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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 computationMon, 21 Nov 2011 14:17:03 -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/2011/Nov/21/t1321903039cjcf0659mvulxmr.htm/, Retrieved Thu, 25 Apr 2024 11:02:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145923, Retrieved Thu, 25 Apr 2024 11:02:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact85

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2011-11-18 14:53:14] [06c08141d7d783218a8164fd2ea166f2]
- R  D  [Multiple Regression] [] [2011-11-21 19:12:09] [06c08141d7d783218a8164fd2ea166f2]
-   P       [Multiple Regression] [] [2011-11-21 19:17:03] [ce4468323d272130d499477f5e05a6d2] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9	13	13	14	13	3
9	12	12	8	13	5
9	8	10	12	16	6
9	12	9	7	12	6
9	10	10	10	11	5
9	12	12	7	12	3
9	15	13	16	18	8
9	9	12	11	11	4
9	12	15	14	14	4
9	11	6	6	9	4
9	11	5	16	14	6
9	11	12	11	12	6
9	15	11	16	11	5
9	7	14	12	12	4
9	11	14	7	13	6
9	11	12	13	11	4
9	10	12	11	12	6
9	14	11	15	16	6
9	10	11	7	9	4
9	6	7	9	11	4
9	11	9	7	13	2
9	15	11	14	15	7
9	11	11	15	10	5
9	12	12	7	11	4
9	14	12	15	13	6
9	15	11	17	16	6
9	9	11	15	15	7
9	13	8	14	14	5
9	13	9	14	14	6
9	16	12	8	14	4
9	13	10	8	8	4
9	12	10	14	13	7
9	14	12	14	15	7
9	11	8	8	13	4
9	9	12	11	11	4
9	16	11	16	15	6
9	12	12	10	15	6
9	10	7	8	9	5
9	13	11	14	13	6
9	16	11	16	16	7
9	14	12	13	13	6
9	15	9	5	11	3
9	5	15	8	12	3
9	8	11	10	12	4
9	11	11	8	12	6
9	16	11	13	14	7
9	17	11	15	14	5
9	9	15	6	8	4
9	9	11	12	13	5
9	13	12	16	16	6
9	10	12	5	13	6
9	6	9	15	11	6
9	12	12	12	14	5
9	8	12	8	13	4
9	14	13	13	13	5
9	12	11	14	13	5
10	11	9	12	12	4
10	16	9	16	16	6
10	8	11	10	15	2
10	15	11	15	15	8
10	7	12	8	12	3
10	16	12	16	14	6
10	14	9	19	12	6
10	16	11	14	15	6
10	9	9	6	12	5
10	14	12	13	13	5
10	11	12	15	12	6
10	13	12	7	12	5
10	15	12	13	13	6
10	5	14	4	5	2
10	15	11	14	13	5
10	13	12	13	13	5
10	11	11	11	14	5
10	11	6	14	17	6
10	12	10	12	13	6
10	12	12	15	13	6
10	12	13	14	12	5
10	12	8	13	13	5
10	14	12	8	14	4
10	6	12	6	11	2
10	7	12	7	12	4
10	14	6	13	12	6
10	14	11	13	16	6
10	10	10	11	12	5
10	13	12	5	12	3
10	12	13	12	12	6
10	9	11	8	10	4
10	12	7	11	15	5
10	16	11	14	15	8
10	10	11	9	12	4
10	14	11	10	16	6
10	10	11	13	15	6
10	16	12	16	16	7
10	15	10	16	13	6
10	12	11	11	12	5
10	10	12	8	11	4
10	8	7	4	13	6
10	8	13	7	10	3
10	11	8	14	15	5
10	13	12	11	13	6
10	16	11	17	16	7
10	16	12	15	15	7
10	14	14	17	18	6
10	11	10	5	13	3
10	4	10	4	10	2
10	14	13	10	16	8
10	9	10	11	13	3
10	14	11	15	15	8
10	8	10	10	14	3
10	8	7	9	15	4
10	11	10	12	14	5
10	12	8	15	13	7
10	11	12	7	13	6
10	14	12	13	15	6
10	15	12	12	16	7
10	16	11	14	14	6
10	16	12	14	14	6
10	11	12	8	16	6
10	14	12	15	14	6
10	14	11	12	12	4
10	12	12	12	13	4
10	14	11	16	12	5
10	8	11	9	12	4
10	13	13	15	14	6
10	16	12	15	14	6
10	12	12	6	14	5
10	16	12	14	16	8
10	12	12	15	13	6
10	11	8	10	14	5
10	4	8	6	4	4
10	16	12	14	16	8
10	15	11	12	13	6
10	10	12	8	16	4
10	13	13	11	15	6
10	15	12	13	14	6
10	12	12	9	13	4
10	14	11	15	14	6
10	7	12	13	12	3
10	19	12	15	15	6
10	12	10	14	14	5
10	12	11	16	13	4
10	13	12	14	14	6
10	15	12	14	16	4
10	8	10	10	6	4
10	12	12	10	13	4
10	10	13	4	13	6
10	8	12	8	14	5
10	10	15	15	15	6
10	15	11	16	14	6
10	16	12	12	15	8
10	13	11	12	13	7
10	16	12	15	16	7
10	9	11	9	12	4
10	14	10	12	15	6
10	14	11	14	12	6
10	12	11	11	14	2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145923&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145923&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145923&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 time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
autonomie[t] = + 2.2728003993154 -0.196918142714503Month[t] + 0.386615956447262Depressie[t] -0.0589369962876584belasting[t] + 0.286394687700643conformistisch[t] + 0.66471758902352agressief[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
autonomie[t] =  +  2.2728003993154 -0.196918142714503Month[t] +  0.386615956447262Depressie[t] -0.0589369962876584belasting[t] +  0.286394687700643conformistisch[t] +  0.66471758902352agressief[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145923&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]autonomie[t] =  +  2.2728003993154 -0.196918142714503Month[t] +  0.386615956447262Depressie[t] -0.0589369962876584belasting[t] +  0.286394687700643conformistisch[t] +  0.66471758902352agressief[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145923&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145923&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
autonomie[t] = + 2.2728003993154 -0.196918142714503Month[t] + 0.386615956447262Depressie[t] -0.0589369962876584belasting[t] + 0.286394687700643conformistisch[t] + 0.66471758902352agressief[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.27280039931544.5185730.5030.6157090.307854
Month-0.1969181427145030.449077-0.43850.6616580.330829
Depressie0.3866159564472620.0957484.03788.6e-054.3e-05
belasting-0.05893699628765840.119887-0.49160.6237160.311858
conformistisch0.2863946877006430.1248992.2930.0232370.011618
agressief0.664717589023520.1999143.3250.0011120.000556

\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) & 2.2728003993154 & 4.518573 & 0.503 & 0.615709 & 0.307854 \tabularnewline
Month & -0.196918142714503 & 0.449077 & -0.4385 & 0.661658 & 0.330829 \tabularnewline
Depressie & 0.386615956447262 & 0.095748 & 4.0378 & 8.6e-05 & 4.3e-05 \tabularnewline
belasting & -0.0589369962876584 & 0.119887 & -0.4916 & 0.623716 & 0.311858 \tabularnewline
conformistisch & 0.286394687700643 & 0.124899 & 2.293 & 0.023237 & 0.011618 \tabularnewline
agressief & 0.66471758902352 & 0.199914 & 3.325 & 0.001112 & 0.000556 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145923&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]2.2728003993154[/C][C]4.518573[/C][C]0.503[/C][C]0.615709[/C][C]0.307854[/C][/ROW]
[ROW][C]Month[/C][C]-0.196918142714503[/C][C]0.449077[/C][C]-0.4385[/C][C]0.661658[/C][C]0.330829[/C][/ROW]
[ROW][C]Depressie[/C][C]0.386615956447262[/C][C]0.095748[/C][C]4.0378[/C][C]8.6e-05[/C][C]4.3e-05[/C][/ROW]
[ROW][C]belasting[/C][C]-0.0589369962876584[/C][C]0.119887[/C][C]-0.4916[/C][C]0.623716[/C][C]0.311858[/C][/ROW]
[ROW][C]conformistisch[/C][C]0.286394687700643[/C][C]0.124899[/C][C]2.293[/C][C]0.023237[/C][C]0.011618[/C][/ROW]
[ROW][C]agressief[/C][C]0.66471758902352[/C][C]0.199914[/C][C]3.325[/C][C]0.001112[/C][C]0.000556[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145923&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145923&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)2.27280039931544.5185730.5030.6157090.307854
Month-0.1969181427145030.449077-0.43850.6616580.330829
Depressie0.3866159564472620.0957484.03788.6e-054.3e-05
belasting-0.05893699628765840.119887-0.49160.6237160.311858
conformistisch0.2863946877006430.1248992.2930.0232370.011618
agressief0.664717589023520.1999143.3250.0011120.000556






Multiple Linear Regression - Regression Statistics
Multiple R0.654678518801336
R-squared0.428603962979911
Adjusted R-squared0.409557428412574
F-TEST (value)22.502989268974
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value1.11022302462516e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.65993428148443
Sum Squared Residuals1061.28755727242

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.654678518801336 \tabularnewline
R-squared & 0.428603962979911 \tabularnewline
Adjusted R-squared & 0.409557428412574 \tabularnewline
F-TEST (value) & 22.502989268974 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 150 \tabularnewline
p-value & 1.11022302462516e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.65993428148443 \tabularnewline
Sum Squared Residuals & 1061.28755727242 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145923&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.654678518801336[/C][/ROW]
[ROW][C]R-squared[/C][C]0.428603962979911[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.409557428412574[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]22.502989268974[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]150[/C][/ROW]
[ROW][C]p-value[/C][C]1.11022302462516e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.65993428148443[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1061.28755727242[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145923&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145923&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 R0.654678518801336
R-squared0.428603962979911
Adjusted R-squared0.409557428412574
F-TEST (value)22.502989268974
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value1.11022302462516e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.65993428148443
Sum Squared Residuals1061.28755727242






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11410.47764730413873.52235269586135
2811.4794035220261-3.47940352202609
31211.57471534093780.425284659062193
4712.0345374122119-5.03453741221194
51010.2512562263056-0.251256226305593
679.8635736562784-2.8635736562784
71616.006440600654-0.00644060065399557
8119.082048688259491.91795131174051
91410.92426963184023.07573036815976
1069.63611320347868-3.63611320347868
111612.45645881631663.5435411836834
121111.4711104669017-0.471110466901702
131612.12539901225423.87460098774575
14128.477337470490293.5226625295097
15711.639631162027-4.63963116202703
16139.855280601154023.14471939884598
171111.0844945104544-0.0844945104544396
181513.83547408333371.16452591666628
1978.95481226559313-1.95481226559313
2098.2168858003560.783114199644003
2179.27544578737124-2.27544578737124
221414.6004129411039-0.60041294110386
231510.29254049876464.70745950123545
24710.2418965576013-3.24189655760128
251512.91735302394412.08264697605587
261714.2220900397812.77790996021902
271512.28071720242032.71928279757971
281412.38816215132461.61183784867537
291412.99394274406051.00605725593951
30812.6475444464923-4.64754444649226
3189.88720244352193-1.88720244352193
321412.92671269264841.07328730735155
331414.1548599883689-0.15485998836894
34810.6638179617059-2.66381796170594
35119.082048688259491.91795131174051
361614.32231130852761.6776886914724
371012.7169104864509-2.71691048645089
3889.85527783976728-1.85527783976728
391412.58967406378451.41032593621547
401615.27342358525180.726576414748233
411312.91735302394410.082646976055867
42510.9138378267825-5.91383782678252
4386.980450972284591.01954902771541
44109.040764415800530.959235584199467
45811.5300474631894-3.53004746318936
461314.7006342098505-1.70063420985048
471513.75781498825071.2421850117493
