FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 14:52:02 +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/t14185687740zrqevr4g98wssn.htm/, Retrieved Thu, 16 May 2024 21:40:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267654, Retrieved Thu, 16 May 2024 21:40:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact76

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [paper29] [2014-12-14 14:52:02] [0015a2406d94cac8c1a56a29b9122359] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
91	80	26	31	57	11
137	99	42	42	84	10
148	137	109	80	189	16
92	77	24	42	66	13
131	108	49	20	69	13
59	62	23	34	57	12
90	72	61	42	103	8
83	58	38	13	51	12
116	97	32	37	69	12
42	88	16	25	41	7
155	104	22	28	50	12
128	80	26	28	54	6
49	25	9	6	15	8
96	99	24	45	69	13
66	60	28	25	53	9
104	66	34	35	69	12
76	90	16	43	59	9
99	75	59	59	118	15
108	69	38	27	65	13
74	81	36	28	64	10
96	54	35	35	70	11
116	46	21	29	50	13
87	106	29	48	77	11
97	34	12	25	37	4
127	60	37	44	81	10
106	95	37	64	101	12
80	57	47	32	79	11
74	62	51	20	71	11
91	36	32	28	60	9
133	56	21	34	55	13
74	54	13	31	44	13
114	64	14	26	40	6
140	76	-2	58	56	10
95	98	20	23	43	9
98	88	24	21	45	8
121	35	11	21	32	9
126	102	23	33	56	7
98	61	24	16	40	11
95	80	14	20	34	14
110	49	52	37	89	8
70	78	15	35	50	11
102	90	23	33	56	10
86	45	19	27	46	8
130	55	35	41	76	10
96	96	24	40	64	14
102	43	39	35	74	9
100	52	29	28	57	14
94	60	13	32	45	5
52	54	8	22	30	6
98	51	18	44	62	10
118	51	24	27	51	12
99	38	19	17	36	11
109	263	37	108	145	17
68	35	14	10	23	13
131	227	93	66	160	19
71	79	10	23	32	12
68	130	15	25	40	15
89	179	2	56	58	14
115	299	29	73	102	15
78	121	45	34	80	12
118	137	25	72	97	18
87	305	4	42	46	16
162	183	66	74	140	20
49	52	61	16	78	11
122	238	32	66	98	18
96	40	31	9	40	15
100	226	39	41	80	15
82	190	19	57	76	11
100	214	31	48	79	16
115	145	36	51	87	18
141	119	42	53	95	15
110	159	25	55	80	18
146	125	28	51	79	15
90	186	41	79	120	13
121	148	29	39	69	14
104	172	17	55	72	15
147	84	13	30	43	13
110	168	32	55	87	16
108	102	30	22	52	17
113	106	34	37	71	13
115	2	59	2	61	12
61	139	13	38	51	13
60	95	23	27	50	10
109	130	10	56	67	15
68	72	5	25	30	10
111	141	31	39	70	18
77	113	19	33	52	14
73	206	32	43	75	15
89	175	25	43	69	14
78	77	48	23	72	15
110	125	35	44	79	16
65	111	15	28	43	14
117	211	18	39	57	16
63	76	46	23	69	15
52	83	14	24	38	13
62	119	23	29	53	12
131	266	12	78	90	19
101	186	38	57	96	17
42	50	12	37	49	11
77	246	12	27	40	13
96	137	34	44	78	16
57	98	20	39	59	11
112	226	44	51	96	16
49	138	7	31	38	8
56	106	24	24	48	15
86	122	60	30	91	16
88	94	25	27	52	13
48	62	13	14	27	12
85	82	34	28	62	12
63	184	17	41	58	11
102	83	45	31	76	15
162	183	66	74	140	20
86	89	48	19	68	11
114	225	29	51	80	15
94	204	19	51	70	15
81	158	16	62	78	15
110	226	40	59	100	17
64	44	27	24	51	8
104	83	49	54	102	13
105	79	39	39	78	8
49	52	61	16	78	11
88	105	19	36	55	12
95	116	67	31	98	10
102	83	45	31	76	15
99	196	30	42	73	17
63	153	8	39	47	13
76	157	19	25	45	14
109	75	52	31	83	16
117	106	22	38	60	17
57	58	17	31	48	11
120	75	33	17	50	15
73	74	34	22	56	12
91	185	22	55	77	15
108	265	30	62	91	18
105	131	25	51	76	15
119	196	26	49	74	17
31	78	13	16	29	8

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'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267654&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267654&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267654&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'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Ex[t] = + 6.32365 + 0.0190786LFM[t] + 0.0304991B[t] + 0.281412PRH[t] + 0.241416CH[t] -0.240847H[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ex[t] =  +  6.32365 +  0.0190786LFM[t] +  0.0304991B[t] +  0.281412PRH[t] +  0.241416CH[t] -0.240847H[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267654&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ex[t] =  +  6.32365 +  0.0190786LFM[t] +  0.0304991B[t] +  0.281412PRH[t] +  0.241416CH[t] -0.240847H[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267654&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267654&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] = + 6.32365 + 0.0190786LFM[t] + 0.0304991B[t] + 0.281412PRH[t] + 0.241416CH[t] -0.240847H[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.323650.8053387.8521.29156e-126.45781e-13
LFM0.01907860.009137392.0880.03873750.0193688
B0.03049910.004729816.4481.99588e-099.97939e-10
PRH0.2814120.5731670.4910.6242640.312132
CH0.2414160.5655620.42690.6701820.335091
H-0.2408470.568909-0.42330.6727350.336368

\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) & 6.32365 & 0.805338 & 7.852 & 1.29156e-12 & 6.45781e-13 \tabularnewline
LFM & 0.0190786 & 0.00913739 & 2.088 & 0.0387375 & 0.0193688 \tabularnewline
B & 0.0304991 & 0.00472981 & 6.448 & 1.99588e-09 & 9.97939e-10 \tabularnewline
PRH & 0.281412 & 0.573167 & 0.491 & 0.624264 & 0.312132 \tabularnewline
CH & 0.241416 & 0.565562 & 0.4269 & 0.670182 & 0.335091 \tabularnewline
H & -0.240847 & 0.568909 & -0.4233 & 0.672735 & 0.336368 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267654&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]6.32365[/C][C]0.805338[/C][C]7.852[/C][C]1.29156e-12[/C][C]6.45781e-13[/C][/ROW]
[ROW][C]LFM[/C][C]0.0190786[/C][C]0.00913739[/C][C]2.088[/C][C]0.0387375[/C][C]0.0193688[/C][/ROW]
[ROW][C]B[/C][C]0.0304991[/C][C]0.00472981[/C][C]6.448[/C][C]1.99588e-09[/C][C]9.97939e-10[/C][/ROW]
[ROW][C]PRH[/C][C]0.281412[/C][C]0.573167[/C][C]0.491[/C][C]0.624264[/C][C]0.312132[/C][/ROW]
[ROW][C]CH[/C][C]0.241416[/C][C]0.565562[/C][C]0.4269[/C][C]0.670182[/C][C]0.335091[/C][/ROW]
[ROW][C]H[/C][C]-0.240847[/C][C]0.568909[/C][C]-0.4233[/C][C]0.672735[/C][C]0.336368[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267654&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267654&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)6.323650.8053387.8521.29156e-126.45781e-13
LFM0.01907860.009137392.0880.03873750.0193688
B0.03049910.004729816.4481.99588e-099.97939e-10
PRH0.2814120.5731670.4910.6242640.312132
CH0.2414160.5655620.42690.6701820.335091
H-0.2408470.568909-0.42330.6727350.336368






Multiple Linear Regression - Regression Statistics
Multiple R0.692788
R-squared0.479956
Adjusted R-squared0.460107
F-TEST (value)24.1803
F-TEST (DF numerator)5
F-TEST (DF denominator)131
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.40911
Sum Squared Residuals760.297

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.692788 \tabularnewline
