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Author's title

multiple regression met 4 maanden minder, totale industrie zonder de bouwni...

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 08:40:21 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258645489yy3ijkj99s8cxl5.htm/, Retrieved Thu, 25 Apr 2024 00:38:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57773, Retrieved Thu, 25 Apr 2024 00:38:34 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact139

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [multiple regressi...] [2009-11-19 15:40:21] [b1ac221d009d6e5c29a4ef1869874933] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101.7	86.2	102.8	105.6	96.9	97.6
104.2	88.8	101.7	102.8	105.6	96.9
92.7	89.6	104.2	101.7	102.8	105.6
91.9	87.8	92.7	104.2	101.7	102.8
106.5	88.3	91.9	92.7	104.2	101.7
112.3	88.6	106.5	91.9	92.7	104.2
102.8	91	112.3	106.5	91.9	92.7
96.5	91.5	102.8	112.3	106.5	91.9
101	95.4	96.5	102.8	112.3	106.5
98.9	98.7	101	96.5	102.8	112.3
105.1	99.9	98.9	101	96.5	102.8
103	98.6	105.1	98.9	101	96.5
99	100.3	103	105.1	98.9	101
104.3	100.2	99	103	105.1	98.9
94.6	100.4	104.3	99	103	105.1
90.4	101.4	94.6	104.3	99	103
108.9	103	90.4	94.6	104.3	99
111.4	109.1	108.9	90.4	94.6	104.3
100.8	111.4	111.4	108.9	90.4	94.6
102.5	114.1	100.8	111.4	108.9	90.4
98.2	121.8	102.5	100.8	111.4	108.9
98.7	127.6	98.2	102.5	100.8	111.4
113.3	129.9	98.7	98.2	102.5	100.8
104.6	128	113.3	98.7	98.2	102.5
99.3	123.5	104.6	113.3	98.7	98.2
111.8	124	99.3	104.6	113.3	98.7
97.3	127.4	111.8	99.3	104.6	113.3
97.7	127.6	97.3	111.8	99.3	104.6
115.6	128.4	97.7	97.3	111.8	99.3
111.9	131.4	115.6	97.7	97.3	111.8
107	135.1	111.9	115.6	97.7	97.3
107.1	134	107	111.9	115.6	97.7
100.6	144.5	107.1	107	111.9	115.6
99.2	147.3	100.6	107.1	107	111.9
108.4	150.9	99.2	100.6	107.1	107
103	148.7	108.4	99.2	100.6	107.1
99.8	141.4	103	108.4	99.2	100.6
115	138.9	99.8	103	108.4	99.2
90.8	139.8	115	99.8	103	108.4
95.9	145.6	90.8	115	99.8	103
114.4	147.9	95.9	90.8	115	99.8
108.2	148.5	114.4	95.9	90.8	115
112.6	151.1	108.2	114.4	95.9	90.8
109.1	157.5	112.6	108.2	114.4	95.9
105	167.5	109.1	112.6	108.2	114.4
105	172.3	105	109.1	112.6	108.2
118.5	173.5	105	105	109.1	112.6
103.7	187.5	118.5	105	105	109.1
112.5	205.5	103.7	118.5	105	105
116.6	195.1	112.5	103.7	118.5	105
96.6	204.5	116.6	112.5	103.7	118.5
101.9	204.5	96.6	116.6	112.5	103.7
116.5	201.7	101.9	96.6	116.6	112.5
119.3	207	116.5	101.9	96.6	116.6
115.4	206.6	119.3	116.5	101.9	96.6
108.5	210.6	115.4	119.3	116.5	101.9
111.5	211.1	108.5	115.4	119.3	116.5
108.8	215	111.5	108.5	115.4	119.3
121.8	223.9	108.8	111.5	108.5	115.4
109.6	238.2	121.8	108.8	111.5	108.5
112.2	238.9	109.6	121.8	108.8	111.5
119.6	229.6	112.2	109.6	121.8	108.8
104.1	232.2	119.6	112.2	109.6	121.8
105.3	222.1	104.1	119.6	112.2	109.6
115	221.6	105.3	104.1	119.6	112.2
124.1	227.3	115	105.3	104.1	119.6
116.8	221	124.1	115	105.3	104.1
107.5	213.6	116.8	124.1	115	105.3
115.6	243.4	107.5	116.8	124.1	115

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57773&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57773&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57773&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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
tot_indus[t] = + 209.298982723361 + 0.200657477896676prijsindex[t] -0.0431033994434085`y(t-1)`[t] -0.6525411392476`y(t-2)`[t] -0.0302790380657440`y(t-3)`[t] -0.535338506090538`y(t-4)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
tot_indus[t] =  +  209.298982723361 +  0.200657477896676prijsindex[t] -0.0431033994434085`y(t-1)`[t] -0.6525411392476`y(t-2)`[t] -0.0302790380657440`y(t-3)`[t] -0.535338506090538`y(t-4)`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57773&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]tot_indus[t] =  +  209.298982723361 +  0.200657477896676prijsindex[t] -0.0431033994434085`y(t-1)`[t] -0.6525411392476`y(t-2)`[t] -0.0302790380657440`y(t-3)`[t] -0.535338506090538`y(t-4)`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57773&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57773&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
tot_indus[t] = + 209.298982723361 + 0.200657477896676prijsindex[t] -0.0431033994434085`y(t-1)`[t] -0.6525411392476`y(t-2)`[t] -0.0302790380657440`y(t-3)`[t] -0.535338506090538`y(t-4)`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)209.29898272336126.0917328.021700
prijsindex0.2006574778966760.0279457.180500
`y(t-1)`-0.04310339944340850.110024-0.39180.6965550.348278
`y(t-2)`-0.65254113924760.112174-5.817200
`y(t-3)`-0.03027903806574400.110954-0.27290.7858250.392912
`y(t-4)`-0.5353385060905380.117064-4.57312.3e-051.2e-05

\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) & 209.298982723361 & 26.091732 & 8.0217 & 0 & 0 \tabularnewline
prijsindex & 0.200657477896676 & 0.027945 & 7.1805 & 0 & 0 \tabularnewline
`y(t-1)` & -0.0431033994434085 & 0.110024 & -0.3918 & 0.696555 & 0.348278 \tabularnewline
`y(t-2)` & -0.6525411392476 & 0.112174 & -5.8172 & 0 & 0 \tabularnewline
