FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 22 Dec 2010 17:13:23 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/22/t1293038134qqam1kjq66o6pf0.htm/, Retrieved Mon, 06 May 2024 03:43:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114419, Retrieved Mon, 06 May 2024 03:43:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact108

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [paper] [2010-12-22 17:13:23] [6df2229e3f2091de42c4a9cf9a617420] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
10554,27	2,08	83,9	61,2	11451	63,96	2,17	69
10532,54	2,09	85,6	62	11964	63,77	2,23	67
10324,31	2,07	87,5	65,1	12574	59,15	2,17	69
10695,25	2,04	88,5	63,2	13031	56,12	2,39	79
10827,81	2,35	91	66,3	13812	57,42	2,6	104
10872,48	2,33	90,6	61,9	14544	63,52	2,67	117
10971,19	2,37	91,2	62,1	14931	61,71	2,63	73
11145,65	2,59	93,2	66,3	14886	63,01	2,85	97
11234,68	2,62	90,1	72	16005	68,18	3,1	124
11333,88	2,6	95	65,3	17064	72,03	3,2	129
10997,97	2,83	95,4	67,6	15168	69,75	3,21	122
11036,89	2,78	93,7	70,5	16050	74,41	3,36	113
11257,35	3,01	93,9	74,2	15839	74,33	3,41	131
11533,59	3,06	92,5	77,8	15137	64,24	3,32	155
11963,12	3,33	89,2	78,5	14954	60,03	3,24	161
12185,15	3,32	93,3	77,8	15648	59,44	3,24	141
12377,62	3,6	93	81,4	15305	62,5	3,26	116
12512,89	3,57	96,1	84,5	15579	55,04	3,48	197
12631,48	3,57	96,7	88	16348	58,34	3,61	163
12268,53	3,83	97,6	93,9	15928	61,92	3,68	154
12754,8	3,84	102,6	98,9	16171	67,65	3,67	143
13407,75	3,8	107,6	96,7	15937	67,68	3,71	165
13480,21	4,07	103,5	98,9	15713	70,3	4,04	169
13673,28	4,05	100,8	102,2	15594	75,26	4,2	194
13239,71	4,272	94,5	105,4	15683	71,44	4,16	185
13557,69	3,858	100,1	105,1	16438	76,36	4,01	160
13901,28	4,067	97,4	116,6	17032	81,71	4,03	135
13200,58	3,964	103	112	17696	92,6	4	125
13406,97	3,782	100,2	108,8	17745	90,6	3,9	131
12538,12	4,114	100,2	106,9	19394	92,23	3,93	73
12419,57	4,009	99	109,5	20148	94,09	3,68	69
12193,88	4,025	102,4	106,7	20108	102,79	3,59	54
12656,63	4,082	99	118,9	18584	109,65	3,55	73
12812,48	4,044	103,7	117,5	18441	124,05	3,88	54
12056,67	3,916	103,4	113,7	18391	132,69	4,61	58
11322,38	4,289	95,3	119,6	19178	135,81	4,87	45
11530,75	4,296	93,6	120,6	18079	116,07	4,9	41
11114,08	4,193	102,4	117,5	18483	101,42	4,54	74
9181,73	3,48	110,5	120,3	19644	75,73	4,47	31
8614,55	2,934	109,1	119,8	19195	55,48	4,04	23
8595,56	2,221	100,9	108	19650	43,8	4,08	123
8396,2	1,211	108,1	98,8	20830	45,29	3,47	46
7690,5	1,28	105	94,6	23595	44,01	3,27	162
7235,47	0,96	111,5	84,6	22937	47,48	2,78	62
7992,12	0,5	109,5	84,4	21814	51,07	2,83	166
8398,37	0,687	110,5	79,1	21928	57,84	2,99	441
8593	0,344	114	73,3	21777	69,04	2,96	313
8679,75	0,346	108,2	74,3	21383	65,61	2,99	224
9374,63	0,334	110,3	67,8	21467	72,87	2,98	175
9634,97	0,34	111,8	64,8	22052	68,41	2,98	305
9857,34	0,328	107,5	66,5	22680	73,25	2,92	400
10238,83	0,344	114,1	57,7	24320	77,43	2,62	299
10433,44	0,341	113,8	53,8	24977	75,28	2,68	204
10471,24	0,32	114,5	51,8	25204	77,33	2,59	164
10214,51	0,314	114,8	50,9	25739	74,31	2,53	133
10677,52	0,325	117,8	49	26434	79,7	2,4	83
11052,15	0,339	116,7	48,1	27525	85,47	2,16	71
10500,19	0,329	122,8	42,6	30695	77,98	2,03	46
