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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 computationTue, 20 Nov 2012 16:50:10 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/20/t1353448254na66hausrgu9w99.htm/, Retrieved Mon, 29 Apr 2024 21:27:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=191310, Retrieved Mon, 29 Apr 2024 21:27:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact77

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [] [2012-11-20 21:31:57] [36b0f91c18c039b2d6c3b5210571f1ca]
-    D      [Multiple Regression] [] [2012-11-20 21:50:10] [c394fd7c96c8a7cd973da7b3be872d6d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
478	184	40	74	11	31	20
494	213	32	72	11	43	18
643	347	57	70	18	16	16
341	565	31	71	11	25	19
773	327	67	72	9	29	24
603	260	25	68	8	32	15
484	325	34	68	12	24	14
546	102	33	62	13	28	11
424	38	36	69	7	25	12
548	226	31	66	9	58	15
506	137	35	60	13	21	9
819	369	30	81	4	77	36
541	109	44	66	9	37	12
491	809	32	67	11	37	16
514	29	30	65	12	35	11
371	245	16	64	10	42	14
457	118	29	64	12	21	10
437	148	36	62	7	81	27
570	387	30	59	15	31	16
432	98	23	56	15	50	15
619	608	33	46	22	24	8
357	218	35	54	14	27	13
623	254	38	54	20	22	11
547	697	44	45	26	18	8
792	827	28	57	12	23	11
799	693	35	57	9	60	18
439	448	31	61	19	14	12
867	942	39	52	17	31	10
912	1017	27	44	21	24	9
462	216	36	43	18	23	8
859	673	38	48	19	22	10
805	989	46	57	14	25	12
652	630	29	47	19	25	9
776	404	32	50	19	21	9
919	692	39	48	16	32	11
732	1517	44	49	13	31	14
657	879	33	72	13	13	22
1419	631	43	59	14	21	13
989	1375	22	49	9	46	13
821	1139	30	54	13	27	12
1740	3545	86	62	22	18	15
815	706	30	47	17	39	11
760	451	32	45	34	15	10
936	433	43	48	26	23	12
863	601	20	69	23	7	12
783	1024	55	42	23	23	11
715	457	44	49	18	30	12
1504	1441	37	57	15	35	13
1324	1022	82	72	22	15	16
940	1244	66	67	26	18	16
478	184	40	74	11	31	20
494	213	32	72	11	43	18
643	347	57	70	18	16	16
341	565	31	71	11	25	19
773	327	67	72	9	29	24
603	260	25	68	8	32	15
484	325	34	68	12	24	14
546	102	33	62	13	28	11
424	38	36	69	7	25	12
548	226	31	66	9	58	15
506	137	35	60	13	21	9
819	369	30	81	4	77	36
541	109	44	66	9	37	12
491	809	32	67	11	37	16
514	29	30	65	12	35	11
371	245	16	64	10	42	14
457	118	29	64	12	21	10
437	148	36	62	7	81	27
570	387	30	59	15	31	16
432	98	23	56	15	50	15
619	608	33	46	22	24	8
357	218	35	54	14	27	13
623	254	38	54	20	22	11
547	697	44	45	26	18	8
792	827	28	57	12	23	11
799	693	35	57	9	60	18
439	448	31	61	19	14	12
867	942	39	52	17	31	10
912	1017	27	44	21	24	9
462	216	36	43	18	23	8
859	673	38	48	19	22	10
805	989	46	57	14	25	12
652	630	29	47	19	25	9
776	404	32	50	19	21	9
919	692	39	48	16	32	11
732	1517	44	49	13	31	14
657	879	33	72	13	13	22
1419	631	43	59	14	21	13
989	1375	22	49	9	46	13
821	1139	30	54	13	27	12
1740	3545	86	62	22	18	15
815	706	30	47	17	39	11
760	451	32	45	34	15	10
936	433	43	48	26	23	12
863	601	20	69	23	7	12
783	1024	55	42	23	23	11
715	457	44	49	18	30	12
1504	1441	37	57	15	35	13
1324	1022	82	72	22	15	16
940	1244	66	67	26	18	16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191310&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191310&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191310&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 time10 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Y1_Total_overal_crime[t] = + 100.393611638968 + 0.332336476662426X1_violent_crime[t] + 3.99817389808975X2_police_fund[t] + 1.85791247072403`X3_%_25+_4yrs_HS`[t] + 7.83886063212366`X4_%_16-19_unschooled`[t] + 2.55876932475728`X5_%_18-24_in_college`[t] -3.23116194241756`X6_%_25+_with_4_yrs_or_more_college`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y1_Total_overal_crime[t] =  +  100.393611638968 +  0.332336476662426X1_violent_crime[t] +  3.99817389808975X2_police_fund[t] +  1.85791247072403`X3_%_25+_4yrs_HS`[t] +  7.83886063212366`X4_%_16-19_unschooled`[t] +  2.55876932475728`X5_%_18-24_in_college`[t] -3.23116194241756`X6_%_25+_with_4_yrs_or_more_college`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191310&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y1_Total_overal_crime[t] =  +  100.393611638968 +  0.332336476662426X1_violent_crime[t] +  3.99817389808975X2_police_fund[t] +  1.85791247072403`X3_%_25+_4yrs_HS`[t] +  7.83886063212366`X4_%_16-19_unschooled`[t] +  2.55876932475728`X5_%_18-24_in_college`[t] -3.23116194241756`X6_%_25+_with_4_yrs_or_more_college`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191310&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191310&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
Y1_Total_overal_crime[t] = + 100.393611638968 + 0.332336476662426X1_violent_crime[t] + 3.99817389808975X2_police_fund[t] + 1.85791247072403`X3_%_25+_4yrs_HS`[t] + 7.83886063212366`X4_%_16-19_unschooled`[t] + 2.55876932475728`X5_%_18-24_in_college`[t] -3.23116194241756`X6_%_25+_with_4_yrs_or_more_college`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)100.393611638968252.061980.39830.691330.345665
X1_violent_crime0.3323364766624260.0405388.198100
X2_police_fund3.998173898089751.824022.1920.0308790.01544
`X3_%_25+_4yrs_HS`1.857912470724033.5636610.52130.6033630.301681
`X4_%_16-19_unschooled`7.838860632123665.2765171.48560.1407640.070382
`X5_%_18-24_in_college`2.558769324757282.3302411.09810.2750080.137504
`X6_%_25+_with_4_yrs_or_more_college`-3.231161942417567.286181-0.44350.658460.32923

\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) & 100.393611638968 & 252.06198 & 0.3983 & 0.69133 & 0.345665 \tabularnewline
X1_violent_crime & 0.332336476662426 & 0.040538 & 8.1981 & 0 & 0 \tabularnewline
X2_police_fund & 3.99817389808975 & 1.82402 & 2.192 & 0.030879 & 0.01544 \tabularnewline
`X3_%_25+_4yrs_HS` & 1.85791247072403 & 3.563661 & 0.5213 & 0.603363 & 0.301681 \tabularnewline
`X4_%_16-19_unschooled` & 7.83886063212366 & 5.276517 & 1.4856 & 0.140764 & 0.070382 \tabularnewline
`X5_%_18-24_in_college` & 2.55876932475728 & 2.330241 & 1.0981 & 0.275008 & 0.137504 \tabularnewline
`X6_%_25+_with_4_yrs_or_more_college` & -3.23116194241756 & 7.286181 & -0.4435 & 0.65846 & 0.32923 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191310&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]100.393611638968[/C][C]252.06198[/C][C]0.3983[/C][C]0.69133[/C][C]0.345665[/C][/ROW]
[ROW][C]X1_violent_crime[/C][C]0.332336476662426[/C][C]0.040538[/C][C]8.1981[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X2_police_fund[/C][C]3.99817389808975[/C][C]1.82402[/C][C]2.192[/C][C]0.030879[/C][C]0.01544[/C][/ROW]
[ROW][C]`X3_%_25+_4yrs_HS`[/C][C]1.85791247072403[/C][C]3.563661[/C][C]0.5213[/C][C]0.603363[/C][C]0.301681[/C][/ROW]
[ROW][C]`X4_%_16-19_unschooled`[/C][C]7.83886063212366[/C][C]5.276517[/C][C]1.4856[/C][C]0.140764[/C][C]0.070382[/C][/ROW]
[ROW][C]`X5_%_18-24_in_college`[/C][C]2.55876932475728[/C][C]2.330241[/C][C]1.0981[/C][C]0.275008[/C][C]0.137504[/C][/ROW]
