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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 17 Dec 2010 16:58:50 +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/17/t1292605035gvsrc82er52kyn4.htm/, Retrieved Fri, 19 Apr 2024 05:21:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=111589, Retrieved Fri, 19 Apr 2024 05:21:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:20:01] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [ws 7] [2010-11-23 19:56:37] [bd591a1ebb67d263a02e7adae3fa1a4d]
-   PD    [Multiple Regression] [WS 7 - minitutorial] [2010-11-24 18:11:33] [bd591a1ebb67d263a02e7adae3fa1a4d]
-    D        [Multiple Regression] [multiple regressi...] [2010-12-17 16:58:50] [09489ba95453d3f5c9e6f2eaeda915af] [Current]
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Dataseries X:
14544.5	94.6	-3.0	14097.8
15116.3	95.9	-3.7	14776.8
17413.2	104.7	-4.7	16833.3
16181.5	102.8	-6.4	15385.5
15607.4	98.1	-7.5	15172.6
17160.9	113.9	-7.8	16858.9
14915.8	80.9	-7.7	14143.5
13768	95.7	-6.6	14731.8
17487.5	113.2	-4.2	16471.6
16198.1	105.9	-2.0	15214
17535.2	108.8	-0.7	17637.4
16571.8	102.3	0.1	17972.4
16198.9	99	0.9	16896.2
16554.2	100.7	2.1	16698
19554.2	115.5	3.5	19691.6
15903.8	100.7	4.9	15930.7
18003.8	109.9	5.7	17444.6
18329.6	114.6	6.2	17699.4
16260.7	85.4	6.5	15189.8
14851.9	100.5	6.5	15672.7
18174.1	114.8	6.3	17180.8
18406.6	116.5	6.2	17664.9
18466.5	112.9	6.4	17862.9
16016.5	102	6.3	16162.3
17428.5	106	5.8	17463.6
17167.2	105.3	5.1	16772.1
19630	118.8	5.1	19106.9
17183.6	106.1	5.8	16721.3
18344.7	109.3	6.7	18161.3
19301.4	117.2	7.1	18509.9
18147.5	92.5	6.7	17802.7
16192.9	104.2	5.5	16409.9
18374.4	112.5	4.2	17967.7
20515.2	122.4	3.0	20286.6
18957.2	113.3	2.2	19537.3
16471.5	100	2.0	18021.9
18746.8	110.7	1.8	20194.3
19009.5	112.8	1.8	19049.6
19211.2	109.8	1.5	20244.7
20547.7	117.3	0.4	21473.3
19325.8	109.1	-0.9	19673.6
20605.5	115.9	-1.7	21053.2
20056.9	96	-2.6	20159.5
16141.4	99.8	-4.4	18203.6
20359.8	116.8	-8.3	21289.5
19711.6	115.7	-14.4	20432.3
15638.6	99.4	-21.3	17180.4
14384.5	94.3	-26.5	15816.8
13855.6	91	-29.2	15071.8
14308.3	93.2	-30.8	14521.1
15290.6	103.1	-30.9	15668.8
14423.8	94.1	-29.5	14346.9
13779.7	91.8	-27.1	13881
15686.3	102.7	-24.4	15465.9
14733.8	82.6	-21.9	14238.2
12522.5	89.1	-19.3	13557.7
16189.4	104.5	-17.0	16127.6
16059.1	105.1	-13.8	16793.9
16007.1	95.1	-9.9	16014
15806.8	88.7	-7.9	16867.9
15160	86.3	-7.2	16014.6
15692.1	91.8	-6.2	15878.6
18908.9	111.5	-4.5	18664.9
16969.9	99.7	-3.9	17962.5
16997.5	97.5	-5.0	17332.7
19858.9	111.7	-6.2	19542.1
17681.2	86.2	-6.1	17203.6
16006.9	95.4	-5.0	16579




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111589&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111589&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111589&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Multiple Linear Regression - Estimated Regression Equation
productie[t] = + 30.8115915919024 + 0.00348754152529101uitvoer[t] -0.167649538311852ondernemersvertrouwen[t] + 0.00103653952403590invoer[t] -0.159945387828218t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
productie[t] =  +  30.8115915919024 +  0.00348754152529101uitvoer[t] -0.167649538311852ondernemersvertrouwen[t] +  0.00103653952403590invoer[t] -0.159945387828218t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111589&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]productie[t] =  +  30.8115915919024 +  0.00348754152529101uitvoer[t] -0.167649538311852ondernemersvertrouwen[t] +  0.00103653952403590invoer[t] -0.159945387828218t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111589&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111589&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
productie[t] = + 30.8115915919024 + 0.00348754152529101uitvoer[t] -0.167649538311852ondernemersvertrouwen[t] + 0.00103653952403590invoer[t] -0.159945387828218t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)30.81159159190248.0305483.83680.0002910.000146
uitvoer0.003487541525291010.0011183.12030.0027260.001363
ondernemersvertrouwen-0.1676495383118520.097799-1.71420.0914050.045702
invoer0.001036539524035900.0010760.96320.3391220.169561
t-0.1599453878282180.046721-3.42340.0010920.000546

\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) & 30.8115915919024 & 8.030548 & 3.8368 & 0.000291 & 0.000146 \tabularnewline
uitvoer & 0.00348754152529101 & 0.001118 & 3.1203 & 0.002726 & 0.001363 \tabularnewline
ondernemersvertrouwen & -0.167649538311852 & 0.097799 & -1.7142 & 0.091405 & 0.045702 \tabularnewline
invoer & 0.00103653952403590 & 0.001076 & 0.9632 & 0.339122 & 0.169561 \tabularnewline
t & -0.159945387828218 & 0.046721 & -3.4234 & 0.001092 & 0.000546 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111589&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]30.8115915919024[/C][C]8.030548[/C][C]3.8368[/C][C]0.000291[/C][C]0.000146[/C][/ROW]
