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multiple regression with seasonal dummies and linear trend, totale industri...

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 08:28:26 -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/t12586447142swiozkgqmuja4x.htm/, Retrieved Fri, 29 Mar 2024 06:52:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57765, Retrieved Fri, 29 Mar 2024 06:52:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact146
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]
-   PD      [Multiple Regression] [multiple regressi...] [2009-11-19 15:28:26] [b1ac221d009d6e5c29a4ef1869874933] [Current]
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Dataseries X:
97.6	82.9
96.9	83.8
105.6	86.2
102.8	86.1
101.7	86.2
104.2	88.8
92.7	89.6
91.9	87.8
106.5	88.3
112.3	88.6
102.8	91
96.5	91.5
101	95.4
98.9	98.7
105.1	99.9
103	98.6
99	100.3
104.3	100.2
94.6	100.4
90.4	101.4
108.9	103
111.4	109.1
100.8	111.4
102.5	114.1
98.2	121.8
98.7	127.6
113.3	129.9
104.6	128
99.3	123.5
111.8	124
97.3	127.4
97.7	127.6
115.6	128.4
111.9	131.4
107	135.1
107.1	134
100.6	144.5
99.2	147.3
108.4	150.9
103	148.7
99.8	141.4
115	138.9
90.8	139.8
95.9	145.6
114.4	147.9
108.2	148.5
112.6	151.1
109.1	157.5
105	167.5
105	172.3
118.5	173.5
103.7	187.5
112.5	205.5
116.6	195.1
96.6	204.5
101.9	204.5
116.5	201.7
119.3	207
115.4	206.6
108.5	210.6
111.5	211.1
108.8	215
121.8	223.9
109.6	238.2
112.2	238.9
119.6	229.6
104.1	232.2
105.3	222.1
115	221.6
124.1	227.3
116.8	221
107.5	213.6
115.6	243.4




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57765&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]4 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=57765&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57765&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
tot_indus[t] = + 90.832085622519 + 0.102540997522223prijsindex[t] -1.02882141864122M1[t] -2.96881131069401M2[t] + 7.59567990197284M3[t] -0.427850750705502M4[t] -0.910410059206618M5[t] + 7.2838462707706M6[t] -8.87902180084571M7[t] -7.59582151496311M8[t] + 8.03783230706095M9[t] + 9.4283972869726M10[t] + 4.08770137665445M11[t] -0.032791804572772t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
tot_indus[t] =  +  90.832085622519 +  0.102540997522223prijsindex[t] -1.02882141864122M1[t] -2.96881131069401M2[t] +  7.59567990197284M3[t] -0.427850750705502M4[t] -0.910410059206618M5[t] +  7.2838462707706M6[t] -8.87902180084571M7[t] -7.59582151496311M8[t] +  8.03783230706095M9[t] +  9.4283972869726M10[t] +  4.08770137665445M11[t] -0.032791804572772t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57765&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]tot_indus[t] =  +  90.832085622519 +  0.102540997522223prijsindex[t] -1.02882141864122M1[t] -2.96881131069401M2[t] +  7.59567990197284M3[t] -0.427850750705502M4[t] -0.910410059206618M5[t] +  7.2838462707706M6[t] -8.87902180084571M7[t] -7.59582151496311M8[t] +  8.03783230706095M9[t] +  9.4283972869726M10[t] +  4.08770137665445M11[t] -0.032791804572772t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57765&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57765&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
tot_indus[t] = + 90.832085622519 + 0.102540997522223prijsindex[t] -1.02882141864122M1[t] -2.96881131069401M2[t] + 7.59567990197284M3[t] -0.427850750705502M4[t] -0.910410059206618M5[t] + 7.2838462707706M6[t] -8.87902180084571M7[t] -7.59582151496311M8[t] + 8.03783230706095M9[t] + 9.4283972869726M10[t] + 4.08770137665445M11[t] -0.032791804572772t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)90.8320856225192.27099839.996500
prijsindex0.1025409975222230.0325933.14610.0025930.001296
M1-1.028821418641221.672555-0.61510.5408410.270421
M2-2.968811310694011.739573-1.70660.0931490.046575
M37.595679901972841.7443594.35445.4e-052.7e-05
M4-0.4278507507055021.753935-0.24390.8081250.404063
M5-0.9104100592066181.74596-0.52140.6040140.302007
M67.28384627077061.7130494.2527.7e-053.8e-05
M7-8.879021800845711.714484-5.17883e-061e-06
M8-7.595821514963111.702849-4.46073.7e-051.9e-05
M98.037832307060951.6985334.73221.4e-057e-06
M109.42839728697261.699975.54621e-060
M114.087701376654451.6973892.40820.0191740.009587
t-0.0327918045727720.078074-0.420.6760060.338003

\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) & 90.832085622519 & 2.270998 & 39.9965 & 0 & 0 \tabularnewline
prijsindex & 0.102540997522223 & 0.032593 & 3.1461 & 0.002593 & 0.001296 \tabularnewline
M1 & -1.02882141864122 & 1.672555 & -0.6151 & 0.540841 & 0.270421 \tabularnewline
M2 & -2.96881131069401 & 1.739573 & -1.7066 & 0.093149 & 0.046575 \tabularnewline
M3 & 7.59567990197284 & 1.744359 & 4.3544 & 5.4e-05 & 2.7e-05 \tabularnewline
M4 & -0.427850750705502 & 1.753935 & -0.2439 & 0.808125 & 0.404063 \tabularnewline