[ROW][C]`X6_%_25+_with_4_yrs_or_more_college`[/C][C]-3.23116194241756[/C][C]7.286181[/C][C]-0.4435[/C][C]0.65846[/C][C]0.32923[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191310&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191310&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)100.393611638968252.061980.39830.691330.345665
X1_violent_crime0.3323364766624260.0405388.198100
X2_police_fund3.998173898089751.824022.1920.0308790.01544
`X3_%_25+_4yrs_HS`1.857912470724033.5636610.52130.6033630.301681
`X4_%_16-19_unschooled`7.838860632123665.2765171.48560.1407640.070382
`X5_%_18-24_in_college`2.558769324757282.3302411.09810.2750080.137504
`X6_%_25+_with_4_yrs_or_more_college`-3.231161942417567.286181-0.44350.658460.32923






Multiple Linear Regression - Regression Statistics
Multiple R0.783045775972243
R-squared0.613160687267972
Adjusted R-squared0.588203312253002
F-TEST (value)24.5683164555645
F-TEST (DF numerator)6
F-TEST (DF denominator)93
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation187.669515253797
Sum Squared Residuals3275445.76687034

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.783045775972243 \tabularnewline
R-squared & 0.613160687267972 \tabularnewline
Adjusted R-squared & 0.588203312253002 \tabularnewline
F-TEST (value) & 24.5683164555645 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 93 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 187.669515253797 \tabularnewline
Sum Squared Residuals & 3275445.76687034 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191310&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.783045775972243[/C][/ROW]
[ROW][C]R-squared[/C][C]0.613160687267972[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.588203312253002[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]24.5683164555645[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]93[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]187.669515253797[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3275445.76687034[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191310&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191310&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 R0.783045775972243
R-squared0.613160687267972
Adjusted R-squared0.588203312253002
F-TEST (value)24.5683164555645
F-TEST (DF numerator)6
F-TEST (DF denominator)93
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation187.669515253797
Sum Squared Residuals3275445.76687034






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1478559.882079274508-81.8820792745077
2494570.986176753474-76.9861767534738
3643704.005363678289-61.0053636782891
4341632.823520381786-291.823520381786
5773677.92115806077795.0788419392228
6603509.21756534563693.7824346543645
7484580.919451284355-96.9194512843549
8546519.43019202460626.5698079753946
9424465.219932798116-41.2199327981163
10548592.558206662016-44.5582066620163
11506523.893430174056-17.8934301740559
12819605.520749206515213.479250793485
13541561.610429575029-20.610429575029
14491750.878862426951-259.878862426951
15514498.82136958732815.1786304126715
16371505.313879684233-134.313879684233
17457489.951621037286-32.9516210372863
18437583.595210986059-146.595210986059
19570603.776048293387-33.7760482933869
20432526.017630951951-94.0176309519513
21619727.874003901545-108.874003901545
22357549.932038770365-192.932038770365
23623614.5923146782738.40768532172736
24547815.576777312715-268.576777312715
25792710.46099850488581.5390014951147
26799742.45487744147456.5451225585262
27439632.543583986624-193.543583986624
28867846.26566354752520.7343364524745
29912825.02473195194986.9752680480505
30462570.104677478719-108.104677478719
31859738.086124885779120.913875114221
32805853.830735861158-48.830735861158
33652696.861648752452-44.8616487524522
34776629.086786834156146.913213165844
35919747.238641249239171.761358750761
367321007.49617940853-275.496179408532
37657722.310437860598-65.3104378605983
381419713.109341221742705.890658778258
39989882.601833249775106.398166750225
40821831.415365596305-10.4153655963048
4117401907.60530244396-167.605302443965
42815739.80012027440675.1998797255943
43760734.41617047456425.5838295254363
44936729.284709842033206.715290157967
45863667.718509057974195.281490942026
46783942.240759548306-159.240759548306
47715698.31737186705716.6826281329432
481504998.258650167046505.741349832954
4913241060.79933102286263.200668977144
509401100.34943462162-160.349434621625
51478559.882079274507-81.8820792745074
52494570.986176753474-76.9861767534742
53643704.005363678289-61.0053636782891
54341632.823520381786-291.823520381786
55773677.92115806077795.0788419392227
56603509.21756534563593.7824346543645
57484580.919451284355-96.9194512843549
58546519.43019202460626.5698079753946
59424465.219932798116-41.2199327981163
60548592.558206662016-44.5582066620163
61506523.893430174056-17.8934301740559
62819605.520749206515213.479250793485
63541561.610429575029-20.610429575029
64491750.878862426951-259.878862426951
65514498.82136958732815.1786304126715
66371505.313879684233-134.313879684233
67457489.951621037286-32.9516210372863
68437583.595210986059-146.595210986059
69570603.776048293387-33.7760482933869
70432526.017630951951-94.0176309519513
71619727.874003901545-108.874003901545
72357549.932038770365-192.932038770365
73623614.5923146782738.40768532172736
74547815.576777312715-268.576777312715
75792710.46099850488581.5390014951147
76799742.45487744147456.5451225585262
77439632.543583986624-193.543583986624
78867846.26566354752520.7343364524745
79912825.02473195194986.9752680480505
80462570.104677478719-108.104677478719
81859738.086124885779120.913875114221
82805853.830735861158-48.830735861158
83652696.861648752452-44.8616487524522
84776629.086786834156146.913213165844
85919747.238641249239171.761358750761
867321007.49617940853-275.496179408532
87657722.310437860598-65.3104378605983
881419713.109341221742705.890658778258
89989882.601833249775106.398166750225
90821831.415365596305-10.4153655963048
9117401907.60530244396-167.605302443965
92815739.80012027440675.1998797255943
93760734.41617047456425.5838295254363
94936729.284709842033206.715290157967
95863667.718509057974195.281490942026
96783942.240759548306-159.240759548306
97715698.31737186705716.6826281329432
981504998.258650167046505.741349832954
9913241060.79933102286263.200668977144
1009401100.34943462162-160.349434621625

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 478 & 559.882079274508 & -81.8820792745077 \tabularnewline
2 & 494 & 570.986176753474 & -76.9861767534738 \tabularnewline
3 & 643 & 704.005363678289 & -61.0053636782891 \tabularnewline
4 & 341 & 632.823520381786 & -291.823520381786 \tabularnewline
5 & 773 & 677.921158060777 & 95.0788419392228 \tabularnewline
6 & 603 & 509.217565345636 & 93.7824346543645 \tabularnewline
7 & 484 & 580.919451284355 & -96.9194512843549 \tabularnewline
8 & 546 & 519.430192024606 & 26.5698079753946 \tabularnewline
9 & 424 & 465.219932798116 & -41.2199327981163 \tabularnewline
10 & 548 & 592.558206662016 & -44.5582066620163 \tabularnewline
11 & 506 & 523.893430174056 & -17.8934301740559 \tabularnewline
