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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software ModulePatrick.Wessarwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 12 Dec 2010 13:04:15 +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/12/t1292159150fwui6aku241avp7.htm/, Retrieved Tue, 07 May 2024 14:04:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108422, Retrieved Tue, 07 May 2024 14:04:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
F RMPD    [Multiple Regression] [workshop 10c] [2010-12-12 13:04:15] [3f56c8f677e988de577e4e00a8180a48] [Current]
Feedback Forum
2010-12-19 14:15:56 [48eb36e2c01435ad7e4ea7854a9d98fe] [reply] 
De student stelt hier op een juiste manier een meervoudig regressiemodel op en ook de interpretatie is correct, maar wel zeer beperkt. Zo wordt bij het bespreken van de 'adjusted R squared' geen aandacht geschonken aan bijhorende P waarde en ook de onderliggende assumpties voor dit model werden (hoewel voldaan) niet besproken.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
11	6	6	4	15	16	2	40	37	15	10	77
26	16	5	4	23	24	1	29	31	9	20	63
26	13	20	10	26	22	1	37	35	12	16	73
15	7	12	6	19	21	1	32	36	15	10	76
10	10	11	5	19	23	1	39	32	17	8	90
21	10	12	8	16	23	1	32	30	14	14	67
27	15	11	9	23	21	2	35	34	9	19	69
21	9	9	9	22	20	2	35	34	12	15	70
21	12	13	8	19	22	1	28	22	11	23	54
21	8	9	11	24	20	1	37	27	13	9	54
22	9	14	6	19	12	2	32	27	16	12	76
29	10	12	8	25	23	2	34	33	16	14	75
29	15	18	11	23	23	2	37	38	15	13	76
29	11	9	5	31	30	1	35	37	10	11	80
30	12	15	10	29	22	2	40	31	16	11	89
19	9	12	7	18	21	1	37	36	12	10	73
19	10	12	7	17	21	1	37	38	15	12	74
22	13	12	13	22	15	2	33	31	13	18	78
18	8	15	10	21	22	2	37	34	18	12	76
28	14	11	8	24	24	1	35	33	13	10	69
17	9	13	6	22	23	2	36	38	17	15	74
18	12	10	8	16	15	2	32	28	14	15	82
20	8	17	7	22	24	1	38	34	13	12	77
16	8	13	5	21	24	2	34	32	13	9	84
17	9	17	9	25	21	2	33	34	15	11	75
25	14	15	11	22	21	2	33	39	15	16	79
22	11	13	11	24	18	2	42	37	13	17	79
34	16	18	11	21	20	2	33	34	14	12	69
31	9	17	9	25	19	2	32	41	13	11	88
38	11	21	7	29	29	2	32	32	16	13	57
18	13	12	6	19	20	2	33	35	14	9	69
25	12	12	7	29	23	1	35	33	12	14	52
20	9	15	6	25	24	2	39	32	18	11	86
23	14	8	5	19	27	1	28	32	9	20	66
12	4	15	4	27	28	1	38	32	16	8	54
20	8	16	10	25	24	2	36	37	16	12	85
15	14	9	8	23	29	1	38	31	17	10	79
21	10	13	6	24	24	1	34	27	13	11	84
21	13	17	11	23	22	2	33	31	15	11	73
20	10	11	4	25	25	2	37	37	17	13	70
30	14	9	9	23	14	2	34	31	15	13	54
22	13	15	10	22	22	2	34	40	14	13	70
33	14	9	6	32	24	1	36	35	10	15	54
25	14	15	9	22	24	1	31	35	13	12	69
20	14	14	10	18	24	2	37	35	11	13	68
10	5	8	6	19	24	1	36	35	16	11	76
15	11	11	8	23	22	1	34	38	16	9	71
21	9	14	13	19	21	2	30	35	11	14	66
16	9	12	8	16	21	2	29	34	15	9	67
23	10	15	10	23	21	2	35	37	15	9	71
25	14	11	5	17	15	2	33	37	12	15	54
18	6	11	8	17	26	2	29	31	17	10	76
33	11	9	6	28	22	1	28	31	15	13	77
18	13	8	9	24	24	1	32	33	16	8	71
18	12	13	9	21	13	2	33	37	14	15	69
13	8	12	7	14	19	2	31	36	17	13	73
24	14	24	20	21	10	2	43	42	10	24	46
19	11	11	8	20	28	1	32	28	11	11	66
20	11	11	8	25	25	2	35	41	15	13	77
21	11	16	7	20	24	1	31	23	15	12	77
18	16	12	7	17	22	2	33	33	7	22	70
29	14	18	10	26	30	1	39	32	17	11	86
13	16	12	5	17	22	1	32	33	14	15	38
26	14	14	8	17	24	1	32	33	18	7	66
22	9	16	9	24	23	1	36	32	14	14	75
28	8	24	20	30	20	1	39	38	14	10	64
28	11	13	6	25	22	2	41	32	9	9	80
23	8	11	10	15	22	2	30	35	14	12	86
22	14	14	11	25	19	2	30	35	11	16	54
28	8	16	12	18	24	2	32	34	15	10	54
28	8	12	7	20	22	2	39	34	16	13	74
31	10	21	12	32	26	2	38	38	17	11	88
15	8	11	8	14	12	2	38	39	16	12	85
15	8	6	6	20	25	1	32	32	12	11	63
24	10	9	6	25	29	2	34	39	15	13	81
22	9	14	9	25	23	2	36	35	15	10	74
17	9	16	5	25	23	2	39	36	16	11	80
25	7	18	11	35	17	2	31	28	16	9	80
32	16	9	6	29	26	1	36	36	11	13	60
23	14	13	10	25	27	2	34	38	12	14	62
20	11	17	8	21	23	1	34	35	14	14	63
20	9	11	7	21	20	2	38	39	15	11	89
28	16	16	8	24	24	2	38	36	17	10	76
20	7	11	9	26	22	2	33	36	19	11	81
20	11	11	8	24	26	2	32	34	15	12	72
23	14	11	10	20	29	1	30	34	16	14	84
20	11	20	13	24	20	2	31	27	14	14	76
21	8	10	7	18	17	2	34	37	16	21	76
14	11	12	7	17	16	2	35	33	15	13	72
31	8	11	8	22	24	1	37	34	17	11	81
21	12	14	9	22	24	2	35	39	12	12	72
18	8	12	9	22	19	2	35	29	18	12	78
26	13	12	8	24	29	2	31	33	13	11	79
25	8	12	7	32	25	2	31	35	14	14	52
9	13	10	6	19	25	1	38	36	14	13	67
18	9	12	8	21	24	1	34	30	14	13	74
19	12	10	8	23	29	1	30	27	12	12	73
29	11	7	4	26	22	2	32	37	14	14	69
31	14	10	8	18	23	1	31	33	12	12	67
24	9	13	10	19	15	2	37	32	15	12	76
16	10	12	7	22	29	2	34	35	11	12	77
19	9	13	8	27	21	1	32	33	11	18	63
19	9	9	7	21	23	2	34	37	15	11	84
22	8	14	10	20	20	2	38	36	14	15	90
31	16	14	9	21	25	1	38	39	15	13	75
20	10	12	8	20	28	2	38	35	16	11	76
26	11	18	5	29	18	2	39	31	14	22	53
17	6	17	8	30	25	2	33	37	18	10	87
16	9	12	9	10	13	2	34	36	13	16	69
16	8	15	9	23	24	2	35	31	14	11	78
9	6	8	11	29	23	2	36	32	13	15	54
19	20	8	7	19	25	1	32	33	14	14	58
22	10	12	8	26	27	2	34	36	14	11	80
15	8	10	4	22	24	2	44	39	17	10	74
25	16	18	16	26	24	2	37	39	12	14	56
30	9	15	9	27	26	2	32	29	16	14	82
30	12	16	10	19	18	2	35	34	15	11	64
24	14	11	12	24	26	1	38	35	10	15	67
20	10	10	8	26	23	1	38	32	13	11	75
12	7	7	4	22	28	1	38	41	15	10	69
31	14	17	11	23	20	2	32	38	16	10	72
25	11	7	8	25	23	2	39	38	14	12	54
23	13	14	12	19	24	1	27	32	13	15	54
23	10	12	8	20	21	2	37	31	17	10	71
26	9	15	6	25	25	2	41	38	14	12	53
14	15	13	8	14	16	2	31	38	16	15	54
18	12	10	8	19	23	1	36	33	15	12	71
28	12	16	14	27	22	2	38	28	12	11	69
19	9	11	10	21	27	1	37	38	16	10	30
21	15	7	5	21	24	1	30	28	8	20	53
18	10	15	8	14	17	1	40	32	9	19	68
29	13	18	12	21	21	2	34	31	13	17	69
16	11	11	11	23	21	2	36	34	19	8	54
22	10	13	8	18	19	2	36	35	11	17	66
15	12	11	8	20	25	1	33	36	15	11	79
21	9	13	9	19	24	1	34	33	11	13	67
17	14	12	6	15	21	1	37	32	15	9	74
17	9	11	5	23	26	1	37	32	16	10	86
33	14	11	8	26	25	2	39	40	15	13	63
17	11	13	7	21	25	2	37	35	12	16	69
20	11	8	4	13	13	1	37	33	16	12	73
17	9	12	9	24	25	1	35	37	15	14	69
16	11	9	5	17	23	1	32	33	13	11	71
18	10	14	9	21	26	2	33	31	14	13	77
32	12	18	12	28	22	2	31	33	11	15	74
22	10	15	6	22	20	2	30	34	15	14	82
19	6	17	8	25	14	2	32	35	12	18	84
29	16	11	6	27	24	2	33	40	14	14	54
23	14	17	7	25	21	2	29	30	13	10	80
17	8	12	9	21	24	2	37	38	15	8	76

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108422&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108422&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108422&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Person-Standards[t] = + 3.84738057444685 + 0.328446319879134Mistakes[t] -0.354503027876362Doubts[t] + 0.163700499059318`P-Expectations`[t] + 0.0505586668721037`P-Criticism`[t] + 0.440539729096576Organization[t] + 0.969401241072715Gender[t] + 0.162183660907544connected[t] -0.0530528042838888separate[t] -0.0554072382256775hapiness[t] + 0.0229805470290063depression[t] -0.0305734635206916sport[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Person-Standards[t] =  +  3.84738057444685 +  0.328446319879134Mistakes[t] -0.354503027876362Doubts[t] +  0.163700499059318`P-Expectations`[t] +  0.0505586668721037`P-Criticism`[t] +  0.440539729096576Organization[t] +  0.969401241072715Gender[t] +  0.162183660907544connected[t] -0.0530528042838888separate[t] -0.0554072382256775hapiness[t] +  0.0229805470290063depression[t] -0.0305734635206916sport[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108422&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Person-Standards[t] =  +  3.84738057444685 +  0.328446319879134Mistakes[t] -0.354503027876362Doubts[t] +  0.163700499059318`P-Expectations`[t] +  0.0505586668721037`P-Criticism`[t] +  0.440539729096576Organization[t] +  0.969401241072715Gender[t] +  0.162183660907544connected[t] -0.0530528042838888separate[t] -0.0554072382256775hapiness[t] +  0.0229805470290063depression[t] -0.0305734635206916sport[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108422&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108422&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
Person-Standards[t] = + 3.84738057444685 + 0.328446319879134Mistakes[t] -0.354503027876362Doubts[t] + 0.163700499059318`P-Expectations`[t] + 0.0505586668721037`P-Criticism`[t] + 0.440539729096576Organization[t] + 0.969401241072715Gender[t] + 0.162183660907544connected[t] -0.0530528042838888separate[t] -0.0554072382256775hapiness[t] + 0.0229805470290063depression[t] -0.0305734635206916sport[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.847380574446855.9056110.65150.5158210.25791
Mistakes0.3284463198791340.0579855.664300
Doubts-0.3545030278763620.12082-2.93420.0039180.001959
`P-Expectations`0.1637004990593180.1080131.51560.1319160.065958
`P-Criticism`0.05055866687210370.1360070.37170.710660.35533
Organization0.4405397290965760.0811995.425500
Gender0.9694012410727150.6815771.42230.1571980.078599
connected0.1621836609075440.0936281.73220.0854710.042736
separate-0.05305280428388880.088316-0.60070.5490160.274508
hapiness-0.05540723822567750.155465-0.35640.7220870.361043
depression0.02298054702900630.1162970.19760.8436470.421824
sport-0.03057346352069160.029014-1.05370.2938470.146923

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 3.84738057444685 & 5.905611 & 0.6515 & 0.515821 & 0.25791 \tabularnewline
Mistakes & 0.328446319879134 & 0.057985 & 5.6643 & 0 & 0 \tabularnewline
Doubts & -0.354503027876362 & 0.12082 & -2.9342 & 0.003918 & 0.001959 \tabularnewline
`P-Expectations` & 0.163700499059318 & 0.108013 & 1.5156 & 0.131916 & 0.065958 \tabularnewline
`P-Criticism` & 0.0505586668721037 & 0.136007 & 0.3717 & 0.71066 & 0.35533 \tabularnewline
Organization & 0.440539729096576 & 0.081199 & 5.4255 & 0 & 0 \tabularnewline
Gender & 0.969401241072715 & 0.681577 & 1.4223 & 0.157198 & 0.078599 \tabularnewline
connected & 0.162183660907544 & 0.093628 & 1.7322 & 0.085471 & 0.042736 \tabularnewline
separate & -0.0530528042838888 & 0.088316 & -0.6007 & 0.549016 & 0.274508 \tabularnewline
hapiness & -0.0554072382256775 & 0.155465 & -0.3564 & 0.722087 & 0.361043 \tabularnewline
depression & 0.0229805470290063 & 0.116297 & 0.1976 & 0.843647 & 0.421824 \tabularnewline
sport & -0.0305734635206916 & 0.029014 & -1.0537 & 0.293847 & 0.146923 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108422&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]3.84738057444685[/C][C]5.905611[/C][C]0.6515[/C][C]0.515821[/C][C]0.25791[/C][/ROW]