[ROW][C]uitvoer[/C][C]0.00348754152529101[/C][C]0.001118[/C][C]3.1203[/C][C]0.002726[/C][C]0.001363[/C][/ROW]
[ROW][C]ondernemersvertrouwen[/C][C]-0.167649538311852[/C][C]0.097799[/C][C]-1.7142[/C][C]0.091405[/C][C]0.045702[/C][/ROW]
[ROW][C]invoer[/C][C]0.00103653952403590[/C][C]0.001076[/C][C]0.9632[/C][C]0.339122[/C][C]0.169561[/C][/ROW]
[ROW][C]t[/C][C]-0.159945387828218[/C][C]0.046721[/C][C]-3.4234[/C][C]0.001092[/C][C]0.000546[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111589&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111589&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)30.81159159190248.0305483.83680.0002910.000146
uitvoer0.003487541525291010.0011183.12030.0027260.001363
ondernemersvertrouwen-0.1676495383118520.097799-1.71420.0914050.045702
invoer0.001036539524035900.0010760.96320.3391220.169561
t-0.1599453878282180.046721-3.42340.0010920.000546







Multiple Linear Regression - Regression Statistics
Multiple R0.81693184425255
R-squared0.667377638153872
Adjusted R-squared0.646258758036657
F-TEST (value)31.6009956233369
F-TEST (DF numerator)4
F-TEST (DF denominator)63
p-value1.92068583260152e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.9179008697521
Sum Squared Residuals2206.3576943654

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.81693184425255 \tabularnewline
R-squared & 0.667377638153872 \tabularnewline
Adjusted R-squared & 0.646258758036657 \tabularnewline
F-TEST (value) & 31.6009956233369 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 63 \tabularnewline
p-value & 1.92068583260152e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5.9179008697521 \tabularnewline
Sum Squared Residuals & 2206.3576943654 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111589&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.81693184425255[/C][/ROW]
[ROW][C]R-squared[/C][C]0.667377638153872[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.646258758036657[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]31.6009956233369[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]63[/C][/ROW]
[ROW][C]p-value[/C][C]1.92068583260152e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]5.9179008697521[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2206.3576943654[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111589&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111589&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.81693184425255
R-squared0.667377638153872
Adjusted R-squared0.646258758036657
F-TEST (value)31.6009956233369
F-TEST (DF numerator)4
F-TEST (DF denominator)63
p-value1.92068583260152e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.9179008697521
Sum Squared Residuals2206.3576943654







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
194.696.4920694355582-1.89206943555816
295.999.14746530553-3.24746530553006
3104.7109.297347116634-4.59734711663445
4102.8103.626099124336-0.826099124336263
598.1101.427691374314-3.32769137431427
6113.9108.4838532069015.41614679309907
780.997.6626439632436-16.7626439632436
895.793.92508012253361.77491987746637
9113.2108.1380580099955.06194199000545
10105.9101.8088954897424.09110451025753
11108.8108.6061473581240.193852641875925
12102.3105.299425574733-2.99942557473304
1399102.589332485707-3.58933248570688
14100.7103.261889022176-2.56188902217642
15115.5116.432843575739-0.93284357573852
16100.799.40894575440481.29105424559523
17109.9108.0079351244761.89206487552386
18114.6109.1645162671565.43548373284383
1985.499.1376017666393-13.7376017666393
20100.594.5649528141385.93504718586194
21114.8107.5880530454937.21194695450745
22116.5108.7575147997117.74248520028854
23112.9108.9781780673453.92182193265509
2410298.52778178180953.47221821819055
25106104.7249186794761.27508132052402
26105.3103.0542662870372.24573371296330
27118.8113.9035506484144.89644935158579
28106.1102.6215603077563.47843969224429
29109.3107.8527317150741.44726828492608
30117.2111.3235951672465.87640483275421
3192.5106.473394677311-13.9733946773108
32104.298.25418782104585.9458121789542
33112.5107.5349799409884.96502005901153
34122.4117.4459743987644.95402560123569
35113.3111.2098798798222.09012012017791
36100100.843710435516-0.843710435516367
37110.7110.904276649861-0.204276649860744
38112.8110.4739816275632.32601837243742
39109.8112.306536612054-2.50653661205443
40117.3118.265597424151-0.965597424151192
41109.1112.196709264968-3.09670926496788
42115.9118.063900325064-2.16390032506397
4396115.215218868311-19.2152188683109
4499.899.67420615210520.125793847894769
45116.8118.078596451203-1.27859645120323
46115.7115.79216715038-0.0921671503800888
4799.499.2135240661810.186475933818985
4894.394.13820515573160.161794844268383
499191.8141308632122-0.814130863212235
5093.292.93041246929560.269587530704357
