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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 12:35:59 -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/t1258745997vs3mo909xxh400d.htm/, Retrieved Thu, 28 Mar 2024 14:33:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58444, Retrieved Thu, 28 Mar 2024 14:33:55 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact142
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]
-    D      [Multiple Regression] [JJ Workshop 7, Mu...] [2009-11-20 19:35:59] [e31f2fa83f4a5291b9a51009566cf69b] [Current]
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Dataseries X:
95,1	93,8	96,9	98,6	111,7	109,8
97	93,8	95,1	96,9	98,6	111,7
112,7	107,6	97	95,1	96,9	98,6
102,9	101	112,7	97	95,1	96,9
97,4	95,4	102,9	112,7	97	95,1
111,4	96,5	97,4	102,9	112,7	97
87,4	89,2	111,4	97,4	102,9	112,7
96,8	87,1	87,4	111,4	97,4	102,9
114,1	110,5	96,8	87,4	111,4	97,4
110,3	110,8	114,1	96,8	87,4	111,4
103,9	104,2	110,3	114,1	96,8	87,4
101,6	88,9	103,9	110,3	114,1	96,8
94,6	89,8	101,6	103,9	110,3	114,1
95,9	90	94,6	101,6	103,9	110,3
104,7	93,9	95,9	94,6	101,6	103,9
102,8	91,3	104,7	95,9	94,6	101,6
98,1	87,8	102,8	104,7	95,9	94,6
113,9	99,7	98,1	102,8	104,7	95,9
80,9	73,5	113,9	98,1	102,8	104,7
95,7	79,2	80,9	113,9	98,1	102,8
113,2	96,9	95,7	80,9	113,9	98,1
105,9	95,2	113,2	95,7	80,9	113,9
108,8	95,6	105,9	113,2	95,7	80,9
102,3	89,7	108,8	105,9	113,2	95,7
99	92,8	102,3	108,8	105,9	113,2
100,7	88	99	102,3	108,8	105,9
115,5	101,1	100,7	99	102,3	108,8
100,7	92,7	115,5	100,7	99	102,3
109,9	95,8	100,7	115,5	100,7	99
114,6	103,8	109,9	100,7	115,5	100,7
85,4	81,8	114,6	109,9	100,7	115,5
100,5	87,1	85,4	114,6	109,9	100,7
114,8	105,9	100,5	85,4	114,6	109,9
116,5	108,1	114,8	100,5	85,4	114,6
112,9	102,6	116,5	114,8	100,5	85,4
102	93,7	112,9	116,5	114,8	100,5
106	103,5	102	112,9	116,5	114,8
105,3	100,6	106	102	112,9	116,5
118,8	113,3	105,3	106	102	112,9
106,1	102,4	118,8	105,3	106	102
109,3	102,1	106,1	118,8	105,3	106
117,2	106,9	109,3	106,1	118,8	105,3
92,5	87,3	117,2	109,3	106,1	118,8
104,2	93,1	92,5	117,2	109,3	106,1
112,5	109,1	104,2	92,5	117,2	109,3
122,4	120,3	112,5	104,2	92,5	117,2
113,3	104,9	122,4	112,5	104,2	92,5
100	92,6	113,3	122,4	112,5	104,2
110,7	109,8	100	113,3	122,4	112,5
112,8	111,4	110,7	100	113,3	122,4
109,8	117,9	112,8	110,7	100	113,3
117,3	121,6	109,8	112,8	110,7	100
109,1	117,8	117,3	109,8	112,8	110,7
115,9	124,2	109,1	117,3	109,8	112,8
96	106,8	115,9	109,1	117,3	109,8
99,8	102,7	96	115,9	109,1	117,3
116,8	116,8	99,8	96	115,9	109,1
115,7	113,6	116,8	99,8	96	115,9
99,4	96,1	115,7	116,8	99,8	96
94,3	85	99,4	115,7	116,8	99,8




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=58444&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=58444&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58444&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
TIA[t] = + 49.3851818122445 + 0.328226028431917IAidM[t] -0.0569153192027402`TIA(t-1)`[t] + 0.0550408037582717`TIA(t-2)`[t] + 0.315315051430443`TIA(t-3)`[t] -0.159716545463239`TIA(t-4)`[t] + 1.07935611922313M1[t] + 5.04024362561899M2[t] + 12.9910144316273M3[t] + 7.48173555248839M4[t] + 5.69904974515526M5[t] + 10.7442308754480M6[t] -5.15002616864018M7[t] + 2.4269950608123M8[t] + 10.1458174187584M9[t] + 19.4896364076166M10[t] + 7.43512929557958M11[t] + 0.0224084944263686t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TIA[t] =  +  49.3851818122445 +  0.328226028431917IAidM[t] -0.0569153192027402`TIA(t-1)`[t] +  0.0550408037582717`TIA(t-2)`[t] +  0.315315051430443`TIA(t-3)`[t] -0.159716545463239`TIA(t-4)`[t] +  1.07935611922313M1[t] +  5.04024362561899M2[t] +  12.9910144316273M3[t] +  7.48173555248839M4[t] +  5.69904974515526M5[t] +  10.7442308754480M6[t] -5.15002616864018M7[t] +  2.4269950608123M8[t] +  10.1458174187584M9[t] +  19.4896364076166M10[t] +  7.43512929557958M11[t] +  0.0224084944263686t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58444&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TIA[t] =  +  49.3851818122445 +  0.328226028431917IAidM[t] -0.0569153192027402`TIA(t-1)`[t] +  0.0550408037582717`TIA(t-2)`[t] +  0.315315051430443`TIA(t-3)`[t] -0.159716545463239`TIA(t-4)`[t] +  1.07935611922313M1[t] +  5.04024362561899M2[t] +  12.9910144316273M3[t] +  7.48173555248839M4[t] +  5.69904974515526M5[t] +  10.7442308754480M6[t] -5.15002616864018M7[t] +  2.4269950608123M8[t] +  10.1458174187584M9[t] +  19.4896364076166M10[t] +  7.43512929557958M11[t] +  0.0224084944263686t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58444&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58444&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
