FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 23:03:08 +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/19/t1292713281vhw0cz1y7ptnz94.htm/, Retrieved Sun, 05 May 2024 05:22:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112215, Retrieved Sun, 05 May 2024 05:22:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact165

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 18:05:27] [87116ee6ef949037dfa02b8eb1a3bf97]
-    D        [Multiple Regression] [MR] [2010-12-18 23:03:08] [66b4703b90a9701067ac75b10c82aca9] [Current]
Feedback Forum

Post a new message

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 time11 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 11 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112215&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112215&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112215&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 time11 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 18.9957165366223 -0.0278024991470845Popularity[t] + 0.0455463905768971KnowingPeople[t] -0.00637402452264036CMistakes[t] -0.280840510374423DAction[t] + 0.181746271876873Tobacco[t] -0.914459276859248Drugs[t] -0.762811775254921Gender[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  18.9957165366223 -0.0278024991470845Popularity[t] +  0.0455463905768971KnowingPeople[t] -0.00637402452264036CMistakes[t] -0.280840510374423DAction[t] +  0.181746271876873Tobacco[t] -0.914459276859248Drugs[t] -0.762811775254921Gender[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112215&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  18.9957165366223 -0.0278024991470845Popularity[t] +  0.0455463905768971KnowingPeople[t] -0.00637402452264036CMistakes[t] -0.280840510374423DAction[t] +  0.181746271876873Tobacco[t] -0.914459276859248Drugs[t] -0.762811775254921Gender[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112215&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112215&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 -0.0278024991470845Popularity[t] + 0.0455463905768971KnowingPeople[t] -0.00637402452264036CMistakes[t] -0.280840510374423DAction[t] + 0.181746271876873Tobacco[t] -0.914459276859248Drugs[t] -0.762811775254921Gender[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-0.02780249914708450.078309-0.3550.7231080.361554
KnowingPeople0.04554639057689710.0661990.6880.4926050.246302
CMistakes-0.006374024522640360.035558-0.17930.8579990.429
DAction-0.2808405103744230.073563-3.81770.0002030.000102
Tobacco0.1817462718768730.2018330.90050.3694470.184723
Drugs-0.9144592768592480.368378-2.48240.0142590.007129
Gender-0.7628117752549210.401175-1.90140.0593430.029672

\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 & -0.0278024991470845 & 0.078309 & -0.355 & 0.723108 & 0.361554 \tabularnewline
KnowingPeople & 0.0455463905768971 & 0.066199 & 0.688 & 0.492605 & 0.246302 \tabularnewline
CMistakes & -0.00637402452264036 & 0.035558 & -0.1793 & 0.857999 & 0.429 \tabularnewline
DAction & -0.280840510374423 & 0.073563 & -3.8177 & 0.000203 & 0.000102 \tabularnewline
Tobacco & 0.181746271876873 & 0.201833 & 0.9005 & 0.369447 & 0.184723 \tabularnewline
Drugs & -0.914459276859248 & 0.368378 & -2.4824 & 0.014259 & 0.007129 \tabularnewline
Gender & -0.762811775254921 & 0.401175 & -1.9014 & 0.059343 & 0.029672 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112215&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]-0.0278024991470845[/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]-0.00637402452264036[/C][C]0.035558[/C][C]-0.1793[/C][C]0.857999[/C][C]0.429[/C][/ROW]
[ROW][C]DAction[/C][C]-0.280840510374423[/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]-0.914459276859248[/C][C]0.368378[/C][C]-2.4824[/C][C]0.014259[/C][C]0.007129[/C][/ROW]
[ROW][C]Gender[/C][C]-0.762811775254921[/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=112215&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112215&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-0.02780249914708450.078309-0.3550.7231080.361554
KnowingPeople0.04554639057689710.0661990.6880.4926050.246302
CMistakes-0.006374024522640360.035558-0.17930.8579990.429
DAction-0.2808405103744230.073563-3.81770.0002030.000102
Tobacco0.1817462718768730.2018330.90050.3694470.184723
Drugs-0.9144592768592480.368378-2.48240.0142590.007129
Gender-0.7628117752549210.401175-1.90140.0593430.029672






Multiple Linear Regression - Regression Statistics
Multiple R0.430048346921455
R-squared0.184941580689876
Adjusted R-squared0.143296259995198
F-TEST (value)4.44087301057838
F-TEST (DF numerator)7
F-TEST (DF denominator)137
p-value0.000177261813259388
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.19879831012477
Sum Squared Residuals662.355819179236

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.430048346921455 \tabularnewline
R-squared & 0.184941580689876 \tabularnewline
Adjusted R-squared & 0.143296259995198 \tabularnewline
F-TEST (value) & 4.44087301057838 \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 & 662.355819179236 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112215&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]0.143296259995198[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.44087301057838[/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]662.355819179236[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112215&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112215&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-squared0.143296259995198
F-TEST (value)4.44087301057838
F-TEST (DF numerator)7
F-TEST (DF denominator)137
p-value0.000177261813259388
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.19879831012477
Sum Squared Residuals662.355819179236






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11415.1838316030315-1.18383160303153
21814.87588780741263.12411219258743
31115.5135306633869-4.51353066338689
41212.7854569899845-0.785456989984458
51614.91733371333571.08266628666430
61815.10618608482562.89381391517442
71414.4705801335575-0.470580133557483
81414.8354603760197-0.835460376019714
91514.89094225751440.109057742485617
101513.41444215163621.5855578483638
111712.87694348419874.12305651580128
121914.55974955392624.44025044607376
131012.5245842728517-2.52458427285174
141816.01849503927771.98150496072231
151411.21239000486922.78760999513077
161414.2168499394085-0.216849939408523
171714.97033832534082.02966167465919
181414.3251804549211-0.325180454921146
191614.43682561542721.56317438457275
201812.62260692996415.37739307003592
211414.5378711160821-0.537871116082101
221213.2521245540801-1.25212455408015
231712.67930975872894.32069024127114
24911.9547441598086-2.95474415980858
251614.20997741351991.79002258648013
261414.1827534970653-0.182753497065282
271113.3744282930834-2.37442829308336
281614.47349553725921.52650446274084
291313.1829533025514-0.182953302551449
301714.83914495921432.16085504078566
311515.0563228200996-0.0563228200995524
321414.3156949272231-0.315694927223066
331613.27574038316662.72425961683337
34912.0634019218240-3.06340192182396
351513.68553478155921.31446521844080
361715.97663323189541.02336676810457
371312.98439130185520.0156086981447623
381513.02907639646191.97092360353807
391613.72393389791552.27606610208450
401615.81374662452520.186253375474806
411213.8711640835198-1.87116408351985
421113.4438827563934-2.44388275639338
431515.2488831074477-0.248883107447689
441715.11268322614771.88731677385229
451314.0853966498101-1.08539664981007
461613.62161765496342.37838234503664
471414.0281642750027-0.0281642750027268
481114.3217528344224-3.32175283442243
491213.5436559631263-1.54365596312634
501215.0266304790701-3.02663047907007
511515.1942063280719-0.194206328071934
521615.35121196592020.648788034079823
531515.1113719669578-0.111371966957814
541213.0078972731827-1.00789727318273
551213.9167541087356-1.91675410873562
56812.6239617231306-4.62396172313061
571314.3862312588195-1.38623125881954
581114.8003556334316-3.80035563343157
591415.3904512549226-1.39045125492264
601513.74451363578221.25548636421785
611013.9796685196258-3.97966851962584
621114.9184285830303-3.91842858303025
631213.3881385221454-1.38813852214537
641513.66210128020751.33789871979248
651514.55942022191670.440579778083251
661413.23195521499930.768044785000675
671612.70636394877423.29363605122582
681515.6057619488741-0.605761948874102
691515.4100005604537-0.410000560453668
701314.0790226252874-1.07902262528743
711714.75711564391122.2428843560888
721313.3163197666959-0.316319766695935
731513.81515992579991.18484007420015
741314.3141936282855-1.31419362828546
751513.13798295276981.86201704723019
761614.21291616183881.78708383816118
771514.72024967891350.279750321086534
781613.85247585401732.14752414598272
791514.50407763268390.495922367316139
801414.8077247998208-0.807724799820837
811512.78675867629602.21324132370396
82713.2415770178752-6.24157701787518
831715.48468344062411.51531655937586
841316.2435482236231-3.24354822362315
851514.54193873954820.458061260451782
861414.0916607159115-0.0916607159114877
871312.48249890681910.517501093180875
881614.97073209470921.02926790529078
891214.5054558148220-2.50545581482197
901415.4631880430515-1.46318804305149
911714.38946587879452.61053412120552
921514.84326634725040.156733652749603
931712.91461703690474.08538296309534
941213.0957942989674-1.09579429896735
951614.13343236834411.86656763165585
961112.7267172565135-1.72671725651346
971514.20228800329440.797711996705624
98913.9461349596996-4.94613495969958
991614.8482084250651.15179157493501
1001012.6384430932132-2.63844309321322
1011011.9097875817659-1.9097875817659
1021514.69113592057650.308864079423518
1031114.1189463288907-3.11894632889067
1041315.0133882751135-2.01338827511348
1051412.54612314809311.45387685190692
1061815.20496414969192.79503585030809
1071614.50662783805801.49337216194197
1081413.07268468325980.927315316740201
1091414.1847437244906-0.184743724490588
1101414.5128154083329-0.512815408332917
1111414.0170569178191-0.0170569178190783
1121213.6018834798003-1.60188347980030
1131414.6330315207020-0.633031520702012
1141515.0048416954307-0.0048416954307306
1151513.99094871813341.00905128186662
1161312.15820211809120.841797881908786
1171714.20474189333062.79525810666936
1181714.59296389757922.40703610242076
1191915.59963717503453.40036282496553
1201514.53463649610720.465363503892837
1211313.8401108452435-0.840110845243483
122911.9320485170236-2.93204851702361
1231515.1418406191236-0.141840619123571
1241514.59146309938640.408536900613567
1251615.2662935966630.733706403337001
1261113.3044149073808-2.30441490738084
1271413.66260500799720.337394992002850
1281113.5778324867961-2.57783248679606
1291512.45378096124422.54621903875583
1301312.80358392168580.196416078314241
1311612.90742475188483.09257524811517
1321415.6099354605564-1.60993546055643
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
1411313.0329331774603-0.0329331774602981
1421316.2435482236231-3.24354822362315
1431414.7118651812982-0.711865181298224
1441915.59963717503453.40036282496553
1451315.3188635876083-2.31886358760825