R-squared & 0.479956 \tabularnewline
Adjusted R-squared & 0.460107 \tabularnewline
F-TEST (value) & 24.1803 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 131 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.40911 \tabularnewline
Sum Squared Residuals & 760.297 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267654&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.692788[/C][/ROW]
[ROW][C]R-squared[/C][C]0.479956[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.460107[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]24.1803[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]131[/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]2.40911[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]760.297[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267654&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267654&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 R0.692788
R-squared0.479956
Adjusted R-squared0.460107
F-TEST (value)24.1803
F-TEST (DF numerator)5
F-TEST (DF denominator)131
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.40911
Sum Squared Residuals760.297






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11111.5721-0.572079
21013.6845-3.68449
31617.7928-1.79283
41311.42481.57521
51314.1159-1.11594
61210.29261.70741
7812.7351-4.73506
81211.2250.774991
91212.8143-0.814337
10710.4722-3.47215
111213.3611-1.36112
12612.2763-6.27628
1388.38948-0.389484
141312.17380.826206
15910.5628-1.56285
161211.71990.280087
17911.1921-2.19207
181512.92682.07319
191312.04540.954567
201011.6822-1.68219
211111.2419-0.24186
221310.80812.19189
231112.4201-1.42012
2449.71226-5.71226
251012.1025-2.10254
261212.7807-0.780749
271111.5132-0.513178
281111.7066-0.706634
29910.4718-1.4718
301311.44031.55972
31139.927423.07258
32611.0333-5.03327
331011.2645-1.26447
34911.9494-2.94943
35811.8628-3.8628
36910.1578-1.1578
37712.7903-5.79025
381111.0365-0.0364766
391411.15532.84465
40812.0472-4.04722
411110.66650.333512
421011.9664-1.96638
43810.123-2.12298
441011.9244-1.92445
451412.07951.92055
46911.1831-2.1831
471411.00982.9902
48510.4926-5.49255
4969.29974-3.29974
501010.504-0.504031
511211.11930.880682
521110.15180.848183
531717.9869-0.986885
54139.502923.49708
551919.3156-0.315555
561210.74731.25275
571512.20862.79141
581413.5940.406003
591518.8549-3.85488
601213.1061-1.10613
611813.80844.19158
621617.4719-1.47189
632017.71522.28484
641111.0872-0.0872093
651817.24570.754306
661510.63784.36219
671516.7297-1.7297
681114.4861-3.48612
691616.0432-0.0431749
701814.42943.57055
711514.3770.622957
721814.31713.6829
731514.08640.913623
741315.4217-2.42171
751414.1038-0.103781
761514.27460.725406
771312.23460.765445
781614.87561.12445
791712.72454.27547
801313.1127-0.112724
811210.97321.02682
821312.27580.724194
831011.3142-1.31416
841512.56482.43521
851010.034-0.03399
861814.02153.97852
871412.02861.97137
881515.3218-0.32178
891414.1568-0.156759
901511.87963.1204
911613.67952.32048
921411.57362.42642
931615.74350.256461
941511.72263.27736
951310.42872.57126
961211.84460.155414
971917.46691.5331
981715.25651.74349
99119.157751.84225
1001315.5568-2.5568
1011613.73782.26215
1021111.2336-0.233554
1031616.9263-0.926319
104811.769-3.76899
1051511.61223.38781
1061613.89552.10454
1071311.8991.10101
108129.665692.33431
1091211.84140.158578
1101113.8504-2.85039
1111512.64422.35582
1122017.71522.28484
1131112.3959-1.39594
1141516.5663-1.56634
1151515.1386-0.138635
1161513.37221.62778
1171716.73050.269546
11889.99557-1.99557
1191313.0985-0.0985415
120812.3406-4.34058
1211111.0872-0.0872093
1221211.99620.00378978
1231014.4095-4.40955
1241512.64422.35582
1251715.19031.80972
1261312.53870.461307
1271413.10610.893885
1281612.81773.1823
1291712.70284.29718
130119.887341.11266
1311512.24892.75115
1321211.36510.634929
1331514.62590.374112
1341817.95950.0404904
1351513.36551.63455
1361715.89531.10473
13789.83048-1.83048

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 11 & 11.5721 & -0.572079 \tabularnewline
2 & 10 & 13.6845 & -3.68449 \tabularnewline
3 & 16 & 17.7928 & -1.79283 \tabularnewline
4 & 13 & 11.4248 & 1.57521 \tabularnewline
5 & 13 & 14.1159 & -1.11594 \tabularnewline
6 & 12 & 10.2926 & 1.70741 \tabularnewline
7 & 8 & 12.7351 & -4.73506 \tabularnewline
8 & 12 & 11.225 & 0.774991 \tabularnewline
9 & 12 & 12.8143 & -0.814337 \tabularnewline
10 & 7 & 10.4722 & -3.47215 \tabularnewline
11 & 12 & 13.3611 & -1.36112 \tabularnewline
12 & 6 & 12.2763 & -6.27628 \tabularnewline
13 & 8 & 8.38948 & -0.389484 \tabularnewline
14 & 13 & 12.1738 & 0.826206 \tabularnewline
15 & 9 & 10.5628 & -1.56285 \tabularnewline
16 & 12 & 11.7199 & 0.280087 \tabularnewline
17 & 9 & 11.1921 & -2.19207 \tabularnewline
18 & 15 & 12.9268 & 2.07319 \tabularnewline
19 & 13 & 12.0454 & 0.954567 \tabularnewline
20 & 10 & 11.6822 & -1.68219 \tabularnewline
21 & 11 & 11.2419 & -0.24186 \tabularnewline
22 & 13 & 10.8081 & 2.19189 \tabularnewline
23 & 11 & 12.4201 & -1.42012 \tabularnewline
24 & 4 & 9.71226 & -5.71226 \tabularnewline
25 & 10 & 12.1025 & -2.10254 \tabularnewline
26 & 12 & 12.7807 & -0.780749 \tabularnewline
27 & 11 & 11.5132 & -0.513178 \tabularnewline
28 & 11 & 11.7066 & -0.706634 \tabularnewline
29 & 9 & 10.4718 & -1.4718 \tabularnewline
30 & 13 & 11.4403 & 1.55972 \tabularnewline
31 & 13 & 9.92742 & 3.07258 \tabularnewline
32 & 6 & 11.0333 & -5.03327 \tabularnewline
33 & 10 & 11.2645 & -1.26447 \tabularnewline
34 & 9 & 11.9494 & -2.94943 \tabularnewline
35 & 8 & 11.8628 & -3.8628 \tabularnewline
36 & 9 & 10.1578 & -1.1578 \tabularnewline
37 & 7 & 12.7903 & -5.79025 \tabularnewline
38 & 11 & 11.0365 & -0.0364766 \tabularnewline
39 & 14 & 11.1553 & 2.84465 \tabularnewline
40 & 8 & 12.0472 & -4.04722 \tabularnewline
41 & 11 & 10.6665 & 0.333512 \tabularnewline
42 & 10 & 11.9664 & -1.96638 \tabularnewline
43 & 8 & 10.123 & -2.12298 \tabularnewline
44 & 10 & 11.9244 & -1.92445 \tabularnewline
45 & 14 & 12.0795 & 1.92055 \tabularnewline
46 & 9 & 11.1831 & -2.1831 \tabularnewline
47 & 14 & 11.0098 & 2.9902 \tabularnewline
48 & 5 & 10.4926 & -5.49255 \tabularnewline
49 & 6 & 9.29974 & -3.29974 \tabularnewline
50 & 10 & 10.504 & -0.504031 \tabularnewline
51 & 12 & 11.1193 & 0.880682 \tabularnewline
52 & 11 & 10.1518 & 0.848183 \tabularnewline
53 & 17 & 17.9869 & -0.986885 \tabularnewline
54 & 13 & 9.50292 & 3.49708 \tabularnewline