51103.197.4026804873285.69731951267202
5294.192.61482315491791.48517684508214
5391.889.3232696144532.47673038554706
54102.797.0028286369475.69717136305295
5582.691.8293165268406-9.22931652684064
5689.182.81611661841926.28388338158083
57104.597.72284623438326.77715376561684
58105.197.26264194807677.83735805192327
5995.195.4591140267216-0.359114026721552
6088.795.150416094328-6.45041609432809
6186.391.7328949952635-5.43289499526352
6291.893.120051539462-1.32005153946191
63111.5106.7819355908814.71806440911909
6499.799.03099210084350.669007899156513
6597.598.4989047590185-0.998904759018524
66111.7110.8095205620370.890479437962851
6786.2100.614043363794-14.4140433637936
6895.493.78307012131471.61692987868527

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 94.6 & 96.4920694355582 & -1.89206943555816 \tabularnewline
2 & 95.9 & 99.14746530553 & -3.24746530553006 \tabularnewline
3 & 104.7 & 109.297347116634 & -4.59734711663445 \tabularnewline
4 & 102.8 & 103.626099124336 & -0.826099124336263 \tabularnewline
5 & 98.1 & 101.427691374314 & -3.32769137431427 \tabularnewline
6 & 113.9 & 108.483853206901 & 5.41614679309907 \tabularnewline
7 & 80.9 & 97.6626439632436 & -16.7626439632436 \tabularnewline
8 & 95.7 & 93.9250801225336 & 1.77491987746637 \tabularnewline
9 & 113.2 & 108.138058009995 & 5.06194199000545 \tabularnewline
10 & 105.9 & 101.808895489742 & 4.09110451025753 \tabularnewline
11 & 108.8 & 108.606147358124 & 0.193852641875925 \tabularnewline
12 & 102.3 & 105.299425574733 & -2.99942557473304 \tabularnewline
13 & 99 & 102.589332485707 & -3.58933248570688 \tabularnewline
14 & 100.7 & 103.261889022176 & -2.56188902217642 \tabularnewline
15 & 115.5 & 116.432843575739 & -0.93284357573852 \tabularnewline
16 & 100.7 & 99.4089457544048 & 1.29105424559523 \tabularnewline
17 & 109.9 & 108.007935124476 & 1.89206487552386 \tabularnewline
18 & 114.6 & 109.164516267156 & 5.43548373284383 \tabularnewline
19 & 85.4 & 99.1376017666393 & -13.7376017666393 \tabularnewline
20 & 100.5 & 94.564952814138 & 5.93504718586194 \tabularnewline
21 & 114.8 & 107.588053045493 & 7.21194695450745 \tabularnewline
22 & 116.5 & 108.757514799711 & 7.74248520028854 \tabularnewline
23 & 112.9 & 108.978178067345 & 3.92182193265509 \tabularnewline
24 & 102 & 98.5277817818095 & 3.47221821819055 \tabularnewline
25 & 106 & 104.724918679476 & 1.27508132052402 \tabularnewline
26 & 105.3 & 103.054266287037 & 2.24573371296330 \tabularnewline
27 & 118.8 & 113.903550648414 & 4.89644935158579 \tabularnewline
28 & 106.1 & 102.621560307756 & 3.47843969224429 \tabularnewline
29 & 109.3 & 107.852731715074 & 1.44726828492608 \tabularnewline
30 & 117.2 & 111.323595167246 & 5.87640483275421 \tabularnewline
31 & 92.5 & 106.473394677311 & -13.9733946773108 \tabularnewline
32 & 104.2 & 98.2541878210458 & 5.9458121789542 \tabularnewline
33 & 112.5 & 107.534979940988 & 4.96502005901153 \tabularnewline
34 & 122.4 & 117.445974398764 & 4.95402560123569 \tabularnewline
35 & 113.3 & 111.209879879822 & 2.09012012017791 \tabularnewline
36 & 100 & 100.843710435516 & -0.843710435516367 \tabularnewline
37 & 110.7 & 110.904276649861 & -0.204276649860744 \tabularnewline
38 & 112.8 & 110.473981627563 & 2.32601837243742 \tabularnewline
39 & 109.8 & 112.306536612054 & -2.50653661205443 \tabularnewline
40 & 117.3 & 118.265597424151 & -0.965597424151192 \tabularnewline
41 & 109.1 & 112.196709264968 & -3.09670926496788 \tabularnewline
42 & 115.9 & 118.063900325064 & -2.16390032506397 \tabularnewline
43 & 96 & 115.215218868311 & -19.2152188683109 \tabularnewline
44 & 99.8 & 99.6742061521052 & 0.125793847894769 \tabularnewline
45 & 116.8 & 118.078596451203 & -1.27859645120323 \tabularnewline
46 & 115.7 & 115.79216715038 & -0.0921671503800888 \tabularnewline
47 & 99.4 & 99.213524066181 & 0.186475933818985 \tabularnewline
48 & 94.3 & 94.1382051557316 & 0.161794844268383 \tabularnewline
49 & 91 & 91.8141308632122 & -0.814130863212235 \tabularnewline
50 & 93.2 & 92.9304124692956 & 0.269587530704357 \tabularnewline
51 & 103.1 & 97.402680487328 & 5.69731951267202 \tabularnewline
52 & 94.1 & 92.6148231549179 & 1.48517684508214 \tabularnewline
53 & 91.8 & 89.323269614453 & 2.47673038554706 \tabularnewline
54 & 102.7 & 97.002828636947 & 5.69717136305295 \tabularnewline
55 & 82.6 & 91.8293165268406 & -9.22931652684064 \tabularnewline
56 & 89.1 & 82.8161166184192 & 6.28388338158083 \tabularnewline
57 & 104.5 & 97.7228462343832 & 6.77715376561684 \tabularnewline
58 & 105.1 & 97.2626419480767 & 7.83735805192327 \tabularnewline
59 & 95.1 & 95.4591140267216 & -0.359114026721552 \tabularnewline
60 & 88.7 & 95.150416094328 & -6.45041609432809 \tabularnewline
61 & 86.3 & 91.7328949952635 & -5.43289499526352 \tabularnewline
62 & 91.8 & 93.120051539462 & -1.32005153946191 \tabularnewline
63 & 111.5 & 106.781935590881 & 4.71806440911909 \tabularnewline
