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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 computationFri, 03 Dec 2010 20:39:07 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/03/t12914087917cf6oqho6mftj1m.htm/, Retrieved Sun, 28 Apr 2024 19:28:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=105003, Retrieved Sun, 28 Apr 2024 19:28:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact133

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [WS 7 - Minitutori...] [2010-11-23 17:27:29] [19f9551d4d95750ef21e9f3cf8fe2131]
-    D      [Multiple Regression] [p_Stress_MR2] [2010-12-03 20:39:07] [fca744d17b21beb005bf086e7071b2bb] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
10	53	7	6	15	11	12	2	4	25	25	3.4
6	86	4	6	15	12	11	4	3	25	24	4
13	66	6	5	14	15	14	7	5	19	21	3.2
12	67	5	4	10	10	12	3	3	18	23	3.2
8	76	4	4	10	12	21	7	6	18	17	2.6
6	78	3	6	12	11	12	2	5	22	19	3.2
10	53	5	7	18	5	22	7	6	29	18	3.8
10	80	6	5	12	16	11	2	6	26	27	3.6
9	74	5	4	14	11	10	1	5	25	23	3.6
9	76	6	6	18	15	13	2	5	23	23	4
7	79	7	1	9	12	10	6	3	23	29	3.4
5	54	6	4	11	9	8	1	5	23	21	2.6
14	67	7	6	11	11	15	1	7	24	26	4.4
6	87	6	6	17	15	10	1	5	30	25	4
10	58	4	5	8	12	14	2	5	19	25	3.8
10	75	6	3	16	16	14	2	3	24	23	3.6
7	88	4	7	21	14	11	2	5	32	26	3.8
10	64	5	2	24	11	10	1	6	30	20	3.6
8	57	3	5	21	10	13	7	5	29	29	3.8
6	66	3	5	14	7	7	1	2	17	24	3.6
10	54	4	3	7	11	12	2	5	25	23	4
12	56	5	5	18	10	14	4	4	26	24	2.8
7	86	3	5	18	11	11	2	6	26	30	5
15	80	7	6	13	16	9	1	3	25	22	4.4
8	76	7	4	11	14	11	1	5	23	22	3.2
10	69	4	4	13	12	15	5	4	21	13	3.4
13	67	4	4	13	12	13	2	5	19	24	3.2
8	80	5	2	18	11	9	1	2	35	17	5
11	54	6	3	14	6	15	3	2	19	24	3.6
7	71	5	6	12	14	10	1	5	20	21	4.8
9	84	4	6	9	9	11	2	2	21	23	3.8
10	74	6	5	12	15	13	5	2	21	24	3.6
8	71	5	3	8	12	8	2	2	24	24	2.6
15	63	5	3	5	12	20	6	5	23	24	3.2
9	71	6	4	10	9	12	4	5	19	23	4
7	76	2	4	11	13	10	1	1	17	26	3.2
11	69	6	5	11	15	10	3	5	24	24	3.4
9	74	7	3	12	11	9	6	2	15	21	3.2
8	75	5	5	12	10	14	7	6	25	23	3.4
8	54	5	4	15	13	8	4	1	27	28	3.4
12	69	5	3	16	16	11	5	3	27	22	3.6
13	68	6	3	14	13	13	3	2	18	24	3.4
9	75	4	4	17	14	11	2	5	25	21	2.8
11	75	6	6	10	16	11	2	3	26	23	3.8
8	72	5	5	17	9	10	2	4	23	20	3
10	67	5	3	12	8	14	2	3	16	23	3.4
13	63	3	4	13	8	18	1	6	27	21	3.6
12	62	4	2	13	12	14	2	4	25	27	3.4
12	63	4	3	11	10	11	1	5	14	12	2.8
9	76	2	5	13	16	12	2	2	19	15	4
8	74	3	5	12	13	13	2	5	20	22	4.2
9	67	6	5	12	11	9	5	5	16	21	3.4
12	73	5	4	12	14	10	5	3	18	21	3.4
12	70	6	5	9	15	15	2	5	22	20	4
16	53	2	3	7	8	20	1	7	21	24	3.2
11	77	3	6	17	9	12	1	4	22	24	3.8
13	77	6	3	12	17	12	2	2	22	29	3.8
10	52	3	2	12	9	14	3	3	32	25	3.4
9	54	6	3	9	13	13	7	6	23	14	3.4
14	80	6	4	9	6	11	4	7	31	30	3.4
13	66	4	3	13	13	17	4	4	18	19	4.8
12	73	7	4	10	8	12	1	4	23	29	3
9	63	6	4	11	12	13	2	4	26	25	4
9	69	3	7	12	13	14	2	5	24	25	4.2
10	67	7	2	10	14	13	2	2	19	25	4
8	54	2	2	13	11	15	5	3	14	16	3.4
9	81	4	5	6	15	13	1	3	20	25	3.8
9	69	6	3	7	7	10	6	4	22	28	3.4
11	84	4	6	13	16	11	2	3	24	24	4.2
7	70	1	6	11	16	13	2	4	25	25	3.2
11	69	4	4	18	14	17	4	6	21	21	3
9	77	7	6	9	11	13	6	2	28	22	4.2
11	54	4	6	9	13	9	2	4	24	20	3.6
9	79	4	4	11	13	11	2	5	20	25	3.2
8	30	4	2	11	7	10	2	2	21	27	3.4
9	71	6	6	15	15	9	1	1	23	21	3.8
8	73	2	3	8	11	12	1	2	13	13	3.6
9	72	3	5	11	15	12	2	5	24	26	3
10	77	4	3	14	13	13	2	4	21	26	3.4
9	75	4	4	14	11	13	3	4	21	25	3.4
17	70	4	6	12	12	22	3	6	17	22	3.8
7	73	6	2	12	10	13	5	1	14	19	3.8
11	54	2	7	8	12	15	2	4	29	23	5
9	77	4	2	11	12	13	5	5	25	25	3.4
10	82	3	3	10	12	15	3	2	16	15	3.2
11	80	7	6	17	14	10	1	3	25	21	3.6
8	80	4	4	16	6	11	2	3	25	23	3.6
12	69	5	4	13	14	16	2	6	21	25	3.8
10	78	6	3	15	15	11	1	5	23	24	3.8
7	81	5	5	11	8	11	2	4	22	24	3.6
9	76	4	4	12	12	10	2	4	19	21	4
7	76	5	5	16	10	10	5	5	24	24	4
12	73	4	5	20	15	16	5	5	26	22	4
8	85	5	7	16	11	12	2	6	25	24	4.4
13	66	7	4	11	9	11	3	6	20	28	3.8
9	79	7	6	15	14	16	5	5	22	21	3.4
15	68	4	3	15	10	19	5	7	14	17	4
8	76	6	6	12	16	11	6	5	20	28	4.4
14	54	4	3	9	5	15	2	5	32	24	4
14	46	1	2	24	8	24	7	7	21	10	4
9	82	3	4	15	13	14	1	5	22	20	3.8
13	74	6	3	18	16	15	1	6	28	22	2.6
11	88	7	3	17	16	11	6	6	25	19	4.5
10	38	6	4	12	14	15	6	4	17	22	3.4
6	76	6	4	15	14	12	2	5	21	22	3.4
8	86	6	5	11	10	10	1	1	23	26	4.4
10	54	4	5	11	9	14	2	6	27	24	4.4
10	69	1	7	12	8	9	1	5	19	20	3.8
10	90	3	7	14	8	15	2	2	20	20	3.2
12	54	7	1	11	16	15	1	1	17	15	3.8
10	76	2	4	20	12	14	3	5	24	20	3.8
9	89	7	6	11	9	11	3	6	21	20	4
9	76	4	5	12	15	8	6	5	21	24	3.4
11	79	5	4	12	12	11	4	5	24	29	3.8
7	90	6	5	11	14	8	1	4	19	23	3.4
7	74	6	5	10	12	10	2	2	22	24	4
5	81	5	6	11	16	11	5	3	26	22	2.4
9	72	5	5	12	12	13	6	3	17	16	3.4
11	71	4	3	9	14	11	3	5	17	23	3.4
15	66	2	4	8	8	20	5	3	19	27	3.4
9	77	2	4	6	15	10	3	2	15	16	3.4
9	74	4	5	12	16	12	2	2	17	21	3.6
8	82	4	6	15	12	14	3	3	27	26	4
13	54	6	2	13	4	23	2	6	19	22	3.2
10	63	5	4	17	8	14	5	5	21	23	3.8
13	54	5	5	14	11	16	5	6	25	19	3
9	64	6	6	16	4	11	7	2	19	18	3.8
11	69	5	6	15	14	12	4	5	22	24	3.6
8	84	7	5	11	14	14	5	5	20	29	3.6
10	86	5	4	11	13	12	1	1	15	22	3.6
9	77	3	5	16	14	12	4	4	20	24	3.6
8	89	5	6	15	7	11	1	2	29	22	4.2
8	76	1	6	14	19	12	4	2	19	12	3.4
13	60	5	5	9	12	13	6	7	29	26	2.8
11	79	7	6	13	10	17	7	6	24	18	3.8
8	76	7	4	11	14	11	1	5	23	22	3.2
12	72	6	5	14	16	12	3	5	22	24	4
15	69	4	5	11	11	19	5	5	23	21	2.8
11	54	2	7	8	12	15	2	4	29	23	5
10	69	6	5	7	12	14	4	3	26	22	3.4
5	81	5	6	11	16	11	5	3	26	22	2.4
11	84	1	6	13	12	9	1	3	21	24	3.2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 13 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105003&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]13 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105003&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105003&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 time13 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
PStress[t] = + 5.25986734334252 -0.0356695703760594BelInSprt[t] + 0.162920650667659KunnenRekRel[t] -0.149394398367188ExtraCurAct[t] -0.0485862018705257Verwouders[t] + 0.0628810522000706Populariteit[t] + 0.399104485306294Depressie[t] -0.180064637594153Slaapgebrek[t] + 0.218592098994588ToekZorgen[t] -0.0278347970425497PersStand[t] + 0.0512352154322369MateGeorgZijn[t] + 0.274485494053298`Eetgewoonten `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PStress[t] =  +  5.25986734334252 -0.0356695703760594BelInSprt[t] +  0.162920650667659KunnenRekRel[t] -0.149394398367188ExtraCurAct[t] -0.0485862018705257Verwouders[t] +  0.0628810522000706Populariteit[t] +  0.399104485306294Depressie[t] -0.180064637594153Slaapgebrek[t] +  0.218592098994588ToekZorgen[t] -0.0278347970425497PersStand[t] +  0.0512352154322369MateGeorgZijn[t] +  0.274485494053298`Eetgewoonten
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105003&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PStress[t] =  +  5.25986734334252 -0.0356695703760594BelInSprt[t] +  0.162920650667659KunnenRekRel[t] -0.149394398367188ExtraCurAct[t] -0.0485862018705257Verwouders[t] +  0.0628810522000706Populariteit[t] +  0.399104485306294Depressie[t] -0.180064637594153Slaapgebrek[t] +  0.218592098994588ToekZorgen[t] -0.0278347970425497PersStand[t] +  0.0512352154322369MateGeorgZijn[t] +  0.274485494053298`Eetgewoonten
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105003&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105003&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
PStress[t] = + 5.25986734334252 -0.0356695703760594BelInSprt[t] + 0.162920650667659KunnenRekRel[t] -0.149394398367188ExtraCurAct[t] -0.0485862018705257Verwouders[t] + 0.0628810522000706Populariteit[t] + 0.399104485306294Depressie[t] -0.180064637594153Slaapgebrek[t] + 0.218592098994588ToekZorgen[t] -0.0278347970425497PersStand[t] + 0.0512352154322369MateGeorgZijn[t] + 0.274485494053298`Eetgewoonten `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.259867343342522.3329392.25460.0258310.012915
BelInSprt-0.03566957037605940.017485-2.040.0433770.021689
KunnenRekRel0.1629206506676590.1112631.46430.1455310.072766
ExtraCurAct-0.1493943983671880.128863-1.15930.2484490.124225
Verwouders-0.04858620187052570.051476-0.94390.3469870.173494
Populariteit0.06288105220007060.0581941.08050.28190.14095
Depressie0.3991044853062940.0641746.219100
Slaapgebrek-0.1800646375941530.095025-1.89490.0603240.030162
ToekZorgen0.2185920989945880.1186931.84170.0678040.033902
PersStand-0.02783479704254970.045309-0.61430.540070.270035
MateGeorgZijn0.05123521543223690.0490321.04490.2979890.148994
`Eetgewoonten `0.2744854940532980.3362250.81640.4157790.20789

\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) & 5.25986734334252 & 2.332939 & 2.2546 & 0.025831 & 0.012915 \tabularnewline
BelInSprt & -0.0356695703760594 & 0.017485 & -2.04 & 0.043377 & 0.021689 \tabularnewline
KunnenRekRel & 0.162920650667659 & 0.111263 & 1.4643 & 0.145531 & 0.072766 \tabularnewline
ExtraCurAct & -0.149394398367188 & 0.128863 & -1.1593 & 0.248449 & 0.124225 \tabularnewline
Verwouders & -0.0485862018705257 & 0.051476 & -0.9439 & 0.346987 & 0.173494 \tabularnewline
Populariteit & 0.0628810522000706 & 0.058194 & 1.0805 & 0.2819 & 0.14095 \tabularnewline
Depressie & 0.399104485306294 & 0.064174 & 6.2191 & 0 & 0 \tabularnewline
Slaapgebrek & -0.180064637594153 & 0.095025 & -1.8949 & 0.060324 & 0.030162 \tabularnewline
ToekZorgen & 0.218592098994588 & 0.118693 & 1.8417 & 0.067804 & 0.033902 \tabularnewline
PersStand & -0.0278347970425497 & 0.045309 & -0.6143 & 0.54007 & 0.270035 \tabularnewline
MateGeorgZijn & 0.0512352154322369 & 0.049032 & 1.0449 & 0.297989 & 0.148994 \tabularnewline
`Eetgewoonten
` & 0.274485494053298 & 0.336225 & 0.8164 & 0.415779 & 0.20789 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105003&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]5.25986734334252[/C][C]2.332939[/C][C]2.2546[/C][C]0.025831[/C][C]0.012915[/C][/ROW]
[ROW][C]BelInSprt[/C][C]-0.0356695703760594[/C][C]0.017485[/C][C]-2.04[/C][C]0.043377[/C][C]0.021689[/C][/ROW]