55 & 19 & 19.3156 & -0.315555 \tabularnewline
56 & 12 & 10.7473 & 1.25275 \tabularnewline
57 & 15 & 12.2086 & 2.79141 \tabularnewline
58 & 14 & 13.594 & 0.406003 \tabularnewline
59 & 15 & 18.8549 & -3.85488 \tabularnewline
60 & 12 & 13.1061 & -1.10613 \tabularnewline
61 & 18 & 13.8084 & 4.19158 \tabularnewline
62 & 16 & 17.4719 & -1.47189 \tabularnewline
63 & 20 & 17.7152 & 2.28484 \tabularnewline
64 & 11 & 11.0872 & -0.0872093 \tabularnewline
65 & 18 & 17.2457 & 0.754306 \tabularnewline
66 & 15 & 10.6378 & 4.36219 \tabularnewline
67 & 15 & 16.7297 & -1.7297 \tabularnewline
68 & 11 & 14.4861 & -3.48612 \tabularnewline
69 & 16 & 16.0432 & -0.0431749 \tabularnewline
70 & 18 & 14.4294 & 3.57055 \tabularnewline
71 & 15 & 14.377 & 0.622957 \tabularnewline
72 & 18 & 14.3171 & 3.6829 \tabularnewline
73 & 15 & 14.0864 & 0.913623 \tabularnewline
74 & 13 & 15.4217 & -2.42171 \tabularnewline
75 & 14 & 14.1038 & -0.103781 \tabularnewline
76 & 15 & 14.2746 & 0.725406 \tabularnewline
77 & 13 & 12.2346 & 0.765445 \tabularnewline
78 & 16 & 14.8756 & 1.12445 \tabularnewline
79 & 17 & 12.7245 & 4.27547 \tabularnewline
80 & 13 & 13.1127 & -0.112724 \tabularnewline
81 & 12 & 10.9732 & 1.02682 \tabularnewline
82 & 13 & 12.2758 & 0.724194 \tabularnewline
83 & 10 & 11.3142 & -1.31416 \tabularnewline
84 & 15 & 12.5648 & 2.43521 \tabularnewline
85 & 10 & 10.034 & -0.03399 \tabularnewline
86 & 18 & 14.0215 & 3.97852 \tabularnewline
87 & 14 & 12.0286 & 1.97137 \tabularnewline
88 & 15 & 15.3218 & -0.32178 \tabularnewline
89 & 14 & 14.1568 & -0.156759 \tabularnewline
90 & 15 & 11.8796 & 3.1204 \tabularnewline
91 & 16 & 13.6795 & 2.32048 \tabularnewline
92 & 14 & 11.5736 & 2.42642 \tabularnewline
93 & 16 & 15.7435 & 0.256461 \tabularnewline
94 & 15 & 11.7226 & 3.27736 \tabularnewline
95 & 13 & 10.4287 & 2.57126 \tabularnewline
96 & 12 & 11.8446 & 0.155414 \tabularnewline
97 & 19 & 17.4669 & 1.5331 \tabularnewline
98 & 17 & 15.2565 & 1.74349 \tabularnewline
99 & 11 & 9.15775 & 1.84225 \tabularnewline
100 & 13 & 15.5568 & -2.5568 \tabularnewline
101 & 16 & 13.7378 & 2.26215 \tabularnewline
102 & 11 & 11.2336 & -0.233554 \tabularnewline
103 & 16 & 16.9263 & -0.926319 \tabularnewline
104 & 8 & 11.769 & -3.76899 \tabularnewline
105 & 15 & 11.6122 & 3.38781 \tabularnewline
106 & 16 & 13.8955 & 2.10454 \tabularnewline
107 & 13 & 11.899 & 1.10101 \tabularnewline
108 & 12 & 9.66569 & 2.33431 \tabularnewline
109 & 12 & 11.8414 & 0.158578 \tabularnewline
110 & 11 & 13.8504 & -2.85039 \tabularnewline
111 & 15 & 12.6442 & 2.35582 \tabularnewline
112 & 20 & 17.7152 & 2.28484 \tabularnewline
113 & 11 & 12.3959 & -1.39594 \tabularnewline
114 & 15 & 16.5663 & -1.56634 \tabularnewline
115 & 15 & 15.1386 & -0.138635 \tabularnewline
116 & 15 & 13.3722 & 1.62778 \tabularnewline
117 & 17 & 16.7305 & 0.269546 \tabularnewline
118 & 8 & 9.99557 & -1.99557 \tabularnewline
119 & 13 & 13.0985 & -0.0985415 \tabularnewline
120 & 8 & 12.3406 & -4.34058 \tabularnewline
121 & 11 & 11.0872 & -0.0872093 \tabularnewline
122 & 12 & 11.9962 & 0.00378978 \tabularnewline
123 & 10 & 14.4095 & -4.40955 \tabularnewline
124 & 15 & 12.6442 & 2.35582 \tabularnewline
125 & 17 & 15.1903 & 1.80972 \tabularnewline
126 & 13 & 12.5387 & 0.461307 \tabularnewline
127 & 14 & 13.1061 & 0.893885 \tabularnewline
128 & 16 & 12.8177 & 3.1823 \tabularnewline
129 & 17 & 12.7028 & 4.29718 \tabularnewline
130 & 11 & 9.88734 & 1.11266 \tabularnewline
131 & 15 & 12.2489 & 2.75115 \tabularnewline
132 & 12 & 11.3651 & 0.634929 \tabularnewline
133 & 15 & 14.6259 & 0.374112 \tabularnewline
134 & 18 & 17.9595 & 0.0404904 \tabularnewline
135 & 15 & 13.3655 & 1.63455 \tabularnewline
136 & 17 & 15.8953 & 1.10473 \tabularnewline
137 & 8 & 9.83048 & -1.83048 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267654&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]11[/C][C]11.5721[/C][C]-0.572079[/C][/ROW]
[ROW][C]2[/C][C]10[/C][C]13.6845[/C][C]-3.68449[/C][/ROW]
[ROW][C]3[/C][C]16[/C][C]17.7928[/C][C]-1.79283[/C][/ROW]
[ROW][C]4[/C][C]13[/C][C]11.4248[/C][C]1.57521[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]14.1159[/C][C]-1.11594[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]10.2926[/C][C]1.70741[/C][/ROW]
[ROW][C]7[/C][C]8[/C][C]12.7351[/C][C]-4.73506[/C][/ROW]
[ROW][C]8[/C][C]12[/C][C]11.225[/C][C]0.774991[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]12.8143[/C][C]-0.814337[/C][/ROW]
[ROW][C]10[/C][C]7[/C][C]10.4722[/C][C]-3.47215[/C][/ROW]
[ROW][C]11[/C][C]12[/C][C]13.3611[/C][C]-1.36112[/C][/ROW]
[ROW][C]12[/C][C]6[/C][C]12.2763[/C][C]-6.27628[/C][/ROW]
[ROW][C]13[/C][C]8[/C][C]8.38948[/C][C]-0.389484[/C][/ROW]
[ROW][C]14[/C][C]13[/C][C]12.1738[/C][C]0.826206[/C][/ROW]
[ROW][C]15[/C][C]9[/C][C]10.5628[/C][C]-1.56285[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]11.7199[/C][C]0.280087[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]11.1921[/C][C]-2.19207[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]12.9268[/C][C]2.07319[/C][/ROW]
[ROW][C]19[/C][C]13[/C][C]12.0454[/C][C]0.954567[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]11.6822[/C][C]-1.68219[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]11.2419[/C][C]-0.24186[/C][/ROW]
[ROW][C]22[/C][C]13[/C][C]10.8081[/C][C]2.19189[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]12.4201[/C][C]-1.42012[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]9.71226[/C][C]-5.71226[/C][/ROW]
[ROW][C]25[/C][C]10[/C][C]12.1025[/C][C]-2.10254[/C][/ROW]
[ROW][C]26[/C][C]12[/C][C]12.7807[/C][C]-0.780749[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]11.5132[/C][C]-0.513178[/C][/ROW]
[ROW][C]28[/C][C]11[/C][C]11.7066[/C][C]-0.706634[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.4718[/C][C]-1.4718[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]11.4403[/C][C]1.55972[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]9.92742[/C][C]3.07258[/C][/ROW]
[ROW][C]32[/C][C]6[/C][C]11.0333[/C][C]-5.03327[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]11.2645[/C][C]-1.26447[/C][/ROW]
[ROW][C]34[/C][C]9[/C][C]11.9494[/C][C]-2.94943[/C][/ROW]
[ROW][C]35[/C][C]8[/C][C]11.8628[/C][C]-3.8628[/C][/ROW]
[ROW][C]36[/C][C]9[/C][C]10.1578[/C][C]-1.1578[/C][/ROW]
[ROW][C]37[/C][C]7[/C][C]12.7903[/C][C]-5.79025[/C][/ROW]
[ROW][C]38[/C][C]11[/C][C]11.0365[/C][C]-0.0364766[/C][/ROW]
[ROW][C]39[/C][C]14[/C][C]11.1553[/C][C]2.84465[/C][/ROW]
