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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 07:31:10 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t135211900970ohfv1ku7n4r7i.htm/, Retrieved Wed, 01 Feb 2023 16:59:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186015, Retrieved Wed, 01 Feb 2023 16:59:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact55
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataseries X:
41	38	13	12	14	12	53	32
39	32	16	11	18	11	86	51
30	35	19	15	11	14	66	42
31	33	15	6	12	12	67	41
34	37	14	13	16	21	76	46
35	29	13	10	18	12	78	47
39	31	19	12	14	22	53	37
34	36	15	14	14	11	80	49
36	35	14	12	15	10	74	45
37	38	15	6	15	13	76	47
38	31	16	10	17	10	79	49
36	34	16	12	19	8	54	33
38	35	16	12	10	15	67	42
39	38	16	11	16	14	54	33
33	37	17	15	18	10	87	53
32	33	15	12	14	14	58	36
36	32	15	10	14	14	75	45
38	38	20	12	17	11	88	54
39	38	18	11	14	10	64	41
32	32	16	12	16	13	57	36
32	33	16	11	18	7	66	41
31	31	16	12	11	14	68	44
39	38	19	13	14	12	54	33
37	39	16	11	12	14	56	37
39	32	17	9	17	11	86	52
41	32	17	13	9	9	80	47
36	35	16	10	16	11	76	43
33	37	15	14	14	15	69	44
33	33	16	12	15	14	78	45
34	33	14	10	11	13	67	44
31	28	15	12	16	9	80	49
27	32	12	8	13	15	54	33
37	31	14	10	17	10	71	43
34	37	16	12	15	11	84	54
34	30	14	12	14	13	74	42
32	33	7	7	16	8	71	44
29	31	10	6	9	20	63	37
36	33	14	12	15	12	71	43
29	31	16	10	17	10	76	46
35	33	16	10	13	10	69	42
37	32	16	10	15	9	74	45
34	33	14	12	16	14	75	44
38	32	20	15	16	8	54	33
35	33	14	10	12	14	52	31
38	28	14	10	12	11	69	42
37	35	11	12	11	13	68	40
38	39	14	13	15	9	65	43
33	34	15	11	15	11	75	46
36	38	16	11	17	15	74	42
38	32	14	12	13	11	75	45
32	38	16	14	16	10	72	44
32	30	14	10	14	14	67	40
32	33	12	12	11	18	63	37
34	38	16	13	12	14	62	46
32	32	9	5	12	11	63	36
37	32	14	6	15	12	76	47
39	34	16	12	16	13	74	45
29	34	16	12	15	9	67	42
37	36	15	11	12	10	73	43
35	34	16	10	12	15	70	43
30	28	12	7	8	20	53	32
38	34	16	12	13	12	77	45
34	35	16	14	11	12	77	45
31	35	14	11	14	14	52	31
34	31	16	12	15	13	54	33
35	37	17	13	10	11	80	49
36	35	18	14	11	17	66	42
30	27	18	11	12	12	73	41
39	40	12	12	15	13	63	38
35	37	16	12	15	14	69	42
38	36	10	8	14	13	67	44
31	38	14	11	16	15	54	33
34	39	18	14	15	13	81	48
38	41	18	14	15	10	69	40
34	27	16	12	13	11	84	50
39	30	17	9	12	19	80	49
37	37	16	13	17	13	70	43
34	31	16	11	13	17	69	44
28	31	13	12	15	13	77	47
37	27	16	12	13	9	54	33
33	36	16	12	15	11	79	46
37	38	20	12	16	10	30	0
35	37	16	12	15	9	71	45
37	33	15	12	16	12	73	43
32	34	15	11	15	12	72	44
33	31	16	10	14	13	77	47
38	39	14	9	15	13	75	45
33	34	16	12	14	12	69	42
29	32	16	12	13	15	54	33
33	33	15	12	7	22	70	43
31	36	12	9	17	13	73	46
36	32	17	15	13	15	54	33
35	41	16	12	15	13	77	46
32	28	15	12	14	15	82	48
29	30	13	12	13	10	80	47
39	36	16	10	16	11	80	47
37	35	16	13	12	16	69	43
35	31	16	9	14	11	78	46
37	34	16	12	17	11	81	48
32	36	14	10	15	10	76	46
38	36	16	14	17	10	76	45
37	35	16	11	12	16	73	45
36	37	20	15	16	12	85	52
32	28	15	11	11	11	66	42
33	39	16	11	15	16	79	47
40	32	13	12	9	19	68	41
38	35	17	12	16	11	76	47
41	39	16	12	15	16	71	43
36	35	16	11	10	15	54	33
43	42	12	7	10	24	46	30
30	34	16	12	15	14	82	49
31	33	16	14	11	15	74	44
32	41	17	11	13	11	88	55
32	33	13	11	14	15	38	11
37	34	12	10	18	12	76	47
37	32	18	13	16	10	86	53
33	40	14	13	14	14	54	33
34	40	14	8	14	13	70	44
33	35	13	11	14	9	69	42
38	36	16	12	14	15	90	55
33	37	13	11	12	15	54	33
31	27	16	13	14	14	76	46
38	39	13	12	15	11	89	54
37	38	16	14	15	8	76	47
33	31	15	13	15	11	73	45
31	33	16	15	13	11	79	47
39	32	15	10	17	8	90	55
44	39	17	11	17	10	74	44
33	36	15	9	19	11	81	53
35	33	12	11	15	13	72	44
32	33	16	10	13	11	71	42
28	32	10	11	9	20	66	40
40	37	16	8	15	10	77	46
27	30	12	11	15	15	65	40
37	38	14	12	15	12	74	46
32	29	15	12	16	14	82	53
28	22	13	9	11	23	54	33
34	35	15	11	14	14	63	42
30	35	11	10	11	16	54	35
35	34	12	8	15	11	64	40
31	35	8	9	13	12	69	41
32	34	16	8	15	10	54	33
30	34	15	9	16	14	84	51
30	35	17	15	14	12	86	53
31	23	16	11	15	12	77	46
40	31	10	8	16	11	89	55
32	27	18	13	16	12	76	47
36	36	13	12	11	13	60	38
32	31	16	12	12	11	75	46
35	32	13	9	9	19	73	46
38	39	10	7	16	12	85	53
42	37	15	13	13	17	79	47
34	38	16	9	16	9	71	41
35	39	16	6	12	12	72	44
35	34	14	8	9	19	69	43
33	31	10	8	13	18	78	51
36	32	17	15	13	15	54	33
32	37	13	6	14	14	69	43
33	36	15	9	19	11	81	53
34	32	16	11	13	9	84	51
32	35	12	8	12	18	84	50
34	36	13	8	13	16	69	46




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186015&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186015&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186015&T=0

As an alternative you can also use a 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 time10 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 4.27566163819705 + 0.108355064457799Connected[t] -0.00635413349300075Separate[t] + 0.522519287456502Software[t] + 0.0866776490697362Happiness[t] -0.0630244321133505Depression[t] + 0.0364344546113714Belonging[t] -0.0458407282060106Belonging_Final[t] + 1.08096691583244M1[t] + 0.439345742284376M2[t] + 0.893787137873395M3[t] + 0.9947450378243M4[t] + 0.722758968784425M5[t] + 0.987731822869931M6[t] + 0.485423725907147M7[t] + 0.964873962815219M8[t] -0.0416032140904913M9[t] + 1.20158576284122M10[t] + 0.498842225135348M11[t] -0.00408762794108934t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  4.27566163819705 +  0.108355064457799Connected[t] -0.00635413349300075Separate[t] +  0.522519287456502Software[t] +  0.0866776490697362Happiness[t] -0.0630244321133505Depression[t] +  0.0364344546113714Belonging[t] -0.0458407282060106Belonging_Final[t] +  1.08096691583244M1[t] +  0.439345742284376M2[t] +  0.893787137873395M3[t] +  0.9947450378243M4[t] +  0.722758968784425M5[t] +  0.987731822869931M6[t] +  0.485423725907147M7[t] +  0.964873962815219M8[t] -0.0416032140904913M9[t] +  1.20158576284122M10[t] +  0.498842225135348M11[t] -0.00408762794108934t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186015&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  4.27566163819705 +  0.108355064457799Connected[t] -0.00635413349300075Separate[t] +  0.522519287456502Software[t] +  0.0866776490697362Happiness[t] -0.0630244321133505Depression[t] +  0.0364344546113714Belonging[t] -0.0458407282060106Belonging_Final[t] +  1.08096691583244M1[t] +  0.439345742284376M2[t] +  0.893787137873395M3[t] +  0.9947450378243M4[t] +  0.722758968784425M5[t] +  0.987731822869931M6[t] +  0.485423725907147M7[t] +  0.964873962815219M8[t] -0.0416032140904913M9[t] +  1.20158576284122M10[t] +  0.498842225135348M11[t] -0.00408762794108934t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186015&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186015&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 4.27566163819705 + 0.108355064457799Connected[t] -0.00635413349300075Separate[t] + 0.522519287456502Software[t] + 0.0866776490697362Happiness[t] -0.0630244321133505Depression[t] + 0.0364344546113714Belonging[t] -0.0458407282060106Belonging_Final[t] + 1.08096691583244M1[t] + 0.439345742284376M2[t] + 0.893787137873395M3[t] + 0.9947450378243M4[t] + 0.722758968784425M5[t] + 0.987731822869931M6[t] + 0.485423725907147M7[t] + 0.964873962815219M8[t] -0.0416032140904913M9[t] + 1.20158576284122M10[t] + 0.498842225135348M11[t] -0.00408762794108934t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.275661638197052.8052511.52420.1296920.064846
Connected0.1083550644577990.0503732.1510.0331650.016583
Separate-0.006354133493000750.047723-0.13310.8942660.447133
Software0.5225192874565020.0721687.240300
Happiness0.08667764906973620.0806331.0750.2842160.142108
Depression-0.06302443211335050.060277-1.04560.2975330.148766
Belonging0.03643445461137140.0467710.7790.4372760.218638
Belonging_Final-0.04584072820601060.066661-0.68770.4927830.246392
M11.080966915832440.73381.47310.1429350.071468
M20.4393457422843760.7360310.59690.5515170.275758
M30.8937871378733950.7394251.20880.2287640.114382
M40.99474503782430.7314331.360.1759870.087994
M50.7227589687844250.7311020.98860.3245460.162273
M60.9877318228699310.7310181.35120.1787890.089394
M70.4854237259071470.740870.65520.5133940.256697
M80.9648739628152190.7571471.27440.2046190.10231
M9-0.04160321409049130.743176-0.0560.9554360.477718
M101.201585762841220.7478651.60670.1103440.055172
M110.4988422251353480.7472530.66760.5054930.252746
t-0.004087627941089340.003308-1.23560.2186480.109324

