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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareMultiple Regression
Date of computationTue, 22 Nov 2011 19:30:44 -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/22/t1322008265fh5wkv0r5l1s3r7.htm/, Retrieved Thu, 25 Apr 2024 20:08:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146456, Retrieved Thu, 25 Apr 2024 20:08:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact122

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [WS7 Tutorial Popu...] [2010-11-22 10:55:52] [afe9379cca749d06b3d6872e02cc47ed]
-   PD    [Multiple Regression] [WS7 - Multiple re...] [2010-11-23 20:19:24] [1f5baf2b24e732d76900bb8178fc04e7]
-    D      [Multiple Regression] [WS7 - Multiple re...] [2010-11-23 20:47:17] [1f5baf2b24e732d76900bb8178fc04e7]
- RM            [Multiple Regression] [] [2011-11-23 00:30:44] [0f9b7c3b8d01420b2751adc6f98a35df] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	13	14	13	3	2	1
12	12	8	13	5	1	1
15	10	12	16	6	0	1
12	9	7	12	6	3	1
10	10	10	11	5	3	1
12	12	7	12	3	1	1
15	13	16	18	8	3	1
9	12	11	11	4	1	1
12	12	14	14	4	4	1
11	6	6	9	4	0	1
11	5	16	14	6	3	1
11	12	11	12	6	2	1
15	11	16	11	5	4	1
7	14	12	12	4	3	1
11	14	7	13	6	1	1
11	12	13	11	4	1	1
10	12	11	12	6	2	1
14	11	15	16	6	3	1
10	11	7	9	4	1	2
6	7	9	11	4	1	2
11	9	7	13	2	2	2
15	11	14	15	7	3	2
11	11	15	10	5	4	2
12	12	7	11	4	2	2
14	12	15	13	6	1	2
15	11	17	16	6	2	2
9	11	15	15	7	2	2
13	8	14	14	5	4	2
13	9	14	14	6	2	2
16	12	8	14	4	3	2
13	10	8	8	4	3	2
12	10	14	13	7	3	2
14	12	14	15	7	4	2
11	8	8	13	4	2	3
9	12	11	11	4	2	3
16	11	16	15	6	4	3
12	12	10	15	6	3	3
10	7	8	9	5	4	3
13	11	14	13	6	2	3
16	11	16	16	7	5	3
14	12	13	13	6	3	3
15	9	5	11	3	1	3
5	15	8	12	3	1	3
8	11	10	12	4	1	3
11	11	8	12	6	2	3
16	11	13	14	7	3	3
17	11	15	14	5	9	3
9	15	6	8	4	0	3
9	11	12	13	5	0	3
13	12	16	16	6	2	3
10	12	5	13	6	2	3
6	9	15	11	6	3	4
12	12	12	14	5	1	4
8	12	8	13	4	2	4
14	13	13	13	5	0	4
12	11	14	13	5	5	4
11	9	12	12	4	2	4
16	9	16	16	6	4	4
8	11	10	15	2	3	4
15	11	15	15	8	0	4
7	12	8	12	3	0	4
16	12	16	14	6	4	4
14	9	19	12	6	1	4
16	11	14	15	6	1	4
9	9	6	12	5	4	4
14	12	13	13	5	2	4
11	12	15	12	6	4	4
13	12	7	12	5	1	4
15	12	13	13	6	4	5
5	14	4	5	2	2	5
15	11	14	13	5	5	5
13	12	13	13	5	4	5
11	11	11	14	5	4	5
11	6	14	17	6	4	5
12	10	12	13	6	4	5
12	12	15	13	6	3	5
12	13	14	12	5	3	5
12	8	13	13	5	3	5
14	12	8	14	4	2	5
6	12	6	11	2	1	5
7	12	7	12	4	1	5
14	6	13	12	6	5	5
14	11	13	16	6	4	5
10	10	11	12	5	2	5
13	12	5	12	3	3	5
12	13	12	12	6	2	5
9	11	8	10	4	2	6
12	7	11	15	5	2	6
16	11	14	15	8	2	6
10	11	9	12	4	3	6
14	11	10	16	6	2	6
10	11	13	15	6	3	6
16	12	16	16	7	4	6
15	10	16	13	6	3	6
12	11	11	12	5	3	6
10	12	8	11	4	0	6
8	7	4	13	6	1	6
8	13	7	10	3	2	6
11	8	14	15	5	2	6
13	12	11	13	6	3	6
16	11	17	16	7	4	6
16	12	15	15	7	4	6
14	14	17	18	6	1	6
11	10	5	13	3	2	6
4	10	4	10	2	2	6
14	13	10	16	8	3	6
9	10	11	13	3	3	7
14	11	15	15	8	3	7
8	10	10	14	3	1	7
8	7	9	15	4	1	7
11	10	12	14	5	1	7
12	8	15	13	7	1	7
11	12	7	13	6	0	7
14	12	13	15	6	1	7
15	12	12	16	7	3	7
16	11	14	14	6	3	7
16	12	14	14	6	0	7
11	12	8	16	6	2	7
14	12	15	14	6	5	7
14	11	12	12	4	2	7
12	12	12	13	4	3	7
14	11	16	12	5	3	7
8	11	9	12	4	5	7
13	13	15	14	6	4	7
16	12	15	14	6	4	7
12	12	6	14	5	0	7
16	12	14	16	8	3	7
12	12	15	13	6	0	7
11	8	10	14	5	2	7
4	8	6	4	4	0	7
16	12	14	16	8	6	7
15	11	12	13	6	3	7
10	12	8	16	4	1	7
13	13	11	15	6	6	7
15	12	13	14	6	2	7
12	12	9	13	4	1	7
14	11	15	14	6	3	7
7	12	13	12	3	1	8
19	12	15	15	6	2	8
12	10	14	14	5	4	8
12	11	16	13	4	1	8
13	12	14	14	6	2	8
15	12	14	16	4	0	8
8	10	10	6	4	5	8
12	12	10	13	4	2	8
10	13	4	13	6	1	8
8	12	8	14	5	1	8
10	15	15	15	6	4	8
15	11	16	14	6	3	8
16	12	12	15	8	0	9
13	11	12	13	7	3	10
16	12	15	16	7	3	10
9	11	9	12	4	0	14
14	10	12	15	6	2	14
14	11	14	12	6	5	14
12	11	11	14	2	2	14

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=146456&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=146456&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146456&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
Popularity[t] = + 0.0341957712676846 + 0.106302633229405FindingFriends[t] + 0.211443525990832KnowingPeople[t] + 0.357643540447476Liked[t] + 0.606004881194799Celebrity[t] + 0.212601130536282Sum_friends[t] + 4.111733341944e-05Day[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  +  0.0341957712676846 +  0.106302633229405FindingFriends[t] +  0.211443525990832KnowingPeople[t] +  0.357643540447476Liked[t] +  0.606004881194799Celebrity[t] +  0.212601130536282Sum_friends[t] +  4.111733341944e-05Day[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146456&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  +  0.0341957712676846 +  0.106302633229405FindingFriends[t] +  0.211443525990832KnowingPeople[t] +  0.357643540447476Liked[t] +  0.606004881194799Celebrity[t] +  0.212601130536282Sum_friends[t] +  4.111733341944e-05Day[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146456&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146456&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
Popularity[t] = + 0.0341957712676846 + 0.106302633229405FindingFriends[t] + 0.211443525990832KnowingPeople[t] + 0.357643540447476Liked[t] + 0.606004881194799Celebrity[t] + 0.212601130536282Sum_friends[t] + 4.111733341944e-05Day[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.03419577126768461.4333610.02390.9809990.490499
FindingFriends0.1063026332294050.0959391.1080.2696350.134818
KnowingPeople0.2114435259908320.0638483.31170.0011640.000582
Liked0.3576435404474760.0971473.68150.0003240.000162
Celebrity0.6060048811947990.1559563.88570.0001537.7e-05
Sum_friends0.2126011305362820.120441.76520.0795770.039789
Day4.111733341944e-050.0625297e-040.9994760.499738

\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) & 0.0341957712676846 & 1.433361 & 0.0239 & 0.980999 & 0.490499 \tabularnewline
FindingFriends & 0.106302633229405 & 0.095939 & 1.108 & 0.269635 & 0.134818 \tabularnewline
KnowingPeople & 0.211443525990832 & 0.063848 & 3.3117 & 0.001164 & 0.000582 \tabularnewline
Liked & 0.357643540447476 & 0.097147 & 3.6815 & 0.000324 & 0.000162 \tabularnewline
Celebrity & 0.606004881194799 & 0.155956 & 3.8857 & 0.000153 & 7.7e-05 \tabularnewline
Sum_friends & 0.212601130536282 & 0.12044 & 1.7652 & 0.079577 & 0.039789 \tabularnewline
Day & 4.111733341944e-05 & 0.062529 & 7e-04 & 0.999476 & 0.499738 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146456&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]0.0341957712676846[/C][C]1.433361[/C][C]0.0239[/C][C]0.980999[/C][C]0.490499[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.106302633229405[/C][C]0.095939[/C][C]1.108[/C][C]0.269635[/C][C]0.134818[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.211443525990832[/C][C]0.063848[/C][C]3.3117[/C][C]0.001164[/C][C]0.000582[/C][/ROW]
[ROW][C]Liked[/C][C]0.357643540447476[/C][C]0.097147[/C][C]3.6815[/C][C]0.000324[/C][C]0.000162[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.606004881194799[/C][C]0.155956[/C][C]3.8857[/C][C]0.000153[/C][C]7.7e-05[/C][/ROW]
[ROW][C]Sum_friends[/C][C]0.212601130536282[/C][C]0.12044[/C][C]1.7652[/C][C]0.079577[/C][C]0.039789[/C][/ROW]
[ROW][C]Day[/C][C]4.111733341944e-05[/C][C]0.062529[/C][C]7e-04[/C][C]0.999476[/C][C]0.499738[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146456&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146456&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)0.03419577126768461.4333610.02390.9809990.490499
FindingFriends0.1063026332294050.0959391.1080.2696350.134818
KnowingPeople0.2114435259908320.0638483.31170.0011640.000582
Liked0.3576435404474760.0971473.68150.0003240.000162
Celebrity0.6060048811947990.1559563.88570.0001537.7e-05
Sum_friends0.2126011305362820.120441.76520.0795770.039789
Day4.111733341944e-050.0625297e-040.9994760.499738






Multiple Linear Regression - Regression Statistics
Multiple R0.713764529068044
R-squared0.509459802955726
Adjusted R-squared0.489706506430454
F-TEST (value)25.7911281949275
F-TEST (DF numerator)6
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.09777472945348
Sum Squared Residuals655.698163514507

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.713764529068044 \tabularnewline
R-squared & 0.509459802955726 \tabularnewline
Adjusted R-squared & 0.489706506430454 \tabularnewline
F-TEST (value) & 25.7911281949275 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.09777472945348 \tabularnewline
Sum Squared Residuals & 655.698163514507 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146456&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.713764529068044[/C][/ROW]
[ROW][C]R-squared[/C][C]0.509459802955726[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.489706506430454[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]25.7911281949275[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]149[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.09777472945348[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]655.698163514507[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146456&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146456&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 R0.713764529068044
R-squared0.509459802955726
Adjusted R-squared0.489706506430454
F-TEST (value)25.7911281949275
F-TEST (DF numerator)6
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.09777472945348
Sum Squared Residuals655.698163514507






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.26896341492921.73103658507082
21210.89340825760811.10659174239191
31512.99291146711362.00708853288645
41211.03662043374890.963379566251072
51010.8136052233086-0.813605223308556
6129.112311428780182.88768857121982
71516.7226937056585-1.72269370565849
8910.2064468734908-1.20644687349083
91212.5515114644146-0.551511464414602
10117.583525232729013.41647476727099
111113.2296887156437-2.22968871564374
121111.9887013068642-0.988701306864189
131512.40117014301922.59882985698077
14711.4133414674605-4.41334146746051
151111.5005748792709-0.500574879270867
161110.62933392547250.370666074527504
171011.9887013068642-1.98870130686419
181414.3713480699243-0.371348069924296
19108.539124172736571.46087582726343
2069.25208777269556-3.25208777269556
21118.757684436214342.24231556378566
221514.40830700201420.591692997985793
231111.8321241939143-0.832124193914343
24129.573315017397212.42668498260279
251412.97955893807211.02044106192787
261514.58167510870310.418324891296904
27914.4071493974688-5.40714939746876
281312.73234693002520.267653069974801
291313.0194521833768-0.0194521833768389
301611.07029029526684.92970970473325
31138.711823786123084.28817621387692
321213.5867172878898-1.58671728788985
331414.7272107657799-0.727210765779895
341110.07487620869880.925123791301211
35910.419130238694-1.41913023869395
361614.43783142067081.56216857932923
371213.0628717674189-1.06287176741891