4868.04605363629459-2.04605363629459
491210.3784926489721.62150735102804
501613.38992113059882.6100788694012
51511.3708891981551-6.37088919815508
52159.428446985827725.57155301417228
531211.76579820972670.234201790273269
5489.26822210721352-1.26822210721352
551312.1936984386330.806301561367046
561411.53834051831372.46165948168625
571210.12156813500311.87843186499687
581614.52966184608911.47033815391094
59108.373595158140921.62640484185908
601515.0682123874129-0.0682123874128782
6187.733575731327590.266424268672412
621613.78006148182482.2199385181752
631912.6108511823926.38914881760804
641414.1253931658131-0.1253931658131
65610.0130538111321-4.01305381113213
661312.05571729220610.944282707793891
671511.27419232418723.7258076758128
68711.3827066480582-4.3827066480582
691313.1070508376769-0.107050837676892
7044.17298942292972-0.172989422929723
711412.5012702449411.49872975505897
721311.66910133575881.33089866424115
731111.2412011068526-0.241201106852624
741413.05978774041640.940212259583634
751212.0650769609104-0.0650769609104216
761511.94720296833513.0527970316649
771410.93715369532333.06284630467672
781311.51823336446221.48176663553778
79811.6773943908832-3.67739439088323
8066.39584749815616-0.395847498156163
8178.39829332035111-1.39829332035111
821312.78766217125490.212337828745064
831313.6385559406192-0.638555940619218
841110.34073277129170.659267228708267
85510.0532714700112-5.05327147001116
861211.60187128434680.398128715653197
8788.65767285413201-0.657672854132005
881112.1499597361512-1.14995973615116
891415.4548283438601-1.45482834386014
9099.61707818598055-0.617078185980554
911013.6385559406192-3.63855594061922
921311.80569742712951.19430257287048
931615.01756844624960.982431553750394
941613.22492483025222.77507516974779
951111.0550276878986-0.0550276878985995
9689.27174650199225-1.27174650199225
97410.6954241239843-6.69542412398435
9877.48846531608591-0.488465316085906
991411.70440678341622.29559321658376
1001112.3338189247824-1.33381892478237
1011715.07650544253731.92349455746274
1021514.7311737585490.268826241451038
1031714.03453432715752.96546567284247
10459.6843082373926-4.6843082373926
10545.45409489013631-1.45409489013631
1061014.8501171260909-4.85011712609094
107118.911076324498072.08892367550193
1081514.68159643096560.318403569034384
109108.810855055751461.18914494424855
11099.93877832133859-0.938778321338593
1111211.30013810314030.699861896859718
1121512.84766854250932.15233145749074
113711.5605870118878-4.56058701188784
1141313.2932242566309-0.293224256630917
1151214.6309524898023-2.63095248980234
1161413.83899847811250.161001521887543
1171413.78006148182480.219938518175202
118812.4197710749898-4.41977107498977
1191513.00682956893031.99317043106973
1201211.16354201176960.836457988230396
1211210.61776779028811.38223220971194
1221611.82825960079314.17174039920688
12398.843846273086030.156153726913971
1241512.56127661619542.43872338380465
1251513.78006148182481.2199385181752
126611.5688800670122-5.56888006701223
1271415.6822860352731-1.68228603527312
1281511.94720296833513.0527970316649
1291011.4180120957156-1.4180120957156
13065.183035934554810.816964065445193
1311415.6822860352731-1.68228603527312
1321213.1659878339646-1.16598783396455
133810.7037199404955-2.70371994049547
1341112.847671303896-1.847671303896
1351313.3934455253775-0.393445525377536
136910.6177677902881-1.61776779028806
1371513.06576656521791.93423343478207
138137.733575731327595.26642426867241
1391515.2263040388672-0.226304038867229
1401411.68675405958752.31324594041246
1411610.67670478657575.32329521342428
1421412.6202136124831.37978638751699
1431412.63679972273181.36320027726822
144107.184415143169832.81558485683017
1451010.6177677902881-0.617767790288064
146411.1150340591529-7.11503405915292
147810.0224162412232-2.02241624122318
1481511.56994944197893.43005055802111
1491613.45238252166522.54761747833481
1501215.3958913475725-3.39589134757248
1511213.0574735100935-1.05747351009355
1521515.0175684462496-0.017568446249605
15399.23046222953329-0.230462229533292
1541213.4110982492062-1.41109824920623
1551412.49297718981661.50702281018336
156119.633664296229331.36633570377067

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 10.4776473041387 & 3.52235269586135 \tabularnewline
2 & 8 & 11.4794035220261 & -3.47940352202609 \tabularnewline
3 & 12 & 11.5747153409378 & 0.425284659062193 \tabularnewline
4 & 7 & 12.0345374122119 & -5.03453741221194 \tabularnewline
5 & 10 & 10.2512562263056 & -0.251256226305593 \tabularnewline
6 & 7 & 9.8635736562784 & -2.8635736562784 \tabularnewline
7 & 16 & 16.006440600654 & -0.00644060065399557 \tabularnewline
8 & 11 & 9.08204868825949 & 1.91795131174051 \tabularnewline
9 & 14 & 10.9242696318402 & 3.07573036815976 \tabularnewline
10 & 6 & 9.63611320347868 & -3.63611320347868 \tabularnewline
11 & 16 & 12.4564588163166 & 3.5435411836834 \tabularnewline
12 & 11 & 11.4711104669017 & -0.471110466901702 \tabularnewline
13 & 16 & 12.1253990122542 & 3.87460098774575 \tabularnewline
14 & 12 & 8.47733747049029 & 3.5226625295097 \tabularnewline
15 & 7 & 11.639631162027 & -4.63963116202703 \tabularnewline
16 & 13 & 9.85528060115402 & 3.14471939884598 \tabularnewline
17 & 11 & 11.0844945104544 & -0.0844945104544396 \tabularnewline
18 & 15 & 13.8354740833337 & 1.16452591666628 \tabularnewline
19 & 7 & 8.95481226559313 & -1.95481226559313 \tabularnewline
20 & 9 & 8.216885800356 & 0.783114199644003 \tabularnewline
21 & 7 & 9.27544578737124 & -2.27544578737124 \tabularnewline
22 & 14 & 14.6004129411039 & -0.60041294110386 \tabularnewline
23 & 15 & 10.2925404987646 & 4.70745950123545 \tabularnewline
24 & 7 & 10.2418965576013 & -3.24189655760128 \tabularnewline
25 & 15 & 12.9173530239441 & 2.08264697605587 \tabularnewline
26 & 17 & 14.222090039781 & 2.77790996021902 \tabularnewline
27 & 15 & 12.2807172024203 & 2.71928279757971 \tabularnewline
28 & 14 & 12.3881621513246 & 1.61183784867537 \tabularnewline
29 & 14 & 12.9939427440605 & 1.00605725593951 \tabularnewline
30 & 8 & 12.6475444464923 & -4.64754444649226 \tabularnewline
31 & 8 & 9.88720244352193 & -1.88720244352193 \tabularnewline
32 & 14 & 12.9267126926484 & 1.07328730735155 \tabularnewline
33 & 14 & 14.1548599883689 & -0.15485998836894 \tabularnewline
34 & 8 & 10.6638179617059 & -2.66381796170594 \tabularnewline
35 & 11 & 9.08204868825949 & 1.91795131174051 \tabularnewline
36 & 16 & 14.3223113085276 & 1.6776886914724 \tabularnewline
37 & 10 & 12.7169104864509 & -2.71691048645089 \tabularnewline
38 & 8 & 9.85527783976728 & -1.85527783976728 \tabularnewline
39 & 14 & 12.5896740637845 & 1.41032593621547 \tabularnewline
40 & 16 & 15.2734235852518 & 0.726576414748233 \tabularnewline
41 & 13 & 12.9173530239441 & 0.082646976055867 \tabularnewline
42 & 5 & 10.9138378267825 & -5.91383782678252 \tabularnewline
43 & 8 & 6.98045097228459 & 1.01954902771541 \tabularnewline
44 & 10 & 9.04076441580053 & 0.959235584199467 \tabularnewline
45 & 8 & 11.5300474631894 & -3.53004746318936 \tabularnewline
46 & 13 & 14.7006342098505 & -1.70063420985048 \tabularnewline
47 & 15 & 13.7578149882507 & 1.2421850117493 \tabularnewline
48 & 6 & 8.04605363629459 & -2.04605363629459 \tabularnewline
49 & 12 & 10.378492648972 & 1.62150735102804 \tabularnewline
50 & 16 & 13.3899211305988 & 2.6100788694012 \tabularnewline
51 & 5 & 11.3708891981551 & -6.37088919815508 \tabularnewline
52 & 15 & 9.42844698582772 & 5.57155301417228 \tabularnewline
53 & 12 & 11.7657982097267 & 0.234201790273269 \tabularnewline
54 & 8 & 9.26822210721352 & -1.26822210721352 \tabularnewline
55 & 13 & 12.193698438633 & 0.806301561367046 \tabularnewline
56 & 14 & 11.5383405183137 & 2.46165948168625 \tabularnewline
57 & 12 & 10.1215681350031 & 1.87843186499687 \tabularnewline
58 & 16 & 14.5296618460891 & 1.47033815391094 \tabularnewline
59 & 10 & 8.37359515814092 & 1.62640484185908 \tabularnewline
60 & 15 & 15.0682123874129 & -0.0682123874128782 \tabularnewline
61 & 8 & 7.73357573132759 & 0.266424268672412 \tabularnewline
62 & 16 & 13.7800614818248 & 2.2199385181752 \tabularnewline
63 & 19 & 12.610851182392 & 6.38914881760804 \tabularnewline
64 & 14 & 14.1253931658131 & -0.1253931658131 \tabularnewline
65 & 6 & 10.0130538111321 & -4.01305381113213 \tabularnewline
66 & 13 & 12.0557172922061 & 0.944282707793891 \tabularnewline
67 & 15 & 11.2741923241872 & 3.7258076758128 \tabularnewline
68 & 7 & 11.3827066480582 & -4.3827066480582 \tabularnewline
69 & 13 & 13.1070508376769 & -0.107050837676892 \tabularnewline
70 & 4 & 4.17298942292972 & -0.172989422929723 \tabularnewline
71 & 14 & 12.501270244941 & 1.49872975505897 \tabularnewline
72 & 13 & 11.6691013357588 & 1.33089866424115 \tabularnewline
73 & 11 & 11.2412011068526 & -0.241201106852624 \tabularnewline
74 & 14 & 13.0597877404164 & 0.940212259583634 \tabularnewline
75 & 12 & 12.0650769609104 & -0.0650769609104216 \tabularnewline
76 & 15 & 11.9472029683351 & 3.0527970316649 \tabularnewline
77 & 14 & 10.9371536953233 & 3.06284630467672 \tabularnewline
78 & 13 & 11.5182333644622 & 1.48176663553778 \tabularnewline
79 & 8 & 11.6773943908832 & -3.67739439088323 \tabularnewline
80 & 6 & 6.39584749815616 & -0.395847498156163 \tabularnewline
81 & 7 & 8.39829332035111 & -1.39829332035111 \tabularnewline
82 & 13 & 12.7876621712549 & 0.212337828745064 \tabularnewline
83 & 13 & 13.6385559406192 & -0.638555940619218 \tabularnewline
84 & 11 & 10.3407327712917 & 0.659267228708267 \tabularnewline
85 & 5 & 10.0532714700112 & -5.05327147001116 \tabularnewline
86 & 12 & 11.6018712843468 & 0.398128715653197 \tabularnewline
87 & 8 & 8.65767285413201 & -0.657672854132005 \tabularnewline
88 & 11 & 12.1499597361512 & -1.14995973615116 \tabularnewline
89 & 14 & 15.4548283438601 & -1.45482834386014 \tabularnewline
90 & 9 & 9.61707818598055 & -0.617078185980554 \tabularnewline
91 & 10 & 13.6385559406192 & -3.63855594061922 \tabularnewline
92 & 13 & 11.8056974271295 & 1.19430257287048 \tabularnewline
93 & 16 & 15.0175684462496 & 0.982431553750394 \tabularnewline
94 & 16 & 13.2249248302522 & 2.77507516974779 \tabularnewline
95 & 11 & 11.0550276878986 & -0.0550276878985995 \tabularnewline
96 & 8 & 9.27174650199225 & -1.27174650199225 \tabularnewline
97 & 4 & 10.6954241239843 & -6.69542412398435 \tabularnewline
98 & 7 & 7.48846531608591 & -0.488465316085906 \tabularnewline
99 & 14 & 11.7044067834162 & 2.29559321658376 \tabularnewline
100 & 11 & 12.3338189247824 & -1.33381892478237 \tabularnewline
101 & 17 & 15.0765054425373 & 1.92349455746274 \tabularnewline
102 & 15 & 14.731173758549 & 0.268826241451038 \tabularnewline