`y(t-3)` & -0.0302790380657440 & 0.110954 & -0.2729 & 0.785825 & 0.392912 \tabularnewline
`y(t-4)` & -0.535338506090538 & 0.117064 & -4.5731 & 2.3e-05 & 1.2e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57773&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]209.298982723361[/C][C]26.091732[/C][C]8.0217[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]prijsindex[/C][C]0.200657477896676[/C][C]0.027945[/C][C]7.1805[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`y(t-1)`[/C][C]-0.0431033994434085[/C][C]0.110024[/C][C]-0.3918[/C][C]0.696555[/C][C]0.348278[/C][/ROW]
[ROW][C]`y(t-2)`[/C][C]-0.6525411392476[/C][C]0.112174[/C][C]-5.8172[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`y(t-3)`[/C][C]-0.0302790380657440[/C][C]0.110954[/C][C]-0.2729[/C][C]0.785825[/C][C]0.392912[/C][/ROW]
[ROW][C]`y(t-4)`[/C][C]-0.535338506090538[/C][C]0.117064[/C][C]-4.5731[/C][C]2.3e-05[/C][C]1.2e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57773&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57773&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)209.29898272336126.0917328.021700
prijsindex0.2006574778966760.0279457.180500
`y(t-1)`-0.04310339944340850.110024-0.39180.6965550.348278
`y(t-2)`-0.65254113924760.112174-5.817200
`y(t-3)`-0.03027903806574400.110954-0.27290.7858250.392912
`y(t-4)`-0.5353385060905380.117064-4.57312.3e-051.2e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.75865628105714
R-squared0.57555935278745
Adjusted R-squared0.54187358713566
F-TEST (value)17.0861294570833
F-TEST (DF numerator)5
F-TEST (DF denominator)63
p-value1.20284671112358e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.35827567184311
Sum Squared Residuals1808.80044505434

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.75865628105714 \tabularnewline
R-squared & 0.57555935278745 \tabularnewline
Adjusted R-squared & 0.54187358713566 \tabularnewline
F-TEST (value) & 17.0861294570833 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 63 \tabularnewline
p-value & 1.20284671112358e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5.35827567184311 \tabularnewline
Sum Squared Residuals & 1808.80044505434 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57773&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.75865628105714[/C][/ROW]
[ROW][C]R-squared[/C][C]0.57555935278745[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.54187358713566[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.0861294570833[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]63[/C][/ROW]
[ROW][C]p-value[/C][C]1.20284671112358e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]5.35827567184311[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1808.80044505434[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57773&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57773&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 R0.75865628105714
R-squared0.57555935278745
Adjusted R-squared0.54187358713566
F-TEST (value)17.0861294570833
F-TEST (DF numerator)5
F-TEST (DF denominator)63
p-value1.20284671112358e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.35827567184311
Sum Squared Residuals1808.80044505434






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1101.798.07320656771683.62679343228316
2104.2100.5807542626223.61924573737824
392.796.7786533030993-4.07865330309934
491.996.8140608472913-4.91406084729132
5106.5104.9662701686771.53372983132296
6112.3103.92905336418.37094663589992
7102.8100.8141470117591.98585298824085
896.597.5254162868964-1.02541628689636
910196.7871120803364.2128879196641
1098.998.5490131634590.350986836541081
11105.1101.2203578968263.87964210317380
12103105.298975408506-2.29897540850587
139999.339417898957-0.339417898956908
14104.3101.7985829681432.50141703185665
1594.6100.964918245840-6.36491824583976
1690.499.3705376753783-8.97053767537831
17108.9108.1831480909910.716851909009263
18111.4108.8068311882552.59316881174482
19100.8102.408529281683-1.60852928168276
20102.5103.464107179349-0.964107179348907
2198.2101.873370098285-3.67337009828478
2298.7101.095819689242-2.39581968924177
23113.3109.9648208872953.33517911270495
24104.6107.848115881123-3.24811588112258
2599.3100.079872229887-0.779872229886675
26111.8105.3760136885346.42398631146593
2797.3101.425410100603-4.12541010060257
2897.798.7517005522524-1.05170055225244
29115.6110.8156378003414.78436219965932
30111.9104.1323576541167.7676423458838
31107101.1040832308295.89591676917102
32107.1102.7528386938184.34716130618157
33100.698.5823566359252.01764336407494
3499.2101.488235315550-2.28823531555028
35108.4109.132595176346-0.732595176345546
36103109.351434941858-6.35143494185842
3799.8105.638106172010-5.83810617200971
38115109.2690222657465.73097773425413
3990.8106.120966519427-15.3209665194275
4095.9101.396977695894-5.49697769589443
41114.4118.682999968578-4.28299996857774
42108.2107.2736291840650.926370815935249
43112.6108.7913373803203.80866261967963