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 15.1838316030315 & -1.18383160303153 \tabularnewline
2 & 18 & 14.8758878074126 & 3.12411219258743 \tabularnewline
3 & 11 & 15.5135306633869 & -4.51353066338689 \tabularnewline
4 & 12 & 12.7854569899845 & -0.785456989984458 \tabularnewline
5 & 16 & 14.9173337133357 & 1.08266628666430 \tabularnewline
6 & 18 & 15.1061860848256 & 2.89381391517442 \tabularnewline
7 & 14 & 14.4705801335575 & -0.470580133557483 \tabularnewline
8 & 14 & 14.8354603760197 & -0.835460376019714 \tabularnewline
9 & 15 & 14.8909422575144 & 0.109057742485617 \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 & 1.98150496072231 \tabularnewline
15 & 14 & 11.2123900048692 & 2.78760999513077 \tabularnewline
16 & 14 & 14.2168499394085 & -0.216849939408523 \tabularnewline
17 & 17 & 14.9703383253408 & 2.02966167465919 \tabularnewline
18 & 14 & 14.3251804549211 & -0.325180454921146 \tabularnewline
19 & 16 & 14.4368256154272 & 1.56317438457275 \tabularnewline
20 & 18 & 12.6226069299641 & 5.37739307003592 \tabularnewline
21 & 14 & 14.5378711160821 & -0.537871116082101 \tabularnewline
22 & 12 & 13.2521245540801 & -1.25212455408015 \tabularnewline
23 & 17 & 12.6793097587289 & 4.32069024127114 \tabularnewline
24 & 9 & 11.9547441598086 & -2.95474415980858 \tabularnewline
25 & 16 & 14.2099774135199 & 1.79002258648013 \tabularnewline
26 & 14 & 14.1827534970653 & -0.182753497065282 \tabularnewline
27 & 11 & 13.3744282930834 & -2.37442829308336 \tabularnewline
28 & 16 & 14.4734955372592 & 1.52650446274084 \tabularnewline
29 & 13 & 13.1829533025514 & -0.182953302551449 \tabularnewline
30 & 17 & 14.8391449592143 & 2.16085504078566 \tabularnewline
31 & 15 & 15.0563228200996 & -0.0563228200995524 \tabularnewline
32 & 14 & 14.3156949272231 & -0.315694927223066 \tabularnewline
33 & 16 & 13.2757403831666 & 2.72425961683337 \tabularnewline
34 & 9 & 12.0634019218240 & -3.06340192182396 \tabularnewline
35 & 15 & 13.6855347815592 & 1.31446521844080 \tabularnewline
36 & 17 & 15.9766332318954 & 1.02336676810457 \tabularnewline
37 & 13 & 12.9843913018552 & 0.0156086981447623 \tabularnewline
38 & 15 & 13.0290763964619 & 1.97092360353807 \tabularnewline
39 & 16 & 13.7239338979155 & 2.27606610208450 \tabularnewline
40 & 16 & 15.8137466245252 & 0.186253375474806 \tabularnewline
41 & 12 & 13.8711640835198 & -1.87116408351985 \tabularnewline
42 & 11 & 13.4438827563934 & -2.44388275639338 \tabularnewline
43 & 15 & 15.2488831074477 & -0.248883107447689 \tabularnewline
44 & 17 & 15.1126832261477 & 1.88731677385229 \tabularnewline
45 & 13 & 14.0853966498101 & -1.08539664981007 \tabularnewline
46 & 16 & 13.6216176549634 & 2.37838234503664 \tabularnewline
47 & 14 & 14.0281642750027 & -0.0281642750027268 \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 & -0.194206328071934 \tabularnewline
52 & 16 & 15.3512119659202 & 0.648788034079823 \tabularnewline
53 & 15 & 15.1113719669578 & -0.111371966957814 \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 & -1.39045125492264 \tabularnewline
60 & 15 & 13.7445136357822 & 1.25548636421785 \tabularnewline
61 & 10 & 13.9796685196258 & -3.97966851962584 \tabularnewline
62 & 11 & 14.9184285830303 & -3.91842858303025 \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 & 0.768044785000675 \tabularnewline
67 & 16 & 12.7063639487742 & 3.29363605122582 \tabularnewline
68 & 15 & 15.6057619488741 & -0.605761948874102 \tabularnewline
69 & 15 & 15.4100005604537 & -0.410000560453668 \tabularnewline
70 & 13 & 14.0790226252874 & -1.07902262528743 \tabularnewline
71 & 17 & 14.7571156439112 & 2.2428843560888 \tabularnewline
72 & 13 & 13.3163197666959 & -0.316319766695935 \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 & 1.78708383816118 \tabularnewline
77 & 15 & 14.7202496789135 & 0.279750321086534 \tabularnewline
78 & 16 & 13.8524758540173 & 2.14752414598272 \tabularnewline
79 & 15 & 14.5040776326839 & 0.495922367316139 \tabularnewline
80 & 14 & 14.8077247998208 & -0.807724799820837 \tabularnewline
81 & 15 & 12.7867586762960 & 2.21324132370396 \tabularnewline
82 & 7 & 13.2415770178752 & -6.24157701787518 \tabularnewline
83 & 17 & 15.4846834406241 & 1.51531655937586 \tabularnewline
84 & 13 & 16.2435482236231 & -3.24354822362315 \tabularnewline
85 & 15 & 14.5419387395482 & 0.458061260451782 \tabularnewline
86 & 14 & 14.0916607159115 & -0.0916607159114877 \tabularnewline
87 & 13 & 12.4824989068191 & 0.517501093180875 \tabularnewline
88 & 16 & 14.9707320947092 & 1.02926790529078 \tabularnewline
89 & 12 & 14.5054558148220 & -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 & -1.72671725651346 \tabularnewline
97 & 15 & 14.2022880032944 & 0.797711996705624 \tabularnewline
98 & 9 & 13.9461349596996 & -4.94613495969958 \tabularnewline
99 & 16 & 14.848208425065 & 1.15179157493501 \tabularnewline
100 & 10 & 12.6384430932132 & -2.63844309321322 \tabularnewline
101 & 10 & 11.9097875817659 & -1.9097875817659 \tabularnewline
102 & 15 & 14.6911359205765 & 0.308864079423518 \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 & 14.5066278380580 & 1.49337216194197 \tabularnewline
108 & 14 & 13.0726846832598 & 0.927315316740201 \tabularnewline
109 & 14 & 14.1847437244906 & -0.184743724490588 \tabularnewline
110 & 14 & 14.5128154083329 & -0.512815408332917 \tabularnewline
111 & 14 & 14.0170569178191 & -0.0170569178190783 \tabularnewline
112 & 12 & 13.6018834798003 & -1.60188347980030 \tabularnewline
113 & 14 & 14.6330315207020 & -0.633031520702012 \tabularnewline
114 & 15 & 15.0048416954307 & -0.0048416954307306 \tabularnewline
115 & 15 & 13.9909487181334 & 1.00905128186662 \tabularnewline
116 & 13 & 12.1582021180912 & 0.841797881908786 \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 & 0.465363503892837 \tabularnewline
121 & 13 & 13.8401108452435 & -0.840110845243483 \tabularnewline
122 & 9 & 11.9320485170236 & -2.93204851702361 \tabularnewline
123 & 15 & 15.1418406191236 & -0.141840619123571 \tabularnewline
124 & 15 & 14.5914630993864 & 0.408536900613567 \tabularnewline
125 & 16 & 15.266293596663 & 0.733706403337001 \tabularnewline
126 & 11 & 13.3044149073808 & -2.30441490738084 \tabularnewline
127 & 14 & 13.6626050079972 & 0.337394992002850 \tabularnewline
128 & 11 & 13.5778324867961 & -2.57783248679606 \tabularnewline
129 & 15 & 12.4537809612442 & 2.54621903875583 \tabularnewline
130 & 13 & 12.8035839216858 & 0.196416078314241 \tabularnewline
131 & 16 & 12.9074247518848 & 3.09257524811517 \tabularnewline
132 & 14 & 15.6099354605564 & -1.60993546055643 \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 & -0.0329331774602981 \tabularnewline
142 & 13 & 16.2435482236231 & -3.24354822362315 \tabularnewline
143 & 14 & 14.7118651812982 & -0.711865181298224 \tabularnewline
144 & 19 & 15.5996371750345 & 3.40036282496553 \tabularnewline
145 & 13 & 15.3188635876083 & -2.31886358760825 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112215&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]15.1838316030315[/C][C]-1.18383160303153[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]14.8758878074126[/C][C]3.12411219258743[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]15.5135306633869[/C][C]-4.51353066338689[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]12.7854569899845[/C][C]-0.785456989984458[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]14.9173337133357[/C][C]1.08266628666430[/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]-0.470580133557483[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]14.8354603760197[/C][C]-0.835460376019714[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]14.8909422575144[/C][C]0.109057742485617[/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]1.98150496072231[/C][/ROW]
[ROW][C]15[/C][C]14[/C][C]11.2123900048692[/C][C]2.78760999513077[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]14.2168499394085[/C][C]-0.216849939408523[/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]-0.325180454921146[/C][/ROW]
[ROW][C]19[/C][C]16[/C][C]14.4368256154272[/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]-0.537871116082101[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]13.2521245540801[/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]-2.95474415980858[/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]-0.182753497065282[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]13.3744282930834[/C][C]-2.37442829308336[/C][/ROW]
[ROW][C]28[/C][C]16[/C][C]14.4734955372592[/C][C]1.52650446274084[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]13.1829533025514[/C][C]-0.182953302551449[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]14.8391449592143[/C][C]2.16085504078566[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.0563228200996[/C][C]-0.0563228200995524[/C][/ROW]
[ROW][C]32[/C][C]14[/C][C]14.3156949272231[/C][C]-0.315694927223066[/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]12.0634019218240[/C][C]-3.06340192182396[/C][/ROW]
[ROW][C]35[/C][C]15[/C][C]13.6855347815592[/C][C]1.31446521844080[/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]0.0156086981447623[/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]2.27606610208450[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.8137466245252[/C][C]0.186253375474806[/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]-0.248883107447689[/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]2.37838234503664[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]14.0281642750027[/C][C]-0.0281642750027268[/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]-0.194206328071934[/C][/ROW]
