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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 11 Dec 2010 09:59:39 +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/11/t12920615639yf6wmxfgq3mrk4.htm/, Retrieved Mon, 06 May 2024 23:20:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108035, Retrieved Mon, 06 May 2024 23:20:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact175

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2010-12-11 09:59:39] [6ff6d3268c67efbfcd6d6506b34b66fb] [Current]
-   PD      [Multiple Regression] [multiple regressi...] [2010-12-14 08:31:57] [f730b099f190102bcd41f590a8dae16d]
-   PD      [Multiple Regression] [workshop 10: Mult...] [2010-12-14 08:37:33] [814f53995537cd15c528d8efbf1cf544]
- R           [Multiple Regression] [ws10-3] [2011-12-13 10:22:13] [f7a862281046b7153543b12c78921b36]
-    D          [Multiple Regression] [paper3-3] [2011-12-22 10:09:46] [f7a862281046b7153543b12c78921b36]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	15	2	9	42	12
12	18	1	9	51	15
15	11	1	9	42	14
12	16	1	8	46	10
10	12	2	14	41	10
12	17	2	14	49	9
15	15	1	15	47	18
9	19	1	11	33	11
11	18	1	8	47	12
11	10	2	14	42	11
11	14	1	9	32	15
15	18	1	6	53	17
7	18	2	14	41	14
11	14	2	8	41	24
11	14	1	11	33	7
10	12	1	16	37	18
14	16	2	11	43	11
6	13	2	13	33	14
11	16	1	7	49	18
15	14	2	9	42	12
11	9	1	15	43	11
12	9	2	16	37	5
14	17	1	10	43	12
15	13	2	14	42	11
9	15	2	12	43	10
13	17	1	6	46	11
13	16	2	4	33	15
16	12	1	12	42	16
13	11	1	14	40	14
12	16	2	13	44	8
14	17	1	9	42	13
11	17	2	14	52	18
9	16	1	14	44	17
16	13	2	10	45	10
12	12	1	14	46	13
10	12	2	8	36	11
13	16	1	8	45	12
16	14	1	10	49	12
14	12	2	9	43	12
15	12	1	9	43	9
5	14	1	11	37	18
8	8	2	15	32	7
11	15	1	9	45	14
16	14	2	9	45	16
17	11	1	10	45	12
9	13	2	8	45	17
9	14	1	8	31	12
13	15	1	14	33	9
10	16	1	10	44	12
6	10	2	11	49	9
12	11	2	9	44	13
8	12	2	12	41	10
14	14	2	13	44	10
12	15	1	14	38	11
11	16	1	15	33	13
16	9	1	11	47	13
8	11	2	9	37	13
15	15	1	8	48	6
7	15	2	7	40	7
16	13	2	10	50	13
14	17	1	10	54	21
16	17	1	10	43	11
9	15	1	9	54	9
14	13	1	13	44	18
11	15	2	11	47	9
13	13	2	8	33	9
15	15	1	10	45	15
5	10	2	14	33	9
15	15	1	11	44	11
13	14	1	10	47	14
11	15	2	16	45	14
11	16	2	11	43	8
12	7	1	16	43	12
12	13	1	6	33	8
12	15	1	11	46	11
14	13	1	14	47	17
6	16	1	9	47	16
7	16	2	9	0	11
14	12	1	11	43	13
13	15	2	12	46	11
12	14	2	20	36	8
9	11	2	11	42	11
12	14	1	12	44	13
16	15	1	9	47	13
10	9	2	10	41	15
14	15	1	14	47	15
10	17	1	8	46	12
16	16	1	10	47	12
15	14	1	8	46	15
12	15	2	7	46	12
10	16	1	11	36	21
8	10	1	14	30	24
8	17	2	8	48	11
11	15	2	14	45	12
13	15	1	10	49	15
16	13	1	9	55	17
14	14	2	16	11	12
11	16	1	8	52	16
4	11	2	12	33	13
14	18	1	8	47	15
9	14	1	16	33	11
14	14	1	13	44	15
8	14	1	13	42	12
8	14	1	8	55	14
11	15	1	9	42	12
12	14	1	11	46	20
14	15	1	9	46	17
15	15	2	8	47	12
16	12	1	14	33	11
16	19	1	7	53	11
14	13	2	11	42	9
12	15	1	11	44	12
14	17	2	10	55	11
8	9	2	14	40	8
16	15	2	10	46	12
12	16	1	9	53	15
12	17	1	8	44	10
11	11	1	14	35	14
4	15	1	12	40	16
16	11	1	12	44	18
15	15	1	6	46	6
10	17	1	16	45	16
13	14	1	8	53	11
15	12	2	13	45	20
12	14	1	12	48	10
14	15	2	11	46	16
7	16	1	12	55	15
19	16	1	9	47	14
12	14	1	11	43	7
12	11	2	16	38	9
8	14	2	10	40	12
12	13	1	13	47	12
10	13	1	11	47	13
8	14	2	11	42	17
10	16	2	9	53	11
14	16	2	11	43	11
16	12	1	12	44	14
13	11	1	10	42	13
16	13	1	13	51	12
9	15	1	9	54	11
14	13	2	14	41	15
14	16	2	14	51	11
12	13	1	8	51	13

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=108035&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=108035&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108035&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
belonging[t] = + 33.7628490052607 + 0.576297541832057popularity[t] + 0.478113214787677hapiness[t] -1.43366386194624gender[t] -0.413115473681592doubsaboutactions[t] + 0.180267632695104parentalexpectations[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
belonging[t] =  +  33.7628490052607 +  0.576297541832057popularity[t] +  0.478113214787677hapiness[t] -1.43366386194624gender[t] -0.413115473681592doubsaboutactions[t] +  0.180267632695104parentalexpectations[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108035&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]belonging[t] =  +  33.7628490052607 +  0.576297541832057popularity[t] +  0.478113214787677hapiness[t] -1.43366386194624gender[t] -0.413115473681592doubsaboutactions[t] +  0.180267632695104parentalexpectations[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108035&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108035&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
belonging[t] = + 33.7628490052607 + 0.576297541832057popularity[t] + 0.478113214787677hapiness[t] -1.43366386194624gender[t] -0.413115473681592doubsaboutactions[t] + 0.180267632695104parentalexpectations[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)33.76284900526076.5885035.12451e-060
popularity0.5762975418320570.1957752.94370.0038110.001906
hapiness0.4781132147876770.2617141.82690.0698980.034949
gender-1.433663861946241.23424-1.16160.2474280.123714
doubsaboutactions-0.4131154736815920.223849-1.84550.0671220.033561
parentalexpectations0.1802676326951040.1724621.04530.2977430.148872

\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) & 33.7628490052607 & 6.588503 & 5.1245 & 1e-06 & 0 \tabularnewline
popularity & 0.576297541832057 & 0.195775 & 2.9437 & 0.003811 & 0.001906 \tabularnewline
hapiness & 0.478113214787677 & 0.261714 & 1.8269 & 0.069898 & 0.034949 \tabularnewline
gender & -1.43366386194624 & 1.23424 & -1.1616 & 0.247428 & 0.123714 \tabularnewline
doubsaboutactions & -0.413115473681592 & 0.223849 & -1.8455 & 0.067122 & 0.033561 \tabularnewline
parentalexpectations & 0.180267632695104 & 0.172462 & 1.0453 & 0.297743 & 0.148872 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108035&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]33.7628490052607[/C][C]6.588503[/C][C]5.1245[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]popularity[/C][C]0.576297541832057[/C][C]0.195775[/C][C]2.9437[/C][C]0.003811[/C][C]0.001906[/C][/ROW]
[ROW][C]hapiness[/C][C]0.478113214787677[/C][C]0.261714[/C][C]1.8269[/C][C]0.069898[/C][C]0.034949[/C][/ROW]
[ROW][C]gender[/C][C]-1.43366386194624[/C][C]1.23424[/C][C]-1.1616[/C][C]0.247428[/C][C]0.123714[/C][/ROW]
[ROW][C]doubsaboutactions[/C][C]-0.413115473681592[/C][C]0.223849[/C][C]-1.8455[/C][C]0.067122[/C][C]0.033561[/C][/ROW]
[ROW][C]parentalexpectations[/C][C]0.180267632695104[/C][C]0.172462[/C][C]1.0453[/C][C]0.297743[/C][C]0.148872[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108035&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108035&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)33.76284900526076.5885035.12451e-060
popularity0.5762975418320570.1957752.94370.0038110.001906
hapiness0.4781132147876770.2617141.82690.0698980.034949
gender-1.433663861946241.23424-1.16160.2474280.123714
doubsaboutactions-0.4131154736815920.223849-1.84550.0671220.033561
parentalexpectations0.1802676326951040.1724621.04530.2977430.148872






Multiple Linear Regression - Regression Statistics
Multiple R0.417432508416781
R-squared0.174249899083126
Adjusted R-squared0.144113034086160
F-TEST (value)5.78195174251427
F-TEST (DF numerator)5
F-TEST (DF denominator)137
p-value7.09848962427984e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.80662163087911
Sum Squared Residuals6347.22342955535

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.417432508416781 \tabularnewline
R-squared & 0.174249899083126 \tabularnewline
Adjusted R-squared & 0.144113034086160 \tabularnewline
F-TEST (value) & 5.78195174251427 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 137 \tabularnewline
p-value & 7.09848962427984e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 6.80662163087911 \tabularnewline
Sum Squared Residuals & 6347.22342955535 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108035&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.417432508416781[/C][/ROW]
[ROW][C]R-squared[/C][C]0.174249899083126[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.144113034086160[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.78195174251427[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]137[/C][/ROW]
[ROW][C]p-value[/C][C]7.09848962427984e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]6.80662163087911[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]6347.22342955535[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108035&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108035&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 R0.417432508416781
R-squared0.174249899083126
Adjusted R-squared0.144113034086160
F-TEST (value)5.78195174251427
F-TEST (DF numerator)5
F-TEST (DF denominator)137
p-value7.09848962427984e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.80662163087911
Sum Squared Residuals6347.22342955535






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14244.0042598762073-2.00425987620725
25146.83676873876964.16323126123041
34245.0386012280569-3.03860122805688
44645.39231961940030.607680380599712
54138.41491497254972.58508502745032
64941.77780849745717.22219150254293
74745.19343177589851.80656822410153
83344.0386878499175-11.0386878499175
94746.13278377253380.867216227466201
104238.21525371750153.78474628249851
113244.3480183377868-12.3480183377868
125350.16554305070072.83445694929927
134140.275772166560.724227833440006
144144.9498786437781-3.9498786437781
153342.0796463288628-9.0796463288628
163740.4644889486936-3.46448894869356
174344.0521720527685-1.05217205276850
183337.7220240244711-4.72202402447114
194946.67127861281072.32872138718934
204244.6787417450835-2.67874174508349
214338.75768889097854.24231110902155
223736.40560130101210.59439869898794
234346.5573322358791-3.55733223587911
244241.95478352919270.0452164708072546