38109.569206555946840.430793444053156
391312.87445502672160.125544973278406
401615.61408097284930.38591902715067
411412.98191526449651.01808473550355
42158.012955171329666.98704482867033
4359.64274508912607-4.64274508912607
44810.2464264893849-2.24642648938491
451111.2481503303291-0.248150330329128
461613.83926105290932.16073894709068
471714.32574512571912.67425487428092
4898.182687626013020.81731237398698
49911.4203608324726-2.42036083247257
501314.4765753332751-1.47657533327509
511011.0777659260335-1.07776592603351
52612.3706484532284-6.37064845322836
531212.0969492540191-0.0969492540191494
54810.5001278589498-2.50012785894983
551411.84445074225562.15554925774437
561212.9062946544691-0.906294654469061
571110.66935052277750.330649477222535
581614.58291081199291.41708918800714
59810.5325907267437-2.53259072674372
601514.58803425225780.41196574774217
6179.11127717623499-2.11127717623499
621614.18653163078611.81346836921388
631413.14886383656660.851136163433398
641613.37718209441372.62281790558632
65910.4318965090998-1.43189650909984
661412.16335037009881.83664962990121
671113.2598010239003-2.25980102390034
681310.324444543172.67555545682996
691513.19459862969961.80540137030043
7055.79384205280933-0.793842052809328
711512.90633577180252.09366422819752
721312.58859374850480.411406251495228
731112.4170476037412-1.41704760374118
741114.1988005181039-3.19880051810387
751212.7705498372499-0.770549837249928
761213.404884551145-1.40488455114495
771212.3360952367413-0.336095236741251
781211.95078208505090.0492179149491326
791410.85781251673073.14218748326928
8067.93738395048075-1.93738395048075
8179.71848077930866-2.71848077930866
821412.41174042041191.58825957958806
831414.1612266178126-0.161226617812593
841011.1702556285443-1.17025562854426
85139.114791107204763.88520889279524
861212.3066119354181-0.306611935418104
8799.32097683904484-0.320976839044836
881211.92431946753190.0756805324681125
891614.80187522200641.1981247779936
901010.4603085764669-0.460308576466901
911413.1017348961010.898265103899047
921013.5910230641623-3.59102306416225
931615.50790582754270.492094172457289
941513.40376392801041.59623607198961
951211.48920050964340.510799490356636
96109.359720751649150.640279248350848
97810.1223314553596-2.12233145535963
9888.71613369831802-0.716133698318016
991112.6649526787338-1.66495267873379
1001312.5591515645150.440848435484955
1011615.61304672030410.386953279695863
1021614.93881876110441.0611812388956
1031415.4034334280837-1.40343342808366
104119.047269367990561.95273063200944
10547.15689033946251-3.15689033946251
1061414.7389510554856-0.738951055485645
107910.5285727718053-1.52857277180526
1081415.2259609958669-1.22596099586694
109810.2495705251893-2.24957052518933
110810.6828675211526-2.68286752115256
1111111.8844673395606-0.884467339560597
1121213.1605588730164-1.1605588730164
1131111.0756151862763-0.0756151862762911
1141413.27216455365250.727835446347485
1151514.44957171037650.550428289623478
1161613.4448641670392.55513583296097
1171612.91336340865963.08663659134041
1181112.7851915946821-1.78519159468211
1191414.1878125873318-0.187812587331831
1201410.88207914123653.11792085876347
1211211.55862644544970.441373554550303
1221412.54645925693091.45354074306906
123810.8855519548729-2.88555195487288
1241314.081514090025-1.08151409002495
1251613.97521145679552.02478854320445
1261210.61581031953811.38418968046186
1271615.4784636435530.521536356447016
1281212.7671633942029-0.767163394202944
1291111.4615761516564-0.461576151656405
13046.00815950095096-2.00815950095096
1311616.1162670351618-0.116267035161831
1321512.66433357460992.33566642539011
1331011.3605807017562-1.36058070175623
1341314.0185857875817-1.01858578758167
1351513.12712214374131.87287785625868
1361210.49909360640461.50090639359536
1371413.65630769302990.343692306970139
138710.3812604060591-3.38126040605911
1391913.90769385350395.09230614649612
1401212.9451989004845-0.945198900484526
1411211.87293677244450.127063227555527
1421313.3386067870656-0.338606787065572
1431512.41668184449842.58331815550164
14488.84487272228288-0.844872722282876
1451210.92317938026521.07682061973483
1461010.7602294894029-0.760229489402903
147811.2513396193895-3.2513396193895
1481014.6518040142647-4.65180401426466
1491513.86779233635411.13220766364589
1501614.06021189418181.93978810581816
1511313.2704618078049-0.270461807804948
1521615.08402564034930.915974359650725
15399.82283412352541-0.82283412352541
1541413.06100471307310.93899528692691
1551413.15506716855060.844932831449422
1561210.1742007550851.82579924491501

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.2689634149292 & 1.73103658507082 \tabularnewline
2 & 12 & 10.8934082576081 & 1.10659174239191 \tabularnewline
3 & 15 & 12.9929114671136 & 2.00708853288645 \tabularnewline
4 & 12 & 11.0366204337489 & 0.963379566251072 \tabularnewline
5 & 10 & 10.8136052233086 & -0.813605223308556 \tabularnewline
6 & 12 & 9.11231142878018 & 2.88768857121982 \tabularnewline
7 & 15 & 16.7226937056585 & -1.72269370565849 \tabularnewline
8 & 9 & 10.2064468734908 & -1.20644687349083 \tabularnewline
9 & 12 & 12.5515114644146 & -0.551511464414602 \tabularnewline
10 & 11 & 7.58352523272901 & 3.41647476727099 \tabularnewline
11 & 11 & 13.2296887156437 & -2.22968871564374 \tabularnewline
12 & 11 & 11.9887013068642 & -0.988701306864189 \tabularnewline
13 & 15 & 12.4011701430192 & 2.59882985698077 \tabularnewline
14 & 7 & 11.4133414674605 & -4.41334146746051 \tabularnewline
15 & 11 & 11.5005748792709 & -0.500574879270867 \tabularnewline
16 & 11 & 10.6293339254725 & 0.370666074527504 \tabularnewline
17 & 10 & 11.9887013068642 & -1.98870130686419 \tabularnewline
18 & 14 & 14.3713480699243 & -0.371348069924296 \tabularnewline
19 & 10 & 8.53912417273657 & 1.46087582726343 \tabularnewline
20 & 6 & 9.25208777269556 & -3.25208777269556 \tabularnewline
21 & 11 & 8.75768443621434 & 2.24231556378566 \tabularnewline
22 & 15 & 14.4083070020142 & 0.591692997985793 \tabularnewline
23 & 11 & 11.8321241939143 & -0.832124193914343 \tabularnewline
24 & 12 & 9.57331501739721 & 2.42668498260279 \tabularnewline
25 & 14 & 12.9795589380721 & 1.02044106192787 \tabularnewline
26 & 15 & 14.5816751087031 & 0.418324891296904 \tabularnewline
27 & 9 & 14.4071493974688 & -5.40714939746876 \tabularnewline
28 & 13 & 12.7323469300252 & 0.267653069974801 \tabularnewline
29 & 13 & 13.0194521833768 & -0.0194521833768389 \tabularnewline
30 & 16 & 11.0702902952668 & 4.92970970473325 \tabularnewline
31 & 13 & 8.71182378612308 & 4.28817621387692 \tabularnewline
32 & 12 & 13.5867172878898 & -1.58671728788985 \tabularnewline
33 & 14 & 14.7272107657799 & -0.727210765779895 \tabularnewline
34 & 11 & 10.0748762086988 & 0.925123791301211 \tabularnewline
35 & 9 & 10.419130238694 & -1.41913023869395 \tabularnewline
36 & 16 & 14.4378314206708 & 1.56216857932923 \tabularnewline
37 & 12 & 13.0628717674189 & -1.06287176741891 \tabularnewline
38 & 10 & 9.56920655594684 & 0.430793444053156 \tabularnewline
39 & 13 & 12.8744550267216 & 0.125544973278406 \tabularnewline
40 & 16 & 15.6140809728493 & 0.38591902715067 \tabularnewline
41 & 14 & 12.9819152644965 & 1.01808473550355 \tabularnewline
42 & 15 & 8.01295517132966 & 6.98704482867033 \tabularnewline
43 & 5 & 9.64274508912607 & -4.64274508912607 \tabularnewline
44 & 8 & 10.2464264893849 & -2.24642648938491 \tabularnewline
45 & 11 & 11.2481503303291 & -0.248150330329128 \tabularnewline
46 & 16 & 13.8392610529093 & 2.16073894709068 \tabularnewline
47 & 17 & 14.3257451257191 & 2.67425487428092 \tabularnewline
48 & 9 & 8.18268762601302 & 0.81731237398698 \tabularnewline
49 & 9 & 11.4203608324726 & -2.42036083247257 \tabularnewline
50 & 13 & 14.4765753332751 & -1.47657533327509 \tabularnewline
51 & 10 & 11.0777659260335 & -1.07776592603351 \tabularnewline
52 & 6 & 12.3706484532284 & -6.37064845322836 \tabularnewline
53 & 12 & 12.0969492540191 & -0.0969492540191494 \tabularnewline
54 & 8 & 10.5001278589498 & -2.50012785894983 \tabularnewline
55 & 14 & 11.8444507422556 & 2.15554925774437 \tabularnewline
56 & 12 & 12.9062946544691 & -0.906294654469061 \tabularnewline
57 & 11 & 10.6693505227775 & 0.330649477222535 \tabularnewline
58 & 16 & 14.5829108119929 & 1.41708918800714 \tabularnewline
59 & 8 & 10.5325907267437 & -2.53259072674372 \tabularnewline
60 & 15 & 14.5880342522578 & 0.41196574774217 \tabularnewline
61 & 7 & 9.11127717623499 & -2.11127717623499 \tabularnewline
62 & 16 & 14.1865316307861 & 1.81346836921388 \tabularnewline
63 & 14 & 13.1488638365666 & 0.851136163433398 \tabularnewline
64 & 16 & 13.3771820944137 & 2.62281790558632 \tabularnewline
65 & 9 & 10.4318965090998 & -1.43189650909984 \tabularnewline
66 & 14 & 12.1633503700988 & 1.83664962990121 \tabularnewline
67 & 11 & 13.2598010239003 & -2.25980102390034 \tabularnewline
68 & 13 & 10.32444454317 & 2.67555545682996 \tabularnewline
69 & 15 & 13.1945986296996 & 1.80540137030043 \tabularnewline
70 & 5 & 5.79384205280933 & -0.793842052809328 \tabularnewline
71 & 15 & 12.9063357718025 & 2.09366422819752 \tabularnewline
72 & 13 & 12.5885937485048 & 0.411406251495228 \tabularnewline
73 & 11 & 12.4170476037412 & -1.41704760374118 \tabularnewline
74 & 11 & 14.1988005181039 & -3.19880051810387 \tabularnewline
75 & 12 & 12.7705498372499 & -0.770549837249928 \tabularnewline
76 & 12 & 13.404884551145 & -1.40488455114495 \tabularnewline
77 & 12 & 12.3360952367413 & -0.336095236741251 \tabularnewline
78 & 12 & 11.9507820850509 & 0.0492179149491326 \tabularnewline
79 & 14 & 10.8578125167307 & 3.14218748326928 \tabularnewline
80 & 6 & 7.93738395048075 & -1.93738395048075 \tabularnewline
81 & 7 & 9.71848077930866 & -2.71848077930866 \tabularnewline
82 & 14 & 12.4117404204119 & 1.58825957958806 \tabularnewline
83 & 14 & 14.1612266178126 & -0.161226617812593 \tabularnewline
84 & 10 & 11.1702556285443 & -1.17025562854426 \tabularnewline
85 & 13 & 9.11479110720476 & 3.88520889279524 \tabularnewline
86 & 12 & 12.3066119354181 & -0.306611935418104 \tabularnewline
87 & 9 & 9.32097683904484 & -0.320976839044836 \tabularnewline
88 & 12 & 11.9243194675319 & 0.0756805324681125 \tabularnewline
89 & 16 & 14.8018752220064 & 1.1981247779936 \tabularnewline
90 & 10 & 10.4603085764669 & -0.460308576466901 \tabularnewline
91 & 14 & 13.101734896101 & 0.898265103899047 \tabularnewline
92 & 10 & 13.5910230641623 & -3.59102306416225 \tabularnewline
93 & 16 & 15.5079058275427 & 0.492094172457289 \tabularnewline
94 & 15 & 13.4037639280104 & 1.59623607198961 \tabularnewline
95 & 12 & 11.4892005096434 & 0.510799490356636 \tabularnewline
96 & 10 & 9.35972075164915 & 0.640279248350848 \tabularnewline
97 & 8 & 10.1223314553596 & -2.12233145535963 \tabularnewline
98 & 8 & 8.71613369831802 & -0.716133698318016 \tabularnewline