[ROW][C]52[/C][C]16[/C][C]15.3512119659202[/C][C]0.648788034079823[/C][/ROW]
[ROW][C]53[/C][C]15[/C][C]15.1113719669578[/C][C]-0.111371966957814[/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]-1.39045125492264[/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]-3.91842858303025[/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]0.768044785000675[/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]-0.605761948874102[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]15.4100005604537[/C][C]-0.410000560453668[/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]-0.316319766695935[/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]1.78708383816118[/C][/ROW]
[ROW][C]77[/C][C]15[/C][C]14.7202496789135[/C][C]0.279750321086534[/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]0.495922367316139[/C][/ROW]
[ROW][C]80[/C][C]14[/C][C]14.8077247998208[/C][C]-0.807724799820837[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]12.7867586762960[/C][C]2.21324132370396[/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]16.2435482236231[/C][C]-3.24354822362315[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.5419387395482[/C][C]0.458061260451782[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.0916607159115[/C][C]-0.0916607159114877[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]12.4824989068191[/C][C]0.517501093180875[/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]14.5054558148220[/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]-1.72671725651346[/C][/ROW]
[ROW][C]97[/C][C]15[/C][C]14.2022880032944[/C][C]0.797711996705624[/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]1.15179157493501[/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]0.308864079423518[/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]14.5066278380580[/C][C]1.49337216194197[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]13.0726846832598[/C][C]0.927315316740201[/C][/ROW]
[ROW][C]109[/C][C]14[/C][C]14.1847437244906[/C][C]-0.184743724490588[/C][/ROW]
[ROW][C]110[/C][C]14[/C][C]14.5128154083329[/C][C]-0.512815408332917[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]14.0170569178191[/C][C]-0.0170569178190783[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]13.6018834798003[/C][C]-1.60188347980030[/C][/ROW]
[ROW][C]113[/C][C]14[/C][C]14.6330315207020[/C][C]-0.633031520702012[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]15.0048416954307[/C][C]-0.0048416954307306[/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]0.841797881908786[/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]0.465363503892837[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]13.8401108452435[/C][C]-0.840110845243483[/C][/ROW]
[ROW][C]122[/C][C]9[/C][C]11.9320485170236[/C][C]-2.93204851702361[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]15.1418406191236[/C][C]-0.141840619123571[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]14.5914630993864[/C][C]0.408536900613567[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]15.266293596663[/C][C]0.733706403337001[/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]0.337394992002850[/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]0.196416078314241[/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]-1.60993546055643[/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]-0.0329331774602981[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]16.2435482236231[/C][C]-3.24354822362315[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]14.7118651812982[/C][C]-0.711865181298224[/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]-2.31886358760825[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112215&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112215&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
11415.1838316030315-1.18383160303153
21814.87588780741263.12411219258743
31115.5135306633869-4.51353066338689
41212.7854569899845-0.785456989984458
51614.91733371333571.08266628666430
61815.10618608482562.89381391517442
71414.4705801335575-0.470580133557483
81414.8354603760197-0.835460376019714
91514.89094225751440.109057742485617
101513.41444215163621.5855578483638
111712.87694348419874.12305651580128
121914.55974955392624.44025044607376
131012.5245842728517-2.52458427285174
141816.01849503927771.98150496072231
151411.21239000486922.78760999513077
161414.2168499394085-0.216849939408523
171714.97033832534082.02966167465919
181414.3251804549211-0.325180454921146
191614.43682561542721.56317438457275
201812.62260692996415.37739307003592
211414.5378711160821-0.537871116082101
221213.2521245540801-1.25212455408015
231712.67930975872894.32069024127114
24911.9547441598086-2.95474415980858
251614.20997741351991.79002258648013
261414.1827534970653-0.182753497065282
271113.3744282930834-2.37442829308336
281614.47349553725921.52650446274084
291313.1829533025514-0.182953302551449
301714.83914495921432.16085504078566
311515.0563228200996-0.0563228200995524
321414.3156949272231-0.315694927223066
331613.27574038316662.72425961683337
34912.0634019218240-3.06340192182396
351513.68553478155921.31446521844080
361715.97663323189541.02336676810457
371312.98439130185520.0156086981447623
381513.02907639646191.97092360353807
391613.72393389791552.27606610208450
401615.81374662452520.186253375474806
411213.8711640835198-1.87116408351985
421113.4438827563934-2.44388275639338
431515.2488831074477-0.248883107447689
441715.11268322614771.88731677385229
451314.0853966498101-1.08539664981007
461613.62161765496342.37838234503664
471414.0281642750027-0.0281642750027268
481114.3217528344224-3.32175283442243
491213.5436559631263-1.54365596312634
501215.0266304790701-3.02663047907007
511515.1942063280719-0.194206328071934
521615.35121196592020.648788034079823
531515.1113719669578-0.111371966957814
541213.0078972731827-1.00789727318273
551213.9167541087356-1.91675410873562
56812.6239617231306-4.62396172313061
571314.3862312588195-1.38623125881954
581114.8003556334316-3.80035563343157
591415.3904512549226-1.39045125492264
601513.74451363578221.25548636421785
611013.9796685196258-3.97966851962584
621114.9184285830303-3.91842858303025
631213.3881385221454-1.38813852214537
641513.66210128020751.33789871979248
651514.55942022191670.440579778083251
661413.23195521499930.768044785000675
671612.70636394877423.29363605122582
681515.6057619488741-0.605761948874102
691515.4100005604537-0.410000560453668
701314.0790226252874-1.07902262528743
711714.75711564391122.2428843560888
721313.3163197666959-0.316319766695935
731513.81515992579991.18484007420015
741314.3141936282855-1.31419362828546
751513.13798295276981.86201704723019
761614.21291616183881.78708383816118
771514.72024967891350.279750321086534
781613.85247585401732.14752414598272
791514.50407763268390.495922367316139
801414.8077247998208-0.807724799820837
811512.78675867629602.21324132370396
82713.2415770178752-6.24157701787518
831715.48468344062411.51531655937586
841316.2435482236231-3.24354822362315
851514.54193873954820.458061260451782
861414.0916607159115-0.0916607159114877
871312.48249890681910.517501093180875
881614.97073209470921.02926790529078
891214.5054558148220-2.50545581482197
901415.4631880430515-1.46318804305149
911714.38946587879452.61053412120552
921514.84326634725040.156733652749603
931712.91461703690474.08538296309534
941213.0957942989674-1.09579429896735
951614.13343236834411.86656763165585
961112.7267172565135-1.72671725651346
971514.20228800329440.797711996705624
98913.9461349596996-4.94613495969958
991614.8482084250651.15179157493501
1001012.6384430932132-2.63844309321322
1011011.9097875817659-1.9097875817659
1021514.69113592057650.308864079423518
1031114.1189463288907-3.11894632889067
1041315.0133882751135-2.01338827511348
1051412.54612314809311.45387685190692
1061815.20496414969192.79503585030809
1071614.50662783805801.49337216194197
1081413.07268468325980.927315316740201
1091414.1847437244906-0.184743724490588
1101414.5128154083329-0.512815408332917
1111414.0170569178191-0.0170569178190783
1121213.6018834798003-1.60188347980030
1131414.6330315207020-0.633031520702012
1141515.0048416954307-0.0048416954307306
1151513.99094871813341.00905128186662
1161312.15820211809120.841797881908786
1171714.20474189333062.79525810666936
1181714.59296389757922.40703610242076
1191915.59963717503453.40036282496553
1201514.53463649610720.465363503892837
1211313.8401108452435-0.840110845243483
122911.9320485170236-2.93204851702361
1231515.1418406191236-0.141840619123571
1241514.59146309938640.408536900613567
1251615.2662935966630.733706403337001
1261113.3044149073808-2.30441490738084
1271413.66260500799720.337394992002850
1281113.5778324867961-2.57783248679606
1291512.45378096124422.54621903875583
1301312.80358392168580.196416078314241
1311612.90742475188483.09257524811517
1321415.6099354605564-1.60993546055643
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
1411313.0329331774603-0.0329331774602981
1421316.2435482236231-3.24354822362315
1431414.7118651812982-0.711865181298224
1441915.59963717503453.40036282496553
1451315.3188635876083-2.31886358760825






