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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 computationWed, 19 Dec 2012 07:35:33 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/19/t1355920575hu2467csayfear0.htm/, Retrieved Thu, 31 Oct 2024 23:00:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=201880, Retrieved Thu, 31 Oct 2024 23:00:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact149

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [regressiemodel co...] [2012-12-15 10:55:28] [2c4ddb4bf62114b8025bb962e2c7a2b5]
- R P     [Multiple Regression] [seasonal dummies] [2012-12-19 12:35:33] [b4b733de199089e913cc2b6ea19b06b9] [Current]
-    D      [Multiple Regression] [seasonal dummies] [2012-12-19 13:28:37] [2c4ddb4bf62114b8025bb962e2c7a2b5]
-    D        [Multiple Regression] [regressie met sea...] [2012-12-19 14:40:27] [2c4ddb4bf62114b8025bb962e2c7a2b5]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-3	-19	53	24	-2	-29
-4	-20	50	24	-4	-29
-7	-21	50	31	-5	-27
-7	-19	51	25	-2	-29
-7	-17	53	28	-4	-24
-3	-16	49	24	-4	-29
0	-10	54	25	-5	-21
-5	-16	57	16	-7	-20
-3	-10	58	17	-5	-26
3	-8	56	11	-6	-19
2	-7	60	12	-4	-22
-7	-15	55	39	-2	-22
-1	-7	54	19	-3	-15
0	-6	52	14	0	-16
-3	-6	55	15	-4	-22
4	2	56	7	-3	-21
2	-4	54	12	-3	-11
3	-4	53	12	-3	-10
0	-8	59	14	-4	-6
-10	-10	62	9	-5	-8
-10	-16	63	8	-5	-15
-9	-14	64	4	-6	-16
-22	-30	75	7	-10	-24
-16	-33	77	3	-11	-27
-18	-40	79	5	-13	-33
-14	-38	77	0	-12	-29
-12	-39	82	-2	-13	-34
-17	-46	83	6	-12	-37
-23	-50	81	11	-15	-31
-28	-55	78	9	-14	-33
-31	-66	79	17	-16	-25
-21	-63	79	21	-16	-27
-19	-56	73	21	-12	-21
-22	-66	72	41	-16	-32
-22	-63	67	57	-15	-31
-25	-69	67	65	-17	-32
-16	-69	50	68	-15	-30
-22	-72	45	73	-14	-34
-21	-69	39	71	-15	-35
-10	-67	39	71	-14	-37
-7	-64	37	70	-16	-32
-5	-61	30	69	-11	-28
-4	-58	24	65	-14	-26
7	-47	27	57	-12	-24
6	-44	19	57	-11	-27
3	-42	19	57	-13	-26
10	-34	25	55	-12	-27
0	-38	16	65	-12	-27
-2	-41	20	65	-10	-24
-1	-38	25	64	-12	-28
2	-37	34	60	-11	-23
8	-22	39	43	-10	-23
-6	-37	40	47	-12	-29
-4	-36	38	40	-12	-25
4	-25	42	31	-11	-24
7	-15	46	27	-12	-20
3	-17	48	24	-9	-22
3	-19	51	23	-6	-24
8	-12	55	17	-7	-27
3	-17	52	16	-7	-25
-3	-21	55	15	-10	-26
4	-10	58	8	-8	-24
-5	-19	72	5	-11	-26
-1	-14	70	6	-12	-22
5	-8	70	5	-11	-20
0	-16	63	12	-11	-26
-6	-14	66	8	-9	-22
-13	-30	65	17	-9	-29
-15	-33	55	22	-12	-30
-8	-37	57	24	-10	-26
-20	-47	60	36	-10	-30
-10	-48	63	31	-13	-33
-22	-50	65	34	-13	-33
-25	-56	61	47	-12	-31
-10	-47	65	33	-14	-36
-8	-37	63	35	-9	-43
-9	-35	59	31	-12	-40
-5	-29	56	35	-10	-38
-7	-28	54	39	-13	-41
-11	-29	56	46	-11	-38
-11	-33	54	40	-11	-40
-16	-41	58	50	-11	-41

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\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 & 8 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201880&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201880&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201880&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 time8 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Y_t[t] = + 27.6311102673683 + 0.414284391942137X_1t[t] -0.46721105468132X_2t[t] -0.0906080774295398X_3t[t] -0.847575818888379X_4t[t] + 0.0492227659404471X_5t[t] -0.880855887263957M1[t] -0.909163478628467M2[t] + 0.29762990266939M3[t] + 3.20092531029737M4[t] + 0.0785997411513749M5[t] -0.232671950536061M6[t] -1.29989292249193M7[t] -0.662633856942808M8[t] -1.7174892402748M9[t] + 0.173268888997232M10[t] + 0.898960134925918M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y_t[t] =  +  27.6311102673683 +  0.414284391942137X_1t[t] -0.46721105468132X_2t[t] -0.0906080774295398X_3t[t] -0.847575818888379X_4t[t] +  0.0492227659404471X_5t[t] -0.880855887263957M1[t] -0.909163478628467M2[t] +  0.29762990266939M3[t] +  3.20092531029737M4[t] +  0.0785997411513749M5[t] -0.232671950536061M6[t] -1.29989292249193M7[t] -0.662633856942808M8[t] -1.7174892402748M9[t] +  0.173268888997232M10[t] +  0.898960134925918M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201880&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y_t[t] =  +  27.6311102673683 +  0.414284391942137X_1t[t] -0.46721105468132X_2t[t] -0.0906080774295398X_3t[t] -0.847575818888379X_4t[t] +  0.0492227659404471X_5t[t] -0.880855887263957M1[t] -0.909163478628467M2[t] +  0.29762990266939M3[t] +  3.20092531029737M4[t] +  0.0785997411513749M5[t] -0.232671950536061M6[t] -1.29989292249193M7[t] -0.662633856942808M8[t] -1.7174892402748M9[t] +  0.173268888997232M10[t] +  0.898960134925918M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201880&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201880&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[t] = + 27.6311102673683 + 0.414284391942137X_1t[t] -0.46721105468132X_2t[t] -0.0906080774295398X_3t[t] -0.847575818888379X_4t[t] + 0.0492227659404471X_5t[t] -0.880855887263957M1[t] -0.909163478628467M2[t] + 0.29762990266939M3[t] + 3.20092531029737M4[t] + 0.0785997411513749M5[t] -0.232671950536061M6[t] -1.29989292249193M7[t] -0.662633856942808M8[t] -1.7174892402748M9[t] + 0.173268888997232M10[t] + 0.898960134925918M11[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)27.63111026736834.8737335.669400
X_1t0.4142843919421370.0643796.435100
X_2t-0.467211054681320.06901-6.770200
X_3t-0.09060807742953980.066685-1.35880.1789210.08946
X_4t-0.8475758188883790.224397-3.77710.0003460.000173
X_5t0.04922276594044710.088560.55580.5802490.290124
M1-0.8808558872639572.355055-0.3740.7096010.3548