64 & 99.7 & 99.0309921008435 & 0.669007899156513 \tabularnewline
65 & 97.5 & 98.4989047590185 & -0.998904759018524 \tabularnewline
66 & 111.7 & 110.809520562037 & 0.890479437962851 \tabularnewline
67 & 86.2 & 100.614043363794 & -14.4140433637936 \tabularnewline
68 & 95.4 & 93.7830701213147 & 1.61692987868527 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111589&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]94.6[/C][C]96.4920694355582[/C][C]-1.89206943555816[/C][/ROW]
[ROW][C]2[/C][C]95.9[/C][C]99.14746530553[/C][C]-3.24746530553006[/C][/ROW]
[ROW][C]3[/C][C]104.7[/C][C]109.297347116634[/C][C]-4.59734711663445[/C][/ROW]
[ROW][C]4[/C][C]102.8[/C][C]103.626099124336[/C][C]-0.826099124336263[/C][/ROW]
[ROW][C]5[/C][C]98.1[/C][C]101.427691374314[/C][C]-3.32769137431427[/C][/ROW]
[ROW][C]6[/C][C]113.9[/C][C]108.483853206901[/C][C]5.41614679309907[/C][/ROW]
[ROW][C]7[/C][C]80.9[/C][C]97.6626439632436[/C][C]-16.7626439632436[/C][/ROW]
[ROW][C]8[/C][C]95.7[/C][C]93.9250801225336[/C][C]1.77491987746637[/C][/ROW]
[ROW][C]9[/C][C]113.2[/C][C]108.138058009995[/C][C]5.06194199000545[/C][/ROW]
[ROW][C]10[/C][C]105.9[/C][C]101.808895489742[/C][C]4.09110451025753[/C][/ROW]
[ROW][C]11[/C][C]108.8[/C][C]108.606147358124[/C][C]0.193852641875925[/C][/ROW]
[ROW][C]12[/C][C]102.3[/C][C]105.299425574733[/C][C]-2.99942557473304[/C][/ROW]
[ROW][C]13[/C][C]99[/C][C]102.589332485707[/C][C]-3.58933248570688[/C][/ROW]
[ROW][C]14[/C][C]100.7[/C][C]103.261889022176[/C][C]-2.56188902217642[/C][/ROW]
[ROW][C]15[/C][C]115.5[/C][C]116.432843575739[/C][C]-0.93284357573852[/C][/ROW]
[ROW][C]16[/C][C]100.7[/C][C]99.4089457544048[/C][C]1.29105424559523[/C][/ROW]
[ROW][C]17[/C][C]109.9[/C][C]108.007935124476[/C][C]1.89206487552386[/C][/ROW]
[ROW][C]18[/C][C]114.6[/C][C]109.164516267156[/C][C]5.43548373284383[/C][/ROW]
[ROW][C]19[/C][C]85.4[/C][C]99.1376017666393[/C][C]-13.7376017666393[/C][/ROW]
[ROW][C]20[/C][C]100.5[/C][C]94.564952814138[/C][C]5.93504718586194[/C][/ROW]
[ROW][C]21[/C][C]114.8[/C][C]107.588053045493[/C][C]7.21194695450745[/C][/ROW]
[ROW][C]22[/C][C]116.5[/C][C]108.757514799711[/C][C]7.74248520028854[/C][/ROW]
[ROW][C]23[/C][C]112.9[/C][C]108.978178067345[/C][C]3.92182193265509[/C][/ROW]
[ROW][C]24[/C][C]102[/C][C]98.5277817818095[/C][C]3.47221821819055[/C][/ROW]
[ROW][C]25[/C][C]106[/C][C]104.724918679476[/C][C]1.27508132052402[/C][/ROW]
[ROW][C]26[/C][C]105.3[/C][C]103.054266287037[/C][C]2.24573371296330[/C][/ROW]
[ROW][C]27[/C][C]118.8[/C][C]113.903550648414[/C][C]4.89644935158579[/C][/ROW]
[ROW][C]28[/C][C]106.1[/C][C]102.621560307756[/C][C]3.47843969224429[/C][/ROW]
[ROW][C]29[/C][C]109.3[/C][C]107.852731715074[/C][C]1.44726828492608[/C][/ROW]
[ROW][C]30[/C][C]117.2[/C][C]111.323595167246[/C][C]5.87640483275421[/C][/ROW]
[ROW][C]31[/C][C]92.5[/C][C]106.473394677311[/C][C]-13.9733946773108[/C][/ROW]
[ROW][C]32[/C][C]104.2[/C][C]98.2541878210458[/C][C]5.9458121789542[/C][/ROW]
[ROW][C]33[/C][C]112.5[/C][C]107.534979940988[/C][C]4.96502005901153[/C][/ROW]
[ROW][C]34[/C][C]122.4[/C][C]117.445974398764[/C][C]4.95402560123569[/C][/ROW]
[ROW][C]35[/C][C]113.3[/C][C]111.209879879822[/C][C]2.09012012017791[/C][/ROW]
[ROW][C]36[/C][C]100[/C][C]100.843710435516[/C][C]-0.843710435516367[/C][/ROW]
[ROW][C]37[/C][C]110.7[/C][C]110.904276649861[/C][C]-0.204276649860744[/C][/ROW]
[ROW][C]38[/C][C]112.8[/C][C]110.473981627563[/C][C]2.32601837243742[/C][/ROW]
[ROW][C]39[/C][C]109.8[/C][C]112.306536612054[/C][C]-2.50653661205443[/C][/ROW]
[ROW][C]40[/C][C]117.3[/C][C]118.265597424151[/C][C]-0.965597424151192[/C][/ROW]
[ROW][C]41[/C][C]109.1[/C][C]112.196709264968[/C][C]-3.09670926496788[/C][/ROW]
[ROW][C]42[/C][C]115.9[/C][C]118.063900325064[/C][C]-2.16390032506397[/C][/ROW]
[ROW][C]43[/C][C]96[/C][C]115.215218868311[/C][C]-19.2152188683109[/C][/ROW]
[ROW][C]44[/C][C]99.8[/C][C]99.6742061521052[/C][C]0.125793847894769[/C][/ROW]
[ROW][C]45[/C][C]116.8[/C][C]118.078596451203[/C][C]-1.27859645120323[/C][/ROW]
[ROW][C]46[/C][C]115.7[/C][C]115.79216715038[/C][C]-0.0921671503800888[/C][/ROW]
[ROW][C]47[/C][C]99.4[/C][C]99.213524066181[/C][C]0.186475933818985[/C][/ROW]
[ROW][C]48[/C][C]94.3[/C][C]94.1382051557316[/C][C]0.161794844268383[/C][/ROW]
[ROW][C]49[/C][C]91[/C][C]91.8141308632122[/C][C]-0.814130863212235[/C][/ROW]
[ROW][C]50[/C][C]93.2[/C][C]92.9304124692956[/C][C]0.269587530704357[/C][/ROW]
[ROW][C]51[/C][C]103.1[/C][C]97.402680487328[/C][C]5.69731951267202[/C][/ROW]
[ROW][C]52[/C][C]94.1[/C][C]92.6148231549179[/C][C]1.48517684508214[/C][/ROW]
[ROW][C]53[/C][C]91.8[/C][C]89.323269614453[/C][C]2.47673038554706[/C][/ROW]
[ROW][C]54[/C][C]102.7[/C][C]97.002828636947[/C][C]5.69717136305295[/C][/ROW]
[ROW][C]55[/C][C]82.6[/C][C]91.8293165268406[/C][C]-9.22931652684064[/C][/ROW]
[ROW][C]56[/C][C]89.1[/C][C]82.8161166184192[/C][C]6.28388338158083[/C][/ROW]