44109.1110.641256759365-1.54125675936521
45105100.2114800970274.78851990297287
46105106.821124886288-1.82112488628781
47118.5107.48781973711111.0121802628893
48103.7111.712957362565-8.0129573625646
49112.5109.3483047715963.15169522840418
50116.6116.1309989333450.469001066654665
5196.6105.319153193628-8.71915319362799
52101.9111.162356866742-9.2623568667424
53116.5118.587767786867-2.08776778686737
54119.3113.9741676361775.32583236382262
55115.4114.7924057136250.607594286375487
56108.5110.656655655108-2.15665565510752
57111.5105.6985847977755.80141520222492
58108.8109.473513055453-0.673513055453239
59121.8111.71486590589510.0851340941052
60109.6119.388783300849-9.78878330084894
61112.2110.0478080828732.15219191712671
62119.6117.0824130702922.51758692970814
63104.1108.998554080123-4.89855408012331
64105.3109.263616089641-3.9636160896411
65115117.610005932177-2.61000593217657
66124.1114.06042135943810.0395786405615
67116.8114.3358012617772.46419873822286
68107.5106.2913534975791.20864650242091
69115.6111.9670355147553.63296448524526

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.7 & 98.0732065677168 & 3.62679343228316 \tabularnewline
2 & 104.2 & 100.580754262622 & 3.61924573737824 \tabularnewline
3 & 92.7 & 96.7786533030993 & -4.07865330309934 \tabularnewline
4 & 91.9 & 96.8140608472913 & -4.91406084729132 \tabularnewline
5 & 106.5 & 104.966270168677 & 1.53372983132296 \tabularnewline
6 & 112.3 & 103.9290533641 & 8.37094663589992 \tabularnewline
7 & 102.8 & 100.814147011759 & 1.98585298824085 \tabularnewline
8 & 96.5 & 97.5254162868964 & -1.02541628689636 \tabularnewline
9 & 101 & 96.787112080336 & 4.2128879196641 \tabularnewline
10 & 98.9 & 98.549013163459 & 0.350986836541081 \tabularnewline
11 & 105.1 & 101.220357896826 & 3.87964210317380 \tabularnewline
12 & 103 & 105.298975408506 & -2.29897540850587 \tabularnewline
13 & 99 & 99.339417898957 & -0.339417898956908 \tabularnewline
14 & 104.3 & 101.798582968143 & 2.50141703185665 \tabularnewline
15 & 94.6 & 100.964918245840 & -6.36491824583976 \tabularnewline
16 & 90.4 & 99.3705376753783 & -8.97053767537831 \tabularnewline
17 & 108.9 & 108.183148090991 & 0.716851909009263 \tabularnewline
18 & 111.4 & 108.806831188255 & 2.59316881174482 \tabularnewline
19 & 100.8 & 102.408529281683 & -1.60852928168276 \tabularnewline
20 & 102.5 & 103.464107179349 & -0.964107179348907 \tabularnewline
21 & 98.2 & 101.873370098285 & -3.67337009828478 \tabularnewline
22 & 98.7 & 101.095819689242 & -2.39581968924177 \tabularnewline
23 & 113.3 & 109.964820887295 & 3.33517911270495 \tabularnewline
24 & 104.6 & 107.848115881123 & -3.24811588112258 \tabularnewline
25 & 99.3 & 100.079872229887 & -0.779872229886675 \tabularnewline
26 & 111.8 & 105.376013688534 & 6.42398631146593 \tabularnewline
27 & 97.3 & 101.425410100603 & -4.12541010060257 \tabularnewline
28 & 97.7 & 98.7517005522524 & -1.05170055225244 \tabularnewline
29 & 115.6 & 110.815637800341 & 4.78436219965932 \tabularnewline
30 & 111.9 & 104.132357654116 & 7.7676423458838 \tabularnewline
31 & 107 & 101.104083230829 & 5.89591676917102 \tabularnewline
32 & 107.1 & 102.752838693818 & 4.34716130618157 \tabularnewline
33 & 100.6 & 98.582356635925 & 2.01764336407494 \tabularnewline
34 & 99.2 & 101.488235315550 & -2.28823531555028 \tabularnewline
35 & 108.4 & 109.132595176346 & -0.732595176345546 \tabularnewline
36 & 103 & 109.351434941858 & -6.35143494185842 \tabularnewline
37 & 99.8 & 105.638106172010 & -5.83810617200971 \tabularnewline
38 & 115 & 109.269022265746 & 5.73097773425413 \tabularnewline
39 & 90.8 & 106.120966519427 & -15.3209665194275 \tabularnewline
40 & 95.9 & 101.396977695894 & -5.49697769589443 \tabularnewline
41 & 114.4 & 118.682999968578 & -4.28299996857774 \tabularnewline
42 & 108.2 & 107.273629184065 & 0.926370815935249 \tabularnewline
43 & 112.6 & 108.791337380320 & 3.80866261967963 \tabularnewline
44 & 109.1 & 110.641256759365 & -1.54125675936521 \tabularnewline
45 & 105 & 100.211480097027 & 4.78851990297287 \tabularnewline
46 & 105 & 106.821124886288 & -1.82112488628781 \tabularnewline
47 & 118.5 & 107.487819737111 & 11.0121802628893 \tabularnewline
48 & 103.7 & 111.712957362565 & -8.0129573625646 \tabularnewline
49 & 112.5 & 109.348304771596 & 3.15169522840418 \tabularnewline
50 & 116.6 & 116.130998933345 & 0.469001066654665 \tabularnewline
51 & 96.6 & 105.319153193628 & -8.71915319362799 \tabularnewline
52 & 101.9 & 111.162356866742 & -9.2623568667424 \tabularnewline
53 & 116.5 & 118.587767786867 & -2.08776778686737 \tabularnewline
54 & 119.3 & 113.974167636177 & 5.32583236382262 \tabularnewline
55 & 115.4 & 114.792405713625 & 0.607594286375487 \tabularnewline
56 & 108.5 & 110.656655655108 & -2.15665565510752 \tabularnewline
57 & 111.5 & 105.698584797775 & 5.80141520222492 \tabularnewline
58 & 108.8 & 109.473513055453 & -0.673513055453239 \tabularnewline
59 & 121.8 & 111.714865905895 & 10.0851340941052 \tabularnewline
60 & 109.6 & 119.388783300849 & -9.78878330084894 \tabularnewline