99 & 11 & 12.6649526787338 & -1.66495267873379 \tabularnewline
100 & 13 & 12.559151564515 & 0.440848435484955 \tabularnewline
101 & 16 & 15.6130467203041 & 0.386953279695863 \tabularnewline
102 & 16 & 14.9388187611044 & 1.0611812388956 \tabularnewline
103 & 14 & 15.4034334280837 & -1.40343342808366 \tabularnewline
104 & 11 & 9.04726936799056 & 1.95273063200944 \tabularnewline
105 & 4 & 7.15689033946251 & -3.15689033946251 \tabularnewline
106 & 14 & 14.7389510554856 & -0.738951055485645 \tabularnewline
107 & 9 & 10.5285727718053 & -1.52857277180526 \tabularnewline
108 & 14 & 15.2259609958669 & -1.22596099586694 \tabularnewline
109 & 8 & 10.2495705251893 & -2.24957052518933 \tabularnewline
110 & 8 & 10.6828675211526 & -2.68286752115256 \tabularnewline
111 & 11 & 11.8844673395606 & -0.884467339560597 \tabularnewline
112 & 12 & 13.1605588730164 & -1.1605588730164 \tabularnewline
113 & 11 & 11.0756151862763 & -0.0756151862762911 \tabularnewline
114 & 14 & 13.2721645536525 & 0.727835446347485 \tabularnewline
115 & 15 & 14.4495717103765 & 0.550428289623478 \tabularnewline
116 & 16 & 13.444864167039 & 2.55513583296097 \tabularnewline
117 & 16 & 12.9133634086596 & 3.08663659134041 \tabularnewline
118 & 11 & 12.7851915946821 & -1.78519159468211 \tabularnewline
119 & 14 & 14.1878125873318 & -0.187812587331831 \tabularnewline
120 & 14 & 10.8820791412365 & 3.11792085876347 \tabularnewline
121 & 12 & 11.5586264454497 & 0.441373554550303 \tabularnewline
122 & 14 & 12.5464592569309 & 1.45354074306906 \tabularnewline
123 & 8 & 10.8855519548729 & -2.88555195487288 \tabularnewline
124 & 13 & 14.081514090025 & -1.08151409002495 \tabularnewline
125 & 16 & 13.9752114567955 & 2.02478854320445 \tabularnewline
126 & 12 & 10.6158103195381 & 1.38418968046186 \tabularnewline
127 & 16 & 15.478463643553 & 0.521536356447016 \tabularnewline
128 & 12 & 12.7671633942029 & -0.767163394202944 \tabularnewline
129 & 11 & 11.4615761516564 & -0.461576151656405 \tabularnewline
130 & 4 & 6.00815950095096 & -2.00815950095096 \tabularnewline
131 & 16 & 16.1162670351618 & -0.116267035161831 \tabularnewline
132 & 15 & 12.6643335746099 & 2.33566642539011 \tabularnewline
133 & 10 & 11.3605807017562 & -1.36058070175623 \tabularnewline
134 & 13 & 14.0185857875817 & -1.01858578758167 \tabularnewline
135 & 15 & 13.1271221437413 & 1.87287785625868 \tabularnewline
136 & 12 & 10.4990936064046 & 1.50090639359536 \tabularnewline
137 & 14 & 13.6563076930299 & 0.343692306970139 \tabularnewline
138 & 7 & 10.3812604060591 & -3.38126040605911 \tabularnewline
139 & 19 & 13.9076938535039 & 5.09230614649612 \tabularnewline
140 & 12 & 12.9451989004845 & -0.945198900484526 \tabularnewline
141 & 12 & 11.8729367724445 & 0.127063227555527 \tabularnewline
142 & 13 & 13.3386067870656 & -0.338606787065572 \tabularnewline
143 & 15 & 12.4166818444984 & 2.58331815550164 \tabularnewline
144 & 8 & 8.84487272228288 & -0.844872722282876 \tabularnewline
145 & 12 & 10.9231793802652 & 1.07682061973483 \tabularnewline
146 & 10 & 10.7602294894029 & -0.760229489402903 \tabularnewline
147 & 8 & 11.2513396193895 & -3.2513396193895 \tabularnewline
148 & 10 & 14.6518040142647 & -4.65180401426466 \tabularnewline
149 & 15 & 13.8677923363541 & 1.13220766364589 \tabularnewline
150 & 16 & 14.0602118941818 & 1.93978810581816 \tabularnewline
151 & 13 & 13.2704618078049 & -0.270461807804948 \tabularnewline
152 & 16 & 15.0840256403493 & 0.915974359650725 \tabularnewline
153 & 9 & 9.82283412352541 & -0.82283412352541 \tabularnewline
154 & 14 & 13.0610047130731 & 0.93899528692691 \tabularnewline
155 & 14 & 13.1550671685506 & 0.844932831449422 \tabularnewline
156 & 12 & 10.174200755085 & 1.82579924491501 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146456&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]13[/C][C]11.2689634149292[/C][C]1.73103658507082[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]10.8934082576081[/C][C]1.10659174239191[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]12.9929114671136[/C][C]2.00708853288645[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]11.0366204337489[/C][C]0.963379566251072[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]10.8136052233086[/C][C]-0.813605223308556[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]9.11231142878018[/C][C]2.88768857121982[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]16.7226937056585[/C][C]-1.72269370565849[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]10.2064468734908[/C][C]-1.20644687349083[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]12.5515114644146[/C][C]-0.551511464414602[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]7.58352523272901[/C][C]3.41647476727099[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]13.2296887156437[/C][C]-2.22968871564374[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]11.9887013068642[/C][C]-0.988701306864189[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.4011701430192[/C][C]2.59882985698077[/C][/ROW]
[ROW][C]14[/C][C]7[/C][C]11.4133414674605[/C][C]-4.41334146746051[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.5005748792709[/C][C]-0.500574879270867[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.6293339254725[/C][C]0.370666074527504[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]11.9887013068642[/C][C]-1.98870130686419[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]14.3713480699243[/C][C]-0.371348069924296[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]8.53912417273657[/C][C]1.46087582726343[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]9.25208777269556[/C][C]-3.25208777269556[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]8.75768443621434[/C][C]2.24231556378566[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.4083070020142[/C][C]0.591692997985793[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]11.8321241939143[/C][C]-0.832124193914343[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]9.57331501739721[/C][C]2.42668498260279[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]12.9795589380721[/C][C]1.02044106192787[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]14.5816751087031[/C][C]0.418324891296904[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]14.4071493974688[/C][C]-5.40714939746876[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]12.7323469300252[/C][C]0.267653069974801[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]13.0194521833768[/C][C]-0.0194521833768389[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]11.0702902952668[/C][C]4.92970970473325[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]8.71182378612308[/C][C]4.28817621387692[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.5867172878898[/C][C]-1.58671728788985[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.7272107657799[/C][C]-0.727210765779895[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]10.0748762086988[/C][C]0.925123791301211[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]10.419130238694[/C][C]-1.41913023869395[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.4378314206708[/C][C]1.56216857932923[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]13.0628717674189[/C][C]-1.06287176741891[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]9.56920655594684[/C][C]0.430793444053156[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]12.8744550267216[/C][C]0.125544973278406[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.6140809728493[/C][C]0.38591902715067[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]12.9819152644965[/C][C]1.01808473550355[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]8.01295517132966[/C][C]6.98704482867033[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]9.64274508912607[/C][C]-4.64274508912607[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]10.2464264893849[/C][C]-2.24642648938491[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]11.2481503303291[/C][C]-0.248150330329128[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.8392610529093[/C][C]2.16073894709068[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]14.3257451257191[/C][C]2.67425487428092[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]8.18268762601302[/C][C]0.81731237398698[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]11.4203608324726[/C][C]-2.42036083247257[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.4765753332751[/C][C]-1.47657533327509[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]11.0777659260335[/C][C]-1.07776592603351[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]12.3706484532284[/C][C]-6.37064845322836[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]12.0969492540191[/C][C]-0.0969492540191494[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]10.5001278589498[/C][C]-2.50012785894983[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]11.8444507422556[/C][C]2.15554925774437[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.9062946544691[/C][C]-0.906294654469061[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]10.6693505227775[/C][C]0.330649477222535[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.5829108119929[/C][C]1.41708918800714[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]10.5325907267437[/C][C]-2.53259072674372[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.5880342522578[/C][C]0.41196574774217[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.11127717623499[/C][C]-2.11127717623499[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]14.1865316307861[/C][C]1.81346836921388[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]13.1488638365666[/C][C]0.851136163433398[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.3771820944137[/C][C]2.62281790558632[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]10.4318965090998[/C][C]-1.43189650909984[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]12.1633503700988[/C][C]1.83664962990121[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]13.2598010239003[/C][C]-2.25980102390034[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]10.32444454317[/C][C]2.67555545682996[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]13.1945986296996[/C][C]1.80540137030043[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]5.79384205280933[/C][C]-0.793842052809328[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]12.9063357718025[/C][C]2.09366422819752[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.5885937485048[/C][C]0.411406251495228[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]12.4170476037412[/C][C]-1.41704760374118[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]14.1988005181039[/C][C]-3.19880051810387[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]12.7705498372