12 & 819 & 605.520749206515 & 213.479250793485 \tabularnewline
13 & 541 & 561.610429575029 & -20.610429575029 \tabularnewline
14 & 491 & 750.878862426951 & -259.878862426951 \tabularnewline
15 & 514 & 498.821369587328 & 15.1786304126715 \tabularnewline
16 & 371 & 505.313879684233 & -134.313879684233 \tabularnewline
17 & 457 & 489.951621037286 & -32.9516210372863 \tabularnewline
18 & 437 & 583.595210986059 & -146.595210986059 \tabularnewline
19 & 570 & 603.776048293387 & -33.7760482933869 \tabularnewline
20 & 432 & 526.017630951951 & -94.0176309519513 \tabularnewline
21 & 619 & 727.874003901545 & -108.874003901545 \tabularnewline
22 & 357 & 549.932038770365 & -192.932038770365 \tabularnewline
23 & 623 & 614.592314678273 & 8.40768532172736 \tabularnewline
24 & 547 & 815.576777312715 & -268.576777312715 \tabularnewline
25 & 792 & 710.460998504885 & 81.5390014951147 \tabularnewline
26 & 799 & 742.454877441474 & 56.5451225585262 \tabularnewline
27 & 439 & 632.543583986624 & -193.543583986624 \tabularnewline
28 & 867 & 846.265663547525 & 20.7343364524745 \tabularnewline
29 & 912 & 825.024731951949 & 86.9752680480505 \tabularnewline
30 & 462 & 570.104677478719 & -108.104677478719 \tabularnewline
31 & 859 & 738.086124885779 & 120.913875114221 \tabularnewline
32 & 805 & 853.830735861158 & -48.830735861158 \tabularnewline
33 & 652 & 696.861648752452 & -44.8616487524522 \tabularnewline
34 & 776 & 629.086786834156 & 146.913213165844 \tabularnewline
35 & 919 & 747.238641249239 & 171.761358750761 \tabularnewline
36 & 732 & 1007.49617940853 & -275.496179408532 \tabularnewline
37 & 657 & 722.310437860598 & -65.3104378605983 \tabularnewline
38 & 1419 & 713.109341221742 & 705.890658778258 \tabularnewline
39 & 989 & 882.601833249775 & 106.398166750225 \tabularnewline
40 & 821 & 831.415365596305 & -10.4153655963048 \tabularnewline
41 & 1740 & 1907.60530244396 & -167.605302443965 \tabularnewline
42 & 815 & 739.800120274406 & 75.1998797255943 \tabularnewline
43 & 760 & 734.416170474564 & 25.5838295254363 \tabularnewline
44 & 936 & 729.284709842033 & 206.715290157967 \tabularnewline
45 & 863 & 667.718509057974 & 195.281490942026 \tabularnewline
46 & 783 & 942.240759548306 & -159.240759548306 \tabularnewline
47 & 715 & 698.317371867057 & 16.6826281329432 \tabularnewline
48 & 1504 & 998.258650167046 & 505.741349832954 \tabularnewline
49 & 1324 & 1060.79933102286 & 263.200668977144 \tabularnewline
50 & 940 & 1100.34943462162 & -160.349434621625 \tabularnewline
51 & 478 & 559.882079274507 & -81.8820792745074 \tabularnewline
52 & 494 & 570.986176753474 & -76.9861767534742 \tabularnewline
53 & 643 & 704.005363678289 & -61.0053636782891 \tabularnewline
54 & 341 & 632.823520381786 & -291.823520381786 \tabularnewline
55 & 773 & 677.921158060777 & 95.0788419392227 \tabularnewline
56 & 603 & 509.217565345635 & 93.7824346543645 \tabularnewline
57 & 484 & 580.919451284355 & -96.9194512843549 \tabularnewline
58 & 546 & 519.430192024606 & 26.5698079753946 \tabularnewline
59 & 424 & 465.219932798116 & -41.2199327981163 \tabularnewline
60 & 548 & 592.558206662016 & -44.5582066620163 \tabularnewline
61 & 506 & 523.893430174056 & -17.8934301740559 \tabularnewline
62 & 819 & 605.520749206515 & 213.479250793485 \tabularnewline
63 & 541 & 561.610429575029 & -20.610429575029 \tabularnewline
64 & 491 & 750.878862426951 & -259.878862426951 \tabularnewline
65 & 514 & 498.821369587328 & 15.1786304126715 \tabularnewline
66 & 371 & 505.313879684233 & -134.313879684233 \tabularnewline
67 & 457 & 489.951621037286 & -32.9516210372863 \tabularnewline
68 & 437 & 583.595210986059 & -146.595210986059 \tabularnewline
69 & 570 & 603.776048293387 & -33.7760482933869 \tabularnewline
70 & 432 & 526.017630951951 & -94.0176309519513 \tabularnewline
71 & 619 & 727.874003901545 & -108.874003901545 \tabularnewline
72 & 357 & 549.932038770365 & -192.932038770365 \tabularnewline
73 & 623 & 614.592314678273 & 8.40768532172736 \tabularnewline
74 & 547 & 815.576777312715 & -268.576777312715 \tabularnewline
75 & 792 & 710.460998504885 & 81.5390014951147 \tabularnewline
76 & 799 & 742.454877441474 & 56.5451225585262 \tabularnewline
77 & 439 & 632.543583986624 & -193.543583986624 \tabularnewline
78 & 867 & 846.265663547525 & 20.7343364524745 \tabularnewline
79 & 912 & 825.024731951949 & 86.9752680480505 \tabularnewline
80 & 462 & 570.104677478719 & -108.104677478719 \tabularnewline
81 & 859 & 738.086124885779 & 120.913875114221 \tabularnewline
82 & 805 & 853.830735861158 & -48.830735861158 \tabularnewline
83 & 652 & 696.861648752452 & -44.8616487524522 \tabularnewline
84 & 776 & 629.086786834156 & 146.913213165844 \tabularnewline
85 & 919 & 747.238641249239 & 171.761358750761 \tabularnewline
86 & 732 & 1007.49617940853 & -275.496179408532 \tabularnewline
87 & 657 & 722.310437860598 & -65.3104378605983 \tabularnewline
88 & 1419 & 713.109341221742 & 705.890658778258 \tabularnewline
89 & 989 & 882.601833249775 & 106.398166750225 \tabularnewline
90 & 821 & 831.415365596305 & -10.4153655963048 \tabularnewline
91 & 1740 & 1907.60530244396 & -167.605302443965 \tabularnewline
92 & 815 & 739.800120274406 & 75.1998797255943 \tabularnewline
93 & 760 & 734.416170474564 & 25.5838295254363 \tabularnewline
94 & 936 & 729.284709842033 & 206.715290157967 \tabularnewline
95 & 863 & 667.718509057974 & 195.281490942026 \tabularnewline
96 & 783 & 942.240759548306 & -159.240759548306 \tabularnewline
97 & 715 & 698.317371867057 & 16.6826281329432 \tabularnewline
98 & 1504 & 998.258650167046 & 505.741349832954 \tabularnewline
99 & 1324 & 1060.79933102286 & 263.200668977144 \tabularnewline
100 & 940 & 1100.34943462162 & -160.349434621625 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191310&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]478[/C][C]559.882079274508[/C][C]-81.8820792745077[/C][/ROW]
[ROW][C]2[/C][C]494[/C][C]570.986176753474[/C][C]-76.9861767534738[/C][/ROW]
[ROW][C]3[/C][C]643[/C][C]704.005363678289[/C][C]-61.0053636782891[/C][/ROW]
[ROW][C]4[/C][C]341[/C][C]632.823520381786[/C][C]-291.823520381786[/C][/ROW]
[ROW][C]5[/C][C]773[/C][C]677.921158060777[/C][C]95.0788419392228[/C][/ROW]
[ROW][C]6[/C][C]603[/C][C]509.217565345636[/C][C]93.7824346543645[/C][/ROW]
[ROW][C]7[/C][C]484[/C][C]580.919451284355[/C][C]-96.9194512843549[/C][/ROW]
[ROW][C]8[/C][C]546[/C][C]519.430192024606[/C][C]26.5698079753946[/C][/ROW]
[ROW][C]9[/C][C]424[/C][C]465.219932798116[/C][C]-41.2199327981163[/C][/ROW]
[ROW][C]10[/C][C]548[/C][C]592.558206662016[/C][C]-44.5582066620163[/C][/ROW]
[ROW][C]11[/C][C]506[/C][C]523.893430174056[/C][C]-17.8934301740559[/C][/ROW]
[ROW][C]12[/C][C]819[/C][C]605.520749206515[/C][C]213.479250793485[/C][/ROW]
[ROW][C]13[/C][C]541[/C][C]561.610429575029[/C][C]-20.610429575029[/C][/ROW]
[ROW][C]14[/C][C]491[/C][C]750.878862426951[/C][C]-259.878862426951[/C][/ROW]
[ROW][C]15[/C][C]514[/C][C]498.821369587328[/C][C]15.1786304126715[/C][/ROW]
[ROW][C]16[/C][C]371[/C][C]505.313879684233[/C][C]-134.313879684233[/C][/ROW]
