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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 08:37:22 -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/20/t1258731534eozeykxx7p8s5eq.htm/, Retrieved Thu, 28 Mar 2024 09:54:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58272, Retrieved Thu, 28 Mar 2024 09:54:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact129
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] [Eco. Crisis] [2009-11-19 19:59:19] [36becc366f59efff5c3495030cea7527]
-   P       [Multiple Regression] [2de model] [2009-11-20 15:08:27] [36becc366f59efff5c3495030cea7527]
-   P           [Multiple Regression] [3de model] [2009-11-20 15:37:22] [e1f26cfd746b288ac2a466939c6f316e] [Current]
-    D            [Multiple Regression] [relatie lichten-o...] [2009-11-23 15:31:21] [74be16979710d4c4e7c6647856088456]
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Dataseries X:
105.7	0
105.7	0
111.1	0
82.4	0
60	0
107.3	0
99.3	0
113.5	0
108.9	0
100.2	0
103.9	0
138.7	0
120.2	0
100.2	0
143.2	0
70.9	0
85.2	0
133	0
136.6	0
117.9	0
106.3	0
122.3	0
125.5	0
148.4	0
126.3	0
99.6	0
140.4	0
80.3	0
92.6	0
138.5	0
110.9	0
119.6	0
105	0
109	0
129.4	0
148.6	0
101.4	0
134.8	0
143.7	0
81.6	0
90.3	0
141.5	0
140.7	0
140.2	0
100.2	0
125.7	0
119.6	0
134.7	0
109	0
116.3	0
146.9	0
97.4	0
89.4	0
132.1	0
139.8	0
129	0
112.5	0
121.9	0
121.7	0
123.1	0
131.6	0
119.3	0
132.5	0
98.3	0
85.1	0
131.7	0
129.3	0
90.7	1
78.6	1
68.9	1
79.1	1
83.5	1
74.1	1
59.7	1
93.3	1
61.3	1
56.6	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58272&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]3 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=58272&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58272&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 126.383760683761 -49.8978632478632X[t] -19.5698836657170M1[t] -24.5135158051825M2[t] + 0.285709198209129M3[t] -48.4007800841134M4[t] -50.5301265092932M5[t] -5.49975579975582M6[t] -10.3552927011260M7[t] -9.92785239451906M8[t] -26.7667226292227M9[t] -20.9555928639262M10[t] -16.0277964319631M11[t] + 0.272203568036901t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  126.383760683761 -49.8978632478632X[t] -19.5698836657170M1[t] -24.5135158051825M2[t] +  0.285709198209129M3[t] -48.4007800841134M4[t] -50.5301265092932M5[t] -5.49975579975582M6[t] -10.3552927011260M7[t] -9.92785239451906M8[t] -26.7667226292227M9[t] -20.9555928639262M10[t] -16.0277964319631M11[t] +  0.272203568036901t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58272&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  126.383760683761 -49.8978632478632X[t] -19.5698836657170M1[t] -24.5135158051825M2[t] +  0.285709198209129M3[t] -48.4007800841134M4[t] -50.5301265092932M5[t] -5.49975579975582M6[t] -10.3552927011260M7[t] -9.92785239451906M8[t] -26.7667226292227M9[t] -20.9555928639262M10[t] -16.0277964319631M11[t] +  0.272203568036901t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58272&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58272&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
Y[t] = + 126.383760683761 -49.8978632478632X[t] -19.5698836657170M1[t] -24.5135158051825M2[t] + 0.285709198209129M3[t] -48.4007800841134M4[t] -50.5301265092932M5[t] -5.49975579975582M6[t] -10.3552927011260M7[t] -9.92785239451906M8[t] -26.7667226292227M9[t] -20.9555928639262M10[t] -16.0277964319631M11[t] + 0.272203568036901t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)126.3837606837614.92435825.66500
X-49.89786324786324.442725-11.231400
M1-19.56988366571705.841073-3.35040.0013670.000684
M2-24.51351580518255.838375-4.19878.6e-054.3e-05
M30.2857091982091295.8364320.0490.9611120.480556
M4-48.40078008411345.835244-8.294600
M5-50.53012650929325.834812-8.660100
M6-5.499755799755826.084279-0.90390.3694770.184738
M7-10.35529270112606.085009-1.70180.0937290.046864
M8-9.927852394519066.060242-1.63820.1063670.053183
M9-26.76672262922276.057693-4.41864e-052e-05
M10-20.95559286392626.055871-3.46040.0009740.000487
M11-16.02779643196316.054778-2.64710.0102420.005121
t0.2722035680369010.0664344.09730.0001226.1e-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) & 126.383760683761 & 4.924358 & 25.665 & 0 & 0 \tabularnewline
X & -49.8978632478632 & 4.442725 & -11.2314 & 0 & 0 \tabularnewline
M1 & -19.5698836657170 & 5.841073 & -3.3504 & 0.001367 & 0.000684 \tabularnewline
M2 & -24.5135158051825 & 5.838375 & -4.1987 & 8.6e-05 & 4.3e-05 \tabularnewline
M3 & 0.285709198209129 & 5.836432 & 0.049 & 0.961112 & 0.480556 \tabularnewline
M4 & -48.4007800841134 & 5.835244 & -8.2946 & 0 & 0 \tabularnewline
M5 & -50.5301265092932 & 5.834812 & -8.6601 & 0 & 0 \tabularnewline
M6 & -5.49975579975582 & 6.084279 & -0.9039 & 0.369477 & 0.184738 \tabularnewline
M7 & -10.3552927011260 & 6.085009 & -1.7018 & 0.093729 & 0.046864 \tabularnewline
M8 & -9.92785239451906 & 6.060242 & -1.6382 & 0.106367 & 0.053183 \tabularnewline
M9 & -26.7667226292227 & 6.057693 & -4.4186 & 4e-05 & 2e-05 \tabularnewline
M10 & -20.9555928639262 & 6.055871 & -3.4604 & 0.000974 & 0.000487 \tabularnewline