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.2497096611911540.4994193223823070.750290338808846
120.2105053697390850.4210107394781710.789494630260915
130.2320845650031270.4641691300062530.767915434996874
140.3979140591442430.7958281182884860.602085940855757
150.3405525336689060.6811050673378120.659447466331094
160.2411307777969680.4822615555939360.758869222203032
170.5685053132763290.8629893734473420.431494686723671
180.4771434188196310.9542868376392610.522856581180369
190.5316197417909810.9367605164180370.468380258209019
200.880272593369570.2394548132608590.119727406630430
210.8588239759314020.2823520481371960.141176024068598
220.8565929125361190.2868141749277630.143407087463881
230.8900575383457280.2198849233085450.109942461654272
240.9269901301326660.1460197397346680.0730098698673339
250.9042259722009970.1915480555980060.0957740277990032
260.886954838147330.2260903237053390.113045161852670
270.9123359150993840.1753281698012320.0876640849006158
280.894054031345210.2118919373095820.105945968654791
290.892513269675930.2149734606481420.107486730324071
300.8840138655915070.2319722688169850.115986134408493
310.8639508344792020.2720983310415960.136049165520798
320.8319344192581560.3361311614836880.168065580741844
330.8195058700368780.3609882599262450.180494129963122
340.8878804741593930.2242390516812150.112119525840607
350.8628699310129430.2742601379741150.137130068987058
360.8296483841415020.3407032317169960.170351615858498
370.7894328476575950.4211343046848100.210567152342405
380.761592806043710.4768143879125810.238407193956290
390.7627963302421390.4744073395157220.237203669757861
400.7196986879833260.5606026240333480.280301312016674
410.6993855338425480.6012289323149050.300614466157452
420.7333436643598330.5333126712803340.266656335640167
430.6890939278443030.6218121443113940.310906072155697
440.6644264719708890.6711470560582220.335573528029111
450.6254526657907360.7490946684185280.374547334209264
460.6147103857535590.7705792284928820.385289614246441
470.5851889966375910.8296220067248180.414811003362409
480.7114668017701240.5770663964597520.288533198229876
490.7024817296600410.5950365406799180.297518270339959
500.7472796836437380.5054406327125230.252720316356262
510.7036336855545690.5927326288908630.296366314445431
520.6593469740424810.6813060519150380.340653025957519
530.618954285730320.762091428539360.38104571426968
540.5854777700427720.8290444599144560.414522229957228
550.5665666486648320.8668667026703370.433433351335168
560.7575765373067740.4848469253864510.242423462693226
570.73118488474120.53763023051760.2688151152588
580.8045270977538530.3909458044922940.195472902246147
590.7908321645848610.4183356708302780.209167835415139
600.7651374312144660.4697251375710680.234862568785534
610.8476961189635440.3046077620729120.152303881036456
620.9008366959400280.1983266081199440.099163304059972
630.8865337017423720.2269325965152570.113466298257628
640.8708934980866720.2582130038266570.129106501913328
650.84457514317190.3108497136561980.155424856828099
660.8163176327890960.3673647344218080.183682367210904
670.8528690979301140.2942618041397720.147130902069886
680.825122749338790.3497545013224210.174877250661210
690.7933451446937760.4133097106124490.206654855306224
700.7687608746399120.4624782507201770.231239125360088
710.76852068284930.4629586343013990.231479317150700
720.730247367631710.539505264736580.26975263236829
730.7006434173953280.5987131652093440.299356582604672
740.683634453040180.632731093919640.31636554695982
750.6699730079417820.6600539841164350.330026992058217
760.6513469602014570.6973060795970870.348653039798543
770.6043218702850230.7913562594299540.395678129714977
780.5993232632244190.8013534735511620.400676736775581
790.5537720098596340.8924559802807310.446227990140366
800.5118626739953570.9762746520092860.488137326004643
810.5156831305950520.9686337388098950.484316869404948
820.7967464231861670.4065071536276660.203253576813833
830.7800976597317660.4398046805364670.219902340268234
840.8211514267674220.3576971464651560.178848573232578
850.788076436374560.4238471272508790.211923563625440
860.7498813850448360.5002372299103270.250118614955164
870.7116297426250020.5767405147499960.288370257374998
880.6843006241517710.6313987516964580.315699375848229
890.6899707672688420.6200584654623170.310029232731158
900.6694441381482680.6611117237034640.330555861851732
910.6994322042886880.6011355914226230.300567795711311
920.6525442151702680.6949115696594630.347455784829732
930.8239344216918190.3521311566163630.176065578308181
940.7941130454958330.4117739090083340.205886954504167
950.7956070166502180.4087859666995640.204392983349782
960.777417712911420.4451645741771590.222582287088580
970.7525588763752060.4948822472495880.247441123624794
980.8995403489469360.2009193021061280.100459651053064
990.879589356371920.2408212872561590.120410643628079
1000.8791532132695720.2416935734608550.120846786730428
1010.863336637303990.273326725392020.13666336269601
1020.8321781795597150.335643640880570.167821820440285
1030.8626847774549580.2746304450900830.137315222545042
1040.8866339560063660.2267320879872680.113366043993634
1050.8917159298620910.2165681402758180.108284070137909
1060.9085860510245460.1828278979509080.091413948975454
1070.8849745832118560.2300508335762890.115025416788144
1080.8989646176273920.2020707647452160.101035382372608
1090.8734008598070190.2531982803859620.126599140192981
1100.902661080841910.1946778383161790.0973389191580897
1110.8829562505870480.2340874988259040.117043749412952
1120.8512056553754820.2975886892490370.148794344624518
1130.810401890037170.379196219925660.18959810996283
1140.7659761699762120.4680476600475770.234023830023788
1150.7244214854028170.5511570291943670.275578514597183
1160.6655754381788590.6688491236422820.334424561821141
1170.7132527411515180.5734945176969640.286747258848482
1180.690028965807620.6199420683847590.309971034192379
1190.7037738182983430.5924523634033150.296226181701657
1200.8022129433252530.3955741133494950.197787056674748
1210.749843005172930.500313989654140.25015699482707
1220.8556847425784970.2886305148430050.144315257421503
1230.821635814449320.3567283711013590.178364185550680
1240.782687972155570.4346240556888590.217312027844430
1250.711952054584440.5760958908311210.288047945415560
1260.6649056992476860.6701886015046290.335094300752314
1270.5768304950714150.846339009857170.423169504928585
1280.5328287288021370.9343425423957250.467171271197863
1290.4376338282128670.8752676564257340.562366171787133
1300.3452989663830080.6905979327660170.654701033616992
1310.4245580443337150.849116088667430.575441955666285
1320.569608386365710.8607832272685790.430391613634290
1330.4337544574270580.8675089148541170.566245542572942
1340.6469732828913420.7060534342173150.353026717108658