61 & 112.2 & 110.047808082873 & 2.15219191712671 \tabularnewline
62 & 119.6 & 117.082413070292 & 2.51758692970814 \tabularnewline
63 & 104.1 & 108.998554080123 & -4.89855408012331 \tabularnewline
64 & 105.3 & 109.263616089641 & -3.9636160896411 \tabularnewline
65 & 115 & 117.610005932177 & -2.61000593217657 \tabularnewline
66 & 124.1 & 114.060421359438 & 10.0395786405615 \tabularnewline
67 & 116.8 & 114.335801261777 & 2.46419873822286 \tabularnewline
68 & 107.5 & 106.291353497579 & 1.20864650242091 \tabularnewline
69 & 115.6 & 111.967035514755 & 3.63296448524526 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57773&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]101.7[/C][C]98.0732065677168[/C][C]3.62679343228316[/C][/ROW]
[ROW][C]2[/C][C]104.2[/C][C]100.580754262622[/C][C]3.61924573737824[/C][/ROW]
[ROW][C]3[/C][C]92.7[/C][C]96.7786533030993[/C][C]-4.07865330309934[/C][/ROW]
[ROW][C]4[/C][C]91.9[/C][C]96.8140608472913[/C][C]-4.91406084729132[/C][/ROW]
[ROW][C]5[/C][C]106.5[/C][C]104.966270168677[/C][C]1.53372983132296[/C][/ROW]
[ROW][C]6[/C][C]112.3[/C][C]103.9290533641[/C][C]8.37094663589992[/C][/ROW]
[ROW][C]7[/C][C]102.8[/C][C]100.814147011759[/C][C]1.98585298824085[/C][/ROW]
[ROW][C]8[/C][C]96.5[/C][C]97.5254162868964[/C][C]-1.02541628689636[/C][/ROW]
[ROW][C]9[/C][C]101[/C][C]96.787112080336[/C][C]4.2128879196641[/C][/ROW]
[ROW][C]10[/C][C]98.9[/C][C]98.549013163459[/C][C]0.350986836541081[/C][/ROW]
[ROW][C]11[/C][C]105.1[/C][C]101.220357896826[/C][C]3.87964210317380[/C][/ROW]
[ROW][C]12[/C][C]103[/C][C]105.298975408506[/C][C]-2.29897540850587[/C][/ROW]
[ROW][C]13[/C][C]99[/C][C]99.339417898957[/C][C]-0.339417898956908[/C][/ROW]
[ROW][C]14[/C][C]104.3[/C][C]101.798582968143[/C][C]2.50141703185665[/C][/ROW]
[ROW][C]15[/C][C]94.6[/C][C]100.964918245840[/C][C]-6.36491824583976[/C][/ROW]
[ROW][C]16[/C][C]90.4[/C][C]99.3705376753783[/C][C]-8.97053767537831[/C][/ROW]
[ROW][C]17[/C][C]108.9[/C][C]108.183148090991[/C][C]0.716851909009263[/C][/ROW]
[ROW][C]18[/C][C]111.4[/C][C]108.806831188255[/C][C]2.59316881174482[/C][/ROW]
[ROW][C]19[/C][C]100.8[/C][C]102.408529281683[/C][C]-1.60852928168276[/C][/ROW]
[ROW][C]20[/C][C]102.5[/C][C]103.464107179349[/C][C]-0.964107179348907[/C][/ROW]
[ROW][C]21[/C][C]98.2[/C][C]101.873370098285[/C][C]-3.67337009828478[/C][/ROW]
[ROW][C]22[/C][C]98.7[/C][C]101.095819689242[/C][C]-2.39581968924177[/C][/ROW]
[ROW][C]23[/C][C]113.3[/C][C]109.964820887295[/C][C]3.33517911270495[/C][/ROW]
[ROW][C]24[/C][C]104.6[/C][C]107.848115881123[/C][C]-3.24811588112258[/C][/ROW]
[ROW][C]25[/C][C]99.3[/C][C]100.079872229887[/C][C]-0.779872229886675[/C][/ROW]
[ROW][C]26[/C][C]111.8[/C][C]105.376013688534[/C][C]6.42398631146593[/C][/ROW]
[ROW][C]27[/C][C]97.3[/C][C]101.425410100603[/C][C]-4.12541010060257[/C][/ROW]
[ROW][C]28[/C][C]97.7[/C][C]98.7517005522524[/C][C]-1.05170055225244[/C][/ROW]
[ROW][C]29[/C][C]115.6[/C][C]110.815637800341[/C][C]4.78436219965932[/C][/ROW]
[ROW][C]30[/C][C]111.9[/C][C]104.132357654116[/C][C]7.7676423458838[/C][/ROW]
[ROW][C]31[/C][C]107[/C][C]101.104083230829[/C][C]5.89591676917102[/C][/ROW]
[ROW][C]32[/C][C]107.1[/C][C]102.752838693818[/C][C]4.34716130618157[/C][/ROW]
[ROW][C]33[/C][C]100.6[/C][C]98.582356635925[/C][C]2.01764336407494[/C][/ROW]
[ROW][C]34[/C][C]99.2[/C][C]101.488235315550[/C][C]-2.28823531555028[/C][/ROW]
[ROW][C]35[/C][C]108.4[/C][C]109.132595176346[/C][C]-0.732595176345546[/C][/ROW]
[ROW][C]36[/C][C]103[/C][C]109.351434941858[/C][C]-6.35143494185842[/C][/ROW]
[ROW][C]37[/C][C]99.8[/C][C]105.638106172010[/C][C]-5.83810617200971[/C][/ROW]
[ROW][C]38[/C][C]115[/C][C]109.269022265746[/C][C]5.73097773425413[/C][/ROW]
[ROW][C]39[/C][C]90.8[/C][C]106.120966519427[/C][C]-15.3209665194275[/C][/ROW]
[ROW][C]40[/C][C]95.9[/C][C]101.396977695894[/C][C]-5.49697769589443[/C][/ROW]
[ROW][C]41[/C][C]114.4[/C][C]118.682999968578[/C][C]-4.28299996857774[/C][/ROW]
[ROW][C]42[/C][C]108.2[/C][C]107.273629184065[/C][C]0.926370815935249[/C][/ROW]
[ROW][C]43[/C][C]112.6[/C][C]108.791337380320[/C][C]3.80866261967963[/C][/ROW]
[ROW][C]44[/C][C]109.1[/C][C]110.641256759365[/C][C]-1.54125675936521[/C][/ROW]
[ROW][C]45[/C][C]105[/C][C]100.211480097027[/C][C]4.78851990297287[/C][/ROW]
[ROW][C]46[/C][C]105[/C][C]106.821124886288[/C][C]-1.82112488628781[/C][/ROW]
[ROW][C]47[/C][C]118.5[/C][C]107.487819737111[/C][C]11.0121802628893[/C][/ROW]
[ROW][C]48[/C][C]103.7[/C][C]111.712957362565[/C][C]-8.0129573625646[/C][/ROW]
[ROW][C]49[/C][C]112.5[/C][C]109.348304771596[/C][C]3.15169522840418[/C][/ROW]
[ROW][C]50[/C][C]116.6[/C][C]116.130998933345[/C][C]0.469001066654665[/C][/ROW]
[ROW][C]51[/C][C]96.6[/C][C]105.319153193628[/C][C]-8.71915319362799[/C][/ROW]
[ROW][C]52[/C][C]101.9[/C][C]111.162356866742[/C][C]-9.2623568667424[/C][/ROW]
[ROW][C]53[/C][C]116.5[/C][C]118.587767786867[/C][C]-2.08776778686737[/C][/ROW]
[ROW][C]54[/C][C]119.3[/C][C]113.974167636177[/C][C]5.32583236382262[/C][/ROW]
[ROW][C]55[/C][C]115.4[/C][C]114.792405713625[/C][C]0.607594286375487[/C][/ROW]