10159,27	0,48	122,3	40,9	32436	75,69	2,05	68
10222,24	0,399	115	43,3	30160	75,2	1,91	51
10350,4	0,37	118,5	43,7	30236	77,21	1,9	38

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114419&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114419&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114419&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 time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
DowJones[t] = + 3294.69304179701 + 1902.00849170329Eonia[t] + 75.8419583730737deposits[t] -40.7278509972586`2JAAR`[t] -0.0314633513384605Goudkoers[t] + 19.7117420762761Brent[t] -859.836876632501gewrentevoet[t] + 5.39966362404075kasbons[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
DowJones[t] =  +  3294.69304179701 +  1902.00849170329Eonia[t] +  75.8419583730737deposits[t] -40.7278509972586`2JAAR`[t] -0.0314633513384605Goudkoers[t] +  19.7117420762761Brent[t] -859.836876632501gewrentevoet[t] +  5.39966362404075kasbons[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114419&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]DowJones[t] =  +  3294.69304179701 +  1902.00849170329Eonia[t] +  75.8419583730737deposits[t] -40.7278509972586`2JAAR`[t] -0.0314633513384605Goudkoers[t] +  19.7117420762761Brent[t] -859.836876632501gewrentevoet[t] +  5.39966362404075kasbons[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114419&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114419&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
DowJones[t] = + 3294.69304179701 + 1902.00849170329Eonia[t] + 75.8419583730737deposits[t] -40.7278509972586`2JAAR`[t] -0.0314633513384605Goudkoers[t] + 19.7117420762761Brent[t] -859.836876632501gewrentevoet[t] + 5.39966362404075kasbons[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3294.693041797011695.7496281.94290.0573470.028673
Eonia1902.00849170329156.88105612.123900
deposits75.841958373073725.3995942.9860.0042720.002136
`2JAAR`-40.727850997258610.002704-4.07170.0001567.8e-05
Goudkoers-0.03146335133846050.056027-0.56160.5767720.288386
Brent19.71174207627615.8863053.34870.00150.00075
gewrentevoet-859.836876632501345.900734-2.48580.0161150.008057
kasbons5.399663624040751.2803364.21749.7e-054.8e-05

\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) & 3294.69304179701 & 1695.749628 & 1.9429 & 0.057347 & 0.028673 \tabularnewline
Eonia & 1902.00849170329 & 156.881056 & 12.1239 & 0 & 0 \tabularnewline
deposits & 75.8419583730737 & 25.399594 & 2.986 & 0.004272 & 0.002136 \tabularnewline
`2JAAR` & -40.7278509972586 & 10.002704 & -4.0717 & 0.000156 & 7.8e-05 \tabularnewline
Goudkoers & -0.0314633513384605 & 0.056027 & -0.5616 & 0.576772 & 0.288386 \tabularnewline
Brent & 19.7117420762761 & 5.886305 & 3.3487 & 0.0015 & 0.00075 \tabularnewline
gewrentevoet & -859.836876632501 & 345.900734 & -2.4858 & 0.016115 & 0.008057 \tabularnewline
kasbons & 5.39966362404075 & 1.280336 & 4.2174 & 9.7e-05 & 4.8e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114419&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]3294.69304179701[/C][C]1695.749628[/C][C]1.9429[/C][C]0.057347[/C][C]0.028673[/C][/ROW]
