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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 computationSun, 11 Dec 2011 11:16:05 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/11/t13236201918u1btrsfvi4m1o0.htm/, Retrieved Mon, 29 Apr 2024 03:16:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=153822, Retrieved Mon, 29 Apr 2024 03:16:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact101

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-12-11 16:16:05] [7dc03dd48c8acabd98b217fada4a6bc0] [Current]
- R PD    [Multiple Regression] [] [2011-12-11 17:01:44] [ee8c3a74bf3b349877806e9a50913c60]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
96560	76	129	17	22996	0	0	78	62	72
112611	41	36	20	26706	3	7	44	56	64
98146	40	37	17	27114	0	1	80	49	66
121848	39	45	17	30594	2	0	73	63	78
31774	23	48	17	4143	1	2	107	67	71
65475	18	24	13	69008	1	0	42	59	71
108446	60	90	17	46300	0	2	76	40	59
76302	31	26	20	30976	2	2	69	34	65
30989	14	35	17	4154	-2	0	62	37	48
150580	77	124	22	45588	-4	0	46	61	72
59382	49	49	12	26263	1	0	133	60	66
341570	168	190	21	117105	0	0	71	57	68
133328	55	79	20	40909	-3	0	46	56	75
101523	42	76	22	61056	0	1	131	67	73
92499	32	57	18	21399	-2	2	47	38	59
118612	46	72	12	30080	-2	0	15	49	72
98104	54	132	17	25568	-3	0	37	32	65
84105	20	45	17	20055	0	1	0	63	69
237213	84	74	38	66198	0	3	79	67	71
133131	55	52	30	57793	4	3	77	43	54
344297	75	86	30	67654	1	5	101	84	84
174415	100	63	31	82753	3	0	105	49	66
294424	77	59	33	101494	4	2	124	58	73
362301	119	715	34	100708	2	0	83	63	69
167488	45	77	28	83737	0	0	106	29	70
152299	53	63	33	61370	2	2	25	58	72
243511	71	65	42	101338	0	2	16	62	63
132487	41	97	36	40735	3	1	22	54	66
172494	52	52	43	86687	3	0	29	53	60
224330	83	52	39	130115	0	0	5	66	66
181633	70	48	30	64466	6	0	27	53	71
210907	56	81	30	112285	2	0	29	26	50
236785	119	75	31	101481	0	5	43	43	52
244052	68	66	44	143558	2	0	158	53	70
143756	46	57	34	69094	4	4	102	54	60
182079	63	63	33	102860	2	0	123	47	76
100750	72	67	30	140867	3	0	105	43	60
152474	65	45	32	65567	0	1	33	57	70
97839	38	42	24	94785	-1	2	96	41	75
149061	44	66	26	116174	0	6	56	37	54
324799	154	108	47	97668	0	5	59	52	65
230964	53	43	30	133824	-1	0	91	52	73
174724	92	135	34	69112	0	1	76	67	42
223632	73	52	33	72654	-1	1	94	70	65
106408	30	32	14	31081	1	2	41	68	75
265769	146	37	32	83122	-2	5	67	43	66
149112	56	65	35	60578	-4	2	100	56	70
152871	58	74	28	79892	2	5	67	74	81
183167	66	66	39	82875	-4	1	135	58	71
218946	41	112	29	80670	2	4	58	63	68
196553	57	50	29	95260	-3	0	56	64	67
143246	103	42	27	106671	2	0	59	53	76
193339	78	47	35	84651	2	1	116	51	71
130585	46	57	29	95364	-4	2	98	54	70
148446	91	63	37	126846	3	8	32	48	65
243060	63	110	29	111813	-1	4	63	50	68
317394	86	53	31	91413	-3	0	113	45	70
244749	95	144	33	76643	0	1	111	61	64
128423	64	89	38	92696	2	10	120	56	70
229242	247	128	31	91721	2	0	25	46	66
324598	110	128	37	135777	2	1	109	51	59
195838	67	50	31	102372	-2	0	37	37	78
254488	83	50	39	103772	0	2	54	42	67
271856	103	91	37	54990	-3	2	55	69	67
95227	34	70	32	34777	3	0	17	56	61

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153822&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153822&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153822&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 time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
test[t] = -0.607455139177964 -9.72917944876327e-06tijd[t] + 0.00235370233505576login[t] + 0.00311041641574067vieuws[t] + 0.0483486821994552revieuws[t] + 1.23734505919451e-05size[t] + 0.124287563521226shared[t] -0.00182273321546957blogged[t] + 0.0197560013060629intrisieke[t] -0.0168624693866774extrisieke[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
test[t] =  -0.607455139177964 -9.72917944876327e-06tijd[t] +  0.00235370233505576login[t] +  0.00311041641574067vieuws[t] +  0.0483486821994552revieuws[t] +  1.23734505919451e-05size[t] +  0.124287563521226shared[t] -0.00182273321546957blogged[t] +  0.0197560013060629intrisieke[t] -0.0168624693866774extrisieke[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153822&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]test[t] =  -0.607455139177964 -9.72917944876327e-06tijd[t] +  0.00235370233505576login[t] +  0.00311041641574067vieuws[t] +  0.0483486821994552revieuws[t] +  1.23734505919451e-05size[t] +  0.124287563521226shared[t] -0.00182273321546957blogged[t] +  0.0197560013060629intrisieke[t] -0.0168624693866774extrisieke[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153822&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153822&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