254340.09918802244382.90081197755616
264647.4532289560783-1.45322895607832
273347.088753357488-14.088753357488
284245.2142008290221-3.21420082902206
294041.8204287759848-1.82042877598483
304441.53254312365592.46745687634411
314247.1507153422558-5.1507153422558
325242.82391964988109.17608035011904
334442.44660758068031.55339241931970
344544.00327533305610.996724666943933
354641.54197681624534.45802318375466
363641.0738754473343-5.07387544733434
374546.3291524266226-1.32915242662256
384946.27558767518022.72441232481981
394343.1462177736761-0.146217773676077
404344.6153762793691-1.61537627936906
413740.6048050375166-3.6048050375166
423234.3959486579680-2.39594865796796
434544.64586391987940.354136080120617
444545.976109817696-0.97610981769596
454545.4175455726492-0.417545572649211
464542.05729691646062.94270308353941
473143.067735829719-12.0677358297190
483342.83154347166-9.83154347166001
494443.77402885376320.225971146236796
504936.212577163995812.7874228360042
514441.69577710791942.30422289208061
524138.08855083624882.91144916375125
534442.08944704313491.91055295686515
543842.6157811952182-4.61578119521817
553342.4650166598824-9.4650166598824
564743.65217376025533.34782623974469
573739.3905869405912-2.39058694059117
584845.92202849932842.07797150067163
594040.4713674091024-0.471367409102381
605044.54407823114145.45592176885862
615448.1797409301355.82025906986496
624347.5296596868481-4.52965968684812
635442.591930672739711.4080693272602
644444.4871387518542-0.487138751854244
654741.48463094709445.51536905290556
663342.920346022228-9.92034602222798
674546.7182062462211-1.71820624622112
683334.3969332011189-1.39693320111894
694445.5840202417591-1.58402024175911
704744.90723031507422.09276968492577
714540.3203917421624.679608257838
724341.7824765291871.21752347081298
734338.14491216224874.85508783775133
743344.4236756570102-11.4236756570102
754643.85512761626292.14487238373706
764743.89375564547753.10624435452245
774742.6030246908974.39697530910302
78040.8443202073073-40.8443202073073
794343.9339183209542-0.933918320954231
804642.58464582246723.41535417753283
813637.6845083783094-1.68450837830939
824238.78011826966983.21988173033017
834443.32443419318390.675565806816119
844747.3470839963446-0.347083996344561
854139.53437538638851.46562461361146
864744.48944680966272.51055319033731
874645.07837301591410.921626984085934
884747.2318141047555-0.231814104755542
894647.0663239787966-1.06632397879663
904644.25419328173821.74580671826182
913644.9833220743376-8.98332207433755
923040.2635041789879-10.2635041789879
934842.31184643760865.68815356239138
944540.7860874241354.21391257586502
954945.5656111625573.43438883744299
965547.11192809754967.88807190245038
971141.2106358874803-30.2106358874803
985245.89762787373896.10237212626114
993335.8460503522182-2.84605035221816
1004748.4024792961153-1.40247929611528
1013339.5825444075711-6.58254440757113
1024444.4244490685566-0.424449068556609
1034240.42586091947901.57413908052104
1045542.851973553277112.1480264467229
1054244.2853286544892-2.28532865448918
1064644.99942309573121.0005769042688
1074646.9155594434609-0.915559443460863
1084745.56997043355281.43002956644724
1093343.4866317181834-10.4866317181834
1105349.72523253746823.27476746253175
1114242.2572971430153-0.257297143015258
1124444.0353952489580-0.0353952489580468
1135544.943400741237810.0565992587622
1144035.46744497913234.53255502086767
1154645.32003702802160.67996297197837
1165345.88054230919427.11945769080578
1174445.870432834188-1.87043283418797
1183540.6678336923207-5.66783369232071
1194039.73296997140040.267030028599579
1204445.0966228796246-1.09662287962459
1214646.7482594466916-0.748259446691554
1224542.49451975724172.50548024275826
1235345.19265836435217.8073416356479
1244543.51219448234261.48780551765740
1254842.78363129509865.21636870490143
1264644.47539700145631.52460299854366
1275541.759708178989213.2402918210108
1284749.7343574693235-2.73435746932351
1294342.65594387069480.344056129305151
1303838.0828982613678-0.0828982613678303
1314040.2315434785775-0.231543478577502
1324742.25293787201954.74706212798049
1334742.10684136841374.89315863158632
1344240.71976616837141.28023383162857
1355342.573212832803510.4267871671965
1364344.0521720527685-1.05217205276850
1374444.8536655636319-0.853665563631855
1384243.2926230380161-1.29262303801609
1395144.55812803934776.44187196065227
1405442.952465938130011.0475340618700
1414142.0995565181411-1.09955651814110
1425142.81282563172378.18717436827628
1435144.49878287312266.50121712687743

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 42 & 44.0042598762073 & -2.00425987620725 \tabularnewline
2 & 51 & 46.8367687387696 & 4.16323126123041 \tabularnewline
3 & 42 & 45.0386012280569 & -3.03860122805688 \tabularnewline
4 & 46 & 45.3923196194003 & 0.607680380599712 \tabularnewline
5 & 41 & 38.4149149725497 & 2.58508502745032 \tabularnewline
6 & 49 & 41.7778084974571 & 7.22219150254293 \tabularnewline
7 & 47 & 45.1934317758985 & 1.80656822410153 \tabularnewline
8 & 33 & 44.0386878499175 & -11.0386878499175 \tabularnewline
9 & 47 & 46.1327837725338 & 0.867216227466201 \tabularnewline
10 & 42 & 38.2152537175015 & 3.78474628249851 \tabularnewline
11 & 32 & 44.3480183377868 & -12.3480183377868 \tabularnewline
12 & 53 & 50.1655430507007 & 2.83445694929927 \tabularnewline
13 & 41 & 40.27577216656 & 0.724227833440006 \tabularnewline
14 & 41 & 44.9498786437781 & -3.9498786437781 \tabularnewline
15 & 33 & 42.0796463288628 & -9.0796463288628 \tabularnewline
16 & 37 & 40.4644889486936 & -3.46448894869356 \tabularnewline
17 & 43 & 44.0521720527685 & -1.05217205276850 \tabularnewline
18 & 33 & 37.7220240244711 & -4.72202402447114 \tabularnewline
19 & 49 & 46.6712786128107 & 2.32872138718934 \tabularnewline
20 & 42 & 44.6787417450835 & -2.67874174508349 \tabularnewline
21 & 43 & 38.7576888909785 & 4.24231110902155 \tabularnewline
22 & 37 & 36.4056013010121 & 0.59439869898794 \tabularnewline
23 & 43 & 46.5573322358791 & -3.55733223587911 \tabularnewline
24 & 42 & 41.9547835291927 & 0.0452164708072546 \tabularnewline
25 & 43 & 40.0991880224438 & 2.90081197755616 \tabularnewline
26 & 46 & 47.4532289560783 & -1.45322895607832 \tabularnewline
27 & 33 & 47.088753357488 & -14.088753357488 \tabularnewline
28 & 42 & 45.2142008290221 & -3.21420082902206 \tabularnewline
29 & 40 & 41.8204287759848 & -1.82042877598483 \tabularnewline
30 & 44 & 41.5325431236559 & 2.46745687634411 \tabularnewline
31 & 42 & 47.1507153422558 & -5.1507153422558 \tabularnewline
32 & 52 & 42.8239196498810 & 9.17608035011904 \tabularnewline
33 & 44 & 42.4466075806803 & 1.55339241931970 \tabularnewline
34 & 45 & 44.0032753330561 & 0.996724666943933 \tabularnewline
35 & 46 & 41.5419768162453 & 4.45802318375466 \tabularnewline
36 & 36 & 41.0738754473343 & -5.07387544733434 \tabularnewline
37 & 45 & 46.3291524266226 & -1.32915242662256 \tabularnewline
38 & 49 & 46.2755876751802 & 2.72441232481981 \tabularnewline
39 & 43 & 43.1462177736761 & -0.146217773676077 \tabularnewline
40 & 43 & 44.6153762793691 & -1.61537627936906 \tabularnewline
41 & 37 & 40.6048050375166 & -3.6048050375166 \tabularnewline
42 & 32 & 34.3959486579680 & -2.39594865796796 \tabularnewline
43 & 45 & 44.6458639198794 & 0.354136080120617 \tabularnewline
44 & 45 & 45.976109817696 & -0.97610981769596 \tabularnewline
45 & 45 & 45.4175455726492 & -0.417545572649211 \tabularnewline
46 & 45 & 42.0572969164606 & 2.94270308353941 \tabularnewline
47 & 31 & 43.067735829719 & -12.0677358297190 \tabularnewline
48 & 33 & 42.83154347166 & -9.83154347166001 \tabularnewline
49 & 44 & 43.7740288537632 & 0.225971146236796 \tabularnewline
50 & 49 & 36.2125771639958 & 12.7874228360042 \tabularnewline
51 & 44 & 41.6957771079194 & 2.30422289208061 \tabularnewline
52 & 41 & 38.0885508362488 & 2.91144916375125 \tabularnewline
53 & 44 & 42.0894470431349 & 1.91055295686515 \tabularnewline
54 & 38 & 42.6157811952182 & -4.61578119521817 \tabularnewline
55 & 33 & 42.4650166598824 & -9.4650166598824 \tabularnewline
56 & 47 & 43.6521737602553 & 3.34782623974469 \tabularnewline
57 & 37 & 39.3905869405912 & -2.39058694059117 \tabularnewline
58 & 48 & 45.9220284993284 & 2.07797150067163 \tabularnewline
59 & 40 & 40.4713674091024 & -0.471367409102381 \tabularnewline
60 & 50 & 44.5440782311414 & 5.45592176885862 \tabularnewline
61 & 54 & 48.179740930135 & 5.82025906986496 \tabularnewline
62 & 43 & 47.5296596868481 & -4.52965968684812 \tabularnewline
63 & 54 & 42.5919306727397 & 11.4080693272602 \tabularnewline
64 & 44 & 44.4871387518542 & -0.487138751854244 \tabularnewline
65 & 47 & 41.4846309470944 & 5.51536905290556 \tabularnewline
66 & 33 & 42.920346022228 & -9.92034602222798 \tabularnewline
67 & 45 & 46.7182062462211 & -1.71820624622112 \tabularnewline
68 & 33 & 34.3969332011189 & -1.39693320111894 \tabularnewline
69 & 44 & 45.5840202417591 & -1.58402024175911 \tabularnewline
70 & 47 & 44.9072303150742 & 2.09276968492577 \tabularnewline
71 & 45 & 40.320391742162 & 4.679608257838 \tabularnewline
72 & 43 & 41.782476529187 & 1.21752347081298 \tabularnewline
73 & 43 & 38.1449121622487 & 4.85508783775133 \tabularnewline
74 & 33 & 44.4236756570102 & -11.4236756570102 \tabularnewline
75 & 46 & 43.8551276162629 & 2.14487238373706 \tabularnewline
76 & 47 & 43.8937556454775 & 3.10624435452245 \tabularnewline
77 & 47 & 42.603024690897 & 4.39697530910302 \tabularnewline
78 & 0 & 40.8443202073073 & -40.8443202073073 \tabularnewline
79 & 43 & 43.9339183209542 & -0.933918320954231 \tabularnewline
80 & 46 & 42.5846458224672 & 3.41535417753283 \tabularnewline
81 & 36 & 37.6845083783094 & -1.68450837830939 \tabularnewline
82 & 42 & 38.7801182696698 & 3.21988173033017 \tabularnewline
83 & 44 & 43.3244341931839 & 0.675565806816119 \tabularnewline
84 & 47 & 47.3470839963446 & -0.347083996344561 \tabularnewline