[ROW][C]Mistakes[/C][C]0.328446319879134[/C][C]0.057985[/C][C]5.6643[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Doubts[/C][C]-0.354503027876362[/C][C]0.12082[/C][C]-2.9342[/C][C]0.003918[/C][C]0.001959[/C][/ROW]
[ROW][C]`P-Expectations`[/C][C]0.163700499059318[/C][C]0.108013[/C][C]1.5156[/C][C]0.131916[/C][C]0.065958[/C][/ROW]
[ROW][C]`P-Criticism`[/C][C]0.0505586668721037[/C][C]0.136007[/C][C]0.3717[/C][C]0.71066[/C][C]0.35533[/C][/ROW]
[ROW][C]Organization[/C][C]0.440539729096576[/C][C]0.081199[/C][C]5.4255[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Gender[/C][C]0.969401241072715[/C][C]0.681577[/C][C]1.4223[/C][C]0.157198[/C][C]0.078599[/C][/ROW]
[ROW][C]connected[/C][C]0.162183660907544[/C][C]0.093628[/C][C]1.7322[/C][C]0.085471[/C][C]0.042736[/C][/ROW]
[ROW][C]separate[/C][C]-0.0530528042838888[/C][C]0.088316[/C][C]-0.6007[/C][C]0.549016[/C][C]0.274508[/C][/ROW]
[ROW][C]hapiness[/C][C]-0.0554072382256775[/C][C]0.155465[/C][C]-0.3564[/C][C]0.722087[/C][C]0.361043[/C][/ROW]
[ROW][C]depression[/C][C]0.0229805470290063[/C][C]0.116297[/C][C]0.1976[/C][C]0.843647[/C][C]0.421824[/C][/ROW]
[ROW][C]sport[/C][C]-0.0305734635206916[/C][C]0.029014[/C][C]-1.0537[/C][C]0.293847[/C][C]0.146923[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108422&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108422&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.847380574446855.9056110.65150.5158210.25791
Mistakes0.3284463198791340.0579855.664300
Doubts-0.3545030278763620.12082-2.93420.0039180.001959
`P-Expectations`0.1637004990593180.1080131.51560.1319160.065958
`P-Criticism`0.05055866687210370.1360070.37170.710660.35533
Organization0.4405397290965760.0811995.425500
Gender0.9694012410727150.6815771.42230.1571980.078599
connected0.1621836609075440.0936281.73220.0854710.042736
separate-0.05305280428388880.088316-0.60070.5490160.274508
hapiness-0.05540723822567750.155465-0.35640.7220870.361043
depression0.02298054702900630.1162970.19760.8436470.421824
sport-0.03057346352069160.029014-1.05370.2938470.146923






Multiple Linear Regression - Regression Statistics
Multiple R0.63489725645034
R-squared0.403094526248169
Adjusted R-squared0.355515104427371
F-TEST (value)8.47203498534248
F-TEST (DF numerator)11
F-TEST (DF denominator)138
p-value2.73046030230262e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.4320284582129
Sum Squared Residuals1625.47706864169

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.63489725645034 \tabularnewline
R-squared & 0.403094526248169 \tabularnewline
Adjusted R-squared & 0.355515104427371 \tabularnewline
F-TEST (value) & 8.47203498534248 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 138 \tabularnewline
p-value & 2.73046030230262e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.4320284582129 \tabularnewline
Sum Squared Residuals & 1625.47706864169 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108422&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.63489725645034[/C][/ROW]
[ROW][C]R-squared[/C][C]0.403094526248169[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.355515104427371[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.47203498534248[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]138[/C][/ROW]
[ROW][C]p-value[/C][C]2.73046030230262e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.4320284582129[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1625.47706864169[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108422&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108422&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 R0.63489725645034
R-squared0.403094526248169
Adjusted R-squared0.355515104427371
F-TEST (value)8.47203498534248
F-TEST (DF numerator)11
F-TEST (DF denominator)138
p-value2.73046030230262e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.4320284582129
Sum Squared Residuals1625.47706864169






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11517.0740806190035-2.07408061900347
22320.37153517572182.62846482427823
32623.83420382040492.16579617959509
41919.1361375836983-0.136137583698324
51917.85990997662461.14009002337539
61621.7663106350127-5.76631063501266
72322.54478513041710.45521486958288
82221.64446728487150.355532715128475
91921.3266548035707-2.32665480357069
102422.12230825315301.87769174684696
111918.52622953880900.473770461190965
122525.173089159465-0.173089159464980
132324.7575932040346-1.75759320403456
143126.35079241083324.64920758916675
152925.52644596905333.47355403094668
161820.8603358841111-2.86033588411107
171720.2488931635272-3.24889316352721
182218.64927679816113.35072320183892
192122.6670139427815-1.6670139427815
202423.15399268563760.846007314362358
212221.70606958682650.29393041317349
221616.8601322325904-0.860132232590415
232223.8199561856778-1.81995618567777
242121.8940678964582-0.89406789645821
252521.34544718471453.6545528152855
262221.70156379963420.298436200365845
272421.83026731843352.16973268156649
282124.533620097611-3.53362009761103
292524.24242236461680.757577635383178
302932.0966144885543-3.09661448855428
311918.98499728908660.0150027109133627
322923.21734019103715.78265980896294
332523.75000609351511.24999390648491
341921.6515247674234-2.65152476742336
352724.44463102255212.55536897744787
362523.88299777079821.11700222920184
372320.82478117775392.17521882224607
382422.2196759813781.780324018622
392322.00318275545940.996817244540579
402523.08103587013401.91896412986595
412320.45869800936262.54130199063740
422221.83146655554610.168533444453873
433225.16351040814276.83648959185733
442222.1651352309697-0.165135230969673
451822.5166334929058-4.51663349290582
461919.5390899878252-0.539089987825227
472318.38882328157044.61117671842957
481922.3964936132109-3.39649361321093
491619.697831673144-3.69783167314402
502322.92632141385460.0736785861454138
511719.1138547918179-2.1138547918179
521723.2533948546980-6.25339485469803
532823.23449501729154.7655049827085
542419.02360896591784.97639103408225
552116.60287638656894.39712361343114
561418.1512862601056-4.15128626010555
572121.3879960128425-0.387996012842517
582023.0278722594801-3.02787225948007
592522.28898920260202.71101079739803
602022.2586736671871-2.25867366718711
611719.6152562545267-2.61525625452672
622627.3560883694443-1.35608836944427
631717.1699587552859-0.169958755285926
641722.2473931186814-5.24739311868136
652423.45266197277440.547338027225605
663026.73459312833443.26540687166560
672525.4206022933960-0.420602293395971
681522.381999432999-7.38199943299899
692520.38307065807724.61692934192279
701827.0793329461060-9.07933294610603
712025.8280089161154-5.82800891611541
723228.68880686266433.31119313733570
731416.2529313064501-2.25293130645009
742020.3604589824629-0.360458982462942
752525.1125461880958-0.112546188095794
762523.81874784005581.18125215994420
772522.51931327740832.48068672259173
783522.924396569918612.0756034300814
792924.66927557301224.33072442698776
802524.16519583512680.834804164873185
812122.0832609370322-1.08326093703216
822120.92455188098380.0754481190161932
832424.1246397385776-0.124639738577637
842621.98695359225074.01304640774934
852422.74423434953761.25576565046242
862022.4187044772761-2.41870447727607
872422.01535424813311.98464575186689
881820.1514057651612-2.15140576516123
891717.0438853055198-0.0438853055197641
902225.9721343619424-3.97213436194241
912222.7662670233775-0.766267023377484
922220.68348436400471.31651563599531
932425.2559147275626-1.25591472756256
943225.62017827315916.37982172684093
951917.84581143187721.15418856812280
962121.6633869624894-0.663386962489435
972322.43245300826830.567546991731743
982623.11498595833192.8850140416681
991823.0488720328140-5.04887203281405
1001921.1443370556301-2.14433705563008
1012223.5597892702048-1.55978927020481
1022720.96782140802116.03217859197892
1032121.2005620328168-0.200562032816824
1042022.8546394220854-2.85463942208538
1052124.3554460231998-3.35544602319977
1062024.8628848564903-4.86288485649034
1072924.34537448345214.65462551654795
1083023.40531428105186.59468571894822
1091017.1394165818265-7.13941658182648
1102322.81293466421320.187065335786822
1112920.72771399950638.27228600049367
1121917.85610927040911.14389072959089
1132624.36597094160661.63402905839344
1142222.3815146209505-0.381514620950453
1152624.53025668793471.46974331206527
1162727.3931475092359-0.393147509235885
1171923.7776546605799-4.77765466057991
1182423.64623828282100.353761717179024
1192621.71933756286554.2806624371345
1202221.23680908736680.763190912633181
1212323.4706958555525-0.470695855552488
1222523.93884875622161.06115124377836
1231921.8886884837636-2.88868848376364
1242022.8889528010285-2.88895280102850
1252527.4208080341128-2.42080803411276
1261415.4670098941009-1.4670098941009
1271920.0104786659160-1.01047866591595
1282725.90337781019591.0966221898041
1292124.4784741301668-3.47847413016677
1302120.1442502163850.855749783615
1311420.1815639394191-6.18156393941906
1322124.9376480249430-3.93764802494304
1332320.26493227113842.73506772886163
1341822.114907220133-4.114907220133
1352019.15664201276220.843357987237819
1361923.0840626345285-4.08406263452846
1371518.3728049300077-3.37280493000771
1382321.7344512954961.26554870450399
1392626.6250982146054-0.625098214605373
1402122.7029277856591-1.70292778565911
1411316.1324708726183-3.13247087261825
1422421.73729369590612.26270630409391
1431718.8318118401506-1.83181184015059
1442123.2303674431162-2.23036744311617
1452826.03735921505291.96264078494714
1462221.08193544212180.91806455787817
1472519.76820004455615.23179995544386
1482723.44109680799103.55890319200903
1492520.94093581618764.05906418381244
1502122.5400752264709-1.54007522647091

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 15 & 17.0740806190035 & -2.07408061900347 \tabularnewline
2 & 23 & 20.3715351757218 & 2.62846482427823 \tabularnewline
3 & 26 & 23.8342038204049 & 2.16579617959509 \tabularnewline
4 & 19 & 19.1361375836983 & -0.136137583698324 \tabularnewline
5 & 19 & 17.8599099766246 & 1.14009002337539 \tabularnewline
6 & 16 & 21.7663106350127 & -5.76631063501266 \tabularnewline
7 & 23 & 22.5447851304171 & 0.45521486958288 \tabularnewline
8 & 22 & 21.6444672848715 & 0.355532715128475 \tabularnewline
9 & 19 & 21.3266548035707 & -2.32665480357069 \tabularnewline
10 & 24 & 22.1223082531530 & 1.87769174684696 \tabularnewline
11 & 19 & 18.5262295388090 & 0.473770461190965 \tabularnewline
