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 16:24:21 +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/t1418574283qq5ucuqlelynjry.htm/, Retrieved Thu, 16 May 2024 12:09:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267729, Retrieved Thu, 16 May 2024 12:09:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact85

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 16:24:21] [04df4205f362f56e0d1a9032a00a5d3d] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
91	80	26	31	5,0
137	99	42	42	3,0
148	137	109	80	7,5
92	77	24	42	7,0
131	108	49	20	6,0
59	62	23	34	6,0
90	72	61	42	1,0
83	58	38	13	6,0
116	97	32	37	5,0
42	88	16	25	1,0
155	104	22	28	6,5
128	80	26	28	0,0
49	25	9	6	3,5
96	99	24	45	7,5
66	60	28	25	3,5
104	66	34	35	6,0
76	90	16	43	3,5
99	75	59	59	7,5
108	69	38	27	6,5
74	81	36	28	3,5
96	54	35	35	4,0
116	46	21	29	7,5
87	106	29	48	4,5
97	34	12	25	0,0
127	60	37	44	3,5
106	95	37	64	5,5
80	57	47	32	5,0
74	62	51	20	4,5
91	36	32	28	2,5
133	56	21	34	7,5
74	54	13	31	7,0
114	64	14	26	0,0
140	76	-2	58	4,5
95	98	20	23	3,0
98	88	24	21	1,5
121	35	11	21	3,5
126	102	23	33	2,5
98	61	24	16	5,5
95	80	14	20	8,0
110	49	52	37	1,0
70	78	15	35	5,0
102	90	23	33	4,5
86	45	19	27	3,0
130	55	35	41	3,0
96	96	24	40	8,0
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
99	38	19	17	5,5
109	263	37	108	8,5
68	35	14	10	7
131	227	93	66	9,5
71	79	10	23	6
68	130	15	25	9
89	179	2	56	7,5
115	299	29	73	7,5
78	121	45	34	6
118	137	25	72	10,5
87	305	4	42	8,5
162	183	66	74	10,5
49	52	61	16	6,5
122	238	32	66	9,5
96	40	31	9	8,5
100	226	39	41	7,5
82	190	19	57	5
100	214	31	48	8
115	145	36	51	10
141	119	42	53	7
110	159	25	55	9,5
146	125	28	51	7
90	186	41	79	6
121	148	29	39	7
104	172	17	55	7
147	84	13	30	3,5
110	168	32	55	8
108	102	30	22	10
113	106	34	37	5,5
115	2	59	2	6
61	139	13	38	6,5
60	95	23	27	6,5
109	130	10	56	8,5
68	72	5	25	4
111	141	31	39	9,5
77	113	19	33	8
73	206	32	43	8,5
89	175	25	43	7
78	77	48	23	9
110	125	35	44	8
65	111	15	28	8
117	211	18	39	8
63	76	46	23	9
52	83	14	24	8,5
62	119	23	29	7
131	266	12	78	9,5
101	186	38	57	8,5
42	50	12	37	7,5
77	246	12	27	7
96	137	34	44	8,5
57	98	20	39	7
112	226	44	51	8
49	138	7	31	3,5
56	106	24	24	8,5
86	122	60	30	10
88	94	25	27	7,5
48	62	13	14	6,5
85	82	34	28	5
63	184	17	41	4
102	83	45	31	8
162	183	66	74	10,5
86	89	48	19	6,5
114	225	29	51	8
94	204	19	51	9
81	158	16	62	8,5
110	226	40	59	9,5
64	44	27	24	3
104	83	49	54	6
105	79	39	39	0,5
49	52	61	16	6,5
88	105	19	36	7,5
95	116	67	31	4,5
102	83	45	31	8
99	196	30	42	9
63	153	8	39	7,5
76	157	19	25	8,5
109	75	52	31	7
117	106	22	38	9,5
57	58	17	31	6,5
120	75	33	17	9,5
73	74	34	22	6
91	185	22	55	8
108	265	30	62	9,5
105	131	25	51	8
119	196	26	49	9
31	78	13	16	5

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'George Udny Yule' @ yule.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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267729&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267729&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267729&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'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Ex[t] = + 3.49992 -0.00127783LFM[t] + 0.0226514B[t] + 0.0234169PRH[t] -0.00854521CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ex[t] =  +  3.49992 -0.00127783LFM[t] +  0.0226514B[t] +  0.0234169PRH[t] -0.00854521CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267729&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ex[t] =  +  3.49992 -0.00127783LFM[t] +  0.0226514B[t] +  0.0234169PRH[t] -0.00854521CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267729&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267729&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] = + 3.49992 -0.00127783LFM[t] + 0.0226514B[t] + 0.0234169PRH[t] -0.00854521CH[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.499920.730084.7944.34275e-062.17137e-06
LFM-0.001277830.00822744-0.15530.8768120.438406
B0.02265140.00412115.4961.92836e-079.64179e-08
PRH0.02341690.01176361.9910.04858830.0242941
CH-0.008545210.0166337-0.51370.60830.30415

\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) & 3.49992 & 0.73008 & 4.794 & 4.34275e-06 & 2.17137e-06 \tabularnewline
LFM & -0.00127783 & 0.00822744 & -0.1553 & 0.876812 & 0.438406 \tabularnewline
B & 0.0226514 & 0.0041211 & 5.496 & 1.92836e-07 & 9.64179e-08 \tabularnewline
PRH & 0.0234169 & 0.0117636 & 1.991 & 0.0485883 & 0.0242941 \tabularnewline
CH & -0.00854521 & 0.0166337 & -0.5137 & 0.6083 & 0.30415 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267729&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]3.49992[/C][C]0.73008[/C][C]4.794[/C][C]4.34275e-06[/C][C]2.17137e-06[/C][/ROW]
[ROW][C]LFM[/C][C]-0.00127783[/C][C]0.00822744[/C][C]-0.1553[/C][C]0.876812[/C][C]0.438406[/C][/ROW]
[ROW][C]B[/C][C]0.0226514[/C][C]0.0041211[/C][C]5.496[/C][C]1.92836e-07[/C][C]9.64179e-08[/C][/ROW]
[ROW][C]PRH[/C][C]0.0234169[/C][C]0.0117636[/C][C]1.991[/C][C]0.0485883[/C][C]0.0242941[/C][/ROW]
[ROW][C]CH[/C][C]-0.00854521[/C][C]0.0166337[/C][C]-0.5137[/C][C]0.6083[/C][C]0.30415[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267729&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267729&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)3.499920.730084.7944.34275e-062.17137e-06
LFM-0.001277830.00822744-0.15530.8768120.438406
B0.02265140.00412115.4961.92836e-079.64179e-08
PRH0.02341690.01176361.9910.04858830.0242941
CH-0.008545210.0166337-0.51370.60830.30415






Multiple Linear Regression - Regression Statistics
Multiple R0.541692
R-squared0.29343
Adjusted R-squared0.272019
F-TEST (value)13.7045
F-TEST (DF numerator)4
F-TEST (DF denominator)132
p-value2.25506e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.18478
Sum Squared Residuals630.07

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.541692 \tabularnewline
R-squared & 0.29343 \tabularnewline
Adjusted R-squared & 0.272019 \tabularnewline
F-TEST (value) & 13.7045 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 132 \tabularnewline
p-value & 2.25506e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.18478 \tabularnewline
Sum Squared Residuals & 630.07 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267729&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.541692[/C][/ROW]
