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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 computationSun, 14 Dec 2014 16:17:27 +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/14/t1418573883fkkx3tja80ea5kg.htm/, Retrieved Sat, 18 May 2024 11:09:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267725, Retrieved Sat, 18 May 2024 11:09:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact79

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-14 16:17:27] [04df4205f362f56e0d1a9032a00a5d3d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
149	96	18	68	7,5
152	75	7	55	2,5
148	88	39	32	6,5
159	114	35	62	8,5
176	141	69	78	5,0
54	35	21	19	2,5
91	80	26	31	5,0
124	97	36	35	3,5
121	84	23	45	4,0
221	107	35	44	4,5
92	77	24	42	7,0
153	93	22	41	5,5
94	105	23	46	2,5
156	131	32	39	5,5
151	77	16	39	4,5
157	168	43	53	5,0
187	121	59	46	4,5
105	40	7	50	7,5
128	80	26	28	0,0
49	25	9	6	3,5
162	58	48	17	6,0
99	63	18	40	1,5
186	50	33	37	3,5
183	152	71	65	4,0
104	66	34	35	6,0
177	127	80	56	5,0
126	67	29	29	5,5
76	90	16	43	3,5
139	128	32	50	6,5
162	146	43	59	6,5
159	186	29	61	7,0
74	81	36	28	3,5
96	54	35	35	4,0
116	46	21	29	7,5
87	106	29	48	4,5
127	60	37	44	3,5
74	62	51	20	4,5
91	36	32	28	2,5
133	56	21	34	7,5
95	98	20	23	3,0
121	35	11	21	3,5
102	90	23	33	4,5
102	43	39	35	2,5
100	52	29	28	7,0
94	60	13	32	0,0
52	54	8	22	1,0
98	51	18	44	3,5
118	51	24	27	5,5
109	263	37	108	8,5
158	214	75	72	10
67	58	15	9	8,5
147	292	29	55	9
165	302	85	78	7,5
150	296	28	69	9
138	145	26	22	8
149	196	36	51	8
145	199	22	67	9
138	91	18	21	5
109	153	31	44	7
132	299	11	45	5,5
169	190	24	36	2
172	269	22	43	8,5
113	190	31	40	9
115	299	29	73	7,5
78	121	45	34	6
118	137	25	72	10,5
173	157	31	61	8
162	183	66	74	10,5
122	238	32	66	9,5
100	226	39	41	7,5
82	190	19	57	5
115	145	36	51	10
158	249	26	43	10
49	122	32	20	3
90	186	41	79	6
121	148	29	39	7
104	172	17	55	7
110	168	32	55	8
108	102	30	22	10
113	106	34	37	5,5
115	2	59	2	6
111	141	31	39	9,5
77	113	19	33	8
151	268	30	57	5,5
89	175	25	43	7
78	77	48	23	9
110	125	35	44	8
141	132	22	36	6
117	211	18	39	8
63	76	46	23	9
131	266	12	78	9,5
107	219	41	61	5
77	246	12	27	7
154	279	31	69	8
154	130	41	21	7,5
112	226	44	51	8
143	234	52	34	8,5
49	138	7	31	3,5
167	236	26	51	10,5
56	106	24	24	8,5
137	135	7	19	8
149	199	20	42	9,5
168	112	52	22	9
140	278	28	85	10
48	62	13	14	6,5
109	207	14	51	6
63	184	17	41	4
162	183	66	74	10,5
164	237	-2	73	8,5
126	221	2	61	7
83	198	20	54	5
81	158	16	62	8,5
110	226	40	59	9,5
93	196	25	26	1,5
104	83	49	54	6
88	105	19	36	7,5
99	196	30	42	9
76	157	19	25	8,5
109	75	52	31	7
120	75	33	17	9,5
91	185	22	55	8
108	265	30	62	9,5
117	139	38	30	8
119	196	26	49	9

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267725&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267725&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267725&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Ex[t] = + 3.41041 + 0.00205953LFM[t] + 0.0158542B[t] + 0.0192537PRH[t] -0.000639947CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ex[t] =  +  3.41041 +  0.00205953LFM[t] +  0.0158542B[t] +  0.0192537PRH[t] -0.000639947CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267725&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ex[t] =  +  3.41041 +  0.00205953LFM[t] +  0.0158542B[t] +  0.0192537PRH[t] -0.000639947CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267725&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267725&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
Ex[t] = + 3.41041 + 0.00205953LFM[t] + 0.0158542B[t] + 0.0192537PRH[t] -0.000639947CH[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.410410.7425854.5931.0952e-055.47601e-06
LFM0.002059530.006419960.32080.7489240.374462
B0.01585420.003502734.5261.43301e-057.16503e-06
PRH0.01925370.0135261.4230.157220.0786102
CH-0.0006399470.0146156-0.043790.9651490.482574

\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) & 3.41041 & 0.742585 & 4.593 & 1.0952e-05 & 5.47601e-06 \tabularnewline
LFM & 0.00205953 & 0.00641996 & 0.3208 & 0.748924 & 0.374462 \tabularnewline
B & 0.0158542 & 0.00350273 & 4.526 & 1.43301e-05 & 7.16503e-06 \tabularnewline
PRH & 0.0192537 & 0.013526 & 1.423 & 0.15722 & 0.0786102 \tabularnewline
CH & -0.000639947 & 0.0146156 & -0.04379 & 0.965149 & 0.482574 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267725&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]3.41041[/C][C]0.742585[/C][C]4.593[/C][C]1.0952e-05[/C][C]5.47601e-06[/C][/ROW]
[ROW][C]LFM[/C][C]0.00205953[/C][C]0.00641996[/C][C]0.3208[/C][C]0.748924[/C][C]0.374462[/C][/ROW]
[ROW][C]B[/C][C]0.0158542[/C][C]0.00350273[/C][C]4.526[/C][C]1.43301e-05[/C][C]7.16503e-06[/C][/ROW]
[ROW][C]PRH[/C][C]0.0192537[/C][C]0.013526[/C][C]1.423[/C][C]0.15722[/C][C]0.0786102[/C][/ROW]
[ROW][C]CH[/C][C]-0.000639947[/C][C]0.0146156[/C][C]-0.04379[/C][C]0.965149[/C][C]0.482574[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267725&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267725&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)3.410410.7425854.5931.0952e-055.47601e-06
LFM0.002059530.006419960.32080.7489240.374462
B0.01585420.003502734.5261.43301e-057.16503e-06
PRH0.01925370.0135261.4230.157220.0786102
CH-0.0006399470.0146156-0.043790.9651490.482574






Multiple Linear Regression - Regression Statistics
Multiple R0.503031
R-squared0.25304
Adjusted R-squared0.227932
F-TEST (value)10.0781
F-TEST (DF numerator)4
F-TEST (DF denominator)119
p-value4.64316e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.19175
Sum Squared Residuals571.647

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.503031 \tabularnewline
R-squared & 0.25304 \tabularnewline