12 & 25 & 25.173089159465 & -0.173089159464980 \tabularnewline
13 & 23 & 24.7575932040346 & -1.75759320403456 \tabularnewline
14 & 31 & 26.3507924108332 & 4.64920758916675 \tabularnewline
15 & 29 & 25.5264459690533 & 3.47355403094668 \tabularnewline
16 & 18 & 20.8603358841111 & -2.86033588411107 \tabularnewline
17 & 17 & 20.2488931635272 & -3.24889316352721 \tabularnewline
18 & 22 & 18.6492767981611 & 3.35072320183892 \tabularnewline
19 & 21 & 22.6670139427815 & -1.6670139427815 \tabularnewline
20 & 24 & 23.1539926856376 & 0.846007314362358 \tabularnewline
21 & 22 & 21.7060695868265 & 0.29393041317349 \tabularnewline
22 & 16 & 16.8601322325904 & -0.860132232590415 \tabularnewline
23 & 22 & 23.8199561856778 & -1.81995618567777 \tabularnewline
24 & 21 & 21.8940678964582 & -0.89406789645821 \tabularnewline
25 & 25 & 21.3454471847145 & 3.6545528152855 \tabularnewline
26 & 22 & 21.7015637996342 & 0.298436200365845 \tabularnewline
27 & 24 & 21.8302673184335 & 2.16973268156649 \tabularnewline
28 & 21 & 24.533620097611 & -3.53362009761103 \tabularnewline
29 & 25 & 24.2424223646168 & 0.757577635383178 \tabularnewline
30 & 29 & 32.0966144885543 & -3.09661448855428 \tabularnewline
31 & 19 & 18.9849972890866 & 0.0150027109133627 \tabularnewline
32 & 29 & 23.2173401910371 & 5.78265980896294 \tabularnewline
33 & 25 & 23.7500060935151 & 1.24999390648491 \tabularnewline
34 & 19 & 21.6515247674234 & -2.65152476742336 \tabularnewline
35 & 27 & 24.4446310225521 & 2.55536897744787 \tabularnewline
36 & 25 & 23.8829977707982 & 1.11700222920184 \tabularnewline
37 & 23 & 20.8247811777539 & 2.17521882224607 \tabularnewline
38 & 24 & 22.219675981378 & 1.780324018622 \tabularnewline
39 & 23 & 22.0031827554594 & 0.996817244540579 \tabularnewline
40 & 25 & 23.0810358701340 & 1.91896412986595 \tabularnewline
41 & 23 & 20.4586980093626 & 2.54130199063740 \tabularnewline
42 & 22 & 21.8314665555461 & 0.168533444453873 \tabularnewline
43 & 32 & 25.1635104081427 & 6.83648959185733 \tabularnewline
44 & 22 & 22.1651352309697 & -0.165135230969673 \tabularnewline
45 & 18 & 22.5166334929058 & -4.51663349290582 \tabularnewline
46 & 19 & 19.5390899878252 & -0.539089987825227 \tabularnewline
47 & 23 & 18.3888232815704 & 4.61117671842957 \tabularnewline
48 & 19 & 22.3964936132109 & -3.39649361321093 \tabularnewline
49 & 16 & 19.697831673144 & -3.69783167314402 \tabularnewline
50 & 23 & 22.9263214138546 & 0.0736785861454138 \tabularnewline
51 & 17 & 19.1138547918179 & -2.1138547918179 \tabularnewline
52 & 17 & 23.2533948546980 & -6.25339485469803 \tabularnewline
53 & 28 & 23.2344950172915 & 4.7655049827085 \tabularnewline
54 & 24 & 19.0236089659178 & 4.97639103408225 \tabularnewline
55 & 21 & 16.6028763865689 & 4.39712361343114 \tabularnewline
56 & 14 & 18.1512862601056 & -4.15128626010555 \tabularnewline
57 & 21 & 21.3879960128425 & -0.387996012842517 \tabularnewline
58 & 20 & 23.0278722594801 & -3.02787225948007 \tabularnewline
59 & 25 & 22.2889892026020 & 2.71101079739803 \tabularnewline
60 & 20 & 22.2586736671871 & -2.25867366718711 \tabularnewline
61 & 17 & 19.6152562545267 & -2.61525625452672 \tabularnewline
62 & 26 & 27.3560883694443 & -1.35608836944427 \tabularnewline
63 & 17 & 17.1699587552859 & -0.169958755285926 \tabularnewline
64 & 17 & 22.2473931186814 & -5.24739311868136 \tabularnewline
65 & 24 & 23.4526619727744 & 0.547338027225605 \tabularnewline
66 & 30 & 26.7345931283344 & 3.26540687166560 \tabularnewline
67 & 25 & 25.4206022933960 & -0.420602293395971 \tabularnewline
68 & 15 & 22.381999432999 & -7.38199943299899 \tabularnewline
69 & 25 & 20.3830706580772 & 4.61692934192279 \tabularnewline
70 & 18 & 27.0793329461060 & -9.07933294610603 \tabularnewline
71 & 20 & 25.8280089161154 & -5.82800891611541 \tabularnewline
72 & 32 & 28.6888068626643 & 3.31119313733570 \tabularnewline
73 & 14 & 16.2529313064501 & -2.25293130645009 \tabularnewline
74 & 20 & 20.3604589824629 & -0.360458982462942 \tabularnewline
75 & 25 & 25.1125461880958 & -0.112546188095794 \tabularnewline
76 & 25 & 23.8187478400558 & 1.18125215994420 \tabularnewline
77 & 25 & 22.5193132774083 & 2.48068672259173 \tabularnewline
78 & 35 & 22.9243965699186 & 12.0756034300814 \tabularnewline
79 & 29 & 24.6692755730122 & 4.33072442698776 \tabularnewline
80 & 25 & 24.1651958351268 & 0.834804164873185 \tabularnewline
81 & 21 & 22.0832609370322 & -1.08326093703216 \tabularnewline
82 & 21 & 20.9245518809838 & 0.0754481190161932 \tabularnewline
83 & 24 & 24.1246397385776 & -0.124639738577637 \tabularnewline
84 & 26 & 21.9869535922507 & 4.01304640774934 \tabularnewline
85 & 24 & 22.7442343495376 & 1.25576565046242 \tabularnewline
86 & 20 & 22.4187044772761 & -2.41870447727607 \tabularnewline
87 & 24 & 22.0153542481331 & 1.98464575186689 \tabularnewline
88 & 18 & 20.1514057651612 & -2.15140576516123 \tabularnewline
89 & 17 & 17.0438853055198 & -0.0438853055197641 \tabularnewline
90 & 22 & 25.9721343619424 & -3.97213436194241 \tabularnewline
91 & 22 & 22.7662670233775 & -0.766267023377484 \tabularnewline
92 & 22 & 20.6834843640047 & 1.31651563599531 \tabularnewline
93 & 24 & 25.2559147275626 & -1.25591472756256 \tabularnewline
94 & 32 & 25.6201782731591 & 6.37982172684093 \tabularnewline
95 & 19 & 17.8458114318772 & 1.15418856812280 \tabularnewline
96 & 21 & 21.6633869624894 & -0.663386962489435 \tabularnewline
97 & 23 & 22.4324530082683 & 0.567546991731743 \tabularnewline
98 & 26 & 23.1149859583319 & 2.8850140416681 \tabularnewline
99 & 18 & 23.0488720328140 & -5.04887203281405 \tabularnewline
100 & 19 & 21.1443370556301 & -2.14433705563008 \tabularnewline
101 & 22 & 23.5597892702048 & -1.55978927020481 \tabularnewline
102 & 27 & 20.9678214080211 & 6.03217859197892 \tabularnewline
103 & 21 & 21.2005620328168 & -0.200562032816824 \tabularnewline
104 & 20 & 22.8546394220854 & -2.85463942208538 \tabularnewline
105 & 21 & 24.3554460231998 & -3.35544602319977 \tabularnewline
106 & 20 & 24.8628848564903 & -4.86288485649034 \tabularnewline
107 & 29 & 24.3453744834521 & 4.65462551654795 \tabularnewline
108 & 30 & 23.4053142810518 & 6.59468571894822 \tabularnewline
109 & 10 & 17.1394165818265 & -7.13941658182648 \tabularnewline
110 & 23 & 22.8129346642132 & 0.187065335786822 \tabularnewline
111 & 29 & 20.7277139995063 & 8.27228600049367 \tabularnewline
112 & 19 & 17.8561092704091 & 1.14389072959089 \tabularnewline
113 & 26 & 24.3659709416066 & 1.63402905839344 \tabularnewline
114 & 22 & 22.3815146209505 & -0.381514620950453 \tabularnewline
115 & 26 & 24.5302566879347 & 1.46974331206527 \tabularnewline
116 & 27 & 27.3931475092359 & -0.393147509235885 \tabularnewline
117 & 19 & 23.7776546605799 & -4.77765466057991 \tabularnewline
118 & 24 & 23.6462382828210 & 0.353761717179024 \tabularnewline
119 & 26 & 21.7193375628655 & 4.2806624371345 \tabularnewline
120 & 22 & 21.2368090873668 & 0.763190912633181 \tabularnewline
121 & 23 & 23.4706958555525 & -0.470695855552488 \tabularnewline
122 & 25 & 23.9388487562216 & 1.06115124377836 \tabularnewline
123 & 19 & 21.8886884837636 & -2.88868848376364 \tabularnewline
124 & 20 & 22.8889528010285 & -2.88895280102850 \tabularnewline
125 & 25 & 27.4208080341128 & -2.42080803411276 \tabularnewline
126 & 14 & 15.4670098941009 & -1.4670098941009 \tabularnewline
127 & 19 & 20.0104786659160 & -1.01047866591595 \tabularnewline
128 & 27 & 25.9033778101959 & 1.0966221898041 \tabularnewline
129 & 21 & 24.4784741301668 & -3.47847413016677 \tabularnewline
130 & 21 & 20.144250216385 & 0.855749783615 \tabularnewline
131 & 14 & 20.1815639394191 & -6.18156393941906 \tabularnewline
132 & 21 & 24.9376480249430 & -3.93764802494304 \tabularnewline
133 & 23 & 20.2649322711384 & 2.73506772886163 \tabularnewline
134 & 18 & 22.114907220133 & -4.114907220133 \tabularnewline
135 & 20 & 19.1566420127622 & 0.843357987237819 \tabularnewline
136 & 19 & 23.0840626345285 & -4.08406263452846 \tabularnewline
137 & 15 & 18.3728049300077 & -3.37280493000771 \tabularnewline
138 & 23 & 21.734451295496 & 1.26554870450399 \tabularnewline
139 & 26 & 26.6250982146054 & -0.625098214605373 \tabularnewline
140 & 21 & 22.7029277856591 & -1.70292778565911 \tabularnewline
141 & 13 & 16.1324708726183 & -3.13247087261825 \tabularnewline
142 & 24 & 21.7372936959061 & 2.26270630409391 \tabularnewline
143 & 17 & 18.8318118401506 & -1.83181184015059 \tabularnewline
144 & 21 & 23.2303674431162 & -2.23036744311617 \tabularnewline
145 & 28 & 26.0373592150529 & 1.96264078494714 \tabularnewline
146 & 22 & 21.0819354421218 & 0.91806455787817 \tabularnewline
147 & 25 & 19.7682000445561 & 5.23179995544386 \tabularnewline
148 & 27 & 23.4410968079910 & 3.55890319200903 \tabularnewline
149 & 25 & 20.9409358161876 & 4.05906418381244 \tabularnewline
150 & 21 & 22.5400752264709 & -1.54007522647091 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108422&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]15[/C][C]17.0740806190035[/C][C]-2.07408061900347[/C][/ROW]
[ROW][C]2[/C][C]23[/C][C]20.3715351757218[/C][C]2.62846482427823[/C][/ROW]
[ROW][C]3[/C][C]26[/C][C]23.8342038204049[/C][C]2.16579617959509[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]19.1361375836983[/C][C]-0.136137583698324[/C][/ROW]
[ROW][C]5[/C][C]19[/C][C]17.8599099766246[/C][C]1.14009002337539[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]21.7663106350127[/C][C]-5.76631063501266[/C][/ROW]
[ROW][C]7[/C][C]23[/C][C]22.5447851304171[/C][C]0.45521486958288[/C][/ROW]
[ROW][C]8[/C][C]22[/C][C]21.6444672848715[/C][C]0.355532715128475[/C][/ROW]
[ROW][C]9[/C][C]19[/C][C]21.3266548035707[/C][C]-2.32665480357069[/C][/ROW]
[ROW][C]10[/C][C]24[/C][C]22.1223082531530[/C][C]1.87769174684696[/C][/ROW]
[ROW][C]11[/C][C]19[/C][C]18.5262295388090[/C][C]0.473770461190965[/C][/ROW]
[ROW][C]12[/C][C]25[/C][C]25.173089159465[/C][C]-0.173089159464980[/C][/ROW]
[ROW][C]13[/C][C]23[/C][C]24.7575932040346[/C][C]-1.75759320403456[/C][/ROW]
[ROW][C]14[/C][C]31[/C][C]26.3507924108332[/C][C]4.64920758916675[/C][/ROW]
[ROW][C]15[/C][C]29[/C][C]25.5264459690533[/C][C]3.47355403094668[/C][/ROW]
[ROW][C]16[/C][C]18[/C][C]20.8603358841111[/C][C]-2.86033588411107[/C][/ROW]
[ROW][C]17[/C][C]17[/C][C]20.2488931635272[/C][C]-3.24889316352721[/C][/ROW]
[ROW][C]18[/C][C]22[/C][C]18.6492767981611[/C][C]3.35072320183892[/C][/ROW]
[ROW][C]19[/C][C]21[/C][C]22.6670139427815[/C][C]-1.6670139427815[/C][/ROW]