[ROW][C]Eonia[/C][C]1902.00849170329[/C][C]156.881056[/C][C]12.1239[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]deposits[/C][C]75.8419583730737[/C][C]25.399594[/C][C]2.986[/C][C]0.004272[/C][C]0.002136[/C][/ROW]
[ROW][C]`2JAAR`[/C][C]-40.7278509972586[/C][C]10.002704[/C][C]-4.0717[/C][C]0.000156[/C][C]7.8e-05[/C][/ROW]
[ROW][C]Goudkoers[/C][C]-0.0314633513384605[/C][C]0.056027[/C][C]-0.5616[/C][C]0.576772[/C][C]0.288386[/C][/ROW]
[ROW][C]Brent[/C][C]19.7117420762761[/C][C]5.886305[/C][C]3.3487[/C][C]0.0015[/C][C]0.00075[/C][/ROW]
[ROW][C]gewrentevoet[/C][C]-859.836876632501[/C][C]345.900734[/C][C]-2.4858[/C][C]0.016115[/C][C]0.008057[/C][/ROW]
[ROW][C]kasbons[/C][C]5.39966362404075[/C][C]1.280336[/C][C]4.2174[/C][C]9.7e-05[/C][C]4.8e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114419&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114419&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)3294.693041797011695.7496281.94290.0573470.028673
Eonia1902.00849170329156.88105612.123900
deposits75.841958373073725.3995942.9860.0042720.002136
`2JAAR`-40.727850997258610.002704-4.07170.0001567.8e-05
Goudkoers-0.03146335133846050.056027-0.56160.5767720.288386
Brent19.71174207627615.8863053.34870.00150.00075
gewrentevoet-859.836876632501345.900734-2.48580.0161150.008057
kasbons5.399663624040751.2803364.21749.7e-054.8e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.921944437296067
R-squared0.849981545461161
Adjusted R-squared0.830167787314522
F-TEST (value)42.8985525699141
F-TEST (DF numerator)7
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation676.704973303932
Sum Squared Residuals24270269.9073966

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.921944437296067 \tabularnewline
R-squared & 0.849981545461161 \tabularnewline
Adjusted R-squared & 0.830167787314522 \tabularnewline
F-TEST (value) & 42.8985525699141 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 53 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 676.704973303932 \tabularnewline
Sum Squared Residuals & 24270269.9073966 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114419&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.921944437296067[/C][/ROW]
[ROW][C]R-squared[/C][C]0.849981545461161[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.830167787314522[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]42.8985525699141[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]53[/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]676.704973303932[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]24270269.9073966[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114419&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114419&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 R0.921944437296067
R-squared0.849981545461161
Adjusted R-squared0.830167787314522
F-TEST (value)42.8985525699141
F-TEST (DF numerator)7
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation676.704973303932
Sum Squared Residuals24270269.9073966






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110554.2710528.673485796725.5965142033081
