]90[/C][C]13.6[/C][C]12.1245[/C][C]1.47554[/C][/ROW]
[ROW][C]91[/C][C]13.6[/C][C]15.6305[/C][C]-2.03049[/C][/ROW]
[ROW][C]92[/C][C]15.65[/C][C]14.9865[/C][C]0.663489[/C][/ROW]
[ROW][C]93[/C][C]12.75[/C][C]14.3264[/C][C]-1.57641[/C][/ROW]
[ROW][C]94[/C][C]14.6[/C][C]11.4959[/C][C]3.10413[/C][/ROW]
[ROW][C]95[/C][C]9.85[/C][C]11.7463[/C][C]-1.89632[/C][/ROW]
[ROW][C]96[/C][C]12.65[/C][C]11.3593[/C][C]1.29071[/C][/ROW]
[ROW][C]97[/C][C]19.2[/C][C]17.1327[/C][C]2.06729[/C][/ROW]
[ROW][C]98[/C][C]16.6[/C][C]15.077[/C][C]1.523[/C][/ROW]
[ROW][C]99[/C][C]11.2[/C][C]11.2963[/C][C]-0.0963266[/C][/ROW]
[ROW][C]100[/C][C]15.25[/C][C]16.0958[/C][C]-0.845832[/C][/ROW]
[ROW][C]101[/C][C]11.9[/C][C]15.14[/C][C]-3.23998[/C][/ROW]
[ROW][C]102[/C][C]13.2[/C][C]13.4367[/C][C]-0.236696[/C][/ROW]
[ROW][C]103[/C][C]16.35[/C][C]17.678[/C][C]-1.32801[/C][/ROW]
[ROW][C]104[/C][C]12.4[/C][C]13.9011[/C][C]-1.50113[/C][/ROW]
[ROW][C]105[/C][C]15.85[/C][C]13.9037[/C][C]1.94625[/C][/ROW]
[ROW][C]106[/C][C]18.15[/C][C]17.1635[/C][C]0.986489[/C][/ROW]
[ROW][C]107[/C][C]11.15[/C][C]12.0783[/C][C]-0.92829[/C][/ROW]
[ROW][C]108[/C][C]15.65[/C][C]15.4085[/C][C]0.241483[/C][/ROW]
[ROW][C]109[/C][C]17.75[/C][C]15.6972[/C][C]2.05285[/C][/ROW]
[ROW][C]110[/C][C]7.65[/C][C]11.6977[/C][C]-4.04766[/C][/ROW]
[ROW][C]111[/C][C]12.35[/C][C]13.7272[/C][C]-1.37722[/C][/ROW]
[ROW][C]112[/C][C]15.6[/C][C]14.2802[/C][C]1.31979[/C][/ROW]
[ROW][C]113[/C][C]19.3[/C][C]17.528[/C][C]1.77197[/C][/ROW]
[ROW][C]114[/C][C]15.2[/C][C]11.5127[/C][C]3.68731[/C][/ROW]
[ROW][C]115[/C][C]17.1[/C][C]15.0175[/C][C]2.08252[/C][/ROW]
[ROW][C]116[/C][C]15.6[/C][C]13.1337[/C][C]2.46634[/C][/ROW]
[ROW][C]117[/C][C]18.4[/C][C]16.6567[/C][C]1.74333[/C][/ROW]
[ROW][C]118[/C][C]19.05[/C][C]16.3953[/C][C]2.65471[/C][/ROW]
[ROW][C]119[/C][C]18.55[/C][C]15.9804[/C][C]2.56964[/C][/ROW]
[ROW][C]120[/C][C]19.1[/C][C]18.0556[/C][C]1.04435[/C][/ROW]
[ROW][C]121[/C][C]13.1[/C][C]12.9301[/C][C]0.169918[/C][/ROW]
[ROW][C]122[/C][C]12.85[/C][C]16.1979[/C][C]-3.34787[/C][/ROW]
[ROW][C]123[/C][C]9.5[/C][C]13.1216[/C][C]-3.62158[/C][/ROW]
[ROW][C]124[/C][C]4.5[/C][C]11.1119[/C][C]-6.61187[/C][/ROW]
[ROW][C]125[/C][C]11.85[/C][C]10.7926[/C][C]1.05745[/C][/ROW]
[ROW][C]126[/C][C]13.6[/C][C]16.0531[/C][C]-2.45309[/C][/ROW]
[ROW][C]127[/C][C]11.7[/C][C]12.883[/C][C]-1.18302[/C][/ROW]
[ROW][C]128[/C][C]12.4[/C][C]12.7231[/C][C]-0.323052[/C][/ROW]
[ROW][C]129[/C][C]13.35[/C][C]15.0792[/C][C]-1.72921[/C][/ROW]
[ROW][C]130[/C][C]11.4[/C][C]12.6874[/C][C]-1.28739[/C][/ROW]
[ROW][C]131[/C][C]14.9[/C][C]13.4563[/C][C]1.44371[/C][/ROW]
[ROW][C]132[/C][C]19.9[/C][C]17.5974[/C][C]2.30258[/C][/ROW]
[ROW][C]133[/C][C]11.2[/C][C]12.7611[/C][C]-1.56112[/C][/ROW]
[ROW][C]134[/C][C]14.6[/C][C]15.217[/C][C]-0.61695[/C][/ROW]
[ROW][C]135[/C][C]17.6[/C][C]18.3968[/C][C]-0.796807[/C][/ROW]
[ROW][C]136[/C][C]14.05[/C][C]14.3477[/C][C]-0.297735[/C][/ROW]
[ROW][C]137[/C][C]16.1[/C][C]16.2891[/C][C]-0.189079[/C][/ROW]
[ROW][C]138[/C][C]13.35[/C][C]14.3716[/C][C]-1.02164[/C][/ROW]
[ROW][C]139[/C][C]11.85[/C][C]14.2393[/C][C]-2.38926[/C][/ROW]
[ROW][C]140[/C][C]11.95[/C][C]13.9982[/C][C]-2.04817[/C][/ROW]
[ROW][C]141[/C][C]14.75[/C][C]14.3897[/C][C]0.360256[/C][/ROW]
[ROW][C]142[/C][C]15.15[/C][C]14.1002[/C][C]1.04976[/C][/ROW]
[ROW][C]143[/C][C]13.2[/C][C]16.7281[/C][C]-3.52811[/C][/ROW]
[ROW][C]144[/C][C]16.85[/C][C]15.6315[/C][C]1.21851[/C][/ROW]
[ROW][C]145[/C][C]7.85[/C][C]11.6394[/C][C]-3.78938[/C][/ROW]
[ROW][C]146[/C][C]7.7[/C][C]13.8193[/C][C]-6.11931[/C][/ROW]
[ROW][C]147[/C][C]12.6[/C][C]13.8906[/C][C]-1.29057[/C][/ROW]
[ROW][C]148[/C][C]7.85[/C][C]13.8969[/C][C]-6.04685[/C][/ROW]
[ROW][C]149[/C][C]10.95[/C][C]10.8626[/C][C]0.087354[/C][/ROW]
[ROW][C]150[/C][C]12.35[/C][C]13.3227[/C][C]-0.972696[/C][/ROW]
[ROW][C]151[/C][C]9.95[/C][C]13.0692[/C][C]-3.11922[/C][/ROW]
[ROW][C]152[/C][C]14.9[/C][C]13.4563[/C][C]1.44371[/C][/ROW]
[ROW][C]153[/C][C]16.65[/C][C]14.5783[/C][C]2.07174[/C][/ROW]
[ROW][C]154[/C][C]13.4[/C][C]12.6028[/C][C]0.797217[/C][/ROW]
[ROW][C]155[/C][C]13.95[/C][C]12.9965[/C][C]0.953455[/C][/ROW]
[ROW][C]156[/C][C]15.7[/C][C]13.6693[/C][C]2.03071[/C][/ROW]
[ROW][C]157[/C][C]16.85[/C][C]14.6005[/C][C]2.2495[/C][/ROW]
[ROW][C]158[/C][C]10.95[/C][C]11.7097[/C][C]-0.759683[/C][/ROW]
[ROW][C]159[/C][C]15.35[/C][C]13.8216[/C][C]1.52842[/C][/ROW]
[ROW][C]160[/C][C]12.2[/C][C]11.9838[/C][C]0.21616[/C][/ROW]
[ROW][C]161[/C][C]15.1[/C][C]14.3158[/C][C]0.784222[/C][/ROW]