\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) & 4.27566163819705 & 2.805251 & 1.5242 & 0.129692 & 0.064846 \tabularnewline
Connected & 0.108355064457799 & 0.050373 & 2.151 & 0.033165 & 0.016583 \tabularnewline
Separate & -0.00635413349300075 & 0.047723 & -0.1331 & 0.894266 & 0.447133 \tabularnewline
Software & 0.522519287456502 & 0.072168 & 7.2403 & 0 & 0 \tabularnewline
Happiness & 0.0866776490697362 & 0.080633 & 1.075 & 0.284216 & 0.142108 \tabularnewline
Depression & -0.0630244321133505 & 0.060277 & -1.0456 & 0.297533 & 0.148766 \tabularnewline
Belonging & 0.0364344546113714 & 0.046771 & 0.779 & 0.437276 & 0.218638 \tabularnewline
Belonging_Final & -0.0458407282060106 & 0.066661 & -0.6877 & 0.492783 & 0.246392 \tabularnewline
M1 & 1.08096691583244 & 0.7338 & 1.4731 & 0.142935 & 0.071468 \tabularnewline
M2 & 0.439345742284376 & 0.736031 & 0.5969 & 0.551517 & 0.275758 \tabularnewline
M3 & 0.893787137873395 & 0.739425 & 1.2088 & 0.228764 & 0.114382 \tabularnewline
M4 & 0.9947450378243 & 0.731433 & 1.36 & 0.175987 & 0.087994 \tabularnewline
M5 & 0.722758968784425 & 0.731102 & 0.9886 & 0.324546 & 0.162273 \tabularnewline
M6 & 0.987731822869931 & 0.731018 & 1.3512 & 0.178789 & 0.089394 \tabularnewline
M7 & 0.485423725907147 & 0.74087 & 0.6552 & 0.513394 & 0.256697 \tabularnewline
M8 & 0.964873962815219 & 0.757147 & 1.2744 & 0.204619 & 0.10231 \tabularnewline
M9 & -0.0416032140904913 & 0.743176 & -0.056 & 0.955436 & 0.477718 \tabularnewline
M10 & 1.20158576284122 & 0.747865 & 1.6067 & 0.110344 & 0.055172 \tabularnewline
M11 & 0.498842225135348 & 0.747253 & 0.6676 & 0.505493 & 0.252746 \tabularnewline
t & -0.00408762794108934 & 0.003308 & -1.2356 & 0.218648 & 0.109324 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186015&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]4.27566163819705[/C][C]2.805251[/C][C]1.5242[/C][C]0.129692[/C][C]0.064846[/C][/ROW]
[ROW][C]Connected[/C][C]0.108355064457799[/C][C]0.050373[/C][C]2.151[/C][C]0.033165[/C][C]0.016583[/C][/ROW]
[ROW][C]Separate[/C][C]-0.00635413349300075[/C][C]0.047723[/C][C]-0.1331[/C][C]0.894266[/C][C]0.447133[/C][/ROW]
[ROW][C]Software[/C][C]0.522519287456502[/C][C]0.072168[/C][C]7.2403[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0866776490697362[/C][C]0.080633[/C][C]1.075[/C][C]0.284216[/C][C]0.142108[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0630244321133505[/C][C]0.060277[/C][C]-1.0456[/C][C]0.297533[/C][C]0.148766[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0364344546113714[/C][C]0.046771[/C][C]0.779[/C][C]0.437276[/C][C]0.218638[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0458407282060106[/C][C]0.066661[/C][C]-0.6877[/C][C]0.492783[/C][C]0.246392[/C][/ROW]
[ROW][C]M1[/C][C]1.08096691583244[/C][C]0.7338[/C][C]1.4731[/C][C]0.142935[/C][C]0.071468[/C][/ROW]
[ROW][C]M2[/C][C]0.439345742284376[/C][C]0.736031[/C][C]0.5969[/C][C]0.551517[/C][C]0.275758[/C][/ROW]
[ROW][C]M3[/C][C]0.893787137873395[/C][C]0.739425[/C][C]1.2088[/C][C]0.228764[/C][C]0.114382[/C][/ROW]
[ROW][C]M4[/C][C]0.9947450378243[/C][C]0.731433[/C][C]1.36[/C][C]0.175987[/C][C]0.087994[/C][/ROW]
[ROW][C]M5[/C][C]0.722758968784425[/C][C]0.731102[/C][C]0.9886[/C][C]0.324546[/C][C]0.162273[/C][/ROW]
[ROW][C]M6[/C][C]0.987731822869931[/C][C]0.731018[/C][C]1.3512[/C][C]0.178789[/C][C]0.089394[/C][/ROW]
[ROW][C]M7[/C][C]0.485423725907147[/C][C]0.74087[/C][C]0.6552[/C][C]0.513394[/C][C]0.256697[/C][/ROW]
[ROW][C]M8[/C][C]0.964873962815219[/C][C]0.757147[/C][C]1.2744[/C][C]0.204619[/C][C]0.10231[/C][/ROW]
[ROW][C]M9[/C][C]-0.0416032140904913[/C][C]0.743176[/C][C]-0.056[/C][C]0.955436[/C][C]0.477718[/C][/ROW]
[ROW][C]M10[/C][C]1.20158576284122[/C][C]0.747865[/C][C]1.6067[/C][C]0.110344[/C][C]0.055172[/C][/ROW]
[ROW][C]M11[/C][C]0.498842225135348[/C][C]0.747253[/C][C]0.6676[/C][C]0.505493[/C][C]0.252746[/C][/ROW]
[ROW][C]t[/C][C]-0.00408762794108934[/C][C]0.003308[/C][C]-1.2356[/C][C]0.218648[/C][C]0.109324[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186015&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186015&T=2

As an alternative you can also use a 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)4.275661638197052.8052511.52420.1296920.064846
Connected0.1083550644577990.0503732.1510.0331650.016583
Separate-0.006354133493000750.047723-0.13310.8942660.447133
Software0.5225192874565020.0721687.240300
Happiness0.08667764906973620.0806331.0750.2842160.142108
Depression-0.06302443211335050.060277-1.04560.2975330.148766
Belonging0.03643445461137140.0467710.7790.4372760.218638
Belonging_Final-0.04584072820601060.066661-0.68770.4927830.246392
M11.080966915832440.73381.47310.1429350.071468
M20.4393457422843760.7360310.59690.5515170.275758
M30.8937871378733950.7394251.20880.2287640.114382
M40.99474503782430.7314331.360.1759870.087994
M50.7227589687844250.7311020.98860.3245460.162273
M60.9877318228699310.7310181.35120.1787890.089394
M70.4854237259071470.740870.65520.5133940.256697
M80.9648739628152190.7571471.27440.2046190.10231
M9-0.04160321409049130.743176-0.0560.9554360.477718
M101.201585762841220.7478651.60670.1103440.055172
M110.4988422251353480.7472530.66760.5054930.252746
t-0.004087627941089340.003308-1.23560.2186480.109324







Multiple Linear Regression - Regression Statistics
Multiple R0.626165599110448
R-squared0.392083357509346
Adjusted R-squared0.31074239830285
F-TEST (value)4.82024506883407
F-TEST (DF numerator)19
F-TEST (DF denominator)142
p-value1.50675444343662e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.87318265399593
Sum Squared Residuals498.251482242834

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.626165599110448 \tabularnewline
R-squared & 0.392083357509346 \tabularnewline
Adjusted R-squared & 0.31074239830285 \tabularnewline
F-TEST (value) & 4.82024506883407 \tabularnewline
F-TEST (DF numerator) & 19 \tabularnewline
F-TEST (DF denominator) & 142 \tabularnewline
p-value & 1.50675444343662e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.87318265399593 \tabularnewline
Sum Squared Residuals & 498.251482242834 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186015&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.626165599110448[/C][/ROW]
[ROW][C]R-squared[/C][C]0.392083357509346[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.31074239830285[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.82024506883407[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]19[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]142[/C][/ROW]
[ROW][C]p-value[/C][C]1.50675444343662e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.87318265399593[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]498.251482242834[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186015&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186015&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.626165599110448
R-squared0.392083357509346
Adjusted R-squared0.31074239830285
F-TEST (value)4.82024506883407
F-TEST (DF numerator)19
F-TEST (DF denominator)142
p-value1.50675444343662e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.87318265399593
Sum Squared Residuals498.251482242834







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.7451896390286-3.74518963902862
21616.1394744167787-0.139474416778698
31916.57370797545192.4262920245481
41512.38396968791082.61603031208918
51415.9433768202578-1.94337682025784
61315.5634956846109-2.56349568461092
71915.09344152482863.90655847517138
81517.1672070082364-2.16720700823636
91415.4491261572241-1.44912615722405
101513.43451860192511.56548139807487
111615.35064908691460.649350913085401
121615.77897972773460.221020272265375
131615.90602650072120.0939734992787753
141615.34910004619240.650899953807597
151717.9567301750605-0.956730175060463
161515.526988925255-0.526988925255041
171514.85247021922450.147529780775512
182016.84716694788253.15283305211751
191815.45110104088932.54889895911072
201615.68706674761610.312933252383945
211614.79783686311161.20216313688838
221615.351242858410.648757141589952
231916.36954001857382.6304599814262
241614.18860916220831.81139083779171
251715.5095231954931.49047680450705
261716.6138262577760.386173742223957
271615.45410421392650.545895786073522
281516.5769432383244-1.57694323832442
291615.71301894488190.286981055118129
301414.3985962238864-0.398596223886415
311515.573874790707-0.573874790707009
321212.648299749556-0.648299749555988
331414.5954895008721-0.595489500872089
341616.2494595998732-0.249459599873202
351415.5601250477392-1.56012504773922
36712.4963188664458-5.49631886644576
371011.404694672196-1.40469467219596
381415.6805713986445-1.68057139864454
391614.68416365796591.31583634203413
401615.0000671840020.999932815998022
411615.22808756812130.77191243187874
421416.0564227136326-2.05642271363265
432017.67463029852992.3253697014701
441414.4999325015782-0.499932501578205
451414.1504145720371-0.150414572037055
461116.1242409855361-5.12424098553609
471416.3748504141116-2.37485041411162
481514.41765082856720.582349171432826
491615.86236480375870.13763519624134
501415.7983096217674-1.79830962176741
511616.865041520318-0.865041520317998
521414.4984053236201-0.498405323620124
531214.7279614915831-2.72796149158309
541615.58607983569230.41392016430772
55911.4048595161903-2.40485951619033
561413.11092514968040.889074850319621
571615.48194369564780.51805630435215
581615.68539548178260.314604518217421
591515.1598858974952-0.159885897495165
601613.50600937063182.49399062936824
611211.73470979984970.265290200150277
621615.74639429659770.253605703402273
631616.628656949695-0.62865694969499
641414.6977970785443-0.697797078544276
651615.4256139303590.574386069640979
661716.18575349493720.814246505062759
671815.84227417820622.15772582179382
681814.8534633261233.14653667387704
691215.228195807044-3.22819580704403
701616.0251586814133-0.0251586814132891
711013.3714661102156-3.37146611021557
721413.74280693554910.257193064450898
731817.04144571226420.958554287735812
741816.93503456827191.06496543172809
751615.84761717893530.15238282106471
761714.20887231416962.79112768583042
771616.4839213647992-0.483921364799152
781614.73174411606541.26825588393456
791314.6771437707371-1.67714377073709
801616.0356386629328-0.0356386629328053
811614.89670473133671.10329526866328
822017.02960537387492.97039462612508
831615.51974486564470.480255134355281
841515.3210963938203-0.321096393820331
851515.1583741065861-0.158374106586065
861614.0125114898331.98748851016705
871414.5367784206284-0.536778420628382
881615.58646414017710.41353585982287
891614.47997724162471.52002275837529
901514.33123566461730.668764335382716
911213.4272871068829-1.4272871068829
921717.0358726654063-0.035872665406333
931614.83367488404481.1663251159552
941515.7080790784194-0.708079078419432
951314.8648907828934-1.86489078289336
961614.55935671361981.44064328638024
971616.1141890237113-0.114189023711278
981613.86597484304312.13402515695694
991616.3591890561284-0.35918905612839
1001414.6557154812792-0.655715481279248
1011617.3390453472166-1.33904534721657
1021615.00953357816380.990466421836217
1032017.18728835426882.81271164573115
1041514.59212958822440.40787041177565
1051613.89605708404232.10394291595769
1061315.6257682838975-2.62576828389754
1071715.81053485690581.18946514309416
1081615.20664449336150.793355506638502
1091614.71330344591081.28669655408918
1101211.97035067005620.0296493299438492
1111615.17981720155660.820182798443439
1121615.9644282221770.0355717778230329
1131714.60960604022622.39039395977375
1141314.9511735654269-1.9511735654269
1151214.7277066942973-2.72770669429725
1161816.82532917558461.17467082441538
1171414.6559700349261-0.655970034926068
1181413.53255770672120.467442293278842
1191314.624044736705-1.62404473670496
1201614.97010284736161.02989715263843
1211314.4998337483978-1.49983374839782
1221615.18800299286090.811997007139067
1231316.1807463520505-3.18074635205048
1241617.2569647518428-1.2569647518428
1251515.862735240263-0.862735240262993
1261616.8928106185355-0.892810618535548
1271515.2168501732006-0.216850173200556
1281716.50727632971950.492723670280494
1291513.23163513585451.76836486414546
1301215.3634445909989-3.36344459099887
1311613.81696951240992.18303048759011
1321012.4050715205078-2.40507152050782
1331614.45892789909921.5410721009008
1341213.539348809812-1.53934880981202
1351415.7868784601568-1.78687846015677
1361515.3303799356065-0.330379935606529
1371311.99384875350881.00615124649115
1381514.60989557978170.390104420218339
1391112.7544535040939-1.75445350409391
1401213.5298806558592-1.52988065585925
141812.5020125617428-4.50201256174277
1421613.45291699007142.54708300992862
1431513.15437543421461.84562456578539
1441715.71408819128211.28591180871787
1451614.96514765073071.03485234926925
1461013.8505924817137-3.85059248171373
1471815.90217218854072.09782781145932
1481315.1859588320983-2.18595883209831
1491614.90075305157011.09924694842993
1501313.0756941618866-0.0756941618866244
1511012.9690890471681-2.96908904716813
1521516.5069784394832-1.50697843948319
1531613.28093897211612.7190610278839
1541612.41761176707643.58238823292364
1551412.02292323617661.97707676382336
1561011.6932649489102-1.69326494891016
1571716.88626980225270.113730197747253
1581311.31050810665241.68949189334757
1591514.04439664958570.955603350414253
1601615.12704488499280.872955115007213
1611212.4395839863638-0.439583986363837
1621312.76039781488080.239602185119241