[ROW][C]R-squared[/C][C]0.29343[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.272019[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]13.7045[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]132[/C][/ROW]
[ROW][C]p-value[/C][C]2.25506e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.18478[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]630.07[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267729&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267729&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 R0.541692
R-squared0.29343
Adjusted R-squared0.272019
F-TEST (value)13.7045
F-TEST (DF numerator)4
F-TEST (DF denominator)132
p-value2.25506e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.18478
Sum Squared Residuals630.07






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
155.53968-0.539685
236.19195-3.19195
37.58.28286-0.782864
475.329621.67038
566.7554-0.755397
665.076960.923036
716.08534-5.08534
865.486390.513608
955.98204-0.982043
1015.60061-4.60061
116.55.933510.566494
1205.51804-5.51804
133.54.16307-0.663069
147.55.797211.70279
153.55.21671-1.71671
1665.359110.640893
173.55.44866-1.94866
187.55.94971.5503
196.55.583980.916021
203.55.84386-2.34386
2145.12093-1.12093
227.54.63762.8624
234.56.05872-1.55872
2404.21349-4.21349
253.55.18715-1.68715
265.55.83588-0.335882
2755.51597-0.515967
284.55.8331-1.3331
292.54.70916-2.20916
307.54.799662.70034
3174.668052.33195
3204.9096-4.9096
334.54.50007-7.30626e-05
3435.87016-2.87016
351.55.75057-4.25057
363.54.21624-0.716235
372.55.90595-3.40595
385.55.181710.318292
3985.347572.65243
4015.37078-4.37078
4155.22945-0.22945
424.55.6648-1.1648
4334.62354-1.62354
4435.04886-2.04886
4585.771982.22802
462.54.95776-2.45776
4774.989832.01017
4804.76986-4.76986
4914.65599-3.65599
503.54.57543-1.07543
515.54.835640.664361
525.54.533820.966183
538.59.2615-0.761499
5474.448212.55179
559.510.0882-0.588179
5665.236280.763718
5796.495332.50467
587.57.00910.490903
597.510.181-2.68103
6066.90429-0.90429
6110.56.422544.07746
628.510.0322-1.5322
6310.58.351292.14871
646.55.906880.593118
659.58.920420.579584
668.54.932323.56768
677.59.05426-1.55426
6857.65675-2.65675
6988.53529-0.535291
70107.044622.95538
7176.545870.454126
729.57.076372.42363
7376.364650.635352
7467.8831-1.8831
7577.04354-0.0435361
7677.19117-0.191168
773.55.26286-1.76286
7887.444150.555852
79106.186873.81313
805.56.23657-0.736573
8164.762771.23723
826.56.55022-0.0502184
836.55.8830.617001
848.56.060962.43904
8544.94738-0.947381
869.56.944592.55541
8786.124061.87594
888.58.454720.0452758
8977.56817-0.568167
9096.071872.92813
9186.634381.36562
9286.043151.95685
9388.2181-0.218102
9496.021562.97844
958.55.436293.06371
9676.406990.593013
979.58.972280.527724
988.57.986780.513215
997.54.543652.95635
10079.02406-2.02406
1018.56.900671.59933
10275.781991.21801
10389.07056-1.07056
1043.56.46222-2.96222
1058.56.186332.31367
106107.302152.69785
1077.55.87141.6286
1086.55.027761.47224
10955.80563-0.805625
11047.63501-3.63501
11186.03851.9615
11210.58.351292.14871
1136.56.367650.13235
11488.6941-0.694098
11598.009810.990194
1168.56.82021.6798
1179.58.911080.588915
11834.84197-1.84197
11965.933080.0669247
1200.55.7352-5.2352
1216.55.906880.593118
1227.55.903161.59684
1234.57.31012-2.81012
12486.03851.9615
12598.15670.843302
1267.56.739150.760847
1278.57.190371.30963
12876.012270.987735
1299.55.941913.55809
1306.54.874051.62595
1319.55.672923.82708
13265.691020.308981
13387.619330.380668
1349.59.53724-0.0372417
13586.48271.5173
13697.977661.02234
13755.39481-0.394811

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 5 & 5.53968 & -0.539685 \tabularnewline
2 & 3 & 6.19195 & -3.19195 \tabularnewline
3 & 7.5 & 8.28286 & -0.782864 \tabularnewline
4 & 7 & 5.32962 & 1.67038 \tabularnewline
5 & 6 & 6.7554 & -0.755397 \tabularnewline
6 & 6 & 5.07696 & 0.923036 \tabularnewline
7 & 1 & 6.08534 & -5.08534 \tabularnewline
8 & 6 & 5.48639 & 0.513608 \tabularnewline
9 & 5 & 5.98204 & -0.982043 \tabularnewline
10 & 1 & 5.60061 & -4.60061 \tabularnewline
11 & 6.5 & 5.93351 & 0.566494 \tabularnewline
12 & 0 & 5.51804 & -5.51804 \tabularnewline
13 & 3.5 & 4.16307 & -0.663069 \tabularnewline
14 & 7.5 & 5.79721 & 1.70279 \tabularnewline
15 & 3.5 & 5.21671 & -1.71671 \tabularnewline
16 & 6 & 5.35911 & 0.640893 \tabularnewline
17 & 3.5 & 5.44866 & -1.94866 \tabularnewline
18 & 7.5 & 5.9497 & 1.5503 \tabularnewline
19 & 6.5 & 5.58398 & 0.916021 \tabularnewline
20 & 3.5 & 5.84386 & -2.34386 \tabularnewline
21 & 4 & 5.12093 & -1.12093 \tabularnewline
22 & 7.5 & 4.6376 & 2.8624 \tabularnewline
23 & 4.5 & 6.05872 & -1.55872 \tabularnewline
24 & 0 & 4.21349 & -4.21349 \tabularnewline
25 & 3.5 & 5.18715 & -1.68715 \tabularnewline
26 & 5.5 & 5.83588 & -0.335882 \tabularnewline
27 & 5 & 5.51597 & -0.515967 \tabularnewline
28 & 4.5 & 5.8331 & -1.3331 \tabularnewline
29 & 2.5 & 4.70916 & -2.20916 \tabularnewline
30 & 7.5 & 4.79966 & 2.70034 \tabularnewline
31 & 7 & 4.66805 & 2.33195 \tabularnewline
32 & 0 & 4.9096 & -4.9096 \tabularnewline
33 & 4.5 & 4.50007 & -7.30626e-05 \tabularnewline
34 & 3 & 5.87016 & -2.87016 \tabularnewline
35 & 1.5 & 5.75057 & -4.25057 \tabularnewline
36 & 3.5 & 4.21624 & -0.716235 \tabularnewline
37 & 2.5 & 5.90595 & -3.40595 \tabularnewline
38 & 5.5 & 5.18171 & 0.318292 \tabularnewline
39 & 8 & 5.34757 & 2.65243 \tabularnewline
40 & 1 & 5.37078 & -4.37078 \tabularnewline
41 & 5 & 5.22945 & -0.22945 \tabularnewline
42 & 4.5 & 5.6648 & -1.1648 \tabularnewline
43 & 3 & 4.62354 & -1.62354 \tabularnewline
44 & 3 & 5.04886 & -2.04886 \tabularnewline
45 & 8 & 5.77198 & 2.22802 \tabularnewline
46 & 2.5 & 4.95776 & -2.45776 \tabularnewline
47 & 7 & 4.98983 & 2.01017 \tabularnewline
48 & 0 & 4.76986 & -4.76986 \tabularnewline
49 & 1 & 4.65599 & -3.65599 \tabularnewline
50 & 3.5 & 4.57543 & -1.07543 \tabularnewline
51 & 5.5 & 4.83564 & 0.664361 \tabularnewline
52 & 5.5 & 4.53382 & 0.966183 \tabularnewline
53 & 8.5 & 9.2615 & -0.761499 \tabularnewline
54 & 7 & 4.44821 & 2.55179 \tabularnewline
55 & 9.5 & 10.0882 & -0.588179 \tabularnewline
56 & 6 & 5.23628 & 0.763718 \tabularnewline
57 & 9 & 6.49533 & 2.50467 \tabularnewline
58 & 7.5 & 7.0091 & 0.490903 \tabularnewline
59 & 7.5 & 10.181 & -2.68103 \tabularnewline
60 & 6 & 6.90429 & -0.90429 \tabularnewline