\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 & 0.499419322382307 & 0.750290338808846 \tabularnewline
12 & 0.210505369739085 & 0.421010739478171 & 0.789494630260915 \tabularnewline
13 & 0.232084565003127 & 0.464169130006253 & 0.767915434996874 \tabularnewline
14 & 0.397914059144243 & 0.795828118288486 & 0.602085940855757 \tabularnewline
15 & 0.340552533668906 & 0.681105067337812 & 0.659447466331094 \tabularnewline
16 & 0.241130777796968 & 0.482261555593936 & 0.758869222203032 \tabularnewline
17 & 0.568505313276329 & 0.862989373447342 & 0.431494686723671 \tabularnewline
18 & 0.477143418819631 & 0.954286837639261 & 0.522856581180369 \tabularnewline
19 & 0.531619741790981 & 0.936760516418037 & 0.468380258209019 \tabularnewline
20 & 0.88027259336957 & 0.239454813260859 & 0.119727406630430 \tabularnewline
21 & 0.858823975931402 & 0.282352048137196 & 0.141176024068598 \tabularnewline
22 & 0.856592912536119 & 0.286814174927763 & 0.143407087463881 \tabularnewline
23 & 0.890057538345728 & 0.219884923308545 & 0.109942461654272 \tabularnewline
24 & 0.926990130132666 & 0.146019739734668 & 0.0730098698673339 \tabularnewline
25 & 0.904225972200997 & 0.191548055598006 & 0.0957740277990032 \tabularnewline
26 & 0.88695483814733 & 0.226090323705339 & 0.113045161852670 \tabularnewline
27 & 0.912335915099384 & 0.175328169801232 & 0.0876640849006158 \tabularnewline
28 & 0.89405403134521 & 0.211891937309582 & 0.105945968654791 \tabularnewline
29 & 0.89251326967593 & 0.214973460648142 & 0.107486730324071 \tabularnewline
30 & 0.884013865591507 & 0.231972268816985 & 0.115986134408493 \tabularnewline
31 & 0.863950834479202 & 0.272098331041596 & 0.136049165520798 \tabularnewline
32 & 0.831934419258156 & 0.336131161483688 & 0.168065580741844 \tabularnewline
33 & 0.819505870036878 & 0.360988259926245 & 0.180494129963122 \tabularnewline
34 & 0.887880474159393 & 0.224239051681215 & 0.112119525840607 \tabularnewline
35 & 0.862869931012943 & 0.274260137974115 & 0.137130068987058 \tabularnewline
36 & 0.829648384141502 & 0.340703231716996 & 0.170351615858498 \tabularnewline
37 & 0.789432847657595 & 0.421134304684810 & 0.210567152342405 \tabularnewline
38 & 0.76159280604371 & 0.476814387912581 & 0.238407193956290 \tabularnewline
39 & 0.762796330242139 & 0.474407339515722 & 0.237203669757861 \tabularnewline
40 & 0.719698687983326 & 0.560602624033348 & 0.280301312016674 \tabularnewline
41 & 0.699385533842548 & 0.601228932314905 & 0.300614466157452 \tabularnewline
42 & 0.733343664359833 & 0.533312671280334 & 0.266656335640167 \tabularnewline
43 & 0.689093927844303 & 0.621812144311394 & 0.310906072155697 \tabularnewline
44 & 0.664426471970889 & 0.671147056058222 & 0.335573528029111 \tabularnewline
45 & 0.625452665790736 & 0.749094668418528 & 0.374547334209264 \tabularnewline
46 & 0.614710385753559 & 0.770579228492882 & 0.385289614246441 \tabularnewline
47 & 0.585188996637591 & 0.829622006724818 & 0.414811003362409 \tabularnewline
48 & 0.711466801770124 & 0.577066396459752 & 0.288533198229876 \tabularnewline
49 & 0.702481729660041 & 0.595036540679918 & 0.297518270339959 \tabularnewline
50 & 0.747279683643738 & 0.505440632712523 & 0.252720316356262 \tabularnewline
51 & 0.703633685554569 & 0.592732628890863 & 0.296366314445431 \tabularnewline
52 & 0.659346974042481 & 0.681306051915038 & 0.340653025957519 \tabularnewline
53 & 0.61895428573032 & 0.76209142853936 & 0.38104571426968 \tabularnewline
54 & 0.585477770042772 & 0.829044459914456 & 0.414522229957228 \tabularnewline
55 & 0.566566648664832 & 0.866866702670337 & 0.433433351335168 \tabularnewline
56 & 0.757576537306774 & 0.484846925386451 & 0.242423462693226 \tabularnewline
57 & 0.7311848847412 & 0.5376302305176 & 0.2688151152588 \tabularnewline
58 & 0.804527097753853 & 0.390945804492294 & 0.195472902246147 \tabularnewline
59 & 0.790832164584861 & 0.418335670830278 & 0.209167835415139 \tabularnewline
60 & 0.765137431214466 & 0.469725137571068 & 0.234862568785534 \tabularnewline
61 & 0.847696118963544 & 0.304607762072912 & 0.152303881036456 \tabularnewline
62 & 0.900836695940028 & 0.198326608119944 & 0.099163304059972 \tabularnewline
63 & 0.886533701742372 & 0.226932596515257 & 0.113466298257628 \tabularnewline
64 & 0.870893498086672 & 0.258213003826657 & 0.129106501913328 \tabularnewline
65 & 0.8445751431719 & 0.310849713656198 & 0.155424856828099 \tabularnewline
66 & 0.816317632789096 & 0.367364734421808 & 0.183682367210904 \tabularnewline
67 & 0.852869097930114 & 0.294261804139772 & 0.147130902069886 \tabularnewline
68 & 0.82512274933879 & 0.349754501322421 & 0.174877250661210 \tabularnewline
69 & 0.793345144693776 & 0.413309710612449 & 0.206654855306224 \tabularnewline
70 & 0.768760874639912 & 0.462478250720177 & 0.231239125360088 \tabularnewline
71 & 0.7685206828493 & 0.462958634301399 & 0.231479317150700 \tabularnewline
72 & 0.73024736763171 & 0.53950526473658 & 0.26975263236829 \tabularnewline
73 & 0.700643417395328 & 0.598713165209344 & 0.299356582604672 \tabularnewline
74 & 0.68363445304018 & 0.63273109391964 & 0.31636554695982 \tabularnewline
75 & 0.669973007941782 & 0.660053984116435 & 0.330026992058217 \tabularnewline
76 & 0.651346960201457 & 0.697306079597087 & 0.348653039798543 \tabularnewline
77 & 0.604321870285023 & 0.791356259429954 & 0.395678129714977 \tabularnewline
78 & 0.599323263224419 & 0.801353473551162 & 0.400676736775581 \tabularnewline
79 & 0.553772009859634 & 0.892455980280731 & 0.446227990140366 \tabularnewline
80 & 0.511862673995357 & 0.976274652009286 & 0.488137326004643 \tabularnewline
81 & 0.515683130595052 & 0.968633738809895 & 0.484316869404948 \tabularnewline
82 & 0.796746423186167 & 0.406507153627666 & 0.203253576813833 \tabularnewline
83 & 0.780097659731766 & 0.439804680536467 & 0.219902340268234 \tabularnewline
84 & 0.821151426767422 & 0.357697146465156 & 0.178848573232578 \tabularnewline
85 & 0.78807643637456 & 0.423847127250879 & 0.211923563625440 \tabularnewline
86 & 0.749881385044836 & 0.500237229910327 & 0.250118614955164 \tabularnewline
87 & 0.711629742625002 & 0.576740514749996 & 0.288370257374998 \tabularnewline
88 & 0.684300624151771 & 0.631398751696458 & 0.315699375848229 \tabularnewline
89 & 0.689970767268842 & 0.620058465462317 & 0.310029232731158 \tabularnewline
90 & 0.669444138148268 & 0.661111723703464 & 0.330555861851732 \tabularnewline
91 & 0.699432204288688 & 0.601135591422623 & 0.300567795711311 \tabularnewline
92 & 0.652544215170268 & 0.694911569659463 & 0.347455784829732 \tabularnewline
93 & 0.823934421691819 & 0.352131156616363 & 0.176065578308181 \tabularnewline
94 & 0.794113045495833 & 0.411773909008334 & 0.205886954504167 \tabularnewline
95 & 0.795607016650218 & 0.408785966699564 & 0.204392983349782 \tabularnewline
96 & 0.77741771291142 & 0.445164574177159 & 0.222582287088580 \tabularnewline
97 & 0.752558876375206 & 0.494882247249588 & 0.247441123624794 \tabularnewline
98 & 0.899540348946936 & 0.200919302106128 & 0.100459651053064 \tabularnewline