[ROW][C]17[/C][C]457[/C][C]489.951621037286[/C][C]-32.9516210372863[/C][/ROW]
[ROW][C]18[/C][C]437[/C][C]583.595210986059[/C][C]-146.595210986059[/C][/ROW]
[ROW][C]19[/C][C]570[/C][C]603.776048293387[/C][C]-33.7760482933869[/C][/ROW]
[ROW][C]20[/C][C]432[/C][C]526.017630951951[/C][C]-94.0176309519513[/C][/ROW]
[ROW][C]21[/C][C]619[/C][C]727.874003901545[/C][C]-108.874003901545[/C][/ROW]
[ROW][C]22[/C][C]357[/C][C]549.932038770365[/C][C]-192.932038770365[/C][/ROW]
[ROW][C]23[/C][C]623[/C][C]614.592314678273[/C][C]8.40768532172736[/C][/ROW]
[ROW][C]24[/C][C]547[/C][C]815.576777312715[/C][C]-268.576777312715[/C][/ROW]
[ROW][C]25[/C][C]792[/C][C]710.460998504885[/C][C]81.5390014951147[/C][/ROW]
[ROW][C]26[/C][C]799[/C][C]742.454877441474[/C][C]56.5451225585262[/C][/ROW]
[ROW][C]27[/C][C]439[/C][C]632.543583986624[/C][C]-193.543583986624[/C][/ROW]
[ROW][C]28[/C][C]867[/C][C]846.265663547525[/C][C]20.7343364524745[/C][/ROW]
[ROW][C]29[/C][C]912[/C][C]825.024731951949[/C][C]86.9752680480505[/C][/ROW]
[ROW][C]30[/C][C]462[/C][C]570.104677478719[/C][C]-108.104677478719[/C][/ROW]
[ROW][C]31[/C][C]859[/C][C]738.086124885779[/C][C]120.913875114221[/C][/ROW]
[ROW][C]32[/C][C]805[/C][C]853.830735861158[/C][C]-48.830735861158[/C][/ROW]
[ROW][C]33[/C][C]652[/C][C]696.861648752452[/C][C]-44.8616487524522[/C][/ROW]
[ROW][C]34[/C][C]776[/C][C]629.086786834156[/C][C]146.913213165844[/C][/ROW]
[ROW][C]35[/C][C]919[/C][C]747.238641249239[/C][C]171.761358750761[/C][/ROW]
[ROW][C]36[/C][C]732[/C][C]1007.49617940853[/C][C]-275.496179408532[/C][/ROW]
[ROW][C]37[/C][C]657[/C][C]722.310437860598[/C][C]-65.3104378605983[/C][/ROW]
[ROW][C]38[/C][C]1419[/C][C]713.109341221742[/C][C]705.890658778258[/C][/ROW]
[ROW][C]39[/C][C]989[/C][C]882.601833249775[/C][C]106.398166750225[/C][/ROW]
[ROW][C]40[/C][C]821[/C][C]831.415365596305[/C][C]-10.4153655963048[/C][/ROW]
[ROW][C]41[/C][C]1740[/C][C]1907.60530244396[/C][C]-167.605302443965[/C][/ROW]
[ROW][C]42[/C][C]815[/C][C]739.800120274406[/C][C]75.1998797255943[/C][/ROW]
[ROW][C]43[/C][C]760[/C][C]734.416170474564[/C][C]25.5838295254363[/C][/ROW]
[ROW][C]44[/C][C]936[/C][C]729.284709842033[/C][C]206.715290157967[/C][/ROW]
[ROW][C]45[/C][C]863[/C][C]667.718509057974[/C][C]195.281490942026[/C][/ROW]
[ROW][C]46[/C][C]783[/C][C]942.240759548306[/C][C]-159.240759548306[/C][/ROW]
[ROW][C]47[/C][C]715[/C][C]698.317371867057[/C][C]16.6826281329432[/C][/ROW]
[ROW][C]48[/C][C]1504[/C][C]998.258650167046[/C][C]505.741349832954[/C][/ROW]
[ROW][C]49[/C][C]1324[/C][C]1060.79933102286[/C][C]263.200668977144[/C][/ROW]
[ROW][C]50[/C][C]940[/C][C]1100.34943462162[/C][C]-160.349434621625[/C][/ROW]
[ROW][C]51[/C][C]478[/C][C]559.882079274507[/C][C]-81.8820792745074[/C][/ROW]
[ROW][C]52[/C][C]494[/C][C]570.986176753474[/C][C]-76.9861767534742[/C][/ROW]
[ROW][C]53[/C][C]643[/C][C]704.005363678289[/C][C]-61.0053636782891[/C][/ROW]
[ROW][C]54[/C][C]341[/C][C]632.823520381786[/C][C]-291.823520381786[/C][/ROW]
[ROW][C]55[/C][C]773[/C][C]677.921158060777[/C][C]95.0788419392227[/C][/ROW]
[ROW][C]56[/C][C]603[/C][C]509.217565345635[/C][C]93.7824346543645[/C][/ROW]
[ROW][C]57[/C][C]484[/C][C]580.919451284355[/C][C]-96.9194512843549[/C][/ROW]
[ROW][C]58[/C][C]546[/C][C]519.430192024606[/C][C]26.5698079753946[/C][/ROW]
[ROW][C]59[/C][C]424[/C][C]465.219932798116[/C][C]-41.2199327981163[/C][/ROW]
[ROW][C]60[/C][C]548[/C][C]592.558206662016[/C][C]-44.5582066620163[/C][/ROW]
[ROW][C]61[/C][C]506[/C][C]523.893430174056[/C][C]-17.8934301740559[/C][/ROW]
[ROW][C]62[/C][C]819[/C][C]605.520749206515[/C][C]213.479250793485[/C][/ROW]
[ROW][C]63[/C][C]541[/C][C]561.610429575029[/C][C]-20.610429575029[/C][/ROW]
[ROW][C]64[/C][C]491[/C][C]750.878862426951[/C][C]-259.878862426951[/C][/ROW]
[ROW][C]65[/C][C]514[/C][C]498.821369587328[/C][C]15.1786304126715[/C][/ROW]
[ROW][C]66[/C][C]371[/C][C]505.313879684233[/C][C]-134.313879684233[/C][/ROW]
[ROW][C]67[/C][C]457[/C][C]489.951621037286[/C][C]-32.9516210372863[/C][/ROW]
[ROW][C]68[/C][C]437[/C][C]583.595210986059[/C][C]-146.595210986059[/C][/ROW]
[ROW][C]69[/C][C]570[/C][C]603.776048293387[/C][C]-33.7760482933869[/C][/ROW]
[ROW][C]70[/C][C]432[/C][C]526.017630951951[/C][C]-94.0176309519513[/C][/ROW]
[ROW][C]71[/C][C]619[/C][C]727.874003901545[/C][C]-108.874003901545[/C][/ROW]
[ROW][C]72[/C][C]357[/C][C]549.932038770365[/C][C]-192.932038770365[/C][/ROW]
[ROW][C]73[/C][C]623[/C][C]614.592314678273[/C][C]8.40768532172736[/C][/ROW]
[ROW][C]74[/C][C]547[/C][C]815.576777312715[/C][C]-268.576777312715[/C][/ROW]
[ROW][C]75[/C][C]792[/C][C]710.460998504885[/C][C]81.5390014951147[/C][/ROW]
[ROW][C]76[/C][C]799[/C][C]742.454877441474[/C][C]56.5451225585262[/C][/ROW]
[ROW][C]77[/C][C]439[/C][C]632.543583986624[/C][C]-193.543583986624[/C][/ROW]
[ROW][C]78[/C][C]867[/C][C]846.265663547525[/C][C]20.7343364524745[/C][/ROW]
[ROW][C]79[/C][C]912[/C][C]825.024731951949[/C][C]86.9752680480505[/C][/ROW]
[ROW][C]80[/C][C]462[/C][C]570.104677478719[/C][C]-108.104677478719[/C][/ROW]
[ROW][C]81[/C][C]859[/C][C]738.086124885779[/C][C]120.913875114221[/C][/ROW]
[ROW][C]82[/C][C]805[/C][C]853.830735861158[/C][C]-48.830735861158[/C][/ROW]
[ROW][C]83[/C][C]652[/C][C]696.861648752452[/C][C]-44.8616487524522[/C][/ROW]
[ROW][C]84[/C][C]776[/C][C]629.086786834156[/C][C]146.913213165844[/C][/ROW]
[ROW][C]85[/C][C]919[/C][C]747.238641249239[/C][C]171.761358750761[/C][/ROW]
[ROW][C]86[/C][C]732[/C][C]1007.49617940853[/C][C]-275.496179408532[/C][/ROW]
[ROW][C]87[/C][C]657[/C][C]722.310437860598[/C][C]-65.3104378605983[/C][/ROW]
[ROW][C]88[/C][C]1419[/C][C]713.109341221742[/C][C]705.890658778258[/C][/ROW]
[ROW][C]89[/C][C]989[/C][C]882.601833249775[/C][C]106.398166750225[/C][/ROW]
[ROW][C]90[/C][C]821[/C][C]831.415365596305[/C][C]-10.4153655963048[/C][/ROW]
[ROW][C]91[/C][C]1740[/C][C]1907.60530244396[/C][C]-167.605302443965[/C][/ROW]
[ROW][C]92[/C][C]815[/C][C]739.800120274406[/C][C]75.1998797255943[/C][/ROW]
[ROW][C]93[/C][C]760[/C][C]734.416170474564[/C][C]25.5838295254363[/C][/ROW]
[ROW][C]94[/C][C]936[/C][C]729.284709842033[/C][C]206.715290157967[/C][/ROW]
[ROW][C]95[/C][C]863[/C][C]667.718509057974[/C][C]195.281490942026[/C][/ROW]
[ROW][C]96[/C][C]783[/C][C]942.240759548306[/C][C]-159.240759548306[/C][/ROW]
[ROW][C]97[/C][C]715[/C][C]698.317371867057[/C][C]16.6826281329432[/C][/ROW]
[ROW][C]98[/C][C]1504[/C][C]998.258650167046[/C][C]505.741349832954[/C][/ROW]
[ROW][C]99[/C][C]1324[/C][C]1060.79933102286[/C][C]263.200668977144[/C][/ROW]
[ROW][C]100[/C][C]940[/C][C]1100.34943462162[/C][C]-160.349434621625[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191310&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191310&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
1478559.882079274508-81.8820792745077
2494570.986176753474-76.9861767534738
3643704.005363678289-61.0053636782891
4341632.823520381786-291.823520381786
5773677.92115806077795.0788419392228
6603509.21756534563693.7824346543645
7484580.919451284355-96.9194512843549
8546519.43019202460626.5698079753946