[ROW][C]20[/C][C]24[/C][C]23.1539926856376[/C][C]0.846007314362358[/C][/ROW]
[ROW][C]21[/C][C]22[/C][C]21.7060695868265[/C][C]0.29393041317349[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]16.8601322325904[/C][C]-0.860132232590415[/C][/ROW]
[ROW][C]23[/C][C]22[/C][C]23.8199561856778[/C][C]-1.81995618567777[/C][/ROW]
[ROW][C]24[/C][C]21[/C][C]21.8940678964582[/C][C]-0.89406789645821[/C][/ROW]
[ROW][C]25[/C][C]25[/C][C]21.3454471847145[/C][C]3.6545528152855[/C][/ROW]
[ROW][C]26[/C][C]22[/C][C]21.7015637996342[/C][C]0.298436200365845[/C][/ROW]
[ROW][C]27[/C][C]24[/C][C]21.8302673184335[/C][C]2.16973268156649[/C][/ROW]
[ROW][C]28[/C][C]21[/C][C]24.533620097611[/C][C]-3.53362009761103[/C][/ROW]
[ROW][C]29[/C][C]25[/C][C]24.2424223646168[/C][C]0.757577635383178[/C][/ROW]
[ROW][C]30[/C][C]29[/C][C]32.0966144885543[/C][C]-3.09661448855428[/C][/ROW]
[ROW][C]31[/C][C]19[/C][C]18.9849972890866[/C][C]0.0150027109133627[/C][/ROW]
[ROW][C]32[/C][C]29[/C][C]23.2173401910371[/C][C]5.78265980896294[/C][/ROW]
[ROW][C]33[/C][C]25[/C][C]23.7500060935151[/C][C]1.24999390648491[/C][/ROW]
[ROW][C]34[/C][C]19[/C][C]21.6515247674234[/C][C]-2.65152476742336[/C][/ROW]
[ROW][C]35[/C][C]27[/C][C]24.4446310225521[/C][C]2.55536897744787[/C][/ROW]
[ROW][C]36[/C][C]25[/C][C]23.8829977707982[/C][C]1.11700222920184[/C][/ROW]
[ROW][C]37[/C][C]23[/C][C]20.8247811777539[/C][C]2.17521882224607[/C][/ROW]
[ROW][C]38[/C][C]24[/C][C]22.219675981378[/C][C]1.780324018622[/C][/ROW]
[ROW][C]39[/C][C]23[/C][C]22.0031827554594[/C][C]0.996817244540579[/C][/ROW]
[ROW][C]40[/C][C]25[/C][C]23.0810358701340[/C][C]1.91896412986595[/C][/ROW]
[ROW][C]41[/C][C]23[/C][C]20.4586980093626[/C][C]2.54130199063740[/C][/ROW]
[ROW][C]42[/C][C]22[/C][C]21.8314665555461[/C][C]0.168533444453873[/C][/ROW]
[ROW][C]43[/C][C]32[/C][C]25.1635104081427[/C][C]6.83648959185733[/C][/ROW]
[ROW][C]44[/C][C]22[/C][C]22.1651352309697[/C][C]-0.165135230969673[/C][/ROW]
[ROW][C]45[/C][C]18[/C][C]22.5166334929058[/C][C]-4.51663349290582[/C][/ROW]
[ROW][C]46[/C][C]19[/C][C]19.5390899878252[/C][C]-0.539089987825227[/C][/ROW]
[ROW][C]47[/C][C]23[/C][C]18.3888232815704[/C][C]4.61117671842957[/C][/ROW]
[ROW][C]48[/C][C]19[/C][C]22.3964936132109[/C][C]-3.39649361321093[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]19.697831673144[/C][C]-3.69783167314402[/C][/ROW]
[ROW][C]50[/C][C]23[/C][C]22.9263214138546[/C][C]0.0736785861454138[/C][/ROW]
[ROW][C]51[/C][C]17[/C][C]19.1138547918179[/C][C]-2.1138547918179[/C][/ROW]
[ROW][C]52[/C][C]17[/C][C]23.2533948546980[/C][C]-6.25339485469803[/C][/ROW]
[ROW][C]53[/C][C]28[/C][C]23.2344950172915[/C][C]4.7655049827085[/C][/ROW]
[ROW][C]54[/C][C]24[/C][C]19.0236089659178[/C][C]4.97639103408225[/C][/ROW]
[ROW][C]55[/C][C]21[/C][C]16.6028763865689[/C][C]4.39712361343114[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]18.1512862601056[/C][C]-4.15128626010555[/C][/ROW]
[ROW][C]57[/C][C]21[/C][C]21.3879960128425[/C][C]-0.387996012842517[/C][/ROW]
[ROW][C]58[/C][C]20[/C][C]23.0278722594801[/C][C]-3.02787225948007[/C][/ROW]
[ROW][C]59[/C][C]25[/C][C]22.2889892026020[/C][C]2.71101079739803[/C][/ROW]
[ROW][C]60[/C][C]20[/C][C]22.2586736671871[/C][C]-2.25867366718711[/C][/ROW]
[ROW][C]61[/C][C]17[/C][C]19.6152562545267[/C][C]-2.61525625452672[/C][/ROW]
[ROW][C]62[/C][C]26[/C][C]27.3560883694443[/C][C]-1.35608836944427[/C][/ROW]
[ROW][C]63[/C][C]17[/C][C]17.1699587552859[/C][C]-0.169958755285926[/C][/ROW]
[ROW][C]64[/C][C]17[/C][C]22.2473931186814[/C][C]-5.24739311868136[/C][/ROW]
[ROW][C]65[/C][C]24[/C][C]23.4526619727744[/C][C]0.547338027225605[/C][/ROW]
[ROW][C]66[/C][C]30[/C][C]26.7345931283344[/C][C]3.26540687166560[/C][/ROW]
[ROW][C]67[/C][C]25[/C][C]25.4206022933960[/C][C]-0.420602293395971[/C][/ROW]
[ROW][C]68[/C][C]15[/C][C]22.381999432999[/C][C]-7.38199943299899[/C][/ROW]
[ROW][C]69[/C][C]25[/C][C]20.3830706580772[/C][C]4.61692934192279[/C][/ROW]
[ROW][C]70[/C][C]18[/C][C]27.0793329461060[/C][C]-9.07933294610603[/C][/ROW]
[ROW][C]71[/C][C]20[/C][C]25.8280089161154[/C][C]-5.82800891611541[/C][/ROW]
[ROW][C]72[/C][C]32[/C][C]28.6888068626643[/C][C]3.31119313733570[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]16.2529313064501[/C][C]-2.25293130645009[/C][/ROW]
[ROW][C]74[/C][C]20[/C][C]20.3604589824629[/C][C]-0.360458982462942[/C][/ROW]
[ROW][C]75[/C][C]25[/C][C]25.1125461880958[/C][C]-0.112546188095794[/C][/ROW]
[ROW][C]76[/C][C]25[/C][C]23.8187478400558[/C][C]1.18125215994420[/C][/ROW]
[ROW][C]77[/C][C]25[/C][C]22.5193132774083[/C][C]2.48068672259173[/C][/ROW]
[ROW][C]78[/C][C]35[/C][C]22.9243965699186[/C][C]12.0756034300814[/C][/ROW]
[ROW][C]79[/C][C]29[/C][C]24.6692755730122[/C][C]4.33072442698776[/C][/ROW]
[ROW][C]80[/C][C]25[/C][C]24.1651958351268[/C][C]0.834804164873185[/C][/ROW]
[ROW][C]81[/C][C]21[/C][C]22.0832609370322[/C][C]-1.08326093703216[/C][/ROW]
[ROW][C]82[/C][C]21[/C][C]20.9245518809838[/C][C]0.0754481190161932[/C][/ROW]
[ROW][C]83[/C][C]24[/C][C]24.1246397385776[/C][C]-0.124639738577637[/C][/ROW]
[ROW][C]84[/C][C]26[/C][C]21.9869535922507[/C][C]4.01304640774934[/C][/ROW]
[ROW][C]85[/C][C]24[/C][C]22.7442343495376[/C][C]1.25576565046242[/C][/ROW]
[ROW][C]86[/C][C]20[/C][C]22.4187044772761[/C][C]-2.41870447727607[/C][/ROW]
[ROW][C]87[/C][C]24[/C][C]22.0153542481331[/C][C]1.98464575186689[/C][/ROW]
[ROW][C]88[/C][C]18[/C][C]20.1514057651612[/C][C]-2.15140576516123[/C][/ROW]
[ROW][C]89[/C][C]17[/C][C]17.0438853055198[/C][C]-0.0438853055197641[/C][/ROW]
[ROW][C]90[/C][C]22[/C][C]25.9721343619424[/C][C]-3.97213436194241[/C][/ROW]
[ROW][C]91[/C][C]22[/C][C]22.7662670233775[/C][C]-0.766267023377484[/C][/ROW]
[ROW][C]92[/C][C]22[/C][C]20.6834843640047[/C][C]1.31651563599531[/C][/ROW]
[ROW][C]93[/C][C]24[/C][C]25.2559147275626[/C][C]-1.25591472756256[/C][/ROW]
[ROW][C]94[/C][C]32[/C][C]25.6201782731591[/C][C]6.37982172684093[/C][/ROW]
[ROW][C]95[/C][C]19[/C][C]17.8458114318772[/C][C]1.15418856812280[/C][/ROW]
[ROW][C]96[/C][C]21[/C][C]21.6633869624894[/C][C]-0.663386962489435[/C][/ROW]
[ROW][C]97[/C][C]23[/C][C]22.4324530082683[/C][C]0.567546991731743[/C][/ROW]
[ROW][C]98[/C][C]26[/C][C]23.1149859583319[/C][C]2.8850140416681[/C][/ROW]
[ROW][C]99[/C][C]18[/C][C]23.0488720328140[/C][C]-5.04887203281405[/C][/ROW]
[ROW][C]100[/C][C]19[/C][C]21.1443370556301[/C][C]-2.14433705563008[/C][/ROW]
[ROW][C]101[/C][C]22[/C][C]23.5597892702048[/C][C]-1.55978927020481[/C][/ROW]
[ROW][C]102[/C][C]27[/C][C]20.9678214080211[/C][C]6.03217859197892[/C][/ROW]
[ROW][C]103[/C][C]21[/C][C]21.2005620328168[/C][C]-0.200562032816824[/C][/ROW]
[ROW][C]104[/C][C]20[/C][C]22.8546394220854[/C][C]-2.85463942208538[/C][/ROW]
[ROW][C]105[/C][C]21[/C][C]24.3554460231998[/C][C]-3.35544602319977[/C][/ROW]
[ROW][C]106[/C][C]20[/C][C]24.8628848564903[/C][C]-4.86288485649034[/C][/ROW]
[ROW][C]107[/C][C]29[/C][C]24.3453744834521[/C][C]4.65462551654795[/C][/ROW]
[ROW][C]108[/C][C]30[/C][C]23.4053142810518[/C][C]6.59468571894822[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]17.1394165818265[/C][C]-7.13941658182648[/C][/ROW]
[ROW][C]110[/C][C]23[/C][C]22.8129346642132[/C][C]0.187065335786822[/C][/ROW]
[ROW][C]111[/C][C]29[/C][C]20.7277139995063[/C][C]8.27228600049367[/C][/ROW]
[ROW][C]112[/C][C]19[/C][C]17.8561092704091[/C][C]1.14389072959089[/C][/ROW]
[ROW][C]113[/C][C]26[/C][C]24.3659709416066[/C][C]1.63402905839344[/C][/ROW]
[ROW][C]114[/C][C]22[/C][C]22.3815146209505[/C][C]-0.381514620950453[/C][/ROW]
[ROW][C]115[/C][C]26[/C][C]24.5302566879347[/C][C]1.46974331206527[/C][/ROW]
[ROW][C]116[/C][C]27[/C][C]27.3931475092359[/C][C]-0.393147509235885[/C][/ROW]
[ROW][C]117[/C][C]19[/C][C]23.7776546605799[/C][C]-4.77765466057991[/C][/ROW]
[ROW][C]118[/C][C]24[/C][C]23.6462382828210[/C][C]0.353761717179024[/C][/ROW]
[ROW][C]119[/C][C]26[/C][C]21.7193375628655[/C][C]4.2806624371345[/C][/ROW]
[ROW][C]120[/C][C]22[/C][C]21.2368090873668[/C][C]0.763190912633181[/C][/ROW]
[ROW][C]121[/C][C]23[/C][C]23.4706958555525[/C][C]-0.470695855552488[/C][/ROW]
[ROW][C]122[/C][C]25[/C][C]23.9388487562216[/C][C]1.06115124377836[/C][/ROW]
[ROW][C]123[/C][C]19[/C][C]21.8886884837636[/C][C]-2.88868848376364[/C][/ROW]
[ROW][C]124[/C][C]20[/C][C]22.8889528010285[/C][C]-2.88895280102850[/C][/ROW]
[ROW][C]125[/C][C]25[/C][C]27.4208080341128[/C][C]-2.42080803411276[/C][/ROW]
[ROW][C]126[/C][C]14[/C][C]15.4670098941009[/C][C]-1.4670098941009[/C][/ROW]
[ROW][C]127[/C][C]19[/C][C]20.0104786659160[/C][C]-1.01047866591595[/C][/ROW]
[ROW][C]128[/C][C]27[/C][C]25.9033778101959[/C][C]1.0966221898041[/C][/ROW]
[ROW][C]129[/C][C]21[/C][C]24.4784741301668[/C][C]-3.47847413016677[/C][/ROW]
[ROW][C]130[/C][C]21[/C][C]20.144250216385[/C][C]0.855749783615[/C][/ROW]
[ROW][C]131[/C][C]14[/C][C]20.1815639394191[/C][C]-6.18156393941906[/C][/ROW]
[ROW][C]132[/C][C]21[/C][C]24.9376480249430[/C][C]-3.93764802494304[/C][/ROW]
[ROW][C]133[/C][C]23[/C][C]20.2649322711384[/C][C]2.73506772886163[/C][/ROW]
[ROW][C]134[/C][C]18[/C][C]22.114907220133[/C][C]-4.114907220133[/C][/ROW]
[ROW][C]135[/C][C]20[/C][C]19.1566420127622[/C][C]0.843357987237819[/C][/ROW]
[ROW][C]136[/C][C]19[/C][C]23.0840626345285[/C][C]-4.08406263452846[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]18.3728049300077[/C][C]-3.37280493000771[/C][/ROW]
[ROW][C]138[/C][C]23[/C][C]21.734451295496[/C][C]1.26554870450399[/C][/ROW]
[ROW][C]139[/C][C]26[/C][C]26.6250982146054[/C][C]-0.625098214605373[/C][/ROW]
[ROW][C]140[/C][C]21[/C][C]22.7029277856591[/C][C]-1.70292778565911[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]16.1324708726183[/C][C]-3.13247087261825[/C][/ROW]
[ROW][C]142[/C][C]24[/C][C]21.7372936959061[/C][C]2.26270630409391[/C][/ROW]
[ROW][C]143[/C][C]17[/C][C]18.8318118401506[/C][C]-1.83181184015059[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]23.2303674431162[/C][C]-2.23036744311617[/C][/ROW]
[ROW][C]145[/C][C]28[/C][C]26.0373592150529[/C][C]1.96264078494714[/C][/ROW]
[ROW][C]146[/C][C]22[/C][C]21.0819354421218[/C][C]0.91806455787817[/C][/ROW]
[ROW][C]147[/C][C]25[/C][C]19.7682000445561[/C][C]5.23179995544386[/C][/ROW]
[ROW][C]148[/C][C]27[/C][C]23.4410968079910[/C][C]3.55890319200903[/C][/ROW]
[ROW][C]149[/C][C]25[/C][C]20.9409358161876[/C][C]4.05906418381244[/C][/ROW]
[ROW][C]150[/C][C]21[/C][C]22.5400752264709[/C][C]-1.54007522647091[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108422&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108422&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
