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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 18 Dec 2010 17:31:06 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/18/t12926934592qyydzseamoeth6.htm/, Retrieved Tue, 30 Apr 2024 03:30:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112125, Retrieved Tue, 30 Apr 2024 03:30:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact138

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [WS7] [2010-11-22 19:33:23] [87116ee6ef949037dfa02b8eb1a3bf97]
-    D      [Multiple Regression] [MR - Happiness] [2010-12-18 17:31:06] [66b4703b90a9701067ac75b10c82aca9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
14	11	11	26	9	2	1	1
18	12	8	20	9	1	1	1
11	15	12	21	9	4	1	1
12	10	10	31	14	1	1	2
16	12	7	21	8	5	2	1
18	11	6	18	8	1	1	1
14	5	8	26	11	1	1	1
14	16	16	22	10	1	1	1
15	11	8	22	9	1	1	1
15	15	16	29	15	1	1	1
17	12	7	15	14	2	1	2
19	9	11	16	11	1	1	1
10	11	16	24	14	3	2	2
18	15	16	17	6	1	1	1
14	12	12	19	20	1	1	2
14	16	13	22	9	1	1	2
17	14	19	31	10	1	1	1
14	11	7	28	8	1	1	2
16	10	8	38	11	2	1	1
18	7	12	26	14	4	2	2
14	11	13	25	11	1	1	1
12	10	11	25	16	2	1	1
17	11	8	29	14	1	1	2
9	16	16	28	11	2	4	1
16	14	15	15	11	3	1	2
14	12	11	18	12	1	1	1
11	12	12	21	9	1	2	2
16	11	7	25	7	1	2	1
13	6	9	23	13	1	1	2
17	14	15	23	10	1	1	1
15	9	6	19	9	2	1	1
14	15	14	18	9	1	1	2
16	12	14	18	13	1	1	2
9	12	7	26	16	1	1	2
15	9	15	18	12	1	1	2
17	13	14	18	6	1	1	1
13	15	17	28	14	1	1	2
15	11	14	17	14	1	1	2
16	10	5	29	10	2	2	1
16	13	14	12	4	1	1	2
12	16	8	28	12	1	1	1
11	13	8	20	14	1	1	1
15	14	13	17	9	2	1	1
17	14	14	17	9	1	1	1
13	16	16	20	10	1	1	2
16	9	11	31	14	1	1	1
14	8	10	21	10	1	1	2
11	8	10	19	9	1	1	2
12	12	10	23	14	1	1	1
12	10	8	15	8	4	1	2
15	16	14	24	9	2	1	1
16	13	14	28	8	1	1	1
15	11	12	16	9	1	1	1
12	14	13	19	9	4	3	2
12	15	5	21	9	2	2	1
8	8	10	21	15	1	1	2
13	9	6	20	8	1	1	2
11	17	15	16	10	1	1	1
14	9	12	25	8	1	1	1
15	13	16	30	14	1	1	1
10	6	15	29	11	1	1	2
11	13	12	22	10	2	1	1
12	8	8	19	12	1	1	2
15	12	14	33	14	1	1	1
15	13	14	17	9	2	1	2
14	14	13	9	13	1	1	2
16	11	12	14	15	2	2	1
15	15	15	15	8	2	1	1
15	7	8	12	7	4	1	2
13	16	16	21	10	1	1	2
17	16	14	20	10	1	1	1
13	14	13	29	13	3	2	1
15	11	15	33	11	1	1	2
13	13	7	21	8	1	1	2
15	13	5	15	12	1	1	2
16	7	7	19	9	1	1	2
15	15	13	23	10	1	1	1
16	11	14	20	11	1	1	2
15	15	14	20	11	1	1	1
14	13	13	18	10	1	1	1
15	11	11	31	16	4	1	2
7	12	15	18	16	1	1	1
17	10	13	13	8	1	1	1
13	12	14	9	6	2	1	1
15	12	13	20	11	1	1	1
14	12	9	18	12	1	1	1
13	14	8	23	14	1	2	1
16	6	6	17	9	1	1	1
12	14	13	17	11	1	1	1
14	15	16	16	8	1	1	1
17	8	7	31	8	1	1	2
15	12	11	15	7	1	1	2
17	10	8	28	16	1	1	1
12	15	13	26	13	1	1	2
16	11	5	20	8	1	2	1
11	9	8	19	11	1	2	2
15	14	10	25	14	5	1	1
9	10	9	18	10	1	1	2
16	16	16	20	10	1	1	1
10	5	4	33	14	1	1	2
10	8	4	24	14	3	3	1
15	13	11	22	10	1	1	1
11	16	14	32	12	1	1	1
13	16	15	31	9	1	1	1
14	14	17	13	16	1	1	2
18	14	10	18	8	1	1	1
16	10	15	17	9	1	1	2
14	9	11	29	16	1	1	1
14	14	15	22	13	2	1	1
14	8	10	18	13	4	1	1
14	8	9	22	8	4	3	1
12	16	14	25	14	1	1	1
14	12	15	20	11	1	1	1
15	9	9	20	9	1	1	1
15	15	12	17	8	4	3	1
13	12	10	26	13	2	3	1
17	14	16	10	10	1	1	2
17	12	15	15	8	1	2	1
19	16	14	20	7	1	1	1
15	12	12	14	11	1	1	1
13	14	15	16	11	1	1	2
9	8	9	23	14	1	2	2
15	15	12	11	6	2	2	1
15	16	15	19	10	4	1	2
16	12	6	30	9	4	1	1
11	4	4	21	12	1	1	2
14	8	8	20	11	1	1	2
11	11	10	22	14	1	1	1
15	4	6	30	12	2	3	1
13	14	12	25	14	1	1	2
16	14	14	23	14	1	1	2
14	13	11	23	8	3	1	1
15	14	15	21	11	2	1	2
16	7	13	30	12	2	1	1
16	19	15	22	9	1	1	1
11	12	16	32	16	1	1	2
13	10	4	22	11	2	2	1
16	14	15	15	11	3	1	2
12	16	12	21	12	1	1	1
9	11	15	27	15	1	1	1
13	16	15	22	13	1	2	1
13	12	14	9	6	2	1	1
14	12	14	29	11	2	1	1
19	16	14	20	7	1	1	1
13	12	11	16	8	1	1	1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 7 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112125&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112125&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112125&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 time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 18.9957165366223 -0.027802499147085Popularity[t] + 0.0455463905768971KnowingPeople[t] -0.00637402452264016CMistakes[t] -0.280840510374424DAction[t] + 0.181746271876873Tobacco[t] -0.914459276859251Drugs[t] -0.762811775254925Gender[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  18.9957165366223 -0.027802499147085Popularity[t] +  0.0455463905768971KnowingPeople[t] -0.00637402452264016CMistakes[t] -0.280840510374424DAction[t] +  0.181746271876873Tobacco[t] -0.914459276859251Drugs[t] -0.762811775254925Gender[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112125&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  18.9957165366223 -0.027802499147085Popularity[t] +  0.0455463905768971KnowingPeople[t] -0.00637402452264016CMistakes[t] -0.280840510374424DAction[t] +  0.181746271876873Tobacco[t] -0.914459276859251Drugs[t] -0.762811775254925Gender[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112125&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112125&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
Happiness[t] = + 18.9957165366223 -0.027802499147085Popularity[t] + 0.0455463905768971KnowingPeople[t] -0.00637402452264016CMistakes[t] -0.280840510374424DAction[t] + 0.181746271876873Tobacco[t] -0.914459276859251Drugs[t] -0.762811775254925Gender[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)18.99571653662231.55945912.18100
Popularity-0.0278024991470850.078309-0.3550.7231080.361554
KnowingPeople0.04554639057689710.0661990.6880.4926050.246302
CMistakes-0.006374024522640160.035558-0.17930.8579990.429
DAction-0.2808405103744240.073563-3.81770.0002030.000102
Tobacco0.1817462718768730.2018330.90050.3694470.184723
Drugs-0.9144592768592510.368378-2.48240.0142590.007129
Gender-0.7628117752549250.401175-1.90140.0593430.029672

\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) & 18.9957165366223 & 1.559459 & 12.181 & 0 & 0 \tabularnewline
Popularity & -0.027802499147085 & 0.078309 & -0.355 & 0.723108 & 0.361554 \tabularnewline
KnowingPeople & 0.0455463905768971 & 0.066199 & 0.688 & 0.492605 & 0.246302 \tabularnewline
CMistakes & -0.00637402452264016 & 0.035558 & -0.1793 & 0.857999 & 0.429 \tabularnewline
DAction & -0.280840510374424 & 0.073563 & -3.8177 & 0.000203 & 0.000102 \tabularnewline
Tobacco & 0.181746271876873 & 0.201833 & 0.9005 & 0.369447 & 0.184723 \tabularnewline
Drugs & -0.914459276859251 & 0.368378 & -2.4824 & 0.014259 & 0.007129 \tabularnewline
Gender & -0.762811775254925 & 0.401175 & -1.9014 & 0.059343 & 0.029672 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112125&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]18.9957165366223[/C][C]1.559459[/C][C]12.181[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Popularity[/C][C]-0.027802499147085[/C][C]0.078309[/C][C]-0.355[/C][C]0.723108[/C][C]0.361554[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.0455463905768971[/C][C]0.066199[/C][C]0.688[/C][C]0.492605[/C][C]0.246302[/C][/ROW]
[ROW][C]CMistakes[/C][C]-0.00637402452264016[/C][C]0.035558[/C][C]-0.1793[/C][C]0.857999[/C][C]0.429[/C][/ROW]
[ROW][C]DAction[/C][C]-0.280840510374424[/C][C]0.073563[/C][C]-3.8177[/C][C]0.000203[/C][C]0.000102[/C][/ROW]
[ROW][C]Tobacco[/C][C]0.181746271876873[/C][C]0.201833[/C][C]0.9005[/C][C]0.369447[/C][C]0.184723[/C][/ROW]
[ROW][C]Drugs[/C][C]-0.914459276859251[/C][C]0.368378[/C][C]-2.4824[/C][C]0.014259[/C][C]0.007129[/C][/ROW]
[ROW][C]Gender[/C][C]-0.762811775254925[/C][C]0.401175[/C][C]-1.9014[/C][C]0.059343[/C][C]0.029672[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112125&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112125&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)18.99571653662231.55945912.18100
Popularity-0.0278024991470850.078309-0.3550.7231080.361554
KnowingPeople0.04554639057689710.0661990.6880.4926050.246302
CMistakes-0.006374024522640160.035558-0.17930.8579990.429
DAction-0.2808405103744240.073563-3.81770.0002030.000102
Tobacco0.1817462718768730.2018330.90050.3694470.184723
Drugs-0.9144592768592510.368378-2.48240.0142590.007129
Gender-0.7628117752549250.401175-1.90140.0593430.029672






Multiple Linear Regression - Regression Statistics
Multiple R0.430048346921455
R-squared0.184941580689876
Adjusted R-squared0.143296259995199
F-TEST (value)4.44087301057839
F-TEST (DF numerator)7
F-TEST (DF denominator)137
p-value0.000177261813259388
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.19879831012477
Sum Squared Residuals662.355819179235

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.430048346921455 \tabularnewline
R-squared & 0.184941580689876 \tabularnewline
Adjusted R-squared & 0.143296259995199 \tabularnewline
F-TEST (value) & 4.44087301057839 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 137 \tabularnewline
p-value & 0.000177261813259388 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.19879831012477 \tabularnewline
Sum Squared Residuals & 662.355819179235 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112125&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.430048346921455[/C][/ROW]
[ROW][C]R-squared[/C][C]0.184941580689876[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.143296259995199[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.44087301057839[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]137[/C][/ROW]
[ROW][C]p-value[/C][C]0.000177261813259388[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.19879831012477[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]662.355819179235[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112125&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112125&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 R0.430048346921455
R-squared0.184941580689876
Adjusted R-squared0.143296259995199
F-TEST (value)4.44087301057839
F-TEST (DF numerator)7
F-TEST (DF denominator)137
p-value0.000177261813259388