test[t] = -0.607455139177964 -9.72917944876327e-06tijd[t] + 0.00235370233505576login[t] + 0.00311041641574067vieuws[t] + 0.0483486821994552revieuws[t] + 1.23734505919451e-05size[t] + 0.124287563521226shared[t] -0.00182273321546957blogged[t] + 0.0197560013060629intrisieke[t] -0.0168624693866774extrisieke[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.6074551391779642.99066-0.20310.8397930.419896
tijd-9.72917944876327e-067e-06-1.47340.1463450.073173
login0.002353702335055760.0104160.2260.8220630.411032
vieuws0.003110416415740670.0038980.7980.4283090.214154
revieuws0.04834868219945520.0515340.93820.3522530.176126
size1.23734505919451e-051.3e-050.97950.33160.1658
shared0.1242875635212260.136780.90870.3674910.183745
blogged-0.001822733215469570.008279-0.22020.8265560.413278
intrisieke0.01975600130606290.0307090.64330.5226830.261341
extrisieke-0.01686246938667740.044255-0.3810.7046480.352324

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -0.607455139177964 & 2.99066 & -0.2031 & 0.839793 & 0.419896 \tabularnewline
tijd & -9.72917944876327e-06 & 7e-06 & -1.4734 & 0.146345 & 0.073173 \tabularnewline
login & 0.00235370233505576 & 0.010416 & 0.226 & 0.822063 & 0.411032 \tabularnewline
vieuws & 0.00311041641574067 & 0.003898 & 0.798 & 0.428309 & 0.214154 \tabularnewline
revieuws & 0.0483486821994552 & 0.051534 & 0.9382 & 0.352253 & 0.176126 \tabularnewline
size & 1.23734505919451e-05 & 1.3e-05 & 0.9795 & 0.3316 & 0.1658 \tabularnewline
shared & 0.124287563521226 & 0.13678 & 0.9087 & 0.367491 & 0.183745 \tabularnewline
blogged & -0.00182273321546957 & 0.008279 & -0.2202 & 0.826556 & 0.413278 \tabularnewline
intrisieke & 0.0197560013060629 & 0.030709 & 0.6433 & 0.522683 & 0.261341 \tabularnewline
extrisieke & -0.0168624693866774 & 0.044255 & -0.381 & 0.704648 & 0.352324 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153822&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-0.607455139177964[/C][C]2.99066[/C][C]-0.2031[/C][C]0.839793[/C][C]0.419896[/C][/ROW]
[ROW][C]tijd[/C][C]-9.72917944876327e-06[/C][C]7e-06[/C][C]-1.4734[/C][C]0.146345[/C][C]0.073173[/C][/ROW]
[ROW][C]login[/C][C]0.00235370233505576[/C][C]0.010416[/C][C]0.226[/C][C]0.822063[/C][C]0.411032[/C][/ROW]
[ROW][C]vieuws[/C][C]0.00311041641574067[/C][C]0.003898[/C][C]0.798[/C][C]0.428309[/C][C]0.214154[/C][/ROW]
[ROW][C]revieuws[/C][C]0.0483486821994552[/C][C]0.051534[/C][C]0.9382[/C][C]0.352253[/C][C]0.176126[/C][/ROW]
[ROW][C]size[/C][C]1.23734505919451e-05[/C][C]1.3e-05[/C][C]0.9795[/C][C]0.3316[/C][C]0.1658[/C][/ROW]
[ROW][C]shared[/C][C]0.124287563521226[/C][C]0.13678[/C][C]0.9087[/C][C]0.367491[/C][C]0.183745[/C][/ROW]
[ROW][C]blogged[/C][C]-0.00182273321546957[/C][C]0.008279[/C][C]-0.2202[/C][C]0.826556[/C][C]0.413278[/C][/ROW]
[ROW][C]intrisieke[/C][C]0.0197560013060629[/C][C]0.030709[/C][C]0.6433[/C][C]0.522683[/C][C]0.261341[/C][/ROW]
[ROW][C]extrisieke[/C][C]-0.0168624693866774[/C][C]0.044255[/C][C]-0.381[/C][C]0.704648[/C][C]0.352324[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153822&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153822&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.6074551391779642.99066-0.20310.8397930.419896
tijd-9.72917944876327e-067e-06-1.47340.1463450.073173
login0.002353702335055760.0104160.2260.8220630.411032
vieuws0.003110416415740670.0038980.7980.4283090.214154
revieuws0.04834868219945520.0515340.93820.3522530.176126
size1.23734505919451e-051.3e-050.97950.33160.1658
shared0.1242875635212260.136780.90870.3674910.183745
blogged-0.001822733215469570.008279-0.22020.8265560.413278
intrisieke0.01975600130606290.0307090.64330.5226830.261341
extrisieke-0.01686246938667740.044255-0.3810.7046480.352324






Multiple Linear Regression - Regression Statistics
Multiple R0.303118269114845
R-squared0.0918806850711794
Adjusted R-squared-0.0567206573717185
F-TEST (value)0.618303196732464
F-TEST (DF numerator)9
F-TEST (DF denominator)55
p-value0.776282486364639
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.33348657980496
Sum Squared Residuals299.483778997142

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.303118269114845 \tabularnewline
R-squared & 0.0918806850711794 \tabularnewline
Adjusted R-squared & -0.0567206573717185 \tabularnewline
F-TEST (value) & 0.618303196732464 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 55 \tabularnewline
p-value & 0.776282486364639 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.33348657980496 \tabularnewline
Sum Squared Residuals & 299.483778997142 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153822&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.303118269114845[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0918806850711794[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0567206573717185[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.618303196732464[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]55[/C][/ROW]