TIA[t] = + 49.3851818122445 + 0.328226028431917IAidM[t] -0.0569153192027402`TIA(t-1)`[t] + 0.0550408037582717`TIA(t-2)`[t] + 0.315315051430443`TIA(t-3)`[t] -0.159716545463239`TIA(t-4)`[t] + 1.07935611922313M1[t] + 5.04024362561899M2[t] + 12.9910144316273M3[t] + 7.48173555248839M4[t] + 5.69904974515526M5[t] + 10.7442308754480M6[t] -5.15002616864018M7[t] + 2.4269950608123M8[t] + 10.1458174187584M9[t] + 19.4896364076166M10[t] + 7.43512929557958M11[t] + 0.0224084944263686t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)49.385181812244534.9129941.41450.164580.08229
IAidM0.3282260284319170.0919883.56810.0009150.000458
`TIA(t-1)`-0.05691531920274020.146032-0.38970.6986950.349347
`TIA(t-2)`0.05504080375827170.1607990.34230.7338350.366918
`TIA(t-3)`0.3153150514304430.1744541.80740.077860.03893
`TIA(t-4)`-0.1597165454632390.168604-0.94730.3489110.174456
M11.079356119223134.0044910.26950.7888370.394418
M25.040243625618994.5049581.11880.2695750.134787
M312.99101443162735.1057352.54440.0147120.007356
M47.481735552488394.4459921.68280.0998340.049917
M55.699049745155263.3657141.69330.0978110.048905
M610.74423087544803.4101413.15070.0030.0015
M7-5.150026168640183.331084-1.54610.1295950.064797
M82.42699506081234.1353880.58690.5604230.280211
M910.14581741875845.9011051.71930.0929210.046461
M1019.48963640761666.8327342.85240.0067050.003352
M117.435129295579585.0265061.47920.1465530.073276
t0.02240849442636860.0678170.33040.7427210.371361

\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) & 49.3851818122445 & 34.912994 & 1.4145 & 0.16458 & 0.08229 \tabularnewline
IAidM & 0.328226028431917 & 0.091988 & 3.5681 & 0.000915 & 0.000458 \tabularnewline
`TIA(t-1)` & -0.0569153192027402 & 0.146032 & -0.3897 & 0.698695 & 0.349347 \tabularnewline
`TIA(t-2)` & 0.0550408037582717 & 0.160799 & 0.3423 & 0.733835 & 0.366918 \tabularnewline
`TIA(t-3)` & 0.315315051430443 & 0.174454 & 1.8074 & 0.07786 & 0.03893 \tabularnewline
`TIA(t-4)` & -0.159716545463239 & 0.168604 & -0.9473 & 0.348911 & 0.174456 \tabularnewline
M1 & 1.07935611922313 & 4.004491 & 0.2695 & 0.788837 & 0.394418 \tabularnewline
M2 & 5.04024362561899 & 4.504958 & 1.1188 & 0.269575 & 0.134787 \tabularnewline
M3 & 12.9910144316273 & 5.105735 & 2.5444 & 0.014712 & 0.007356 \tabularnewline
M4 & 7.48173555248839 & 4.445992 & 1.6828 & 0.099834 & 0.049917 \tabularnewline
M5 & 5.69904974515526 & 3.365714 & 1.6933 & 0.097811 & 0.048905 \tabularnewline
M6 & 10.7442308754480 & 3.410141 & 3.1507 & 0.003 & 0.0015 \tabularnewline
M7 & -5.15002616864018 & 3.331084 & -1.5461 & 0.129595 & 0.064797 \tabularnewline
M8 & 2.4269950608123 & 4.135388 & 0.5869 & 0.560423 & 0.280211 \tabularnewline
M9 & 10.1458174187584 & 5.901105 & 1.7193 & 0.092921 & 0.046461 \tabularnewline
M10 & 19.4896364076166 & 6.832734 & 2.8524 & 0.006705 & 0.003352 \tabularnewline
M11 & 7.43512929557958 & 5.026506 & 1.4792 & 0.146553 & 0.073276 \tabularnewline
t & 0.0224084944263686 & 0.067817 & 0.3304 & 0.742721 & 0.371361 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58444&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]49.3851818122445[/C][C]34.912994[/C][C]1.4145[/C][C]0.16458[/C][C]0.08229[/C][/ROW]
[ROW][C]IAidM[/C][C]0.328226028431917[/C][C]0.091988[/C][C]3.5681[/C][C]0.000915[/C][C]0.000458[/C][/ROW]
[ROW][C]`TIA(t-1)`[/C][C]-0.0569153192027402[/C][C]0.146032[/C][C]-0.3897[/C][C]0.698695[/C][C]0.349347[/C][/ROW]
[ROW][C]`TIA(t-2)`[/C][C]0.0550408037582717[/C][C]0.160799[/C][C]0.3423[/C][C]0.733835[/C][C]0.366918[/C][/ROW]