[ROW][C]162[/C][C]17.75[/C][C]15.0477[/C][C]2.70225[/C][/ROW]
[ROW][C]163[/C][C]15.2[/C][C]14.5848[/C][C]0.615166[/C][/ROW]
[ROW][C]164[/C][C]14.6[/C][C]14.289[/C][C]0.311006[/C][/ROW]
[ROW][C]165[/C][C]16.65[/C][C]14.8965[/C][C]1.75346[/C][/ROW]
[ROW][C]166[/C][C]8.1[/C][C]10.187[/C][C]-2.08696[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271215&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271215&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.3510.172-5.82196
212.712.16550.534479
318.116.10351.99646
417.8516.31231.53766
516.616.9993-0.399349
612.611.01271.58727
717.119.6463-2.54631
819.117.37531.7247
916.120.255-4.15502
1013.3511.39961.95038
1118.416.95841.44158
1214.710.58224.11777
1310.613.9732-3.37315
1412.614.1525-1.55247
1516.215.72250.477489
1613.613.9269-0.326913
1718.916.30992.59009
1814.113.22490.875057
1914.513.75440.74565
2016.1518.0578-1.90779
2114.7513.93120.818798
2214.814.69850.101503
2312.4512.5166-0.0665522
2412.6513.4777-0.827666
2517.3514.69412.65586
268.610.7658-2.16577
2718.417.58530.814738
2816.116.2295-0.12952
2911.611.7521-0.152113
3017.7515.13612.61391
3115.2516.7787-1.52865
3217.6515.56842.08156
3316.3516.15360.196446
3417.6517.06290.587123
3513.614.5681-0.96812
3614.3514.5303-0.180312
3714.7516.5579-1.80792
3818.2517.03181.21824
399.916.9783-7.07833
401615.73010.269939
4118.2516.48311.76689
4216.8517.6011-0.751142
4314.612.20682.39321
4413.8514.419-0.569019
4518.9518.39440.555631
4615.614.67540.924647
4714.8516.2353-1.38531
4811.7513.0566-1.3066
4918.4515.87332.57665
5015.914.2191.68099
5117.118.2457-1.14569
5216.19.394816.70519
5319.917.59742.30258
5410.9510.86260.087354
5518.4516.08322.3668
5615.112.43972.6603
571514.19710.802885
5811.3514.0427-2.69268
5915.9514.4691.48096
6018.114.95743.14263
6114.616.0003-1.40026
6215.416.2889-0.888931
6315.416.2889-0.888931
6417.615.01872.58132
6513.3515.2823-1.9323
6619.116.91142.18861
6715.3516.283-0.93299
687.611.058-3.45799
6913.414.9333-1.53332
7013.915.274-1.374
7119.117.41471.68535
7215.2514.87190.378097
7312.915.6127-2.71268
7416.114.98561.11437
7517.3513.59013.75992
7613.1514.293-1.14305
7712.1512.6518-0.501793
7812.612.39830.201749
7910.3511.7606-1.41058
8015.415.4545-0.0545041
819.612.1176-2.51756
8218.214.40813.79193
8313.612.77690.823091
8414.8513.11811.73195
8514.7517.1156-2.36561
8614.114.1671-0.06711
8714.912.52172.37831
8816.2514.45371.79627
8919.2519.5871-0.337093
9013.612.12451.47554
9113.615.6305-2.03049
9215.6514.98650.663489
9312.7514.3264-1.57641
9414.611.49593.10413
959.8511.7463-1.89632
9612.6511.35931.29071
9719.217.13272.06729
9816.615.0771.523
9911.211.2963-0.0963266
10015.2516.0958-0.845832
10111.915.14-3.23998
10213.213.4367-0.236696
10316.3517.678-1.32801
10412.413.9011-1.50113
10515.8513.90371.94625
10618.1517.16350.986489
10711.1512.0783-0.92829
10815.6515.40850.241483
10917.7515.69722.05285
1107.6511.6977-4.04766
11112.3513.7272-1.37722
11215.614.28021.31979
11319.317.5281.77197
11415.211.51273.68731
11517.115.01752.08252
11615.613.13372.46634
11718.416.65671.74333
11819.0516.39532.65471
11918.5515.98042.56964
12019.118.05561.04435
12113.112.93010.169918
12212.8516.1979-3.34787
1239.513.1216-3.62158
1244.511.1119-6.61187
12511.8510.79261.05745
12613.616.0531-2.45309
12711.712.883-1.18302
12812.412.7231-0.323052
12913.3515.0792-1.72921
13011.412.6874-1.28739
13114.913.45631.44371
13219.917.59742.30258
13311.212.7611-1.56112
13414.615.217-0.61695
13517.618.3968-0.796807
13614.0514.3477-0.297735
13716.116.2891-0.189079
13813.3514.3716-1.02164
13911.8514.2393-2.38926
14011.9513.9982-2.04817
14114.7514.38970.360256
14215.1514.10021.04976
14313.216.7281-3.52811
14416.8515.63151.21851
1457.8511.6394-3.78938
1467.713.8193-6.11931
14712.613.8906-1.29057
1487.8513.8969-6.04685
14910.9510.86260.087354
15012.3513.3227-0.972696
1519.9513.0692-3.11922
15214.913.45631.44371
15316.6514.57832.07174
15413.412.60280.797217
15513.9512.99650.953455
15615.713.66932.03071
15716.8514.60052.2495
15810.9511.7097-0.759683
15915.3513.82161.52842
16012.211.98380.21616
16115.114.31580.784222
16217.7515.04772.70225
16315.214.58480.615166
16414.614.2890.311006
16516.6514.89651.75346
1668.110.187-2.08696






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.836850.32630.16315
100.7439590.5120810.256041
110.6575790.6848420.342421
120.6879490.6241010.312051
130.7605370.4789260.239463
140.856080.2878410.14392
150.8082310.3835390.191769
160.8915610.2168780.108439
170.8657930.2684140.134207
180.819720.360560.18028