Adjusted R-squared & 0.227932 \tabularnewline
F-TEST (value) & 10.0781 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 119 \tabularnewline
p-value & 4.64316e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.19175 \tabularnewline
Sum Squared Residuals & 571.647 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267725&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.503031[/C][/ROW]
[ROW][C]R-squared[/C][C]0.25304[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.227932[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.0781[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]119[/C][/ROW]
[ROW][C]p-value[/C][C]4.64316e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.19175[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]571.647[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267725&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267725&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 R0.503031
R-squared0.25304
Adjusted R-squared0.227932
F-TEST (value)10.0781
F-TEST (DF numerator)4
F-TEST (DF denominator)119
p-value4.64316e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.19175
Sum Squared Residuals571.647






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
17.55.542341.95766
22.55.01211-2.51211
36.55.840810.659189
48.56.179462.32054
557.28693-2.28693
62.54.46869-1.96869
755.34693-0.346925
83.55.87439-2.37439
945.40541-1.40541
104.56.20769-1.70769
1175.255881.74412
125.55.59731-0.0973069
132.55.6821-3.1821
145.56.39976-0.899764
154.55.22528-0.725277
1657.19126-2.19126
174.56.82044-2.32044
187.54.363613.13639
1905.42505-5.42505
203.54.07713-0.577127
2165.57690.4231
221.54.93409-3.43409
233.55.19789-1.69789
2447.52257-3.52257
2565.303210.69679
2657.2929-2.2929
275.55.271940.228055
283.55.27436-1.77436
296.56.310150.18985
306.56.84893-0.348927
3177.20609-0.206086
323.55.52222-2.02222
3345.11574-1.11574
347.54.764382.73562
354.55.79778-1.29778
363.55.30746-1.80746
374.55.51492-1.01492
382.54.76678-2.26678
397.54.954742.54526
4035.53014-2.53014
413.54.41286-0.912864
424.55.46908-0.969082
432.55.03071-2.53071
4474.981222.01878
4504.78508-4.78508
4614.51359-3.51359
473.54.73922-1.23922
485.54.906810.593188
498.58.447840.0521611
50108.526581.47342
518.54.750993.74901
5298.865760.134239
537.510.1249-2.62487
5498.907140.0928561
5586.480011.51999
5687.485210.514791
5797.244741.75526
5855.47049-0.470489
5976.629310.370694
605.58.60568-3.10568
6127.20983-5.20983
628.58.425510.0744949
6397.226711.77329
647.58.89932-1.39932
6566.33408-0.334077
6610.56.260734.23927
6786.813651.18635
6810.57.868772.63123
699.58.008871.49113
707.57.92408-0.42408
7156.92094-1.92094
72106.606623.39338
73108.15661.8434
7436.04887-3.04887
7567.2835-1.2835
7676.539440.460558
7776.643650.356353
7886.881391.11861
79105.813514.18649
805.55.95464-0.454636
8164.813661.18634
829.56.446373.05363
8385.705232.29477
845.58.51147-3.01147
8576.822030.177974
8695.701293.29871
8786.264461.73554
8866.19411-0.194108
8987.318230.68177
9095.616043.38396
919.58.078571.42143
9257.85323-2.85323
9377.6829-0.682904
9488.70362-0.703621
957.56.564590.935406
9688.03866-0.0386637
978.58.394250.105748
983.55.81415-2.31415
9910.57.963912.53609
1008.55.653022.84698
10185.955512.04449
1029.57.230472.26953
10396.51922.4808
104108.590931.40907
1056.54.733571.76643
10667.15364-1.15364
10746.75842-2.75842
10810.57.868772.63123
1098.57.42041.0796
11077.17317-0.173169
11157.07101-2.07101
1128.56.350592.14941
1139.57.952411.54759
1141.57.17408-5.67408
11565.849380.150621
1167.55.599131.90087
11797.272471.72753
1188.56.405872.09413
11975.805321.19468
1209.55.471124.02888
12186.919251.08075
1229.58.372151.12785
12386.567561.43244
12497.232171.76783

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7.5 & 5.54234 & 1.95766 \tabularnewline
2 & 2.5 & 5.01211 & -2.51211 \tabularnewline
3 & 6.5 & 5.84081 & 0.659189 \tabularnewline
4 & 8.5 & 6.17946 & 2.32054 \tabularnewline
5 & 5 & 7.28693 & -2.28693 \tabularnewline
6 & 2.5 & 4.46869 & -1.96869 \tabularnewline
7 & 5 & 5.34693 & -0.346925 \tabularnewline
8 & 3.5 & 5.87439 & -2.37439 \tabularnewline
9 & 4 & 5.40541 & -1.40541 \tabularnewline
10 & 4.5 & 6.20769 & -1.70769 \tabularnewline
11 & 7 & 5.25588 & 1.74412 \tabularnewline
12 & 5.5 & 5.59731 & -0.0973069 \tabularnewline
13 & 2.5 & 5.6821 & -3.1821 \tabularnewline
14 & 5.5 & 6.39976 & -0.899764 \tabularnewline
15 & 4.5 & 5.22528 & -0.725277 \tabularnewline
16 & 5 & 7.19126 & -2.19126 \tabularnewline
17 & 4.5 & 6.82044 & -2.32044 \tabularnewline
18 & 7.5 & 4.36361 & 3.13639 \tabularnewline
19 & 0 & 5.42505 & -5.42505 \tabularnewline
20 & 3.5 & 4.07713 & -0.577127 \tabularnewline
21 & 6 & 5.5769 & 0.4231 \tabularnewline
22 & 1.5 & 4.93409 & -3.43409 \tabularnewline
23 & 3.5 & 5.19789 & -1.69789 \tabularnewline
24 & 4 & 7.52257 & -3.52257 \tabularnewline
25 & 6 & 5.30321 & 0.69679 \tabularnewline
26 & 5 & 7.2929 & -2.2929 \tabularnewline
27 & 5.5 & 5.27194 & 0.228055 \tabularnewline
28 & 3.5 & 5.27436 & -1.77436 \tabularnewline
29 & 6.5 & 6.31015 & 0.18985 \tabularnewline
30 & 6.5 & 6.84893 & -0.348927 \tabularnewline
31 & 7 & 7.20609 & -0.206086 \tabularnewline
32 & 3.5 & 5.52222 & -2.02222 \tabularnewline
33 & 4 & 5.11574 & -1.11574 \tabularnewline
34 & 7.5 & 4.76438 & 2.73562 \tabularnewline
35 & 4.5 & 5.79778 & -1.29778 \tabularnewline
36 & 3.5 & 5.30746 & -1.80746 \tabularnewline
37 & 4.5 & 5.51492 & -1.01492 \tabularnewline
38 & 2.5 & 4.76678 & -2.26678 \tabularnewline
39 & 7.5 & 4.95474 & 2.54526 \tabularnewline
40 & 3 & 5.53014 & -2.53014 \tabularnewline
41 & 3.5 & 4.41286 & -0.912864 \tabularnewline
42 & 4.5 & 5.46908 & -0.969082 \tabularnewline
