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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 15 Dec 2009 09:20:27 -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/Dec/15/t12608940942z0yzpd9ajtwfaq.htm/, Retrieved Wed, 08 May 2024 19:41:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=68024, Retrieved Wed, 08 May 2024 19:41:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact120

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2009-12-14 15:04:18] [69bbb86d5181c362d5647cae31af3dc7]
-   PD      [Multiple Regression] [] [2009-12-15 12:01:39] [69bbb86d5181c362d5647cae31af3dc7]
-   P           [Multiple Regression] [] [2009-12-15 16:20:27] [14869f38c4320b00c96ca15cc00142de] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
106.2	431	436	460	467
81	484	431	448	460
94.7	510	484	443	448
101	513	510	436	443
109.4	503	513	431	436
102.3	471	503	484	431
90.7	471	471	510	484
96.2	476	471	513	510
96.1	475	476	503	513
106	470	475	471	503
103.1	461	470	471	471
102	455	461	476	471
104.7	456	455	475	476
86	517	456	470	475
92.1	525	517	461	470
106.9	523	525	455	461
112.6	519	523	456	455
101.7	509	519	517	456
92	512	509	525	517
97.4	519	512	523	525
97	517	519	519	523
105.4	510	517	509	519
102.7	509	510	512	509
98.1	501	509	519	512
104.5	507	501	517	519
87.4	569	507	510	517
89.9	580	569	509	510
109.8	578	580	501	509
111.7	565	578	507	501
98.6	547	565	569	507
96.9	555	547	580	569
95.1	562	555	578	580
97	561	562	565	578
112.7	555	561	547	565
102.9	544	555	555	547
97.4	537	544	562	555
111.4	543	537	561	562
87.4	594	543	555	561
96.8	611	594	544	555
114.1	613	611	537	544
110.3	611	613	543	537
103.9	594	611	594	543
101.6	595	594	611	594
94.6	591	595	613	611
95.9	589	591	611	613
104.7	584	589	594	611
102.8	573	584	595	594
98.1	567	573	591	595
113.9	569	567	589	591
80.9	621	569	584	589
95.7	629	621	573	584
113.2	628	629	567	573
105.9	612	628	569	567
108.8	595	612	621	569
102.3	597	595	629	621
99	593	597	628	629
100.7	590	593	612	628
115.5	580	590	595	612
100.7	574	580	597	595
109.9	573	574	593	597
114.6	573	573	590	593
85.4	620	573	580	590
100.5	626	620	574	580
114.8	620	626	573	574
116.5	588	620	573	573
112.9	566	588	620	573
102	557	566	626	620

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68024&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]5 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=68024&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68024&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Y[t] = -99.7566815768365 + 0.382755975409948X[t] + 1.02354018150553`Y(t-1)`[t] + 0.104099099931590`Y(t-4)`[t] + 0.00510435475890887`Y(t-5)`[t] + 4.0412038052447M1[t] + 67.3233309731705M2[t] + 21.7024053188966M3[t] + 3.10372942599408M4[t] -8.80075905104333M5[t] -17.7562793542933M6[t] + 4.46357831698049M7[t] + 4.86982749424487M8[t] + 1.88816508082327M9[t] -4.80574981277827M10[t] -2.9185475181839M11[t] -0.470059344867009t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  -99.7566815768365 +  0.382755975409948X[t] +  1.02354018150553`Y(t-1)`[t] +  0.104099099931590`Y(t-4)`[t] +  0.00510435475890887`Y(t-5)`[t] +  4.0412038052447M1[t] +  67.3233309731705M2[t] +  21.7024053188966M3[t] +  3.10372942599408M4[t] -8.80075905104333M5[t] -17.7562793542933M6[t] +  4.46357831698049M7[t] +  4.86982749424487M8[t] +  1.88816508082327M9[t] -4.80574981277827M10[t] -2.9185475181839M11[t] -0.470059344867009t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68024&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  -99.7566815768365 +  0.382755975409948X[t] +  1.02354018150553`Y(t-1)`[t] +  0.104099099931590`Y(t-4)`[t] +  0.00510435475890887`Y(t-5)`[t] +  4.0412038052447M1[t] +  67.3233309731705M2[t] +  21.7024053188966M3[t] +  3.10372942599408M4[t] -8.80075905104333M5[t] -17.7562793542933M6[t] +  4.46357831698049M7[t] +  4.86982749424487M8[t] +  1.88816508082327M9[t] -4.80574981277827M10[t] -2.9185475181839M11[t] -0.470059344867009t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68024&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68024&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = -99.7566815768365 + 0.382755975409948X[t] + 1.02354018150553`Y(t-1)`[t] + 0.104099099931590`Y(t-4)`[t] + 0.00510435475890887`Y(t-5)`[t] + 4.0412038052447M1[t] + 67.3233309731705M2[t] + 21.7024053188966M3[t] + 3.10372942599408M4[t] -8.80075905104333M5[t] -17.7562793542933M6[t] + 4.46357831698049M7[t] + 4.86982749424487M8[t] + 1.88816508082327M9[t] -4.80574981277827M10[t] -2.9185475181839M11[t] -0.470059344867009t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-99.756681576836541.611218-2.39740.0202910.010146