190.7675680.4648630.232432
200.7372670.5254670.262733
210.7303370.5393260.269663
220.6657260.6685470.334274
230.6032610.7934790.396739
240.539360.9212810.46064
250.5319520.9360950.468048
260.5133040.9733910.486696
270.480940.961880.51906
280.4354530.8709060.564547
290.3784690.7569380.621531
300.3475990.6951980.652401
310.373350.74670.62665
320.3619160.7238330.638084
330.3128140.6256270.687186
340.2624360.5248730.737564
350.2498490.4996980.750151
360.2073540.4147080.792646
370.2237330.4474670.776267
380.1854650.3709290.814535
390.7379050.5241890.262095
400.694980.610040.30502
410.6700260.6599480.329974
420.6232710.7534580.376729
430.6230770.7538450.376923
440.5725890.8548230.427411
450.5293770.9412460.470623
460.483910.9678210.51609
470.4447380.8894760.555262
480.4434790.8869570.556521
490.4585290.9170580.541471
500.4585690.9171370.541431
510.4325830.8651670.567417
520.7638750.472250.236125
530.7644110.4711780.235589
540.7338290.5323420.266171
550.7349550.5300910.265045
560.7394420.5211160.260558
570.7018320.5963360.298168
580.7295960.5408080.270404
590.7031550.593690.296845
600.7290530.5418940.270947
610.7130010.5739980.286999
620.6758030.6483940.324197
630.636410.7271810.36359
640.6421150.7157690.357885
650.6399240.7201510.360076
660.6381860.7236270.361814
670.6057120.7885760.394288
680.6874770.6250460.312523
690.675410.649180.32459
700.6577720.6844550.342228
710.6378860.7242280.362114
720.5943130.8113740.405687
730.6197780.7604440.380222
740.5830580.8338840.416942
750.6596320.6807360.340368
760.6322080.7355850.367792
770.5947620.8104760.405238
780.5520630.8958730.447937
790.5292360.9415280.470764
800.4842610.9685220.515739
810.497320.994640.50268
820.5780420.8439150.421958
830.5405570.9188870.459443
840.52190.9561990.4781
850.5297870.9404260.470213
860.4848640.9697290.515136
870.4849910.9699830.515009
880.4656080.9312150.534392
890.4276140.8552280.572386
900.4104980.8209950.589502
910.4033930.8067850.596607
920.3645340.7290680.635466
930.3457580.6915170.654242
940.3947480.7894950.605252
950.3813880.7627760.618612
960.3676870.7353750.632313
970.3557860.7115720.644214
980.3271010.6542010.672899
990.2926680.5853360.707332
1000.260920.521840.73908
1010.3176960.6353920.682304
1020.2796330.5592660.720367
1030.2701060.5402120.729894
1040.2488670.4977340.751133
1050.2397260.4794510.760274
1060.209620.4192390.79038
1070.1818780.3637560.818122
1080.1524170.3048340.847583
1090.1422510.2845020.857749
1100.189360.3787190.81064
1110.1672670.3345340.832733
1120.1608020.3216030.839198
1130.1463330.2926650.853667
1140.2316390.4632790.768361
1150.266420.5328410.73358
1160.2756840.5513680.724316
1170.2485130.4970270.751487
1180.2857040.5714080.714296
1190.3466610.6933230.653339
1200.3052480.6104960.694752
1210.2738460.5476930.726154
1220.2860920.5721840.713908
1230.3067690.6135380.693231
1240.6106980.7786040.389302
1250.6067880.7864240.393212
1260.6277950.744410.372205
1270.5852860.8294290.414714
1280.5322620.9354760.467738
1290.504440.991120.49556
1300.4615280.9230550.538472
1310.4450560.8901120.554944
1320.4091110.8182220.590889
1330.3624430.7248870.637557
1340.3157170.6314340.684283
1350.2739920.5479830.726008
1360.2301650.4603290.769835
1370.1880420.3760850.811958
1380.1501270.3002540.849873
1390.1290290.2580590.870971
1400.1345840.2691680.865416
1410.1024750.204950.897525
1420.07671540.1534310.923285
1430.1163560.2327110.883644
1440.08789870.1757970.912101
1450.1023570.2047140.897643
1460.6261890.7476220.373811
1470.5540710.8918580.445929
1480.9762910.04741870.0237094
1490.9831810.0336380.016819
1500.9865040.02699180.0134959
1510.9990910.001818890.000909447
1520.9974860.00502790.00251395
1530.993170.01365960.0068298
1540.988070.02385960.0119298
1550.9862160.02756710.0137835
1560.9636180.07276340.0363817
1570.9491040.1017910.0508955

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.83685 & 0.3263 & 0.16315 \tabularnewline
10 & 0.743959 & 0.512081 & 0.256041 \tabularnewline
11 & 0.657579 & 0.684842 & 0.342421 \tabularnewline
12 & 0.687949 & 0.624101 & 0.312051 \tabularnewline
13 & 0.760537 & 0.478926 & 0.239463 \tabularnewline
14 & 0.85608 & 0.287841 & 0.14392 \tabularnewline
15 & 0.808231 & 0.383539 & 0.191769 \tabularnewline
16 & 0.891561 & 0.216878 & 0.108439 \tabularnewline
17 & 0.865793 & 0.268414 & 0.134207 \tabularnewline
18 & 0.81972 & 0.36056 & 0.18028 \tabularnewline
19 & 0.767568 & 0.464863 & 0.232432 \tabularnewline
20 & 0.737267 & 0.525467 & 0.262733 \tabularnewline