43 & 2.5 & 5.03071 & -2.53071 \tabularnewline
44 & 7 & 4.98122 & 2.01878 \tabularnewline
45 & 0 & 4.78508 & -4.78508 \tabularnewline
46 & 1 & 4.51359 & -3.51359 \tabularnewline
47 & 3.5 & 4.73922 & -1.23922 \tabularnewline
48 & 5.5 & 4.90681 & 0.593188 \tabularnewline
49 & 8.5 & 8.44784 & 0.0521611 \tabularnewline
50 & 10 & 8.52658 & 1.47342 \tabularnewline
51 & 8.5 & 4.75099 & 3.74901 \tabularnewline
52 & 9 & 8.86576 & 0.134239 \tabularnewline
53 & 7.5 & 10.1249 & -2.62487 \tabularnewline
54 & 9 & 8.90714 & 0.0928561 \tabularnewline
55 & 8 & 6.48001 & 1.51999 \tabularnewline
56 & 8 & 7.48521 & 0.514791 \tabularnewline
57 & 9 & 7.24474 & 1.75526 \tabularnewline
58 & 5 & 5.47049 & -0.470489 \tabularnewline
59 & 7 & 6.62931 & 0.370694 \tabularnewline
60 & 5.5 & 8.60568 & -3.10568 \tabularnewline
61 & 2 & 7.20983 & -5.20983 \tabularnewline
62 & 8.5 & 8.42551 & 0.0744949 \tabularnewline
63 & 9 & 7.22671 & 1.77329 \tabularnewline
64 & 7.5 & 8.89932 & -1.39932 \tabularnewline
65 & 6 & 6.33408 & -0.334077 \tabularnewline
66 & 10.5 & 6.26073 & 4.23927 \tabularnewline
67 & 8 & 6.81365 & 1.18635 \tabularnewline
68 & 10.5 & 7.86877 & 2.63123 \tabularnewline
69 & 9.5 & 8.00887 & 1.49113 \tabularnewline
70 & 7.5 & 7.92408 & -0.42408 \tabularnewline
71 & 5 & 6.92094 & -1.92094 \tabularnewline
72 & 10 & 6.60662 & 3.39338 \tabularnewline
73 & 10 & 8.1566 & 1.8434 \tabularnewline
74 & 3 & 6.04887 & -3.04887 \tabularnewline
75 & 6 & 7.2835 & -1.2835 \tabularnewline
76 & 7 & 6.53944 & 0.460558 \tabularnewline
77 & 7 & 6.64365 & 0.356353 \tabularnewline
78 & 8 & 6.88139 & 1.11861 \tabularnewline
79 & 10 & 5.81351 & 4.18649 \tabularnewline
80 & 5.5 & 5.95464 & -0.454636 \tabularnewline
81 & 6 & 4.81366 & 1.18634 \tabularnewline
82 & 9.5 & 6.44637 & 3.05363 \tabularnewline
83 & 8 & 5.70523 & 2.29477 \tabularnewline
84 & 5.5 & 8.51147 & -3.01147 \tabularnewline
85 & 7 & 6.82203 & 0.177974 \tabularnewline
86 & 9 & 5.70129 & 3.29871 \tabularnewline
87 & 8 & 6.26446 & 1.73554 \tabularnewline
88 & 6 & 6.19411 & -0.194108 \tabularnewline
89 & 8 & 7.31823 & 0.68177 \tabularnewline
90 & 9 & 5.61604 & 3.38396 \tabularnewline
91 & 9.5 & 8.07857 & 1.42143 \tabularnewline
92 & 5 & 7.85323 & -2.85323 \tabularnewline
93 & 7 & 7.6829 & -0.682904 \tabularnewline
94 & 8 & 8.70362 & -0.703621 \tabularnewline
95 & 7.5 & 6.56459 & 0.935406 \tabularnewline
96 & 8 & 8.03866 & -0.0386637 \tabularnewline
97 & 8.5 & 8.39425 & 0.105748 \tabularnewline
98 & 3.5 & 5.81415 & -2.31415 \tabularnewline
99 & 10.5 & 7.96391 & 2.53609 \tabularnewline
100 & 8.5 & 5.65302 & 2.84698 \tabularnewline
101 & 8 & 5.95551 & 2.04449 \tabularnewline
102 & 9.5 & 7.23047 & 2.26953 \tabularnewline
103 & 9 & 6.5192 & 2.4808 \tabularnewline
104 & 10 & 8.59093 & 1.40907 \tabularnewline
105 & 6.5 & 4.73357 & 1.76643 \tabularnewline
106 & 6 & 7.15364 & -1.15364 \tabularnewline
107 & 4 & 6.75842 & -2.75842 \tabularnewline
108 & 10.5 & 7.86877 & 2.63123 \tabularnewline
109 & 8.5 & 7.4204 & 1.0796 \tabularnewline
110 & 7 & 7.17317 & -0.173169 \tabularnewline
111 & 5 & 7.07101 & -2.07101 \tabularnewline
112 & 8.5 & 6.35059 & 2.14941 \tabularnewline
113 & 9.5 & 7.95241 & 1.54759 \tabularnewline
114 & 1.5 & 7.17408 & -5.67408 \tabularnewline
115 & 6 & 5.84938 & 0.150621 \tabularnewline
116 & 7.5 & 5.59913 & 1.90087 \tabularnewline
117 & 9 & 7.27247 & 1.72753 \tabularnewline
118 & 8.5 & 6.40587 & 2.09413 \tabularnewline
119 & 7 & 5.80532 & 1.19468 \tabularnewline
120 & 9.5 & 5.47112 & 4.02888 \tabularnewline
121 & 8 & 6.91925 & 1.08075 \tabularnewline
122 & 9.5 & 8.37215 & 1.12785 \tabularnewline
123 & 8 & 6.56756 & 1.43244 \tabularnewline
124 & 9 & 7.23217 & 1.76783 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267725&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]7.5[/C][C]5.54234[/C][C]1.95766[/C][/ROW]
[ROW][C]2[/C][C]2.5[/C][C]5.01211[/C][C]-2.51211[/C][/ROW]
[ROW][C]3[/C][C]6.5[/C][C]5.84081[/C][C]0.659189[/C][/ROW]
[ROW][C]4[/C][C]8.5[/C][C]6.17946[/C][C]2.32054[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]7.28693[/C][C]-2.28693[/C][/ROW]
[ROW][C]6[/C][C]2.5[/C][C]4.46869[/C][C]-1.96869[/C][/ROW]
[ROW][C]7[/C][C]5[/C][C]5.34693[/C][C]-0.346925[/C][/ROW]
[ROW][C]8[/C][C]3.5[/C][C]5.87439[/C][C]-2.37439[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]5.40541[/C][C]-1.40541[/C][/ROW]
[ROW][C]10[/C][C]4.5[/C][C]6.20769[/C][C]-1.70769[/C][/ROW]
[ROW][C]11[/C][C]7[/C][C]5.25588[/C][C]1.74412[/C][/ROW]
[ROW][C]12[/C][C]5.5[/C][C]5.59731[/C][C]-0.0973069[/C][/ROW]
[ROW][C]13[/C][C]2.5[/C][C]5.6821[/C][C]-3.1821[/C][/ROW]
[ROW][C]14[/C][C]5.5[/C][C]6.39976[/C][C]-0.899764[/C][/ROW]
[ROW][C]15[/C][C]4.5[/C][C]5.22528[/C][C]-0.725277[/C][/ROW]
[ROW][C]16[/C][C]5[/C][C]7.19126[/C][C]-2.19126[/C][/ROW]
[ROW][C]17[/C][C]4.5[/C][C]6.82044[/C][C]-2.32044[/C][/ROW]
[ROW][C]18[/C][C]7.5[/C][C]4.36361[/C][C]3.13639[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]5.42505[/C][C]-5.42505[/C][/ROW]
[ROW][C]20[/C][C]3.5[/C][C]4.07713[/C][C]-0.577127[/C][/ROW]
[ROW][C]21[/C][C]6[/C][C]5.5769[/C][C]0.4231[/C][/ROW]
[ROW][C]22[/C][C]1.5[/C][C]4.93409[/C][C]-3.43409[/C][/ROW]
[ROW][C]23[/C][C]3.5[/C][C]5.19789[/C][C]-1.69789[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]7.52257[/C][C]-3.52257[/C][/ROW]
[ROW][C]25[/C][C]6[/C][C]5.30321[/C][C]0.69679[/C][/ROW]
[ROW][C]26[/C][C]5[/C][C]7.2929[/C][C]-2.2929[/C][/ROW]
[ROW][C]27[/C][C]5.5[/C][C]5.27194[/C][C]0.228055[/C][/ROW]
[ROW][C]28[/C][C]3.5[/C][C]5.27436[/C][C]-1.77436[/C][/ROW]
[ROW][C]29[/C][C]6.5[/C][C]6.31015[/C][C]0.18985[/C][/ROW]
[ROW][C]30[/C][C]6.5[/C][C]6.84893[/C][C]-0.348927[/C][/ROW]
[ROW][C]31[/C][C]7[/C][C]7.20609[/C][C]-0.206086[/C][/ROW]