M2-0.9091634786284672.370966-0.38350.7026320.351316
M30.297629902669392.3498930.12670.8996030.449802
M43.200925310297372.3820211.34380.183690.091845
M50.07859974115137492.3521260.03340.9734450.486722
M6-0.2326719505360612.382495-0.09770.9225040.461252
M7-1.299892922491932.373917-0.54760.5858610.29293
M8-0.6626338569428082.367914-0.27980.780490.390245
M9-1.71748924027482.384242-0.72040.4738920.236946
M100.1732688889972322.3553020.07360.9415820.470791
M110.8989601349259182.424710.37070.712030.356015

\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) & 27.6311102673683 & 4.873733 & 5.6694 & 0 & 0 \tabularnewline
X_1t & 0.414284391942137 & 0.064379 & 6.4351 & 0 & 0 \tabularnewline
X_2t & -0.46721105468132 & 0.06901 & -6.7702 & 0 & 0 \tabularnewline
X_3t & -0.0906080774295398 & 0.066685 & -1.3588 & 0.178921 & 0.08946 \tabularnewline
X_4t & -0.847575818888379 & 0.224397 & -3.7771 & 0.000346 & 0.000173 \tabularnewline
X_5t & 0.0492227659404471 & 0.08856 & 0.5558 & 0.580249 & 0.290124 \tabularnewline
M1 & -0.880855887263957 & 2.355055 & -0.374 & 0.709601 & 0.3548 \tabularnewline
M2 & -0.909163478628467 & 2.370966 & -0.3835 & 0.702632 & 0.351316 \tabularnewline
M3 & 0.29762990266939 & 2.349893 & 0.1267 & 0.899603 & 0.449802 \tabularnewline
M4 & 3.20092531029737 & 2.382021 & 1.3438 & 0.18369 & 0.091845 \tabularnewline
M5 & 0.0785997411513749 & 2.352126 & 0.0334 & 0.973445 & 0.486722 \tabularnewline
M6 & -0.232671950536061 & 2.382495 & -0.0977 & 0.922504 & 0.461252 \tabularnewline
M7 & -1.29989292249193 & 2.373917 & -0.5476 & 0.585861 & 0.29293 \tabularnewline
M8 & -0.662633856942808 & 2.367914 & -0.2798 & 0.78049 & 0.390245 \tabularnewline
M9 & -1.7174892402748 & 2.384242 & -0.7204 & 0.473892 & 0.236946 \tabularnewline
M10 & 0.173268888997232 & 2.355302 & 0.0736 & 0.941582 & 0.470791 \tabularnewline
M11 & 0.898960134925918 & 2.42471 & 0.3707 & 0.71203 & 0.356015 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201880&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]27.6311102673683[/C][C]4.873733[/C][C]5.6694[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X_1t[/C][C]0.414284391942137[/C][C]0.064379[/C][C]6.4351[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X_2t[/C][C]-0.46721105468132[/C][C]0.06901[/C][C]-6.7702[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X_3t[/C][C]-0.0906080774295398[/C][C]0.066685[/C][C]-1.3588[/C][C]0.178921[/C][C]0.08946[/C][/ROW]
[ROW][C]X_4t[/C][C]-0.847575818888379[/C][C]0.224397[/C][C]-3.7771[/C][C]0.000346[/C][C]0.000173[/C][/ROW]
[ROW][C]X_5t[/C][C]0.0492227659404471[/C][C]0.08856[/C][C]0.5558[/C][C]0.580249[/C][C]0.290124[/C][/ROW]
[ROW][C]M1[/C][C]-0.880855887263957[/C][C]2.355055[/C][C]-0.374[/C][C]0.709601[/C][C]0.3548[/C][/ROW]
[ROW][C]M2[/C][C]-0.909163478628467[/C][C]2.370966[/C][C]-0.3835[/C][C]0.702632[/C][C]0.351316[/C][/ROW]
[ROW][C]M3[/C][C]0.29762990266939[/C][C]2.349893[/C][C]0.1267[/C][C]0.899603[/C][C]0.449802[/C][/ROW]
[ROW][C]M4[/C][C]3.20092531029737[/C][C]2.382021[/C][C]1.3438[/C][C]0.18369[/C][C]0.091845[/C][/ROW]
[ROW][C]M5[/C][C]0.0785997411513749[/C][C]2.352126[/C][C]0.0334[/C][C]0.973445[/C][C]0.486722[/C][/ROW]
[ROW][C]M6[/C][C]-0.232671950536061[/C][C]2.382495[/C][C]-0.0977[/C][C]0.922504[/C][C]0.461252[/C][/ROW]
[ROW][C]M7[/C][C]-1.29989292249193[/C][C]2.373917[/C][C]-0.5476[/C][C]0.585861[/C][C]0.29293[/C][/ROW]
[ROW][C]M8[/C][C]-0.662633856942808[/C][C]2.367914[/C][C]-0.2798[/C][C]0.78049[/C][C]0.390245[/C][/ROW]
[ROW][C]M9[/C][C]-1.7174892402748[/C][C]2.384242[/C][C]-0.7204[/C][C]0.473892[/C][C]0.236946[/C][/ROW]
[ROW][C]M10[/C][C]0.173268888997232[/C][C]2.355302[/C][C]0.0736[/C][C]0.941582[/C][C]0.470791[/C][/ROW]
[ROW][C]M11[/C][C]0.898960134925918[/C][C]2.42471[/C][C]0.3707[/C][C]0.71203[/C][C]0.356015[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201880&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201880&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)27.63111026736834.8737335.669400
X_1t0.4142843919421370.0643796.435100
X_2t-0.467211054681320.06901-6.770200
X_3t-0.09060807742953980.066685-1.35880.1789210.08946
X_4t-0.8475758188883790.224397-3.77710.0003460.000173
X_5t0.04922276594044710.088560.55580.5802490.290124
M1-0.8808558872639572.355055-0.3740.7096010.3548
M2-0.9091634786284672.370966-0.38350.7026320.351316
M30.297629902669392.3498930.12670.8996030.449802
M43.200925310297372.3820211.34380.183690.091845
M50.07859974115137492.3521260.03340.9734450.486722
M6-0.2326719505360612.382495-0.09770.9225040.461252
M7-1.299892922491932.373917-0.54760.5858610.29293
M8-0.6626338569428082.367914-0.27980.780490.390245
M9-1.71748924027482.384242-0.72040.4738920.236946
M100.1732688889972322.3553020.07360.9415820.470791
M110.8989601349259182.424710.37070.712030.356015






Multiple Linear Regression - Regression Statistics
Multiple R0.922061804389825
R-squared0.850197971114621
Adjusted R-squared0.813323625542835
F-TEST (value)23.0566253564957
F-TEST (DF numerator)16
F-TEST (DF denominator)65
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.19270177080869
Sum Squared Residuals1142.61862903125

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.922061804389825 \tabularnewline
R-squared & 0.850197971114621 \tabularnewline
Adjusted R-squared & 0.813323625542835 \tabularnewline
F-TEST (value) & 23.0566253564957 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 65 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.19270177080869 \tabularnewline