X0.3827559754099480.2400111.59470.1170720.058536
`Y(t-1)`1.023540181505530.07489613.666100
`Y(t-4)`0.1040990999315900.1740310.59820.552430.276215
`Y(t-5)`0.005104354758908870.1599460.03190.9746690.487334
M14.04120380524473.8732521.04340.3017990.1509
M267.32333097317055.60369312.014100
M321.70240531889666.5484583.31410.0017150.000858
M43.103729425994087.7258190.40170.6895910.344796
M5-8.800759051043337.69218-1.14410.2580230.129011
M6-17.75627935429339.010969-1.97050.0543290.027164
M74.463578316980494.0857681.09250.2798630.139931
M84.869827494244874.1340051.1780.2443760.122188
M91.888165080823274.2630690.44290.659740.32987
M10-4.805749812778275.161882-0.9310.3563230.178162
M11-2.91854751818393.500481-0.83380.4083840.204192
t-0.4700593448670090.172063-2.73190.0086790.004339

\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) & -99.7566815768365 & 41.611218 & -2.3974 & 0.020291 & 0.010146 \tabularnewline
X & 0.382755975409948 & 0.240011 & 1.5947 & 0.117072 & 0.058536 \tabularnewline
`Y(t-1)` & 1.02354018150553 & 0.074896 & 13.6661 & 0 & 0 \tabularnewline
`Y(t-4)` & 0.104099099931590 & 0.174031 & 0.5982 & 0.55243 & 0.276215 \tabularnewline
`Y(t-5)` & 0.00510435475890887 & 0.159946 & 0.0319 & 0.974669 & 0.487334 \tabularnewline
M1 & 4.0412038052447 & 3.873252 & 1.0434 & 0.301799 & 0.1509 \tabularnewline
M2 & 67.3233309731705 & 5.603693 & 12.0141 & 0 & 0 \tabularnewline
M3 & 21.7024053188966 & 6.548458 & 3.3141 & 0.001715 & 0.000858 \tabularnewline
M4 & 3.10372942599408 & 7.725819 & 0.4017 & 0.689591 & 0.344796 \tabularnewline
M5 & -8.80075905104333 & 7.69218 & -1.1441 & 0.258023 & 0.129011 \tabularnewline
M6 & -17.7562793542933 & 9.010969 & -1.9705 & 0.054329 & 0.027164 \tabularnewline
M7 & 4.46357831698049 & 4.085768 & 1.0925 & 0.279863 & 0.139931 \tabularnewline
M8 & 4.86982749424487 & 4.134005 & 1.178 & 0.244376 & 0.122188 \tabularnewline
M9 & 1.88816508082327 & 4.263069 & 0.4429 & 0.65974 & 0.32987 \tabularnewline
M10 & -4.80574981277827 & 5.161882 & -0.931 & 0.356323 & 0.178162 \tabularnewline
M11 & -2.9185475181839 & 3.500481 & -0.8338 & 0.408384 & 0.204192 \tabularnewline
t & -0.470059344867009 & 0.172063 & -2.7319 & 0.008679 & 0.004339 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68024&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]-99.7566815768365[/C][C]41.611218[/C][C]-2.3974[/C][C]0.020291[/C][C]0.010146[/C][/ROW]
[ROW][C]X[/C][C]0.382755975409948[/C][C]0.240011[/C][C]1.5947[/C][C]0.117072[/C][C]0.058536[/C][/ROW]
[ROW][C]`Y(t-1)`[/C][C]1.02354018150553[/C][C]0.074896[/C][C]13.6661[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Y(t-4)`[/C][C]0.104099099931590[/C][C]0.174031[/C][C]0.5982[/C][C]0.55243[/C][C]0.276215[/C][/ROW]
[ROW][C]`Y(t-5)`[/C][C]0.00510435475890887[/C][C]0.159946[/C][C]0.0319[/C][C]0.974669[/C][C]0.487334[/C][/ROW]
[ROW][C]M1[/C][C]4.0412038052447[/C][C]3.873252[/C][C]1.0434[/C][C]0.301799[/C][C]0.1509[/C][/ROW]
[ROW][C]M2[/C][C]67.3233309731705[/C][C]5.603693[/C][C]12.0141[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M3[/C][C]21.7024053188966[/C][C]6.548458[/C][C]3.3141[/C][C]0.001715[/C][C]0.000858[/C][/ROW]
[ROW][C]M4[/C][C]3.10372942599408[/C][C]7.725819[/C][C]0.4017[/C][C]0.689591[/C][C]0.344796[/C][/ROW]
[ROW][C]M5[/C][C]-8.80075905104333[/C][C]7.69218[/C][C]-1.1441[/C][C]0.258023[/C][C]0.129011[/C][/ROW]
[ROW][C]M6[/C][C]-17.7562793542933[/C][C]9.010969[/C][C]-1.9705[/C][C]0.054329[/C][C]0.027164[/C][/ROW]
[ROW][C]M7[/C][C]4.46357831698049[/C][C]4.085768[/C][C]1.0925[/C][C]0.279863[/C][C]0.139931[/C][/ROW]
[ROW][C]M8[/C][C]4.86982749424487[/C][C]4.134005[/C][C]1.178[/C][C]0.244376[/C][C]0.122188[/C][/ROW]
[ROW][C]M9[/C][C]1.88816508082327[/C][C]4.263069[/C][C]0.4429[/C][C]0.65974[/C][C]0.32987[/C][/ROW]
[ROW][C]M10[/C][C]-4.80574981277827[/C][C]5.161882[/C][C]-0.931[/C][C]0.356323[/C][C]0.178162[/C][/ROW]
[ROW][C]M11[/C][C]-2.9185475181839[/C][C]3.500481[/C][C]-0.8338[/C][C]0.408384[/C][C]0.204192[/C][/ROW]
[ROW][C]t[/C][C]-0.470059344867009[/C][C]0.172063[/C][C]-2.7319[/C][C]0.008679[/C][C]0.004339[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68024&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68024&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-99.756681576836541.611218-2.39740.0202910.010146