21 & 0.730337 & 0.539326 & 0.269663 \tabularnewline
22 & 0.665726 & 0.668547 & 0.334274 \tabularnewline
23 & 0.603261 & 0.793479 & 0.396739 \tabularnewline
24 & 0.53936 & 0.921281 & 0.46064 \tabularnewline
25 & 0.531952 & 0.936095 & 0.468048 \tabularnewline
26 & 0.513304 & 0.973391 & 0.486696 \tabularnewline
27 & 0.48094 & 0.96188 & 0.51906 \tabularnewline
28 & 0.435453 & 0.870906 & 0.564547 \tabularnewline
29 & 0.378469 & 0.756938 & 0.621531 \tabularnewline
30 & 0.347599 & 0.695198 & 0.652401 \tabularnewline
31 & 0.37335 & 0.7467 & 0.62665 \tabularnewline
32 & 0.361916 & 0.723833 & 0.638084 \tabularnewline
33 & 0.312814 & 0.625627 & 0.687186 \tabularnewline
34 & 0.262436 & 0.524873 & 0.737564 \tabularnewline
35 & 0.249849 & 0.499698 & 0.750151 \tabularnewline
36 & 0.207354 & 0.414708 & 0.792646 \tabularnewline
37 & 0.223733 & 0.447467 & 0.776267 \tabularnewline
38 & 0.185465 & 0.370929 & 0.814535 \tabularnewline
39 & 0.737905 & 0.524189 & 0.262095 \tabularnewline
40 & 0.69498 & 0.61004 & 0.30502 \tabularnewline
41 & 0.670026 & 0.659948 & 0.329974 \tabularnewline
42 & 0.623271 & 0.753458 & 0.376729 \tabularnewline
43 & 0.623077 & 0.753845 & 0.376923 \tabularnewline
44 & 0.572589 & 0.854823 & 0.427411 \tabularnewline
45 & 0.529377 & 0.941246 & 0.470623 \tabularnewline
46 & 0.48391 & 0.967821 & 0.51609 \tabularnewline
47 & 0.444738 & 0.889476 & 0.555262 \tabularnewline
48 & 0.443479 & 0.886957 & 0.556521 \tabularnewline
49 & 0.458529 & 0.917058 & 0.541471 \tabularnewline
50 & 0.458569 & 0.917137 & 0.541431 \tabularnewline
51 & 0.432583 & 0.865167 & 0.567417 \tabularnewline
52 & 0.763875 & 0.47225 & 0.236125 \tabularnewline
53 & 0.764411 & 0.471178 & 0.235589 \tabularnewline
54 & 0.733829 & 0.532342 & 0.266171 \tabularnewline
55 & 0.734955 & 0.530091 & 0.265045 \tabularnewline
56 & 0.739442 & 0.521116 & 0.260558 \tabularnewline
57 & 0.701832 & 0.596336 & 0.298168 \tabularnewline
58 & 0.729596 & 0.540808 & 0.270404 \tabularnewline
59 & 0.703155 & 0.59369 & 0.296845 \tabularnewline
60 & 0.729053 & 0.541894 & 0.270947 \tabularnewline
61 & 0.713001 & 0.573998 & 0.286999 \tabularnewline
62 & 0.675803 & 0.648394 & 0.324197 \tabularnewline
63 & 0.63641 & 0.727181 & 0.36359 \tabularnewline
64 & 0.642115 & 0.715769 & 0.357885 \tabularnewline
65 & 0.639924 & 0.720151 & 0.360076 \tabularnewline
66 & 0.638186 & 0.723627 & 0.361814 \tabularnewline
67 & 0.605712 & 0.788576 & 0.394288 \tabularnewline
68 & 0.687477 & 0.625046 & 0.312523 \tabularnewline
69 & 0.67541 & 0.64918 & 0.32459 \tabularnewline
70 & 0.657772 & 0.684455 & 0.342228 \tabularnewline
71 & 0.637886 & 0.724228 & 0.362114 \tabularnewline
72 & 0.594313 & 0.811374 & 0.405687 \tabularnewline
73 & 0.619778 & 0.760444 & 0.380222 \tabularnewline
74 & 0.583058 & 0.833884 & 0.416942 \tabularnewline
75 & 0.659632 & 0.680736 & 0.340368 \tabularnewline
76 & 0.632208 & 0.735585 & 0.367792 \tabularnewline
77 & 0.594762 & 0.810476 & 0.405238 \tabularnewline
78 & 0.552063 & 0.895873 & 0.447937 \tabularnewline
79 & 0.529236 & 0.941528 & 0.470764 \tabularnewline
80 & 0.484261 & 0.968522 & 0.515739 \tabularnewline
81 & 0.49732 & 0.99464 & 0.50268 \tabularnewline
82 & 0.578042 & 0.843915 & 0.421958 \tabularnewline
83 & 0.540557 & 0.918887 & 0.459443 \tabularnewline
84 & 0.5219 & 0.956199 & 0.4781 \tabularnewline
85 & 0.529787 & 0.940426 & 0.470213 \tabularnewline
86 & 0.484864 & 0.969729 & 0.515136 \tabularnewline
87 & 0.484991 & 0.969983 & 0.515009 \tabularnewline
88 & 0.465608 & 0.931215 & 0.534392 \tabularnewline
89 & 0.427614 & 0.855228 & 0.572386 \tabularnewline
90 & 0.410498 & 0.820995 & 0.589502 \tabularnewline
91 & 0.403393 & 0.806785 & 0.596607 \tabularnewline
92 & 0.364534 & 0.729068 & 0.635466 \tabularnewline
93 & 0.345758 & 0.691517 & 0.654242 \tabularnewline
94 & 0.394748 & 0.789495 & 0.605252 \tabularnewline
95 & 0.381388 & 0.762776 & 0.618612 \tabularnewline
96 & 0.367687 & 0.735375 & 0.632313 \tabularnewline
97 & 0.355786 & 0.711572 & 0.644214 \tabularnewline
98 & 0.327101 & 0.654201 & 0.672899 \tabularnewline
99 & 0.292668 & 0.585336 & 0.707332 \tabularnewline
100 & 0.26092 & 0.52184 & 0.73908 \tabularnewline
101 & 0.317696 & 0.635392 & 0.682304 \tabularnewline
102 & 0.279633 & 0.559266 & 0.720367 \tabularnewline
103 & 0.270106 & 0.540212 & 0.729894 \tabularnewline
104 & 0.248867 & 0.497734 & 0.751133 \tabularnewline
105 & 0.239726 & 0.479451 & 0.760274 \tabularnewline
106 & 0.20962 & 0.419239 & 0.79038 \tabularnewline
107 & 0.181878 & 0.363756 & 0.818122 \tabularnewline