M11 & -16.0277964319631 & 6.054778 & -2.6471 & 0.010242 & 0.005121 \tabularnewline
t & 0.272203568036901 & 0.066434 & 4.0973 & 0.000122 & 6.1e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58272&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]126.383760683761[/C][C]4.924358[/C][C]25.665[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-49.8978632478632[/C][C]4.442725[/C][C]-11.2314[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-19.5698836657170[/C][C]5.841073[/C][C]-3.3504[/C][C]0.001367[/C][C]0.000684[/C][/ROW]
[ROW][C]M2[/C][C]-24.5135158051825[/C][C]5.838375[/C][C]-4.1987[/C][C]8.6e-05[/C][C]4.3e-05[/C][/ROW]
[ROW][C]M3[/C][C]0.285709198209129[/C][C]5.836432[/C][C]0.049[/C][C]0.961112[/C][C]0.480556[/C][/ROW]
[ROW][C]M4[/C][C]-48.4007800841134[/C][C]5.835244[/C][C]-8.2946[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M5[/C][C]-50.5301265092932[/C][C]5.834812[/C][C]-8.6601[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M6[/C][C]-5.49975579975582[/C][C]6.084279[/C][C]-0.9039[/C][C]0.369477[/C][C]0.184738[/C][/ROW]
[ROW][C]M7[/C][C]-10.3552927011260[/C][C]6.085009[/C][C]-1.7018[/C][C]0.093729[/C][C]0.046864[/C][/ROW]
[ROW][C]M8[/C][C]-9.92785239451906[/C][C]6.060242[/C][C]-1.6382[/C][C]0.106367[/C][C]0.053183[/C][/ROW]
[ROW][C]M9[/C][C]-26.7667226292227[/C][C]6.057693[/C][C]-4.4186[/C][C]4e-05[/C][C]2e-05[/C][/ROW]
[ROW][C]M10[/C][C]-20.9555928639262[/C][C]6.055871[/C][C]-3.4604[/C][C]0.000974[/C][C]0.000487[/C][/ROW]
[ROW][C]M11[/C][C]-16.0277964319631[/C][C]6.054778[/C][C]-2.6471[/C][C]0.010242[/C][C]0.005121[/C][/ROW]
[ROW][C]t[/C][C]0.272203568036901[/C][C]0.066434[/C][C]4.0973[/C][C]0.000122[/C][C]6.1e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58272&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58272&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)126.3837606837614.92435825.66500
X-49.89786324786324.442725-11.231400
M1-19.56988366571705.841073-3.35040.0013670.000684
M2-24.51351580518255.838375-4.19878.6e-054.3e-05
M30.2857091982091295.8364320.0490.9611120.480556
M4-48.40078008411345.835244-8.294600
M5-50.53012650929325.834812-8.660100
M6-5.499755799755826.084279-0.90390.3694770.184738
M7-10.35529270112606.085009-1.70180.0937290.046864
M8-9.927852394519066.060242-1.63820.1063670.053183
M9-26.76672262922276.057693-4.41864e-052e-05
M10-20.95559286392626.055871-3.46040.0009740.000487
M11-16.02779643196316.054778-2.64710.0102420.005121
t0.2722035680369010.0664344.09730.0001226.1e-05







Multiple Linear Regression - Regression Statistics
Multiple R0.917996846433517
R-squared0.842718210061882
Adjusted R-squared0.81026323753497
F-TEST (value)25.9657656269178
F-TEST (DF numerator)13
F-TEST (DF denominator)63
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation10.4865511345852
Sum Squared Residuals6927.96854599104

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.917996846433517 \tabularnewline
R-squared & 0.842718210061882 \tabularnewline
Adjusted R-squared & 0.81026323753497 \tabularnewline
F-TEST (value) & 25.9657656269178 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 63 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 10.4865511345852 \tabularnewline
Sum Squared Residuals & 6927.96854599104 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58272&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.917996846433517[/C][/ROW]
[ROW][C]R-squared[/C][C]0.842718210061882[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.81026323753497[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]25.9657656269178[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]63[/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]10.4865511345852[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]6927.96854599104[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58272&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58272&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 R0.917996846433517
R-squared0.842718210061882
Adjusted R-squared0.81026323753497
F-TEST (value)25.9657656269178
F-TEST (DF numerator)13
F-TEST (DF denominator)63
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation10.4865511345852
Sum Squared Residuals6927.96854599104







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1105.7107.086080586081-1.38608058608068
2105.7102.4146520146523.28534798534793
3111.1127.486080586081-16.3860805860806
482.479.07179487179493.32820512820513
56077.2146520146519-17.2146520146519
6107.3122.517226292226-15.2172262922263
799.3117.933892958893-18.6338929588929
8113.5118.633536833537-5.13353683353683
9108.9102.0668701668706.83312983312984
10100.2108.150203500204-7.95020350020353
11103.9113.350203500203-9.45020350020345
12138.7129.6502035002049.04979649979646
13120.2110.3525234025239.84747659747661
14100.2105.681094831095-5.48109483109483
15143.2130.75252340252312.4474765974766
1670.982.3382376882377-11.4382376882377
1785.280.48109483109494.71890516890516
18133125.7836691086697.2163308913309
19136.6121.20033577533615.3996642246642