M5 & -0.910410059206618 & 1.74596 & -0.5214 & 0.604014 & 0.302007 \tabularnewline
M6 & 7.2838462707706 & 1.713049 & 4.252 & 7.7e-05 & 3.8e-05 \tabularnewline
M7 & -8.87902180084571 & 1.714484 & -5.1788 & 3e-06 & 1e-06 \tabularnewline
M8 & -7.59582151496311 & 1.702849 & -4.4607 & 3.7e-05 & 1.9e-05 \tabularnewline
M9 & 8.03783230706095 & 1.698533 & 4.7322 & 1.4e-05 & 7e-06 \tabularnewline
M10 & 9.4283972869726 & 1.69997 & 5.5462 & 1e-06 & 0 \tabularnewline
M11 & 4.08770137665445 & 1.697389 & 2.4082 & 0.019174 & 0.009587 \tabularnewline
t & -0.032791804572772 & 0.078074 & -0.42 & 0.676006 & 0.338003 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57765&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]90.832085622519[/C][C]2.270998[/C][C]39.9965[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]prijsindex[/C][C]0.102540997522223[/C][C]0.032593[/C][C]3.1461[/C][C]0.002593[/C][C]0.001296[/C][/ROW]
[ROW][C]M1[/C][C]-1.02882141864122[/C][C]1.672555[/C][C]-0.6151[/C][C]0.540841[/C][C]0.270421[/C][/ROW]
[ROW][C]M2[/C][C]-2.96881131069401[/C][C]1.739573[/C][C]-1.7066[/C][C]0.093149[/C][C]0.046575[/C][/ROW]
[ROW][C]M3[/C][C]7.59567990197284[/C][C]1.744359[/C][C]4.3544[/C][C]5.4e-05[/C][C]2.7e-05[/C][/ROW]
[ROW][C]M4[/C][C]-0.427850750705502[/C][C]1.753935[/C][C]-0.2439[/C][C]0.808125[/C][C]0.404063[/C][/ROW]
[ROW][C]M5[/C][C]-0.910410059206618[/C][C]1.74596[/C][C]-0.5214[/C][C]0.604014[/C][C]0.302007[/C][/ROW]
[ROW][C]M6[/C][C]7.2838462707706[/C][C]1.713049[/C][C]4.252[/C][C]7.7e-05[/C][C]3.8e-05[/C][/ROW]
[ROW][C]M7[/C][C]-8.87902180084571[/C][C]1.714484[/C][C]-5.1788[/C][C]3e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M8[/C][C]-7.59582151496311[/C][C]1.702849[/C][C]-4.4607[/C][C]3.7e-05[/C][C]1.9e-05[/C][/ROW]
[ROW][C]M9[/C][C]8.03783230706095[/C][C]1.698533[/C][C]4.7322[/C][C]1.4e-05[/C][C]7e-06[/C][/ROW]
[ROW][C]M10[/C][C]9.4283972869726[/C][C]1.69997[/C][C]5.5462[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]M11[/C][C]4.08770137665445[/C][C]1.697389[/C][C]2.4082[/C][C]0.019174[/C][C]0.009587[/C][/ROW]
[ROW][C]t[/C][C]-0.032791804572772[/C][C]0.078074[/C][C]-0.42[/C][C]0.676006[/C][C]0.338003[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57765&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57765&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)90.8320856225192.27099839.996500
prijsindex0.1025409975222230.0325933.14610.0025930.001296
M1-1.028821418641221.672555-0.61510.5408410.270421
M2-2.968811310694011.739573-1.70660.0931490.046575
M37.595679901972841.7443594.35445.4e-052.7e-05
M4-0.4278507507055021.753935-0.24390.8081250.404063
M5-0.9104100592066181.74596-0.52140.6040140.302007
M67.28384627077061.7130494.2527.7e-053.8e-05
M7-8.879021800845711.714484-5.17883e-061e-06
M8-7.595821514963111.702849-4.46073.7e-051.9e-05
M98.037832307060951.6985334.73221.4e-057e-06
M109.42839728697261.699975.54621e-060
M114.087701376654451.6973892.40820.0191740.009587
t-0.0327918045727720.078074-0.420.6760060.338003







Multiple Linear Regression - Regression Statistics
Multiple R0.940800839498478
R-squared0.88510621960104
Adjusted R-squared0.859790640869066
F-TEST (value)34.9629067923750
F-TEST (DF numerator)13
F-TEST (DF denominator)59
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.93862008885469
Sum Squared Residuals509.493793570601

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.940800839498478 \tabularnewline
R-squared & 0.88510621960104 \tabularnewline
Adjusted R-squared & 0.859790640869066 \tabularnewline
F-TEST (value) & 34.9629067923750 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.93862008885469 \tabularnewline
Sum Squared Residuals & 509.493793570601 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57765&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.940800839498478[/C][/ROW]
[ROW][C]R-squared[/C][C]0.88510621960104[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.859790640869066[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]34.9629067923750[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.93862008885469[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]509.493793570601[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57765&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57765&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 R0.940800839498478
R-squared0.88510621960104
Adjusted R-squared0.859790640869066
F-TEST (value)34.9629067923750
F-TEST (DF numerator)13
F-TEST (DF denominator)59
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.93862008885469
Sum Squared Residuals509.493793570601







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
197.698.2711210938972-0.671121093897202
296.996.39062629504180.509373704958178
3105.6107.168424097189-1.56842409718921
4102.899.1018475401863.69815245981413