[ROW][C]32[/C][C]3.5[/C][C]5.52222[/C][C]-2.02222[/C][/ROW]
[ROW][C]33[/C][C]4[/C][C]5.11574[/C][C]-1.11574[/C][/ROW]
[ROW][C]34[/C][C]7.5[/C][C]4.76438[/C][C]2.73562[/C][/ROW]
[ROW][C]35[/C][C]4.5[/C][C]5.79778[/C][C]-1.29778[/C][/ROW]
[ROW][C]36[/C][C]3.5[/C][C]5.30746[/C][C]-1.80746[/C][/ROW]
[ROW][C]37[/C][C]4.5[/C][C]5.51492[/C][C]-1.01492[/C][/ROW]
[ROW][C]38[/C][C]2.5[/C][C]4.76678[/C][C]-2.26678[/C][/ROW]
[ROW][C]39[/C][C]7.5[/C][C]4.95474[/C][C]2.54526[/C][/ROW]
[ROW][C]40[/C][C]3[/C][C]5.53014[/C][C]-2.53014[/C][/ROW]
[ROW][C]41[/C][C]3.5[/C][C]4.41286[/C][C]-0.912864[/C][/ROW]
[ROW][C]42[/C][C]4.5[/C][C]5.46908[/C][C]-0.969082[/C][/ROW]
[ROW][C]43[/C][C]2.5[/C][C]5.03071[/C][C]-2.53071[/C][/ROW]
[ROW][C]44[/C][C]7[/C][C]4.98122[/C][C]2.01878[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]4.78508[/C][C]-4.78508[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]4.51359[/C][C]-3.51359[/C][/ROW]
[ROW][C]47[/C][C]3.5[/C][C]4.73922[/C][C]-1.23922[/C][/ROW]
[ROW][C]48[/C][C]5.5[/C][C]4.90681[/C][C]0.593188[/C][/ROW]
[ROW][C]49[/C][C]8.5[/C][C]8.44784[/C][C]0.0521611[/C][/ROW]
[ROW][C]50[/C][C]10[/C][C]8.52658[/C][C]1.47342[/C][/ROW]
[ROW][C]51[/C][C]8.5[/C][C]4.75099[/C][C]3.74901[/C][/ROW]
[ROW][C]52[/C][C]9[/C][C]8.86576[/C][C]0.134239[/C][/ROW]
[ROW][C]53[/C][C]7.5[/C][C]10.1249[/C][C]-2.62487[/C][/ROW]
[ROW][C]54[/C][C]9[/C][C]8.90714[/C][C]0.0928561[/C][/ROW]
[ROW][C]55[/C][C]8[/C][C]6.48001[/C][C]1.51999[/C][/ROW]
[ROW][C]56[/C][C]8[/C][C]7.48521[/C][C]0.514791[/C][/ROW]
[ROW][C]57[/C][C]9[/C][C]7.24474[/C][C]1.75526[/C][/ROW]
[ROW][C]58[/C][C]5[/C][C]5.47049[/C][C]-0.470489[/C][/ROW]
[ROW][C]59[/C][C]7[/C][C]6.62931[/C][C]0.370694[/C][/ROW]
[ROW][C]60[/C][C]5.5[/C][C]8.60568[/C][C]-3.10568[/C][/ROW]
[ROW][C]61[/C][C]2[/C][C]7.20983[/C][C]-5.20983[/C][/ROW]
[ROW][C]62[/C][C]8.5[/C][C]8.42551[/C][C]0.0744949[/C][/ROW]
[ROW][C]63[/C][C]9[/C][C]7.22671[/C][C]1.77329[/C][/ROW]
[ROW][C]64[/C][C]7.5[/C][C]8.89932[/C][C]-1.39932[/C][/ROW]
[ROW][C]65[/C][C]6[/C][C]6.33408[/C][C]-0.334077[/C][/ROW]
[ROW][C]66[/C][C]10.5[/C][C]6.26073[/C][C]4.23927[/C][/ROW]
[ROW][C]67[/C][C]8[/C][C]6.81365[/C][C]1.18635[/C][/ROW]
[ROW][C]68[/C][C]10.5[/C][C]7.86877[/C][C]2.63123[/C][/ROW]
[ROW][C]69[/C][C]9.5[/C][C]8.00887[/C][C]1.49113[/C][/ROW]
[ROW][C]70[/C][C]7.5[/C][C]7.92408[/C][C]-0.42408[/C][/ROW]
[ROW][C]71[/C][C]5[/C][C]6.92094[/C][C]-1.92094[/C][/ROW]
[ROW][C]72[/C][C]10[/C][C]6.60662[/C][C]3.39338[/C][/ROW]
[ROW][C]73[/C][C]10[/C][C]8.1566[/C][C]1.8434[/C][/ROW]
[ROW][C]74[/C][C]3[/C][C]6.04887[/C][C]-3.04887[/C][/ROW]
[ROW][C]75[/C][C]6[/C][C]7.2835[/C][C]-1.2835[/C][/ROW]
[ROW][C]76[/C][C]7[/C][C]6.53944[/C][C]0.460558[/C][/ROW]
[ROW][C]77[/C][C]7[/C][C]6.64365[/C][C]0.356353[/C][/ROW]
[ROW][C]78[/C][C]8[/C][C]6.88139[/C][C]1.11861[/C][/ROW]
[ROW][C]79[/C][C]10[/C][C]5.81351[/C][C]4.18649[/C][/ROW]
[ROW][C]80[/C][C]5.5[/C][C]5.95464[/C][C]-0.454636[/C][/ROW]
[ROW][C]81[/C][C]6[/C][C]4.81366[/C][C]1.18634[/C][/ROW]
[ROW][C]82[/C][C]9.5[/C][C]6.44637[/C][C]3.05363[/C][/ROW]
[ROW][C]83[/C][C]8[/C][C]5.70523[/C][C]2.29477[/C][/ROW]
[ROW][C]84[/C][C]5.5[/C][C]8.51147[/C][C]-3.01147[/C][/ROW]
[ROW][C]85[/C][C]7[/C][C]6.82203[/C][C]0.177974[/C][/ROW]
[ROW][C]86[/C][C]9[/C][C]5.70129[/C][C]3.29871[/C][/ROW]
[ROW][C]87[/C][C]8[/C][C]6.26446[/C][C]1.73554[/C][/ROW]
[ROW][C]88[/C][C]6[/C][C]6.19411[/C][C]-0.194108[/C][/ROW]
[ROW][C]89[/C][C]8[/C][C]7.31823[/C][C]0.68177[/C][/ROW]
[ROW][C]90[/C][C]9[/C][C]5.61604[/C][C]3.38396[/C][/ROW]
[ROW][C]91[/C][C]9.5[/C][C]8.07857[/C][C]1.42143[/C][/ROW]
[ROW][C]92[/C][C]5[/C][C]7.85323[/C][C]-2.85323[/C][/ROW]
[ROW][C]93[/C][C]7[/C][C]7.6829[/C][C]-0.682904[/C][/ROW]
[ROW][C]94[/C][C]8[/C][C]8.70362[/C][C]-0.703621[/C][/ROW]
[ROW][C]95[/C][C]7.5[/C][C]6.56459[/C][C]0.935406[/C][/ROW]
[ROW][C]96[/C][C]8[/C][C]8.03866[/C][C]-0.0386637[/C][/ROW]
[ROW][C]97[/C][C]8.5[/C][C]8.39425[/C][C]0.105748[/C][/ROW]
[ROW][C]98[/C][C]3.5[/C][C]5.81415[/C][C]-2.31415[/C][/ROW]
[ROW][C]99[/C][C]10.5[/C][C]7.96391[/C][C]2.53609[/C][/ROW]
[ROW][C]100[/C][C]8.5[/C][C]5.65302[/C][C]2.84698[/C][/ROW]
[ROW][C]101[/C][C]8[/C][C]5.95551[/C][C]2.04449[/C][/ROW]
[ROW][C]102[/C][C]9.5[/C][C]7.23047[/C][C]2.26953[/C][/ROW]
[ROW][C]103[/C][C]9[/C][C]6.5192[/C][C]2.4808[/C][/ROW]
[ROW][C]104[/C][C]10[/C][C]8.59093[/C][C]1.40907[/C][/ROW]
[ROW][C]105[/C][C]6.5[/C][C]4.73357[/C][C]1.76643[/C][/ROW]
[ROW][C]106[/C][C]6[/C][C]7.15364[/C][C]-1.15364[/C][/ROW]
[ROW][C]107[/C][C]4[/C][C]6.75842[/C][C]-2.75842[/C][/ROW]
[ROW][C]108[/C][C]10.5[/C][C]7.86877[/C][C]2.63123[/C][/ROW]
[ROW][C]109[/C][C]8.5[/C][C]7.4204[/C][C]1.0796[/C][/ROW]
[ROW][C]110[/C][C]7[/C][C]7.17317[/C][C]-0.173169[/C][/ROW]
[ROW][C]111[/C][C]5[/C][C]7.07101[/C][C]-2.07101[/C][/ROW]
[ROW][C]112[/C][C]8.5[/C][C]6.35059[/C][C]2.14941[/C][/ROW]
[ROW][C]113[/C][C]9.5[/C][C]7.95241[/C][C]1.54759[/C][/ROW]
[ROW][C]114[/C][C]1.5[/C][C]7.17408[/C][C]-5.67408[/C][/ROW]
[ROW][C]115[/C][C]6[/C][C]5.84938[/C][C]0.150621[/C][/ROW]
[ROW][C]116[/C][C]7.5[/C][C]5.59913[/C][C]1.90087[/C][/ROW]
[ROW][C]117[/C][C]9[/C][C]7.27247[/C][C]1.72753[/C][/ROW]
[ROW][C]118[/C][C]8.5[/C][C]6.40587[/C][C]2.09413[/C][/ROW]
[ROW][C]119[/C][C]7[/C][C]5.80532[/C][C]1.19468[/C][/ROW]
[ROW][C]120[/C][C]9.5[/C][C]5.47112[/C][C]4.02888[/C][/ROW]
[ROW][C]121[/C][C]8[/C][C]6.91925[/C][C]1.08075[/C][/ROW]
[ROW][C]122[/C][C]9.5[/C][C]8.37215[/C][C]1.12785[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]6.56756[/C][C]1.43244[/C][/ROW]
[ROW][C]124[/C][C]9[/C][C]7.23217[/C][C]1.76783[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267725&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267725&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