[ROW][C]KunnenRekRel[/C][C]0.162920650667659[/C][C]0.111263[/C][C]1.4643[/C][C]0.145531[/C][C]0.072766[/C][/ROW]
[ROW][C]ExtraCurAct[/C][C]-0.149394398367188[/C][C]0.128863[/C][C]-1.1593[/C][C]0.248449[/C][C]0.124225[/C][/ROW]
[ROW][C]Verwouders[/C][C]-0.0485862018705257[/C][C]0.051476[/C][C]-0.9439[/C][C]0.346987[/C][C]0.173494[/C][/ROW]
[ROW][C]Populariteit[/C][C]0.0628810522000706[/C][C]0.058194[/C][C]1.0805[/C][C]0.2819[/C][C]0.14095[/C][/ROW]
[ROW][C]Depressie[/C][C]0.399104485306294[/C][C]0.064174[/C][C]6.2191[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Slaapgebrek[/C][C]-0.180064637594153[/C][C]0.095025[/C][C]-1.8949[/C][C]0.060324[/C][C]0.030162[/C][/ROW]
[ROW][C]ToekZorgen[/C][C]0.218592098994588[/C][C]0.118693[/C][C]1.8417[/C][C]0.067804[/C][C]0.033902[/C][/ROW]
[ROW][C]PersStand[/C][C]-0.0278347970425497[/C][C]0.045309[/C][C]-0.6143[/C][C]0.54007[/C][C]0.270035[/C][/ROW]
[ROW][C]MateGeorgZijn[/C][C]0.0512352154322369[/C][C]0.049032[/C][C]1.0449[/C][C]0.297989[/C][C]0.148994[/C][/ROW]
[ROW][C]`Eetgewoonten
`[/C][C]0.274485494053298[/C][C]0.336225[/C][C]0.8164[/C][C]0.415779[/C][C]0.20789[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105003&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105003&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)5.259867343342522.3329392.25460.0258310.012915
BelInSprt-0.03566957037605940.017485-2.040.0433770.021689
KunnenRekRel0.1629206506676590.1112631.46430.1455310.072766
ExtraCurAct-0.1493943983671880.128863-1.15930.2484490.124225
Verwouders-0.04858620187052570.051476-0.94390.3469870.173494
Populariteit0.06288105220007060.0581941.08050.28190.14095
Depressie0.3991044853062940.0641746.219100
Slaapgebrek-0.1800646375941530.095025-1.89490.0603240.030162
ToekZorgen0.2185920989945880.1186931.84170.0678040.033902
PersStand-0.02783479704254970.045309-0.61430.540070.270035
MateGeorgZijn0.05123521543223690.0490321.04490.2979890.148994
`Eetgewoonten `0.2744854940532980.3362250.81640.4157790.20789






Multiple Linear Regression - Regression Statistics
Multiple R0.627502539554768
R-squared0.393759437147683
Adjusted R-squared0.342462158752487
F-TEST (value)7.67602979078433
F-TEST (DF numerator)11
F-TEST (DF denominator)130
p-value4.1419778717966e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.93510610595773
Sum Squared Residuals486.802633370934

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.627502539554768 \tabularnewline
R-squared & 0.393759437147683 \tabularnewline
Adjusted R-squared & 0.342462158752487 \tabularnewline
F-TEST (value) & 7.67602979078433 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 130 \tabularnewline
p-value & 4.1419778717966e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.93510610595773 \tabularnewline
Sum Squared Residuals & 486.802633370934 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105003&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.627502539554768[/C][/ROW]
[ROW][C]R-squared[/C][C]0.393759437147683[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.342462158752487[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.67602979078433[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]130[/C][/ROW]
[ROW][C]p-value[/C][C]4.1419778717966e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.93510610595773[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]486.802633370934[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105003&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105003&T=3

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.627502539554768
R-squared0.393759437147683
Adjusted R-squared0.342462158752487
F-TEST (value)7.67602979078433
F-TEST (DF numerator)11
F-TEST (DF denominator)130
p-value4.1419778717966e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.93510610595773
Sum Squared Residuals486.802633370934






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11010.3981109080137-0.398110908013729
267.9307644073114-1.9307644073114
31310.24463935484742.75536064515261
4129.690553688334482.30944631166553
5812.3877245340208-4.38772453402081
668.94023575027133-2.94023575027133
71012.5675446540060-2.56754465400602
81010.0492825674870-0.0492825674870383
999.2234580613084-0.223458061308392
10910.2561427858389-1.25614278583888
1179.0956231693256-2.09562316932559
1259.00027131926342-4.00027131926342
131412.47979162725591.52020837274413
1468.80274174681465-2.80274174681465
151011.5769840172752-1.57698401727518
161010.7243402999109-0.724340299910876
1778.19431898730253-1.19431898730253
18109.318793010583240.681206989416764
1989.319515426293-1.31951542629299
2067.20287855468407-1.20287855468407
211010.9913651602625-0.991365160262543
221210.10033402262331.89966597737672
2379.27856605897553-2.27856605897553
24158.731549103038796.26845089696121
25810.1061066504412-2.10610665044122
26109.951114228789070.0488857712109341
271310.54739027573472.45260972426531
2887.857524274669950.142475725330050
291111.1326201089110-0.132620108911009
3079.6835796832557-2.68357968325569
3198.251721652807490.748278347192514
32109.569533938311140.430466061688862
3388.00479404745797-0.0047940474579675
341513.35320687983521.64679312016478
35910.0967881638929-1.09678816389294
3678.32709910760245-1.32709910760245
37119.476658618376841.52334138162316
3897.906745536430831.09325446356917
3989.75241055995031-1.75241055995031
4087.946863476251220.0531365237487764
41128.903190096238353.09680990376165
421310.24814221666602.75185778333403
4398.964733412858340.0352665871416619
44119.369588364839151.63041163516085
4588.21249800844928-0.212498008449276
461010.7058538796630-0.70585387966297
471312.40321303587540.596786964124592
481211.24663327351310.75336672648686
49129.607286124333182.39271387566682
5098.706243325510380.293756674489619
51810.2410357542843-2.2410357542843
5298.557626318007240.442373681992759
53128.424976493282643.57502350671736
541211.72916854465560.270831455344415
551614.26562099909651.73437900090345
56118.989552119087352.01044788091265
571310.31140393472292.68859606527706
581010.6043806251253-0.604380625125327
59910.4930309173501-1.49303091735006
60149.533722435544684.46627756445532
611312.02386737408690.976132625913069
621210.36867704576621.63132295423380
63910.9704701946977-1.97047019469774
64910.5620657528630-1.56206575286303
651011.2215090481749-1.22150904817487
66810.5261813542289-2.52618135422892
67910.3583406162232-1.35834061622320
6898.968568249029760.0314317509702444
69118.793661331646372.20633866835363
7079.66716176259284-2.66716176259284
711111.5494906558691-0.549490655869072
7299.05759852497023-0.0575985249702288
73118.920201064031122.07979893596888
7499.5046003349402-0.504600334940199
75810.2485637172436-2.24856371724359
7698.132160014046250.867839985953749
7789.17537537308887-1.17537537308887
7899.85183779669558-0.851837796695581
791010.2374896572663-0.237489657266274
8099.80237244222467-0.802372442224675
811713.93853725855233.06146274144766
8279.51395394474114-2.51395394474114
831111.2241087785954-0.224108778595412
8499.98558539165466-0.985585391654661
85109.72933456167710.270665438322909
86118.539723065787962.46027693421204
8788.21679797336594-0.216797973365939
881212.3408963627430-0.340896362742957
891010.1569392294030-0.156939229403048
9078.9166794745976-1.91667947459759
9198.924927538253140.0750724617468596
9278.31127672596746-1.31127672596746
931210.61191212683441.38808787316558
9489.39329723101702-1.39329723101702
951310.56246689716082.43753310283921
96910.8127248916724-1.81272489167239
971512.72991366505882.27008633494117
9889.54154819418306-1.54154819418306
991411.57061153537432.42938846462566
1001413.69414180248110.305858197518861
101910.270549884361-1.27054988436099
1021311.46072298877441.53927701122564
103119.127435910383981.87256408961602
1041011.9494520075191-1.94945200751907
105610.0784477328994-4.07844773289936
10688.44642226215375-0.446422262153754
1071011.4946303402216-1.49463034022156
108107.879565112020292.12043488797971
109108.725432915502621.27456708449738
1101212.1598856141247-0.159885614124670
111109.600035725123580.399964274876418
11299.06045856008726-0.0604585600872634
11397.597645630271181.40235436972881
114119.454217426339681.54578257366032
11578.09598356364117-1.09598356364117
11678.90290304289432-1.90290304289432
11757.9683552701553-2.96835527015529
11898.974397099097470.0256029009025346
119119.955271173736671.04472882626333
1201512.27358137680762.72641862319243
12198.116800013358840.883199986641165
12299.40529665852555-0.405296658525544
12389.49762161910805-1.49762161910805
1241315.2398428922856-2.23984289228564
1251010.3238172510627-0.323817251062680
1261311.31078337275721.68921662724279
12797.535616016523111.46438398347689
128119.635828735574021.36417126442598
129810.7003556913825-2.70035569138245
130109.21789725643490.782102743565107
13198.96251656281740.0374834371826047
13287.83496037941440.165039620585609
13387.855459013288220.144540986711776
1341310.43620223857992.5637977614201
1351110.81635935296520.183640647034757
136810.1061066504412-2.10610665044122
1371210.30534221496681.69465778503318
1381511.84054205512343.15945794487656
1391111.2241087785954-0.224108778595412
1401010.303389349951-0.303389349951004
14157.9683552701553-2.96835527015529
142117.244249734899033.75575026510097

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 10 & 10.3981109080137 & -0.398110908013729 \tabularnewline
2 & 6 & 7.9307644073114 & -1.9307644073114 \tabularnewline
3 & 13 & 10.2446393548474 & 2.75536064515261 \tabularnewline
4 & 12 & 9.69055368833448 & 2.30944631166553 \tabularnewline
5 & 8 & 12.3877245340208 & -4.38772453402081 \tabularnewline
6 & 6 & 8.94023575027133 & -2.94023575027133 \tabularnewline
7 & 10 & 12.5675446540060 & -2.56754465400602 \tabularnewline
