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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of 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 Fri, 01 Nov 2024 00:59:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186015, Retrieved Fri, 01 Nov 2024 00:59:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact147

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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

Tables (Output of Computation)





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:  
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:  
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:  
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:  
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:  
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:  
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:  
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


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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')
}