[ROW][C]56[/C][C]108.5[/C][C]110.656655655108[/C][C]-2.15665565510752[/C][/ROW]
[ROW][C]57[/C][C]111.5[/C][C]105.698584797775[/C][C]5.80141520222492[/C][/ROW]
[ROW][C]58[/C][C]108.8[/C][C]109.473513055453[/C][C]-0.673513055453239[/C][/ROW]
[ROW][C]59[/C][C]121.8[/C][C]111.714865905895[/C][C]10.0851340941052[/C][/ROW]
[ROW][C]60[/C][C]109.6[/C][C]119.388783300849[/C][C]-9.78878330084894[/C][/ROW]
[ROW][C]61[/C][C]112.2[/C][C]110.047808082873[/C][C]2.15219191712671[/C][/ROW]
[ROW][C]62[/C][C]119.6[/C][C]117.082413070292[/C][C]2.51758692970814[/C][/ROW]
[ROW][C]63[/C][C]104.1[/C][C]108.998554080123[/C][C]-4.89855408012331[/C][/ROW]
[ROW][C]64[/C][C]105.3[/C][C]109.263616089641[/C][C]-3.9636160896411[/C][/ROW]
[ROW][C]65[/C][C]115[/C][C]117.610005932177[/C][C]-2.61000593217657[/C][/ROW]
[ROW][C]66[/C][C]124.1[/C][C]114.060421359438[/C][C]10.0395786405615[/C][/ROW]
[ROW][C]67[/C][C]116.8[/C][C]114.335801261777[/C][C]2.46419873822286[/C][/ROW]
[ROW][C]68[/C][C]107.5[/C][C]106.291353497579[/C][C]1.20864650242091[/C][/ROW]
[ROW][C]69[/C][C]115.6[/C][C]111.967035514755[/C][C]3.63296448524526[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57773&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57773&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
1101.798.07320656771683.62679343228316
2104.2100.5807542626223.61924573737824
392.796.7786533030993-4.07865330309934
491.996.8140608472913-4.91406084729132
5106.5104.9662701686771.53372983132296
6112.3103.92905336418.37094663589992
7102.8100.8141470117591.98585298824085
896.597.5254162868964-1.02541628689636
910196.7871120803364.2128879196641
1098.998.5490131634590.350986836541081
11105.1101.2203578968263.87964210317380
12103105.298975408506-2.29897540850587
139999.339417898957-0.339417898956908
14104.3101.7985829681432.50141703185665
1594.6100.964918245840-6.36491824583976
1690.499.3705376753783-8.97053767537831
17108.9108.1831480909910.716851909009263
18111.4108.8068311882552.59316881174482
19100.8102.408529281683-1.60852928168276
20102.5103.464107179349-0.964107179348907
2198.2101.873370098285-3.67337009828478
2298.7101.095819689242-2.39581968924177
23113.3109.9648208872953.33517911270495
24104.6107.848115881123-3.24811588112258
2599.3100.079872229887-0.779872229886675
26111.8105.3760136885346.42398631146593
2797.3101.425410100603-4.12541010060257
2897.798.7517005522524-1.05170055225244
29115.6110.8156378003414.78436219965932
30111.9104.1323576541167.7676423458838
31107101.1040832308295.89591676917102
32107.1102.7528386938184.34716130618157
33100.698.5823566359252.01764336407494
3499.2101.488235315550-2.28823531555028
35108.4109.132595176346-0.732595176345546
36103109.351434941858-6.35143494185842
3799.8105.638106172010-5.83810617200971
38115109.2690222657465.73097773425413
3990.8106.120966519427-15.3209665194275
4095.9101.396977695894-5.49697769589443
41114.4118.682999968578-4.28299996857774
42108.2107.2736291840650.926370815935249
43112.6108.7913373803203.80866261967963
44109.1110.641256759365-1.54125675936521
45105100.2114800970274.78851990297287
46105106.821124886288-1.82112488628781
47118.5107.48781973711111.0121802628893
48103.7111.712957362565-8.0129573625646
49112.5109.3483047715963.15169522840418
50116.6116.1309989333450.469001066654665
5196.6105.319153193628-8.71915319362799
52101.9111.162356866742-9.2623568667424
53116.5118.587767786867-2.08776778686737
54119.3113.9741676361775.32583236382262
55115.4114.7924057136250.607594286375487
56108.5110.656655655108-2.15665565510752
57111.5105.6985847977755.80141520222492
58108.8109.473513055453-0.673513055453239
59121.8111.71486590589510.0851340941052
60109.6119.388783300849-9.78878330084894
61112.2110.0478080828732.15219191712671
62119.6117.0824130702922.51758692970814
63104.1108.998554080123-4.89855408012331
64105.3109.263616089641-3.9636160896411
65115117.610005932177-2.61000593217657
66124.1114.06042135943810.0395786405615
67116.8114.3358012617772.46419873822286
68107.5106.2913534975791.20864650242091
69115.6111.9670355147553.63296448524526






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.5074262272720450.9851475454559110.492573772727955
100.3574638336441030.7149276672882060.642536166355897
110.2417081040142020.4834162080284050.758291895985798
120.3470430514337190.6940861028674380.652956948566281
130.2367337253786260.4734674507572520.763266274621374
140.1673626117807250.3347252235614510.832637388219275
150.2447875626666590.4895751253333170.755212437333342
160.2988509704179610.5977019408359210.701149029582039
170.2186821844211830.4373643688423670.781317815578817
180.1568217812027180.3136435624054350.843178218797282
190.1079242999258420.2158485998516840.892075700074158
200.07504169128856370.1500833825771270.924958308711436
210.04901281459626060.09802562919252120.95098718540374
220.03942729048557810.07885458097115620.960572709514422
230.03378433771456790.06756867542913570.966215662285432
240.02612607425694730.05225214851389460.973873925743053
250.01873905810708810.03747811621417630.981260941892912