[ROW][C]57[/C][C]104.5[/C][C]97.7228462343832[/C][C]6.77715376561684[/C][/ROW]
[ROW][C]58[/C][C]105.1[/C][C]97.2626419480767[/C][C]7.83735805192327[/C][/ROW]
[ROW][C]59[/C][C]95.1[/C][C]95.4591140267216[/C][C]-0.359114026721552[/C][/ROW]
[ROW][C]60[/C][C]88.7[/C][C]95.150416094328[/C][C]-6.45041609432809[/C][/ROW]
[ROW][C]61[/C][C]86.3[/C][C]91.7328949952635[/C][C]-5.43289499526352[/C][/ROW]
[ROW][C]62[/C][C]91.8[/C][C]93.120051539462[/C][C]-1.32005153946191[/C][/ROW]
[ROW][C]63[/C][C]111.5[/C][C]106.781935590881[/C][C]4.71806440911909[/C][/ROW]
[ROW][C]64[/C][C]99.7[/C][C]99.0309921008435[/C][C]0.669007899156513[/C][/ROW]
[ROW][C]65[/C][C]97.5[/C][C]98.4989047590185[/C][C]-0.998904759018524[/C][/ROW]
[ROW][C]66[/C][C]111.7[/C][C]110.809520562037[/C][C]0.890479437962851[/C][/ROW]
[ROW][C]67[/C][C]86.2[/C][C]100.614043363794[/C][C]-14.4140433637936[/C][/ROW]
[ROW][C]68[/C][C]95.4[/C][C]93.7830701213147[/C][C]1.61692987868527[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111589&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111589&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
194.696.4920694355582-1.89206943555816
295.999.14746530553-3.24746530553006
3104.7109.297347116634-4.59734711663445
4102.8103.626099124336-0.826099124336263
598.1101.427691374314-3.32769137431427
6113.9108.4838532069015.41614679309907
780.997.6626439632436-16.7626439632436
895.793.92508012253361.77491987746637
9113.2108.1380580099955.06194199000545
10105.9101.8088954897424.09110451025753
11108.8108.6061473581240.193852641875925
12102.3105.299425574733-2.99942557473304
1399102.589332485707-3.58933248570688
14100.7103.261889022176-2.56188902217642
15115.5116.432843575739-0.93284357573852
16100.799.40894575440481.29105424559523
17109.9108.0079351244761.89206487552386
18114.6109.1645162671565.43548373284383
1985.499.1376017666393-13.7376017666393
20100.594.5649528141385.93504718586194
21114.8107.5880530454937.21194695450745
22116.5108.7575147997117.74248520028854
23112.9108.9781780673453.92182193265509
2410298.52778178180953.47221821819055
25106104.7249186794761.27508132052402
26105.3103.0542662870372.24573371296330
27118.8113.9035506484144.89644935158579
28106.1102.6215603077563.47843969224429
29109.3107.8527317150741.44726828492608
30117.2111.3235951672465.87640483275421
3192.5106.473394677311-13.9733946773108
32104.298.25418782104585.9458121789542
33112.5107.5349799409884.96502005901153
34122.4117.4459743987644.95402560123569
35113.3111.2098798798222.09012012017791
36100100.843710435516-0.843710435516367
37110.7110.904276649861-0.204276649860744
38112.8110.4739816275632.32601837243742
39109.8112.306536612054-2.50653661205443
40117.3118.265597424151-0.965597424151192
41109.1112.196709264968-3.09670926496788
42115.9118.063900325064-2.16390032506397
4396115.215218868311-19.2152188683109
4499.899.67420615210520.125793847894769
45116.8118.078596451203-1.27859645120323
46115.7115.79216715038-0.0921671503800888
4799.499.2135240661810.186475933818985
4894.394.13820515573160.161794844268383
499191.8141308632122-0.814130863212235
5093.292.93041246929560.269587530704357
51103.197.4026804873285.69731951267202
5294.192.61482315491791.48517684508214
5391.889.3232696144532.47673038554706
54102.797.0028286369475.69717136305295
5582.691.8293165268406-9.22931652684064
5689.182.81611661841926.28388338158083
57104.597.72284623438326.77715376561684
58105.197.26264194807677.83735805192327
5995.195.4591140267216-0.359114026721552
6088.795.150416094328-6.45041609432809
6186.391.7328949952635-5.43289499526352
6291.893.120051539462-1.32005153946191
63111.5106.7819355908814.71806440911909
6499.799.03099210084350.669007899156513
6597.598.4989047590185-0.998904759018524
66111.7110.8095205620370.890479437962851
6786.2100.614043363794-14.4140433637936
6895.493.78307012131471.61692987868527







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.3482187298526980.6964374597053960.651781270147302
90.6750714465348810.6498571069302370.324928553465118
100.5420017656699670.9159964686600660.457998234330033
110.7636628102682720.4726743794634550.236337189731728
120.7770992406824830.4458015186350330.222900759317516
130.7220149963149450.555970007370110.277985003685055
140.6446663017112560.7106673965774870.355333698288744
150.5761982859912030.8476034280175930.423801714008797
160.5059332042148190.9881335915703620.494066795785181
170.4124774512314330.8249549024628650.587522548768567
180.3508998681058890.7017997362117780.649100131894111
190.6959278490374240.6081443019251520.304072150962576
200.7396631909828750.520673618034250.260336809017125
210.7400723598774070.5198552802451850.259927640122593
220.7193663339889260.5612673320221490.280633666011074
230.6497895275763730.7004209448472540.350210472423627
240.5771499370667430.8457001258665140.422850062933257
250.5079331131021850.984133773795630.492066886897815