8 & 10 & 10.0492825674870 & -0.0492825674870383 \tabularnewline
9 & 9 & 9.2234580613084 & -0.223458061308392 \tabularnewline
10 & 9 & 10.2561427858389 & -1.25614278583888 \tabularnewline
11 & 7 & 9.0956231693256 & -2.09562316932559 \tabularnewline
12 & 5 & 9.00027131926342 & -4.00027131926342 \tabularnewline
13 & 14 & 12.4797916272559 & 1.52020837274413 \tabularnewline
14 & 6 & 8.80274174681465 & -2.80274174681465 \tabularnewline
15 & 10 & 11.5769840172752 & -1.57698401727518 \tabularnewline
16 & 10 & 10.7243402999109 & -0.724340299910876 \tabularnewline
17 & 7 & 8.19431898730253 & -1.19431898730253 \tabularnewline
18 & 10 & 9.31879301058324 & 0.681206989416764 \tabularnewline
19 & 8 & 9.319515426293 & -1.31951542629299 \tabularnewline
20 & 6 & 7.20287855468407 & -1.20287855468407 \tabularnewline
21 & 10 & 10.9913651602625 & -0.991365160262543 \tabularnewline
22 & 12 & 10.1003340226233 & 1.89966597737672 \tabularnewline
23 & 7 & 9.27856605897553 & -2.27856605897553 \tabularnewline
24 & 15 & 8.73154910303879 & 6.26845089696121 \tabularnewline
25 & 8 & 10.1061066504412 & -2.10610665044122 \tabularnewline
26 & 10 & 9.95111422878907 & 0.0488857712109341 \tabularnewline
27 & 13 & 10.5473902757347 & 2.45260972426531 \tabularnewline
28 & 8 & 7.85752427466995 & 0.142475725330050 \tabularnewline
29 & 11 & 11.1326201089110 & -0.132620108911009 \tabularnewline
30 & 7 & 9.6835796832557 & -2.68357968325569 \tabularnewline
31 & 9 & 8.25172165280749 & 0.748278347192514 \tabularnewline
32 & 10 & 9.56953393831114 & 0.430466061688862 \tabularnewline
33 & 8 & 8.00479404745797 & -0.0047940474579675 \tabularnewline
34 & 15 & 13.3532068798352 & 1.64679312016478 \tabularnewline
35 & 9 & 10.0967881638929 & -1.09678816389294 \tabularnewline
36 & 7 & 8.32709910760245 & -1.32709910760245 \tabularnewline
37 & 11 & 9.47665861837684 & 1.52334138162316 \tabularnewline
38 & 9 & 7.90674553643083 & 1.09325446356917 \tabularnewline
39 & 8 & 9.75241055995031 & -1.75241055995031 \tabularnewline
40 & 8 & 7.94686347625122 & 0.0531365237487764 \tabularnewline
41 & 12 & 8.90319009623835 & 3.09680990376165 \tabularnewline
42 & 13 & 10.2481422166660 & 2.75185778333403 \tabularnewline
43 & 9 & 8.96473341285834 & 0.0352665871416619 \tabularnewline
44 & 11 & 9.36958836483915 & 1.63041163516085 \tabularnewline
45 & 8 & 8.21249800844928 & -0.212498008449276 \tabularnewline
46 & 10 & 10.7058538796630 & -0.70585387966297 \tabularnewline
47 & 13 & 12.4032130358754 & 0.596786964124592 \tabularnewline
48 & 12 & 11.2466332735131 & 0.75336672648686 \tabularnewline
49 & 12 & 9.60728612433318 & 2.39271387566682 \tabularnewline
50 & 9 & 8.70624332551038 & 0.293756674489619 \tabularnewline
51 & 8 & 10.2410357542843 & -2.2410357542843 \tabularnewline
52 & 9 & 8.55762631800724 & 0.442373681992759 \tabularnewline
53 & 12 & 8.42497649328264 & 3.57502350671736 \tabularnewline
54 & 12 & 11.7291685446556 & 0.270831455344415 \tabularnewline
55 & 16 & 14.2656209990965 & 1.73437900090345 \tabularnewline
56 & 11 & 8.98955211908735 & 2.01044788091265 \tabularnewline
57 & 13 & 10.3114039347229 & 2.68859606527706 \tabularnewline
58 & 10 & 10.6043806251253 & -0.604380625125327 \tabularnewline
59 & 9 & 10.4930309173501 & -1.49303091735006 \tabularnewline
60 & 14 & 9.53372243554468 & 4.46627756445532 \tabularnewline
61 & 13 & 12.0238673740869 & 0.976132625913069 \tabularnewline
62 & 12 & 10.3686770457662 & 1.63132295423380 \tabularnewline
63 & 9 & 10.9704701946977 & -1.97047019469774 \tabularnewline
64 & 9 & 10.5620657528630 & -1.56206575286303 \tabularnewline
65 & 10 & 11.2215090481749 & -1.22150904817487 \tabularnewline
66 & 8 & 10.5261813542289 & -2.52618135422892 \tabularnewline
67 & 9 & 10.3583406162232 & -1.35834061622320 \tabularnewline
68 & 9 & 8.96856824902976 & 0.0314317509702444 \tabularnewline
69 & 11 & 8.79366133164637 & 2.20633866835363 \tabularnewline
70 & 7 & 9.66716176259284 & -2.66716176259284 \tabularnewline
71 & 11 & 11.5494906558691 & -0.549490655869072 \tabularnewline
72 & 9 & 9.05759852497023 & -0.0575985249702288 \tabularnewline
73 & 11 & 8.92020106403112 & 2.07979893596888 \tabularnewline
74 & 9 & 9.5046003349402 & -0.504600334940199 \tabularnewline
75 & 8 & 10.2485637172436 & -2.24856371724359 \tabularnewline
76 & 9 & 8.13216001404625 & 0.867839985953749 \tabularnewline
77 & 8 & 9.17537537308887 & -1.17537537308887 \tabularnewline
78 & 9 & 9.85183779669558 & -0.851837796695581 \tabularnewline
79 & 10 & 10.2374896572663 & -0.237489657266274 \tabularnewline
80 & 9 & 9.80237244222467 & -0.802372442224675 \tabularnewline
81 & 17 & 13.9385372585523 & 3.06146274144766 \tabularnewline
82 & 7 & 9.51395394474114 & -2.51395394474114 \tabularnewline
83 & 11 & 11.2241087785954 & -0.224108778595412 \tabularnewline
84 & 9 & 9.98558539165466 & -0.985585391654661 \tabularnewline
85 & 10 & 9.7293345616771 & 0.270665438322909 \tabularnewline
86 & 11 & 8.53972306578796 & 2.46027693421204 \tabularnewline
87 & 8 & 8.21679797336594 & -0.216797973365939 \tabularnewline
88 & 12 & 12.3408963627430 & -0.340896362742957 \tabularnewline
89 & 10 & 10.1569392294030 & -0.156939229403048 \tabularnewline
90 & 7 & 8.9166794745976 & -1.91667947459759 \tabularnewline
91 & 9 & 8.92492753825314 & 0.0750724617468596 \tabularnewline
92 & 7 & 8.31127672596746 & -1.31127672596746 \tabularnewline
93 & 12 & 10.6119121268344 & 1.38808787316558 \tabularnewline
94 & 8 & 9.39329723101702 & -1.39329723101702 \tabularnewline
95 & 13 & 10.5624668971608 & 2.43753310283921 \tabularnewline
96 & 9 & 10.8127248916724 & -1.81272489167239 \tabularnewline
97 & 15 & 12.7299136650588 & 2.27008633494117 \tabularnewline
98 & 8 & 9.54154819418306 & -1.54154819418306 \tabularnewline
99 & 14 & 11.5706115353743 & 2.42938846462566 \tabularnewline
100 & 14 & 13.6941418024811 & 0.305858197518861 \tabularnewline
101 & 9 & 10.270549884361 & -1.27054988436099 \tabularnewline
102 & 13 & 11.4607229887744 & 1.53927701122564 \tabularnewline
103 & 11 & 9.12743591038398 & 1.87256408961602 \tabularnewline
104 & 10 & 11.9494520075191 & -1.94945200751907 \tabularnewline
105 & 6 & 10.0784477328994 & -4.07844773289936 \tabularnewline
106 & 8 & 8.44642226215375 & -0.446422262153754 \tabularnewline
107 & 10 & 11.4946303402216 & -1.49463034022156 \tabularnewline
108 & 10 & 7.87956511202029 & 2.12043488797971 \tabularnewline
109 & 10 & 8.72543291550262 & 1.27456708449738 \tabularnewline
110 & 12 & 12.1598856141247 & -0.159885614124670 \tabularnewline
111 & 10 & 9.60003572512358 & 0.399964274876418 \tabularnewline
112 & 9 & 9.06045856008726 & -0.0604585600872634 \tabularnewline
113 & 9 & 7.59764563027118 & 1.40235436972881 \tabularnewline
114 & 11 & 9.45421742633968 & 1.54578257366032 \tabularnewline
115 & 7 & 8.09598356364117 & -1.09598356364117 \tabularnewline
116 & 7 & 8.90290304289432 & -1.90290304289432 \tabularnewline
117 & 5 & 7.9683552701553 & -2.96835527015529 \tabularnewline
118 & 9 & 8.97439709909747 & 0.0256029009025346 \tabularnewline
119 & 11 & 9.95527117373667 & 1.04472882626333 \tabularnewline
120 & 15 & 12.2735813768076 & 2.72641862319243 \tabularnewline
121 & 9 & 8.11680001335884 & 0.883199986641165 \tabularnewline
122 & 9 & 9.40529665852555 & -0.405296658525544 \tabularnewline
123 & 8 & 9.49762161910805 & -1.49762161910805 \tabularnewline
124 & 13 & 15.2398428922856 & -2.23984289228564 \tabularnewline
125 & 10 & 10.3238172510627 & -0.323817251062680 \tabularnewline
126 & 13 & 11.3107833727572 & 1.68921662724279 \tabularnewline
127 & 9 & 7.53561601652311 & 1.46438398347689 \tabularnewline
128 & 11 & 9.63582873557402 & 1.36417126442598 \tabularnewline
129 & 8 & 10.7003556913825 & -2.70035569138245 \tabularnewline
130 & 10 & 9.2178972564349 & 0.782102743565107 \tabularnewline
131 & 9 & 8.9625165628174 & 0.0374834371826047 \tabularnewline
132 & 8 & 7.8349603794144 & 0.165039620585609 \tabularnewline
133 & 8 & 7.85545901328822 & 0.144540986711776 \tabularnewline
134 & 13 & 10.4362022385799 & 2.5637977614201 \tabularnewline
135 & 11 & 10.8163593529652 & 0.183640647034757 \tabularnewline
136 & 8 & 10.1061066504412 & -2.10610665044122 \tabularnewline
137 & 12 & 10.3053422149668 & 1.69465778503318 \tabularnewline
138 & 15 & 11.8405420551234 & 3.15945794487656 \tabularnewline
139 & 11 & 11.2241087785954 & -0.224108778595412 \tabularnewline
140 & 10 & 10.303389349951 & -0.303389349951004 \tabularnewline
141 & 5 & 7.9683552701553 & -2.96835527015529 \tabularnewline
142 & 11 & 7.24424973489903 & 3.75575026510097 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105003&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]10[/C][C]10.3981109080137[/C][C]-0.398110908013729[/C][/ROW]
[ROW][C]2[/C][C]6[/C][C]7.9307644073114[/C][C]-1.9307644073114[/C][/ROW]
[ROW][C]3[/C][C]13[/C][C]10.2446393548474[/C][C]2.75536064515261[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]9.69055368833448[/C][C]2.30944631166553[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]12.3877245340208[/C][C]-4.38772453402081[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]8.94023575027133[/C][C]-2.94023575027133[/C][/ROW]
[ROW][C]7[/C][C]10[/C][C]12.5675446540060[/C][C]-2.56754465400602[/C][/ROW]
[ROW][C]8[/C][C]10[/C][C]10.0492825674870[/C][C]-0.0492825674870383[/C][/ROW]
[ROW][C]9[/C][C]9[/C][C]9.2234580613084[/C][C]-0.223458061308392[/C][/ROW]
[ROW][C]10[/C][C]9[/C][C]10.2561427858389[/C][C]-1.25614278583888[/C][/ROW]
[ROW][C]11[/C][C]7[/C][C]9.0956231693256[/C][C]-2.09562316932559[/C][/ROW]
[ROW][C]12[/C][C]5[/C][C]9.00027131926342[/C][C]-4.00027131926342[/C][/ROW]
[ROW][C]13[/C][C]14[/C][C]12.4797916272559[/C][C]1.52020837274413[/C][/ROW]
[ROW][C]14[/C][C]6[/C][C]8.80274174681465[/C][C]-2.80274174681465[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]11.5769840172752[/C][C]-1.57698401727518[/C][/ROW]