85 & 41 & 39.5343753863885 & 1.46562461361146 \tabularnewline
86 & 47 & 44.4894468096627 & 2.51055319033731 \tabularnewline
87 & 46 & 45.0783730159141 & 0.921626984085934 \tabularnewline
88 & 47 & 47.2318141047555 & -0.231814104755542 \tabularnewline
89 & 46 & 47.0663239787966 & -1.06632397879663 \tabularnewline
90 & 46 & 44.2541932817382 & 1.74580671826182 \tabularnewline
91 & 36 & 44.9833220743376 & -8.98332207433755 \tabularnewline
92 & 30 & 40.2635041789879 & -10.2635041789879 \tabularnewline
93 & 48 & 42.3118464376086 & 5.68815356239138 \tabularnewline
94 & 45 & 40.786087424135 & 4.21391257586502 \tabularnewline
95 & 49 & 45.565611162557 & 3.43438883744299 \tabularnewline
96 & 55 & 47.1119280975496 & 7.88807190245038 \tabularnewline
97 & 11 & 41.2106358874803 & -30.2106358874803 \tabularnewline
98 & 52 & 45.8976278737389 & 6.10237212626114 \tabularnewline
99 & 33 & 35.8460503522182 & -2.84605035221816 \tabularnewline
100 & 47 & 48.4024792961153 & -1.40247929611528 \tabularnewline
101 & 33 & 39.5825444075711 & -6.58254440757113 \tabularnewline
102 & 44 & 44.4244490685566 & -0.424449068556609 \tabularnewline
103 & 42 & 40.4258609194790 & 1.57413908052104 \tabularnewline
104 & 55 & 42.8519735532771 & 12.1480264467229 \tabularnewline
105 & 42 & 44.2853286544892 & -2.28532865448918 \tabularnewline
106 & 46 & 44.9994230957312 & 1.0005769042688 \tabularnewline
107 & 46 & 46.9155594434609 & -0.915559443460863 \tabularnewline
108 & 47 & 45.5699704335528 & 1.43002956644724 \tabularnewline
109 & 33 & 43.4866317181834 & -10.4866317181834 \tabularnewline
110 & 53 & 49.7252325374682 & 3.27476746253175 \tabularnewline
111 & 42 & 42.2572971430153 & -0.257297143015258 \tabularnewline
112 & 44 & 44.0353952489580 & -0.0353952489580468 \tabularnewline
113 & 55 & 44.9434007412378 & 10.0565992587622 \tabularnewline
114 & 40 & 35.4674449791323 & 4.53255502086767 \tabularnewline
115 & 46 & 45.3200370280216 & 0.67996297197837 \tabularnewline
116 & 53 & 45.8805423091942 & 7.11945769080578 \tabularnewline
117 & 44 & 45.870432834188 & -1.87043283418797 \tabularnewline
118 & 35 & 40.6678336923207 & -5.66783369232071 \tabularnewline
119 & 40 & 39.7329699714004 & 0.267030028599579 \tabularnewline
120 & 44 & 45.0966228796246 & -1.09662287962459 \tabularnewline
121 & 46 & 46.7482594466916 & -0.748259446691554 \tabularnewline
122 & 45 & 42.4945197572417 & 2.50548024275826 \tabularnewline
123 & 53 & 45.1926583643521 & 7.8073416356479 \tabularnewline
124 & 45 & 43.5121944823426 & 1.48780551765740 \tabularnewline
125 & 48 & 42.7836312950986 & 5.21636870490143 \tabularnewline
126 & 46 & 44.4753970014563 & 1.52460299854366 \tabularnewline
127 & 55 & 41.7597081789892 & 13.2402918210108 \tabularnewline
128 & 47 & 49.7343574693235 & -2.73435746932351 \tabularnewline
129 & 43 & 42.6559438706948 & 0.344056129305151 \tabularnewline
130 & 38 & 38.0828982613678 & -0.0828982613678303 \tabularnewline
131 & 40 & 40.2315434785775 & -0.231543478577502 \tabularnewline
132 & 47 & 42.2529378720195 & 4.74706212798049 \tabularnewline
133 & 47 & 42.1068413684137 & 4.89315863158632 \tabularnewline
134 & 42 & 40.7197661683714 & 1.28023383162857 \tabularnewline
135 & 53 & 42.5732128328035 & 10.4267871671965 \tabularnewline
136 & 43 & 44.0521720527685 & -1.05217205276850 \tabularnewline
137 & 44 & 44.8536655636319 & -0.853665563631855 \tabularnewline
138 & 42 & 43.2926230380161 & -1.29262303801609 \tabularnewline
139 & 51 & 44.5581280393477 & 6.44187196065227 \tabularnewline
140 & 54 & 42.9524659381300 & 11.0475340618700 \tabularnewline
141 & 41 & 42.0995565181411 & -1.09955651814110 \tabularnewline
142 & 51 & 42.8128256317237 & 8.18717436827628 \tabularnewline
143 & 51 & 44.4987828731226 & 6.50121712687743 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108035&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]42[/C][C]44.0042598762073[/C][C]-2.00425987620725[/C][/ROW]
[ROW][C]2[/C][C]51[/C][C]46.8367687387696[/C][C]4.16323126123041[/C][/ROW]
[ROW][C]3[/C][C]42[/C][C]45.0386012280569[/C][C]-3.03860122805688[/C][/ROW]
[ROW][C]4[/C][C]46[/C][C]45.3923196194003[/C][C]0.607680380599712[/C][/ROW]
[ROW][C]5[/C][C]41[/C][C]38.4149149725497[/C][C]2.58508502745032[/C][/ROW]
[ROW][C]6[/C][C]49[/C][C]41.7778084974571[/C][C]7.22219150254293[/C][/ROW]
[ROW][C]7[/C][C]47[/C][C]45.1934317758985[/C][C]1.80656822410153[/C][/ROW]
[ROW][C]8[/C][C]33[/C][C]44.0386878499175[/C][C]-11.0386878499175[/C][/ROW]
[ROW][C]9[/C][C]47[/C][C]46.1327837725338[/C][C]0.867216227466201[/C][/ROW]
[ROW][C]10[/C][C]42[/C][C]38.2152537175015[/C][C]3.78474628249851[/C][/ROW]
[ROW][C]11[/C][C]32[/C][C]44.3480183377868[/C][C]-12.3480183377868[/C][/ROW]
[ROW][C]12[/C][C]53[/C][C]50.1655430507007[/C][C]2.83445694929927[/C][/ROW]
[ROW][C]13[/C][C]41[/C][C]40.27577216656[/C][C]0.724227833440006[/C][/ROW]
[ROW][C]14[/C][C]41[/C][C]44.9498786437781[/C][C]-3.9498786437781[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]42.0796463288628[/C][C]-9.0796463288628[/C][/ROW]
[ROW][C]16[/C][C]37[/C][C]40.4644889486936[/C][C]-3.46448894869356[/C][/ROW]
[ROW][C]17[/C][C]43[/C][C]44.0521720527685[/C][C]-1.05217205276850[/C][/ROW]
[ROW][C]18[/C][C]33[/C][C]37.7220240244711[/C][C]-4.72202402447114[/C][/ROW]
[ROW][C]19[/C][C]49[/C][C]46.6712786128107[/C][C]2.32872138718934[/C][/ROW]
[ROW][C]20[/C][C]42[/C][C]44.6787417450835[/C][C]-2.67874174508349[/C][/ROW]
[ROW][C]21[/C][C]43[/C][C]38.7576888909785[/C][C]4.24231110902155[/C][/ROW]
[ROW][C]22[/C][C]37[/C][C]36.4056013010121[/C][C]0.59439869898794[/C][/ROW]
[ROW][C]23[/C][C]43[/C][C]46.5573322358791[/C][C]-3.55733223587911[/C][/ROW]
[ROW][C]24[/C][C]42[/C][C]41.9547835291927[/C][C]0.0452164708072546[/C][/ROW]
[ROW][C]25[/C][C]43[/C][C]40.0991880224438[/C][C]2.90081197755616[/C][/ROW]
[ROW][C]26[/C][C]46[/C][C]47.4532289560783[/C][C]-1.45322895607832[/C][/ROW]
[ROW][C]27[/C][C]33[/C][C]47.088753357488[/C][C]-14.088753357488[/C][/ROW]
[ROW][C]28[/C][C]42[/C][C]45.2142008290221[/C][C]-3.21420082902206[/C][/ROW]
[ROW][C]29[/C][C]40[/C][C]41.8204287759848[/C][C]-1.82042877598483[/C][/ROW]
[ROW][C]30[/C][C]44[/C][C]41.5325431236559[/C][C]2.46745687634411[/C][/ROW]
[ROW][C]31[/C][C]42[/C][C]47.1507153422558[/C][C]-5.1507153422558[/C][/ROW]
[ROW][C]32[/C][C]52[/C][C]42.8239196498810[/C][C]9.17608035011904[/C][/ROW]
[ROW][C]33[/C][C]44[/C][C]42.4466075806803[/C][C]1.55339241931970[/C][/ROW]
[ROW][C]34[/C][C]45[/C][C]44.0032753330561[/C][C]0.996724666943933[/C][/ROW]
[ROW][C]35[/C][C]46[/C][C]41.5419768162453[/C][C]4.45802318375466[/C][/ROW]
[ROW][C]36[/C][C]36[/C][C]41.0738754473343[/C][C]-5.07387544733434[/C][/ROW]
[ROW][C]37[/C][C]45[/C][C]46.3291524266226[/C][C]-1.32915242662256[/C][/ROW]
[ROW][C]38[/C][C]49[/C][C]46.2755876751802[/C][C]2.72441232481981[/C][/ROW]
[ROW][C]39[/C][C]43[/C][C]43.1462177736761[/C][C]-0.146217773676077[/C][/ROW]
[ROW][C]40[/C][C]43[/C][C]44.6153762793691[/C][C]-1.61537627936906[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]40.6048050375166[/C][C]-3.6048050375166[/C][/ROW]
[ROW][C]42[/C][C]32[/C][C]34.3959486579680[/C][C]-2.39594865796796[/C][/ROW]
[ROW][C]43[/C][C]45[/C][C]44.6458639198794[/C][C]0.354136080120617[/C][/ROW]
[ROW][C]44[/C][C]45[/C][C]45.976109817696[/C][C]-0.97610981769596[/C][/ROW]
[ROW][C]45[/C][C]45[/C][C]45.4175455726492[/C][C]-0.417545572649211[/C][/ROW]
[ROW][C]46[/C][C]45[/C][C]42.0572969164606[/C][C]2.94270308353941[/C][/ROW]
[ROW][C]47[/C][C]31[/C][C]43.067735829719[/C][C]-12.0677358297190[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]42.83154347166[/C][C]-9.83154347166001[/C][/ROW]
[ROW][C]49[/C][C]44[/C][C]43.7740288537632[/C][C]0.225971146236796[/C][/ROW]
[ROW][C]50[/C][C]49[/C][C]36.2125771639958[/C][C]12.7874228360042[/C][/ROW]
[ROW][C]51[/C][C]44[/C][C]41.6957771079194[/C][C]2.30422289208061[/C][/ROW]
[ROW][C]52[/C][C]41[/C][C]38.0885508362488[/C][C]2.91144916375125[/C][/ROW]
[ROW][C]53[/C][C]44[/C][C]42.0894470431349[/C][C]1.91055295686515[/C][/ROW]
[ROW][C]54[/C][C]38[/C][C]42.6157811952182[/C][C]-4.61578119521817[/C][/ROW]
[ROW][C]55[/C][C]33[/C][C]42.4650166598824[/C][C]-9.4650166598824[/C][/ROW]
[ROW][C]56[/C][C]47[/C][C]43.6521737602553[/C][C]3.34782623974469[/C][/ROW]
[ROW][C]57[/C][C]37[/C][C]39.3905869405912[/C][C]-2.39058694059117[/C][/ROW]
[ROW][C]58[/C][C]48[/C][C]45.9220284993284[/C][C]2.07797150067163[/C][/ROW]
[ROW][C]59[/C][C]40[/C][C]40.4713674091024[/C][C]-0.471367409102381[/C][/ROW]
[ROW][C]60[/C][C]50[/C][C]44.5440782311414[/C][C]5.45592176885862[/C][/ROW]
[ROW][C]61[/C][C]54[/C][C]48.179740930135[/C][C]5.82025906986496[/C][/ROW]
[ROW][C]62[/C][C]43[/C][C]47.5296596868481[/C][C]-4.52965968684812[/C][/ROW]
[ROW][C]63[/C][C]54[/C][C]42.5919306727397[/C][C]11.4080693272602[/C][/ROW]
[ROW][C]64[/C][C]44[/C][C]44.4871387518542[/C][C]-0.487138751854244[/C][/ROW]
[ROW][C]65[/C][C]47[/C][C]41.4846309470944[/C][C]5.51536905290556[/C][/ROW]
[ROW][C]66[/C][C]33[/C][C]42.920346022228[/C][C]-9.92034602222798[/C][/ROW]
[ROW][C]67[/C][C]45[/C][C]46.7182062462211[/C][C]-1.71820624622112[/C][/ROW]
[ROW][C]68[/C][C]33[/C][C]34.3969332011189[/C][C]-1.39693320111894[/C][/ROW]
[ROW][C]69[/C][C]44[/C][C]45.5840202417591[/C][C]-1.58402024175911[/C][/ROW]
[ROW][C]70[/C][C]47[/C][C]44.9072303150742[/C][C]2.09276968492577[/C][/ROW]
[ROW][C]71[/C][C]45[/C][C]40.320391742162[/C][C]4.679608257838[/C][/ROW]
[ROW][C]72[/C][C]43[/C][C]41.782476529187[/C][C]1.21752347081298[/C][/ROW]
[ROW][C]73[/C][C]43[/C][C]38.1449121622487[/C][C]4.85508783775133[/C][/ROW]
[ROW][C]74[/C][C]33[/C][C]44.4236756570102[/C][C]-11.4236756570102[/C][/ROW]
[ROW][C]75[/C][C]46[/C][C]43.8551276162629[/C][C]2.14487238373706[/C][/ROW]
[ROW][C]76[/C][C]47[/C][C]43.8937556454775[/C][C]3.10624435452245[/C][/ROW]