210532.5410561.767149073-29.2271490730023
310324.3110493.6990091934-169.389009193449
410695.2510380.5908230387314.65917696132
510827.8110989.0402471199-161.230247119897
610872.4811207.0833375583-334.603337558325
710971.1911069.4769875323-98.2869875323122
811145.6511436.0147278919-290.364727891915
911234.6811023.3500760797211.329923920305
1011333.8811613.3972523385-279.517252338512
1110997.9711955.837659555-957.867659555005
1211036.8911500.2286759272-463.338675927247
1311257.3511861.4299011712-604.079901171181
1411533.5911733.9043614078-200.314361407788
1511963.1211992.6149868671-29.494986867111
1612185.1512171.597660843113.5523391569473
1712377.6212453.7087195467-76.0887195467481
1812512.8912598.0402841931-85.1502841930693
1912631.4812246.4840552193384.995944780666
2012268.5312543.9667949287-275.436794928708
2112754.812793.0621733485-38.2621733485098
2213407.7513278.1457988788129.6042011212
2313480.2113187.6798302622292.530169737786
2413673.2812909.3965343773763.883465622682
2513239.7112631.2063680423608.503631957706
2613557.6912347.91905631411209.77094368588
2713901.2812006.87551828371894.4044817163
2813200.5812588.5994010953611.980598904663
2913406.9712237.82197639131169.14802360868
3012538.1212587.9431892655-49.82318926547
3112419.5712397.630573010721.9394269892975
3212193.8812969.1044047922-775.224404792186
3312656.6312642.936230189413.6937698105921
3412812.4812886.1446702486-73.6646702485682
3512056.6712340.5011832486-283.831183248593
3611322.3811938.3219296858-615.94192968583
3711530.7511380.0514826364150.698517363592
3811114.0812164.0521395884-1049.97213958838
399181.7310593.2814057205-1411.55140572055
408614.559410.4667686929-795.916768692905
418595.568174.04321224578421.516787754221
428396.27274.743101895321121.45689810468
437690.58028.02975046156-337.529750461559
447235.478290.09460984531-1054.62460984531
457992.127896.3040277906395.8159722093657
468398.3710020.8744525515-1622.5044525515
4785939430.3145697153-837.314569715294
488679.758391.92749340474287.822506595261
499374.638677.5817127196697.048287280401
509634.979520.5760951533114.393904846689
519857.349742.59832944376114.741670556242
5210238.8310375.3727019946-136.542701994567
5310433.449878.14278372166555.29721627834
5410471.249867.4113426894603.828657310608
5510214.519723.24523136488491.264768635117
5610677.529975.25099015872702.269009841283
5711052.1510176.0831431029876.06685689712
5810500.1910573.1096162135-72.9196162134961
5910159.2710893.3075441308-734.03754413081
6010222.2410178.386432934843.8535670651953
6110350.410348.01602910762.38397089234955

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 10554.27 & 10528.6734857967 & 25.5965142033081 \tabularnewline
2 & 10532.54 & 10561.767149073 & -29.2271490730023 \tabularnewline
3 & 10324.31 & 10493.6990091934 & -169.389009193449 \tabularnewline
4 & 10695.25 & 10380.5908230387 & 314.65917696132 \tabularnewline
5 & 10827.81 & 10989.0402471199 & -161.230247119897 \tabularnewline
6 & 10872.48 & 11207.0833375583 & -334.603337558325 \tabularnewline
7 & 10971.19 & 11069.4769875323 & -98.2869875323122 \tabularnewline