X0.3827559754099480.2400111.59470.1170720.058536
`Y(t-1)`1.023540181505530.07489613.666100
`Y(t-4)`0.1040990999315900.1740310.59820.552430.276215
`Y(t-5)`0.005104354758908870.1599460.03190.9746690.487334
M14.04120380524473.8732521.04340.3017990.1509
M267.32333097317055.60369312.014100
M321.70240531889666.5484583.31410.0017150.000858
M43.103729425994087.7258190.40170.6895910.344796
M5-8.800759051043337.69218-1.14410.2580230.129011
M6-17.75627935429339.010969-1.97050.0543290.027164
M74.463578316980494.0857681.09250.2798630.139931
M84.869827494244874.1340051.1780.2443760.122188
M91.888165080823274.2630690.44290.659740.32987
M10-4.805749812778275.161882-0.9310.3563230.178162
M11-2.91854751818393.500481-0.83380.4083840.204192
t-0.4700593448670090.172063-2.73190.0086790.004339






Multiple Linear Regression - Regression Statistics
Multiple R0.995733449711533
R-squared0.99148510287443
Adjusted R-squared0.988760335794248
F-TEST (value)363.878846777648
F-TEST (DF numerator)16
F-TEST (DF denominator)50
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.38069327362718
Sum Squared Residuals1447.59300524284

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.995733449711533 \tabularnewline
R-squared & 0.99148510287443 \tabularnewline
Adjusted R-squared & 0.988760335794248 \tabularnewline
F-TEST (value) & 363.878846777648 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 50 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5.38069327362718 \tabularnewline
Sum Squared Residuals & 1447.59300524284 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68024&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.995733449711533[/C][/ROW]
[ROW][C]R-squared[/C][C]0.99148510287443[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.988760335794248[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]363.878846777648[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]50[/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]5.38069327362718[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1447.59300524284[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68024&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68024&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.995733449711533
R-squared0.99148510287443
Adjusted R-squared0.988760335794248
F-TEST (value)363.878846777648
F-TEST (DF numerator)16
F-TEST (DF denominator)50
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.38069327362718
Sum Squared Residuals1447.59300524284






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1431440.995986249429-9.99598624942896
2484487.759982902138-3.75998290213822
3510500.5786366291429.42136337085846
4513509.7790932822833.22090671771729
5503503.134090215368-0.134090215368056
6471486.247271849365-15.2472718493649
7471473.780922453283-2.78092245328301
8476476.267280673961-0.267280673961445
9475476.86930629062-1.86930629062032
10470469.0888612818050.911138718195271
11461464.114971643031-3.11497164303055
12455457.451062109505-2.45106210950471
13456455.8358292883190.164170711680960
14517511.9883006983015.01169930169949
15525529.705664547819-4.70566454781862
16523523.819505405741-0.819505405740737
17519511.653059252047.34694074795991
18509500.3164281965188.68357180348152
19512509.262250186142.73774981386033
20519514.1685794684754.83142053152457
21517517.301931481317-0.301931481317353
22510510.24461865493-0.244618654929754
23509503.7247929527175.27520704728275
24501504.133070221441-3.13307022144072
25507501.7930637558475.20693624415297
26569563.462343079395.53765692061006
27580581.647909688873-1.64790968887270
28578580.61706320411-2.61706320411020
29565567.506431133992-2.5064311339919
30547546.2454961727450.754503827255213
31555550.3824461681554.61755383184498
32562557.6659463993444.33405360065648
33561560.7427452562440.257254743755970
34555556.624359239572-1.62435923957166
35544548.890166955041-4.89016695504074
36537538.744083804635-1.74408380463454
37543540.4406620335942.55933796640645
38594599.578128581498-5.57812858149836
39611608.1098827801922.89011721980806
40613612.2781674007390.721832599260754
41611601.0850913515659.91490864843475
42594592.5024733228781.49752667712190
43595598.00175661179-3.00175661178942
44591596.577167028587-5.57716702858733
45589589.330877821964-0.330877821964188
46584581.7081823957372.29181760426274
47573577.297713153688-4.29771315368823