260.02871203528408480.05742407056816960.971287964715915
270.01990067144127460.03980134288254930.980099328558725
280.01420872159264460.02841744318528930.985791278407355
290.01073537392777710.02147074785555420.989264626072223
300.02659285905012730.05318571810025450.973407140949873
310.03478948241031530.06957896482063070.965210517589685
320.02995070443514160.05990140887028310.970049295564858
330.02208426349966390.04416852699932780.977915736500336
340.01428772840589300.02857545681178610.985712271594107
350.009206890277236830.01841378055447370.990793109722763
360.01362310698974850.0272462139794970.986376893010251
370.01264365432311700.02528730864623390.987356345676883
380.01502332377184760.03004664754369520.984976676228152
390.1782248922943030.3564497845886060.821775107705697
400.1769549023464830.3539098046929670.823045097653517
410.1651512623971590.3303025247943180.83484873760284
420.1365695882346070.2731391764692140.863430411765393
430.1138803485450900.2277606970901800.88611965145491
440.08352893729495230.1670578745899050.916471062705048
450.07990673988722240.1598134797744450.920093260112778
460.0562607885055870.1125215770111740.943739211494413
470.1677751051626480.3355502103252960.832224894837352
480.1874906017983920.3749812035967840.812509398201608
490.1574519384786460.3149038769572930.842548061521354
500.1183559696559520.2367119393119040.881644030344048
510.2495126709467050.499025341893410.750487329053295
520.3849067617296160.7698135234592320.615093238270384
530.3350159174547580.6700318349095150.664984082545242
540.2801305417079990.5602610834159980.719869458292001
550.1994374475946460.3988748951892930.800562552405354
560.1350560389662170.2701120779324330.864943961033783
570.1069380969332960.2138761938665910.893061903066704
580.07055408589468350.1411081717893670.929445914105317
590.08364003253167190.1672800650633440.916359967468328
600.2469023754998420.4938047509996850.753097624500158

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.507426227272045 & 0.985147545455911 & 0.492573772727955 \tabularnewline
10 & 0.357463833644103 & 0.714927667288206 & 0.642536166355897 \tabularnewline
11 & 0.241708104014202 & 0.483416208028405 & 0.758291895985798 \tabularnewline
12 & 0.347043051433719 & 0.694086102867438 & 0.652956948566281 \tabularnewline
13 & 0.236733725378626 & 0.473467450757252 & 0.763266274621374 \tabularnewline
14 & 0.167362611780725 & 0.334725223561451 & 0.832637388219275 \tabularnewline
15 & 0.244787562666659 & 0.489575125333317 & 0.755212437333342 \tabularnewline
16 & 0.298850970417961 & 0.597701940835921 & 0.701149029582039 \tabularnewline
17 & 0.218682184421183 & 0.437364368842367 & 0.781317815578817 \tabularnewline
18 & 0.156821781202718 & 0.313643562405435 & 0.843178218797282 \tabularnewline
19 & 0.107924299925842 & 0.215848599851684 & 0.892075700074158 \tabularnewline
20 & 0.0750416912885637 & 0.150083382577127 & 0.924958308711436 \tabularnewline
21 & 0.0490128145962606 & 0.0980256291925212 & 0.95098718540374 \tabularnewline
22 & 0.0394272904855781 & 0.0788545809711562 & 0.960572709514422 \tabularnewline
23 & 0.0337843377145679 & 0.0675686754291357 & 0.966215662285432 \tabularnewline
24 & 0.0261260742569473 & 0.0522521485138946 & 0.973873925743053 \tabularnewline
25 & 0.0187390581070881 & 0.0374781162141763 & 0.981260941892912 \tabularnewline
26 & 0.0287120352840848 & 0.0574240705681696 & 0.971287964715915 \tabularnewline
27 & 0.0199006714412746 & 0.0398013428825493 & 0.980099328558725 \tabularnewline
28 & 0.0142087215926446 & 0.0284174431852893 & 0.985791278407355 \tabularnewline
29 & 0.0107353739277771 & 0.0214707478555542 & 0.989264626072223 \tabularnewline
30 & 0.0265928590501273 & 0.0531857181002545 & 0.973407140949873 \tabularnewline
31 & 0.0347894824103153 & 0.0695789648206307 & 0.965210517589685 \tabularnewline
32 & 0.0299507044351416 & 0.0599014088702831 & 0.970049295564858 \tabularnewline
33 & 0.0220842634996639 & 0.0441685269993278 & 0.977915736500336 \tabularnewline
34 & 0.0142877284058930 & 0.0285754568117861 & 0.985712271594107 \tabularnewline
35 & 0.00920689027723683 & 0.0184137805544737 & 0.990793109722763 \tabularnewline
36 & 0.0136231069897485 & 0.027246213979497 & 0.986376893010251 \tabularnewline
37 & 0.0126436543231170 & 0.0252873086462339 & 0.987356345676883 \tabularnewline
38 & 0.0150233237718476 & 0.0300466475436952 & 0.984976676228152 \tabularnewline
39 & 0.178224892294303 & 0.356449784588606 & 0.821775107705697 \tabularnewline
40 & 0.176954902346483 & 0.353909804692967 & 0.823045097653517 \tabularnewline
41 & 0.165151262397159 & 0.330302524794318 & 0.83484873760284 \tabularnewline
42 & 0.136569588234607 & 0.273139176469214 & 0.863430411765393 \tabularnewline
43 & 0.113880348545090 & 0.227760697090180 & 0.88611965145491 \tabularnewline
44 & 0.0835289372949523 & 0.167057874589905 & 0.916471062705048 \tabularnewline
45 & 0.0799067398872224 & 0.159813479774445 & 0.920093260112778 \tabularnewline
46 & 0.056260788505587 & 0.112521577011174 & 0.943739211494413 \tabularnewline
