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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 20:24:39 +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/t1291407869ze7kt3waqxz7w9u.htm/, Retrieved Mon, 29 Apr 2024 06:11:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=105001, Retrieved Mon, 29 Apr 2024 06:11:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact98

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_MR1] [2010-12-03 20:24:39] [fca744d17b21beb005bf086e7071b2bb] [Current]
-   PD        [Multiple Regression] [p_Stress_MR3v2] [2010-12-04 13:59:58] [19f9551d4d95750ef21e9f3cf8fe2131]
-   PD        [Multiple Regression] [p_Stress_MR2v2] [2010-12-04 14:03:11] [19f9551d4d95750ef21e9f3cf8fe2131]
-   PD        [Multiple Regression] [p_Stress_MR3v3] [2010-12-04 14:09:41] [19f9551d4d95750ef21e9f3cf8fe2131]
-   PD        [Multiple Regression] [p_Stress_MR4] [2010-12-04 14:16:09] [19f9551d4d95750ef21e9f3cf8fe2131]
-    D          [Multiple Regression] [p_Stress_MR1v2] [2010-12-04 14:53:22] [19f9551d4d95750ef21e9f3cf8fe2131]
-   PD            [Multiple Regression] [p_Stress_MR2v3] [2010-12-05 16:08:01] [19f9551d4d95750ef21e9f3cf8fe2131]
-   PD              [Multiple Regression] [Multiple Regressi...] [2010-12-25 15:04:54] [8ec018d7298e4a3ae278d8b7199e08b6]
-                 [Multiple Regression] [PAPER BAEYENS (Mu...] [2010-12-21 11:01:30] [e4076051fbfb461c886b1e223cd7862f]
-    D            [Multiple Regression] [PAPER BAEYENS (Mu...] [2010-12-21 11:33:46] [e4076051fbfb461c886b1e223cd7862f]
-   P               [Multiple Regression] [PAPER BAEYENS (Mu...] [2010-12-21 12:37:56] [e4076051fbfb461c886b1e223cd7862f]
-    D                [Multiple Regression] [PAPER BAEYENS (Mu...] [2010-12-21 14:03:10] [e4076051fbfb461c886b1e223cd7862f]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
10	53	7	6	7	6	15	11	11	12	2	4	25	25	2	2	3.4
6	86	4	6	5	6	15	12	8	11	4	3	25	24	1	2	4
13	66	6	5	7	13	14	15	12	14	7	5	19	21	4	3.666666667	3.2
12	67	5	4	3	8	10	10	10	12	3	3	18	23	1	2.333333333	3.2
8	76	4	4	7	7	10	12	7	21	7	6	18	17	5	4	2.6
6	78	3	6	7	9	12	11	6	12	2	5	22	19	1	2.666666667	3.2
10	53	5	7	7	5	18	5	8	22	7	6	29	18	1	2.333333333	3.8
10	80	6	5	1	8	12	16	16	11	2	6	26	27	1	3.666666667	3.6
9	74	5	4	4	9	14	11	8	10	1	5	25	23	1	2.666666667	3.6
9	76	6	6	5	11	18	15	16	13	2	5	23	23	1	3	4
7	79	7	1	6	8	9	12	7	10	6	3	23	29	2	3	3.4
5	54	6	4	4	11	11	9	11	8	1	5	23	21	1	2	2.6
14	67	7	6	7	12	11	11	16	15	1	7	24	26	3	3	4.4
6	87	6	6	6	8	17	15	16	10	1	5	30	25	1	1.666666667	4
10	58	4	5	2	7	8	12	12	14	2	5	19	25	1	3	3.8
10	75	6	3	2	9	16	16	13	14	2	3	24	23	1	1.333333333	3.6
7	88	4	7	6	12	21	14	19	11	2	5	32	26	1	3	3.8
10	64	5	2	7	20	24	11	7	10	1	6	30	20	1	2	3.6
8	57	3	5	5	7	21	10	8	13	7	5	29	29	2	2.666666667	3.8
6	66	3	5	2	8	14	7	12	7	1	2	17	24	4	4	3.6
10	54	4	3	7	8	7	11	13	12	2	5	25	23	1	2.333333333	4
12	56	5	5	4	16	18	10	11	14	4	4	26	24	2	2.666666667	2.8
7	86	3	5	5	10	18	11	8	11	2	6	26	30	1	1	5
15	80	7	6	5	6	13	16	16	9	1	3	25	22	2	3	4.4
8	76	7	4	5	8	11	14	15	11	1	5	23	22	3	2.333333333	3.2
10	69	4	4	3	9	13	12	11	15	5	4	21	13	1	3	3.4
13	67	4	4	5	9	13	12	12	13	2	5	19	24	1	3	3.2
8	80	5	2	1	11	18	11	7	9	1	2	35	17	1	2.333333333	5
11	54	6	3	1	12	14	6	9	15	3	2	19	24	1	1.666666667	3.6
7	71	5	6	3	8	12	14	15	10	1	5	20	21	1	2.666666667	4.8
9	84	4	6	2	7	9	9	6	11	2	2	21	23	2	2.333333333	3.8
10	74	6	5	3	8	12	15	14	13	5	2	21	24	1	2	3.6
8	71	5	3	2	9	8	12	14	8	2	2	24	24	1	2	2.6
15	63	5	3	5	4	5	12	7	20	6	5	23	24	1	1.333333333	3.2
9	71	6	4	2	8	10	9	15	12	4	5	19	23	1	2.666666667	4
7	76	2	4	3	8	11	13	14	10	1	1	17	26	1	2.666666667	3.2
11	69	6	5	4	8	11	15	17	10	3	5	24	24	1	1	3.4
9	74	7	3	6	6	12	11	14	9	6	2	15	21	1	2.666666667	3.2
8	75	5	5	2	8	12	10	5	14	7	6	25	23	2	3	3.4
8	54	5	4	7	4	15	13	14	8	4	1	27	28	1	2	3.4
12	69	5	3	5	14	16	16	8	11	5	3	27	22	1	1.666666667	3.6
13	68	6	3	3	10	14	13	8	13	3	2	18	24	1	2.666666667	3.4
9	75	4	4	3	9	17	14	13	11	2	5	25	21	2	2	2.8
11	75	6	6	4	8	10	16	16	11	2	3	26	23	1	3	3.8
8	72	5	5	5	11	17	9	11	10	2	4	23	20	1	2.666666667	3
10	67	5	3	2	8	12	8	10	14	2	3	16	23	1	1.666666667	3.4
13	63	3	4	7	8	13	8	10	18	1	6	27	21	1	3	3.6
12	62	4	2	6	10	13	12	10	14	2	4	25	27	1	2.666666667	3.4
12	63	4	3	5	8	11	10	8	11	1	5	14	12	4	3.666666667	2.8
9	76	2	5	6	10	13	16	14	12	2	2	19	15	2	2.333333333	4
8	74	3	5	5	7	12	13	14	13	2	5	20	22	1	3	4.2
9	67	6	5	2	8	12	11	12	9	5	5	16	21	1	3.666666667	3.4
12	73	5	4	3	7	12	14	13	10	5	3	18	21	4	3	3.4
12	70	6	5	5	9	9	15	5	15	2	5	22	20	2	3.333333333	4
16	53	2	3	7	5	7	8	10	20	1	7	21	24	1	2	3.2
11	77	3	6	4	7	17	9	6	12	1	4	22	24	1	3	3.8
13	77	6	3	7	7	12	17	15	12	2	2	22	29	1	3	3.8
10	52	3	2	5	7	12	9	12	14	3	3	32	25	1	1	3.4
9	54	6	3	6	9	9	13	16	13	7	6	23	14	1	1	3.4
14	80	6	4	6	5	9	6	15	11	4	7	31	30	1	1	3.4
13	66	4	3	3	8	13	13	12	17	4	4	18	19	2	4	4.8
12	73	7	4	5	8	10	8	8	12	1	4	23	29	1	2.666666667	3
9	63	6	4	7	8	11	12	14	13	2	4	26	25	1	3	4
9	69	3	7	7	9	12	13	14	14	2	5	24	25	2	3.333333333	4.2
10	67	7	2	5	6	10	14	13	13	2	2	19	25	1	1.333333333	4
8	54	2	2	6	8	13	11	12	15	5	3	14	16	2	4.666666667	3.4
9	81	4	5	5	6	6	15	15	13	1	3	20	25	2	2.666666667	3.8
9	69	6	3	5	4	7	7	8	10	6	4	22	28	4	2	3.4
11	84	4	6	2	6	13	16	16	11	2	3	24	24	1	3	4.2
7	70	1	6	5	4	11	16	14	13	2	4	25	25	1	3.333333333	3.2
11	69	4	4	4	12	18	14	13	17	4	6	21	21	3	3.333333333	3
9	77	7	6	6	6	9	11	15	13	6	2	28	22	1	2.333333333	4.2
11	54	4	6	5	11	9	13	7	9	2	4	24	20	1	1	3.6
9	79	4	4	3	8	11	13	5	11	2	5	20	25	1	2	3.2
8	30	4	2	3	10	11	7	7	10	2	2	21	27	1	1.333333333	3.4
9	71	6	6	4	10	15	15	13	9	1	1	23	21	1	3	3.8
8	73	2	3	2	4	8	11	14	12	1	2	13	13	1	3.666666667	3.6
9	72	3	5	2	8	11	15	14	12	2	5	24	26	1	2	3
10	77	4	3	5	9	14	13	13	13	2	4	21	26	1	2.333333333	3.4
9	75	4	4	4	9	14	11	11	13	3	4	21	25	4	2.666666667	3.4
17	70	4	6	6	7	12	12	15	22	3	6	17	22	1	3.666666667	3.8
7	73	6	2	4	7	12	10	13	13	5	1	14	19	1	3	3.8
11	54	2	7	6	11	8	12	14	15	2	4	29	23	2	4	5
9	77	4	2	4	8	11	12	13	13	5	5	25	25	1	2.333333333	3.4
10	82	3	3	2	8	10	12	9	15	3	2	16	15	1	3	3.2
11	80	7	6	5	7	17	14	8	10	1	3	25	21	1	3.333333333	3.6
8	80	4	4	2	5	16	6	6	11	2	3	25	23	1	2.666666667	3.6
12	69	5	4	7	7	13	14	13	16	2	6	21	25	1	3	3.8
10	78	6	3	1	9	15	15	16	11	1	5	23	24	1	3	3.8
7	81	5	5	3	8	11	8	7	11	2	4	22	24	1	3	3.6
9	76	4	4	5	6	12	12	11	10	2	4	19	21	1	3	4
7	76	5	5	6	8	16	10	8	10	5	5	24	24	1	3	4
12	73	4	5	6	10	20	15	13	16	5	5	26	22	1	2.333333333	4
8	85	5	7	2	10	16	11	5	12	2	6	25	24	1	3.666666667	4.4
13	66	7	4	5	8	11	9	8	11	3	6	20	28	1	2	3.8
9	79	7	6	5	11	15	14	10	16	5	5	22	21	5	3.666666667	3.4
15	68	4	3	3	8	15	10	9	19	5	7	14	17	1	3	4
8	76	6	6	6	8	12	16	16	11	6	5	20	28	1	2.333333333	4.4
14	54	4	3	5	6	9	5	4	15	2	5	32	24	1	1.666666667	4
14	46	1	2	7	20	24	8	4	24	7	7	21	10	3	3	4
9	82	3	4	1	6	15	13	11	14	1	5	22	20	1	2.333333333	3.8
13	74	6	3	6	12	18	16	14	15	1	6	28	22	1	3	2.6
11	88	7	3	4	9	17	16	15	11	6	6	25	19	1	3	4.5
10	38	6	4	7	5	12	14	17	15	6	4	17	22	1	1	3.4
6	76	6	4	2	10	15	14	10	12	2	5	21	22	1	3.666666667	3.4
8	86	6	5	6	5	11	10	15	10	1	1	23	26	1	2.333333333	4.4
10	54	4	5	7	6	11	9	11	14	2	6	27	24	1	2	4.4
10	69	1	7	5	6	12	8	10	9	1	5	19	20	4	3.333333333	3.8
10	90	3	7	2	10	14	8	9	15	2	2	20	20	4	2.666666667	3.2
12	54	7	1	1	5	11	16	14	15	1	1	17	15	1	3	3.8
10	76	2	4	3	13	20	12	15	14	3	5	24	20	1	2.666666667	3.8
9	89	7	6	3	7	11	9	9	11	3	6	21	20	1	3.333333333	4
9	76	4	5	3	9	12	15	12	8	6	5	21	24	4	3.333333333	3.4
11	79	5	4	5	8	12	12	10	11	4	5	24	29	2	3	3.8
7	90	6	5	2	5	11	14	16	8	1	4	19	23	1	3	3.4
7	74	6	5	4	4	10	12	15	10	2	2	22	24	1	2.333333333	4
5	81	5	6	6	9	11	16	14	11	5	3	26	22	1	3	2.4
9	72	5	5	5	7	12	12	12	13	6	3	17	16	1	4	3.4
11	71	4	3	5	5	9	14	15	11	3	5	17	23	1	3.333333333	3.4
15	66	2	4	2	5	8	8	9	20	5	3	19	27	1	3	3.4
9	77	2	4	3	4	6	15	12	10	3	2	15	16	2	4	3.4
9	74	4	5	2	7	12	16	15	12	2	2	17	21	4	3.333333333	3.6
8	82	4	6	6	9	15	12	6	14	3	3	27	26	4	3.333333333	4
13	54	6	2	5	8	13	4	4	23	2	6	19	22	1	3	3.2
10	63	5	4	4	8	17	8	8	14	5	5	21	23	1	1	3.8
13	54	5	5	6	11	14	11	10	16	5	6	25	19	1	2.333333333	3
9	64	6	6	4	10	16	4	6	11	7	2	19	18	2	3.333333333	3.8
11	69	5	6	6	9	15	14	12	12	4	5	22	24	1	3	3.6
8	84	7	5	0	10	11	14	14	14	5	5	20	29	1	3.666666667	3.6
10	86	5	4	1	10	11	13	11	12	1	1	15	22	3	3.333333333	3.6
9	77	3	5	5	7	16	14	15	12	4	4	20	24	2	3.666666667	3.6
8	89	5	6	2	10	15	7	13	11	1	2	29	22	2	2.333333333	4.2
8	76	1	6	5	6	14	19	15	12	4	2	19	12	1	3.333333333	3.4
13	60	5	5	6	6	9	12	16	13	6	7	29	26	1	1.666666667	2.8
11	79	7	6	7	11	13	10	4	17	7	6	24	18	2	2.666666667	3.8
8	76	7	4	5	8	11	14	15	11	1	5	23	22	3	2.333333333	3.2
12	72	6	5	5	9	14	16	12	12	3	5	22	24	1	3.333333333	4
15	69	4	5	5	9	11	11	15	19	5	5	23	21	1	3.666666667	2.8
11	54	2	7	6	11	8	12	14	15	2	4	29	23	2	4	5
10	69	6	5	6	4	7	12	14	14	4	3	26	22	2	2.666666667	3.4
5	81	5	6	6	9	11	16	14	11	5	3	26	22	1	3	2.4
11	84	1	6	1	5	13	12	11	9	1	3	21	24	1	2.333333333	3.2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 9 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105001&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105001&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105001&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 time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Multiple Linear Regression - Estimated Regression Equation