[ROW][C]p-value[/C][C]0.776282486364639[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.33348657980496[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]299.483778997142[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153822&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153822&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 R0.303118269114845
R-squared0.0918806850711794
Adjusted R-squared-0.0567206573717185
F-TEST (value)0.618303196732464
F-TEST (DF numerator)9
F-TEST (DF denominator)55
p-value0.776282486364639
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.33348657980496
Sum Squared Residuals299.483778997142






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.00828894987584876-0.00828894987584876
230.6197787516789882.38022124832101
30-0.3620903570708740.362090357070874
42-0.564396176682812.56439617668281
510.3399952919113630.660004708088637
610.2467573750188020.753242624981198
700.0588340047345725-0.0588340047345725
82-0.1472716713091572.14727167130916
9-2-0.0852453043196066-1.91475469568039
10-40.0293761646694719-4.02937616466947
111-0.1822897539400681.18228975394007
120-0.6299044966004650.629904496600465
13-3-0.298486375968988-2.70151362403101
1400.537402263617169-0.537402263617169
15-2-0.200977528051081-1.79902247194892
16-2-0.750249432485341-1.24975056751466
17-3-0.417269282035457-2.58273071796454
1800.0367974158278948-0.0367974158278948
1900.524169960610631-0.524169960610631
2040.7254909919719023.2745090080281
211-0.5451866125627331.54518661256273
2230.3134398968336432.68656010316636
234-0.3184278950633254.31842789506332
2421.19148322719420.8085167728058
250-0.3023370838318010.302337083831801
2620.7211252698807391.27887473011926
2701.05916587056016-1.05916587056016
2830.7844408440887252.21555915591128
2931.132525244841071.86747475515893
3001.2445265526609-1.2445265526609
316-0.01178976079217716.01178976079218
3220.1818305383351971.8181694616648
3300.872392139197523-0.872392139197523
3420.8658116011793461.13418839882065
3541.14457474651052.8554252534895
3620.2563329202350561.74366707976494
3731.630038735192721.36996126480728
3800.57036179417179-0.57036179417179
39-10.612819325977304-1.6128193259773
4001.40974403795055-1.40974403795055
4100.856938589004909-0.856938589004909
42-10.140756986332889-1.14075698633289
4301.42926786626973-1.42926786626973
44-10.284648340860456-1.28464834086046
451-0.1585465519462981.1585465519463
46-20.0771200755001109-2.07712007550011
47-40.709819641151698-4.7098196411517
4821.209622604282070.790377395717929
49-40.708991738599569-4.70899173859957
5020.5969383061453371.40306169385466
51-30.383259568301718-3.38325956830172
5220.6552295550761581.34477044492384
5320.3040934827733021.6959065172267
54-40.946116017035935-4.94611601703594
5532.505054788050140.494945211949856
56-10.527294180186386-1.52729418018639
57-3-1.19558611478774-1.80441388521226
5800.274566707440687-0.274566707440687
5922.50488862399626-0.50488862399626
6020.5257052437005781.47429475629942
6120.2987244864159731.70127551358403
62-2-0.0858169674562639-1.91418303254374
6300.287194015822263-0.287194015822263
64-30.124109017775678-3.12410901777568
6530.788027615456262.21197238454374

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.00828894987584876 & -0.00828894987584876 \tabularnewline
2 & 3 & 0.619778751678988 & 2.38022124832101 \tabularnewline
3 & 0 & -0.362090357070874 & 0.362090357070874 \tabularnewline
4 & 2 & -0.56439617668281 & 2.56439617668281 \tabularnewline
5 & 1 & 0.339995291911363 & 0.660004708088637 \tabularnewline
6 & 1 & 0.246757375018802 & 0.753242624981198 \tabularnewline
7 & 0 & 0.0588340047345725 & -0.0588340047345725 \tabularnewline
8 & 2 & -0.147271671309157 & 2.14727167130916 \tabularnewline
9 & -2 & -0.0852453043196066 & -1.91475469568039 \tabularnewline
10 & -4 & 0.0293761646694719 & -4.02937616466947 \tabularnewline
11 & 1 & -0.182289753940068 & 1.18228975394007 \tabularnewline
12 & 0 & -0.629904496600465 & 0.629904496600465 \tabularnewline
13 & -3 & -0.298486375968988 & -2.70151362403101 \tabularnewline
14 & 0 & 0.537402263617169 & -0.537402263617169 \tabularnewline
15 & -2 & -0.200977528051081 & -1.79902247194892 \tabularnewline
16 & -2 & -0.750249432485341 & -1.24975056751466 \tabularnewline
17 & -3 & -0.417269282035457 & -2.58273071796454 \tabularnewline
18 & 0 & 0.0367974158278948 & -0.0367974158278948 \tabularnewline
19 & 0 & 0.524169960610631 & -0.524169960610631 \tabularnewline
20 & 4 & 0.725490991971902 & 3.2745090080281 \tabularnewline