99 & 0.87958935637192 & 0.240821287256159 & 0.120410643628079 \tabularnewline
100 & 0.879153213269572 & 0.241693573460855 & 0.120846786730428 \tabularnewline
101 & 0.86333663730399 & 0.27332672539202 & 0.13666336269601 \tabularnewline
102 & 0.832178179559715 & 0.33564364088057 & 0.167821820440285 \tabularnewline
103 & 0.862684777454958 & 0.274630445090083 & 0.137315222545042 \tabularnewline
104 & 0.886633956006366 & 0.226732087987268 & 0.113366043993634 \tabularnewline
105 & 0.891715929862091 & 0.216568140275818 & 0.108284070137909 \tabularnewline
106 & 0.908586051024546 & 0.182827897950908 & 0.091413948975454 \tabularnewline
107 & 0.884974583211856 & 0.230050833576289 & 0.115025416788144 \tabularnewline
108 & 0.898964617627392 & 0.202070764745216 & 0.101035382372608 \tabularnewline
109 & 0.873400859807019 & 0.253198280385962 & 0.126599140192981 \tabularnewline
110 & 0.90266108084191 & 0.194677838316179 & 0.0973389191580897 \tabularnewline
111 & 0.882956250587048 & 0.234087498825904 & 0.117043749412952 \tabularnewline
112 & 0.851205655375482 & 0.297588689249037 & 0.148794344624518 \tabularnewline
113 & 0.81040189003717 & 0.37919621992566 & 0.18959810996283 \tabularnewline
114 & 0.765976169976212 & 0.468047660047577 & 0.234023830023788 \tabularnewline
115 & 0.724421485402817 & 0.551157029194367 & 0.275578514597183 \tabularnewline
116 & 0.665575438178859 & 0.668849123642282 & 0.334424561821141 \tabularnewline
117 & 0.713252741151518 & 0.573494517696964 & 0.286747258848482 \tabularnewline
118 & 0.69002896580762 & 0.619942068384759 & 0.309971034192379 \tabularnewline
119 & 0.703773818298343 & 0.592452363403315 & 0.296226181701657 \tabularnewline
120 & 0.802212943325253 & 0.395574113349495 & 0.197787056674748 \tabularnewline
121 & 0.74984300517293 & 0.50031398965414 & 0.25015699482707 \tabularnewline
122 & 0.855684742578497 & 0.288630514843005 & 0.144315257421503 \tabularnewline
123 & 0.82163581444932 & 0.356728371101359 & 0.178364185550680 \tabularnewline
124 & 0.78268797215557 & 0.434624055688859 & 0.217312027844430 \tabularnewline
125 & 0.71195205458444 & 0.576095890831121 & 0.288047945415560 \tabularnewline
126 & 0.664905699247686 & 0.670188601504629 & 0.335094300752314 \tabularnewline
127 & 0.576830495071415 & 0.84633900985717 & 0.423169504928585 \tabularnewline
128 & 0.532828728802137 & 0.934342542395725 & 0.467171271197863 \tabularnewline
129 & 0.437633828212867 & 0.875267656425734 & 0.562366171787133 \tabularnewline
130 & 0.345298966383008 & 0.690597932766017 & 0.654701033616992 \tabularnewline
131 & 0.424558044333715 & 0.84911608866743 & 0.575441955666285 \tabularnewline
132 & 0.56960838636571 & 0.860783227268579 & 0.430391613634290 \tabularnewline
133 & 0.433754457427058 & 0.867508914854117 & 0.566245542572942 \tabularnewline
134 & 0.646973282891342 & 0.706053434217315 & 0.353026717108658 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112215&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]0.499419322382307[/C][C]0.750290338808846[/C][/ROW]
[ROW][C]12[/C][C]0.210505369739085[/C][C]0.421010739478171[/C][C]0.789494630260915[/C][/ROW]
[ROW][C]13[/C][C]0.232084565003127[/C][C]0.464169130006253[/C][C]0.767915434996874[/C][/ROW]
[ROW][C]14[/C][C]0.397914059144243[/C][C]0.795828118288486[/C][C]0.602085940855757[/C][/ROW]
[ROW][C]15[/C][C]0.340552533668906[/C][C]0.681105067337812[/C][C]0.659447466331094[/C][/ROW]
[ROW][C]16[/C][C]0.241130777796968[/C][C]0.482261555593936[/C][C]0.758869222203032[/C][/ROW]
[ROW][C]17[/C][C]0.568505313276329[/C][C]0.862989373447342[/C][C]0.431494686723671[/C][/ROW]
[ROW][C]18[/C][C]0.477143418819631[/C][C]0.954286837639261[/C][C]0.522856581180369[/C][/ROW]
[ROW][C]19[/C][C]0.531619741790981[/C][C]0.936760516418037[/C][C]0.468380258209019[/C][/ROW]
[ROW][C]20[/C][C]0.88027259336957[/C][C]0.239454813260859[/C][C]0.119727406630430[/C][/ROW]
[ROW][C]21[/C][C]0.858823975931402[/C][C]0.282352048137196[/C][C]0.141176024068598[/C][/ROW]
[ROW][C]22[/C][C]0.856592912536119[/C][C]0.286814174927763[/C][C]0.143407087463881[/C][/ROW]
[ROW][C]23[/C][C]0.890057538345728[/C][C]0.219884923308545[/C][C]0.109942461654272[/C][/ROW]
[ROW][C]24[/C][C]0.926990130132666[/C][C]0.146019739734668[/C][C]0.0730098698673339[/C][/ROW]
[ROW][C]25[/C][C]0.904225972200997[/C][C]0.191548055598006[/C][C]0.0957740277990032[/C][/ROW]
[ROW][C]26[/C][C]0.88695483814733[/C][C]0.226090323705339[/C][C]0.113045161852670[/C][/ROW]
[ROW][C]27[/C][C]0.912335915099384[/C][C]0.175328169801232[/C][C]0.0876640849006158[/C][/ROW]
[ROW][C]28[/C][C]0.89405403134521[/C][C]0.211891937309582[/C][C]0.105945968654791[/C][/ROW]
[ROW][C]29[/C][C]0.89251326967593[/C][C]0.214973460648142[/C][C]0.107486730324071[/C][/ROW]
[ROW][C]30[/C][C]0.884013865591507[/C][C]0.231972268816985[/C][C]0.115986134408493[/C][/ROW]
[ROW][C]31[/C][C]0.863950834479202[/C][C]0.272098331041596[/C][C]0.136049165520798[/C][/ROW]
[ROW][C]32[/C][C]0.831934419258156[/C][C]0.336131161483688[/C][C]0.168065580741844[/C][/ROW]
[ROW][C]33[/C][C]0.819505870036878[/C][C]0.360988259926245[/C][C]0.180494129963122[/C][/ROW]
[ROW][C]34[/C][C]0.887880474159393[/C][C]0.224239051681215[/C][C]0.112119525840607[/C][/ROW]
[ROW][C]35[/C][C]0.862869931012943[/C][C]0.274260137974115[/C][C]0.137130068987058[/C][/ROW]
[ROW][C]36[/C][C]0.829648384141502[/C][C]0.340703231716996[/C][C]0.170351615858498[/C][/ROW]
[ROW][C]37[/C][C]0.789432847657595[/C][C]0.421134304684810[/C][C]0.210567152342405[/C][/ROW]
[ROW][C]38[/C][C]0.76159280604371[/C][C]0.476814387912581[/C][C]0.238407193956290[/C][/ROW]
[ROW][C]39[/C][C]0.762796330242139[/C][C]0.474407339515722[/C][C]0.237203669757861[/C][/ROW]
[ROW][C]40[/C][C]0.719698687983326[/C][C]0.560602624033348[/C][C]0.280301312016674[/C][/ROW]
[ROW][C]41[/C][C]0.699385533842548[/C][C]0.601228932314905[/C][C]0.300614466157452[/C][/ROW]
[ROW][C]42[/C][C]0.733343664359833[/C][C]0.533312671280334[/C][C]0.266656335640167[/C][/ROW]
[ROW][C]43[/C][C]0.689093927844303[/C][C]0.621812144311394[/C][C]0.310906072155697[/C][/ROW]
[ROW][C]44[/C][C]0.664426471970889[/C][C]0.671147056058222[/C][C]0.335573528029111[/C][/ROW]
[ROW][C]45[/C][C]0.625452665790736[/C][C]0.749094668418528[/C][C]0.374547334209264[/C][/ROW]
[ROW][C]46[/C][C]0.614710385753559[/C][C]0.770579228492882[/C][C]0.385289614246441[/C][/ROW]
[ROW][C]47[/C][C]0.585188996637591[/C][C]0.829622006724818[/C][C]0.414811003362409[/C][/ROW]
[ROW][C]48[/C][C]0.711466801770124[/C][C]0.577066396459752[/C][C]0.288533198229876[/C][/ROW]
[ROW][C]49[/C][C]0.702481729660041[/C][C]0.595036540679918[/C][C]0.297518270339959[/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]0.659346974042481[/C][C]0.681306051915038[/C][C]0.340653025957519[/C][/ROW]
[ROW][C]53[/C][C]0.61895428573032[/C][C]0.76209142853936[/C][C]0.38104571426968[/C][/ROW]
[ROW][C]54[/C][C]0.585477770042772[/C][C]0.829044459914456[/C][C]0.414522229957228[/C][/ROW]
[ROW][C]55[/C][C]0.566566648664832[/C][C]0.866866702670337[/C][C]0.433433351335168[/C][/ROW]
[ROW][C]56[/C][C]0.757576537306774[/C][C]0.484846925386451[/C][C]0.242423462693226[/C][/ROW]