5101.798.59675052686423.10324947313581
6104.2107.024821645826-2.82482164582643
792.790.91119456765511.78880543234487
891.991.977029253425-0.0770292534249414
9106.5107.629161769637-1.12916176963735
10112.3109.0176972442333.28230275576709
11102.8103.890307923395-1.09030792339531
1296.599.8210852409292-3.3210852409292
1310199.15938190805191.84061809194812
1498.997.52498550324971.37501449675035
15105.1108.179734108370-3.07973410837040
1610399.99010835434043.00989164565961
179999.6490769370543-0.649076937054284
18104.3107.800287362707-3.50028736270651
1994.691.62513568602192.97486431397812
2090.492.978085164854-2.57808516485391
21108.9108.7430127783410.156987221659233
22111.4110.7262860385650.673713961434789
23100.8105.588642617975-4.78864261797541
24102.5101.7450101300580.754989869941816
2598.2101.472962587765-3.27296258776531
2698.7100.094918676769-1.39491867676865
27113.3110.8624623791642.43753762083616
28104.6102.6113120266201.9886879733795
2999.3101.634526424697-2.33452642469660
30111.8109.8472614488621.95273855113784
3197.394.00024096424863.29975903575136
3297.795.27115764506292.42884235493710
33115.6110.9540524605324.64594753946801
34111.9112.619448628438-0.719448628437527
35107107.625362604379-0.625362604378833
36107.1103.3920743258773.70792567412283
37100.6103.407141576647-2.80714157664653
3899.2101.721474673083-2.52147467308318
39108.4112.622321672257-4.22232167225726
40103104.340409020457-1.34040902045726
4199.8103.076508625471-3.27650862547114
42115110.981620657074.01837934292998
4390.894.878247678651-4.07824767865095
4495.996.7233939455897-0.823393945589662
45114.4112.5601002573421.83989974265793
46108.2113.979398031194-5.77939803119428
47112.6108.8725169098613.72748309013885
48109.1105.4082861127763.69171388722384
49105105.372082864784-0.372082864784393
50105103.8914979562661.10850204373449
51118.5114.5462465613863.95375343861376
52103.7107.925498069446-4.22549806944626
53112.5109.2558849117723.24411508822761
54116.6116.3509230629460.249076937054285
5596.6101.119148563466-4.51914856346554
56101.9102.369557044775-0.469557044775349
57116.5117.683304269164-1.18330426916442
58119.3119.584544731371-0.284544731371096
59115.4114.1700406174711.22995938252873
60108.5110.459711426333-1.95971142633294
61111.5109.449368701882.05063129811993
62108.8107.8764968955910.923503104408818
63121.8119.3208111816332.47918881836696
64109.6112.730824988950-3.13082498894972
65112.2112.287252574141-0.087252574141383
66119.6119.4950858225890.104914177410842
67104.1103.5660325399580.53396746004214
68105.3103.7807769462931.51922305370677
69115119.330368464983-4.33036846498340
70124.1121.2726253261992.82737467380103
71116.8115.2531293269181.54687067308197
72107.5110.373832764026-2.87383276402635
73115.6112.3679412669753.23205873302538

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 97.6 & 98.2711210938972 & -0.671121093897202 \tabularnewline
2 & 96.9 & 96.3906262950418 & 0.509373704958178 \tabularnewline
3 & 105.6 & 107.168424097189 & -1.56842409718921 \tabularnewline
4 & 102.8 & 99.101847540186 & 3.69815245981413 \tabularnewline
5 & 101.7 & 98.5967505268642 & 3.10324947313581 \tabularnewline
6 & 104.2 & 107.024821645826 & -2.82482164582643 \tabularnewline
7 & 92.7 & 90.9111945676551 & 1.78880543234487 \tabularnewline
8 & 91.9 & 91.977029253425 & -0.0770292534249414 \tabularnewline
9 & 106.5 & 107.629161769637 & -1.12916176963735 \tabularnewline
10 & 112.3 & 109.017697244233 & 3.28230275576709 \tabularnewline
11 & 102.8 & 103.890307923395 & -1.09030792339531 \tabularnewline
12 & 96.5 & 99.8210852409292 & -3.3210852409292 \tabularnewline
13 & 101 & 99.1593819080519 & 1.84061809194812 \tabularnewline
14 & 98.9 & 97.5249855032497 & 1.37501449675035 \tabularnewline
15 & 105.1 & 108.179734108370 & -3.07973410837040 \tabularnewline
16 & 103 & 99.9901083543404 & 3.00989164565961 \tabularnewline
17 & 99 & 99.6490769370543 & -0.649076937054284 \tabularnewline
18 & 104.3 & 107.800287362707 & -3.50028736270651 \tabularnewline
19 & 94.6 & 91.6251356860219 & 2.97486431397812 \tabularnewline
20 & 90.4 & 92.978085164854 & -2.57808516485391 \tabularnewline
21 & 108.9 & 108.743012778341 & 0.156987221659233 \tabularnewline
22 & 111.4 & 110.726286038565 & 0.673713961434789 \tabularnewline
23 & 100.8 & 105.588642617975 & -4.78864261797541 \tabularnewline
24 & 102.5 & 101.745010130058 & 0.754989869941816 \tabularnewline
25 & 98.2 & 101.472962587765 & -3.27296258776531 \tabularnewline
26 & 98.7 & 100.094918676769 & -1.39491867676865 \tabularnewline
27 & 113.3 & 110.862462379164 & 2.43753762083616 \tabularnewline
28 & 104.6 & 102.611312026620 & 1.9886879733795 \tabularnewline
29 & 99.3 & 101.634526424697 & -2.33452642469660 \tabularnewline
30 & 111.8 & 109.847261448862 & 1.95273855113784 \tabularnewline