[ROW][C]40[/C][C]8[/C][C]12.0472[/C][C]-4.04722[/C][/ROW]
[ROW][C]41[/C][C]11[/C][C]10.6665[/C][C]0.333512[/C][/ROW]
[ROW][C]42[/C][C]10[/C][C]11.9664[/C][C]-1.96638[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]10.123[/C][C]-2.12298[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]11.9244[/C][C]-1.92445[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]12.0795[/C][C]1.92055[/C][/ROW]
[ROW][C]46[/C][C]9[/C][C]11.1831[/C][C]-2.1831[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]11.0098[/C][C]2.9902[/C][/ROW]
[ROW][C]48[/C][C]5[/C][C]10.4926[/C][C]-5.49255[/C][/ROW]
[ROW][C]49[/C][C]6[/C][C]9.29974[/C][C]-3.29974[/C][/ROW]
[ROW][C]50[/C][C]10[/C][C]10.504[/C][C]-0.504031[/C][/ROW]
[ROW][C]51[/C][C]12[/C][C]11.1193[/C][C]0.880682[/C][/ROW]
[ROW][C]52[/C][C]11[/C][C]10.1518[/C][C]0.848183[/C][/ROW]
[ROW][C]53[/C][C]17[/C][C]17.9869[/C][C]-0.986885[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]9.50292[/C][C]3.49708[/C][/ROW]
[ROW][C]55[/C][C]19[/C][C]19.3156[/C][C]-0.315555[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]10.7473[/C][C]1.25275[/C][/ROW]
[ROW][C]57[/C][C]15[/C][C]12.2086[/C][C]2.79141[/C][/ROW]
[ROW][C]58[/C][C]14[/C][C]13.594[/C][C]0.406003[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]18.8549[/C][C]-3.85488[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]13.1061[/C][C]-1.10613[/C][/ROW]
[ROW][C]61[/C][C]18[/C][C]13.8084[/C][C]4.19158[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]17.4719[/C][C]-1.47189[/C][/ROW]
[ROW][C]63[/C][C]20[/C][C]17.7152[/C][C]2.28484[/C][/ROW]
[ROW][C]64[/C][C]11[/C][C]11.0872[/C][C]-0.0872093[/C][/ROW]
[ROW][C]65[/C][C]18[/C][C]17.2457[/C][C]0.754306[/C][/ROW]
[ROW][C]66[/C][C]15[/C][C]10.6378[/C][C]4.36219[/C][/ROW]
[ROW][C]67[/C][C]15[/C][C]16.7297[/C][C]-1.7297[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]14.4861[/C][C]-3.48612[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]16.0432[/C][C]-0.0431749[/C][/ROW]
[ROW][C]70[/C][C]18[/C][C]14.4294[/C][C]3.57055[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]14.377[/C][C]0.622957[/C][/ROW]
[ROW][C]72[/C][C]18[/C][C]14.3171[/C][C]3.6829[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.0864[/C][C]0.913623[/C][/ROW]
[ROW][C]74[/C][C]13[/C][C]15.4217[/C][C]-2.42171[/C][/ROW]
[ROW][C]75[/C][C]14[/C][C]14.1038[/C][C]-0.103781[/C][/ROW]
[ROW][C]76[/C][C]15[/C][C]14.2746[/C][C]0.725406[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]12.2346[/C][C]0.765445[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.8756[/C][C]1.12445[/C][/ROW]
[ROW][C]79[/C][C]17[/C][C]12.7245[/C][C]4.27547[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]13.1127[/C][C]-0.112724[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]10.9732[/C][C]1.02682[/C][/ROW]
[ROW][C]82[/C][C]13[/C][C]12.2758[/C][C]0.724194[/C][/ROW]
[ROW][C]83[/C][C]10[/C][C]11.3142[/C][C]-1.31416[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]12.5648[/C][C]2.43521[/C][/ROW]
[ROW][C]85[/C][C]10[/C][C]10.034[/C][C]-0.03399[/C][/ROW]
[ROW][C]86[/C][C]18[/C][C]14.0215[/C][C]3.97852[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]12.0286[/C][C]1.97137[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]15.3218[/C][C]-0.32178[/C][/ROW]
[ROW][C]89[/C][C]14[/C][C]14.1568[/C][C]-0.156759[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]11.8796[/C][C]3.1204[/C][/ROW]
[ROW][C]91[/C][C]16[/C][C]13.6795[/C][C]2.32048[/C][/ROW]
[ROW][C]92[/C][C]14[/C][C]11.5736[/C][C]2.42642[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.7435[/C][C]0.256461[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]11.7226[/C][C]3.27736[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]10.4287[/C][C]2.57126[/C][/ROW]
[ROW][C]96[/C][C]12[/C][C]11.8446[/C][C]0.155414[/C][/ROW]
[ROW][C]97[/C][C]19[/C][C]17.4669[/C][C]1.5331[/C][/ROW]
[ROW][C]98[/C][C]17[/C][C]15.2565[/C][C]1.74349[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]9.15775[/C][C]1.84225[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]15.5568[/C][C]-2.5568[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]13.7378[/C][C]2.26215[/C][/ROW]
[ROW][C]102[/C][C]11[/C][C]11.2336[/C][C]-0.233554[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]16.9263[/C][C]-0.926319[/C][/ROW]
[ROW][C]104[/C][C]8[/C][C]11.769[/C][C]-3.76899[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]11.6122[/C][C]3.38781[/C][/ROW]
[ROW][C]106[/C][C]16[/C][C]13.8955[/C][C]2.10454[/C][/ROW]
[ROW][C]107[/C][C]13[/C][C]11.899[/C][C]1.10101[/C][/ROW]
[ROW][C]108[/C][C]12[/C][C]9.66569[/C][C]2.33431[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]11.8414[/C][C]0.158578[/C][/ROW]
[ROW][C]110[/C][C]11[/C][C]13.8504[/C][C]-2.85039[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]12.6442[/C][C]2.35582[/C][/ROW]
[ROW][C]112[/C][C]20[/C][C]17.7152[/C][C]2.28484[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]12.3959[/C][C]-1.39594[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]16.5663[/C][C]-1.56634[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]15.1386[/C][C]-0.138635[/C][/ROW]
[ROW][C]116[/C][C]15[/C][C]13.3722[/C][C]1.62778[/C][/ROW]
[ROW][C]117[/C][C]17[/C][C]16.7305[/C][C]0.269546[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]9.99557[/C][C]-1.99557[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]13.0985[/C][C]-0.0985415[/C][/ROW]
[ROW][C]120[/C][C]8[/C][C]12.3406[/C][C]-4.34058[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]11.0872[/C][C]-0.0872093[/C][/ROW]
[ROW][C]122[/C][C]12[/C][C]11.9962[/C][C]0.00378978[/C][/ROW]
[ROW][C]123[/C][C]10[/C][C]14.4095[/C][C]-4.40955[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]12.6442[/C][C]2.35582[/C][/ROW]
[ROW][C]125[/C][C]17[/C][C]15.1903[/C][C]1.80972[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]12.5387[/C][C]0.461307[/C][/ROW]
[ROW][C]127[/C][C]14[/C][C]13.1061[/C][C]0.893885[/C][/ROW]
[ROW][C]128[/C][C]16[/C][C]12.8177[/C][C]3.1823[/C][/ROW]
[ROW][C]129[/C][C]17[/C][C]12.7028[/C][C]4.29718[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]9.88734[/C][C]1.11266[/C][/ROW]
[ROW][C]131[/C][C]15[/C][C]12.2489[/C][C]2.75115[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]11.3651[/C][C]0.634929[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]14.6259[/C][C]0.374112[/C][/ROW]
[ROW][C]134[/C][C]18[/C][C]17.9595[/C][C]0.0404904[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]13.3655[/C][C]1.63455[/C][/ROW]