48567566.277014201050.722985798949873
49569569.525846364973-0.525846364972995
50621621.223343153339-0.223343153338821
51629632.85062415551-3.85062415551058
52628627.9876974375220.0123025624782818
53612611.9730628849290.0269371150711113
54595592.7041945673732.29580543262719
55597595.6641152149371.33588478506305
56593596.321026429632-3.32102642963226
57590587.7551391498542.2448608501459
58580581.333978427957-1.33397842795659
59574566.9723552955237.02764470447678
60573566.394769663376.6052303366301
61573570.4086123078382.59138769216159
62620620.987901585334-0.987901585334136
63626628.107282198465-2.10728219846462
64620620.518473269605-0.518473269605384
65588602.648265162106-14.6482651621058
66566563.9841358911212.01586410887912
67557559.908509365696-2.90850936569592

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 431 & 440.995986249429 & -9.99598624942896 \tabularnewline
2 & 484 & 487.759982902138 & -3.75998290213822 \tabularnewline
3 & 510 & 500.578636629142 & 9.42136337085846 \tabularnewline
4 & 513 & 509.779093282283 & 3.22090671771729 \tabularnewline
5 & 503 & 503.134090215368 & -0.134090215368056 \tabularnewline
6 & 471 & 486.247271849365 & -15.2472718493649 \tabularnewline
7 & 471 & 473.780922453283 & -2.78092245328301 \tabularnewline
8 & 476 & 476.267280673961 & -0.267280673961445 \tabularnewline
9 & 475 & 476.86930629062 & -1.86930629062032 \tabularnewline
10 & 470 & 469.088861281805 & 0.911138718195271 \tabularnewline
11 & 461 & 464.114971643031 & -3.11497164303055 \tabularnewline
12 & 455 & 457.451062109505 & -2.45106210950471 \tabularnewline
13 & 456 & 455.835829288319 & 0.164170711680960 \tabularnewline
14 & 517 & 511.988300698301 & 5.01169930169949 \tabularnewline
15 & 525 & 529.705664547819 & -4.70566454781862 \tabularnewline
16 & 523 & 523.819505405741 & -0.819505405740737 \tabularnewline
17 & 519 & 511.65305925204 & 7.34694074795991 \tabularnewline
18 & 509 & 500.316428196518 & 8.68357180348152 \tabularnewline
19 & 512 & 509.26225018614 & 2.73774981386033 \tabularnewline
20 & 519 & 514.168579468475 & 4.83142053152457 \tabularnewline
21 & 517 & 517.301931481317 & -0.301931481317353 \tabularnewline
22 & 510 & 510.24461865493 & -0.244618654929754 \tabularnewline
23 & 509 & 503.724792952717 & 5.27520704728275 \tabularnewline
24 & 501 & 504.133070221441 & -3.13307022144072 \tabularnewline
25 & 507 & 501.793063755847 & 5.20693624415297 \tabularnewline
26 & 569 & 563.46234307939 & 5.53765692061006 \tabularnewline
27 & 580 & 581.647909688873 & -1.64790968887270 \tabularnewline
28 & 578 & 580.61706320411 & -2.61706320411020 \tabularnewline
29 & 565 & 567.506431133992 & -2.5064311339919 \tabularnewline
30 & 547 & 546.245496172745 & 0.754503827255213 \tabularnewline
31 & 555 & 550.382446168155 & 4.61755383184498 \tabularnewline
32 & 562 & 557.665946399344 & 4.33405360065648 \tabularnewline
33 & 561 & 560.742745256244 & 0.257254743755970 \tabularnewline
34 & 555 & 556.624359239572 & -1.62435923957166 \tabularnewline
35 & 544 & 548.890166955041 & -4.89016695504074 \tabularnewline
36 & 537 & 538.744083804635 & -1.74408380463454 \tabularnewline
37 & 543 & 540.440662033594 & 2.55933796640645 \tabularnewline
38 & 594 & 599.578128581498 & -5.57812858149836 \tabularnewline
39 & 611 & 608.109882780192 & 2.89011721980806 \tabularnewline
40 & 613 & 612.278167400739 & 0.721832599260754 \tabularnewline
41 & 611 & 601.085091351565 & 9.91490864843475 \tabularnewline
42 & 594 & 592.502473322878 & 1.49752667712190 \tabularnewline
43 & 595 & 598.00175661179 & -3.00175661178942 \tabularnewline
44 & 591 & 596.577167028587 & -5.57716702858733 \tabularnewline
45 & 589 & 589.330877821964 & -0.330877821964188 \tabularnewline
46 & 584 & 581.708182395737 & 2.29181760426274 \tabularnewline
47 & 573 & 577.297713153688 & -4.29771315368823 \tabularnewline
48 & 567 & 566.27701420105 & 0.722985798949873 \tabularnewline
49 & 569 & 569.525846364973 & -0.525846364972995 \tabularnewline
50 & 621 & 621.223343153339 & -0.223343153338821 \tabularnewline
51 & 629 & 632.85062415551 & -3.85062415551058 \tabularnewline
52 & 628 & 627.987697437522 & 0.0123025624782818 \tabularnewline
53 & 612 & 611.973062884929 & 0.0269371150711113 \tabularnewline
54 & 595 & 592.704194567373 & 2.29580543262719 \tabularnewline
55 & 597 & 595.664115214937 & 1.33588478506305 \tabularnewline
56 & 593 & 596.321026429632 & -3.32102642963226 \tabularnewline