[ROW][C]77[/C][C]47[/C][C]42.603024690897[/C][C]4.39697530910302[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]40.8443202073073[/C][C]-40.8443202073073[/C][/ROW]
[ROW][C]79[/C][C]43[/C][C]43.9339183209542[/C][C]-0.933918320954231[/C][/ROW]
[ROW][C]80[/C][C]46[/C][C]42.5846458224672[/C][C]3.41535417753283[/C][/ROW]
[ROW][C]81[/C][C]36[/C][C]37.6845083783094[/C][C]-1.68450837830939[/C][/ROW]
[ROW][C]82[/C][C]42[/C][C]38.7801182696698[/C][C]3.21988173033017[/C][/ROW]
[ROW][C]83[/C][C]44[/C][C]43.3244341931839[/C][C]0.675565806816119[/C][/ROW]
[ROW][C]84[/C][C]47[/C][C]47.3470839963446[/C][C]-0.347083996344561[/C][/ROW]
[ROW][C]85[/C][C]41[/C][C]39.5343753863885[/C][C]1.46562461361146[/C][/ROW]
[ROW][C]86[/C][C]47[/C][C]44.4894468096627[/C][C]2.51055319033731[/C][/ROW]
[ROW][C]87[/C][C]46[/C][C]45.0783730159141[/C][C]0.921626984085934[/C][/ROW]
[ROW][C]88[/C][C]47[/C][C]47.2318141047555[/C][C]-0.231814104755542[/C][/ROW]
[ROW][C]89[/C][C]46[/C][C]47.0663239787966[/C][C]-1.06632397879663[/C][/ROW]
[ROW][C]90[/C][C]46[/C][C]44.2541932817382[/C][C]1.74580671826182[/C][/ROW]
[ROW][C]91[/C][C]36[/C][C]44.9833220743376[/C][C]-8.98332207433755[/C][/ROW]
[ROW][C]92[/C][C]30[/C][C]40.2635041789879[/C][C]-10.2635041789879[/C][/ROW]
[ROW][C]93[/C][C]48[/C][C]42.3118464376086[/C][C]5.68815356239138[/C][/ROW]
[ROW][C]94[/C][C]45[/C][C]40.786087424135[/C][C]4.21391257586502[/C][/ROW]
[ROW][C]95[/C][C]49[/C][C]45.565611162557[/C][C]3.43438883744299[/C][/ROW]
[ROW][C]96[/C][C]55[/C][C]47.1119280975496[/C][C]7.88807190245038[/C][/ROW]
[ROW][C]97[/C][C]11[/C][C]41.2106358874803[/C][C]-30.2106358874803[/C][/ROW]
[ROW][C]98[/C][C]52[/C][C]45.8976278737389[/C][C]6.10237212626114[/C][/ROW]
[ROW][C]99[/C][C]33[/C][C]35.8460503522182[/C][C]-2.84605035221816[/C][/ROW]
[ROW][C]100[/C][C]47[/C][C]48.4024792961153[/C][C]-1.40247929611528[/C][/ROW]
[ROW][C]101[/C][C]33[/C][C]39.5825444075711[/C][C]-6.58254440757113[/C][/ROW]
[ROW][C]102[/C][C]44[/C][C]44.4244490685566[/C][C]-0.424449068556609[/C][/ROW]
[ROW][C]103[/C][C]42[/C][C]40.4258609194790[/C][C]1.57413908052104[/C][/ROW]
[ROW][C]104[/C][C]55[/C][C]42.8519735532771[/C][C]12.1480264467229[/C][/ROW]
[ROW][C]105[/C][C]42[/C][C]44.2853286544892[/C][C]-2.28532865448918[/C][/ROW]
[ROW][C]106[/C][C]46[/C][C]44.9994230957312[/C][C]1.0005769042688[/C][/ROW]
[ROW][C]107[/C][C]46[/C][C]46.9155594434609[/C][C]-0.915559443460863[/C][/ROW]
[ROW][C]108[/C][C]47[/C][C]45.5699704335528[/C][C]1.43002956644724[/C][/ROW]
[ROW][C]109[/C][C]33[/C][C]43.4866317181834[/C][C]-10.4866317181834[/C][/ROW]
[ROW][C]110[/C][C]53[/C][C]49.7252325374682[/C][C]3.27476746253175[/C][/ROW]
[ROW][C]111[/C][C]42[/C][C]42.2572971430153[/C][C]-0.257297143015258[/C][/ROW]
[ROW][C]112[/C][C]44[/C][C]44.0353952489580[/C][C]-0.0353952489580468[/C][/ROW]
[ROW][C]113[/C][C]55[/C][C]44.9434007412378[/C][C]10.0565992587622[/C][/ROW]
[ROW][C]114[/C][C]40[/C][C]35.4674449791323[/C][C]4.53255502086767[/C][/ROW]
[ROW][C]115[/C][C]46[/C][C]45.3200370280216[/C][C]0.67996297197837[/C][/ROW]
[ROW][C]116[/C][C]53[/C][C]45.8805423091942[/C][C]7.11945769080578[/C][/ROW]
[ROW][C]117[/C][C]44[/C][C]45.870432834188[/C][C]-1.87043283418797[/C][/ROW]
[ROW][C]118[/C][C]35[/C][C]40.6678336923207[/C][C]-5.66783369232071[/C][/ROW]
[ROW][C]119[/C][C]40[/C][C]39.7329699714004[/C][C]0.267030028599579[/C][/ROW]
[ROW][C]120[/C][C]44[/C][C]45.0966228796246[/C][C]-1.09662287962459[/C][/ROW]
[ROW][C]121[/C][C]46[/C][C]46.7482594466916[/C][C]-0.748259446691554[/C][/ROW]
[ROW][C]122[/C][C]45[/C][C]42.4945197572417[/C][C]2.50548024275826[/C][/ROW]
[ROW][C]123[/C][C]53[/C][C]45.1926583643521[/C][C]7.8073416356479[/C][/ROW]
[ROW][C]124[/C][C]45[/C][C]43.5121944823426[/C][C]1.48780551765740[/C][/ROW]
[ROW][C]125[/C][C]48[/C][C]42.7836312950986[/C][C]5.21636870490143[/C][/ROW]
[ROW][C]126[/C][C]46[/C][C]44.4753970014563[/C][C]1.52460299854366[/C][/ROW]
[ROW][C]127[/C][C]55[/C][C]41.7597081789892[/C][C]13.2402918210108[/C][/ROW]
[ROW][C]128[/C][C]47[/C][C]49.7343574693235[/C][C]-2.73435746932351[/C][/ROW]
[ROW][C]129[/C][C]43[/C][C]42.6559438706948[/C][C]0.344056129305151[/C][/ROW]
[ROW][C]130[/C][C]38[/C][C]38.0828982613678[/C][C]-0.0828982613678303[/C][/ROW]
[ROW][C]131[/C][C]40[/C][C]40.2315434785775[/C][C]-0.231543478577502[/C][/ROW]
[ROW][C]132[/C][C]47[/C][C]42.2529378720195[/C][C]4.74706212798049[/C][/ROW]
[ROW][C]133[/C][C]47[/C][C]42.1068413684137[/C][C]4.89315863158632[/C][/ROW]
[ROW][C]134[/C][C]42[/C][C]40.7197661683714[/C][C]1.28023383162857[/C][/ROW]
[ROW][C]135[/C][C]53[/C][C]42.5732128328035[/C][C]10.4267871671965[/C][/ROW]
[ROW][C]136[/C][C]43[/C][C]44.0521720527685[/C][C]-1.05217205276850[/C][/ROW]
[ROW][C]137[/C][C]44[/C][C]44.8536655636319[/C][C]-0.853665563631855[/C][/ROW]
[ROW][C]138[/C][C]42[/C][C]43.2926230380161[/C][C]-1.29262303801609[/C][/ROW]
[ROW][C]139[/C][C]51[/C][C]44.5581280393477[/C][C]6.44187196065227[/C][/ROW]
[ROW][C]140[/C][C]54[/C][C]42.9524659381300[/C][C]11.0475340618700[/C][/ROW]
[ROW][C]141[/C][C]41[/C][C]42.0995565181411[/C][C]-1.09955651814110[/C][/ROW]
[ROW][C]142[/C][C]51[/C][C]42.8128256317237[/C][C]8.18717436827628[/C][/ROW]
[ROW][C]143[/C][C]51[/C][C]44.4987828731226[/C][C]6.50121712687743[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108035&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108035&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
14244.0042598762073-2.00425987620725
25146.83676873876964.16323126123041
34245.0386012280569-3.03860122805688
44645.39231961940030.607680380599712
54138.41491497254972.58508502745032
64941.77780849745717.22219150254293
74745.19343177589851.80656822410153
83344.0386878499175-11.0386878499175
94746.13278377253380.867216227466201
104238.21525371750153.78474628249851
113244.3480183377868-12.3480183377868
125350.16554305070072.83445694929927
134140.275772166560.724227833440006
144144.9498786437781-3.9498786437781
153342.0796463288628-9.0796463288628
163740.4644889486936-3.46448894869356
174344.0521720527685-1.05217205276850
183337.7220240244711-4.72202402447114
194946.67127861281072.32872138718934
204244.6787417450835-2.67874174508349
214338.75768889097854.24231110902155
223736.40560130101210.59439869898794
234346.5573322358791-3.55733223587911
244241.95478352919270.0452164708072546
254340.09918802244382.90081197755616
264647.4532289560783-1.45322895607832
273347.088753357488-14.088753357488
284245.2142008290221-3.21420082902206
294041.8204287759848-1.82042877598483
304441.53254312365592.46745687634411
314247.1507153422558-5.1507153422558
325242.82391964988109.17608035011904
334442.44660758068031.55339241931970
344544.00327533305610.996724666943933
354641.54197681624534.45802318375466
363641.0738754473343-5.07387544733434
374546.3291524266226-1.32915242662256
384946.27558767518022.72441232481981
394343.1462177736761-0.146217773676077
404344.6153762793691-1.61537627936906
413740.6048050375166-3.6048050375166
423234.3959486579680-2.39594865796796
434544.64586391987940.354136080120617
444545.976109817696-0.97610981769596
454545.4175455726492-0.417545572649211
464542.05729691646062.94270308353941
473143.067735829719-12.0677358297190
483342.83154347166-9.83154347166001
494443.77402885376320.225971146236796
504936.212577163995812.7874228360042
514441.69577710791942.30422289208061
524138.08855083624882.91144916375125
534442.08944704313491.91055295686515
543842.6157811952182-4.61578119521817
553342.4650166598824-9.4650166598824
564743.65217376025533.34782623974469
573739.3905869405912-2.39058694059117
584845.92202849932842.07797150067163
594040.4713674091024-0.471367409102381
605044.54407823114145.45592176885862
615448.1797409301355.82025906986496
624347.5296596868481-4.52965968684812
635442.591930672739711.4080693272602
644444.4871387518542-0.487138751854244
654741.48463094709445.51536905290556
663342.920346022228-9.92034602222798
674546.7182062462211-1.71820624622112
683334.3969332011189-1.39693320111894
694445.5840202417591-1.58402024175911
704744.90723031507422.09276968492577
714540.3203917421624.679608257838
724341.7824765291871.21752347081298
734338.14491216224874.85508783775133
743344.4236756570102-11.4236756570102
754643.85512761626292.14487238373706
764743.89375564547753.10624435452245
774742.6030246908974.39697530910302
78040.8443202073073-40.8443202073073
794343.9339183209542-0.933918320954231
804642.58464582246723.41535417753283
813637.6845083783094-1.68450837830939
824238.78011826966983.21988173033017
834443.32443419318390.675565806816119
844747.3470839963446-0.347083996344561
854139.53437538638851.46562461361146
864744.48944680966272.51055319033731
874645.07837301591410.921626984085934
884747.2318141047555-0.231814104755542
894647.0663239787966-1.06632397879663
904644.25419328173821.74580671826182
913644.9833220743376-8.98332207433755
923040.2635041789879-10.2635041789879
934842.31184643760865.68815356239138
944540.7860874241354.21391257586502
954945.5656111625573.43438883744299
965547.11192809754967.88807190245038
971141.2106358874803-30.2106358874803
985245.89762787373896.10237212626114
993335.8460503522182-2.84605035221816
1004748.4024792961153-1.40247929611528
1013339.5825444075711-6.58254440757113
1024444.4244490685566-0.424449068556609
1034240.42586091947901.57413908052104
1045542.851973553277112.1480264467229
1054244.2853286544892-2.28532865448918
1064644.99942309573121.0005769042688
1074646.9155594434609-0.915559443460863
1084745.56997043355281.43002956644724
1093343.4866317181834-10.4866317181834
1105349.72523253746823.27476746253175
1114242.2572971430153-0.257297143015258
1124444.0353952489580-0.0353952489580468
1135544.943400741237810.0565992587622
1144035.46744497913234.53255502086767
1154645.32003702802160.67996297197837
1165345.88054230919427.11945769080578
1174445.870432834188-1.87043283418797
1183540.6678336923207-5.66783369232071
1194039.73296997140040.267030028599579