PStress[t] = + 5.22652616884922 -0.0342689806008217BelInSprt[t] + 0.156751517105329KunnenRekRel[t] -0.137087238624758ExtraCurAct[t] -0.0131171061742578Verandvorigjaar[t] + 0.0306992360511323Kritouders[t] -0.060701263441551Verwouders[t] + 0.0651926129660706Populariteit[t] + 0.000472082504175796KenMedeStud[t] + 0.402890559410430Depressie[t] -0.177299512348825Slaapgebrek[t] + 0.215782954579822ToekZorgen[t] -0.0326727049326701PersStand[t] + 0.0520518310861078MateGeorgZijn[t] + 0.0264275257714054Rookgedrag[t] -0.0883024675116791MateAlcoholCon[t] + 0.295536842036837MateGezondGevarEetg[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PStress[t] =  +  5.22652616884922 -0.0342689806008217BelInSprt[t] +  0.156751517105329KunnenRekRel[t] -0.137087238624758ExtraCurAct[t] -0.0131171061742578Verandvorigjaar[t] +  0.0306992360511323Kritouders[t] -0.060701263441551Verwouders[t] +  0.0651926129660706Populariteit[t] +  0.000472082504175796KenMedeStud[t] +  0.402890559410430Depressie[t] -0.177299512348825Slaapgebrek[t] +  0.215782954579822ToekZorgen[t] -0.0326727049326701PersStand[t] +  0.0520518310861078MateGeorgZijn[t] +  0.0264275257714054Rookgedrag[t] -0.0883024675116791MateAlcoholCon[t] +  0.295536842036837MateGezondGevarEetg[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105001&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PStress[t] =  +  5.22652616884922 -0.0342689806008217BelInSprt[t] +  0.156751517105329KunnenRekRel[t] -0.137087238624758ExtraCurAct[t] -0.0131171061742578Verandvorigjaar[t] +  0.0306992360511323Kritouders[t] -0.060701263441551Verwouders[t] +  0.0651926129660706Populariteit[t] +  0.000472082504175796KenMedeStud[t] +  0.402890559410430Depressie[t] -0.177299512348825Slaapgebrek[t] +  0.215782954579822ToekZorgen[t] -0.0326727049326701PersStand[t] +  0.0520518310861078MateGeorgZijn[t] +  0.0264275257714054Rookgedrag[t] -0.0883024675116791MateAlcoholCon[t] +  0.295536842036837MateGezondGevarEetg[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105001&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105001&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] = + 5.22652616884922 -0.0342689806008217BelInSprt[t] + 0.156751517105329KunnenRekRel[t] -0.137087238624758ExtraCurAct[t] -0.0131171061742578Verandvorigjaar[t] + 0.0306992360511323Kritouders[t] -0.060701263441551Verwouders[t] + 0.0651926129660706Populariteit[t] + 0.000472082504175796KenMedeStud[t] + 0.402890559410430Depressie[t] -0.177299512348825Slaapgebrek[t] + 0.215782954579822ToekZorgen[t] -0.0326727049326701PersStand[t] + 0.0520518310861078MateGeorgZijn[t] + 0.0264275257714054Rookgedrag[t] -0.0883024675116791MateAlcoholCon[t] + 0.295536842036837MateGezondGevarEetg[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.226526168849222.530352.06550.0409390.020469
BelInSprt-0.03426898060082170.019649-1.7440.0836090.041804
KunnenRekRel0.1567515171053290.1151071.36180.1757130.087856
ExtraCurAct-0.1370872386247580.138554-0.98940.3243720.162186
Verandvorigjaar-0.01311710617425780.108761-0.12060.9041970.452099
Kritouders0.03069923605113230.0833530.36830.7132690.356635
Verwouders-0.0607012634415510.06191-0.98050.3287460.164373
Populariteit0.06519261296607060.0732740.88970.375330.187665
KenMedeStud0.0004720825041757960.0613760.00770.9938750.496938
Depressie0.4028905594104300.0666146.048200
Slaapgebrek-0.1772995123488250.099054-1.78990.0758870.037943
ToekZorgen0.2157829545798220.1237951.74310.0837820.041891
PersStand-0.03267270493267010.051558-0.63370.5274260.263713
MateGeorgZijn0.05205183108610780.0511261.01810.3105930.155296
Rookgedrag0.02642752577140540.1890980.13980.8890780.444539
MateAlcoholCon-0.08830246751167910.270555-0.32640.7446860.372343
MateGezondGevarEetg0.2955368420368370.3461670.85370.3948820.197441

\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) & 5.22652616884922 & 2.53035 & 2.0655 & 0.040939 & 0.020469 \tabularnewline
BelInSprt & -0.0342689806008217 & 0.019649 & -1.744 & 0.083609 & 0.041804 \tabularnewline
KunnenRekRel & 0.156751517105329 & 0.115107 & 1.3618 & 0.175713 & 0.087856 \tabularnewline
ExtraCurAct & -0.137087238624758 & 0.138554 & -0.9894 & 0.324372 & 0.162186 \tabularnewline
Verandvorigjaar & -0.0131171061742578 & 0.108761 & -0.1206 & 0.904197 & 0.452099 \tabularnewline
Kritouders & 0.0306992360511323 & 0.083353 & 0.3683 & 0.713269 & 0.356635 \tabularnewline
Verwouders & -0.060701263441551 & 0.06191 & -0.9805 & 0.328746 & 0.164373 \tabularnewline
Populariteit & 0.0651926129660706 & 0.073274 & 0.8897 & 0.37533 & 0.187665 \tabularnewline
KenMedeStud & 0.000472082504175796 & 0.061376 & 0.0077 & 0.993875 & 0.496938 \tabularnewline
Depressie & 0.402890559410430 & 0.066614 & 6.0482 & 0 & 0 \tabularnewline
Slaapgebrek & -0.177299512348825 & 0.099054 & -1.7899 & 0.075887 & 0.037943 \tabularnewline
ToekZorgen & 0.215782954579822 & 0.123795 & 1.7431 & 0.083782 & 0.041891 \tabularnewline
PersStand & -0.0326727049326701 & 0.051558 & -0.6337 & 0.527426 & 0.263713 \tabularnewline
MateGeorgZijn & 0.0520518310861078 & 0.051126 & 1.0181 & 0.310593 & 0.155296 \tabularnewline
Rookgedrag & 0.0264275257714054 & 0.189098 & 0.1398 & 0.889078 & 0.444539 \tabularnewline
MateAlcoholCon & -0.0883024675116791 & 0.270555 & -0.3264 & 0.744686 & 0.372343 \tabularnewline
MateGezondGevarEetg & 0.295536842036837 & 0.346167 & 0.8537 & 0.394882 & 0.197441 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105001&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]5.22652616884922[/C][C]2.53035[/C][C]2.0655[/C][C]0.040939[/C][C]0.020469[/C][/ROW]
[ROW][C]BelInSprt[/C][C]-0.0342689806008217[/C][C]0.019649[/C][C]-1.744[/C][C]0.083609[/C][C]0.041804[/C][/ROW]
[ROW][C]KunnenRekRel[/C][C]0.156751517105329[/C][C]0.115107[/C][C]1.3618[/C][C]0.175713[/C][C]0.087856[/C][/ROW]
[ROW][C]ExtraCurAct[/C][C]-0.137087238624758[/C][C]0.138554[/C][C]-0.9894[/C][C]0.324372[/C][C]0.162186[/C][/ROW]
[ROW][C]Verandvorigjaar[/C][C]-0.0131171061742578[/C][C]0.108761[/C][C]-0.1206[/C][C]0.904197[/C][C]0.452099[/C][/ROW]
[ROW][C]Kritouders[/C][C]0.0306992360511323[/C][C]0.083353[/C][C]0.3683[/C][C]0.713269[/C][C]0.356635[/C][/ROW]
[ROW][C]Verwouders[/C][C]-0.060701263441551[/C][C]0.06191[/C][C]-0.9805[/C][C]0.328746[/C][C]0.164373[/C][/ROW]
[ROW][C]Populariteit[/C][C]0.0651926129660706[/C][C]0.073274[/C][C]0.8897[/C][C]0.37533[/C][C]0.187665[/C][/ROW]
[ROW][C]KenMedeStud[/C][C]0.000472082504175796[/C][C]0.061376[/C][C]0.0077[/C][C]0.993875[/C][C]0.496938[/C][/ROW]
[ROW][C]Depressie[/C][C]0.402890559410430[/C][C]0.066614[/C][C]6.0482[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Slaapgebrek[/C][C]-0.177299512348825[/C][C]0.099054[/C][C]-1.7899[/C][C]0.075887[/C][C]0.037943[/C][/ROW]
[ROW][C]ToekZorgen[/C][C]0.215782954579822[/C][C]0.123795[/C][C]1.7431[/C][C]0.083782[/C][C]0.041891[/C][/ROW]
[ROW][C]PersStand[/C][C]-0.0326727049326701[/C][C]0.051558[/C][C]-0.6337[/C][C]0.527426[/C][C]0.263713[/C][/ROW]
[ROW][C]MateGeorgZijn[/C][C]0.0520518310861078[/C][C]0.051126[/C][C]1.0181[/C][C]0.310593[/C][C]0.155296[/C][/ROW]
[ROW][C]Rookgedrag[/C][C]0.0264275257714054[/C][C]0.189098[/C][C]0.1398[/C][C]0.889078[/C][C]0.444539[/C][/ROW]
[ROW][C]MateAlcoholCon[/C][C]-0.0883024675116791[/C][C]0.270555[/C][C]-0.3264[/C][C]0.744686[/C][C]0.372343[/C][/ROW]
[ROW][C]MateGezondGevarEetg[/C][C]0.295536842036837[/C][C]0.346167[/C][C]0.8537[/C][C]0.394882[/C][C]0.197441[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105001&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105001&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)5.226526168849222.530352.06550.0409390.020469
BelInSprt-0.03426898060082170.019649-1.7440.0836090.041804
KunnenRekRel0.1567515171053290.1151071.36180.1757130.087856
ExtraCurAct-0.1370872386247580.138554-0.98940.3243720.162186
Verandvorigjaar-0.01311710617425780.108761-0.12060.9041970.452099
Kritouders0.03069923605113230.0833530.36830.7132690.356635
Verwouders-0.0607012634415510.06191-0.98050.3287460.164373
Populariteit0.06519261296607060.0732740.88970.375330.187665
KenMedeStud0.0004720825041757960.0613760.00770.9938750.496938
Depressie0.4028905594104300.0666146.048200
Slaapgebrek-0.1772995123488250.099054-1.78990.0758870.037943
ToekZorgen0.2157829545798220.1237951.74310.0837820.041891
PersStand-0.03267270493267010.051558-0.63370.5274260.263713
MateGeorgZijn0.05205183108610780.0511261.01810.3105930.155296
Rookgedrag0.02642752577140540.1890980.13980.8890780.444539
MateAlcoholCon-0.08830246751167910.270555-0.32640.7446860.372343
MateGezondGevarEetg0.2955368420368370.3461670.85370.3948820.197441






Multiple Linear Regression - Regression Statistics
Multiple R0.628535092629106
R-squared0.395056362666279
Adjusted R-squared0.317623577087563
F-TEST (value)5.10192626693862
F-TEST (DF numerator)16
F-TEST (DF denominator)125
p-value4.64995227877907e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.97131675881084
Sum Squared Residuals485.761220446058

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.628535092629106 \tabularnewline
R-squared & 0.395056362666279 \tabularnewline
Adjusted R-squared & 0.317623577087563 \tabularnewline
F-TEST (value) & 5.10192626693862 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 125 \tabularnewline
p-value & 4.64995227877907e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.97131675881084 \tabularnewline
Sum Squared Residuals & 485.761220446058 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105001&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.628535092629106[/C][/ROW]
[ROW][C]R-squared[/C][C]0.395056362666279[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.317623577087563[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.10192626693862[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]125[/C][/ROW]
[ROW][C]p-value[/C][C]4.64995227877907e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.97131675881084[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]485.761220446058[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105001&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105001&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.628535092629106
R-squared0.395056362666279
Adjusted R-squared0.317623577087563
F-TEST (value)5.10192626693862
F-TEST (DF numerator)16
F-TEST (DF denominator)125
p-value4.64995227877907e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.97131675881084
Sum Squared Residuals485.761220446058






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11010.2979487964583-0.297948796458302
267.91239867279394-1.91239867279394
31310.3390985411632.660901458837
4129.747116785063722.25288321493628
5812.3608178413918-4.36081784139178
668.94314617595213-2.94314617595213
71012.3832569313601-2.38325693136006
8109.989165788988390.0108342110116108
999.18671490635274-0.186714906352737
10910.2562755658211-1.25627556582115
1179.04890707973803-2.04890707973803
1259.06217141802112-4.06217141802112
131412.59925044127561.40074955872441
1468.79656241340654-2.79656241340653
151011.6013799337748-1.60137993377483
161010.8325459887723-0.832545988772341
1778.16337216151061-1.16337216151061
18109.44956699909250.550433000907498
1989.16610993350092-1.16610993350092
2067.16588821534516-1.16588821534516