8 & 11145.65 & 11436.0147278919 & -290.364727891915 \tabularnewline
9 & 11234.68 & 11023.3500760797 & 211.329923920305 \tabularnewline
10 & 11333.88 & 11613.3972523385 & -279.517252338512 \tabularnewline
11 & 10997.97 & 11955.837659555 & -957.867659555005 \tabularnewline
12 & 11036.89 & 11500.2286759272 & -463.338675927247 \tabularnewline
13 & 11257.35 & 11861.4299011712 & -604.079901171181 \tabularnewline
14 & 11533.59 & 11733.9043614078 & -200.314361407788 \tabularnewline
15 & 11963.12 & 11992.6149868671 & -29.494986867111 \tabularnewline
16 & 12185.15 & 12171.5976608431 & 13.5523391569473 \tabularnewline
17 & 12377.62 & 12453.7087195467 & -76.0887195467481 \tabularnewline
18 & 12512.89 & 12598.0402841931 & -85.1502841930693 \tabularnewline
19 & 12631.48 & 12246.4840552193 & 384.995944780666 \tabularnewline
20 & 12268.53 & 12543.9667949287 & -275.436794928708 \tabularnewline
21 & 12754.8 & 12793.0621733485 & -38.2621733485098 \tabularnewline
22 & 13407.75 & 13278.1457988788 & 129.6042011212 \tabularnewline
23 & 13480.21 & 13187.6798302622 & 292.530169737786 \tabularnewline
24 & 13673.28 & 12909.3965343773 & 763.883465622682 \tabularnewline
25 & 13239.71 & 12631.2063680423 & 608.503631957706 \tabularnewline
26 & 13557.69 & 12347.9190563141 & 1209.77094368588 \tabularnewline
27 & 13901.28 & 12006.8755182837 & 1894.4044817163 \tabularnewline
28 & 13200.58 & 12588.5994010953 & 611.980598904663 \tabularnewline
29 & 13406.97 & 12237.8219763913 & 1169.14802360868 \tabularnewline
30 & 12538.12 & 12587.9431892655 & -49.82318926547 \tabularnewline
31 & 12419.57 & 12397.6305730107 & 21.9394269892975 \tabularnewline
32 & 12193.88 & 12969.1044047922 & -775.224404792186 \tabularnewline
33 & 12656.63 & 12642.9362301894 & 13.6937698105921 \tabularnewline
34 & 12812.48 & 12886.1446702486 & -73.6646702485682 \tabularnewline
35 & 12056.67 & 12340.5011832486 & -283.831183248593 \tabularnewline
36 & 11322.38 & 11938.3219296858 & -615.94192968583 \tabularnewline
37 & 11530.75 & 11380.0514826364 & 150.698517363592 \tabularnewline
38 & 11114.08 & 12164.0521395884 & -1049.97213958838 \tabularnewline
39 & 9181.73 & 10593.2814057205 & -1411.55140572055 \tabularnewline
40 & 8614.55 & 9410.4667686929 & -795.916768692905 \tabularnewline
41 & 8595.56 & 8174.04321224578 & 421.516787754221 \tabularnewline
42 & 8396.2 & 7274.74310189532 & 1121.45689810468 \tabularnewline
43 & 7690.5 & 8028.02975046156 & -337.529750461559 \tabularnewline
44 & 7235.47 & 8290.09460984531 & -1054.62460984531 \tabularnewline
45 & 7992.12 & 7896.30402779063 & 95.8159722093657 \tabularnewline
46 & 8398.37 & 10020.8744525515 & -1622.5044525515 \tabularnewline
47 & 8593 & 9430.3145697153 & -837.314569715294 \tabularnewline
48 & 8679.75 & 8391.92749340474 & 287.822506595261 \tabularnewline
49 & 9374.63 & 8677.5817127196 & 697.048287280401 \tabularnewline
50 & 9634.97 & 9520.5760951533 & 114.393904846689 \tabularnewline
51 & 9857.34 & 9742.59832944376 & 114.741670556242 \tabularnewline
52 & 10238.83 & 10375.3727019946 & -136.542701994567 \tabularnewline
53 & 10433.44 & 9878.14278372166 & 555.29721627834 \tabularnewline