499[/C][C]-0.770549837249928[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]13.404884551145[/C][C]-1.40488455114495[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]12.3360952367413[/C][C]-0.336095236741251[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]11.9507820850509[/C][C]0.0492179149491326[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]10.8578125167307[/C][C]3.14218748326928[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]7.93738395048075[/C][C]-1.93738395048075[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]9.71848077930866[/C][C]-2.71848077930866[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]12.4117404204119[/C][C]1.58825957958806[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]14.1612266178126[/C][C]-0.161226617812593[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]11.1702556285443[/C][C]-1.17025562854426[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]9.11479110720476[/C][C]3.88520889279524[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]12.3066119354181[/C][C]-0.306611935418104[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]9.32097683904484[/C][C]-0.320976839044836[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.9243194675319[/C][C]0.0756805324681125[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.8018752220064[/C][C]1.1981247779936[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.4603085764669[/C][C]-0.460308576466901[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]13.101734896101[/C][C]0.898265103899047[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.5910230641623[/C][C]-3.59102306416225[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.5079058275427[/C][C]0.492094172457289[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.4037639280104[/C][C]1.59623607198961[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]11.4892005096434[/C][C]0.510799490356636[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]9.35972075164915[/C][C]0.640279248350848[/C][/ROW]
[ROW][C]97[/C][C]8[/C][C]10.1223314553596[/C][C]-2.12233145535963[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]8.71613369831802[/C][C]-0.716133698318016[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]12.6649526787338[/C][C]-1.66495267873379[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]12.559151564515[/C][C]0.440848435484955[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.6130467203041[/C][C]0.386953279695863[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.9388187611044[/C][C]1.0611812388956[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]15.4034334280837[/C][C]-1.40343342808366[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]9.04726936799056[/C][C]1.95273063200944[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]7.15689033946251[/C][C]-3.15689033946251[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]14.7389510554856[/C][C]-0.738951055485645[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.5285727718053[/C][C]-1.52857277180526[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]15.2259609958669[/C][C]-1.22596099586694[/C][/ROW]
[ROW][C]109[/C][C]8[/C][C]10.2495705251893[/C][C]-2.24957052518933[/C][/ROW]
[ROW][C]110[/C][C]8[/C][C]10.6828675211526[/C][C]-2.68286752115256[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]11.8844673395606[/C][C]-0.884467339560597[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]13.1605588730164[/C][C]-1.1605588730164[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]11.0756151862763[/C][C]-0.0756151862762911[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]13.2721645536525[/C][C]0.727835446347485[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]14.4495717103765[/C][C]0.550428289623478[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]13.444864167039[/C][C]2.55513583296097[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]12.9133634086596[/C][C]3.08663659134041[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]12.7851915946821[/C][C]-1.78519159468211[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]14.1878125873318[/C][C]-0.187812587331831[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]10.8820791412365[/C][C]3.11792085876347[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]11.5586264454497[/C][C]0.441373554550303[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.5464592569309[/C][C]1.45354074306906[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]10.8855519548729[/C][C]-2.88555195487288[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]14.081514090025[/C][C]-1.08151409002495[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]13.9752114567955[/C][C]2.02478854320445[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]10.6158103195381[/C][C]1.38418968046186[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]15.478463643553[/C][C]0.521536356447016[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]12.7671633942029[/C][C]-0.767163394202944[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]11.4615761516564[/C][C]-0.461576151656405[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]6.00815950095096[/C][C]-2.00815950095096[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]16.1162670351618[/C][C]-0.116267035161831[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]12.6643335746099[/C][C]2.33566642539011[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.3605807017562[/C][C]-1.36058070175623[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]14.0185857875817[/C][C]-1.01858578758167[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]13.1271221437413[/C][C]1.87287785625868[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.4990936064046[/C][C]1.50090639359536[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]13.6563076930299[/C][C]0.343692306970139[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]10.3812604060591[/C][C]-3.38126040605911[/C][/ROW]
[ROW][C]139[/C][C]19[/C][C]13.9076938535039[/C][C]5.09230614649612[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]12.9451989004845[/C][C]-0.945198900484526[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]11.8729367724445[/C][C]0.127063227555527[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]13.3386067870656[/C][C]-0.338606787065572[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]12.4166818444984[/C][C]2.58331815550164[/C][/ROW]
[ROW][C]144[/C][C]8[/C][C]8.84487272228288[/C][C]-0.844872722282876[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]10.9231793802652[/C][C]1.07682061973483[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]10.7602294894029[/C][C]-0.760229489402903[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]11.2513396193895[/C][C]-3.2513396193895[/C][/ROW]
[ROW][C]148[/C][C]10[/C][C]14.6518040142647[/C][C]-4.65180401426466[/C][/ROW]
[ROW][C]149[/C][C]15[/C][C]13.8677923363541[/C][C]1.13220766364589[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]14.0602118941818[/C][C]1.93978810581816[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]13.2704618078049[/C][C]-0.270461807804948[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.0840256403493[/C][C]0.915974359650725[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]9.82283412352541[/C][C]-0.82283412352541[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]13.0610047130731[/C][C]0.93899528692691[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]13.1550671685506[/C][C]0.844932831449422[/C][/ROW]
[ROW][C]156[/C][C]12[/C][C]10.174200755085[/C][C]1.82579924491501[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146456&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146456&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
11311.26896341492921.73103658507082
21210.89340825760811.10659174239191
31512.99291146711362.00708853288645
41211.03662043374890.963379566251072
51010.8136052233086-0.813605223308556
6129.112311428780182.88768857121982
71516.7226937056585-1.72269370565849
8910.2064468734908-1.20644687349083
91212.5515114644146-0.551511464414602
10117.583525232729013.41647476727099
111113.2296887156437-2.22968871564374
121111.9887013068642-0.988701306864189
131512.40117014301922.59882985698077
14711.4133414674605-4.41334146746051
151111.5005748792709-0.500574879270867
161110.62933392547250.370666074527504
171011.9887013068642-1.98870130686419
181414.3713480699243-0.371348069924296
19108.539124172736571.46087582726343
2069.25208777269556-3.25208777269556
21118.757684436214342.24231556378566
221514.40830700201420.591692997985793
231111.8321241939143-0.832124193914343
24129.573315017397212.42668498260279
251412.97955893807211.02044106192787
261514.58167510870310.418324891296904
27914.4071493974688-5.40714939746876
281312.73234693002520.267653069974801
291313.0194521833768-0.0194521833768389
301611.07029029526684.92970970473325
31138.711823786123084.28817621387692
321213.5867172878898-1.58671728788985
331414.7272107657799-0.727210765779895
341110.07487620869880.925123791301211
35910.419130238694-1.41913023869395
361614.43783142067081.56216857932923
371213.0628717674189-1.06287176741891
38109.569206555946840.430793444053156
391312.87445502672160.125544973278406
401615.61408097284930.38591902715067
411412.98191526449651.01808473550355
42158.012955171329666.98704482867033
4359.64274508912607-4.64274508912607
44810.2464264893849-2.24642648938491
451111.2481503303291-0.248150330329128
461613.83926105290932.16073894709068
471714.32574512571912.67425487428092
4898.182687626013020.81731237398698
49911.4203608324726-2.42036083247257
501314.4765753332751-1.47657533327509
511011.0777659260335-1.07776592603351
52612.3706484532284-6.37064845322836
531212.0969492540191-0.0969492540191494
54810.5001278589498-2.50012785894983
551411.84445074225562.15554925774437
561212.9062946544691-0.906294654469061
571110.66935052277750.330649477222535
581614.58291081199291.41708918800714
59810.5325907267437-2.53259072674372
601514.58803425225780.41196574774217
6179.11127717623499-2.11127717623499
621614.18653163078611.81346836921388
631413.14886383656660.851136163433398
641613.37718209441372.62281790558632
65910.4318965090998-1.43189650909984
661412.16335037009881.83664962990121
671113.2598010239003-2.25980102390034
681310.324444543172.67555545682996
691513.19459862969961.80540137030043
7055.79384205280933-0.793842052809328
711512.90633577180252.09366422819752
721312.58859374850480.411406251495228
731112.4170476037412-1.41704760374118
741114.1988005181039-3.19880051810387
751212.7705498372499-0.770549837249928
761213.404884551145-1.40488455114495
771212.3360952367413-0.336095236741251
781211.95078208505090.0492179149491326
791410.85781251673073.14218748326928
8067.93738395048075-1.93738395048075
8179.71848077930866-2.71848077930866