[ROW][C]`TIA(t-3)`[/C][C]0.315315051430443[/C][C]0.174454[/C][C]1.8074[/C][C]0.07786[/C][C]0.03893[/C][/ROW]
[ROW][C]`TIA(t-4)`[/C][C]-0.159716545463239[/C][C]0.168604[/C][C]-0.9473[/C][C]0.348911[/C][C]0.174456[/C][/ROW]
[ROW][C]M1[/C][C]1.07935611922313[/C][C]4.004491[/C][C]0.2695[/C][C]0.788837[/C][C]0.394418[/C][/ROW]
[ROW][C]M2[/C][C]5.04024362561899[/C][C]4.504958[/C][C]1.1188[/C][C]0.269575[/C][C]0.134787[/C][/ROW]
[ROW][C]M3[/C][C]12.9910144316273[/C][C]5.105735[/C][C]2.5444[/C][C]0.014712[/C][C]0.007356[/C][/ROW]
[ROW][C]M4[/C][C]7.48173555248839[/C][C]4.445992[/C][C]1.6828[/C][C]0.099834[/C][C]0.049917[/C][/ROW]
[ROW][C]M5[/C][C]5.69904974515526[/C][C]3.365714[/C][C]1.6933[/C][C]0.097811[/C][C]0.048905[/C][/ROW]
[ROW][C]M6[/C][C]10.7442308754480[/C][C]3.410141[/C][C]3.1507[/C][C]0.003[/C][C]0.0015[/C][/ROW]
[ROW][C]M7[/C][C]-5.15002616864018[/C][C]3.331084[/C][C]-1.5461[/C][C]0.129595[/C][C]0.064797[/C][/ROW]
[ROW][C]M8[/C][C]2.4269950608123[/C][C]4.135388[/C][C]0.5869[/C][C]0.560423[/C][C]0.280211[/C][/ROW]
[ROW][C]M9[/C][C]10.1458174187584[/C][C]5.901105[/C][C]1.7193[/C][C]0.092921[/C][C]0.046461[/C][/ROW]
[ROW][C]M10[/C][C]19.4896364076166[/C][C]6.832734[/C][C]2.8524[/C][C]0.006705[/C][C]0.003352[/C][/ROW]
[ROW][C]M11[/C][C]7.43512929557958[/C][C]5.026506[/C][C]1.4792[/C][C]0.146553[/C][C]0.073276[/C][/ROW]
[ROW][C]t[/C][C]0.0224084944263686[/C][C]0.067817[/C][C]0.3304[/C][C]0.742721[/C][C]0.371361[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58444&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58444&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)49.385181812244534.9129941.41450.164580.08229
IAidM0.3282260284319170.0919883.56810.0009150.000458
`TIA(t-1)`-0.05691531920274020.146032-0.38970.6986950.349347
`TIA(t-2)`0.05504080375827170.1607990.34230.7338350.366918
`TIA(t-3)`0.3153150514304430.1744541.80740.077860.03893
`TIA(t-4)`-0.1597165454632390.168604-0.94730.3489110.174456
M11.079356119223134.0044910.26950.7888370.394418
M25.040243625618994.5049581.11880.2695750.134787
M312.99101443162735.1057352.54440.0147120.007356
M47.481735552488394.4459921.68280.0998340.049917
M55.699049745155263.3657141.69330.0978110.048905
M610.74423087544803.4101413.15070.0030.0015
M7-5.150026168640183.331084-1.54610.1295950.064797
M82.42699506081234.1353880.58690.5604230.280211
M910.14581741875845.9011051.71930.0929210.046461
M1019.48963640761666.8327342.85240.0067050.003352
M117.435129295579585.0265061.47920.1465530.073276
t0.02240849442636860.0678170.33040.7427210.371361







Multiple Linear Regression - Regression Statistics
Multiple R0.94203267354235
R-squared0.887425558021349
Adjusted R-squared0.841859712458562
F-TEST (value)19.4756740945041
F-TEST (DF numerator)17
F-TEST (DF denominator)42
p-value1.17683640610267e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.57889345058774
Sum Squared Residuals537.956089887712

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.94203267354235 \tabularnewline
R-squared & 0.887425558021349 \tabularnewline
Adjusted R-squared & 0.841859712458562 \tabularnewline
F-TEST (value) & 19.4756740945041 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 42 \tabularnewline
p-value & 1.17683640610267e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.57889345058774 \tabularnewline
Sum Squared Residuals & 537.956089887712 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58444&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.94203267354235[/C][/ROW]
[ROW][C]R-squared[/C][C]0.887425558021349[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.841859712458562[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]19.4756740945041[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]42[/C][/ROW]
[ROW][C]p-value[/C][C]1.17683640610267e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.57889345058774[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]537.956089887712[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58444&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58444&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 R0.94203267354235
R-squared0.887425558021349
Adjusted R-squared0.841859712458562