31 & 97.3 & 94.0002409642486 & 3.29975903575136 \tabularnewline
32 & 97.7 & 95.2711576450629 & 2.42884235493710 \tabularnewline
33 & 115.6 & 110.954052460532 & 4.64594753946801 \tabularnewline
34 & 111.9 & 112.619448628438 & -0.719448628437527 \tabularnewline
35 & 107 & 107.625362604379 & -0.625362604378833 \tabularnewline
36 & 107.1 & 103.392074325877 & 3.70792567412283 \tabularnewline
37 & 100.6 & 103.407141576647 & -2.80714157664653 \tabularnewline
38 & 99.2 & 101.721474673083 & -2.52147467308318 \tabularnewline
39 & 108.4 & 112.622321672257 & -4.22232167225726 \tabularnewline
40 & 103 & 104.340409020457 & -1.34040902045726 \tabularnewline
41 & 99.8 & 103.076508625471 & -3.27650862547114 \tabularnewline
42 & 115 & 110.98162065707 & 4.01837934292998 \tabularnewline
43 & 90.8 & 94.878247678651 & -4.07824767865095 \tabularnewline
44 & 95.9 & 96.7233939455897 & -0.823393945589662 \tabularnewline
45 & 114.4 & 112.560100257342 & 1.83989974265793 \tabularnewline
46 & 108.2 & 113.979398031194 & -5.77939803119428 \tabularnewline
47 & 112.6 & 108.872516909861 & 3.72748309013885 \tabularnewline
48 & 109.1 & 105.408286112776 & 3.69171388722384 \tabularnewline
49 & 105 & 105.372082864784 & -0.372082864784393 \tabularnewline
50 & 105 & 103.891497956266 & 1.10850204373449 \tabularnewline
51 & 118.5 & 114.546246561386 & 3.95375343861376 \tabularnewline
52 & 103.7 & 107.925498069446 & -4.22549806944626 \tabularnewline
53 & 112.5 & 109.255884911772 & 3.24411508822761 \tabularnewline
54 & 116.6 & 116.350923062946 & 0.249076937054285 \tabularnewline
55 & 96.6 & 101.119148563466 & -4.51914856346554 \tabularnewline
56 & 101.9 & 102.369557044775 & -0.469557044775349 \tabularnewline
57 & 116.5 & 117.683304269164 & -1.18330426916442 \tabularnewline
58 & 119.3 & 119.584544731371 & -0.284544731371096 \tabularnewline
59 & 115.4 & 114.170040617471 & 1.22995938252873 \tabularnewline
60 & 108.5 & 110.459711426333 & -1.95971142633294 \tabularnewline
61 & 111.5 & 109.44936870188 & 2.05063129811993 \tabularnewline
62 & 108.8 & 107.876496895591 & 0.923503104408818 \tabularnewline
63 & 121.8 & 119.320811181633 & 2.47918881836696 \tabularnewline
64 & 109.6 & 112.730824988950 & -3.13082498894972 \tabularnewline
65 & 112.2 & 112.287252574141 & -0.087252574141383 \tabularnewline
66 & 119.6 & 119.495085822589 & 0.104914177410842 \tabularnewline
67 & 104.1 & 103.566032539958 & 0.53396746004214 \tabularnewline
68 & 105.3 & 103.780776946293 & 1.51922305370677 \tabularnewline
69 & 115 & 119.330368464983 & -4.33036846498340 \tabularnewline
70 & 124.1 & 121.272625326199 & 2.82737467380103 \tabularnewline
71 & 116.8 & 115.253129326918 & 1.54687067308197 \tabularnewline
72 & 107.5 & 110.373832764026 & -2.87383276402635 \tabularnewline
73 & 115.6 & 112.367941266975 & 3.23205873302538 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57765&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]97.6[/C][C]98.2711210938972[/C][C]-0.671121093897202[/C][/ROW]
[ROW][C]2[/C][C]96.9[/C][C]96.3906262950418[/C][C]0.509373704958178[/C][/ROW]
[ROW][C]3[/C][C]105.6[/C][C]107.168424097189[/C][C]-1.56842409718921[/C][/ROW]
[ROW][C]4[/C][C]102.8[/C][C]99.101847540186[/C][C]3.69815245981413[/C][/ROW]
[ROW][C]5[/C][C]101.7[/C][C]98.5967505268642[/C][C]3.10324947313581[/C][/ROW]
[ROW][C]6[/C][C]104.2[/C][C]107.024821645826[/C][C]-2.82482164582643[/C][/ROW]
[ROW][C]7[/C][C]92.7[/C][C]90.9111945676551[/C][C]1.78880543234487[/C][/ROW]
[ROW][C]8[/C][C]91.9[/C][C]91.977029253425[/C][C]-0.0770292534249414[/C][/ROW]
[ROW][C]9[/C][C]106.5[/C][C]107.629161769637[/C][C]-1.12916176963735[/C][/ROW]
[ROW][C]10[/C][C]112.3[/C][C]109.017697244233[/C][C]3.28230275576709[/C][/ROW]
[ROW][C]11[/C][C]102.8[/C][C]103.890307923395[/C][C]-1.09030792339531[/C][/ROW]
[ROW][C]12[/C][C]96.5[/C][C]99.8210852409292[/C][C]-3.3210852409292[/C][/ROW]
[ROW][C]13[/C][C]101[/C][C]99.1593819080519[/C][C]1.84061809194812[/C][/ROW]
[ROW][C]14[/C][C]98.9[/C][C]97.5249855032497[/C][C]1.37501449675035[/C][/ROW]
[ROW][C]15[/C][C]105.1[/C][C]108.179734108370[/C][C]-3.07973410837040[/C][/ROW]
[ROW][C]16[/C][C]103[/C][C]99.9901083543404[/C][C]3.00989164565961[/C][/ROW]
[ROW][C]17[/C][C]99[/C][C]99.6490769370543[/C][C]-0.649076937054284[/C][/ROW]
[ROW][C]18[/C][C]104.3[/C][C]107.800287362707[/C][C]-3.50028736270651[/C][/ROW]
[ROW][C]19[/C][C]94.6[/C][C]91.6251356860219[/C][C]2.97486431397812[/C][/ROW]
[ROW][C]20[/C][C]90.4[/C][C]92.978085164854[/C][C]-2.57808516485391[/C][/ROW]
[ROW][C]21[/C][C]108.9[/C][C]108.743012778341[/C][C]0.156987221659233[/C][/ROW]
[ROW][C]22[/C][C]111.4[/C][C]110.726286038565[/C][C]0.673713961434789[/C][/ROW]
[ROW][C]23[/C][C]100.8[/C][C]105.588642617975[/C][C]-4.78864261797541[/C][/ROW]
[ROW][C]24[/C][C]102.5[/C][C]101.745010130058[/C][C]0.754989869941816[/C][/ROW]
[ROW][C]25[/C][C]98.2[/C][C]101.472962587765[/C][C]-3.27296258776531[/C][/ROW]