11517.0740806190035-2.07408061900347
22320.37153517572182.62846482427823
32623.83420382040492.16579617959509
41919.1361375836983-0.136137583698324
51917.85990997662461.14009002337539
61621.7663106350127-5.76631063501266
72322.54478513041710.45521486958288
82221.64446728487150.355532715128475
91921.3266548035707-2.32665480357069
102422.12230825315301.87769174684696
111918.52622953880900.473770461190965
122525.173089159465-0.173089159464980
132324.7575932040346-1.75759320403456
143126.35079241083324.64920758916675
152925.52644596905333.47355403094668
161820.8603358841111-2.86033588411107
171720.2488931635272-3.24889316352721
182218.64927679816113.35072320183892
192122.6670139427815-1.6670139427815
202423.15399268563760.846007314362358
212221.70606958682650.29393041317349
221616.8601322325904-0.860132232590415
232223.8199561856778-1.81995618567777
242121.8940678964582-0.89406789645821
252521.34544718471453.6545528152855
262221.70156379963420.298436200365845
272421.83026731843352.16973268156649
282124.533620097611-3.53362009761103
292524.24242236461680.757577635383178
302932.0966144885543-3.09661448855428
311918.98499728908660.0150027109133627
322923.21734019103715.78265980896294
332523.75000609351511.24999390648491
341921.6515247674234-2.65152476742336
352724.44463102255212.55536897744787
362523.88299777079821.11700222920184
372320.82478117775392.17521882224607
382422.2196759813781.780324018622
392322.00318275545940.996817244540579
402523.08103587013401.91896412986595
412320.45869800936262.54130199063740
422221.83146655554610.168533444453873
433225.16351040814276.83648959185733
442222.1651352309697-0.165135230969673
451822.5166334929058-4.51663349290582
461919.5390899878252-0.539089987825227
472318.38882328157044.61117671842957
481922.3964936132109-3.39649361321093
491619.697831673144-3.69783167314402
502322.92632141385460.0736785861454138
511719.1138547918179-2.1138547918179
521723.2533948546980-6.25339485469803
532823.23449501729154.7655049827085
542419.02360896591784.97639103408225
552116.60287638656894.39712361343114
561418.1512862601056-4.15128626010555
572121.3879960128425-0.387996012842517
582023.0278722594801-3.02787225948007
592522.28898920260202.71101079739803
602022.2586736671871-2.25867366718711
611719.6152562545267-2.61525625452672
622627.3560883694443-1.35608836944427
631717.1699587552859-0.169958755285926
641722.2473931186814-5.24739311868136
652423.45266197277440.547338027225605
663026.73459312833443.26540687166560
672525.4206022933960-0.420602293395971
681522.381999432999-7.38199943299899
692520.38307065807724.61692934192279
701827.0793329461060-9.07933294610603
712025.8280089161154-5.82800891611541
723228.68880686266433.31119313733570
731416.2529313064501-2.25293130645009
742020.3604589824629-0.360458982462942
752525.1125461880958-0.112546188095794
762523.81874784005581.18125215994420
772522.51931327740832.48068672259173
783522.924396569918612.0756034300814
792924.66927557301224.33072442698776
802524.16519583512680.834804164873185
812122.0832609370322-1.08326093703216
822120.92455188098380.0754481190161932
832424.1246397385776-0.124639738577637
842621.98695359225074.01304640774934
852422.74423434953761.25576565046242
862022.4187044772761-2.41870447727607
872422.01535424813311.98464575186689
881820.1514057651612-2.15140576516123
891717.0438853055198-0.0438853055197641
902225.9721343619424-3.97213436194241
912222.7662670233775-0.766267023377484
922220.68348436400471.31651563599531
932425.2559147275626-1.25591472756256
943225.62017827315916.37982172684093
951917.84581143187721.15418856812280
962121.6633869624894-0.663386962489435
972322.43245300826830.567546991731743
982623.11498595833192.8850140416681
991823.0488720328140-5.04887203281405
1001921.1443370556301-2.14433705563008
1012223.5597892702048-1.55978927020481
1022720.96782140802116.03217859197892
1032121.2005620328168-0.200562032816824
1042022.8546394220854-2.85463942208538
1052124.3554460231998-3.35544602319977
1062024.8628848564903-4.86288485649034
1072924.34537448345214.65462551654795
1083023.40531428105186.59468571894822
1091017.1394165818265-7.13941658182648
1102322.81293466421320.187065335786822
1112920.72771399950638.27228600049367
1121917.85610927040911.14389072959089
1132624.36597094160661.63402905839344
1142222.3815146209505-0.381514620950453
1152624.53025668793471.46974331206527
1162727.3931475092359-0.393147509235885
1171923.7776546605799-4.77765466057991
1182423.64623828282100.353761717179024
1192621.71933756286554.2806624371345
1202221.23680908736680.763190912633181
1212323.4706958555525-0.470695855552488
1222523.93884875622161.06115124377836
1231921.8886884837636-2.88868848376364
1242022.8889528010285-2.88895280102850
1252527.4208080341128-2.42080803411276
1261415.4670098941009-1.4670098941009
1271920.0104786659160-1.01047866591595
1282725.90337781019591.0966221898041
1292124.4784741301668-3.47847413016677
1302120.1442502163850.855749783615
1311420.1815639394191-6.18156393941906
1322124.9376480249430-3.93764802494304
1332320.26493227113842.73506772886163
1341822.114907220133-4.114907220133
1352019.15664201276220.843357987237819
1361923.0840626345285-4.08406263452846
1371518.3728049300077-3.37280493000771
1382321.7344512954961.26554870450399
1392626.6250982146054-0.625098214605373
1402122.7029277856591-1.70292778565911
1411316.1324708726183-3.13247087261825
1422421.73729369590612.26270630409391
1431718.8318118401506-1.83181184015059
1442123.2303674431162-2.23036744311617
1452826.03735921505291.96264078494714
1462221.08193544212180.91806455787817
1472519.76820004455615.23179995544386
1482723.44109680799103.55890319200903
1492520.94093581618764.05906418381244
1502122.5400752264709-1.54007522647091






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.505704339954830.988591320090340.49429566004517
160.566741726202390.866516547595220.43325827379761
170.4522988135335190.9045976270670380.547701186466481
180.3321108149921270.6642216299842540.667889185007873
190.2673039097819780.5346078195639560.732696090218022
200.1803894736733020.3607789473466050.819610526326698
210.2436897323855660.4873794647711320.756310267614434
220.2013817836046530.4027635672093070.798618216395347
230.1513835186684020.3027670373368030.848616481331598
240.1017745366444200.2035490732888400.89822546335558
250.1783926995639390.3567853991278790.82160730043606
260.1275366162132360.2550732324264710.872463383786764
270.09634595762297290.1926919152459460.903654042377027
280.1017731359770760.2035462719541510.898226864022924
290.07087321611080770.1417464322216150.929126783889192
300.05038889805576160.1007777961115230.949611101944238
310.03719845580103420.07439691160206830.962801544198966
320.1399578300499390.2799156600998780.860042169950061
330.110207833209750.22041566641950.88979216679025
340.1025684209828430.2051368419656860.897431579017157
350.1041324665278740.2082649330557470.895867533472126
360.07851486373074270.1570297274614850.921485136269257
370.05929689616645510.1185937923329100.940703103833545
380.04367127486080080.08734254972160150.9563287251392
390.03088475139908780.06176950279817550.969115248600912
400.02475977153920340.04951954307840680.975240228460797
410.0208250461045660.0416500922091320.979174953895434
420.01401229265747500.02802458531494990.985987707342525
430.02626480315619950.0525296063123990.9737351968438
440.01815812066990400.03631624133980790.981841879330096
450.03436769627224970.06873539254449930.96563230372775
460.02480171011822320.04960342023644650.975198289881777
470.03302213205352620.06604426410705240.966977867946474
480.02746507164353830.05493014328707660.972534928356462
490.02420335276976560.04840670553953120.975796647230234
500.01707472274850340.03414944549700680.982925277251497
510.01412493770005970.02824987540011950.98587506229994
520.02259058086295250.04518116172590490.977409419137047
530.02604224505955020.05208449011910050.97395775494045
540.03190354164212340.06380708328424690.968096458357877
550.04593659485314740.09187318970629490.954063405146853
560.04740046479384510.09480092958769020.952599535206155
570.03547909228411080.07095818456822160.96452090771589
580.03111873598735250.06223747197470510.968881264012647
590.02825622347077710.05651244694155430.971743776529223
600.02296214665719500.04592429331439010.977037853342805
610.01795803165937500.03591606331874990.982041968340625
620.01750864277648340.03501728555296690.982491357223517
630.01270622286893860.02541244573787730.987293777131061
640.02943272496429810.05886544992859610.970567275035702
650.02178592319992350.04357184639984710.978214076800076
660.02325696155610860.04651392311221730.976743038443891
670.01804927668595830.03609855337191650.981950723314042
680.04814817253526380.09629634507052750.951851827464736
690.07692068951316970.1538413790263390.92307931048683
700.2543967860461520.5087935720923040.745603213953848
710.357136225787790.714272451575580.64286377421221
720.3749817267676870.7499634535353740.625018273232313
730.3601481361710890.7202962723421780.639851863828911
740.3189188952800340.6378377905600690.681081104719966
750.2768701002450210.5537402004900420.723129899754979
760.2461457124540390.4922914249080780.753854287545961
770.2305669893476880.4611339786953770.769433010652312
780.8018917046093650.3962165907812700.198108295390635
790.8497229246816180.3005541506367650.150277075318382
800.8234984650899750.353003069820050.176501534910025
810.7919686724538140.4160626550923720.208031327546186
820.7598871836689970.4802256326620070.240112816331003
830.7271559700321220.5456880599357560.272844029967878
840.7393445895546570.5213108208906860.260655410445343
850.7058305741915750.588338851616850.294169425808425
860.6878347175884440.6243305648231110.312165282411555
870.660359592819590.679280814360820.33964040718041
880.6559347112495670.6881305775008650.344065288750433
890.6069056855810630.7861886288378730.393094314418937
900.6174712691047130.7650574617905740.382528730895287
910.5670455311609290.8659089376781420.432954468839071
920.5191864766440150.961627046711970.480813523355985
930.4816320378629270.9632640757258540.518367962137073
940.5719645562358160.8560708875283690.428035443764184
950.5259161962073050.948167607585390.474083803792695
960.4746467574732580.9492935149465150.525353242526742