F-TEST (value)19.4756740945041
F-TEST (DF numerator)17
F-TEST (DF denominator)42
p-value1.17683640610267e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.57889345058774
Sum Squared Residuals537.956089887712







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
195.198.8702912655445-3.77029126554452
29798.4283768644238-1.4283768644238
3112.7112.2801139621060.41988603789447
4102.9103.541909840113-0.641909840112737
597.4102.252065894731-4.85206589473064
6111.4112.101323400587-0.701323400586641
787.487.13624868607180.263751313928171
896.896.01393202639750.786067973602462
9114.1114.872520373447-0.772520373446601
10110.3114.066371327565-3.76637132756515
11103.9107.833623614856-3.93362361485621
12101.699.5077624297032.09223757029699
1394.696.7222811271049-2.12228112710488
1495.999.6319422629941-3.73194226299409
15104.7108.722888805716-4.02288880571649
16102.8100.1134716775352.68652832246503
1798.199.3248268297767-1.22482682977674
18113.9111.0283716094332.87162839056653
1980.983.394443095994-2.49444309599395
2095.794.4340921096631.26590789033707
21113.2111.0588759933352.14112400666497
22105.9106.756786922336-0.856786922336051
23108.8106.1719833735052.62801662649468
24102.399.41008523865732.88991476134270
259996.96207902511362.03792097488638
26100.7101.280289849433-0.580289849432837
27115.5110.7621136111374.7378863888626
28100.7101.766985105576-1.06698510557587
29109.9103.7442592880916.15574071190854
30114.6114.4945769418610.105423058138700
3185.484.6101615269940.789838473005926
32100.5101.134511645962-0.634511645961985
33114.8112.5923675666132.20763243338713
34116.5112.7400521191523.75994788084775
35112.9109.0180182003913.88198179960910
36102101.0798356606730.920164339327007
37106104.0725344260431.92746557395719
38105.3104.8697165941990.430283405800751
39118.8114.409415897274.3905841027299
40106.1107.540166982053-1.44016698205297
41109.3106.2877105473743.01228945262617
42117.2116.4181926555230.781807344477457
4392.587.67893898199974.82106101800034
44104.2102.0601186967422.13988130325779
45112.5115.007444877342-2.50744487734211
46122.4119.1713416541623.22865834583831
47113.3109.6121239844643.68787601553581
4810099.9734877405030.0265122594969877
49110.7108.7728141561941.92718584380584
50112.8107.489674428955.31032557104998
51109.8115.325467723770-5.52546772377049
52117.3116.8374663947230.462533605276545
53109.1112.191137440027-3.09113744002732
54115.9118.957535392596-3.05753539259604
559699.3802077089405-3.38020770894048
5699.8103.357345521235-3.55734552123534
57116.8117.868791189263-1.06879118926339
58115.7118.065447976785-2.36544797678486
5999.4105.664250826783-6.26425082678338
6094.3100.228828930464-5.92882893046369

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 95.1 & 98.8702912655445 & -3.77029126554452 \tabularnewline
2 & 97 & 98.4283768644238 & -1.4283768644238 \tabularnewline
3 & 112.7 & 112.280113962106 & 0.41988603789447 \tabularnewline
4 & 102.9 & 103.541909840113 & -0.641909840112737 \tabularnewline
5 & 97.4 & 102.252065894731 & -4.85206589473064 \tabularnewline
6 & 111.4 & 112.101323400587 & -0.701323400586641 \tabularnewline
7 & 87.4 & 87.1362486860718 & 0.263751313928171 \tabularnewline
8 & 96.8 & 96.0139320263975 & 0.786067973602462 \tabularnewline
9 & 114.1 & 114.872520373447 & -0.772520373446601 \tabularnewline
10 & 110.3 & 114.066371327565 & -3.76637132756515 \tabularnewline
11 & 103.9 & 107.833623614856 & -3.93362361485621 \tabularnewline
12 & 101.6 & 99.507762429703 & 2.09223757029699 \tabularnewline
13 & 94.6 & 96.7222811271049 & -2.12228112710488 \tabularnewline
14 & 95.9 & 99.6319422629941 & -3.73194226299409 \tabularnewline
15 & 104.7 & 108.722888805716 & -4.02288880571649 \tabularnewline
16 & 102.8 & 100.113471677535 & 2.68652832246503 \tabularnewline
17 & 98.1 & 99.3248268297767 & -1.22482682977674 \tabularnewline
18 & 113.9 & 111.028371609433 & 2.87162839056653 \tabularnewline
19 & 80.9 & 83.394443095994 & -2.49444309599395 \tabularnewline
20 & 95.7 & 94.434092109663 & 1.26590789033707 \tabularnewline
21 & 113.2 & 111.058875993335 & 2.14112400666497 \tabularnewline