[ROW][C]26[/C][C]98.7[/C][C]100.094918676769[/C][C]-1.39491867676865[/C][/ROW]
[ROW][C]27[/C][C]113.3[/C][C]110.862462379164[/C][C]2.43753762083616[/C][/ROW]
[ROW][C]28[/C][C]104.6[/C][C]102.611312026620[/C][C]1.9886879733795[/C][/ROW]
[ROW][C]29[/C][C]99.3[/C][C]101.634526424697[/C][C]-2.33452642469660[/C][/ROW]
[ROW][C]30[/C][C]111.8[/C][C]109.847261448862[/C][C]1.95273855113784[/C][/ROW]
[ROW][C]31[/C][C]97.3[/C][C]94.0002409642486[/C][C]3.29975903575136[/C][/ROW]
[ROW][C]32[/C][C]97.7[/C][C]95.2711576450629[/C][C]2.42884235493710[/C][/ROW]
[ROW][C]33[/C][C]115.6[/C][C]110.954052460532[/C][C]4.64594753946801[/C][/ROW]
[ROW][C]34[/C][C]111.9[/C][C]112.619448628438[/C][C]-0.719448628437527[/C][/ROW]
[ROW][C]35[/C][C]107[/C][C]107.625362604379[/C][C]-0.625362604378833[/C][/ROW]
[ROW][C]36[/C][C]107.1[/C][C]103.392074325877[/C][C]3.70792567412283[/C][/ROW]
[ROW][C]37[/C][C]100.6[/C][C]103.407141576647[/C][C]-2.80714157664653[/C][/ROW]
[ROW][C]38[/C][C]99.2[/C][C]101.721474673083[/C][C]-2.52147467308318[/C][/ROW]
[ROW][C]39[/C][C]108.4[/C][C]112.622321672257[/C][C]-4.22232167225726[/C][/ROW]
[ROW][C]40[/C][C]103[/C][C]104.340409020457[/C][C]-1.34040902045726[/C][/ROW]
[ROW][C]41[/C][C]99.8[/C][C]103.076508625471[/C][C]-3.27650862547114[/C][/ROW]
[ROW][C]42[/C][C]115[/C][C]110.98162065707[/C][C]4.01837934292998[/C][/ROW]
[ROW][C]43[/C][C]90.8[/C][C]94.878247678651[/C][C]-4.07824767865095[/C][/ROW]
[ROW][C]44[/C][C]95.9[/C][C]96.7233939455897[/C][C]-0.823393945589662[/C][/ROW]
[ROW][C]45[/C][C]114.4[/C][C]112.560100257342[/C][C]1.83989974265793[/C][/ROW]
[ROW][C]46[/C][C]108.2[/C][C]113.979398031194[/C][C]-5.77939803119428[/C][/ROW]
[ROW][C]47[/C][C]112.6[/C][C]108.872516909861[/C][C]3.72748309013885[/C][/ROW]
[ROW][C]48[/C][C]109.1[/C][C]105.408286112776[/C][C]3.69171388722384[/C][/ROW]
[ROW][C]49[/C][C]105[/C][C]105.372082864784[/C][C]-0.372082864784393[/C][/ROW]
[ROW][C]50[/C][C]105[/C][C]103.891497956266[/C][C]1.10850204373449[/C][/ROW]
[ROW][C]51[/C][C]118.5[/C][C]114.546246561386[/C][C]3.95375343861376[/C][/ROW]
[ROW][C]52[/C][C]103.7[/C][C]107.925498069446[/C][C]-4.22549806944626[/C][/ROW]
[ROW][C]53[/C][C]112.5[/C][C]109.255884911772[/C][C]3.24411508822761[/C][/ROW]
[ROW][C]54[/C][C]116.6[/C][C]116.350923062946[/C][C]0.249076937054285[/C][/ROW]
[ROW][C]55[/C][C]96.6[/C][C]101.119148563466[/C][C]-4.51914856346554[/C][/ROW]
[ROW][C]56[/C][C]101.9[/C][C]102.369557044775[/C][C]-0.469557044775349[/C][/ROW]
[ROW][C]57[/C][C]116.5[/C][C]117.683304269164[/C][C]-1.18330426916442[/C][/ROW]
[ROW][C]58[/C][C]119.3[/C][C]119.584544731371[/C][C]-0.284544731371096[/C][/ROW]
[ROW][C]59[/C][C]115.4[/C][C]114.170040617471[/C][C]1.22995938252873[/C][/ROW]
[ROW][C]60[/C][C]108.5[/C][C]110.459711426333[/C][C]-1.95971142633294[/C][/ROW]
[ROW][C]61[/C][C]111.5[/C][C]109.44936870188[/C][C]2.05063129811993[/C][/ROW]
[ROW][C]62[/C][C]108.8[/C][C]107.876496895591[/C][C]0.923503104408818[/C][/ROW]
[ROW][C]63[/C][C]121.8[/C][C]119.320811181633[/C][C]2.47918881836696[/C][/ROW]
[ROW][C]64[/C][C]109.6[/C][C]112.730824988950[/C][C]-3.13082498894972[/C][/ROW]
[ROW][C]65[/C][C]112.2[/C][C]112.287252574141[/C][C]-0.087252574141383[/C][/ROW]
[ROW][C]66[/C][C]119.6[/C][C]119.495085822589[/C][C]0.104914177410842[/C][/ROW]
[ROW][C]67[/C][C]104.1[/C][C]103.566032539958[/C][C]0.53396746004214[/C][/ROW]
[ROW][C]68[/C][C]105.3[/C][C]103.780776946293[/C][C]1.51922305370677[/C][/ROW]
[ROW][C]69[/C][C]115[/C][C]119.330368464983[/C][C]-4.33036846498340[/C][/ROW]
[ROW][C]70[/C][C]124.1[/C][C]121.272625326199[/C][C]2.82737467380103[/C][/ROW]
[ROW][C]71[/C][C]116.8[/C][C]115.253129326918[/C][C]1.54687067308197[/C][/ROW]
[ROW][C]72[/C][C]107.5[/C][C]110.373832764026[/C][C]-2.87383276402635[/C][/ROW]
[ROW][C]73[/C][C]115.6[/C][C]112.367941266975[/C][C]3.23205873302538[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57765&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57765&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
197.698.2711210938972-0.671121093897202
296.996.39062629504180.509373704958178
3105.6107.168424097189-1.56842409718921
4102.899.1018475401863.69815245981413
5101.798.59675052686423.10324947313581
6104.2107.024821645826-2.82482164582643
792.790.91119456765511.78880543234487
891.991.977029253425-0.0770292534249414
9106.5107.629161769637-1.12916176963735
10112.3109.0176972442333.28230275576709
11102.8103.890307923395-1.09030792339531
1296.599.8210852409292-3.3210852409292
1310199.15938190805191.84061809194812
1498.997.52498550324971.37501449675035
15105.1108.179734108370-3.07973410837040
1610399.99010835434043.00989164565961
179999.6490769370543-0.649076937054284
18104.3107.800287362707-3.50028736270651
1994.691.62513568602192.97486431397812
2090.492.978085164854-2.57808516485391