211010.9857794411093-0.985779441109293
221210.24804047599171.75195952400826
2379.43366041479678-2.43366041479678
24158.65257029762356.3474297023765
25810.1491084527023-2.14910845270229
26109.95283830007740.0471616999226065
271310.51632268764532.48367731235471
2887.86665643010640.133343569893595
291111.3207867552581-0.320786755258090
3079.7319542572747-2.73195425727469
3198.374013825630880.625986174369125
32109.667538256346070.332461743653928
3388.10270249854559-0.102702498545585
341513.40450670649711.59549329350287
35910.1456662951063-1.14566629510635
3678.38185235282574-1.38185235282574
37119.612336758642131.38766324135787
3897.810879946040131.18912005395987
3989.76712144951388-1.76712144951388
4087.745626691781480.254373308218522
41129.073118487425982.92688151257402
421310.28874630084692.71125369915311
4398.99499972465050.00500027534950619
44119.367894153755421.63210584624458
4588.19415957056963-0.19415957056963
461010.8119350981055-0.811935098105464
471312.28621189574680.713788104253187
481211.21424842547550.78575157452449
49129.561375057510082.43862494248992
5098.839627744331890.160372255668115
51810.2068011119551-2.20680111195511
5298.495540119733290.504459880266712
53128.466625666228333.53337433377167
541211.75798837983540.242011620164587
551614.22519082272231.77480917727766
56118.891140122447422.10885987755258
571310.21399476851092.78600523148912
581010.6085494175370-0.608549417536961
59910.6229671645408-1.62296716454078
60149.528617143545254.47138285645474
611311.97298430951941.02701569048063
621210.32823024920771.67176975079226
63910.8763043033581-1.87630430335811
64910.5644844003651-1.56448440036514
651011.2699183270100-1.26991832701005
66810.3411115868558-2.34111158685581
67910.4275714591011-1.42757145910115
6898.984463945398930.0155360546010725
69118.764314221825152.23568577817485
7079.50950617187987-2.50950617187987
711111.5959093237277-0.595909323727745
7299.04322706372827-0.0432270637282655
73119.150584315128891.84941568487111
7499.58582093389701-0.585820933897015
75810.3495296341019-2.34952963410189
7698.130120307568740.86987969243126
7789.06927861793022-1.06927861793022
7899.94476031937515-0.94476031937515
791010.2566345301288-0.256634530128837
8099.89037004592268-0.890370045922676
811713.87113721940713.12886278059293
8279.46628918867527-2.46628918867527
831111.2710971912694-0.271097191269415
8499.99419258295186-0.994192582951863
85109.782647703191770.217352296808229
86118.364850167058542.63514983294146
8788.07350215496662-0.0735021549666187
881212.2362440267885-0.236244026788495
891010.1366359196199-0.136635919619902
9078.90866042512573-1.90866042512573
9198.831852682581910.168147317418089
9278.20186818590794-1.20186818590794
931210.60160310242131.39839689757873
9489.3770524761292-1.37705247612920
951310.58794485703482.41205514296516
96910.8991606935624-1.89916069356244
971512.71391283090472.28608716909534
9889.60084209563988-1.60084209563988
991411.52469112054272.47530887945727
1001413.89116556168690.108834438313056
101910.2593802311929-1.25938023119287
1021311.38760247188011.61239752811987
103119.065220932738641.93477906726136
1041011.9457960705909-1.94579607059089
105610.0273934103186-4.02739341031865
10688.39056873028816-0.390568730288156
1071011.4319914342948-1.43199143429479
108107.857857874760112.14214212523989
109108.933554451528641.06644554847136
1101212.0502741750573-0.0502741750573411
111109.680713847085670.319286152914328
11299.01387427623435-0.0138742762343448
11397.673287471902421.32671252809758
114119.431169418165021.56883058183498
11578.0289016846006-1.02890168460060
11678.8372557113458-1.83725571134581
11757.95064640131373-2.95064640131373
11898.842916557978270.157083442021736
119119.828029747082231.17197025291777
1201512.26699495751722.73300504248278
12198.031543105897670.968456894102328
12299.46720704583193-0.467207045831931
12389.51378642904974-1.51378642904975
1241315.1340650336149-2.13406503361495
1251010.3946259832878-0.394625983287812
1261311.33894869248321.66105130751682
12797.518174283920641.48182571607936
128119.588934397082891.41106560291711
129810.7738589128412-2.77385891284124
130109.37075421615550.629245783844505
13198.847922044186420.152077955813578
13287.942310697449370.0576893025506331
13387.76865119207120.231348807928797
1341310.41461475890152.58538524109854
1351110.90053427438880.0994657256111754
136810.1491084527023-2.14910845270229
1371210.25024989991421.74975010008577
1381511.78983910715693.21016089284307
1391111.2710971912694-0.271097191269415
1401010.2216210600961-0.221621060096082
14157.95064640131373-2.95064640131373
142117.246214654240163.75378534575984

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 10 & 10.2979487964583 & -0.297948796458302 \tabularnewline
2 & 6 & 7.91239867279394 & -1.91239867279394 \tabularnewline
3 & 13 & 10.339098541163 & 2.660901458837 \tabularnewline
4 & 12 & 9.74711678506372 & 2.25288321493628 \tabularnewline
5 & 8 & 12.3608178413918 & -4.36081784139178 \tabularnewline
6 & 6 & 8.94314617595213 & -2.94314617595213 \tabularnewline
7 & 10 & 12.3832569313601 & -2.38325693136006 \tabularnewline
8 & 10 & 9.98916578898839 & 0.0108342110116108 \tabularnewline
9 & 9 & 9.18671490635274 & -0.186714906352737 \tabularnewline
10 & 9 & 10.2562755658211 & -1.25627556582115 \tabularnewline
11 & 7 & 9.04890707973803 & -2.04890707973803 \tabularnewline
12 & 5 & 9.06217141802112 & -4.06217141802112 \tabularnewline
13 & 14 & 12.5992504412756 & 1.40074955872441 \tabularnewline
14 & 6 & 8.79656241340654 & -2.79656241340653 \tabularnewline
15 & 10 & 11.6013799337748 & -1.60137993377483 \tabularnewline
16 & 10 & 10.8325459887723 & -0.832545988772341 \tabularnewline
17 & 7 & 8.16337216151061 & -1.16337216151061 \tabularnewline
18 & 10 & 9.4495669990925 & 0.550433000907498 \tabularnewline
19 & 8 & 9.16610993350092 & -1.16610993350092 \tabularnewline
20 & 6 & 7.16588821534516 & -1.16588821534516 \tabularnewline
21 & 10 & 10.9857794411093 & -0.985779441109293 \tabularnewline
22 & 12 & 10.2480404759917 & 1.75195952400826 \tabularnewline
23 & 7 & 9.43366041479678 & -2.43366041479678 \tabularnewline
24 & 15 & 8.6525702976235 & 6.3474297023765 \tabularnewline
25 & 8 & 10.1491084527023 & -2.14910845270229 \tabularnewline
26 & 10 & 9.9528383000774 & 0.0471616999226065 \tabularnewline
27 & 13 & 10.5163226876453 & 2.48367731235471 \tabularnewline
28 & 8 & 7.8666564301064 & 0.133343569893595 \tabularnewline
29 & 11 & 11.3207867552581 & -0.320786755258090 \tabularnewline
30 & 7 & 9.7319542572747 & -2.73195425727469 \tabularnewline
31 & 9 & 8.37401382563088 & 0.625986174369125 \tabularnewline
32 & 10 & 9.66753825634607 & 0.332461743653928 \tabularnewline
33 & 8 & 8.10270249854559 & -0.102702498545585 \tabularnewline
34 & 15 & 13.4045067064971 & 1.59549329350287 \tabularnewline
35 & 9 & 10.1456662951063 & -1.14566629510635 \tabularnewline
36 & 7 & 8.38185235282574 & -1.38185235282574 \tabularnewline
37 & 11 & 9.61233675864213 & 1.38766324135787 \tabularnewline
38 & 9 & 7.81087994604013 & 1.18912005395987 \tabularnewline
39 & 8 & 9.76712144951388 & -1.76712144951388 \tabularnewline
40 & 8 & 7.74562669178148 & 0.254373308218522 \tabularnewline
41 & 12 & 9.07311848742598 & 2.92688151257402 \tabularnewline
42 & 13 & 10.2887463008469 & 2.71125369915311 \tabularnewline
43 & 9 & 8.9949997246505 & 0.00500027534950619 \tabularnewline
44 & 11 & 9.36789415375542 & 1.63210584624458 \tabularnewline
45 & 8 & 8.19415957056963 & -0.19415957056963 \tabularnewline
46 & 10 & 10.8119350981055 & -0.811935098105464 \tabularnewline
47 & 13 & 12.2862118957468 & 0.713788104253187 \tabularnewline
48 & 12 & 11.2142484254755 & 0.78575157452449 \tabularnewline
49 & 12 & 9.56137505751008 & 2.43862494248992 \tabularnewline
50 & 9 & 8.83962774433189 & 0.160372255668115 \tabularnewline
51 & 8 & 10.2068011119551 & -2.20680111195511 \tabularnewline
52 & 9 & 8.49554011973329 & 0.504459880266712 \tabularnewline
53 & 12 & 8.46662566622833 & 3.53337433377167 \tabularnewline
54 & 12 & 11.7579883798354 & 0.242011620164587 \tabularnewline
55 & 16 & 14.2251908227223 & 1.77480917727766 \tabularnewline
56 & 11 & 8.89114012244742 & 2.10885987755258 \tabularnewline
57 & 13 & 10.2139947685109 & 2.78600523148912 \tabularnewline
58 & 10 & 10.6085494175370 & -0.608549417536961 \tabularnewline
59 & 9 & 10.6229671645408 & -1.62296716454078 \tabularnewline
60 & 14 & 9.52861714354525 & 4.47138285645474 \tabularnewline
61 & 13 & 11.9729843095194 & 1.02701569048063 \tabularnewline
62 & 12 & 10.3282302492077 & 1.67176975079226 \tabularnewline
63 & 9 & 10.8763043033581 & -1.87630430335811 \tabularnewline
64 & 9 & 10.5644844003651 & -1.56448440036514 \tabularnewline
65 & 10 & 11.2699183270100 & -1.26991832701005 \tabularnewline
66 & 8 & 10.3411115868558 & -2.34111158685581 \tabularnewline
67 & 9 & 10.4275714591011 & -1.42757145910115 \tabularnewline
68 & 9 & 8.98446394539893 & 0.0155360546010725 \tabularnewline
69 & 11 & 8.76431422182515 & 2.23568577817485 \tabularnewline
70 & 7 & 9.50950617187987 & -2.50950617187987 \tabularnewline
71 & 11 & 11.5959093237277 & -0.595909323727745 \tabularnewline
72 & 9 & 9.04322706372827 & -0.0432270637282655 \tabularnewline
73 & 11 & 9.15058431512889 & 1.84941568487111 \tabularnewline
74 & 9 & 9.58582093389701 & -0.585820933897015 \tabularnewline
75 & 8 & 10.3495296341019 & -2.34952963410189 \tabularnewline
76 & 9 & 8.13012030756874 & 0.86987969243126 \tabularnewline
77 & 8 & 9.06927861793022 & -1.06927861793022 \tabularnewline
78 & 9 & 9.94476031937515 & -0.94476031937515 \tabularnewline
79 & 10 & 10.2566345301288 & -0.256634530128837 \tabularnewline
80 & 9 & 9.89037004592268 & -0.890370045922676 \tabularnewline
81 & 17 & 13.8711372194071 & 3.12886278059293 \tabularnewline
82 & 7 & 9.46628918867527 & -2.46628918867527 \tabularnewline
83 & 11 & 11.2710971912694 & -0.271097191269415 \tabularnewline
84 & 9 & 9.99419258295186 & -0.994192582951863 \tabularnewline
85 & 10 & 9.78264770319177 & 0.217352296808229 \tabularnewline
86 & 11 & 8.36485016705854 & 2.63514983294146 \tabularnewline
87 & 8 & 8.07350215496662 & -0.0735021549666187 \tabularnewline
88 & 12 & 12.2362440267885 & -0.236244026788495 \tabularnewline