61 & 10.5 & 6.42254 & 4.07746 \tabularnewline
62 & 8.5 & 10.0322 & -1.5322 \tabularnewline
63 & 10.5 & 8.35129 & 2.14871 \tabularnewline
64 & 6.5 & 5.90688 & 0.593118 \tabularnewline
65 & 9.5 & 8.92042 & 0.579584 \tabularnewline
66 & 8.5 & 4.93232 & 3.56768 \tabularnewline
67 & 7.5 & 9.05426 & -1.55426 \tabularnewline
68 & 5 & 7.65675 & -2.65675 \tabularnewline
69 & 8 & 8.53529 & -0.535291 \tabularnewline
70 & 10 & 7.04462 & 2.95538 \tabularnewline
71 & 7 & 6.54587 & 0.454126 \tabularnewline
72 & 9.5 & 7.07637 & 2.42363 \tabularnewline
73 & 7 & 6.36465 & 0.635352 \tabularnewline
74 & 6 & 7.8831 & -1.8831 \tabularnewline
75 & 7 & 7.04354 & -0.0435361 \tabularnewline
76 & 7 & 7.19117 & -0.191168 \tabularnewline
77 & 3.5 & 5.26286 & -1.76286 \tabularnewline
78 & 8 & 7.44415 & 0.555852 \tabularnewline
79 & 10 & 6.18687 & 3.81313 \tabularnewline
80 & 5.5 & 6.23657 & -0.736573 \tabularnewline
81 & 6 & 4.76277 & 1.23723 \tabularnewline
82 & 6.5 & 6.55022 & -0.0502184 \tabularnewline
83 & 6.5 & 5.883 & 0.617001 \tabularnewline
84 & 8.5 & 6.06096 & 2.43904 \tabularnewline
85 & 4 & 4.94738 & -0.947381 \tabularnewline
86 & 9.5 & 6.94459 & 2.55541 \tabularnewline
87 & 8 & 6.12406 & 1.87594 \tabularnewline
88 & 8.5 & 8.45472 & 0.0452758 \tabularnewline
89 & 7 & 7.56817 & -0.568167 \tabularnewline
90 & 9 & 6.07187 & 2.92813 \tabularnewline
91 & 8 & 6.63438 & 1.36562 \tabularnewline
92 & 8 & 6.04315 & 1.95685 \tabularnewline
93 & 8 & 8.2181 & -0.218102 \tabularnewline
94 & 9 & 6.02156 & 2.97844 \tabularnewline
95 & 8.5 & 5.43629 & 3.06371 \tabularnewline
96 & 7 & 6.40699 & 0.593013 \tabularnewline
97 & 9.5 & 8.97228 & 0.527724 \tabularnewline
98 & 8.5 & 7.98678 & 0.513215 \tabularnewline
99 & 7.5 & 4.54365 & 2.95635 \tabularnewline
100 & 7 & 9.02406 & -2.02406 \tabularnewline
101 & 8.5 & 6.90067 & 1.59933 \tabularnewline
102 & 7 & 5.78199 & 1.21801 \tabularnewline
103 & 8 & 9.07056 & -1.07056 \tabularnewline
104 & 3.5 & 6.46222 & -2.96222 \tabularnewline
105 & 8.5 & 6.18633 & 2.31367 \tabularnewline
106 & 10 & 7.30215 & 2.69785 \tabularnewline
107 & 7.5 & 5.8714 & 1.6286 \tabularnewline
108 & 6.5 & 5.02776 & 1.47224 \tabularnewline
109 & 5 & 5.80563 & -0.805625 \tabularnewline
110 & 4 & 7.63501 & -3.63501 \tabularnewline
111 & 8 & 6.0385 & 1.9615 \tabularnewline
112 & 10.5 & 8.35129 & 2.14871 \tabularnewline
113 & 6.5 & 6.36765 & 0.13235 \tabularnewline
114 & 8 & 8.6941 & -0.694098 \tabularnewline
115 & 9 & 8.00981 & 0.990194 \tabularnewline
116 & 8.5 & 6.8202 & 1.6798 \tabularnewline
117 & 9.5 & 8.91108 & 0.588915 \tabularnewline
118 & 3 & 4.84197 & -1.84197 \tabularnewline
119 & 6 & 5.93308 & 0.0669247 \tabularnewline
120 & 0.5 & 5.7352 & -5.2352 \tabularnewline
121 & 6.5 & 5.90688 & 0.593118 \tabularnewline
122 & 7.5 & 5.90316 & 1.59684 \tabularnewline
123 & 4.5 & 7.31012 & -2.81012 \tabularnewline
124 & 8 & 6.0385 & 1.9615 \tabularnewline
125 & 9 & 8.1567 & 0.843302 \tabularnewline
126 & 7.5 & 6.73915 & 0.760847 \tabularnewline
127 & 8.5 & 7.19037 & 1.30963 \tabularnewline
128 & 7 & 6.01227 & 0.987735 \tabularnewline
129 & 9.5 & 5.94191 & 3.55809 \tabularnewline
130 & 6.5 & 4.87405 & 1.62595 \tabularnewline
131 & 9.5 & 5.67292 & 3.82708 \tabularnewline
132 & 6 & 5.69102 & 0.308981 \tabularnewline
133 & 8 & 7.61933 & 0.380668 \tabularnewline
134 & 9.5 & 9.53724 & -0.0372417 \tabularnewline
135 & 8 & 6.4827 & 1.5173 \tabularnewline
136 & 9 & 7.97766 & 1.02234 \tabularnewline
137 & 5 & 5.39481 & -0.394811 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267729&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]5[/C][C]5.53968[/C][C]-0.539685[/C][/ROW]
[ROW][C]2[/C][C]3[/C][C]6.19195[/C][C]-3.19195[/C][/ROW]
[ROW][C]3[/C][C]7.5[/C][C]8.28286[/C][C]-0.782864[/C][/ROW]
[ROW][C]4[/C][C]7[/C][C]5.32962[/C][C]1.67038[/C][/ROW]
[ROW][C]5[/C][C]6[/C][C]6.7554[/C][C]-0.755397[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]5.07696[/C][C]0.923036[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]6.08534[/C][C]-5.08534[/C][/ROW]
[ROW][C]8[/C][C]6[/C][C]5.48639[/C][C]0.513608[/C][/ROW]
[ROW][C]9[/C][C]5[/C][C]5.98204[/C][C]-0.982043[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]5.60061[/C][C]-4.60061[/C][/ROW]
[ROW][C]11[/C][C]6.5[/C][C]5.93351[/C][C]0.566494[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]5.51804[/C][C]-5.51804[/C][/ROW]
[ROW][C]13[/C][C]3.5[/C][C]4.16307[/C][C]-0.663069[/C][/ROW]
[ROW][C]14[/C][C]7.5[/C][C]5.79721[/C][C]1.70279[/C][/ROW]
[ROW][C]15[/C][C]3.5[/C][C]5.21671[/C][C]-1.71671[/C][/ROW]
[ROW][C]16[/C][C]6[/C][C]5.35911[/C][C]0.640893[/C][/ROW]
[ROW][C]17[/C][C]3.5[/C][C]5.44866[/C][C]-1.94866[/C][/ROW]
[ROW][C]18[/C][C]7.5[/C][C]5.9497[/C][C]1.5503[/C][/ROW]
[ROW][C]19[/C][C]6.5[/C][C]5.58398[/C][C]0.916021[/C][/ROW]
[ROW][C]20[/C][C]3.5[/C][C]5.84386[/C][C]-2.34386[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]5.12093[/C][C]-1.12093[/C][/ROW]
[ROW][C]22[/C][C]7.5[/C][C]4.6376[/C][C]2.8624[/C][/ROW]
[ROW][C]23[/C][C]4.5[/C][C]6.05872[/C][C]-1.55872[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]4.21349[/C][C]-4.21349[/C][/ROW]
[ROW][C]25[/C][C]3.5[/C][C]5.18715[/C][C]-1.68715[/C][/ROW]
[ROW][C]26[/C][C]5.5[/C][C]5.83588[/C][C]-0.335882[/C][/ROW]
[ROW][C]27[/C][C]5[/C][C]5.51597[/C][C]-0.515967[/C][/ROW]
[ROW][C]28[/C][C]4.5[/C][C]5.8331[/C][C]-1.3331[/C][/ROW]
[ROW][C]29[/C][C]2.5[/C][C]4.70916[/C][C]-2.20916[/C][/ROW]
[ROW][C]30[/C][C]7.5[/C][C]4.79966[/C][C]2.70034[/C][/ROW]
[ROW][C]31[/C][C]7[/C][C]4.66805[/C][C]2.33195[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]4.9096[/C][C]-4.9096[/C][/ROW]
[ROW][C]33[/C][C]4.5[/C][C]4.50007[/C][C]-7.30626e-05[/C][/ROW]
[ROW][C]34[/C][C]3[/C][C]5.87016[/C][C]-2.87016[/C][/ROW]
[ROW][C]35[/C][C]1.5[/C][C]5.75057[/C][C]-4.25057[/C][/ROW]
[ROW][C]36[/C][C]3.5[/C][C]4.21624[/C][C]-0.716235[/C][/ROW]
[ROW][C]37[/C][C]2.5[/C][C]5.90595[/C][C]-3.40595[/C][/ROW]
[ROW][C]38[/C][C]5.5[/C][C]5.18171[/C][C]0.318292[/C][/ROW]
[ROW][C]39[/C][C]8[/C][C]5.34757[/C][C]2.65243[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]5.37078[/C][C]-4.37078[/C][/ROW]
[ROW][C]41[/C][C]5[/C][C]5.22945[/C][C]-0.22945[/C][/ROW]
[ROW][C]42[/C][C]4.5[/C][C]5.6648[/C][C]-1.1648[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]4.62354[/C][C]-1.62354[/C][/ROW]
[ROW][C]44[/C][C]3[/C][C]5.04886[/C][C]-2.04886[/C][/ROW]