[ROW][C]136[/C][C]17[/C][C]15.8953[/C][C]1.10473[/C][/ROW]
[ROW][C]137[/C][C]8[/C][C]9.83048[/C][C]-1.83048[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267654&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267654&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
11111.5721-0.572079
21013.6845-3.68449
31617.7928-1.79283
41311.42481.57521
51314.1159-1.11594
61210.29261.70741
7812.7351-4.73506
81211.2250.774991
91212.8143-0.814337
10710.4722-3.47215
111213.3611-1.36112
12612.2763-6.27628
1388.38948-0.389484
141312.17380.826206
15910.5628-1.56285
161211.71990.280087
17911.1921-2.19207
181512.92682.07319
191312.04540.954567
201011.6822-1.68219
211111.2419-0.24186
221310.80812.19189
231112.4201-1.42012
2449.71226-5.71226
251012.1025-2.10254
261212.7807-0.780749
271111.5132-0.513178
281111.7066-0.706634
29910.4718-1.4718
301311.44031.55972
31139.927423.07258
32611.0333-5.03327
331011.2645-1.26447
34911.9494-2.94943
35811.8628-3.8628
36910.1578-1.1578
37712.7903-5.79025
381111.0365-0.0364766
391411.15532.84465
40812.0472-4.04722
411110.66650.333512
421011.9664-1.96638
43810.123-2.12298
441011.9244-1.92445
451412.07951.92055
46911.1831-2.1831
471411.00982.9902
48510.4926-5.49255
4969.29974-3.29974
501010.504-0.504031
511211.11930.880682
521110.15180.848183
531717.9869-0.986885
54139.502923.49708
551919.3156-0.315555
561210.74731.25275
571512.20862.79141
581413.5940.406003
591518.8549-3.85488
601213.1061-1.10613
611813.80844.19158
621617.4719-1.47189
632017.71522.28484
641111.0872-0.0872093
651817.24570.754306
661510.63784.36219
671516.7297-1.7297
681114.4861-3.48612
691616.0432-0.0431749
701814.42943.57055
711514.3770.622957
721814.31713.6829
731514.08640.913623
741315.4217-2.42171
751414.1038-0.103781
761514.27460.725406
771312.23460.765445
781614.87561.12445
791712.72454.27547
801313.1127-0.112724
811210.97321.02682
821312.27580.724194
831011.3142-1.31416
841512.56482.43521
851010.034-0.03399
861814.02153.97852
871412.02861.97137
881515.3218-0.32178
891414.1568-0.156759
901511.87963.1204
911613.67952.32048
921411.57362.42642
931615.74350.256461
941511.72263.27736
951310.42872.57126
961211.84460.155414
971917.46691.5331
981715.25651.74349
99119.157751.84225
1001315.5568-2.5568
1011613.73782.26215
1021111.2336-0.233554
1031616.9263-0.926319
104811.769-3.76899
1051511.61223.38781
1061613.89552.10454
1071311.8991.10101
108129.665692.33431
1091211.84140.158578
1101113.8504-2.85039
1111512.64422.35582
1122017.71522.28484
1131112.3959-1.39594
1141516.5663-1.56634
1151515.1386-0.138635
1161513.37221.62778
1171716.73050.269546
11889.99557-1.99557
1191313.0985-0.0985415
120812.3406-4.34058
1211111.0872-0.0872093
1221211.99620.00378978
1231014.4095-4.40955
1241512.64422.35582
1251715.19031.80972
1261312.53870.461307
1271413.10610.893885
1281612.81773.1823
1291712.70284.29718
130119.887341.11266
1311512.24892.75115
1321211.36510.634929
1331514.62590.374112
1341817.95950.0404904
1351513.36551.63455
1361715.89531.10473
13789.83048-1.83048






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.6260280.7479450.373972
100.8210340.3579320.178966
110.7261240.5477520.273876
120.9193180.1613640.0806818
130.8704930.2590140.129507
140.8361330.3277340.163867
150.7751660.4496680.224834
160.7223920.5552160.277608
170.6797580.6404850.320242
180.6717770.6564460.328223
190.6408270.7183450.359173
200.5676560.8646880.432344
210.4891190.9782380.510881
220.4487330.8974660.551267
230.3775170.7550340.622483
240.7335160.5329680.266484
250.6991920.6016160.300808
260.6386330.7227340.361367
270.5747840.8504320.425216
280.5111190.9777620.488881
290.4594470.9188940.540553
300.4619620.9239240.538038
310.5257910.9484180.474209
320.6598220.6803550.340178
330.6133070.7733860.386693
340.590260.819480.40974
350.6107220.7785570.389278
360.569340.8613190.43066
370.7296550.5406910.270345
380.7040530.5918940.295947
390.8019150.396170.198085
400.8720940.2558120.127906
410.8441310.3117380.155869
420.8313740.3372520.168626
430.8320250.3359510.167975
440.8378090.3243830.162191
450.8463080.3073840.153692
460.8554650.2890710.144535
470.8878810.2242390.112119
480.9778370.04432520.0221626
490.9870910.02581760.0129088
500.9863090.02738180.0136909
510.9862490.02750290.0137514
520.9852190.02956260.0147813
530.9801920.03961520.0198076
540.9760010.04799890.0239994
550.9815620.03687630.0184381
560.9749980.05000380.0250019
570.9811690.03766270.0188314
580.9750930.04981390.024907
590.9803530.03929480.0196474
600.9758950.04821010.024105
610.9876490.02470190.0123509
620.9829460.03410730.0170536
630.9842110.03157830.0157892
640.9797390.04052280.0202614
650.9748950.05020930.0251046
660.986990.02602030.0130101
670.9833090.03338150.0166907
680.9887770.02244510.0112226
690.9847350.03052950.0152647
700.9898350.0203290.0101645
710.9875270.02494580.0124729
720.9919280.01614310.00807153
730.9903210.01935810.00967903
740.9912230.0175540.00877702
750.9899860.02002830.0100142
760.986510.02697930.0134897
770.987620.02475920.0123796
780.9836410.0327190.0163595
790.9902610.01947860.00973929
800.9884340.02313160.0115658
810.9854660.02906850.0145342
820.9807860.03842720.0192136
830.9768990.04620290.0231014
840.9746970.05060660.0253033
850.9699480.06010440.0300522
860.9782470.04350510.0217525
870.9739840.05203190.026016
880.966260.06748070.0337404
890.9561890.08762190.0438109
900.9606280.07874310.0393716
910.9532650.09347020.0467351
920.9520430.09591310.0479566
930.9363640.1272720.0636362
940.9626840.07463250.0373162
950.9651770.06964690.0348234
960.9522950.09541030.0477051
970.9378170.1243650.0621827
980.9235110.1529770.0764887
990.9098240.1803520.0901762
1000.9068530.1862940.093147
1010.899390.2012210.10061
1020.8693640.2612710.130636
1030.8436460.3127080.156354
1040.887610.224780.11239
1050.9385920.1228170.0614084
1060.9496150.100770.0503851
1070.9300850.1398310.0699153
1080.9361580.1276840.0638422
1090.9110950.177810.0889049
1100.9033420.1933160.0966582
1110.8948760.2102480.105124
1120.8690350.2619310.130965
1130.8464480.3071050.153552
1140.8490750.301850.150925
1150.8060330.3879330.193967
1160.7693530.4612940.230647
1170.7064420.5871170.293558
1180.6856870.6286260.314313
1190.6178560.7642880.382144
1200.9661470.06770550.0338528
1210.9729220.05415620.0270781
1220.9802920.03941680.0197084
1230.9997190.0005611230.000280561
1240.9989640.002071660.00103583
1250.9963890.007222040.00361102
1260.991770.01645940.00822971