260.4300938126421650.860187625284330.569906187357835
270.3705954032632290.7411908065264580.629404596736771
280.3042177959590670.6084355919181340.695782204040933
290.2534167978237070.5068335956474140.746583202176293
300.2287289776683150.4574579553366290.771271022331685
310.6704378281644250.659124343671150.329562171835575
320.6516329636882550.696734072623490.348367036311745
330.6185520993856390.7628958012287220.381447900614361
340.6179436680864170.7641126638271660.382056331913583
350.5833194998328520.8333610003342960.416680500167148
360.5171693466354330.9656613067291330.482830653364567
370.4591015404822010.9182030809644010.540898459517799
380.4975913490987210.9951826981974420.502408650901279
390.4592945276521850.918589055304370.540705472347815
400.4220776261031630.8441552522063260.577922373896837
410.4107541301023310.8215082602046630.589245869897669
420.4195680812023010.8391361624046030.580431918797699
430.7214068490387620.5571863019224770.278593150961239
440.6880656406978690.6238687186042630.311934359302131
450.6244413018959240.7511173962081510.375558698104076
460.5785132476962270.8429735046075460.421486752303773
470.5510906483170520.8978187033658960.448909351682948
480.5377601016076390.9244797967847210.462239898392361
490.5909584981890720.8180830036218550.409041501810928
500.5576097410823510.8847805178352970.442390258917649
510.5289423327451320.9421153345097360.471057667254868
520.4601457409632460.9202914819264910.539854259036755
530.3685254020993970.7370508041987930.631474597900603
540.2873971061539280.5747942123078570.712602893846072
550.4635297320443780.9270594640887560.536470267955622
560.3740636066629360.7481272133258730.625936393337064
570.2927485260273260.5854970520546520.707251473972674
580.3573314673982890.7146629347965780.642668532601711
590.4321022887101930.8642045774203850.567897711289807
600.3105287152526480.6210574305052960.689471284747352

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.348218729852698 & 0.696437459705396 & 0.651781270147302 \tabularnewline
9 & 0.675071446534881 & 0.649857106930237 & 0.324928553465118 \tabularnewline
10 & 0.542001765669967 & 0.915996468660066 & 0.457998234330033 \tabularnewline
11 & 0.763662810268272 & 0.472674379463455 & 0.236337189731728 \tabularnewline
12 & 0.777099240682483 & 0.445801518635033 & 0.222900759317516 \tabularnewline
13 & 0.722014996314945 & 0.55597000737011 & 0.277985003685055 \tabularnewline
14 & 0.644666301711256 & 0.710667396577487 & 0.355333698288744 \tabularnewline
15 & 0.576198285991203 & 0.847603428017593 & 0.423801714008797 \tabularnewline
16 & 0.505933204214819 & 0.988133591570362 & 0.494066795785181 \tabularnewline
17 & 0.412477451231433 & 0.824954902462865 & 0.587522548768567 \tabularnewline
18 & 0.350899868105889 & 0.701799736211778 & 0.649100131894111 \tabularnewline
19 & 0.695927849037424 & 0.608144301925152 & 0.304072150962576 \tabularnewline
20 & 0.739663190982875 & 0.52067361803425 & 0.260336809017125 \tabularnewline
21 & 0.740072359877407 & 0.519855280245185 & 0.259927640122593 \tabularnewline
22 & 0.719366333988926 & 0.561267332022149 & 0.280633666011074 \tabularnewline
23 & 0.649789527576373 & 0.700420944847254 & 0.350210472423627 \tabularnewline
24 & 0.577149937066743 & 0.845700125866514 & 0.422850062933257 \tabularnewline
25 & 0.507933113102185 & 0.98413377379563 & 0.492066886897815 \tabularnewline
26 & 0.430093812642165 & 0.86018762528433 & 0.569906187357835 \tabularnewline
27 & 0.370595403263229 & 0.741190806526458 & 0.629404596736771 \tabularnewline
28 & 0.304217795959067 & 0.608435591918134 & 0.695782204040933 \tabularnewline
29 & 0.253416797823707 & 0.506833595647414 & 0.746583202176293 \tabularnewline
30 & 0.228728977668315 & 0.457457955336629 & 0.771271022331685 \tabularnewline
31 & 0.670437828164425 & 0.65912434367115 & 0.329562171835575 \tabularnewline
32 & 0.651632963688255 & 0.69673407262349 & 0.348367036311745 \tabularnewline
33 & 0.618552099385639 & 0.762895801228722 & 0.381447900614361 \tabularnewline
34 & 0.617943668086417 & 0.764112663827166 & 0.382056331913583 \tabularnewline
35 & 0.583319499832852 & 0.833361000334296 & 0.416680500167148 \tabularnewline
36 & 0.517169346635433 & 0.965661306729133 & 0.482830653364567 \tabularnewline
37 & 0.459101540482201 & 0.918203080964401 & 0.540898459517799 \tabularnewline
38 & 0.497591349098721 & 0.995182698197442 & 0.502408650901279 \tabularnewline
39 & 0.459294527652185 & 0.91858905530437 & 0.540705472347815 \tabularnewline
40 & 0.422077626103163 & 0.844155252206326 & 0.577922373896837 \tabularnewline
41 & 0.410754130102331 & 0.821508260204663 & 0.589245869897669 \tabularnewline
42 & 0.419568081202301 & 0.839136162404603 & 0.580431918797699 \tabularnewline
43 & 0.721406849038762 & 0.557186301922477 & 0.278593150961239 \tabularnewline
44 & 0.688065640697869 & 0.623868718604263 & 0.311934359302131 \tabularnewline