57 & 590 & 587.755139149854 & 2.2448608501459 \tabularnewline
58 & 580 & 581.333978427957 & -1.33397842795659 \tabularnewline
59 & 574 & 566.972355295523 & 7.02764470447678 \tabularnewline
60 & 573 & 566.39476966337 & 6.6052303366301 \tabularnewline
61 & 573 & 570.408612307838 & 2.59138769216159 \tabularnewline
62 & 620 & 620.987901585334 & -0.987901585334136 \tabularnewline
63 & 626 & 628.107282198465 & -2.10728219846462 \tabularnewline
64 & 620 & 620.518473269605 & -0.518473269605384 \tabularnewline
65 & 588 & 602.648265162106 & -14.6482651621058 \tabularnewline
66 & 566 & 563.984135891121 & 2.01586410887912 \tabularnewline
67 & 557 & 559.908509365696 & -2.90850936569592 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68024&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]431[/C][C]440.995986249429[/C][C]-9.99598624942896[/C][/ROW]
[ROW][C]2[/C][C]484[/C][C]487.759982902138[/C][C]-3.75998290213822[/C][/ROW]
[ROW][C]3[/C][C]510[/C][C]500.578636629142[/C][C]9.42136337085846[/C][/ROW]
[ROW][C]4[/C][C]513[/C][C]509.779093282283[/C][C]3.22090671771729[/C][/ROW]
[ROW][C]5[/C][C]503[/C][C]503.134090215368[/C][C]-0.134090215368056[/C][/ROW]
[ROW][C]6[/C][C]471[/C][C]486.247271849365[/C][C]-15.2472718493649[/C][/ROW]
[ROW][C]7[/C][C]471[/C][C]473.780922453283[/C][C]-2.78092245328301[/C][/ROW]
[ROW][C]8[/C][C]476[/C][C]476.267280673961[/C][C]-0.267280673961445[/C][/ROW]
[ROW][C]9[/C][C]475[/C][C]476.86930629062[/C][C]-1.86930629062032[/C][/ROW]
[ROW][C]10[/C][C]470[/C][C]469.088861281805[/C][C]0.911138718195271[/C][/ROW]
[ROW][C]11[/C][C]461[/C][C]464.114971643031[/C][C]-3.11497164303055[/C][/ROW]
[ROW][C]12[/C][C]455[/C][C]457.451062109505[/C][C]-2.45106210950471[/C][/ROW]
[ROW][C]13[/C][C]456[/C][C]455.835829288319[/C][C]0.164170711680960[/C][/ROW]
[ROW][C]14[/C][C]517[/C][C]511.988300698301[/C][C]5.01169930169949[/C][/ROW]
[ROW][C]15[/C][C]525[/C][C]529.705664547819[/C][C]-4.70566454781862[/C][/ROW]
[ROW][C]16[/C][C]523[/C][C]523.819505405741[/C][C]-0.819505405740737[/C][/ROW]
[ROW][C]17[/C][C]519[/C][C]511.65305925204[/C][C]7.34694074795991[/C][/ROW]
[ROW][C]18[/C][C]509[/C][C]500.316428196518[/C][C]8.68357180348152[/C][/ROW]
[ROW][C]19[/C][C]512[/C][C]509.26225018614[/C][C]2.73774981386033[/C][/ROW]
[ROW][C]20[/C][C]519[/C][C]514.168579468475[/C][C]4.83142053152457[/C][/ROW]
[ROW][C]21[/C][C]517[/C][C]517.301931481317[/C][C]-0.301931481317353[/C][/ROW]
[ROW][C]22[/C][C]510[/C][C]510.24461865493[/C][C]-0.244618654929754[/C][/ROW]
[ROW][C]23[/C][C]509[/C][C]503.724792952717[/C][C]5.27520704728275[/C][/ROW]
[ROW][C]24[/C][C]501[/C][C]504.133070221441[/C][C]-3.13307022144072[/C][/ROW]
[ROW][C]25[/C][C]507[/C][C]501.793063755847[/C][C]5.20693624415297[/C][/ROW]
[ROW][C]26[/C][C]569[/C][C]563.46234307939[/C][C]5.53765692061006[/C][/ROW]
[ROW][C]27[/C][C]580[/C][C]581.647909688873[/C][C]-1.64790968887270[/C][/ROW]
[ROW][C]28[/C][C]578[/C][C]580.61706320411[/C][C]-2.61706320411020[/C][/ROW]
[ROW][C]29[/C][C]565[/C][C]567.506431133992[/C][C]-2.5064311339919[/C][/ROW]
[ROW][C]30[/C][C]547[/C][C]546.245496172745[/C][C]0.754503827255213[/C][/ROW]
[ROW][C]31[/C][C]555[/C][C]550.382446168155[/C][C]4.61755383184498[/C][/ROW]
[ROW][C]32[/C][C]562[/C][C]557.665946399344[/C][C]4.33405360065648[/C][/ROW]
[ROW][C]33[/C][C]561[/C][C]560.742745256244[/C][C]0.257254743755970[/C][/ROW]
[ROW][C]34[/C][C]555[/C][C]556.624359239572[/C][C]-1.62435923957166[/C][/ROW]
[ROW][C]35[/C][C]544[/C][C]548.890166955041[/C][C]-4.89016695504074[/C][/ROW]
[ROW][C]36[/C][C]537[/C][C]538.744083804635[/C][C]-1.74408380463454[/C][/ROW]
[ROW][C]37[/C][C]543[/C][C]540.440662033594[/C][C]2.55933796640645[/C][/ROW]
[ROW][C]38[/C][C]594[/C][C]599.578128581498[/C][C]-5.57812858149836[/C][/ROW]
[ROW][C]39[/C][C]611[/C][C]608.109882780192[/C][C]2.89011721980806[/C][/ROW]
[ROW][C]40[/C][C]613[/C][C]612.278167400739[/C][C]0.721832599260754[/C][/ROW]
[ROW][C]41[/C][C]611[/C][C]601.085091351565[/C][C]9.91490864843475[/C][/ROW]
[ROW][C]42[/C][C]594[/C][C]592.502473322878[/C][C]1.49752667712190[/C][/ROW]
[ROW][C]43[/C][C]595[/C][C]598.00175661179[/C][C]-3.00175661178942[/C][/ROW]
[ROW][C]44[/C][C]591[/C][C]596.577167028587[/C][C]-5.57716702858733[/C][/ROW]
[ROW][C]45[/C][C]589[/C][C]589.330877821964[/C][C]-0.330877821964188[/C][/ROW]
[ROW][C]46[/C][C]584[/C][C]581.708182395737[/C][C]2.29181760426274[/C][/ROW]
[ROW][C]47[/C][C]573[/C][C]577.297713153688[/C][C]-4.29771315368823[/C][/ROW]