21 & 1 & -0.545186612562733 & 1.54518661256273 \tabularnewline
22 & 3 & 0.313439896833643 & 2.68656010316636 \tabularnewline
23 & 4 & -0.318427895063325 & 4.31842789506332 \tabularnewline
24 & 2 & 1.1914832271942 & 0.8085167728058 \tabularnewline
25 & 0 & -0.302337083831801 & 0.302337083831801 \tabularnewline
26 & 2 & 0.721125269880739 & 1.27887473011926 \tabularnewline
27 & 0 & 1.05916587056016 & -1.05916587056016 \tabularnewline
28 & 3 & 0.784440844088725 & 2.21555915591128 \tabularnewline
29 & 3 & 1.13252524484107 & 1.86747475515893 \tabularnewline
30 & 0 & 1.2445265526609 & -1.2445265526609 \tabularnewline
31 & 6 & -0.0117897607921771 & 6.01178976079218 \tabularnewline
32 & 2 & 0.181830538335197 & 1.8181694616648 \tabularnewline
33 & 0 & 0.872392139197523 & -0.872392139197523 \tabularnewline
34 & 2 & 0.865811601179346 & 1.13418839882065 \tabularnewline
35 & 4 & 1.1445747465105 & 2.8554252534895 \tabularnewline
36 & 2 & 0.256332920235056 & 1.74366707976494 \tabularnewline
37 & 3 & 1.63003873519272 & 1.36996126480728 \tabularnewline
38 & 0 & 0.57036179417179 & -0.57036179417179 \tabularnewline
39 & -1 & 0.612819325977304 & -1.6128193259773 \tabularnewline
40 & 0 & 1.40974403795055 & -1.40974403795055 \tabularnewline
41 & 0 & 0.856938589004909 & -0.856938589004909 \tabularnewline
42 & -1 & 0.140756986332889 & -1.14075698633289 \tabularnewline
43 & 0 & 1.42926786626973 & -1.42926786626973 \tabularnewline
44 & -1 & 0.284648340860456 & -1.28464834086046 \tabularnewline
45 & 1 & -0.158546551946298 & 1.1585465519463 \tabularnewline
46 & -2 & 0.0771200755001109 & -2.07712007550011 \tabularnewline
47 & -4 & 0.709819641151698 & -4.7098196411517 \tabularnewline
48 & 2 & 1.20962260428207 & 0.790377395717929 \tabularnewline
49 & -4 & 0.708991738599569 & -4.70899173859957 \tabularnewline
50 & 2 & 0.596938306145337 & 1.40306169385466 \tabularnewline
51 & -3 & 0.383259568301718 & -3.38325956830172 \tabularnewline
52 & 2 & 0.655229555076158 & 1.34477044492384 \tabularnewline
53 & 2 & 0.304093482773302 & 1.6959065172267 \tabularnewline
54 & -4 & 0.946116017035935 & -4.94611601703594 \tabularnewline
55 & 3 & 2.50505478805014 & 0.494945211949856 \tabularnewline
56 & -1 & 0.527294180186386 & -1.52729418018639 \tabularnewline
57 & -3 & -1.19558611478774 & -1.80441388521226 \tabularnewline
58 & 0 & 0.274566707440687 & -0.274566707440687 \tabularnewline
59 & 2 & 2.50488862399626 & -0.50488862399626 \tabularnewline
60 & 2 & 0.525705243700578 & 1.47429475629942 \tabularnewline
61 & 2 & 0.298724486415973 & 1.70127551358403 \tabularnewline
62 & -2 & -0.0858169674562639 & -1.91418303254374 \tabularnewline
63 & 0 & 0.287194015822263 & -0.287194015822263 \tabularnewline
64 & -3 & 0.124109017775678 & -3.12410901777568 \tabularnewline
65 & 3 & 0.78802761545626 & 2.21197238454374 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153822&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]0[/C][C]0.00828894987584876[/C][C]-0.00828894987584876[/C][/ROW]
[ROW][C]2[/C][C]3[/C][C]0.619778751678988[/C][C]2.38022124832101[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]-0.362090357070874[/C][C]0.362090357070874[/C][/ROW]
[ROW][C]4[/C][C]2[/C][C]-0.56439617668281[/C][C]2.56439617668281[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]0.339995291911363[/C][C]0.660004708088637[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]0.246757375018802[/C][C]0.753242624981198[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.0588340047345725[/C][C]-0.0588340047345725[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]-0.147271671309157[/C][C]2.14727167130916[/C][/ROW]
[ROW][C]9[/C][C]-2[/C][C]-0.0852453043196066[/C][C]-1.91475469568039[/C][/ROW]
[ROW][C]10[/C][C]-4[/C][C]0.0293761646694719[/C][C]-4.02937616466947[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]-0.182289753940068[/C][C]1.18228975394007[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]-0.629904496600465[/C][C]0.629904496600465[/C][/ROW]
[ROW][C]13[/C][C]-3[/C][C]-0.298486375968988[/C][C]-2.70151362403101[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.537402263617169[/C][C]-0.537402263617169[/C][/ROW]
[ROW][C]15[/C][C]-2[/C][C]-0.200977528051081[/C][C]-1.79902247194892[/C][/ROW]
[ROW][C]16[/C][C]-2[/C][C]-0.750249432485341[/C][C]-1.24975056751466[/C][/ROW]
[ROW][C]17[/C][C]-3[/C][C]-0.417269282035457[/C][C]-2.58273071796454[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.0367974158278948[/C][C]-0.0367974158278948[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.524169960610631[/C][C]-0.524169960610631[/C][/ROW]