[ROW][C]16[/C][C]10[/C][C]10.7243402999109[/C][C]-0.724340299910876[/C][/ROW]
[ROW][C]17[/C][C]7[/C][C]8.19431898730253[/C][C]-1.19431898730253[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]9.31879301058324[/C][C]0.681206989416764[/C][/ROW]
[ROW][C]19[/C][C]8[/C][C]9.319515426293[/C][C]-1.31951542629299[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]7.20287855468407[/C][C]-1.20287855468407[/C][/ROW]
[ROW][C]21[/C][C]10[/C][C]10.9913651602625[/C][C]-0.991365160262543[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]10.1003340226233[/C][C]1.89966597737672[/C][/ROW]
[ROW][C]23[/C][C]7[/C][C]9.27856605897553[/C][C]-2.27856605897553[/C][/ROW]
[ROW][C]24[/C][C]15[/C][C]8.73154910303879[/C][C]6.26845089696121[/C][/ROW]
[ROW][C]25[/C][C]8[/C][C]10.1061066504412[/C][C]-2.10610665044122[/C][/ROW]
[ROW][C]26[/C][C]10[/C][C]9.95111422878907[/C][C]0.0488857712109341[/C][/ROW]
[ROW][C]27[/C][C]13[/C][C]10.5473902757347[/C][C]2.45260972426531[/C][/ROW]
[ROW][C]28[/C][C]8[/C][C]7.85752427466995[/C][C]0.142475725330050[/C][/ROW]
[ROW][C]29[/C][C]11[/C][C]11.1326201089110[/C][C]-0.132620108911009[/C][/ROW]
[ROW][C]30[/C][C]7[/C][C]9.6835796832557[/C][C]-2.68357968325569[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]8.25172165280749[/C][C]0.748278347192514[/C][/ROW]
[ROW][C]32[/C][C]10[/C][C]9.56953393831114[/C][C]0.430466061688862[/C][/ROW]
[ROW][C]33[/C][C]8[/C][C]8.00479404745797[/C][C]-0.0047940474579675[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]13.3532068798352[/C][C]1.64679312016478[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]10.0967881638929[/C][C]-1.09678816389294[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]8.32709910760245[/C][C]-1.32709910760245[/C][/ROW]
[ROW][C]37[/C][C]11[/C][C]9.47665861837684[/C][C]1.52334138162316[/C][/ROW]
[ROW][C]38[/C][C]9[/C][C]7.90674553643083[/C][C]1.09325446356917[/C][/ROW]
[ROW][C]39[/C][C]8[/C][C]9.75241055995031[/C][C]-1.75241055995031[/C][/ROW]
[ROW][C]40[/C][C]8[/C][C]7.94686347625122[/C][C]0.0531365237487764[/C][/ROW]
[ROW][C]41[/C][C]12[/C][C]8.90319009623835[/C][C]3.09680990376165[/C][/ROW]
[ROW][C]42[/C][C]13[/C][C]10.2481422166660[/C][C]2.75185778333403[/C][/ROW]
[ROW][C]43[/C][C]9[/C][C]8.96473341285834[/C][C]0.0352665871416619[/C][/ROW]
[ROW][C]44[/C][C]11[/C][C]9.36958836483915[/C][C]1.63041163516085[/C][/ROW]
[ROW][C]45[/C][C]8[/C][C]8.21249800844928[/C][C]-0.212498008449276[/C][/ROW]
[ROW][C]46[/C][C]10[/C][C]10.7058538796630[/C][C]-0.70585387966297[/C][/ROW]
[ROW][C]47[/C][C]13[/C][C]12.4032130358754[/C][C]0.596786964124592[/C][/ROW]
[ROW][C]48[/C][C]12[/C][C]11.2466332735131[/C][C]0.75336672648686[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]9.60728612433318[/C][C]2.39271387566682[/C][/ROW]
[ROW][C]50[/C][C]9[/C][C]8.70624332551038[/C][C]0.293756674489619[/C][/ROW]
[ROW][C]51[/C][C]8[/C][C]10.2410357542843[/C][C]-2.2410357542843[/C][/ROW]
[ROW][C]52[/C][C]9[/C][C]8.55762631800724[/C][C]0.442373681992759[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]8.42497649328264[/C][C]3.57502350671736[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]11.7291685446556[/C][C]0.270831455344415[/C][/ROW]
[ROW][C]55[/C][C]16[/C][C]14.2656209990965[/C][C]1.73437900090345[/C][/ROW]
[ROW][C]56[/C][C]11[/C][C]8.98955211908735[/C][C]2.01044788091265[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]10.3114039347229[/C][C]2.68859606527706[/C][/ROW]
[ROW][C]58[/C][C]10[/C][C]10.6043806251253[/C][C]-0.604380625125327[/C][/ROW]
[ROW][C]59[/C][C]9[/C][C]10.4930309173501[/C][C]-1.49303091735006[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]9.53372243554468[/C][C]4.46627756445532[/C][/ROW]
[ROW][C]61[/C][C]13[/C][C]12.0238673740869[/C][C]0.976132625913069[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]10.3686770457662[/C][C]1.63132295423380[/C][/ROW]
[ROW][C]63[/C][C]9[/C][C]10.9704701946977[/C][C]-1.97047019469774[/C][/ROW]
[ROW][C]64[/C][C]9[/C][C]10.5620657528630[/C][C]-1.56206575286303[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]11.2215090481749[/C][C]-1.22150904817487[/C][/ROW]
[ROW][C]66[/C][C]8[/C][C]10.5261813542289[/C][C]-2.52618135422892[/C][/ROW]
[ROW][C]67[/C][C]9[/C][C]10.3583406162232[/C][C]-1.35834061622320[/C][/ROW]
[ROW][C]68[/C][C]9[/C][C]8.96856824902976[/C][C]0.0314317509702444[/C][/ROW]
[ROW][C]69[/C][C]11[/C][C]8.79366133164637[/C][C]2.20633866835363[/C][/ROW]
[ROW][C]70[/C][C]7[/C][C]9.66716176259284[/C][C]-2.66716176259284[/C][/ROW]
[ROW][C]71[/C][C]11[/C][C]11.5494906558691[/C][C]-0.549490655869072[/C][/ROW]
[ROW][C]72[/C][C]9[/C][C]9.05759852497023[/C][C]-0.0575985249702288[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]8.92020106403112[/C][C]2.07979893596888[/C][/ROW]
[ROW][C]74[/C][C]9[/C][C]9.5046003349402[/C][C]-0.504600334940199[/C][/ROW]
[ROW][C]75[/C][C]8[/C][C]10.2485637172436[/C][C]-2.24856371724359[/C][/ROW]
[ROW][C]76[/C][C]9[/C][C]8.13216001404625[/C][C]0.867839985953749[/C][/ROW]
[ROW][C]77[/C][C]8[/C][C]9.17537537308887[/C][C]-1.17537537308887[/C][/ROW]
[ROW][C]78[/C][C]9[/C][C]9.85183779669558[/C][C]-0.851837796695581[/C][/ROW]
[ROW][C]79[/C][C]10[/C][C]10.2374896572663[/C][C]-0.237489657266274[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]9.80237244222467[/C][C]-0.802372442224675[/C][/ROW]
[ROW][C]81[/C][C]17[/C][C]13.9385372585523[/C][C]3.06146274144766[/C][/ROW]
[ROW][C]82[/C][C]7[/C][C]9.51395394474114[/C][C]-2.51395394474114[/C][/ROW]
[ROW][C]83[/C][C]11[/C][C]11.2241087785954[/C][C]-0.224108778595412[/C][/ROW]
[ROW][C]84[/C][C]9[/C][C]9.98558539165466[/C][C]-0.985585391654661[/C][/ROW]
[ROW][C]85[/C][C]10[/C][C]9.7293345616771[/C][C]0.270665438322909[/C][/ROW]
[ROW][C]86[/C][C]11[/C][C]8.53972306578796[/C][C]2.46027693421204[/C][/ROW]
[ROW][C]87[/C][C]8[/C][C]8.21679797336594[/C][C]-0.216797973365939[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.3408963627430[/C][C]-0.340896362742957[/C][/ROW]
[ROW][C]89[/C][C]10[/C][C]10.1569392294030[/C][C]-0.156939229403048[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]8.9166794745976[/C][C]-1.91667947459759[/C][/ROW]
[ROW][C]91[/C][C]9[/C][C]8.92492753825314[/C][C]0.0750724617468596[/C][/ROW]
[ROW][C]92[/C][C]7[/C][C]8.31127672596746[/C][C]-1.31127672596746[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]10.6119121268344[/C][C]1.38808787316558[/C][/ROW]
[ROW][C]94[/C][C]8[/C][C]9.39329723101702[/C][C]-1.39329723101702[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]10.5624668971608[/C][C]2.43753310283921[/C][/ROW]
[ROW][C]96[/C][C]9[/C][C]10.8127248916724[/C][C]-1.81272489167239[/C][/ROW]
[ROW][C]97[/C][C]15[/C][C]12.7299136650588[/C][C]2.27008633494117[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]9.54154819418306[/C][C]-1.54154819418306[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]11.5706115353743[/C][C]2.42938846462566[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]13.6941418024811[/C][C]0.305858197518861[/C][/ROW]
[ROW][C]101[/C][C]9[/C][C]10.270549884361[/C][C]-1.27054988436099[/C][/ROW]
[ROW][C]102[/C][C]13[/C][C]11.4607229887744[/C][C]1.53927701122564[/C][/ROW]
[ROW][C]103[/C][C]11[/C][C]9.12743591038398[/C][C]1.87256408961602[/C][/ROW]
[ROW][C]104[/C][C]10[/C][C]11.9494520075191[/C][C]-1.94945200751907[/C][/ROW]
[ROW][C]105[/C][C]6[/C][C]10.0784477328994[/C][C]-4.07844773289936[/C][/ROW]
[ROW][C]106[/C][C]8[/C][C]8.44642226215375[/C][C]-0.446422262153754[/C][/ROW]
[ROW][C]107[/C][C]10[/C][C]11.4946303402216[/C][C]-1.49463034022156[/C][/ROW]
[ROW][C]108[/C][C]10[/C][C]7.87956511202029[/C][C]2.12043488797971[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]8.72543291550262[/C][C]1.27456708449738[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.1598856141247[/C][C]-0.159885614124670[/C][/ROW]
[ROW][C]111[/C][C]10[/C][C]9.60003572512358[/C][C]0.399964274876418[/C][/ROW]
[ROW][C]112[/C][C]9[/C][C]9.06045856008726[/C][C]-0.0604585600872634[/C][/ROW]
[ROW][C]113[/C][C]9[/C][C]7.59764563027118[/C][C]1.40235436972881[/C][/ROW]
[ROW][C]114[/C][C]11[/C][C]9.45421742633968[/C][C]1.54578257366032[/C][/ROW]
[ROW][C]115[/C][C]7[/C][C]8.09598356364117[/C][C]-1.09598356364117[/C][/ROW]
[ROW][C]116[/C][C]7[/C][C]8.90290304289432[/C][C]-1.90290304289432[/C][/ROW]
[ROW][C]117[/C][C]5[/C][C]7.9683552701553[/C][C]-2.96835527015529[/C][/ROW]
[ROW][C]118[/C][C]9[/C][C]8.97439709909747[/C][C]0.0256029009025346[/C][/ROW]
[ROW][C]119[/C][C]11[/C][C]9.95527117373667[/C][C]1.04472882626333[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]12.2735813768076[/C][C]2.72641862319243[/C][/ROW]
[ROW][C]121[/C][C]9[/C][C]8.11680001335884[/C][C]0.883199986641165[/C][/ROW]
[ROW][C]122[/C][C]9[/C][C]9.40529665852555[/C][C]-0.405296658525544[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]9.49762161910805[/C][C]-1.49762161910805[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]15.2398428922856[/C][C]-2.23984289228564[/C][/ROW]
[ROW][C]125[/C][C]10[/C][C]10.3238172510627[/C][C]-0.323817251062680[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]11.3107833727572[/C][C]1.68921662724279[/C][/ROW]
[ROW][C]127[/C][C]9[/C][C]7.53561601652311[/C][C]1.46438398347689[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]9.63582873557402[/C][C]1.36417126442598[/C][/ROW]
[ROW][C]129[/C][C]8[/C][C]10.7003556913825[/C][C]-2.70035569138245[/C][/ROW]
[ROW][C]130[/C][C]10[/C][C]9.2178972564349[/C][C]0.782102743565107[/C][/ROW]
[ROW][C]131[/C][C]9[/C][C]8.9625165628174[/C][C]0.0374834371826047[/C][/ROW]
[ROW][C]132[/C][C]8[/C][C]7.8349603794144[/C][C]0.165039620585609[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]7.85545901328822[/C][C]0.144540986711776[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]10.4362022385799[/C][C]2.5637977614201[/C][/ROW]