47 & 0.167775105162648 & 0.335550210325296 & 0.832224894837352 \tabularnewline
48 & 0.187490601798392 & 0.374981203596784 & 0.812509398201608 \tabularnewline
49 & 0.157451938478646 & 0.314903876957293 & 0.842548061521354 \tabularnewline
50 & 0.118355969655952 & 0.236711939311904 & 0.881644030344048 \tabularnewline
51 & 0.249512670946705 & 0.49902534189341 & 0.750487329053295 \tabularnewline
52 & 0.384906761729616 & 0.769813523459232 & 0.615093238270384 \tabularnewline
53 & 0.335015917454758 & 0.670031834909515 & 0.664984082545242 \tabularnewline
54 & 0.280130541707999 & 0.560261083415998 & 0.719869458292001 \tabularnewline
55 & 0.199437447594646 & 0.398874895189293 & 0.800562552405354 \tabularnewline
56 & 0.135056038966217 & 0.270112077932433 & 0.864943961033783 \tabularnewline
57 & 0.106938096933296 & 0.213876193866591 & 0.893061903066704 \tabularnewline
58 & 0.0705540858946835 & 0.141108171789367 & 0.929445914105317 \tabularnewline
59 & 0.0836400325316719 & 0.167280065063344 & 0.916359967468328 \tabularnewline
60 & 0.246902375499842 & 0.493804750999685 & 0.753097624500158 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57773&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]9[/C][C]0.507426227272045[/C][C]0.985147545455911[/C][C]0.492573772727955[/C][/ROW]
[ROW][C]10[/C][C]0.357463833644103[/C][C]0.714927667288206[/C][C]0.642536166355897[/C][/ROW]
[ROW][C]11[/C][C]0.241708104014202[/C][C]0.483416208028405[/C][C]0.758291895985798[/C][/ROW]
[ROW][C]12[/C][C]0.347043051433719[/C][C]0.694086102867438[/C][C]0.652956948566281[/C][/ROW]
[ROW][C]13[/C][C]0.236733725378626[/C][C]0.473467450757252[/C][C]0.763266274621374[/C][/ROW]
[ROW][C]14[/C][C]0.167362611780725[/C][C]0.334725223561451[/C][C]0.832637388219275[/C][/ROW]
[ROW][C]15[/C][C]0.244787562666659[/C][C]0.489575125333317[/C][C]0.755212437333342[/C][/ROW]
[ROW][C]16[/C][C]0.298850970417961[/C][C]0.597701940835921[/C][C]0.701149029582039[/C][/ROW]
[ROW][C]17[/C][C]0.218682184421183[/C][C]0.437364368842367[/C][C]0.781317815578817[/C][/ROW]
[ROW][C]18[/C][C]0.156821781202718[/C][C]0.313643562405435[/C][C]0.843178218797282[/C][/ROW]
[ROW][C]19[/C][C]0.107924299925842[/C][C]0.215848599851684[/C][C]0.892075700074158[/C][/ROW]
[ROW][C]20[/C][C]0.0750416912885637[/C][C]0.150083382577127[/C][C]0.924958308711436[/C][/ROW]
[ROW][C]21[/C][C]0.0490128145962606[/C][C]0.0980256291925212[/C][C]0.95098718540374[/C][/ROW]
[ROW][C]22[/C][C]0.0394272904855781[/C][C]0.0788545809711562[/C][C]0.960572709514422[/C][/ROW]
[ROW][C]23[/C][C]0.0337843377145679[/C][C]0.0675686754291357[/C][C]0.966215662285432[/C][/ROW]
[ROW][C]24[/C][C]0.0261260742569473[/C][C]0.0522521485138946[/C][C]0.973873925743053[/C][/ROW]
[ROW][C]25[/C][C]0.0187390581070881[/C][C]0.0374781162141763[/C][C]0.981260941892912[/C][/ROW]
[ROW][C]26[/C][C]0.0287120352840848[/C][C]0.0574240705681696[/C][C]0.971287964715915[/C][/ROW]
[ROW][C]27[/C][C]0.0199006714412746[/C][C]0.0398013428825493[/C][C]0.980099328558725[/C][/ROW]
[ROW][C]28[/C][C]0.0142087215926446[/C][C]0.0284174431852893[/C][C]0.985791278407355[/C][/ROW]
[ROW][C]29[/C][C]0.0107353739277771[/C][C]0.0214707478555542[/C][C]0.989264626072223[/C][/ROW]
[ROW][C]30[/C][C]0.0265928590501273[/C][C]0.0531857181002545[/C][C]0.973407140949873[/C][/ROW]
[ROW][C]31[/C][C]0.0347894824103153[/C][C]0.0695789648206307[/C][C]0.965210517589685[/C][/ROW]
[ROW][C]32[/C][C]0.0299507044351416[/C][C]0.0599014088702831[/C][C]0.970049295564858[/C][/ROW]
[ROW][C]33[/C][C]0.0220842634996639[/C][C]0.0441685269993278[/C][C]0.977915736500336[/C][/ROW]
[ROW][C]34[/C][C]0.0142877284058930[/C][C]0.0285754568117861[/C][C]0.985712271594107[/C][/ROW]
[ROW][C]35[/C][C]0.00920689027723683[/C][C]0.0184137805544737[/C][C]0.990793109722763[/C][/ROW]
[ROW][C]36[/C][C]0.0136231069897485[/C][C]0.027246213979497[/C][C]0.986376893010251[/C][/ROW]
[ROW][C]37[/C][C]0.0126436543231170[/C][C]0.0252873086462339[/C][C]0.987356345676883[/C][/ROW]
[ROW][C]38[/C][C]0.0150233237718476[/C][C]0.0300466475436952[/C][C]0.984976676228152[/C][/ROW]
[ROW][C]39[/C][C]0.178224892294303[/C][C]0.356449784588606[/C][C]0.821775107705697[/C][/ROW]
[ROW][C]40[/C][C]0.176954902346483[/C][C]0.353909804692967[/C][C]0.823045097653517[/C][/ROW]
[ROW][C]41[/C][C]0.165151262397159[/C][C]0.330302524794318[/C][C]0.83484873760284[/C][/ROW]
[ROW][C]42[/C][C]0.136569588234607[/C][C]0.273139176469214[/C][C]0.863430411765393[/C][/ROW]
[ROW][C]43[/C][C]0.113880348545090[/C][C]0.227760697090180[/C][C]0.88611965145491[/C][/ROW]
[ROW][C]44[/C][C]0.0835289372949523[/C][C]0.167057874589905[/C][C]0.916471062705048[/C][/ROW]
[ROW][C]45[/C][C]0.0799067398872224[/C][C]0.159813479774445[/C][C]0.920093260112778[/C][/ROW]
[ROW][C]46[/C][C]0.056260788505587[/C][C]0.112521577011174[/C][C]0.943739211494413[/C][/ROW]
[ROW][C]47[/C][C]0.167775105162648[/C][C]0.335550210325296[/C][C]0.832224894837352[/C][/ROW]
[ROW][C]48[/C][C]0.187490601798392[/C][C]0.374981203596784[/C][C]0.812509398201608[/C][/ROW]