45 & 0.624441301895924 & 0.751117396208151 & 0.375558698104076 \tabularnewline
46 & 0.578513247696227 & 0.842973504607546 & 0.421486752303773 \tabularnewline
47 & 0.551090648317052 & 0.897818703365896 & 0.448909351682948 \tabularnewline
48 & 0.537760101607639 & 0.924479796784721 & 0.462239898392361 \tabularnewline
49 & 0.590958498189072 & 0.818083003621855 & 0.409041501810928 \tabularnewline
50 & 0.557609741082351 & 0.884780517835297 & 0.442390258917649 \tabularnewline
51 & 0.528942332745132 & 0.942115334509736 & 0.471057667254868 \tabularnewline
52 & 0.460145740963246 & 0.920291481926491 & 0.539854259036755 \tabularnewline
53 & 0.368525402099397 & 0.737050804198793 & 0.631474597900603 \tabularnewline
54 & 0.287397106153928 & 0.574794212307857 & 0.712602893846072 \tabularnewline
55 & 0.463529732044378 & 0.927059464088756 & 0.536470267955622 \tabularnewline
56 & 0.374063606662936 & 0.748127213325873 & 0.625936393337064 \tabularnewline
57 & 0.292748526027326 & 0.585497052054652 & 0.707251473972674 \tabularnewline
58 & 0.357331467398289 & 0.714662934796578 & 0.642668532601711 \tabularnewline
59 & 0.432102288710193 & 0.864204577420385 & 0.567897711289807 \tabularnewline
60 & 0.310528715252648 & 0.621057430505296 & 0.689471284747352 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111589&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]8[/C][C]0.348218729852698[/C][C]0.696437459705396[/C][C]0.651781270147302[/C][/ROW]
[ROW][C]9[/C][C]0.675071446534881[/C][C]0.649857106930237[/C][C]0.324928553465118[/C][/ROW]
[ROW][C]10[/C][C]0.542001765669967[/C][C]0.915996468660066[/C][C]0.457998234330033[/C][/ROW]
[ROW][C]11[/C][C]0.763662810268272[/C][C]0.472674379463455[/C][C]0.236337189731728[/C][/ROW]
[ROW][C]12[/C][C]0.777099240682483[/C][C]0.445801518635033[/C][C]0.222900759317516[/C][/ROW]
[ROW][C]13[/C][C]0.722014996314945[/C][C]0.55597000737011[/C][C]0.277985003685055[/C][/ROW]
[ROW][C]14[/C][C]0.644666301711256[/C][C]0.710667396577487[/C][C]0.355333698288744[/C][/ROW]
[ROW][C]15[/C][C]0.576198285991203[/C][C]0.847603428017593[/C][C]0.423801714008797[/C][/ROW]
[ROW][C]16[/C][C]0.505933204214819[/C][C]0.988133591570362[/C][C]0.494066795785181[/C][/ROW]
[ROW][C]17[/C][C]0.412477451231433[/C][C]0.824954902462865[/C][C]0.587522548768567[/C][/ROW]
[ROW][C]18[/C][C]0.350899868105889[/C][C]0.701799736211778[/C][C]0.649100131894111[/C][/ROW]
[ROW][C]19[/C][C]0.695927849037424[/C][C]0.608144301925152[/C][C]0.304072150962576[/C][/ROW]
[ROW][C]20[/C][C]0.739663190982875[/C][C]0.52067361803425[/C][C]0.260336809017125[/C][/ROW]
[ROW][C]21[/C][C]0.740072359877407[/C][C]0.519855280245185[/C][C]0.259927640122593[/C][/ROW]
[ROW][C]22[/C][C]0.719366333988926[/C][C]0.561267332022149[/C][C]0.280633666011074[/C][/ROW]
[ROW][C]23[/C][C]0.649789527576373[/C][C]0.700420944847254[/C][C]0.350210472423627[/C][/ROW]
[ROW][C]24[/C][C]0.577149937066743[/C][C]0.845700125866514[/C][C]0.422850062933257[/C][/ROW]
[ROW][C]25[/C][C]0.507933113102185[/C][C]0.98413377379563[/C][C]0.492066886897815[/C][/ROW]
[ROW][C]26[/C][C]0.430093812642165[/C][C]0.86018762528433[/C][C]0.569906187357835[/C][/ROW]
[ROW][C]27[/C][C]0.370595403263229[/C][C]0.741190806526458[/C][C]0.629404596736771[/C][/ROW]
[ROW][C]28[/C][C]0.304217795959067[/C][C]0.608435591918134[/C][C]0.695782204040933[/C][/ROW]
[ROW][C]29[/C][C]0.253416797823707[/C][C]0.506833595647414[/C][C]0.746583202176293[/C][/ROW]
[ROW][C]30[/C][C]0.228728977668315[/C][C]0.457457955336629[/C][C]0.771271022331685[/C][/ROW]
[ROW][C]31[/C][C]0.670437828164425[/C][C]0.65912434367115[/C][C]0.329562171835575[/C][/ROW]
[ROW][C]32[/C][C]0.651632963688255[/C][C]0.69673407262349[/C][C]0.348367036311745[/C][/ROW]
[ROW][C]33[/C][C]0.618552099385639[/C][C]0.762895801228722[/C][C]0.381447900614361[/C][/ROW]
[ROW][C]34[/C][C]0.617943668086417[/C][C]0.764112663827166[/C][C]0.382056331913583[/C][/ROW]
[ROW][C]35[/C][C]0.583319499832852[/C][C]0.833361000334296[/C][C]0.416680500167148[/C][/ROW]
[ROW][C]36[/C][C]0.517169346635433[/C][C]0.965661306729133[/C][C]0.482830653364567[/C][/ROW]
[ROW][C]37[/C][C]0.459101540482201[/C][C]0.918203080964401[/C][C]0.540898459517799[/C][/ROW]
[ROW][C]38[/C][C]0.497591349098721[/C][C]0.995182698197442[/C][C]0.502408650901279[/C][/ROW]
[ROW][C]39[/C][C]0.459294527652185[/C][C]0.91858905530437[/C][C]0.540705472347815[/C][/ROW]
[ROW][C]40[/C][C]0.422077626103163[/C][C]0.844155252206326[/C][C]0.577922373896837[/C][/ROW]
[ROW][C]41[/C][C]0.410754130102331[/C][C]0.821508260204663[/C][C]0.589245869897669[/C][/ROW]
[ROW][C]42[/C][C]0.419568081202301[/C][C]0.839136162404603[/C][C]0.580431918797699[/C][/ROW]
[ROW][C]43[/C][C]0.721406849038762[/C][C]0.557186301922477[/C][C]0.278593150961239[/C][/ROW]
[ROW][C]44[/C][C]0.688065640697869[/C][C]0.623868718604263[/C][C]0.311934359302131[/C][/ROW]
[ROW][C]45[/C][C]0.624441301895924[/C][C]0.751117396208151[/C][C]0.375558698104076[/C][/ROW]