970.4235356236899690.8470712473799380.576464376310031
980.4005140372916150.801028074583230.599485962708385
990.4306302082428260.8612604164856520.569369791757174
1000.3899049212141650.779809842428330.610095078785835
1010.3664671331756970.7329342663513930.633532866824303
1020.4729925757883640.9459851515767280.527007424211636
1030.421104957834570.842209915669140.57889504216543
1040.3982437402690890.7964874805381780.601756259730911
1050.3721899565121270.7443799130242540.627810043487873
1060.5031551344905230.9936897310189550.496844865509477
1070.6808330845656090.6383338308687830.319166915434391
1080.7695868992173570.4608262015652850.230413100782643
1090.9257776936909120.1484446126181750.0742223063090876
1100.9019738739349440.1960522521301110.0980261260650557
1110.957410690296510.08517861940698230.0425893097034911
1120.94173695504560.1165260899088010.0582630449544005
1130.9233752585187790.1532494829624420.0766247414812212
1140.8973745123527150.2052509752945700.102625487647285
1150.878187084248830.2436258315023390.121812915751169
1160.8439471182023280.3121057635953450.156052881797672
1170.8557984775972750.2884030448054500.144201522402725
1180.817824910817640.3643501783647190.182175089182360
1190.8909484298572290.2181031402855420.109051570142771
1200.8536627542729160.2926744914541670.146337245727083
1210.8214536421513880.3570927156972240.178546357848612
1220.7732501071042970.4534997857914060.226749892895703
1230.7877323643980660.4245352712038670.212267635601933
1240.7708945767921140.4582108464157710.229105423207885
1250.7142980733958430.5714038532083150.285701926604157
1260.6829262548291130.6341474903417740.317073745170887
1270.5982215405398090.8035569189203820.401778459460191
1280.7015348193770310.5969303612459390.298465180622970
1290.6546426220288610.6907147559422770.345357377971139
1300.7802031562522180.4395936874955650.219796843747782
1310.7052994515979220.5894010968041560.294700548402078
1320.7328309951006790.5343380097986420.267169004899321
1330.742394965380050.5152100692399010.257605034619950
1340.6325282594684020.7349434810631960.367471740531598
1350.4993431992436520.9986863984873040.500656800756348

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.50570433995483 & 0.98859132009034 & 0.49429566004517 \tabularnewline
16 & 0.56674172620239 & 0.86651654759522 & 0.43325827379761 \tabularnewline
17 & 0.452298813533519 & 0.904597627067038 & 0.547701186466481 \tabularnewline
18 & 0.332110814992127 & 0.664221629984254 & 0.667889185007873 \tabularnewline
19 & 0.267303909781978 & 0.534607819563956 & 0.732696090218022 \tabularnewline
20 & 0.180389473673302 & 0.360778947346605 & 0.819610526326698 \tabularnewline
21 & 0.243689732385566 & 0.487379464771132 & 0.756310267614434 \tabularnewline
22 & 0.201381783604653 & 0.402763567209307 & 0.798618216395347 \tabularnewline
23 & 0.151383518668402 & 0.302767037336803 & 0.848616481331598 \tabularnewline
24 & 0.101774536644420 & 0.203549073288840 & 0.89822546335558 \tabularnewline
25 & 0.178392699563939 & 0.356785399127879 & 0.82160730043606 \tabularnewline
26 & 0.127536616213236 & 0.255073232426471 & 0.872463383786764 \tabularnewline
27 & 0.0963459576229729 & 0.192691915245946 & 0.903654042377027 \tabularnewline
28 & 0.101773135977076 & 0.203546271954151 & 0.898226864022924 \tabularnewline
29 & 0.0708732161108077 & 0.141746432221615 & 0.929126783889192 \tabularnewline
30 & 0.0503888980557616 & 0.100777796111523 & 0.949611101944238 \tabularnewline
31 & 0.0371984558010342 & 0.0743969116020683 & 0.962801544198966 \tabularnewline
32 & 0.139957830049939 & 0.279915660099878 & 0.860042169950061 \tabularnewline
33 & 0.11020783320975 & 0.2204156664195 & 0.88979216679025 \tabularnewline
34 & 0.102568420982843 & 0.205136841965686 & 0.897431579017157 \tabularnewline
35 & 0.104132466527874 & 0.208264933055747 & 0.895867533472126 \tabularnewline
36 & 0.0785148637307427 & 0.157029727461485 & 0.921485136269257 \tabularnewline
37 & 0.0592968961664551 & 0.118593792332910 & 0.940703103833545 \tabularnewline
38 & 0.0436712748608008 & 0.0873425497216015 & 0.9563287251392 \tabularnewline
39 & 0.0308847513990878 & 0.0617695027981755 & 0.969115248600912 \tabularnewline
40 & 0.0247597715392034 & 0.0495195430784068 & 0.975240228460797 \tabularnewline
41 & 0.020825046104566 & 0.041650092209132 & 0.979174953895434 \tabularnewline
42 & 0.0140122926574750 & 0.0280245853149499 & 0.985987707342525 \tabularnewline
43 & 0.0262648031561995 & 0.052529606312399 & 0.9737351968438 \tabularnewline
44 & 0.0181581206699040 & 0.0363162413398079 & 0.981841879330096 \tabularnewline
45 & 0.0343676962722497 & 0.0687353925444993 & 0.96563230372775 \tabularnewline
46 & 0.0248017101182232 & 0.0496034202364465 & 0.975198289881777 \tabularnewline
47 & 0.0330221320535262 & 0.0660442641070524 & 0.966977867946474 \tabularnewline
48 & 0.0274650716435383 & 0.0549301432870766 & 0.972534928356462 \tabularnewline
49 & 0.0242033527697656 & 0.0484067055395312 & 0.975796647230234 \tabularnewline
50 & 0.0170747227485034 & 0.0341494454970068 & 0.982925277251497 \tabularnewline
51 & 0.0141249377000597 & 0.0282498754001195 & 0.98587506229994 \tabularnewline
52 & 0.0225905808629525 & 0.0451811617259049 & 0.977409419137047 \tabularnewline
53 & 0.0260422450595502 & 0.0520844901191005 & 0.97395775494045 \tabularnewline
54 & 0.0319035416421234 & 0.0638070832842469 & 0.968096458357877 \tabularnewline
55 & 0.0459365948531474 & 0.0918731897062949 & 0.954063405146853 \tabularnewline
56 & 0.0474004647938451 & 0.0948009295876902 & 0.952599535206155 \tabularnewline
57 & 0.0354790922841108 & 0.0709581845682216 & 0.96452090771589 \tabularnewline
58 & 0.0311187359873525 & 0.0622374719747051 & 0.968881264012647 \tabularnewline
59 & 0.0282562234707771 & 0.0565124469415543 & 0.971743776529223 \tabularnewline
60 & 0.0229621466571950 & 0.0459242933143901 & 0.977037853342805 \tabularnewline
61 & 0.0179580316593750 & 0.0359160633187499 & 0.982041968340625 \tabularnewline
62 & 0.0175086427764834 & 0.0350172855529669 & 0.982491357223517 \tabularnewline
63 & 0.0127062228689386 & 0.0254124457378773 & 0.987293777131061 \tabularnewline
64 & 0.0294327249642981 & 0.0588654499285961 & 0.970567275035702 \tabularnewline
65 & 0.0217859231999235 & 0.0435718463998471 & 0.978214076800076 \tabularnewline
66 & 0.0232569615561086 & 0.0465139231122173 & 0.976743038443891 \tabularnewline
67 & 0.0180492766859583 & 0.0360985533719165 & 0.981950723314042 \tabularnewline
68 & 0.0481481725352638 & 0.0962963450705275 & 0.951851827464736 \tabularnewline
69 & 0.0769206895131697 & 0.153841379026339 & 0.92307931048683 \tabularnewline
70 & 0.254396786046152 & 0.508793572092304 & 0.745603213953848 \tabularnewline
71 & 0.35713622578779 & 0.71427245157558 & 0.64286377421221 \tabularnewline
72 & 0.374981726767687 & 0.749963453535374 & 0.625018273232313 \tabularnewline
73 & 0.360148136171089 & 0.720296272342178 & 0.639851863828911 \tabularnewline
74 & 0.318918895280034 & 0.637837790560069 & 0.681081104719966 \tabularnewline
75 & 0.276870100245021 & 0.553740200490042 & 0.723129899754979 \tabularnewline
76 & 0.246145712454039 & 0.492291424908078 & 0.753854287545961 \tabularnewline
77 & 0.230566989347688 & 0.461133978695377 & 0.769433010652312 \tabularnewline
78 & 0.801891704609365 & 0.396216590781270 & 0.198108295390635 \tabularnewline
79 & 0.849722924681618 & 0.300554150636765 & 0.150277075318382 \tabularnewline
80 & 0.823498465089975 & 0.35300306982005 & 0.176501534910025 \tabularnewline
81 & 0.791968672453814 & 0.416062655092372 & 0.208031327546186 \tabularnewline
82 & 0.759887183668997 & 0.480225632662007 & 0.240112816331003 \tabularnewline
83 & 0.727155970032122 & 0.545688059935756 & 0.272844029967878 \tabularnewline
84 & 0.739344589554657 & 0.521310820890686 & 0.260655410445343 \tabularnewline
85 & 0.705830574191575 & 0.58833885161685 & 0.294169425808425 \tabularnewline
86 & 0.687834717588444 & 0.624330564823111 & 0.312165282411555 \tabularnewline
87 & 0.66035959281959 & 0.67928081436082 & 0.33964040718041 \tabularnewline
88 & 0.655934711249567 & 0.688130577500865 & 0.344065288750433 \tabularnewline
89 & 0.606905685581063 & 0.786188628837873 & 0.393094314418937 \tabularnewline
90 & 0.617471269104713 & 0.765057461790574 & 0.382528730895287 \tabularnewline
91 & 0.567045531160929 & 0.865908937678142 & 0.432954468839071 \tabularnewline
92 & 0.519186476644015 & 0.96162704671197 & 0.480813523355985 \tabularnewline
93 & 0.481632037862927 & 0.963264075725854 & 0.518367962137073 \tabularnewline
94 & 0.571964556235816 & 0.856070887528369 & 0.428035443764184 \tabularnewline
95 & 0.525916196207305 & 0.94816760758539 & 0.474083803792695 \tabularnewline
96 & 0.474646757473258 & 0.949293514946515 & 0.525353242526742 \tabularnewline
97 & 0.423535623689969 & 0.847071247379938 & 0.576464376310031 \tabularnewline
98 & 0.400514037291615 & 0.80102807458323 & 0.599485962708385 \tabularnewline
99 & 0.430630208242826 & 0.861260416485652 & 0.569369791757174 \tabularnewline
100 & 0.389904921214165 & 0.77980984242833 & 0.610095078785835 \tabularnewline
101 & 0.366467133175697 & 0.732934266351393 & 0.633532866824303 \tabularnewline
102 & 0.472992575788364 & 0.945985151576728 & 0.527007424211636 \tabularnewline
103 & 0.42110495783457 & 0.84220991566914 & 0.57889504216543 \tabularnewline
104 & 0.398243740269089 & 0.796487480538178 & 0.601756259730911 \tabularnewline
105 & 0.372189956512127 & 0.744379913024254 & 0.627810043487873 \tabularnewline
106 & 0.503155134490523 & 0.993689731018955 & 0.496844865509477 \tabularnewline
107 & 0.680833084565609 & 0.638333830868783 & 0.319166915434391 \tabularnewline
108 & 0.769586899217357 & 0.460826201565285 & 0.230413100782643 \tabularnewline
109 & 0.925777693690912 & 0.148444612618175 & 0.0742223063090876 \tabularnewline
110 & 0.901973873934944 & 0.196052252130111 & 0.0980261260650557 \tabularnewline
111 & 0.95741069029651 & 0.0851786194069823 & 0.0425893097034911 \tabularnewline
112 & 0.9417369550456 & 0.116526089908801 & 0.0582630449544005 \tabularnewline
113 & 0.923375258518779 & 0.153249482962442 & 0.0766247414812212 \tabularnewline
114 & 0.897374512352715 & 0.205250975294570 & 0.102625487647285 \tabularnewline
115 & 0.87818708424883 & 0.243625831502339 & 0.121812915751169 \tabularnewline
116 & 0.843947118202328 & 0.312105763595345 & 0.156052881797672 \tabularnewline