[ROW][C]57[/C][C]0.7311848847412[/C][C]0.5376302305176[/C][C]0.2688151152588[/C][/ROW]
[ROW][C]58[/C][C]0.804527097753853[/C][C]0.390945804492294[/C][C]0.195472902246147[/C][/ROW]
[ROW][C]59[/C][C]0.790832164584861[/C][C]0.418335670830278[/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]0.847696118963544[/C][C]0.304607762072912[/C][C]0.152303881036456[/C][/ROW]
[ROW][C]62[/C][C]0.900836695940028[/C][C]0.198326608119944[/C][C]0.099163304059972[/C][/ROW]
[ROW][C]63[/C][C]0.886533701742372[/C][C]0.226932596515257[/C][C]0.113466298257628[/C][/ROW]
[ROW][C]64[/C][C]0.870893498086672[/C][C]0.258213003826657[/C][C]0.129106501913328[/C][/ROW]
[ROW][C]65[/C][C]0.8445751431719[/C][C]0.310849713656198[/C][C]0.155424856828099[/C][/ROW]
[ROW][C]66[/C][C]0.816317632789096[/C][C]0.367364734421808[/C][C]0.183682367210904[/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]0.174877250661210[/C][/ROW]
[ROW][C]69[/C][C]0.793345144693776[/C][C]0.413309710612449[/C][C]0.206654855306224[/C][/ROW]
[ROW][C]70[/C][C]0.768760874639912[/C][C]0.462478250720177[/C][C]0.231239125360088[/C][/ROW]
[ROW][C]71[/C][C]0.7685206828493[/C][C]0.462958634301399[/C][C]0.231479317150700[/C][/ROW]
[ROW][C]72[/C][C]0.73024736763171[/C][C]0.53950526473658[/C][C]0.26975263236829[/C][/ROW]
[ROW][C]73[/C][C]0.700643417395328[/C][C]0.598713165209344[/C][C]0.299356582604672[/C][/ROW]
[ROW][C]74[/C][C]0.68363445304018[/C][C]0.63273109391964[/C][C]0.31636554695982[/C][/ROW]
[ROW][C]75[/C][C]0.669973007941782[/C][C]0.660053984116435[/C][C]0.330026992058217[/C][/ROW]
[ROW][C]76[/C][C]0.651346960201457[/C][C]0.697306079597087[/C][C]0.348653039798543[/C][/ROW]
[ROW][C]77[/C][C]0.604321870285023[/C][C]0.791356259429954[/C][C]0.395678129714977[/C][/ROW]
[ROW][C]78[/C][C]0.599323263224419[/C][C]0.801353473551162[/C][C]0.400676736775581[/C][/ROW]
[ROW][C]79[/C][C]0.553772009859634[/C][C]0.892455980280731[/C][C]0.446227990140366[/C][/ROW]
[ROW][C]80[/C][C]0.511862673995357[/C][C]0.976274652009286[/C][C]0.488137326004643[/C][/ROW]
[ROW][C]81[/C][C]0.515683130595052[/C][C]0.968633738809895[/C][C]0.484316869404948[/C][/ROW]
[ROW][C]82[/C][C]0.796746423186167[/C][C]0.406507153627666[/C][C]0.203253576813833[/C][/ROW]
[ROW][C]83[/C][C]0.780097659731766[/C][C]0.439804680536467[/C][C]0.219902340268234[/C][/ROW]
[ROW][C]84[/C][C]0.821151426767422[/C][C]0.357697146465156[/C][C]0.178848573232578[/C][/ROW]
[ROW][C]85[/C][C]0.78807643637456[/C][C]0.423847127250879[/C][C]0.211923563625440[/C][/ROW]
[ROW][C]86[/C][C]0.749881385044836[/C][C]0.500237229910327[/C][C]0.250118614955164[/C][/ROW]
[ROW][C]87[/C][C]0.711629742625002[/C][C]0.576740514749996[/C][C]0.288370257374998[/C][/ROW]
[ROW][C]88[/C][C]0.684300624151771[/C][C]0.631398751696458[/C][C]0.315699375848229[/C][/ROW]
[ROW][C]89[/C][C]0.689970767268842[/C][C]0.620058465462317[/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]0.601135591422623[/C][C]0.300567795711311[/C][/ROW]
[ROW][C]92[/C][C]0.652544215170268[/C][C]0.694911569659463[/C][C]0.347455784829732[/C][/ROW]
[ROW][C]93[/C][C]0.823934421691819[/C][C]0.352131156616363[/C][C]0.176065578308181[/C][/ROW]
[ROW][C]94[/C][C]0.794113045495833[/C][C]0.411773909008334[/C][C]0.205886954504167[/C][/ROW]
[ROW][C]95[/C][C]0.795607016650218[/C][C]0.408785966699564[/C][C]0.204392983349782[/C][/ROW]
[ROW][C]96[/C][C]0.77741771291142[/C][C]0.445164574177159[/C][C]0.222582287088580[/C][/ROW]
[ROW][C]97[/C][C]0.752558876375206[/C][C]0.494882247249588[/C][C]0.247441123624794[/C][/ROW]
[ROW][C]98[/C][C]0.899540348946936[/C][C]0.200919302106128[/C][C]0.100459651053064[/C][/ROW]
[ROW][C]99[/C][C]0.87958935637192[/C][C]0.240821287256159[/C][C]0.120410643628079[/C][/ROW]
[ROW][C]100[/C][C]0.879153213269572[/C][C]0.241693573460855[/C][C]0.120846786730428[/C][/ROW]
[ROW][C]101[/C][C]0.86333663730399[/C][C]0.27332672539202[/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]0.862684777454958[/C][C]0.274630445090083[/C][C]0.137315222545042[/C][/ROW]
[ROW][C]104[/C][C]0.886633956006366[/C][C]0.226732087987268[/C][C]0.113366043993634[/C][/ROW]
[ROW][C]105[/C][C]0.891715929862091[/C][C]0.216568140275818[/C][C]0.108284070137909[/C][/ROW]
[ROW][C]106[/C][C]0.908586051024546[/C][C]0.182827897950908[/C][C]0.091413948975454[/C][/ROW]
[ROW][C]107[/C][C]0.884974583211856[/C][C]0.230050833576289[/C][C]0.115025416788144[/C][/ROW]
[ROW][C]108[/C][C]0.898964617627392[/C][C]0.202070764745216[/C][C]0.101035382372608[/C][/ROW]
[ROW][C]109[/C][C]0.873400859807019[/C][C]0.253198280385962[/C][C]0.126599140192981[/C][/ROW]
[ROW][C]110[/C][C]0.90266108084191[/C][C]0.194677838316179[/C][C]0.0973389191580897[/C][/ROW]
[ROW][C]111[/C][C]0.882956250587048[/C][C]0.234087498825904[/C][C]0.117043749412952[/C][/ROW]
[ROW][C]112[/C][C]0.851205655375482[/C][C]0.297588689249037[/C][C]0.148794344624518[/C][/ROW]
[ROW][C]113[/C][C]0.81040189003717[/C][C]0.37919621992566[/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]0.724421485402817[/C][C]0.551157029194367[/C][C]0.275578514597183[/C][/ROW]
[ROW][C]116[/C][C]0.665575438178859[/C][C]0.668849123642282[/C][C]0.334424561821141[/C][/ROW]
[ROW][C]117[/C][C]0.713252741151518[/C][C]0.573494517696964[/C][C]0.286747258848482[/C][/ROW]
[ROW][C]118[/C][C]0.69002896580762[/C][C]0.619942068384759[/C][C]0.309971034192379[/C][/ROW]
[ROW][C]119[/C][C]0.703773818298343[/C][C]0.592452363403315[/C][C]0.296226181701657[/C][/ROW]
[ROW][C]120[/C][C]0.802212943325253[/C][C]0.395574113349495[/C][C]0.197787056674748[/C][/ROW]
[ROW][C]121[/C][C]0.74984300517293[/C][C]0.50031398965414[/C][C]0.25015699482707[/C][/ROW]
[ROW][C]122[/C][C]0.855684742578497[/C][C]0.288630514843005[/C][C]0.144315257421503[/C][/ROW]
[ROW][C]123[/C][C]0.82163581444932[/C][C]0.356728371101359[/C][C]0.178364185550680[/C][/ROW]
[ROW][C]124[/C][C]0.78268797215557[/C][C]0.434624055688859[/C][C]0.217312027844430[/C][/ROW]
[ROW][C]125[/C][C]0.71195205458444[/C][C]0.576095890831121[/C][C]0.288047945415560[/C][/ROW]
[ROW][C]126[/C][C]0.664905699247686[/C][C]0.670188601504629[/C][C]0.335094300752314[/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]0.934342542395725[/C][C]0.467171271197863[/C][/ROW]
[ROW][C]129[/C][C]0.437633828212867[/C][C]0.875267656425734[/C][C]0.562366171787133[/C][/ROW]
[ROW][C]130[/C][C]0.345298966383008[/C][C]0.690597932766017[/C][C]0.654701033616992[/C][/ROW]
[ROW][C]131[/C][C]0.424558044333715[/C][C]0.84911608866743[/C][C]0.575441955666285[/C][/ROW]
[ROW][C]132[/C][C]0.56960838636571[/C][C]0.860783227268579[/C][C]0.430391613634290[/C][/ROW]
[ROW][C]133[/C][C]0.433754457427058[/C][C]0.867508914854117[/C][C]0.566245542572942[/C][/ROW]
[ROW][C]134[/C][C]0.646973282891342[/C][C]0.706053434217315[/C][C]0.353026717108658[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112215&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112215&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
110.2497096611911540.4994193223823070.750290338808846
120.2105053697390850.4210107394781710.789494630260915
130.2320845650031270.4641691300062530.767915434996874