[ROW][C]135[/C][C]11[/C][C]10.8163593529652[/C][C]0.183640647034757[/C][/ROW]
[ROW][C]136[/C][C]8[/C][C]10.1061066504412[/C][C]-2.10610665044122[/C][/ROW]
[ROW][C]137[/C][C]12[/C][C]10.3053422149668[/C][C]1.69465778503318[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]11.8405420551234[/C][C]3.15945794487656[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]11.2241087785954[/C][C]-0.224108778595412[/C][/ROW]
[ROW][C]140[/C][C]10[/C][C]10.303389349951[/C][C]-0.303389349951004[/C][/ROW]
[ROW][C]141[/C][C]5[/C][C]7.9683552701553[/C][C]-2.96835527015529[/C][/ROW]
[ROW][C]142[/C][C]11[/C][C]7.24424973489903[/C][C]3.75575026510097[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105003&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105003&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
11010.3981109080137-0.398110908013729
267.9307644073114-1.9307644073114
31310.24463935484742.75536064515261
4129.690553688334482.30944631166553
5812.3877245340208-4.38772453402081
668.94023575027133-2.94023575027133
71012.5675446540060-2.56754465400602
81010.0492825674870-0.0492825674870383
999.2234580613084-0.223458061308392
10910.2561427858389-1.25614278583888
1179.0956231693256-2.09562316932559
1259.00027131926342-4.00027131926342
131412.47979162725591.52020837274413
1468.80274174681465-2.80274174681465
151011.5769840172752-1.57698401727518
161010.7243402999109-0.724340299910876
1778.19431898730253-1.19431898730253
18109.318793010583240.681206989416764
1989.319515426293-1.31951542629299
2067.20287855468407-1.20287855468407
211010.9913651602625-0.991365160262543
221210.10033402262331.89966597737672
2379.27856605897553-2.27856605897553
24158.731549103038796.26845089696121
25810.1061066504412-2.10610665044122
26109.951114228789070.0488857712109341
271310.54739027573472.45260972426531
2887.857524274669950.142475725330050
291111.1326201089110-0.132620108911009
3079.6835796832557-2.68357968325569
3198.251721652807490.748278347192514
32109.569533938311140.430466061688862
3388.00479404745797-0.0047940474579675
341513.35320687983521.64679312016478
35910.0967881638929-1.09678816389294
3678.32709910760245-1.32709910760245
37119.476658618376841.52334138162316
3897.906745536430831.09325446356917
3989.75241055995031-1.75241055995031
4087.946863476251220.0531365237487764
41128.903190096238353.09680990376165
421310.24814221666602.75185778333403
4398.964733412858340.0352665871416619
44119.369588364839151.63041163516085
4588.21249800844928-0.212498008449276
461010.7058538796630-0.70585387966297
471312.40321303587540.596786964124592
481211.24663327351310.75336672648686
49129.607286124333182.39271387566682
5098.706243325510380.293756674489619
51810.2410357542843-2.2410357542843
5298.557626318007240.442373681992759
53128.424976493282643.57502350671736
541211.72916854465560.270831455344415
551614.26562099909651.73437900090345
56118.989552119087352.01044788091265
571310.31140393472292.68859606527706
581010.6043806251253-0.604380625125327
59910.4930309173501-1.49303091735006
60149.533722435544684.46627756445532
611312.02386737408690.976132625913069
621210.36867704576621.63132295423380
63910.9704701946977-1.97047019469774
64910.5620657528630-1.56206575286303
651011.2215090481749-1.22150904817487
66810.5261813542289-2.52618135422892
67910.3583406162232-1.35834061622320
6898.968568249029760.0314317509702444
69118.793661331646372.20633866835363
7079.66716176259284-2.66716176259284
711111.5494906558691-0.549490655869072
7299.05759852497023-0.0575985249702288
73118.920201064031122.07979893596888
7499.5046003349402-0.504600334940199
75810.2485637172436-2.24856371724359
7698.132160014046250.867839985953749
7789.17537537308887-1.17537537308887
7899.85183779669558-0.851837796695581
791010.2374896572663-0.237489657266274
8099.80237244222467-0.802372442224675
811713.93853725855233.06146274144766
8279.51395394474114-2.51395394474114
831111.2241087785954-0.224108778595412
8499.98558539165466-0.985585391654661
85109.72933456167710.270665438322909
86118.539723065787962.46027693421204
8788.21679797336594-0.216797973365939
881212.3408963627430-0.340896362742957
891010.1569392294030-0.156939229403048
9078.9166794745976-1.91667947459759
9198.924927538253140.0750724617468596
9278.31127672596746-1.31127672596746
931210.61191212683441.38808787316558
9489.39329723101702-1.39329723101702
951310.56246689716082.43753310283921
96910.8127248916724-1.81272489167239
971512.72991366505882.27008633494117
9889.54154819418306-1.54154819418306
991411.57061153537432.42938846462566
1001413.69414180248110.305858197518861
101910.270549884361-1.27054988436099
1021311.46072298877441.53927701122564
103119.127435910383981.87256408961602
1041011.9494520075191-1.94945200751907
105610.0784477328994-4.07844773289936
10688.44642226215375-0.446422262153754
1071011.4946303402216-1.49463034022156
108107.879565112020292.12043488797971
109108.725432915502621.27456708449738
1101212.1598856141247-0.159885614124670
111109.600035725123580.399964274876418
11299.06045856008726-0.0604585600872634
11397.597645630271181.40235436972881
114119.454217426339681.54578257366032
11578.09598356364117-1.09598356364117
11678.90290304289432-1.90290304289432
11757.9683552701553-2.96835527015529
11898.974397099097470.0256029009025346
119119.955271173736671.04472882626333
1201512.27358137680762.72641862319243
12198.116800013358840.883199986641165
12299.40529665852555-0.405296658525544
12389.49762161910805-1.49762161910805
1241315.2398428922856-2.23984289228564
1251010.3238172510627-0.323817251062680
1261311.31078337275721.68921662724279
12797.535616016523111.46438398347689
128119.635828735574021.36417126442598
129810.7003556913825-2.70035569138245
130109.21789725643490.782102743565107
13198.96251656281740.0374834371826047
13287.83496037941440.165039620585609
13387.855459013288220.144540986711776
1341310.43620223857992.5637977614201
1351110.81635935296520.183640647034757
136810.1061066504412-2.10610665044122
1371210.30534221496681.69465778503318
1381511.84054205512343.15945794487656
1391111.2241087785954-0.224108778595412
1401010.303389349951-0.303389349951004
14157.9683552701553-2.96835527015529
142117.244249734899033.75575026510097






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.929714778750610.1405704424987790.0702852212493896
160.9236247984547550.1527504030904900.0763752015452451
170.887440753265730.225118493468540.11255924673427
180.8256548986386060.3486902027227880.174345101361394
190.8058548028633170.3882903942733660.194145197136683
200.7822110869546850.4355778260906310.217788913045315
210.7113687654385260.5772624691229470.288631234561474
220.8197115130075940.3605769739848110.180288486992406
230.7917470463480540.4165059073038910.208252953651946
240.9506863787440250.09862724251194920.0493136212559746
250.9406265420896820.1187469158206370.0593734579103184
260.914123700222150.1717525995557010.0858762997778506
270.9579992653816670.08400146923666660.0420007346183333
280.938811658124730.1223766837505400.06118834187527
290.9153508571878830.1692982856242330.0846491428121165
300.969994593418240.0600108131635190.0300054065817595
310.9691398625867620.06172027482647550.0308601374132378
320.959409014828960.08118197034207890.0405909851710394
330.9440036400174490.1119927199651030.0559963599825513
340.9500667338187390.09986653236252260.0499332661812613
350.9342464550006510.1315070899986980.0657535449993488
360.9240982063193730.1518035873612540.0759017936806268
370.9120400133900850.1759199732198300.0879599866099148
380.8897596716829520.2204806566340970.110240328317048
390.8714245713320920.2571508573358160.128575428667908
400.8544228647134950.291154270573010.145577135286505
410.8730820473052220.2538359053895560.126917952694778
420.8790023371002780.2419953257994450.120997662899722
430.8543948395192120.2912103209615760.145605160480788
440.8329467970330820.3341064059338370.167053202966918
450.804032811940840.391934376118320.19596718805916
460.7644239859694240.4711520280611510.235576014030576
470.771789784733090.4564204305338220.228210215266911
480.7329223206480860.5341553587038280.267077679351914
490.7620220371735520.4759559256528950.237977962826448
500.7274216808296430.5451566383407130.272578319170357
510.7320258700333910.5359482599332180.267974129966609
520.6910883798112130.6178232403775740.308911620188787
530.7838243334501390.4323513330997220.216175666549861
540.7455374436430840.5089251127138320.254462556356916
550.777385785697180.4452284286056400.222614214302820
560.8130467700740560.3739064598518870.186953229925944
570.8416784355786540.3166431288426930.158321564421347
580.808771017992770.3824579640144610.191228982007231
590.79630471255910.4073905748817990.203695287440900
600.9537713843472920.09245723130541540.0462286156527077
610.9438463942009540.1123072115980910.0561536057990457
620.9391819441502730.1216361116994540.060818055849727
630.9412855065876880.1174289868246250.0587144934123124
640.9362852530105110.1274294939789780.0637147469894889
650.935714227278020.1285715454439610.0642857727219803
660.942095394212830.1158092115743400.0579046057871702
670.9331271627160670.1337456745678650.0668728372839326
680.9150587171059690.1698825657880620.084941282894031
690.9241779903442990.1516440193114020.0758220096557012
700.93950115187740.1209976962452010.0604988481226003
710.924712278419320.1505754431613590.0752877215806795
720.9060161492729720.1879677014540560.0939838507270279
730.910513010183740.1789739796325200.0894869898162599
740.8892128975630230.2215742048739540.110787102436977
750.8915001216584630.2169997566830750.108499878341537
760.8800424346917250.2399151306165490.119957565308275
770.8678774854269010.2642450291461970.132122514573099
780.8483606144024930.3032787711950150.151639385597507
790.8155397453836730.3689205092326530.184460254616326