1270.9810810.03783860.0189193
1280.9460030.1079950.0539974

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.626028 & 0.747945 & 0.373972 \tabularnewline
10 & 0.821034 & 0.357932 & 0.178966 \tabularnewline
11 & 0.726124 & 0.547752 & 0.273876 \tabularnewline
12 & 0.919318 & 0.161364 & 0.0806818 \tabularnewline
13 & 0.870493 & 0.259014 & 0.129507 \tabularnewline
14 & 0.836133 & 0.327734 & 0.163867 \tabularnewline
15 & 0.775166 & 0.449668 & 0.224834 \tabularnewline
16 & 0.722392 & 0.555216 & 0.277608 \tabularnewline
17 & 0.679758 & 0.640485 & 0.320242 \tabularnewline
18 & 0.671777 & 0.656446 & 0.328223 \tabularnewline
19 & 0.640827 & 0.718345 & 0.359173 \tabularnewline
20 & 0.567656 & 0.864688 & 0.432344 \tabularnewline
21 & 0.489119 & 0.978238 & 0.510881 \tabularnewline
22 & 0.448733 & 0.897466 & 0.551267 \tabularnewline
23 & 0.377517 & 0.755034 & 0.622483 \tabularnewline
24 & 0.733516 & 0.532968 & 0.266484 \tabularnewline
25 & 0.699192 & 0.601616 & 0.300808 \tabularnewline
26 & 0.638633 & 0.722734 & 0.361367 \tabularnewline
27 & 0.574784 & 0.850432 & 0.425216 \tabularnewline
28 & 0.511119 & 0.977762 & 0.488881 \tabularnewline
29 & 0.459447 & 0.918894 & 0.540553 \tabularnewline
30 & 0.461962 & 0.923924 & 0.538038 \tabularnewline
31 & 0.525791 & 0.948418 & 0.474209 \tabularnewline
32 & 0.659822 & 0.680355 & 0.340178 \tabularnewline
33 & 0.613307 & 0.773386 & 0.386693 \tabularnewline
34 & 0.59026 & 0.81948 & 0.40974 \tabularnewline
35 & 0.610722 & 0.778557 & 0.389278 \tabularnewline
36 & 0.56934 & 0.861319 & 0.43066 \tabularnewline
37 & 0.729655 & 0.540691 & 0.270345 \tabularnewline
38 & 0.704053 & 0.591894 & 0.295947 \tabularnewline
39 & 0.801915 & 0.39617 & 0.198085 \tabularnewline
40 & 0.872094 & 0.255812 & 0.127906 \tabularnewline
41 & 0.844131 & 0.311738 & 0.155869 \tabularnewline
42 & 0.831374 & 0.337252 & 0.168626 \tabularnewline
43 & 0.832025 & 0.335951 & 0.167975 \tabularnewline
44 & 0.837809 & 0.324383 & 0.162191 \tabularnewline
45 & 0.846308 & 0.307384 & 0.153692 \tabularnewline
46 & 0.855465 & 0.289071 & 0.144535 \tabularnewline
47 & 0.887881 & 0.224239 & 0.112119 \tabularnewline
48 & 0.977837 & 0.0443252 & 0.0221626 \tabularnewline
49 & 0.987091 & 0.0258176 & 0.0129088 \tabularnewline
50 & 0.986309 & 0.0273818 & 0.0136909 \tabularnewline
51 & 0.986249 & 0.0275029 & 0.0137514 \tabularnewline
52 & 0.985219 & 0.0295626 & 0.0147813 \tabularnewline
53 & 0.980192 & 0.0396152 & 0.0198076 \tabularnewline
54 & 0.976001 & 0.0479989 & 0.0239994 \tabularnewline
55 & 0.981562 & 0.0368763 & 0.0184381 \tabularnewline
56 & 0.974998 & 0.0500038 & 0.0250019 \tabularnewline
57 & 0.981169 & 0.0376627 & 0.0188314 \tabularnewline
58 & 0.975093 & 0.0498139 & 0.024907 \tabularnewline
59 & 0.980353 & 0.0392948 & 0.0196474 \tabularnewline
60 & 0.975895 & 0.0482101 & 0.024105 \tabularnewline
61 & 0.987649 & 0.0247019 & 0.0123509 \tabularnewline
62 & 0.982946 & 0.0341073 & 0.0170536 \tabularnewline
63 & 0.984211 & 0.0315783 & 0.0157892 \tabularnewline
64 & 0.979739 & 0.0405228 & 0.0202614 \tabularnewline
65 & 0.974895 & 0.0502093 & 0.0251046 \tabularnewline
66 & 0.98699 & 0.0260203 & 0.0130101 \tabularnewline
67 & 0.983309 & 0.0333815 & 0.0166907 \tabularnewline
68 & 0.988777 & 0.0224451 & 0.0112226 \tabularnewline
69 & 0.984735 & 0.0305295 & 0.0152647 \tabularnewline
70 & 0.989835 & 0.020329 & 0.0101645 \tabularnewline
71 & 0.987527 & 0.0249458 & 0.0124729 \tabularnewline
72 & 0.991928 & 0.0161431 & 0.00807153 \tabularnewline
73 & 0.990321 & 0.0193581 & 0.00967903 \tabularnewline
74 & 0.991223 & 0.017554 & 0.00877702 \tabularnewline
75 & 0.989986 & 0.0200283 & 0.0100142 \tabularnewline
76 & 0.98651 & 0.0269793 & 0.0134897 \tabularnewline
77 & 0.98762 & 0.0247592 & 0.0123796 \tabularnewline
78 & 0.983641 & 0.032719 & 0.0163595 \tabularnewline
79 & 0.990261 & 0.0194786 & 0.00973929 \tabularnewline
80 & 0.988434 & 0.0231316 & 0.0115658 \tabularnewline
81 & 0.985466 & 0.0290685 & 0.0145342 \tabularnewline
82 & 0.980786 & 0.0384272 & 0.0192136 \tabularnewline
83 & 0.976899 & 0.0462029 & 0.0231014 \tabularnewline
84 & 0.974697 & 0.0506066 & 0.0253033 \tabularnewline
85 & 0.969948 & 0.0601044 & 0.0300522 \tabularnewline
86 & 0.978247 & 0.0435051 & 0.0217525 \tabularnewline
87 & 0.973984 & 0.0520319 & 0.026016 \tabularnewline
88 & 0.96626 & 0.0674807 & 0.0337404 \tabularnewline
89 & 0.956189 & 0.0876219 & 0.0438109 \tabularnewline
90 & 0.960628 & 0.0787431 & 0.0393716 \tabularnewline
91 & 0.953265 & 0.0934702 & 0.0467351 \tabularnewline
92 & 0.952043 & 0.0959131 & 0.0479566 \tabularnewline
93 & 0.936364 & 0.127272 & 0.0636362 \tabularnewline
94 & 0.962684 & 0.0746325 & 0.0373162 \tabularnewline
95 & 0.965177 & 0.0696469 & 0.0348234 \tabularnewline
96 & 0.952295 & 0.0954103 & 0.0477051 \tabularnewline
97 & 0.937817 & 0.124365 & 0.0621827 \tabularnewline
98 & 0.923511 & 0.152977 & 0.0764887 \tabularnewline
99 & 0.909824 & 0.180352 & 0.0901762 \tabularnewline
100 & 0.906853 & 0.186294 & 0.093147 \tabularnewline
101 & 0.89939 & 0.201221 & 0.10061 \tabularnewline
102 & 0.869364 & 0.261271 & 0.130636 \tabularnewline
103 & 0.843646 & 0.312708 & 0.156354 \tabularnewline
104 & 0.88761 & 0.22478 & 0.11239 \tabularnewline
105 & 0.938592 & 0.122817 & 0.0614084 \tabularnewline
106 & 0.949615 & 0.10077 & 0.0503851 \tabularnewline
107 & 0.930085 & 0.139831 & 0.0699153 \tabularnewline
108 & 0.936158 & 0.127684 & 0.0638422 \tabularnewline
109 & 0.911095 & 0.17781 & 0.0889049 \tabularnewline
110 & 0.903342 & 0.193316 & 0.0966582 \tabularnewline
111 & 0.894876 & 0.210248 & 0.105124 \tabularnewline
112 & 0.869035 & 0.261931 & 0.130965 \tabularnewline
113 & 0.846448 & 0.307105 & 0.153552 \tabularnewline
114 & 0.849075 & 0.30185 & 0.150925 \tabularnewline
115 & 0.806033 & 0.387933 & 0.193967 \tabularnewline
116 & 0.769353 & 0.461294 & 0.230647 \tabularnewline
117 & 0.706442 & 0.587117 & 0.293558 \tabularnewline
118 & 0.685687 & 0.628626 & 0.314313 \tabularnewline
119 & 0.617856 & 0.764288 & 0.382144 \tabularnewline
120 & 0.966147 & 0.0677055 & 0.0338528 \tabularnewline
121 & 0.972922 & 0.0541562 & 0.0270781 \tabularnewline
122 & 0.980292 & 0.0394168 & 0.0197084 \tabularnewline
123 & 0.999719 & 0.000561123 & 0.000280561 \tabularnewline
124 & 0.998964 & 0.00207166 & 0.00103583 \tabularnewline
125 & 0.996389 & 0.00722204 & 0.00361102 \tabularnewline
126 & 0.99177 & 0.0164594 & 0.00822971 \tabularnewline