[ROW][C]20[/C][C]4[/C][C]0.725490991971902[/C][C]3.2745090080281[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]-0.545186612562733[/C][C]1.54518661256273[/C][/ROW]
[ROW][C]22[/C][C]3[/C][C]0.313439896833643[/C][C]2.68656010316636[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]-0.318427895063325[/C][C]4.31842789506332[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]1.1914832271942[/C][C]0.8085167728058[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]-0.302337083831801[/C][C]0.302337083831801[/C][/ROW]
[ROW][C]26[/C][C]2[/C][C]0.721125269880739[/C][C]1.27887473011926[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]1.05916587056016[/C][C]-1.05916587056016[/C][/ROW]
[ROW][C]28[/C][C]3[/C][C]0.784440844088725[/C][C]2.21555915591128[/C][/ROW]
[ROW][C]29[/C][C]3[/C][C]1.13252524484107[/C][C]1.86747475515893[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]1.2445265526609[/C][C]-1.2445265526609[/C][/ROW]
[ROW][C]31[/C][C]6[/C][C]-0.0117897607921771[/C][C]6.01178976079218[/C][/ROW]
[ROW][C]32[/C][C]2[/C][C]0.181830538335197[/C][C]1.8181694616648[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.872392139197523[/C][C]-0.872392139197523[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]0.865811601179346[/C][C]1.13418839882065[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]1.1445747465105[/C][C]2.8554252534895[/C][/ROW]
[ROW][C]36[/C][C]2[/C][C]0.256332920235056[/C][C]1.74366707976494[/C][/ROW]
[ROW][C]37[/C][C]3[/C][C]1.63003873519272[/C][C]1.36996126480728[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.57036179417179[/C][C]-0.57036179417179[/C][/ROW]
[ROW][C]39[/C][C]-1[/C][C]0.612819325977304[/C][C]-1.6128193259773[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]1.40974403795055[/C][C]-1.40974403795055[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0.856938589004909[/C][C]-0.856938589004909[/C][/ROW]
[ROW][C]42[/C][C]-1[/C][C]0.140756986332889[/C][C]-1.14075698633289[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]1.42926786626973[/C][C]-1.42926786626973[/C][/ROW]
[ROW][C]44[/C][C]-1[/C][C]0.284648340860456[/C][C]-1.28464834086046[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]-0.158546551946298[/C][C]1.1585465519463[/C][/ROW]
[ROW][C]46[/C][C]-2[/C][C]0.0771200755001109[/C][C]-2.07712007550011[/C][/ROW]
[ROW][C]47[/C][C]-4[/C][C]0.709819641151698[/C][C]-4.7098196411517[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]1.20962260428207[/C][C]0.790377395717929[/C][/ROW]
[ROW][C]49[/C][C]-4[/C][C]0.708991738599569[/C][C]-4.70899173859957[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]0.596938306145337[/C][C]1.40306169385466[/C][/ROW]
[ROW][C]51[/C][C]-3[/C][C]0.383259568301718[/C][C]-3.38325956830172[/C][/ROW]
[ROW][C]52[/C][C]2[/C][C]0.655229555076158[/C][C]1.34477044492384[/C][/ROW]
[ROW][C]53[/C][C]2[/C][C]0.304093482773302[/C][C]1.6959065172267[/C][/ROW]
[ROW][C]54[/C][C]-4[/C][C]0.946116017035935[/C][C]-4.94611601703594[/C][/ROW]
[ROW][C]55[/C][C]3[/C][C]2.50505478805014[/C][C]0.494945211949856[/C][/ROW]
[ROW][C]56[/C][C]-1[/C][C]0.527294180186386[/C][C]-1.52729418018639[/C][/ROW]
[ROW][C]57[/C][C]-3[/C][C]-1.19558611478774[/C][C]-1.80441388521226[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.274566707440687[/C][C]-0.274566707440687[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]2.50488862399626[/C][C]-0.50488862399626[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]0.525705243700578[/C][C]1.47429475629942[/C][/ROW]
[ROW][C]61[/C][C]2[/C][C]0.298724486415973[/C][C]1.70127551358403[/C][/ROW]
[ROW][C]62[/C][C]-2[/C][C]-0.0858169674562639[/C][C]-1.91418303254374[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.287194015822263[/C][C]-0.287194015822263[/C][/ROW]
[ROW][C]64[/C][C]-3[/C][C]0.124109017775678[/C][C]-3.12410901777568[/C][/ROW]
[ROW][C]65[/C][C]3[/C][C]0.78802761545626[/C][C]2.21197238454374[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153822&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153822&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
100.00828894987584876-0.00828894987584876
230.6197787516789882.38022124832101
30-0.3620903570708740.362090357070874
42-0.564396176682812.56439617668281
510.3399952919113630.660004708088637
610.2467573750188020.753242624981198
700.0588340047345725-0.0588340047345725
82-0.1472716713091572.14727167130916
9-2-0.0852453043196066-1.91475469568039
10-40.0293761646694719-4.02937616466947
111-0.1822897539400681.18228975394007
120-0.6299044966004650.629904496600465
13-3-0.298486375968988-2.70151362403101
1400.537402263617169-0.537402263617169
15-2-0.200977528051081-1.79902247194892
16-2-0.750249432485341-1.24975056751466
17-3-0.417269282035457-2.58273071796454
1800.0367974158278948-0.0367974158278948