22 & 105.9 & 106.756786922336 & -0.856786922336051 \tabularnewline
23 & 108.8 & 106.171983373505 & 2.62801662649468 \tabularnewline
24 & 102.3 & 99.4100852386573 & 2.88991476134270 \tabularnewline
25 & 99 & 96.9620790251136 & 2.03792097488638 \tabularnewline
26 & 100.7 & 101.280289849433 & -0.580289849432837 \tabularnewline
27 & 115.5 & 110.762113611137 & 4.7378863888626 \tabularnewline
28 & 100.7 & 101.766985105576 & -1.06698510557587 \tabularnewline
29 & 109.9 & 103.744259288091 & 6.15574071190854 \tabularnewline
30 & 114.6 & 114.494576941861 & 0.105423058138700 \tabularnewline
31 & 85.4 & 84.610161526994 & 0.789838473005926 \tabularnewline
32 & 100.5 & 101.134511645962 & -0.634511645961985 \tabularnewline
33 & 114.8 & 112.592367566613 & 2.20763243338713 \tabularnewline
34 & 116.5 & 112.740052119152 & 3.75994788084775 \tabularnewline
35 & 112.9 & 109.018018200391 & 3.88198179960910 \tabularnewline
36 & 102 & 101.079835660673 & 0.920164339327007 \tabularnewline
37 & 106 & 104.072534426043 & 1.92746557395719 \tabularnewline
38 & 105.3 & 104.869716594199 & 0.430283405800751 \tabularnewline
39 & 118.8 & 114.40941589727 & 4.3905841027299 \tabularnewline
40 & 106.1 & 107.540166982053 & -1.44016698205297 \tabularnewline
41 & 109.3 & 106.287710547374 & 3.01228945262617 \tabularnewline
42 & 117.2 & 116.418192655523 & 0.781807344477457 \tabularnewline
43 & 92.5 & 87.6789389819997 & 4.82106101800034 \tabularnewline
44 & 104.2 & 102.060118696742 & 2.13988130325779 \tabularnewline
45 & 112.5 & 115.007444877342 & -2.50744487734211 \tabularnewline
46 & 122.4 & 119.171341654162 & 3.22865834583831 \tabularnewline
47 & 113.3 & 109.612123984464 & 3.68787601553581 \tabularnewline
48 & 100 & 99.973487740503 & 0.0265122594969877 \tabularnewline
49 & 110.7 & 108.772814156194 & 1.92718584380584 \tabularnewline
50 & 112.8 & 107.48967442895 & 5.31032557104998 \tabularnewline
51 & 109.8 & 115.325467723770 & -5.52546772377049 \tabularnewline
52 & 117.3 & 116.837466394723 & 0.462533605276545 \tabularnewline
53 & 109.1 & 112.191137440027 & -3.09113744002732 \tabularnewline
54 & 115.9 & 118.957535392596 & -3.05753539259604 \tabularnewline
55 & 96 & 99.3802077089405 & -3.38020770894048 \tabularnewline
56 & 99.8 & 103.357345521235 & -3.55734552123534 \tabularnewline
57 & 116.8 & 117.868791189263 & -1.06879118926339 \tabularnewline
58 & 115.7 & 118.065447976785 & -2.36544797678486 \tabularnewline
59 & 99.4 & 105.664250826783 & -6.26425082678338 \tabularnewline
60 & 94.3 & 100.228828930464 & -5.92882893046369 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58444&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]95.1[/C][C]98.8702912655445[/C][C]-3.77029126554452[/C][/ROW]
[ROW][C]2[/C][C]97[/C][C]98.4283768644238[/C][C]-1.4283768644238[/C][/ROW]
[ROW][C]3[/C][C]112.7[/C][C]112.280113962106[/C][C]0.41988603789447[/C][/ROW]
[ROW][C]4[/C][C]102.9[/C][C]103.541909840113[/C][C]-0.641909840112737[/C][/ROW]
[ROW][C]5[/C][C]97.4[/C][C]102.252065894731[/C][C]-4.85206589473064[/C][/ROW]
[ROW][C]6[/C][C]111.4[/C][C]112.101323400587[/C][C]-0.701323400586641[/C][/ROW]
[ROW][C]7[/C][C]87.4[/C][C]87.1362486860718[/C][C]0.263751313928171[/C][/ROW]
[ROW][C]8[/C][C]96.8[/C][C]96.0139320263975[/C][C]0.786067973602462[/C][/ROW]
[ROW][C]9[/C][C]114.1[/C][C]114.872520373447[/C][C]-0.772520373446601[/C][/ROW]
[ROW][C]10[/C][C]110.3[/C][C]114.066371327565[/C][C]-3.76637132756515[/C][/ROW]
[ROW][C]11[/C][C]103.9[/C][C]107.833623614856[/C][C]-3.93362361485621[/C][/ROW]
[ROW][C]12[/C][C]101.6[/C][C]99.507762429703[/C][C]2.09223757029699[/C][/ROW]
[ROW][C]13[/C][C]94.6[/C][C]96.7222811271049[/C][C]-2.12228112710488[/C][/ROW]
[ROW][C]14[/C][C]95.9[/C][C]99.6319422629941[/C][C]-3.73194226299409[/C][/ROW]
[ROW][C]15[/C][C]104.7[/C][C]108.722888805716[/C][C]-4.02288880571649[/C][/ROW]
[ROW][C]16[/C][C]102.8[/C][C]100.113471677535[/C][C]2.68652832246503[/C][/ROW]
[ROW][C]17[/C][C]98.1[/C][C]99.3248268297767[/C][C]-1.22482682977674[/C][/ROW]
[ROW][C]18[/C][C]113.9[/C][C]111.028371609433[/C][C]2.87162839056653[/C][/ROW]
[ROW][C]19[/C][C]80.9[/C][C]83.394443095994[/C][C]-2.49444309599395[/C][/ROW]