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.7451896390286 & -3.74518963902862 \tabularnewline
2 & 16 & 16.1394744167787 & -0.139474416778698 \tabularnewline
3 & 19 & 16.5737079754519 & 2.4262920245481 \tabularnewline
4 & 15 & 12.3839696879108 & 2.61603031208918 \tabularnewline
5 & 14 & 15.9433768202578 & -1.94337682025784 \tabularnewline
6 & 13 & 15.5634956846109 & -2.56349568461092 \tabularnewline
7 & 19 & 15.0934415248286 & 3.90655847517138 \tabularnewline
8 & 15 & 17.1672070082364 & -2.16720700823636 \tabularnewline
9 & 14 & 15.4491261572241 & -1.44912615722405 \tabularnewline
10 & 15 & 13.4345186019251 & 1.56548139807487 \tabularnewline
11 & 16 & 15.3506490869146 & 0.649350913085401 \tabularnewline
12 & 16 & 15.7789797277346 & 0.221020272265375 \tabularnewline
13 & 16 & 15.9060265007212 & 0.0939734992787753 \tabularnewline
14 & 16 & 15.3491000461924 & 0.650899953807597 \tabularnewline
15 & 17 & 17.9567301750605 & -0.956730175060463 \tabularnewline
16 & 15 & 15.526988925255 & -0.526988925255041 \tabularnewline
17 & 15 & 14.8524702192245 & 0.147529780775512 \tabularnewline
18 & 20 & 16.8471669478825 & 3.15283305211751 \tabularnewline
19 & 18 & 15.4511010408893 & 2.54889895911072 \tabularnewline
20 & 16 & 15.6870667476161 & 0.312933252383945 \tabularnewline
21 & 16 & 14.7978368631116 & 1.20216313688838 \tabularnewline
22 & 16 & 15.35124285841 & 0.648757141589952 \tabularnewline
23 & 19 & 16.3695400185738 & 2.6304599814262 \tabularnewline
24 & 16 & 14.1886091622083 & 1.81139083779171 \tabularnewline
25 & 17 & 15.509523195493 & 1.49047680450705 \tabularnewline
26 & 17 & 16.613826257776 & 0.386173742223957 \tabularnewline
27 & 16 & 15.4541042139265 & 0.545895786073522 \tabularnewline
28 & 15 & 16.5769432383244 & -1.57694323832442 \tabularnewline
29 & 16 & 15.7130189448819 & 0.286981055118129 \tabularnewline
30 & 14 & 14.3985962238864 & -0.398596223886415 \tabularnewline
31 & 15 & 15.573874790707 & -0.573874790707009 \tabularnewline
32 & 12 & 12.648299749556 & -0.648299749555988 \tabularnewline
33 & 14 & 14.5954895008721 & -0.595489500872089 \tabularnewline
34 & 16 & 16.2494595998732 & -0.249459599873202 \tabularnewline
35 & 14 & 15.5601250477392 & -1.56012504773922 \tabularnewline
36 & 7 & 12.4963188664458 & -5.49631886644576 \tabularnewline
37 & 10 & 11.404694672196 & -1.40469467219596 \tabularnewline
38 & 14 & 15.6805713986445 & -1.68057139864454 \tabularnewline
39 & 16 & 14.6841636579659 & 1.31583634203413 \tabularnewline
40 & 16 & 15.000067184002 & 0.999932815998022 \tabularnewline
41 & 16 & 15.2280875681213 & 0.77191243187874 \tabularnewline
42 & 14 & 16.0564227136326 & -2.05642271363265 \tabularnewline
43 & 20 & 17.6746302985299 & 2.3253697014701 \tabularnewline
44 & 14 & 14.4999325015782 & -0.499932501578205 \tabularnewline
45 & 14 & 14.1504145720371 & -0.150414572037055 \tabularnewline
46 & 11 & 16.1242409855361 & -5.12424098553609 \tabularnewline
47 & 14 & 16.3748504141116 & -2.37485041411162 \tabularnewline
48 & 15 & 14.4176508285672 & 0.582349171432826 \tabularnewline
49 & 16 & 15.8623648037587 & 0.13763519624134 \tabularnewline
50 & 14 & 15.7983096217674 & -1.79830962176741 \tabularnewline
51 & 16 & 16.865041520318 & -0.865041520317998 \tabularnewline
52 & 14 & 14.4984053236201 & -0.498405323620124 \tabularnewline
53 & 12 & 14.7279614915831 & -2.72796149158309 \tabularnewline
54 & 16 & 15.5860798356923 & 0.41392016430772 \tabularnewline
55 & 9 & 11.4048595161903 & -2.40485951619033 \tabularnewline
56 & 14 & 13.1109251496804 & 0.889074850319621 \tabularnewline
57 & 16 & 15.4819436956478 & 0.51805630435215 \tabularnewline
58 & 16 & 15.6853954817826 & 0.314604518217421 \tabularnewline
59 & 15 & 15.1598858974952 & -0.159885897495165 \tabularnewline
60 & 16 & 13.5060093706318 & 2.49399062936824 \tabularnewline
61 & 12 & 11.7347097998497 & 0.265290200150277 \tabularnewline
62 & 16 & 15.7463942965977 & 0.253605703402273 \tabularnewline
63 & 16 & 16.628656949695 & -0.62865694969499 \tabularnewline
64 & 14 & 14.6977970785443 & -0.697797078544276 \tabularnewline
65 & 16 & 15.425613930359 & 0.574386069640979 \tabularnewline
66 & 17 & 16.1857534949372 & 0.814246505062759 \tabularnewline
67 & 18 & 15.8422741782062 & 2.15772582179382 \tabularnewline
68 & 18 & 14.853463326123 & 3.14653667387704 \tabularnewline
69 & 12 & 15.228195807044 & -3.22819580704403 \tabularnewline
70 & 16 & 16.0251586814133 & -0.0251586814132891 \tabularnewline
71 & 10 & 13.3714661102156 & -3.37146611021557 \tabularnewline
72 & 14 & 13.7428069355491 & 0.257193064450898 \tabularnewline
73 & 18 & 17.0414457122642 & 0.958554287735812 \tabularnewline
74 & 18 & 16.9350345682719 & 1.06496543172809 \tabularnewline
75 & 16 & 15.8476171789353 & 0.15238282106471 \tabularnewline
76 & 17 & 14.2088723141696 & 2.79112768583042 \tabularnewline
77 & 16 & 16.4839213647992 & -0.483921364799152 \tabularnewline
78 & 16 & 14.7317441160654 & 1.26825588393456 \tabularnewline
79 & 13 & 14.6771437707371 & -1.67714377073709 \tabularnewline
80 & 16 & 16.0356386629328 & -0.0356386629328053 \tabularnewline
81 & 16 & 14.8967047313367 & 1.10329526866328 \tabularnewline
82 & 20 & 17.0296053738749 & 2.97039462612508 \tabularnewline
83 & 16 & 15.5197448656447 & 0.480255134355281 \tabularnewline
84 & 15 & 15.3210963938203 & -0.321096393820331 \tabularnewline
85 & 15 & 15.1583741065861 & -0.158374106586065 \tabularnewline
86 & 16 & 14.012511489833 & 1.98748851016705 \tabularnewline
87 & 14 & 14.5367784206284 & -0.536778420628382 \tabularnewline
88 & 16 & 15.5864641401771 & 0.41353585982287 \tabularnewline
89 & 16 & 14.4799772416247 & 1.52002275837529 \tabularnewline
90 & 15 & 14.3312356646173 & 0.668764335382716 \tabularnewline
91 & 12 & 13.4272871068829 & -1.4272871068829 \tabularnewline
92 & 17 & 17.0358726654063 & -0.035872665406333 \tabularnewline
93 & 16 & 14.8336748840448 & 1.1663251159552 \tabularnewline
94 & 15 & 15.7080790784194 & -0.708079078419432 \tabularnewline
95 & 13 & 14.8648907828934 & -1.86489078289336 \tabularnewline
96 & 16 & 14.5593567136198 & 1.44064328638024 \tabularnewline
97 & 16 & 16.1141890237113 & -0.114189023711278 \tabularnewline
98 & 16 & 13.8659748430431 & 2.13402515695694 \tabularnewline
99 & 16 & 16.3591890561284 & -0.35918905612839 \tabularnewline
100 & 14 & 14.6557154812792 & -0.655715481279248 \tabularnewline
101 & 16 & 17.3390453472166 & -1.33904534721657 \tabularnewline
102 & 16 & 15.0095335781638 & 0.990466421836217 \tabularnewline
103 & 20 & 17.1872883542688 & 2.81271164573115 \tabularnewline
104 & 15 & 14.5921295882244 & 0.40787041177565 \tabularnewline
105 & 16 & 13.8960570840423 & 2.10394291595769 \tabularnewline
106 & 13 & 15.6257682838975 & -2.62576828389754 \tabularnewline
107 & 17 & 15.8105348569058 & 1.18946514309416 \tabularnewline
108 & 16 & 15.2066444933615 & 0.793355506638502 \tabularnewline
109 & 16 & 14.7133034459108 & 1.28669655408918 \tabularnewline
110 & 12 & 11.9703506700562 & 0.0296493299438492 \tabularnewline
111 & 16 & 15.1798172015566 & 0.820182798443439 \tabularnewline
112 & 16 & 15.964428222177 & 0.0355717778230329 \tabularnewline
113 & 17 & 14.6096060402262 & 2.39039395977375 \tabularnewline
114 & 13 & 14.9511735654269 & -1.9511735654269 \tabularnewline
115 & 12 & 14.7277066942973 & -2.72770669429725 \tabularnewline
116 & 18 & 16.8253291755846 & 1.17467082441538 \tabularnewline
117 & 14 & 14.6559700349261 & -0.655970034926068 \tabularnewline
118 & 14 & 13.5325577067212 & 0.467442293278842 \tabularnewline
119 & 13 & 14.624044736705 & -1.62404473670496 \tabularnewline
120 & 16 & 14.9701028473616 & 1.02989715263843 \tabularnewline
121 & 13 & 14.4998337483978 & -1.49983374839782 \tabularnewline
122 & 16 & 15.1880029928609 & 0.811997007139067 \tabularnewline
123 & 13 & 16.1807463520505 & -3.18074635205048 \tabularnewline
124 & 16 & 17.2569647518428 & -1.2569647518428 \tabularnewline
125 & 15 & 15.862735240263 & -0.862735240262993 \tabularnewline
126 & 16 & 16.8928106185355 & -0.892810618535548 \tabularnewline
127 & 15 & 15.2168501732006 & -0.216850173200556 \tabularnewline
128 & 17 & 16.5072763297195 & 0.492723670280494 \tabularnewline
129 & 15 & 13.2316351358545 & 1.76836486414546 \tabularnewline
130 & 12 & 15.3634445909989 & -3.36344459099887 \tabularnewline
131 & 16 & 13.8169695124099 & 2.18303048759011 \tabularnewline
132 & 10 & 12.4050715205078 & -2.40507152050782 \tabularnewline
133 & 16 & 14.4589278990992 & 1.5410721009008 \tabularnewline
134 & 12 & 13.539348809812 & -1.53934880981202 \tabularnewline
135 & 14 & 15.7868784601568 & -1.78687846015677 \tabularnewline
136 & 15 & 15.3303799356065 & -0.330379935606529 \tabularnewline
137 & 13 & 11.9938487535088 & 1.00615124649115 \tabularnewline
138 & 15 & 14.6098955797817 & 0.390104420218339 \tabularnewline
139 & 11 & 12.7544535040939 & -1.75445350409391 \tabularnewline
140 & 12 & 13.5298806558592 & -1.52988065585925 \tabularnewline
141 & 8 & 12.5020125617428 & -4.50201256174277 \tabularnewline
142 & 16 & 13.4529169900714 & 2.54708300992862 \tabularnewline
143 & 15 & 13.1543754342146 & 1.84562456578539 \tabularnewline
144 & 17 & 15.7140881912821 & 1.28591180871787 \tabularnewline
145 & 16 & 14.9651476507307 & 1.03485234926925 \tabularnewline
146 & 10 & 13.8505924817137 & -3.85059248171373 \tabularnewline
147 & 18 & 15.9021721885407 & 2.09782781145932 \tabularnewline
148 & 13 & 15.1859588320983 & -2.18595883209831 \tabularnewline
149 & 16 & 14.9007530515701 & 1.09924694842993 \tabularnewline
150 & 13 & 13.0756941618866 & -0.0756941618866244 \tabularnewline
151 & 10 & 12.9690890471681 & -2.96908904716813 \tabularnewline
152 & 15 & 16.5069784394832 & -1.50697843948319 \tabularnewline
153 & 16 & 13.2809389721161 & 2.7190610278839 \tabularnewline
154 & 16 & 12.4176117670764 & 3.58238823292364 \tabularnewline
155 & 14 & 12.0229232361766 & 1.97707676382336 \tabularnewline
156 & 10 & 11.6932649489102 & -1.69326494891016 \tabularnewline
157 & 17 & 16.8862698022527 & 0.113730197747253 \tabularnewline
158 & 13 & 11.3105081066524 & 1.68949189334757 \tabularnewline
159 & 15 & 14.0443966495857 & 0.955603350414253 \tabularnewline
160 & 16 & 15.1270448849928 & 0.872955115007213 \tabularnewline
161 & 12 & 12.4395839863638 & -0.439583986363837 \tabularnewline
162 & 13 & 12.7603978148808 & 0.239602185119241 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186015&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]13[/C][C]16.7451896390286[/C][C]-3.74518963902862[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.1394744167787[/C][C]-0.139474416778698[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.5737079754519[/C][C]2.4262920245481[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.3839696879108[/C][C]2.61603031208918[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.9433768202578[/C][C]-1.94337682025784[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.5634956846109[/C][C]-2.56349568461092[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.0934415248286[/C][C]3.90655847517138[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]17.1672070082364[/C][C]-2.16720700823636[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.4491261572241[/C][C]-1.44912615722405[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]13.4345186019251[/C][C]1.56548139807487[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.3506490869146[/C][C]0.649350913085401[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]15.7789797277346[/C][C]0.221020272265375[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.9060265007212[/C][C]0.0939734992787753[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.3491000461924[/C][C]0.650899953807597[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.9567301750605[/C][C]-0.956730175060463[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.526988925255[/C][C]-0.526988925255041[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.8524702192245[/C][C]0.147529780775512[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.8471669478825[/C][C]3.15283305211751[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.4511010408893[/C][C]2.54889895911072[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.6870667476161[/C][C]0.312933252383945[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]14.7978368631116[/C][C]1.20216313688838[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.35124285841[/C][C]0.648757141589952[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.3695400185738[/C][C]2.6304599814262[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.1886091622083[/C][C]1.81139083779171[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.509523195493[/C][C]1.49047680450705[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.613826257776[/C][C]0.386173742223957[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.4541042139265[/C][C]0.545895786073522[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.5769432383244[/C][C]-1.57694323832442[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.7130189448819[/C][C]0.286981055118129[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.3985962238864[/C][C]-0.398596223886415[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.573874790707[/C][C]-0.573874790707009[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.648299749556[/C][C]-0.648299749555988[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.5954895008721[/C][C]-0.595489500872089[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]16.2494595998732[/C][C]-0.249459599873202[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.5601250477392[/C][C]-1.56012504773922[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]12.4963188664458[/C][C]-5.49631886644576[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]11.404694672196[/C][C]-1.40469467219596[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.6805713986445[/C][C]-1.68057139864454[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.6841636579659[/C][C]1.31583634203413[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.000067184002[/C][C]0.999932815998022[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.2280875681213[/C][C]0.77191243187874[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]16.0564227136326[/C][C]-2.05642271363265[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.6746302985299[/C][C]2.3253697014701[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.4999325015782[/C][C]-0.499932501578205[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.1504145720371[/C][C]-0.150414572037055[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]16.1242409855361[/C][C]-5.12424098553609[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.3748504141116[/C][C]-2.37485041411162[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.4176508285672[/C][C]0.582349171432826[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.8623648037587[/C][C]0.13763519624134[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.7983096217674[/C][C]-1.79830962176741[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.865041520318[/C][C]-0.865041520317998[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.4984053236201[/C][C]-0.498405323620124[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.7279614915831[/C][C]-2.72796149158309[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.5860798356923[/C][C]0.41392016430772[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.4048595161903[/C][C]-2.40485951619033[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]13.1109251496804[/C][C]0.889074850319621[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.4819436956478[/C][C]0.51805630435215[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.6853954817826[/C][C]0.314604518217421[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.1598858974952[/C][C]-0.159885897495165[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]13.5060093706318[