1900.524169960610631-0.524169960610631
2040.7254909919719023.2745090080281
211-0.5451866125627331.54518661256273
2230.3134398968336432.68656010316636
234-0.3184278950633254.31842789506332
2421.19148322719420.8085167728058
250-0.3023370838318010.302337083831801
2620.7211252698807391.27887473011926
2701.05916587056016-1.05916587056016
2830.7844408440887252.21555915591128
2931.132525244841071.86747475515893
3001.2445265526609-1.2445265526609
316-0.01178976079217716.01178976079218
3220.1818305383351971.8181694616648
3300.872392139197523-0.872392139197523
3420.8658116011793461.13418839882065
3541.14457474651052.8554252534895
3620.2563329202350561.74366707976494
3731.630038735192721.36996126480728
3800.57036179417179-0.57036179417179
39-10.612819325977304-1.6128193259773
4001.40974403795055-1.40974403795055
4100.856938589004909-0.856938589004909
42-10.140756986332889-1.14075698633289
4301.42926786626973-1.42926786626973
44-10.284648340860456-1.28464834086046
451-0.1585465519462981.1585465519463
46-20.0771200755001109-2.07712007550011
47-40.709819641151698-4.7098196411517
4821.209622604282070.790377395717929
49-40.708991738599569-4.70899173859957
5020.5969383061453371.40306169385466
51-30.383259568301718-3.38325956830172
5220.6552295550761581.34477044492384
5320.3040934827733021.6959065172267
54-40.946116017035935-4.94611601703594
5532.505054788050140.494945211949856
56-10.527294180186386-1.52729418018639
57-3-1.19558611478774-1.80441388521226
5800.274566707440687-0.274566707440687
5922.50488862399626-0.50488862399626
6020.5257052437005781.47429475629942
6120.2987244864159731.70127551358403
62-2-0.0858169674562639-1.91418303254374
6300.287194015822263-0.287194015822263
64-30.124109017775678-3.12410901777568
6530.788027615456262.21197238454374






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.4027912080992270.8055824161984550.597208791900773
140.2445168858468520.4890337716937040.755483114153148
150.1852212537611420.3704425075222830.814778746238858
160.1168138076837950.2336276153675910.883186192316205
170.07587612898495270.1517522579699050.924123871015047
180.0747231381504530.1494462763009060.925276861849547
190.04056160415185880.08112320830371760.959438395848141
200.08738980960787470.1747796192157490.912610190392125
210.06281497845135860.1256299569027170.937185021548641
220.04089030806946930.08178061613893870.959109691930531
230.06281137250850640.1256227450170130.937188627491494
240.2011245026085340.4022490052170670.798875497391466
250.1669609415865940.3339218831731880.833039058413406
260.1286415464946730.2572830929893460.871358453505327
270.1052994565828970.2105989131657940.894700543417103
280.1281396461187920.2562792922375830.871860353881208
290.1002782993885960.2005565987771920.899721700611404
300.08422502803107490.168450056062150.915774971968925
310.4748178219805020.9496356439610040.525182178019498
320.4176469363274280.8352938726548550.582353063672572
330.3615782487533020.7231564975066050.638421751246698
340.3601163447720920.7202326895441840.639883655227908
350.4542064619702860.9084129239405710.545793538029714
360.4621320559570170.9242641119140340.537867944042983
370.4278467126355670.8556934252711340.572153287364433
380.3522921268089820.7045842536179640.647707873191018
390.3175754255898610.6351508511797210.682424574410139
400.2712730229945760.5425460459891530.728726977005424
410.2201534368409690.4403068736819390.779846563159031
420.181108860431930.3622177208638610.81889113956807
430.1573973763892860.3147947527785720.842602623610714
440.1260072188423720.2520144376847440.873992781157628
450.09870729085858270.1974145817171650.901292709141417
460.07225828867961450.1445165773592290.927741711320386
470.1374727934910830.2749455869821660.862527206508917
480.1495854300149660.2991708600299310.850414569985035
490.212693328209220.4253866564184390.78730667179078
500.313848007876560.6276960157531190.68615199212344
510.2493393693233020.4986787386466040.750660630676698
520.446706552757020.893413105514040.55329344724298

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.402791208099227 & 0.805582416198455 & 0.597208791900773 \tabularnewline
14 & 0.244516885846852 & 0.489033771693704 & 0.755483114153148 \tabularnewline
15 & 0.185221253761142 & 0.370442507522283 & 0.814778746238858 \tabularnewline
16 & 0.116813807683795 & 0.233627615367591 & 0.883186192316205 \tabularnewline
17 & 0.0758761289849527 & 0.151752257969905 & 0.924123871015047 \tabularnewline
18 & 0.074723138150453 & 0.149446276300906 & 0.925276861849547 \tabularnewline
19 & 0.0405616041518588 & 0.0811232083037176 & 0.959438395848141 \tabularnewline
20 & 0.0873898096078747 & 0.174779619215749 & 0.912610190392125 \tabularnewline