17.55.542341.95766
22.55.01211-2.51211
36.55.840810.659189
48.56.179462.32054
557.28693-2.28693
62.54.46869-1.96869
755.34693-0.346925
83.55.87439-2.37439
945.40541-1.40541
104.56.20769-1.70769
1175.255881.74412
125.55.59731-0.0973069
132.55.6821-3.1821
145.56.39976-0.899764
154.55.22528-0.725277
1657.19126-2.19126
174.56.82044-2.32044
187.54.363613.13639
1905.42505-5.42505
203.54.07713-0.577127
2165.57690.4231
221.54.93409-3.43409
233.55.19789-1.69789
2447.52257-3.52257
2565.303210.69679
2657.2929-2.2929
275.55.271940.228055
283.55.27436-1.77436
296.56.310150.18985
306.56.84893-0.348927
3177.20609-0.206086
323.55.52222-2.02222
3345.11574-1.11574
347.54.764382.73562
354.55.79778-1.29778
363.55.30746-1.80746
374.55.51492-1.01492
382.54.76678-2.26678
397.54.954742.54526
4035.53014-2.53014
413.54.41286-0.912864
424.55.46908-0.969082
432.55.03071-2.53071
4474.981222.01878
4504.78508-4.78508
4614.51359-3.51359
473.54.73922-1.23922
485.54.906810.593188
498.58.447840.0521611
50108.526581.47342
518.54.750993.74901
5298.865760.134239
537.510.1249-2.62487
5498.907140.0928561
5586.480011.51999
5687.485210.514791
5797.244741.75526
5855.47049-0.470489
5976.629310.370694
605.58.60568-3.10568
6127.20983-5.20983
628.58.425510.0744949
6397.226711.77329
647.58.89932-1.39932
6566.33408-0.334077
6610.56.260734.23927
6786.813651.18635
6810.57.868772.63123
699.58.008871.49113
707.57.92408-0.42408
7156.92094-1.92094
72106.606623.39338
73108.15661.8434
7436.04887-3.04887
7567.2835-1.2835
7676.539440.460558
7776.643650.356353
7886.881391.11861
79105.813514.18649
805.55.95464-0.454636
8164.813661.18634
829.56.446373.05363
8385.705232.29477
845.58.51147-3.01147
8576.822030.177974
8695.701293.29871
8786.264461.73554
8866.19411-0.194108
8987.318230.68177
9095.616043.38396
919.58.078571.42143
9257.85323-2.85323
9377.6829-0.682904
9488.70362-0.703621
957.56.564590.935406
9688.03866-0.0386637
978.58.394250.105748
983.55.81415-2.31415
9910.57.963912.53609
1008.55.653022.84698
10185.955512.04449
1029.57.230472.26953
10396.51922.4808
104108.590931.40907
1056.54.733571.76643
10667.15364-1.15364
10746.75842-2.75842
10810.57.868772.63123
1098.57.42041.0796
11077.17317-0.173169
11157.07101-2.07101
1128.56.350592.14941
1139.57.952411.54759
1141.57.17408-5.67408
11565.849380.150621
1167.55.599131.90087
11797.272471.72753
1188.56.405872.09413
11975.805321.19468
1209.55.471124.02888
12186.919251.08075
1229.58.372151.12785
12386.567561.43244
12497.232171.76783






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.8009390.3981220.199061
90.698270.603460.30173
100.584750.83050.41525
110.5511360.8977290.448864
120.4306620.8613230.569338
130.571920.8561590.42808
140.4734170.9468330.526583
150.3768610.7537230.623139
160.3029660.6059320.697034
170.2473090.4946170.752691
180.2608930.5217850.739107
190.4921330.9842660.507867
200.4227750.845550.577225
210.4068740.8137470.593126
220.5056890.9886220.494311
230.4899540.9799080.510046
240.5048780.9902430.495122
250.4686920.9373850.531308
260.4393720.8787440.560628
270.3935960.7871920.606404
280.3466430.6932860.653357
290.3271680.6543350.672832
300.2937660.5875330.706234
310.2651710.5303410.734829
320.2279750.4559510.772025
330.1906580.3813150.809342
340.2458450.4916910.754155
350.20660.41320.7934
360.2010430.4020860.798957
370.1740810.3481630.825919
380.1788540.3577070.821146
390.2045210.4090430.795479
400.1959720.3919430.804028
410.1747440.3494880.825256
420.1478240.2956490.852176
430.1784420.3568830.821558
440.2020030.4040070.797997
450.4777860.9555730.522214
460.589570.8208590.41043
470.6271530.7456930.372847
480.6178590.7642830.382141
490.5902690.8194610.409731
500.6353340.7293320.364666
510.7930490.4139030.206951
520.7629940.4740120.237006
530.7524460.4951090.247554
540.7118490.5763030.288151
550.69660.60680.3034
560.656440.6871210.34356
570.6298640.7402710.370136
580.6166220.7667550.383378
590.5740060.8519880.425994
600.6108050.7783910.389195
610.9023280.1953440.0976718
620.8811940.2376130.118806
630.8857310.2285380.114269
640.8603830.2792330.139617
650.8360920.3278170.163908
660.8879340.2241320.112066
670.8789820.2420370.121018
680.8831150.233770.116885
690.8732480.2535050.126752
700.8444060.3111880.155594
710.8406340.3187330.159366
720.8725010.2549980.127499
730.8703390.2593220.129661
740.8940920.2118160.105908
750.8879940.2240120.112006
760.8663550.2672910.133645
770.8368260.3263470.163174
780.8064560.3870870.193544
790.8784250.243150.121575
800.8800520.2398970.119948
810.8908210.2183580.109179
820.8991490.2017020.100851
830.8899580.2200850.110042
840.9184220.1631560.0815779
850.8939740.2120530.106026
860.9057120.1885750.0942875
870.8840470.2319060.115953
880.8878330.2243330.112167
890.8585080.2829840.141492
900.8872230.2255550.112777
910.8654250.2691510.134575
920.8986850.2026310.101315
930.8755760.2488470.124424
940.8504240.2991520.149576
950.8293930.3412150.170607
960.7826070.4347860.217393
970.7300960.5398080.269904
980.7292050.5415890.270795
990.7060950.5878090.293905
1000.7430130.5139740.256987
1010.6904520.6190960.309548
1020.6482640.7034720.351736
1030.5898140.8203730.410186
1040.5300970.9398050.469903
1050.4828040.9656070.517196
1060.4301350.860270.569865
1070.4240910.8481820.575909
1080.3541950.708390.645805
1090.2879610.5759210.712039
1100.3526890.7053780.647311
1110.3579230.7158460.642077
1120.2703620.5407240.729638
1130.221450.4429010.77855
1140.9974250.0051490.0025745
1150.9890770.02184610.0109231
1160.9610410.07791710.0389586

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.800939 & 0.398122 & 0.199061 \tabularnewline
9 & 0.69827 & 0.60346 & 0.30173 \tabularnewline