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.19879831012477
Sum Squared Residuals662.355819179235






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11415.1838316030313-1.18383160303134
21814.87588780741263.12411219258742
31115.5135306633869-4.5135306633869
41212.7854569899845-0.785456989984462
51614.91733371333571.08266628666429
61815.10618608482562.89381391517442
71414.4705801335575-0.47058013355749
81414.8354603760197-0.835460376019715
91514.89094225751440.109057742485613
101513.41444215163621.5855578483638
111712.87694348419874.12305651580128
121914.55974955392624.44025044607376
131012.5245842728517-2.52458427285174
141816.01849503927771.9815049607223
151411.21239000486922.78760999513078
161414.2168499394085-0.216849939408522
171714.97033832534082.02966167465919
181414.3251804549211-0.325180454921148
191614.43682561542731.56317438457275
201812.62260692996415.37739307003592
211414.5378711160821-0.537871116082105
221213.2521245540802-1.25212455408015
231712.67930975872894.32069024127114
24911.9547441598086-2.95474415980857
251614.20997741351991.79002258648013
261414.1827534970653-0.182753497065283
271113.3744282930834-2.37442829308335
281614.47349553725921.52650446274083
291313.1829533025515-0.182953302551449
301714.83914495921432.16085504078565
311515.0563228200996-0.0563228200995568
321414.3156949272231-0.315694927223065
331613.27574038316662.72425961683337
34912.063401921824-3.06340192182395
351513.68553478155921.3144652184408
361715.97663323189541.02336676810457
371312.98439130185520.0156086981447637
381513.02907639646191.97092360353807
391613.72393389791552.2760661020845
401615.81374662452520.186253375474805
411213.8711640835198-1.87116408351985
421113.4438827563934-2.44388275639338
431515.2488831074477-0.248883107447692
441715.11268322614771.88731677385229
451314.0853966498101-1.08539664981007
461613.62161765496342.37838234503663
471414.0281642750027-0.0281642750027282
481114.3217528344224-3.32175283442243
491213.5436559631263-1.54365596312634
501215.0266304790701-3.02663047907007
511515.1942063280719-0.194206328071937
521615.35121196592020.648788034079818
531515.1113719669578-0.111371966957816
541213.0078972731827-1.00789727318273
551213.9167541087356-1.91675410873562
56812.6239617231306-4.62396172313061
571314.3862312588195-1.38623125881954
581114.8003556334316-3.80035563343157
591415.3904512549226-1.39045125492265
601513.74451363578221.25548636421785
611013.9796685196258-3.97966851962584
621114.9184285830303-3.91842858303026
631213.3881385221454-1.38813852214537
641513.66210128020751.33789871979248
651514.55942022191670.440579778083251
661413.23195521499930.76804478500068
671612.70636394877423.29363605122582
681515.6057619488741-0.605761948874105
691515.4100005604537-0.410000560453672
701314.0790226252874-1.07902262528743
711714.75711564391122.2428843560888
721313.3163197666959-0.316319766695937
731513.81515992579991.18484007420015
741314.3141936282855-1.31419362828546
751513.13798295276981.86201704723019
761614.21291616183881.78708383816117
771514.72024967891350.279750321086532
781613.85247585401732.14752414598272
791514.50407763268390.495922367316138
801414.8077247998208-0.807724799820839
811512.7867586762962.21324132370395
82713.2415770178752-6.24157701787518
831715.48468344062411.51531655937586
841316.2435482236232-3.24354822362315
851514.54193873954820.45806126045178
861414.0916607159115-0.0916607159114886
871312.48249890681910.517501093180879
881614.97073209470921.02926790529078
891214.505455814822-2.50545581482197
901415.4631880430515-1.46318804305149
911714.38946587879452.61053412120552
921514.84326634725040.156733652749603
931712.91461703690474.08538296309534
941213.0957942989674-1.09579429896735
951614.13343236834411.86656763165585
961112.7267172565135-1.72671725651345
971514.20228800329440.79771199670562
98913.9461349596996-4.94613495969958
991614.8482084250651.151791574935
1001012.6384430932132-2.63844309321322
1011011.9097875817659-1.9097875817659
1021514.69113592057650.308864079423516
1031114.1189463288907-3.11894632889067
1041315.0133882751135-2.01338827511348
1051412.54612314809311.45387685190692
1061815.20496414969192.79503585030809
1071614.5066278380581.49337216194197
1081413.07268468325980.927315316740199
1091414.1847437244906-0.18474372449059
1101414.5128154083329-0.512815408332923
1111414.0170569178191-0.0170569178190805
1121213.6018834798003-1.6018834798003
1131414.633031520702-0.633031520702015
1141515.0048416954307-0.00484169543073433
1151513.99094871813341.00905128186662
1161312.15820211809120.84179788190879
1171714.20474189333062.79525810666936
1181714.59296389757922.40703610242076
1191915.59963717503453.40036282496553
1201514.53463649610720.465363503892836
1211313.8401108452435-0.84011084524348
122911.9320485170236-2.9320485170236
1231515.1418406191236-0.14184061912357
1241514.59146309938640.408536900613566
1251615.2662935966630.733706403336993
1261113.3044149073808-2.30441490738084
1271413.66260500799720.33739499200285
1281113.5778324867961-2.57783248679606
1291512.45378096124422.54621903875583
1301312.80358392168580.196416078314244
1311612.90742475188483.09257524811517
1321415.6099354605564-1.60993546055644
1331513.98998699450721.01001300549285
1341614.51811675155971.48188324844031
1351614.9873469983761.01265300162401
1361112.4350752898802-1.43507528988019
1371313.4421651686227-0.442165168622658
1381614.20997741351991.79002258648013
1391214.0979678174859-2.09796781748592
140913.4928538066929-4.49285380669292
1411313.0329331774603-0.0329331774602952
1421316.2435482236232-3.24354822362315
1431414.7118651812982-0.71186518129823
1441915.59963717503453.40036282496553
1451315.3188635876083-2.31886358760826

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 15.1838316030313 & -1.18383160303134 \tabularnewline
2 & 18 & 14.8758878074126 & 3.12411219258742 \tabularnewline
3 & 11 & 15.5135306633869 & -4.5135306633869 \tabularnewline
4 & 12 & 12.7854569899845 & -0.785456989984462 \tabularnewline
5 & 16 & 14.9173337133357 & 1.08266628666429 \tabularnewline
6 & 18 & 15.1061860848256 & 2.89381391517442 \tabularnewline
7 & 14 & 14.4705801335575 & -0.47058013355749 \tabularnewline
8 & 14 & 14.8354603760197 & -0.835460376019715 \tabularnewline
9 & 15 & 14.8909422575144 & 0.109057742485613 \tabularnewline
10 & 15 & 13.4144421516362 & 1.5855578483638 \tabularnewline
11 & 17 & 12.8769434841987 & 4.12305651580128 \tabularnewline
12 & 19 & 14.5597495539262 & 4.44025044607376 \tabularnewline
13 & 10 & 12.5245842728517 & -2.52458427285174 \tabularnewline
14 & 18 & 16.0184950392777 & 1.9815049607223 \tabularnewline
15 & 14 & 11.2123900048692 & 2.78760999513078 \tabularnewline
16 & 14 & 14.2168499394085 & -0.216849939408522 \tabularnewline
17 & 17 & 14.9703383253408 & 2.02966167465919 \tabularnewline
18 & 14 & 14.3251804549211 & -0.325180454921148 \tabularnewline
19 & 16 & 14.4368256154273 & 1.56317438457275 \tabularnewline
20 & 18 & 12.6226069299641 & 5.37739307003592 \tabularnewline
21 & 14 & 14.5378711160821 & -0.537871116082105 \tabularnewline
22 & 12 & 13.2521245540802 & -1.25212455408015 \tabularnewline
23 & 17 & 12.6793097587289 & 4.32069024127114 \tabularnewline
24 & 9 & 11.9547441598086 & -2.95474415980857 \tabularnewline
25 & 16 & 14.2099774135199 & 1.79002258648013 \tabularnewline
26 & 14 & 14.1827534970653 & -0.182753497065283 \tabularnewline
27 & 11 & 13.3744282930834 & -2.37442829308335 \tabularnewline
28 & 16 & 14.4734955372592 & 1.52650446274083 \tabularnewline
29 & 13 & 13.1829533025515 & -0.182953302551449 \tabularnewline
30 & 17 & 14.8391449592143 & 2.16085504078565 \tabularnewline
31 & 15 & 15.0563228200996 & -0.0563228200995568 \tabularnewline
32 & 14 & 14.3156949272231 & -0.315694927223065 \tabularnewline
33 & 16 & 13.2757403831666 & 2.72425961683337 \tabularnewline
34 & 9 & 12.063401921824 & -3.06340192182395 \tabularnewline
35 & 15 & 13.6855347815592 & 1.3144652184408 \tabularnewline
36 & 17 & 15.9766332318954 & 1.02336676810457 \tabularnewline
37 & 13 & 12.9843913018552 & 0.0156086981447637 \tabularnewline
38 & 15 & 13.0290763964619 & 1.97092360353807 \tabularnewline
39 & 16 & 13.7239338979155 & 2.2760661020845 \tabularnewline
40 & 16 & 15.8137466245252 & 0.186253375474805 \tabularnewline
41 & 12 & 13.8711640835198 & -1.87116408351985 \tabularnewline
42 & 11 & 13.4438827563934 & -2.44388275639338 \tabularnewline
43 & 15 & 15.2488831074477 & -0.248883107447692 \tabularnewline
44 & 17 & 15.1126832261477 & 1.88731677385229 \tabularnewline
45 & 13 & 14.0853966498101 & -1.08539664981007 \tabularnewline
46 & 16 & 13.6216176549634 & 2.37838234503663 \tabularnewline
47 & 14 & 14.0281642750027 & -0.0281642750027282 \tabularnewline
48 & 11 & 14.3217528344224 & -3.32175283442243 \tabularnewline
49 & 12 & 13.5436559631263 & -1.54365596312634 \tabularnewline
50 & 12 & 15.0266304790701 & -3.02663047907007 \tabularnewline
51 & 15 & 15.1942063280719 & -0.194206328071937 \tabularnewline
52 & 16 & 15.3512119659202 & 0.648788034079818 \tabularnewline
53 & 15 & 15.1113719669578 & -0.111371966957816 \tabularnewline
54 & 12 & 13.0078972731827 & -1.00789727318273 \tabularnewline
55 & 12 & 13.9167541087356 & -1.91675410873562 \tabularnewline
56 & 8 & 12.6239617231306 & -4.62396172313061 \tabularnewline
57 & 13 & 14.3862312588195 & -1.38623125881954 \tabularnewline
58 & 11 & 14.8003556334316 & -3.80035563343157 \tabularnewline
59 & 14 & 15.3904512549226 & -1.39045125492265 \tabularnewline
60 & 15 & 13.7445136357822 & 1.25548636421785 \tabularnewline
61 & 10 & 13.9796685196258 & -3.97966851962584 \tabularnewline
62 & 11 & 14.9184285830303 & -3.91842858303026 \tabularnewline
63 & 12 & 13.3881385221454 & -1.38813852214537 \tabularnewline
64 & 15 & 13.6621012802075 & 1.33789871979248 \tabularnewline
65 & 15 & 14.5594202219167 & 0.440579778083251 \tabularnewline