127 & 0.981081 & 0.0378386 & 0.0189193 \tabularnewline
128 & 0.946003 & 0.107995 & 0.0539974 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267654&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]0.626028[/C][C]0.747945[/C][C]0.373972[/C][/ROW]
[ROW][C]10[/C][C]0.821034[/C][C]0.357932[/C][C]0.178966[/C][/ROW]
[ROW][C]11[/C][C]0.726124[/C][C]0.547752[/C][C]0.273876[/C][/ROW]
[ROW][C]12[/C][C]0.919318[/C][C]0.161364[/C][C]0.0806818[/C][/ROW]
[ROW][C]13[/C][C]0.870493[/C][C]0.259014[/C][C]0.129507[/C][/ROW]
[ROW][C]14[/C][C]0.836133[/C][C]0.327734[/C][C]0.163867[/C][/ROW]
[ROW][C]15[/C][C]0.775166[/C][C]0.449668[/C][C]0.224834[/C][/ROW]
[ROW][C]16[/C][C]0.722392[/C][C]0.555216[/C][C]0.277608[/C][/ROW]
[ROW][C]17[/C][C]0.679758[/C][C]0.640485[/C][C]0.320242[/C][/ROW]
[ROW][C]18[/C][C]0.671777[/C][C]0.656446[/C][C]0.328223[/C][/ROW]
[ROW][C]19[/C][C]0.640827[/C][C]0.718345[/C][C]0.359173[/C][/ROW]
[ROW][C]20[/C][C]0.567656[/C][C]0.864688[/C][C]0.432344[/C][/ROW]
[ROW][C]21[/C][C]0.489119[/C][C]0.978238[/C][C]0.510881[/C][/ROW]
[ROW][C]22[/C][C]0.448733[/C][C]0.897466[/C][C]0.551267[/C][/ROW]
[ROW][C]23[/C][C]0.377517[/C][C]0.755034[/C][C]0.622483[/C][/ROW]
[ROW][C]24[/C][C]0.733516[/C][C]0.532968[/C][C]0.266484[/C][/ROW]
[ROW][C]25[/C][C]0.699192[/C][C]0.601616[/C][C]0.300808[/C][/ROW]
[ROW][C]26[/C][C]0.638633[/C][C]0.722734[/C][C]0.361367[/C][/ROW]
[ROW][C]27[/C][C]0.574784[/C][C]0.850432[/C][C]0.425216[/C][/ROW]
[ROW][C]28[/C][C]0.511119[/C][C]0.977762[/C][C]0.488881[/C][/ROW]
[ROW][C]29[/C][C]0.459447[/C][C]0.918894[/C][C]0.540553[/C][/ROW]
[ROW][C]30[/C][C]0.461962[/C][C]0.923924[/C][C]0.538038[/C][/ROW]
[ROW][C]31[/C][C]0.525791[/C][C]0.948418[/C][C]0.474209[/C][/ROW]
[ROW][C]32[/C][C]0.659822[/C][C]0.680355[/C][C]0.340178[/C][/ROW]
[ROW][C]33[/C][C]0.613307[/C][C]0.773386[/C][C]0.386693[/C][/ROW]
[ROW][C]34[/C][C]0.59026[/C][C]0.81948[/C][C]0.40974[/C][/ROW]
[ROW][C]35[/C][C]0.610722[/C][C]0.778557[/C][C]0.389278[/C][/ROW]
[ROW][C]36[/C][C]0.56934[/C][C]0.861319[/C][C]0.43066[/C][/ROW]
[ROW][C]37[/C][C]0.729655[/C][C]0.540691[/C][C]0.270345[/C][/ROW]
[ROW][C]38[/C][C]0.704053[/C][C]0.591894[/C][C]0.295947[/C][/ROW]
[ROW][C]39[/C][C]0.801915[/C][C]0.39617[/C][C]0.198085[/C][/ROW]
[ROW][C]40[/C][C]0.872094[/C][C]0.255812[/C][C]0.127906[/C][/ROW]
[ROW][C]41[/C][C]0.844131[/C][C]0.311738[/C][C]0.155869[/C][/ROW]
[ROW][C]42[/C][C]0.831374[/C][C]0.337252[/C][C]0.168626[/C][/ROW]
[ROW][C]43[/C][C]0.832025[/C][C]0.335951[/C][C]0.167975[/C][/ROW]
[ROW][C]44[/C][C]0.837809[/C][C]0.324383[/C][C]0.162191[/C][/ROW]
[ROW][C]45[/C][C]0.846308[/C][C]0.307384[/C][C]0.153692[/C][/ROW]
[ROW][C]46[/C][C]0.855465[/C][C]0.289071[/C][C]0.144535[/C][/ROW]
[ROW][C]47[/C][C]0.887881[/C][C]0.224239[/C][C]0.112119[/C][/ROW]
[ROW][C]48[/C][C]0.977837[/C][C]0.0443252[/C][C]0.0221626[/C][/ROW]
[ROW][C]49[/C][C]0.987091[/C][C]0.0258176[/C][C]0.0129088[/C][/ROW]
[ROW][C]50[/C][C]0.986309[/C][C]0.0273818[/C][C]0.0136909[/C][/ROW]
[ROW][C]51[/C][C]0.986249[/C][C]0.0275029[/C][C]0.0137514[/C][/ROW]
[ROW][C]52[/C][C]0.985219[/C][C]0.0295626[/C][C]0.0147813[/C][/ROW]
[ROW][C]53[/C][C]0.980192[/C][C]0.0396152[/C][C]0.0198076[/C][/ROW]
[ROW][C]54[/C][C]0.976001[/C][C]0.0479989[/C][C]0.0239994[/C][/ROW]
[ROW][C]55[/C][C]0.981562[/C][C]0.0368763[/C][C]0.0184381[/C][/ROW]
[ROW][C]56[/C][C]0.974998[/C][C]0.0500038[/C][C]0.0250019[/C][/ROW]
[ROW][C]57[/C][C]0.981169[/C][C]0.0376627[/C][C]0.0188314[/C][/ROW]
[ROW][C]58[/C][C]0.975093[/C][C]0.0498139[/C][C]0.024907[/C][/ROW]
[ROW][C]59[/C][C]0.980353[/C][C]0.0392948[/C][C]0.0196474[/C][/ROW]
[ROW][C]60[/C][C]0.975895[/C][C]0.0482101[/C][C]0.024105[/C][/ROW]
[ROW][C]61[/C][C]0.987649[/C][C]0.0247019[/C][C]0.0123509[/C][/ROW]
[ROW][C]62[/C][C]0.982946[/C][C]0.0341073[/C][C]0.0170536[/C][/ROW]
[ROW][C]63[/C][C]0.984211[/C][C]0.0315783[/C][C]0.0157892[/C][/ROW]
[ROW][C]64[/C][C]0.979739[/C][C]0.0405228[/C][C]0.0202614[/C][/ROW]
[ROW][C]65[/C][C]0.974895[/C][C]0.0502093[/C][C]0.0251046[/C][/ROW]
[ROW][C]66[/C][C]0.98699[/C][C]0.0260203[/C][C]0.0130101[/C][/ROW]
[ROW][C]67[/C][C]0.983309[/C][C]0.0333815[/C][C]0.0166907[/C][/ROW]
[ROW][C]68[/C][C]0.988777[/C][C]0.0224451[/C][C]0.0112226[/C][/ROW]
[ROW][C]69[/C][C]0.984735[/C][C]0.0305295[/C][C]0.0152647[/C][/ROW]
[ROW][C]70[/C][C]0.989835[/C][C]0.020329[/C][C]0.0101645[/C][/ROW]
[ROW][C]71[/C][C]0.987527[/C][C]0.0249458[/C][C]0.0124729[/C][/ROW]
[ROW][C]72[/C][C]0.991928[/C][C]0.0161431[/C][C]0.00807153[/C][/ROW]
[ROW][C]73[/C][C]0.990321[/C][C]0.0193581[/C][C]0.00967903[/C][/ROW]
[ROW][C]74[/C][C]0.991223[/C][C]0.017554[/C][C]0.00877702[/C][/ROW]
[ROW][C]75[/C][C]0.989986[/C][C]0.0200283[/C][C]0.0100142[/C][/ROW]
[ROW][C]76[/C][C]0.98651[/C][C]0.0269793[/C][C]0.0134897[/C][/ROW]
[ROW][C]77[/C][C]0.98762[/C][C]0.0247592[/C][C]0.0123796[/C][/ROW]
[ROW][C]78[/C][C]0.983641[/C][C]0.032719[/C][C]0.0163595[/C][/ROW]
[ROW][C]79[/C][C]0.990261[/C][C]0.0194786[/C][C]0.00973929[/C][/ROW]
[ROW][C]80[/C][C]0.988434[/C][C]0.0231316[/C][C]0.0115658[/C][/ROW]
[ROW][C]81[/C][C]0.985466[/C][C]0.0290685[/C][C]0.0145342[/C][/ROW]
[ROW][C]82[/C][C]0.980786[/C][C]0.0384272[/C][C]0.0192136[/C][/ROW]
[ROW][C]83[/C][C]0.976899[/C][C]0.0462029[/C][C]0.0231014[/C][/ROW]
[ROW][C]84[/C][C]0.974697[/C][C]0.0506066[/C][C]0.0253033[/C][/ROW]
[ROW][C]85[/C][C]0.969948[/C][C]0.0601044[/C][C]0.0300522[/C][/ROW]
[ROW][C]86[/C][C]0.978247[/C][C]0.0435051[/C][C]0.0217525[/C][/ROW]
[ROW][C]87[/C][C]0.973984[/C][C]0.0520319[/C][C]0.026016[/C][/ROW]
[ROW][C]88[/C][C]0.96626[/C][C]0.0674807[/C][C]0.0337404[/C][/ROW]
[ROW][C]89[/C][C]0.956189[/C][C]0.0876219[/C][C]0.0438109[/C][/ROW]
[ROW][C]90[/C][C]0.960628[/C][C]0.0787431[/C][C]0.0393716[/C][/ROW]
[ROW][C]91[/C][C]0.953265[/C][C]0.0934702[/C][C]0.0467351[/C][/ROW]
[ROW][C]92[/C][C]0.952043[/C][C]0.0959131[/C][C]0.0479566[/C][/ROW]
[ROW][C]93[/C][C]0.936364[/C][C]0.127272[/C][C]0.0636362[/C][/ROW]
[ROW][C]94[/C][C]0.962684[/C][C]0.0746325[/C][C]0.0373162[/C][/ROW]
[ROW][C]95[/C][C]0.965177[/C][C]0.0696469[/C][C]0.0348234[/C][/ROW]
[ROW][C]96[/C][C]0.952295[/C][C]0.0954103[/C][C]0.0477051[/C][/ROW]
[ROW][C]97[/C][C]0.937817[/C][C]0.124365[/C][C]0.0621827[/C][/ROW]
[ROW][C]98[/C][C]0.923511[/C][C]0.152977[/C][C]0.0764887[/C][/ROW]
[ROW][C]99[/C][C]0.909824[/C][C]0.180352[/C][C]0.0901762[/C][/ROW]
[ROW][C]100[/C][C]0.906853[/C][C]0.186294[/C][C]0.093147[/C][/ROW]