800.7868156087895140.4263687824209730.213184391210486
810.8380148027242510.3239703945514970.161985197275749
820.8553501761119010.2892996477761970.144649823888099
830.8257785993298180.3484428013403630.174221400670182
840.8139855186024510.3720289627950980.186014481397549
850.777849522587660.4443009548246800.222150477412340
860.8651776035989350.269644792802130.134822396401065
870.8415522622395660.3168954755208670.158447737760434
880.8067508360966940.3864983278066120.193249163903306
890.7689351343857050.462129731228590.231064865614295
900.7883252606266420.4233494787467160.211674739373358
910.7507269146220840.4985461707558310.249273085377916
920.7526918035306920.4946163929386160.247308196469308
930.7522509694490420.4954980611019150.247749030550958
940.7168107233453910.5663785533092170.283189276654609
950.7412922766781790.5174154466436430.258707723321821
960.7156346539641310.5687306920717370.284365346035869
970.7164784026920040.5670431946159910.283521597307996
980.6801932394970660.6396135210058690.319806760502934
990.6621470686808940.6757058626382120.337852931319106
1000.6660443106146490.6679113787707030.333955689385351
1010.6755642447600690.6488715104798620.324435755239931
1020.7334386189650270.5331227620699460.266561381034973
1030.7276811436483520.5446377127032960.272318856351648
1040.7215663058941130.5568673882117750.278433694105887
1050.8353398384564050.3293203230871890.164660161543595
1060.7960052752126150.4079894495747700.203994724787385
1070.817297460487520.365405079024960.18270253951248
1080.8110349168830640.3779301662338720.188965083116936
1090.7778660373115340.4442679253769330.222133962688466
1100.8305573054852850.3388853890294310.169442694514715
1110.7870466692846470.4259066614307060.212953330715353
1120.7443794803654270.5112410392691470.255620519634573
1130.6908950467749840.6182099064500320.309104953225016
1140.6589285424053520.6821429151892950.341071457594648
1150.6200226769244290.7599546461511420.379977323075571
1160.5568284058925570.8863431882148850.443171594107443
1170.5870780015009140.8258439969981710.412921998499086
1180.505549975802180.988900048395640.49445002419782
1190.4201522018927290.8403044037854570.579847798107271
1200.4092490325501560.8184980651003120.590750967449844
1210.3313635999997210.6627271999994410.66863640000028
1220.2456401763159160.4912803526318310.754359823684084
1230.1734556476448810.3469112952897630.826544352355119
1240.2833859227774390.5667718455548780.716614077222561
1250.3047758632227500.6095517264454990.69522413677725
1260.2321584802093270.4643169604186540.767841519790673
1270.2120775140855220.4241550281710450.787922485914478

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.92971477875061 & 0.140570442498779 & 0.0702852212493896 \tabularnewline
16 & 0.923624798454755 & 0.152750403090490 & 0.0763752015452451 \tabularnewline
17 & 0.88744075326573 & 0.22511849346854 & 0.11255924673427 \tabularnewline
18 & 0.825654898638606 & 0.348690202722788 & 0.174345101361394 \tabularnewline
19 & 0.805854802863317 & 0.388290394273366 & 0.194145197136683 \tabularnewline
20 & 0.782211086954685 & 0.435577826090631 & 0.217788913045315 \tabularnewline
21 & 0.711368765438526 & 0.577262469122947 & 0.288631234561474 \tabularnewline
22 & 0.819711513007594 & 0.360576973984811 & 0.180288486992406 \tabularnewline
23 & 0.791747046348054 & 0.416505907303891 & 0.208252953651946 \tabularnewline
24 & 0.950686378744025 & 0.0986272425119492 & 0.0493136212559746 \tabularnewline
25 & 0.940626542089682 & 0.118746915820637 & 0.0593734579103184 \tabularnewline
26 & 0.91412370022215 & 0.171752599555701 & 0.0858762997778506 \tabularnewline
27 & 0.957999265381667 & 0.0840014692366666 & 0.0420007346183333 \tabularnewline
28 & 0.93881165812473 & 0.122376683750540 & 0.06118834187527 \tabularnewline
29 & 0.915350857187883 & 0.169298285624233 & 0.0846491428121165 \tabularnewline
30 & 0.96999459341824 & 0.060010813163519 & 0.0300054065817595 \tabularnewline
31 & 0.969139862586762 & 0.0617202748264755 & 0.0308601374132378 \tabularnewline
32 & 0.95940901482896 & 0.0811819703420789 & 0.0405909851710394 \tabularnewline
33 & 0.944003640017449 & 0.111992719965103 & 0.0559963599825513 \tabularnewline
34 & 0.950066733818739 & 0.0998665323625226 & 0.0499332661812613 \tabularnewline
35 & 0.934246455000651 & 0.131507089998698 & 0.0657535449993488 \tabularnewline
36 & 0.924098206319373 & 0.151803587361254 & 0.0759017936806268 \tabularnewline
37 & 0.912040013390085 & 0.175919973219830 & 0.0879599866099148 \tabularnewline
38 & 0.889759671682952 & 0.220480656634097 & 0.110240328317048 \tabularnewline
39 & 0.871424571332092 & 0.257150857335816 & 0.128575428667908 \tabularnewline
40 & 0.854422864713495 & 0.29115427057301 & 0.145577135286505 \tabularnewline
41 & 0.873082047305222 & 0.253835905389556 & 0.126917952694778 \tabularnewline
42 & 0.879002337100278 & 0.241995325799445 & 0.120997662899722 \tabularnewline
43 & 0.854394839519212 & 0.291210320961576 & 0.145605160480788 \tabularnewline
44 & 0.832946797033082 & 0.334106405933837 & 0.167053202966918 \tabularnewline
45 & 0.80403281194084 & 0.39193437611832 & 0.19596718805916 \tabularnewline
46 & 0.764423985969424 & 0.471152028061151 & 0.235576014030576 \tabularnewline
47 & 0.77178978473309 & 0.456420430533822 & 0.228210215266911 \tabularnewline
48 & 0.732922320648086 & 0.534155358703828 & 0.267077679351914 \tabularnewline
49 & 0.762022037173552 & 0.475955925652895 & 0.237977962826448 \tabularnewline
50 & 0.727421680829643 & 0.545156638340713 & 0.272578319170357 \tabularnewline
51 & 0.732025870033391 & 0.535948259933218 & 0.267974129966609 \tabularnewline
52 & 0.691088379811213 & 0.617823240377574 & 0.308911620188787 \tabularnewline
53 & 0.783824333450139 & 0.432351333099722 & 0.216175666549861 \tabularnewline
54 & 0.745537443643084 & 0.508925112713832 & 0.254462556356916 \tabularnewline
55 & 0.77738578569718 & 0.445228428605640 & 0.222614214302820 \tabularnewline
56 & 0.813046770074056 & 0.373906459851887 & 0.186953229925944 \tabularnewline
57 & 0.841678435578654 & 0.316643128842693 & 0.158321564421347 \tabularnewline
58 & 0.80877101799277 & 0.382457964014461 & 0.191228982007231 \tabularnewline
59 & 0.7963047125591 & 0.407390574881799 & 0.203695287440900 \tabularnewline
60 & 0.953771384347292 & 0.0924572313054154 & 0.0462286156527077 \tabularnewline
61 & 0.943846394200954 & 0.112307211598091 & 0.0561536057990457 \tabularnewline
62 & 0.939181944150273 & 0.121636111699454 & 0.060818055849727 \tabularnewline
63 & 0.941285506587688 & 0.117428986824625 & 0.0587144934123124 \tabularnewline
64 & 0.936285253010511 & 0.127429493978978 & 0.0637147469894889 \tabularnewline
65 & 0.93571422727802 & 0.128571545443961 & 0.0642857727219803 \tabularnewline
66 & 0.94209539421283 & 0.115809211574340 & 0.0579046057871702 \tabularnewline
67 & 0.933127162716067 & 0.133745674567865 & 0.0668728372839326 \tabularnewline
68 & 0.915058717105969 & 0.169882565788062 & 0.084941282894031 \tabularnewline
69 & 0.924177990344299 & 0.151644019311402 & 0.0758220096557012 \tabularnewline
70 & 0.9395011518774 & 0.120997696245201 & 0.0604988481226003 \tabularnewline
71 & 0.92471227841932 & 0.150575443161359 & 0.0752877215806795 \tabularnewline
72 & 0.906016149272972 & 0.187967701454056 & 0.0939838507270279 \tabularnewline
73 & 0.91051301018374 & 0.178973979632520 & 0.0894869898162599 \tabularnewline
74 & 0.889212897563023 & 0.221574204873954 & 0.110787102436977 \tabularnewline
75 & 0.891500121658463 & 0.216999756683075 & 0.108499878341537 \tabularnewline
76 & 0.880042434691725 & 0.239915130616549 & 0.119957565308275 \tabularnewline
77 & 0.867877485426901 & 0.264245029146197 & 0.132122514573099 \tabularnewline
78 & 0.848360614402493 & 0.303278771195015 & 0.151639385597507 \tabularnewline
79 & 0.815539745383673 & 0.368920509232653 & 0.184460254616326 \tabularnewline
80 & 0.786815608789514 & 0.426368782420973 & 0.213184391210486 \tabularnewline
81 & 0.838014802724251 & 0.323970394551497 & 0.161985197275749 \tabularnewline
82 & 0.855350176111901 & 0.289299647776197 & 0.144649823888099 \tabularnewline
83 & 0.825778599329818 & 0.348442801340363 & 0.174221400670182 \tabularnewline
84 & 0.813985518602451 & 0.372028962795098 & 0.186014481397549 \tabularnewline
85 & 0.77784952258766 & 0.444300954824680 & 0.222150477412340 \tabularnewline
86 & 0.865177603598935 & 0.26964479280213 & 0.134822396401065 \tabularnewline
87 & 0.841552262239566 & 0.316895475520867 & 0.158447737760434 \tabularnewline
88 & 0.806750836096694 & 0.386498327806612 & 0.193249163903306 \tabularnewline
89 & 0.768935134385705 & 0.46212973122859 & 0.231064865614295 \tabularnewline
90 & 0.788325260626642 & 0.423349478746716 & 0.211674739373358 \tabularnewline
91 & 0.750726914622084 & 0.498546170755831 & 0.249273085377916 \tabularnewline
92 & 0.752691803530692 & 0.494616392938616 & 0.247308196469308 \tabularnewline
93 & 0.752250969449042 & 0.495498061101915 & 0.247749030550958 \tabularnewline
94 & 0.716810723345391 & 0.566378553309217 & 0.283189276654609 \tabularnewline
95 & 0.741292276678179 & 0.517415446643643 & 0.258707723321821 \tabularnewline
96 & 0.715634653964131 & 0.568730692071737 & 0.284365346035869 \tabularnewline
97 & 0.716478402692004 & 0.567043194615991 & 0.283521597307996 \tabularnewline
98 & 0.680193239497066 & 0.639613521005869 & 0.319806760502934 \tabularnewline
99 & 0.662147068680894 & 0.675705862638212 & 0.337852931319106 \tabularnewline
100 & 0.666044310614649 & 0.667911378770703 & 0.333955689385351 \tabularnewline
101 & 0.675564244760069 & 0.648871510479862 & 0.324435755239931 \tabularnewline
102 & 0.733438618965027 & 0.533122762069946 & 0.266561381034973 \tabularnewline
103 & 0.727681143648352 & 0.544637712703296 & 0.272318856351648 \tabularnewline
104 & 0.721566305894113 & 0.556867388211775 & 0.278433694105887 \tabularnewline