821412.41174042041191.58825957958806
831414.1612266178126-0.161226617812593
841011.1702556285443-1.17025562854426
85139.114791107204763.88520889279524
861212.3066119354181-0.306611935418104
8799.32097683904484-0.320976839044836
881211.92431946753190.0756805324681125
891614.80187522200641.1981247779936
901010.4603085764669-0.460308576466901
911413.1017348961010.898265103899047
921013.5910230641623-3.59102306416225
931615.50790582754270.492094172457289
941513.40376392801041.59623607198961
951211.48920050964340.510799490356636
96109.359720751649150.640279248350848
97810.1223314553596-2.12233145535963
9888.71613369831802-0.716133698318016
991112.6649526787338-1.66495267873379
1001312.5591515645150.440848435484955
1011615.61304672030410.386953279695863
1021614.93881876110441.0611812388956
1031415.4034334280837-1.40343342808366
104119.047269367990561.95273063200944
10547.15689033946251-3.15689033946251
1061414.7389510554856-0.738951055485645
107910.5285727718053-1.52857277180526
1081415.2259609958669-1.22596099586694
109810.2495705251893-2.24957052518933
110810.6828675211526-2.68286752115256
1111111.8844673395606-0.884467339560597
1121213.1605588730164-1.1605588730164
1131111.0756151862763-0.0756151862762911
1141413.27216455365250.727835446347485
1151514.44957171037650.550428289623478
1161613.4448641670392.55513583296097
1171612.91336340865963.08663659134041
1181112.7851915946821-1.78519159468211
1191414.1878125873318-0.187812587331831
1201410.88207914123653.11792085876347
1211211.55862644544970.441373554550303
1221412.54645925693091.45354074306906
123810.8855519548729-2.88555195487288
1241314.081514090025-1.08151409002495
1251613.97521145679552.02478854320445
1261210.61581031953811.38418968046186
1271615.4784636435530.521536356447016
1281212.7671633942029-0.767163394202944
1291111.4615761516564-0.461576151656405
13046.00815950095096-2.00815950095096
1311616.1162670351618-0.116267035161831
1321512.66433357460992.33566642539011
1331011.3605807017562-1.36058070175623
1341314.0185857875817-1.01858578758167
1351513.12712214374131.87287785625868
1361210.49909360640461.50090639359536
1371413.65630769302990.343692306970139
138710.3812604060591-3.38126040605911
1391913.90769385350395.09230614649612
1401212.9451989004845-0.945198900484526
1411211.87293677244450.127063227555527
1421313.3386067870656-0.338606787065572
1431512.41668184449842.58331815550164
14488.84487272228288-0.844872722282876
1451210.92317938026521.07682061973483
1461010.7602294894029-0.760229489402903
147811.2513396193895-3.2513396193895
1481014.6518040142647-4.65180401426466
1491513.86779233635411.13220766364589
1501614.06021189418181.93978810581816
1511313.2704618078049-0.270461807804948
1521615.08402564034930.915974359650725
15399.82283412352541-0.82283412352541
1541413.06100471307310.93899528692691
1551413.15506716855060.844932831449422
1561210.1742007550851.82579924491501






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.1280798671545630.2561597343091260.871920132845437
110.1487047738945520.2974095477891030.851295226105448
120.07793726609842320.1558745321968460.922062733901577
130.5662377992522580.8675244014954830.433762200747742
140.8330554465519840.3338891068960320.166944553448016
150.7631989829703910.4736020340592180.236801017029609
160.6802623825284910.6394752349430190.319737617471509
170.623779273990840.752441452018320.37622072600916
180.5362767334060840.9274465331878320.463723266593916
190.4510202822834630.9020405645669260.548979717716537
200.6318547919229070.7362904161541870.368145208077093
210.600749433801970.7985011323960610.39925056619803
220.6396190348772660.7207619302454680.360380965122734
230.57505359056190.8498928188761990.4249464094381
240.5610791063354970.8778417873290060.438920893664503
250.5140137161932050.9719725676135890.485986283806795
260.4460214350287510.8920428700575020.553978564971249
270.7056200850075340.5887598299849310.294379914992466
280.6518615721920650.696276855615870.348138427807935
290.5920369941375740.8159260117248510.407963005862426
300.7268883311028320.5462233377943350.273111668897168
310.8103417605010730.3793164789978540.189658239498927
320.7741440881444990.4517118237110020.225855911855501
330.7272920899944340.5454158200111310.272707910005566
340.6969049858429060.6061900283141880.303095014157094
350.6930274743143270.6139450513713450.306972525685673
360.6897414245139670.6205171509720670.310258575486033
370.6631920139336890.6736159721326220.336807986066311
380.6144888502933780.7710222994132430.385511149706622
390.5657583755528350.8684832488943310.434241624447165
400.5224581239542370.9550837520915270.477541876045763
410.4838662013850590.9677324027701180.516133798614941
420.784511647238390.4309767055232190.21548835276161
430.9521114048303690.09577719033926180.0478885951696309
440.9563325976328020.08733480473439610.0436674023671981
450.9432977029135360.1134045941729290.0567022970864644
460.9508262798500530.09834744029989480.0491737201499474
470.950688128494580.09862374301084090.0493118715054204
480.9402432804988540.1195134390022930.0597567195011463
490.9378088497472030.1243823005055940.0621911502527969
500.926077997148780.1478440057024410.0739220028512203
510.9193336921810010.1613326156379990.0806663078189994
520.986584972607550.02683005478490020.0134150273924501
530.9825938764978370.03481224700432550.0174061235021628
540.9856328792678560.02873424146428870.0143671207321444
550.9911247448812080.01775051023758350.00887525511879177
560.9882047632762930.0235904734474130.0117952367237065
570.9842023489237620.03159530215247650.0157976510762383
580.9825917311777980.03481653764440480.0174082688222024
590.9880609455281150.02387810894376950.0119390544718847
600.9870767006148690.02584659877026220.0129232993851311
610.9866736934625260.02665261307494790.0133263065374739
620.9873673993573830.02526520128523420.0126326006426171
630.9858505365024480.02829892699510420.0141494634975521
640.9891045553931540.02179088921369150.0108954446068458
650.987761731146360.02447653770728070.0122382688536403
660.9873814589380280.02523708212394310.0126185410619716
670.9874463989286570.02510720214268620.0125536010713431
680.9903601998657870.01927960026842630.00963980013421316
690.989955781392620.02008843721476090.0100442186073805
700.9868606269274090.02627874614518210.013139373072591
710.9874638817877590.02507223642448170.0125361182122409
720.9833716089348050.03325678213038940.0166283910651947
730.9802986265962520.03940274680749630.0197013734037482
740.9861815553875990.02763688922480170.0138184446124008
750.9817395419448510.03652091611029740.0182604580551487
760.9783032247770730.04339355044585410.0216967752229271
770.9715658214287820.05686835714243510.0284341785712175
780.9629429493523550.074114101295290.037057050647645
790.9769704621913560.0460590756172880.023029537808644
800.9749562880944580.0500874238110840.025043711905542
810.9774160420298470.04516791594030550.0225839579701528
820.9773586423516640.04528271529667280.0226413576483364
830.9700572495383050.05988550092338960.0299427504616948
840.9627367719924790.0745264560150420.037263228007521
850.9888269325828240.02234613483435310.0111730674171765
860.9848921819944220.03021563601115650.0151078180055783
870.9798321424549120.04033571509017560.0201678575450878
880.9740041117370150.05199177652596940.0259958882629847
890.9693173001471670.06136539970566560.0306826998528328
900.96049215711530.07901568576940030.0395078428847002
910.9537813617131280.09243727657374380.0462186382868719
920.9715917772830560.05681644543388770.0284082227169439
930.9636637912626210.07267241747475730.0363362087373787
940.9604094187496070.07918116250078510.0395905812503926
950.9511576551340610.09768468973187790.0488423448659389
960.9404298920709240.1191402158581530.0595701079290764
970.9337281319670630.1325437360658740.0662718680329371
980.9171463183739780.1657073632520450.0828536816260223
990.9080640043752260.1838719912495470.0919359956247736
1000.8889248528611520.2221502942776960.111075147138848
1010.8646362951944980.2707274096110040.135363704805502
1020.8439869226677520.3120261546644960.156013077332248
1030.8496934075064060.3006131849871880.150306592493594
1040.8989552802539770.2020894394920460.101044719746023
1050.8966865681955490.2066268636089020.103313431804451
1060.8724634428635650.2550731142728690.127536557136435
1070.8476840389679640.3046319220640720.152315961032036
1080.8401218532342380.3197562935315240.159878146765762
1090.8305268399472470.3389463201055060.169473160052753
1100.8485349710403570.3029300579192850.151465028959643
1110.8325736363273330.3348527273453340.167426363672667
1120.8759241065532210.2481517868935580.124075893446779
1130.8457392568794540.3085214862410920.154260743120546
1140.8157565892037970.3684868215924060.184243410796203
1150.7774879484253710.4450241031492570.222512051574629
1160.7811674284195430.4376651431609140.218832571580457
1170.7997765856130420.4004468287739150.200223414386958
1180.7854121413124590.4291757173750810.214587858687541
1190.7395291390649950.520941721870010.260470860935005
1200.8114100862240540.3771798275518930.188589913775946
1210.7805727933466120.4388544133067770.219427206653388
1220.7547577345747970.4904845308504060.245242265425203
1230.7460395637952740.5079208724094510.253960436204726
1240.6993311527012780.6013376945974430.300668847298722
1250.7001036675022640.5997926649954720.299896332497736
1260.687135614960150.6257287700797010.31286438503985
1270.6278678393902230.7442643212195530.372132160609777
1280.5902877709216020.8194244581567950.409712229078398
1290.5941428410310010.8117143179379990.405857158968999
1300.5855860850351240.8288278299297530.414413914964876
1310.5170993973444640.9658012053110710.482900602655536
1320.5012118516118160.9975762967763690.498788148388184
1330.4686788045386420.9373576090772840.531321195461358
1340.3985345514955780.7970691029911550.601465448504422
1350.3704257349245640.7408514698491270.629574265075436
1360.3671276785842870.7342553571685750.632872321415713
1370.2948201469901370.5896402939802740.705179853009863
1380.3662729509388210.7325459018776420.633727049061179
1390.7328671654956460.5342656690087080.267132834504354
1400.7453243834512390.5093512330975230.254675616548761
1410.6943162301609940.6113675396780130.305683769839006
1420.5980682823594280.8038634352811440.401931717640572
1430.5327591583961280.9344816832077450.467240841603872
1440.4102607879723090.8205215759446180.589739212027691
1450.3877643495158410.7755286990316830.612235650484159
1460.5667234466098070.8665531067803850.433276553390193

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.128079867154563 & 0.256159734309126 & 0.871920132845437 \tabularnewline
11 & 0.148704773894552 & 0.297409547789103 & 0.851295226105448 \tabularnewline
12 & 0.0779372660984232 & 0.155874532196846 & 0.922062733901577 \tabularnewline