89 & 10 & 10.1366359196199 & -0.136635919619902 \tabularnewline
90 & 7 & 8.90866042512573 & -1.90866042512573 \tabularnewline
91 & 9 & 8.83185268258191 & 0.168147317418089 \tabularnewline
92 & 7 & 8.20186818590794 & -1.20186818590794 \tabularnewline
93 & 12 & 10.6016031024213 & 1.39839689757873 \tabularnewline
94 & 8 & 9.3770524761292 & -1.37705247612920 \tabularnewline
95 & 13 & 10.5879448570348 & 2.41205514296516 \tabularnewline
96 & 9 & 10.8991606935624 & -1.89916069356244 \tabularnewline
97 & 15 & 12.7139128309047 & 2.28608716909534 \tabularnewline
98 & 8 & 9.60084209563988 & -1.60084209563988 \tabularnewline
99 & 14 & 11.5246911205427 & 2.47530887945727 \tabularnewline
100 & 14 & 13.8911655616869 & 0.108834438313056 \tabularnewline
101 & 9 & 10.2593802311929 & -1.25938023119287 \tabularnewline
102 & 13 & 11.3876024718801 & 1.61239752811987 \tabularnewline
103 & 11 & 9.06522093273864 & 1.93477906726136 \tabularnewline
104 & 10 & 11.9457960705909 & -1.94579607059089 \tabularnewline
105 & 6 & 10.0273934103186 & -4.02739341031865 \tabularnewline
106 & 8 & 8.39056873028816 & -0.390568730288156 \tabularnewline
107 & 10 & 11.4319914342948 & -1.43199143429479 \tabularnewline
108 & 10 & 7.85785787476011 & 2.14214212523989 \tabularnewline
109 & 10 & 8.93355445152864 & 1.06644554847136 \tabularnewline
110 & 12 & 12.0502741750573 & -0.0502741750573411 \tabularnewline
111 & 10 & 9.68071384708567 & 0.319286152914328 \tabularnewline
112 & 9 & 9.01387427623435 & -0.0138742762343448 \tabularnewline
113 & 9 & 7.67328747190242 & 1.32671252809758 \tabularnewline
114 & 11 & 9.43116941816502 & 1.56883058183498 \tabularnewline
115 & 7 & 8.0289016846006 & -1.02890168460060 \tabularnewline
116 & 7 & 8.8372557113458 & -1.83725571134581 \tabularnewline
117 & 5 & 7.95064640131373 & -2.95064640131373 \tabularnewline
118 & 9 & 8.84291655797827 & 0.157083442021736 \tabularnewline
119 & 11 & 9.82802974708223 & 1.17197025291777 \tabularnewline
120 & 15 & 12.2669949575172 & 2.73300504248278 \tabularnewline
121 & 9 & 8.03154310589767 & 0.968456894102328 \tabularnewline
122 & 9 & 9.46720704583193 & -0.467207045831931 \tabularnewline
123 & 8 & 9.51378642904974 & -1.51378642904975 \tabularnewline
124 & 13 & 15.1340650336149 & -2.13406503361495 \tabularnewline
125 & 10 & 10.3946259832878 & -0.394625983287812 \tabularnewline
126 & 13 & 11.3389486924832 & 1.66105130751682 \tabularnewline
127 & 9 & 7.51817428392064 & 1.48182571607936 \tabularnewline
128 & 11 & 9.58893439708289 & 1.41106560291711 \tabularnewline
129 & 8 & 10.7738589128412 & -2.77385891284124 \tabularnewline
130 & 10 & 9.3707542161555 & 0.629245783844505 \tabularnewline
131 & 9 & 8.84792204418642 & 0.152077955813578 \tabularnewline
132 & 8 & 7.94231069744937 & 0.0576893025506331 \tabularnewline
133 & 8 & 7.7686511920712 & 0.231348807928797 \tabularnewline
134 & 13 & 10.4146147589015 & 2.58538524109854 \tabularnewline
135 & 11 & 10.9005342743888 & 0.0994657256111754 \tabularnewline
136 & 8 & 10.1491084527023 & -2.14910845270229 \tabularnewline
137 & 12 & 10.2502498999142 & 1.74975010008577 \tabularnewline
138 & 15 & 11.7898391071569 & 3.21016089284307 \tabularnewline
139 & 11 & 11.2710971912694 & -0.271097191269415 \tabularnewline
140 & 10 & 10.2216210600961 & -0.221621060096082 \tabularnewline
141 & 5 & 7.95064640131373 & -2.95064640131373 \tabularnewline
142 & 11 & 7.24621465424016 & 3.75378534575984 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105001&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]10.2979487964583[/C][C]-0.297948796458302[/C][/ROW]
[ROW][C]2[/C][C]6[/C][C]7.91239867279394[/C][C]-1.91239867279394[/C][/ROW]
[ROW][C]3[/C][C]13[/C][C]10.339098541163[/C][C]2.660901458837[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]9.74711678506372[/C][C]2.25288321493628[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]12.3608178413918[/C][C]-4.36081784139178[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]8.94314617595213[/C][C]-2.94314617595213[/C][/ROW]
[ROW][C]7[/C][C]10[/C][C]12.3832569313601[/C][C]-2.38325693136006[/C][/ROW]
[ROW][C]8[/C][C]10[/C][C]9.98916578898839[/C][C]0.0108342110116108[/C][/ROW]
[ROW][C]9[/C][C]9[/C][C]9.18671490635274[/C][C]-0.186714906352737[/C][/ROW]
[ROW][C]10[/C][C]9[/C][C]10.2562755658211[/C][C]-1.25627556582115[/C][/ROW]
[ROW][C]11[/C][C]7[/C][C]9.04890707973803[/C][C]-2.04890707973803[/C][/ROW]
[ROW][C]12[/C][C]5[/C][C]9.06217141802112[/C][C]-4.06217141802112[/C][/ROW]
[ROW][C]13[/C][C]14[/C][C]12.5992504412756[/C][C]1.40074955872441[/C][/ROW]
[ROW][C]14[/C][C]6[/C][C]8.79656241340654[/C][C]-2.79656241340653[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]11.6013799337748[/C][C]-1.60137993377483[/C][/ROW]
[ROW][C]16[/C][C]10[/C][C]10.8325459887723[/C][C]-0.832545988772341[/C][/ROW]
[ROW][C]17[/C][C]7[/C][C]8.16337216151061[/C][C]-1.16337216151061[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]9.4495669990925[/C][C]0.550433000907498[/C][/ROW]
[ROW][C]19[/C][C]8[/C][C]9.16610993350092[/C][C]-1.16610993350092[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]7.16588821534516[/C][C]-1.16588821534516[/C][/ROW]
[ROW][C]21[/C][C]10[/C][C]10.9857794411093[/C][C]-0.985779441109293[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]10.2480404759917[/C][C]1.75195952400826[/C][/ROW]
[ROW][C]23[/C][C]7[/C][C]9.43366041479678[/C][C]-2.43366041479678[/C][/ROW]
[ROW][C]24[/C][C]15[/C][C]8.6525702976235[/C][C]6.3474297023765[/C][/ROW]
[ROW][C]25[/C][C]8[/C][C]10.1491084527023[/C][C]-2.14910845270229[/C][/ROW]
[ROW][C]26[/C][C]10[/C][C]9.9528383000774[/C][C]0.0471616999226065[/C][/ROW]
[ROW][C]27[/C][C]13[/C][C]10.5163226876453[/C][C]2.48367731235471[/C][/ROW]
[ROW][C]28[/C][C]8[/C][C]7.8666564301064[/C][C]0.133343569893595[/C][/ROW]
[ROW][C]29[/C][C]11[/C][C]11.3207867552581[/C][C]-0.320786755258090[/C][/ROW]
[ROW][C]30[/C][C]7[/C][C]9.7319542572747[/C][C]-2.73195425727469[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]8.37401382563088[/C][C]0.625986174369125[/C][/ROW]
[ROW][C]32[/C][C]10[/C][C]9.66753825634607[/C][C]0.332461743653928[/C][/ROW]
[ROW][C]33[/C][C]8[/C][C]8.10270249854559[/C][C]-0.102702498545585[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]13.4045067064971[/C][C]1.59549329350287[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]10.1456662951063[/C][C]-1.14566629510635[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]8.38185235282574[/C][C]-1.38185235282574[/C][/ROW]
[ROW][C]37[/C][C]11[/C][C]9.61233675864213[/C][C]1.38766324135787[/C][/ROW]
[ROW][C]38[/C][C]9[/C][C]7.81087994604013[/C][C]1.18912005395987[/C][/ROW]
[ROW][C]39[/C][C]8[/C][C]9.76712144951388[/C][C]-1.76712144951388[/C][/ROW]
[ROW][C]40[/C][C]8[/C][C]7.74562669178148[/C][C]0.254373308218522[/C][/ROW]
[ROW][C]41[/C][C]12[/C][C]9.07311848742598[/C][C]2.92688151257402[/C][/ROW]
[ROW][C]42[/C][C]13[/C][C]10.2887463008469[/C][C]2.71125369915311[/C][/ROW]
[ROW][C]43[/C][C]9[/C][C]8.9949997246505[/C][C]0.00500027534950619[/C][/ROW]
[ROW][C]44[/C][C]11[/C][C]9.36789415375542[/C][C]1.63210584624458[/C][/ROW]
[ROW][C]45[/C][C]8[/C][C]8.19415957056963[/C][C]-0.19415957056963[/C][/ROW]
[ROW][C]46[/C][C]10[/C][C]10.8119350981055[/C][C]-0.811935098105464[/C][/ROW]
[ROW][C]47[/C][C]13[/C][C]12.2862118957468[/C][C]0.713788104253187[/C][/ROW]
[ROW][C]48[/C][C]12[/C][C]11.2142484254755[/C][C]0.78575157452449[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]9.56137505751008[/C][C]2.43862494248992[/C][/ROW]
[ROW][C]50[/C][C]9[/C][C]8.83962774433189[/C][C]0.160372255668115[/C][/ROW]
[ROW][C]51[/C][C]8[/C][C]10.2068011119551[/C][C]-2.20680111195511[/C][/ROW]
[ROW][C]52[/C][C]9[/C][C]8.49554011973329[/C][C]0.504459880266712[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]8.46662566622833[/C][C]3.53337433377167[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]11.7579883798354[/C][C]0.242011620164587[/C][/ROW]
[ROW][C]55[/C][C]16[/C][C]14.2251908227223[/C][C]1.77480917727766[/C][/ROW]
[ROW][C]56[/C][C]11[/C][C]8.89114012244742[/C][C]2.10885987755258[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]10.2139947685109[/C][C]2.78600523148912[/C][/ROW]
[ROW][C]58[/C][C]10[/C][C]10.6085494175370[/C][C]-0.608549417536961[/C][/ROW]
[ROW][C]59[/C][C]9[/C][C]10.6229671645408[/C][C]-1.62296716454078[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]9.52861714354525[/C][C]4.47138285645474[/C][/ROW]
[ROW][C]61[/C][C]13[/C][C]11.9729843095194[/C][C]1.02701569048063[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]10.3282302492077[/C][C]1.67176975079226[/C][/ROW]
[ROW][C]63[/C][C]9[/C][C]10.8763043033581[/C][C]-1.87630430335811[/C][/ROW]
[ROW][C]64[/C][C]9[/C][C]10.5644844003651[/C][C]-1.56448440036514[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]11.2699183270100[/C][C]-1.26991832701005[/C][/ROW]
[ROW][C]66[/C][C]8[/C][C]10.3411115868558[/C][C]-2.34111158685581[/C][/ROW]
[ROW][C]67[/C][C]9[/C][C]10.4275714591011[/C][C]-1.42757145910115[/C][/ROW]
[ROW][C]68[/C][C]9[/C][C]8.98446394539893[/C][C]0.0155360546010725[/C][/ROW]
[ROW][C]69[/C][C]11[/C][C]8.76431422182515[/C][C]2.23568577817485[/C][/ROW]
[ROW][C]70[/C][C]7[/C][C]9.50950617187987[/C][C]-2.50950617187987[/C][/ROW]
[ROW][C]71[/C][C]11[/C][C]11.5959093237277[/C][C]-0.595909323727745[/C][/ROW]
[ROW][C]72[/C][C]9[/C][C]9.04322706372827[/C][C]-0.0432270637282655[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]9.15058431512889[/C][C]1.84941568487111[/C][/ROW]
[ROW][C]74[/C][C]9[/C][C]9.58582093389701[/C][C]-0.585820933897015[/C][/ROW]
[ROW][C]75[/C][C]8[/C][C]10.3495296341019[/C][C]-2.34952963410189[/C][/ROW]
[ROW][C]76[/C][C]9[/C][C]8.13012030756874[/C][C]0.86987969243126[/C][/ROW]
[ROW][C]77[/C][C]8[/C][C]9.06927861793022[/C][C]-1.06927861793022[/C][/ROW]
[ROW][C]78[/C][C]9[/C][C]9.94476031937515[/C][C]-0.94476031937515[/C][/ROW]
[ROW][C]79[/C][C]10[/C][C]10.2566345301288[/C][C]-0.256634530128837[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]9.89037004592268[/C][C]-0.890370045922676[/C][/ROW]
[ROW][C]81[/C][C]17[/C][C]13.8711372194071[/C][C]3.12886278059293[/C][/ROW]
[ROW][C]82[/C][C]7[/C][C]9.46628918867527[/C][C]-2.46628918867527[/C][/ROW]