/C][C]2.49399062936824[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.7347097998497[/C][C]0.265290200150277[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.7463942965977[/C][C]0.253605703402273[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.628656949695[/C][C]-0.62865694969499[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.6977970785443[/C][C]-0.697797078544276[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.425613930359[/C][C]0.574386069640979[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.1857534949372[/C][C]0.814246505062759[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]15.8422741782062[/C][C]2.15772582179382[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.853463326123[/C][C]3.14653667387704[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.228195807044[/C][C]-3.22819580704403[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]16.0251586814133[/C][C]-0.0251586814132891[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.3714661102156[/C][C]-3.37146611021557[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]13.7428069355491[/C][C]0.257193064450898[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]17.0414457122642[/C][C]0.958554287735812[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]16.9350345682719[/C][C]1.06496543172809[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.8476171789353[/C][C]0.15238282106471[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]14.2088723141696[/C][C]2.79112768583042[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.4839213647992[/C][C]-0.483921364799152[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.7317441160654[/C][C]1.26825588393456[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.6771437707371[/C][C]-1.67714377073709[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.0356386629328[/C][C]-0.0356386629328053[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]14.8967047313367[/C][C]1.10329526866328[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]17.0296053738749[/C][C]2.97039462612508[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.5197448656447[/C][C]0.480255134355281[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.3210963938203[/C][C]-0.321096393820331[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]15.1583741065861[/C][C]-0.158374106586065[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.012511489833[/C][C]1.98748851016705[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.5367784206284[/C][C]-0.536778420628382[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.5864641401771[/C][C]0.41353585982287[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.4799772416247[/C][C]1.52002275837529[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.3312356646173[/C][C]0.668764335382716[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.4272871068829[/C][C]-1.4272871068829[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]17.0358726654063[/C][C]-0.035872665406333[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]14.8336748840448[/C][C]1.1663251159552[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.7080790784194[/C][C]-0.708079078419432[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]14.8648907828934[/C][C]-1.86489078289336[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]14.5593567136198[/C][C]1.44064328638024[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]16.1141890237113[/C][C]-0.114189023711278[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]13.8659748430431[/C][C]2.13402515695694[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.3591890561284[/C][C]-0.35918905612839[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.6557154812792[/C][C]-0.655715481279248[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.3390453472166[/C][C]-1.33904534721657[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]15.0095335781638[/C][C]0.990466421836217[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.1872883542688[/C][C]2.81271164573115[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.5921295882244[/C][C]0.40787041177565[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]13.8960570840423[/C][C]2.10394291595769[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.6257682838975[/C][C]-2.62576828389754[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.8105348569058[/C][C]1.18946514309416[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.2066444933615[/C][C]0.793355506638502[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.7133034459108[/C][C]1.28669655408918[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]11.9703506700562[/C][C]0.0296493299438492[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.1798172015566[/C][C]0.820182798443439[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.964428222177[/C][C]0.0355717778230329[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.6096060402262[/C][C]2.39039395977375[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.9511735654269[/C][C]-1.9511735654269[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.7277066942973[/C][C]-2.72770669429725[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.8253291755846[/C][C]1.17467082441538[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]14.6559700349261[/C][C]-0.655970034926068[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.5325577067212[/C][C]0.467442293278842[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.624044736705[/C][C]-1.62404473670496[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]14.9701028473616[/C][C]1.02989715263843[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.4998337483978[/C][C]-1.49983374839782[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.1880029928609[/C][C]0.811997007139067[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]16.1807463520505[/C][C]-3.18074635205048[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]17.2569647518428[/C][C]-1.2569647518428[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.862735240263[/C][C]-0.862735240262993[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.8928106185355[/C][C]-0.892810618535548[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.2168501732006[/C][C]-0.216850173200556[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.5072763297195[/C][C]0.492723670280494[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.2316351358545[/C][C]1.76836486414546[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]15.3634445909989[/C][C]-3.36344459099887[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]13.8169695124099[/C][C]2.18303048759011[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]12.4050715205078[/C][C]-2.40507152050782[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]14.4589278990992[/C][C]1.5410721009008[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.539348809812[/C][C]-1.53934880981202[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.7868784601568[/C][C]-1.78687846015677[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.3303799356065[/C][C]-0.330379935606529[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]11.9938487535088[/C][C]1.00615124649115[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.6098955797817[/C][C]0.390104420218339[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]12.7544535040939[/C][C]-1.75445350409391[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.5298806558592[/C][C]-1.52988065585925[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]12.5020125617428[/C][C]-4.50201256174277[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.4529169900714[/C][C]2.54708300992862[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.1543754342146[/C][C]1.84562456578539[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]15.7140881912821[/C][C]1.28591180871787[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.9651476507307[/C][C]1.03485234926925[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]13.8505924817137[/C][C]-3.85059248171373[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.9021721885407[/C][C]2.09782781145932[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.1859588320983[/C][C]-2.18595883209831[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.9007530515701[/C][C]1.09924694842993[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.0756941618866[/C][C]-0.0756941618866244[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]12.9690890471681[/C][C]-2.96908904716813[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.5069784394832[/C][C]-1.50697843948319[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]13.2809389721161[/C][C]2.7190610278839[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]12.4176117670764[/C][C]3.58238823292364[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.0229232361766[/C][C]1.97707676382336[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]11.6932649489102[/C][C]-1.69326494891016[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.8862698022527[/C][C]0.113730197747253[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.3105081066524[/C][C]1.68949189334757[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]14.0443966495857[/C][C]0.955603350414253[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.1270448849928[/C][C]0.872955115007213[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.4395839863638[/C][C]-0.439583986363837[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.7603978148808[/C][C]0.239602185119241[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186015&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186015&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.7451896390286-3.74518963902862
21616.1394744167787-0.139474416778698
31916.57370797545192.4262920245481
41512.38396968791082.61603031208918
51415.9433768202578-1.94337682025784
61315.5634956846109-2.56349568461092
71915.09344152482863.90655847517138
81517.1672070082364-2.16720700823636
91415.4491261572241-1.44912615722405
101513.43451860192511.56548139807487
111615.35064908691460.649350913085401
121615.77897972773460.221020272265375
131615.90602650072120.0939734992787753
141615.34910004619240.650899953807597
151717.9567301750605-0.956730175060463
161515.526988925255-0.526988925255041
171514.85247021922450.147529780775512
182016.84716694788253.15283305211751
191815.45110104088932.54889895911072
201615.68706674761610.312933252383945
211614.79783686311161.20216313688838
221615.351242858410.648757141589952
231916.36954001857382.6304599814262
241614.18860916220831.81139083779171
251715.5095231954931.49047680450705
261716.6138262577760.386173742223957
271615.45410421392650.545895786073522
281516.5769432383244-1.57694323832442
291615.71301894488190.286981055118129
301414.3985962238864-0.398596223886415
311515.573874790707-0.573874790707009
321212.648299749556-0.648299749555988
331414.5954895008721-0.595489500872089
341616.2494595998732-0.249459599873202
351415.5601250477392-1.56012504773922
36712.4963188664458-5.49631886644576
371011.404694672196-1.40469467219596
381415.6805713986445-1.68057139864454
391614.68416365796591.31583634203413
401615.0000671840020.999932815998022
411615.22808756812130.77191243187874
421416.0564227136326-2.05642271363265
432017.67463029852992.3253697014701
441414.4999325015782-0.499932501578205
451414.1504145720371-0.150414572037055
461116.1242409855361-5.12424098553609
471416.3748504141116-2.37485041411162
481514.41765082856720.582349171432826
491615.86236480375870.13763519624134
501415.7983096217674-1.79830962176741
511616.865041520318-0.865041520317998
521414.4984053236201-0.498405323620124
531214.7279614915831-2.72796149158309
541615.58607983569230.41392016430772
55911.4048595161903-2.40485951619033
561413.11092514968040.889074850319621
571615.48194369564780.51805630435215
581615.68539548178260.314604518217421
591515.1598858974952-0.159885897495165
601613.50600937063182.49399062936824
611211.73470979984970.265290200150277
621615.74639429659770.253605703402273
631616.628656949695-0.62865694969499
641414.6977970785443-0.697797078544276
651615.4256139303590.574386069640979
661716.18575349493720.814246505062759
671815.84227417820622.15772582179382
681814.8534633261233.14653667387704
691215.228195807044-3.22819580704403
701616.0251586814133-0.0251586814132891
711013.3714661102156-3.37146611021557
721413.74280693554910.257193064450898
731817.04144571226420.958554287735812
741816.93503456827191.06496543172809
751615.84761717893530.15238282106471
761714.20887231416962.79112768583042
771616.4839213647992-0.483921364799152
781614.73174411606541.26825588393456
791314.6771437707371-1.67714377073709
801616.0356386629328-0.0356386629328053
811614.89670473133671.10329526866328
822017.02960537387492.97039462612508
831615.51974486564470.480255134355281
841515.3210963938203-0.321096393820331
851515.1583741065861-0.158374106586065
861614.0125114898331.98748851016705
871414.5367784206284-0.536778420628382
881615.58646414017710.41353585982287
891614.47997724162471.52002275837529
901514.33123566461730.668764335382716
911213.4272871068829-1.4272871068829
921717.0358726654063-0.035872665406333
931614.83367488404481.1663251159552
941515.7080790784194-0.708079078419432
951314.8648907828934-1.86489078289336
961614.55935671361981.44064328638024
971616.1141890237113-0.114189023711278
981613.86597484304312.13402515695694
991616.3591890561284-0.35918905612839
1001414.6557154812792-0.655715481279248
1011617.3390453472166-1.33904534721657
1021615.00953357816380.990466421836217
1032017.18728835426882.81271164573115
1041514.59212958822440.40787041177565
1051613.89605708404232.10394291595769
1061315.6257682838975-2.62576828389754
1071715.81053485690581.18946514309416
1081615.20664449336150.793355506638502
1091614.71330344591081.28669655408918
1101211.97035067005620.0296493299438492
1111615.17981720155660.820182798443439
1121615.9644282221770.0355717778230329
1131714.60960604022622.39039395977375
1141314.9511735654269-1.9511735654269
1151214.7277066942973-2.72770669429725
1161816.82532917558461.17467082441538
1171414.6559700349261-0.655970034926068
1181413.53255770672120.467442293278842
1191314.624044736705-1.62404473670496
1201614.97010284736161.02989715263843
1211314.4998337483978-1.49983374839782
1221615.18800299286090.811997007139067
1231316.1807463520505-3.18074635205048
1241617.2569647518428-1.2569647518428
1251515.862735240263-0.862735240262993
1261616.8928106185355-0.892810618535548
1271515.2168501732006-0.216850173200556
1281716.50727632971950.492723670280494
1291513.23163513585451.76836486414546
1301215.3634445909989-3.36344459099887
1311613.81696951240992.18303048759011
1321012.4050715205078-2.40507152050782
1331614.45892789909921.5410721009008
1341213.539348809812-1.53934880981202
1351415.7868784601568-1.78687846015677
1361515.3303799356065-0.330379935606529
1371311.99384875350881.00615124649115
1381514.60989557978170.390104420218339
1391112.7544535040939-1.75445350409391
1401213.5298806558592-1.52988065585925
141812.5020125617428-4.50201256174277
1421613.45291699007142.54708300992862
1431513.15437543421461.84562456578539
1441715.71408819128211.28591180871787
1451614.96514765073071.03485234926925
1461013.8505924817137-3.85059248171373
1471815.90217218854072.09782781145932
1481315.1859588320983-2.18595883209831
1491614.90075305157011.09924694842993
1501313.0756941618866-0.0756941618866244
1511012.9690890471681-2.96908904716813
1521516.5069784394832-1.50697843948319
1531613.28093897211612.7190610278839
1541612.41761176707643.58238823292364
1551412.02292323617661.97707676382336
1561011.6932649489102-1.69326494891016
1571716.88626980225270.113730197747253
1581311.31050810665241.68949189334757
1591514.04439664958570.955603350414253
1601615.12704488499280.872955115007213
1611212.4395839863638-0.439583986363837
1621312.76039781488080.239602185119241