103 & 17 & 14.0345343271575 & 2.96546567284247 \tabularnewline
104 & 5 & 9.6843082373926 & -4.6843082373926 \tabularnewline
105 & 4 & 5.45409489013631 & -1.45409489013631 \tabularnewline
106 & 10 & 14.8501171260909 & -4.85011712609094 \tabularnewline
107 & 11 & 8.91107632449807 & 2.08892367550193 \tabularnewline
108 & 15 & 14.6815964309656 & 0.318403569034384 \tabularnewline
109 & 10 & 8.81085505575146 & 1.18914494424855 \tabularnewline
110 & 9 & 9.93877832133859 & -0.938778321338593 \tabularnewline
111 & 12 & 11.3001381031403 & 0.699861896859718 \tabularnewline
112 & 15 & 12.8476685425093 & 2.15233145749074 \tabularnewline
113 & 7 & 11.5605870118878 & -4.56058701188784 \tabularnewline
114 & 13 & 13.2932242566309 & -0.293224256630917 \tabularnewline
115 & 12 & 14.6309524898023 & -2.63095248980234 \tabularnewline
116 & 14 & 13.8389984781125 & 0.161001521887543 \tabularnewline
117 & 14 & 13.7800614818248 & 0.219938518175202 \tabularnewline
118 & 8 & 12.4197710749898 & -4.41977107498977 \tabularnewline
119 & 15 & 13.0068295689303 & 1.99317043106973 \tabularnewline
120 & 12 & 11.1635420117696 & 0.836457988230396 \tabularnewline
121 & 12 & 10.6177677902881 & 1.38223220971194 \tabularnewline
122 & 16 & 11.8282596007931 & 4.17174039920688 \tabularnewline
123 & 9 & 8.84384627308603 & 0.156153726913971 \tabularnewline
124 & 15 & 12.5612766161954 & 2.43872338380465 \tabularnewline
125 & 15 & 13.7800614818248 & 1.2199385181752 \tabularnewline
126 & 6 & 11.5688800670122 & -5.56888006701223 \tabularnewline
127 & 14 & 15.6822860352731 & -1.68228603527312 \tabularnewline
128 & 15 & 11.9472029683351 & 3.0527970316649 \tabularnewline
129 & 10 & 11.4180120957156 & -1.4180120957156 \tabularnewline
130 & 6 & 5.18303593455481 & 0.816964065445193 \tabularnewline
131 & 14 & 15.6822860352731 & -1.68228603527312 \tabularnewline
132 & 12 & 13.1659878339646 & -1.16598783396455 \tabularnewline
133 & 8 & 10.7037199404955 & -2.70371994049547 \tabularnewline
134 & 11 & 12.847671303896 & -1.847671303896 \tabularnewline
135 & 13 & 13.3934455253775 & -0.393445525377536 \tabularnewline
136 & 9 & 10.6177677902881 & -1.61776779028806 \tabularnewline
137 & 15 & 13.0657665652179 & 1.93423343478207 \tabularnewline
138 & 13 & 7.73357573132759 & 5.26642426867241 \tabularnewline
139 & 15 & 15.2263040388672 & -0.226304038867229 \tabularnewline
140 & 14 & 11.6867540595875 & 2.31324594041246 \tabularnewline
141 & 16 & 10.6767047865757 & 5.32329521342428 \tabularnewline
142 & 14 & 12.620213612483 & 1.37978638751699 \tabularnewline
143 & 14 & 12.6367997227318 & 1.36320027726822 \tabularnewline
144 & 10 & 7.18441514316983 & 2.81558485683017 \tabularnewline
145 & 10 & 10.6177677902881 & -0.617767790288064 \tabularnewline
146 & 4 & 11.1150340591529 & -7.11503405915292 \tabularnewline
147 & 8 & 10.0224162412232 & -2.02241624122318 \tabularnewline
148 & 15 & 11.5699494419789 & 3.43005055802111 \tabularnewline
149 & 16 & 13.4523825216652 & 2.54761747833481 \tabularnewline
150 & 12 & 15.3958913475725 & -3.39589134757248 \tabularnewline
151 & 12 & 13.0574735100935 & -1.05747351009355 \tabularnewline
152 & 15 & 15.0175684462496 & -0.017568446249605 \tabularnewline
153 & 9 & 9.23046222953329 & -0.230462229533292 \tabularnewline
154 & 12 & 13.4110982492062 & -1.41109824920623 \tabularnewline
155 & 14 & 12.4929771898166 & 1.50702281018336 \tabularnewline
156 & 11 & 9.63366429622933 & 1.36633570377067 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145923&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]14[/C][C]10.4776473041387[/C][C]3.52235269586135[/C][/ROW]
[ROW][C]2[/C][C]8[/C][C]11.4794035220261[/C][C]-3.47940352202609[/C][/ROW]
[ROW][C]3[/C][C]12[/C][C]11.5747153409378[/C][C]0.425284659062193[/C][/ROW]
[ROW][C]4[/C][C]7[/C][C]12.0345374122119[/C][C]-5.03453741221194[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]10.2512562263056[/C][C]-0.251256226305593[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]9.8635736562784[/C][C]-2.8635736562784[/C][/ROW]
[ROW][C]7[/C][C]16[/C][C]16.006440600654[/C][C]-0.00644060065399557[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]9.08204868825949[/C][C]1.91795131174051[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]10.9242696318402[/C][C]3.07573036815976[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]9.63611320347868[/C][C]-3.63611320347868[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]12.4564588163166[/C][C]3.5435411836834[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]11.4711104669017[/C][C]-0.471110466901702[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]12.1253990122542[/C][C]3.87460098774575[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]8.47733747049029[/C][C]3.5226625295097[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]11.639631162027[/C][C]-4.63963116202703[/C][/ROW]
[ROW][C]16[/C][C]13[/C][C]9.85528060115402[/C][C]3.14471939884598[/C][/ROW]
[ROW][C]17[/C][C]11[/C][C]11.0844945104544[/C][C]-0.0844945104544396[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]13.8354740833337[/C][C]1.16452591666628[/C][/ROW]
[ROW][C]19[/C][C]7[/C][C]8.95481226559313[/C][C]-1.95481226559313[/C][/ROW]
[ROW][C]20[/C][C]9[/C][C]8.216885800356[/C][C]0.783114199644003[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]9.27544578737124[/C][C]-2.27544578737124[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]14.6004129411039[/C][C]-0.60041294110386[/C][/ROW]
[ROW][C]23[/C][C]15[/C][C]10.2925404987646[/C][C]4.70745950123545[/C][/ROW]
[ROW][C]24[/C][C]7[/C][C]10.2418965576013[/C][C]-3.24189655760128[/C][/ROW]
[ROW][C]25[/C][C]15[/C][C]12.9173530239441[/C][C]2.08264697605587[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]14.222090039781[/C][C]2.77790996021902[/C][/ROW]
[ROW][C]27[/C][C]15[/C][C]12.2807172024203[/C][C]2.71928279757971[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]12.3881621513246[/C][C]1.61183784867537[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.9939427440605[/C][C]1.00605725593951[/C][/ROW]
[ROW][C]30[/C][C]8[/C][C]12.6475444464923[/C][C]-4.64754444649226[/C][/ROW]
[ROW][C]31[/C][C]8[/C][C]9.88720244352193[/C][C]-1.88720244352193[/C][/ROW]
[ROW][C]32[/C][C]14[/C][C]12.9267126926484[/C][C]1.07328730735155[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.1548599883689[/C][C]-0.15485998836894[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]10.6638179617059[/C][C]-2.66381796170594[/C][/ROW]
[ROW][C]35[/C][C]11[/C][C]9.08204868825949[/C][C]1.91795131174051[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.3223113085276[/C][C]1.6776886914724[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]12.7169104864509[/C][C]-2.71691048645089[/C][/ROW]
[ROW][C]38[/C][C]8[/C][C]9.85527783976728[/C][C]-1.85527783976728[/C][/ROW]
[ROW][C]39[/C][C]14[/C][C]12.5896740637845[/C][C]1.41032593621547[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.2734235852518[/C][C]0.726576414748233[/C][/ROW]
[ROW][C]41[/C][C]13[/C][C]12.9173530239441[/C][C]0.082646976055867[/C][/ROW]
[ROW][C]42[/C][C]5[/C][C]10.9138378267825[/C][C]-5.91383782678252[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]6.98045097228459[/C][C]1.01954902771541[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]9.04076441580053[/C][C]0.959235584199467[/C][/ROW]
[ROW][C]45[/C][C]8[/C][C]11.5300474631894[/C][C]-3.53004746318936[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]14.7006342098505[/C][C]-1.70063420985048[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]13.7578149882507[/C][C]1.2421850117493[/C][/ROW]
[ROW][C]48[/C][C]6[/C][C]8.04605363629459[/C][C]-2.04605363629459[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]10.378492648972[/C][C]1.62150735102804[/C][/ROW]
[ROW][C]50[/C][C]16[/C][C]13.3899211305988[/C][C]2.6100788694012[/C][/ROW]
[ROW][C]51[/C][C]5[/C][C]11.3708891981551[/C][C]-6.37088919815508[/C][/ROW]
[ROW][C]52[/C][C]15[/C][C]9.42844698582772[/C][C]5.57155301417228[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]11.7657982097267[/C][C]0.234201790273269[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]9.26822210721352[/C][C]-1.26822210721352[/C][/ROW]
[ROW][C]55[/C][C]13[/C][C]12.193698438633[/C][C]0.806301561367046[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]11.5383405183137[/C][C]2.46165948168625[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]10.1215681350031[/C][C]1.87843186499687[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.5296618460891[/C][C]1.47033815391094[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]8.37359515814092[/C][C]1.62640484185908[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]15.0682123874129[/C][C]-0.0682123874128782[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]7.73357573132759[/C][C]0.266424268672412[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]13.7800614818248[/C][C]2.2199385181752[/C][/ROW]
[ROW][C]63[/C][C]19[/C][C]12.610851182392[/C][C]6.38914881760804[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.1253931658131[/C][C]-0.1253931658131[/C][/ROW]
[ROW][C]65[/C][C]6[/C][C]10.0130538111321[/C][C]-4.01305381113213[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]12.0557172922061[/C][C]0.944282707793891[/C][/ROW]
[ROW][C]67[/C][C]15[/C][C]11.2741923241872[/C][C]3.7258076758128[/C][/ROW]
[ROW][C]68[/C][C]7[/C][C]11.3827066480582[/C][C]-4.3827066480582[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]13.1070508376769[/C][C]-0.107050837676892[/C][/ROW]
[ROW][C]70[/C][C]4[/C][C]4.17298942292972[/C][C]-0.172989422929723[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]12.501270244941[/C][C]1.49872975505897[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]11.6691013357588[/C][C]1.33089866424115[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]11.2412011068526[/C][C]-0.241201106852624[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]13.0597877404164[/C][C]0.940212259583634[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]12.0650769609104[/C][C]-0.0650769609104216[/C][/ROW]
[ROW][C]76[/C][C]15[/C][C]11.9472029683351[/C][C]3.0527970316649[/C][/ROW]
[ROW][C]77[/C][C]14[/C][C]10.9371536953233[/C][C]3.06284630467672[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]11.5182333644622[/C][C]1.48176663553778[/C][/ROW]
[ROW][C]79[/C][C]8[/C][C]11.6773943908832[/C][C]-3.67739439088323[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]6.39584749815616[/C][C]-0.395847498156163[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]8.39829332035111[/C][C]-1.39829332035111[/C][/ROW]