[ROW][C]83[/C][C]11[/C][C]11.2710971912694[/C][C]-0.271097191269415[/C][/ROW]
[ROW][C]84[/C][C]9[/C][C]9.99419258295186[/C][C]-0.994192582951863[/C][/ROW]
[ROW][C]85[/C][C]10[/C][C]9.78264770319177[/C][C]0.217352296808229[/C][/ROW]
[ROW][C]86[/C][C]11[/C][C]8.36485016705854[/C][C]2.63514983294146[/C][/ROW]
[ROW][C]87[/C][C]8[/C][C]8.07350215496662[/C][C]-0.0735021549666187[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.2362440267885[/C][C]-0.236244026788495[/C][/ROW]
[ROW][C]89[/C][C]10[/C][C]10.1366359196199[/C][C]-0.136635919619902[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]8.90866042512573[/C][C]-1.90866042512573[/C][/ROW]
[ROW][C]91[/C][C]9[/C][C]8.83185268258191[/C][C]0.168147317418089[/C][/ROW]
[ROW][C]92[/C][C]7[/C][C]8.20186818590794[/C][C]-1.20186818590794[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]10.6016031024213[/C][C]1.39839689757873[/C][/ROW]
[ROW][C]94[/C][C]8[/C][C]9.3770524761292[/C][C]-1.37705247612920[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]10.5879448570348[/C][C]2.41205514296516[/C][/ROW]
[ROW][C]96[/C][C]9[/C][C]10.8991606935624[/C][C]-1.89916069356244[/C][/ROW]
[ROW][C]97[/C][C]15[/C][C]12.7139128309047[/C][C]2.28608716909534[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]9.60084209563988[/C][C]-1.60084209563988[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]11.5246911205427[/C][C]2.47530887945727[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]13.8911655616869[/C][C]0.108834438313056[/C][/ROW]
[ROW][C]101[/C][C]9[/C][C]10.2593802311929[/C][C]-1.25938023119287[/C][/ROW]
[ROW][C]102[/C][C]13[/C][C]11.3876024718801[/C][C]1.61239752811987[/C][/ROW]
[ROW][C]103[/C][C]11[/C][C]9.06522093273864[/C][C]1.93477906726136[/C][/ROW]
[ROW][C]104[/C][C]10[/C][C]11.9457960705909[/C][C]-1.94579607059089[/C][/ROW]
[ROW][C]105[/C][C]6[/C][C]10.0273934103186[/C][C]-4.02739341031865[/C][/ROW]
[ROW][C]106[/C][C]8[/C][C]8.39056873028816[/C][C]-0.390568730288156[/C][/ROW]
[ROW][C]107[/C][C]10[/C][C]11.4319914342948[/C][C]-1.43199143429479[/C][/ROW]
[ROW][C]108[/C][C]10[/C][C]7.85785787476011[/C][C]2.14214212523989[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]8.93355445152864[/C][C]1.06644554847136[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.0502741750573[/C][C]-0.0502741750573411[/C][/ROW]
[ROW][C]111[/C][C]10[/C][C]9.68071384708567[/C][C]0.319286152914328[/C][/ROW]
[ROW][C]112[/C][C]9[/C][C]9.01387427623435[/C][C]-0.0138742762343448[/C][/ROW]
[ROW][C]113[/C][C]9[/C][C]7.67328747190242[/C][C]1.32671252809758[/C][/ROW]
[ROW][C]114[/C][C]11[/C][C]9.43116941816502[/C][C]1.56883058183498[/C][/ROW]
[ROW][C]115[/C][C]7[/C][C]8.0289016846006[/C][C]-1.02890168460060[/C][/ROW]
[ROW][C]116[/C][C]7[/C][C]8.8372557113458[/C][C]-1.83725571134581[/C][/ROW]
[ROW][C]117[/C][C]5[/C][C]7.95064640131373[/C][C]-2.95064640131373[/C][/ROW]
[ROW][C]118[/C][C]9[/C][C]8.84291655797827[/C][C]0.157083442021736[/C][/ROW]
[ROW][C]119[/C][C]11[/C][C]9.82802974708223[/C][C]1.17197025291777[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]12.2669949575172[/C][C]2.73300504248278[/C][/ROW]
[ROW][C]121[/C][C]9[/C][C]8.03154310589767[/C][C]0.968456894102328[/C][/ROW]
[ROW][C]122[/C][C]9[/C][C]9.46720704583193[/C][C]-0.467207045831931[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]9.51378642904974[/C][C]-1.51378642904975[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]15.1340650336149[/C][C]-2.13406503361495[/C][/ROW]
[ROW][C]125[/C][C]10[/C][C]10.3946259832878[/C][C]-0.394625983287812[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]11.3389486924832[/C][C]1.66105130751682[/C][/ROW]
[ROW][C]127[/C][C]9[/C][C]7.51817428392064[/C][C]1.48182571607936[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]9.58893439708289[/C][C]1.41106560291711[/C][/ROW]
[ROW][C]129[/C][C]8[/C][C]10.7738589128412[/C][C]-2.77385891284124[/C][/ROW]
[ROW][C]130[/C][C]10[/C][C]9.3707542161555[/C][C]0.629245783844505[/C][/ROW]
[ROW][C]131[/C][C]9[/C][C]8.84792204418642[/C][C]0.152077955813578[/C][/ROW]
[ROW][C]132[/C][C]8[/C][C]7.94231069744937[/C][C]0.0576893025506331[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]7.7686511920712[/C][C]0.231348807928797[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]10.4146147589015[/C][C]2.58538524109854[/C][/ROW]
[ROW][C]135[/C][C]11[/C][C]10.9005342743888[/C][C]0.0994657256111754[/C][/ROW]
[ROW][C]136[/C][C]8[/C][C]10.1491084527023[/C][C]-2.14910845270229[/C][/ROW]
[ROW][C]137[/C][C]12[/C][C]10.2502498999142[/C][C]1.74975010008577[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]11.7898391071569[/C][C]3.21016089284307[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]11.2710971912694[/C][C]-0.271097191269415[/C][/ROW]
[ROW][C]140[/C][C]10[/C][C]10.2216210600961[/C][C]-0.221621060096082[/C][/ROW]
[ROW][C]141[/C][C]5[/C][C]7.95064640131373[/C][C]-2.95064640131373[/C][/ROW]
[ROW][C]142[/C][C]11[/C][C]7.24621465424016[/C][C]3.75378534575984[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105001&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105001&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
11010.2979487964583-0.297948796458302
267.91239867279394-1.91239867279394
31310.3390985411632.660901458837
4129.747116785063722.25288321493628
5812.3608178413918-4.36081784139178
668.94314617595213-2.94314617595213
71012.3832569313601-2.38325693136006
8109.989165788988390.0108342110116108
999.18671490635274-0.186714906352737
10910.2562755658211-1.25627556582115
1179.04890707973803-2.04890707973803
1259.06217141802112-4.06217141802112
131412.59925044127561.40074955872441
1468.79656241340654-2.79656241340653
151011.6013799337748-1.60137993377483
161010.8325459887723-0.832545988772341
1778.16337216151061-1.16337216151061
18109.44956699909250.550433000907498
1989.16610993350092-1.16610993350092
2067.16588821534516-1.16588821534516
211010.9857794411093-0.985779441109293
221210.24804047599171.75195952400826
2379.43366041479678-2.43366041479678
24158.65257029762356.3474297023765
25810.1491084527023-2.14910845270229
26109.95283830007740.0471616999226065
271310.51632268764532.48367731235471
2887.86665643010640.133343569893595
291111.3207867552581-0.320786755258090
3079.7319542572747-2.73195425727469
3198.374013825630880.625986174369125
32109.667538256346070.332461743653928
3388.10270249854559-0.102702498545585
341513.40450670649711.59549329350287
35910.1456662951063-1.14566629510635
3678.38185235282574-1.38185235282574
37119.612336758642131.38766324135787
3897.810879946040131.18912005395987
3989.76712144951388-1.76712144951388
4087.745626691781480.254373308218522
41129.073118487425982.92688151257402
421310.28874630084692.71125369915311
4398.99499972465050.00500027534950619
44119.367894153755421.63210584624458
4588.19415957056963-0.19415957056963
461010.8119350981055-0.811935098105464
471312.28621189574680.713788104253187
481211.21424842547550.78575157452449
49129.561375057510082.43862494248992
5098.839627744331890.160372255668115
51810.2068011119551-2.20680111195511
5298.495540119733290.504459880266712
53128.466625666228333.53337433377167
541211.75798837983540.242011620164587
551614.22519082272231.77480917727766
56118.891140122447422.10885987755258
571310.21399476851092.78600523148912
581010.6085494175370-0.608549417536961
59910.6229671645408-1.62296716454078
60149.528617143545254.47138285645474
611311.97298430951941.02701569048063
621210.32823024920771.67176975079226
63910.8763043033581-1.87630430335811
64910.5644844003651-1.56448440036514
651011.2699183270100-1.26991832701005
66810.3411115868558-2.34111158685581
67910.4275714591011-1.42757145910115
6898.984463945398930.0155360546010725
69118.764314221825152.23568577817485
7079.50950617187987-2.50950617187987
711111.5959093237277-0.595909323727745
7299.04322706372827-0.0432270637282655
73119.150584315128891.84941568487111
7499.58582093389701-0.585820933897015
75810.3495296341019-2.34952963410189
7698.130120307568740.86987969243126
7789.06927861793022-1.06927861793022
7899.94476031937515-0.94476031937515
791010.2566345301288-0.256634530128837
8099.89037004592268-0.890370045922676
811713.87113721940713.12886278059293
8279.46628918867527-2.46628918867527
831111.2710971912694-0.271097191269415
8499.99419258295186-0.994192582951863
85109.782647703191770.217352296808229
86118.364850167058542.63514983294146
8788.07350215496662-0.0735021549666187
881212.2362440267885-0.236244026788495
891010.1366359196199-0.136635919619902
9078.90866042512573-1.90866042512573
9198.831852682581910.168147317418089
9278.20186818590794-1.20186818590794
931210.60160310242131.39839689757873
9489.3770524761292-1.37705247612920
951310.58794485703482.41205514296516
96910.8991606935624-1.89916069356244
971512.71391283090472.28608716909534
9889.60084209563988-1.60084209563988
991411.52469112054272.47530887945727
1001413.89116556168690.108834438313056
101910.2593802311929-1.25938023119287
1021311.38760247188011.61239752811987
103119.065220932738641.93477906726136
1041011.9457960705909-1.94579607059089
105610.0273934103186-4.02739341031865
10688.39056873028816-0.390568730288156
1071011.4319914342948-1.43199143429479
108107.857857874760112.14214212523989
109108.933554451528641.06644554847136
1101212.0502741750573-0.0502741750573411
111109.680713847085670.319286152914328
11299.01387427623435-0.0138742762343448
11397.673287471902421.32671252809758
114119.431169418165021.56883058183498
11578.0289016846006-1.02890168460060
11678.8372557113458-1.83725571134581
11757.95064640131373-2.95064640131373
11898.842916557978270.157083442021736
119119.828029747082231.17197025291777
1201512.26699495751722.73300504248278
12198.031543105897670.968456894102328
12299.46720704583193-0.467207045831931
12389.51378642904974-1.51378642904975
1241315.1340650336149-2.13406503361495
1251010.3946259832878-0.394625983287812
1261311.33894869248321.66105130751682
12797.518174283920641.48182571607936
128119.588934397082891.41106560291711
129810.7738589128412-2.77385891284124
130109.37075421615550.629245783844505
13198.847922044186420.152077955813578
13287.942310697449370.0576893025506331
13387.76865119207120.231348807928797