21108.9108.7430127783410.156987221659233
22111.4110.7262860385650.673713961434789
23100.8105.588642617975-4.78864261797541
24102.5101.7450101300580.754989869941816
2598.2101.472962587765-3.27296258776531
2698.7100.094918676769-1.39491867676865
27113.3110.8624623791642.43753762083616
28104.6102.6113120266201.9886879733795
2999.3101.634526424697-2.33452642469660
30111.8109.8472614488621.95273855113784
3197.394.00024096424863.29975903575136
3297.795.27115764506292.42884235493710
33115.6110.9540524605324.64594753946801
34111.9112.619448628438-0.719448628437527
35107107.625362604379-0.625362604378833
36107.1103.3920743258773.70792567412283
37100.6103.407141576647-2.80714157664653
3899.2101.721474673083-2.52147467308318
39108.4112.622321672257-4.22232167225726
40103104.340409020457-1.34040902045726
4199.8103.076508625471-3.27650862547114
42115110.981620657074.01837934292998
4390.894.878247678651-4.07824767865095
4495.996.7233939455897-0.823393945589662
45114.4112.5601002573421.83989974265793
46108.2113.979398031194-5.77939803119428
47112.6108.8725169098613.72748309013885
48109.1105.4082861127763.69171388722384
49105105.372082864784-0.372082864784393
50105103.8914979562661.10850204373449
51118.5114.5462465613863.95375343861376
52103.7107.925498069446-4.22549806944626
53112.5109.2558849117723.24411508822761
54116.6116.3509230629460.249076937054285
5596.6101.119148563466-4.51914856346554
56101.9102.369557044775-0.469557044775349
57116.5117.683304269164-1.18330426916442
58119.3119.584544731371-0.284544731371096
59115.4114.1700406174711.22995938252873
60108.5110.459711426333-1.95971142633294
61111.5109.449368701882.05063129811993
62108.8107.8764968955910.923503104408818
63121.8119.3208111816332.47918881836696
64109.6112.730824988950-3.13082498894972
65112.2112.287252574141-0.087252574141383
66119.6119.4950858225890.104914177410842
67104.1103.5660325399580.53396746004214
68105.3103.7807769462931.51922305370677
69115119.330368464983-4.33036846498340
70124.1121.2726253261992.82737467380103
71116.8115.2531293269181.54687067308197
72107.5110.373832764026-2.87383276402635
73115.6112.3679412669753.23205873302538







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.2192758807721190.4385517615442390.780724119227881
180.1205016719890010.2410033439780020.879498328010999
190.06019831398440170.1203966279688030.939801686015598
200.03542103312462860.07084206624925730.964578966875371
210.027048897319030.054097794638060.97295110268097
220.01177181017690250.02354362035380510.988228189823097
230.007220371374030940.01444074274806190.99277962862597
240.05023075563371140.1004615112674230.949769244366289
250.04436207534386630.08872415068773250.955637924656134
260.02554574378082910.05109148756165820.974454256219171
270.09673625423884320.1934725084776860.903263745761157
280.07621704192835110.1524340838567020.92378295807165
290.06785615042157060.1357123008431410.93214384957843
300.1273130646046640.2546261292093280.872686935395336
310.1258595263649890.2517190527299780.874140473635011
320.1304648769403590.2609297538807170.869535123059641
330.2265033693838750.453006738767750.773496630616125
340.2055044080008440.4110088160016890.794495591999156
350.1650385713093740.3300771426187490.834961428690626
360.2347919716790110.4695839433580230.765208028320989
370.2323962222204530.4647924444409060.767603777779547
380.2258073667951070.4516147335902150.774192633204893
390.3797057134953840.7594114269907680.620294286504616
400.3876620827776130.7753241655552270.612337917222387
410.4189670230085440.8379340460170870.581032976991456
420.5323718913551680.9352562172896640.467628108644832
430.5823543603772080.8352912792455840.417645639622792
440.5010153337956730.9979693324086540.498984666204327
450.542462646982320.915074706035360.45753735301768
460.8286088646561340.3427822706877310.171391135343865
470.837601589687210.3247968206255790.162398410312789
480.9624429008950.07511419820999940.0375570991049997
490.9531213921539460.09375721569210820.0468786078460541
500.9185055485339450.1629889029321090.0814944514660547
510.8997446928011040.2005106143977930.100255307198896
520.863308407166060.2733831856678790.136691592833939
530.9044822632840980.1910354734318050.0955177367159023
540.8700983250170040.2598033499659910.129901674982996
550.8828417118806230.2343165762387540.117158288119377
560.7940724584115950.4118550831768090.205927541588405

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.219275880772119 & 0.438551761544239 & 0.780724119227881 \tabularnewline
18 & 0.120501671989001 & 0.241003343978002 & 0.879498328010999 \tabularnewline
19 & 0.0601983139844017 & 0.120396627968803 & 0.939801686015598 \tabularnewline
20 & 0.0354210331246286 & 0.0708420662492573 & 0.964578966875371 \tabularnewline
21 & 0.02704889731903 & 0.05409779463806 & 0.97295110268097 \tabularnewline