117 & 0.855798477597275 & 0.288403044805450 & 0.144201522402725 \tabularnewline
118 & 0.81782491081764 & 0.364350178364719 & 0.182175089182360 \tabularnewline
119 & 0.890948429857229 & 0.218103140285542 & 0.109051570142771 \tabularnewline
120 & 0.853662754272916 & 0.292674491454167 & 0.146337245727083 \tabularnewline
121 & 0.821453642151388 & 0.357092715697224 & 0.178546357848612 \tabularnewline
122 & 0.773250107104297 & 0.453499785791406 & 0.226749892895703 \tabularnewline
123 & 0.787732364398066 & 0.424535271203867 & 0.212267635601933 \tabularnewline
124 & 0.770894576792114 & 0.458210846415771 & 0.229105423207885 \tabularnewline
125 & 0.714298073395843 & 0.571403853208315 & 0.285701926604157 \tabularnewline
126 & 0.682926254829113 & 0.634147490341774 & 0.317073745170887 \tabularnewline
127 & 0.598221540539809 & 0.803556918920382 & 0.401778459460191 \tabularnewline
128 & 0.701534819377031 & 0.596930361245939 & 0.298465180622970 \tabularnewline
129 & 0.654642622028861 & 0.690714755942277 & 0.345357377971139 \tabularnewline
130 & 0.780203156252218 & 0.439593687495565 & 0.219796843747782 \tabularnewline
131 & 0.705299451597922 & 0.589401096804156 & 0.294700548402078 \tabularnewline
132 & 0.732830995100679 & 0.534338009798642 & 0.267169004899321 \tabularnewline
133 & 0.74239496538005 & 0.515210069239901 & 0.257605034619950 \tabularnewline
134 & 0.632528259468402 & 0.734943481063196 & 0.367471740531598 \tabularnewline
135 & 0.499343199243652 & 0.998686398487304 & 0.500656800756348 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108422&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]15[/C][C]0.50570433995483[/C][C]0.98859132009034[/C][C]0.49429566004517[/C][/ROW]
[ROW][C]16[/C][C]0.56674172620239[/C][C]0.86651654759522[/C][C]0.43325827379761[/C][/ROW]
[ROW][C]17[/C][C]0.452298813533519[/C][C]0.904597627067038[/C][C]0.547701186466481[/C][/ROW]
[ROW][C]18[/C][C]0.332110814992127[/C][C]0.664221629984254[/C][C]0.667889185007873[/C][/ROW]
[ROW][C]19[/C][C]0.267303909781978[/C][C]0.534607819563956[/C][C]0.732696090218022[/C][/ROW]
[ROW][C]20[/C][C]0.180389473673302[/C][C]0.360778947346605[/C][C]0.819610526326698[/C][/ROW]
[ROW][C]21[/C][C]0.243689732385566[/C][C]0.487379464771132[/C][C]0.756310267614434[/C][/ROW]
[ROW][C]22[/C][C]0.201381783604653[/C][C]0.402763567209307[/C][C]0.798618216395347[/C][/ROW]
[ROW][C]23[/C][C]0.151383518668402[/C][C]0.302767037336803[/C][C]0.848616481331598[/C][/ROW]
[ROW][C]24[/C][C]0.101774536644420[/C][C]0.203549073288840[/C][C]0.89822546335558[/C][/ROW]
[ROW][C]25[/C][C]0.178392699563939[/C][C]0.356785399127879[/C][C]0.82160730043606[/C][/ROW]
[ROW][C]26[/C][C]0.127536616213236[/C][C]0.255073232426471[/C][C]0.872463383786764[/C][/ROW]
[ROW][C]27[/C][C]0.0963459576229729[/C][C]0.192691915245946[/C][C]0.903654042377027[/C][/ROW]
[ROW][C]28[/C][C]0.101773135977076[/C][C]0.203546271954151[/C][C]0.898226864022924[/C][/ROW]
[ROW][C]29[/C][C]0.0708732161108077[/C][C]0.141746432221615[/C][C]0.929126783889192[/C][/ROW]
[ROW][C]30[/C][C]0.0503888980557616[/C][C]0.100777796111523[/C][C]0.949611101944238[/C][/ROW]
[ROW][C]31[/C][C]0.0371984558010342[/C][C]0.0743969116020683[/C][C]0.962801544198966[/C][/ROW]
[ROW][C]32[/C][C]0.139957830049939[/C][C]0.279915660099878[/C][C]0.860042169950061[/C][/ROW]
[ROW][C]33[/C][C]0.11020783320975[/C][C]0.2204156664195[/C][C]0.88979216679025[/C][/ROW]
[ROW][C]34[/C][C]0.102568420982843[/C][C]0.205136841965686[/C][C]0.897431579017157[/C][/ROW]
[ROW][C]35[/C][C]0.104132466527874[/C][C]0.208264933055747[/C][C]0.895867533472126[/C][/ROW]
[ROW][C]36[/C][C]0.0785148637307427[/C][C]0.157029727461485[/C][C]0.921485136269257[/C][/ROW]
[ROW][C]37[/C][C]0.0592968961664551[/C][C]0.118593792332910[/C][C]0.940703103833545[/C][/ROW]
[ROW][C]38[/C][C]0.0436712748608008[/C][C]0.0873425497216015[/C][C]0.9563287251392[/C][/ROW]
[ROW][C]39[/C][C]0.0308847513990878[/C][C]0.0617695027981755[/C][C]0.969115248600912[/C][/ROW]
[ROW][C]40[/C][C]0.0247597715392034[/C][C]0.0495195430784068[/C][C]0.975240228460797[/C][/ROW]
[ROW][C]41[/C][C]0.020825046104566[/C][C]0.041650092209132[/C][C]0.979174953895434[/C][/ROW]
[ROW][C]42[/C][C]0.0140122926574750[/C][C]0.0280245853149499[/C][C]0.985987707342525[/C][/ROW]
[ROW][C]43[/C][C]0.0262648031561995[/C][C]0.052529606312399[/C][C]0.9737351968438[/C][/ROW]
[ROW][C]44[/C][C]0.0181581206699040[/C][C]0.0363162413398079[/C][C]0.981841879330096[/C][/ROW]
[ROW][C]45[/C][C]0.0343676962722497[/C][C]0.0687353925444993[/C][C]0.96563230372775[/C][/ROW]
[ROW][C]46[/C][C]0.0248017101182232[/C][C]0.0496034202364465[/C][C]0.975198289881777[/C][/ROW]
[ROW][C]47[/C][C]0.0330221320535262[/C][C]0.0660442641070524[/C][C]0.966977867946474[/C][/ROW]
[ROW][C]48[/C][C]0.0274650716435383[/C][C]0.0549301432870766[/C][C]0.972534928356462[/C][/ROW]
[ROW][C]49[/C][C]0.0242033527697656[/C][C]0.0484067055395312[/C][C]0.975796647230234[/C][/ROW]
[ROW][C]50[/C][C]0.0170747227485034[/C][C]0.0341494454970068[/C][C]0.982925277251497[/C][/ROW]
[ROW][C]51[/C][C]0.0141249377000597[/C][C]0.0282498754001195[/C][C]0.98587506229994[/C][/ROW]
[ROW][C]52[/C][C]0.0225905808629525[/C][C]0.0451811617259049[/C][C]0.977409419137047[/C][/ROW]
[ROW][C]53[/C][C]0.0260422450595502[/C][C]0.0520844901191005[/C][C]0.97395775494045[/C][/ROW]
[ROW][C]54[/C][C]0.0319035416421234[/C][C]0.0638070832842469[/C][C]0.968096458357877[/C][/ROW]
[ROW][C]55[/C][C]0.0459365948531474[/C][C]0.0918731897062949[/C][C]0.954063405146853[/C][/ROW]
[ROW][C]56[/C][C]0.0474004647938451[/C][C]0.0948009295876902[/C][C]0.952599535206155[/C][/ROW]
[ROW][C]57[/C][C]0.0354790922841108[/C][C]0.0709581845682216[/C][C]0.96452090771589[/C][/ROW]
[ROW][C]58[/C][C]0.0311187359873525[/C][C]0.0622374719747051[/C][C]0.968881264012647[/C][/ROW]
[ROW][C]59[/C][C]0.0282562234707771[/C][C]0.0565124469415543[/C][C]0.971743776529223[/C][/ROW]
[ROW][C]60[/C][C]0.0229621466571950[/C][C]0.0459242933143901[/C][C]0.977037853342805[/C][/ROW]
[ROW][C]61[/C][C]0.0179580316593750[/C][C]0.0359160633187499[/C][C]0.982041968340625[/C][/ROW]
[ROW][C]62[/C][C]0.0175086427764834[/C][C]0.0350172855529669[/C][C]0.982491357223517[/C][/ROW]
[ROW][C]63[/C][C]0.0127062228689386[/C][C]0.0254124457378773[/C][C]0.987293777131061[/C][/ROW]
[ROW][C]64[/C][C]0.0294327249642981[/C][C]0.0588654499285961[/C][C]0.970567275035702[/C][/ROW]
[ROW][C]65[/C][C]0.0217859231999235[/C][C]0.0435718463998471[/C][C]0.978214076800076[/C][/ROW]
[ROW][C]66[/C][C]0.0232569615561086[/C][C]0.0465139231122173[/C][C]0.976743038443891[/C][/ROW]
[ROW][C]67[/C][C]0.0180492766859583[/C][C]0.0360985533719165[/C][C]0.981950723314042[/C][/ROW]
[ROW][C]68[/C][C]0.0481481725352638[/C][C]0.0962963450705275[/C][C]0.951851827464736[/C][/ROW]
[ROW][C]69[/C][C]0.0769206895131697[/C][C]0.153841379026339[/C][C]0.92307931048683[/C][/ROW]
[ROW][C]70[/C][C]0.254396786046152[/C][C]0.508793572092304[/C][C]0.745603213953848[/C][/ROW]
[ROW][C]71[/C][C]0.35713622578779[/C][C]0.71427245157558[/C][C]0.64286377421221[/C][/ROW]
[ROW][C]72[/C][C]0.374981726767687[/C][C]0.749963453535374[/C][C]0.625018273232313[/C][/ROW]
[ROW][C]73[/C][C]0.360148136171089[/C][C]0.720296272342178[/C][C]0.639851863828911[/C][/ROW]
[ROW][C]74[/C][C]0.318918895280034[/C][C]0.637837790560069[/C][C]0.681081104719966[/C][/ROW]
[ROW][C]75[/C][C]0.276870100245021[/C][C]0.553740200490042[/C][C]0.723129899754979[/C][/ROW]
[ROW][C]76[/C][C]0.246145712454039[/C][C]0.492291424908078[/C][C]0.753854287545961[/C][/ROW]
[ROW][C]77[/C][C]0.230566989347688[/C][C]0.461133978695377[/C][C]0.769433010652312[/C][/ROW]
[ROW][C]78[/C][C]0.801891704609365[/C][C]0.396216590781270[/C][C]0.198108295390635[/C][/ROW]
[ROW][C]79[/C][C]0.849722924681618[/C][C]0.300554150636765[/C][C]0.150277075318382[/C][/ROW]
[ROW][C]80[/C][C]0.823498465089975[/C][C]0.35300306982005[/C][C]0.176501534910025[/C][/ROW]
[ROW][C]81[/C][C]0.791968672453814[/C][C]0.416062655092372[/C][C]0.208031327546186[/C][/ROW]
[ROW][C]82[/C][C]0.759887183668997[/C][C]0.480225632662007[/C][C]0.240112816331003[/C][/ROW]
[ROW][C]83[/C][C]0.727155970032122[/C][C]0.545688059935756[/C][C]0.272844029967878[/C][/ROW]
[ROW][C]84[/C][C]0.739344589554657[/C][C]0.521310820890686[/C][C]0.260655410445343[/C][/ROW]
[ROW][C]85[/C][C]0.705830574191575[/C][C]0.58833885161685[/C][C]0.294169425808425[/C][/ROW]
[ROW][C]86[/C][C]0.687834717588444[/C][C]0.624330564823111[/C][C]0.312165282411555[/C][/ROW]
[ROW][C]87[/C][C]0.66035959281959[/C][C]0.67928081436082[/C][C]0.33964040718041[/C][/ROW]
[ROW][C]88[/C][C]0.655934711249567[/C][C]0.688130577500865[/C][C]0.344065288750433[/C][/ROW]
[ROW][C]89[/C][C]0.606905685581063[/C][C]0.786188628837873[/C][C]0.393094314418937[/C][/ROW]
[ROW][C]90[/C][C]0.617471269104713[/C][C]0.765057461790574[/C][C]0.382528730895287[/C][/ROW]
[ROW][C]91[/C][C]0.567045531160929[/C][C]0.865908937678142[/C][C]0.432954468839071[/C][/ROW]
[ROW][C]92[/C][C]0.519186476644015[/C][C]0.96162704671197[/C][C]0.480813523355985[/C][/ROW]
[ROW][C]93[/C][C]0.481632037862927[/C][C]0.963264075725854[/C][C]0.518367962137073[/C][/ROW]
[ROW][C]94[/C][C]0.571964556235816[/C][C]0.856070887528369[/C][C]0.428035443764184[/C][/ROW]
[ROW][C]95[/C][C]0.525916196207305[/C][C]0.94816760758539[/C][C]0.474083803792695[/C][/ROW]
[ROW][C]96[/C][C]0.474646757473258[/C][C]0.949293514946515[/C][C]0.525353242526742[/C][/ROW]
[ROW][C]97[/C][C]0.423535623689969[/C][C]0.847071247379938[/C][C]0.576464376310031[/C][/ROW]
[ROW][C]98[/C][C]0.400514037291615[/C][C]0.80102807458323[/C][C]0.599485962708385[/C][/ROW]
[ROW][C]99[/C][C]0.430630208242826[/C][C]0.861260416485652[/C][C]0.569369791757174[/C][/ROW]
[ROW][C]100[/C][C]0.389904921214165[/C][C]0.77980984242833[/C][C]0.610095078785835[/C][/ROW]
[ROW][C]101[/C][C]0.366467133175697[/C][C]0.732934266351393[/C][C]0.633532866824303[/C][/ROW]
[ROW][C]102[/C][C]0.472992575788364[/C][C]0.945985151576728[/C][C]0.527007424211636[/C][/ROW]
[ROW][C]103[/C][C]0.42110495783457[/C][C]0.84220991566914[/C][C]0.57889504216543[/C][/ROW]
[ROW][C]104[/C][C]0.398243740269089[/C][C]0.796487480538178[/C][C]0.601756259730911[/C][/ROW]
[ROW][C]105[/C][C]0.372189956512127[/C][C]0.744379913024254[/C][C]0.627810043487873[/C][/ROW]
[ROW][C]106[/C][C]0.503155134490523[/C][C]0.993689731018955[/C][C]0.496844865509477[/C][/ROW]
[ROW][C]107[/C][C]0.680833084565609[/C][C]0.638333830868783[/C][C]0.319166915434391[/C][/ROW]