66 & 14 & 13.2319552149993 & 0.76804478500068 \tabularnewline
67 & 16 & 12.7063639487742 & 3.29363605122582 \tabularnewline
68 & 15 & 15.6057619488741 & -0.605761948874105 \tabularnewline
69 & 15 & 15.4100005604537 & -0.410000560453672 \tabularnewline
70 & 13 & 14.0790226252874 & -1.07902262528743 \tabularnewline
71 & 17 & 14.7571156439112 & 2.2428843560888 \tabularnewline
72 & 13 & 13.3163197666959 & -0.316319766695937 \tabularnewline
73 & 15 & 13.8151599257999 & 1.18484007420015 \tabularnewline
74 & 13 & 14.3141936282855 & -1.31419362828546 \tabularnewline
75 & 15 & 13.1379829527698 & 1.86201704723019 \tabularnewline
76 & 16 & 14.2129161618388 & 1.78708383816117 \tabularnewline
77 & 15 & 14.7202496789135 & 0.279750321086532 \tabularnewline
78 & 16 & 13.8524758540173 & 2.14752414598272 \tabularnewline
79 & 15 & 14.5040776326839 & 0.495922367316138 \tabularnewline
80 & 14 & 14.8077247998208 & -0.807724799820839 \tabularnewline
81 & 15 & 12.786758676296 & 2.21324132370395 \tabularnewline
82 & 7 & 13.2415770178752 & -6.24157701787518 \tabularnewline
83 & 17 & 15.4846834406241 & 1.51531655937586 \tabularnewline
84 & 13 & 16.2435482236232 & -3.24354822362315 \tabularnewline
85 & 15 & 14.5419387395482 & 0.45806126045178 \tabularnewline
86 & 14 & 14.0916607159115 & -0.0916607159114886 \tabularnewline
87 & 13 & 12.4824989068191 & 0.517501093180879 \tabularnewline
88 & 16 & 14.9707320947092 & 1.02926790529078 \tabularnewline
89 & 12 & 14.505455814822 & -2.50545581482197 \tabularnewline
90 & 14 & 15.4631880430515 & -1.46318804305149 \tabularnewline
91 & 17 & 14.3894658787945 & 2.61053412120552 \tabularnewline
92 & 15 & 14.8432663472504 & 0.156733652749603 \tabularnewline
93 & 17 & 12.9146170369047 & 4.08538296309534 \tabularnewline
94 & 12 & 13.0957942989674 & -1.09579429896735 \tabularnewline
95 & 16 & 14.1334323683441 & 1.86656763165585 \tabularnewline
96 & 11 & 12.7267172565135 & -1.72671725651345 \tabularnewline
97 & 15 & 14.2022880032944 & 0.79771199670562 \tabularnewline
98 & 9 & 13.9461349596996 & -4.94613495969958 \tabularnewline
99 & 16 & 14.848208425065 & 1.151791574935 \tabularnewline
100 & 10 & 12.6384430932132 & -2.63844309321322 \tabularnewline
101 & 10 & 11.9097875817659 & -1.9097875817659 \tabularnewline
102 & 15 & 14.6911359205765 & 0.308864079423516 \tabularnewline
103 & 11 & 14.1189463288907 & -3.11894632889067 \tabularnewline
104 & 13 & 15.0133882751135 & -2.01338827511348 \tabularnewline
105 & 14 & 12.5461231480931 & 1.45387685190692 \tabularnewline
106 & 18 & 15.2049641496919 & 2.79503585030809 \tabularnewline
107 & 16 & 14.506627838058 & 1.49337216194197 \tabularnewline
108 & 14 & 13.0726846832598 & 0.927315316740199 \tabularnewline
109 & 14 & 14.1847437244906 & -0.18474372449059 \tabularnewline
110 & 14 & 14.5128154083329 & -0.512815408332923 \tabularnewline
111 & 14 & 14.0170569178191 & -0.0170569178190805 \tabularnewline
112 & 12 & 13.6018834798003 & -1.6018834798003 \tabularnewline
113 & 14 & 14.633031520702 & -0.633031520702015 \tabularnewline
114 & 15 & 15.0048416954307 & -0.00484169543073433 \tabularnewline
115 & 15 & 13.9909487181334 & 1.00905128186662 \tabularnewline
116 & 13 & 12.1582021180912 & 0.84179788190879 \tabularnewline
117 & 17 & 14.2047418933306 & 2.79525810666936 \tabularnewline
118 & 17 & 14.5929638975792 & 2.40703610242076 \tabularnewline
119 & 19 & 15.5996371750345 & 3.40036282496553 \tabularnewline
120 & 15 & 14.5346364961072 & 0.465363503892836 \tabularnewline
121 & 13 & 13.8401108452435 & -0.84011084524348 \tabularnewline
122 & 9 & 11.9320485170236 & -2.9320485170236 \tabularnewline
123 & 15 & 15.1418406191236 & -0.14184061912357 \tabularnewline
124 & 15 & 14.5914630993864 & 0.408536900613566 \tabularnewline
125 & 16 & 15.266293596663 & 0.733706403336993 \tabularnewline
126 & 11 & 13.3044149073808 & -2.30441490738084 \tabularnewline
127 & 14 & 13.6626050079972 & 0.33739499200285 \tabularnewline
128 & 11 & 13.5778324867961 & -2.57783248679606 \tabularnewline
129 & 15 & 12.4537809612442 & 2.54621903875583 \tabularnewline
130 & 13 & 12.8035839216858 & 0.196416078314244 \tabularnewline
131 & 16 & 12.9074247518848 & 3.09257524811517 \tabularnewline
132 & 14 & 15.6099354605564 & -1.60993546055644 \tabularnewline
133 & 15 & 13.9899869945072 & 1.01001300549285 \tabularnewline
134 & 16 & 14.5181167515597 & 1.48188324844031 \tabularnewline
135 & 16 & 14.987346998376 & 1.01265300162401 \tabularnewline
136 & 11 & 12.4350752898802 & -1.43507528988019 \tabularnewline
137 & 13 & 13.4421651686227 & -0.442165168622658 \tabularnewline
138 & 16 & 14.2099774135199 & 1.79002258648013 \tabularnewline
139 & 12 & 14.0979678174859 & -2.09796781748592 \tabularnewline
140 & 9 & 13.4928538066929 & -4.49285380669292 \tabularnewline
141 & 13 & 13.0329331774603 & -0.0329331774602952 \tabularnewline
142 & 13 & 16.2435482236232 & -3.24354822362315 \tabularnewline
143 & 14 & 14.7118651812982 & -0.71186518129823 \tabularnewline
144 & 19 & 15.5996371750345 & 3.40036282496553 \tabularnewline
145 & 13 & 15.3188635876083 & -2.31886358760826 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112125&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]14[/C][C]15.1838316030313[/C][C]-1.18383160303134[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]14.8758878074126[/C][C]3.12411219258742[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]15.5135306633869[/C][C]-4.5135306633869[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]12.7854569899845[/C][C]-0.785456989984462[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]14.9173337133357[/C][C]1.08266628666429[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]15.1061860848256[/C][C]2.89381391517442[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]14.4705801335575[/C][C]-0.47058013355749[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]14.8354603760197[/C][C]-0.835460376019715[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]14.8909422575144[/C][C]0.109057742485613[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]13.4144421516362[/C][C]1.5855578483638[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]12.8769434841987[/C][C]4.12305651580128[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]14.5597495539262[/C][C]4.44025044607376[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]12.5245842728517[/C][C]-2.52458427285174[/C][/ROW]
[ROW][C]14[/C][C]18[/C][C]16.0184950392777[/C][C]1.9815049607223[/C][/ROW]
[ROW][C]15[/C][C]14[/C][C]11.2123900048692[/C][C]2.78760999513078[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]14.2168499394085[/C][C]-0.216849939408522[/C][/ROW]
[ROW][C]17[/C][C]17[/C][C]14.9703383253408[/C][C]2.02966167465919[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]14.3251804549211[/C][C]-0.325180454921148[/C][/ROW]
[ROW][C]19[/C][C]16[/C][C]14.4368256154273[/C][C]1.56317438457275[/C][/ROW]
[ROW][C]20[/C][C]18[/C][C]12.6226069299641[/C][C]5.37739307003592[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]14.5378711160821[/C][C]-0.537871116082105[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]13.2521245540802[/C][C]-1.25212455408015[/C][/ROW]
[ROW][C]23[/C][C]17[/C][C]12.6793097587289[/C][C]4.32069024127114[/C][/ROW]
[ROW][C]24[/C][C]9[/C][C]11.9547441598086[/C][C]-2.95474415980857[/C][/ROW]
[ROW][C]25[/C][C]16[/C][C]14.2099774135199[/C][C]1.79002258648013[/C][/ROW]
[ROW][C]26[/C][C]14[/C][C]14.1827534970653[/C][C]-0.182753497065283[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]13.3744282930834[/C][C]-2.37442829308335[/C][/ROW]
[ROW][C]28[/C][C]16[/C][C]14.4734955372592[/C][C]1.52650446274083[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]13.1829533025515[/C][C]-0.182953302551449[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]14.8391449592143[/C][C]2.16085504078565[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.0563228200996[/C][C]-0.0563228200995568[/C][/ROW]
[ROW][C]32[/C][C]14[/C][C]14.3156949272231[/C][C]-0.315694927223065[/C][/ROW]
[ROW][C]33[/C][C]16[/C][C]13.2757403831666[/C][C]2.72425961683337[/C][/ROW]
[ROW][C]34[/C][C]9[/C][C]12.063401921824[/C][C]-3.06340192182395[/C][/ROW]
[ROW][C]35[/C][C]15[/C][C]13.6855347815592[/C][C]1.3144652184408[/C][/ROW]
[ROW][C]36[/C][C]17[/C][C]15.9766332318954[/C][C]1.02336676810457[/C][/ROW]
[ROW][C]37[/C][C]13[/C][C]12.9843913018552[/C][C]0.0156086981447637[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]13.0290763964619[/C][C]1.97092360353807[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]13.7239338979155[/C][C]2.2760661020845[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.8137466245252[/C][C]0.186253375474805[/C][/ROW]
[ROW][C]41[/C][C]12[/C][C]13.8711640835198[/C][C]-1.87116408351985[/C][/ROW]
[ROW][C]42[/C][C]11[/C][C]13.4438827563934[/C][C]-2.44388275639338[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]15.2488831074477[/C][C]-0.248883107447692[/C][/ROW]
[ROW][C]44[/C][C]17[/C][C]15.1126832261477[/C][C]1.88731677385229[/C][/ROW]
[ROW][C]45[/C][C]13[/C][C]14.0853966498101[/C][C]-1.08539664981007[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.6216176549634[/C][C]2.37838234503663[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]14.0281642750027[/C][C]-0.0281642750027282[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]14.3217528344224[/C][C]-3.32175283442243[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]13.5436559631263[/C][C]-1.54365596312634[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]15.0266304790701[/C][C]-3.02663047907007[/C][/ROW]
[ROW][C]51[/C][C]15[/C][C]15.1942063280719[/C][C]-0.194206328071937[/C][/ROW]
[ROW][C]52[/C][C]16[/C][C]15.3512119659202[/C][C]0.648788034079818[/C][/ROW]