[ROW][C]20[/C][C]95.7[/C][C]94.434092109663[/C][C]1.26590789033707[/C][/ROW]
[ROW][C]21[/C][C]113.2[/C][C]111.058875993335[/C][C]2.14112400666497[/C][/ROW]
[ROW][C]22[/C][C]105.9[/C][C]106.756786922336[/C][C]-0.856786922336051[/C][/ROW]
[ROW][C]23[/C][C]108.8[/C][C]106.171983373505[/C][C]2.62801662649468[/C][/ROW]
[ROW][C]24[/C][C]102.3[/C][C]99.4100852386573[/C][C]2.88991476134270[/C][/ROW]
[ROW][C]25[/C][C]99[/C][C]96.9620790251136[/C][C]2.03792097488638[/C][/ROW]
[ROW][C]26[/C][C]100.7[/C][C]101.280289849433[/C][C]-0.580289849432837[/C][/ROW]
[ROW][C]27[/C][C]115.5[/C][C]110.762113611137[/C][C]4.7378863888626[/C][/ROW]
[ROW][C]28[/C][C]100.7[/C][C]101.766985105576[/C][C]-1.06698510557587[/C][/ROW]
[ROW][C]29[/C][C]109.9[/C][C]103.744259288091[/C][C]6.15574071190854[/C][/ROW]
[ROW][C]30[/C][C]114.6[/C][C]114.494576941861[/C][C]0.105423058138700[/C][/ROW]
[ROW][C]31[/C][C]85.4[/C][C]84.610161526994[/C][C]0.789838473005926[/C][/ROW]
[ROW][C]32[/C][C]100.5[/C][C]101.134511645962[/C][C]-0.634511645961985[/C][/ROW]
[ROW][C]33[/C][C]114.8[/C][C]112.592367566613[/C][C]2.20763243338713[/C][/ROW]
[ROW][C]34[/C][C]116.5[/C][C]112.740052119152[/C][C]3.75994788084775[/C][/ROW]
[ROW][C]35[/C][C]112.9[/C][C]109.018018200391[/C][C]3.88198179960910[/C][/ROW]
[ROW][C]36[/C][C]102[/C][C]101.079835660673[/C][C]0.920164339327007[/C][/ROW]
[ROW][C]37[/C][C]106[/C][C]104.072534426043[/C][C]1.92746557395719[/C][/ROW]
[ROW][C]38[/C][C]105.3[/C][C]104.869716594199[/C][C]0.430283405800751[/C][/ROW]
[ROW][C]39[/C][C]118.8[/C][C]114.40941589727[/C][C]4.3905841027299[/C][/ROW]
[ROW][C]40[/C][C]106.1[/C][C]107.540166982053[/C][C]-1.44016698205297[/C][/ROW]
[ROW][C]41[/C][C]109.3[/C][C]106.287710547374[/C][C]3.01228945262617[/C][/ROW]
[ROW][C]42[/C][C]117.2[/C][C]116.418192655523[/C][C]0.781807344477457[/C][/ROW]
[ROW][C]43[/C][C]92.5[/C][C]87.6789389819997[/C][C]4.82106101800034[/C][/ROW]
[ROW][C]44[/C][C]104.2[/C][C]102.060118696742[/C][C]2.13988130325779[/C][/ROW]
[ROW][C]45[/C][C]112.5[/C][C]115.007444877342[/C][C]-2.50744487734211[/C][/ROW]
[ROW][C]46[/C][C]122.4[/C][C]119.171341654162[/C][C]3.22865834583831[/C][/ROW]
[ROW][C]47[/C][C]113.3[/C][C]109.612123984464[/C][C]3.68787601553581[/C][/ROW]
[ROW][C]48[/C][C]100[/C][C]99.973487740503[/C][C]0.0265122594969877[/C][/ROW]
[ROW][C]49[/C][C]110.7[/C][C]108.772814156194[/C][C]1.92718584380584[/C][/ROW]
[ROW][C]50[/C][C]112.8[/C][C]107.48967442895[/C][C]5.31032557104998[/C][/ROW]
[ROW][C]51[/C][C]109.8[/C][C]115.325467723770[/C][C]-5.52546772377049[/C][/ROW]
[ROW][C]52[/C][C]117.3[/C][C]116.837466394723[/C][C]0.462533605276545[/C][/ROW]
[ROW][C]53[/C][C]109.1[/C][C]112.191137440027[/C][C]-3.09113744002732[/C][/ROW]
[ROW][C]54[/C][C]115.9[/C][C]118.957535392596[/C][C]-3.05753539259604[/C][/ROW]
[ROW][C]55[/C][C]96[/C][C]99.3802077089405[/C][C]-3.38020770894048[/C][/ROW]
[ROW][C]56[/C][C]99.8[/C][C]103.357345521235[/C][C]-3.55734552123534[/C][/ROW]
[ROW][C]57[/C][C]116.8[/C][C]117.868791189263[/C][C]-1.06879118926339[/C][/ROW]
[ROW][C]58[/C][C]115.7[/C][C]118.065447976785[/C][C]-2.36544797678486[/C][/ROW]
[ROW][C]59[/C][C]99.4[/C][C]105.664250826783[/C][C]-6.26425082678338[/C][/ROW]
[ROW][C]60[/C][C]94.3[/C][C]100.228828930464[/C][C]-5.92882893046369[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58444&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58444&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
195.198.8702912655445-3.77029126554452
29798.4283768644238-1.4283768644238
3112.7112.2801139621060.41988603789447
4102.9103.541909840113-0.641909840112737
597.4102.252065894731-4.85206589473064
6111.4112.101323400587-0.701323400586641
787.487.13624868607180.263751313928171
896.896.01393202639750.786067973602462
9114.1114.872520373447-0.772520373446601
10110.3114.066371327565-3.76637132756515
11103.9107.833623614856-3.93362361485621
12101.699.5077624297032.09223757029699
1394.696.7222811271049-2.12228112710488
1495.999.6319422629941-3.73194226299409
15104.7108.722888805716-4.02288880571649
16102.8100.1134716775352.68652832246503
1798.199.3248268297767-1.22482682977674