9424465.219932798116-41.2199327981163
10548592.558206662016-44.5582066620163
11506523.893430174056-17.8934301740559
12819605.520749206515213.479250793485
13541561.610429575029-20.610429575029
14491750.878862426951-259.878862426951
15514498.82136958732815.1786304126715
16371505.313879684233-134.313879684233
17457489.951621037286-32.9516210372863
18437583.595210986059-146.595210986059
19570603.776048293387-33.7760482933869
20432526.017630951951-94.0176309519513
21619727.874003901545-108.874003901545
22357549.932038770365-192.932038770365
23623614.5923146782738.40768532172736
24547815.576777312715-268.576777312715
25792710.46099850488581.5390014951147
26799742.45487744147456.5451225585262
27439632.543583986624-193.543583986624
28867846.26566354752520.7343364524745
29912825.02473195194986.9752680480505
30462570.104677478719-108.104677478719
31859738.086124885779120.913875114221
32805853.830735861158-48.830735861158
33652696.861648752452-44.8616487524522
34776629.086786834156146.913213165844
35919747.238641249239171.761358750761
367321007.49617940853-275.496179408532
37657722.310437860598-65.3104378605983
381419713.109341221742705.890658778258
39989882.601833249775106.398166750225
40821831.415365596305-10.4153655963048
4117401907.60530244396-167.605302443965
42815739.80012027440675.1998797255943
43760734.41617047456425.5838295254363
44936729.284709842033206.715290157967
45863667.718509057974195.281490942026
46783942.240759548306-159.240759548306
47715698.31737186705716.6826281329432
481504998.258650167046505.741349832954
4913241060.79933102286263.200668977144
509401100.34943462162-160.349434621625
51478559.882079274507-81.8820792745074
52494570.986176753474-76.9861767534742
53643704.005363678289-61.0053636782891
54341632.823520381786-291.823520381786
55773677.92115806077795.0788419392227
56603509.21756534563593.7824346543645
57484580.919451284355-96.9194512843549
58546519.43019202460626.5698079753946
59424465.219932798116-41.2199327981163
60548592.558206662016-44.5582066620163
61506523.893430174056-17.8934301740559
62819605.520749206515213.479250793485
63541561.610429575029-20.610429575029
64491750.878862426951-259.878862426951
65514498.82136958732815.1786304126715
66371505.313879684233-134.313879684233
67457489.951621037286-32.9516210372863
68437583.595210986059-146.595210986059
69570603.776048293387-33.7760482933869
70432526.017630951951-94.0176309519513
71619727.874003901545-108.874003901545
72357549.932038770365-192.932038770365
73623614.5923146782738.40768532172736
74547815.576777312715-268.576777312715
75792710.46099850488581.5390014951147
76799742.45487744147456.5451225585262
77439632.543583986624-193.543583986624
78867846.26566354752520.7343364524745
79912825.02473195194986.9752680480505
80462570.104677478719-108.104677478719
81859738.086124885779120.913875114221
82805853.830735861158-48.830735861158
83652696.861648752452-44.8616487524522
84776629.086786834156146.913213165844
85919747.238641249239171.761358750761
867321007.49617940853-275.496179408532
87657722.310437860598-65.3104378605983
881419713.109341221742705.890658778258
89989882.601833249775106.398166750225
90821831.415365596305-10.4153655963048
9117401907.60530244396-167.605302443965
92815739.80012027440675.1998797255943
93760734.41617047456425.5838295254363
94936729.284709842033206.715290157967
95863667.718509057974195.281490942026
96783942.240759548306-159.240759548306
97715698.31737186705716.6826281329432
981504998.258650167046505.741349832954
9913241060.79933102286263.200668977144
1009401100.34943462162-160.349434621625






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.2140359076545410.4280718153090820.785964092345459
110.1161951064467450.2323902128934910.883804893553255
120.06700630769723680.1340126153944740.932993692302763
130.02905493877291520.05810987754583050.970945061227085
140.01415404676983030.02830809353966060.98584595323017
150.00560834051502580.01121668103005160.994391659484974
160.004576301973776770.009152603947553540.995423698026223
170.001880867102977670.003761734205955340.998119132897022
180.01564458087270120.03128916174540240.984355419127299
190.01298190460151260.02596380920302530.987018095398487
200.006731045395185530.01346209079037110.993268954604814
210.006672548107081950.01334509621416390.993327451892918
220.006131808174690630.01226361634938130.993868191825309
230.003919024212737960.007838048425475920.996080975787262
240.002490549002279580.004981098004559160.99750945099772
250.008558523561234210.01711704712246840.991441476438766
260.006496737181462130.01299347436292430.993503262818538
270.004275124036270730.008550248072541450.995724875963729
280.00344729525616930.00689459051233860.996552704743831
290.004591562688018230.009183125376036460.995408437311982
300.002863033846795410.005726067693590830.997136966153205
310.003140392268841250.006280784537682510.996859607731159
320.001895601265473720.003791202530947440.998104398734526
330.001062665191479290.002125330382958570.998937334808521
340.00159818910343330.00319637820686660.998401810896567
350.001589408359270970.003178816718541930.998410591640729
360.004766245590389330.009532491180778660.995233754409611
370.002954170207275830.005908340414551660.997045829792724
380.3693913695577850.738782739115570.630608630442215
390.323771382589610.647542765179220.67622861741039
400.2686963173565780.5373926347131560.731303682643422
410.2362047282986350.4724094565972690.763795271701365
420.1994587701784230.3989175403568460.800541229821577
430.1774112537871210.3548225075742410.822588746212879
440.1894309740961350.3788619481922690.810569025903865
450.2146539766235690.4293079532471390.785346023376431
460.1992890553132430.3985781106264860.800710944686757
470.1591308764674440.3182617529348880.840869123532556
480.4663879602798260.9327759205596510.533612039720174
490.5184938276650210.9630123446699590.481506172334979
500.50.9999999999999990.5
510.4494262038789150.8988524077578290.550573796121085
520.3986919766344550.7973839532689110.601308023365545
530.3467080768323710.6934161536647430.653291923167629
540.4247277825972770.8494555651945540.575272217402723
550.3762780124934890.7525560249869770.623721987506511
560.3306851785180610.6613703570361220.669314821481939
570.2925102610859830.5850205221719660.707489738914017
580.2420650577536810.4841301155073610.757934942246319
590.2001896509123470.4003793018246940.799810349087653
600.1619362326724860.3238724653449720.838063767327514
610.1276001417988950.255200283597790.872399858201105
620.138257110948470.2765142218969410.86174288905153
630.1093634805708470.2187269611416950.890636519429153
640.1401336158169220.2802672316338440.859866384183078
650.1133097401238090.2266194802476190.886690259876191
660.1170421614016990.2340843228033990.882957838598301
670.1192409776169130.2384819552338260.880759022383087
680.1006717847648280.2013435695296550.899328215235172
690.07625961021353780.1525192204270760.923740389786462
700.07960398682182670.1592079736436530.920396013178173
710.06708743374923560.1341748674984710.932912566250764