[ROW][C]48[/C][C]567[/C][C]566.27701420105[/C][C]0.722985798949873[/C][/ROW]
[ROW][C]49[/C][C]569[/C][C]569.525846364973[/C][C]-0.525846364972995[/C][/ROW]
[ROW][C]50[/C][C]621[/C][C]621.223343153339[/C][C]-0.223343153338821[/C][/ROW]
[ROW][C]51[/C][C]629[/C][C]632.85062415551[/C][C]-3.85062415551058[/C][/ROW]
[ROW][C]52[/C][C]628[/C][C]627.987697437522[/C][C]0.0123025624782818[/C][/ROW]
[ROW][C]53[/C][C]612[/C][C]611.973062884929[/C][C]0.0269371150711113[/C][/ROW]
[ROW][C]54[/C][C]595[/C][C]592.704194567373[/C][C]2.29580543262719[/C][/ROW]
[ROW][C]55[/C][C]597[/C][C]595.664115214937[/C][C]1.33588478506305[/C][/ROW]
[ROW][C]56[/C][C]593[/C][C]596.321026429632[/C][C]-3.32102642963226[/C][/ROW]
[ROW][C]57[/C][C]590[/C][C]587.755139149854[/C][C]2.2448608501459[/C][/ROW]
[ROW][C]58[/C][C]580[/C][C]581.333978427957[/C][C]-1.33397842795659[/C][/ROW]
[ROW][C]59[/C][C]574[/C][C]566.972355295523[/C][C]7.02764470447678[/C][/ROW]
[ROW][C]60[/C][C]573[/C][C]566.39476966337[/C][C]6.6052303366301[/C][/ROW]
[ROW][C]61[/C][C]573[/C][C]570.408612307838[/C][C]2.59138769216159[/C][/ROW]
[ROW][C]62[/C][C]620[/C][C]620.987901585334[/C][C]-0.987901585334136[/C][/ROW]
[ROW][C]63[/C][C]626[/C][C]628.107282198465[/C][C]-2.10728219846462[/C][/ROW]
[ROW][C]64[/C][C]620[/C][C]620.518473269605[/C][C]-0.518473269605384[/C][/ROW]
[ROW][C]65[/C][C]588[/C][C]602.648265162106[/C][C]-14.6482651621058[/C][/ROW]
[ROW][C]66[/C][C]566[/C][C]563.984135891121[/C][C]2.01586410887912[/C][/ROW]
[ROW][C]67[/C][C]557[/C][C]559.908509365696[/C][C]-2.90850936569592[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68024&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68024&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1431440.995986249429-9.99598624942896
2484487.759982902138-3.75998290213822
3510500.5786366291429.42136337085846
4513509.7790932822833.22090671771729
5503503.134090215368-0.134090215368056
6471486.247271849365-15.2472718493649
7471473.780922453283-2.78092245328301
8476476.267280673961-0.267280673961445
9475476.86930629062-1.86930629062032
10470469.0888612818050.911138718195271
11461464.114971643031-3.11497164303055
12455457.451062109505-2.45106210950471
13456455.8358292883190.164170711680960
14517511.9883006983015.01169930169949
15525529.705664547819-4.70566454781862
16523523.819505405741-0.819505405740737
17519511.653059252047.34694074795991
18509500.3164281965188.68357180348152
19512509.262250186142.73774981386033
20519514.1685794684754.83142053152457
21517517.301931481317-0.301931481317353
22510510.24461865493-0.244618654929754
23509503.7247929527175.27520704728275
24501504.133070221441-3.13307022144072
25507501.7930637558475.20693624415297
26569563.462343079395.53765692061006
27580581.647909688873-1.64790968887270
28578580.61706320411-2.61706320411020
29565567.506431133992-2.5064311339919
30547546.2454961727450.754503827255213
31555550.3824461681554.61755383184498
32562557.6659463993444.33405360065648
33561560.7427452562440.257254743755970
34555556.624359239572-1.62435923957166
35544548.890166955041-4.89016695504074
36537538.744083804635-1.74408380463454
37543540.4406620335942.55933796640645
38594599.578128581498-5.57812858149836
39611608.1098827801922.89011721980806
40613612.2781674007390.721832599260754
41611601.0850913515659.91490864843475
42594592.5024733228781.49752667712190
43595598.00175661179-3.00175661178942
44591596.577167028587-5.57716702858733
45589589.330877821964-0.330877821964188
46584581.7081823957372.29181760426274
47573577.297713153688-4.29771315368823
48567566.277014201050.722985798949873
49569569.525846364973-0.525846364972995
50621621.223343153339-0.223343153338821
51629632.85062415551-3.85062415551058
52628627.9876974375220.0123025624782818
53612611.9730628849290.0269371150711113
54595592.7041945673732.29580543262719
55597595.6641152149371.33588478506305
56593596.321026429632-3.32102642963226
57590587.7551391498542.2448608501459
58580581.333978427957-1.33397842795659
59574566.9723552955237.02764470447678
60573566.394769663376.6052303366301
61573570.4086123078382.59138769216159
62620620.987901585334-0.987901585334136
63626628.107282198465-2.10728219846462
64620620.518473269605-0.518473269605384
65588602.648265162106-14.6482651621058
66566563.9841358911212.01586410887912
67557559.908509365696-2.90850936569592






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.9910310904189660.01793781916206890.00896890958103446