[ROW][C]53[/C][C]15[/C][C]15.1113719669578[/C][C]-0.111371966957816[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]13.0078972731827[/C][C]-1.00789727318273[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]13.9167541087356[/C][C]-1.91675410873562[/C][/ROW]
[ROW][C]56[/C][C]8[/C][C]12.6239617231306[/C][C]-4.62396172313061[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]14.3862312588195[/C][C]-1.38623125881954[/C][/ROW]
[ROW][C]58[/C][C]11[/C][C]14.8003556334316[/C][C]-3.80035563343157[/C][/ROW]
[ROW][C]59[/C][C]14[/C][C]15.3904512549226[/C][C]-1.39045125492265[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]13.7445136357822[/C][C]1.25548636421785[/C][/ROW]
[ROW][C]61[/C][C]10[/C][C]13.9796685196258[/C][C]-3.97966851962584[/C][/ROW]
[ROW][C]62[/C][C]11[/C][C]14.9184285830303[/C][C]-3.91842858303026[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]13.3881385221454[/C][C]-1.38813852214537[/C][/ROW]
[ROW][C]64[/C][C]15[/C][C]13.6621012802075[/C][C]1.33789871979248[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]14.5594202219167[/C][C]0.440579778083251[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]13.2319552149993[/C][C]0.76804478500068[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]12.7063639487742[/C][C]3.29363605122582[/C][/ROW]
[ROW][C]68[/C][C]15[/C][C]15.6057619488741[/C][C]-0.605761948874105[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]15.4100005604537[/C][C]-0.410000560453672[/C][/ROW]
[ROW][C]70[/C][C]13[/C][C]14.0790226252874[/C][C]-1.07902262528743[/C][/ROW]
[ROW][C]71[/C][C]17[/C][C]14.7571156439112[/C][C]2.2428843560888[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]13.3163197666959[/C][C]-0.316319766695937[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]13.8151599257999[/C][C]1.18484007420015[/C][/ROW]
[ROW][C]74[/C][C]13[/C][C]14.3141936282855[/C][C]-1.31419362828546[/C][/ROW]
[ROW][C]75[/C][C]15[/C][C]13.1379829527698[/C][C]1.86201704723019[/C][/ROW]
[ROW][C]76[/C][C]16[/C][C]14.2129161618388[/C][C]1.78708383816117[/C][/ROW]
[ROW][C]77[/C][C]15[/C][C]14.7202496789135[/C][C]0.279750321086532[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]13.8524758540173[/C][C]2.14752414598272[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]14.5040776326839[/C][C]0.495922367316138[/C][/ROW]
[ROW][C]80[/C][C]14[/C][C]14.8077247998208[/C][C]-0.807724799820839[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]12.786758676296[/C][C]2.21324132370395[/C][/ROW]
[ROW][C]82[/C][C]7[/C][C]13.2415770178752[/C][C]-6.24157701787518[/C][/ROW]
[ROW][C]83[/C][C]17[/C][C]15.4846834406241[/C][C]1.51531655937586[/C][/ROW]
[ROW][C]84[/C][C]13[/C][C]16.2435482236232[/C][C]-3.24354822362315[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.5419387395482[/C][C]0.45806126045178[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.0916607159115[/C][C]-0.0916607159114886[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]12.4824989068191[/C][C]0.517501093180879[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]14.9707320947092[/C][C]1.02926790529078[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]14.505455814822[/C][C]-2.50545581482197[/C][/ROW]
[ROW][C]90[/C][C]14[/C][C]15.4631880430515[/C][C]-1.46318804305149[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]14.3894658787945[/C][C]2.61053412120552[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]14.8432663472504[/C][C]0.156733652749603[/C][/ROW]
[ROW][C]93[/C][C]17[/C][C]12.9146170369047[/C][C]4.08538296309534[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]13.0957942989674[/C][C]-1.09579429896735[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.1334323683441[/C][C]1.86656763165585[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]12.7267172565135[/C][C]-1.72671725651345[/C][/ROW]
[ROW][C]97[/C][C]15[/C][C]14.2022880032944[/C][C]0.79771199670562[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]13.9461349596996[/C][C]-4.94613495969958[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]14.848208425065[/C][C]1.151791574935[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]12.6384430932132[/C][C]-2.63844309321322[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]11.9097875817659[/C][C]-1.9097875817659[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]14.6911359205765[/C][C]0.308864079423516[/C][/ROW]
[ROW][C]103[/C][C]11[/C][C]14.1189463288907[/C][C]-3.11894632889067[/C][/ROW]
[ROW][C]104[/C][C]13[/C][C]15.0133882751135[/C][C]-2.01338827511348[/C][/ROW]
[ROW][C]105[/C][C]14[/C][C]12.5461231480931[/C][C]1.45387685190692[/C][/ROW]
[ROW][C]106[/C][C]18[/C][C]15.2049641496919[/C][C]2.79503585030809[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]14.506627838058[/C][C]1.49337216194197[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]13.0726846832598[/C][C]0.927315316740199[/C][/ROW]
[ROW][C]109[/C][C]14[/C][C]14.1847437244906[/C][C]-0.18474372449059[/C][/ROW]
[ROW][C]110[/C][C]14[/C][C]14.5128154083329[/C][C]-0.512815408332923[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]14.0170569178191[/C][C]-0.0170569178190805[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]13.6018834798003[/C][C]-1.6018834798003[/C][/ROW]
[ROW][C]113[/C][C]14[/C][C]14.633031520702[/C][C]-0.633031520702015[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]15.0048416954307[/C][C]-0.00484169543073433[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]13.9909487181334[/C][C]1.00905128186662[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]12.1582021180912[/C][C]0.84179788190879[/C][/ROW]
[ROW][C]117[/C][C]17[/C][C]14.2047418933306[/C][C]2.79525810666936[/C][/ROW]
[ROW][C]118[/C][C]17[/C][C]14.5929638975792[/C][C]2.40703610242076[/C][/ROW]
[ROW][C]119[/C][C]19[/C][C]15.5996371750345[/C][C]3.40036282496553[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]14.5346364961072[/C][C]0.465363503892836[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]13.8401108452435[/C][C]-0.84011084524348[/C][/ROW]
[ROW][C]122[/C][C]9[/C][C]11.9320485170236[/C][C]-2.9320485170236[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]15.1418406191236[/C][C]-0.14184061912357[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]14.5914630993864[/C][C]0.408536900613566[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]15.266293596663[/C][C]0.733706403336993[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]13.3044149073808[/C][C]-2.30441490738084[/C][/ROW]
[ROW][C]127[/C][C]14[/C][C]13.6626050079972[/C][C]0.33739499200285[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]13.5778324867961[/C][C]-2.57783248679606[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]12.4537809612442[/C][C]2.54621903875583[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]12.8035839216858[/C][C]0.196416078314244[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]12.9074247518848[/C][C]3.09257524811517[/C][/ROW]
[ROW][C]132[/C][C]14[/C][C]15.6099354605564[/C][C]-1.60993546055644[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]13.9899869945072[/C][C]1.01001300549285[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]14.5181167515597[/C][C]1.48188324844031[/C][/ROW]
[ROW][C]135[/C][C]16[/C][C]14.987346998376[/C][C]1.01265300162401[/C][/ROW]
[ROW][C]136[/C][C]11[/C][C]12.4350752898802[/C][C]-1.43507528988019[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]13.4421651686227[/C][C]-0.442165168622658[/C][/ROW]
[ROW][C]138[/C][C]16[/C][C]14.2099774135199[/C][C]1.79002258648013[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]14.0979678174859[/C][C]-2.09796781748592[/C][/ROW]
[ROW][C]140[/C][C]9[/C][C]13.4928538066929[/C][C]-4.49285380669292[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]13.0329331774603[/C][C]-0.0329331774602952[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]16.2435482236232[/C][C]-3.24354822362315[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]14.7118651812982[/C][C]-0.71186518129823[/C][/ROW]
[ROW][C]144[/C][C]19[/C][C]15.5996371750345[/C][C]3.40036282496553[/C][/ROW]
[ROW][C]145[/C][C]13[/C][C]15.3188635876083[/C][C]-2.31886358760826[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112125&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112125&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
11415.1838316030313-1.18383160303134
21814.87588780741263.12411219258742
31115.5135306633869-4.5135306633869
41212.7854569899845-0.785456989984462
51614.91733371333571.08266628666429
61815.10618608482562.89381391517442
71414.4705801335575-0.47058013355749
81414.8354603760197-0.835460376019715
91514.89094225751440.109057742485613
101513.41444215163621.5855578483638
111712.87694348419874.12305651580128
121914.55974955392624.44025044607376
131012.5245842728517-2.52458427285174
141816.01849503927771.9815049607223
151411.21239000486922.78760999513078
161414.2168499394085-0.216849939408522
171714.97033832534082.02966167465919
181414.3251804549211-0.325180454921148
191614.43682561542731.56317438457275
201812.62260692996415.37739307003592
211414.5378711160821-0.537871116082105
221213.2521245540802-1.25212455408015
231712.67930975872894.32069024127114
24911.9547441598086-2.95474415980857
251614.20997741351991.79002258648013
261414.1827534970653-0.182753497065283
271113.3744282930834-2.37442829308335
281614.47349553725921.52650446274083
291313.1829533025515-0.182953302551449
301714.83914495921432.16085504078565
311515.0563228200996-0.0563228200995568
321414.3156949272231-0.315694927223065
331613.27574038316662.72425961683337
34912.063401921824-3.06340192182395
351513.68553478155921.3144652184408
361715.97663323189541.02336676810457
371312.98439130185520.0156086981447637
381513.02907639646191.97092360353807
391613.72393389791552.2760661020845
401615.81374662452520.186253375474805
411213.8711640835198-1.87116408351985
421113.4438827563934-2.44388275639338
431515.2488831074477-0.248883107447692
441715.11268322614771.88731677385229