720.07913671851561880.1582734370312380.920863281484381
730.0602842295254270.1205684590508540.939715770474573
740.08033346458998050.1606669291799610.91966653541002
750.05981006891824110.1196201378364820.940189931081759
760.05421342257198050.1084268451439610.945786577428019
770.0902580186603530.1805160373207060.909741981339647
780.07875123909358320.1575024781871660.921248760906417
790.06129767388609840.1225953477721970.938702326113902
800.05770189782515430.1154037956503090.942298102174846
810.04050673266051570.08101346532103130.959493267339484
820.04540577655153230.09081155310306460.954594223448468
830.04245073140401330.08490146280802670.957549268595987
840.03384108587412040.06768217174824080.96615891412588
850.021466825501160.04293365100231990.97853317449884
860.02801401888166770.05602803776333530.971985981118332
870.01496253060979560.02992506121959120.985037469390204
880.2553402784849280.5106805569698550.744659721515072
890.16053249774060.32106499548120.8394675022594
900.09168371992063740.1833674398412750.908316280079363

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.214035907654541 & 0.428071815309082 & 0.785964092345459 \tabularnewline
11 & 0.116195106446745 & 0.232390212893491 & 0.883804893553255 \tabularnewline
12 & 0.0670063076972368 & 0.134012615394474 & 0.932993692302763 \tabularnewline
13 & 0.0290549387729152 & 0.0581098775458305 & 0.970945061227085 \tabularnewline
14 & 0.0141540467698303 & 0.0283080935396606 & 0.98584595323017 \tabularnewline
15 & 0.0056083405150258 & 0.0112166810300516 & 0.994391659484974 \tabularnewline
16 & 0.00457630197377677 & 0.00915260394755354 & 0.995423698026223 \tabularnewline
17 & 0.00188086710297767 & 0.00376173420595534 & 0.998119132897022 \tabularnewline
18 & 0.0156445808727012 & 0.0312891617454024 & 0.984355419127299 \tabularnewline
19 & 0.0129819046015126 & 0.0259638092030253 & 0.987018095398487 \tabularnewline
20 & 0.00673104539518553 & 0.0134620907903711 & 0.993268954604814 \tabularnewline
21 & 0.00667254810708195 & 0.0133450962141639 & 0.993327451892918 \tabularnewline
22 & 0.00613180817469063 & 0.0122636163493813 & 0.993868191825309 \tabularnewline
23 & 0.00391902421273796 & 0.00783804842547592 & 0.996080975787262 \tabularnewline
24 & 0.00249054900227958 & 0.00498109800455916 & 0.99750945099772 \tabularnewline
25 & 0.00855852356123421 & 0.0171170471224684 & 0.991441476438766 \tabularnewline
26 & 0.00649673718146213 & 0.0129934743629243 & 0.993503262818538 \tabularnewline
27 & 0.00427512403627073 & 0.00855024807254145 & 0.995724875963729 \tabularnewline
28 & 0.0034472952561693 & 0.0068945905123386 & 0.996552704743831 \tabularnewline
29 & 0.00459156268801823 & 0.00918312537603646 & 0.995408437311982 \tabularnewline
30 & 0.00286303384679541 & 0.00572606769359083 & 0.997136966153205 \tabularnewline
31 & 0.00314039226884125 & 0.00628078453768251 & 0.996859607731159 \tabularnewline
32 & 0.00189560126547372 & 0.00379120253094744 & 0.998104398734526 \tabularnewline
33 & 0.00106266519147929 & 0.00212533038295857 & 0.998937334808521 \tabularnewline
34 & 0.0015981891034333 & 0.0031963782068666 & 0.998401810896567 \tabularnewline
35 & 0.00158940835927097 & 0.00317881671854193 & 0.998410591640729 \tabularnewline
36 & 0.00476624559038933 & 0.00953249118077866 & 0.995233754409611 \tabularnewline
37 & 0.00295417020727583 & 0.00590834041455166 & 0.997045829792724 \tabularnewline
38 & 0.369391369557785 & 0.73878273911557 & 0.630608630442215 \tabularnewline
39 & 0.32377138258961 & 0.64754276517922 & 0.67622861741039 \tabularnewline
40 & 0.268696317356578 & 0.537392634713156 & 0.731303682643422 \tabularnewline
41 & 0.236204728298635 & 0.472409456597269 & 0.763795271701365 \tabularnewline
42 & 0.199458770178423 & 0.398917540356846 & 0.800541229821577 \tabularnewline
43 & 0.177411253787121 & 0.354822507574241 & 0.822588746212879 \tabularnewline
44 & 0.189430974096135 & 0.378861948192269 & 0.810569025903865 \tabularnewline
45 & 0.214653976623569 & 0.429307953247139 & 0.785346023376431 \tabularnewline
46 & 0.199289055313243 & 0.398578110626486 & 0.800710944686757 \tabularnewline
47 & 0.159130876467444 & 0.318261752934888 & 0.840869123532556 \tabularnewline
48 & 0.466387960279826 & 0.932775920559651 & 0.533612039720174 \tabularnewline
49 & 0.518493827665021 & 0.963012344669959 & 0.481506172334979 \tabularnewline
50 & 0.5 & 0.999999999999999 & 0.5 \tabularnewline
51 & 0.449426203878915 & 0.898852407757829 & 0.550573796121085 \tabularnewline
52 & 0.398691976634455 & 0.797383953268911 & 0.601308023365545 \tabularnewline
53 & 0.346708076832371 & 0.693416153664743 & 0.653291923167629 \tabularnewline
54 & 0.424727782597277 & 0.849455565194554 & 0.575272217402723 \tabularnewline
55 & 0.376278012493489 & 0.752556024986977 & 0.623721987506511 \tabularnewline
56 & 0.330685178518061 & 0.661370357036122 & 0.669314821481939 \tabularnewline
57 & 0.292510261085983 & 0.585020522171966 & 0.707489738914017 \tabularnewline
58 & 0.242065057753681 & 0.484130115507361 & 0.757934942246319 \tabularnewline
59 & 0.200189650912347 & 0.400379301824694 & 0.799810349087653 \tabularnewline
60 & 0.161936232672486 & 0.323872465344972 & 0.838063767327514 \tabularnewline
61 & 0.127600141798895 & 0.25520028359779 & 0.872399858201105 \tabularnewline
62 & 0.13825711094847 & 0.276514221896941 & 0.86174288905153 \tabularnewline
63 & 0.109363480570847 & 0.218726961141695 & 0.890636519429153 \tabularnewline
64 & 0.140133615816922 & 0.280267231633844 & 0.859866384183078 \tabularnewline
65 & 0.113309740123809 & 0.226619480247619 & 0.886690259876191 \tabularnewline
66 & 0.117042161401699 & 0.234084322803399 & 0.882957838598301 \tabularnewline
67 & 0.119240977616913 & 0.238481955233826 & 0.880759022383087 \tabularnewline
68 & 0.100671784764828 & 0.201343569529655 & 0.899328215235172 \tabularnewline
69 & 0.0762596102135378 & 0.152519220427076 & 0.923740389786462 \tabularnewline
70 & 0.0796039868218267 & 0.159207973643653 & 0.920396013178173 \tabularnewline
71 & 0.0670874337492356 & 0.134174867498471 & 0.932912566250764 \tabularnewline
72 & 0.0791367185156188 & 0.158273437031238 & 0.920863281484381 \tabularnewline
73 & 0.060284229525427 & 0.120568459050854 & 0.939715770474573 \tabularnewline
74 & 0.0803334645899805 & 0.160666929179961 & 0.91966653541002 \tabularnewline
75 & 0.0598100689182411 & 0.119620137836482 & 0.940189931081759 \tabularnewline
76 & 0.0542134225719805 & 0.108426845143961 & 0.945786577428019 \tabularnewline
77 & 0.090258018660353 & 0.180516037320706 & 0.909741981339647 \tabularnewline
78 & 0.0787512390935832 & 0.157502478187166 & 0.921248760906417 \tabularnewline
79 & 0.0612976738860984 & 0.122595347772197 & 0.938702326113902 \tabularnewline
80 & 0.0577018978251543 & 0.115403795650309 & 0.942298102174846 \tabularnewline
81 & 0.0405067326605157 & 0.0810134653210313 & 0.959493267339484 \tabularnewline
82 & 0.0454057765515323 & 0.0908115531030646 & 0.954594223448468 \tabularnewline
83 & 0.0424507314040133 & 0.0849014628080267 & 0.957549268595987 \tabularnewline