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
230.6973026943974240.6053946112051520.302697305602576
240.8635891391852060.2728217216295890.136410860814794
250.8246587953236340.3506824093527320.175341204676366
260.735328318049810.5293433639003790.26467168195019
270.636405753131080.7271884937378410.36359424686892
280.6467438906012330.7065122187975350.353256109398767
290.558408366499840.8831832670003190.441591633500159
300.7202226506293630.5595546987412750.279777349370637
310.7713611644160050.4572776711679890.228638835583995
320.7199986849133350.560002630173330.280001315086665
330.6622918057883890.6754163884232230.337708194211611
340.6052520506007630.7894958987984750.394747949399237
350.5424891649120920.9150216701758150.457510835087908
360.9084905064793830.1830189870412350.0915094935206175
370.8831422727178560.2337154545642870.116857727282144
380.8594509749394240.2810980501211520.140549025060576
390.8276515804118730.3446968391762540.172348419588127
400.7857203902978310.4285592194043380.214279609702169
410.7349912583158980.5300174833682050.265008741684102
420.6982926021932130.6034147956135730.301707397806787
430.7024022162786340.5951955674427310.297597783721366
440.6507053730315750.698589253936850.349294626968425
450.5956285099306980.8087429801386040.404371490069302
460.7931977169945340.4136045660109310.206802283005466
470.868210823178280.263578353643440.13178917682172
480.8871744821291840.2256510357416320.112825517870816
490.8960910889527060.2078178220945880.103908911047294
500.8818021302680350.236395739463930.118197869731965
510.8581795380120690.2836409239758620.141820461987931
520.8244471591462120.3511056817075770.175552840853788
530.8318031521648760.3363936956702480.168196847835124
540.795125358587050.4097492828258990.20487464141295
550.8279052889020060.3441894221959880.172094711097994
560.791368708794640.417262582410720.20863129120536
570.7557196213927570.4885607572144860.244280378607243
580.7604648308605580.4790703382788840.239535169139442
590.7273677505051660.5452644989896690.272632249494834
600.7785176988825890.4429646022348210.22148230111741
610.7493116442413690.5013767115172630.250688355758631
620.7186053138357820.5627893723284370.281394686164218
630.6770797976312740.6458404047374520.322920202368726
640.6314257808671820.7371484382656370.368574219132818
650.592298567627170.815402864745660.40770143237283
660.5783554739783660.8432890520432670.421644526021634
670.6064114638997850.787177072200430.393588536100215
680.7259039405900530.5481921188198930.274096059409947
690.7713251340530160.4573497318939670.228674865946984
700.7358179312316750.5283641375366490.264182068768325
710.8383061597483420.3233876805033160.161693840251658
720.8133163934863570.3733672130272870.186683606513643
730.7984139810582580.4031720378834850.201586018941742
740.7864999143516020.4270001712967950.213500085648398
750.7496475112511190.5007049774977620.250352488748881
760.7638288390972920.4723423218054160.236171160902708
770.7313078179290680.5373843641418640.268692182070932
780.7005742403649830.5988515192700340.299425759635017
790.6865992016868920.6268015966262160.313400798313108
800.6454510733554340.7090978532891330.354548926644566
810.6310928793727320.7378142412545360.368907120627268
820.7145918065614810.5708163868770370.285408193438519
830.684752177561740.630495644876520.31524782243826
840.642832441097770.714335117804460.35716755890223
850.6120257416431420.7759485167137150.387974258356857
860.6147040764954530.7705918470090950.385295923504547
870.5750094530538440.8499810938923130.424990546946156
880.5276405973727290.9447188052545430.472359402627271
890.5105366578769970.9789266842460060.489463342123003
900.4654312344923320.9308624689846650.534568765507668
910.4386248052577290.8772496105154580.561375194742271
920.397504417622250.79500883524450.60249558237775
930.3663549425108270.7327098850216550.633645057489173
940.3267575720304730.6535151440609450.673242427969527
950.3533561711682030.7067123423364050.646643828831797
960.3253068049138890.6506136098277780.674693195086111
970.2845601947424490.5691203894848970.715439805257551
980.2997239403605520.5994478807211030.700276059639448
990.2596526953510250.5193053907020510.740347304648975
1000.2242700623191160.4485401246382320.775729937680884
1010.2091319980789520.4182639961579030.790868001921048
1020.180723230429870.3614464608597410.81927676957013
1030.2799101505641980.5598203011283960.720089849435802
1040.2397965962857980.4795931925715970.760203403714202
1050.2511449237025650.5022898474051290.748855076297436
1060.2671684819359880.5343369638719760.732831518064012
1070.2321742527734390.4643485055468780.767825747226561
1080.2120404628031970.4240809256063930.787959537196803
1090.190499287334190.380998574668380.80950071266581
1100.2206963884148070.4413927768296130.779303611585193
1110.1945052712035780.3890105424071560.805494728796422
1120.1782164352103830.3564328704207670.821783564789617
1130.1979613323761060.3959226647522120.802038667623894
1140.1762014979161020.3524029958322040.823798502083898
1150.1753866523678430.3507733047356860.824613347632157
1160.1737597127601870.3475194255203750.826240287239813
1170.1451774753269930.2903549506539870.854822524673007
1180.1197085182072330.2394170364144670.880291481792767
1190.1424822950860640.2849645901721290.857517704913936
1200.1672903452905360.3345806905810720.832709654709464
1210.1347672827710070.2695345655420150.865232717228993
1220.1846449522314670.3692899044629330.815355047768533
1230.1785933473606730.3571866947213450.821406652639327
1240.1413724753607120.2827449507214240.858627524639288
1250.1153252778650350.2306505557300710.884674722134965
1260.08828815404107170.1765763080821430.911711845958928
1270.08525384606502940.1705076921300590.914746153934971
1280.08382645512626780.1676529102525360.916173544873732
1290.2016058463081790.4032116926163580.798394153691821
1300.2979307734695860.5958615469391720.702069226530414
1310.243291622027790.4865832440555790.75670837797221
1320.2261109263623010.4522218527246020.773889073637699
1330.3157087852009190.6314175704018380.684291214799081
1340.3035888439195120.6071776878390240.696411156080488
1350.2254892735548730.4509785471097470.774510726445127
1360.3236169452133320.6472338904266650.676383054786668
1370.4393583298092540.8787166596185090.560641670190746
1380.4843647635536570.9687295271073140.515635236446343
1390.4574660662259430.9149321324518860.542533933774057