451314.0853966498101-1.08539664981007
461613.62161765496342.37838234503663
471414.0281642750027-0.0281642750027282
481114.3217528344224-3.32175283442243
491213.5436559631263-1.54365596312634
501215.0266304790701-3.02663047907007
511515.1942063280719-0.194206328071937
521615.35121196592020.648788034079818
531515.1113719669578-0.111371966957816
541213.0078972731827-1.00789727318273
551213.9167541087356-1.91675410873562
56812.6239617231306-4.62396172313061
571314.3862312588195-1.38623125881954
581114.8003556334316-3.80035563343157
591415.3904512549226-1.39045125492265
601513.74451363578221.25548636421785
611013.9796685196258-3.97966851962584
621114.9184285830303-3.91842858303026
631213.3881385221454-1.38813852214537
641513.66210128020751.33789871979248
651514.55942022191670.440579778083251
661413.23195521499930.76804478500068
671612.70636394877423.29363605122582
681515.6057619488741-0.605761948874105
691515.4100005604537-0.410000560453672
701314.0790226252874-1.07902262528743
711714.75711564391122.2428843560888
721313.3163197666959-0.316319766695937
731513.81515992579991.18484007420015
741314.3141936282855-1.31419362828546
751513.13798295276981.86201704723019
761614.21291616183881.78708383816117
771514.72024967891350.279750321086532
781613.85247585401732.14752414598272
791514.50407763268390.495922367316138
801414.8077247998208-0.807724799820839
811512.7867586762962.21324132370395
82713.2415770178752-6.24157701787518
831715.48468344062411.51531655937586
841316.2435482236232-3.24354822362315
851514.54193873954820.45806126045178
861414.0916607159115-0.0916607159114886
871312.48249890681910.517501093180879
881614.97073209470921.02926790529078
891214.505455814822-2.50545581482197
901415.4631880430515-1.46318804305149
911714.38946587879452.61053412120552
921514.84326634725040.156733652749603
931712.91461703690474.08538296309534
941213.0957942989674-1.09579429896735
951614.13343236834411.86656763165585
961112.7267172565135-1.72671725651345
971514.20228800329440.79771199670562
98913.9461349596996-4.94613495969958
991614.8482084250651.151791574935
1001012.6384430932132-2.63844309321322
1011011.9097875817659-1.9097875817659
1021514.69113592057650.308864079423516
1031114.1189463288907-3.11894632889067
1041315.0133882751135-2.01338827511348
1051412.54612314809311.45387685190692
1061815.20496414969192.79503585030809
1071614.5066278380581.49337216194197
1081413.07268468325980.927315316740199
1091414.1847437244906-0.18474372449059
1101414.5128154083329-0.512815408332923
1111414.0170569178191-0.0170569178190805
1121213.6018834798003-1.6018834798003
1131414.633031520702-0.633031520702015
1141515.0048416954307-0.00484169543073433
1151513.99094871813341.00905128186662
1161312.15820211809120.84179788190879
1171714.20474189333062.79525810666936
1181714.59296389757922.40703610242076
1191915.59963717503453.40036282496553
1201514.53463649610720.465363503892836
1211313.8401108452435-0.84011084524348
122911.9320485170236-2.9320485170236
1231515.1418406191236-0.14184061912357
1241514.59146309938640.408536900613566
1251615.2662935966630.733706403336993
1261113.3044149073808-2.30441490738084
1271413.66260500799720.33739499200285
1281113.5778324867961-2.57783248679606
1291512.45378096124422.54621903875583
1301312.80358392168580.196416078314244
1311612.90742475188483.09257524811517
1321415.6099354605564-1.60993546055644
1331513.98998699450721.01001300549285
1341614.51811675155971.48188324844031
1351614.9873469983761.01265300162401
1361112.4350752898802-1.43507528988019
1371313.4421651686227-0.442165168622658
1381614.20997741351991.79002258648013
1391214.0979678174859-2.09796781748592
140913.4928538066929-4.49285380669292
1411313.0329331774603-0.0329331774602952
1421316.2435482236232-3.24354822362315
1431414.7118651812982-0.71186518129823
1441915.59963717503453.40036282496553
1451315.3188635876083-2.31886358760826






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.2497096611911540.4994193223823090.750290338808846
120.2105053697390870.4210107394781730.789494630260913
130.2320845650031270.4641691300062550.767915434996873
140.3979140591442420.7958281182884850.602085940855758
150.3405525336689040.6811050673378070.659447466331096
160.2411307777969670.4822615555939330.758869222203033
170.5685053132763290.8629893734473420.431494686723671
180.477143418819630.954286837639260.52285658118037
190.5316197417909790.9367605164180420.468380258209021
200.880272593369570.239454813260860.11972740663043
210.8588239759314020.2823520481371960.141176024068598
220.8565929125361130.2868141749277730.143407087463887
230.8900575383457260.2198849233085470.109942461654274
240.9269901301326660.1460197397346670.0730098698673336
250.9042259722009960.1915480555980080.0957740277990038
260.886954838147330.2260903237053390.11304516185267
270.9123359150993830.1753281698012330.0876640849006165
280.8940540313452090.2118919373095830.105945968654791
290.8925132696759280.2149734606481430.107486730324072
300.8840138655915070.2319722688169870.115986134408493
310.8639508344792030.2720983310415950.136049165520797
320.8319344192581550.336131161483690.168065580741845
330.8195058700368740.3609882599262520.180494129963126
340.8878804741593930.2242390516812130.112119525840607
350.8628699310129430.2742601379741140.137130068987057
360.8296483841415020.3407032317169960.170351615858498
370.7894328476575960.4211343046848090.210567152342404
380.7615928060437120.4768143879125770.238407193956288
390.7627963302421380.4744073395157230.237203669757862
400.7196986879833260.5606026240333480.280301312016674
410.6993855338425490.6012289323149020.300614466157451
420.733343664359830.5333126712803390.26665633564017
430.6890939278443040.6218121443113920.310906072155696
440.6644264719708880.6711470560582240.335573528029112
450.6254526657907390.7490946684185220.374547334209261
460.6147103857535580.7705792284928840.385289614246442
470.5851889966375930.8296220067248140.414811003362407
480.7114668017701230.5770663964597540.288533198229877
490.7024817296600430.5950365406799150.297518270339957
500.7472796836437380.5054406327125230.252720316356262
510.7036336855545690.5927326288908630.296366314445431
520.6593469740424820.6813060519150370.340653025957518
530.6189542857303170.7620914285393650.381045714269683
540.5854777700427740.8290444599144510.414522229957226
550.5665666486648290.8668667026703430.433433351335171
560.7575765373067720.4848469253864550.242423462693228
570.7311848847411960.5376302305176080.268815115258804
580.8045270977538560.3909458044922890.195472902246144
590.7908321645848610.4183356708302770.209167835415139
600.7651374312144660.4697251375710680.234862568785534
610.847696118963540.3046077620729190.152303881036459
620.9008366959400280.1983266081199440.099163304059972
630.8865337017423710.2269325965152570.113466298257629
640.8708934980866720.2582130038266570.129106501913328
650.8445751431719010.3108497136561980.155424856828099
660.8163176327890970.3673647344218060.183682367210903
670.8528690979301140.2942618041397720.147130902069886
680.825122749338790.3497545013224210.17487725066121
690.7933451446937760.4133097106124470.206654855306224
700.7687608746399120.4624782507201750.231239125360088
710.76852068284930.46295863430140.2314793171507
720.730247367631710.5395052647365810.26975263236829
730.7006434173953280.5987131652093450.299356582604672
740.6836344530401810.6327310939196370.316365546959819
750.6699730079417850.6600539841164310.330026992058215
760.6513469602014580.6973060795970840.348653039798542
770.6043218702850250.791356259429950.395678129714975
780.5993232632244180.8013534735511630.400676736775582
790.5537720098596330.8924559802807340.446227990140367
800.5118626739953560.9762746520092890.488137326004644
810.5156831305950540.968633738809890.484316869404946
820.7967464231861680.4065071536276630.203253576813832
830.7800976597317650.439804680536470.219902340268235
840.8211514267674210.3576971464651580.178848573232579
850.788076436374560.4238471272508780.211923563625439
860.7498813850448350.5002372299103290.250118614955165
870.7116297426250020.5767405147499950.288370257374998
880.6843006241517720.6313987516964560.315699375848228
890.6899707672688420.6200584654623160.310029232731158
900.6694441381482680.6611117237034640.330555861851732
910.6994322042886880.6011355914226240.300567795711312
920.6525442151702640.6949115696594720.347455784829736
930.8239344216918190.3521311566163630.176065578308181
940.7941130454958340.4117739090083330.205886954504166
950.7956070166502170.4087859666995650.204392983349783
960.777417712911420.445164574177160.22258228708858
970.7525588763752090.4948822472495830.247441123624791
980.8995403489469360.2009193021061280.100459651053064
990.8795893563719210.2408212872561570.120410643628079
1000.8791532132695710.2416935734608570.120846786730429
1010.863336637303990.2733267253920210.13666336269601
1020.8321781795597150.335643640880570.167821820440285
1030.862684777454960.274630445090080.13731522254504
1040.8866339560063670.2267320879872670.113366043993633
1050.891715929862090.216568140275820.10828407013791
1060.9085860510245450.1828278979509090.0914139489754545
1070.8849745832118570.2300508335762870.115025416788143
1080.8989646176273920.2020707647452160.101035382372608
1090.873400859807020.253198280385960.12659914019298
1100.902661080841910.194677838316180.0973389191580902
1110.8829562505870480.2340874988259040.117043749412952
1120.8512056553754830.2975886892490340.148794344624517
1130.810401890037170.3791962199256610.18959810996283
1140.7659761699762120.4680476600475770.234023830023788
1150.7244214854028160.5511570291943680.275578514597184
1160.6655754381788580.6688491236422850.334424561821142
1170.7132527411515180.5734945176969650.286747258848482
1180.690028965807620.619942068384760.30997103419238
1190.7037738182983440.5924523634033130.296226181701656