10 & 0.58475 & 0.8305 & 0.41525 \tabularnewline
11 & 0.551136 & 0.897729 & 0.448864 \tabularnewline
12 & 0.430662 & 0.861323 & 0.569338 \tabularnewline
13 & 0.57192 & 0.856159 & 0.42808 \tabularnewline
14 & 0.473417 & 0.946833 & 0.526583 \tabularnewline
15 & 0.376861 & 0.753723 & 0.623139 \tabularnewline
16 & 0.302966 & 0.605932 & 0.697034 \tabularnewline
17 & 0.247309 & 0.494617 & 0.752691 \tabularnewline
18 & 0.260893 & 0.521785 & 0.739107 \tabularnewline
19 & 0.492133 & 0.984266 & 0.507867 \tabularnewline
20 & 0.422775 & 0.84555 & 0.577225 \tabularnewline
21 & 0.406874 & 0.813747 & 0.593126 \tabularnewline
22 & 0.505689 & 0.988622 & 0.494311 \tabularnewline
23 & 0.489954 & 0.979908 & 0.510046 \tabularnewline
24 & 0.504878 & 0.990243 & 0.495122 \tabularnewline
25 & 0.468692 & 0.937385 & 0.531308 \tabularnewline
26 & 0.439372 & 0.878744 & 0.560628 \tabularnewline
27 & 0.393596 & 0.787192 & 0.606404 \tabularnewline
28 & 0.346643 & 0.693286 & 0.653357 \tabularnewline
29 & 0.327168 & 0.654335 & 0.672832 \tabularnewline
30 & 0.293766 & 0.587533 & 0.706234 \tabularnewline
31 & 0.265171 & 0.530341 & 0.734829 \tabularnewline
32 & 0.227975 & 0.455951 & 0.772025 \tabularnewline
33 & 0.190658 & 0.381315 & 0.809342 \tabularnewline
34 & 0.245845 & 0.491691 & 0.754155 \tabularnewline
35 & 0.2066 & 0.4132 & 0.7934 \tabularnewline
36 & 0.201043 & 0.402086 & 0.798957 \tabularnewline
37 & 0.174081 & 0.348163 & 0.825919 \tabularnewline
38 & 0.178854 & 0.357707 & 0.821146 \tabularnewline
39 & 0.204521 & 0.409043 & 0.795479 \tabularnewline
40 & 0.195972 & 0.391943 & 0.804028 \tabularnewline
41 & 0.174744 & 0.349488 & 0.825256 \tabularnewline
42 & 0.147824 & 0.295649 & 0.852176 \tabularnewline
43 & 0.178442 & 0.356883 & 0.821558 \tabularnewline
44 & 0.202003 & 0.404007 & 0.797997 \tabularnewline
45 & 0.477786 & 0.955573 & 0.522214 \tabularnewline
46 & 0.58957 & 0.820859 & 0.41043 \tabularnewline
47 & 0.627153 & 0.745693 & 0.372847 \tabularnewline
48 & 0.617859 & 0.764283 & 0.382141 \tabularnewline
49 & 0.590269 & 0.819461 & 0.409731 \tabularnewline
50 & 0.635334 & 0.729332 & 0.364666 \tabularnewline
51 & 0.793049 & 0.413903 & 0.206951 \tabularnewline
52 & 0.762994 & 0.474012 & 0.237006 \tabularnewline
53 & 0.752446 & 0.495109 & 0.247554 \tabularnewline
54 & 0.711849 & 0.576303 & 0.288151 \tabularnewline
55 & 0.6966 & 0.6068 & 0.3034 \tabularnewline
56 & 0.65644 & 0.687121 & 0.34356 \tabularnewline
57 & 0.629864 & 0.740271 & 0.370136 \tabularnewline
58 & 0.616622 & 0.766755 & 0.383378 \tabularnewline
59 & 0.574006 & 0.851988 & 0.425994 \tabularnewline
60 & 0.610805 & 0.778391 & 0.389195 \tabularnewline
61 & 0.902328 & 0.195344 & 0.0976718 \tabularnewline
62 & 0.881194 & 0.237613 & 0.118806 \tabularnewline
63 & 0.885731 & 0.228538 & 0.114269 \tabularnewline
64 & 0.860383 & 0.279233 & 0.139617 \tabularnewline
65 & 0.836092 & 0.327817 & 0.163908 \tabularnewline
66 & 0.887934 & 0.224132 & 0.112066 \tabularnewline
67 & 0.878982 & 0.242037 & 0.121018 \tabularnewline
68 & 0.883115 & 0.23377 & 0.116885 \tabularnewline
69 & 0.873248 & 0.253505 & 0.126752 \tabularnewline
70 & 0.844406 & 0.311188 & 0.155594 \tabularnewline
71 & 0.840634 & 0.318733 & 0.159366 \tabularnewline
72 & 0.872501 & 0.254998 & 0.127499 \tabularnewline
73 & 0.870339 & 0.259322 & 0.129661 \tabularnewline
74 & 0.894092 & 0.211816 & 0.105908 \tabularnewline
75 & 0.887994 & 0.224012 & 0.112006 \tabularnewline
76 & 0.866355 & 0.267291 & 0.133645 \tabularnewline
77 & 0.836826 & 0.326347 & 0.163174 \tabularnewline
78 & 0.806456 & 0.387087 & 0.193544 \tabularnewline
79 & 0.878425 & 0.24315 & 0.121575 \tabularnewline
80 & 0.880052 & 0.239897 & 0.119948 \tabularnewline
81 & 0.890821 & 0.218358 & 0.109179 \tabularnewline
82 & 0.899149 & 0.201702 & 0.100851 \tabularnewline
83 & 0.889958 & 0.220085 & 0.110042 \tabularnewline
84 & 0.918422 & 0.163156 & 0.0815779 \tabularnewline
85 & 0.893974 & 0.212053 & 0.106026 \tabularnewline
86 & 0.905712 & 0.188575 & 0.0942875 \tabularnewline
87 & 0.884047 & 0.231906 & 0.115953 \tabularnewline
88 & 0.887833 & 0.224333 & 0.112167 \tabularnewline
89 & 0.858508 & 0.282984 & 0.141492 \tabularnewline
90 & 0.887223 & 0.225555 & 0.112777 \tabularnewline
91 & 0.865425 & 0.269151 & 0.134575 \tabularnewline
92 & 0.898685 & 0.202631 & 0.101315 \tabularnewline
93 & 0.875576 & 0.248847 & 0.124424 \tabularnewline
94 & 0.850424 & 0.299152 & 0.149576 \tabularnewline
95 & 0.829393 & 0.341215 & 0.170607 \tabularnewline
96 & 0.782607 & 0.434786 & 0.217393 \tabularnewline
97 & 0.730096 & 0.539808 & 0.269904 \tabularnewline
98 & 0.729205 & 0.541589 & 0.270795 \tabularnewline
99 & 0.706095 & 0.587809 & 0.293905 \tabularnewline
100 & 0.743013 & 0.513974 & 0.256987 \tabularnewline
101 & 0.690452 & 0.619096 & 0.309548 \tabularnewline
102 & 0.648264 & 0.703472 & 0.351736 \tabularnewline
103 & 0.589814 & 0.820373 & 0.410186 \tabularnewline
104 & 0.530097 & 0.939805 & 0.469903 \tabularnewline
105 & 0.482804 & 0.965607 & 0.517196 \tabularnewline
106 & 0.430135 & 0.86027 & 0.569865 \tabularnewline
107 & 0.424091 & 0.848182 & 0.575909 \tabularnewline
108 & 0.354195 & 0.70839 & 0.645805 \tabularnewline
109 & 0.287961 & 0.575921 & 0.712039 \tabularnewline
110 & 0.352689 & 0.705378 & 0.647311 \tabularnewline
111 & 0.357923 & 0.715846 & 0.642077 \tabularnewline
112 & 0.270362 & 0.540724 & 0.729638 \tabularnewline
113 & 0.22145 & 0.442901 & 0.77855 \tabularnewline
114 & 0.997425 & 0.005149 & 0.0025745 \tabularnewline
115 & 0.989077 & 0.0218461 & 0.0109231 \tabularnewline
116 & 0.961041 & 0.0779171 & 0.0389586 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267725&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]8[/C][C]0.800939[/C][C]0.398122[/C][C]0.199061[/C][/ROW]