[ROW][C]45[/C][C]8[/C][C]5.77198[/C][C]2.22802[/C][/ROW]
[ROW][C]46[/C][C]2.5[/C][C]4.95776[/C][C]-2.45776[/C][/ROW]
[ROW][C]47[/C][C]7[/C][C]4.98983[/C][C]2.01017[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]4.76986[/C][C]-4.76986[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]4.65599[/C][C]-3.65599[/C][/ROW]
[ROW][C]50[/C][C]3.5[/C][C]4.57543[/C][C]-1.07543[/C][/ROW]
[ROW][C]51[/C][C]5.5[/C][C]4.83564[/C][C]0.664361[/C][/ROW]
[ROW][C]52[/C][C]5.5[/C][C]4.53382[/C][C]0.966183[/C][/ROW]
[ROW][C]53[/C][C]8.5[/C][C]9.2615[/C][C]-0.761499[/C][/ROW]
[ROW][C]54[/C][C]7[/C][C]4.44821[/C][C]2.55179[/C][/ROW]
[ROW][C]55[/C][C]9.5[/C][C]10.0882[/C][C]-0.588179[/C][/ROW]
[ROW][C]56[/C][C]6[/C][C]5.23628[/C][C]0.763718[/C][/ROW]
[ROW][C]57[/C][C]9[/C][C]6.49533[/C][C]2.50467[/C][/ROW]
[ROW][C]58[/C][C]7.5[/C][C]7.0091[/C][C]0.490903[/C][/ROW]
[ROW][C]59[/C][C]7.5[/C][C]10.181[/C][C]-2.68103[/C][/ROW]
[ROW][C]60[/C][C]6[/C][C]6.90429[/C][C]-0.90429[/C][/ROW]
[ROW][C]61[/C][C]10.5[/C][C]6.42254[/C][C]4.07746[/C][/ROW]
[ROW][C]62[/C][C]8.5[/C][C]10.0322[/C][C]-1.5322[/C][/ROW]
[ROW][C]63[/C][C]10.5[/C][C]8.35129[/C][C]2.14871[/C][/ROW]
[ROW][C]64[/C][C]6.5[/C][C]5.90688[/C][C]0.593118[/C][/ROW]
[ROW][C]65[/C][C]9.5[/C][C]8.92042[/C][C]0.579584[/C][/ROW]
[ROW][C]66[/C][C]8.5[/C][C]4.93232[/C][C]3.56768[/C][/ROW]
[ROW][C]67[/C][C]7.5[/C][C]9.05426[/C][C]-1.55426[/C][/ROW]
[ROW][C]68[/C][C]5[/C][C]7.65675[/C][C]-2.65675[/C][/ROW]
[ROW][C]69[/C][C]8[/C][C]8.53529[/C][C]-0.535291[/C][/ROW]
[ROW][C]70[/C][C]10[/C][C]7.04462[/C][C]2.95538[/C][/ROW]
[ROW][C]71[/C][C]7[/C][C]6.54587[/C][C]0.454126[/C][/ROW]
[ROW][C]72[/C][C]9.5[/C][C]7.07637[/C][C]2.42363[/C][/ROW]
[ROW][C]73[/C][C]7[/C][C]6.36465[/C][C]0.635352[/C][/ROW]
[ROW][C]74[/C][C]6[/C][C]7.8831[/C][C]-1.8831[/C][/ROW]
[ROW][C]75[/C][C]7[/C][C]7.04354[/C][C]-0.0435361[/C][/ROW]
[ROW][C]76[/C][C]7[/C][C]7.19117[/C][C]-0.191168[/C][/ROW]
[ROW][C]77[/C][C]3.5[/C][C]5.26286[/C][C]-1.76286[/C][/ROW]
[ROW][C]78[/C][C]8[/C][C]7.44415[/C][C]0.555852[/C][/ROW]
[ROW][C]79[/C][C]10[/C][C]6.18687[/C][C]3.81313[/C][/ROW]
[ROW][C]80[/C][C]5.5[/C][C]6.23657[/C][C]-0.736573[/C][/ROW]
[ROW][C]81[/C][C]6[/C][C]4.76277[/C][C]1.23723[/C][/ROW]
[ROW][C]82[/C][C]6.5[/C][C]6.55022[/C][C]-0.0502184[/C][/ROW]
[ROW][C]83[/C][C]6.5[/C][C]5.883[/C][C]0.617001[/C][/ROW]
[ROW][C]84[/C][C]8.5[/C][C]6.06096[/C][C]2.43904[/C][/ROW]
[ROW][C]85[/C][C]4[/C][C]4.94738[/C][C]-0.947381[/C][/ROW]
[ROW][C]86[/C][C]9.5[/C][C]6.94459[/C][C]2.55541[/C][/ROW]
[ROW][C]87[/C][C]8[/C][C]6.12406[/C][C]1.87594[/C][/ROW]
[ROW][C]88[/C][C]8.5[/C][C]8.45472[/C][C]0.0452758[/C][/ROW]
[ROW][C]89[/C][C]7[/C][C]7.56817[/C][C]-0.568167[/C][/ROW]
[ROW][C]90[/C][C]9[/C][C]6.07187[/C][C]2.92813[/C][/ROW]
[ROW][C]91[/C][C]8[/C][C]6.63438[/C][C]1.36562[/C][/ROW]
[ROW][C]92[/C][C]8[/C][C]6.04315[/C][C]1.95685[/C][/ROW]
[ROW][C]93[/C][C]8[/C][C]8.2181[/C][C]-0.218102[/C][/ROW]
[ROW][C]94[/C][C]9[/C][C]6.02156[/C][C]2.97844[/C][/ROW]
[ROW][C]95[/C][C]8.5[/C][C]5.43629[/C][C]3.06371[/C][/ROW]
[ROW][C]96[/C][C]7[/C][C]6.40699[/C][C]0.593013[/C][/ROW]
[ROW][C]97[/C][C]9.5[/C][C]8.97228[/C][C]0.527724[/C][/ROW]
[ROW][C]98[/C][C]8.5[/C][C]7.98678[/C][C]0.513215[/C][/ROW]
[ROW][C]99[/C][C]7.5[/C][C]4.54365[/C][C]2.95635[/C][/ROW]
[ROW][C]100[/C][C]7[/C][C]9.02406[/C][C]-2.02406[/C][/ROW]
[ROW][C]101[/C][C]8.5[/C][C]6.90067[/C][C]1.59933[/C][/ROW]
[ROW][C]102[/C][C]7[/C][C]5.78199[/C][C]1.21801[/C][/ROW]
[ROW][C]103[/C][C]8[/C][C]9.07056[/C][C]-1.07056[/C][/ROW]
[ROW][C]104[/C][C]3.5[/C][C]6.46222[/C][C]-2.96222[/C][/ROW]
[ROW][C]105[/C][C]8.5[/C][C]6.18633[/C][C]2.31367[/C][/ROW]
[ROW][C]106[/C][C]10[/C][C]7.30215[/C][C]2.69785[/C][/ROW]
[ROW][C]107[/C][C]7.5[/C][C]5.8714[/C][C]1.6286[/C][/ROW]
[ROW][C]108[/C][C]6.5[/C][C]5.02776[/C][C]1.47224[/C][/ROW]
[ROW][C]109[/C][C]5[/C][C]5.80563[/C][C]-0.805625[/C][/ROW]
[ROW][C]110[/C][C]4[/C][C]7.63501[/C][C]-3.63501[/C][/ROW]
[ROW][C]111[/C][C]8[/C][C]6.0385[/C][C]1.9615[/C][/ROW]
[ROW][C]112[/C][C]10.5[/C][C]8.35129[/C][C]2.14871[/C][/ROW]
[ROW][C]113[/C][C]6.5[/C][C]6.36765[/C][C]0.13235[/C][/ROW]
[ROW][C]114[/C][C]8[/C][C]8.6941[/C][C]-0.694098[/C][/ROW]
[ROW][C]115[/C][C]9[/C][C]8.00981[/C][C]0.990194[/C][/ROW]
[ROW][C]116[/C][C]8.5[/C][C]6.8202[/C][C]1.6798[/C][/ROW]
[ROW][C]117[/C][C]9.5[/C][C]8.91108[/C][C]0.588915[/C][/ROW]
[ROW][C]118[/C][C]3[/C][C]4.84197[/C][C]-1.84197[/C][/ROW]
[ROW][C]119[/C][C]6[/C][C]5.93308[/C][C]0.0669247[/C][/ROW]
[ROW][C]120[/C][C]0.5[/C][C]5.7352[/C][C]-5.2352[/C][/ROW]
[ROW][C]121[/C][C]6.5[/C][C]5.90688[/C][C]0.593118[/C][/ROW]
[ROW][C]122[/C][C]7.5[/C][C]5.90316[/C][C]1.59684[/C][/ROW]
[ROW][C]123[/C][C]4.5[/C][C]7.31012[/C][C]-2.81012[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]6.0385[/C][C]1.9615[/C][/ROW]
[ROW][C]125[/C][C]9[/C][C]8.1567[/C][C]0.843302[/C][/ROW]
[ROW][C]126[/C][C]7.5[/C][C]6.73915[/C][C]0.760847[/C][/ROW]
[ROW][C]127[/C][C]8.5[/C][C]7.19037[/C][C]1.30963[/C][/ROW]
[ROW][C]128[/C][C]7[/C][C]6.01227[/C][C]0.987735[/C][/ROW]
[ROW][C]129[/C][C]9.5[/C][C]5.94191[/C][C]3.55809[/C][/ROW]
[ROW][C]130[/C][C]6.5[/C][C]4.87405[/C][C]1.62595[/C][/ROW]
[ROW][C]131[/C][C]9.5[/C][C]5.67292[/C][C]3.82708[/C][/ROW]
[ROW][C]132[/C][C]6[/C][C]5.69102[/C][C]0.308981[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]7.61933[/C][C]0.380668[/C][/ROW]
[ROW][C]134[/C][C]9.5[/C][C]9.53724[/C][C]-0.0372417[/C][/ROW]
[ROW][C]135[/C][C]8[/C][C]6.4827[/C][C]1.5173[/C][/ROW]
[ROW][C]136[/C][C]9[/C][C]7.97766[/C][C]1.02234[/C][/ROW]
[ROW][C]137[/C][C]5[/C][C]5.39481[/C][C]-0.394811[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267729&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267729&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
155.53968-0.539685
236.19195-3.19195
37.58.28286-0.782864
475.329621.67038
566.7554-0.755397
665.076960.923036
716.08534-5.08534
865.486390.513608
955.98204-0.982043
1015.60061-4.60061
116.55.933510.566494
1205.51804-5.51804
133.54.16307-0.663069
147.55.797211.70279
153.55.21671-1.71671
1665.359110.640893
173.55.44866-1.94866
187.55.94971.5503
196.55.583980.916021
203.55.84386-2.34386
2145.12093-1.12093
227.54.63762.8624
234.56.05872-1.55872
2404.21349-4.21349
253.55.18715-1.68715
265.55.83588-0.335882
2755.51597-0.515967
284.55.8331-1.3331