1200.8022129433252520.3955741133494950.197787056674748
1210.7498430051729310.5003139896541380.250156994827069
1220.8556847425784980.2886305148430030.144315257421502
1230.8216358144493190.3567283711013630.178364185550681
1240.782687972155570.4346240556888590.217312027844429
1250.7119520545844410.5760958908311180.288047945415559
1260.6649056992476850.670188601504630.335094300752315
1270.5768304950714150.846339009857170.423169504928585
1280.5328287288021370.9343425423957260.467171271197863
1290.4376338282128660.8752676564257310.562366171787134
1300.3452989663830090.6905979327660180.654701033616991
1310.4245580443337150.849116088667430.575441955666285
1320.5696083863657090.8607832272685810.430391613634291
1330.4337544574270570.8675089148541140.566245542572943
1340.6469732828913410.7060534342173180.353026717108659

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.249709661191154 & 0.499419322382309 & 0.750290338808846 \tabularnewline
12 & 0.210505369739087 & 0.421010739478173 & 0.789494630260913 \tabularnewline
13 & 0.232084565003127 & 0.464169130006255 & 0.767915434996873 \tabularnewline
14 & 0.397914059144242 & 0.795828118288485 & 0.602085940855758 \tabularnewline
15 & 0.340552533668904 & 0.681105067337807 & 0.659447466331096 \tabularnewline
16 & 0.241130777796967 & 0.482261555593933 & 0.758869222203033 \tabularnewline
17 & 0.568505313276329 & 0.862989373447342 & 0.431494686723671 \tabularnewline
18 & 0.47714341881963 & 0.95428683763926 & 0.52285658118037 \tabularnewline
19 & 0.531619741790979 & 0.936760516418042 & 0.468380258209021 \tabularnewline
20 & 0.88027259336957 & 0.23945481326086 & 0.11972740663043 \tabularnewline
21 & 0.858823975931402 & 0.282352048137196 & 0.141176024068598 \tabularnewline
22 & 0.856592912536113 & 0.286814174927773 & 0.143407087463887 \tabularnewline
23 & 0.890057538345726 & 0.219884923308547 & 0.109942461654274 \tabularnewline
24 & 0.926990130132666 & 0.146019739734667 & 0.0730098698673336 \tabularnewline
25 & 0.904225972200996 & 0.191548055598008 & 0.0957740277990038 \tabularnewline
26 & 0.88695483814733 & 0.226090323705339 & 0.11304516185267 \tabularnewline
27 & 0.912335915099383 & 0.175328169801233 & 0.0876640849006165 \tabularnewline
28 & 0.894054031345209 & 0.211891937309583 & 0.105945968654791 \tabularnewline
29 & 0.892513269675928 & 0.214973460648143 & 0.107486730324072 \tabularnewline
30 & 0.884013865591507 & 0.231972268816987 & 0.115986134408493 \tabularnewline
31 & 0.863950834479203 & 0.272098331041595 & 0.136049165520797 \tabularnewline
32 & 0.831934419258155 & 0.33613116148369 & 0.168065580741845 \tabularnewline
33 & 0.819505870036874 & 0.360988259926252 & 0.180494129963126 \tabularnewline
34 & 0.887880474159393 & 0.224239051681213 & 0.112119525840607 \tabularnewline
35 & 0.862869931012943 & 0.274260137974114 & 0.137130068987057 \tabularnewline
36 & 0.829648384141502 & 0.340703231716996 & 0.170351615858498 \tabularnewline
37 & 0.789432847657596 & 0.421134304684809 & 0.210567152342404 \tabularnewline
38 & 0.761592806043712 & 0.476814387912577 & 0.238407193956288 \tabularnewline
39 & 0.762796330242138 & 0.474407339515723 & 0.237203669757862 \tabularnewline
40 & 0.719698687983326 & 0.560602624033348 & 0.280301312016674 \tabularnewline
41 & 0.699385533842549 & 0.601228932314902 & 0.300614466157451 \tabularnewline
42 & 0.73334366435983 & 0.533312671280339 & 0.26665633564017 \tabularnewline
43 & 0.689093927844304 & 0.621812144311392 & 0.310906072155696 \tabularnewline
44 & 0.664426471970888 & 0.671147056058224 & 0.335573528029112 \tabularnewline
45 & 0.625452665790739 & 0.749094668418522 & 0.374547334209261 \tabularnewline
46 & 0.614710385753558 & 0.770579228492884 & 0.385289614246442 \tabularnewline
47 & 0.585188996637593 & 0.829622006724814 & 0.414811003362407 \tabularnewline
48 & 0.711466801770123 & 0.577066396459754 & 0.288533198229877 \tabularnewline
49 & 0.702481729660043 & 0.595036540679915 & 0.297518270339957 \tabularnewline
50 & 0.747279683643738 & 0.505440632712523 & 0.252720316356262 \tabularnewline
51 & 0.703633685554569 & 0.592732628890863 & 0.296366314445431 \tabularnewline
52 & 0.659346974042482 & 0.681306051915037 & 0.340653025957518 \tabularnewline
53 & 0.618954285730317 & 0.762091428539365 & 0.381045714269683 \tabularnewline
54 & 0.585477770042774 & 0.829044459914451 & 0.414522229957226 \tabularnewline
55 & 0.566566648664829 & 0.866866702670343 & 0.433433351335171 \tabularnewline
56 & 0.757576537306772 & 0.484846925386455 & 0.242423462693228 \tabularnewline
57 & 0.731184884741196 & 0.537630230517608 & 0.268815115258804 \tabularnewline
58 & 0.804527097753856 & 0.390945804492289 & 0.195472902246144 \tabularnewline
59 & 0.790832164584861 & 0.418335670830277 & 0.209167835415139 \tabularnewline
60 & 0.765137431214466 & 0.469725137571068 & 0.234862568785534 \tabularnewline
61 & 0.84769611896354 & 0.304607762072919 & 0.152303881036459 \tabularnewline
62 & 0.900836695940028 & 0.198326608119944 & 0.099163304059972 \tabularnewline
63 & 0.886533701742371 & 0.226932596515257 & 0.113466298257629 \tabularnewline
64 & 0.870893498086672 & 0.258213003826657 & 0.129106501913328 \tabularnewline
65 & 0.844575143171901 & 0.310849713656198 & 0.155424856828099 \tabularnewline
66 & 0.816317632789097 & 0.367364734421806 & 0.183682367210903 \tabularnewline
67 & 0.852869097930114 & 0.294261804139772 & 0.147130902069886 \tabularnewline
68 & 0.82512274933879 & 0.349754501322421 & 0.17487725066121 \tabularnewline
69 & 0.793345144693776 & 0.413309710612447 & 0.206654855306224 \tabularnewline
70 & 0.768760874639912 & 0.462478250720175 & 0.231239125360088 \tabularnewline
71 & 0.7685206828493 & 0.4629586343014 & 0.2314793171507 \tabularnewline
72 & 0.73024736763171 & 0.539505264736581 & 0.26975263236829 \tabularnewline
73 & 0.700643417395328 & 0.598713165209345 & 0.299356582604672 \tabularnewline
74 & 0.683634453040181 & 0.632731093919637 & 0.316365546959819 \tabularnewline
75 & 0.669973007941785 & 0.660053984116431 & 0.330026992058215 \tabularnewline
76 & 0.651346960201458 & 0.697306079597084 & 0.348653039798542 \tabularnewline
77 & 0.604321870285025 & 0.79135625942995 & 0.395678129714975 \tabularnewline
78 & 0.599323263224418 & 0.801353473551163 & 0.400676736775582 \tabularnewline
79 & 0.553772009859633 & 0.892455980280734 & 0.446227990140367 \tabularnewline
80 & 0.511862673995356 & 0.976274652009289 & 0.488137326004644 \tabularnewline
81 & 0.515683130595054 & 0.96863373880989 & 0.484316869404946 \tabularnewline
82 & 0.796746423186168 & 0.406507153627663 & 0.203253576813832 \tabularnewline
83 & 0.780097659731765 & 0.43980468053647 & 0.219902340268235 \tabularnewline
84 & 0.821151426767421 & 0.357697146465158 & 0.178848573232579 \tabularnewline
85 & 0.78807643637456 & 0.423847127250878 & 0.211923563625439 \tabularnewline
86 & 0.749881385044835 & 0.500237229910329 & 0.250118614955165 \tabularnewline
87 & 0.711629742625002 & 0.576740514749995 & 0.288370257374998 \tabularnewline
88 & 0.684300624151772 & 0.631398751696456 & 0.315699375848228 \tabularnewline
89 & 0.689970767268842 & 0.620058465462316 & 0.310029232731158 \tabularnewline
90 & 0.669444138148268 & 0.661111723703464 & 0.330555861851732 \tabularnewline
91 & 0.699432204288688 & 0.601135591422624 & 0.300567795711312 \tabularnewline
92 & 0.652544215170264 & 0.694911569659472 & 0.347455784829736 \tabularnewline
93 & 0.823934421691819 & 0.352131156616363 & 0.176065578308181 \tabularnewline
94 & 0.794113045495834 & 0.411773909008333 & 0.205886954504166 \tabularnewline
95 & 0.795607016650217 & 0.408785966699565 & 0.204392983349783 \tabularnewline
96 & 0.77741771291142 & 0.44516457417716 & 0.22258228708858 \tabularnewline
97 & 0.752558876375209 & 0.494882247249583 & 0.247441123624791 \tabularnewline
98 & 0.899540348946936 & 0.200919302106128 & 0.100459651053064 \tabularnewline
99 & 0.879589356371921 & 0.240821287256157 & 0.120410643628079 \tabularnewline
100 & 0.879153213269571 & 0.241693573460857 & 0.120846786730429 \tabularnewline
101 & 0.86333663730399 & 0.273326725392021 & 0.13666336269601 \tabularnewline
102 & 0.832178179559715 & 0.33564364088057 & 0.167821820440285 \tabularnewline
103 & 0.86268477745496 & 0.27463044509008 & 0.13731522254504 \tabularnewline
104 & 0.886633956006367 & 0.226732087987267 & 0.113366043993633 \tabularnewline
105 & 0.89171592986209 & 0.21656814027582 & 0.10828407013791 \tabularnewline
106 & 0.908586051024545 & 0.182827897950909 & 0.0914139489754545 \tabularnewline
107 & 0.884974583211857 & 0.230050833576287 & 0.115025416788143 \tabularnewline
108 & 0.898964617627392 & 0.202070764745216 & 0.101035382372608 \tabularnewline
109 & 0.87340085980702 & 0.25319828038596 & 0.12659914019298 \tabularnewline
110 & 0.90266108084191 & 0.19467783831618 & 0.0973389191580902 \tabularnewline
111 & 0.882956250587048 & 0.234087498825904 & 0.117043749412952 \tabularnewline
112 & 0.851205655375483 & 0.297588689249034 & 0.148794344624517 \tabularnewline
113 & 0.81040189003717 & 0.379196219925661 & 0.18959810996283 \tabularnewline
114 & 0.765976169976212 & 0.468047660047577 & 0.234023830023788 \tabularnewline
115 & 0.724421485402816 & 0.551157029194368 & 0.275578514597184 \tabularnewline
116 & 0.665575438178858 & 0.668849123642285 & 0.334424561821142 \tabularnewline
117 & 0.713252741151518 & 0.573494517696965 & 0.286747258848482 \tabularnewline
118 & 0.69002896580762 & 0.61994206838476 & 0.30997103419238 \tabularnewline
119 & 0.703773818298344 & 0.592452363403313 & 0.296226181701656 \tabularnewline
120 & 0.802212943325252 & 0.395574113349495 & 0.197787056674748 \tabularnewline
121 & 0.749843005172931 & 0.500313989654138 & 0.250156994827069 \tabularnewline
122 & 0.855684742578498 & 0.288630514843003 & 0.144315257421502 \tabularnewline
123 & 0.821635814449319 & 0.356728371101363 & 0.178364185550681 \tabularnewline