[ROW][C]9[/C][C]0.69827[/C][C]0.60346[/C][C]0.30173[/C][/ROW]
[ROW][C]10[/C][C]0.58475[/C][C]0.8305[/C][C]0.41525[/C][/ROW]
[ROW][C]11[/C][C]0.551136[/C][C]0.897729[/C][C]0.448864[/C][/ROW]
[ROW][C]12[/C][C]0.430662[/C][C]0.861323[/C][C]0.569338[/C][/ROW]
[ROW][C]13[/C][C]0.57192[/C][C]0.856159[/C][C]0.42808[/C][/ROW]
[ROW][C]14[/C][C]0.473417[/C][C]0.946833[/C][C]0.526583[/C][/ROW]
[ROW][C]15[/C][C]0.376861[/C][C]0.753723[/C][C]0.623139[/C][/ROW]
[ROW][C]16[/C][C]0.302966[/C][C]0.605932[/C][C]0.697034[/C][/ROW]
[ROW][C]17[/C][C]0.247309[/C][C]0.494617[/C][C]0.752691[/C][/ROW]
[ROW][C]18[/C][C]0.260893[/C][C]0.521785[/C][C]0.739107[/C][/ROW]
[ROW][C]19[/C][C]0.492133[/C][C]0.984266[/C][C]0.507867[/C][/ROW]
[ROW][C]20[/C][C]0.422775[/C][C]0.84555[/C][C]0.577225[/C][/ROW]
[ROW][C]21[/C][C]0.406874[/C][C]0.813747[/C][C]0.593126[/C][/ROW]
[ROW][C]22[/C][C]0.505689[/C][C]0.988622[/C][C]0.494311[/C][/ROW]
[ROW][C]23[/C][C]0.489954[/C][C]0.979908[/C][C]0.510046[/C][/ROW]
[ROW][C]24[/C][C]0.504878[/C][C]0.990243[/C][C]0.495122[/C][/ROW]
[ROW][C]25[/C][C]0.468692[/C][C]0.937385[/C][C]0.531308[/C][/ROW]
[ROW][C]26[/C][C]0.439372[/C][C]0.878744[/C][C]0.560628[/C][/ROW]
[ROW][C]27[/C][C]0.393596[/C][C]0.787192[/C][C]0.606404[/C][/ROW]
[ROW][C]28[/C][C]0.346643[/C][C]0.693286[/C][C]0.653357[/C][/ROW]
[ROW][C]29[/C][C]0.327168[/C][C]0.654335[/C][C]0.672832[/C][/ROW]
[ROW][C]30[/C][C]0.293766[/C][C]0.587533[/C][C]0.706234[/C][/ROW]
[ROW][C]31[/C][C]0.265171[/C][C]0.530341[/C][C]0.734829[/C][/ROW]
[ROW][C]32[/C][C]0.227975[/C][C]0.455951[/C][C]0.772025[/C][/ROW]
[ROW][C]33[/C][C]0.190658[/C][C]0.381315[/C][C]0.809342[/C][/ROW]
[ROW][C]34[/C][C]0.245845[/C][C]0.491691[/C][C]0.754155[/C][/ROW]
[ROW][C]35[/C][C]0.2066[/C][C]0.4132[/C][C]0.7934[/C][/ROW]
[ROW][C]36[/C][C]0.201043[/C][C]0.402086[/C][C]0.798957[/C][/ROW]
[ROW][C]37[/C][C]0.174081[/C][C]0.348163[/C][C]0.825919[/C][/ROW]
[ROW][C]38[/C][C]0.178854[/C][C]0.357707[/C][C]0.821146[/C][/ROW]
[ROW][C]39[/C][C]0.204521[/C][C]0.409043[/C][C]0.795479[/C][/ROW]
[ROW][C]40[/C][C]0.195972[/C][C]0.391943[/C][C]0.804028[/C][/ROW]
[ROW][C]41[/C][C]0.174744[/C][C]0.349488[/C][C]0.825256[/C][/ROW]
[ROW][C]42[/C][C]0.147824[/C][C]0.295649[/C][C]0.852176[/C][/ROW]
[ROW][C]43[/C][C]0.178442[/C][C]0.356883[/C][C]0.821558[/C][/ROW]
[ROW][C]44[/C][C]0.202003[/C][C]0.404007[/C][C]0.797997[/C][/ROW]
[ROW][C]45[/C][C]0.477786[/C][C]0.955573[/C][C]0.522214[/C][/ROW]
[ROW][C]46[/C][C]0.58957[/C][C]0.820859[/C][C]0.41043[/C][/ROW]
[ROW][C]47[/C][C]0.627153[/C][C]0.745693[/C][C]0.372847[/C][/ROW]
[ROW][C]48[/C][C]0.617859[/C][C]0.764283[/C][C]0.382141[/C][/ROW]
[ROW][C]49[/C][C]0.590269[/C][C]0.819461[/C][C]0.409731[/C][/ROW]
[ROW][C]50[/C][C]0.635334[/C][C]0.729332[/C][C]0.364666[/C][/ROW]
[ROW][C]51[/C][C]0.793049[/C][C]0.413903[/C][C]0.206951[/C][/ROW]
[ROW][C]52[/C][C]0.762994[/C][C]0.474012[/C][C]0.237006[/C][/ROW]
[ROW][C]53[/C][C]0.752446[/C][C]0.495109[/C][C]0.247554[/C][/ROW]
[ROW][C]54[/C][C]0.711849[/C][C]0.576303[/C][C]0.288151[/C][/ROW]
[ROW][C]55[/C][C]0.6966[/C][C]0.6068[/C][C]0.3034[/C][/ROW]
[ROW][C]56[/C][C]0.65644[/C][C]0.687121[/C][C]0.34356[/C][/ROW]
[ROW][C]57[/C][C]0.629864[/C][C]0.740271[/C][C]0.370136[/C][/ROW]
[ROW][C]58[/C][C]0.616622[/C][C]0.766755[/C][C]0.383378[/C][/ROW]
[ROW][C]59[/C][C]0.574006[/C][C]0.851988[/C][C]0.425994[/C][/ROW]
[ROW][C]60[/C][C]0.610805[/C][C]0.778391[/C][C]0.389195[/C][/ROW]
[ROW][C]61[/C][C]0.902328[/C][C]0.195344[/C][C]0.0976718[/C][/ROW]
[ROW][C]62[/C][C]0.881194[/C][C]0.237613[/C][C]0.118806[/C][/ROW]
[ROW][C]63[/C][C]0.885731[/C][C]0.228538[/C][C]0.114269[/C][/ROW]
[ROW][C]64[/C][C]0.860383[/C][C]0.279233[/C][C]0.139617[/C][/ROW]
[ROW][C]65[/C][C]0.836092[/C][C]0.327817[/C][C]0.163908[/C][/ROW]
[ROW][C]66[/C][C]0.887934[/C][C]0.224132[/C][C]0.112066[/C][/ROW]
[ROW][C]67[/C][C]0.878982[/C][C]0.242037[/C][C]0.121018[/C][/ROW]
[ROW][C]68[/C][C]0.883115[/C][C]0.23377[/C][C]0.116885[/C][/ROW]
[ROW][C]69[/C][C]0.873248[/C][C]0.253505[/C][C]0.126752[/C][/ROW]
[ROW][C]70[/C][C]0.844406[/C][C]0.311188[/C][C]0.155594[/C][/ROW]
[ROW][C]71[/C][C]0.840634[/C][C]0.318733[/C][C]0.159366[/C][/ROW]
[ROW][C]72[/C][C]0.872501[/C][C]0.254998[/C][C]0.127499[/C][/ROW]
[ROW][C]73[/C][C]0.870339[/C][C]0.259322[/C][C]0.129661[/C][/ROW]
[ROW][C]74[/C][C]0.894092[/C][C]0.211816[/C][C]0.105908[/C][/ROW]
[ROW][C]75[/C][C]0.887994[/C][C]0.224012[/C][C]0.112006[/C][/ROW]
[ROW][C]76[/C][C]0.866355[/C][C]0.267291[/C][C]0.133645[/C][/ROW]
[ROW][C]77[/C][C]0.836826[/C][C]0.326347[/C][C]0.163174[/C][/ROW]
[ROW][C]78[/C][C]0.806456[/C][C]0.387087[/C][C]0.193544[/C][/ROW]
[ROW][C]79[/C][C]0.878425[/C][C]0.24315[/C][C]0.121575[/C][/ROW]
[ROW][C]80[/C][C]0.880052[/C][C]0.239897[/C][C]0.119948[/C][/ROW]
[ROW][C]81[/C][C]0.890821[/C][C]0.218358[/C][C]0.109179[/C][/ROW]
[ROW][C]82[/C][C]0.899149[/C][C]0.201702[/C][C]0.100851[/C][/ROW]
[ROW][C]83[/C][C]0.889958[/C][C]0.220085[/C][C]0.110042[/C][/ROW]
[ROW][C]84[/C][C]0.918422[/C][C]0.163156[/C][C]0.0815779[/C][/ROW]
[ROW][C]85[/C][C]0.893974[/C][C]0.212053[/C][C]0.106026[/C][/ROW]
[ROW][C]86[/C][C]0.905712[/C][C]0.188575[/C][C]0.0942875[/C][/ROW]
[ROW][C]87[/C][C]0.884047[/C][C]0.231906[/C][C]0.115953[/C][/ROW]
[ROW][C]88[/C][C]0.887833[/C][C]0.224333[/C][C]0.112167[/C][/ROW]
[ROW][C]89[/C][C]0.858508[/C][C]0.282984[/C][C]0.141492[/C][/ROW]
[ROW][C]90[/C][C]0.887223[/C][C]0.225555[/C][C]0.112777[/C][/ROW]
[ROW][C]91[/C][C]0.865425[/C][C]0.269151[/C][C]0.134575[/C][/ROW]
[ROW][C]92[/C][C]0.898685[/C][C]0.202631[/C][C]0.101315[/C][/ROW]
[ROW][C]93[/C][C]0.875576[/C][C]0.248847[/C][C]0.124424[/C][/ROW]