292.54.70916-2.20916
307.54.799662.70034
3174.668052.33195
3204.9096-4.9096
334.54.50007-7.30626e-05
3435.87016-2.87016
351.55.75057-4.25057
363.54.21624-0.716235
372.55.90595-3.40595
385.55.181710.318292
3985.347572.65243
4015.37078-4.37078
4155.22945-0.22945
424.55.6648-1.1648
4334.62354-1.62354
4435.04886-2.04886
4585.771982.22802
462.54.95776-2.45776
4774.989832.01017
4804.76986-4.76986
4914.65599-3.65599
503.54.57543-1.07543
515.54.835640.664361
525.54.533820.966183
538.59.2615-0.761499
5474.448212.55179
559.510.0882-0.588179
5665.236280.763718
5796.495332.50467
587.57.00910.490903
597.510.181-2.68103
6066.90429-0.90429
6110.56.422544.07746
628.510.0322-1.5322
6310.58.351292.14871
646.55.906880.593118
659.58.920420.579584
668.54.932323.56768
677.59.05426-1.55426
6857.65675-2.65675
6988.53529-0.535291
70107.044622.95538
7176.545870.454126
729.57.076372.42363
7376.364650.635352
7467.8831-1.8831
7577.04354-0.0435361
7677.19117-0.191168
773.55.26286-1.76286
7887.444150.555852
79106.186873.81313
805.56.23657-0.736573
8164.762771.23723
826.56.55022-0.0502184
836.55.8830.617001
848.56.060962.43904
8544.94738-0.947381
869.56.944592.55541
8786.124061.87594
888.58.454720.0452758
8977.56817-0.568167
9096.071872.92813
9186.634381.36562
9286.043151.95685
9388.2181-0.218102
9496.021562.97844
958.55.436293.06371
9676.406990.593013
979.58.972280.527724
988.57.986780.513215
997.54.543652.95635
10079.02406-2.02406
1018.56.900671.59933
10275.781991.21801
10389.07056-1.07056
1043.56.46222-2.96222
1058.56.186332.31367
106107.302152.69785
1077.55.87141.6286
1086.55.027761.47224
10955.80563-0.805625
11047.63501-3.63501
11186.03851.9615
11210.58.351292.14871
1136.56.367650.13235
11488.6941-0.694098
11598.009810.990194
1168.56.82021.6798
1179.58.911080.588915
11834.84197-1.84197
11965.933080.0669247
1200.55.7352-5.2352
1216.55.906880.593118
1227.55.903161.59684
1234.57.31012-2.81012
12486.03851.9615
12598.15670.843302
1267.56.739150.760847
1278.57.190371.30963
12876.012270.987735
1299.55.941913.55809
1306.54.874051.62595
1319.55.672923.82708
13265.691020.308981
13387.619330.380668
1349.59.53724-0.0372417
13586.48271.5173
13697.977661.02234
13755.39481-0.394811






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.7642570.4714860.235743
90.6454030.7091940.354597
100.8712660.2574680.128734
110.7975170.4049660.202483
120.9566610.08667810.0433391
130.9304190.1391630.0695814
140.9297280.1405450.0702724
150.8974780.2050440.102522
160.8679570.2640860.132043
170.8379580.3240840.162042
180.8142970.3714060.185703
190.7863070.4273860.213693
200.739320.521360.26068
210.6929740.6140510.307026
220.6797380.6405250.320262
230.6187810.7624380.381219
240.8214450.357110.178555
250.8040760.3918480.195924
260.7552910.4894190.244709
270.7060320.5879350.293968
280.6565190.6869630.343481
290.6391170.7217650.360883
300.6675420.6649150.332458
310.6969180.6061650.303082
320.8475280.3049440.152472
330.8124490.3751030.187551
340.8017380.3965250.198262
350.8460580.3078830.153942
360.8155780.3688440.184422
370.8365160.3269670.163484
380.8208960.3582080.179104
390.8844450.231110.115555
400.9473580.1052830.0526417
410.9326430.1347130.0673567
420.9192460.1615080.0807541
430.9120950.175810.0879051
440.9187290.1625420.0812708
450.9287730.1424540.0712268
460.9395410.1209170.0604586
470.9440660.1118680.0559342
480.9875810.02483710.0124186
490.994690.01061920.0053096
500.9949420.01011630.00505816
510.9941290.01174140.00587072
520.9933720.01325660.00662829
530.9906340.0187330.00936649
540.9927880.01442340.00721171
550.9905430.01891340.0094567
560.9883370.02332680.0116634
570.9913340.01733290.00866644
580.9880060.02398850.0119943
590.9877650.02447010.012235
600.984490.03101930.0155097
610.9924820.01503670.00751837
620.989550.02090050.0104503
630.9898090.02038110.0101905
640.9877990.0244020.012201
650.9842010.03159830.0157992
660.9903790.01924230.00962116
670.9876970.02460630.0123031
680.9896280.02074340.0103717
690.9858540.02829210.0141461
700.9892650.02146980.0107349
710.9857190.02856240.0142812
720.9865090.02698190.013491
730.9820040.03599150.0179958
740.9818860.03622780.0181139
750.9761650.04767060.0238353
760.9690160.06196820.0309841
770.9819990.03600110.0180005
780.9760710.0478580.023929
790.9854110.02917770.0145888
800.9845480.03090350.0154518
810.9811050.03778990.018895
820.9746330.05073440.0253672
830.9670270.06594560.0329728
840.962940.07411980.0370599
850.964240.07152090.0357604
860.96340.07319980.0365999
870.9577480.0845050.0422525
880.9456940.1086110.0543056
890.9312630.1374730.0687365
900.9419410.1161180.058059
910.9271680.1456650.0728324
920.9188620.1622760.0811378
930.8984770.2030470.101523
940.9224480.1551040.0775522
950.9353860.1292280.0646138
960.9167020.1665960.0832978
970.8934480.2131040.106552
980.8657210.2685570.134279
990.8821090.2357820.117891
1000.8752910.2494180.124709
1010.8541410.2917180.145859
1020.8349520.3300950.165048
1030.8074940.3850120.192506
1040.8458470.3083060.154153
1050.8538210.2923570.146179
1060.9040420.1919150.0959577
1070.8775820.2448360.122418
1080.8521610.2956770.147839
1090.8286050.3427910.171395
1100.8957250.208550.104275
1110.8778860.2442280.122114
1120.8814040.2371920.118596
1130.8404650.3190690.159535
1140.8223490.3553020.177651
1150.7698760.4602470.230124
1160.7529830.4940330.247017
1170.6968880.6062250.303112
1180.7172370.5655260.282763
1190.6750310.6499370.324969
1200.9988210.002357920.00117896
1210.9999862.83202e-051.41601e-05
1220.9999637.35826e-053.67913e-05
1230.9999764.79837e-052.39918e-05
1240.9999656.95774e-053.47887e-05
1250.9998710.0002583870.000129194
1260.999370.001260340.000630171
1270.9980310.003938790.0019694
1280.9912150.01757040.00878518
1290.9666660.06666750.0333338

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.764257 & 0.471486 & 0.235743 \tabularnewline
9 & 0.645403 & 0.709194 & 0.354597 \tabularnewline
10 & 0.871266 & 0.257468 & 0.128734 \tabularnewline
11 & 0.797517 & 0.404966 & 0.202483 \tabularnewline
12 & 0.956661 & 0.0866781 & 0.0433391 \tabularnewline
13 & 0.930419 & 0.139163 & 0.0695814 \tabularnewline
14 & 0.929728 & 0.140545 & 0.0702724 \tabularnewline
15 & 0.897478 & 0.205044 & 0.102522 \tabularnewline
16 & 0.867957 & 0.264086 & 0.132043 \tabularnewline
17 & 0.837958 & 0.324084 & 0.162042 \tabularnewline
18 & 0.814297 & 0.371406 & 0.185703 \tabularnewline