[ROW][C]82[/C][C]13[/C][C]12.7876621712549[/C][C]0.212337828745064[/C][/ROW]
[ROW][C]83[/C][C]13[/C][C]13.6385559406192[/C][C]-0.638555940619218[/C][/ROW]
[ROW][C]84[/C][C]11[/C][C]10.3407327712917[/C][C]0.659267228708267[/C][/ROW]
[ROW][C]85[/C][C]5[/C][C]10.0532714700112[/C][C]-5.05327147001116[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]11.6018712843468[/C][C]0.398128715653197[/C][/ROW]
[ROW][C]87[/C][C]8[/C][C]8.65767285413201[/C][C]-0.657672854132005[/C][/ROW]
[ROW][C]88[/C][C]11[/C][C]12.1499597361512[/C][C]-1.14995973615116[/C][/ROW]
[ROW][C]89[/C][C]14[/C][C]15.4548283438601[/C][C]-1.45482834386014[/C][/ROW]
[ROW][C]90[/C][C]9[/C][C]9.61707818598055[/C][C]-0.617078185980554[/C][/ROW]
[ROW][C]91[/C][C]10[/C][C]13.6385559406192[/C][C]-3.63855594061922[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]11.8056974271295[/C][C]1.19430257287048[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.0175684462496[/C][C]0.982431553750394[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]13.2249248302522[/C][C]2.77507516974779[/C][/ROW]
[ROW][C]95[/C][C]11[/C][C]11.0550276878986[/C][C]-0.0550276878985995[/C][/ROW]
[ROW][C]96[/C][C]8[/C][C]9.27174650199225[/C][C]-1.27174650199225[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]10.6954241239843[/C][C]-6.69542412398435[/C][/ROW]
[ROW][C]98[/C][C]7[/C][C]7.48846531608591[/C][C]-0.488465316085906[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]11.7044067834162[/C][C]2.29559321658376[/C][/ROW]
[ROW][C]100[/C][C]11[/C][C]12.3338189247824[/C][C]-1.33381892478237[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]15.0765054425373[/C][C]1.92349455746274[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]14.731173758549[/C][C]0.268826241451038[/C][/ROW]
[ROW][C]103[/C][C]17[/C][C]14.0345343271575[/C][C]2.96546567284247[/C][/ROW]
[ROW][C]104[/C][C]5[/C][C]9.6843082373926[/C][C]-4.6843082373926[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]5.45409489013631[/C][C]-1.45409489013631[/C][/ROW]
[ROW][C]106[/C][C]10[/C][C]14.8501171260909[/C][C]-4.85011712609094[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]8.91107632449807[/C][C]2.08892367550193[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]14.6815964309656[/C][C]0.318403569034384[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]8.81085505575146[/C][C]1.18914494424855[/C][/ROW]
[ROW][C]110[/C][C]9[/C][C]9.93877832133859[/C][C]-0.938778321338593[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]11.3001381031403[/C][C]0.699861896859718[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]12.8476685425093[/C][C]2.15233145749074[/C][/ROW]
[ROW][C]113[/C][C]7[/C][C]11.5605870118878[/C][C]-4.56058701188784[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]13.2932242566309[/C][C]-0.293224256630917[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.6309524898023[/C][C]-2.63095248980234[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]13.8389984781125[/C][C]0.161001521887543[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.7800614818248[/C][C]0.219938518175202[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]12.4197710749898[/C][C]-4.41977107498977[/C][/ROW]
[ROW][C]119[/C][C]15[/C][C]13.0068295689303[/C][C]1.99317043106973[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]11.1635420117696[/C][C]0.836457988230396[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]10.6177677902881[/C][C]1.38223220971194[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]11.8282596007931[/C][C]4.17174039920688[/C][/ROW]
[ROW][C]123[/C][C]9[/C][C]8.84384627308603[/C][C]0.156153726913971[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]12.5612766161954[/C][C]2.43872338380465[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]13.7800614818248[/C][C]1.2199385181752[/C][/ROW]
[ROW][C]126[/C][C]6[/C][C]11.5688800670122[/C][C]-5.56888006701223[/C][/ROW]
[ROW][C]127[/C][C]14[/C][C]15.6822860352731[/C][C]-1.68228603527312[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]11.9472029683351[/C][C]3.0527970316649[/C][/ROW]
[ROW][C]129[/C][C]10[/C][C]11.4180120957156[/C][C]-1.4180120957156[/C][/ROW]
[ROW][C]130[/C][C]6[/C][C]5.18303593455481[/C][C]0.816964065445193[/C][/ROW]
[ROW][C]131[/C][C]14[/C][C]15.6822860352731[/C][C]-1.68228603527312[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]13.1659878339646[/C][C]-1.16598783396455[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]10.7037199404955[/C][C]-2.70371994049547[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]12.847671303896[/C][C]-1.847671303896[/C][/ROW]
[ROW][C]135[/C][C]13[/C][C]13.3934455253775[/C][C]-0.393445525377536[/C][/ROW]
[ROW][C]136[/C][C]9[/C][C]10.6177677902881[/C][C]-1.61776779028806[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]13.0657665652179[/C][C]1.93423343478207[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]7.73357573132759[/C][C]5.26642426867241[/C][/ROW]
[ROW][C]139[/C][C]15[/C][C]15.2263040388672[/C][C]-0.226304038867229[/C][/ROW]
[ROW][C]140[/C][C]14[/C][C]11.6867540595875[/C][C]2.31324594041246[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]10.6767047865757[/C][C]5.32329521342428[/C][/ROW]
[ROW][C]142[/C][C]14[/C][C]12.620213612483[/C][C]1.37978638751699[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]12.6367997227318[/C][C]1.36320027726822[/C][/ROW]
[ROW][C]144[/C][C]10[/C][C]7.18441514316983[/C][C]2.81558485683017[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]10.6177677902881[/C][C]-0.617767790288064[/C][/ROW]
[ROW][C]146[/C][C]4[/C][C]11.1150340591529[/C][C]-7.11503405915292[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]10.0224162412232[/C][C]-2.02241624122318[/C][/ROW]
[ROW][C]148[/C][C]15[/C][C]11.5699494419789[/C][C]3.43005055802111[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]13.4523825216652[/C][C]2.54761747833481[/C][/ROW]
[ROW][C]150[/C][C]12[/C][C]15.3958913475725[/C][C]-3.39589134757248[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]13.0574735100935[/C][C]-1.05747351009355[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]15.0175684462496[/C][C]-0.017568446249605[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]9.23046222953329[/C][C]-0.230462229533292[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]13.4110982492062[/C][C]-1.41109824920623[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.4929771898166[/C][C]1.50702281018336[/C][/ROW]
[ROW][C]156[/C][C]11[/C][C]9.63366429622933[/C][C]1.36633570377067[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145923&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145923&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
11410.47764730413873.52235269586135
2811.4794035220261-3.47940352202609
31211.57471534093780.425284659062193
4712.0345374122119-5.03453741221194
51010.2512562263056-0.251256226305593
679.8635736562784-2.8635736562784
71616.006440600654-0.00644060065399557
8119.082048688259491.91795131174051
91410.92426963184023.07573036815976
1069.63611320347868-3.63611320347868
111612.45645881631663.5435411836834
121111.4711104669017-0.471110466901702
131612.12539901225423.87460098774575
14128.477337470490293.5226625295097
15711.639631162027-4.63963116202703
16139.855280601154023.14471939884598
171111.0844945104544-0.0844945104544396
181513.83547408333371.16452591666628
1978.95481226559313-1.95481226559313
2098.2168858003560.783114199644003
2179.27544578737124-2.27544578737124
221414.6004129411039-0.60041294110386
231510.29254049876464.70745950123545
24710.2418965576013-3.24189655760128
251512.91735302394412.08264697605587
261714.2220900397812.77790996021902
271512.28071720242032.71928279757971
281412.38816215132461.61183784867537
291412.99394274406051.00605725593951
30812.6475444464923-4.64754444649226
3189.88720244352193-1.88720244352193
321412.92671269264841.07328730735155
331414.1548599883689-0.15485998836894
34810.6638179617059-2.66381796170594
35119.082048688259491.91795131174051
361614.32231130852761.6776886914724
371012.7169104864509-2.71691048645089
3889.85527783976728-1.85527783976728
391412.58967406378451.41032593621547
401615.27342358525180.726576414748233
411312.91735302394410.082646976055867
42510.9138378267825-5.91383782678252
4386.980450972284591.01954902771541
44109.040764415800530.959235584199467
45811.5300474631894-3.53004746318936
461314.7006342098505-1.70063420985048
471513.75781498825071.2421850117493
4868.04605363629459-2.04605363629459
491210.3784926489721.62150735102804
501613.38992113059882.6100788694012
51511.3708891981551-6.37088919815508
52159.428446985827725.57155301417228
531211.76579820972670.234201790273269
5489.26822210721352-1.26822210721352
551312.1936984386330.806301561367046
561411.53834051831372.46165948168625
571210.12156813500311.87843186499687
581614.52966184608911.47033815391094
59108.373595158140921.62640484185908
601515.0682123874129-0.0682123874128782
6187.733575731327590.266424268672412
621613.78006148182482.2199385181752
631912.6108511823926.38914881760804
641414.1253931658131-0.1253931658131
65610.0130538111321-4.01305381113213
661312.05571729220610.944282707793891
671511.27419232418723.7258076758128
68711.3827066480582-4.3827066480582
691313.1070508376769-0.107050837676892
7044.17298942292972-0.172989422929723
711412.5012702449411.49872975505897
721311.66910133575881.33089866424115
731111.2412011068526-0.241201106852624
741413.05978774041640.940212259583634
751212.0650769609104-0.0650769609104216
761511.94720296833513.0527970316649
771410.93715369532333.06284630467672
781311.51823336446221.48176663553778
79811.6773943908832-3.67739439088323
8066.39584749815616-0.395847498156163
8178.39829332035111-1.39829332035111
821312.78766217125490.212337828745064
831313.6385559406192-0.638555940619218
841110.34073277129170.659267228708267
85510.0532714700112-5.05327147001116
861211.60187128434680.398128715653197
8788.65767285413201-0.657672854132005
881112.1499597361512-1.14995973615116
891415.4548283438601-1.45482834386014
9099.61707818598055-0.617078185980554
911013.6385559406192-3.63855594061922
921311.80569742712951.19430257287048
931615.01756844624960.982431553750394
941613.22492483025222.77507516974779
951111.0550276878986-0.0550276878985995
9689.27174650199225-1.27174650199225
97410.6954241239843-6.69542412398435
9877.48846531608591-0.488465316085906
991411.70440678341622.29559321658376
1001112.3338189247824-1.33381892478237
1011715.07650544253731.92349455746274
1021514.7311737585490.268826241451038
1031714.03453432715752.96546567284247
10459.6843082373926-4.6843082373926