13 & 0.566237799252258 & 0.867524401495483 & 0.433762200747742 \tabularnewline
14 & 0.833055446551984 & 0.333889106896032 & 0.166944553448016 \tabularnewline
15 & 0.763198982970391 & 0.473602034059218 & 0.236801017029609 \tabularnewline
16 & 0.680262382528491 & 0.639475234943019 & 0.319737617471509 \tabularnewline
17 & 0.62377927399084 & 0.75244145201832 & 0.37622072600916 \tabularnewline
18 & 0.536276733406084 & 0.927446533187832 & 0.463723266593916 \tabularnewline
19 & 0.451020282283463 & 0.902040564566926 & 0.548979717716537 \tabularnewline
20 & 0.631854791922907 & 0.736290416154187 & 0.368145208077093 \tabularnewline
21 & 0.60074943380197 & 0.798501132396061 & 0.39925056619803 \tabularnewline
22 & 0.639619034877266 & 0.720761930245468 & 0.360380965122734 \tabularnewline
23 & 0.5750535905619 & 0.849892818876199 & 0.4249464094381 \tabularnewline
24 & 0.561079106335497 & 0.877841787329006 & 0.438920893664503 \tabularnewline
25 & 0.514013716193205 & 0.971972567613589 & 0.485986283806795 \tabularnewline
26 & 0.446021435028751 & 0.892042870057502 & 0.553978564971249 \tabularnewline
27 & 0.705620085007534 & 0.588759829984931 & 0.294379914992466 \tabularnewline
28 & 0.651861572192065 & 0.69627685561587 & 0.348138427807935 \tabularnewline
29 & 0.592036994137574 & 0.815926011724851 & 0.407963005862426 \tabularnewline
30 & 0.726888331102832 & 0.546223337794335 & 0.273111668897168 \tabularnewline
31 & 0.810341760501073 & 0.379316478997854 & 0.189658239498927 \tabularnewline
32 & 0.774144088144499 & 0.451711823711002 & 0.225855911855501 \tabularnewline
33 & 0.727292089994434 & 0.545415820011131 & 0.272707910005566 \tabularnewline
34 & 0.696904985842906 & 0.606190028314188 & 0.303095014157094 \tabularnewline
35 & 0.693027474314327 & 0.613945051371345 & 0.306972525685673 \tabularnewline
36 & 0.689741424513967 & 0.620517150972067 & 0.310258575486033 \tabularnewline
37 & 0.663192013933689 & 0.673615972132622 & 0.336807986066311 \tabularnewline
38 & 0.614488850293378 & 0.771022299413243 & 0.385511149706622 \tabularnewline
39 & 0.565758375552835 & 0.868483248894331 & 0.434241624447165 \tabularnewline
40 & 0.522458123954237 & 0.955083752091527 & 0.477541876045763 \tabularnewline
41 & 0.483866201385059 & 0.967732402770118 & 0.516133798614941 \tabularnewline
42 & 0.78451164723839 & 0.430976705523219 & 0.21548835276161 \tabularnewline
43 & 0.952111404830369 & 0.0957771903392618 & 0.0478885951696309 \tabularnewline
44 & 0.956332597632802 & 0.0873348047343961 & 0.0436674023671981 \tabularnewline
45 & 0.943297702913536 & 0.113404594172929 & 0.0567022970864644 \tabularnewline
46 & 0.950826279850053 & 0.0983474402998948 & 0.0491737201499474 \tabularnewline
47 & 0.95068812849458 & 0.0986237430108409 & 0.0493118715054204 \tabularnewline
48 & 0.940243280498854 & 0.119513439002293 & 0.0597567195011463 \tabularnewline
49 & 0.937808849747203 & 0.124382300505594 & 0.0621911502527969 \tabularnewline
50 & 0.92607799714878 & 0.147844005702441 & 0.0739220028512203 \tabularnewline
51 & 0.919333692181001 & 0.161332615637999 & 0.0806663078189994 \tabularnewline
52 & 0.98658497260755 & 0.0268300547849002 & 0.0134150273924501 \tabularnewline
53 & 0.982593876497837 & 0.0348122470043255 & 0.0174061235021628 \tabularnewline
54 & 0.985632879267856 & 0.0287342414642887 & 0.0143671207321444 \tabularnewline
55 & 0.991124744881208 & 0.0177505102375835 & 0.00887525511879177 \tabularnewline
56 & 0.988204763276293 & 0.023590473447413 & 0.0117952367237065 \tabularnewline
57 & 0.984202348923762 & 0.0315953021524765 & 0.0157976510762383 \tabularnewline
58 & 0.982591731177798 & 0.0348165376444048 & 0.0174082688222024 \tabularnewline
59 & 0.988060945528115 & 0.0238781089437695 & 0.0119390544718847 \tabularnewline
60 & 0.987076700614869 & 0.0258465987702622 & 0.0129232993851311 \tabularnewline
61 & 0.986673693462526 & 0.0266526130749479 & 0.0133263065374739 \tabularnewline
62 & 0.987367399357383 & 0.0252652012852342 & 0.0126326006426171 \tabularnewline
63 & 0.985850536502448 & 0.0282989269951042 & 0.0141494634975521 \tabularnewline
64 & 0.989104555393154 & 0.0217908892136915 & 0.0108954446068458 \tabularnewline
65 & 0.98776173114636 & 0.0244765377072807 & 0.0122382688536403 \tabularnewline
66 & 0.987381458938028 & 0.0252370821239431 & 0.0126185410619716 \tabularnewline
67 & 0.987446398928657 & 0.0251072021426862 & 0.0125536010713431 \tabularnewline
68 & 0.990360199865787 & 0.0192796002684263 & 0.00963980013421316 \tabularnewline
69 & 0.98995578139262 & 0.0200884372147609 & 0.0100442186073805 \tabularnewline
70 & 0.986860626927409 & 0.0262787461451821 & 0.013139373072591 \tabularnewline
71 & 0.987463881787759 & 0.0250722364244817 & 0.0125361182122409 \tabularnewline
72 & 0.983371608934805 & 0.0332567821303894 & 0.0166283910651947 \tabularnewline
73 & 0.980298626596252 & 0.0394027468074963 & 0.0197013734037482 \tabularnewline
74 & 0.986181555387599 & 0.0276368892248017 & 0.0138184446124008 \tabularnewline
75 & 0.981739541944851 & 0.0365209161102974 & 0.0182604580551487 \tabularnewline
76 & 0.978303224777073 & 0.0433935504458541 & 0.0216967752229271 \tabularnewline
77 & 0.971565821428782 & 0.0568683571424351 & 0.0284341785712175 \tabularnewline
78 & 0.962942949352355 & 0.07411410129529 & 0.037057050647645 \tabularnewline
79 & 0.976970462191356 & 0.046059075617288 & 0.023029537808644 \tabularnewline
80 & 0.974956288094458 & 0.050087423811084 & 0.025043711905542 \tabularnewline
81 & 0.977416042029847 & 0.0451679159403055 & 0.0225839579701528 \tabularnewline
82 & 0.977358642351664 & 0.0452827152966728 & 0.0226413576483364 \tabularnewline
83 & 0.970057249538305 & 0.0598855009233896 & 0.0299427504616948 \tabularnewline
84 & 0.962736771992479 & 0.074526456015042 & 0.037263228007521 \tabularnewline
85 & 0.988826932582824 & 0.0223461348343531 & 0.0111730674171765 \tabularnewline
86 & 0.984892181994422 & 0.0302156360111565 & 0.0151078180055783 \tabularnewline
87 & 0.979832142454912 & 0.0403357150901756 & 0.0201678575450878 \tabularnewline
88 & 0.974004111737015 & 0.0519917765259694 & 0.0259958882629847 \tabularnewline
89 & 0.969317300147167 & 0.0613653997056656 & 0.0306826998528328 \tabularnewline
90 & 0.9604921571153 & 0.0790156857694003 & 0.0395078428847002 \tabularnewline
91 & 0.953781361713128 & 0.0924372765737438 & 0.0462186382868719 \tabularnewline
92 & 0.971591777283056 & 0.0568164454338877 & 0.0284082227169439 \tabularnewline
93 & 0.963663791262621 & 0.0726724174747573 & 0.0363362087373787 \tabularnewline
94 & 0.960409418749607 & 0.0791811625007851 & 0.0395905812503926 \tabularnewline
95 & 0.951157655134061 & 0.0976846897318779 & 0.0488423448659389 \tabularnewline
96 & 0.940429892070924 & 0.119140215858153 & 0.0595701079290764 \tabularnewline
97 & 0.933728131967063 & 0.132543736065874 & 0.0662718680329371 \tabularnewline
98 & 0.917146318373978 & 0.165707363252045 & 0.0828536816260223 \tabularnewline
99 & 0.908064004375226 & 0.183871991249547 & 0.0919359956247736 \tabularnewline
100 & 0.888924852861152 & 0.222150294277696 & 0.111075147138848 \tabularnewline
101 & 0.864636295194498 & 0.270727409611004 & 0.135363704805502 \tabularnewline
102 & 0.843986922667752 & 0.312026154664496 & 0.156013077332248 \tabularnewline
103 & 0.849693407506406 & 0.300613184987188 & 0.150306592493594 \tabularnewline
104 & 0.898955280253977 & 0.202089439492046 & 0.101044719746023 \tabularnewline
105 & 0.896686568195549 & 0.206626863608902 & 0.103313431804451 \tabularnewline
106 & 0.872463442863565 & 0.255073114272869 & 0.127536557136435 \tabularnewline
107 & 0.847684038967964 & 0.304631922064072 & 0.152315961032036 \tabularnewline
108 & 0.840121853234238 & 0.319756293531524 & 0.159878146765762 \tabularnewline
109 & 0.830526839947247 & 0.338946320105506 & 0.169473160052753 \tabularnewline
110 & 0.848534971040357 & 0.302930057919285 & 0.151465028959643 \tabularnewline
111 & 0.832573636327333 & 0.334852727345334 & 0.167426363672667 \tabularnewline
112 & 0.875924106553221 & 0.248151786893558 & 0.124075893446779 \tabularnewline
113 & 0.845739256879454 & 0.308521486241092 & 0.154260743120546 \tabularnewline
114 & 0.815756589203797 & 0.368486821592406 & 0.184243410796203 \tabularnewline
115 & 0.777487948425371 & 0.445024103149257 & 0.222512051574629 \tabularnewline
116 & 0.781167428419543 & 0.437665143160914 & 0.218832571580457 \tabularnewline
117 & 0.799776585613042 & 0.400446828773915 & 0.200223414386958 \tabularnewline
118 & 0.785412141312459 & 0.429175717375081 & 0.214587858687541 \tabularnewline
119 & 0.739529139064995 & 0.52094172187001 & 0.260470860935005 \tabularnewline
120 & 0.811410086224054 & 0.377179827551893 & 0.188589913775946 \tabularnewline
121 & 0.780572793346612 & 0.438854413306777 & 0.219427206653388 \tabularnewline
122 & 0.754757734574797 & 0.490484530850406 & 0.245242265425203 \tabularnewline
123 & 0.746039563795274 & 0.507920872409451 & 0.253960436204726 \tabularnewline
124 & 0.699331152701278 & 0.601337694597443 & 0.300668847298722 \tabularnewline
125 & 0.700103667502264 & 0.599792664995472 & 0.299896332497736 \tabularnewline
126 & 0.68713561496015 & 0.625728770079701 & 0.31286438503985 \tabularnewline
127 & 0.627867839390223 & 0.744264321219553 & 0.372132160609777 \tabularnewline
128 & 0.590287770921602 & 0.819424458156795 & 0.409712229078398 \tabularnewline
129 & 0.594142841031001 & 0.811714317937999 & 0.405857158968999 \tabularnewline
130 & 0.585586085035124 & 0.828827829929753 & 0.414413914964876 \tabularnewline
131 & 0.517099397344464 & 0.965801205311071 & 0.482900602655536 \tabularnewline
132 & 0.501211851611816 & 0.997576296776369 & 0.498788148388184 \tabularnewline
133 & 0.468678804538642 & 0.937357609077284 & 0.531321195461358 \tabularnewline
134 & 0.398534551495578 & 0.797069102991155 & 0.601465448504422 \tabularnewline
135 & 0.370425734924564 & 0.740851469849127 & 0.629574265075436 \tabularnewline
136 & 0.367127678584287 & 0.734255357168575 & 0.632872321415713 \tabularnewline
137 & 0.294820146990137 & 0.589640293980274 & 0.705179853009863 \tabularnewline
138 & 0.366272950938821 & 0.732545901877642 & 0.633727049061179 \tabularnewline
139 & 0.732867165495646 & 0.534265669008708 & 0.267132834504354 \tabularnewline
140 & 0.745324383451239 & 0.509351233097523 & 0.254675616548761 \tabularnewline
141 & 0.694316230160994 & 0.611367539678013 & 0.305683769839006 \tabularnewline
142 & 0.598068282359428 & 0.803863435281144 & 0.401931717640572 \tabularnewline
143 & 0.532759158396128 & 0.934481683207745 & 0.467240841603872 \tabularnewline
144 & 0.410260787972309 & 0.820521575944618 & 0.589739212027691 \tabularnewline
145 & 0.387764349515841 & 0.775528699031683 & 0.612235650484159 \tabularnewline