19 & 0.786307 & 0.427386 & 0.213693 \tabularnewline
20 & 0.73932 & 0.52136 & 0.26068 \tabularnewline
21 & 0.692974 & 0.614051 & 0.307026 \tabularnewline
22 & 0.679738 & 0.640525 & 0.320262 \tabularnewline
23 & 0.618781 & 0.762438 & 0.381219 \tabularnewline
24 & 0.821445 & 0.35711 & 0.178555 \tabularnewline
25 & 0.804076 & 0.391848 & 0.195924 \tabularnewline
26 & 0.755291 & 0.489419 & 0.244709 \tabularnewline
27 & 0.706032 & 0.587935 & 0.293968 \tabularnewline
28 & 0.656519 & 0.686963 & 0.343481 \tabularnewline
29 & 0.639117 & 0.721765 & 0.360883 \tabularnewline
30 & 0.667542 & 0.664915 & 0.332458 \tabularnewline
31 & 0.696918 & 0.606165 & 0.303082 \tabularnewline
32 & 0.847528 & 0.304944 & 0.152472 \tabularnewline
33 & 0.812449 & 0.375103 & 0.187551 \tabularnewline
34 & 0.801738 & 0.396525 & 0.198262 \tabularnewline
35 & 0.846058 & 0.307883 & 0.153942 \tabularnewline
36 & 0.815578 & 0.368844 & 0.184422 \tabularnewline
37 & 0.836516 & 0.326967 & 0.163484 \tabularnewline
38 & 0.820896 & 0.358208 & 0.179104 \tabularnewline
39 & 0.884445 & 0.23111 & 0.115555 \tabularnewline
40 & 0.947358 & 0.105283 & 0.0526417 \tabularnewline
41 & 0.932643 & 0.134713 & 0.0673567 \tabularnewline
42 & 0.919246 & 0.161508 & 0.0807541 \tabularnewline
43 & 0.912095 & 0.17581 & 0.0879051 \tabularnewline
44 & 0.918729 & 0.162542 & 0.0812708 \tabularnewline
45 & 0.928773 & 0.142454 & 0.0712268 \tabularnewline
46 & 0.939541 & 0.120917 & 0.0604586 \tabularnewline
47 & 0.944066 & 0.111868 & 0.0559342 \tabularnewline
48 & 0.987581 & 0.0248371 & 0.0124186 \tabularnewline
49 & 0.99469 & 0.0106192 & 0.0053096 \tabularnewline
50 & 0.994942 & 0.0101163 & 0.00505816 \tabularnewline
51 & 0.994129 & 0.0117414 & 0.00587072 \tabularnewline
52 & 0.993372 & 0.0132566 & 0.00662829 \tabularnewline
53 & 0.990634 & 0.018733 & 0.00936649 \tabularnewline
54 & 0.992788 & 0.0144234 & 0.00721171 \tabularnewline
55 & 0.990543 & 0.0189134 & 0.0094567 \tabularnewline
56 & 0.988337 & 0.0233268 & 0.0116634 \tabularnewline
57 & 0.991334 & 0.0173329 & 0.00866644 \tabularnewline
58 & 0.988006 & 0.0239885 & 0.0119943 \tabularnewline
59 & 0.987765 & 0.0244701 & 0.012235 \tabularnewline
60 & 0.98449 & 0.0310193 & 0.0155097 \tabularnewline
61 & 0.992482 & 0.0150367 & 0.00751837 \tabularnewline
62 & 0.98955 & 0.0209005 & 0.0104503 \tabularnewline
63 & 0.989809 & 0.0203811 & 0.0101905 \tabularnewline
64 & 0.987799 & 0.024402 & 0.012201 \tabularnewline
65 & 0.984201 & 0.0315983 & 0.0157992 \tabularnewline
66 & 0.990379 & 0.0192423 & 0.00962116 \tabularnewline
67 & 0.987697 & 0.0246063 & 0.0123031 \tabularnewline
68 & 0.989628 & 0.0207434 & 0.0103717 \tabularnewline
69 & 0.985854 & 0.0282921 & 0.0141461 \tabularnewline
70 & 0.989265 & 0.0214698 & 0.0107349 \tabularnewline
71 & 0.985719 & 0.0285624 & 0.0142812 \tabularnewline
72 & 0.986509 & 0.0269819 & 0.013491 \tabularnewline
73 & 0.982004 & 0.0359915 & 0.0179958 \tabularnewline
74 & 0.981886 & 0.0362278 & 0.0181139 \tabularnewline
75 & 0.976165 & 0.0476706 & 0.0238353 \tabularnewline
76 & 0.969016 & 0.0619682 & 0.0309841 \tabularnewline
77 & 0.981999 & 0.0360011 & 0.0180005 \tabularnewline
78 & 0.976071 & 0.047858 & 0.023929 \tabularnewline
79 & 0.985411 & 0.0291777 & 0.0145888 \tabularnewline
80 & 0.984548 & 0.0309035 & 0.0154518 \tabularnewline
81 & 0.981105 & 0.0377899 & 0.018895 \tabularnewline
82 & 0.974633 & 0.0507344 & 0.0253672 \tabularnewline
83 & 0.967027 & 0.0659456 & 0.0329728 \tabularnewline
84 & 0.96294 & 0.0741198 & 0.0370599 \tabularnewline
85 & 0.96424 & 0.0715209 & 0.0357604 \tabularnewline
86 & 0.9634 & 0.0731998 & 0.0365999 \tabularnewline
87 & 0.957748 & 0.084505 & 0.0422525 \tabularnewline
88 & 0.945694 & 0.108611 & 0.0543056 \tabularnewline
89 & 0.931263 & 0.137473 & 0.0687365 \tabularnewline
90 & 0.941941 & 0.116118 & 0.058059 \tabularnewline
91 & 0.927168 & 0.145665 & 0.0728324 \tabularnewline
92 & 0.918862 & 0.162276 & 0.0811378 \tabularnewline
93 & 0.898477 & 0.203047 & 0.101523 \tabularnewline
94 & 0.922448 & 0.155104 & 0.0775522 \tabularnewline
95 & 0.935386 & 0.129228 & 0.0646138 \tabularnewline
96 & 0.916702 & 0.166596 & 0.0832978 \tabularnewline
97 & 0.893448 & 0.213104 & 0.106552 \tabularnewline
98 & 0.865721 & 0.268557 & 0.134279 \tabularnewline
99 & 0.882109 & 0.235782 & 0.117891 \tabularnewline
100 & 0.875291 & 0.249418 & 0.124709 \tabularnewline
101 & 0.854141 & 0.291718 & 0.145859 \tabularnewline
102 & 0.834952 & 0.330095 & 0.165048 \tabularnewline
103 & 0.807494 & 0.385012 & 0.192506 \tabularnewline
104 & 0.845847 & 0.308306 & 0.154153 \tabularnewline
105 & 0.853821 & 0.292357 & 0.146179 \tabularnewline
106 & 0.904042 & 0.191915 & 0.0959577 \tabularnewline
107 & 0.877582 & 0.244836 & 0.122418 \tabularnewline
108 & 0.852161 & 0.295677 & 0.147839 \tabularnewline
109 & 0.828605 & 0.342791 & 0.171395 \tabularnewline
110 & 0.895725 & 0.20855 & 0.104275 \tabularnewline
111 & 0.877886 & 0.244228 & 0.122114 \tabularnewline
112 & 0.881404 & 0.237192 & 0.118596 \tabularnewline
113 & 0.840465 & 0.319069 & 0.159535 \tabularnewline
114 & 0.822349 & 0.355302 & 0.177651 \tabularnewline
115 & 0.769876 & 0.460247 & 0.230124 \tabularnewline
116 & 0.752983 & 0.494033 & 0.247017 \tabularnewline
117 & 0.696888 & 0.606225 & 0.303112 \tabularnewline
118 & 0.717237 & 0.565526 & 0.282763 \tabularnewline
119 & 0.675031 & 0.649937 & 0.324969 \tabularnewline
120 & 0.998821 & 0.00235792 & 0.00117896 \tabularnewline
121 & 0.999986 & 2.83202e-05 & 1.41601e-05 \tabularnewline
122 & 0.999963 & 7.35826e-05 & 3.67913e-05 \tabularnewline
123 & 0.999976 & 4.79837e-05 & 2.39918e-05 \tabularnewline
124 & 0.999965 & 6.95774e-05 & 3.47887e-05 \tabularnewline
125 & 0.999871 & 0.000258387 & 0.000129194 \tabularnewline
126 & 0.99937 & 0.00126034 & 0.000630171 \tabularnewline
127 & 0.998031 & 0.00393879 & 0.0019694 \tabularnewline
128 & 0.991215 & 0.0175704 & 0.00878518 \tabularnewline
129 & 0.966666 & 0.0666675 & 0.0333338 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267729&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]0.764257[/C][C]0.471486[/C][C]0.235743[/C][/ROW]
[ROW][C]9[/C][C]0.645403[/C][C]0.709194[/C][C]0.354597[/C][/ROW]
[ROW][C]10[/C][C]0.871266[/C][C]0.257468[/C][C]0.128734[/C][/ROW]
[ROW][C]11[/C][C]0.797517[/C][C]0.404966[/C][C]0.202483[/C][/ROW]