10545.45409489013631-1.45409489013631
1061014.8501171260909-4.85011712609094
107118.911076324498072.08892367550193
1081514.68159643096560.318403569034384
109108.810855055751461.18914494424855
11099.93877832133859-0.938778321338593
1111211.30013810314030.699861896859718
1121512.84766854250932.15233145749074
113711.5605870118878-4.56058701188784
1141313.2932242566309-0.293224256630917
1151214.6309524898023-2.63095248980234
1161413.83899847811250.161001521887543
1171413.78006148182480.219938518175202
118812.4197710749898-4.41977107498977
1191513.00682956893031.99317043106973
1201211.16354201176960.836457988230396
1211210.61776779028811.38223220971194
1221611.82825960079314.17174039920688
12398.843846273086030.156153726913971
1241512.56127661619542.43872338380465
1251513.78006148182481.2199385181752
126611.5688800670122-5.56888006701223
1271415.6822860352731-1.68228603527312
1281511.94720296833513.0527970316649
1291011.4180120957156-1.4180120957156
13065.183035934554810.816964065445193
1311415.6822860352731-1.68228603527312
1321213.1659878339646-1.16598783396455
133810.7037199404955-2.70371994049547
1341112.847671303896-1.847671303896
1351313.3934455253775-0.393445525377536
136910.6177677902881-1.61776779028806
1371513.06576656521791.93423343478207
138137.733575731327595.26642426867241
1391515.2263040388672-0.226304038867229
1401411.68675405958752.31324594041246
1411610.67670478657575.32329521342428
1421412.6202136124831.37978638751699
1431412.63679972273181.36320027726822
144107.184415143169832.81558485683017
1451010.6177677902881-0.617767790288064
146411.1150340591529-7.11503405915292
147810.0224162412232-2.02241624122318
1481511.56994944197893.43005055802111
1491613.45238252166522.54761747833481
1501215.3958913475725-3.39589134757248
1511213.0574735100935-1.05747351009355
1521515.0175684462496-0.017568446249605
15399.23046222953329-0.230462229533292
1541213.4110982492062-1.41109824920623
1551412.49297718981661.50702281018336
156119.633664296229331.36633570377067






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.7532022906134050.493595418773190.246797709386595
100.7127295456792490.5745409086415030.287270454320751
110.8713108493535510.2573783012928990.128689150646449
120.8345242234650640.3309515530698720.165475776534936
130.9411111757045920.1177776485908150.0588888242954077
140.9380057980582430.1239884038835150.0619942019417573
150.9564474852890990.08710502942180260.0435525147109013
160.9563682923187810.08726341536243790.043631707681219
170.9365642143111570.1268715713776860.0634357856888431
180.9077159767118180.1845680465763640.0922840232881818
190.8769644172689690.2460711654620610.123035582731031
200.8329205522960720.3341588954078570.167079447703928
210.8821467507247610.2357064985504780.117853249275239
220.8419822453887020.3160355092225970.158017754611298
230.9168986773942880.1662026452114240.083101322605712
240.9238930873667370.1522138252665260.0761069126332628
250.9131756760511550.173648647897690.0868243239488448
260.9053150680468640.1893698639062720.0946849319531362
270.8938671548236160.2122656903527690.106132845176384
280.8707102037416930.2585795925166150.129289796258307
290.8378474643532180.3243050712935630.162152535646782
300.8929530887304160.2140938225391680.107046911269584
310.8676085803726380.2647828392547250.132391419627362
320.8365812534561770.3268374930876470.163418746543823
330.7989235980847630.4021528038304730.201076401915237
340.7934641001867040.4130717996265920.206535899813296
350.766012276024490.467975447951020.23398772397551
360.7407982188219110.5184035623561780.259201781178089
370.7574534460277040.4850931079445920.242546553972296
380.7248283320232770.5503433359534470.275171667976723
390.6890321493856290.6219357012287430.310967850614371
400.6413484402127640.7173031195744710.358651559787236
410.5886337368664350.822732526267130.411366263133565
420.7205135641233590.5589728717532820.279486435876641
430.6824548264006170.6350903471987660.317545173599383
440.6363608992336350.7272782015327290.363639100766365
450.6846348928487830.6307302143024330.315365107151217
460.6579298915884460.6841402168231070.342070108411554
470.6347873069405220.7304253861189560.365212693059478
480.6167388260434730.7665223479130530.383261173956527
490.5742447746316890.8515104507366220.425755225368311
500.551785472737060.8964290545258790.44821452726294
510.8039074723894550.392185055221090.196092527610545
520.8800797568158250.2398404863683510.119920243184176
530.8533204760542330.2933590478915350.146679523945767
540.8384772491177430.3230455017645130.161522750882257
550.8146131638939230.3707736722121530.185386836106077
560.7990699361883460.4018601276233080.200930063811654
570.7660774422636170.4678451154727670.233922557736383
580.7304320480440810.5391359039118380.269567951955919
590.7031541007558340.5936917984883330.296845899244166
600.6700698981574520.6598602036850960.329930101842548
610.6317020543149160.7365958913701680.368297945685084
620.6052787488754030.7894425022491940.394721251124597
630.7507690442306090.4984619115387810.249230955769391
640.7220193685946810.5559612628106380.277980631405319
650.8067511910267050.3864976179465890.193248808973295
660.7731489088770350.4537021822459290.226851091122965
670.7915705919279930.4168588161440130.208429408072007
680.864481978302650.2710360433947010.135518021697351
690.8381134106361070.3237731787277860.161886589363893
700.8099349446719060.3801301106561880.190065055328094
710.7810288330936820.4379423338126370.218971166906318
720.7486725975403440.5026548049193120.251327402459656
730.7158995257662070.5682009484675860.284100474233793
740.6921252712907770.6157494574184460.307874728709223
750.6522863574198410.6954272851603180.347713642580159
760.6576697261026090.6846605477947810.342330273897391
770.6576638863510450.6846722272979110.342336113648955
780.6219003550024410.7561992899951180.378099644997559
790.6913206609842990.6173586780314010.308679339015701
800.6514922861100660.6970154277798670.348507713889934
810.6260630212687970.7478739574624070.373936978731204
820.5804800700860820.8390398598278350.419519929913918
830.5427577325328150.9144845349343710.457242267467185
840.4988914960313610.9977829920627210.501108503968639
850.6847771038122810.6304457923754370.315222896187719
860.6427535462650630.7144929074698740.357246453734937
870.6041627438587580.7916745122824840.395837256141242
880.567252779499160.8654944410016810.43274722050084
890.540079112960940.9198417740781210.45992088703906
900.4976169745153650.995233949030730.502383025484635
910.542488174366240.9150236512675190.45751182563376
920.5306864403690210.9386271192619590.469313559630979
930.4910726364479640.9821452728959280.508927363552036
940.4832795143565990.9665590287131990.5167204856434
950.4361154522117250.872230904423450.563884547788275
960.4089600552343430.8179201104686860.591039944765657
970.6179565285533860.7640869428932290.382043471446614
980.5768139194708410.8463721610583180.423186080529159
990.5735600035121730.8528799929756530.426439996487827
1000.5393780252760580.9212439494478840.460621974723942
1010.52460570870180.9507885825964010.4753942912982
1020.4763888730391970.9527777460783940.523611126960803
1030.5365305096933380.9269389806133230.463469490306662
1040.7107603231269370.5784793537461260.289239676873063
1050.7015573350985840.5968853298028330.298442664901416
1060.752194034740750.49561193051850.24780596525925
1070.7258611198065170.5482777603869670.274138880193483
1080.705008221507920.5899835569841610.29499177849208
1090.6656468345216740.6687063309566530.334353165478326
1100.6180501246408030.7638997507183940.381949875359197
1110.5731674858501290.8536650282997420.426832514149871
1120.6143963843718370.7712072312563260.385603615628163
1130.6925292749362380.6149414501275230.307470725063762
1140.6431134465976280.7137731068047450.356886553402372
1150.6151595090910660.7696809818178680.384840490908934
1160.5616312943159950.876737411368010.438368705684005
1170.5082443492858530.9835113014282940.491755650714147
1180.5331054956067180.9337890087865640.466894504393282
1190.5066225189066840.9867549621866330.493377481093316
1200.4657322875988240.9314645751976470.534267712401176
1210.4127522084825510.8255044169651010.587247791517449
1220.4351206592236840.8702413184473680.564879340776316
1230.3770671841865760.7541343683731510.622932815813424
1240.369620690603890.739241381207780.63037930939611
1250.3188847353050570.6377694706101150.681115264694943
1260.5332195449001430.9335609101997130.466780455099857
1270.4750952951080510.9501905902161020.524904704891949
1280.5086848078157870.9826303843684260.491315192184213
1290.4510583045105360.9021166090210730.548941695489464
1300.3978114827476120.7956229654952240.602188517252388
1310.339137913583390.6782758271667790.66086208641661
1320.2954942055502190.5909884111004370.704505794449781
1330.3021122476067860.6042244952135720.697887752393214
1340.2556882462380280.5113764924760560.744311753761972
1350.2005727853316120.4011455706632250.799427214668388
1360.2146625463164640.4293250926329270.785337453683536
1370.1854170369625980.3708340739251950.814582963037403
1380.2324025700495680.4648051400991360.767597429950432
1390.1980120273879730.3960240547759450.801987972612027
1400.1931126641850710.3862253283701410.806887335814929
1410.2891758232890840.5783516465781680.710824176710916
1420.2373921443178210.4747842886356420.762607855682179
1430.1658685654161930.3317371308323870.834131434583807
1440.1450674910359110.2901349820718210.854932508964089
1450.105068432345310.2101368646906190.89493156765469
1460.6436271537691780.7127456924616450.356372846230822
1470.4937409578701610.9874819157403210.506259042129839

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.753202290613405 & 0.49359541877319 & 0.246797709386595 \tabularnewline
10 & 0.712729545679249 & 0.574540908641503 & 0.287270454320751 \tabularnewline
11 & 0.871310849353551 & 0.257378301292899 & 0.128689150646449 \tabularnewline
12 & 0.834524223465064 & 0.330951553069872 & 0.165475776534936 \tabularnewline
13 & 0.941111175704592 & 0.117777648590815 & 0.0588888242954077 \tabularnewline
14 & 0.938005798058243 & 0.123988403883515 & 0.0619942019417573 \tabularnewline
15 & 0.956447485289099 & 0.0871050294218026 & 0.0435525147109013 \tabularnewline
16 & 0.956368292318781 & 0.0872634153624379 & 0.043631707681219 \tabularnewline
17 & 0.936564214311157 & 0.126871571377686 & 0.0634357856888431 \tabularnewline
18 & 0.907715976711818 & 0.184568046576364 & 0.0922840232881818 \tabularnewline
19 & 0.876964417268969 & 0.246071165462061 & 0.123035582731031 \tabularnewline