21 & 0.0628149784513586 & 0.125629956902717 & 0.937185021548641 \tabularnewline
22 & 0.0408903080694693 & 0.0817806161389387 & 0.959109691930531 \tabularnewline
23 & 0.0628113725085064 & 0.125622745017013 & 0.937188627491494 \tabularnewline
24 & 0.201124502608534 & 0.402249005217067 & 0.798875497391466 \tabularnewline
25 & 0.166960941586594 & 0.333921883173188 & 0.833039058413406 \tabularnewline
26 & 0.128641546494673 & 0.257283092989346 & 0.871358453505327 \tabularnewline
27 & 0.105299456582897 & 0.210598913165794 & 0.894700543417103 \tabularnewline
28 & 0.128139646118792 & 0.256279292237583 & 0.871860353881208 \tabularnewline
29 & 0.100278299388596 & 0.200556598777192 & 0.899721700611404 \tabularnewline
30 & 0.0842250280310749 & 0.16845005606215 & 0.915774971968925 \tabularnewline
31 & 0.474817821980502 & 0.949635643961004 & 0.525182178019498 \tabularnewline
32 & 0.417646936327428 & 0.835293872654855 & 0.582353063672572 \tabularnewline
33 & 0.361578248753302 & 0.723156497506605 & 0.638421751246698 \tabularnewline
34 & 0.360116344772092 & 0.720232689544184 & 0.639883655227908 \tabularnewline
35 & 0.454206461970286 & 0.908412923940571 & 0.545793538029714 \tabularnewline
36 & 0.462132055957017 & 0.924264111914034 & 0.537867944042983 \tabularnewline
37 & 0.427846712635567 & 0.855693425271134 & 0.572153287364433 \tabularnewline
38 & 0.352292126808982 & 0.704584253617964 & 0.647707873191018 \tabularnewline
39 & 0.317575425589861 & 0.635150851179721 & 0.682424574410139 \tabularnewline
40 & 0.271273022994576 & 0.542546045989153 & 0.728726977005424 \tabularnewline
41 & 0.220153436840969 & 0.440306873681939 & 0.779846563159031 \tabularnewline
42 & 0.18110886043193 & 0.362217720863861 & 0.81889113956807 \tabularnewline
43 & 0.157397376389286 & 0.314794752778572 & 0.842602623610714 \tabularnewline
44 & 0.126007218842372 & 0.252014437684744 & 0.873992781157628 \tabularnewline
45 & 0.0987072908585827 & 0.197414581717165 & 0.901292709141417 \tabularnewline
46 & 0.0722582886796145 & 0.144516577359229 & 0.927741711320386 \tabularnewline
47 & 0.137472793491083 & 0.274945586982166 & 0.862527206508917 \tabularnewline
48 & 0.149585430014966 & 0.299170860029931 & 0.850414569985035 \tabularnewline
49 & 0.21269332820922 & 0.425386656418439 & 0.78730667179078 \tabularnewline
50 & 0.31384800787656 & 0.627696015753119 & 0.68615199212344 \tabularnewline
51 & 0.249339369323302 & 0.498678738646604 & 0.750660630676698 \tabularnewline
52 & 0.44670655275702 & 0.89341310551404 & 0.55329344724298 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153822&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]13[/C][C]0.402791208099227[/C][C]0.805582416198455[/C][C]0.597208791900773[/C][/ROW]
[ROW][C]14[/C][C]0.244516885846852[/C][C]0.489033771693704[/C][C]0.755483114153148[/C][/ROW]
[ROW][C]15[/C][C]0.185221253761142[/C][C]0.370442507522283[/C][C]0.814778746238858[/C][/ROW]
[ROW][C]16[/C][C]0.116813807683795[/C][C]0.233627615367591[/C][C]0.883186192316205[/C][/ROW]
[ROW][C]17[/C][C]0.0758761289849527[/C][C]0.151752257969905[/C][C]0.924123871015047[/C][/ROW]
[ROW][C]18[/C][C]0.074723138150453[/C][C]0.149446276300906[/C][C]0.925276861849547[/C][/ROW]
[ROW][C]19[/C][C]0.0405616041518588[/C][C]0.0811232083037176[/C][C]0.959438395848141[/C][/ROW]
[ROW][C]20[/C][C]0.0873898096078747[/C][C]0.174779619215749[/C][C]0.912610190392125[/C][/ROW]
[ROW][C]21[/C][C]0.0628149784513586[/C][C]0.125629956902717[/C][C]0.937185021548641[/C][/ROW]
[ROW][C]22[/C][C]0.0408903080694693[/C][C]0.0817806161389387[/C][C]0.959109691930531[/C][/ROW]
[ROW][C]23[/C][C]0.0628113725085064[/C][C]0.125622745017013[/C][C]0.937188627491494[/C][/ROW]
[ROW][C]24[/C][C]0.201124502608534[/C][C]0.402249005217067[/C][C]0.798875497391466[/C][/ROW]
[ROW][C]25[/C][C]0.166960941586594[/C][C]0.333921883173188[/C][C]0.833039058413406[/C][/ROW]
[ROW][C]26[/C][C]0.128641546494673[/C][C]0.257283092989346[/C][C]0.871358453505327[/C][/ROW]
[ROW][C]27[/C][C]0.105299456582897[/C][C]0.210598913165794[/C][C]0.894700543417103[/C][/ROW]
[ROW][C]28[/C][C]0.128139646118792[/C][C]0.256279292237583[/C][C]0.871860353881208[/C][/ROW]
[ROW][C]29[/C][C]0.100278299388596[/C][C]0.200556598777192[/C][C]0.899721700611404[/C][/ROW]
[ROW][C]30[/C][C]0.0842250280310749[/C][C]0.16845005606215[/C][C]0.915774971968925[/C][/ROW]
[ROW][C]31[/C][C]0.474817821980502[/C][C]0.949635643961004[/C][C]0.525182178019498[/C][/ROW]
[ROW][C]32[/C][C]0.417646936327428[/C][C]0.835293872654855[/C][C]0.582353063672572[/C][/ROW]
[ROW][C]33[/C][C]0.361578248753302[/C][C]0.723156497506605[/C][C]0.638421751246698[/C][/ROW]
[ROW][C]34[/C][C]0.360116344772092[/C][C]0.720232689544184[/C][C]0.639883655227908[/C][/ROW]