20117.9121.899979649980-3.99997964997965
21106.3105.3333129833130.966687016687016
22122.3111.41664631664610.8833536833537
23125.5116.6166463166468.88335368335368
24148.4132.91664631664615.4833536833537
25126.3113.61896621896612.6810337810338
2699.6108.947537647538-9.34753764753764
27140.4134.0189662189666.3810337810338
2880.385.6046805046805-5.30468050468051
2992.683.74753764753778.85246235246234
30138.5129.0501119251129.44988807488808
31110.9124.466778591779-13.5667785917786
32119.6125.166422466422-5.56642246642248
33105108.599755799756-3.5997557997558
34109114.683089133089-5.68308913308913
35129.4119.8830891330899.51691086691087
36148.6136.18308913308912.4169108669108
37101.4116.885409035409-15.485409035409
38134.8112.21398046398022.5860195360196
39143.7137.2854090354096.41459096459096
4081.688.8711233211233-7.27112332112333
4190.387.01398046398053.28601953601952
42141.5132.3165547415559.18344525844527
43140.7127.73322140822112.9667785917786
44140.2128.43286528286511.7671347171347
45100.2111.866198616199-11.6661986161986
46125.7117.9495319495327.75046805046806
47119.6123.149531949532-3.54953194953197
48134.7139.449531949532-4.74953194953197
49109120.151851851852-11.1518518518518
50116.3115.4804232804230.81957671957673
51146.9140.5518518518526.34814814814816
5297.492.13756613756615.26243386243387
5389.490.2804232804233-0.880423280423285
54132.1135.582997557998-3.48299755799756
55139.8130.9996642246648.80033577533578
56129131.699308099308-2.6993080993081
57112.5115.132641432641-2.63264143264143
58121.9121.2159747659750.684025234025239
59121.7126.415974765975-4.71597476597477
60123.1142.715974765975-19.6159747659748
61131.6123.4182946682958.18170533170535
62119.3118.7468660968660.553133903133916
63132.5143.818294668295-11.3182946682947
6498.395.4040089540092.89599104599105
6585.193.5468660968661-8.44686609686611
66131.7138.849440374440-7.14944037444039
67129.3134.266107041107-4.96610704110704
6890.785.06788766788775.63211233211233
6978.668.50122100122110.098778998779
7068.974.5845543345543-5.68455433455433
7179.179.7845543345543-0.684554334554351
7283.596.0845543345543-12.5845543345543
7374.176.7868742368742-2.68687423687422
7459.772.1154456654457-12.4154456654457
7593.397.1868742368742-3.88687423687423
7661.348.772588522588512.5274114774115
7756.646.91544566544579.68455433455432

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 105.7 & 107.086080586081 & -1.38608058608068 \tabularnewline
2 & 105.7 & 102.414652014652 & 3.28534798534793 \tabularnewline
3 & 111.1 & 127.486080586081 & -16.3860805860806 \tabularnewline
4 & 82.4 & 79.0717948717949 & 3.32820512820513 \tabularnewline
5 & 60 & 77.2146520146519 & -17.2146520146519 \tabularnewline
6 & 107.3 & 122.517226292226 & -15.2172262922263 \tabularnewline
7 & 99.3 & 117.933892958893 & -18.6338929588929 \tabularnewline
8 & 113.5 & 118.633536833537 & -5.13353683353683 \tabularnewline
9 & 108.9 & 102.066870166870 & 6.83312983312984 \tabularnewline
10 & 100.2 & 108.150203500204 & -7.95020350020353 \tabularnewline
11 & 103.9 & 113.350203500203 & -9.45020350020345 \tabularnewline
12 & 138.7 & 129.650203500204 & 9.04979649979646 \tabularnewline
13 & 120.2 & 110.352523402523 & 9.84747659747661 \tabularnewline
14 & 100.2 & 105.681094831095 & -5.48109483109483 \tabularnewline
15 & 143.2 & 130.752523402523 & 12.4474765974766 \tabularnewline
16 & 70.9 & 82.3382376882377 & -11.4382376882377 \tabularnewline
17 & 85.2 & 80.4810948310949 & 4.71890516890516 \tabularnewline
18 & 133 & 125.783669108669 & 7.2163308913309 \tabularnewline
19 & 136.6 & 121.200335775336 & 15.3996642246642 \tabularnewline
20 & 117.9 & 121.899979649980 & -3.99997964997965 \tabularnewline
21 & 106.3 & 105.333312983313 & 0.966687016687016 \tabularnewline
22 & 122.3 & 111.416646316646 & 10.8833536833537 \tabularnewline
23 & 125.5 & 116.616646316646 & 8.88335368335368 \tabularnewline
24 & 148.4 & 132.916646316646 & 15.4833536833537 \tabularnewline
25 & 126.3 & 113.618966218966 & 12.6810337810338 \tabularnewline
26 & 99.6 & 108.947537647538 & -9.34753764753764 \tabularnewline
27 & 140.4 & 134.018966218966 & 6.3810337810338 \tabularnewline
28 & 80.3 & 85.6046805046805 & -5.30468050468051 \tabularnewline
29 & 92.6 & 83.7475376475377 & 8.85246235246234 \tabularnewline
30 & 138.5 & 129.050111925112 & 9.44988807488808 \tabularnewline
31 & 110.9 & 124.466778591779 & -13.5667785917786 \tabularnewline
32 & 119.6 & 125.166422466422 & -5.56642246642248 \tabularnewline
33 & 105 & 108.599755799756 & -3.5997557997558 \tabularnewline
34 & 109 & 114.683089133089 & -5.68308913308913 \tabularnewline
35 & 129.4 & 119.883089133089 & 9.51691086691087 \tabularnewline
36 & 148.6 & 136.183089133089 & 12.4169108669108 \tabularnewline
37 & 101.4 & 116.885409035409 & -15.485409035409 \tabularnewline
38 & 134.8 & 112.213980463980 & 22.5860195360196 \tabularnewline
39 & 143.7 & 137.285409035409 & 6.41459096459096 \tabularnewline
40 & 81.6 & 88.8711233211233 & -7.27112332112333 \tabularnewline
41 & 90.3 & 87.0139804639805 & 3.28601953601952 \tabularnewline