Sum Squared Residuals & 1142.61862903125 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201880&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.922061804389825[/C][/ROW]
[ROW][C]R-squared[/C][C]0.850197971114621[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.813323625542835[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]23.0566253564957[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]65[/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]4.19270177080869[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1142.61862903125[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201880&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201880&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 R0.922061804389825
R-squared0.850197971114621
Adjusted R-squared0.813323625542835
F-TEST (value)23.0566253564957
F-TEST (DF numerator)16
F-TEST (DF denominator)65
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.19270177080869
Sum Squared Residuals1142.61862903125






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-3-7.790237397711414.79023739771141
2-4-5.136044579197321.13604457919732
3-7-4.0317707810791-2.9682292189209
4-7-2.86464216821697-4.13535783178303
5-7-4.42337982765096-2.57662017234904
6-3-2.33520442865505-0.66479557134495
70-2.102024453382272.10202445338227
8-5-2.79225780294688-2.20774219705312
9-3-3.909714200156340.909714200156343
1031.479818467411351.52018153258865
112-1.182478126470743.18247812647074
12-7-7.201227851901490.201227851901495
13-1-1.296300819884730.296300819884727
140-2.114811745402342.11481174540234
15-30.694707074332846-3.69470707433285
1646.371578129305-2.371578129305
1721.737155590125580.262844409874415
1831.942317719059921.05768228094008
190-2.722056420961332.72205642096133
20-10-3.11282862918526-6.88717137081474
21-10-7.37455270300499-2.62544729699501
22-9-3.96165148186391-5.03834851813609
23-22-12.2791351927626-9.72086480723738
24-16-14.2930307820924-1.7069692179076
25-18-17.789700635039-0.210299364961043
26-14-16.25266170113542.25266170113545
27-12-17.01352984114115.0135298411411
28-17-19.19754496793542.19754496793544
29-23-20.6575623303272-2.34243766967277
30-28-22.4034280135916-5.59657198640841
31-31-27.1309192057283-3.86908079427173
32-21-25.71168480595184.71168480595179
33-19-24.15824979751175.15824979751174
34-22-24.9064332313622.90643323136196
35-22-22.84991582802080.849915828020826
36-25-25.31351806219960.313518062199576
37-16-20.12031635806564.12031635806558
38-22-20.5529291216478-1.44707087835223
39-21-14.3204470286286-6.67955297137143
40-10-11.53460418788561.53460418788559
41-7-10.4477809269343.447780926934
42-5-10.19610201327645.19610201327636
43-4-4.21359818305370.213598183053699
447-1.292685456644588.29268545664458
4561.637756656590684.36224334340932
4636.10145797346419-3.10145797346419
47106.622575596872323.37742440312768
4807.36529661201433-7.36529661201433
49-21.82525999024326-3.82525999024326
50-12.29261895274308-3.29261895274308
512-0.5302324456167882.53023244561679
5286.944035065150451.05596493484955
53-6-1.82238470539301-4.17761529460699
54-40.0461977099928985-4.0461977099929
5541.684380474593192.31561952540681
5676.002538433206730.997461566793265
5730.8153434003704142.18465659962959
583-2.074665329402275.07466532940227
5980.9257284270403737.07427157295963
603-0.4539668942418423.45396689424184
61-3-1.80948074516408-1.19051925483592
6240.355257246901883.64474275309812
63-5-5.991357507745110.991357507745113
64-10.871640774176823-1.87164077417682
655-0.4235006528942975.4235006528943
660-1.708163234999051.70816323499905
67-6-4.48427685141141-1.51572314858859
68-13-11.1683890607042-1.83161093929584
69-15-6.75352276947239-8.24647723052761
70-8-9.133801046205591.13380104620559
71-20-15.2367748766585-4.7632251233415
72-10-15.1035530215795.10355302157902
73-22-18.0192240343785-3.98077596562149
74-25-20.5914290522621-4.40857094773793
75-10-14.80736947012224.80736947012218
76-8-11.59046264459433.59046264459428
77-9-8.96254714692609-0.037452853073911
78-5-7.345617738530762.34561773853076
79-7-5.03150536005622-1.96849463994378
80-11-7.92469267777407-3.07530732222593
81-11-9.25706058681563-1.74293941318437
82-16-13.5047253520418-2.49527464795818

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -3 & -7.79023739771141 & 4.79023739771141 \tabularnewline
2 & -4 & -5.13604457919732 & 1.13604457919732 \tabularnewline
3 & -7 & -4.0317707810791 & -2.9682292189209 \tabularnewline
4 & -7 & -2.86464216821697 & -4.13535783178303 \tabularnewline
5 & -7 & -4.42337982765096 & -2.57662017234904 \tabularnewline
6 & -3 & -2.33520442865505 & -0.66479557134495 \tabularnewline
7 & 0 & -2.10202445338227 & 2.10202445338227 \tabularnewline
8 & -5 & -2.79225780294688 & -2.20774219705312 \tabularnewline
9 & -3 & -3.90971420015634 & 0.909714200156343 \tabularnewline
10 & 3 & 1.47981846741135 & 1.52018153258865 \tabularnewline
11 & 2 & -1.18247812647074 & 3.18247812647074 \tabularnewline
12 & -7 & -7.20122785190149 & 0.201227851901495 \tabularnewline
13 & -1 & -1.29630081988473 & 0.296300819884727 \tabularnewline
14 & 0 & -2.11481174540234 & 2.11481174540234 \tabularnewline
15 & -3 & 0.694707074332846 & -3.69470707433285 \tabularnewline
16 & 4 & 6.371578129305 & -2.371578129305 \tabularnewline
17 & 2 & 1.73715559012558 & 0.262844409874415 \tabularnewline
18 & 3 & 1.94231771905992 & 1.05768228094008 \tabularnewline
19 & 0 & -2.72205642096133 & 2.72205642096133 \tabularnewline