22 & 0.0117718101769025 & 0.0235436203538051 & 0.988228189823097 \tabularnewline
23 & 0.00722037137403094 & 0.0144407427480619 & 0.99277962862597 \tabularnewline
24 & 0.0502307556337114 & 0.100461511267423 & 0.949769244366289 \tabularnewline
25 & 0.0443620753438663 & 0.0887241506877325 & 0.955637924656134 \tabularnewline
26 & 0.0255457437808291 & 0.0510914875616582 & 0.974454256219171 \tabularnewline
27 & 0.0967362542388432 & 0.193472508477686 & 0.903263745761157 \tabularnewline
28 & 0.0762170419283511 & 0.152434083856702 & 0.92378295807165 \tabularnewline
29 & 0.0678561504215706 & 0.135712300843141 & 0.93214384957843 \tabularnewline
30 & 0.127313064604664 & 0.254626129209328 & 0.872686935395336 \tabularnewline
31 & 0.125859526364989 & 0.251719052729978 & 0.874140473635011 \tabularnewline
32 & 0.130464876940359 & 0.260929753880717 & 0.869535123059641 \tabularnewline
33 & 0.226503369383875 & 0.45300673876775 & 0.773496630616125 \tabularnewline
34 & 0.205504408000844 & 0.411008816001689 & 0.794495591999156 \tabularnewline
35 & 0.165038571309374 & 0.330077142618749 & 0.834961428690626 \tabularnewline
36 & 0.234791971679011 & 0.469583943358023 & 0.765208028320989 \tabularnewline
37 & 0.232396222220453 & 0.464792444440906 & 0.767603777779547 \tabularnewline
38 & 0.225807366795107 & 0.451614733590215 & 0.774192633204893 \tabularnewline
39 & 0.379705713495384 & 0.759411426990768 & 0.620294286504616 \tabularnewline
40 & 0.387662082777613 & 0.775324165555227 & 0.612337917222387 \tabularnewline
41 & 0.418967023008544 & 0.837934046017087 & 0.581032976991456 \tabularnewline
42 & 0.532371891355168 & 0.935256217289664 & 0.467628108644832 \tabularnewline
43 & 0.582354360377208 & 0.835291279245584 & 0.417645639622792 \tabularnewline
44 & 0.501015333795673 & 0.997969332408654 & 0.498984666204327 \tabularnewline
45 & 0.54246264698232 & 0.91507470603536 & 0.45753735301768 \tabularnewline
46 & 0.828608864656134 & 0.342782270687731 & 0.171391135343865 \tabularnewline
47 & 0.83760158968721 & 0.324796820625579 & 0.162398410312789 \tabularnewline
48 & 0.962442900895 & 0.0751141982099994 & 0.0375570991049997 \tabularnewline
49 & 0.953121392153946 & 0.0937572156921082 & 0.0468786078460541 \tabularnewline
50 & 0.918505548533945 & 0.162988902932109 & 0.0814944514660547 \tabularnewline
51 & 0.899744692801104 & 0.200510614397793 & 0.100255307198896 \tabularnewline
52 & 0.86330840716606 & 0.273383185667879 & 0.136691592833939 \tabularnewline
53 & 0.904482263284098 & 0.191035473431805 & 0.0955177367159023 \tabularnewline
54 & 0.870098325017004 & 0.259803349965991 & 0.129901674982996 \tabularnewline
55 & 0.882841711880623 & 0.234316576238754 & 0.117158288119377 \tabularnewline
56 & 0.794072458411595 & 0.411855083176809 & 0.205927541588405 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57765&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]17[/C][C]0.219275880772119[/C][C]0.438551761544239[/C][C]0.780724119227881[/C][/ROW]
[ROW][C]18[/C][C]0.120501671989001[/C][C]0.241003343978002[/C][C]0.879498328010999[/C][/ROW]
[ROW][C]19[/C][C]0.0601983139844017[/C][C]0.120396627968803[/C][C]0.939801686015598[/C][/ROW]
[ROW][C]20[/C][C]0.0354210331246286[/C][C]0.0708420662492573[/C][C]0.964578966875371[/C][/ROW]
[ROW][C]21[/C][C]0.02704889731903[/C][C]0.05409779463806[/C][C]0.97295110268097[/C][/ROW]
[ROW][C]22[/C][C]0.0117718101769025[/C][C]0.0235436203538051[/C][C]0.988228189823097[/C][/ROW]
[ROW][C]23[/C][C]0.00722037137403094[/C][C]0.0144407427480619[/C][C]0.99277962862597[/C][/ROW]
[ROW][C]24[/C][C]0.0502307556337114[/C][C]0.100461511267423[/C][C]0.949769244366289[/C][/ROW]
[ROW][C]25[/C][C]0.0443620753438663[/C][C]0.0887241506877325[/C][C]0.955637924656134[/C][/ROW]
[ROW][C]26[/C][C]0.0255457437808291[/C][C]0.0510914875616582[/C][C]0.974454256219171[/C][/ROW]
[ROW][C]27[/C][C]0.0967362542388432[/C][C]0.193472508477686[/C][C]0.903263745761157[/C][/ROW]
[ROW][C]28[/C][C]0.0762170419283511[/C][C]0.152434083856702[/C][C]0.92378295807165[/C][/ROW]
[ROW][C]29[/C][C]0.0678561504215706[/C][C]0.135712300843141[/C][C]0.93214384957843[/C][/ROW]
[ROW][C]30[/C][C]0.127313064604664[/C][C]0.254626129209328[/C][C]0.872686935395336[/C][/ROW]
[ROW][C]31[/C][C]0.125859526364989[/C][C]0.251719052729978[/C][C]0.874140473635011[/C][/ROW]
[ROW][C]32[/C][C]0.130464876940359[/C][C]0.260929753880717[/C][C]0.869535123059641[/C][/ROW]
[ROW][C]33[/C][C]0.226503369383875[/C][C]0.45300673876775[/C][C]0.773496630616125[/C][/ROW]
[ROW][C]34[/C][C]0.205504408000844[/C][C]0.411008816001689[/C][C]0.794495591999156[/C][/ROW]
[ROW][C]35[/C][C]0.165038571309374[/C][C]0.330077142618749[/C][C]0.834961428690626[/C][/ROW]
[ROW][C]36[/C][C]0.234791971679011[/C][C]0.469583943358023[/C][C]0.765208028320989[/C][/ROW]
[ROW][C]37[/C][C]0.232396222220453[/C][C]0.464792444440906[/C][C]0.767603777779547[/C][/ROW]