42 & 141.5 & 132.316554741555 & 9.18344525844527 \tabularnewline
43 & 140.7 & 127.733221408221 & 12.9667785917786 \tabularnewline
44 & 140.2 & 128.432865282865 & 11.7671347171347 \tabularnewline
45 & 100.2 & 111.866198616199 & -11.6661986161986 \tabularnewline
46 & 125.7 & 117.949531949532 & 7.75046805046806 \tabularnewline
47 & 119.6 & 123.149531949532 & -3.54953194953197 \tabularnewline
48 & 134.7 & 139.449531949532 & -4.74953194953197 \tabularnewline
49 & 109 & 120.151851851852 & -11.1518518518518 \tabularnewline
50 & 116.3 & 115.480423280423 & 0.81957671957673 \tabularnewline
51 & 146.9 & 140.551851851852 & 6.34814814814816 \tabularnewline
52 & 97.4 & 92.1375661375661 & 5.26243386243387 \tabularnewline
53 & 89.4 & 90.2804232804233 & -0.880423280423285 \tabularnewline
54 & 132.1 & 135.582997557998 & -3.48299755799756 \tabularnewline
55 & 139.8 & 130.999664224664 & 8.80033577533578 \tabularnewline
56 & 129 & 131.699308099308 & -2.6993080993081 \tabularnewline
57 & 112.5 & 115.132641432641 & -2.63264143264143 \tabularnewline
58 & 121.9 & 121.215974765975 & 0.684025234025239 \tabularnewline
59 & 121.7 & 126.415974765975 & -4.71597476597477 \tabularnewline
60 & 123.1 & 142.715974765975 & -19.6159747659748 \tabularnewline
61 & 131.6 & 123.418294668295 & 8.18170533170535 \tabularnewline
62 & 119.3 & 118.746866096866 & 0.553133903133916 \tabularnewline
63 & 132.5 & 143.818294668295 & -11.3182946682947 \tabularnewline
64 & 98.3 & 95.404008954009 & 2.89599104599105 \tabularnewline
65 & 85.1 & 93.5468660968661 & -8.44686609686611 \tabularnewline
66 & 131.7 & 138.849440374440 & -7.14944037444039 \tabularnewline
67 & 129.3 & 134.266107041107 & -4.96610704110704 \tabularnewline
68 & 90.7 & 85.0678876678877 & 5.63211233211233 \tabularnewline
69 & 78.6 & 68.501221001221 & 10.098778998779 \tabularnewline
70 & 68.9 & 74.5845543345543 & -5.68455433455433 \tabularnewline
71 & 79.1 & 79.7845543345543 & -0.684554334554351 \tabularnewline
72 & 83.5 & 96.0845543345543 & -12.5845543345543 \tabularnewline
73 & 74.1 & 76.7868742368742 & -2.68687423687422 \tabularnewline
74 & 59.7 & 72.1154456654457 & -12.4154456654457 \tabularnewline
75 & 93.3 & 97.1868742368742 & -3.88687423687423 \tabularnewline
76 & 61.3 & 48.7725885225885 & 12.5274114774115 \tabularnewline
77 & 56.6 & 46.9154456654457 & 9.68455433455432 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58272&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]105.7[/C][C]107.086080586081[/C][C]-1.38608058608068[/C][/ROW]
[ROW][C]2[/C][C]105.7[/C][C]102.414652014652[/C][C]3.28534798534793[/C][/ROW]
[ROW][C]3[/C][C]111.1[/C][C]127.486080586081[/C][C]-16.3860805860806[/C][/ROW]
[ROW][C]4[/C][C]82.4[/C][C]79.0717948717949[/C][C]3.32820512820513[/C][/ROW]
[ROW][C]5[/C][C]60[/C][C]77.2146520146519[/C][C]-17.2146520146519[/C][/ROW]
[ROW][C]6[/C][C]107.3[/C][C]122.517226292226[/C][C]-15.2172262922263[/C][/ROW]
[ROW][C]7[/C][C]99.3[/C][C]117.933892958893[/C][C]-18.6338929588929[/C][/ROW]
[ROW][C]8[/C][C]113.5[/C][C]118.633536833537[/C][C]-5.13353683353683[/C][/ROW]
[ROW][C]9[/C][C]108.9[/C][C]102.066870166870[/C][C]6.83312983312984[/C][/ROW]
[ROW][C]10[/C][C]100.2[/C][C]108.150203500204[/C][C]-7.95020350020353[/C][/ROW]
[ROW][C]11[/C][C]103.9[/C][C]113.350203500203[/C][C]-9.45020350020345[/C][/ROW]
[ROW][C]12[/C][C]138.7[/C][C]129.650203500204[/C][C]9.04979649979646[/C][/ROW]
[ROW][C]13[/C][C]120.2[/C][C]110.352523402523[/C][C]9.84747659747661[/C][/ROW]
[ROW][C]14[/C][C]100.2[/C][C]105.681094831095[/C][C]-5.48109483109483[/C][/ROW]
[ROW][C]15[/C][C]143.2[/C][C]130.752523402523[/C][C]12.4474765974766[/C][/ROW]
[ROW][C]16[/C][C]70.9[/C][C]82.3382376882377[/C][C]-11.4382376882377[/C][/ROW]
[ROW][C]17[/C][C]85.2[/C][C]80.4810948310949[/C][C]4.71890516890516[/C][/ROW]
[ROW][C]18[/C][C]133[/C][C]125.783669108669[/C][C]7.2163308913309[/C][/ROW]
[ROW][C]19[/C][C]136.6[/C][C]121.200335775336[/C][C]15.3996642246642[/C][/ROW]
[ROW][C]20[/C][C]117.9[/C][C]121.899979649980[/C][C]-3.99997964997965[/C][/ROW]
[ROW][C]21[/C][C]106.3[/C][C]105.333312983313[/C][C]0.966687016687016[/C][/ROW]
[ROW][C]22[/C][C]122.3[/C][C]111.416646316646[/C][C]10.8833536833537[/C][/ROW]
[ROW][C]23[/C][C]125.5[/C][C]116.616646316646[/C][C]8.88335368335368[/C][/ROW]
[ROW][C]24[/C][C]148.4[/C][C]132.916646316646[/C][C]15.4833536833537[/C][/ROW]
[ROW][C]25[/C][C]126.3[/C][C]113.618966218966[/C][C]12.6810337810338[/C][/ROW]
[ROW][C]26[/C][C]99.6[/C][C]108.947537647538[/C][C]-9.34753764753764[/C][/ROW]
[ROW][C]27[/C][C]140.4[/C][C]134.018966218966[/C][C]6.3810337810338[/C][/ROW]
[ROW][C]28[/C][C]80.3[/C][C]85.6046805046805[/C][C]-5.30468050468051[/C][/ROW]
[ROW][C]29[/C][C]92.6[/C][C]83.7475376475377[/C][C]8.85246235246234[/C][/ROW]
[ROW][C]30[/C][C]138.5[/C][C]129.050111925112[/C][C]9.44988807488808[/C][/ROW]
[ROW][C]31[/C][C]110.9[/C][C]124.466778591779[/C][C]-13.5667785917786[/C][/ROW]
[ROW][C]32[/C][C]119.6[/C][C]125.166422466422[/C][C]-5.56642246642248[/C][/ROW]
[ROW][C]33[/C][C]105[/C][C]108.599755799756[/C][C]-3.5997557997558[/C][/ROW]
[ROW][C]34[/C][C]109[/C][C]114.683089133089[/C][C]-5.68308913308913[/C][/ROW]