146 & 0.566723446609807 & 0.866553106780385 & 0.433276553390193 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146456&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]10[/C][C]0.128079867154563[/C][C]0.256159734309126[/C][C]0.871920132845437[/C][/ROW]
[ROW][C]11[/C][C]0.148704773894552[/C][C]0.297409547789103[/C][C]0.851295226105448[/C][/ROW]
[ROW][C]12[/C][C]0.0779372660984232[/C][C]0.155874532196846[/C][C]0.922062733901577[/C][/ROW]
[ROW][C]13[/C][C]0.566237799252258[/C][C]0.867524401495483[/C][C]0.433762200747742[/C][/ROW]
[ROW][C]14[/C][C]0.833055446551984[/C][C]0.333889106896032[/C][C]0.166944553448016[/C][/ROW]
[ROW][C]15[/C][C]0.763198982970391[/C][C]0.473602034059218[/C][C]0.236801017029609[/C][/ROW]
[ROW][C]16[/C][C]0.680262382528491[/C][C]0.639475234943019[/C][C]0.319737617471509[/C][/ROW]
[ROW][C]17[/C][C]0.62377927399084[/C][C]0.75244145201832[/C][C]0.37622072600916[/C][/ROW]
[ROW][C]18[/C][C]0.536276733406084[/C][C]0.927446533187832[/C][C]0.463723266593916[/C][/ROW]
[ROW][C]19[/C][C]0.451020282283463[/C][C]0.902040564566926[/C][C]0.548979717716537[/C][/ROW]
[ROW][C]20[/C][C]0.631854791922907[/C][C]0.736290416154187[/C][C]0.368145208077093[/C][/ROW]
[ROW][C]21[/C][C]0.60074943380197[/C][C]0.798501132396061[/C][C]0.39925056619803[/C][/ROW]
[ROW][C]22[/C][C]0.639619034877266[/C][C]0.720761930245468[/C][C]0.360380965122734[/C][/ROW]
[ROW][C]23[/C][C]0.5750535905619[/C][C]0.849892818876199[/C][C]0.4249464094381[/C][/ROW]
[ROW][C]24[/C][C]0.561079106335497[/C][C]0.877841787329006[/C][C]0.438920893664503[/C][/ROW]
[ROW][C]25[/C][C]0.514013716193205[/C][C]0.971972567613589[/C][C]0.485986283806795[/C][/ROW]
[ROW][C]26[/C][C]0.446021435028751[/C][C]0.892042870057502[/C][C]0.553978564971249[/C][/ROW]
[ROW][C]27[/C][C]0.705620085007534[/C][C]0.588759829984931[/C][C]0.294379914992466[/C][/ROW]
[ROW][C]28[/C][C]0.651861572192065[/C][C]0.69627685561587[/C][C]0.348138427807935[/C][/ROW]
[ROW][C]29[/C][C]0.592036994137574[/C][C]0.815926011724851[/C][C]0.407963005862426[/C][/ROW]
[ROW][C]30[/C][C]0.726888331102832[/C][C]0.546223337794335[/C][C]0.273111668897168[/C][/ROW]
[ROW][C]31[/C][C]0.810341760501073[/C][C]0.379316478997854[/C][C]0.189658239498927[/C][/ROW]
[ROW][C]32[/C][C]0.774144088144499[/C][C]0.451711823711002[/C][C]0.225855911855501[/C][/ROW]
[ROW][C]33[/C][C]0.727292089994434[/C][C]0.545415820011131[/C][C]0.272707910005566[/C][/ROW]
[ROW][C]34[/C][C]0.696904985842906[/C][C]0.606190028314188[/C][C]0.303095014157094[/C][/ROW]
[ROW][C]35[/C][C]0.693027474314327[/C][C]0.613945051371345[/C][C]0.306972525685673[/C][/ROW]
[ROW][C]36[/C][C]0.689741424513967[/C][C]0.620517150972067[/C][C]0.310258575486033[/C][/ROW]
[ROW][C]37[/C][C]0.663192013933689[/C][C]0.673615972132622[/C][C]0.336807986066311[/C][/ROW]
[ROW][C]38[/C][C]0.614488850293378[/C][C]0.771022299413243[/C][C]0.385511149706622[/C][/ROW]
[ROW][C]39[/C][C]0.565758375552835[/C][C]0.868483248894331[/C][C]0.434241624447165[/C][/ROW]
[ROW][C]40[/C][C]0.522458123954237[/C][C]0.955083752091527[/C][C]0.477541876045763[/C][/ROW]
[ROW][C]41[/C][C]0.483866201385059[/C][C]0.967732402770118[/C][C]0.516133798614941[/C][/ROW]
[ROW][C]42[/C][C]0.78451164723839[/C][C]0.430976705523219[/C][C]0.21548835276161[/C][/ROW]
[ROW][C]43[/C][C]0.952111404830369[/C][C]0.0957771903392618[/C][C]0.0478885951696309[/C][/ROW]
[ROW][C]44[/C][C]0.956332597632802[/C][C]0.0873348047343961[/C][C]0.0436674023671981[/C][/ROW]
[ROW][C]45[/C][C]0.943297702913536[/C][C]0.113404594172929[/C][C]0.0567022970864644[/C][/ROW]
[ROW][C]46[/C][C]0.950826279850053[/C][C]0.0983474402998948[/C][C]0.0491737201499474[/C][/ROW]
[ROW][C]47[/C][C]0.95068812849458[/C][C]0.0986237430108409[/C][C]0.0493118715054204[/C][/ROW]
[ROW][C]48[/C][C]0.940243280498854[/C][C]0.119513439002293[/C][C]0.0597567195011463[/C][/ROW]
[ROW][C]49[/C][C]0.937808849747203[/C][C]0.124382300505594[/C][C]0.0621911502527969[/C][/ROW]
[ROW][C]50[/C][C]0.92607799714878[/C][C]0.147844005702441[/C][C]0.0739220028512203[/C][/ROW]
[ROW][C]51[/C][C]0.919333692181001[/C][C]0.161332615637999[/C][C]0.0806663078189994[/C][/ROW]
[ROW][C]52[/C][C]0.98658497260755[/C][C]0.0268300547849002[/C][C]0.0134150273924501[/C][/ROW]
[ROW][C]53[/C][C]0.982593876497837[/C][C]0.0348122470043255[/C][C]0.0174061235021628[/C][/ROW]
[ROW][C]54[/C][C]0.985632879267856[/C][C]0.0287342414642887[/C][C]0.0143671207321444[/C][/ROW]
[ROW][C]55[/C][C]0.991124744881208[/C][C]0.0177505102375835[/C][C]0.00887525511879177[/C][/ROW]
[ROW][C]56[/C][C]0.988204763276293[/C][C]0.023590473447413[/C][C]0.0117952367237065[/C][/ROW]
[ROW][C]57[/C][C]0.984202348923762[/C][C]0.0315953021524765[/C][C]0.0157976510762383[/C][/ROW]
[ROW][C]58[/C][C]0.982591731177798[/C][C]0.0348165376444048[/C][C]0.0174082688222024[/C][/ROW]
[ROW][C]59[/C][C]0.988060945528115[/C][C]0.0238781089437695[/C][C]0.0119390544718847[/C][/ROW]
[ROW][C]60[/C][C]0.987076700614869[/C][C]0.0258465987702622[/C][C]0.0129232993851311[/C][/ROW]
[ROW][C]61[/C][C]0.986673693462526[/C][C]0.0266526130749479[/C][C]0.0133263065374739[/C][/ROW]
[ROW][C]62[/C][C]0.987367399357383[/C][C]0.0252652012852342[/C][C]0.0126326006426171[/C][/ROW]
[ROW][C]63[/C][C]0.985850536502448[/C][C]0.0282989269951042[/C][C]0.0141494634975521[/C][/ROW]
[ROW][C]64[/C][C]0.989104555393154[/C][C]0.0217908892136915[/C][C]0.0108954446068458[/C][/ROW]
[ROW][C]65[/C][C]0.98776173114636[/C][C]0.0244765377072807[/C][C]0.0122382688536403[/C][/ROW]
[ROW][C]66[/C][C]0.987381458938028[/C][C]0.0252370821239431[/C][C]0.0126185410619716[/C][/ROW]
[ROW][C]67[/C][C]0.987446398928657[/C][C]0.0251072021426862[/C][C]0.0125536010713431[/C][/ROW]
[ROW][C]68[/C][C]0.990360199865787[/C][C]0.0192796002684263[/C][C]0.00963980013421316[/C][/ROW]
[ROW][C]69[/C][C]0.98995578139262[/C][C]0.0200884372147609[/C][C]0.0100442186073805[/C][/ROW]
[ROW][C]70[/C][C]0.986860626927409[/C][C]0.0262787461451821[/C][C]0.013139373072591[/C][/ROW]
[ROW][C]71[/C][C]0.987463881787759[/C][C]0.0250722364244817[/C][C]0.0125361182122409[/C][/ROW]
[ROW][C]72[/C][C]0.983371608934805[/C][C]0.0332567821303894[/C][C]0.0166283910651947[/C][/ROW]
[ROW][C]73[/C][C]0.980298626596252[/C][C]0.0394027468074963[/C][C]0.0197013734037482[/C][/ROW]
[ROW][C]74[/C][C]0.986181555387599[/C][C]0.0276368892248017[/C][C]0.0138184446124008[/C][/ROW]
[ROW][C]75[/C][C]0.981739541944851[/C][C]0.0365209161102974[/C][C]0.0182604580551487[/C][/ROW]
[ROW][C]76[/C][C]0.978303224777073[/C][C]0.0433935504458541[/C][C]0.0216967752229271[/C][/ROW]
[ROW][C]77[/C][C]0.971565821428782[/C][C]0.0568683571424351[/C][C]0.0284341785712175[/C][/ROW]
[ROW][C]78[/C][C]0.962942949352355[/C][C]0.07411410129529[/C][C]0.037057050647645[/C][/ROW]
[ROW][C]79[/C][C]0.976970462191356[/C][C]0.046059075617288[/C][C]0.023029537808644[/C][/ROW]
[ROW][C]80[/C][C]0.974956288094458[/C][C]0.050087423811084[/C][C]0.025043711905542[/C][/ROW]
[ROW][C]81[/C][C]0.977416042029847[/C][C]0.0451679159403055[/C][C]0.0225839579701528[/C][/ROW]
[ROW][C]82[/C][C]0.977358642351664[/C][C]0.0452827152966728[/C][C]0.0226413576483364[/C][/ROW]
[ROW][C]83[/C][C]0.970057249538305[/C][C]0.0598855009233896[/C][C]0.0299427504616948[/C][/ROW]
[ROW][C]84[/C][C]0.962736771992479[/C][C]0.074526456015042[/C][C]0.037263228007521[/C][/ROW]
[ROW][C]85[/C][C]0.988826932582824[/C][C]0.0223461348343531[/C][C]0.0111730674171765[/C][/ROW]
[ROW][C]86[/C][C]0.984892181994422[/C][C]0.0302156360111565[/C][C]0.0151078180055783[/C][/ROW]
[ROW][C]87[/C][C]0.979832142454912[/C][C]0.0403357150901756[/C][C]0.0201678575450878[/C][/ROW]
[ROW][C]88[/C][C]0.974004111737015[/C][C]0.0519917765259694[/C][C]0.0259958882629847[/C][/ROW]
[ROW][C]89[/C][C]0.969317300147167[/C][C]0.0613653997056656[/C][C]0.0306826998528328[/C][/ROW]
[ROW][C]90[/C][C]0.9604921571153[/C][C]0.0790156857694003[/C][C]0.0395078428847002[/C][/ROW]
[ROW][C]91[/C][C]0.953781361713128[/C][C]0.0924372765737438[/C][C]0.0462186382868719[/C][/ROW]
[ROW][C]92[/C][C]0.971591777283056[/C][C]0.0568164454338877[/C][C]0.0284082227169439[/C][/ROW]
[ROW][C]93[/C][C]0.963663791262621[/C][C]0.0726724174747573[/C][C]0.0363362087373787[/C][/ROW]
[ROW][C]94[/C][C]0.960409418749607[/C][C]0.0791811625007851[/C][C]0.0395905812503926[/C][/ROW]
[ROW][C]95[/C][C]0.951157655134061[/C][C]0.0976846897318779[/C][C]0.0488423448659389[/C][/ROW]
[ROW][C]96[/C][C]0.940429892070924[/C][C]0.119140215858153[/C][C]0.0595701079290764[/C][/ROW]
[ROW][C]97[/C][C]0.933728131967063[/C][C]0.132543736065874[/C][C]0.0662718680329371[/C][/ROW]
[ROW][C]98[/C][C]0.917146318373978[/C][C]0.165707363252045[/C][C]0.0828536816260223[/C][/ROW]
[ROW][C]99[/C][C]0.908064004375226[/C][C]0.183871991249547[/C][C]0.0919359956247736[/C][/ROW]
[ROW][C]100[/C][C]0.888924852861152[/C][C]0.222150294277696[/C][C]0.111075147138848[/C][/ROW]
[ROW][C]101[/C][C]0.864636295194498[/C][C]0.270727409611004[/C][C]0.135363704805502[/C][/ROW]
[ROW][C]102[/C][C]0.843986922667752[/C][C]0.312026154664496[/C][C]0.156013077332248[/C][/ROW]
[ROW][C]103[/C][C]0.849693407506406[/C][C]0.300613184987188[/C][C]0.150306592493594[/C][/ROW]
[ROW][C]104[/C][C]0.898955280253977[/C][C]0.202089439492046[/C][C]0.101044719746023[/C][/ROW]
[ROW][C]105[/C][C]0.896686568195549[/C][C]0.206626863608902[/C][C]0.103313431804451[/C][/ROW]
[ROW][C]106[/C][C]0.872463442863565[/C][C]0.255073114272869[/C][C]0.127536557136435[/C][/ROW]
[ROW][C]107[/C][C]0.847684038967964[/C][C]0.304631922064072[/C][C]0.152315961032036[/C][/ROW]
[ROW][C]108[/C][C]0.840121853234238[/C][C]0.319756293531524[/C][C]0.159878146765762[/C][/ROW]
[ROW][C]109[/C][C]0.830526839947247[/C][C]0.338946320105506[/C][C]0.169473160052753[/C][/ROW]
[ROW][C]110[/C][C]0.848534971040357[/C][C]0.302930057919285[/C][C]0.151465028959643[/C][/ROW]
[ROW][C]111[/C][C]0.832573636327333[/C][C]0.334852727345334[/C][C]0.167426363672667[/C][/ROW]
[ROW][C]112[/C][C]0.875924106553221[/C][C]0.248151786893558[/C][C]0.124075893446779[/C][/ROW]
[ROW][C]113[/C][C]0.845739256879454[/C][C]0.308521486241092[/C][C]0.154260743120546[/C][/ROW]
[ROW][C]114[/C][C]0.815756589203797[/C][C]0.368486821592406[/C][C]0.184243410796203[/C][/ROW]
[ROW][C]115[/C][C]0.777487948425371[/C][C]0.445024103149257[/C][C]0.222512051574629[/C][/ROW]
[ROW][C]116[/C][C]0.781167428419543[/C][C]0.437665143160914[/C][C]0.218832571580457[/C][/ROW]
[ROW][C]117[/C][C]0.799776585613042[/C][C]0.400446828773915[/C][C]0.200223414386958[/C][/ROW]
[ROW][C]118[/C][C]0.785412141312459[/C][C]0.429175717375081[/C][C]0.214587858687541[/C][/ROW]
[ROW][C]119[/C][C]0.739529139064995[/C][C]0.52094172187001[/C][C]0.260470860935005[/C][/ROW]
[ROW][C]120[/C][C]0.811410086224054[/C][C]0.377179827551893[/C][C]0.188589913775946[/C][/ROW]
[ROW][C]121[/C][C]0.780572793346612[/C][C]0.438854413306777[/C][C]0.219427206653388[/C][/ROW]
[ROW][C]122[/C][C]0.754757734574797[/C][C]0.490484530850406[/C][C]0.245242265425203[/C][/ROW]