[ROW][C]12[/C][C]0.956661[/C][C]0.0866781[/C][C]0.0433391[/C][/ROW]
[ROW][C]13[/C][C]0.930419[/C][C]0.139163[/C][C]0.0695814[/C][/ROW]
[ROW][C]14[/C][C]0.929728[/C][C]0.140545[/C][C]0.0702724[/C][/ROW]
[ROW][C]15[/C][C]0.897478[/C][C]0.205044[/C][C]0.102522[/C][/ROW]
[ROW][C]16[/C][C]0.867957[/C][C]0.264086[/C][C]0.132043[/C][/ROW]
[ROW][C]17[/C][C]0.837958[/C][C]0.324084[/C][C]0.162042[/C][/ROW]
[ROW][C]18[/C][C]0.814297[/C][C]0.371406[/C][C]0.185703[/C][/ROW]
[ROW][C]19[/C][C]0.786307[/C][C]0.427386[/C][C]0.213693[/C][/ROW]
[ROW][C]20[/C][C]0.73932[/C][C]0.52136[/C][C]0.26068[/C][/ROW]
[ROW][C]21[/C][C]0.692974[/C][C]0.614051[/C][C]0.307026[/C][/ROW]
[ROW][C]22[/C][C]0.679738[/C][C]0.640525[/C][C]0.320262[/C][/ROW]
[ROW][C]23[/C][C]0.618781[/C][C]0.762438[/C][C]0.381219[/C][/ROW]
[ROW][C]24[/C][C]0.821445[/C][C]0.35711[/C][C]0.178555[/C][/ROW]
[ROW][C]25[/C][C]0.804076[/C][C]0.391848[/C][C]0.195924[/C][/ROW]
[ROW][C]26[/C][C]0.755291[/C][C]0.489419[/C][C]0.244709[/C][/ROW]
[ROW][C]27[/C][C]0.706032[/C][C]0.587935[/C][C]0.293968[/C][/ROW]
[ROW][C]28[/C][C]0.656519[/C][C]0.686963[/C][C]0.343481[/C][/ROW]
[ROW][C]29[/C][C]0.639117[/C][C]0.721765[/C][C]0.360883[/C][/ROW]
[ROW][C]30[/C][C]0.667542[/C][C]0.664915[/C][C]0.332458[/C][/ROW]
[ROW][C]31[/C][C]0.696918[/C][C]0.606165[/C][C]0.303082[/C][/ROW]
[ROW][C]32[/C][C]0.847528[/C][C]0.304944[/C][C]0.152472[/C][/ROW]
[ROW][C]33[/C][C]0.812449[/C][C]0.375103[/C][C]0.187551[/C][/ROW]
[ROW][C]34[/C][C]0.801738[/C][C]0.396525[/C][C]0.198262[/C][/ROW]
[ROW][C]35[/C][C]0.846058[/C][C]0.307883[/C][C]0.153942[/C][/ROW]
[ROW][C]36[/C][C]0.815578[/C][C]0.368844[/C][C]0.184422[/C][/ROW]
[ROW][C]37[/C][C]0.836516[/C][C]0.326967[/C][C]0.163484[/C][/ROW]
[ROW][C]38[/C][C]0.820896[/C][C]0.358208[/C][C]0.179104[/C][/ROW]
[ROW][C]39[/C][C]0.884445[/C][C]0.23111[/C][C]0.115555[/C][/ROW]
[ROW][C]40[/C][C]0.947358[/C][C]0.105283[/C][C]0.0526417[/C][/ROW]
[ROW][C]41[/C][C]0.932643[/C][C]0.134713[/C][C]0.0673567[/C][/ROW]
[ROW][C]42[/C][C]0.919246[/C][C]0.161508[/C][C]0.0807541[/C][/ROW]
[ROW][C]43[/C][C]0.912095[/C][C]0.17581[/C][C]0.0879051[/C][/ROW]
[ROW][C]44[/C][C]0.918729[/C][C]0.162542[/C][C]0.0812708[/C][/ROW]
[ROW][C]45[/C][C]0.928773[/C][C]0.142454[/C][C]0.0712268[/C][/ROW]
[ROW][C]46[/C][C]0.939541[/C][C]0.120917[/C][C]0.0604586[/C][/ROW]
[ROW][C]47[/C][C]0.944066[/C][C]0.111868[/C][C]0.0559342[/C][/ROW]
[ROW][C]48[/C][C]0.987581[/C][C]0.0248371[/C][C]0.0124186[/C][/ROW]
[ROW][C]49[/C][C]0.99469[/C][C]0.0106192[/C][C]0.0053096[/C][/ROW]
[ROW][C]50[/C][C]0.994942[/C][C]0.0101163[/C][C]0.00505816[/C][/ROW]
[ROW][C]51[/C][C]0.994129[/C][C]0.0117414[/C][C]0.00587072[/C][/ROW]
[ROW][C]52[/C][C]0.993372[/C][C]0.0132566[/C][C]0.00662829[/C][/ROW]
[ROW][C]53[/C][C]0.990634[/C][C]0.018733[/C][C]0.00936649[/C][/ROW]
[ROW][C]54[/C][C]0.992788[/C][C]0.0144234[/C][C]0.00721171[/C][/ROW]
[ROW][C]55[/C][C]0.990543[/C][C]0.0189134[/C][C]0.0094567[/C][/ROW]
[ROW][C]56[/C][C]0.988337[/C][C]0.0233268[/C][C]0.0116634[/C][/ROW]
[ROW][C]57[/C][C]0.991334[/C][C]0.0173329[/C][C]0.00866644[/C][/ROW]
[ROW][C]58[/C][C]0.988006[/C][C]0.0239885[/C][C]0.0119943[/C][/ROW]
[ROW][C]59[/C][C]0.987765[/C][C]0.0244701[/C][C]0.012235[/C][/ROW]
[ROW][C]60[/C][C]0.98449[/C][C]0.0310193[/C][C]0.0155097[/C][/ROW]
[ROW][C]61[/C][C]0.992482[/C][C]0.0150367[/C][C]0.00751837[/C][/ROW]
[ROW][C]62[/C][C]0.98955[/C][C]0.0209005[/C][C]0.0104503[/C][/ROW]
[ROW][C]63[/C][C]0.989809[/C][C]0.0203811[/C][C]0.0101905[/C][/ROW]
[ROW][C]64[/C][C]0.987799[/C][C]0.024402[/C][C]0.012201[/C][/ROW]
[ROW][C]65[/C][C]0.984201[/C][C]0.0315983[/C][C]0.0157992[/C][/ROW]
[ROW][C]66[/C][C]0.990379[/C][C]0.0192423[/C][C]0.00962116[/C][/ROW]
[ROW][C]67[/C][C]0.987697[/C][C]0.0246063[/C][C]0.0123031[/C][/ROW]
[ROW][C]68[/C][C]0.989628[/C][C]0.0207434[/C][C]0.0103717[/C][/ROW]
[ROW][C]69[/C][C]0.985854[/C][C]0.0282921[/C][C]0.0141461[/C][/ROW]
[ROW][C]70[/C][C]0.989265[/C][C]0.0214698[/C][C]0.0107349[/C][/ROW]
[ROW][C]71[/C][C]0.985719[/C][C]0.0285624[/C][C]0.0142812[/C][/ROW]
[ROW][C]72[/C][C]0.986509[/C][C]0.0269819[/C][C]0.013491[/C][/ROW]
[ROW][C]73[/C][C]0.982004[/C][C]0.0359915[/C][C]0.0179958[/C][/ROW]
[ROW][C]74[/C][C]0.981886[/C][C]0.0362278[/C][C]0.0181139[/C][/ROW]
[ROW][C]75[/C][C]0.976165[/C][C]0.0476706[/C][C]0.0238353[/C][/ROW]
[ROW][C]76[/C][C]0.969016[/C][C]0.0619682[/C][C]0.0309841[/C][/ROW]
[ROW][C]77[/C][C]0.981999[/C][C]0.0360011[/C][C]0.0180005[/C][/ROW]
[ROW][C]78[/C][C]0.976071[/C][C]0.047858[/C][C]0.023929[/C][/ROW]
[ROW][C]79[/C][C]0.985411[/C][C]0.0291777[/C][C]0.0145888[/C][/ROW]
[ROW][C]80[/C][C]0.984548[/C][C]0.0309035[/C][C]0.0154518[/C][/ROW]
[ROW][C]81[/C][C]0.981105[/C][C]0.0377899[/C][C]0.018895[/C][/ROW]
[ROW][C]82[/C][C]0.974633[/C][C]0.0507344[/C][C]0.0253672[/C][/ROW]
[ROW][C]83[/C][C]0.967027[/C][C]0.0659456[/C][C]0.0329728[/C][/ROW]
[ROW][C]84[/C][C]0.96294[/C][C]0.0741198[/C][C]0.0370599[/C][/ROW]
[ROW][C]85[/C][C]0.96424[/C][C]0.0715209[/C][C]0.0357604[/C][/ROW]
[ROW][C]86[/C][C]0.9634[/C][C]0.0731998[/C][C]0.0365999[/C][/ROW]
[ROW][C]87[/C][C]0.957748[/C][C]0.084505[/C][C]0.0422525[/C][/ROW]
[ROW][C]88[/C][C]0.945694[/C][C]0.108611[/C][C]0.0543056[/C][/ROW]
[ROW][C]89[/C][C]0.931263[/C][C]0.137473[/C][C]0.0687365[/C][/ROW]
[ROW][C]90[/C][C]0.941941[/C][C]0.116118[/C][C]0.058059[/C][/ROW]
[ROW][C]91[/C][C]0.927168[/C][C]0.145665[/C][C]0.0728324[/C][/ROW]
[ROW][C]92[/C][C]0.918862[/C][C]0.162276[/C][C]0.0811378[/C][/ROW]
[ROW][C]93[/C][C]0.898477[/C][C]0.203047[/C][C]0.101523[/C][/ROW]
[ROW][C]94[/C][C]0.922448[/C][C]0.155104[/C][C]0.0775522[/C][/ROW]
[ROW][C]95[/C][C]0.935386[/C][C]0.129228[/C][C]0.0646138[/C][/ROW]
[ROW][C]96[/C][C]0.916702[/C][C]0.166596[/C][C]0.0832978[/C][/ROW]
[ROW][C]97[/C][C]0.893448[/C][C]0.213104[/C][C]0.106552[/C][/ROW]
[ROW][C]98[/C][C]0.865721[/C][C]0.268557[/C][C]0.134279[/C][/ROW]
[ROW][C]99[/C][C]0.882109[/C][C]0.235782[/C][C]0.117891[/C][/ROW]
[ROW][C]100[/C][C]0.875291[/C][C]0.249418[/C][C]0.124709[/C][/ROW]
[ROW][C]101[/C][C]0.854141[/C][C]0.291718[/C][C]0.145859[/C][/ROW]
[ROW][C]102[/C][C]0.834952[/C][C]0.330095[/C][C]0.165048[/C][/ROW]
[ROW][C]103[/C][C]0.807494[/C][C]0.385012[/C][C]0.192506[/C][/ROW]
[ROW][C]104[/C][C]0.845847[/C][C]0.308306[/C][C]0.154153[/C][/ROW]
[ROW][C]105[/C][C]0.853821[/C][C]0.292357[/C][C]0.146179[/C][/ROW]