20 & 0.832920552296072 & 0.334158895407857 & 0.167079447703928 \tabularnewline
21 & 0.882146750724761 & 0.235706498550478 & 0.117853249275239 \tabularnewline
22 & 0.841982245388702 & 0.316035509222597 & 0.158017754611298 \tabularnewline
23 & 0.916898677394288 & 0.166202645211424 & 0.083101322605712 \tabularnewline
24 & 0.923893087366737 & 0.152213825266526 & 0.0761069126332628 \tabularnewline
25 & 0.913175676051155 & 0.17364864789769 & 0.0868243239488448 \tabularnewline
26 & 0.905315068046864 & 0.189369863906272 & 0.0946849319531362 \tabularnewline
27 & 0.893867154823616 & 0.212265690352769 & 0.106132845176384 \tabularnewline
28 & 0.870710203741693 & 0.258579592516615 & 0.129289796258307 \tabularnewline
29 & 0.837847464353218 & 0.324305071293563 & 0.162152535646782 \tabularnewline
30 & 0.892953088730416 & 0.214093822539168 & 0.107046911269584 \tabularnewline
31 & 0.867608580372638 & 0.264782839254725 & 0.132391419627362 \tabularnewline
32 & 0.836581253456177 & 0.326837493087647 & 0.163418746543823 \tabularnewline
33 & 0.798923598084763 & 0.402152803830473 & 0.201076401915237 \tabularnewline
34 & 0.793464100186704 & 0.413071799626592 & 0.206535899813296 \tabularnewline
35 & 0.76601227602449 & 0.46797544795102 & 0.23398772397551 \tabularnewline
36 & 0.740798218821911 & 0.518403562356178 & 0.259201781178089 \tabularnewline
37 & 0.757453446027704 & 0.485093107944592 & 0.242546553972296 \tabularnewline
38 & 0.724828332023277 & 0.550343335953447 & 0.275171667976723 \tabularnewline
39 & 0.689032149385629 & 0.621935701228743 & 0.310967850614371 \tabularnewline
40 & 0.641348440212764 & 0.717303119574471 & 0.358651559787236 \tabularnewline
41 & 0.588633736866435 & 0.82273252626713 & 0.411366263133565 \tabularnewline
42 & 0.720513564123359 & 0.558972871753282 & 0.279486435876641 \tabularnewline
43 & 0.682454826400617 & 0.635090347198766 & 0.317545173599383 \tabularnewline
44 & 0.636360899233635 & 0.727278201532729 & 0.363639100766365 \tabularnewline
45 & 0.684634892848783 & 0.630730214302433 & 0.315365107151217 \tabularnewline
46 & 0.657929891588446 & 0.684140216823107 & 0.342070108411554 \tabularnewline
47 & 0.634787306940522 & 0.730425386118956 & 0.365212693059478 \tabularnewline
48 & 0.616738826043473 & 0.766522347913053 & 0.383261173956527 \tabularnewline
49 & 0.574244774631689 & 0.851510450736622 & 0.425755225368311 \tabularnewline
50 & 0.55178547273706 & 0.896429054525879 & 0.44821452726294 \tabularnewline
51 & 0.803907472389455 & 0.39218505522109 & 0.196092527610545 \tabularnewline
52 & 0.880079756815825 & 0.239840486368351 & 0.119920243184176 \tabularnewline
53 & 0.853320476054233 & 0.293359047891535 & 0.146679523945767 \tabularnewline
54 & 0.838477249117743 & 0.323045501764513 & 0.161522750882257 \tabularnewline
55 & 0.814613163893923 & 0.370773672212153 & 0.185386836106077 \tabularnewline
56 & 0.799069936188346 & 0.401860127623308 & 0.200930063811654 \tabularnewline
57 & 0.766077442263617 & 0.467845115472767 & 0.233922557736383 \tabularnewline
58 & 0.730432048044081 & 0.539135903911838 & 0.269567951955919 \tabularnewline
59 & 0.703154100755834 & 0.593691798488333 & 0.296845899244166 \tabularnewline
60 & 0.670069898157452 & 0.659860203685096 & 0.329930101842548 \tabularnewline
61 & 0.631702054314916 & 0.736595891370168 & 0.368297945685084 \tabularnewline
62 & 0.605278748875403 & 0.789442502249194 & 0.394721251124597 \tabularnewline
63 & 0.750769044230609 & 0.498461911538781 & 0.249230955769391 \tabularnewline
64 & 0.722019368594681 & 0.555961262810638 & 0.277980631405319 \tabularnewline
65 & 0.806751191026705 & 0.386497617946589 & 0.193248808973295 \tabularnewline
66 & 0.773148908877035 & 0.453702182245929 & 0.226851091122965 \tabularnewline
67 & 0.791570591927993 & 0.416858816144013 & 0.208429408072007 \tabularnewline
68 & 0.86448197830265 & 0.271036043394701 & 0.135518021697351 \tabularnewline
69 & 0.838113410636107 & 0.323773178727786 & 0.161886589363893 \tabularnewline
70 & 0.809934944671906 & 0.380130110656188 & 0.190065055328094 \tabularnewline
71 & 0.781028833093682 & 0.437942333812637 & 0.218971166906318 \tabularnewline
72 & 0.748672597540344 & 0.502654804919312 & 0.251327402459656 \tabularnewline
73 & 0.715899525766207 & 0.568200948467586 & 0.284100474233793 \tabularnewline
74 & 0.692125271290777 & 0.615749457418446 & 0.307874728709223 \tabularnewline
75 & 0.652286357419841 & 0.695427285160318 & 0.347713642580159 \tabularnewline
76 & 0.657669726102609 & 0.684660547794781 & 0.342330273897391 \tabularnewline
77 & 0.657663886351045 & 0.684672227297911 & 0.342336113648955 \tabularnewline
78 & 0.621900355002441 & 0.756199289995118 & 0.378099644997559 \tabularnewline
79 & 0.691320660984299 & 0.617358678031401 & 0.308679339015701 \tabularnewline
80 & 0.651492286110066 & 0.697015427779867 & 0.348507713889934 \tabularnewline
81 & 0.626063021268797 & 0.747873957462407 & 0.373936978731204 \tabularnewline
82 & 0.580480070086082 & 0.839039859827835 & 0.419519929913918 \tabularnewline
83 & 0.542757732532815 & 0.914484534934371 & 0.457242267467185 \tabularnewline
84 & 0.498891496031361 & 0.997782992062721 & 0.501108503968639 \tabularnewline
85 & 0.684777103812281 & 0.630445792375437 & 0.315222896187719 \tabularnewline
86 & 0.642753546265063 & 0.714492907469874 & 0.357246453734937 \tabularnewline
87 & 0.604162743858758 & 0.791674512282484 & 0.395837256141242 \tabularnewline
88 & 0.56725277949916 & 0.865494441001681 & 0.43274722050084 \tabularnewline
89 & 0.54007911296094 & 0.919841774078121 & 0.45992088703906 \tabularnewline
90 & 0.497616974515365 & 0.99523394903073 & 0.502383025484635 \tabularnewline
91 & 0.54248817436624 & 0.915023651267519 & 0.45751182563376 \tabularnewline
92 & 0.530686440369021 & 0.938627119261959 & 0.469313559630979 \tabularnewline
93 & 0.491072636447964 & 0.982145272895928 & 0.508927363552036 \tabularnewline
94 & 0.483279514356599 & 0.966559028713199 & 0.5167204856434 \tabularnewline
95 & 0.436115452211725 & 0.87223090442345 & 0.563884547788275 \tabularnewline
96 & 0.408960055234343 & 0.817920110468686 & 0.591039944765657 \tabularnewline
97 & 0.617956528553386 & 0.764086942893229 & 0.382043471446614 \tabularnewline
98 & 0.576813919470841 & 0.846372161058318 & 0.423186080529159 \tabularnewline
99 & 0.573560003512173 & 0.852879992975653 & 0.426439996487827 \tabularnewline
100 & 0.539378025276058 & 0.921243949447884 & 0.460621974723942 \tabularnewline
101 & 0.5246057087018 & 0.950788582596401 & 0.4753942912982 \tabularnewline
102 & 0.476388873039197 & 0.952777746078394 & 0.523611126960803 \tabularnewline
103 & 0.536530509693338 & 0.926938980613323 & 0.463469490306662 \tabularnewline
104 & 0.710760323126937 & 0.578479353746126 & 0.289239676873063 \tabularnewline
105 & 0.701557335098584 & 0.596885329802833 & 0.298442664901416 \tabularnewline
106 & 0.75219403474075 & 0.4956119305185 & 0.24780596525925 \tabularnewline
107 & 0.725861119806517 & 0.548277760386967 & 0.274138880193483 \tabularnewline
108 & 0.70500822150792 & 0.589983556984161 & 0.29499177849208 \tabularnewline
109 & 0.665646834521674 & 0.668706330956653 & 0.334353165478326 \tabularnewline
110 & 0.618050124640803 & 0.763899750718394 & 0.381949875359197 \tabularnewline
111 & 0.573167485850129 & 0.853665028299742 & 0.426832514149871 \tabularnewline
112 & 0.614396384371837 & 0.771207231256326 & 0.385603615628163 \tabularnewline
113 & 0.692529274936238 & 0.614941450127523 & 0.307470725063762 \tabularnewline
114 & 0.643113446597628 & 0.713773106804745 & 0.356886553402372 \tabularnewline
115 & 0.615159509091066 & 0.769680981817868 & 0.384840490908934 \tabularnewline
116 & 0.561631294315995 & 0.87673741136801 & 0.438368705684005 \tabularnewline
117 & 0.508244349285853 & 0.983511301428294 & 0.491755650714147 \tabularnewline
118 & 0.533105495606718 & 0.933789008786564 & 0.466894504393282 \tabularnewline
119 & 0.506622518906684 & 0.986754962186633 & 0.493377481093316 \tabularnewline
120 & 0.465732287598824 & 0.931464575197647 & 0.534267712401176 \tabularnewline
121 & 0.412752208482551 & 0.825504416965101 & 0.587247791517449 \tabularnewline
122 & 0.435120659223684 & 0.870241318447368 & 0.564879340776316 \tabularnewline
123 & 0.377067184186576 & 0.754134368373151 & 0.622932815813424 \tabularnewline
124 & 0.36962069060389 & 0.73924138120778 & 0.63037930939611 \tabularnewline
125 & 0.318884735305057 & 0.637769470610115 & 0.681115264694943 \tabularnewline
126 & 0.533219544900143 & 0.933560910199713 & 0.466780455099857 \tabularnewline
127 & 0.475095295108051 & 0.950190590216102 & 0.524904704891949 \tabularnewline
128 & 0.508684807815787 & 0.982630384368426 & 0.491315192184213 \tabularnewline
129 & 0.451058304510536 & 0.902116609021073 & 0.548941695489464 \tabularnewline
130 & 0.397811482747612 & 0.795622965495224 & 0.602188517252388 \tabularnewline
131 & 0.33913791358339 & 0.678275827166779 & 0.66086208641661 \tabularnewline
132 & 0.295494205550219 & 0.590988411100437 & 0.704505794449781 \tabularnewline
133 & 0.302112247606786 & 0.604224495213572 & 0.697887752393214 \tabularnewline
134 & 0.255688246238028 & 0.511376492476056 & 0.744311753761972 \tabularnewline
135 & 0.200572785331612 & 0.401145570663225 & 0.799427214668388 \tabularnewline
136 & 0.214662546316464 & 0.429325092632927 & 0.785337453683536 \tabularnewline
137 & 0.185417036962598 & 0.370834073925195 & 0.814582963037403 \tabularnewline
138 & 0.232402570049568 & 0.464805140099136 & 0.767597429950432 \tabularnewline
139 & 0.198012027387973 & 0.396024054775945 & 0.801987972612027 \tabularnewline
140 & 0.193112664185071 & 0.386225328370141 & 0.806887335814929 \tabularnewline
141 & 0.289175823289084 & 0.578351646578168 & 0.710824176710916 \tabularnewline
142 & 0.237392144317821 & 0.474784288635642 & 0.762607855682179 \tabularnewline
143 & 0.165868565416193 & 0.331737130832387 & 0.834131434583807 \tabularnewline
144 & 0.145067491035911 & 0.290134982071821 & 0.854932508964089 \tabularnewline
145 & 0.10506843234531 & 0.210136864690619 & 0.89493156765469 \tabularnewline
146 & 0.643627153769178 & 0.712745692461645 & 0.356372846230822 \tabularnewline
147 & 0.493740957870161 & 0.987481915740321 & 0.506259042129839 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145923&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]9[/C][C]0.753202290613405[/C][C]0.49359541877319[/C][C]0.246797709386595[/C][/ROW]
[ROW][C]10[/C][C]0.712729545679249[/C][C]0.574540908641503[/C][C]0.287270454320751[/C][/ROW]
[ROW][C]11[/C][C]0.871310849353551[/C][C]0.257378301292899[/C][C]0.128689150646449[/C][/ROW]