[ROW][C]123[/C][C]0.746039563795274[/C][C]0.507920872409451[/C][C]0.253960436204726[/C][/ROW]
[ROW][C]124[/C][C]0.699331152701278[/C][C]0.601337694597443[/C][C]0.300668847298722[/C][/ROW]
[ROW][C]125[/C][C]0.700103667502264[/C][C]0.599792664995472[/C][C]0.299896332497736[/C][/ROW]
[ROW][C]126[/C][C]0.68713561496015[/C][C]0.625728770079701[/C][C]0.31286438503985[/C][/ROW]
[ROW][C]127[/C][C]0.627867839390223[/C][C]0.744264321219553[/C][C]0.372132160609777[/C][/ROW]
[ROW][C]128[/C][C]0.590287770921602[/C][C]0.819424458156795[/C][C]0.409712229078398[/C][/ROW]
[ROW][C]129[/C][C]0.594142841031001[/C][C]0.811714317937999[/C][C]0.405857158968999[/C][/ROW]
[ROW][C]130[/C][C]0.585586085035124[/C][C]0.828827829929753[/C][C]0.414413914964876[/C][/ROW]
[ROW][C]131[/C][C]0.517099397344464[/C][C]0.965801205311071[/C][C]0.482900602655536[/C][/ROW]
[ROW][C]132[/C][C]0.501211851611816[/C][C]0.997576296776369[/C][C]0.498788148388184[/C][/ROW]
[ROW][C]133[/C][C]0.468678804538642[/C][C]0.937357609077284[/C][C]0.531321195461358[/C][/ROW]
[ROW][C]134[/C][C]0.398534551495578[/C][C]0.797069102991155[/C][C]0.601465448504422[/C][/ROW]
[ROW][C]135[/C][C]0.370425734924564[/C][C]0.740851469849127[/C][C]0.629574265075436[/C][/ROW]
[ROW][C]136[/C][C]0.367127678584287[/C][C]0.734255357168575[/C][C]0.632872321415713[/C][/ROW]
[ROW][C]137[/C][C]0.294820146990137[/C][C]0.589640293980274[/C][C]0.705179853009863[/C][/ROW]
[ROW][C]138[/C][C]0.366272950938821[/C][C]0.732545901877642[/C][C]0.633727049061179[/C][/ROW]
[ROW][C]139[/C][C]0.732867165495646[/C][C]0.534265669008708[/C][C]0.267132834504354[/C][/ROW]
[ROW][C]140[/C][C]0.745324383451239[/C][C]0.509351233097523[/C][C]0.254675616548761[/C][/ROW]
[ROW][C]141[/C][C]0.694316230160994[/C][C]0.611367539678013[/C][C]0.305683769839006[/C][/ROW]
[ROW][C]142[/C][C]0.598068282359428[/C][C]0.803863435281144[/C][C]0.401931717640572[/C][/ROW]
[ROW][C]143[/C][C]0.532759158396128[/C][C]0.934481683207745[/C][C]0.467240841603872[/C][/ROW]
[ROW][C]144[/C][C]0.410260787972309[/C][C]0.820521575944618[/C][C]0.589739212027691[/C][/ROW]
[ROW][C]145[/C][C]0.387764349515841[/C][C]0.775528699031683[/C][C]0.612235650484159[/C][/ROW]
[ROW][C]146[/C][C]0.566723446609807[/C][C]0.866553106780385[/C][C]0.433276553390193[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146456&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146456&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
100.1280798671545630.2561597343091260.871920132845437
110.1487047738945520.2974095477891030.851295226105448
120.07793726609842320.1558745321968460.922062733901577
130.5662377992522580.8675244014954830.433762200747742
140.8330554465519840.3338891068960320.166944553448016
150.7631989829703910.4736020340592180.236801017029609
160.6802623825284910.6394752349430190.319737617471509
170.623779273990840.752441452018320.37622072600916
180.5362767334060840.9274465331878320.463723266593916
190.4510202822834630.9020405645669260.548979717716537
200.6318547919229070.7362904161541870.368145208077093
210.600749433801970.7985011323960610.39925056619803
220.6396190348772660.7207619302454680.360380965122734
230.57505359056190.8498928188761990.4249464094381
240.5610791063354970.8778417873290060.438920893664503
250.5140137161932050.9719725676135890.485986283806795
260.4460214350287510.8920428700575020.553978564971249
270.7056200850075340.5887598299849310.294379914992466
280.6518615721920650.696276855615870.348138427807935
290.5920369941375740.8159260117248510.407963005862426
300.7268883311028320.5462233377943350.273111668897168
310.8103417605010730.3793164789978540.189658239498927
320.7741440881444990.4517118237110020.225855911855501
330.7272920899944340.5454158200111310.272707910005566
340.6969049858429060.6061900283141880.303095014157094
350.6930274743143270.6139450513713450.306972525685673
360.6897414245139670.6205171509720670.310258575486033
370.6631920139336890.6736159721326220.336807986066311
380.6144888502933780.7710222994132430.385511149706622
390.5657583755528350.8684832488943310.434241624447165
400.5224581239542370.9550837520915270.477541876045763
410.4838662013850590.9677324027701180.516133798614941
420.784511647238390.4309767055232190.21548835276161
430.9521114048303690.09577719033926180.0478885951696309
440.9563325976328020.08733480473439610.0436674023671981
450.9432977029135360.1134045941729290.0567022970864644
460.9508262798500530.09834744029989480.0491737201499474
470.950688128494580.09862374301084090.0493118715054204
480.9402432804988540.1195134390022930.0597567195011463
490.9378088497472030.1243823005055940.0621911502527969
500.926077997148780.1478440057024410.0739220028512203
510.9193336921810010.1613326156379990.0806663078189994
520.986584972607550.02683005478490020.0134150273924501
530.9825938764978370.03481224700432550.0174061235021628
540.9856328792678560.02873424146428870.0143671207321444
550.9911247448812080.01775051023758350.00887525511879177
560.9882047632762930.0235904734474130.0117952367237065
570.9842023489237620.03159530215247650.0157976510762383
580.9825917311777980.03481653764440480.0174082688222024
590.9880609455281150.02387810894376950.0119390544718847
600.9870767006148690.02584659877026220.0129232993851311
610.9866736934625260.02665261307494790.0133263065374739
620.9873673993573830.02526520128523420.0126326006426171
630.9858505365024480.02829892699510420.0141494634975521
640.9891045553931540.02179088921369150.0108954446068458
650.987761731146360.02447653770728070.0122382688536403
660.9873814589380280.02523708212394310.0126185410619716
670.9874463989286570.02510720214268620.0125536010713431
680.9903601998657870.01927960026842630.00963980013421316
690.989955781392620.02008843721476090.0100442186073805
700.9868606269274090.02627874614518210.013139373072591
710.9874638817877590.02507223642448170.0125361182122409
720.9833716089348050.03325678213038940.0166283910651947
730.9802986265962520.03940274680749630.0197013734037482
740.9861815553875990.02763688922480170.0138184446124008
750.9817395419448510.03652091611029740.0182604580551487
760.9783032247770730.04339355044585410.0216967752229271
770.9715658214287820.05686835714243510.0284341785712175
780.9629429493523550.074114101295290.037057050647645
790.9769704621913560.0460590756172880.023029537808644
800.9749562880944580.0500874238110840.025043711905542
810.9774160420298470.04516791594030550.0225839579701528
820.9773586423516640.04528271529667280.0226413576483364
830.9700572495383050.05988550092338960.0299427504616948
840.9627367719924790.0745264560150420.037263228007521
850.9888269325828240.02234613483435310.0111730674171765
860.9848921819944220.03021563601115650.0151078180055783
870.9798321424549120.04033571509017560.0201678575450878
880.9740041117370150.05199177652596940.0259958882629847
890.9693173001471670.06136539970566560.0306826998528328
900.96049215711530.07901568576940030.0395078428847002
910.9537813617131280.09243727657374380.0462186382868719
920.9715917772830560.05681644543388770.0284082227169439
930.9636637912626210.07267241747475730.0363362087373787
940.9604094187496070.07918116250078510.0395905812503926
950.9511576551340610.09768468973187790.0488423448659389
960.9404298920709240.1191402158581530.0595701079290764
970.9337281319670630.1325437360658740.0662718680329371
980.9171463183739780.1657073632520450.0828536816260223
990.9080640043752260.1838719912495470.0919359956247736
1000.8889248528611520.2221502942776960.111075147138848
1010.8646362951944980.2707274096110040.135363704805502
1020.8439869226677520.3120261546644960.156013077332248
1030.8496934075064060.3006131849871880.150306592493594
1040.8989552802539770.2020894394920460.101044719746023
1050.8966865681955490.2066268636089020.103313431804451
1060.8724634428635650.2550731142728690.127536557136435
1070.8476840389679640.3046319220640720.152315961032036
1080.8401218532342380.3197562935315240.159878146765762
1090.8305268399472470.3389463201055060.169473160052753
1100.8485349710403570.3029300579192850.151465028959643
1110.8325736363273330.3348527273453340.167426363672667
1120.8759241065532210.2481517868935580.124075893446779
1130.8457392568794540.3085214862410920.154260743120546
1140.8157565892037970.3684868215924060.184243410796203
1150.7774879484253710.4450241031492570.222512051574629
1160.7811674284195430.4376651431609140.218832571580457
1170.7997765856130420.4004468287739150.200223414386958
1180.7854121413124590.4291757173750810.214587858687541
1190.7395291390649950.520941721870010.260470860935005
1200.8114100862240540.3771798275518930.188589913775946
1210.7805727933466120.4388544133067770.219427206653388
1220.7547577345747970.4904845308504060.245242265425203
1230.7460395637952740.5079208724094510.253960436204726
1240.6993311527012780.6013376945974430.300668847298722
1250.7001036675022640.5997926649954720.299896332497736
1260.687135614960150.6257287700797010.31286438503985
1270.6278678393902230.7442643212195530.372132160609777
1280.5902877709216020.8194244581567950.409712229078398
1290.5941428410310010.8117143179379990.405857158968999
1300.5855860850351240.8288278299297530.414413914964876
1310.5170993973444640.9658012053110710.482900602655536
1320.5012118516118160.9975762967763690.498788148388184
1330.4686788045386420.9373576090772840.531321195461358
1340.3985345514955780.7970691029911550.601465448504422
1350.3704257349245640.7408514698491270.629574265075436
1360.3671276785842870.7342553571685750.632872321415713
1370.2948201469901370.5896402939802740.705179853009863
1380.3662729509388210.7325459018776420.633727049061179
1390.7328671654956460.5342656690087080.267132834504354
1400.7453243834512390.5093512330975230.254675616548761
1410.6943162301609940.6113675396780130.305683769839006
1420.5980682823594280.8038634352811440.401931717640572
1430.5327591583961280.9344816832077450.467240841603872
1440.4102607879723090.8205215759446180.589739212027691
1450.3877643495158410.7755286990316830.612235650484159
1460.5667234466098070.8665531067803850.433276553390193






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level310.226277372262774NOK
10% type I error level480.35036496350365NOK

\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 & 31 & 0.226277372262774 & NOK \tabularnewline
10% type I error level & 48 & 0.35036496350365 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146456&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]31[/C][C]0.226277372262774[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]48[/C][C]0.35036496350365[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146456&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146456&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 level310.226277372262774NOK
10% type I error level480.35036496350365NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}