84 & 0.0338410858741204 & 0.0676821717482408 & 0.96615891412588 \tabularnewline
85 & 0.02146682550116 & 0.0429336510023199 & 0.97853317449884 \tabularnewline
86 & 0.0280140188816677 & 0.0560280377633353 & 0.971985981118332 \tabularnewline
87 & 0.0149625306097956 & 0.0299250612195912 & 0.985037469390204 \tabularnewline
88 & 0.255340278484928 & 0.510680556969855 & 0.744659721515072 \tabularnewline
89 & 0.1605324977406 & 0.3210649954812 & 0.8394675022594 \tabularnewline
90 & 0.0916837199206374 & 0.183367439841275 & 0.908316280079363 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191310&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.214035907654541[/C][C]0.428071815309082[/C][C]0.785964092345459[/C][/ROW]
[ROW][C]11[/C][C]0.116195106446745[/C][C]0.232390212893491[/C][C]0.883804893553255[/C][/ROW]
[ROW][C]12[/C][C]0.0670063076972368[/C][C]0.134012615394474[/C][C]0.932993692302763[/C][/ROW]
[ROW][C]13[/C][C]0.0290549387729152[/C][C]0.0581098775458305[/C][C]0.970945061227085[/C][/ROW]
[ROW][C]14[/C][C]0.0141540467698303[/C][C]0.0283080935396606[/C][C]0.98584595323017[/C][/ROW]
[ROW][C]15[/C][C]0.0056083405150258[/C][C]0.0112166810300516[/C][C]0.994391659484974[/C][/ROW]
[ROW][C]16[/C][C]0.00457630197377677[/C][C]0.00915260394755354[/C][C]0.995423698026223[/C][/ROW]
[ROW][C]17[/C][C]0.00188086710297767[/C][C]0.00376173420595534[/C][C]0.998119132897022[/C][/ROW]
[ROW][C]18[/C][C]0.0156445808727012[/C][C]0.0312891617454024[/C][C]0.984355419127299[/C][/ROW]
[ROW][C]19[/C][C]0.0129819046015126[/C][C]0.0259638092030253[/C][C]0.987018095398487[/C][/ROW]
[ROW][C]20[/C][C]0.00673104539518553[/C][C]0.0134620907903711[/C][C]0.993268954604814[/C][/ROW]
[ROW][C]21[/C][C]0.00667254810708195[/C][C]0.0133450962141639[/C][C]0.993327451892918[/C][/ROW]
[ROW][C]22[/C][C]0.00613180817469063[/C][C]0.0122636163493813[/C][C]0.993868191825309[/C][/ROW]
[ROW][C]23[/C][C]0.00391902421273796[/C][C]0.00783804842547592[/C][C]0.996080975787262[/C][/ROW]
[ROW][C]24[/C][C]0.00249054900227958[/C][C]0.00498109800455916[/C][C]0.99750945099772[/C][/ROW]
[ROW][C]25[/C][C]0.00855852356123421[/C][C]0.0171170471224684[/C][C]0.991441476438766[/C][/ROW]
[ROW][C]26[/C][C]0.00649673718146213[/C][C]0.0129934743629243[/C][C]0.993503262818538[/C][/ROW]
[ROW][C]27[/C][C]0.00427512403627073[/C][C]0.00855024807254145[/C][C]0.995724875963729[/C][/ROW]
[ROW][C]28[/C][C]0.0034472952561693[/C][C]0.0068945905123386[/C][C]0.996552704743831[/C][/ROW]
[ROW][C]29[/C][C]0.00459156268801823[/C][C]0.00918312537603646[/C][C]0.995408437311982[/C][/ROW]
[ROW][C]30[/C][C]0.00286303384679541[/C][C]0.00572606769359083[/C][C]0.997136966153205[/C][/ROW]
[ROW][C]31[/C][C]0.00314039226884125[/C][C]0.00628078453768251[/C][C]0.996859607731159[/C][/ROW]
[ROW][C]32[/C][C]0.00189560126547372[/C][C]0.00379120253094744[/C][C]0.998104398734526[/C][/ROW]
[ROW][C]33[/C][C]0.00106266519147929[/C][C]0.00212533038295857[/C][C]0.998937334808521[/C][/ROW]
[ROW][C]34[/C][C]0.0015981891034333[/C][C]0.0031963782068666[/C][C]0.998401810896567[/C][/ROW]
[ROW][C]35[/C][C]0.00158940835927097[/C][C]0.00317881671854193[/C][C]0.998410591640729[/C][/ROW]
[ROW][C]36[/C][C]0.00476624559038933[/C][C]0.00953249118077866[/C][C]0.995233754409611[/C][/ROW]
[ROW][C]37[/C][C]0.00295417020727583[/C][C]0.00590834041455166[/C][C]0.997045829792724[/C][/ROW]
[ROW][C]38[/C][C]0.369391369557785[/C][C]0.73878273911557[/C][C]0.630608630442215[/C][/ROW]
[ROW][C]39[/C][C]0.32377138258961[/C][C]0.64754276517922[/C][C]0.67622861741039[/C][/ROW]
[ROW][C]40[/C][C]0.268696317356578[/C][C]0.537392634713156[/C][C]0.731303682643422[/C][/ROW]
[ROW][C]41[/C][C]0.236204728298635[/C][C]0.472409456597269[/C][C]0.763795271701365[/C][/ROW]
[ROW][C]42[/C][C]0.199458770178423[/C][C]0.398917540356846[/C][C]0.800541229821577[/C][/ROW]
[ROW][C]43[/C][C]0.177411253787121[/C][C]0.354822507574241[/C][C]0.822588746212879[/C][/ROW]
[ROW][C]44[/C][C]0.189430974096135[/C][C]0.378861948192269[/C][C]0.810569025903865[/C][/ROW]
[ROW][C]45[/C][C]0.214653976623569[/C][C]0.429307953247139[/C][C]0.785346023376431[/C][/ROW]
[ROW][C]46[/C][C]0.199289055313243[/C][C]0.398578110626486[/C][C]0.800710944686757[/C][/ROW]
[ROW][C]47[/C][C]0.159130876467444[/C][C]0.318261752934888[/C][C]0.840869123532556[/C][/ROW]
[ROW][C]48[/C][C]0.466387960279826[/C][C]0.932775920559651[/C][C]0.533612039720174[/C][/ROW]
[ROW][C]49[/C][C]0.518493827665021[/C][C]0.963012344669959[/C][C]0.481506172334979[/C][/ROW]
[ROW][C]50[/C][C]0.5[/C][C]0.999999999999999[/C][C]0.5[/C][/ROW]
[ROW][C]51[/C][C]0.449426203878915[/C][C]0.898852407757829[/C][C]0.550573796121085[/C][/ROW]
[ROW][C]52[/C][C]0.398691976634455[/C][C]0.797383953268911[/C][C]0.601308023365545[/C][/ROW]
[ROW][C]53[/C][C]0.346708076832371[/C][C]0.693416153664743[/C][C]0.653291923167629[/C][/ROW]
[ROW][C]54[/C][C]0.424727782597277[/C][C]0.849455565194554[/C][C]0.575272217402723[/C][/ROW]
[ROW][C]55[/C][C]0.376278012493489[/C][C]0.752556024986977[/C][C]0.623721987506511[/C][/ROW]
[ROW][C]56[/C][C]0.330685178518061[/C][C]0.661370357036122[/C][C]0.669314821481939[/C][/ROW]
[ROW][C]57[/C][C]0.292510261085983[/C][C]0.585020522171966[/C][C]0.707489738914017[/C][/ROW]
[ROW][C]58[/C][C]0.242065057753681[/C][C]0.484130115507361[/C][C]0.757934942246319[/C][/ROW]
[ROW][C]59[/C][C]0.200189650912347[/C][C]0.400379301824694[/C][C]0.799810349087653[/C][/ROW]
[ROW][C]60[/C][C]0.161936232672486[/C][C]0.323872465344972[/C][C]0.838063767327514[/C][/ROW]
[ROW][C]61[/C][C]0.127600141798895[/C][C]0.25520028359779[/C][C]0.872399858201105[/C][/ROW]
[ROW][C]62[/C][C]0.13825711094847[/C][C]0.276514221896941[/C][C]0.86174288905153[/C][/ROW]
[ROW][C]63[/C][C]0.109363480570847[/C][C]0.218726961141695[/C][C]0.890636519429153[/C][/ROW]
[ROW][C]64[/C][C]0.140133615816922[/C][C]0.280267231633844[/C][C]0.859866384183078[/C][/ROW]
[ROW][C]65[/C][C]0.113309740123809[/C][C]0.226619480247619[/C][C]0.886690259876191[/C][/ROW]
[ROW][C]66[/C][C]0.117042161401699[/C][C]0.234084322803399[/C][C]0.882957838598301[/C][/ROW]
[ROW][C]67[/C][C]0.119240977616913[/C][C]0.238481955233826[/C][C]0.880759022383087[/C][/ROW]
[ROW][C]68[/C][C]0.100671784764828[/C][C]0.201343569529655[/C][C]0.899328215235172[/C][/ROW]
[ROW][C]69[/C][C]0.0762596102135378[/C][C]0.152519220427076[/C][C]0.923740389786462[/C][/ROW]
[ROW][C]70[/C][C]0.0796039868218267[/C][C]0.159207973643653[/C][C]0.920396013178173[/C][/ROW]
[ROW][C]71[/C][C]0.0670874337492356[/C][C]0.134174867498471[/C][C]0.932912566250764[/C][/ROW]
[ROW][C]72[/C][C]0.0791367185156188[/C][C]0.158273437031238[/C][C]0.920863281484381[/C][/ROW]
[ROW][C]73[/C][C]0.060284229525427[/C][C]0.120568459050854[/C][C]0.939715770474573[/C][/ROW]
[ROW][C]74[/C][C]0.0803334645899805[/C][C]0.160666929179961[/C][C]0.91966653541002[/C][/ROW]
[ROW][C]75[/C][C]0.0598100689182411[/C][C]0.119620137836482[/C][C]0.940189931081759[/C][/ROW]
[ROW][C]76[/C][C]0.0542134225719805[/C][C]0.108426845143961[/C][C]0.945786577428019[/C][/ROW]