108 & 0.152417 & 0.304834 & 0.847583 \tabularnewline
109 & 0.142251 & 0.284502 & 0.857749 \tabularnewline
110 & 0.18936 & 0.378719 & 0.81064 \tabularnewline
111 & 0.167267 & 0.334534 & 0.832733 \tabularnewline
112 & 0.160802 & 0.321603 & 0.839198 \tabularnewline
113 & 0.146333 & 0.292665 & 0.853667 \tabularnewline
114 & 0.231639 & 0.463279 & 0.768361 \tabularnewline
115 & 0.26642 & 0.532841 & 0.73358 \tabularnewline
116 & 0.275684 & 0.551368 & 0.724316 \tabularnewline
117 & 0.248513 & 0.497027 & 0.751487 \tabularnewline
118 & 0.285704 & 0.571408 & 0.714296 \tabularnewline
119 & 0.346661 & 0.693323 & 0.653339 \tabularnewline
120 & 0.305248 & 0.610496 & 0.694752 \tabularnewline
121 & 0.273846 & 0.547693 & 0.726154 \tabularnewline
122 & 0.286092 & 0.572184 & 0.713908 \tabularnewline
123 & 0.306769 & 0.613538 & 0.693231 \tabularnewline
124 & 0.610698 & 0.778604 & 0.389302 \tabularnewline
125 & 0.606788 & 0.786424 & 0.393212 \tabularnewline
126 & 0.627795 & 0.74441 & 0.372205 \tabularnewline
127 & 0.585286 & 0.829429 & 0.414714 \tabularnewline
128 & 0.532262 & 0.935476 & 0.467738 \tabularnewline
129 & 0.50444 & 0.99112 & 0.49556 \tabularnewline
130 & 0.461528 & 0.923055 & 0.538472 \tabularnewline
131 & 0.445056 & 0.890112 & 0.554944 \tabularnewline
132 & 0.409111 & 0.818222 & 0.590889 \tabularnewline
133 & 0.362443 & 0.724887 & 0.637557 \tabularnewline
134 & 0.315717 & 0.631434 & 0.684283 \tabularnewline
135 & 0.273992 & 0.547983 & 0.726008 \tabularnewline
136 & 0.230165 & 0.460329 & 0.769835 \tabularnewline
137 & 0.188042 & 0.376085 & 0.811958 \tabularnewline
138 & 0.150127 & 0.300254 & 0.849873 \tabularnewline
139 & 0.129029 & 0.258059 & 0.870971 \tabularnewline
140 & 0.134584 & 0.269168 & 0.865416 \tabularnewline
141 & 0.102475 & 0.20495 & 0.897525 \tabularnewline
142 & 0.0767154 & 0.153431 & 0.923285 \tabularnewline
143 & 0.116356 & 0.232711 & 0.883644 \tabularnewline
144 & 0.0878987 & 0.175797 & 0.912101 \tabularnewline
145 & 0.102357 & 0.204714 & 0.897643 \tabularnewline
146 & 0.626189 & 0.747622 & 0.373811 \tabularnewline
147 & 0.554071 & 0.891858 & 0.445929 \tabularnewline
148 & 0.976291 & 0.0474187 & 0.0237094 \tabularnewline
149 & 0.983181 & 0.033638 & 0.016819 \tabularnewline
150 & 0.986504 & 0.0269918 & 0.0134959 \tabularnewline
151 & 0.999091 & 0.00181889 & 0.000909447 \tabularnewline
152 & 0.997486 & 0.0050279 & 0.00251395 \tabularnewline
153 & 0.99317 & 0.0136596 & 0.0068298 \tabularnewline
154 & 0.98807 & 0.0238596 & 0.0119298 \tabularnewline
155 & 0.986216 & 0.0275671 & 0.0137835 \tabularnewline
156 & 0.963618 & 0.0727634 & 0.0363817 \tabularnewline
157 & 0.949104 & 0.101791 & 0.0508955 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271215&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]0.83685[/C][C]0.3263[/C][C]0.16315[/C][/ROW]
[ROW][C]10[/C][C]0.743959[/C][C]0.512081[/C][C]0.256041[/C][/ROW]
[ROW][C]11[/C][C]0.657579[/C][C]0.684842[/C][C]0.342421[/C][/ROW]
[ROW][C]12[/C][C]0.687949[/C][C]0.624101[/C][C]0.312051[/C][/ROW]
[ROW][C]13[/C][C]0.760537[/C][C]0.478926[/C][C]0.239463[/C][/ROW]
[ROW][C]14[/C][C]0.85608[/C][C]0.287841[/C][C]0.14392[/C][/ROW]
[ROW][C]15[/C][C]0.808231[/C][C]0.383539[/C][C]0.191769[/C][/ROW]
[ROW][C]16[/C][C]0.891561[/C][C]0.216878[/C][C]0.108439[/C][/ROW]
[ROW][C]17[/C][C]0.865793[/C][C]0.268414[/C][C]0.134207[/C][/ROW]
[ROW][C]18[/C][C]0.81972[/C][C]0.36056[/C][C]0.18028[/C][/ROW]
[ROW][C]19[/C][C]0.767568[/C][C]0.464863[/C][C]0.232432[/C][/ROW]
[ROW][C]20[/C][C]0.737267[/C][C]0.525467[/C][C]0.262733[/C][/ROW]
[ROW][C]21[/C][C]0.730337[/C][C]0.539326[/C][C]0.269663[/C][/ROW]
[ROW][C]22[/C][C]0.665726[/C][C]0.668547[/C][C]0.334274[/C][/ROW]
[ROW][C]23[/C][C]0.603261[/C][C]0.793479[/C][C]0.396739[/C][/ROW]
[ROW][C]24[/C][C]0.53936[/C][C]0.921281[/C][C]0.46064[/C][/ROW]
[ROW][C]25[/C][C]0.531952[/C][C]0.936095[/C][C]0.468048[/C][/ROW]
[ROW][C]26[/C][C]0.513304[/C][C]0.973391[/C][C]0.486696[/C][/ROW]
[ROW][C]27[/C][C]0.48094[/C][C]0.96188[/C][C]0.51906[/C][/ROW]
[ROW][C]28[/C][C]0.435453[/C][C]0.870906[/C][C]0.564547[/C][/ROW]
[ROW][C]29[/C][C]0.378469[/C][C]0.756938[/C][C]0.621531[/C][/ROW]
[ROW][C]30[/C][C]0.347599[/C][C]0.695198[/C][C]0.652401[/C][/ROW]
[ROW][C]31[/C][C]0.37335[/C][C]0.7467[/C][C]0.62665[/C][/ROW]
[ROW][C]32[/C][C]0.361916[/C][C]0.723833[/C][C]0.638084[/C][/ROW]
[ROW][C]33[/C][C]0.312814[/C][C]0.625627[/C][C]0.687186[/C][/ROW]
[ROW][C]34[/C][C]0.262436[/C][C]0.524873[/C][C]0.737564[/C][/ROW]