20 & -10 & -3.11282862918526 & -6.88717137081474 \tabularnewline
21 & -10 & -7.37455270300499 & -2.62544729699501 \tabularnewline
22 & -9 & -3.96165148186391 & -5.03834851813609 \tabularnewline
23 & -22 & -12.2791351927626 & -9.72086480723738 \tabularnewline
24 & -16 & -14.2930307820924 & -1.7069692179076 \tabularnewline
25 & -18 & -17.789700635039 & -0.210299364961043 \tabularnewline
26 & -14 & -16.2526617011354 & 2.25266170113545 \tabularnewline
27 & -12 & -17.0135298411411 & 5.0135298411411 \tabularnewline
28 & -17 & -19.1975449679354 & 2.19754496793544 \tabularnewline
29 & -23 & -20.6575623303272 & -2.34243766967277 \tabularnewline
30 & -28 & -22.4034280135916 & -5.59657198640841 \tabularnewline
31 & -31 & -27.1309192057283 & -3.86908079427173 \tabularnewline
32 & -21 & -25.7116848059518 & 4.71168480595179 \tabularnewline
33 & -19 & -24.1582497975117 & 5.15824979751174 \tabularnewline
34 & -22 & -24.906433231362 & 2.90643323136196 \tabularnewline
35 & -22 & -22.8499158280208 & 0.849915828020826 \tabularnewline
36 & -25 & -25.3135180621996 & 0.313518062199576 \tabularnewline
37 & -16 & -20.1203163580656 & 4.12031635806558 \tabularnewline
38 & -22 & -20.5529291216478 & -1.44707087835223 \tabularnewline
39 & -21 & -14.3204470286286 & -6.67955297137143 \tabularnewline
40 & -10 & -11.5346041878856 & 1.53460418788559 \tabularnewline
41 & -7 & -10.447780926934 & 3.447780926934 \tabularnewline
42 & -5 & -10.1961020132764 & 5.19610201327636 \tabularnewline
43 & -4 & -4.2135981830537 & 0.213598183053699 \tabularnewline
44 & 7 & -1.29268545664458 & 8.29268545664458 \tabularnewline
45 & 6 & 1.63775665659068 & 4.36224334340932 \tabularnewline
46 & 3 & 6.10145797346419 & -3.10145797346419 \tabularnewline
47 & 10 & 6.62257559687232 & 3.37742440312768 \tabularnewline
48 & 0 & 7.36529661201433 & -7.36529661201433 \tabularnewline
49 & -2 & 1.82525999024326 & -3.82525999024326 \tabularnewline
50 & -1 & 2.29261895274308 & -3.29261895274308 \tabularnewline
51 & 2 & -0.530232445616788 & 2.53023244561679 \tabularnewline
52 & 8 & 6.94403506515045 & 1.05596493484955 \tabularnewline
53 & -6 & -1.82238470539301 & -4.17761529460699 \tabularnewline
54 & -4 & 0.0461977099928985 & -4.0461977099929 \tabularnewline
55 & 4 & 1.68438047459319 & 2.31561952540681 \tabularnewline
56 & 7 & 6.00253843320673 & 0.997461566793265 \tabularnewline
57 & 3 & 0.815343400370414 & 2.18465659962959 \tabularnewline
58 & 3 & -2.07466532940227 & 5.07466532940227 \tabularnewline
59 & 8 & 0.925728427040373 & 7.07427157295963 \tabularnewline
60 & 3 & -0.453966894241842 & 3.45396689424184 \tabularnewline
61 & -3 & -1.80948074516408 & -1.19051925483592 \tabularnewline
62 & 4 & 0.35525724690188 & 3.64474275309812 \tabularnewline
63 & -5 & -5.99135750774511 & 0.991357507745113 \tabularnewline
64 & -1 & 0.871640774176823 & -1.87164077417682 \tabularnewline
65 & 5 & -0.423500652894297 & 5.4235006528943 \tabularnewline
66 & 0 & -1.70816323499905 & 1.70816323499905 \tabularnewline
67 & -6 & -4.48427685141141 & -1.51572314858859 \tabularnewline
68 & -13 & -11.1683890607042 & -1.83161093929584 \tabularnewline
69 & -15 & -6.75352276947239 & -8.24647723052761 \tabularnewline
70 & -8 & -9.13380104620559 & 1.13380104620559 \tabularnewline
71 & -20 & -15.2367748766585 & -4.7632251233415 \tabularnewline
72 & -10 & -15.103553021579 & 5.10355302157902 \tabularnewline
73 & -22 & -18.0192240343785 & -3.98077596562149 \tabularnewline
74 & -25 & -20.5914290522621 & -4.40857094773793 \tabularnewline
75 & -10 & -14.8073694701222 & 4.80736947012218 \tabularnewline
76 & -8 & -11.5904626445943 & 3.59046264459428 \tabularnewline
77 & -9 & -8.96254714692609 & -0.037452853073911 \tabularnewline
78 & -5 & -7.34561773853076 & 2.34561773853076 \tabularnewline
79 & -7 & -5.03150536005622 & -1.96849463994378 \tabularnewline
80 & -11 & -7.92469267777407 & -3.07530732222593 \tabularnewline
81 & -11 & -9.25706058681563 & -1.74293941318437 \tabularnewline
82 & -16 & -13.5047253520418 & -2.49527464795818 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201880&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]-3[/C][C]-7.79023739771141[/C][C]4.79023739771141[/C][/ROW]
[ROW][C]2[/C][C]-4[/C][C]-5.13604457919732[/C][C]1.13604457919732[/C][/ROW]
[ROW][C]3[/C][C]-7[/C][C]-4.0317707810791[/C][C]-2.9682292189209[/C][/ROW]
[ROW][C]4[/C][C]-7[/C][C]-2.86464216821697[/C][C]-4.13535783178303[/C][/ROW]
[ROW][C]5[/C][C]-7[/C][C]-4.42337982765096[/C][C]-2.57662017234904[/C][/ROW]
[ROW][C]6[/C][C]-3[/C][C]-2.33520442865505[/C][C]-0.66479557134495[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]-2.10202445338227[/C][C]2.10202445338227[/C][/ROW]
[ROW][C]8[/C][C]-5[/C][C]-2.79225780294688[/C][C]-2.20774219705312[/C][/ROW]
[ROW][C]9[/C][C]-3[/C][C]-3.90971420015634[/C][C]0.909714200156343[/C][/ROW]
[ROW][C]10[/C][C]3[/C][C]1.47981846741135[/C][C]1.52018153258865[/C][/ROW]
[ROW][C]11[/C][C]2[/C][C]-1.18247812647074[/C][C]3.18247812647074[/C][/ROW]
[ROW][C]12[/C][C]-7[/C][C]-7.20122785190149[/C][C]0.201227851901495[/C][/ROW]
[ROW][C]13[/C][C]-1[/C][C]-1.29630081988473[/C][C]0.296300819884727[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]-2.11481174540234[/C][C]2.11481174540234[/C][/ROW]
[ROW][C]15[/C][C]-3[/C][C]0.694707074332846[/C][C]-3.69470707433285[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]6.371578129305[/C][C]-2.371578129305[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]1.73715559012558[/C][C]0.262844409874415[/C][/ROW]