[ROW][C]35[/C][C]129.4[/C][C]119.883089133089[/C][C]9.51691086691087[/C][/ROW]
[ROW][C]36[/C][C]148.6[/C][C]136.183089133089[/C][C]12.4169108669108[/C][/ROW]
[ROW][C]37[/C][C]101.4[/C][C]116.885409035409[/C][C]-15.485409035409[/C][/ROW]
[ROW][C]38[/C][C]134.8[/C][C]112.213980463980[/C][C]22.5860195360196[/C][/ROW]
[ROW][C]39[/C][C]143.7[/C][C]137.285409035409[/C][C]6.41459096459096[/C][/ROW]
[ROW][C]40[/C][C]81.6[/C][C]88.8711233211233[/C][C]-7.27112332112333[/C][/ROW]
[ROW][C]41[/C][C]90.3[/C][C]87.0139804639805[/C][C]3.28601953601952[/C][/ROW]
[ROW][C]42[/C][C]141.5[/C][C]132.316554741555[/C][C]9.18344525844527[/C][/ROW]
[ROW][C]43[/C][C]140.7[/C][C]127.733221408221[/C][C]12.9667785917786[/C][/ROW]
[ROW][C]44[/C][C]140.2[/C][C]128.432865282865[/C][C]11.7671347171347[/C][/ROW]
[ROW][C]45[/C][C]100.2[/C][C]111.866198616199[/C][C]-11.6661986161986[/C][/ROW]
[ROW][C]46[/C][C]125.7[/C][C]117.949531949532[/C][C]7.75046805046806[/C][/ROW]
[ROW][C]47[/C][C]119.6[/C][C]123.149531949532[/C][C]-3.54953194953197[/C][/ROW]
[ROW][C]48[/C][C]134.7[/C][C]139.449531949532[/C][C]-4.74953194953197[/C][/ROW]
[ROW][C]49[/C][C]109[/C][C]120.151851851852[/C][C]-11.1518518518518[/C][/ROW]
[ROW][C]50[/C][C]116.3[/C][C]115.480423280423[/C][C]0.81957671957673[/C][/ROW]
[ROW][C]51[/C][C]146.9[/C][C]140.551851851852[/C][C]6.34814814814816[/C][/ROW]
[ROW][C]52[/C][C]97.4[/C][C]92.1375661375661[/C][C]5.26243386243387[/C][/ROW]
[ROW][C]53[/C][C]89.4[/C][C]90.2804232804233[/C][C]-0.880423280423285[/C][/ROW]
[ROW][C]54[/C][C]132.1[/C][C]135.582997557998[/C][C]-3.48299755799756[/C][/ROW]
[ROW][C]55[/C][C]139.8[/C][C]130.999664224664[/C][C]8.80033577533578[/C][/ROW]
[ROW][C]56[/C][C]129[/C][C]131.699308099308[/C][C]-2.6993080993081[/C][/ROW]
[ROW][C]57[/C][C]112.5[/C][C]115.132641432641[/C][C]-2.63264143264143[/C][/ROW]
[ROW][C]58[/C][C]121.9[/C][C]121.215974765975[/C][C]0.684025234025239[/C][/ROW]
[ROW][C]59[/C][C]121.7[/C][C]126.415974765975[/C][C]-4.71597476597477[/C][/ROW]
[ROW][C]60[/C][C]123.1[/C][C]142.715974765975[/C][C]-19.6159747659748[/C][/ROW]
[ROW][C]61[/C][C]131.6[/C][C]123.418294668295[/C][C]8.18170533170535[/C][/ROW]
[ROW][C]62[/C][C]119.3[/C][C]118.746866096866[/C][C]0.553133903133916[/C][/ROW]
[ROW][C]63[/C][C]132.5[/C][C]143.818294668295[/C][C]-11.3182946682947[/C][/ROW]
[ROW][C]64[/C][C]98.3[/C][C]95.404008954009[/C][C]2.89599104599105[/C][/ROW]
[ROW][C]65[/C][C]85.1[/C][C]93.5468660968661[/C][C]-8.44686609686611[/C][/ROW]
[ROW][C]66[/C][C]131.7[/C][C]138.849440374440[/C][C]-7.14944037444039[/C][/ROW]
[ROW][C]67[/C][C]129.3[/C][C]134.266107041107[/C][C]-4.96610704110704[/C][/ROW]
[ROW][C]68[/C][C]90.7[/C][C]85.0678876678877[/C][C]5.63211233211233[/C][/ROW]
[ROW][C]69[/C][C]78.6[/C][C]68.501221001221[/C][C]10.098778998779[/C][/ROW]
[ROW][C]70[/C][C]68.9[/C][C]74.5845543345543[/C][C]-5.68455433455433[/C][/ROW]
[ROW][C]71[/C][C]79.1[/C][C]79.7845543345543[/C][C]-0.684554334554351[/C][/ROW]
[ROW][C]72[/C][C]83.5[/C][C]96.0845543345543[/C][C]-12.5845543345543[/C][/ROW]
[ROW][C]73[/C][C]74.1[/C][C]76.7868742368742[/C][C]-2.68687423687422[/C][/ROW]
[ROW][C]74[/C][C]59.7[/C][C]72.1154456654457[/C][C]-12.4154456654457[/C][/ROW]
[ROW][C]75[/C][C]93.3[/C][C]97.1868742368742[/C][C]-3.88687423687423[/C][/ROW]
[ROW][C]76[/C][C]61.3[/C][C]48.7725885225885[/C][C]12.5274114774115[/C][/ROW]
[ROW][C]77[/C][C]56.6[/C][C]46.9154456654457[/C][C]9.68455433455432[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58272&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58272&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
1105.7107.086080586081-1.38608058608068
2105.7102.4146520146523.28534798534793
3111.1127.486080586081-16.3860805860806
482.479.07179487179493.32820512820513
56077.2146520146519-17.2146520146519
6107.3122.517226292226-15.2172262922263
799.3117.933892958893-18.6338929588929
8113.5118.633536833537-5.13353683353683
9108.9102.0668701668706.83312983312984
10100.2108.150203500204-7.95020350020353
11103.9113.350203500203-9.45020350020345
12138.7129.6502035002049.04979649979646
13120.2110.3525234025239.84747659747661
14100.2105.681094831095-5.48109483109483
15143.2130.75252340252312.4474765974766
1670.982.3382376882377-11.4382376882377
1785.280.48109483109494.71890516890516
18133125.7836691086697.2163308913309
19136.6121.20033577533615.3996642246642
20117.9121.899979649980-3.99997964997965
21106.3105.3333129833130.966687016687016
22122.3111.41664631664610.8833536833537
23125.5116.6166463166468.88335368335368
24148.4132.91664631664615.4833536833537
25126.3113.61896621896612.6810337810338
2699.6108.947537647538-9.34753764753764
27140.4134.0189662189666.3810337810338
2880.385.6046805046805-5.30468050468051
2992.683.74753764753778.85246235246234
30138.5129.0501119251129.44988807488808
31110.9124.466778591779-13.5667785917786
32119.6125.166422466422-5.56642246642248
33105108.599755799756-3.5997557997558
34109114.683089133089-5.68308913308913
35129.4119.8830891330899.51691086691087
36148.6136.18308913308912.4169108669108