[ROW][C]35[/C][C]0.249849[/C][C]0.499698[/C][C]0.750151[/C][/ROW]
[ROW][C]36[/C][C]0.207354[/C][C]0.414708[/C][C]0.792646[/C][/ROW]
[ROW][C]37[/C][C]0.223733[/C][C]0.447467[/C][C]0.776267[/C][/ROW]
[ROW][C]38[/C][C]0.185465[/C][C]0.370929[/C][C]0.814535[/C][/ROW]
[ROW][C]39[/C][C]0.737905[/C][C]0.524189[/C][C]0.262095[/C][/ROW]
[ROW][C]40[/C][C]0.69498[/C][C]0.61004[/C][C]0.30502[/C][/ROW]
[ROW][C]41[/C][C]0.670026[/C][C]0.659948[/C][C]0.329974[/C][/ROW]
[ROW][C]42[/C][C]0.623271[/C][C]0.753458[/C][C]0.376729[/C][/ROW]
[ROW][C]43[/C][C]0.623077[/C][C]0.753845[/C][C]0.376923[/C][/ROW]
[ROW][C]44[/C][C]0.572589[/C][C]0.854823[/C][C]0.427411[/C][/ROW]
[ROW][C]45[/C][C]0.529377[/C][C]0.941246[/C][C]0.470623[/C][/ROW]
[ROW][C]46[/C][C]0.48391[/C][C]0.967821[/C][C]0.51609[/C][/ROW]
[ROW][C]47[/C][C]0.444738[/C][C]0.889476[/C][C]0.555262[/C][/ROW]
[ROW][C]48[/C][C]0.443479[/C][C]0.886957[/C][C]0.556521[/C][/ROW]
[ROW][C]49[/C][C]0.458529[/C][C]0.917058[/C][C]0.541471[/C][/ROW]
[ROW][C]50[/C][C]0.458569[/C][C]0.917137[/C][C]0.541431[/C][/ROW]
[ROW][C]51[/C][C]0.432583[/C][C]0.865167[/C][C]0.567417[/C][/ROW]
[ROW][C]52[/C][C]0.763875[/C][C]0.47225[/C][C]0.236125[/C][/ROW]
[ROW][C]53[/C][C]0.764411[/C][C]0.471178[/C][C]0.235589[/C][/ROW]
[ROW][C]54[/C][C]0.733829[/C][C]0.532342[/C][C]0.266171[/C][/ROW]
[ROW][C]55[/C][C]0.734955[/C][C]0.530091[/C][C]0.265045[/C][/ROW]
[ROW][C]56[/C][C]0.739442[/C][C]0.521116[/C][C]0.260558[/C][/ROW]
[ROW][C]57[/C][C]0.701832[/C][C]0.596336[/C][C]0.298168[/C][/ROW]
[ROW][C]58[/C][C]0.729596[/C][C]0.540808[/C][C]0.270404[/C][/ROW]
[ROW][C]59[/C][C]0.703155[/C][C]0.59369[/C][C]0.296845[/C][/ROW]
[ROW][C]60[/C][C]0.729053[/C][C]0.541894[/C][C]0.270947[/C][/ROW]
[ROW][C]61[/C][C]0.713001[/C][C]0.573998[/C][C]0.286999[/C][/ROW]
[ROW][C]62[/C][C]0.675803[/C][C]0.648394[/C][C]0.324197[/C][/ROW]
[ROW][C]63[/C][C]0.63641[/C][C]0.727181[/C][C]0.36359[/C][/ROW]
[ROW][C]64[/C][C]0.642115[/C][C]0.715769[/C][C]0.357885[/C][/ROW]
[ROW][C]65[/C][C]0.639924[/C][C]0.720151[/C][C]0.360076[/C][/ROW]
[ROW][C]66[/C][C]0.638186[/C][C]0.723627[/C][C]0.361814[/C][/ROW]
[ROW][C]67[/C][C]0.605712[/C][C]0.788576[/C][C]0.394288[/C][/ROW]
[ROW][C]68[/C][C]0.687477[/C][C]0.625046[/C][C]0.312523[/C][/ROW]
[ROW][C]69[/C][C]0.67541[/C][C]0.64918[/C][C]0.32459[/C][/ROW]
[ROW][C]70[/C][C]0.657772[/C][C]0.684455[/C][C]0.342228[/C][/ROW]
[ROW][C]71[/C][C]0.637886[/C][C]0.724228[/C][C]0.362114[/C][/ROW]
[ROW][C]72[/C][C]0.594313[/C][C]0.811374[/C][C]0.405687[/C][/ROW]
[ROW][C]73[/C][C]0.619778[/C][C]0.760444[/C][C]0.380222[/C][/ROW]
[ROW][C]74[/C][C]0.583058[/C][C]0.833884[/C][C]0.416942[/C][/ROW]
[ROW][C]75[/C][C]0.659632[/C][C]0.680736[/C][C]0.340368[/C][/ROW]
[ROW][C]76[/C][C]0.632208[/C][C]0.735585[/C][C]0.367792[/C][/ROW]
[ROW][C]77[/C][C]0.594762[/C][C]0.810476[/C][C]0.405238[/C][/ROW]
[ROW][C]78[/C][C]0.552063[/C][C]0.895873[/C][C]0.447937[/C][/ROW]
[ROW][C]79[/C][C]0.529236[/C][C]0.941528[/C][C]0.470764[/C][/ROW]
[ROW][C]80[/C][C]0.484261[/C][C]0.968522[/C][C]0.515739[/C][/ROW]
[ROW][C]81[/C][C]0.49732[/C][C]0.99464[/C][C]0.50268[/C][/ROW]
[ROW][C]82[/C][C]0.578042[/C][C]0.843915[/C][C]0.421958[/C][/ROW]
[ROW][C]83[/C][C]0.540557[/C][C]0.918887[/C][C]0.459443[/C][/ROW]
[ROW][C]84[/C][C]0.5219[/C][C]0.956199[/C][C]0.4781[/C][/ROW]
[ROW][C]85[/C][C]0.529787[/C][C]0.940426[/C][C]0.470213[/C][/ROW]
[ROW][C]86[/C][C]0.484864[/C][C]0.969729[/C][C]0.515136[/C][/ROW]
[ROW][C]87[/C][C]0.484991[/C][C]0.969983[/C][C]0.515009[/C][/ROW]
[ROW][C]88[/C][C]0.465608[/C][C]0.931215[/C][C]0.534392[/C][/ROW]
[ROW][C]89[/C][C]0.427614[/C][C]0.855228[/C][C]0.572386[/C][/ROW]
[ROW][C]90[/C][C]0.410498[/C][C]0.820995[/C][C]0.589502[/C][/ROW]
[ROW][C]91[/C][C]0.403393[/C][C]0.806785[/C][C]0.596607[/C][/ROW]
[ROW][C]92[/C][C]0.364534[/C][C]0.729068[/C][C]0.635466[/C][/ROW]
[ROW][C]93[/C][C]0.345758[/C][C]0.691517[/C][C]0.654242[/C][/ROW]
[ROW][C]94[/C][C]0.394748[/C][C]0.789495[/C][C]0.605252[/C][/ROW]
[ROW][C]95[/C][C]0.381388[/C][C]0.762776[/C][C]0.618612[/C][/ROW]
[ROW][C]96[/C][C]0.367687[/C][C]0.735375[/C][C]0.632313[/C][/ROW]
[ROW][C]97[/C][C]0.355786[/C][C]0.711572[/C][C]0.644214[/C][/ROW]
[ROW][C]98[/C][C]0.327101[/C][C]0.654201[/C][C]0.672899[/C][/ROW]
[ROW][C]99[/C][C]0.292668[/C][C]0.585336[/C][C]0.707332[/C][/ROW]
[ROW][C]100[/C][C]0.26092[/C][C]0.52184[/C][C]0.73908[/C][/ROW]
[ROW][C]101[/C][C]0.317696[/C][C]0.635392[/C][C]0.682304[/C][/ROW]
[ROW][C]102[/C][C]0.279633[/C][C]0.559266[/C][C]0.720367[/C][/ROW]