[ROW][C]18[/C][C]3[/C][C]1.94231771905992[/C][C]1.05768228094008[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]-2.72205642096133[/C][C]2.72205642096133[/C][/ROW]
[ROW][C]20[/C][C]-10[/C][C]-3.11282862918526[/C][C]-6.88717137081474[/C][/ROW]
[ROW][C]21[/C][C]-10[/C][C]-7.37455270300499[/C][C]-2.62544729699501[/C][/ROW]
[ROW][C]22[/C][C]-9[/C][C]-3.96165148186391[/C][C]-5.03834851813609[/C][/ROW]
[ROW][C]23[/C][C]-22[/C][C]-12.2791351927626[/C][C]-9.72086480723738[/C][/ROW]
[ROW][C]24[/C][C]-16[/C][C]-14.2930307820924[/C][C]-1.7069692179076[/C][/ROW]
[ROW][C]25[/C][C]-18[/C][C]-17.789700635039[/C][C]-0.210299364961043[/C][/ROW]
[ROW][C]26[/C][C]-14[/C][C]-16.2526617011354[/C][C]2.25266170113545[/C][/ROW]
[ROW][C]27[/C][C]-12[/C][C]-17.0135298411411[/C][C]5.0135298411411[/C][/ROW]
[ROW][C]28[/C][C]-17[/C][C]-19.1975449679354[/C][C]2.19754496793544[/C][/ROW]
[ROW][C]29[/C][C]-23[/C][C]-20.6575623303272[/C][C]-2.34243766967277[/C][/ROW]
[ROW][C]30[/C][C]-28[/C][C]-22.4034280135916[/C][C]-5.59657198640841[/C][/ROW]
[ROW][C]31[/C][C]-31[/C][C]-27.1309192057283[/C][C]-3.86908079427173[/C][/ROW]
[ROW][C]32[/C][C]-21[/C][C]-25.7116848059518[/C][C]4.71168480595179[/C][/ROW]
[ROW][C]33[/C][C]-19[/C][C]-24.1582497975117[/C][C]5.15824979751174[/C][/ROW]
[ROW][C]34[/C][C]-22[/C][C]-24.906433231362[/C][C]2.90643323136196[/C][/ROW]
[ROW][C]35[/C][C]-22[/C][C]-22.8499158280208[/C][C]0.849915828020826[/C][/ROW]
[ROW][C]36[/C][C]-25[/C][C]-25.3135180621996[/C][C]0.313518062199576[/C][/ROW]
[ROW][C]37[/C][C]-16[/C][C]-20.1203163580656[/C][C]4.12031635806558[/C][/ROW]
[ROW][C]38[/C][C]-22[/C][C]-20.5529291216478[/C][C]-1.44707087835223[/C][/ROW]
[ROW][C]39[/C][C]-21[/C][C]-14.3204470286286[/C][C]-6.67955297137143[/C][/ROW]
[ROW][C]40[/C][C]-10[/C][C]-11.5346041878856[/C][C]1.53460418788559[/C][/ROW]
[ROW][C]41[/C][C]-7[/C][C]-10.447780926934[/C][C]3.447780926934[/C][/ROW]
[ROW][C]42[/C][C]-5[/C][C]-10.1961020132764[/C][C]5.19610201327636[/C][/ROW]
[ROW][C]43[/C][C]-4[/C][C]-4.2135981830537[/C][C]0.213598183053699[/C][/ROW]
[ROW][C]44[/C][C]7[/C][C]-1.29268545664458[/C][C]8.29268545664458[/C][/ROW]
[ROW][C]45[/C][C]6[/C][C]1.63775665659068[/C][C]4.36224334340932[/C][/ROW]
[ROW][C]46[/C][C]3[/C][C]6.10145797346419[/C][C]-3.10145797346419[/C][/ROW]
[ROW][C]47[/C][C]10[/C][C]6.62257559687232[/C][C]3.37742440312768[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]7.36529661201433[/C][C]-7.36529661201433[/C][/ROW]
[ROW][C]49[/C][C]-2[/C][C]1.82525999024326[/C][C]-3.82525999024326[/C][/ROW]
[ROW][C]50[/C][C]-1[/C][C]2.29261895274308[/C][C]-3.29261895274308[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]-0.530232445616788[/C][C]2.53023244561679[/C][/ROW]
[ROW][C]52[/C][C]8[/C][C]6.94403506515045[/C][C]1.05596493484955[/C][/ROW]
[ROW][C]53[/C][C]-6[/C][C]-1.82238470539301[/C][C]-4.17761529460699[/C][/ROW]
[ROW][C]54[/C][C]-4[/C][C]0.0461977099928985[/C][C]-4.0461977099929[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]1.68438047459319[/C][C]2.31561952540681[/C][/ROW]
[ROW][C]56[/C][C]7[/C][C]6.00253843320673[/C][C]0.997461566793265[/C][/ROW]
[ROW][C]57[/C][C]3[/C][C]0.815343400370414[/C][C]2.18465659962959[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]-2.07466532940227[/C][C]5.07466532940227[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]0.925728427040373[/C][C]7.07427157295963[/C][/ROW]
[ROW][C]60[/C][C]3[/C][C]-0.453966894241842[/C][C]3.45396689424184[/C][/ROW]
[ROW][C]61[/C][C]-3[/C][C]-1.80948074516408[/C][C]-1.19051925483592[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]0.35525724690188[/C][C]3.64474275309812[/C][/ROW]
[ROW][C]63[/C][C]-5[/C][C]-5.99135750774511[/C][C]0.991357507745113[/C][/ROW]
[ROW][C]64[/C][C]-1[/C][C]0.871640774176823[/C][C]-1.87164077417682[/C][/ROW]
[ROW][C]65[/C][C]5[/C][C]-0.423500652894297[/C][C]5.4235006528943[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]-1.70816323499905[/C][C]1.70816323499905[/C][/ROW]
[ROW][C]67[/C][C]-6[/C][C]-4.48427685141141[/C][C]-1.51572314858859[/C][/ROW]
[ROW][C]68[/C][C]-13[/C][C]-11.1683890607042[/C][C]-1.83161093929584[/C][/ROW]
[ROW][C]69[/C][C]-15[/C][C]-6.75352276947239[/C][C]-8.24647723052761[/C][/ROW]
[ROW][C]70[/C][C]-8[/C][C]-9.13380104620559[/C][C]1.13380104620559[/C][/ROW]
[ROW][C]71[/C][C]-20[/C][C]-15.2367748766585[/C][C]-4.7632251233415[/C][/ROW]
[ROW][C]72[/C][C]-10[/C][C]-15.103553021579[/C][C]5.10355302157902[/C][/ROW]
[ROW][C]73[/C][C]-22[/C][C]-18.0192240343785[/C][C]-3.98077596562149[/C][/ROW]
[ROW][C]74[/C][C]-25[/C][C]-20.5914290522621[/C][C]-4.40857094773793[/C][/ROW]
[ROW][C]75[/C][C]-10[/C][C]-14.8073694701222[/C][C]4.80736947012218[/C][/ROW]
[ROW][C]76[/C][C]-8[/C][C]-11.5904626445943[/C][C]3.59046264459428[/C][/ROW]
[ROW][C]77[/C][C]-9[/C][C]-8.96254714692609[/C][C]-0.037452853073911[/C][/ROW]
[ROW][C]78[/C][C]-5[/C][C]-7.34561773853076[/C][C]2.34561773853076[/C][/ROW]
[ROW][C]79[/C][C]-7[/C][C]-5.03150536005622[/C][C]-1.96849463994378[/C][/ROW]
[ROW][C]80[/C][C]-11[/C][C]-7.92469267777407[/C][C]-3.07530732222593[/C][/ROW]
[ROW][C]81[/C][C]-11[/C][C]-9.25706058681563[/C][C]-1.74293941318437[/C][/ROW]
[ROW][C]82[/C][C]-16[/C][C]-13.5047253520418[/C][C]-2.49527464795818[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201880&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201880&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
1-3-7.790237397711414.79023739771141
2-4-5.136044579197321.13604457919732