37101.4116.885409035409-15.485409035409
38134.8112.21398046398022.5860195360196
39143.7137.2854090354096.41459096459096
4081.688.8711233211233-7.27112332112333
4190.387.01398046398053.28601953601952
42141.5132.3165547415559.18344525844527
43140.7127.73322140822112.9667785917786
44140.2128.43286528286511.7671347171347
45100.2111.866198616199-11.6661986161986
46125.7117.9495319495327.75046805046806
47119.6123.149531949532-3.54953194953197
48134.7139.449531949532-4.74953194953197
49109120.151851851852-11.1518518518518
50116.3115.4804232804230.81957671957673
51146.9140.5518518518526.34814814814816
5297.492.13756613756615.26243386243387
5389.490.2804232804233-0.880423280423285
54132.1135.582997557998-3.48299755799756
55139.8130.9996642246648.80033577533578
56129131.699308099308-2.6993080993081
57112.5115.132641432641-2.63264143264143
58121.9121.2159747659750.684025234025239
59121.7126.415974765975-4.71597476597477
60123.1142.715974765975-19.6159747659748
61131.6123.4182946682958.18170533170535
62119.3118.7468660968660.553133903133916
63132.5143.818294668295-11.3182946682947
6498.395.4040089540092.89599104599105
6585.193.5468660968661-8.44686609686611
66131.7138.849440374440-7.14944037444039
67129.3134.266107041107-4.96610704110704
6890.785.06788766788775.63211233211233
6978.668.50122100122110.098778998779
7068.974.5845543345543-5.68455433455433
7179.179.7845543345543-0.684554334554351
7283.596.0845543345543-12.5845543345543
7374.176.7868742368742-2.68687423687422
7459.772.1154456654457-12.4154456654457
7593.397.1868742368742-3.88687423687423
7661.348.772588522588512.5274114774115
7756.646.91544566544579.68455433455432







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.9362055660265840.1275888679468320.063794433973416
180.9092769357316820.1814461285366360.090723064268318
190.9298978833589470.1402042332821060.0701021166410528
200.9081727390034290.1836545219931430.0918272609965713
210.8984991467235340.2030017065529310.101500853276466
220.8571724750967070.2856550498065860.142827524903293
230.8016935699860580.3966128600278840.198306430013942
240.7687374052932720.4625251894134550.231262594706728
250.7394636881464750.5210726237070510.260536311853525
260.8316568950426870.3366862099146270.168343104957313
270.7705449373446070.4589101253107870.229455062655393
280.7637950913897550.472409817220490.236204908610245
290.7009594854603330.5980810290793340.299040514539667
300.6300148750159230.7399702499681540.369985124984077
310.8015869153527530.3968261692944940.198413084647247
320.8046716225566930.3906567548866140.195328377443307
330.801714638095240.3965707238095220.198285361904761
340.8132391544030940.3735216911938120.186760845596906
350.7631689665683060.4736620668633870.236831033431694
360.7908434892283780.4183130215432440.209156510771622
370.9318286703541260.1363426592917480.0681713296458738
380.9757167761798140.04856644764037190.0242832238201859
390.9633190418891110.07336191622177750.0366809581108887
400.9829873311209440.03402533775811170.0170126688790559
410.973670785533150.05265842893370130.0263292144668507
420.9623979883083420.07520402338331550.0376020116916578
430.9515659249918170.09686815001636550.0484340750081828
440.942403297153190.1151934056936220.0575967028468108
450.9756128640347390.04877427193052280.0243871359652614
460.965940263999740.06811947200052170.0340597360002608
470.9529764431415580.09404711371688320.0470235568584416
480.958101155487020.08379768902595920.0418988445129796
490.982325513346490.03534897330701880.0176744866535094
500.9692446736243920.06151065275121660.0307553263756083
510.9672778443783970.06544431124320550.0327221556216028
520.9493106734390720.1013786531218560.0506893265609278
530.9270951797393130.1458096405213730.0729048202606866
540.898748505749420.202502988501160.10125149425058
550.8378819167652280.3242361664695440.162118083234772
560.7636100189738270.4727799620523460.236389981026173
570.7116458943910590.5767082112178830.288354105608941
580.6342500220103070.7314999559793860.365749977989693
590.489833818568050.97966763713610.51016618143195
600.39634268192360.79268536384720.6036573180764

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.936205566026584 & 0.127588867946832 & 0.063794433973416 \tabularnewline
18 & 0.909276935731682 & 0.181446128536636 & 0.090723064268318 \tabularnewline
19 & 0.929897883358947 & 0.140204233282106 & 0.0701021166410528 \tabularnewline
20 & 0.908172739003429 & 0.183654521993143 & 0.0918272609965713 \tabularnewline
21 & 0.898499146723534 & 0.203001706552931 & 0.101500853276466 \tabularnewline
22 & 0.857172475096707 & 0.285655049806586 & 0.142827524903293 \tabularnewline
23 & 0.801693569986058 & 0.396612860027884 & 0.198306430013942 \tabularnewline
24 & 0.768737405293272 & 0.462525189413455 & 0.231262594706728 \tabularnewline
25 & 0.739463688146475 & 0.521072623707051 & 0.260536311853525 \tabularnewline
26 & 0.831656895042687 & 0.336686209914627 & 0.168343104957313 \tabularnewline
27 & 0.770544937344607 & 0.458910125310787 & 0.229455062655393 \tabularnewline
28 & 0.763795091389755 & 0.47240981722049 & 0.236204908610245 \tabularnewline