105 & 0.835339838456405 & 0.329320323087189 & 0.164660161543595 \tabularnewline
106 & 0.796005275212615 & 0.407989449574770 & 0.203994724787385 \tabularnewline
107 & 0.81729746048752 & 0.36540507902496 & 0.18270253951248 \tabularnewline
108 & 0.811034916883064 & 0.377930166233872 & 0.188965083116936 \tabularnewline
109 & 0.777866037311534 & 0.444267925376933 & 0.222133962688466 \tabularnewline
110 & 0.830557305485285 & 0.338885389029431 & 0.169442694514715 \tabularnewline
111 & 0.787046669284647 & 0.425906661430706 & 0.212953330715353 \tabularnewline
112 & 0.744379480365427 & 0.511241039269147 & 0.255620519634573 \tabularnewline
113 & 0.690895046774984 & 0.618209906450032 & 0.309104953225016 \tabularnewline
114 & 0.658928542405352 & 0.682142915189295 & 0.341071457594648 \tabularnewline
115 & 0.620022676924429 & 0.759954646151142 & 0.379977323075571 \tabularnewline
116 & 0.556828405892557 & 0.886343188214885 & 0.443171594107443 \tabularnewline
117 & 0.587078001500914 & 0.825843996998171 & 0.412921998499086 \tabularnewline
118 & 0.50554997580218 & 0.98890004839564 & 0.49445002419782 \tabularnewline
119 & 0.420152201892729 & 0.840304403785457 & 0.579847798107271 \tabularnewline
120 & 0.409249032550156 & 0.818498065100312 & 0.590750967449844 \tabularnewline
121 & 0.331363599999721 & 0.662727199999441 & 0.66863640000028 \tabularnewline
122 & 0.245640176315916 & 0.491280352631831 & 0.754359823684084 \tabularnewline
123 & 0.173455647644881 & 0.346911295289763 & 0.826544352355119 \tabularnewline
124 & 0.283385922777439 & 0.566771845554878 & 0.716614077222561 \tabularnewline
125 & 0.304775863222750 & 0.609551726445499 & 0.69522413677725 \tabularnewline
126 & 0.232158480209327 & 0.464316960418654 & 0.767841519790673 \tabularnewline
127 & 0.212077514085522 & 0.424155028171045 & 0.787922485914478 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105003&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]15[/C][C]0.92971477875061[/C][C]0.140570442498779[/C][C]0.0702852212493896[/C][/ROW]
[ROW][C]16[/C][C]0.923624798454755[/C][C]0.152750403090490[/C][C]0.0763752015452451[/C][/ROW]
[ROW][C]17[/C][C]0.88744075326573[/C][C]0.22511849346854[/C][C]0.11255924673427[/C][/ROW]
[ROW][C]18[/C][C]0.825654898638606[/C][C]0.348690202722788[/C][C]0.174345101361394[/C][/ROW]
[ROW][C]19[/C][C]0.805854802863317[/C][C]0.388290394273366[/C][C]0.194145197136683[/C][/ROW]
[ROW][C]20[/C][C]0.782211086954685[/C][C]0.435577826090631[/C][C]0.217788913045315[/C][/ROW]
[ROW][C]21[/C][C]0.711368765438526[/C][C]0.577262469122947[/C][C]0.288631234561474[/C][/ROW]
[ROW][C]22[/C][C]0.819711513007594[/C][C]0.360576973984811[/C][C]0.180288486992406[/C][/ROW]
[ROW][C]23[/C][C]0.791747046348054[/C][C]0.416505907303891[/C][C]0.208252953651946[/C][/ROW]
[ROW][C]24[/C][C]0.950686378744025[/C][C]0.0986272425119492[/C][C]0.0493136212559746[/C][/ROW]
[ROW][C]25[/C][C]0.940626542089682[/C][C]0.118746915820637[/C][C]0.0593734579103184[/C][/ROW]
[ROW][C]26[/C][C]0.91412370022215[/C][C]0.171752599555701[/C][C]0.0858762997778506[/C][/ROW]
[ROW][C]27[/C][C]0.957999265381667[/C][C]0.0840014692366666[/C][C]0.0420007346183333[/C][/ROW]
[ROW][C]28[/C][C]0.93881165812473[/C][C]0.122376683750540[/C][C]0.06118834187527[/C][/ROW]
[ROW][C]29[/C][C]0.915350857187883[/C][C]0.169298285624233[/C][C]0.0846491428121165[/C][/ROW]
[ROW][C]30[/C][C]0.96999459341824[/C][C]0.060010813163519[/C][C]0.0300054065817595[/C][/ROW]
[ROW][C]31[/C][C]0.969139862586762[/C][C]0.0617202748264755[/C][C]0.0308601374132378[/C][/ROW]
[ROW][C]32[/C][C]0.95940901482896[/C][C]0.0811819703420789[/C][C]0.0405909851710394[/C][/ROW]
[ROW][C]33[/C][C]0.944003640017449[/C][C]0.111992719965103[/C][C]0.0559963599825513[/C][/ROW]
[ROW][C]34[/C][C]0.950066733818739[/C][C]0.0998665323625226[/C][C]0.0499332661812613[/C][/ROW]
[ROW][C]35[/C][C]0.934246455000651[/C][C]0.131507089998698[/C][C]0.0657535449993488[/C][/ROW]
[ROW][C]36[/C][C]0.924098206319373[/C][C]0.151803587361254[/C][C]0.0759017936806268[/C][/ROW]
[ROW][C]37[/C][C]0.912040013390085[/C][C]0.175919973219830[/C][C]0.0879599866099148[/C][/ROW]
[ROW][C]38[/C][C]0.889759671682952[/C][C]0.220480656634097[/C][C]0.110240328317048[/C][/ROW]
[ROW][C]39[/C][C]0.871424571332092[/C][C]0.257150857335816[/C][C]0.128575428667908[/C][/ROW]
[ROW][C]40[/C][C]0.854422864713495[/C][C]0.29115427057301[/C][C]0.145577135286505[/C][/ROW]
[ROW][C]41[/C][C]0.873082047305222[/C][C]0.253835905389556[/C][C]0.126917952694778[/C][/ROW]
[ROW][C]42[/C][C]0.879002337100278[/C][C]0.241995325799445[/C][C]0.120997662899722[/C][/ROW]
[ROW][C]43[/C][C]0.854394839519212[/C][C]0.291210320961576[/C][C]0.145605160480788[/C][/ROW]
[ROW][C]44[/C][C]0.832946797033082[/C][C]0.334106405933837[/C][C]0.167053202966918[/C][/ROW]
[ROW][C]45[/C][C]0.80403281194084[/C][C]0.39193437611832[/C][C]0.19596718805916[/C][/ROW]
[ROW][C]46[/C][C]0.764423985969424[/C][C]0.471152028061151[/C][C]0.235576014030576[/C][/ROW]
[ROW][C]47[/C][C]0.77178978473309[/C][C]0.456420430533822[/C][C]0.228210215266911[/C][/ROW]
[ROW][C]48[/C][C]0.732922320648086[/C][C]0.534155358703828[/C][C]0.267077679351914[/C][/ROW]
[ROW][C]49[/C][C]0.762022037173552[/C][C]0.475955925652895[/C][C]0.237977962826448[/C][/ROW]
[ROW][C]50[/C][C]0.727421680829643[/C][C]0.545156638340713[/C][C]0.272578319170357[/C][/ROW]
[ROW][C]51[/C][C]0.732025870033391[/C][C]0.535948259933218[/C][C]0.267974129966609[/C][/ROW]
[ROW][C]52[/C][C]0.691088379811213[/C][C]0.617823240377574[/C][C]0.308911620188787[/C][/ROW]
[ROW][C]53[/C][C]0.783824333450139[/C][C]0.432351333099722[/C][C]0.216175666549861[/C][/ROW]
[ROW][C]54[/C][C]0.745537443643084[/C][C]0.508925112713832[/C][C]0.254462556356916[/C][/ROW]
[ROW][C]55[/C][C]0.77738578569718[/C][C]0.445228428605640[/C][C]0.222614214302820[/C][/ROW]
[ROW][C]56[/C][C]0.813046770074056[/C][C]0.373906459851887[/C][C]0.186953229925944[/C][/ROW]
[ROW][C]57[/C][C]0.841678435578654[/C][C]0.316643128842693[/C][C]0.158321564421347[/C][/ROW]
[ROW][C]58[/C][C]0.80877101799277[/C][C]0.382457964014461[/C][C]0.191228982007231[/C][/ROW]
[ROW][C]59[/C][C]0.7963047125591[/C][C]0.407390574881799[/C][C]0.203695287440900[/C][/ROW]
[ROW][C]60[/C][C]0.953771384347292[/C][C]0.0924572313054154[/C][C]0.0462286156527077[/C][/ROW]
[ROW][C]61[/C][C]0.943846394200954[/C][C]0.112307211598091[/C][C]0.0561536057990457[/C][/ROW]
[ROW][C]62[/C][C]0.939181944150273[/C][C]0.121636111699454[/C][C]0.060818055849727[/C][/ROW]
[ROW][C]63[/C][C]0.941285506587688[/C][C]0.117428986824625[/C][C]0.0587144934123124[/C][/ROW]
[ROW][C]64[/C][C]0.936285253010511[/C][C]0.127429493978978[/C][C]0.0637147469894889[/C][/ROW]
[ROW][C]65[/C][C]0.93571422727802[/C][C]0.128571545443961[/C][C]0.0642857727219803[/C][/ROW]
[ROW][C]66[/C][C]0.94209539421283[/C][C]0.115809211574340[/C][C]0.0579046057871702[/C][/ROW]
[ROW][C]67[/C][C]0.933127162716067[/C][C]0.133745674567865[/C][C]0.0668728372839326[/C][/ROW]
[ROW][C]68[/C][C]0.915058717105969[/C][C]0.169882565788062[/C][C]0.084941282894031[/C][/ROW]
[ROW][C]69[/C][C]0.924177990344299[/C][C]0.151644019311402[/C][C]0.0758220096557012[/C][/ROW]
[ROW][C]70[/C][C]0.9395011518774[/C][C]0.120997696245201[/C][C]0.0604988481226003[/C][/ROW]
[ROW][C]71[/C][C]0.92471227841932[/C][C]0.150575443161359[/C][C]0.0752877215806795[/C][/ROW]
[ROW][C]72[/C][C]0.906016149272972[/C][C]0.187967701454056[/C][C]0.0939838507270279[/C][/ROW]
[ROW][C]73[/C][C]0.91051301018374[/C][C]0.178973979632520[/C][C]0.0894869898162599[/C][/ROW]
[ROW][C]74[/C][C]0.889212897563023[/C][C]0.221574204873954[/C][C]0.110787102436977[/C][/ROW]
[ROW][C]75[/C][C]0.891500121658463[/C][C]0.216999756683075[/C][C]0.108499878341537[/C][/ROW]
[ROW][C]76[/C][C]0.880042434691725[/C][C]0.239915130616549[/C][C]0.119957565308275[/C][/ROW]
[ROW][C]77[/C][C]0.867877485426901[/C][C]0.264245029146197[/C][C]0.132122514573099[/C][/ROW]
[ROW][C]78[/C][C]0.848360614402493[/C][C]0.303278771195015[/C][C]0.151639385597507[/C][/ROW]
[ROW][C]79[/C][C]0.815539745383673[/C][C]0.368920509232653[/C][C]0.184460254616326[/C][/ROW]
[ROW][C]80[/C][C]0.786815608789514[/C][C]0.426368782420973[/C][C]0.213184391210486[/C][/ROW]
[ROW][C]81[/C][C]0.838014802724251[/C][C]0.323970394551497[/C][C]0.161985197275749[/C][/ROW]
[ROW][C]82[/C][C]0.855350176111901[/C][C]0.289299647776197[/C][C]0.144649823888099[/C][/ROW]
[ROW][C]83[/C][C]0.825778599329818[/C][C]0.348442801340363[/C][C]0.174221400670182[/C][/ROW]
[ROW][C]84[/C][C]0.813985518602451[/C][C]0.372028962795098[/C][C]0.186014481397549[/C][/ROW]
[ROW][C]85[/C][C]0.77784952258766[/C][C]0.444300954824680[/C][C]0.222150477412340[/C][/ROW]
[ROW][C]86[/C][C]0.865177603598935[/C][C]0.26964479280213[/C][C]0.134822396401065[/C][/ROW]
[ROW][C]87[/C][C]0.841552262239566[/C][C]0.316895475520867[/C][C]0.158447737760434[/C][/ROW]
[ROW][C]88[/C][C]0.806750836096694[/C][C]0.386498327806612[/C][C]0.193249163903306[/C][/ROW]
[ROW][C]89[/C][C]0.768935134385705[/C][C]0.46212973122859[/C][C]0.231064865614295[/C][/ROW]
[ROW][C]90[/C][C]0.788325260626642[/C][C]0.423349478746716[/C][C]0.211674739373358[/C][/ROW]
[ROW][C]91[/C][C]0.750726914622084[/C][C]0.498546170755831[/C][C]0.249273085377916[/C][/ROW]
[ROW][C]92[/C][C]0.752691803530692[/C][C]0.494616392938616[/C][C]0.247308196469308[/C][/ROW]
[ROW][C]93[/C][C]0.752250969449042[/C][C]0.495498061101915[/C][C]0.247749030550958[/C][/ROW]
[ROW][C]94[/C][C]0.716810723345391[/C][C]0.566378553309217[/C][C]0.283189276654609[/C][/ROW]
[ROW][C]95[/C][C]0.741292276678179[/C][C]0.517415446643643[/C][C]0.258707723321821[/C][/ROW]
[ROW][C]96[/C][C]0.715634653964131[/C][C]0.568730692071737[/C][C]0.284365346035869[/C][/ROW]
[ROW][C]97[/C][C]0.716478402692004[/C][C]0.567043194615991[/C][C]0.283521597307996[/C][/ROW]
[ROW][C]98[/C][C]0.680193239497066[/C][C]0.639613521005869[/C][C]0.319806760502934[/C][/ROW]