18113.9111.0283716094332.87162839056653
1980.983.394443095994-2.49444309599395
2095.794.4340921096631.26590789033707
21113.2111.0588759933352.14112400666497
22105.9106.756786922336-0.856786922336051
23108.8106.1719833735052.62801662649468
24102.399.41008523865732.88991476134270
259996.96207902511362.03792097488638
26100.7101.280289849433-0.580289849432837
27115.5110.7621136111374.7378863888626
28100.7101.766985105576-1.06698510557587
29109.9103.7442592880916.15574071190854
30114.6114.4945769418610.105423058138700
3185.484.6101615269940.789838473005926
32100.5101.134511645962-0.634511645961985
33114.8112.5923675666132.20763243338713
34116.5112.7400521191523.75994788084775
35112.9109.0180182003913.88198179960910
36102101.0798356606730.920164339327007
37106104.0725344260431.92746557395719
38105.3104.8697165941990.430283405800751
39118.8114.409415897274.3905841027299
40106.1107.540166982053-1.44016698205297
41109.3106.2877105473743.01228945262617
42117.2116.4181926555230.781807344477457
4392.587.67893898199974.82106101800034
44104.2102.0601186967422.13988130325779
45112.5115.007444877342-2.50744487734211
46122.4119.1713416541623.22865834583831
47113.3109.6121239844643.68787601553581
4810099.9734877405030.0265122594969877
49110.7108.7728141561941.92718584380584
50112.8107.489674428955.31032557104998
51109.8115.325467723770-5.52546772377049
52117.3116.8374663947230.462533605276545
53109.1112.191137440027-3.09113744002732
54115.9118.957535392596-3.05753539259604
559699.3802077089405-3.38020770894048
5699.8103.357345521235-3.55734552123534
57116.8117.868791189263-1.06879118926339
58115.7118.065447976785-2.36544797678486
5999.4105.664250826783-6.26425082678338
6094.3100.228828930464-5.92882893046369







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.253367449398110.506734898796220.74663255060189
220.1398694748150400.2797389496300800.86013052518496
230.2199108854907720.4398217709815450.780089114509228
240.1329719051855750.2659438103711490.867028094814425
250.09679227593892240.1935845518778450.903207724061078
260.1043426349524600.2086852699049210.89565736504754
270.1312568128282660.2625136256565320.868743187171734
280.1164137760130510.2328275520261010.88358622398695
290.1232016739944150.246403347988830.876798326005585
300.0853442440589330.1706884881178660.914655755941067
310.06565657579113390.1313131515822680.934343424208866
320.08405827870593470.1681165574118690.915941721294065
330.05677224647789190.1135444929557840.943227753522108
340.03086387526192340.06172775052384680.969136124738077
350.02245114244811250.0449022848962250.977548857551887
360.02189441927703180.04378883855406360.978105580722968
370.0205576124292380.0411152248584760.979442387570762
380.07442312578632760.1488462515726550.925576874213672
390.04024513518798620.08049027037597250.959754864812014

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.25336744939811 & 0.50673489879622 & 0.74663255060189 \tabularnewline
22 & 0.139869474815040 & 0.279738949630080 & 0.86013052518496 \tabularnewline
23 & 0.219910885490772 & 0.439821770981545 & 0.780089114509228 \tabularnewline
24 & 0.132971905185575 & 0.265943810371149 & 0.867028094814425 \tabularnewline
25 & 0.0967922759389224 & 0.193584551877845 & 0.903207724061078 \tabularnewline
26 & 0.104342634952460 & 0.208685269904921 & 0.89565736504754 \tabularnewline
27 & 0.131256812828266 & 0.262513625656532 & 0.868743187171734 \tabularnewline
28 & 0.116413776013051 & 0.232827552026101 & 0.88358622398695 \tabularnewline
29 & 0.123201673994415 & 0.24640334798883 & 0.876798326005585 \tabularnewline
30 & 0.085344244058933 & 0.170688488117866 & 0.914655755941067 \tabularnewline
31 & 0.0656565757911339 & 0.131313151582268 & 0.934343424208866 \tabularnewline
32 & 0.0840582787059347 & 0.168116557411869 & 0.915941721294065 \tabularnewline
33 & 0.0567722464778919 & 0.113544492955784 & 0.943227753522108 \tabularnewline
34 & 0.0308638752619234 & 0.0617277505238468 & 0.969136124738077 \tabularnewline
35 & 0.0224511424481125 & 0.044902284896225 & 0.977548857551887 \tabularnewline
36 & 0.0218944192770318 & 0.0437888385540636 & 0.978105580722968 \tabularnewline