29 & 0.700959485460333 & 0.598081029079334 & 0.299040514539667 \tabularnewline
30 & 0.630014875015923 & 0.739970249968154 & 0.369985124984077 \tabularnewline
31 & 0.801586915352753 & 0.396826169294494 & 0.198413084647247 \tabularnewline
32 & 0.804671622556693 & 0.390656754886614 & 0.195328377443307 \tabularnewline
33 & 0.80171463809524 & 0.396570723809522 & 0.198285361904761 \tabularnewline
34 & 0.813239154403094 & 0.373521691193812 & 0.186760845596906 \tabularnewline
35 & 0.763168966568306 & 0.473662066863387 & 0.236831033431694 \tabularnewline
36 & 0.790843489228378 & 0.418313021543244 & 0.209156510771622 \tabularnewline
37 & 0.931828670354126 & 0.136342659291748 & 0.0681713296458738 \tabularnewline
38 & 0.975716776179814 & 0.0485664476403719 & 0.0242832238201859 \tabularnewline
39 & 0.963319041889111 & 0.0733619162217775 & 0.0366809581108887 \tabularnewline
40 & 0.982987331120944 & 0.0340253377581117 & 0.0170126688790559 \tabularnewline
41 & 0.97367078553315 & 0.0526584289337013 & 0.0263292144668507 \tabularnewline
42 & 0.962397988308342 & 0.0752040233833155 & 0.0376020116916578 \tabularnewline
43 & 0.951565924991817 & 0.0968681500163655 & 0.0484340750081828 \tabularnewline
44 & 0.94240329715319 & 0.115193405693622 & 0.0575967028468108 \tabularnewline
45 & 0.975612864034739 & 0.0487742719305228 & 0.0243871359652614 \tabularnewline
46 & 0.96594026399974 & 0.0681194720005217 & 0.0340597360002608 \tabularnewline
47 & 0.952976443141558 & 0.0940471137168832 & 0.0470235568584416 \tabularnewline
48 & 0.95810115548702 & 0.0837976890259592 & 0.0418988445129796 \tabularnewline
49 & 0.98232551334649 & 0.0353489733070188 & 0.0176744866535094 \tabularnewline
50 & 0.969244673624392 & 0.0615106527512166 & 0.0307553263756083 \tabularnewline
51 & 0.967277844378397 & 0.0654443112432055 & 0.0327221556216028 \tabularnewline
52 & 0.949310673439072 & 0.101378653121856 & 0.0506893265609278 \tabularnewline
53 & 0.927095179739313 & 0.145809640521373 & 0.0729048202606866 \tabularnewline
54 & 0.89874850574942 & 0.20250298850116 & 0.10125149425058 \tabularnewline
55 & 0.837881916765228 & 0.324236166469544 & 0.162118083234772 \tabularnewline
56 & 0.763610018973827 & 0.472779962052346 & 0.236389981026173 \tabularnewline
57 & 0.711645894391059 & 0.576708211217883 & 0.288354105608941 \tabularnewline
58 & 0.634250022010307 & 0.731499955979386 & 0.365749977989693 \tabularnewline
59 & 0.48983381856805 & 0.9796676371361 & 0.51016618143195 \tabularnewline
60 & 0.3963426819236 & 0.7926853638472 & 0.6036573180764 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58272&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]0.936205566026584[/C][C]0.127588867946832[/C][C]0.063794433973416[/C][/ROW]
[ROW][C]18[/C][C]0.909276935731682[/C][C]0.181446128536636[/C][C]0.090723064268318[/C][/ROW]
[ROW][C]19[/C][C]0.929897883358947[/C][C]0.140204233282106[/C][C]0.0701021166410528[/C][/ROW]
[ROW][C]20[/C][C]0.908172739003429[/C][C]0.183654521993143[/C][C]0.0918272609965713[/C][/ROW]
[ROW][C]21[/C][C]0.898499146723534[/C][C]0.203001706552931[/C][C]0.101500853276466[/C][/ROW]
[ROW][C]22[/C][C]0.857172475096707[/C][C]0.285655049806586[/C][C]0.142827524903293[/C][/ROW]
[ROW][C]23[/C][C]0.801693569986058[/C][C]0.396612860027884[/C][C]0.198306430013942[/C][/ROW]
[ROW][C]24[/C][C]0.768737405293272[/C][C]0.462525189413455[/C][C]0.231262594706728[/C][/ROW]
[ROW][C]25[/C][C]0.739463688146475[/C][C]0.521072623707051[/C][C]0.260536311853525[/C][/ROW]
[ROW][C]26[/C][C]0.831656895042687[/C][C]0.336686209914627[/C][C]0.168343104957313[/C][/ROW]
[ROW][C]27[/C][C]0.770544937344607[/C][C]0.458910125310787[/C][C]0.229455062655393[/C][/ROW]
[ROW][C]28[/C][C]0.763795091389755[/C][C]0.47240981722049[/C][C]0.236204908610245[/C][/ROW]
[ROW][C]29[/C][C]0.700959485460333[/C][C]0.598081029079334[/C][C]0.299040514539667[/C][/ROW]
[ROW][C]30[/C][C]0.630014875015923[/C][C]0.739970249968154[/C][C]0.369985124984077[/C][/ROW]
[ROW][C]31[/C][C]0.801586915352753[/C][C]0.396826169294494[/C][C]0.198413084647247[/C][/ROW]
[ROW][C]32[/C][C]0.804671622556693[/C][C]0.390656754886614[/C][C]0.195328377443307[/C][/ROW]
[ROW][C]33[/C][C]0.80171463809524[/C][C]0.396570723809522[/C][C]0.198285361904761[/C][/ROW]
[ROW][C]34[/C][C]0.813239154403094[/C][C]0.373521691193812[/C][C]0.186760845596906[/C][/ROW]
[ROW][C]35[/C][C]0.763168966568306[/C][C]0.473662066863387[/C][C]0.236831033431694[/C][/ROW]
[ROW][C]36[/C][C]0.790843489228378[/C][C]0.418313021543244[/C][C]0.209156510771622[/C][/ROW]
[ROW][C]37[/C][C]0.931828670354126[/C][C]0.136342659291748[/C][C]0.0681713296458738[/C][/ROW]
[ROW][C]38[/C][C]0.975716776179814[/C][C]0.0485664476403719[/C][C]0.0242832238201859[/C][/ROW]
[ROW][C]39[/C][C]0.963319041889111[/C][C]0.0733619162217775[/C][C]0.0366809581108887[/C][/ROW]
[ROW][C]40[/C][C]0.982987331120944[/C][C]0.0340253377581117[/C][C]0.0170126688790559[/C][/ROW]
[ROW][C]41[/C][C]0.97367078553315[/C][C]0.0526584289337013[/C][C]0.0263292144668507[/C][/ROW]
[ROW][C]42[/C][C]0.962397988308342[/C][C]0.0752040233833155[/C][C]0.0376020116916578[/C][/ROW]