[ROW][C]101[/C][C]0.89939[/C][C]0.201221[/C][C]0.10061[/C][/ROW]
[ROW][C]102[/C][C]0.869364[/C][C]0.261271[/C][C]0.130636[/C][/ROW]
[ROW][C]103[/C][C]0.843646[/C][C]0.312708[/C][C]0.156354[/C][/ROW]
[ROW][C]104[/C][C]0.88761[/C][C]0.22478[/C][C]0.11239[/C][/ROW]
[ROW][C]105[/C][C]0.938592[/C][C]0.122817[/C][C]0.0614084[/C][/ROW]
[ROW][C]106[/C][C]0.949615[/C][C]0.10077[/C][C]0.0503851[/C][/ROW]
[ROW][C]107[/C][C]0.930085[/C][C]0.139831[/C][C]0.0699153[/C][/ROW]
[ROW][C]108[/C][C]0.936158[/C][C]0.127684[/C][C]0.0638422[/C][/ROW]
[ROW][C]109[/C][C]0.911095[/C][C]0.17781[/C][C]0.0889049[/C][/ROW]
[ROW][C]110[/C][C]0.903342[/C][C]0.193316[/C][C]0.0966582[/C][/ROW]
[ROW][C]111[/C][C]0.894876[/C][C]0.210248[/C][C]0.105124[/C][/ROW]
[ROW][C]112[/C][C]0.869035[/C][C]0.261931[/C][C]0.130965[/C][/ROW]
[ROW][C]113[/C][C]0.846448[/C][C]0.307105[/C][C]0.153552[/C][/ROW]
[ROW][C]114[/C][C]0.849075[/C][C]0.30185[/C][C]0.150925[/C][/ROW]
[ROW][C]115[/C][C]0.806033[/C][C]0.387933[/C][C]0.193967[/C][/ROW]
[ROW][C]116[/C][C]0.769353[/C][C]0.461294[/C][C]0.230647[/C][/ROW]
[ROW][C]117[/C][C]0.706442[/C][C]0.587117[/C][C]0.293558[/C][/ROW]
[ROW][C]118[/C][C]0.685687[/C][C]0.628626[/C][C]0.314313[/C][/ROW]
[ROW][C]119[/C][C]0.617856[/C][C]0.764288[/C][C]0.382144[/C][/ROW]
[ROW][C]120[/C][C]0.966147[/C][C]0.0677055[/C][C]0.0338528[/C][/ROW]
[ROW][C]121[/C][C]0.972922[/C][C]0.0541562[/C][C]0.0270781[/C][/ROW]
[ROW][C]122[/C][C]0.980292[/C][C]0.0394168[/C][C]0.0197084[/C][/ROW]
[ROW][C]123[/C][C]0.999719[/C][C]0.000561123[/C][C]0.000280561[/C][/ROW]
[ROW][C]124[/C][C]0.998964[/C][C]0.00207166[/C][C]0.00103583[/C][/ROW]
[ROW][C]125[/C][C]0.996389[/C][C]0.00722204[/C][C]0.00361102[/C][/ROW]
[ROW][C]126[/C][C]0.99177[/C][C]0.0164594[/C][C]0.00822971[/C][/ROW]
[ROW][C]127[/C][C]0.981081[/C][C]0.0378386[/C][C]0.0189193[/C][/ROW]
[ROW][C]128[/C][C]0.946003[/C][C]0.107995[/C][C]0.0539974[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267654&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267654&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
90.6260280.7479450.373972
100.8210340.3579320.178966
110.7261240.5477520.273876
120.9193180.1613640.0806818
130.8704930.2590140.129507
140.8361330.3277340.163867
150.7751660.4496680.224834
160.7223920.5552160.277608
170.6797580.6404850.320242
180.6717770.6564460.328223
190.6408270.7183450.359173
200.5676560.8646880.432344
210.4891190.9782380.510881
220.4487330.8974660.551267
230.3775170.7550340.622483
240.7335160.5329680.266484
250.6991920.6016160.300808
260.6386330.7227340.361367
270.5747840.8504320.425216
280.5111190.9777620.488881
290.4594470.9188940.540553
300.4619620.9239240.538038
310.5257910.9484180.474209
320.6598220.6803550.340178
330.6133070.7733860.386693
340.590260.819480.40974
350.6107220.7785570.389278
360.569340.8613190.43066
370.7296550.5406910.270345
380.7040530.5918940.295947
390.8019150.396170.198085
400.8720940.2558120.127906
410.8441310.3117380.155869
420.8313740.3372520.168626
430.8320250.3359510.167975
440.8378090.3243830.162191
450.8463080.3073840.153692
460.8554650.2890710.144535
470.8878810.2242390.112119
480.9778370.04432520.0221626
490.9870910.02581760.0129088
500.9863090.02738180.0136909
510.9862490.02750290.0137514
520.9852190.02956260.0147813
530.9801920.03961520.0198076
540.9760010.04799890.0239994
550.9815620.03687630.0184381
560.9749980.05000380.0250019
570.9811690.03766270.0188314
580.9750930.04981390.024907
590.9803530.03929480.0196474
600.9758950.04821010.024105
610.9876490.02470190.0123509
620.9829460.03410730.0170536
630.9842110.03157830.0157892
640.9797390.04052280.0202614
650.9748950.05020930.0251046
660.986990.02602030.0130101
670.9833090.03338150.0166907
680.9887770.02244510.0112226
690.9847350.03052950.0152647
700.9898350.0203290.0101645
710.9875270.02494580.0124729
720.9919280.01614310.00807153
730.9903210.01935810.00967903
740.9912230.0175540.00877702
750.9899860.02002830.0100142
760.986510.02697930.0134897
770.987620.02475920.0123796
780.9836410.0327190.0163595
790.9902610.01947860.00973929
800.9884340.02313160.0115658
810.9854660.02906850.0145342
820.9807860.03842720.0192136
830.9768990.04620290.0231014
840.9746970.05060660.0253033
850.9699480.06010440.0300522
860.9782470.04350510.0217525
870.9739840.05203190.026016
880.966260.06748070.0337404
890.9561890.08762190.0438109
900.9606280.07874310.0393716
910.9532650.09347020.0467351
920.9520430.09591310.0479566
930.9363640.1272720.0636362
940.9626840.07463250.0373162
950.9651770.06964690.0348234
960.9522950.09541030.0477051
970.9378170.1243650.0621827
980.9235110.1529770.0764887
990.9098240.1803520.0901762
1000.9068530.1862940.093147
1010.899390.2012210.10061
1020.8693640.2612710.130636
1030.8436460.3127080.156354
1040.887610.224780.11239
1050.9385920.1228170.0614084
1060.9496150.100770.0503851
1070.9300850.1398310.0699153
1080.9361580.1276840.0638422
1090.9110950.177810.0889049
1100.9033420.1933160.0966582
1110.8948760.2102480.105124
1120.8690350.2619310.130965
1130.8464480.3071050.153552
1140.8490750.301850.150925
1150.8060330.3879330.193967
1160.7693530.4612940.230647
1170.7064420.5871170.293558
1180.6856870.6286260.314313
1190.6178560.7642880.382144
1200.9661470.06770550.0338528
1210.9729220.05415620.0270781
1220.9802920.03941680.0197084
1230.9997190.0005611230.000280561
1240.9989640.002071660.00103583
1250.9963890.007222040.00361102
1260.991770.01645940.00822971
1270.9810810.03783860.0189193
1280.9460030.1079950.0539974






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level30.025NOK
5% type I error level410.341667NOK
10% type I error level560.466667NOK

\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 & 3 & 0.025 & NOK \tabularnewline
5% type I error level & 41 & 0.341667 & NOK \tabularnewline
10% type I error level & 56 & 0.466667 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267654&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]3[/C][C]0.025[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]41[/C][C]0.341667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]56[/C][C]0.466667[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267654&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267654&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 level30.025NOK
5% type I error level410.341667NOK
10% type I error level560.466667NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

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