[ROW][C]35[/C][C]0.454206461970286[/C][C]0.908412923940571[/C][C]0.545793538029714[/C][/ROW]
[ROW][C]36[/C][C]0.462132055957017[/C][C]0.924264111914034[/C][C]0.537867944042983[/C][/ROW]
[ROW][C]37[/C][C]0.427846712635567[/C][C]0.855693425271134[/C][C]0.572153287364433[/C][/ROW]
[ROW][C]38[/C][C]0.352292126808982[/C][C]0.704584253617964[/C][C]0.647707873191018[/C][/ROW]
[ROW][C]39[/C][C]0.317575425589861[/C][C]0.635150851179721[/C][C]0.682424574410139[/C][/ROW]
[ROW][C]40[/C][C]0.271273022994576[/C][C]0.542546045989153[/C][C]0.728726977005424[/C][/ROW]
[ROW][C]41[/C][C]0.220153436840969[/C][C]0.440306873681939[/C][C]0.779846563159031[/C][/ROW]
[ROW][C]42[/C][C]0.18110886043193[/C][C]0.362217720863861[/C][C]0.81889113956807[/C][/ROW]
[ROW][C]43[/C][C]0.157397376389286[/C][C]0.314794752778572[/C][C]0.842602623610714[/C][/ROW]
[ROW][C]44[/C][C]0.126007218842372[/C][C]0.252014437684744[/C][C]0.873992781157628[/C][/ROW]
[ROW][C]45[/C][C]0.0987072908585827[/C][C]0.197414581717165[/C][C]0.901292709141417[/C][/ROW]
[ROW][C]46[/C][C]0.0722582886796145[/C][C]0.144516577359229[/C][C]0.927741711320386[/C][/ROW]
[ROW][C]47[/C][C]0.137472793491083[/C][C]0.274945586982166[/C][C]0.862527206508917[/C][/ROW]
[ROW][C]48[/C][C]0.149585430014966[/C][C]0.299170860029931[/C][C]0.850414569985035[/C][/ROW]
[ROW][C]49[/C][C]0.21269332820922[/C][C]0.425386656418439[/C][C]0.78730667179078[/C][/ROW]
[ROW][C]50[/C][C]0.31384800787656[/C][C]0.627696015753119[/C][C]0.68615199212344[/C][/ROW]
[ROW][C]51[/C][C]0.249339369323302[/C][C]0.498678738646604[/C][C]0.750660630676698[/C][/ROW]
[ROW][C]52[/C][C]0.44670655275702[/C][C]0.89341310551404[/C][C]0.55329344724298[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153822&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153822&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
130.4027912080992270.8055824161984550.597208791900773
140.2445168858468520.4890337716937040.755483114153148
150.1852212537611420.3704425075222830.814778746238858
160.1168138076837950.2336276153675910.883186192316205
170.07587612898495270.1517522579699050.924123871015047
180.0747231381504530.1494462763009060.925276861849547
190.04056160415185880.08112320830371760.959438395848141
200.08738980960787470.1747796192157490.912610190392125
210.06281497845135860.1256299569027170.937185021548641
220.04089030806946930.08178061613893870.959109691930531
230.06281137250850640.1256227450170130.937188627491494
240.2011245026085340.4022490052170670.798875497391466
250.1669609415865940.3339218831731880.833039058413406
260.1286415464946730.2572830929893460.871358453505327
270.1052994565828970.2105989131657940.894700543417103
280.1281396461187920.2562792922375830.871860353881208
290.1002782993885960.2005565987771920.899721700611404
300.08422502803107490.168450056062150.915774971968925
310.4748178219805020.9496356439610040.525182178019498
320.4176469363274280.8352938726548550.582353063672572
330.3615782487533020.7231564975066050.638421751246698
340.3601163447720920.7202326895441840.639883655227908
350.4542064619702860.9084129239405710.545793538029714
360.4621320559570170.9242641119140340.537867944042983
370.4278467126355670.8556934252711340.572153287364433
380.3522921268089820.7045842536179640.647707873191018
390.3175754255898610.6351508511797210.682424574410139
400.2712730229945760.5425460459891530.728726977005424
410.2201534368409690.4403068736819390.779846563159031
420.181108860431930.3622177208638610.81889113956807
430.1573973763892860.3147947527785720.842602623610714
440.1260072188423720.2520144376847440.873992781157628
450.09870729085858270.1974145817171650.901292709141417
460.07225828867961450.1445165773592290.927741711320386
470.1374727934910830.2749455869821660.862527206508917
480.1495854300149660.2991708600299310.850414569985035
490.212693328209220.4253866564184390.78730667179078
500.313848007876560.6276960157531190.68615199212344
510.2493393693233020.4986787386466040.750660630676698
520.446706552757020.893413105514040.55329344724298






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.05OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 2 & 0.05 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153822&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.05[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153822&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153822&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.05OK


Figures (Output of Computation)

Figure 1
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Figure 6
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; 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')
}