3-7-4.0317707810791-2.9682292189209
4-7-2.86464216821697-4.13535783178303
5-7-4.42337982765096-2.57662017234904
6-3-2.33520442865505-0.66479557134495
70-2.102024453382272.10202445338227
8-5-2.79225780294688-2.20774219705312
9-3-3.909714200156340.909714200156343
1031.479818467411351.52018153258865
112-1.182478126470743.18247812647074
12-7-7.201227851901490.201227851901495
13-1-1.296300819884730.296300819884727
140-2.114811745402342.11481174540234
15-30.694707074332846-3.69470707433285
1646.371578129305-2.371578129305
1721.737155590125580.262844409874415
1831.942317719059921.05768228094008
190-2.722056420961332.72205642096133
20-10-3.11282862918526-6.88717137081474
21-10-7.37455270300499-2.62544729699501
22-9-3.96165148186391-5.03834851813609
23-22-12.2791351927626-9.72086480723738
24-16-14.2930307820924-1.7069692179076
25-18-17.789700635039-0.210299364961043
26-14-16.25266170113542.25266170113545
27-12-17.01352984114115.0135298411411
28-17-19.19754496793542.19754496793544
29-23-20.6575623303272-2.34243766967277
30-28-22.4034280135916-5.59657198640841
31-31-27.1309192057283-3.86908079427173
32-21-25.71168480595184.71168480595179
33-19-24.15824979751175.15824979751174
34-22-24.9064332313622.90643323136196
35-22-22.84991582802080.849915828020826
36-25-25.31351806219960.313518062199576
37-16-20.12031635806564.12031635806558
38-22-20.5529291216478-1.44707087835223
39-21-14.3204470286286-6.67955297137143
40-10-11.53460418788561.53460418788559
41-7-10.4477809269343.447780926934
42-5-10.19610201327645.19610201327636
43-4-4.21359818305370.213598183053699
447-1.292685456644588.29268545664458
4561.637756656590684.36224334340932
4636.10145797346419-3.10145797346419
47106.622575596872323.37742440312768
4807.36529661201433-7.36529661201433
49-21.82525999024326-3.82525999024326
50-12.29261895274308-3.29261895274308
512-0.5302324456167882.53023244561679
5286.944035065150451.05596493484955
53-6-1.82238470539301-4.17761529460699
54-40.0461977099928985-4.0461977099929
5541.684380474593192.31561952540681
5676.002538433206730.997461566793265
5730.8153434003704142.18465659962959
583-2.074665329402275.07466532940227
5980.9257284270403737.07427157295963
603-0.4539668942418423.45396689424184
61-3-1.80948074516408-1.19051925483592
6240.355257246901883.64474275309812
63-5-5.991357507745110.991357507745113
64-10.871640774176823-1.87164077417682
655-0.4235006528942975.4235006528943
660-1.708163234999051.70816323499905
67-6-4.48427685141141-1.51572314858859
68-13-11.1683890607042-1.83161093929584
69-15-6.75352276947239-8.24647723052761
70-8-9.133801046205591.13380104620559
71-20-15.2367748766585-4.7632251233415
72-10-15.1035530215795.10355302157902
73-22-18.0192240343785-3.98077596562149
74-25-20.5914290522621-4.40857094773793
75-10-14.80736947012224.80736947012218
76-8-11.59046264459433.59046264459428
77-9-8.96254714692609-0.037452853073911
78-5-7.345617738530762.34561773853076
79-7-5.03150536005622-1.96849463994378
80-11-7.92469267777407-3.07530732222593
81-11-9.25706058681563-1.74293941318437
82-16-13.5047253520418-2.49527464795818






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.1074406519095770.2148813038191540.892559348090423
210.06891797882444870.1378359576488970.931082021175551
220.03356445550257730.06712891100515470.966435544497423
230.01915236725982440.03830473451964890.980847632740176
240.009934636924397680.01986927384879540.990065363075602
250.1001956720441920.2003913440883850.899804327955808
260.1603799398758520.3207598797517040.839620060124148
270.3546101815846880.7092203631693770.645389818415312
280.368200964201410.7364019284028210.63179903579859
290.2845202479539380.5690404959078760.715479752046062
300.2655609433554250.5311218867108510.734439056644575
310.2319379146319090.4638758292638180.768062085368091
320.5390737796044740.9218524407910530.460926220395526
330.5932253629177090.8135492741645820.406774637082291
340.5268232294316060.9463535411367870.473176770568394
350.4407106263502140.8814212527004280.559289373649786
360.3656762603074370.7313525206148740.634323739692563
370.3965993816292130.7931987632584260.603400618370787
380.3592622590811260.7185245181622510.640737740918874
390.3789106263677610.7578212527355210.621089373632239
400.3746853013994070.7493706027988140.625314698600593
410.4174109035413240.8348218070826480.582589096458676
420.4591998495786260.9183996991572510.540800150421374
430.3882017115551070.7764034231102150.611798288444892
440.6401670539246150.719665892150770.359832946075385
450.7495581037731630.5008837924536730.250441896226837
460.7053438005549270.5893123988901450.294656199445073
470.728803583492480.542392833015040.27119641650752
480.8533560414865860.2932879170268280.146643958513414
490.8241697066717190.3516605866565630.175830293328281
500.77889965330490.4422006933901990.2211003466951
510.7281284356654690.5437431286690610.271871564334531
520.6508865756206970.6982268487586050.349113424379303
530.6685124232074350.6629751535851310.331487576792565
540.7133702865743420.5732594268513150.286629713425658
550.6307274325624860.7385451348750290.369272567437514
560.5361139479058970.9277721041882060.463886052094103
570.529916951477290.940166097045420.47008304852271
580.4913759724442170.9827519448884340.508624027555783
590.5998990430032370.8002019139935270.400100956996763
600.634171117465570.7316577650688590.36582888253443
610.5302176851007620.9395646297984770.469782314899238
620.5052422414210080.9895155171579840.494757758578992