1341310.41461475890152.58538524109854
1351110.90053427438880.0994657256111754
136810.1491084527023-2.14910845270229
1371210.25024989991421.74975010008577
1381511.78983910715693.21016089284307
1391111.2710971912694-0.271097191269415
1401010.2216210600961-0.221621060096082
14157.95064640131373-2.95064640131373
142117.246214654240163.75378534575984






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.8258484809910680.3483030380178630.174151519008932
210.8792721987070350.241455602585930.120727801292965
220.8152568986899070.3694862026201860.184743101310093
230.7393273850052780.5213452299894450.260672614994722
240.8590773242224840.2818453515550320.140922675777516
250.8034675734950010.3930648530099980.196532426504999
260.7303336262260640.5393327475478720.269666373773936
270.9507840414455150.0984319171089710.0492159585544855
280.9302443586538060.1395112826923880.069755641346194
290.9046953469639150.1906093060721700.0953046530360849
300.9562798738910180.08744025221796460.0437201261089823
310.945627908056650.1087441838867010.0543720919433504
320.9248384601300350.1503230797399310.0751615398699654
330.9059508892627550.1880982214744910.0940491107372453
340.9351384028835070.1297231942329860.064861597116493
350.9255313676305620.1489372647388750.0744686323694376
360.9147325196784180.1705349606431630.0852674803215817
370.9219413387829570.1561173224340860.0780586612170432
380.909136927971740.1817261440565210.0908630720282606
390.8908892499617820.2182215000764370.109110750038218
400.8624079437634370.2751841124731270.137592056236563
410.8531850979471880.2936298041056240.146814902052812
420.8519801311654170.2960397376691660.148019868834583
430.8560446729995870.2879106540008260.143955327000413
440.8326579010160990.3346841979678020.167342098983901
450.8002841535564750.399431692887050.199715846443525
460.7575657015340980.4848685969318040.242434298465902
470.7725962524411460.4548074951177090.227403747558854
480.7315422165622980.5369155668754030.268457783437702
490.7858673572836390.4282652854327220.214132642716361
500.752098820960360.4958023580792790.247901179039640
510.7492849142919760.5014301714160490.250715085708024
520.7068887737261270.5862224525477460.293111226273873
530.8009569862584010.3980860274831970.199043013741599
540.7743389541876020.4513220916247960.225661045812398
550.8194030232931580.3611939534136830.180596976706842
560.8451503383175170.3096993233649650.154849661682483
570.8735047910690030.2529904178619940.126495208930997
580.8439974352319890.3120051295360230.156002564768011
590.829968249712390.3400635005752200.170031750287610
600.9643481554725160.0713036890549690.0356518445274845
610.9554350017403470.08912999651930560.0445649982596528
620.9552029089689650.0895941820620710.0447970910310355
630.956269555847840.08746088830431940.0437304441521597
640.9500130134645410.09997397307091730.0499869865354587
650.9467315015582660.1065369968834680.0532684984417338
660.9459067394083770.1081865211832450.0540932605916226
670.936720134477620.1265597310447590.0632798655223796
680.9190414345643370.1619171308713260.080958565435663
690.922751122262290.1544977554754210.0772488777377105
700.9411101543664560.1177796912670880.0588898456335438
710.9250562659276820.1498874681446360.074943734072318
720.9044570899349380.1910858201301240.0955429100650622
730.9139619814554760.1720760370890470.0860380185445236
740.8906255955766120.2187488088467770.109374404423388
750.885300933645680.2293981327086410.114699066354320
760.8785802347795310.2428395304409370.121419765220469
770.8664838216337860.2670323567324290.133516178366214
780.842297643047110.3154047139057790.157702356952890
790.8065555879126170.3868888241747670.193444412087383
800.7738443210801620.4523113578396750.226155678919838
810.8137633552904070.3724732894191850.186236644709593
820.8213454744246990.3573090511506030.178654525575302
830.7824249076848360.4351501846303280.217575092315164
840.7631007003815410.4737985992369180.236899299618459
850.7181529385527150.563694122894570.281847061447285
860.8262657478088870.3474685043822250.173734252191113
870.7972293863068720.4055412273862570.202770613693128
880.7545638165870950.490872366825810.245436183412905
890.7071442255041110.5857115489917780.292855774495889
900.713208802377750.5735823952445010.286791197622250
910.6644373976491560.6711252047016890.335562602350844
920.6541383232317450.6917233535365090.345861676768255
930.6451143486816990.7097713026366020.354885651318301
940.6007192189876650.798561562024670.399280781012335
950.6469581739801710.7060836520396580.353041826019829
960.6191725905969920.7616548188060170.380827409403008
970.6088033670753640.7823932658492710.391196632924636
980.5591475600787670.8817048798424650.440852439921233
990.5399552718024070.9200894563951870.460044728197593
1000.5288180209190960.9423639581618080.471181979080904
1010.5310041011761970.9379917976476060.468995898823803
1020.6144838396873490.7710323206253030.385516160312651
1030.5836422491859710.8327155016280590.416357750814029
1040.6076749924368220.7846500151263560.392325007563178
1050.7058277537197630.5883444925604730.294172246280237
1060.6769102985440170.6461794029119660.323089701455983
1070.6870548776076240.6258902447847530.312945122392376
1080.6609525893351920.6780948213296160.339047410664808
1090.5987011279442010.8025977441115980.401298872055799
1100.6310330481033150.7379339037933710.368966951896685
1110.5653681384452830.8692637231094340.434631861554717
1120.4916104591301770.9832209182603540.508389540869823
1130.4194269178896320.8388538357792650.580573082110368
1140.3972581521310750.7945163042621510.602741847868925
1150.3472697542229340.6945395084458680.652730245777066
1160.2978675831568590.5957351663137180.702132416843141
1170.2768312620497450.553662524099490.723168737950255
1180.1997582693836250.399516538767250.800241730616375
1190.133767822923250.26753564584650.86623217707675
1200.1007988819891720.2015977639783440.899201118010828
1210.05642092915594080.1128418583118820.94357907084406
1220.02825891859208230.05651783718416470.971741081407918

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.825848480991068 & 0.348303038017863 & 0.174151519008932 \tabularnewline
21 & 0.879272198707035 & 0.24145560258593 & 0.120727801292965 \tabularnewline
22 & 0.815256898689907 & 0.369486202620186 & 0.184743101310093 \tabularnewline
23 & 0.739327385005278 & 0.521345229989445 & 0.260672614994722 \tabularnewline
24 & 0.859077324222484 & 0.281845351555032 & 0.140922675777516 \tabularnewline
25 & 0.803467573495001 & 0.393064853009998 & 0.196532426504999 \tabularnewline
26 & 0.730333626226064 & 0.539332747547872 & 0.269666373773936 \tabularnewline
27 & 0.950784041445515 & 0.098431917108971 & 0.0492159585544855 \tabularnewline
28 & 0.930244358653806 & 0.139511282692388 & 0.069755641346194 \tabularnewline
29 & 0.904695346963915 & 0.190609306072170 & 0.0953046530360849 \tabularnewline
30 & 0.956279873891018 & 0.0874402522179646 & 0.0437201261089823 \tabularnewline
31 & 0.94562790805665 & 0.108744183886701 & 0.0543720919433504 \tabularnewline
32 & 0.924838460130035 & 0.150323079739931 & 0.0751615398699654 \tabularnewline
33 & 0.905950889262755 & 0.188098221474491 & 0.0940491107372453 \tabularnewline
34 & 0.935138402883507 & 0.129723194232986 & 0.064861597116493 \tabularnewline
35 & 0.925531367630562 & 0.148937264738875 & 0.0744686323694376 \tabularnewline
36 & 0.914732519678418 & 0.170534960643163 & 0.0852674803215817 \tabularnewline
37 & 0.921941338782957 & 0.156117322434086 & 0.0780586612170432 \tabularnewline
38 & 0.90913692797174 & 0.181726144056521 & 0.0908630720282606 \tabularnewline
39 & 0.890889249961782 & 0.218221500076437 & 0.109110750038218 \tabularnewline
40 & 0.862407943763437 & 0.275184112473127 & 0.137592056236563 \tabularnewline
41 & 0.853185097947188 & 0.293629804105624 & 0.146814902052812 \tabularnewline
42 & 0.851980131165417 & 0.296039737669166 & 0.148019868834583 \tabularnewline
43 & 0.856044672999587 & 0.287910654000826 & 0.143955327000413 \tabularnewline
44 & 0.832657901016099 & 0.334684197967802 & 0.167342098983901 \tabularnewline
45 & 0.800284153556475 & 0.39943169288705 & 0.199715846443525 \tabularnewline
46 & 0.757565701534098 & 0.484868596931804 & 0.242434298465902 \tabularnewline
47 & 0.772596252441146 & 0.454807495117709 & 0.227403747558854 \tabularnewline
48 & 0.731542216562298 & 0.536915566875403 & 0.268457783437702 \tabularnewline
49 & 0.785867357283639 & 0.428265285432722 & 0.214132642716361 \tabularnewline
50 & 0.75209882096036 & 0.495802358079279 & 0.247901179039640 \tabularnewline
51 & 0.749284914291976 & 0.501430171416049 & 0.250715085708024 \tabularnewline
52 & 0.706888773726127 & 0.586222452547746 & 0.293111226273873 \tabularnewline
53 & 0.800956986258401 & 0.398086027483197 & 0.199043013741599 \tabularnewline
54 & 0.774338954187602 & 0.451322091624796 & 0.225661045812398 \tabularnewline
55 & 0.819403023293158 & 0.361193953413683 & 0.180596976706842 \tabularnewline
56 & 0.845150338317517 & 0.309699323364965 & 0.154849661682483 \tabularnewline
57 & 0.873504791069003 & 0.252990417861994 & 0.126495208930997 \tabularnewline
58 & 0.843997435231989 & 0.312005129536023 & 0.156002564768011 \tabularnewline
59 & 0.82996824971239 & 0.340063500575220 & 0.170031750287610 \tabularnewline
60 & 0.964348155472516 & 0.071303689054969 & 0.0356518445274845 \tabularnewline
61 & 0.955435001740347 & 0.0891299965193056 & 0.0445649982596528 \tabularnewline
62 & 0.955202908968965 & 0.089594182062071 & 0.0447970910310355 \tabularnewline
63 & 0.95626955584784 & 0.0874608883043194 & 0.0437304441521597 \tabularnewline
64 & 0.950013013464541 & 0.0999739730709173 & 0.0499869865354587 \tabularnewline
65 & 0.946731501558266 & 0.106536996883468 & 0.0532684984417338 \tabularnewline
66 & 0.945906739408377 & 0.108186521183245 & 0.0540932605916226 \tabularnewline
67 & 0.93672013447762 & 0.126559731044759 & 0.0632798655223796 \tabularnewline
68 & 0.919041434564337 & 0.161917130871326 & 0.080958565435663 \tabularnewline
69 & 0.92275112226229 & 0.154497755475421 & 0.0772488777377105 \tabularnewline
70 & 0.941110154366456 & 0.117779691267088 & 0.0588898456335438 \tabularnewline