124 & 0.78268797215557 & 0.434624055688859 & 0.217312027844429 \tabularnewline
125 & 0.711952054584441 & 0.576095890831118 & 0.288047945415559 \tabularnewline
126 & 0.664905699247685 & 0.67018860150463 & 0.335094300752315 \tabularnewline
127 & 0.576830495071415 & 0.84633900985717 & 0.423169504928585 \tabularnewline
128 & 0.532828728802137 & 0.934342542395726 & 0.467171271197863 \tabularnewline
129 & 0.437633828212866 & 0.875267656425731 & 0.562366171787134 \tabularnewline
130 & 0.345298966383009 & 0.690597932766018 & 0.654701033616991 \tabularnewline
131 & 0.424558044333715 & 0.84911608866743 & 0.575441955666285 \tabularnewline
132 & 0.569608386365709 & 0.860783227268581 & 0.430391613634291 \tabularnewline
133 & 0.433754457427057 & 0.867508914854114 & 0.566245542572943 \tabularnewline
134 & 0.646973282891341 & 0.706053434217318 & 0.353026717108659 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112125&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]11[/C][C]0.249709661191154[/C][C]0.499419322382309[/C][C]0.750290338808846[/C][/ROW]
[ROW][C]12[/C][C]0.210505369739087[/C][C]0.421010739478173[/C][C]0.789494630260913[/C][/ROW]
[ROW][C]13[/C][C]0.232084565003127[/C][C]0.464169130006255[/C][C]0.767915434996873[/C][/ROW]
[ROW][C]14[/C][C]0.397914059144242[/C][C]0.795828118288485[/C][C]0.602085940855758[/C][/ROW]
[ROW][C]15[/C][C]0.340552533668904[/C][C]0.681105067337807[/C][C]0.659447466331096[/C][/ROW]
[ROW][C]16[/C][C]0.241130777796967[/C][C]0.482261555593933[/C][C]0.758869222203033[/C][/ROW]
[ROW][C]17[/C][C]0.568505313276329[/C][C]0.862989373447342[/C][C]0.431494686723671[/C][/ROW]
[ROW][C]18[/C][C]0.47714341881963[/C][C]0.95428683763926[/C][C]0.52285658118037[/C][/ROW]
[ROW][C]19[/C][C]0.531619741790979[/C][C]0.936760516418042[/C][C]0.468380258209021[/C][/ROW]
[ROW][C]20[/C][C]0.88027259336957[/C][C]0.23945481326086[/C][C]0.11972740663043[/C][/ROW]
[ROW][C]21[/C][C]0.858823975931402[/C][C]0.282352048137196[/C][C]0.141176024068598[/C][/ROW]
[ROW][C]22[/C][C]0.856592912536113[/C][C]0.286814174927773[/C][C]0.143407087463887[/C][/ROW]
[ROW][C]23[/C][C]0.890057538345726[/C][C]0.219884923308547[/C][C]0.109942461654274[/C][/ROW]
[ROW][C]24[/C][C]0.926990130132666[/C][C]0.146019739734667[/C][C]0.0730098698673336[/C][/ROW]
[ROW][C]25[/C][C]0.904225972200996[/C][C]0.191548055598008[/C][C]0.0957740277990038[/C][/ROW]
[ROW][C]26[/C][C]0.88695483814733[/C][C]0.226090323705339[/C][C]0.11304516185267[/C][/ROW]
[ROW][C]27[/C][C]0.912335915099383[/C][C]0.175328169801233[/C][C]0.0876640849006165[/C][/ROW]
[ROW][C]28[/C][C]0.894054031345209[/C][C]0.211891937309583[/C][C]0.105945968654791[/C][/ROW]
[ROW][C]29[/C][C]0.892513269675928[/C][C]0.214973460648143[/C][C]0.107486730324072[/C][/ROW]
[ROW][C]30[/C][C]0.884013865591507[/C][C]0.231972268816987[/C][C]0.115986134408493[/C][/ROW]
[ROW][C]31[/C][C]0.863950834479203[/C][C]0.272098331041595[/C][C]0.136049165520797[/C][/ROW]
[ROW][C]32[/C][C]0.831934419258155[/C][C]0.33613116148369[/C][C]0.168065580741845[/C][/ROW]
[ROW][C]33[/C][C]0.819505870036874[/C][C]0.360988259926252[/C][C]0.180494129963126[/C][/ROW]
[ROW][C]34[/C][C]0.887880474159393[/C][C]0.224239051681213[/C][C]0.112119525840607[/C][/ROW]
[ROW][C]35[/C][C]0.862869931012943[/C][C]0.274260137974114[/C][C]0.137130068987057[/C][/ROW]
[ROW][C]36[/C][C]0.829648384141502[/C][C]0.340703231716996[/C][C]0.170351615858498[/C][/ROW]
[ROW][C]37[/C][C]0.789432847657596[/C][C]0.421134304684809[/C][C]0.210567152342404[/C][/ROW]
[ROW][C]38[/C][C]0.761592806043712[/C][C]0.476814387912577[/C][C]0.238407193956288[/C][/ROW]
[ROW][C]39[/C][C]0.762796330242138[/C][C]0.474407339515723[/C][C]0.237203669757862[/C][/ROW]
[ROW][C]40[/C][C]0.719698687983326[/C][C]0.560602624033348[/C][C]0.280301312016674[/C][/ROW]
[ROW][C]41[/C][C]0.699385533842549[/C][C]0.601228932314902[/C][C]0.300614466157451[/C][/ROW]
[ROW][C]42[/C][C]0.73334366435983[/C][C]0.533312671280339[/C][C]0.26665633564017[/C][/ROW]
[ROW][C]43[/C][C]0.689093927844304[/C][C]0.621812144311392[/C][C]0.310906072155696[/C][/ROW]
[ROW][C]44[/C][C]0.664426471970888[/C][C]0.671147056058224[/C][C]0.335573528029112[/C][/ROW]
[ROW][C]45[/C][C]0.625452665790739[/C][C]0.749094668418522[/C][C]0.374547334209261[/C][/ROW]
[ROW][C]46[/C][C]0.614710385753558[/C][C]0.770579228492884[/C][C]0.385289614246442[/C][/ROW]
[ROW][C]47[/C][C]0.585188996637593[/C][C]0.829622006724814[/C][C]0.414811003362407[/C][/ROW]
[ROW][C]48[/C][C]0.711466801770123[/C][C]0.577066396459754[/C][C]0.288533198229877[/C][/ROW]
[ROW][C]49[/C][C]0.702481729660043[/C][C]0.595036540679915[/C][C]0.297518270339957[/C][/ROW]
[ROW][C]50[/C][C]0.747279683643738[/C][C]0.505440632712523[/C][C]0.252720316356262[/C][/ROW]
[ROW][C]51[/C][C]0.703633685554569[/C][C]0.592732628890863[/C][C]0.296366314445431[/C][/ROW]
[ROW][C]52[/C][C]0.659346974042482[/C][C]0.681306051915037[/C][C]0.340653025957518[/C][/ROW]
[ROW][C]53[/C][C]0.618954285730317[/C][C]0.762091428539365[/C][C]0.381045714269683[/C][/ROW]
[ROW][C]54[/C][C]0.585477770042774[/C][C]0.829044459914451[/C][C]0.414522229957226[/C][/ROW]
[ROW][C]55[/C][C]0.566566648664829[/C][C]0.866866702670343[/C][C]0.433433351335171[/C][/ROW]
[ROW][C]56[/C][C]0.757576537306772[/C][C]0.484846925386455[/C][C]0.242423462693228[/C][/ROW]
[ROW][C]57[/C][C]0.731184884741196[/C][C]0.537630230517608[/C][C]0.268815115258804[/C][/ROW]
[ROW][C]58[/C][C]0.804527097753856[/C][C]0.390945804492289[/C][C]0.195472902246144[/C][/ROW]
[ROW][C]59[/C][C]0.790832164584861[/C][C]0.418335670830277[/C][C]0.209167835415139[/C][/ROW]
[ROW][C]60[/C][C]0.765137431214466[/C][C]0.469725137571068[/C][C]0.234862568785534[/C][/ROW]
[ROW][C]61[/C][C]0.84769611896354[/C][C]0.304607762072919[/C][C]0.152303881036459[/C][/ROW]
[ROW][C]62[/C][C]0.900836695940028[/C][C]0.198326608119944[/C][C]0.099163304059972[/C][/ROW]
[ROW][C]63[/C][C]0.886533701742371[/C][C]0.226932596515257[/C][C]0.113466298257629[/C][/ROW]
[ROW][C]64[/C][C]0.870893498086672[/C][C]0.258213003826657[/C][C]0.129106501913328[/C][/ROW]
[ROW][C]65[/C][C]0.844575143171901[/C][C]0.310849713656198[/C][C]0.155424856828099[/C][/ROW]
[ROW][C]66[/C][C]0.816317632789097[/C][C]0.367364734421806[/C][C]0.183682367210903[/C][/ROW]
[ROW][C]67[/C][C]0.852869097930114[/C][C]0.294261804139772[/C][C]0.147130902069886[/C][/ROW]
[ROW][C]68[/C][C]0.82512274933879[/C][C]0.349754501322421[/C][C]0.17487725066121[/C][/ROW]
[ROW][C]69[/C][C]0.793345144693776[/C][C]0.413309710612447[/C][C]0.206654855306224[/C][/ROW]
[ROW][C]70[/C][C]0.768760874639912[/C][C]0.462478250720175[/C][C]0.231239125360088[/C][/ROW]
[ROW][C]71[/C][C]0.7685206828493[/C][C]0.4629586343014[/C][C]0.2314793171507[/C][/ROW]
[ROW][C]72[/C][C]0.73024736763171[/C][C]0.539505264736581[/C][C]0.26975263236829[/C][/ROW]
[ROW][C]73[/C][C]0.700643417395328[/C][C]0.598713165209345[/C][C]0.299356582604672[/C][/ROW]
[ROW][C]74[/C][C]0.683634453040181[/C][C]0.632731093919637[/C][C]0.316365546959819[/C][/ROW]
[ROW][C]75[/C][C]0.669973007941785[/C][C]0.660053984116431[/C][C]0.330026992058215[/C][/ROW]
[ROW][C]76[/C][C]0.651346960201458[/C][C]0.697306079597084[/C][C]0.348653039798542[/C][/ROW]
[ROW][C]77[/C][C]0.604321870285025[/C][C]0.79135625942995[/C][C]0.395678129714975[/C][/ROW]
[ROW][C]78[/C][C]0.599323263224418[/C][C]0.801353473551163[/C][C]0.400676736775582[/C][/ROW]
[ROW][C]79[/C][C]0.553772009859633[/C][C]0.892455980280734[/C][C]0.446227990140367[/C][/ROW]
[ROW][C]80[/C][C]0.511862673995356[/C][C]0.976274652009289[/C][C]0.488137326004644[/C][/ROW]
[ROW][C]81[/C][C]0.515683130595054[/C][C]0.96863373880989[/C][C]0.484316869404946[/C][/ROW]
[ROW][C]82[/C][C]0.796746423186168[/C][C]0.406507153627663[/C][C]0.203253576813832[/C][/ROW]
[ROW][C]83[/C][C]0.780097659731765[/C][C]0.43980468053647[/C][C]0.219902340268235[/C][/ROW]
[ROW][C]84[/C][C]0.821151426767421[/C][C]0.357697146465158[/C][C]0.178848573232579[/C][/ROW]
[ROW][C]85[/C][C]0.78807643637456[/C][C]0.423847127250878[/C][C]0.211923563625439[/C][/ROW]
[ROW][C]86[/C][C]0.749881385044835[/C][C]0.500237229910329[/C][C]0.250118614955165[/C][/ROW]
[ROW][C]87[/C][C]0.711629742625002[/C][C]0.576740514749995[/C][C]0.288370257374998[/C][/ROW]
[ROW][C]88[/C][C]0.684300624151772[/C][C]0.631398751696456[/C][C]0.315699375848228[/C][/ROW]
[ROW][C]89[/C][C]0.689970767268842[/C][C]0.620058465462316[/C][C]0.310029232731158[/C][/ROW]
[ROW][C]90[/C][C]0.669444138148268[/C][C]0.661111723703464[/C][C]0.330555861851732[/C][/ROW]
[ROW][C]91[/C][C]0.699432204288688[/C][C]0.601135591422624[/C][C]0.300567795711312[/C][/ROW]
[ROW][C]92[/C][C]0.652544215170264[/C][C]0.694911569659472[/C][C]0.347455784829736[/C][/ROW]
[ROW][C]93[/C][C]0.823934421691819[/C][C]0.352131156616363[/C][C]0.176065578308181[/C][/ROW]
[ROW][C]94[/C][C]0.794113045495834[/C][C]0.411773909008333[/C][C]0.205886954504166[/C][/ROW]
[ROW][C]95[/C][C]0.795607016650217[/C][C]0.408785966699565[/C][C]0.204392983349783[/C][/ROW]
[ROW][C]96[/C][C]0.77741771291142[/C][C]0.44516457417716[/C][C]0.22258228708858[/C][/ROW]
[ROW][C]97[/C][C]0.752558876375209[/C][C]0.494882247249583[/C][C]0.247441123624791[/C][/ROW]
[ROW][C]98[/C][C]0.899540348946936[/C][C]0.200919302106128[/C][C]0.100459651053064[/C][/ROW]
[ROW][C]99[/C][C]0.879589356371921[/C][C]0.240821287256157[/C][C]0.120410643628079[/C][/ROW]
[ROW][C]100[/C][C]0.879153213269571[/C][C]0.241693573460857[/C][C]0.120846786730429[/C][/ROW]
[ROW][C]101[/C][C]0.86333663730399[/C][C]0.273326725392021[/C][C]0.13666336269601[/C][/ROW]
[ROW][C]102[/C][C]0.832178179559715[/C][C]0.33564364088057[/C][C]0.167821820440285[/C][/ROW]
[ROW][C]103[/C][C]0.86268477745496[/C][C]0.27463044509008[/C][C]0.13731522254504[/C][/ROW]
[ROW][C]104[/C][C]0.886633956006367[/C][C]0.226732087987267[/C][C]0.113366043993633[/C][/ROW]
[ROW][C]105[/C][C]0.89171592986209[/C][C]0.21656814027582[/C][C]0.10828407013791[/C][/ROW]