[ROW][C]106[/C][C]0.904042[/C][C]0.191915[/C][C]0.0959577[/C][/ROW]
[ROW][C]107[/C][C]0.877582[/C][C]0.244836[/C][C]0.122418[/C][/ROW]
[ROW][C]108[/C][C]0.852161[/C][C]0.295677[/C][C]0.147839[/C][/ROW]
[ROW][C]109[/C][C]0.828605[/C][C]0.342791[/C][C]0.171395[/C][/ROW]
[ROW][C]110[/C][C]0.895725[/C][C]0.20855[/C][C]0.104275[/C][/ROW]
[ROW][C]111[/C][C]0.877886[/C][C]0.244228[/C][C]0.122114[/C][/ROW]
[ROW][C]112[/C][C]0.881404[/C][C]0.237192[/C][C]0.118596[/C][/ROW]
[ROW][C]113[/C][C]0.840465[/C][C]0.319069[/C][C]0.159535[/C][/ROW]
[ROW][C]114[/C][C]0.822349[/C][C]0.355302[/C][C]0.177651[/C][/ROW]
[ROW][C]115[/C][C]0.769876[/C][C]0.460247[/C][C]0.230124[/C][/ROW]
[ROW][C]116[/C][C]0.752983[/C][C]0.494033[/C][C]0.247017[/C][/ROW]
[ROW][C]117[/C][C]0.696888[/C][C]0.606225[/C][C]0.303112[/C][/ROW]
[ROW][C]118[/C][C]0.717237[/C][C]0.565526[/C][C]0.282763[/C][/ROW]
[ROW][C]119[/C][C]0.675031[/C][C]0.649937[/C][C]0.324969[/C][/ROW]
[ROW][C]120[/C][C]0.998821[/C][C]0.00235792[/C][C]0.00117896[/C][/ROW]
[ROW][C]121[/C][C]0.999986[/C][C]2.83202e-05[/C][C]1.41601e-05[/C][/ROW]
[ROW][C]122[/C][C]0.999963[/C][C]7.35826e-05[/C][C]3.67913e-05[/C][/ROW]
[ROW][C]123[/C][C]0.999976[/C][C]4.79837e-05[/C][C]2.39918e-05[/C][/ROW]
[ROW][C]124[/C][C]0.999965[/C][C]6.95774e-05[/C][C]3.47887e-05[/C][/ROW]
[ROW][C]125[/C][C]0.999871[/C][C]0.000258387[/C][C]0.000129194[/C][/ROW]
[ROW][C]126[/C][C]0.99937[/C][C]0.00126034[/C][C]0.000630171[/C][/ROW]
[ROW][C]127[/C][C]0.998031[/C][C]0.00393879[/C][C]0.0019694[/C][/ROW]
[ROW][C]128[/C][C]0.991215[/C][C]0.0175704[/C][C]0.00878518[/C][/ROW]
[ROW][C]129[/C][C]0.966666[/C][C]0.0666675[/C][C]0.0333338[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267729&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267729&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
80.7642570.4714860.235743
90.6454030.7091940.354597
100.8712660.2574680.128734
110.7975170.4049660.202483
120.9566610.08667810.0433391
130.9304190.1391630.0695814
140.9297280.1405450.0702724
150.8974780.2050440.102522
160.8679570.2640860.132043
170.8379580.3240840.162042
180.8142970.3714060.185703
190.7863070.4273860.213693
200.739320.521360.26068
210.6929740.6140510.307026
220.6797380.6405250.320262
230.6187810.7624380.381219
240.8214450.357110.178555
250.8040760.3918480.195924
260.7552910.4894190.244709
270.7060320.5879350.293968
280.6565190.6869630.343481
290.6391170.7217650.360883
300.6675420.6649150.332458
310.6969180.6061650.303082
320.8475280.3049440.152472
330.8124490.3751030.187551
340.8017380.3965250.198262
350.8460580.3078830.153942
360.8155780.3688440.184422
370.8365160.3269670.163484
380.8208960.3582080.179104
390.8844450.231110.115555
400.9473580.1052830.0526417
410.9326430.1347130.0673567
420.9192460.1615080.0807541
430.9120950.175810.0879051
440.9187290.1625420.0812708
450.9287730.1424540.0712268
460.9395410.1209170.0604586
470.9440660.1118680.0559342
480.9875810.02483710.0124186
490.994690.01061920.0053096
500.9949420.01011630.00505816
510.9941290.01174140.00587072
520.9933720.01325660.00662829
530.9906340.0187330.00936649
540.9927880.01442340.00721171
550.9905430.01891340.0094567
560.9883370.02332680.0116634
570.9913340.01733290.00866644
580.9880060.02398850.0119943
590.9877650.02447010.012235
600.984490.03101930.0155097
610.9924820.01503670.00751837
620.989550.02090050.0104503
630.9898090.02038110.0101905
640.9877990.0244020.012201
650.9842010.03159830.0157992
660.9903790.01924230.00962116
670.9876970.02460630.0123031
680.9896280.02074340.0103717
690.9858540.02829210.0141461
700.9892650.02146980.0107349
710.9857190.02856240.0142812
720.9865090.02698190.013491
730.9820040.03599150.0179958
740.9818860.03622780.0181139
750.9761650.04767060.0238353
760.9690160.06196820.0309841
770.9819990.03600110.0180005
780.9760710.0478580.023929
790.9854110.02917770.0145888
800.9845480.03090350.0154518
810.9811050.03778990.018895
820.9746330.05073440.0253672
830.9670270.06594560.0329728
840.962940.07411980.0370599
850.964240.07152090.0357604
860.96340.07319980.0365999
870.9577480.0845050.0422525
880.9456940.1086110.0543056
890.9312630.1374730.0687365
900.9419410.1161180.058059
910.9271680.1456650.0728324
920.9188620.1622760.0811378
930.8984770.2030470.101523
940.9224480.1551040.0775522
950.9353860.1292280.0646138
960.9167020.1665960.0832978
970.8934480.2131040.106552
980.8657210.2685570.134279
990.8821090.2357820.117891
1000.8752910.2494180.124709
1010.8541410.2917180.145859
1020.8349520.3300950.165048
1030.8074940.3850120.192506
1040.8458470.3083060.154153
1050.8538210.2923570.146179
1060.9040420.1919150.0959577
1070.8775820.2448360.122418
1080.8521610.2956770.147839
1090.8286050.3427910.171395
1100.8957250.208550.104275
1110.8778860.2442280.122114
1120.8814040.2371920.118596
1130.8404650.3190690.159535
1140.8223490.3553020.177651
1150.7698760.4602470.230124
1160.7529830.4940330.247017
1170.6968880.6062250.303112
1180.7172370.5655260.282763
1190.6750310.6499370.324969
1200.9988210.002357920.00117896
1210.9999862.83202e-051.41601e-05
1220.9999637.35826e-053.67913e-05
1230.9999764.79837e-052.39918e-05
1240.9999656.95774e-053.47887e-05
1250.9998710.0002583870.000129194
1260.999370.001260340.000630171
1270.9980310.003938790.0019694
1280.9912150.01757040.00878518
1290.9666660.06666750.0333338






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.0655738NOK
5% type I error level420.344262NOK
10% type I error level510.418033NOK

\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 & 8 & 0.0655738 & NOK \tabularnewline
5% type I error level & 42 & 0.344262 & NOK \tabularnewline
10% type I error level & 51 & 0.418033 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267729&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]8[/C][C]0.0655738[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]42[/C][C]0.344262[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]51[/C][C]0.418033[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267729&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267729&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 level80.0655738NOK
5% type I error level420.344262NOK
10% type I error level510.418033NOK


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 = 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')
}