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
23 & 0.697302694397424 & 0.605394611205152 & 0.302697305602576 \tabularnewline
24 & 0.863589139185206 & 0.272821721629589 & 0.136410860814794 \tabularnewline
25 & 0.824658795323634 & 0.350682409352732 & 0.175341204676366 \tabularnewline
26 & 0.73532831804981 & 0.529343363900379 & 0.26467168195019 \tabularnewline
27 & 0.63640575313108 & 0.727188493737841 & 0.36359424686892 \tabularnewline
28 & 0.646743890601233 & 0.706512218797535 & 0.353256109398767 \tabularnewline
29 & 0.55840836649984 & 0.883183267000319 & 0.441591633500159 \tabularnewline
30 & 0.720222650629363 & 0.559554698741275 & 0.279777349370637 \tabularnewline
31 & 0.771361164416005 & 0.457277671167989 & 0.228638835583995 \tabularnewline
32 & 0.719998684913335 & 0.56000263017333 & 0.280001315086665 \tabularnewline
33 & 0.662291805788389 & 0.675416388423223 & 0.337708194211611 \tabularnewline
34 & 0.605252050600763 & 0.789495898798475 & 0.394747949399237 \tabularnewline
35 & 0.542489164912092 & 0.915021670175815 & 0.457510835087908 \tabularnewline
36 & 0.908490506479383 & 0.183018987041235 & 0.0915094935206175 \tabularnewline
37 & 0.883142272717856 & 0.233715454564287 & 0.116857727282144 \tabularnewline
38 & 0.859450974939424 & 0.281098050121152 & 0.140549025060576 \tabularnewline
39 & 0.827651580411873 & 0.344696839176254 & 0.172348419588127 \tabularnewline
40 & 0.785720390297831 & 0.428559219404338 & 0.214279609702169 \tabularnewline
41 & 0.734991258315898 & 0.530017483368205 & 0.265008741684102 \tabularnewline
42 & 0.698292602193213 & 0.603414795613573 & 0.301707397806787 \tabularnewline
43 & 0.702402216278634 & 0.595195567442731 & 0.297597783721366 \tabularnewline
44 & 0.650705373031575 & 0.69858925393685 & 0.349294626968425 \tabularnewline
45 & 0.595628509930698 & 0.808742980138604 & 0.404371490069302 \tabularnewline
46 & 0.793197716994534 & 0.413604566010931 & 0.206802283005466 \tabularnewline
47 & 0.86821082317828 & 0.26357835364344 & 0.13178917682172 \tabularnewline
48 & 0.887174482129184 & 0.225651035741632 & 0.112825517870816 \tabularnewline
49 & 0.896091088952706 & 0.207817822094588 & 0.103908911047294 \tabularnewline
50 & 0.881802130268035 & 0.23639573946393 & 0.118197869731965 \tabularnewline
51 & 0.858179538012069 & 0.283640923975862 & 0.141820461987931 \tabularnewline
52 & 0.824447159146212 & 0.351105681707577 & 0.175552840853788 \tabularnewline
53 & 0.831803152164876 & 0.336393695670248 & 0.168196847835124 \tabularnewline
54 & 0.79512535858705 & 0.409749282825899 & 0.20487464141295 \tabularnewline
55 & 0.827905288902006 & 0.344189422195988 & 0.172094711097994 \tabularnewline
56 & 0.79136870879464 & 0.41726258241072 & 0.20863129120536 \tabularnewline
57 & 0.755719621392757 & 0.488560757214486 & 0.244280378607243 \tabularnewline
58 & 0.760464830860558 & 0.479070338278884 & 0.239535169139442 \tabularnewline
59 & 0.727367750505166 & 0.545264498989669 & 0.272632249494834 \tabularnewline
60 & 0.778517698882589 & 0.442964602234821 & 0.22148230111741 \tabularnewline
61 & 0.749311644241369 & 0.501376711517263 & 0.250688355758631 \tabularnewline
62 & 0.718605313835782 & 0.562789372328437 & 0.281394686164218 \tabularnewline
63 & 0.677079797631274 & 0.645840404737452 & 0.322920202368726 \tabularnewline
64 & 0.631425780867182 & 0.737148438265637 & 0.368574219132818 \tabularnewline
65 & 0.59229856762717 & 0.81540286474566 & 0.40770143237283 \tabularnewline
66 & 0.578355473978366 & 0.843289052043267 & 0.421644526021634 \tabularnewline
67 & 0.606411463899785 & 0.78717707220043 & 0.393588536100215 \tabularnewline
68 & 0.725903940590053 & 0.548192118819893 & 0.274096059409947 \tabularnewline
69 & 0.771325134053016 & 0.457349731893967 & 0.228674865946984 \tabularnewline
70 & 0.735817931231675 & 0.528364137536649 & 0.264182068768325 \tabularnewline
71 & 0.838306159748342 & 0.323387680503316 & 0.161693840251658 \tabularnewline
72 & 0.813316393486357 & 0.373367213027287 & 0.186683606513643 \tabularnewline
73 & 0.798413981058258 & 0.403172037883485 & 0.201586018941742 \tabularnewline
74 & 0.786499914351602 & 0.427000171296795 & 0.213500085648398 \tabularnewline
75 & 0.749647511251119 & 0.500704977497762 & 0.250352488748881 \tabularnewline
76 & 0.763828839097292 & 0.472342321805416 & 0.236171160902708 \tabularnewline
77 & 0.731307817929068 & 0.537384364141864 & 0.268692182070932 \tabularnewline
78 & 0.700574240364983 & 0.598851519270034 & 0.299425759635017 \tabularnewline
79 & 0.686599201686892 & 0.626801596626216 & 0.313400798313108 \tabularnewline
80 & 0.645451073355434 & 0.709097853289133 & 0.354548926644566 \tabularnewline
81 & 0.631092879372732 & 0.737814241254536 & 0.368907120627268 \tabularnewline
82 & 0.714591806561481 & 0.570816386877037 & 0.285408193438519 \tabularnewline
83 & 0.68475217756174 & 0.63049564487652 & 0.31524782243826 \tabularnewline
84 & 0.64283244109777 & 0.71433511780446 & 0.35716755890223 \tabularnewline
85 & 0.612025741643142 & 0.775948516713715 & 0.387974258356857 \tabularnewline
86 & 0.614704076495453 & 0.770591847009095 & 0.385295923504547 \tabularnewline
87 & 0.575009453053844 & 0.849981093892313 & 0.424990546946156 \tabularnewline
88 & 0.527640597372729 & 0.944718805254543 & 0.472359402627271 \tabularnewline
89 & 0.510536657876997 & 0.978926684246006 & 0.489463342123003 \tabularnewline
90 & 0.465431234492332 & 0.930862468984665 & 0.534568765507668 \tabularnewline
91 & 0.438624805257729 & 0.877249610515458 & 0.561375194742271 \tabularnewline
92 & 0.39750441762225 & 0.7950088352445 & 0.60249558237775 \tabularnewline
93 & 0.366354942510827 & 0.732709885021655 & 0.633645057489173 \tabularnewline
94 & 0.326757572030473 & 0.653515144060945 & 0.673242427969527 \tabularnewline
95 & 0.353356171168203 & 0.706712342336405 & 0.646643828831797 \tabularnewline
96 & 0.325306804913889 & 0.650613609827778 & 0.674693195086111 \tabularnewline
97 & 0.284560194742449 & 0.569120389484897 & 0.715439805257551 \tabularnewline
98 & 0.299723940360552 & 0.599447880721103 & 0.700276059639448 \tabularnewline
99 & 0.259652695351025 & 0.519305390702051 & 0.740347304648975 \tabularnewline
100 & 0.224270062319116 & 0.448540124638232 & 0.775729937680884 \tabularnewline
101 & 0.209131998078952 & 0.418263996157903 & 0.790868001921048 \tabularnewline
102 & 0.18072323042987 & 0.361446460859741 & 0.81927676957013 \tabularnewline
103 & 0.279910150564198 & 0.559820301128396 & 0.720089849435802 \tabularnewline
104 & 0.239796596285798 & 0.479593192571597 & 0.760203403714202 \tabularnewline
105 & 0.251144923702565 & 0.502289847405129 & 0.748855076297436 \tabularnewline
106 & 0.267168481935988 & 0.534336963871976 & 0.732831518064012 \tabularnewline
107 & 0.232174252773439 & 0.464348505546878 & 0.767825747226561 \tabularnewline
108 & 0.212040462803197 & 0.424080925606393 & 0.787959537196803 \tabularnewline
109 & 0.19049928733419 & 0.38099857466838 & 0.80950071266581 \tabularnewline
110 & 0.220696388414807 & 0.441392776829613 & 0.779303611585193 \tabularnewline
111 & 0.194505271203578 & 0.389010542407156 & 0.805494728796422 \tabularnewline
112 & 0.178216435210383 & 0.356432870420767 & 0.821783564789617 \tabularnewline
113 & 0.197961332376106 & 0.395922664752212 & 0.802038667623894 \tabularnewline
114 & 0.176201497916102 & 0.352402995832204 & 0.823798502083898 \tabularnewline
115 & 0.175386652367843 & 0.350773304735686 & 0.824613347632157 \tabularnewline
116 & 0.173759712760187 & 0.347519425520375 & 0.826240287239813 \tabularnewline
117 & 0.145177475326993 & 0.290354950653987 & 0.854822524673007 \tabularnewline
118 & 0.119708518207233 & 0.239417036414467 & 0.880291481792767 \tabularnewline
119 & 0.142482295086064 & 0.284964590172129 & 0.857517704913936 \tabularnewline
120 & 0.167290345290536 & 0.334580690581072 & 0.832709654709464 \tabularnewline
121 & 0.134767282771007 & 0.269534565542015 & 0.865232717228993 \tabularnewline
122 & 0.184644952231467 & 0.369289904462933 & 0.815355047768533 \tabularnewline
123 & 0.178593347360673 & 0.357186694721345 & 0.821406652639327 \tabularnewline
124 & 0.141372475360712 & 0.282744950721424 & 0.858627524639288 \tabularnewline
125 & 0.115325277865035 & 0.230650555730071 & 0.884674722134965 \tabularnewline
126 & 0.0882881540410717 & 0.176576308082143 & 0.911711845958928 \tabularnewline
127 & 0.0852538460650294 & 0.170507692130059 & 0.914746153934971 \tabularnewline
128 & 0.0838264551262678 & 0.167652910252536 & 0.916173544873732 \tabularnewline
129 & 0.201605846308179 & 0.403211692616358 & 0.798394153691821 \tabularnewline
130 & 0.297930773469586 & 0.595861546939172 & 0.702069226530414 \tabularnewline
131 & 0.24329162202779 & 0.486583244055579 & 0.75670837797221 \tabularnewline
132 & 0.226110926362301 & 0.452221852724602 & 0.773889073637699 \tabularnewline
133 & 0.315708785200919 & 0.631417570401838 & 0.684291214799081 \tabularnewline
134 & 0.303588843919512 & 0.607177687839024 & 0.696411156080488 \tabularnewline
135 & 0.225489273554873 & 0.450978547109747 & 0.774510726445127 \tabularnewline
136 & 0.323616945213332 & 0.647233890426665 & 0.676383054786668 \tabularnewline
137 & 0.439358329809254 & 0.878716659618509 & 0.560641670190746 \tabularnewline