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.107440651909577 & 0.214881303819154 & 0.892559348090423 \tabularnewline
21 & 0.0689179788244487 & 0.137835957648897 & 0.931082021175551 \tabularnewline
22 & 0.0335644555025773 & 0.0671289110051547 & 0.966435544497423 \tabularnewline
23 & 0.0191523672598244 & 0.0383047345196489 & 0.980847632740176 \tabularnewline
24 & 0.00993463692439768 & 0.0198692738487954 & 0.990065363075602 \tabularnewline
25 & 0.100195672044192 & 0.200391344088385 & 0.899804327955808 \tabularnewline
26 & 0.160379939875852 & 0.320759879751704 & 0.839620060124148 \tabularnewline
27 & 0.354610181584688 & 0.709220363169377 & 0.645389818415312 \tabularnewline
28 & 0.36820096420141 & 0.736401928402821 & 0.63179903579859 \tabularnewline
29 & 0.284520247953938 & 0.569040495907876 & 0.715479752046062 \tabularnewline
30 & 0.265560943355425 & 0.531121886710851 & 0.734439056644575 \tabularnewline
31 & 0.231937914631909 & 0.463875829263818 & 0.768062085368091 \tabularnewline
32 & 0.539073779604474 & 0.921852440791053 & 0.460926220395526 \tabularnewline
33 & 0.593225362917709 & 0.813549274164582 & 0.406774637082291 \tabularnewline
34 & 0.526823229431606 & 0.946353541136787 & 0.473176770568394 \tabularnewline
35 & 0.440710626350214 & 0.881421252700428 & 0.559289373649786 \tabularnewline
36 & 0.365676260307437 & 0.731352520614874 & 0.634323739692563 \tabularnewline
37 & 0.396599381629213 & 0.793198763258426 & 0.603400618370787 \tabularnewline
38 & 0.359262259081126 & 0.718524518162251 & 0.640737740918874 \tabularnewline
39 & 0.378910626367761 & 0.757821252735521 & 0.621089373632239 \tabularnewline
40 & 0.374685301399407 & 0.749370602798814 & 0.625314698600593 \tabularnewline
41 & 0.417410903541324 & 0.834821807082648 & 0.582589096458676 \tabularnewline
42 & 0.459199849578626 & 0.918399699157251 & 0.540800150421374 \tabularnewline
43 & 0.388201711555107 & 0.776403423110215 & 0.611798288444892 \tabularnewline
44 & 0.640167053924615 & 0.71966589215077 & 0.359832946075385 \tabularnewline
45 & 0.749558103773163 & 0.500883792453673 & 0.250441896226837 \tabularnewline
46 & 0.705343800554927 & 0.589312398890145 & 0.294656199445073 \tabularnewline
47 & 0.72880358349248 & 0.54239283301504 & 0.27119641650752 \tabularnewline
48 & 0.853356041486586 & 0.293287917026828 & 0.146643958513414 \tabularnewline
49 & 0.824169706671719 & 0.351660586656563 & 0.175830293328281 \tabularnewline
50 & 0.7788996533049 & 0.442200693390199 & 0.2211003466951 \tabularnewline
51 & 0.728128435665469 & 0.543743128669061 & 0.271871564334531 \tabularnewline
52 & 0.650886575620697 & 0.698226848758605 & 0.349113424379303 \tabularnewline
53 & 0.668512423207435 & 0.662975153585131 & 0.331487576792565 \tabularnewline
54 & 0.713370286574342 & 0.573259426851315 & 0.286629713425658 \tabularnewline
55 & 0.630727432562486 & 0.738545134875029 & 0.369272567437514 \tabularnewline
56 & 0.536113947905897 & 0.927772104188206 & 0.463886052094103 \tabularnewline
57 & 0.52991695147729 & 0.94016609704542 & 0.47008304852271 \tabularnewline
58 & 0.491375972444217 & 0.982751944888434 & 0.508624027555783 \tabularnewline
59 & 0.599899043003237 & 0.800201913993527 & 0.400100956996763 \tabularnewline
60 & 0.63417111746557 & 0.731657765068859 & 0.36582888253443 \tabularnewline
61 & 0.530217685100762 & 0.939564629798477 & 0.469782314899238 \tabularnewline
62 & 0.505242241421008 & 0.989515517157984 & 0.494757758578992 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201880&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]0.107440651909577[/C][C]0.214881303819154[/C][C]0.892559348090423[/C][/ROW]
[ROW][C]21[/C][C]0.0689179788244487[/C][C]0.137835957648897[/C][C]0.931082021175551[/C][/ROW]
[ROW][C]22[/C][C]0.0335644555025773[/C][C]0.0671289110051547[/C][C]0.966435544497423[/C][/ROW]
[ROW][C]23[/C][C]0.0191523672598244[/C][C]0.0383047345196489[/C][C]0.980847632740176[/C][/ROW]
[ROW][C]24[/C][C]0.00993463692439768[/C][C]0.0198692738487954[/C][C]0.990065363075602[/C][/ROW]
[ROW][C]25[/C][C]0.100195672044192[/C][C]0.200391344088385[/C][C]0.899804327955808[/C][/ROW]
[ROW][C]26[/C][C]0.160379939875852[/C][C]0.320759879751704[/C][C]0.839620060124148[/C][/ROW]
[ROW][C]27[/C][C]0.354610181584688[/C][C]0.709220363169377[/C][C]0.645389818415312[/C][/ROW]
[ROW][C]28[/C][C]0.36820096420141[/C][C]0.736401928402821[/C][C]0.63179903579859[/C][/ROW]
[ROW][C]29[/C][C]0.284520247953938[/C][C]0.569040495907876[/C][C]0.715479752046062[/C][/ROW]
[ROW][C]30[/C][C]0.265560943355425[/C][C]0.531121886710851[/C][C]0.734439056644575[/C][/ROW]
[ROW][C]31[/C][C]0.231937914631909[/C][C]0.463875829263818[/C][C]0.768062085368091[/C][/ROW]
[ROW][C]32[/C][C]0.539073779604474[/C][C]0.921852440791053[/C][C]0.460926220395526[/C][/ROW]
[ROW][C]33[/C][C]0.593225362917709[/C][C]0.813549274164582[/C][C]0.406774637082291[/C][/ROW]
[ROW][C]34[/C][C]0.526823229431606[/C][C]0.946353541136787[/C][C]0.473176770568394[/C][/ROW]
[ROW][C]35[/C][C]0.440710626350214[/C][C]0.881421252700428[/C][C]0.559289373649786[/C][/ROW]
[ROW][C]36[/C][C]0.365676260307437[/C][C]0.731352520614874[/C][C]0.634323739692563[/C][/ROW]
[ROW][C]37[/C][C]0.396599381629213[/C][C]0.793198763258426[/C][C]0.603400618370787[/C][/ROW]
[ROW][C]38[/C][C]0.359262259081126[/C][C]0.718524518162251[/C][C]0.640737740918874[/C][/ROW]
[ROW][C]39[/C][C]0.378910626367761[/C][C]0.757821252735521[/C][C]0.621089373632239[/C][/ROW]
[ROW][C]40[/C][C]0.374685301399407[/C][C]0.749370602798814[/C][C]0.625314698600593[/C][/ROW]