71 & 0.925056265927682 & 0.149887468144636 & 0.074943734072318 \tabularnewline
72 & 0.904457089934938 & 0.191085820130124 & 0.0955429100650622 \tabularnewline
73 & 0.913961981455476 & 0.172076037089047 & 0.0860380185445236 \tabularnewline
74 & 0.890625595576612 & 0.218748808846777 & 0.109374404423388 \tabularnewline
75 & 0.88530093364568 & 0.229398132708641 & 0.114699066354320 \tabularnewline
76 & 0.878580234779531 & 0.242839530440937 & 0.121419765220469 \tabularnewline
77 & 0.866483821633786 & 0.267032356732429 & 0.133516178366214 \tabularnewline
78 & 0.84229764304711 & 0.315404713905779 & 0.157702356952890 \tabularnewline
79 & 0.806555587912617 & 0.386888824174767 & 0.193444412087383 \tabularnewline
80 & 0.773844321080162 & 0.452311357839675 & 0.226155678919838 \tabularnewline
81 & 0.813763355290407 & 0.372473289419185 & 0.186236644709593 \tabularnewline
82 & 0.821345474424699 & 0.357309051150603 & 0.178654525575302 \tabularnewline
83 & 0.782424907684836 & 0.435150184630328 & 0.217575092315164 \tabularnewline
84 & 0.763100700381541 & 0.473798599236918 & 0.236899299618459 \tabularnewline
85 & 0.718152938552715 & 0.56369412289457 & 0.281847061447285 \tabularnewline
86 & 0.826265747808887 & 0.347468504382225 & 0.173734252191113 \tabularnewline
87 & 0.797229386306872 & 0.405541227386257 & 0.202770613693128 \tabularnewline
88 & 0.754563816587095 & 0.49087236682581 & 0.245436183412905 \tabularnewline
89 & 0.707144225504111 & 0.585711548991778 & 0.292855774495889 \tabularnewline
90 & 0.71320880237775 & 0.573582395244501 & 0.286791197622250 \tabularnewline
91 & 0.664437397649156 & 0.671125204701689 & 0.335562602350844 \tabularnewline
92 & 0.654138323231745 & 0.691723353536509 & 0.345861676768255 \tabularnewline
93 & 0.645114348681699 & 0.709771302636602 & 0.354885651318301 \tabularnewline
94 & 0.600719218987665 & 0.79856156202467 & 0.399280781012335 \tabularnewline
95 & 0.646958173980171 & 0.706083652039658 & 0.353041826019829 \tabularnewline
96 & 0.619172590596992 & 0.761654818806017 & 0.380827409403008 \tabularnewline
97 & 0.608803367075364 & 0.782393265849271 & 0.391196632924636 \tabularnewline
98 & 0.559147560078767 & 0.881704879842465 & 0.440852439921233 \tabularnewline
99 & 0.539955271802407 & 0.920089456395187 & 0.460044728197593 \tabularnewline
100 & 0.528818020919096 & 0.942363958161808 & 0.471181979080904 \tabularnewline
101 & 0.531004101176197 & 0.937991797647606 & 0.468995898823803 \tabularnewline
102 & 0.614483839687349 & 0.771032320625303 & 0.385516160312651 \tabularnewline
103 & 0.583642249185971 & 0.832715501628059 & 0.416357750814029 \tabularnewline
104 & 0.607674992436822 & 0.784650015126356 & 0.392325007563178 \tabularnewline
105 & 0.705827753719763 & 0.588344492560473 & 0.294172246280237 \tabularnewline
106 & 0.676910298544017 & 0.646179402911966 & 0.323089701455983 \tabularnewline
107 & 0.687054877607624 & 0.625890244784753 & 0.312945122392376 \tabularnewline
108 & 0.660952589335192 & 0.678094821329616 & 0.339047410664808 \tabularnewline
109 & 0.598701127944201 & 0.802597744111598 & 0.401298872055799 \tabularnewline
110 & 0.631033048103315 & 0.737933903793371 & 0.368966951896685 \tabularnewline
111 & 0.565368138445283 & 0.869263723109434 & 0.434631861554717 \tabularnewline
112 & 0.491610459130177 & 0.983220918260354 & 0.508389540869823 \tabularnewline
113 & 0.419426917889632 & 0.838853835779265 & 0.580573082110368 \tabularnewline
114 & 0.397258152131075 & 0.794516304262151 & 0.602741847868925 \tabularnewline
115 & 0.347269754222934 & 0.694539508445868 & 0.652730245777066 \tabularnewline
116 & 0.297867583156859 & 0.595735166313718 & 0.702132416843141 \tabularnewline
117 & 0.276831262049745 & 0.55366252409949 & 0.723168737950255 \tabularnewline
118 & 0.199758269383625 & 0.39951653876725 & 0.800241730616375 \tabularnewline
119 & 0.13376782292325 & 0.2675356458465 & 0.86623217707675 \tabularnewline
120 & 0.100798881989172 & 0.201597763978344 & 0.899201118010828 \tabularnewline
121 & 0.0564209291559408 & 0.112841858311882 & 0.94357907084406 \tabularnewline
122 & 0.0282589185920823 & 0.0565178371841647 & 0.971741081407918 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105001&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]20[/C][C]0.825848480991068[/C][C]0.348303038017863[/C][C]0.174151519008932[/C][/ROW]
[ROW][C]21[/C][C]0.879272198707035[/C][C]0.24145560258593[/C][C]0.120727801292965[/C][/ROW]
[ROW][C]22[/C][C]0.815256898689907[/C][C]0.369486202620186[/C][C]0.184743101310093[/C][/ROW]
[ROW][C]23[/C][C]0.739327385005278[/C][C]0.521345229989445[/C][C]0.260672614994722[/C][/ROW]
[ROW][C]24[/C][C]0.859077324222484[/C][C]0.281845351555032[/C][C]0.140922675777516[/C][/ROW]
[ROW][C]25[/C][C]0.803467573495001[/C][C]0.393064853009998[/C][C]0.196532426504999[/C][/ROW]
[ROW][C]26[/C][C]0.730333626226064[/C][C]0.539332747547872[/C][C]0.269666373773936[/C][/ROW]
[ROW][C]27[/C][C]0.950784041445515[/C][C]0.098431917108971[/C][C]0.0492159585544855[/C][/ROW]
[ROW][C]28[/C][C]0.930244358653806[/C][C]0.139511282692388[/C][C]0.069755641346194[/C][/ROW]
[ROW][C]29[/C][C]0.904695346963915[/C][C]0.190609306072170[/C][C]0.0953046530360849[/C][/ROW]
[ROW][C]30[/C][C]0.956279873891018[/C][C]0.0874402522179646[/C][C]0.0437201261089823[/C][/ROW]
[ROW][C]31[/C][C]0.94562790805665[/C][C]0.108744183886701[/C][C]0.0543720919433504[/C][/ROW]
[ROW][C]32[/C][C]0.924838460130035[/C][C]0.150323079739931[/C][C]0.0751615398699654[/C][/ROW]
[ROW][C]33[/C][C]0.905950889262755[/C][C]0.188098221474491[/C][C]0.0940491107372453[/C][/ROW]
[ROW][C]34[/C][C]0.935138402883507[/C][C]0.129723194232986[/C][C]0.064861597116493[/C][/ROW]
[ROW][C]35[/C][C]0.925531367630562[/C][C]0.148937264738875[/C][C]0.0744686323694376[/C][/ROW]
[ROW][C]36[/C][C]0.914732519678418[/C][C]0.170534960643163[/C][C]0.0852674803215817[/C][/ROW]
[ROW][C]37[/C][C]0.921941338782957[/C][C]0.156117322434086[/C][C]0.0780586612170432[/C][/ROW]
[ROW][C]38[/C][C]0.90913692797174[/C][C]0.181726144056521[/C][C]0.0908630720282606[/C][/ROW]
[ROW][C]39[/C][C]0.890889249961782[/C][C]0.218221500076437[/C][C]0.109110750038218[/C][/ROW]
[ROW][C]40[/C][C]0.862407943763437[/C][C]0.275184112473127[/C][C]0.137592056236563[/C][/ROW]
[ROW][C]41[/C][C]0.853185097947188[/C][C]0.293629804105624[/C][C]0.146814902052812[/C][/ROW]
[ROW][C]42[/C][C]0.851980131165417[/C][C]0.296039737669166[/C][C]0.148019868834583[/C][/ROW]
[ROW][C]43[/C][C]0.856044672999587[/C][C]0.287910654000826[/C][C]0.143955327000413[/C][/ROW]
[ROW][C]44[/C][C]0.832657901016099[/C][C]0.334684197967802[/C][C]0.167342098983901[/C][/ROW]
[ROW][C]45[/C][C]0.800284153556475[/C][C]0.39943169288705[/C][C]0.199715846443525[/C][/ROW]
[ROW][C]46[/C][C]0.757565701534098[/C][C]0.484868596931804[/C][C]0.242434298465902[/C][/ROW]
[ROW][C]47[/C][C]0.772596252441146[/C][C]0.454807495117709[/C][C]0.227403747558854[/C][/ROW]
[ROW][C]48[/C][C]0.731542216562298[/C][C]0.536915566875403[/C][C]0.268457783437702[/C][/ROW]
[ROW][C]49[/C][C]0.785867357283639[/C][C]0.428265285432722[/C][C]0.214132642716361[/C][/ROW]
[ROW][C]50[/C][C]0.75209882096036[/C][C]0.495802358079279[/C][C]0.247901179039640[/C][/ROW]
[ROW][C]51[/C][C]0.749284914291976[/C][C]0.501430171416049[/C][C]0.250715085708024[/C][/ROW]
[ROW][C]52[/C][C]0.706888773726127[/C][C]0.586222452547746[/C][C]0.293111226273873[/C][/ROW]
[ROW][C]53[/C][C]0.800956986258401[/C][C]0.398086027483197[/C][C]0.199043013741599[/C][/ROW]
[ROW][C]54[/C][C]0.774338954187602[/C][C]0.451322091624796[/C][C]0.225661045812398[/C][/ROW]
[ROW][C]55[/C][C]0.819403023293158[/C][C]0.361193953413683[/C][C]0.180596976706842[/C][/ROW]
[ROW][C]56[/C][C]0.845150338317517[/C][C]0.309699323364965[/C][C]0.154849661682483[/C][/ROW]
[ROW][C]57[/C][C]0.873504791069003[/C][C]0.252990417861994[/C][C]0.126495208930997[/C][/ROW]
[ROW][C]58[/C][C]0.843997435231989[/C][C]0.312005129536023[/C][C]0.156002564768011[/C][/ROW]
[ROW][C]59[/C][C]0.82996824971239[/C][C]0.340063500575220[/C][C]0.170031750287610[/C][/ROW]
[ROW][C]60[/C][C]0.964348155472516[/C][C]0.071303689054969[/C][C]0.0356518445274845[/C][/ROW]
[ROW][C]61[/C][C]0.955435001740347[/C][C]0.0891299965193056[/C][C]0.0445649982596528[/C][/ROW]
[ROW][C]62[/C][C]0.955202908968965[/C][C]0.089594182062071[/C][C]0.0447970910310355[/C][/ROW]
[ROW][C]63[/C][C]0.95626955584784[/C][C]0.0874608883043194[/C][C]0.0437304441521597[/C][/ROW]
[ROW][C]64[/C][C]0.950013013464541[/C][C]0.0999739730709173[/C][C]0.0499869865354587[/C][/ROW]
[ROW][C]65[/C][C]0.946731501558266[/C][C]0.106536996883468[/C][C]0.0532684984417338[/C][/ROW]
[ROW][C]66[/C][C]0.945906739408377[/C][C]0.108186521183245[/C][C]0.0540932605916226[/C][/ROW]
[ROW][C]67[/C][C]0.93672013447762[/C][C]0.126559731044759[/C][C]0.0632798655223796[/C][/ROW]
[ROW][C]68[/C][C]0.919041434564337[/C][C]0.161917130871326[/C][C]0.080958565435663[/C][/ROW]
[ROW][C]69[/C][C]0.92275112226229[/C][C]0.154497755475421[/C][C]0.0772488777377105[/C][/ROW]
[ROW][C]70[/C][C]0.941110154366456[/C][C]0.117779691267088[/C][C]0.0588898456335438[/C][/ROW]
[ROW][C]71[/C][C]0.925056265927682[/C][C]0.149887468144636[/C][C]0.074943734072318[/C][/ROW]
[ROW][C]72[/C][C]0.904457089934938[/C][C]0.191085820130124[/C][C]0.0955429100650622[/C][/ROW]
[ROW][C]73[/C][C]0.913961981455476[/C][C]0.172076037089047[/C][C]0.0860380185445236[/C][/ROW]
[ROW][C]74[/C][C]0.890625595576612[/C][C]0.218748808846777[/C][C]0.109374404423388[/C][/ROW]
[ROW][C]75[/C][C]0.88530093364568[/C][C]0.229398132708641[/C][C]0.114699066354320[/C][/ROW]
[ROW][C]76[/C][C]0.878580234779531[/C][C]0.242839530440937[/C][C]0.121419765220469[/C][/ROW]
[ROW][C]77[/C][C]0.866483821633786[/C][C]0.267032356732429[/C][C]0.133516178366214[/C][/ROW]
[ROW][C]78[/C][C]0.84229764304711[/C][C]0.315404713905779[/C][C]0.157702356952890[/C][/ROW]
[ROW][C]79[/C][C]0.806555587912617[/C][C]0.386888824174767[/C][C]0.193444412087383[/C][/ROW]
[ROW][C]80[/C][C]0.773844321080162[/C][C]0.452311357839675[/C][C]0.226155678919838[/C][/ROW]
[ROW][C]81[/C][C]0.813763355290407[/C][C]0.372473289419185[/C][C]0.186236644709593[/C][/ROW]
[ROW][C]82[/C][C]0.821345474424699[/C][C]0.357309051150603[/C][C]0.178654525575302[/C][/ROW]
[ROW][C]83[/C][C]0.782424907684836[/C][C]0.435150184630328[/C][C]0.217575092315164[/C][/ROW]