1204445.0966228796246-1.09662287962459
1214646.7482594466916-0.748259446691554
1224542.49451975724172.50548024275826
1235345.19265836435217.8073416356479
1244543.51219448234261.48780551765740
1254842.78363129509865.21636870490143
1264644.47539700145631.52460299854366
1275541.759708178989213.2402918210108
1284749.7343574693235-2.73435746932351
1294342.65594387069480.344056129305151
1303838.0828982613678-0.0828982613678303
1314040.2315434785775-0.231543478577502
1324742.25293787201954.74706212798049
1334742.10684136841374.89315863158632
1344240.71976616837141.28023383162857
1355342.573212832803510.4267871671965
1364344.0521720527685-1.05217205276850
1374444.8536655636319-0.853665563631855
1384243.2926230380161-1.29262303801609
1395144.55812803934776.44187196065227
1405442.952465938130011.0475340618700
1414142.0995565181411-1.09955651814110
1425142.81282563172378.18717436827628
1435144.49878287312266.50121712687743






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.6254795988306770.7490408023386460.374520401169323
100.489788634623320.979577269246640.51021136537668
110.4743386605142870.9486773210285730.525661339485713
120.3692425475725020.7384850951450030.630757452427498
130.281391045728540.562782091457080.71860895427146
140.1920752796108920.3841505592217840.807924720389108
150.1716614271174800.3433228542349610.82833857288252
160.1258440224503310.2516880449006620.874155977549669
170.1447216499687460.2894432999374920.855278350031254
180.09830617258488490.1966123451697700.901693827415115
190.1143386256607290.2286772513214580.88566137433927
200.1003553450617090.2007106901234170.899644654938291
210.1174731368421530.2349462736843050.882526863157847
220.08147744267452840.1629548853490570.918522557325472
230.06260623250938020.1252124650187600.93739376749062
240.04612215757594480.09224431515188950.953877842424055
250.03644229388389640.07288458776779280.963557706116104
260.02427333529268430.04854667058536860.975726664707316
270.06003284751220060.1200656950244010.9399671524878
280.04655300337647040.09310600675294080.95344699662353
290.03226442901692150.0645288580338430.967735570983078
300.0218854009227320.0437708018454640.978114599077268
310.01718534668290180.03437069336580370.982814653317098
320.01925896118380210.03851792236760430.980741038816198
330.01283395380115620.02566790760231240.987166046198844
340.008484613820603350.01696922764120670.991515386179397
350.007305373343641580.01461074668728320.992694626656358
360.004911772416412320.009823544832824650.995088227583588
370.00325279116521320.00650558233042640.996747208834787
380.002362955077232690.004725910154465380.997637044922767
390.001524242285843830.003048484571687650.998475757714156
400.0009462642886371140.001892528577274230.999053735711363
410.0005956514089634670.001191302817926930.999404348591037
420.0003478625337812180.0006957250675624370.999652137466219
430.0002435853957484130.0004871707914968260.999756414604252
440.0001398953560230470.0002797907120460940.999860104643977
457.86207430430635e-050.0001572414860861270.999921379256957
469.93624592464402e-050.0001987249184928800.999900637540754
470.0001770555102022180.0003541110204044350.999822944489798
480.0005667046996346880.001133409399269380.999433295300365
490.0003971702717912370.0007943405435824730.999602829728209
500.00442443954420870.00884887908841740.995575560455791
510.003213092094603190.006426184189206380.996786907905397
520.002302525490017350.004605050980034700.997697474509983
530.001522038839881410.003044077679762830.998477961160119
540.001203959137175370.002407918274350750.998796040862825
550.002043568652818310.004087137305636620.997956431347182
560.001638600734975790.003277201469951580.998361399265024
570.001108063847691460.002216127695382930.998891936152309
580.0009177396502502780.001835479300500560.99908226034975
590.0005992813382319050.001198562676463810.999400718661768
600.0004976246900264180.0009952493800528360.999502375309974
610.0005333292539051360.001066658507810270.999466670746095
620.0004005589657339790.0008011179314679580.999599441034266
630.001883418613539100.003766837227078210.998116581386461
640.001242047511616950.00248409502323390.998757952488383
650.001078898340910870.002157796681821750.99892110165909
660.001865922726562330.003731845453124670.998134077273438
670.001265699577933230.002531399155866470.998734300422067
680.0008555900400425570.001711180080085110.999144409959957
690.000564573422286360.001129146844572720.999435426577714
700.0003989118269954690.0007978236539909370.999601088173005
710.000301337008530650.00060267401706130.99969866299147
720.0001905515690503730.0003811031381007460.99980944843095
730.0001551923162215300.0003103846324430600.999844807683778
740.0003399272243440430.0006798544486880860.999660072775656
750.0002379529793958250.0004759059587916490.999762047020604
760.0001641717635065510.0003283435270131020.999835828236493
770.0001476131373634270.0002952262747268530.999852386862637
780.8957658792393660.2084682415212670.104234120760634
790.8715366858512110.2569266282975780.128463314148789
800.8490333155509440.3019333688981120.150966684449056
810.8323112900341440.3353774199317110.167688709965856
820.8046667089162570.3906665821674850.195333291083743
830.7683700562349550.4632598875300890.231629943765045
840.7301956164946120.5396087670107760.269804383505388
850.6871063667964660.6257872664070680.312893633203534
860.6601073097291170.6797853805417670.339892690270883
870.6388655254862420.7222689490275170.361134474513758
880.5911360082304440.8177279835391110.408863991769556
890.5499629715649270.9000740568701450.450037028435073
900.5183011772549850.963397645490030.481698822745015
910.5875639849710850.824872030057830.412436015028915
920.6624728769370750.6750542461258510.337527123062925
930.6436193350867920.7127613298264150.356380664913208
940.6215592958566430.7568814082867140.378440704143357
950.5793267053804570.8413465892390850.420673294619543
960.5848877520333630.8302244959332740.415112247966637
970.9985293041122510.002941391775497450.00147069588774873
980.9980213771074740.003957245785052370.00197862289252619
990.9985170006510730.002965998697853160.00148299934892658
1000.9982837914786680.003432417042663080.00171620852133154
1010.998854101659780.002291796680439730.00114589834021987
1020.9981292106475060.003741578704988060.00187078935249403
1030.997267212183810.005465575632379860.00273278781618993
1040.9984130108779180.003173978244163770.00158698912208188
1050.9983939826537140.003212034692572010.00160601734628601
1060.9973642960804380.005271407839123680.00263570391956184
1070.996165261126110.007669477747780480.00383473887389024
1080.9940588550979420.01188228980411550.00594114490205773
1090.9975460256149960.004907948770008090.00245397438500404
1100.9961538219466470.007692356106705080.00384617805335254
1110.9941325553037360.01173488939252700.00586744469626352
1120.9921052864862730.01578942702745440.0078947135137272
1130.9932340582816360.01353188343672740.00676594171836368
1140.990765940058310.01846811988337850.00923405994168925
1150.9852989887409020.02940202251819550.0147010112590977
1160.9805379969741470.03892400605170690.0194620030258534
1170.9858866361014660.02822672779706880.0141133638985344
1180.9887526181930240.02249476361395240.0112473818069762
1190.9949159742573210.01016805148535730.00508402574267863
1200.9909662490350180.01806750192996350.00903375096498175
1210.9891878786452530.02162424270949380.0108121213547469
1220.9937584734187650.01248305316247070.00624152658123534
1230.993789602467650.01242079506469970.00621039753234983
1240.9918706737564060.01625865248718710.00812932624359357
1250.9851177129020620.02976457419587610.0148822870979380
1260.9729650535536150.05406989289277080.0270349464463854
1270.9571885670548050.08562286589038970.0428114329451948
1280.9614714894150250.07705702116994970.0385285105849748
1290.9831762798304870.03364744033902650.0168237201695132
1300.9717961687261390.05640766254772260.0282038312738613
1310.950390500986620.09921899802676220.0496094990133811
1320.9062131232716430.1875737534567150.0937868767283573
1330.8469838480706590.3060323038586820.153016151929341
1340.7289845903779580.5420308192440830.271015409622042

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.625479598830677 & 0.749040802338646 & 0.374520401169323 \tabularnewline
10 & 0.48978863462332 & 0.97957726924664 & 0.51021136537668 \tabularnewline
11 & 0.474338660514287 & 0.948677321028573 & 0.525661339485713 \tabularnewline
12 & 0.369242547572502 & 0.738485095145003 & 0.630757452427498 \tabularnewline
13 & 0.28139104572854 & 0.56278209145708 & 0.71860895427146 \tabularnewline
14 & 0.192075279610892 & 0.384150559221784 & 0.807924720389108 \tabularnewline
15 & 0.171661427117480 & 0.343322854234961 & 0.82833857288252 \tabularnewline
16 & 0.125844022450331 & 0.251688044900662 & 0.874155977549669 \tabularnewline
17 & 0.144721649968746 & 0.289443299937492 & 0.855278350031254 \tabularnewline
18 & 0.0983061725848849 & 0.196612345169770 & 0.901693827415115 \tabularnewline
19 & 0.114338625660729 & 0.228677251321458 & 0.88566137433927 \tabularnewline
20 & 0.100355345061709 & 0.200710690123417 & 0.899644654938291 \tabularnewline
21 & 0.117473136842153 & 0.234946273684305 & 0.882526863157847 \tabularnewline
22 & 0.0814774426745284 & 0.162954885349057 & 0.918522557325472 \tabularnewline
23 & 0.0626062325093802 & 0.125212465018760 & 0.93739376749062 \tabularnewline
24 & 0.0461221575759448 & 0.0922443151518895 & 0.953877842424055 \tabularnewline
25 & 0.0364422938838964 & 0.0728845877677928 & 0.963557706116104 \tabularnewline
26 & 0.0242733352926843 & 0.0485466705853686 & 0.975726664707316 \tabularnewline
27 & 0.0600328475122006 & 0.120065695024401 & 0.9399671524878 \tabularnewline
28 & 0.0465530033764704 & 0.0931060067529408 & 0.95344699662353 \tabularnewline