[ROW][C]38[/C][C]0.225807366795107[/C][C]0.451614733590215[/C][C]0.774192633204893[/C][/ROW]
[ROW][C]39[/C][C]0.379705713495384[/C][C]0.759411426990768[/C][C]0.620294286504616[/C][/ROW]
[ROW][C]40[/C][C]0.387662082777613[/C][C]0.775324165555227[/C][C]0.612337917222387[/C][/ROW]
[ROW][C]41[/C][C]0.418967023008544[/C][C]0.837934046017087[/C][C]0.581032976991456[/C][/ROW]
[ROW][C]42[/C][C]0.532371891355168[/C][C]0.935256217289664[/C][C]0.467628108644832[/C][/ROW]
[ROW][C]43[/C][C]0.582354360377208[/C][C]0.835291279245584[/C][C]0.417645639622792[/C][/ROW]
[ROW][C]44[/C][C]0.501015333795673[/C][C]0.997969332408654[/C][C]0.498984666204327[/C][/ROW]
[ROW][C]45[/C][C]0.54246264698232[/C][C]0.91507470603536[/C][C]0.45753735301768[/C][/ROW]
[ROW][C]46[/C][C]0.828608864656134[/C][C]0.342782270687731[/C][C]0.171391135343865[/C][/ROW]
[ROW][C]47[/C][C]0.83760158968721[/C][C]0.324796820625579[/C][C]0.162398410312789[/C][/ROW]
[ROW][C]48[/C][C]0.962442900895[/C][C]0.0751141982099994[/C][C]0.0375570991049997[/C][/ROW]
[ROW][C]49[/C][C]0.953121392153946[/C][C]0.0937572156921082[/C][C]0.0468786078460541[/C][/ROW]
[ROW][C]50[/C][C]0.918505548533945[/C][C]0.162988902932109[/C][C]0.0814944514660547[/C][/ROW]
[ROW][C]51[/C][C]0.899744692801104[/C][C]0.200510614397793[/C][C]0.100255307198896[/C][/ROW]
[ROW][C]52[/C][C]0.86330840716606[/C][C]0.273383185667879[/C][C]0.136691592833939[/C][/ROW]
[ROW][C]53[/C][C]0.904482263284098[/C][C]0.191035473431805[/C][C]0.0955177367159023[/C][/ROW]
[ROW][C]54[/C][C]0.870098325017004[/C][C]0.259803349965991[/C][C]0.129901674982996[/C][/ROW]
[ROW][C]55[/C][C]0.882841711880623[/C][C]0.234316576238754[/C][C]0.117158288119377[/C][/ROW]
[ROW][C]56[/C][C]0.794072458411595[/C][C]0.411855083176809[/C][C]0.205927541588405[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57765&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57765&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
170.2192758807721190.4385517615442390.780724119227881
180.1205016719890010.2410033439780020.879498328010999
190.06019831398440170.1203966279688030.939801686015598
200.03542103312462860.07084206624925730.964578966875371
210.027048897319030.054097794638060.97295110268097
220.01177181017690250.02354362035380510.988228189823097
230.007220371374030940.01444074274806190.99277962862597
240.05023075563371140.1004615112674230.949769244366289
250.04436207534386630.08872415068773250.955637924656134
260.02554574378082910.05109148756165820.974454256219171
270.09673625423884320.1934725084776860.903263745761157
280.07621704192835110.1524340838567020.92378295807165
290.06785615042157060.1357123008431410.93214384957843
300.1273130646046640.2546261292093280.872686935395336
310.1258595263649890.2517190527299780.874140473635011
320.1304648769403590.2609297538807170.869535123059641
330.2265033693838750.453006738767750.773496630616125
340.2055044080008440.4110088160016890.794495591999156
350.1650385713093740.3300771426187490.834961428690626
360.2347919716790110.4695839433580230.765208028320989
370.2323962222204530.4647924444409060.767603777779547
380.2258073667951070.4516147335902150.774192633204893
390.3797057134953840.7594114269907680.620294286504616
400.3876620827776130.7753241655552270.612337917222387
410.4189670230085440.8379340460170870.581032976991456
420.5323718913551680.9352562172896640.467628108644832
430.5823543603772080.8352912792455840.417645639622792
440.5010153337956730.9979693324086540.498984666204327
450.542462646982320.915074706035360.45753735301768
460.8286088646561340.3427822706877310.171391135343865
470.837601589687210.3247968206255790.162398410312789
480.9624429008950.07511419820999940.0375570991049997
490.9531213921539460.09375721569210820.0468786078460541
500.9185055485339450.1629889029321090.0814944514660547
510.8997446928011040.2005106143977930.100255307198896
520.863308407166060.2733831856678790.136691592833939
530.9044822632840980.1910354734318050.0955177367159023
540.8700983250170040.2598033499659910.129901674982996
550.8828417118806230.2343165762387540.117158288119377
560.7940724584115950.4118550831768090.205927541588405







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.05NOK
10% type I error level80.2NOK

\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 & 2 & 0.05 & NOK \tabularnewline
10% type I error level & 8 & 0.2 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57765&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]2[/C][C]0.05[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]8[/C][C]0.2[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57765&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57765&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 level20.05NOK
10% type I error level80.2NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}