[ROW][C]103[/C][C]0.270106[/C][C]0.540212[/C][C]0.729894[/C][/ROW]
[ROW][C]104[/C][C]0.248867[/C][C]0.497734[/C][C]0.751133[/C][/ROW]
[ROW][C]105[/C][C]0.239726[/C][C]0.479451[/C][C]0.760274[/C][/ROW]
[ROW][C]106[/C][C]0.20962[/C][C]0.419239[/C][C]0.79038[/C][/ROW]
[ROW][C]107[/C][C]0.181878[/C][C]0.363756[/C][C]0.818122[/C][/ROW]
[ROW][C]108[/C][C]0.152417[/C][C]0.304834[/C][C]0.847583[/C][/ROW]
[ROW][C]109[/C][C]0.142251[/C][C]0.284502[/C][C]0.857749[/C][/ROW]
[ROW][C]110[/C][C]0.18936[/C][C]0.378719[/C][C]0.81064[/C][/ROW]
[ROW][C]111[/C][C]0.167267[/C][C]0.334534[/C][C]0.832733[/C][/ROW]
[ROW][C]112[/C][C]0.160802[/C][C]0.321603[/C][C]0.839198[/C][/ROW]
[ROW][C]113[/C][C]0.146333[/C][C]0.292665[/C][C]0.853667[/C][/ROW]
[ROW][C]114[/C][C]0.231639[/C][C]0.463279[/C][C]0.768361[/C][/ROW]
[ROW][C]115[/C][C]0.26642[/C][C]0.532841[/C][C]0.73358[/C][/ROW]
[ROW][C]116[/C][C]0.275684[/C][C]0.551368[/C][C]0.724316[/C][/ROW]
[ROW][C]117[/C][C]0.248513[/C][C]0.497027[/C][C]0.751487[/C][/ROW]
[ROW][C]118[/C][C]0.285704[/C][C]0.571408[/C][C]0.714296[/C][/ROW]
[ROW][C]119[/C][C]0.346661[/C][C]0.693323[/C][C]0.653339[/C][/ROW]
[ROW][C]120[/C][C]0.305248[/C][C]0.610496[/C][C]0.694752[/C][/ROW]
[ROW][C]121[/C][C]0.273846[/C][C]0.547693[/C][C]0.726154[/C][/ROW]
[ROW][C]122[/C][C]0.286092[/C][C]0.572184[/C][C]0.713908[/C][/ROW]
[ROW][C]123[/C][C]0.306769[/C][C]0.613538[/C][C]0.693231[/C][/ROW]
[ROW][C]124[/C][C]0.610698[/C][C]0.778604[/C][C]0.389302[/C][/ROW]
[ROW][C]125[/C][C]0.606788[/C][C]0.786424[/C][C]0.393212[/C][/ROW]
[ROW][C]126[/C][C]0.627795[/C][C]0.74441[/C][C]0.372205[/C][/ROW]
[ROW][C]127[/C][C]0.585286[/C][C]0.829429[/C][C]0.414714[/C][/ROW]
[ROW][C]128[/C][C]0.532262[/C][C]0.935476[/C][C]0.467738[/C][/ROW]
[ROW][C]129[/C][C]0.50444[/C][C]0.99112[/C][C]0.49556[/C][/ROW]
[ROW][C]130[/C][C]0.461528[/C][C]0.923055[/C][C]0.538472[/C][/ROW]
[ROW][C]131[/C][C]0.445056[/C][C]0.890112[/C][C]0.554944[/C][/ROW]
[ROW][C]132[/C][C]0.409111[/C][C]0.818222[/C][C]0.590889[/C][/ROW]
[ROW][C]133[/C][C]0.362443[/C][C]0.724887[/C][C]0.637557[/C][/ROW]
[ROW][C]134[/C][C]0.315717[/C][C]0.631434[/C][C]0.684283[/C][/ROW]
[ROW][C]135[/C][C]0.273992[/C][C]0.547983[/C][C]0.726008[/C][/ROW]
[ROW][C]136[/C][C]0.230165[/C][C]0.460329[/C][C]0.769835[/C][/ROW]
[ROW][C]137[/C][C]0.188042[/C][C]0.376085[/C][C]0.811958[/C][/ROW]
[ROW][C]138[/C][C]0.150127[/C][C]0.300254[/C][C]0.849873[/C][/ROW]
[ROW][C]139[/C][C]0.129029[/C][C]0.258059[/C][C]0.870971[/C][/ROW]
[ROW][C]140[/C][C]0.134584[/C][C]0.269168[/C][C]0.865416[/C][/ROW]
[ROW][C]141[/C][C]0.102475[/C][C]0.20495[/C][C]0.897525[/C][/ROW]
[ROW][C]142[/C][C]0.0767154[/C][C]0.153431[/C][C]0.923285[/C][/ROW]
[ROW][C]143[/C][C]0.116356[/C][C]0.232711[/C][C]0.883644[/C][/ROW]
[ROW][C]144[/C][C]0.0878987[/C][C]0.175797[/C][C]0.912101[/C][/ROW]
[ROW][C]145[/C][C]0.102357[/C][C]0.204714[/C][C]0.897643[/C][/ROW]
[ROW][C]146[/C][C]0.626189[/C][C]0.747622[/C][C]0.373811[/C][/ROW]
[ROW][C]147[/C][C]0.554071[/C][C]0.891858[/C][C]0.445929[/C][/ROW]
[ROW][C]148[/C][C]0.976291[/C][C]0.0474187[/C][C]0.0237094[/C][/ROW]
[ROW][C]149[/C][C]0.983181[/C][C]0.033638[/C][C]0.016819[/C][/ROW]
[ROW][C]150[/C][C]0.986504[/C][C]0.0269918[/C][C]0.0134959[/C][/ROW]
[ROW][C]151[/C][C]0.999091[/C][C]0.00181889[/C][C]0.000909447[/C][/ROW]
[ROW][C]152[/C][C]0.997486[/C][C]0.0050279[/C][C]0.00251395[/C][/ROW]
[ROW][C]153[/C][C]0.99317[/C][C]0.0136596[/C][C]0.0068298[/C][/ROW]
[ROW][C]154[/C][C]0.98807[/C][C]0.0238596[/C][C]0.0119298[/C][/ROW]
[ROW][C]155[/C][C]0.986216[/C][C]0.0275671[/C][C]0.0137835[/C][/ROW]
[ROW][C]156[/C][C]0.963618[/C][C]0.0727634[/C][C]0.0363817[/C][/ROW]
[ROW][C]157[/C][C]0.949104[/C][C]0.101791[/C][C]0.0508955[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271215&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271215&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
90.836850.32630.16315
100.7439590.5120810.256041
110.6575790.6848420.342421
120.6879490.6241010.312051
130.7605370.4789260.239463
140.856080.2878410.14392
150.8082310.3835390.191769
160.8915610.2168780.108439
170.8657930.2684140.134207
180.819720.360560.18028
190.7675680.4648630.232432
200.7372670.5254670.262733
210.7303370.5393260.269663
220.6657260.6685470.334274
230.6032610.7934790.396739
240.539360.9212810.46064
250.5319520.9360950.468048
260.5133040.9733910.486696
270.480940.961880.51906
280.4354530.8709060.564547
290.3784690.7569380.621531
300.3475990.6951980.652401
310.373350.74670.62665
320.3619160.7238330.638084