[ROW][C]99[/C][C]0.662147068680894[/C][C]0.675705862638212[/C][C]0.337852931319106[/C][/ROW]
[ROW][C]100[/C][C]0.666044310614649[/C][C]0.667911378770703[/C][C]0.333955689385351[/C][/ROW]
[ROW][C]101[/C][C]0.675564244760069[/C][C]0.648871510479862[/C][C]0.324435755239931[/C][/ROW]
[ROW][C]102[/C][C]0.733438618965027[/C][C]0.533122762069946[/C][C]0.266561381034973[/C][/ROW]
[ROW][C]103[/C][C]0.727681143648352[/C][C]0.544637712703296[/C][C]0.272318856351648[/C][/ROW]
[ROW][C]104[/C][C]0.721566305894113[/C][C]0.556867388211775[/C][C]0.278433694105887[/C][/ROW]
[ROW][C]105[/C][C]0.835339838456405[/C][C]0.329320323087189[/C][C]0.164660161543595[/C][/ROW]
[ROW][C]106[/C][C]0.796005275212615[/C][C]0.407989449574770[/C][C]0.203994724787385[/C][/ROW]
[ROW][C]107[/C][C]0.81729746048752[/C][C]0.36540507902496[/C][C]0.18270253951248[/C][/ROW]
[ROW][C]108[/C][C]0.811034916883064[/C][C]0.377930166233872[/C][C]0.188965083116936[/C][/ROW]
[ROW][C]109[/C][C]0.777866037311534[/C][C]0.444267925376933[/C][C]0.222133962688466[/C][/ROW]
[ROW][C]110[/C][C]0.830557305485285[/C][C]0.338885389029431[/C][C]0.169442694514715[/C][/ROW]
[ROW][C]111[/C][C]0.787046669284647[/C][C]0.425906661430706[/C][C]0.212953330715353[/C][/ROW]
[ROW][C]112[/C][C]0.744379480365427[/C][C]0.511241039269147[/C][C]0.255620519634573[/C][/ROW]
[ROW][C]113[/C][C]0.690895046774984[/C][C]0.618209906450032[/C][C]0.309104953225016[/C][/ROW]
[ROW][C]114[/C][C]0.658928542405352[/C][C]0.682142915189295[/C][C]0.341071457594648[/C][/ROW]
[ROW][C]115[/C][C]0.620022676924429[/C][C]0.759954646151142[/C][C]0.379977323075571[/C][/ROW]
[ROW][C]116[/C][C]0.556828405892557[/C][C]0.886343188214885[/C][C]0.443171594107443[/C][/ROW]
[ROW][C]117[/C][C]0.587078001500914[/C][C]0.825843996998171[/C][C]0.412921998499086[/C][/ROW]
[ROW][C]118[/C][C]0.50554997580218[/C][C]0.98890004839564[/C][C]0.49445002419782[/C][/ROW]
[ROW][C]119[/C][C]0.420152201892729[/C][C]0.840304403785457[/C][C]0.579847798107271[/C][/ROW]
[ROW][C]120[/C][C]0.409249032550156[/C][C]0.818498065100312[/C][C]0.590750967449844[/C][/ROW]
[ROW][C]121[/C][C]0.331363599999721[/C][C]0.662727199999441[/C][C]0.66863640000028[/C][/ROW]
[ROW][C]122[/C][C]0.245640176315916[/C][C]0.491280352631831[/C][C]0.754359823684084[/C][/ROW]
[ROW][C]123[/C][C]0.173455647644881[/C][C]0.346911295289763[/C][C]0.826544352355119[/C][/ROW]
[ROW][C]124[/C][C]0.283385922777439[/C][C]0.566771845554878[/C][C]0.716614077222561[/C][/ROW]
[ROW][C]125[/C][C]0.304775863222750[/C][C]0.609551726445499[/C][C]0.69522413677725[/C][/ROW]
[ROW][C]126[/C][C]0.232158480209327[/C][C]0.464316960418654[/C][C]0.767841519790673[/C][/ROW]
[ROW][C]127[/C][C]0.212077514085522[/C][C]0.424155028171045[/C][C]0.787922485914478[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105003&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105003&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
150.929714778750610.1405704424987790.0702852212493896
160.9236247984547550.1527504030904900.0763752015452451
170.887440753265730.225118493468540.11255924673427
180.8256548986386060.3486902027227880.174345101361394
190.8058548028633170.3882903942733660.194145197136683
200.7822110869546850.4355778260906310.217788913045315
210.7113687654385260.5772624691229470.288631234561474
220.8197115130075940.3605769739848110.180288486992406
230.7917470463480540.4165059073038910.208252953651946
240.9506863787440250.09862724251194920.0493136212559746
250.9406265420896820.1187469158206370.0593734579103184
260.914123700222150.1717525995557010.0858762997778506
270.9579992653816670.08400146923666660.0420007346183333
280.938811658124730.1223766837505400.06118834187527
290.9153508571878830.1692982856242330.0846491428121165
300.969994593418240.0600108131635190.0300054065817595
310.9691398625867620.06172027482647550.0308601374132378
320.959409014828960.08118197034207890.0405909851710394
330.9440036400174490.1119927199651030.0559963599825513
340.9500667338187390.09986653236252260.0499332661812613
350.9342464550006510.1315070899986980.0657535449993488
360.9240982063193730.1518035873612540.0759017936806268
370.9120400133900850.1759199732198300.0879599866099148
380.8897596716829520.2204806566340970.110240328317048
390.8714245713320920.2571508573358160.128575428667908
400.8544228647134950.291154270573010.145577135286505
410.8730820473052220.2538359053895560.126917952694778
420.8790023371002780.2419953257994450.120997662899722
430.8543948395192120.2912103209615760.145605160480788
440.8329467970330820.3341064059338370.167053202966918
450.804032811940840.391934376118320.19596718805916
460.7644239859694240.4711520280611510.235576014030576
470.771789784733090.4564204305338220.228210215266911
480.7329223206480860.5341553587038280.267077679351914
490.7620220371735520.4759559256528950.237977962826448
500.7274216808296430.5451566383407130.272578319170357
510.7320258700333910.5359482599332180.267974129966609
520.6910883798112130.6178232403775740.308911620188787
530.7838243334501390.4323513330997220.216175666549861
540.7455374436430840.5089251127138320.254462556356916
550.777385785697180.4452284286056400.222614214302820
560.8130467700740560.3739064598518870.186953229925944
570.8416784355786540.3166431288426930.158321564421347
580.808771017992770.3824579640144610.191228982007231
590.79630471255910.4073905748817990.203695287440900
600.9537713843472920.09245723130541540.0462286156527077
610.9438463942009540.1123072115980910.0561536057990457
620.9391819441502730.1216361116994540.060818055849727
630.9412855065876880.1174289868246250.0587144934123124
640.9362852530105110.1274294939789780.0637147469894889
650.935714227278020.1285715454439610.0642857727219803
660.942095394212830.1158092115743400.0579046057871702
670.9331271627160670.1337456745678650.0668728372839326
680.9150587171059690.1698825657880620.084941282894031
690.9241779903442990.1516440193114020.0758220096557012
700.93950115187740.1209976962452010.0604988481226003
710.924712278419320.1505754431613590.0752877215806795
720.9060161492729720.1879677014540560.0939838507270279
730.910513010183740.1789739796325200.0894869898162599
740.8892128975630230.2215742048739540.110787102436977
750.8915001216584630.2169997566830750.108499878341537
760.8800424346917250.2399151306165490.119957565308275
770.8678774854269010.2642450291461970.132122514573099
780.8483606144024930.3032787711950150.151639385597507
790.8155397453836730.3689205092326530.184460254616326
800.7868156087895140.4263687824209730.213184391210486
810.8380148027242510.3239703945514970.161985197275749
820.8553501761119010.2892996477761970.144649823888099
830.8257785993298180.3484428013403630.174221400670182
840.8139855186024510.3720289627950980.186014481397549
850.777849522587660.4443009548246800.222150477412340
860.8651776035989350.269644792802130.134822396401065
870.8415522622395660.3168954755208670.158447737760434
880.8067508360966940.3864983278066120.193249163903306
890.7689351343857050.462129731228590.231064865614295
900.7883252606266420.4233494787467160.211674739373358
910.7507269146220840.4985461707558310.249273085377916
920.7526918035306920.4946163929386160.247308196469308
930.7522509694490420.4954980611019150.247749030550958
940.7168107233453910.5663785533092170.283189276654609
950.7412922766781790.5174154466436430.258707723321821
960.7156346539641310.5687306920717370.284365346035869
970.7164784026920040.5670431946159910.283521597307996
980.6801932394970660.6396135210058690.319806760502934
990.6621470686808940.6757058626382120.337852931319106
1000.6660443106146490.6679113787707030.333955689385351
1010.6755642447600690.6488715104798620.324435755239931
1020.7334386189650270.5331227620699460.266561381034973
1030.7276811436483520.5446377127032960.272318856351648
1040.7215663058941130.5568673882117750.278433694105887
1050.8353398384564050.3293203230871890.164660161543595
1060.7960052752126150.4079894495747700.203994724787385
1070.817297460487520.365405079024960.18270253951248
1080.8110349168830640.3779301662338720.188965083116936
1090.7778660373115340.4442679253769330.222133962688466
1100.8305573054852850.3388853890294310.169442694514715
1110.7870466692846470.4259066614307060.212953330715353
1120.7443794803654270.5112410392691470.255620519634573
1130.6908950467749840.6182099064500320.309104953225016
1140.6589285424053520.6821429151892950.341071457594648
1150.6200226769244290.7599546461511420.379977323075571
1160.5568284058925570.8863431882148850.443171594107443
1170.5870780015009140.8258439969981710.412921998499086
1180.505549975802180.988900048395640.49445002419782
1190.4201522018927290.8403044037854570.579847798107271
1200.4092490325501560.8184980651003120.590750967449844
1210.3313635999997210.6627271999994410.66863640000028
1220.2456401763159160.4912803526318310.754359823684084
1230.1734556476448810.3469112952897630.826544352355119
1240.2833859227774390.5667718455548780.716614077222561
1250.3047758632227500.6095517264454990.69522413677725
1260.2321584802093270.4643169604186540.767841519790673
1270.2120775140855220.4241550281710450.787922485914478






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 level70.0619469026548673OK

\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 & 7 & 0.0619469026548673 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105003&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]7[/C][C]0.0619469026548673[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105003&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105003&T=6

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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 level70.0619469026548673OK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$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')
}