[ROW][C]84[/C][C]0.763100700381541[/C][C]0.473798599236918[/C][C]0.236899299618459[/C][/ROW]
[ROW][C]85[/C][C]0.718152938552715[/C][C]0.56369412289457[/C][C]0.281847061447285[/C][/ROW]
[ROW][C]86[/C][C]0.826265747808887[/C][C]0.347468504382225[/C][C]0.173734252191113[/C][/ROW]
[ROW][C]87[/C][C]0.797229386306872[/C][C]0.405541227386257[/C][C]0.202770613693128[/C][/ROW]
[ROW][C]88[/C][C]0.754563816587095[/C][C]0.49087236682581[/C][C]0.245436183412905[/C][/ROW]
[ROW][C]89[/C][C]0.707144225504111[/C][C]0.585711548991778[/C][C]0.292855774495889[/C][/ROW]
[ROW][C]90[/C][C]0.71320880237775[/C][C]0.573582395244501[/C][C]0.286791197622250[/C][/ROW]
[ROW][C]91[/C][C]0.664437397649156[/C][C]0.671125204701689[/C][C]0.335562602350844[/C][/ROW]
[ROW][C]92[/C][C]0.654138323231745[/C][C]0.691723353536509[/C][C]0.345861676768255[/C][/ROW]
[ROW][C]93[/C][C]0.645114348681699[/C][C]0.709771302636602[/C][C]0.354885651318301[/C][/ROW]
[ROW][C]94[/C][C]0.600719218987665[/C][C]0.79856156202467[/C][C]0.399280781012335[/C][/ROW]
[ROW][C]95[/C][C]0.646958173980171[/C][C]0.706083652039658[/C][C]0.353041826019829[/C][/ROW]
[ROW][C]96[/C][C]0.619172590596992[/C][C]0.761654818806017[/C][C]0.380827409403008[/C][/ROW]
[ROW][C]97[/C][C]0.608803367075364[/C][C]0.782393265849271[/C][C]0.391196632924636[/C][/ROW]
[ROW][C]98[/C][C]0.559147560078767[/C][C]0.881704879842465[/C][C]0.440852439921233[/C][/ROW]
[ROW][C]99[/C][C]0.539955271802407[/C][C]0.920089456395187[/C][C]0.460044728197593[/C][/ROW]
[ROW][C]100[/C][C]0.528818020919096[/C][C]0.942363958161808[/C][C]0.471181979080904[/C][/ROW]
[ROW][C]101[/C][C]0.531004101176197[/C][C]0.937991797647606[/C][C]0.468995898823803[/C][/ROW]
[ROW][C]102[/C][C]0.614483839687349[/C][C]0.771032320625303[/C][C]0.385516160312651[/C][/ROW]
[ROW][C]103[/C][C]0.583642249185971[/C][C]0.832715501628059[/C][C]0.416357750814029[/C][/ROW]
[ROW][C]104[/C][C]0.607674992436822[/C][C]0.784650015126356[/C][C]0.392325007563178[/C][/ROW]
[ROW][C]105[/C][C]0.705827753719763[/C][C]0.588344492560473[/C][C]0.294172246280237[/C][/ROW]
[ROW][C]106[/C][C]0.676910298544017[/C][C]0.646179402911966[/C][C]0.323089701455983[/C][/ROW]
[ROW][C]107[/C][C]0.687054877607624[/C][C]0.625890244784753[/C][C]0.312945122392376[/C][/ROW]
[ROW][C]108[/C][C]0.660952589335192[/C][C]0.678094821329616[/C][C]0.339047410664808[/C][/ROW]
[ROW][C]109[/C][C]0.598701127944201[/C][C]0.802597744111598[/C][C]0.401298872055799[/C][/ROW]
[ROW][C]110[/C][C]0.631033048103315[/C][C]0.737933903793371[/C][C]0.368966951896685[/C][/ROW]
[ROW][C]111[/C][C]0.565368138445283[/C][C]0.869263723109434[/C][C]0.434631861554717[/C][/ROW]
[ROW][C]112[/C][C]0.491610459130177[/C][C]0.983220918260354[/C][C]0.508389540869823[/C][/ROW]
[ROW][C]113[/C][C]0.419426917889632[/C][C]0.838853835779265[/C][C]0.580573082110368[/C][/ROW]
[ROW][C]114[/C][C]0.397258152131075[/C][C]0.794516304262151[/C][C]0.602741847868925[/C][/ROW]
[ROW][C]115[/C][C]0.347269754222934[/C][C]0.694539508445868[/C][C]0.652730245777066[/C][/ROW]
[ROW][C]116[/C][C]0.297867583156859[/C][C]0.595735166313718[/C][C]0.702132416843141[/C][/ROW]
[ROW][C]117[/C][C]0.276831262049745[/C][C]0.55366252409949[/C][C]0.723168737950255[/C][/ROW]
[ROW][C]118[/C][C]0.199758269383625[/C][C]0.39951653876725[/C][C]0.800241730616375[/C][/ROW]
[ROW][C]119[/C][C]0.13376782292325[/C][C]0.2675356458465[/C][C]0.86623217707675[/C][/ROW]
[ROW][C]120[/C][C]0.100798881989172[/C][C]0.201597763978344[/C][C]0.899201118010828[/C][/ROW]
[ROW][C]121[/C][C]0.0564209291559408[/C][C]0.112841858311882[/C][C]0.94357907084406[/C][/ROW]
[ROW][C]122[/C][C]0.0282589185920823[/C][C]0.0565178371841647[/C][C]0.971741081407918[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105001&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105001&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
200.8258484809910680.3483030380178630.174151519008932
210.8792721987070350.241455602585930.120727801292965
220.8152568986899070.3694862026201860.184743101310093
230.7393273850052780.5213452299894450.260672614994722
240.8590773242224840.2818453515550320.140922675777516
250.8034675734950010.3930648530099980.196532426504999
260.7303336262260640.5393327475478720.269666373773936
270.9507840414455150.0984319171089710.0492159585544855
280.9302443586538060.1395112826923880.069755641346194
290.9046953469639150.1906093060721700.0953046530360849
300.9562798738910180.08744025221796460.0437201261089823
310.945627908056650.1087441838867010.0543720919433504
320.9248384601300350.1503230797399310.0751615398699654
330.9059508892627550.1880982214744910.0940491107372453
340.9351384028835070.1297231942329860.064861597116493
350.9255313676305620.1489372647388750.0744686323694376
360.9147325196784180.1705349606431630.0852674803215817
370.9219413387829570.1561173224340860.0780586612170432
380.909136927971740.1817261440565210.0908630720282606
390.8908892499617820.2182215000764370.109110750038218
400.8624079437634370.2751841124731270.137592056236563
410.8531850979471880.2936298041056240.146814902052812
420.8519801311654170.2960397376691660.148019868834583
430.8560446729995870.2879106540008260.143955327000413
440.8326579010160990.3346841979678020.167342098983901
450.8002841535564750.399431692887050.199715846443525
460.7575657015340980.4848685969318040.242434298465902
470.7725962524411460.4548074951177090.227403747558854
480.7315422165622980.5369155668754030.268457783437702
490.7858673572836390.4282652854327220.214132642716361
500.752098820960360.4958023580792790.247901179039640
510.7492849142919760.5014301714160490.250715085708024
520.7068887737261270.5862224525477460.293111226273873
530.8009569862584010.3980860274831970.199043013741599
540.7743389541876020.4513220916247960.225661045812398
550.8194030232931580.3611939534136830.180596976706842
560.8451503383175170.3096993233649650.154849661682483
570.8735047910690030.2529904178619940.126495208930997
580.8439974352319890.3120051295360230.156002564768011
590.829968249712390.3400635005752200.170031750287610
600.9643481554725160.0713036890549690.0356518445274845
610.9554350017403470.08912999651930560.0445649982596528
620.9552029089689650.0895941820620710.0447970910310355
630.956269555847840.08746088830431940.0437304441521597
640.9500130134645410.09997397307091730.0499869865354587
650.9467315015582660.1065369968834680.0532684984417338
660.9459067394083770.1081865211832450.0540932605916226
670.936720134477620.1265597310447590.0632798655223796
680.9190414345643370.1619171308713260.080958565435663
690.922751122262290.1544977554754210.0772488777377105
700.9411101543664560.1177796912670880.0588898456335438
710.9250562659276820.1498874681446360.074943734072318
720.9044570899349380.1910858201301240.0955429100650622
730.9139619814554760.1720760370890470.0860380185445236
740.8906255955766120.2187488088467770.109374404423388
750.885300933645680.2293981327086410.114699066354320
760.8785802347795310.2428395304409370.121419765220469
770.8664838216337860.2670323567324290.133516178366214
780.842297643047110.3154047139057790.157702356952890
790.8065555879126170.3868888241747670.193444412087383
800.7738443210801620.4523113578396750.226155678919838
810.8137633552904070.3724732894191850.186236644709593
820.8213454744246990.3573090511506030.178654525575302
830.7824249076848360.4351501846303280.217575092315164
840.7631007003815410.4737985992369180.236899299618459
850.7181529385527150.563694122894570.281847061447285
860.8262657478088870.3474685043822250.173734252191113
870.7972293863068720.4055412273862570.202770613693128
880.7545638165870950.490872366825810.245436183412905
890.7071442255041110.5857115489917780.292855774495889
900.713208802377750.5735823952445010.286791197622250
910.6644373976491560.6711252047016890.335562602350844
920.6541383232317450.6917233535365090.345861676768255
930.6451143486816990.7097713026366020.354885651318301
940.6007192189876650.798561562024670.399280781012335
950.6469581739801710.7060836520396580.353041826019829
960.6191725905969920.7616548188060170.380827409403008
970.6088033670753640.7823932658492710.391196632924636
980.5591475600787670.8817048798424650.440852439921233
990.5399552718024070.9200894563951870.460044728197593
1000.5288180209190960.9423639581618080.471181979080904
1010.5310041011761970.9379917976476060.468995898823803
1020.6144838396873490.7710323206253030.385516160312651
1030.5836422491859710.8327155016280590.416357750814029
1040.6076749924368220.7846500151263560.392325007563178
1050.7058277537197630.5883444925604730.294172246280237
1060.6769102985440170.6461794029119660.323089701455983
1070.6870548776076240.6258902447847530.312945122392376
1080.6609525893351920.6780948213296160.339047410664808
1090.5987011279442010.8025977441115980.401298872055799
1100.6310330481033150.7379339037933710.368966951896685
1110.5653681384452830.8692637231094340.434631861554717
1120.4916104591301770.9832209182603540.508389540869823
1130.4194269178896320.8388538357792650.580573082110368
1140.3972581521310750.7945163042621510.602741847868925
1150.3472697542229340.6945395084458680.652730245777066
1160.2978675831568590.5957351663137180.702132416843141
1170.2768312620497450.553662524099490.723168737950255
1180.1997582693836250.399516538767250.800241730616375
1190.133767822923250.26753564584650.86623217707675
1200.1007988819891720.2015977639783440.899201118010828
1210.05642092915594080.1128418583118820.94357907084406
1220.02825891859208230.05651783718416470.971741081407918






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 level80.0776699029126214OK

\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 & 8 & 0.0776699029126214 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105001&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]8[/C][C]0.0776699029126214[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105001&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105001&T=6

As an alternative you can also use a QR Code:  
QR Code


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level80.0776699029126214OK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 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')
}