[ROW][C]46[/C][C]0.578513247696227[/C][C]0.842973504607546[/C][C]0.421486752303773[/C][/ROW]
[ROW][C]47[/C][C]0.551090648317052[/C][C]0.897818703365896[/C][C]0.448909351682948[/C][/ROW]
[ROW][C]48[/C][C]0.537760101607639[/C][C]0.924479796784721[/C][C]0.462239898392361[/C][/ROW]
[ROW][C]49[/C][C]0.590958498189072[/C][C]0.818083003621855[/C][C]0.409041501810928[/C][/ROW]
[ROW][C]50[/C][C]0.557609741082351[/C][C]0.884780517835297[/C][C]0.442390258917649[/C][/ROW]
[ROW][C]51[/C][C]0.528942332745132[/C][C]0.942115334509736[/C][C]0.471057667254868[/C][/ROW]
[ROW][C]52[/C][C]0.460145740963246[/C][C]0.920291481926491[/C][C]0.539854259036755[/C][/ROW]
[ROW][C]53[/C][C]0.368525402099397[/C][C]0.737050804198793[/C][C]0.631474597900603[/C][/ROW]
[ROW][C]54[/C][C]0.287397106153928[/C][C]0.574794212307857[/C][C]0.712602893846072[/C][/ROW]
[ROW][C]55[/C][C]0.463529732044378[/C][C]0.927059464088756[/C][C]0.536470267955622[/C][/ROW]
[ROW][C]56[/C][C]0.374063606662936[/C][C]0.748127213325873[/C][C]0.625936393337064[/C][/ROW]
[ROW][C]57[/C][C]0.292748526027326[/C][C]0.585497052054652[/C][C]0.707251473972674[/C][/ROW]
[ROW][C]58[/C][C]0.357331467398289[/C][C]0.714662934796578[/C][C]0.642668532601711[/C][/ROW]
[ROW][C]59[/C][C]0.432102288710193[/C][C]0.864204577420385[/C][C]0.567897711289807[/C][/ROW]
[ROW][C]60[/C][C]0.310528715252648[/C][C]0.621057430505296[/C][C]0.689471284747352[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111589&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111589&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.3482187298526980.6964374597053960.651781270147302
90.6750714465348810.6498571069302370.324928553465118
100.5420017656699670.9159964686600660.457998234330033
110.7636628102682720.4726743794634550.236337189731728
120.7770992406824830.4458015186350330.222900759317516
130.7220149963149450.555970007370110.277985003685055
140.6446663017112560.7106673965774870.355333698288744
150.5761982859912030.8476034280175930.423801714008797
160.5059332042148190.9881335915703620.494066795785181
170.4124774512314330.8249549024628650.587522548768567
180.3508998681058890.7017997362117780.649100131894111
190.6959278490374240.6081443019251520.304072150962576
200.7396631909828750.520673618034250.260336809017125
210.7400723598774070.5198552802451850.259927640122593
220.7193663339889260.5612673320221490.280633666011074
230.6497895275763730.7004209448472540.350210472423627
240.5771499370667430.8457001258665140.422850062933257
250.5079331131021850.984133773795630.492066886897815
260.4300938126421650.860187625284330.569906187357835
270.3705954032632290.7411908065264580.629404596736771
280.3042177959590670.6084355919181340.695782204040933
290.2534167978237070.5068335956474140.746583202176293
300.2287289776683150.4574579553366290.771271022331685
310.6704378281644250.659124343671150.329562171835575
320.6516329636882550.696734072623490.348367036311745
330.6185520993856390.7628958012287220.381447900614361
340.6179436680864170.7641126638271660.382056331913583
350.5833194998328520.8333610003342960.416680500167148
360.5171693466354330.9656613067291330.482830653364567
370.4591015404822010.9182030809644010.540898459517799
380.4975913490987210.9951826981974420.502408650901279
390.4592945276521850.918589055304370.540705472347815
400.4220776261031630.8441552522063260.577922373896837
410.4107541301023310.8215082602046630.589245869897669
420.4195680812023010.8391361624046030.580431918797699
430.7214068490387620.5571863019224770.278593150961239
440.6880656406978690.6238687186042630.311934359302131
450.6244413018959240.7511173962081510.375558698104076
460.5785132476962270.8429735046075460.421486752303773
470.5510906483170520.8978187033658960.448909351682948
480.5377601016076390.9244797967847210.462239898392361
490.5909584981890720.8180830036218550.409041501810928
500.5576097410823510.8847805178352970.442390258917649
510.5289423327451320.9421153345097360.471057667254868
520.4601457409632460.9202914819264910.539854259036755
530.3685254020993970.7370508041987930.631474597900603
540.2873971061539280.5747942123078570.712602893846072
550.4635297320443780.9270594640887560.536470267955622
560.3740636066629360.7481272133258730.625936393337064
570.2927485260273260.5854970520546520.707251473972674
580.3573314673982890.7146629347965780.642668532601711
590.4321022887101930.8642045774203850.567897711289807
600.3105287152526480.6210574305052960.689471284747352







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111589&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111589&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111589&T=6

As an alternative you can also use a QR Code:  

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

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



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}