29 & 0.0322644290169215 & 0.064528858033843 & 0.967735570983078 \tabularnewline
30 & 0.021885400922732 & 0.043770801845464 & 0.978114599077268 \tabularnewline
31 & 0.0171853466829018 & 0.0343706933658037 & 0.982814653317098 \tabularnewline
32 & 0.0192589611838021 & 0.0385179223676043 & 0.980741038816198 \tabularnewline
33 & 0.0128339538011562 & 0.0256679076023124 & 0.987166046198844 \tabularnewline
34 & 0.00848461382060335 & 0.0169692276412067 & 0.991515386179397 \tabularnewline
35 & 0.00730537334364158 & 0.0146107466872832 & 0.992694626656358 \tabularnewline
36 & 0.00491177241641232 & 0.00982354483282465 & 0.995088227583588 \tabularnewline
37 & 0.0032527911652132 & 0.0065055823304264 & 0.996747208834787 \tabularnewline
38 & 0.00236295507723269 & 0.00472591015446538 & 0.997637044922767 \tabularnewline
39 & 0.00152424228584383 & 0.00304848457168765 & 0.998475757714156 \tabularnewline
40 & 0.000946264288637114 & 0.00189252857727423 & 0.999053735711363 \tabularnewline
41 & 0.000595651408963467 & 0.00119130281792693 & 0.999404348591037 \tabularnewline
42 & 0.000347862533781218 & 0.000695725067562437 & 0.999652137466219 \tabularnewline
43 & 0.000243585395748413 & 0.000487170791496826 & 0.999756414604252 \tabularnewline
44 & 0.000139895356023047 & 0.000279790712046094 & 0.999860104643977 \tabularnewline
45 & 7.86207430430635e-05 & 0.000157241486086127 & 0.999921379256957 \tabularnewline
46 & 9.93624592464402e-05 & 0.000198724918492880 & 0.999900637540754 \tabularnewline
47 & 0.000177055510202218 & 0.000354111020404435 & 0.999822944489798 \tabularnewline
48 & 0.000566704699634688 & 0.00113340939926938 & 0.999433295300365 \tabularnewline
49 & 0.000397170271791237 & 0.000794340543582473 & 0.999602829728209 \tabularnewline
50 & 0.0044244395442087 & 0.0088488790884174 & 0.995575560455791 \tabularnewline
51 & 0.00321309209460319 & 0.00642618418920638 & 0.996786907905397 \tabularnewline
52 & 0.00230252549001735 & 0.00460505098003470 & 0.997697474509983 \tabularnewline
53 & 0.00152203883988141 & 0.00304407767976283 & 0.998477961160119 \tabularnewline
54 & 0.00120395913717537 & 0.00240791827435075 & 0.998796040862825 \tabularnewline
55 & 0.00204356865281831 & 0.00408713730563662 & 0.997956431347182 \tabularnewline
56 & 0.00163860073497579 & 0.00327720146995158 & 0.998361399265024 \tabularnewline
57 & 0.00110806384769146 & 0.00221612769538293 & 0.998891936152309 \tabularnewline
58 & 0.000917739650250278 & 0.00183547930050056 & 0.99908226034975 \tabularnewline
59 & 0.000599281338231905 & 0.00119856267646381 & 0.999400718661768 \tabularnewline
60 & 0.000497624690026418 & 0.000995249380052836 & 0.999502375309974 \tabularnewline
61 & 0.000533329253905136 & 0.00106665850781027 & 0.999466670746095 \tabularnewline
62 & 0.000400558965733979 & 0.000801117931467958 & 0.999599441034266 \tabularnewline
63 & 0.00188341861353910 & 0.00376683722707821 & 0.998116581386461 \tabularnewline
64 & 0.00124204751161695 & 0.0024840950232339 & 0.998757952488383 \tabularnewline
65 & 0.00107889834091087 & 0.00215779668182175 & 0.99892110165909 \tabularnewline
66 & 0.00186592272656233 & 0.00373184545312467 & 0.998134077273438 \tabularnewline
67 & 0.00126569957793323 & 0.00253139915586647 & 0.998734300422067 \tabularnewline
68 & 0.000855590040042557 & 0.00171118008008511 & 0.999144409959957 \tabularnewline
69 & 0.00056457342228636 & 0.00112914684457272 & 0.999435426577714 \tabularnewline
70 & 0.000398911826995469 & 0.000797823653990937 & 0.999601088173005 \tabularnewline
71 & 0.00030133700853065 & 0.0006026740170613 & 0.99969866299147 \tabularnewline
72 & 0.000190551569050373 & 0.000381103138100746 & 0.99980944843095 \tabularnewline
73 & 0.000155192316221530 & 0.000310384632443060 & 0.999844807683778 \tabularnewline
74 & 0.000339927224344043 & 0.000679854448688086 & 0.999660072775656 \tabularnewline
75 & 0.000237952979395825 & 0.000475905958791649 & 0.999762047020604 \tabularnewline
76 & 0.000164171763506551 & 0.000328343527013102 & 0.999835828236493 \tabularnewline
77 & 0.000147613137363427 & 0.000295226274726853 & 0.999852386862637 \tabularnewline
78 & 0.895765879239366 & 0.208468241521267 & 0.104234120760634 \tabularnewline
79 & 0.871536685851211 & 0.256926628297578 & 0.128463314148789 \tabularnewline
80 & 0.849033315550944 & 0.301933368898112 & 0.150966684449056 \tabularnewline
81 & 0.832311290034144 & 0.335377419931711 & 0.167688709965856 \tabularnewline
82 & 0.804666708916257 & 0.390666582167485 & 0.195333291083743 \tabularnewline
83 & 0.768370056234955 & 0.463259887530089 & 0.231629943765045 \tabularnewline
84 & 0.730195616494612 & 0.539608767010776 & 0.269804383505388 \tabularnewline
85 & 0.687106366796466 & 0.625787266407068 & 0.312893633203534 \tabularnewline
86 & 0.660107309729117 & 0.679785380541767 & 0.339892690270883 \tabularnewline
87 & 0.638865525486242 & 0.722268949027517 & 0.361134474513758 \tabularnewline
88 & 0.591136008230444 & 0.817727983539111 & 0.408863991769556 \tabularnewline
89 & 0.549962971564927 & 0.900074056870145 & 0.450037028435073 \tabularnewline
90 & 0.518301177254985 & 0.96339764549003 & 0.481698822745015 \tabularnewline
91 & 0.587563984971085 & 0.82487203005783 & 0.412436015028915 \tabularnewline
92 & 0.662472876937075 & 0.675054246125851 & 0.337527123062925 \tabularnewline
93 & 0.643619335086792 & 0.712761329826415 & 0.356380664913208 \tabularnewline
94 & 0.621559295856643 & 0.756881408286714 & 0.378440704143357 \tabularnewline
95 & 0.579326705380457 & 0.841346589239085 & 0.420673294619543 \tabularnewline
96 & 0.584887752033363 & 0.830224495933274 & 0.415112247966637 \tabularnewline
97 & 0.998529304112251 & 0.00294139177549745 & 0.00147069588774873 \tabularnewline
98 & 0.998021377107474 & 0.00395724578505237 & 0.00197862289252619 \tabularnewline
99 & 0.998517000651073 & 0.00296599869785316 & 0.00148299934892658 \tabularnewline
100 & 0.998283791478668 & 0.00343241704266308 & 0.00171620852133154 \tabularnewline
101 & 0.99885410165978 & 0.00229179668043973 & 0.00114589834021987 \tabularnewline
102 & 0.998129210647506 & 0.00374157870498806 & 0.00187078935249403 \tabularnewline
103 & 0.99726721218381 & 0.00546557563237986 & 0.00273278781618993 \tabularnewline
104 & 0.998413010877918 & 0.00317397824416377 & 0.00158698912208188 \tabularnewline
105 & 0.998393982653714 & 0.00321203469257201 & 0.00160601734628601 \tabularnewline
106 & 0.997364296080438 & 0.00527140783912368 & 0.00263570391956184 \tabularnewline
107 & 0.99616526112611 & 0.00766947774778048 & 0.00383473887389024 \tabularnewline
108 & 0.994058855097942 & 0.0118822898041155 & 0.00594114490205773 \tabularnewline
109 & 0.997546025614996 & 0.00490794877000809 & 0.00245397438500404 \tabularnewline
110 & 0.996153821946647 & 0.00769235610670508 & 0.00384617805335254 \tabularnewline
111 & 0.994132555303736 & 0.0117348893925270 & 0.00586744469626352 \tabularnewline
112 & 0.992105286486273 & 0.0157894270274544 & 0.0078947135137272 \tabularnewline
113 & 0.993234058281636 & 0.0135318834367274 & 0.00676594171836368 \tabularnewline
114 & 0.99076594005831 & 0.0184681198833785 & 0.00923405994168925 \tabularnewline
115 & 0.985298988740902 & 0.0294020225181955 & 0.0147010112590977 \tabularnewline
116 & 0.980537996974147 & 0.0389240060517069 & 0.0194620030258534 \tabularnewline
117 & 0.985886636101466 & 0.0282267277970688 & 0.0141133638985344 \tabularnewline
118 & 0.988752618193024 & 0.0224947636139524 & 0.0112473818069762 \tabularnewline
119 & 0.994915974257321 & 0.0101680514853573 & 0.00508402574267863 \tabularnewline
120 & 0.990966249035018 & 0.0180675019299635 & 0.00903375096498175 \tabularnewline
121 & 0.989187878645253 & 0.0216242427094938 & 0.0108121213547469 \tabularnewline
122 & 0.993758473418765 & 0.0124830531624707 & 0.00624152658123534 \tabularnewline
123 & 0.99378960246765 & 0.0124207950646997 & 0.00621039753234983 \tabularnewline
124 & 0.991870673756406 & 0.0162586524871871 & 0.00812932624359357 \tabularnewline
125 & 0.985117712902062 & 0.0297645741958761 & 0.0148822870979380 \tabularnewline
126 & 0.972965053553615 & 0.0540698928927708 & 0.0270349464463854 \tabularnewline
127 & 0.957188567054805 & 0.0856228658903897 & 0.0428114329451948 \tabularnewline
128 & 0.961471489415025 & 0.0770570211699497 & 0.0385285105849748 \tabularnewline
129 & 0.983176279830487 & 0.0336474403390265 & 0.0168237201695132 \tabularnewline
130 & 0.971796168726139 & 0.0564076625477226 & 0.0282038312738613 \tabularnewline
131 & 0.95039050098662 & 0.0992189980267622 & 0.0496094990133811 \tabularnewline
132 & 0.906213123271643 & 0.187573753456715 & 0.0937868767283573 \tabularnewline
133 & 0.846983848070659 & 0.306032303858682 & 0.153016151929341 \tabularnewline
134 & 0.728984590377958 & 0.542030819244083 & 0.271015409622042 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108035&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.625479598830677[/C][C]0.749040802338646[/C][C]0.374520401169323[/C][/ROW]
[ROW][C]10[/C][C]0.48978863462332[/C][C]0.97957726924664[/C][C]0.51021136537668[/C][/ROW]
[ROW][C]11[/C][C]0.474338660514287[/C][C]0.948677321028573[/C][C]0.525661339485713[/C][/ROW]
[ROW][C]12[/C][C]0.369242547572502[/C][C]0.738485095145003[/C][C]0.630757452427498[/C][/ROW]
[ROW][C]13[/C][C]0.28139104572854[/C][C]0.56278209145708[/C][C]0.71860895427146[/C][/ROW]
[ROW][C]14[/C][C]0.192075279610892[/C][C]0.384150559221784[/C][C]0.807924720389108[/C][/ROW]
[ROW][C]15[/C][C]0.171661427117480[/C][C]0.343322854234961[/C][C]0.82833857288252[/C][/ROW]
[ROW][C]16[/C][C]0.125844022450331[/C][C]0.251688044900662[/C][C]0.874155977549669[/C][/ROW]
[ROW][C]17[/C][C]0.144721649968746[/C][C]0.289443299937492[/C][C]0.855278350031254[/C][/ROW]
[ROW][C]18[/C][C]0.0983061725848849[/C][C]0.196612345169770[/C][C]0.901693827415115[/C][/ROW]
[ROW][C]19[/C][C]0.114338625660729[/C][C]0.228677251321458[/C][C]0.88566137433927[/C][/ROW]