[ROW][C]94[/C][C]0.850424[/C][C]0.299152[/C][C]0.149576[/C][/ROW]
[ROW][C]95[/C][C]0.829393[/C][C]0.341215[/C][C]0.170607[/C][/ROW]
[ROW][C]96[/C][C]0.782607[/C][C]0.434786[/C][C]0.217393[/C][/ROW]
[ROW][C]97[/C][C]0.730096[/C][C]0.539808[/C][C]0.269904[/C][/ROW]
[ROW][C]98[/C][C]0.729205[/C][C]0.541589[/C][C]0.270795[/C][/ROW]
[ROW][C]99[/C][C]0.706095[/C][C]0.587809[/C][C]0.293905[/C][/ROW]
[ROW][C]100[/C][C]0.743013[/C][C]0.513974[/C][C]0.256987[/C][/ROW]
[ROW][C]101[/C][C]0.690452[/C][C]0.619096[/C][C]0.309548[/C][/ROW]
[ROW][C]102[/C][C]0.648264[/C][C]0.703472[/C][C]0.351736[/C][/ROW]
[ROW][C]103[/C][C]0.589814[/C][C]0.820373[/C][C]0.410186[/C][/ROW]
[ROW][C]104[/C][C]0.530097[/C][C]0.939805[/C][C]0.469903[/C][/ROW]
[ROW][C]105[/C][C]0.482804[/C][C]0.965607[/C][C]0.517196[/C][/ROW]
[ROW][C]106[/C][C]0.430135[/C][C]0.86027[/C][C]0.569865[/C][/ROW]
[ROW][C]107[/C][C]0.424091[/C][C]0.848182[/C][C]0.575909[/C][/ROW]
[ROW][C]108[/C][C]0.354195[/C][C]0.70839[/C][C]0.645805[/C][/ROW]
[ROW][C]109[/C][C]0.287961[/C][C]0.575921[/C][C]0.712039[/C][/ROW]
[ROW][C]110[/C][C]0.352689[/C][C]0.705378[/C][C]0.647311[/C][/ROW]
[ROW][C]111[/C][C]0.357923[/C][C]0.715846[/C][C]0.642077[/C][/ROW]
[ROW][C]112[/C][C]0.270362[/C][C]0.540724[/C][C]0.729638[/C][/ROW]
[ROW][C]113[/C][C]0.22145[/C][C]0.442901[/C][C]0.77855[/C][/ROW]
[ROW][C]114[/C][C]0.997425[/C][C]0.005149[/C][C]0.0025745[/C][/ROW]
[ROW][C]115[/C][C]0.989077[/C][C]0.0218461[/C][C]0.0109231[/C][/ROW]
[ROW][C]116[/C][C]0.961041[/C][C]0.0779171[/C][C]0.0389586[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267725&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267725&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
80.8009390.3981220.199061
90.698270.603460.30173
100.584750.83050.41525
110.5511360.8977290.448864
120.4306620.8613230.569338
130.571920.8561590.42808
140.4734170.9468330.526583
150.3768610.7537230.623139
160.3029660.6059320.697034
170.2473090.4946170.752691
180.2608930.5217850.739107
190.4921330.9842660.507867
200.4227750.845550.577225
210.4068740.8137470.593126
220.5056890.9886220.494311
230.4899540.9799080.510046
240.5048780.9902430.495122
250.4686920.9373850.531308
260.4393720.8787440.560628
270.3935960.7871920.606404
280.3466430.6932860.653357
290.3271680.6543350.672832
300.2937660.5875330.706234
310.2651710.5303410.734829
320.2279750.4559510.772025
330.1906580.3813150.809342
340.2458450.4916910.754155
350.20660.41320.7934
360.2010430.4020860.798957
370.1740810.3481630.825919
380.1788540.3577070.821146
390.2045210.4090430.795479
400.1959720.3919430.804028
410.1747440.3494880.825256
420.1478240.2956490.852176
430.1784420.3568830.821558
440.2020030.4040070.797997
450.4777860.9555730.522214
460.589570.8208590.41043
470.6271530.7456930.372847
480.6178590.7642830.382141
490.5902690.8194610.409731
500.6353340.7293320.364666
510.7930490.4139030.206951
520.7629940.4740120.237006
530.7524460.4951090.247554
540.7118490.5763030.288151
550.69660.60680.3034
560.656440.6871210.34356
570.6298640.7402710.370136
580.6166220.7667550.383378
590.5740060.8519880.425994
600.6108050.7783910.389195
610.9023280.1953440.0976718
620.8811940.2376130.118806
630.8857310.2285380.114269
640.8603830.2792330.139617
650.8360920.3278170.163908
660.8879340.2241320.112066
670.8789820.2420370.121018
680.8831150.233770.116885
690.8732480.2535050.126752
700.8444060.3111880.155594
710.8406340.3187330.159366
720.8725010.2549980.127499
730.8703390.2593220.129661
740.8940920.2118160.105908
750.8879940.2240120.112006
760.8663550.2672910.133645
770.8368260.3263470.163174
780.8064560.3870870.193544
790.8784250.243150.121575
800.8800520.2398970.119948
810.8908210.2183580.109179
820.8991490.2017020.100851
830.8899580.2200850.110042
840.9184220.1631560.0815779
850.8939740.2120530.106026
860.9057120.1885750.0942875
870.8840470.2319060.115953
880.8878330.2243330.112167
890.8585080.2829840.141492
900.8872230.2255550.112777
910.8654250.2691510.134575
920.8986850.2026310.101315
930.8755760.2488470.124424
940.8504240.2991520.149576
950.8293930.3412150.170607
960.7826070.4347860.217393
970.7300960.5398080.269904
980.7292050.5415890.270795
990.7060950.5878090.293905
1000.7430130.5139740.256987
1010.6904520.6190960.309548
1020.6482640.7034720.351736
1030.5898140.8203730.410186
1040.5300970.9398050.469903
1050.4828040.9656070.517196
1060.4301350.860270.569865
1070.4240910.8481820.575909
1080.3541950.708390.645805
1090.2879610.5759210.712039
1100.3526890.7053780.647311
1110.3579230.7158460.642077
1120.2703620.5407240.729638
1130.221450.4429010.77855
1140.9974250.0051490.0025745
1150.9890770.02184610.0109231
1160.9610410.07791710.0389586






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.00917431OK
5% type I error level20.0183486OK
10% type I error level30.0275229OK

\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 & 1 & 0.00917431 & OK \tabularnewline
5% type I error level & 2 & 0.0183486 & OK \tabularnewline
10% type I error level & 3 & 0.0275229 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267725&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]1[/C][C]0.00917431[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]2[/C][C]0.0183486[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0275229[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267725&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267725&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 level10.00917431OK
5% type I error level20.0183486OK
10% type I error level30.0275229OK


Figures (Output of Computation)

Figure 1
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 5 ; 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')
}