140.3979140591442430.7958281182884860.602085940855757
150.3405525336689060.6811050673378120.659447466331094
160.2411307777969680.4822615555939360.758869222203032
170.5685053132763290.8629893734473420.431494686723671
180.4771434188196310.9542868376392610.522856581180369
190.5316197417909810.9367605164180370.468380258209019
200.880272593369570.2394548132608590.119727406630430
210.8588239759314020.2823520481371960.141176024068598
220.8565929125361190.2868141749277630.143407087463881
230.8900575383457280.2198849233085450.109942461654272
240.9269901301326660.1460197397346680.0730098698673339
250.9042259722009970.1915480555980060.0957740277990032
260.886954838147330.2260903237053390.113045161852670
270.9123359150993840.1753281698012320.0876640849006158
280.894054031345210.2118919373095820.105945968654791
290.892513269675930.2149734606481420.107486730324071
300.8840138655915070.2319722688169850.115986134408493
310.8639508344792020.2720983310415960.136049165520798
320.8319344192581560.3361311614836880.168065580741844
330.8195058700368780.3609882599262450.180494129963122
340.8878804741593930.2242390516812150.112119525840607
350.8628699310129430.2742601379741150.137130068987058
360.8296483841415020.3407032317169960.170351615858498
370.7894328476575950.4211343046848100.210567152342405
380.761592806043710.4768143879125810.238407193956290
390.7627963302421390.4744073395157220.237203669757861
400.7196986879833260.5606026240333480.280301312016674
410.6993855338425480.6012289323149050.300614466157452
420.7333436643598330.5333126712803340.266656335640167
430.6890939278443030.6218121443113940.310906072155697
440.6644264719708890.6711470560582220.335573528029111
450.6254526657907360.7490946684185280.374547334209264
460.6147103857535590.7705792284928820.385289614246441
470.5851889966375910.8296220067248180.414811003362409
480.7114668017701240.5770663964597520.288533198229876
490.7024817296600410.5950365406799180.297518270339959
500.7472796836437380.5054406327125230.252720316356262
510.7036336855545690.5927326288908630.296366314445431
520.6593469740424810.6813060519150380.340653025957519
530.618954285730320.762091428539360.38104571426968
540.5854777700427720.8290444599144560.414522229957228
550.5665666486648320.8668667026703370.433433351335168
560.7575765373067740.4848469253864510.242423462693226
570.73118488474120.53763023051760.2688151152588
580.8045270977538530.3909458044922940.195472902246147
590.7908321645848610.4183356708302780.209167835415139
600.7651374312144660.4697251375710680.234862568785534
610.8476961189635440.3046077620729120.152303881036456
620.9008366959400280.1983266081199440.099163304059972
630.8865337017423720.2269325965152570.113466298257628
640.8708934980866720.2582130038266570.129106501913328
650.84457514317190.3108497136561980.155424856828099
660.8163176327890960.3673647344218080.183682367210904
670.8528690979301140.2942618041397720.147130902069886
680.825122749338790.3497545013224210.174877250661210
690.7933451446937760.4133097106124490.206654855306224
700.7687608746399120.4624782507201770.231239125360088
710.76852068284930.4629586343013990.231479317150700
720.730247367631710.539505264736580.26975263236829
730.7006434173953280.5987131652093440.299356582604672
740.683634453040180.632731093919640.31636554695982
750.6699730079417820.6600539841164350.330026992058217
760.6513469602014570.6973060795970870.348653039798543
770.6043218702850230.7913562594299540.395678129714977
780.5993232632244190.8013534735511620.400676736775581
790.5537720098596340.8924559802807310.446227990140366
800.5118626739953570.9762746520092860.488137326004643
810.5156831305950520.9686337388098950.484316869404948
820.7967464231861670.4065071536276660.203253576813833
830.7800976597317660.4398046805364670.219902340268234
840.8211514267674220.3576971464651560.178848573232578
850.788076436374560.4238471272508790.211923563625440
860.7498813850448360.5002372299103270.250118614955164
870.7116297426250020.5767405147499960.288370257374998
880.6843006241517710.6313987516964580.315699375848229
890.6899707672688420.6200584654623170.310029232731158
900.6694441381482680.6611117237034640.330555861851732
910.6994322042886880.6011355914226230.300567795711311
920.6525442151702680.6949115696594630.347455784829732
930.8239344216918190.3521311566163630.176065578308181
940.7941130454958330.4117739090083340.205886954504167
950.7956070166502180.4087859666995640.204392983349782
960.777417712911420.4451645741771590.222582287088580
970.7525588763752060.4948822472495880.247441123624794
980.8995403489469360.2009193021061280.100459651053064
990.879589356371920.2408212872561590.120410643628079
1000.8791532132695720.2416935734608550.120846786730428
1010.863336637303990.273326725392020.13666336269601
1020.8321781795597150.335643640880570.167821820440285
1030.8626847774549580.2746304450900830.137315222545042
1040.8866339560063660.2267320879872680.113366043993634
1050.8917159298620910.2165681402758180.108284070137909
1060.9085860510245460.1828278979509080.091413948975454
1070.8849745832118560.2300508335762890.115025416788144
1080.8989646176273920.2020707647452160.101035382372608
1090.8734008598070190.2531982803859620.126599140192981
1100.902661080841910.1946778383161790.0973389191580897
1110.8829562505870480.2340874988259040.117043749412952
1120.8512056553754820.2975886892490370.148794344624518
1130.810401890037170.379196219925660.18959810996283
1140.7659761699762120.4680476600475770.234023830023788
1150.7244214854028170.5511570291943670.275578514597183
1160.6655754381788590.6688491236422820.334424561821141
1170.7132527411515180.5734945176969640.286747258848482
1180.690028965807620.6199420683847590.309971034192379
1190.7037738182983430.5924523634033150.296226181701657
1200.8022129433252530.3955741133494950.197787056674748
1210.749843005172930.500313989654140.25015699482707
1220.8556847425784970.2886305148430050.144315257421503
1230.821635814449320.3567283711013590.178364185550680
1240.782687972155570.4346240556888590.217312027844430
1250.711952054584440.5760958908311210.288047945415560
1260.6649056992476860.6701886015046290.335094300752314
1270.5768304950714150.846339009857170.423169504928585
1280.5328287288021370.9343425423957250.467171271197863
1290.4376338282128670.8752676564257340.562366171787133
1300.3452989663830080.6905979327660170.654701033616992
1310.4245580443337150.849116088667430.575441955666285
1320.569608386365710.8607832272685790.430391613634290
1330.4337544574270580.8675089148541170.566245542572942
1340.6469732828913420.7060534342173150.353026717108658






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=112215&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=112215&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112215&T=6

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


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

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


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


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')
}