[ROW][C]12[/C][C]0.834524223465064[/C][C]0.330951553069872[/C][C]0.165475776534936[/C][/ROW]
[ROW][C]13[/C][C]0.941111175704592[/C][C]0.117777648590815[/C][C]0.0588888242954077[/C][/ROW]
[ROW][C]14[/C][C]0.938005798058243[/C][C]0.123988403883515[/C][C]0.0619942019417573[/C][/ROW]
[ROW][C]15[/C][C]0.956447485289099[/C][C]0.0871050294218026[/C][C]0.0435525147109013[/C][/ROW]
[ROW][C]16[/C][C]0.956368292318781[/C][C]0.0872634153624379[/C][C]0.043631707681219[/C][/ROW]
[ROW][C]17[/C][C]0.936564214311157[/C][C]0.126871571377686[/C][C]0.0634357856888431[/C][/ROW]
[ROW][C]18[/C][C]0.907715976711818[/C][C]0.184568046576364[/C][C]0.0922840232881818[/C][/ROW]
[ROW][C]19[/C][C]0.876964417268969[/C][C]0.246071165462061[/C][C]0.123035582731031[/C][/ROW]
[ROW][C]20[/C][C]0.832920552296072[/C][C]0.334158895407857[/C][C]0.167079447703928[/C][/ROW]
[ROW][C]21[/C][C]0.882146750724761[/C][C]0.235706498550478[/C][C]0.117853249275239[/C][/ROW]
[ROW][C]22[/C][C]0.841982245388702[/C][C]0.316035509222597[/C][C]0.158017754611298[/C][/ROW]
[ROW][C]23[/C][C]0.916898677394288[/C][C]0.166202645211424[/C][C]0.083101322605712[/C][/ROW]
[ROW][C]24[/C][C]0.923893087366737[/C][C]0.152213825266526[/C][C]0.0761069126332628[/C][/ROW]
[ROW][C]25[/C][C]0.913175676051155[/C][C]0.17364864789769[/C][C]0.0868243239488448[/C][/ROW]
[ROW][C]26[/C][C]0.905315068046864[/C][C]0.189369863906272[/C][C]0.0946849319531362[/C][/ROW]
[ROW][C]27[/C][C]0.893867154823616[/C][C]0.212265690352769[/C][C]0.106132845176384[/C][/ROW]
[ROW][C]28[/C][C]0.870710203741693[/C][C]0.258579592516615[/C][C]0.129289796258307[/C][/ROW]
[ROW][C]29[/C][C]0.837847464353218[/C][C]0.324305071293563[/C][C]0.162152535646782[/C][/ROW]
[ROW][C]30[/C][C]0.892953088730416[/C][C]0.214093822539168[/C][C]0.107046911269584[/C][/ROW]
[ROW][C]31[/C][C]0.867608580372638[/C][C]0.264782839254725[/C][C]0.132391419627362[/C][/ROW]
[ROW][C]32[/C][C]0.836581253456177[/C][C]0.326837493087647[/C][C]0.163418746543823[/C][/ROW]
[ROW][C]33[/C][C]0.798923598084763[/C][C]0.402152803830473[/C][C]0.201076401915237[/C][/ROW]
[ROW][C]34[/C][C]0.793464100186704[/C][C]0.413071799626592[/C][C]0.206535899813296[/C][/ROW]
[ROW][C]35[/C][C]0.76601227602449[/C][C]0.46797544795102[/C][C]0.23398772397551[/C][/ROW]
[ROW][C]36[/C][C]0.740798218821911[/C][C]0.518403562356178[/C][C]0.259201781178089[/C][/ROW]
[ROW][C]37[/C][C]0.757453446027704[/C][C]0.485093107944592[/C][C]0.242546553972296[/C][/ROW]
[ROW][C]38[/C][C]0.724828332023277[/C][C]0.550343335953447[/C][C]0.275171667976723[/C][/ROW]
[ROW][C]39[/C][C]0.689032149385629[/C][C]0.621935701228743[/C][C]0.310967850614371[/C][/ROW]
[ROW][C]40[/C][C]0.641348440212764[/C][C]0.717303119574471[/C][C]0.358651559787236[/C][/ROW]
[ROW][C]41[/C][C]0.588633736866435[/C][C]0.82273252626713[/C][C]0.411366263133565[/C][/ROW]
[ROW][C]42[/C][C]0.720513564123359[/C][C]0.558972871753282[/C][C]0.279486435876641[/C][/ROW]
[ROW][C]43[/C][C]0.682454826400617[/C][C]0.635090347198766[/C][C]0.317545173599383[/C][/ROW]
[ROW][C]44[/C][C]0.636360899233635[/C][C]0.727278201532729[/C][C]0.363639100766365[/C][/ROW]
[ROW][C]45[/C][C]0.684634892848783[/C][C]0.630730214302433[/C][C]0.315365107151217[/C][/ROW]
[ROW][C]46[/C][C]0.657929891588446[/C][C]0.684140216823107[/C][C]0.342070108411554[/C][/ROW]
[ROW][C]47[/C][C]0.634787306940522[/C][C]0.730425386118956[/C][C]0.365212693059478[/C][/ROW]
[ROW][C]48[/C][C]0.616738826043473[/C][C]0.766522347913053[/C][C]0.383261173956527[/C][/ROW]
[ROW][C]49[/C][C]0.574244774631689[/C][C]0.851510450736622[/C][C]0.425755225368311[/C][/ROW]
[ROW][C]50[/C][C]0.55178547273706[/C][C]0.896429054525879[/C][C]0.44821452726294[/C][/ROW]
[ROW][C]51[/C][C]0.803907472389455[/C][C]0.39218505522109[/C][C]0.196092527610545[/C][/ROW]
[ROW][C]52[/C][C]0.880079756815825[/C][C]0.239840486368351[/C][C]0.119920243184176[/C][/ROW]
[ROW][C]53[/C][C]0.853320476054233[/C][C]0.293359047891535[/C][C]0.146679523945767[/C][/ROW]
[ROW][C]54[/C][C]0.838477249117743[/C][C]0.323045501764513[/C][C]0.161522750882257[/C][/ROW]
[ROW][C]55[/C][C]0.814613163893923[/C][C]0.370773672212153[/C][C]0.185386836106077[/C][/ROW]
[ROW][C]56[/C][C]0.799069936188346[/C][C]0.401860127623308[/C][C]0.200930063811654[/C][/ROW]
[ROW][C]57[/C][C]0.766077442263617[/C][C]0.467845115472767[/C][C]0.233922557736383[/C][/ROW]
[ROW][C]58[/C][C]0.730432048044081[/C][C]0.539135903911838[/C][C]0.269567951955919[/C][/ROW]
[ROW][C]59[/C][C]0.703154100755834[/C][C]0.593691798488333[/C][C]0.296845899244166[/C][/ROW]
[ROW][C]60[/C][C]0.670069898157452[/C][C]0.659860203685096[/C][C]0.329930101842548[/C][/ROW]
[ROW][C]61[/C][C]0.631702054314916[/C][C]0.736595891370168[/C][C]0.368297945685084[/C][/ROW]
[ROW][C]62[/C][C]0.605278748875403[/C][C]0.789442502249194[/C][C]0.394721251124597[/C][/ROW]
[ROW][C]63[/C][C]0.750769044230609[/C][C]0.498461911538781[/C][C]0.249230955769391[/C][/ROW]
[ROW][C]64[/C][C]0.722019368594681[/C][C]0.555961262810638[/C][C]0.277980631405319[/C][/ROW]
[ROW][C]65[/C][C]0.806751191026705[/C][C]0.386497617946589[/C][C]0.193248808973295[/C][/ROW]
[ROW][C]66[/C][C]0.773148908877035[/C][C]0.453702182245929[/C][C]0.226851091122965[/C][/ROW]
[ROW][C]67[/C][C]0.791570591927993[/C][C]0.416858816144013[/C][C]0.208429408072007[/C][/ROW]
[ROW][C]68[/C][C]0.86448197830265[/C][C]0.271036043394701[/C][C]0.135518021697351[/C][/ROW]
[ROW][C]69[/C][C]0.838113410636107[/C][C]0.323773178727786[/C][C]0.161886589363893[/C][/ROW]
[ROW][C]70[/C][C]0.809934944671906[/C][C]0.380130110656188[/C][C]0.190065055328094[/C][/ROW]
[ROW][C]71[/C][C]0.781028833093682[/C][C]0.437942333812637[/C][C]0.218971166906318[/C][/ROW]
[ROW][C]72[/C][C]0.748672597540344[/C][C]0.502654804919312[/C][C]0.251327402459656[/C][/ROW]
[ROW][C]73[/C][C]0.715899525766207[/C][C]0.568200948467586[/C][C]0.284100474233793[/C][/ROW]
[ROW][C]74[/C][C]0.692125271290777[/C][C]0.615749457418446[/C][C]0.307874728709223[/C][/ROW]
[ROW][C]75[/C][C]0.652286357419841[/C][C]0.695427285160318[/C][C]0.347713642580159[/C][/ROW]
[ROW][C]76[/C][C]0.657669726102609[/C][C]0.684660547794781[/C][C]0.342330273897391[/C][/ROW]
[ROW][C]77[/C][C]0.657663886351045[/C][C]0.684672227297911[/C][C]0.342336113648955[/C][/ROW]
[ROW][C]78[/C][C]0.621900355002441[/C][C]0.756199289995118[/C][C]0.378099644997559[/C][/ROW]
[ROW][C]79[/C][C]0.691320660984299[/C][C]0.617358678031401[/C][C]0.308679339015701[/C][/ROW]
[ROW][C]80[/C][C]0.651492286110066[/C][C]0.697015427779867[/C][C]0.348507713889934[/C][/ROW]
[ROW][C]81[/C][C]0.626063021268797[/C][C]0.747873957462407[/C][C]0.373936978731204[/C][/ROW]
[ROW][C]82[/C][C]0.580480070086082[/C][C]0.839039859827835[/C][C]0.419519929913918[/C][/ROW]
[ROW][C]83[/C][C]0.542757732532815[/C][C]0.914484534934371[/C][C]0.457242267467185[/C][/ROW]
[ROW][C]84[/C][C]0.498891496031361[/C][C]0.997782992062721[/C][C]0.501108503968639[/C][/ROW]
[ROW][C]85[/C][C]0.684777103812281[/C][C]0.630445792375437[/C][C]0.315222896187719[/C][/ROW]
[ROW][C]86[/C][C]0.642753546265063[/C][C]0.714492907469874[/C][C]0.357246453734937[/C][/ROW]
[ROW][C]87[/C][C]0.604162743858758[/C][C]0.791674512282484[/C][C]0.395837256141242[/C][/ROW]
[ROW][C]88[/C][C]0.56725277949916[/C][C]0.865494441001681[/C][C]0.43274722050084[/C][/ROW]
[ROW][C]89[/C][C]0.54007911296094[/C][C]0.919841774078121[/C][C]0.45992088703906[/C][/ROW]
[ROW][C]90[/C][C]0.497616974515365[/C][C]0.99523394903073[/C][C]0.502383025484635[/C][/ROW]
[ROW][C]91[/C][C]0.54248817436624[/C][C]0.915023651267519[/C][C]0.45751182563376[/C][/ROW]
[ROW][C]92[/C][C]0.530686440369021[/C][C]0.938627119261959[/C][C]0.469313559630979[/C][/ROW]
[ROW][C]93[/C][C]0.491072636447964[/C][C]0.982145272895928[/C][C]0.508927363552036[/C][/ROW]
[ROW][C]94[/C][C]0.483279514356599[/C][C]0.966559028713199[/C][C]0.5167204856434[/C][/ROW]
[ROW][C]95[/C][C]0.436115452211725[/C][C]0.87223090442345[/C][C]0.563884547788275[/C][/ROW]
[ROW][C]96[/C][C]0.408960055234343[/C][C]0.817920110468686[/C][C]0.591039944765657[/C][/ROW]
[ROW][C]97[/C][C]0.617956528553386[/C][C]0.764086942893229[/C][C]0.382043471446614[/C][/ROW]
[ROW][C]98[/C][C]0.576813919470841[/C][C]0.846372161058318[/C][C]0.423186080529159[/C][/ROW]
[ROW][C]99[/C][C]0.573560003512173[/C][C]0.852879992975653[/C][C]0.426439996487827[/C][/ROW]
[ROW][C]100[/C][C]0.539378025276058[/C][C]0.921243949447884[/C][C]0.460621974723942[/C][/ROW]
[ROW][C]101[/C][C]0.5246057087018[/C][C]0.950788582596401[/C][C]0.4753942912982[/C][/ROW]
[ROW][C]102[/C][C]0.476388873039197[/C][C]0.952777746078394[/C][C]0.523611126960803[/C][/ROW]
[ROW][C]103[/C][C]0.536530509693338[/C][C]0.926938980613323[/C][C]0.463469490306662[/C][/ROW]
[ROW][C]104[/C][C]0.710760323126937[/C][C]0.578479353746126[/C][C]0.289239676873063[/C][/ROW]
[ROW][C]105[/C][C]0.701557335098584[/C][C]0.596885329802833[/C][C]0.298442664901416[/C][/ROW]
[ROW][C]106[/C][C]0.75219403474075[/C][C]0.4956119305185[/C][C]0.24780596525925[/C][/ROW]
[ROW][C]107[/C][C]0.725861119806517[/C][C]0.548277760386967[/C][C]0.274138880193483[/C][/ROW]
[ROW][C]108[/C][C]0.70500822150792[/C][C]0.589983556984161[/C][C]0.29499177849208[/C][/ROW]
[ROW][C]109[/C][C]0.665646834521674[/C][C]0.668706330956653[/C][C]0.334353165478326[/C][/ROW]
[ROW][C]110[/C][C]0.618050124640803[/C][C]0.763899750718394[/C][C]0.381949875359197[/C][/ROW]
[ROW][C]111[/C][C]0.573167485850129[/C][C]0.853665028299742[/C][C]0.426832514149871[/C][/ROW]
[ROW][C]112[/C][C]0.614396384371837[/C][C]0.771207231256326[/C][C]0.385603615628163[/C][/ROW]
[ROW][C]113[/C][C]0.692529274936238[/C][C]0.614941450127523[/C][C]0.307470725063762[/C][/ROW]
[ROW][C]114[/C][C]0.643113446597628[/C][C]0.713773106804745[/C][C]0.356886553402372[/C][/ROW]
[ROW][C]115[/C][C]0.615159509091066[/C][C]0.769680981817868[/C][C]0.384840490908934[/C][/ROW]
[ROW][C]116[/C][C]0.561631294315995[/C][C]0.87673741136801[/C][C]0.438368705684005[/C][/ROW]
[ROW][C]117[/C][C]0.508244349285853[/C][C]0.983511301428294[/C][C]0.491755650714147[/C][/ROW]
[ROW][C]118[/C][C]0.533105495606718[/C][C]0.933789008786564[/C][C]0.466894504393282[/C][/ROW]
[ROW][C]119[/C][C]0.506622518906684[/C][C]0.986754962186633[/C][C]0.493377481093316[/C][/ROW]
[ROW][C]120[/C][C]0.465732287598824[/C][C]0.931464575197647[/C][C]0.534267712401176[/C][/ROW]
[ROW][C]121[/C][C]0.412752208482551[/C][C]0.825504416965101[/C][C]0.587247791517449[/C][/ROW]
[ROW][C]122[/C][C]0.435120659223684[/C][C]0.870241318447368[/C][C]0.564879340776316[/C][/ROW]
[ROW][C]123[/C][C]0.377067184186576[/C][C]0.754134368373151[/C][C]0.622932815813424[/C][/ROW]
[ROW][C]124[/C][C]0.36962069060389[/C][C]0.73924138120778[/C][C]0.63037930939611[/C][/ROW]