138 & 0.484364763553657 & 0.968729527107314 & 0.515635236446343 \tabularnewline
139 & 0.457466066225943 & 0.914932132451886 & 0.542533933774057 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186015&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]23[/C][C]0.697302694397424[/C][C]0.605394611205152[/C][C]0.302697305602576[/C][/ROW]
[ROW][C]24[/C][C]0.863589139185206[/C][C]0.272821721629589[/C][C]0.136410860814794[/C][/ROW]
[ROW][C]25[/C][C]0.824658795323634[/C][C]0.350682409352732[/C][C]0.175341204676366[/C][/ROW]
[ROW][C]26[/C][C]0.73532831804981[/C][C]0.529343363900379[/C][C]0.26467168195019[/C][/ROW]
[ROW][C]27[/C][C]0.63640575313108[/C][C]0.727188493737841[/C][C]0.36359424686892[/C][/ROW]
[ROW][C]28[/C][C]0.646743890601233[/C][C]0.706512218797535[/C][C]0.353256109398767[/C][/ROW]
[ROW][C]29[/C][C]0.55840836649984[/C][C]0.883183267000319[/C][C]0.441591633500159[/C][/ROW]
[ROW][C]30[/C][C]0.720222650629363[/C][C]0.559554698741275[/C][C]0.279777349370637[/C][/ROW]
[ROW][C]31[/C][C]0.771361164416005[/C][C]0.457277671167989[/C][C]0.228638835583995[/C][/ROW]
[ROW][C]32[/C][C]0.719998684913335[/C][C]0.56000263017333[/C][C]0.280001315086665[/C][/ROW]
[ROW][C]33[/C][C]0.662291805788389[/C][C]0.675416388423223[/C][C]0.337708194211611[/C][/ROW]
[ROW][C]34[/C][C]0.605252050600763[/C][C]0.789495898798475[/C][C]0.394747949399237[/C][/ROW]
[ROW][C]35[/C][C]0.542489164912092[/C][C]0.915021670175815[/C][C]0.457510835087908[/C][/ROW]
[ROW][C]36[/C][C]0.908490506479383[/C][C]0.183018987041235[/C][C]0.0915094935206175[/C][/ROW]
[ROW][C]37[/C][C]0.883142272717856[/C][C]0.233715454564287[/C][C]0.116857727282144[/C][/ROW]
[ROW][C]38[/C][C]0.859450974939424[/C][C]0.281098050121152[/C][C]0.140549025060576[/C][/ROW]
[ROW][C]39[/C][C]0.827651580411873[/C][C]0.344696839176254[/C][C]0.172348419588127[/C][/ROW]
[ROW][C]40[/C][C]0.785720390297831[/C][C]0.428559219404338[/C][C]0.214279609702169[/C][/ROW]
[ROW][C]41[/C][C]0.734991258315898[/C][C]0.530017483368205[/C][C]0.265008741684102[/C][/ROW]
[ROW][C]42[/C][C]0.698292602193213[/C][C]0.603414795613573[/C][C]0.301707397806787[/C][/ROW]
[ROW][C]43[/C][C]0.702402216278634[/C][C]0.595195567442731[/C][C]0.297597783721366[/C][/ROW]
[ROW][C]44[/C][C]0.650705373031575[/C][C]0.69858925393685[/C][C]0.349294626968425[/C][/ROW]
[ROW][C]45[/C][C]0.595628509930698[/C][C]0.808742980138604[/C][C]0.404371490069302[/C][/ROW]
[ROW][C]46[/C][C]0.793197716994534[/C][C]0.413604566010931[/C][C]0.206802283005466[/C][/ROW]
[ROW][C]47[/C][C]0.86821082317828[/C][C]0.26357835364344[/C][C]0.13178917682172[/C][/ROW]
[ROW][C]48[/C][C]0.887174482129184[/C][C]0.225651035741632[/C][C]0.112825517870816[/C][/ROW]
[ROW][C]49[/C][C]0.896091088952706[/C][C]0.207817822094588[/C][C]0.103908911047294[/C][/ROW]
[ROW][C]50[/C][C]0.881802130268035[/C][C]0.23639573946393[/C][C]0.118197869731965[/C][/ROW]
[ROW][C]51[/C][C]0.858179538012069[/C][C]0.283640923975862[/C][C]0.141820461987931[/C][/ROW]
[ROW][C]52[/C][C]0.824447159146212[/C][C]0.351105681707577[/C][C]0.175552840853788[/C][/ROW]
[ROW][C]53[/C][C]0.831803152164876[/C][C]0.336393695670248[/C][C]0.168196847835124[/C][/ROW]
[ROW][C]54[/C][C]0.79512535858705[/C][C]0.409749282825899[/C][C]0.20487464141295[/C][/ROW]
[ROW][C]55[/C][C]0.827905288902006[/C][C]0.344189422195988[/C][C]0.172094711097994[/C][/ROW]
[ROW][C]56[/C][C]0.79136870879464[/C][C]0.41726258241072[/C][C]0.20863129120536[/C][/ROW]
[ROW][C]57[/C][C]0.755719621392757[/C][C]0.488560757214486[/C][C]0.244280378607243[/C][/ROW]
[ROW][C]58[/C][C]0.760464830860558[/C][C]0.479070338278884[/C][C]0.239535169139442[/C][/ROW]
[ROW][C]59[/C][C]0.727367750505166[/C][C]0.545264498989669[/C][C]0.272632249494834[/C][/ROW]
[ROW][C]60[/C][C]0.778517698882589[/C][C]0.442964602234821[/C][C]0.22148230111741[/C][/ROW]
[ROW][C]61[/C][C]0.749311644241369[/C][C]0.501376711517263[/C][C]0.250688355758631[/C][/ROW]
[ROW][C]62[/C][C]0.718605313835782[/C][C]0.562789372328437[/C][C]0.281394686164218[/C][/ROW]
[ROW][C]63[/C][C]0.677079797631274[/C][C]0.645840404737452[/C][C]0.322920202368726[/C][/ROW]
[ROW][C]64[/C][C]0.631425780867182[/C][C]0.737148438265637[/C][C]0.368574219132818[/C][/ROW]
[ROW][C]65[/C][C]0.59229856762717[/C][C]0.81540286474566[/C][C]0.40770143237283[/C][/ROW]
[ROW][C]66[/C][C]0.578355473978366[/C][C]0.843289052043267[/C][C]0.421644526021634[/C][/ROW]
[ROW][C]67[/C][C]0.606411463899785[/C][C]0.78717707220043[/C][C]0.393588536100215[/C][/ROW]
[ROW][C]68[/C][C]0.725903940590053[/C][C]0.548192118819893[/C][C]0.274096059409947[/C][/ROW]
[ROW][C]69[/C][C]0.771325134053016[/C][C]0.457349731893967[/C][C]0.228674865946984[/C][/ROW]
[ROW][C]70[/C][C]0.735817931231675[/C][C]0.528364137536649[/C][C]0.264182068768325[/C][/ROW]
[ROW][C]71[/C][C]0.838306159748342[/C][C]0.323387680503316[/C][C]0.161693840251658[/C][/ROW]
[ROW][C]72[/C][C]0.813316393486357[/C][C]0.373367213027287[/C][C]0.186683606513643[/C][/ROW]
[ROW][C]73[/C][C]0.798413981058258[/C][C]0.403172037883485[/C][C]0.201586018941742[/C][/ROW]
[ROW][C]74[/C][C]0.786499914351602[/C][C]0.427000171296795[/C][C]0.213500085648398[/C][/ROW]
[ROW][C]75[/C][C]0.749647511251119[/C][C]0.500704977497762[/C][C]0.250352488748881[/C][/ROW]
[ROW][C]76[/C][C]0.763828839097292[/C][C]0.472342321805416[/C][C]0.236171160902708[/C][/ROW]
[ROW][C]77[/C][C]0.731307817929068[/C][C]0.537384364141864[/C][C]0.268692182070932[/C][/ROW]
[ROW][C]78[/C][C]0.700574240364983[/C][C]0.598851519270034[/C][C]0.299425759635017[/C][/ROW]
[ROW][C]79[/C][C]0.686599201686892[/C][C]0.626801596626216[/C][C]0.313400798313108[/C][/ROW]
[ROW][C]80[/C][C]0.645451073355434[/C][C]0.709097853289133[/C][C]0.354548926644566[/C][/ROW]
[ROW][C]81[/C][C]0.631092879372732[/C][C]0.737814241254536[/C][C]0.368907120627268[/C][/ROW]
[ROW][C]82[/C][C]0.714591806561481[/C][C]0.570816386877037[/C][C]0.285408193438519[/C][/ROW]
[ROW][C]83[/C][C]0.68475217756174[/C][C]0.63049564487652[/C][C]0.31524782243826[/C][/ROW]
[ROW][C]84[/C][C]0.64283244109777[/C][C]0.71433511780446[/C][C]0.35716755890223[/C][/ROW]
[ROW][C]85[/C][C]0.612025741643142[/C][C]0.775948516713715[/C][C]0.387974258356857[/C][/ROW]
[ROW][C]86[/C][C]0.614704076495453[/C][C]0.770591847009095[/C][C]0.385295923504547[/C][/ROW]
[ROW][C]87[/C][C]0.575009453053844[/C][C]0.849981093892313[/C][C]0.424990546946156[/C][/ROW]
[ROW][C]88[/C][C]0.527640597372729[/C][C]0.944718805254543[/C][C]0.472359402627271[/C][/ROW]
[ROW][C]89[/C][C]0.510536657876997[/C][C]0.978926684246006[/C][C]0.489463342123003[/C][/ROW]
[ROW][C]90[/C][C]0.465431234492332[/C][C]0.930862468984665[/C][C]0.534568765507668[/C][/ROW]
[ROW][C]91[/C][C]0.438624805257729[/C][C]0.877249610515458[/C][C]0.561375194742271[/C][/ROW]
[ROW][C]92[/C][C]0.39750441762225[/C][C]0.7950088352445[/C][C]0.60249558237775[/C][/ROW]
[ROW][C]93[/C][C]0.366354942510827[/C][C]0.732709885021655[/C][C]0.633645057489173[/C][/ROW]
[ROW][C]94[/C][C]0.326757572030473[/C][C]0.653515144060945[/C][C]0.673242427969527[/C][/ROW]
[ROW][C]95[/C][C]0.353356171168203[/C][C]0.706712342336405[/C][C]0.646643828831797[/C][/ROW]
[ROW][C]96[/C][C]0.325306804913889[/C][C]0.650613609827778[/C][C]0.674693195086111[/C][/ROW]
[ROW][C]97[/C][C]0.284560194742449[/C][C]0.569120389484897[/C][C]0.715439805257551[/C][/ROW]
[ROW][C]98[/C][C]0.299723940360552[/C][C]0.599447880721103[/C][C]0.700276059639448[/C][/ROW]
[ROW][C]99[/C][C]0.259652695351025[/C][C]0.519305390702051[/C][C]0.740347304648975[/C][/ROW]
[ROW][C]100[/C][C]0.224270062319116[/C][C]0.448540124638232[/C][C]0.775729937680884[/C][/ROW]
[ROW][C]101[/C][C]0.209131998078952[/C][C]0.418263996157903[/C][C]0.790868001921048[/C][/ROW]
[ROW][C]102[/C][C]0.18072323042987[/C][C]0.361446460859741[/C][C]0.81927676957013[/C][/ROW]
[ROW][C]103[/C][C]0.279910150564198[/C][C]0.559820301128396[/C][C]0.720089849435802[/C][/ROW]
[ROW][C]104[/C][C]0.239796596285798[/C][C]0.479593192571597[/C][C]0.760203403714202[/C][/ROW]
[ROW][C]105[/C][C]0.251144923702565[/C][C]0.502289847405129[/C][C]0.748855076297436[/C][/ROW]
[ROW][C]106[/C][C]0.267168481935988[/C][C]0.534336963871976[/C][C]0.732831518064012[/C][/ROW]
[ROW][C]107[/C][C]0.232174252773439[/C][C]0.464348505546878[/C][C]0.767825747226561[/C][/ROW]
[ROW][C]108[/C][C]0.212040462803197[/C][C]0.424080925606393[/C][C]0.787959537196803[/C][/ROW]
[ROW][C]109[/C][C]0.19049928733419[/C][C]0.38099857466838[/C][C]0.80950071266581[/C][/ROW]
[ROW][C]110[/C][C]0.220696388414807[/C][C]0.441392776829613[/C][C]0.779303611585193[/C][/ROW]
[ROW][C]111[/C][C]0.194505271203578[/C][C]0.389010542407156[/C][C]0.805494728796422[/C][/ROW]
[ROW][C]112[/C][C]0.178216435210383[/C][C]0.356432870420767[/C][C]0.821783564789617[/C][/ROW]
[ROW][C]113[/C][C]0.197961332376106[/C][C]0.395922664752212[/C][C]0.802038667623894[/C][/ROW]
[ROW][C]114[/C][C]0.176201497916102[/C][C]0.352402995832204[/C][C]0.823798502083898[/C][/ROW]
[ROW][C]115[/C][C]0.175386652367843[/C][C]0.350773304735686[/C][C]0.824613347632157[/C][/ROW]
[ROW][C]116[/C][C]0.173759712760187[/C][C]0.347519425520375[/C][C]0.826240287239813[/C][/ROW]
[ROW][C]117[/C][C]0.145177475326993[/C][C]0.290354950653987[/C][C]0.854822524673007[/C][/ROW]
[ROW][C]118[/C][C]0.119708518207233[/C][C]0.239417036414467[/C][C]0.880291481792767[/C][/ROW]