210.9804807394971570.03903852100568620.0195192605028431
220.9799651994315410.04006960113691740.0200348005684587
230.9631254015020.0737491969959990.0368745984979995
240.9472837471000460.1054325057999080.052716252899954
250.9137580355703760.1724839288592480.086241964429624
260.8902891255258150.2194217489483690.109710874474185
270.8726306864632130.2547386270735740.127369313536787
280.8655234996709820.2689530006580360.134476500329018
290.857405351799690.2851892964006190.142594648200310
300.7978452193166330.4043095613667350.202154780683367
310.741027383736710.5179452325265810.258972616263290
320.7158319032553220.5683361934893550.284168096744678
330.6308182684576850.7383634630846310.369181731542315
340.5428664062107820.9142671875784370.457133593789219
350.5341597905565870.9316804188868270.465840209443413
360.53219234960780.93561530078440.4678076503922
370.4328330403379260.8656660806758520.567166959662074
380.4626848751281740.9253697502563480.537315124871826
390.3693018317572890.7386036635145770.630698168242711
400.286974339700620.573948679401240.71302566029938
410.8020598014339820.3958803971320360.197940198566018
420.7217725221362850.5564549557274290.278227477863715
430.6093160343267970.7813679313464050.390683965673203
440.5428563236679310.9142873526641370.457143676332069
450.4349575796450480.8699151592900970.565042420354952
460.2987467103271820.5974934206543630.701253289672819
470.3335033294564930.6670066589129860.666496670543507

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.991031090418966 & 0.0179378191620689 & 0.00896890958103446 \tabularnewline
21 & 0.980480739497157 & 0.0390385210056862 & 0.0195192605028431 \tabularnewline
22 & 0.979965199431541 & 0.0400696011369174 & 0.0200348005684587 \tabularnewline
23 & 0.963125401502 & 0.073749196995999 & 0.0368745984979995 \tabularnewline
24 & 0.947283747100046 & 0.105432505799908 & 0.052716252899954 \tabularnewline
25 & 0.913758035570376 & 0.172483928859248 & 0.086241964429624 \tabularnewline
26 & 0.890289125525815 & 0.219421748948369 & 0.109710874474185 \tabularnewline
27 & 0.872630686463213 & 0.254738627073574 & 0.127369313536787 \tabularnewline
28 & 0.865523499670982 & 0.268953000658036 & 0.134476500329018 \tabularnewline
29 & 0.85740535179969 & 0.285189296400619 & 0.142594648200310 \tabularnewline
30 & 0.797845219316633 & 0.404309561366735 & 0.202154780683367 \tabularnewline
31 & 0.74102738373671 & 0.517945232526581 & 0.258972616263290 \tabularnewline
32 & 0.715831903255322 & 0.568336193489355 & 0.284168096744678 \tabularnewline
33 & 0.630818268457685 & 0.738363463084631 & 0.369181731542315 \tabularnewline
34 & 0.542866406210782 & 0.914267187578437 & 0.457133593789219 \tabularnewline
35 & 0.534159790556587 & 0.931680418886827 & 0.465840209443413 \tabularnewline
36 & 0.5321923496078 & 0.9356153007844 & 0.4678076503922 \tabularnewline
37 & 0.432833040337926 & 0.865666080675852 & 0.567166959662074 \tabularnewline
38 & 0.462684875128174 & 0.925369750256348 & 0.537315124871826 \tabularnewline
39 & 0.369301831757289 & 0.738603663514577 & 0.630698168242711 \tabularnewline
40 & 0.28697433970062 & 0.57394867940124 & 0.71302566029938 \tabularnewline
41 & 0.802059801433982 & 0.395880397132036 & 0.197940198566018 \tabularnewline
42 & 0.721772522136285 & 0.556454955727429 & 0.278227477863715 \tabularnewline
43 & 0.609316034326797 & 0.781367931346405 & 0.390683965673203 \tabularnewline
44 & 0.542856323667931 & 0.914287352664137 & 0.457143676332069 \tabularnewline
45 & 0.434957579645048 & 0.869915159290097 & 0.565042420354952 \tabularnewline
46 & 0.298746710327182 & 0.597493420654363 & 0.701253289672819 \tabularnewline
47 & 0.333503329456493 & 0.667006658912986 & 0.666496670543507 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68024&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]20[/C][C]0.991031090418966[/C][C]0.0179378191620689[/C][C]0.00896890958103446[/C][/ROW]
[ROW][C]21[/C][C]0.980480739497157[/C][C]0.0390385210056862[/C][C]0.0195192605028431[/C][/ROW]
[ROW][C]22[/C][C]0.979965199431541[/C][C]0.0400696011369174[/C][C]0.0200348005684587[/C][/ROW]
[ROW][C]23[/C][C]0.963125401502[/C][C]0.073749196995999[/C][C]0.0368745984979995[/C][/ROW]
[ROW][C]24[/C][C]0.947283747100046[/C][C]0.105432505799908[/C][C]0.052716252899954[/C][/ROW]
[ROW][C]25[/C][C]0.913758035570376[/C][C]0.172483928859248[/C][C]0.086241964429624[/C][/ROW]
[ROW][C]26[/C][C]0.890289125525815[/C][C]0.219421748948369[/C][C]0.109710874474185[/C][/ROW]