[ROW][C]125[/C][C]0.318884735305057[/C][C]0.637769470610115[/C][C]0.681115264694943[/C][/ROW]
[ROW][C]126[/C][C]0.533219544900143[/C][C]0.933560910199713[/C][C]0.466780455099857[/C][/ROW]
[ROW][C]127[/C][C]0.475095295108051[/C][C]0.950190590216102[/C][C]0.524904704891949[/C][/ROW]
[ROW][C]128[/C][C]0.508684807815787[/C][C]0.982630384368426[/C][C]0.491315192184213[/C][/ROW]
[ROW][C]129[/C][C]0.451058304510536[/C][C]0.902116609021073[/C][C]0.548941695489464[/C][/ROW]
[ROW][C]130[/C][C]0.397811482747612[/C][C]0.795622965495224[/C][C]0.602188517252388[/C][/ROW]
[ROW][C]131[/C][C]0.33913791358339[/C][C]0.678275827166779[/C][C]0.66086208641661[/C][/ROW]
[ROW][C]132[/C][C]0.295494205550219[/C][C]0.590988411100437[/C][C]0.704505794449781[/C][/ROW]
[ROW][C]133[/C][C]0.302112247606786[/C][C]0.604224495213572[/C][C]0.697887752393214[/C][/ROW]
[ROW][C]134[/C][C]0.255688246238028[/C][C]0.511376492476056[/C][C]0.744311753761972[/C][/ROW]
[ROW][C]135[/C][C]0.200572785331612[/C][C]0.401145570663225[/C][C]0.799427214668388[/C][/ROW]
[ROW][C]136[/C][C]0.214662546316464[/C][C]0.429325092632927[/C][C]0.785337453683536[/C][/ROW]
[ROW][C]137[/C][C]0.185417036962598[/C][C]0.370834073925195[/C][C]0.814582963037403[/C][/ROW]
[ROW][C]138[/C][C]0.232402570049568[/C][C]0.464805140099136[/C][C]0.767597429950432[/C][/ROW]
[ROW][C]139[/C][C]0.198012027387973[/C][C]0.396024054775945[/C][C]0.801987972612027[/C][/ROW]
[ROW][C]140[/C][C]0.193112664185071[/C][C]0.386225328370141[/C][C]0.806887335814929[/C][/ROW]
[ROW][C]141[/C][C]0.289175823289084[/C][C]0.578351646578168[/C][C]0.710824176710916[/C][/ROW]
[ROW][C]142[/C][C]0.237392144317821[/C][C]0.474784288635642[/C][C]0.762607855682179[/C][/ROW]
[ROW][C]143[/C][C]0.165868565416193[/C][C]0.331737130832387[/C][C]0.834131434583807[/C][/ROW]
[ROW][C]144[/C][C]0.145067491035911[/C][C]0.290134982071821[/C][C]0.854932508964089[/C][/ROW]
[ROW][C]145[/C][C]0.10506843234531[/C][C]0.210136864690619[/C][C]0.89493156765469[/C][/ROW]
[ROW][C]146[/C][C]0.643627153769178[/C][C]0.712745692461645[/C][C]0.356372846230822[/C][/ROW]
[ROW][C]147[/C][C]0.493740957870161[/C][C]0.987481915740321[/C][C]0.506259042129839[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145923&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145923&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
90.7532022906134050.493595418773190.246797709386595
100.7127295456792490.5745409086415030.287270454320751
110.8713108493535510.2573783012928990.128689150646449
120.8345242234650640.3309515530698720.165475776534936
130.9411111757045920.1177776485908150.0588888242954077
140.9380057980582430.1239884038835150.0619942019417573
150.9564474852890990.08710502942180260.0435525147109013
160.9563682923187810.08726341536243790.043631707681219
170.9365642143111570.1268715713776860.0634357856888431
180.9077159767118180.1845680465763640.0922840232881818
190.8769644172689690.2460711654620610.123035582731031
200.8329205522960720.3341588954078570.167079447703928
210.8821467507247610.2357064985504780.117853249275239
220.8419822453887020.3160355092225970.158017754611298
230.9168986773942880.1662026452114240.083101322605712
240.9238930873667370.1522138252665260.0761069126332628
250.9131756760511550.173648647897690.0868243239488448
260.9053150680468640.1893698639062720.0946849319531362
270.8938671548236160.2122656903527690.106132845176384
280.8707102037416930.2585795925166150.129289796258307
290.8378474643532180.3243050712935630.162152535646782
300.8929530887304160.2140938225391680.107046911269584
310.8676085803726380.2647828392547250.132391419627362
320.8365812534561770.3268374930876470.163418746543823
330.7989235980847630.4021528038304730.201076401915237
340.7934641001867040.4130717996265920.206535899813296
350.766012276024490.467975447951020.23398772397551
360.7407982188219110.5184035623561780.259201781178089
370.7574534460277040.4850931079445920.242546553972296
380.7248283320232770.5503433359534470.275171667976723
390.6890321493856290.6219357012287430.310967850614371
400.6413484402127640.7173031195744710.358651559787236
410.5886337368664350.822732526267130.411366263133565
420.7205135641233590.5589728717532820.279486435876641
430.6824548264006170.6350903471987660.317545173599383
440.6363608992336350.7272782015327290.363639100766365
450.6846348928487830.6307302143024330.315365107151217
460.6579298915884460.6841402168231070.342070108411554
470.6347873069405220.7304253861189560.365212693059478
480.6167388260434730.7665223479130530.383261173956527
490.5742447746316890.8515104507366220.425755225368311
500.551785472737060.8964290545258790.44821452726294
510.8039074723894550.392185055221090.196092527610545
520.8800797568158250.2398404863683510.119920243184176
530.8533204760542330.2933590478915350.146679523945767
540.8384772491177430.3230455017645130.161522750882257
550.8146131638939230.3707736722121530.185386836106077
560.7990699361883460.4018601276233080.200930063811654
570.7660774422636170.4678451154727670.233922557736383
580.7304320480440810.5391359039118380.269567951955919
590.7031541007558340.5936917984883330.296845899244166
600.6700698981574520.6598602036850960.329930101842548
610.6317020543149160.7365958913701680.368297945685084
620.6052787488754030.7894425022491940.394721251124597
630.7507690442306090.4984619115387810.249230955769391
640.7220193685946810.5559612628106380.277980631405319
650.8067511910267050.3864976179465890.193248808973295
660.7731489088770350.4537021822459290.226851091122965
670.7915705919279930.4168588161440130.208429408072007
680.864481978302650.2710360433947010.135518021697351
690.8381134106361070.3237731787277860.161886589363893
700.8099349446719060.3801301106561880.190065055328094
710.7810288330936820.4379423338126370.218971166906318
720.7486725975403440.5026548049193120.251327402459656
730.7158995257662070.5682009484675860.284100474233793
740.6921252712907770.6157494574184460.307874728709223
750.6522863574198410.6954272851603180.347713642580159
760.6576697261026090.6846605477947810.342330273897391
770.6576638863510450.6846722272979110.342336113648955
780.6219003550024410.7561992899951180.378099644997559
790.6913206609842990.6173586780314010.308679339015701
800.6514922861100660.6970154277798670.348507713889934
810.6260630212687970.7478739574624070.373936978731204
820.5804800700860820.8390398598278350.419519929913918
830.5427577325328150.9144845349343710.457242267467185
840.4988914960313610.9977829920627210.501108503968639
850.6847771038122810.6304457923754370.315222896187719
860.6427535462650630.7144929074698740.357246453734937
870.6041627438587580.7916745122824840.395837256141242
880.567252779499160.8654944410016810.43274722050084
890.540079112960940.9198417740781210.45992088703906
900.4976169745153650.995233949030730.502383025484635
910.542488174366240.9150236512675190.45751182563376
920.5306864403690210.9386271192619590.469313559630979
930.4910726364479640.9821452728959280.508927363552036
940.4832795143565990.9665590287131990.5167204856434
950.4361154522117250.872230904423450.563884547788275
960.4089600552343430.8179201104686860.591039944765657
970.6179565285533860.7640869428932290.382043471446614
980.5768139194708410.8463721610583180.423186080529159
990.5735600035121730.8528799929756530.426439996487827
1000.5393780252760580.9212439494478840.460621974723942
1010.52460570870180.9507885825964010.4753942912982
1020.4763888730391970.9527777460783940.523611126960803
1030.5365305096933380.9269389806133230.463469490306662
1040.7107603231269370.5784793537461260.289239676873063
1050.7015573350985840.5968853298028330.298442664901416
1060.752194034740750.49561193051850.24780596525925
1070.7258611198065170.5482777603869670.274138880193483
1080.705008221507920.5899835569841610.29499177849208
1090.6656468345216740.6687063309566530.334353165478326
1100.6180501246408030.7638997507183940.381949875359197
1110.5731674858501290.8536650282997420.426832514149871
1120.6143963843718370.7712072312563260.385603615628163
1130.6925292749362380.6149414501275230.307470725063762
1140.6431134465976280.7137731068047450.356886553402372
1150.6151595090910660.7696809818178680.384840490908934
1160.5616312943159950.876737411368010.438368705684005
1170.5082443492858530.9835113014282940.491755650714147
1180.5331054956067180.9337890087865640.466894504393282
1190.5066225189066840.9867549621866330.493377481093316
1200.4657322875988240.9314645751976470.534267712401176
1210.4127522084825510.8255044169651010.587247791517449
1220.4351206592236840.8702413184473680.564879340776316
1230.3770671841865760.7541343683731510.622932815813424
1240.369620690603890.739241381207780.63037930939611
1250.3188847353050570.6377694706101150.681115264694943
1260.5332195449001430.9335609101997130.466780455099857
1270.4750952951080510.9501905902161020.524904704891949
1280.5086848078157870.9826303843684260.491315192184213
1290.4510583045105360.9021166090210730.548941695489464
1300.3978114827476120.7956229654952240.602188517252388
1310.339137913583390.6782758271667790.66086208641661
1320.2954942055502190.5909884111004370.704505794449781
1330.3021122476067860.6042244952135720.697887752393214
1340.2556882462380280.5113764924760560.744311753761972
1350.2005727853316120.4011455706632250.799427214668388
1360.2146625463164640.4293250926329270.785337453683536
1370.1854170369625980.3708340739251950.814582963037403
1380.2324025700495680.4648051400991360.767597429950432
1390.1980120273879730.3960240547759450.801987972612027
1400.1931126641850710.3862253283701410.806887335814929
1410.2891758232890840.5783516465781680.710824176710916
1420.2373921443178210.4747842886356420.762607855682179
1430.1658685654161930.3317371308323870.834131434583807
1440.1450674910359110.2901349820718210.854932508964089
1450.105068432345310.2101368646906190.89493156765469
1460.6436271537691780.7127456924616450.356372846230822
1470.4937409578701610.9874819157403210.506259042129839






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.0143884892086331OK

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 2 & 0.0143884892086331 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145923&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0143884892086331[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145923&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145923&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.0143884892086331OK


Figures (Output of Computation)

Figure 1
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; par2 = Do not include Seasonal 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')
}