[ROW][C]119[/C][C]0.142482295086064[/C][C]0.284964590172129[/C][C]0.857517704913936[/C][/ROW]
[ROW][C]120[/C][C]0.167290345290536[/C][C]0.334580690581072[/C][C]0.832709654709464[/C][/ROW]
[ROW][C]121[/C][C]0.134767282771007[/C][C]0.269534565542015[/C][C]0.865232717228993[/C][/ROW]
[ROW][C]122[/C][C]0.184644952231467[/C][C]0.369289904462933[/C][C]0.815355047768533[/C][/ROW]
[ROW][C]123[/C][C]0.178593347360673[/C][C]0.357186694721345[/C][C]0.821406652639327[/C][/ROW]
[ROW][C]124[/C][C]0.141372475360712[/C][C]0.282744950721424[/C][C]0.858627524639288[/C][/ROW]
[ROW][C]125[/C][C]0.115325277865035[/C][C]0.230650555730071[/C][C]0.884674722134965[/C][/ROW]
[ROW][C]126[/C][C]0.0882881540410717[/C][C]0.176576308082143[/C][C]0.911711845958928[/C][/ROW]
[ROW][C]127[/C][C]0.0852538460650294[/C][C]0.170507692130059[/C][C]0.914746153934971[/C][/ROW]
[ROW][C]128[/C][C]0.0838264551262678[/C][C]0.167652910252536[/C][C]0.916173544873732[/C][/ROW]
[ROW][C]129[/C][C]0.201605846308179[/C][C]0.403211692616358[/C][C]0.798394153691821[/C][/ROW]
[ROW][C]130[/C][C]0.297930773469586[/C][C]0.595861546939172[/C][C]0.702069226530414[/C][/ROW]
[ROW][C]131[/C][C]0.24329162202779[/C][C]0.486583244055579[/C][C]0.75670837797221[/C][/ROW]
[ROW][C]132[/C][C]0.226110926362301[/C][C]0.452221852724602[/C][C]0.773889073637699[/C][/ROW]
[ROW][C]133[/C][C]0.315708785200919[/C][C]0.631417570401838[/C][C]0.684291214799081[/C][/ROW]
[ROW][C]134[/C][C]0.303588843919512[/C][C]0.607177687839024[/C][C]0.696411156080488[/C][/ROW]
[ROW][C]135[/C][C]0.225489273554873[/C][C]0.450978547109747[/C][C]0.774510726445127[/C][/ROW]
[ROW][C]136[/C][C]0.323616945213332[/C][C]0.647233890426665[/C][C]0.676383054786668[/C][/ROW]
[ROW][C]137[/C][C]0.439358329809254[/C][C]0.878716659618509[/C][C]0.560641670190746[/C][/ROW]
[ROW][C]138[/C][C]0.484364763553657[/C][C]0.968729527107314[/C][C]0.515635236446343[/C][/ROW]
[ROW][C]139[/C][C]0.457466066225943[/C][C]0.914932132451886[/C][C]0.542533933774057[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186015&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186015&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
230.6973026943974240.6053946112051520.302697305602576
240.8635891391852060.2728217216295890.136410860814794
250.8246587953236340.3506824093527320.175341204676366
260.735328318049810.5293433639003790.26467168195019
270.636405753131080.7271884937378410.36359424686892
280.6467438906012330.7065122187975350.353256109398767
290.558408366499840.8831832670003190.441591633500159
300.7202226506293630.5595546987412750.279777349370637
310.7713611644160050.4572776711679890.228638835583995
320.7199986849133350.560002630173330.280001315086665
330.6622918057883890.6754163884232230.337708194211611
340.6052520506007630.7894958987984750.394747949399237
350.5424891649120920.9150216701758150.457510835087908
360.9084905064793830.1830189870412350.0915094935206175
370.8831422727178560.2337154545642870.116857727282144
380.8594509749394240.2810980501211520.140549025060576
390.8276515804118730.3446968391762540.172348419588127
400.7857203902978310.4285592194043380.214279609702169
410.7349912583158980.5300174833682050.265008741684102
420.6982926021932130.6034147956135730.301707397806787
430.7024022162786340.5951955674427310.297597783721366
440.6507053730315750.698589253936850.349294626968425
450.5956285099306980.8087429801386040.404371490069302
460.7931977169945340.4136045660109310.206802283005466
470.868210823178280.263578353643440.13178917682172
480.8871744821291840.2256510357416320.112825517870816
490.8960910889527060.2078178220945880.103908911047294
500.8818021302680350.236395739463930.118197869731965
510.8581795380120690.2836409239758620.141820461987931
520.8244471591462120.3511056817075770.175552840853788
530.8318031521648760.3363936956702480.168196847835124
540.795125358587050.4097492828258990.20487464141295
550.8279052889020060.3441894221959880.172094711097994
560.791368708794640.417262582410720.20863129120536
570.7557196213927570.4885607572144860.244280378607243
580.7604648308605580.4790703382788840.239535169139442
590.7273677505051660.5452644989896690.272632249494834
600.7785176988825890.4429646022348210.22148230111741
610.7493116442413690.5013767115172630.250688355758631
620.7186053138357820.5627893723284370.281394686164218
630.6770797976312740.6458404047374520.322920202368726
640.6314257808671820.7371484382656370.368574219132818
650.592298567627170.815402864745660.40770143237283
660.5783554739783660.8432890520432670.421644526021634
670.6064114638997850.787177072200430.393588536100215
680.7259039405900530.5481921188198930.274096059409947
690.7713251340530160.4573497318939670.228674865946984
700.7358179312316750.5283641375366490.264182068768325
710.8383061597483420.3233876805033160.161693840251658
720.8133163934863570.3733672130272870.186683606513643
730.7984139810582580.4031720378834850.201586018941742
740.7864999143516020.4270001712967950.213500085648398
750.7496475112511190.5007049774977620.250352488748881
760.7638288390972920.4723423218054160.236171160902708
770.7313078179290680.5373843641418640.268692182070932
780.7005742403649830.5988515192700340.299425759635017
790.6865992016868920.6268015966262160.313400798313108
800.6454510733554340.7090978532891330.354548926644566
810.6310928793727320.7378142412545360.368907120627268
820.7145918065614810.5708163868770370.285408193438519
830.684752177561740.630495644876520.31524782243826
840.642832441097770.714335117804460.35716755890223
850.6120257416431420.7759485167137150.387974258356857
860.6147040764954530.7705918470090950.385295923504547
870.5750094530538440.8499810938923130.424990546946156
880.5276405973727290.9447188052545430.472359402627271
890.5105366578769970.9789266842460060.489463342123003
900.4654312344923320.9308624689846650.534568765507668
910.4386248052577290.8772496105154580.561375194742271
920.397504417622250.79500883524450.60249558237775
930.3663549425108270.7327098850216550.633645057489173
940.3267575720304730.6535151440609450.673242427969527
950.3533561711682030.7067123423364050.646643828831797
960.3253068049138890.6506136098277780.674693195086111
970.2845601947424490.5691203894848970.715439805257551
980.2997239403605520.5994478807211030.700276059639448
990.2596526953510250.5193053907020510.740347304648975
1000.2242700623191160.4485401246382320.775729937680884
1010.2091319980789520.4182639961579030.790868001921048
1020.180723230429870.3614464608597410.81927676957013
1030.2799101505641980.5598203011283960.720089849435802
1040.2397965962857980.4795931925715970.760203403714202
1050.2511449237025650.5022898474051290.748855076297436
1060.2671684819359880.5343369638719760.732831518064012
1070.2321742527734390.4643485055468780.767825747226561
1080.2120404628031970.4240809256063930.787959537196803
1090.190499287334190.380998574668380.80950071266581
1100.2206963884148070.4413927768296130.779303611585193
1110.1945052712035780.3890105424071560.805494728796422
1120.1782164352103830.3564328704207670.821783564789617
1130.1979613323761060.3959226647522120.802038667623894
1140.1762014979161020.3524029958322040.823798502083898
1150.1753866523678430.3507733047356860.824613347632157
1160.1737597127601870.3475194255203750.826240287239813
1170.1451774753269930.2903549506539870.854822524673007
1180.1197085182072330.2394170364144670.880291481792767
1190.1424822950860640.2849645901721290.857517704913936
1200.1672903452905360.3345806905810720.832709654709464
1210.1347672827710070.2695345655420150.865232717228993
1220.1846449522314670.3692899044629330.815355047768533
1230.1785933473606730.3571866947213450.821406652639327
1240.1413724753607120.2827449507214240.858627524639288
1250.1153252778650350.2306505557300710.884674722134965
1260.08828815404107170.1765763080821430.911711845958928
1270.08525384606502940.1705076921300590.914746153934971
1280.08382645512626780.1676529102525360.916173544873732
1290.2016058463081790.4032116926163580.798394153691821
1300.2979307734695860.5958615469391720.702069226530414
1310.243291622027790.4865832440555790.75670837797221
1320.2261109263623010.4522218527246020.773889073637699
1330.3157087852009190.6314175704018380.684291214799081
1340.3035888439195120.6071776878390240.696411156080488
1350.2254892735548730.4509785471097470.774510726445127
1360.3236169452133320.6472338904266650.676383054786668
1370.4393583298092540.8787166596185090.560641670190746
1380.4843647635536570.9687295271073140.515635236446343
1390.4574660662259430.9149321324518860.542533933774057







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186015&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186015&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186015&T=6

As an alternative you can also use a QR Code:  

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

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



Parameters (Session):
par1 = 3 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 3 ; par2 = Include Monthly Dummies ; par3 = 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], 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,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}