[ROW][C]27[/C][C]0.872630686463213[/C][C]0.254738627073574[/C][C]0.127369313536787[/C][/ROW]
[ROW][C]28[/C][C]0.865523499670982[/C][C]0.268953000658036[/C][C]0.134476500329018[/C][/ROW]
[ROW][C]29[/C][C]0.85740535179969[/C][C]0.285189296400619[/C][C]0.142594648200310[/C][/ROW]
[ROW][C]30[/C][C]0.797845219316633[/C][C]0.404309561366735[/C][C]0.202154780683367[/C][/ROW]
[ROW][C]31[/C][C]0.74102738373671[/C][C]0.517945232526581[/C][C]0.258972616263290[/C][/ROW]
[ROW][C]32[/C][C]0.715831903255322[/C][C]0.568336193489355[/C][C]0.284168096744678[/C][/ROW]
[ROW][C]33[/C][C]0.630818268457685[/C][C]0.738363463084631[/C][C]0.369181731542315[/C][/ROW]
[ROW][C]34[/C][C]0.542866406210782[/C][C]0.914267187578437[/C][C]0.457133593789219[/C][/ROW]
[ROW][C]35[/C][C]0.534159790556587[/C][C]0.931680418886827[/C][C]0.465840209443413[/C][/ROW]
[ROW][C]36[/C][C]0.5321923496078[/C][C]0.9356153007844[/C][C]0.4678076503922[/C][/ROW]
[ROW][C]37[/C][C]0.432833040337926[/C][C]0.865666080675852[/C][C]0.567166959662074[/C][/ROW]
[ROW][C]38[/C][C]0.462684875128174[/C][C]0.925369750256348[/C][C]0.537315124871826[/C][/ROW]
[ROW][C]39[/C][C]0.369301831757289[/C][C]0.738603663514577[/C][C]0.630698168242711[/C][/ROW]
[ROW][C]40[/C][C]0.28697433970062[/C][C]0.57394867940124[/C][C]0.71302566029938[/C][/ROW]
[ROW][C]41[/C][C]0.802059801433982[/C][C]0.395880397132036[/C][C]0.197940198566018[/C][/ROW]
[ROW][C]42[/C][C]0.721772522136285[/C][C]0.556454955727429[/C][C]0.278227477863715[/C][/ROW]
[ROW][C]43[/C][C]0.609316034326797[/C][C]0.781367931346405[/C][C]0.390683965673203[/C][/ROW]
[ROW][C]44[/C][C]0.542856323667931[/C][C]0.914287352664137[/C][C]0.457143676332069[/C][/ROW]
[ROW][C]45[/C][C]0.434957579645048[/C][C]0.869915159290097[/C][C]0.565042420354952[/C][/ROW]
[ROW][C]46[/C][C]0.298746710327182[/C][C]0.597493420654363[/C][C]0.701253289672819[/C][/ROW]
[ROW][C]47[/C][C]0.333503329456493[/C][C]0.667006658912986[/C][C]0.666496670543507[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68024&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68024&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.9910310904189660.01793781916206890.00896890958103446
210.9804807394971570.03903852100568620.0195192605028431
220.9799651994315410.04006960113691740.0200348005684587
230.9631254015020.0737491969959990.0368745984979995
240.9472837471000460.1054325057999080.052716252899954
250.9137580355703760.1724839288592480.086241964429624
260.8902891255258150.2194217489483690.109710874474185
270.8726306864632130.2547386270735740.127369313536787
280.8655234996709820.2689530006580360.134476500329018
290.857405351799690.2851892964006190.142594648200310
300.7978452193166330.4043095613667350.202154780683367
310.741027383736710.5179452325265810.258972616263290
320.7158319032553220.5683361934893550.284168096744678
330.6308182684576850.7383634630846310.369181731542315
340.5428664062107820.9142671875784370.457133593789219
350.5341597905565870.9316804188868270.465840209443413
360.53219234960780.93561530078440.4678076503922
370.4328330403379260.8656660806758520.567166959662074
380.4626848751281740.9253697502563480.537315124871826
390.3693018317572890.7386036635145770.630698168242711
400.286974339700620.573948679401240.71302566029938
410.8020598014339820.3958803971320360.197940198566018
420.7217725221362850.5564549557274290.278227477863715
430.6093160343267970.7813679313464050.390683965673203
440.5428563236679310.9142873526641370.457143676332069
450.4349575796450480.8699151592900970.565042420354952
460.2987467103271820.5974934206543630.701253289672819
470.3335033294564930.6670066589129860.666496670543507






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.107142857142857NOK
10% type I error level40.142857142857143NOK

\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 & 3 & 0.107142857142857 & NOK \tabularnewline
10% type I error level & 4 & 0.142857142857143 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68024&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]3[/C][C]0.107142857142857[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.142857142857143[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68024&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68024&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.107142857142857NOK
10% type I error level40.142857142857143NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}