37 & 0.020557612429238 & 0.041115224858476 & 0.979442387570762 \tabularnewline
38 & 0.0744231257863276 & 0.148846251572655 & 0.925576874213672 \tabularnewline
39 & 0.0402451351879862 & 0.0804902703759725 & 0.959754864812014 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58444&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]21[/C][C]0.25336744939811[/C][C]0.50673489879622[/C][C]0.74663255060189[/C][/ROW]
[ROW][C]22[/C][C]0.139869474815040[/C][C]0.279738949630080[/C][C]0.86013052518496[/C][/ROW]
[ROW][C]23[/C][C]0.219910885490772[/C][C]0.439821770981545[/C][C]0.780089114509228[/C][/ROW]
[ROW][C]24[/C][C]0.132971905185575[/C][C]0.265943810371149[/C][C]0.867028094814425[/C][/ROW]
[ROW][C]25[/C][C]0.0967922759389224[/C][C]0.193584551877845[/C][C]0.903207724061078[/C][/ROW]
[ROW][C]26[/C][C]0.104342634952460[/C][C]0.208685269904921[/C][C]0.89565736504754[/C][/ROW]
[ROW][C]27[/C][C]0.131256812828266[/C][C]0.262513625656532[/C][C]0.868743187171734[/C][/ROW]
[ROW][C]28[/C][C]0.116413776013051[/C][C]0.232827552026101[/C][C]0.88358622398695[/C][/ROW]
[ROW][C]29[/C][C]0.123201673994415[/C][C]0.24640334798883[/C][C]0.876798326005585[/C][/ROW]
[ROW][C]30[/C][C]0.085344244058933[/C][C]0.170688488117866[/C][C]0.914655755941067[/C][/ROW]
[ROW][C]31[/C][C]0.0656565757911339[/C][C]0.131313151582268[/C][C]0.934343424208866[/C][/ROW]
[ROW][C]32[/C][C]0.0840582787059347[/C][C]0.168116557411869[/C][C]0.915941721294065[/C][/ROW]
[ROW][C]33[/C][C]0.0567722464778919[/C][C]0.113544492955784[/C][C]0.943227753522108[/C][/ROW]
[ROW][C]34[/C][C]0.0308638752619234[/C][C]0.0617277505238468[/C][C]0.969136124738077[/C][/ROW]
[ROW][C]35[/C][C]0.0224511424481125[/C][C]0.044902284896225[/C][C]0.977548857551887[/C][/ROW]
[ROW][C]36[/C][C]0.0218944192770318[/C][C]0.0437888385540636[/C][C]0.978105580722968[/C][/ROW]
[ROW][C]37[/C][C]0.020557612429238[/C][C]0.041115224858476[/C][C]0.979442387570762[/C][/ROW]
[ROW][C]38[/C][C]0.0744231257863276[/C][C]0.148846251572655[/C][C]0.925576874213672[/C][/ROW]
[ROW][C]39[/C][C]0.0402451351879862[/C][C]0.0804902703759725[/C][C]0.959754864812014[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58444&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58444&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
210.253367449398110.506734898796220.74663255060189
220.1398694748150400.2797389496300800.86013052518496
230.2199108854907720.4398217709815450.780089114509228
240.1329719051855750.2659438103711490.867028094814425
250.09679227593892240.1935845518778450.903207724061078
260.1043426349524600.2086852699049210.89565736504754
270.1312568128282660.2625136256565320.868743187171734
280.1164137760130510.2328275520261010.88358622398695
290.1232016739944150.246403347988830.876798326005585
300.0853442440589330.1706884881178660.914655755941067
310.06565657579113390.1313131515822680.934343424208866
320.08405827870593470.1681165574118690.915941721294065
330.05677224647789190.1135444929557840.943227753522108
340.03086387526192340.06172775052384680.969136124738077
350.02245114244811250.0449022848962250.977548857551887
360.02189441927703180.04378883855406360.978105580722968
370.0205576124292380.0411152248584760.979442387570762
380.07442312578632760.1488462515726550.925576874213672
390.04024513518798620.08049027037597250.959754864812014







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.157894736842105NOK
10% type I error level50.263157894736842NOK

\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 & 0.157894736842105 & NOK \tabularnewline
10% type I error level & 5 & 0.263157894736842 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58444&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]0.157894736842105[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]5[/C][C]0.263157894736842[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58444&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58444&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 level30.157894736842105NOK
10% type I error level50.263157894736842NOK



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')
}