330.3128140.6256270.687186
340.2624360.5248730.737564
350.2498490.4996980.750151
360.2073540.4147080.792646
370.2237330.4474670.776267
380.1854650.3709290.814535
390.7379050.5241890.262095
400.694980.610040.30502
410.6700260.6599480.329974
420.6232710.7534580.376729
430.6230770.7538450.376923
440.5725890.8548230.427411
450.5293770.9412460.470623
460.483910.9678210.51609
470.4447380.8894760.555262
480.4434790.8869570.556521
490.4585290.9170580.541471
500.4585690.9171370.541431
510.4325830.8651670.567417
520.7638750.472250.236125
530.7644110.4711780.235589
540.7338290.5323420.266171
550.7349550.5300910.265045
560.7394420.5211160.260558
570.7018320.5963360.298168
580.7295960.5408080.270404
590.7031550.593690.296845
600.7290530.5418940.270947
610.7130010.5739980.286999
620.6758030.6483940.324197
630.636410.7271810.36359
640.6421150.7157690.357885
650.6399240.7201510.360076
660.6381860.7236270.361814
670.6057120.7885760.394288
680.6874770.6250460.312523
690.675410.649180.32459
700.6577720.6844550.342228
710.6378860.7242280.362114
720.5943130.8113740.405687
730.6197780.7604440.380222
740.5830580.8338840.416942
750.6596320.6807360.340368
760.6322080.7355850.367792
770.5947620.8104760.405238
780.5520630.8958730.447937
790.5292360.9415280.470764
800.4842610.9685220.515739
810.497320.994640.50268
820.5780420.8439150.421958
830.5405570.9188870.459443
840.52190.9561990.4781
850.5297870.9404260.470213
860.4848640.9697290.515136
870.4849910.9699830.515009
880.4656080.9312150.534392
890.4276140.8552280.572386
900.4104980.8209950.589502
910.4033930.8067850.596607
920.3645340.7290680.635466
930.3457580.6915170.654242
940.3947480.7894950.605252
950.3813880.7627760.618612
960.3676870.7353750.632313
970.3557860.7115720.644214
980.3271010.6542010.672899
990.2926680.5853360.707332
1000.260920.521840.73908
1010.3176960.6353920.682304
1020.2796330.5592660.720367
1030.2701060.5402120.729894
1040.2488670.4977340.751133
1050.2397260.4794510.760274
1060.209620.4192390.79038
1070.1818780.3637560.818122
1080.1524170.3048340.847583
1090.1422510.2845020.857749
1100.189360.3787190.81064
1110.1672670.3345340.832733
1120.1608020.3216030.839198
1130.1463330.2926650.853667
1140.2316390.4632790.768361
1150.266420.5328410.73358
1160.2756840.5513680.724316
1170.2485130.4970270.751487
1180.2857040.5714080.714296
1190.3466610.6933230.653339
1200.3052480.6104960.694752
1210.2738460.5476930.726154
1220.2860920.5721840.713908
1230.3067690.6135380.693231
1240.6106980.7786040.389302
1250.6067880.7864240.393212
1260.6277950.744410.372205
1270.5852860.8294290.414714
1280.5322620.9354760.467738
1290.504440.991120.49556
1300.4615280.9230550.538472
1310.4450560.8901120.554944
1320.4091110.8182220.590889
1330.3624430.7248870.637557
1340.3157170.6314340.684283
1350.2739920.5479830.726008
1360.2301650.4603290.769835
1370.1880420.3760850.811958
1380.1501270.3002540.849873
1390.1290290.2580590.870971
1400.1345840.2691680.865416
1410.1024750.204950.897525
1420.07671540.1534310.923285
1430.1163560.2327110.883644
1440.08789870.1757970.912101
1450.1023570.2047140.897643
1460.6261890.7476220.373811
1470.5540710.8918580.445929
1480.9762910.04741870.0237094
1490.9831810.0336380.016819
1500.9865040.02699180.0134959
1510.9990910.001818890.000909447
1520.9974860.00502790.00251395
1530.993170.01365960.0068298
1540.988070.02385960.0119298
1550.9862160.02756710.0137835
1560.9636180.07276340.0363817
1570.9491040.1017910.0508955






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0134228NOK
5% type I error level80.0536913NOK
10% type I error level90.0604027OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 2 & 0.0134228 & NOK \tabularnewline
5% type I error level & 8 & 0.0536913 & NOK \tabularnewline
10% type I error level & 9 & 0.0604027 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271215&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]2[/C][C]0.0134228[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.0536913[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.0604027[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271215&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271215&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0134228NOK
5% type I error level80.0536913NOK
10% type I error level90.0604027OK


Figures (Output of Computation)

Figure 1
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Figure 6
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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')
}