[ROW][C]77[/C][C]0.090258018660353[/C][C]0.180516037320706[/C][C]0.909741981339647[/C][/ROW]
[ROW][C]78[/C][C]0.0787512390935832[/C][C]0.157502478187166[/C][C]0.921248760906417[/C][/ROW]
[ROW][C]79[/C][C]0.0612976738860984[/C][C]0.122595347772197[/C][C]0.938702326113902[/C][/ROW]
[ROW][C]80[/C][C]0.0577018978251543[/C][C]0.115403795650309[/C][C]0.942298102174846[/C][/ROW]
[ROW][C]81[/C][C]0.0405067326605157[/C][C]0.0810134653210313[/C][C]0.959493267339484[/C][/ROW]
[ROW][C]82[/C][C]0.0454057765515323[/C][C]0.0908115531030646[/C][C]0.954594223448468[/C][/ROW]
[ROW][C]83[/C][C]0.0424507314040133[/C][C]0.0849014628080267[/C][C]0.957549268595987[/C][/ROW]
[ROW][C]84[/C][C]0.0338410858741204[/C][C]0.0676821717482408[/C][C]0.96615891412588[/C][/ROW]
[ROW][C]85[/C][C]0.02146682550116[/C][C]0.0429336510023199[/C][C]0.97853317449884[/C][/ROW]
[ROW][C]86[/C][C]0.0280140188816677[/C][C]0.0560280377633353[/C][C]0.971985981118332[/C][/ROW]
[ROW][C]87[/C][C]0.0149625306097956[/C][C]0.0299250612195912[/C][C]0.985037469390204[/C][/ROW]
[ROW][C]88[/C][C]0.255340278484928[/C][C]0.510680556969855[/C][C]0.744659721515072[/C][/ROW]
[ROW][C]89[/C][C]0.1605324977406[/C][C]0.3210649954812[/C][C]0.8394675022594[/C][/ROW]
[ROW][C]90[/C][C]0.0916837199206374[/C][C]0.183367439841275[/C][C]0.908316280079363[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191310&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191310&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.2140359076545410.4280718153090820.785964092345459
110.1161951064467450.2323902128934910.883804893553255
120.06700630769723680.1340126153944740.932993692302763
130.02905493877291520.05810987754583050.970945061227085
140.01415404676983030.02830809353966060.98584595323017
150.00560834051502580.01121668103005160.994391659484974
160.004576301973776770.009152603947553540.995423698026223
170.001880867102977670.003761734205955340.998119132897022
180.01564458087270120.03128916174540240.984355419127299
190.01298190460151260.02596380920302530.987018095398487
200.006731045395185530.01346209079037110.993268954604814
210.006672548107081950.01334509621416390.993327451892918
220.006131808174690630.01226361634938130.993868191825309
230.003919024212737960.007838048425475920.996080975787262
240.002490549002279580.004981098004559160.99750945099772
250.008558523561234210.01711704712246840.991441476438766
260.006496737181462130.01299347436292430.993503262818538
270.004275124036270730.008550248072541450.995724875963729
280.00344729525616930.00689459051233860.996552704743831
290.004591562688018230.009183125376036460.995408437311982
300.002863033846795410.005726067693590830.997136966153205
310.003140392268841250.006280784537682510.996859607731159
320.001895601265473720.003791202530947440.998104398734526
330.001062665191479290.002125330382958570.998937334808521
340.00159818910343330.00319637820686660.998401810896567
350.001589408359270970.003178816718541930.998410591640729
360.004766245590389330.009532491180778660.995233754409611
370.002954170207275830.005908340414551660.997045829792724
380.3693913695577850.738782739115570.630608630442215
390.323771382589610.647542765179220.67622861741039
400.2686963173565780.5373926347131560.731303682643422
410.2362047282986350.4724094565972690.763795271701365
420.1994587701784230.3989175403568460.800541229821577
430.1774112537871210.3548225075742410.822588746212879
440.1894309740961350.3788619481922690.810569025903865
450.2146539766235690.4293079532471390.785346023376431
460.1992890553132430.3985781106264860.800710944686757
470.1591308764674440.3182617529348880.840869123532556
480.4663879602798260.9327759205596510.533612039720174
490.5184938276650210.9630123446699590.481506172334979
500.50.9999999999999990.5
510.4494262038789150.8988524077578290.550573796121085
520.3986919766344550.7973839532689110.601308023365545
530.3467080768323710.6934161536647430.653291923167629
540.4247277825972770.8494555651945540.575272217402723
550.3762780124934890.7525560249869770.623721987506511
560.3306851785180610.6613703570361220.669314821481939
570.2925102610859830.5850205221719660.707489738914017
580.2420650577536810.4841301155073610.757934942246319
590.2001896509123470.4003793018246940.799810349087653
600.1619362326724860.3238724653449720.838063767327514
610.1276001417988950.255200283597790.872399858201105
620.138257110948470.2765142218969410.86174288905153
630.1093634805708470.2187269611416950.890636519429153
640.1401336158169220.2802672316338440.859866384183078
650.1133097401238090.2266194802476190.886690259876191
660.1170421614016990.2340843228033990.882957838598301
670.1192409776169130.2384819552338260.880759022383087
680.1006717847648280.2013435695296550.899328215235172
690.07625961021353780.1525192204270760.923740389786462
700.07960398682182670.1592079736436530.920396013178173
710.06708743374923560.1341748674984710.932912566250764
720.07913671851561880.1582734370312380.920863281484381
730.0602842295254270.1205684590508540.939715770474573
740.08033346458998050.1606669291799610.91966653541002
750.05981006891824110.1196201378364820.940189931081759
760.05421342257198050.1084268451439610.945786577428019
770.0902580186603530.1805160373207060.909741981339647
780.07875123909358320.1575024781871660.921248760906417
790.06129767388609840.1225953477721970.938702326113902
800.05770189782515430.1154037956503090.942298102174846
810.04050673266051570.08101346532103130.959493267339484
820.04540577655153230.09081155310306460.954594223448468
830.04245073140401330.08490146280802670.957549268595987
840.03384108587412040.06768217174824080.96615891412588
850.021466825501160.04293365100231990.97853317449884
860.02801401888166770.05602803776333530.971985981118332
870.01496253060979560.02992506121959120.985037469390204
880.2553402784849280.5106805569698550.744659721515072
890.16053249774060.32106499548120.8394675022594
900.09168371992063740.1833674398412750.908316280079363






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level150.185185185185185NOK
5% type I error level260.320987654320988NOK
10% type I error level320.395061728395062NOK

\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 & 15 & 0.185185185185185 & NOK \tabularnewline
5% type I error level & 26 & 0.320987654320988 & NOK \tabularnewline
10% type I error level & 32 & 0.395061728395062 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191310&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]15[/C][C]0.185185185185185[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]26[/C][C]0.320987654320988[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]32[/C][C]0.395061728395062[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191310&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191310&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level150.185185185185185NOK
5% type I error level260.320987654320988NOK
10% type I error level320.395061728395062NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
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')
}