[ROW][C]106[/C][C]0.908586051024545[/C][C]0.182827897950909[/C][C]0.0914139489754545[/C][/ROW]
[ROW][C]107[/C][C]0.884974583211857[/C][C]0.230050833576287[/C][C]0.115025416788143[/C][/ROW]
[ROW][C]108[/C][C]0.898964617627392[/C][C]0.202070764745216[/C][C]0.101035382372608[/C][/ROW]
[ROW][C]109[/C][C]0.87340085980702[/C][C]0.25319828038596[/C][C]0.12659914019298[/C][/ROW]
[ROW][C]110[/C][C]0.90266108084191[/C][C]0.19467783831618[/C][C]0.0973389191580902[/C][/ROW]
[ROW][C]111[/C][C]0.882956250587048[/C][C]0.234087498825904[/C][C]0.117043749412952[/C][/ROW]
[ROW][C]112[/C][C]0.851205655375483[/C][C]0.297588689249034[/C][C]0.148794344624517[/C][/ROW]
[ROW][C]113[/C][C]0.81040189003717[/C][C]0.379196219925661[/C][C]0.18959810996283[/C][/ROW]
[ROW][C]114[/C][C]0.765976169976212[/C][C]0.468047660047577[/C][C]0.234023830023788[/C][/ROW]
[ROW][C]115[/C][C]0.724421485402816[/C][C]0.551157029194368[/C][C]0.275578514597184[/C][/ROW]
[ROW][C]116[/C][C]0.665575438178858[/C][C]0.668849123642285[/C][C]0.334424561821142[/C][/ROW]
[ROW][C]117[/C][C]0.713252741151518[/C][C]0.573494517696965[/C][C]0.286747258848482[/C][/ROW]
[ROW][C]118[/C][C]0.69002896580762[/C][C]0.61994206838476[/C][C]0.30997103419238[/C][/ROW]
[ROW][C]119[/C][C]0.703773818298344[/C][C]0.592452363403313[/C][C]0.296226181701656[/C][/ROW]
[ROW][C]120[/C][C]0.802212943325252[/C][C]0.395574113349495[/C][C]0.197787056674748[/C][/ROW]
[ROW][C]121[/C][C]0.749843005172931[/C][C]0.500313989654138[/C][C]0.250156994827069[/C][/ROW]
[ROW][C]122[/C][C]0.855684742578498[/C][C]0.288630514843003[/C][C]0.144315257421502[/C][/ROW]
[ROW][C]123[/C][C]0.821635814449319[/C][C]0.356728371101363[/C][C]0.178364185550681[/C][/ROW]
[ROW][C]124[/C][C]0.78268797215557[/C][C]0.434624055688859[/C][C]0.217312027844429[/C][/ROW]
[ROW][C]125[/C][C]0.711952054584441[/C][C]0.576095890831118[/C][C]0.288047945415559[/C][/ROW]
[ROW][C]126[/C][C]0.664905699247685[/C][C]0.67018860150463[/C][C]0.335094300752315[/C][/ROW]
[ROW][C]127[/C][C]0.576830495071415[/C][C]0.84633900985717[/C][C]0.423169504928585[/C][/ROW]
[ROW][C]128[/C][C]0.532828728802137[/C][C]0.934342542395726[/C][C]0.467171271197863[/C][/ROW]
[ROW][C]129[/C][C]0.437633828212866[/C][C]0.875267656425731[/C][C]0.562366171787134[/C][/ROW]
[ROW][C]130[/C][C]0.345298966383009[/C][C]0.690597932766018[/C][C]0.654701033616991[/C][/ROW]
[ROW][C]131[/C][C]0.424558044333715[/C][C]0.84911608866743[/C][C]0.575441955666285[/C][/ROW]
[ROW][C]132[/C][C]0.569608386365709[/C][C]0.860783227268581[/C][C]0.430391613634291[/C][/ROW]
[ROW][C]133[/C][C]0.433754457427057[/C][C]0.867508914854114[/C][C]0.566245542572943[/C][/ROW]
[ROW][C]134[/C][C]0.646973282891341[/C][C]0.706053434217318[/C][C]0.353026717108659[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112125&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112125&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
110.2497096611911540.4994193223823090.750290338808846
120.2105053697390870.4210107394781730.789494630260913
130.2320845650031270.4641691300062550.767915434996873
140.3979140591442420.7958281182884850.602085940855758
150.3405525336689040.6811050673378070.659447466331096
160.2411307777969670.4822615555939330.758869222203033
170.5685053132763290.8629893734473420.431494686723671
180.477143418819630.954286837639260.52285658118037
190.5316197417909790.9367605164180420.468380258209021
200.880272593369570.239454813260860.11972740663043
210.8588239759314020.2823520481371960.141176024068598
220.8565929125361130.2868141749277730.143407087463887
230.8900575383457260.2198849233085470.109942461654274
240.9269901301326660.1460197397346670.0730098698673336
250.9042259722009960.1915480555980080.0957740277990038
260.886954838147330.2260903237053390.11304516185267
270.9123359150993830.1753281698012330.0876640849006165
280.8940540313452090.2118919373095830.105945968654791
290.8925132696759280.2149734606481430.107486730324072
300.8840138655915070.2319722688169870.115986134408493
310.8639508344792030.2720983310415950.136049165520797
320.8319344192581550.336131161483690.168065580741845
330.8195058700368740.3609882599262520.180494129963126
340.8878804741593930.2242390516812130.112119525840607
350.8628699310129430.2742601379741140.137130068987057
360.8296483841415020.3407032317169960.170351615858498
370.7894328476575960.4211343046848090.210567152342404
380.7615928060437120.4768143879125770.238407193956288
390.7627963302421380.4744073395157230.237203669757862
400.7196986879833260.5606026240333480.280301312016674
410.6993855338425490.6012289323149020.300614466157451
420.733343664359830.5333126712803390.26665633564017
430.6890939278443040.6218121443113920.310906072155696
440.6644264719708880.6711470560582240.335573528029112
450.6254526657907390.7490946684185220.374547334209261
460.6147103857535580.7705792284928840.385289614246442
470.5851889966375930.8296220067248140.414811003362407
480.7114668017701230.5770663964597540.288533198229877
490.7024817296600430.5950365406799150.297518270339957
500.7472796836437380.5054406327125230.252720316356262
510.7036336855545690.5927326288908630.296366314445431
520.6593469740424820.6813060519150370.340653025957518
530.6189542857303170.7620914285393650.381045714269683
540.5854777700427740.8290444599144510.414522229957226
550.5665666486648290.8668667026703430.433433351335171
560.7575765373067720.4848469253864550.242423462693228
570.7311848847411960.5376302305176080.268815115258804
580.8045270977538560.3909458044922890.195472902246144
590.7908321645848610.4183356708302770.209167835415139
600.7651374312144660.4697251375710680.234862568785534
610.847696118963540.3046077620729190.152303881036459
620.9008366959400280.1983266081199440.099163304059972
630.8865337017423710.2269325965152570.113466298257629
640.8708934980866720.2582130038266570.129106501913328
650.8445751431719010.3108497136561980.155424856828099
660.8163176327890970.3673647344218060.183682367210903
670.8528690979301140.2942618041397720.147130902069886
680.825122749338790.3497545013224210.17487725066121
690.7933451446937760.4133097106124470.206654855306224
700.7687608746399120.4624782507201750.231239125360088
710.76852068284930.46295863430140.2314793171507
720.730247367631710.5395052647365810.26975263236829
730.7006434173953280.5987131652093450.299356582604672
740.6836344530401810.6327310939196370.316365546959819
750.6699730079417850.6600539841164310.330026992058215
760.6513469602014580.6973060795970840.348653039798542
770.6043218702850250.791356259429950.395678129714975
780.5993232632244180.8013534735511630.400676736775582
790.5537720098596330.8924559802807340.446227990140367
800.5118626739953560.9762746520092890.488137326004644
810.5156831305950540.968633738809890.484316869404946
820.7967464231861680.4065071536276630.203253576813832
830.7800976597317650.439804680536470.219902340268235
840.8211514267674210.3576971464651580.178848573232579
850.788076436374560.4238471272508780.211923563625439
860.7498813850448350.5002372299103290.250118614955165
870.7116297426250020.5767405147499950.288370257374998
880.6843006241517720.6313987516964560.315699375848228
890.6899707672688420.6200584654623160.310029232731158
900.6694441381482680.6611117237034640.330555861851732
910.6994322042886880.6011355914226240.300567795711312
920.6525442151702640.6949115696594720.347455784829736
930.8239344216918190.3521311566163630.176065578308181
940.7941130454958340.4117739090083330.205886954504166
950.7956070166502170.4087859666995650.204392983349783
960.777417712911420.445164574177160.22258228708858
970.7525588763752090.4948822472495830.247441123624791
980.8995403489469360.2009193021061280.100459651053064
990.8795893563719210.2408212872561570.120410643628079
1000.8791532132695710.2416935734608570.120846786730429
1010.863336637303990.2733267253920210.13666336269601
1020.8321781795597150.335643640880570.167821820440285
1030.862684777454960.274630445090080.13731522254504
1040.8866339560063670.2267320879872670.113366043993633
1050.891715929862090.216568140275820.10828407013791
1060.9085860510245450.1828278979509090.0914139489754545
1070.8849745832118570.2300508335762870.115025416788143
1080.8989646176273920.2020707647452160.101035382372608
1090.873400859807020.253198280385960.12659914019298
1100.902661080841910.194677838316180.0973389191580902
1110.8829562505870480.2340874988259040.117043749412952
1120.8512056553754830.2975886892490340.148794344624517
1130.810401890037170.3791962199256610.18959810996283
1140.7659761699762120.4680476600475770.234023830023788
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1200.8022129433252520.3955741133494950.197787056674748
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1230.8216358144493190.3567283711013630.178364185550681
1240.782687972155570.4346240556888590.217312027844429
1250.7119520545844410.5760958908311180.288047945415559
1260.6649056992476850.670188601504630.335094300752315
1270.5768304950714150.846339009857170.423169504928585
1280.5328287288021370.9343425423957260.467171271197863
1290.4376338282128660.8752676564257310.562366171787134
1300.3452989663830090.6905979327660180.654701033616991
1310.4245580443337150.849116088667430.575441955666285
1320.5696083863657090.8607832272685810.430391613634291
1330.4337544574270570.8675089148541140.566245542572943
1340.6469732828913410.7060534342173180.353026717108659






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112125&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112125&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112125&T=6

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

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


Figures (Output of Computation)

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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 ;
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')
}