[ROW][C]20[/C][C]0.100355345061709[/C][C]0.200710690123417[/C][C]0.899644654938291[/C][/ROW]
[ROW][C]21[/C][C]0.117473136842153[/C][C]0.234946273684305[/C][C]0.882526863157847[/C][/ROW]
[ROW][C]22[/C][C]0.0814774426745284[/C][C]0.162954885349057[/C][C]0.918522557325472[/C][/ROW]
[ROW][C]23[/C][C]0.0626062325093802[/C][C]0.125212465018760[/C][C]0.93739376749062[/C][/ROW]
[ROW][C]24[/C][C]0.0461221575759448[/C][C]0.0922443151518895[/C][C]0.953877842424055[/C][/ROW]
[ROW][C]25[/C][C]0.0364422938838964[/C][C]0.0728845877677928[/C][C]0.963557706116104[/C][/ROW]
[ROW][C]26[/C][C]0.0242733352926843[/C][C]0.0485466705853686[/C][C]0.975726664707316[/C][/ROW]
[ROW][C]27[/C][C]0.0600328475122006[/C][C]0.120065695024401[/C][C]0.9399671524878[/C][/ROW]
[ROW][C]28[/C][C]0.0465530033764704[/C][C]0.0931060067529408[/C][C]0.95344699662353[/C][/ROW]
[ROW][C]29[/C][C]0.0322644290169215[/C][C]0.064528858033843[/C][C]0.967735570983078[/C][/ROW]
[ROW][C]30[/C][C]0.021885400922732[/C][C]0.043770801845464[/C][C]0.978114599077268[/C][/ROW]
[ROW][C]31[/C][C]0.0171853466829018[/C][C]0.0343706933658037[/C][C]0.982814653317098[/C][/ROW]
[ROW][C]32[/C][C]0.0192589611838021[/C][C]0.0385179223676043[/C][C]0.980741038816198[/C][/ROW]
[ROW][C]33[/C][C]0.0128339538011562[/C][C]0.0256679076023124[/C][C]0.987166046198844[/C][/ROW]
[ROW][C]34[/C][C]0.00848461382060335[/C][C]0.0169692276412067[/C][C]0.991515386179397[/C][/ROW]
[ROW][C]35[/C][C]0.00730537334364158[/C][C]0.0146107466872832[/C][C]0.992694626656358[/C][/ROW]
[ROW][C]36[/C][C]0.00491177241641232[/C][C]0.00982354483282465[/C][C]0.995088227583588[/C][/ROW]
[ROW][C]37[/C][C]0.0032527911652132[/C][C]0.0065055823304264[/C][C]0.996747208834787[/C][/ROW]
[ROW][C]38[/C][C]0.00236295507723269[/C][C]0.00472591015446538[/C][C]0.997637044922767[/C][/ROW]
[ROW][C]39[/C][C]0.00152424228584383[/C][C]0.00304848457168765[/C][C]0.998475757714156[/C][/ROW]
[ROW][C]40[/C][C]0.000946264288637114[/C][C]0.00189252857727423[/C][C]0.999053735711363[/C][/ROW]
[ROW][C]41[/C][C]0.000595651408963467[/C][C]0.00119130281792693[/C][C]0.999404348591037[/C][/ROW]
[ROW][C]42[/C][C]0.000347862533781218[/C][C]0.000695725067562437[/C][C]0.999652137466219[/C][/ROW]
[ROW][C]43[/C][C]0.000243585395748413[/C][C]0.000487170791496826[/C][C]0.999756414604252[/C][/ROW]
[ROW][C]44[/C][C]0.000139895356023047[/C][C]0.000279790712046094[/C][C]0.999860104643977[/C][/ROW]
[ROW][C]45[/C][C]7.86207430430635e-05[/C][C]0.000157241486086127[/C][C]0.999921379256957[/C][/ROW]
[ROW][C]46[/C][C]9.93624592464402e-05[/C][C]0.000198724918492880[/C][C]0.999900637540754[/C][/ROW]
[ROW][C]47[/C][C]0.000177055510202218[/C][C]0.000354111020404435[/C][C]0.999822944489798[/C][/ROW]
[ROW][C]48[/C][C]0.000566704699634688[/C][C]0.00113340939926938[/C][C]0.999433295300365[/C][/ROW]
[ROW][C]49[/C][C]0.000397170271791237[/C][C]0.000794340543582473[/C][C]0.999602829728209[/C][/ROW]
[ROW][C]50[/C][C]0.0044244395442087[/C][C]0.0088488790884174[/C][C]0.995575560455791[/C][/ROW]
[ROW][C]51[/C][C]0.00321309209460319[/C][C]0.00642618418920638[/C][C]0.996786907905397[/C][/ROW]
[ROW][C]52[/C][C]0.00230252549001735[/C][C]0.00460505098003470[/C][C]0.997697474509983[/C][/ROW]
[ROW][C]53[/C][C]0.00152203883988141[/C][C]0.00304407767976283[/C][C]0.998477961160119[/C][/ROW]
[ROW][C]54[/C][C]0.00120395913717537[/C][C]0.00240791827435075[/C][C]0.998796040862825[/C][/ROW]
[ROW][C]55[/C][C]0.00204356865281831[/C][C]0.00408713730563662[/C][C]0.997956431347182[/C][/ROW]
[ROW][C]56[/C][C]0.00163860073497579[/C][C]0.00327720146995158[/C][C]0.998361399265024[/C][/ROW]
[ROW][C]57[/C][C]0.00110806384769146[/C][C]0.00221612769538293[/C][C]0.998891936152309[/C][/ROW]
[ROW][C]58[/C][C]0.000917739650250278[/C][C]0.00183547930050056[/C][C]0.99908226034975[/C][/ROW]
[ROW][C]59[/C][C]0.000599281338231905[/C][C]0.00119856267646381[/C][C]0.999400718661768[/C][/ROW]
[ROW][C]60[/C][C]0.000497624690026418[/C][C]0.000995249380052836[/C][C]0.999502375309974[/C][/ROW]
[ROW][C]61[/C][C]0.000533329253905136[/C][C]0.00106665850781027[/C][C]0.999466670746095[/C][/ROW]
[ROW][C]62[/C][C]0.000400558965733979[/C][C]0.000801117931467958[/C][C]0.999599441034266[/C][/ROW]
[ROW][C]63[/C][C]0.00188341861353910[/C][C]0.00376683722707821[/C][C]0.998116581386461[/C][/ROW]
[ROW][C]64[/C][C]0.00124204751161695[/C][C]0.0024840950232339[/C][C]0.998757952488383[/C][/ROW]
[ROW][C]65[/C][C]0.00107889834091087[/C][C]0.00215779668182175[/C][C]0.99892110165909[/C][/ROW]
[ROW][C]66[/C][C]0.00186592272656233[/C][C]0.00373184545312467[/C][C]0.998134077273438[/C][/ROW]
[ROW][C]67[/C][C]0.00126569957793323[/C][C]0.00253139915586647[/C][C]0.998734300422067[/C][/ROW]
[ROW][C]68[/C][C]0.000855590040042557[/C][C]0.00171118008008511[/C][C]0.999144409959957[/C][/ROW]
[ROW][C]69[/C][C]0.00056457342228636[/C][C]0.00112914684457272[/C][C]0.999435426577714[/C][/ROW]
[ROW][C]70[/C][C]0.000398911826995469[/C][C]0.000797823653990937[/C][C]0.999601088173005[/C][/ROW]
[ROW][C]71[/C][C]0.00030133700853065[/C][C]0.0006026740170613[/C][C]0.99969866299147[/C][/ROW]
[ROW][C]72[/C][C]0.000190551569050373[/C][C]0.000381103138100746[/C][C]0.99980944843095[/C][/ROW]
[ROW][C]73[/C][C]0.000155192316221530[/C][C]0.000310384632443060[/C][C]0.999844807683778[/C][/ROW]
[ROW][C]74[/C][C]0.000339927224344043[/C][C]0.000679854448688086[/C][C]0.999660072775656[/C][/ROW]
[ROW][C]75[/C][C]0.000237952979395825[/C][C]0.000475905958791649[/C][C]0.999762047020604[/C][/ROW]
[ROW][C]76[/C][C]0.000164171763506551[/C][C]0.000328343527013102[/C][C]0.999835828236493[/C][/ROW]
[ROW][C]77[/C][C]0.000147613137363427[/C][C]0.000295226274726853[/C][C]0.999852386862637[/C][/ROW]
[ROW][C]78[/C][C]0.895765879239366[/C][C]0.208468241521267[/C][C]0.104234120760634[/C][/ROW]
[ROW][C]79[/C][C]0.871536685851211[/C][C]0.256926628297578[/C][C]0.128463314148789[/C][/ROW]
[ROW][C]80[/C][C]0.849033315550944[/C][C]0.301933368898112[/C][C]0.150966684449056[/C][/ROW]
[ROW][C]81[/C][C]0.832311290034144[/C][C]0.335377419931711[/C][C]0.167688709965856[/C][/ROW]
[ROW][C]82[/C][C]0.804666708916257[/C][C]0.390666582167485[/C][C]0.195333291083743[/C][/ROW]
[ROW][C]83[/C][C]0.768370056234955[/C][C]0.463259887530089[/C][C]0.231629943765045[/C][/ROW]
[ROW][C]84[/C][C]0.730195616494612[/C][C]0.539608767010776[/C][C]0.269804383505388[/C][/ROW]
[ROW][C]85[/C][C]0.687106366796466[/C][C]0.625787266407068[/C][C]0.312893633203534[/C][/ROW]
[ROW][C]86[/C][C]0.660107309729117[/C][C]0.679785380541767[/C][C]0.339892690270883[/C][/ROW]
[ROW][C]87[/C][C]0.638865525486242[/C][C]0.722268949027517[/C][C]0.361134474513758[/C][/ROW]
[ROW][C]88[/C][C]0.591136008230444[/C][C]0.817727983539111[/C][C]0.408863991769556[/C][/ROW]
[ROW][C]89[/C][C]0.549962971564927[/C][C]0.900074056870145[/C][C]0.450037028435073[/C][/ROW]
[ROW][C]90[/C][C]0.518301177254985[/C][C]0.96339764549003[/C][C]0.481698822745015[/C][/ROW]
[ROW][C]91[/C][C]0.587563984971085[/C][C]0.82487203005783[/C][C]0.412436015028915[/C][/ROW]
[ROW][C]92[/C][C]0.662472876937075[/C][C]0.675054246125851[/C][C]0.337527123062925[/C][/ROW]
[ROW][C]93[/C][C]0.643619335086792[/C][C]0.712761329826415[/C][C]0.356380664913208[/C][/ROW]
[ROW][C]94[/C][C]0.621559295856643[/C][C]0.756881408286714[/C][C]0.378440704143357[/C][/ROW]
[ROW][C]95[/C][C]0.579326705380457[/C][C]0.841346589239085[/C][C]0.420673294619543[/C][/ROW]
[ROW][C]96[/C][C]0.584887752033363[/C][C]0.830224495933274[/C][C]0.415112247966637[/C][/ROW]
[ROW][C]97[/C][C]0.998529304112251[/C][C]0.00294139177549745[/C][C]0.00147069588774873[/C][/ROW]
[ROW][C]98[/C][C]0.998021377107474[/C][C]0.00395724578505237[/C][C]0.00197862289252619[/C][/ROW]
[ROW][C]99[/C][C]0.998517000651073[/C][C]0.00296599869785316[/C][C]0.00148299934892658[/C][/ROW]
[ROW][C]100[/C][C]0.998283791478668[/C][C]0.00343241704266308[/C][C]0.00171620852133154[/C][/ROW]
[ROW][C]101[/C][C]0.99885410165978[/C][C]0.00229179668043973[/C][C]0.00114589834021987[/C][/ROW]
[ROW][C]102[/C][C]0.998129210647506[/C][C]0.00374157870498806[/C][C]0.00187078935249403[/C][/ROW]
[ROW][C]103[/C][C]0.99726721218381[/C][C]0.00546557563237986[/C][C]0.00273278781618993[/C][/ROW]
[ROW][C]104[/C][C]0.998413010877918[/C][C]0.00317397824416377[/C][C]0.00158698912208188[/C][/ROW]
[ROW][C]105[/C][C]0.998393982653714[/C][C]0.00321203469257201[/C][C]0.00160601734628601[/C][/ROW]
[ROW][C]106[/C][C]0.997364296080438[/C][C]0.00527140783912368[/C][C]0.00263570391956184[/C][/ROW]
[ROW][C]107[/C][C]0.99616526112611[/C][C]0.00766947774778048[/C][C]0.00383473887389024[/C][/ROW]
[ROW][C]108[/C][C]0.994058855097942[/C][C]0.0118822898041155[/C][C]0.00594114490205773[/C][/ROW]
[ROW][C]109[/C][C]0.997546025614996[/C][C]0.00490794877000809[/C][C]0.00245397438500404[/C][/ROW]
[ROW][C]110[/C][C]0.996153821946647[/C][C]0.00769235610670508[/C][C]0.00384617805335254[/C][/ROW]
[ROW][C]111[/C][C]0.994132555303736[/C][C]0.0117348893925270[/C][C]0.00586744469626352[/C][/ROW]
[ROW][C]112[/C][C]0.992105286486273[/C][C]0.0157894270274544[/C][C]0.0078947135137272[/C][/ROW]
[ROW][C]113[/C][C]0.993234058281636[/C][C]0.0135318834367274[/C][C]0.00676594171836368[/C][/ROW]
[ROW][C]114[/C][C]0.99076594005831[/C][C]0.0184681198833785[/C][C]0.00923405994168925[/C][/ROW]
[ROW][C]115[/C][C]0.985298988740902[/C][C]0.0294020225181955[/C][C]0.0147010112590977[/C][/ROW]
[ROW][C]116[/C][C]0.980537996974147[/C][C]0.0389240060517069[/C][C]0.0194620030258534[/C][/ROW]
[ROW][C]117[/C][C]0.985886636101466[/C][C]0.0282267277970688[/C][C]0.0141133638985344[/C][/ROW]
[ROW][C]118[/C][C]0.988752618193024[/C][C]0.0224947636139524[/C][C]0.0112473818069762[/C][/ROW]
[ROW][C]119[/C][C]0.994915974257321[/C][C]0.0101680514853573[/C][C]0.00508402574267863[/C][/ROW]
[ROW][C]120[/C][C]0.990966249035018[/C][C]0.0180675019299635[/C][C]0.00903375096498175[/C][/ROW]
[ROW][C]121[/C][C]0.989187878645253[/C][C]0.0216242427094938[/C][C]0.0108121213547469[/C][/ROW]