[ROW][C]41[/C][C]0.417410903541324[/C][C]0.834821807082648[/C][C]0.582589096458676[/C][/ROW]
[ROW][C]42[/C][C]0.459199849578626[/C][C]0.918399699157251[/C][C]0.540800150421374[/C][/ROW]
[ROW][C]43[/C][C]0.388201711555107[/C][C]0.776403423110215[/C][C]0.611798288444892[/C][/ROW]
[ROW][C]44[/C][C]0.640167053924615[/C][C]0.71966589215077[/C][C]0.359832946075385[/C][/ROW]
[ROW][C]45[/C][C]0.749558103773163[/C][C]0.500883792453673[/C][C]0.250441896226837[/C][/ROW]
[ROW][C]46[/C][C]0.705343800554927[/C][C]0.589312398890145[/C][C]0.294656199445073[/C][/ROW]
[ROW][C]47[/C][C]0.72880358349248[/C][C]0.54239283301504[/C][C]0.27119641650752[/C][/ROW]
[ROW][C]48[/C][C]0.853356041486586[/C][C]0.293287917026828[/C][C]0.146643958513414[/C][/ROW]
[ROW][C]49[/C][C]0.824169706671719[/C][C]0.351660586656563[/C][C]0.175830293328281[/C][/ROW]
[ROW][C]50[/C][C]0.7788996533049[/C][C]0.442200693390199[/C][C]0.2211003466951[/C][/ROW]
[ROW][C]51[/C][C]0.728128435665469[/C][C]0.543743128669061[/C][C]0.271871564334531[/C][/ROW]
[ROW][C]52[/C][C]0.650886575620697[/C][C]0.698226848758605[/C][C]0.349113424379303[/C][/ROW]
[ROW][C]53[/C][C]0.668512423207435[/C][C]0.662975153585131[/C][C]0.331487576792565[/C][/ROW]
[ROW][C]54[/C][C]0.713370286574342[/C][C]0.573259426851315[/C][C]0.286629713425658[/C][/ROW]
[ROW][C]55[/C][C]0.630727432562486[/C][C]0.738545134875029[/C][C]0.369272567437514[/C][/ROW]
[ROW][C]56[/C][C]0.536113947905897[/C][C]0.927772104188206[/C][C]0.463886052094103[/C][/ROW]
[ROW][C]57[/C][C]0.52991695147729[/C][C]0.94016609704542[/C][C]0.47008304852271[/C][/ROW]
[ROW][C]58[/C][C]0.491375972444217[/C][C]0.982751944888434[/C][C]0.508624027555783[/C][/ROW]
[ROW][C]59[/C][C]0.599899043003237[/C][C]0.800201913993527[/C][C]0.400100956996763[/C][/ROW]
[ROW][C]60[/C][C]0.63417111746557[/C][C]0.731657765068859[/C][C]0.36582888253443[/C][/ROW]
[ROW][C]61[/C][C]0.530217685100762[/C][C]0.939564629798477[/C][C]0.469782314899238[/C][/ROW]
[ROW][C]62[/C][C]0.505242241421008[/C][C]0.989515517157984[/C][C]0.494757758578992[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201880&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201880&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
200.1074406519095770.2148813038191540.892559348090423
210.06891797882444870.1378359576488970.931082021175551
220.03356445550257730.06712891100515470.966435544497423
230.01915236725982440.03830473451964890.980847632740176
240.009934636924397680.01986927384879540.990065363075602
250.1001956720441920.2003913440883850.899804327955808
260.1603799398758520.3207598797517040.839620060124148
270.3546101815846880.7092203631693770.645389818415312
280.368200964201410.7364019284028210.63179903579859
290.2845202479539380.5690404959078760.715479752046062
300.2655609433554250.5311218867108510.734439056644575
310.2319379146319090.4638758292638180.768062085368091
320.5390737796044740.9218524407910530.460926220395526
330.5932253629177090.8135492741645820.406774637082291
340.5268232294316060.9463535411367870.473176770568394
350.4407106263502140.8814212527004280.559289373649786
360.3656762603074370.7313525206148740.634323739692563
370.3965993816292130.7931987632584260.603400618370787
380.3592622590811260.7185245181622510.640737740918874
390.3789106263677610.7578212527355210.621089373632239
400.3746853013994070.7493706027988140.625314698600593
410.4174109035413240.8348218070826480.582589096458676
420.4591998495786260.9183996991572510.540800150421374
430.3882017115551070.7764034231102150.611798288444892
440.6401670539246150.719665892150770.359832946075385
450.7495581037731630.5008837924536730.250441896226837
460.7053438005549270.5893123988901450.294656199445073
470.728803583492480.542392833015040.27119641650752
480.8533560414865860.2932879170268280.146643958513414
490.8241697066717190.3516605866565630.175830293328281
500.77889965330490.4422006933901990.2211003466951
510.7281284356654690.5437431286690610.271871564334531
520.6508865756206970.6982268487586050.349113424379303
530.6685124232074350.6629751535851310.331487576792565
540.7133702865743420.5732594268513150.286629713425658
550.6307274325624860.7385451348750290.369272567437514
560.5361139479058970.9277721041882060.463886052094103
570.529916951477290.940166097045420.47008304852271
580.4913759724442170.9827519448884340.508624027555783
590.5998990430032370.8002019139935270.400100956996763
600.634171117465570.7316577650688590.36582888253443
610.5302176851007620.9395646297984770.469782314899238
620.5052422414210080.9895155171579840.494757758578992






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0465116279069767OK
10% type I error level30.0697674418604651OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 2 & 0.0465116279069767 & OK \tabularnewline
10% type I error level & 3 & 0.0697674418604651 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201880&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]2[/C][C]0.0465116279069767[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0697674418604651[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201880&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201880&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0465116279069767OK
10% type I error level30.0697674418604651OK


Figures (Output of Computation)

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Input Parameters & R Code

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