[ROW][C]43[/C][C]0.951565924991817[/C][C]0.0968681500163655[/C][C]0.0484340750081828[/C][/ROW]
[ROW][C]44[/C][C]0.94240329715319[/C][C]0.115193405693622[/C][C]0.0575967028468108[/C][/ROW]
[ROW][C]45[/C][C]0.975612864034739[/C][C]0.0487742719305228[/C][C]0.0243871359652614[/C][/ROW]
[ROW][C]46[/C][C]0.96594026399974[/C][C]0.0681194720005217[/C][C]0.0340597360002608[/C][/ROW]
[ROW][C]47[/C][C]0.952976443141558[/C][C]0.0940471137168832[/C][C]0.0470235568584416[/C][/ROW]
[ROW][C]48[/C][C]0.95810115548702[/C][C]0.0837976890259592[/C][C]0.0418988445129796[/C][/ROW]
[ROW][C]49[/C][C]0.98232551334649[/C][C]0.0353489733070188[/C][C]0.0176744866535094[/C][/ROW]
[ROW][C]50[/C][C]0.969244673624392[/C][C]0.0615106527512166[/C][C]0.0307553263756083[/C][/ROW]
[ROW][C]51[/C][C]0.967277844378397[/C][C]0.0654443112432055[/C][C]0.0327221556216028[/C][/ROW]
[ROW][C]52[/C][C]0.949310673439072[/C][C]0.101378653121856[/C][C]0.0506893265609278[/C][/ROW]
[ROW][C]53[/C][C]0.927095179739313[/C][C]0.145809640521373[/C][C]0.0729048202606866[/C][/ROW]
[ROW][C]54[/C][C]0.89874850574942[/C][C]0.20250298850116[/C][C]0.10125149425058[/C][/ROW]
[ROW][C]55[/C][C]0.837881916765228[/C][C]0.324236166469544[/C][C]0.162118083234772[/C][/ROW]
[ROW][C]56[/C][C]0.763610018973827[/C][C]0.472779962052346[/C][C]0.236389981026173[/C][/ROW]
[ROW][C]57[/C][C]0.711645894391059[/C][C]0.576708211217883[/C][C]0.288354105608941[/C][/ROW]
[ROW][C]58[/C][C]0.634250022010307[/C][C]0.731499955979386[/C][C]0.365749977989693[/C][/ROW]
[ROW][C]59[/C][C]0.48983381856805[/C][C]0.9796676371361[/C][C]0.51016618143195[/C][/ROW]
[ROW][C]60[/C][C]0.3963426819236[/C][C]0.7926853638472[/C][C]0.6036573180764[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58272&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58272&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
170.9362055660265840.1275888679468320.063794433973416
180.9092769357316820.1814461285366360.090723064268318
190.9298978833589470.1402042332821060.0701021166410528
200.9081727390034290.1836545219931430.0918272609965713
210.8984991467235340.2030017065529310.101500853276466
220.8571724750967070.2856550498065860.142827524903293
230.8016935699860580.3966128600278840.198306430013942
240.7687374052932720.4625251894134550.231262594706728
250.7394636881464750.5210726237070510.260536311853525
260.8316568950426870.3366862099146270.168343104957313
270.7705449373446070.4589101253107870.229455062655393
280.7637950913897550.472409817220490.236204908610245
290.7009594854603330.5980810290793340.299040514539667
300.6300148750159230.7399702499681540.369985124984077
310.8015869153527530.3968261692944940.198413084647247
320.8046716225566930.3906567548866140.195328377443307
330.801714638095240.3965707238095220.198285361904761
340.8132391544030940.3735216911938120.186760845596906
350.7631689665683060.4736620668633870.236831033431694
360.7908434892283780.4183130215432440.209156510771622
370.9318286703541260.1363426592917480.0681713296458738
380.9757167761798140.04856644764037190.0242832238201859
390.9633190418891110.07336191622177750.0366809581108887
400.9829873311209440.03402533775811170.0170126688790559
410.973670785533150.05265842893370130.0263292144668507
420.9623979883083420.07520402338331550.0376020116916578
430.9515659249918170.09686815001636550.0484340750081828
440.942403297153190.1151934056936220.0575967028468108
450.9756128640347390.04877427193052280.0243871359652614
460.965940263999740.06811947200052170.0340597360002608
470.9529764431415580.09404711371688320.0470235568584416
480.958101155487020.08379768902595920.0418988445129796
490.982325513346490.03534897330701880.0176744866535094
500.9692446736243920.06151065275121660.0307553263756083
510.9672778443783970.06544431124320550.0327221556216028
520.9493106734390720.1013786531218560.0506893265609278
530.9270951797393130.1458096405213730.0729048202606866
540.898748505749420.202502988501160.10125149425058
550.8378819167652280.3242361664695440.162118083234772
560.7636100189738270.4727799620523460.236389981026173
570.7116458943910590.5767082112178830.288354105608941
580.6342500220103070.7314999559793860.365749977989693
590.489833818568050.97966763713610.51016618143195
600.39634268192360.79268536384720.6036573180764







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level40.090909090909091NOK
10% type I error level130.295454545454545NOK

\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 & 4 & 0.090909090909091 & NOK \tabularnewline
10% type I error level & 13 & 0.295454545454545 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58272&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]4[/C][C]0.090909090909091[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.295454545454545[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58272&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58272&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 level40.090909090909091NOK
10% type I error level130.295454545454545NOK



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')
}