[ROW][C]49[/C][C]0.157451938478646[/C][C]0.314903876957293[/C][C]0.842548061521354[/C][/ROW]
[ROW][C]50[/C][C]0.118355969655952[/C][C]0.236711939311904[/C][C]0.881644030344048[/C][/ROW]
[ROW][C]51[/C][C]0.249512670946705[/C][C]0.49902534189341[/C][C]0.750487329053295[/C][/ROW]
[ROW][C]52[/C][C]0.384906761729616[/C][C]0.769813523459232[/C][C]0.615093238270384[/C][/ROW]
[ROW][C]53[/C][C]0.335015917454758[/C][C]0.670031834909515[/C][C]0.664984082545242[/C][/ROW]
[ROW][C]54[/C][C]0.280130541707999[/C][C]0.560261083415998[/C][C]0.719869458292001[/C][/ROW]
[ROW][C]55[/C][C]0.199437447594646[/C][C]0.398874895189293[/C][C]0.800562552405354[/C][/ROW]
[ROW][C]56[/C][C]0.135056038966217[/C][C]0.270112077932433[/C][C]0.864943961033783[/C][/ROW]
[ROW][C]57[/C][C]0.106938096933296[/C][C]0.213876193866591[/C][C]0.893061903066704[/C][/ROW]
[ROW][C]58[/C][C]0.0705540858946835[/C][C]0.141108171789367[/C][C]0.929445914105317[/C][/ROW]
[ROW][C]59[/C][C]0.0836400325316719[/C][C]0.167280065063344[/C][C]0.916359967468328[/C][/ROW]
[ROW][C]60[/C][C]0.246902375499842[/C][C]0.493804750999685[/C][C]0.753097624500158[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57773&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57773&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
90.5074262272720450.9851475454559110.492573772727955
100.3574638336441030.7149276672882060.642536166355897
110.2417081040142020.4834162080284050.758291895985798
120.3470430514337190.6940861028674380.652956948566281
130.2367337253786260.4734674507572520.763266274621374
140.1673626117807250.3347252235614510.832637388219275
150.2447875626666590.4895751253333170.755212437333342
160.2988509704179610.5977019408359210.701149029582039
170.2186821844211830.4373643688423670.781317815578817
180.1568217812027180.3136435624054350.843178218797282
190.1079242999258420.2158485998516840.892075700074158
200.07504169128856370.1500833825771270.924958308711436
210.04901281459626060.09802562919252120.95098718540374
220.03942729048557810.07885458097115620.960572709514422
230.03378433771456790.06756867542913570.966215662285432
240.02612607425694730.05225214851389460.973873925743053
250.01873905810708810.03747811621417630.981260941892912
260.02871203528408480.05742407056816960.971287964715915
270.01990067144127460.03980134288254930.980099328558725
280.01420872159264460.02841744318528930.985791278407355
290.01073537392777710.02147074785555420.989264626072223
300.02659285905012730.05318571810025450.973407140949873
310.03478948241031530.06957896482063070.965210517589685
320.02995070443514160.05990140887028310.970049295564858
330.02208426349966390.04416852699932780.977915736500336
340.01428772840589300.02857545681178610.985712271594107
350.009206890277236830.01841378055447370.990793109722763
360.01362310698974850.0272462139794970.986376893010251
370.01264365432311700.02528730864623390.987356345676883
380.01502332377184760.03004664754369520.984976676228152
390.1782248922943030.3564497845886060.821775107705697
400.1769549023464830.3539098046929670.823045097653517
410.1651512623971590.3303025247943180.83484873760284
420.1365695882346070.2731391764692140.863430411765393
430.1138803485450900.2277606970901800.88611965145491
440.08352893729495230.1670578745899050.916471062705048
450.07990673988722240.1598134797744450.920093260112778
460.0562607885055870.1125215770111740.943739211494413
470.1677751051626480.3355502103252960.832224894837352
480.1874906017983920.3749812035967840.812509398201608
490.1574519384786460.3149038769572930.842548061521354
500.1183559696559520.2367119393119040.881644030344048
510.2495126709467050.499025341893410.750487329053295
520.3849067617296160.7698135234592320.615093238270384
530.3350159174547580.6700318349095150.664984082545242
540.2801305417079990.5602610834159980.719869458292001
550.1994374475946460.3988748951892930.800562552405354
560.1350560389662170.2701120779324330.864943961033783
570.1069380969332960.2138761938665910.893061903066704
580.07055408589468350.1411081717893670.929445914105317
590.08364003253167190.1672800650633440.916359967468328
600.2469023754998420.4938047509996850.753097624500158






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level100.192307692307692NOK
10% type I error level180.346153846153846NOK

\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 & 10 & 0.192307692307692 & NOK \tabularnewline
10% type I error level & 18 & 0.346153846153846 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57773&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]10[/C][C]0.192307692307692[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]18[/C][C]0.346153846153846[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57773&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57773&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 level100.192307692307692NOK
10% type I error level180.346153846153846NOK


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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}