[ROW][C]108[/C][C]0.769586899217357[/C][C]0.460826201565285[/C][C]0.230413100782643[/C][/ROW]
[ROW][C]109[/C][C]0.925777693690912[/C][C]0.148444612618175[/C][C]0.0742223063090876[/C][/ROW]
[ROW][C]110[/C][C]0.901973873934944[/C][C]0.196052252130111[/C][C]0.0980261260650557[/C][/ROW]
[ROW][C]111[/C][C]0.95741069029651[/C][C]0.0851786194069823[/C][C]0.0425893097034911[/C][/ROW]
[ROW][C]112[/C][C]0.9417369550456[/C][C]0.116526089908801[/C][C]0.0582630449544005[/C][/ROW]
[ROW][C]113[/C][C]0.923375258518779[/C][C]0.153249482962442[/C][C]0.0766247414812212[/C][/ROW]
[ROW][C]114[/C][C]0.897374512352715[/C][C]0.205250975294570[/C][C]0.102625487647285[/C][/ROW]
[ROW][C]115[/C][C]0.87818708424883[/C][C]0.243625831502339[/C][C]0.121812915751169[/C][/ROW]
[ROW][C]116[/C][C]0.843947118202328[/C][C]0.312105763595345[/C][C]0.156052881797672[/C][/ROW]
[ROW][C]117[/C][C]0.855798477597275[/C][C]0.288403044805450[/C][C]0.144201522402725[/C][/ROW]
[ROW][C]118[/C][C]0.81782491081764[/C][C]0.364350178364719[/C][C]0.182175089182360[/C][/ROW]
[ROW][C]119[/C][C]0.890948429857229[/C][C]0.218103140285542[/C][C]0.109051570142771[/C][/ROW]
[ROW][C]120[/C][C]0.853662754272916[/C][C]0.292674491454167[/C][C]0.146337245727083[/C][/ROW]
[ROW][C]121[/C][C]0.821453642151388[/C][C]0.357092715697224[/C][C]0.178546357848612[/C][/ROW]
[ROW][C]122[/C][C]0.773250107104297[/C][C]0.453499785791406[/C][C]0.226749892895703[/C][/ROW]
[ROW][C]123[/C][C]0.787732364398066[/C][C]0.424535271203867[/C][C]0.212267635601933[/C][/ROW]
[ROW][C]124[/C][C]0.770894576792114[/C][C]0.458210846415771[/C][C]0.229105423207885[/C][/ROW]
[ROW][C]125[/C][C]0.714298073395843[/C][C]0.571403853208315[/C][C]0.285701926604157[/C][/ROW]
[ROW][C]126[/C][C]0.682926254829113[/C][C]0.634147490341774[/C][C]0.317073745170887[/C][/ROW]
[ROW][C]127[/C][C]0.598221540539809[/C][C]0.803556918920382[/C][C]0.401778459460191[/C][/ROW]
[ROW][C]128[/C][C]0.701534819377031[/C][C]0.596930361245939[/C][C]0.298465180622970[/C][/ROW]
[ROW][C]129[/C][C]0.654642622028861[/C][C]0.690714755942277[/C][C]0.345357377971139[/C][/ROW]
[ROW][C]130[/C][C]0.780203156252218[/C][C]0.439593687495565[/C][C]0.219796843747782[/C][/ROW]
[ROW][C]131[/C][C]0.705299451597922[/C][C]0.589401096804156[/C][C]0.294700548402078[/C][/ROW]
[ROW][C]132[/C][C]0.732830995100679[/C][C]0.534338009798642[/C][C]0.267169004899321[/C][/ROW]
[ROW][C]133[/C][C]0.74239496538005[/C][C]0.515210069239901[/C][C]0.257605034619950[/C][/ROW]
[ROW][C]134[/C][C]0.632528259468402[/C][C]0.734943481063196[/C][C]0.367471740531598[/C][/ROW]
[ROW][C]135[/C][C]0.499343199243652[/C][C]0.998686398487304[/C][C]0.500656800756348[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108422&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108422&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.505704339954830.988591320090340.49429566004517
160.566741726202390.866516547595220.43325827379761
170.4522988135335190.9045976270670380.547701186466481
180.3321108149921270.6642216299842540.667889185007873
190.2673039097819780.5346078195639560.732696090218022
200.1803894736733020.3607789473466050.819610526326698
210.2436897323855660.4873794647711320.756310267614434
220.2013817836046530.4027635672093070.798618216395347
230.1513835186684020.3027670373368030.848616481331598
240.1017745366444200.2035490732888400.89822546335558
250.1783926995639390.3567853991278790.82160730043606
260.1275366162132360.2550732324264710.872463383786764
270.09634595762297290.1926919152459460.903654042377027
280.1017731359770760.2035462719541510.898226864022924
290.07087321611080770.1417464322216150.929126783889192
300.05038889805576160.1007777961115230.949611101944238
310.03719845580103420.07439691160206830.962801544198966
320.1399578300499390.2799156600998780.860042169950061
330.110207833209750.22041566641950.88979216679025
340.1025684209828430.2051368419656860.897431579017157
350.1041324665278740.2082649330557470.895867533472126
360.07851486373074270.1570297274614850.921485136269257
370.05929689616645510.1185937923329100.940703103833545
380.04367127486080080.08734254972160150.9563287251392
390.03088475139908780.06176950279817550.969115248600912
400.02475977153920340.04951954307840680.975240228460797
410.0208250461045660.0416500922091320.979174953895434
420.01401229265747500.02802458531494990.985987707342525
430.02626480315619950.0525296063123990.9737351968438
440.01815812066990400.03631624133980790.981841879330096
450.03436769627224970.06873539254449930.96563230372775
460.02480171011822320.04960342023644650.975198289881777
470.03302213205352620.06604426410705240.966977867946474
480.02746507164353830.05493014328707660.972534928356462
490.02420335276976560.04840670553953120.975796647230234
500.01707472274850340.03414944549700680.982925277251497
510.01412493770005970.02824987540011950.98587506229994
520.02259058086295250.04518116172590490.977409419137047
530.02604224505955020.05208449011910050.97395775494045
540.03190354164212340.06380708328424690.968096458357877
550.04593659485314740.09187318970629490.954063405146853
560.04740046479384510.09480092958769020.952599535206155
570.03547909228411080.07095818456822160.96452090771589
580.03111873598735250.06223747197470510.968881264012647
590.02825622347077710.05651244694155430.971743776529223
600.02296214665719500.04592429331439010.977037853342805
610.01795803165937500.03591606331874990.982041968340625
620.01750864277648340.03501728555296690.982491357223517
630.01270622286893860.02541244573787730.987293777131061
640.02943272496429810.05886544992859610.970567275035702
650.02178592319992350.04357184639984710.978214076800076
660.02325696155610860.04651392311221730.976743038443891
670.01804927668595830.03609855337191650.981950723314042
680.04814817253526380.09629634507052750.951851827464736
690.07692068951316970.1538413790263390.92307931048683
700.2543967860461520.5087935720923040.745603213953848
710.357136225787790.714272451575580.64286377421221
720.3749817267676870.7499634535353740.625018273232313
730.3601481361710890.7202962723421780.639851863828911
740.3189188952800340.6378377905600690.681081104719966
750.2768701002450210.5537402004900420.723129899754979
760.2461457124540390.4922914249080780.753854287545961
770.2305669893476880.4611339786953770.769433010652312
780.8018917046093650.3962165907812700.198108295390635
790.8497229246816180.3005541506367650.150277075318382
800.8234984650899750.353003069820050.176501534910025
810.7919686724538140.4160626550923720.208031327546186
820.7598871836689970.4802256326620070.240112816331003
830.7271559700321220.5456880599357560.272844029967878
840.7393445895546570.5213108208906860.260655410445343
850.7058305741915750.588338851616850.294169425808425
860.6878347175884440.6243305648231110.312165282411555
870.660359592819590.679280814360820.33964040718041
880.6559347112495670.6881305775008650.344065288750433
890.6069056855810630.7861886288378730.393094314418937
900.6174712691047130.7650574617905740.382528730895287
910.5670455311609290.8659089376781420.432954468839071
920.5191864766440150.961627046711970.480813523355985
930.4816320378629270.9632640757258540.518367962137073
940.5719645562358160.8560708875283690.428035443764184
950.5259161962073050.948167607585390.474083803792695
960.4746467574732580.9492935149465150.525353242526742
970.4235356236899690.8470712473799380.576464376310031
980.4005140372916150.801028074583230.599485962708385
990.4306302082428260.8612604164856520.569369791757174
1000.3899049212141650.779809842428330.610095078785835
1010.3664671331756970.7329342663513930.633532866824303
1020.4729925757883640.9459851515767280.527007424211636
1030.421104957834570.842209915669140.57889504216543
1040.3982437402690890.7964874805381780.601756259730911
1050.3721899565121270.7443799130242540.627810043487873
1060.5031551344905230.9936897310189550.496844865509477
1070.6808330845656090.6383338308687830.319166915434391
1080.7695868992173570.4608262015652850.230413100782643
1090.9257776936909120.1484446126181750.0742223063090876
1100.9019738739349440.1960522521301110.0980261260650557
1110.957410690296510.08517861940698230.0425893097034911
1120.94173695504560.1165260899088010.0582630449544005
1130.9233752585187790.1532494829624420.0766247414812212
1140.8973745123527150.2052509752945700.102625487647285
1150.878187084248830.2436258315023390.121812915751169
1160.8439471182023280.3121057635953450.156052881797672
1170.8557984775972750.2884030448054500.144201522402725
1180.817824910817640.3643501783647190.182175089182360
1190.8909484298572290.2181031402855420.109051570142771
1200.8536627542729160.2926744914541670.146337245727083
1210.8214536421513880.3570927156972240.178546357848612
1220.7732501071042970.4534997857914060.226749892895703
1230.7877323643980660.4245352712038670.212267635601933
1240.7708945767921140.4582108464157710.229105423207885
1250.7142980733958430.5714038532083150.285701926604157
1260.6829262548291130.6341474903417740.317073745170887
1270.5982215405398090.8035569189203820.401778459460191
1280.7015348193770310.5969303612459390.298465180622970
1290.6546426220288610.6907147559422770.345357377971139
1300.7802031562522180.4395936874955650.219796843747782
1310.7052994515979220.5894010968041560.294700548402078
1320.7328309951006790.5343380097986420.267169004899321
1330.742394965380050.5152100692399010.257605034619950
1340.6325282594684020.7349434810631960.367471740531598
1350.4993431992436520.9986863984873040.500656800756348






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level160.132231404958678NOK
10% type I error level330.272727272727273NOK

\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 & 16 & 0.132231404958678 & NOK \tabularnewline
10% type I error level & 33 & 0.272727272727273 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108422&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]16[/C][C]0.132231404958678[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]33[/C][C]0.272727272727273[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108422&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108422&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 level160.132231404958678NOK
10% type I error level330.272727272727273NOK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 0 ;
Parameters (R input):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 0 ;
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')
}