[ROW][C]122[/C][C]0.993758473418765[/C][C]0.0124830531624707[/C][C]0.00624152658123534[/C][/ROW]
[ROW][C]123[/C][C]0.99378960246765[/C][C]0.0124207950646997[/C][C]0.00621039753234983[/C][/ROW]
[ROW][C]124[/C][C]0.991870673756406[/C][C]0.0162586524871871[/C][C]0.00812932624359357[/C][/ROW]
[ROW][C]125[/C][C]0.985117712902062[/C][C]0.0297645741958761[/C][C]0.0148822870979380[/C][/ROW]
[ROW][C]126[/C][C]0.972965053553615[/C][C]0.0540698928927708[/C][C]0.0270349464463854[/C][/ROW]
[ROW][C]127[/C][C]0.957188567054805[/C][C]0.0856228658903897[/C][C]0.0428114329451948[/C][/ROW]
[ROW][C]128[/C][C]0.961471489415025[/C][C]0.0770570211699497[/C][C]0.0385285105849748[/C][/ROW]
[ROW][C]129[/C][C]0.983176279830487[/C][C]0.0336474403390265[/C][C]0.0168237201695132[/C][/ROW]
[ROW][C]130[/C][C]0.971796168726139[/C][C]0.0564076625477226[/C][C]0.0282038312738613[/C][/ROW]
[ROW][C]131[/C][C]0.95039050098662[/C][C]0.0992189980267622[/C][C]0.0496094990133811[/C][/ROW]
[ROW][C]132[/C][C]0.906213123271643[/C][C]0.187573753456715[/C][C]0.0937868767283573[/C][/ROW]
[ROW][C]133[/C][C]0.846983848070659[/C][C]0.306032303858682[/C][C]0.153016151929341[/C][/ROW]
[ROW][C]134[/C][C]0.728984590377958[/C][C]0.542030819244083[/C][C]0.271015409622042[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108035&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108035&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.6254795988306770.7490408023386460.374520401169323
100.489788634623320.979577269246640.51021136537668
110.4743386605142870.9486773210285730.525661339485713
120.3692425475725020.7384850951450030.630757452427498
130.281391045728540.562782091457080.71860895427146
140.1920752796108920.3841505592217840.807924720389108
150.1716614271174800.3433228542349610.82833857288252
160.1258440224503310.2516880449006620.874155977549669
170.1447216499687460.2894432999374920.855278350031254
180.09830617258488490.1966123451697700.901693827415115
190.1143386256607290.2286772513214580.88566137433927
200.1003553450617090.2007106901234170.899644654938291
210.1174731368421530.2349462736843050.882526863157847
220.08147744267452840.1629548853490570.918522557325472
230.06260623250938020.1252124650187600.93739376749062
240.04612215757594480.09224431515188950.953877842424055
250.03644229388389640.07288458776779280.963557706116104
260.02427333529268430.04854667058536860.975726664707316
270.06003284751220060.1200656950244010.9399671524878
280.04655300337647040.09310600675294080.95344699662353
290.03226442901692150.0645288580338430.967735570983078
300.0218854009227320.0437708018454640.978114599077268
310.01718534668290180.03437069336580370.982814653317098
320.01925896118380210.03851792236760430.980741038816198
330.01283395380115620.02566790760231240.987166046198844
340.008484613820603350.01696922764120670.991515386179397
350.007305373343641580.01461074668728320.992694626656358
360.004911772416412320.009823544832824650.995088227583588
370.00325279116521320.00650558233042640.996747208834787
380.002362955077232690.004725910154465380.997637044922767
390.001524242285843830.003048484571687650.998475757714156
400.0009462642886371140.001892528577274230.999053735711363
410.0005956514089634670.001191302817926930.999404348591037
420.0003478625337812180.0006957250675624370.999652137466219
430.0002435853957484130.0004871707914968260.999756414604252
440.0001398953560230470.0002797907120460940.999860104643977
457.86207430430635e-050.0001572414860861270.999921379256957
469.93624592464402e-050.0001987249184928800.999900637540754
470.0001770555102022180.0003541110204044350.999822944489798
480.0005667046996346880.001133409399269380.999433295300365
490.0003971702717912370.0007943405435824730.999602829728209
500.00442443954420870.00884887908841740.995575560455791
510.003213092094603190.006426184189206380.996786907905397
520.002302525490017350.004605050980034700.997697474509983
530.001522038839881410.003044077679762830.998477961160119
540.001203959137175370.002407918274350750.998796040862825
550.002043568652818310.004087137305636620.997956431347182
560.001638600734975790.003277201469951580.998361399265024
570.001108063847691460.002216127695382930.998891936152309
580.0009177396502502780.001835479300500560.99908226034975
590.0005992813382319050.001198562676463810.999400718661768
600.0004976246900264180.0009952493800528360.999502375309974
610.0005333292539051360.001066658507810270.999466670746095
620.0004005589657339790.0008011179314679580.999599441034266
630.001883418613539100.003766837227078210.998116581386461
640.001242047511616950.00248409502323390.998757952488383
650.001078898340910870.002157796681821750.99892110165909
660.001865922726562330.003731845453124670.998134077273438
670.001265699577933230.002531399155866470.998734300422067
680.0008555900400425570.001711180080085110.999144409959957
690.000564573422286360.001129146844572720.999435426577714
700.0003989118269954690.0007978236539909370.999601088173005
710.000301337008530650.00060267401706130.99969866299147
720.0001905515690503730.0003811031381007460.99980944843095
730.0001551923162215300.0003103846324430600.999844807683778
740.0003399272243440430.0006798544486880860.999660072775656
750.0002379529793958250.0004759059587916490.999762047020604
760.0001641717635065510.0003283435270131020.999835828236493
770.0001476131373634270.0002952262747268530.999852386862637
780.8957658792393660.2084682415212670.104234120760634
790.8715366858512110.2569266282975780.128463314148789
800.8490333155509440.3019333688981120.150966684449056
810.8323112900341440.3353774199317110.167688709965856
820.8046667089162570.3906665821674850.195333291083743
830.7683700562349550.4632598875300890.231629943765045
840.7301956164946120.5396087670107760.269804383505388
850.6871063667964660.6257872664070680.312893633203534
860.6601073097291170.6797853805417670.339892690270883
870.6388655254862420.7222689490275170.361134474513758
880.5911360082304440.8177279835391110.408863991769556
890.5499629715649270.9000740568701450.450037028435073
900.5183011772549850.963397645490030.481698822745015
910.5875639849710850.824872030057830.412436015028915
920.6624728769370750.6750542461258510.337527123062925
930.6436193350867920.7127613298264150.356380664913208
940.6215592958566430.7568814082867140.378440704143357
950.5793267053804570.8413465892390850.420673294619543
960.5848877520333630.8302244959332740.415112247966637
970.9985293041122510.002941391775497450.00147069588774873
980.9980213771074740.003957245785052370.00197862289252619
990.9985170006510730.002965998697853160.00148299934892658
1000.9982837914786680.003432417042663080.00171620852133154
1010.998854101659780.002291796680439730.00114589834021987
1020.9981292106475060.003741578704988060.00187078935249403
1030.997267212183810.005465575632379860.00273278781618993
1040.9984130108779180.003173978244163770.00158698912208188
1050.9983939826537140.003212034692572010.00160601734628601
1060.9973642960804380.005271407839123680.00263570391956184
1070.996165261126110.007669477747780480.00383473887389024
1080.9940588550979420.01188228980411550.00594114490205773
1090.9975460256149960.004907948770008090.00245397438500404
1100.9961538219466470.007692356106705080.00384617805335254
1110.9941325553037360.01173488939252700.00586744469626352
1120.9921052864862730.01578942702745440.0078947135137272
1130.9932340582816360.01353188343672740.00676594171836368
1140.990765940058310.01846811988337850.00923405994168925
1150.9852989887409020.02940202251819550.0147010112590977
1160.9805379969741470.03892400605170690.0194620030258534
1170.9858866361014660.02822672779706880.0141133638985344
1180.9887526181930240.02249476361395240.0112473818069762
1190.9949159742573210.01016805148535730.00508402574267863
1200.9909662490350180.01806750192996350.00903375096498175
1210.9891878786452530.02162424270949380.0108121213547469
1220.9937584734187650.01248305316247070.00624152658123534
1230.993789602467650.01242079506469970.00621039753234983
1240.9918706737564060.01625865248718710.00812932624359357
1250.9851177129020620.02976457419587610.0148822870979380
1260.9729650535536150.05406989289277080.0270349464463854
1270.9571885670548050.08562286589038970.0428114329451948
1280.9614714894150250.07705702116994970.0385285105849748
1290.9831762798304870.03364744033902650.0168237201695132
1300.9717961687261390.05640766254772260.0282038312738613
1310.950390500986620.09921899802676220.0496094990133811
1320.9062131232716430.1875737534567150.0937868767283573
1330.8469838480706590.3060323038586820.153016151929341
1340.7289845903779580.5420308192440830.271015409622042






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level550.436507936507937NOK
5% type I error level790.626984126984127NOK
10% type I error level880.698412698412698NOK

\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 & 55 & 0.436507936507937 & NOK \tabularnewline
5% type I error level & 79 & 0.626984126984127 & NOK \tabularnewline
10% type I error level & 88 & 0.698412698412698 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108035&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]55[/C][C]0.436507936507937[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]79[/C][C]0.626984126984127[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]88[/C][C]0.698412698412698[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108035&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108035&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level550.436507936507937NOK
5% type I error level790.626984126984127NOK
10% type I error level880.698412698412698NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 5 ; par2 = equal ; par3 = 4 ; par4 = no ;
Parameters (R input):
par1 = 5 ; 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')
}