54 & 10471.24 & 9867.4113426894 & 603.828657310608 \tabularnewline
55 & 10214.51 & 9723.24523136488 & 491.264768635117 \tabularnewline
56 & 10677.52 & 9975.25099015872 & 702.269009841283 \tabularnewline
57 & 11052.15 & 10176.0831431029 & 876.06685689712 \tabularnewline
58 & 10500.19 & 10573.1096162135 & -72.9196162134961 \tabularnewline
59 & 10159.27 & 10893.3075441308 & -734.03754413081 \tabularnewline
60 & 10222.24 & 10178.3864329348 & 43.8535670651953 \tabularnewline
61 & 10350.4 & 10348.0160291076 & 2.38397089234955 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114419&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]10554.27[/C][C]10528.6734857967[/C][C]25.5965142033081[/C][/ROW]
[ROW][C]2[/C][C]10532.54[/C][C]10561.767149073[/C][C]-29.2271490730023[/C][/ROW]
[ROW][C]3[/C][C]10324.31[/C][C]10493.6990091934[/C][C]-169.389009193449[/C][/ROW]
[ROW][C]4[/C][C]10695.25[/C][C]10380.5908230387[/C][C]314.65917696132[/C][/ROW]
[ROW][C]5[/C][C]10827.81[/C][C]10989.0402471199[/C][C]-161.230247119897[/C][/ROW]
[ROW][C]6[/C][C]10872.48[/C][C]11207.0833375583[/C][C]-334.603337558325[/C][/ROW]
[ROW][C]7[/C][C]10971.19[/C][C]11069.4769875323[/C][C]-98.2869875323122[/C][/ROW]
[ROW][C]8[/C][C]11145.65[/C][C]11436.0147278919[/C][C]-290.364727891915[/C][/ROW]
[ROW][C]9[/C][C]11234.68[/C][C]11023.3500760797[/C][C]211.329923920305[/C][/ROW]
[ROW][C]10[/C][C]11333.88[/C][C]11613.3972523385[/C][C]-279.517252338512[/C][/ROW]
[ROW][C]11[/C][C]10997.97[/C][C]11955.837659555[/C][C]-957.867659555005[/C][/ROW]
[ROW][C]12[/C][C]11036.89[/C][C]11500.2286759272[/C][C]-463.338675927247[/C][/ROW]
[ROW][C]13[/C][C]11257.35[/C][C]11861.4299011712[/C][C]-604.079901171181[/C][/ROW]
[ROW][C]14[/C][C]11533.59[/C][C]11733.9043614078[/C][C]-200.314361407788[/C][/ROW]
[ROW][C]15[/C][C]11963.12[/C][C]11992.6149868671[/C][C]-29.494986867111[/C][/ROW]
[ROW][C]16[/C][C]12185.15[/C][C]12171.5976608431[/C][C]13.5523391569473[/C][/ROW]
[ROW][C]17[/C][C]12377.62[/C][C]12453.7087195467[/C][C]-76.0887195467481[/C][/ROW]
[ROW][C]18[/C][C]12512.89[/C][C]12598.0402841931[/C][C]-85.1502841930693[/C][/ROW]
[ROW][C]19[/C][C]12631.48[/C][C]12246.4840552193[/C][C]384.995944780666[/C][/ROW]
[ROW][C]20[/C][C]12268.53[/C][C]12543.9667949287[/C][C]-275.436794928708[/C][/ROW]
[ROW][C]21[/C][C]12754.8[/C][C]12793.0621733485[/C][C]-38.2621733485098[/C][/ROW]
[ROW][C]22[/C][C]13407.75[/C][C]13278.1457988788[/C][C]129.6042011212[/C][/ROW]
[ROW][C]23[/C][C]13480.21[/C][C]13187.6798302622[/C][C]292.530169737786[/C][/ROW]
[ROW][C]24[/C][C]13673.28[/C][C]12909.3965343773[/C][C]763.883465622682[/C][/ROW]
[ROW][C]25[/C][C]13239.71[/C][C]12631.2063680423[/C][C]608.503631957706[/C][/ROW]
[ROW][C]26[/C][C]13557.69[/C][C]12347.9190563141[/C][C]1209.77094368588[/C][/ROW]
[ROW][C]27[/C][C]13901.28[/C][C]12006.8755182837[/C][C]1894.4044817163[/C][/ROW]
[ROW][C]28[/C][C]13200.58[/C][C]12588.5994010953[/C][C]611.980598904663[/C][/ROW]
[ROW][C]29[/C][C]13406.97[/C][C]12237.8219763913[/C][C]1169.14802360868[/C][/ROW]
[ROW][C]30[/C][C]12538.12[/C][C]12587.9431892655[/C][C]-49.82318926547[/C][/ROW]
[ROW][C]31[/C][C]12419.57[/C][C]12397.6305730107[/C][C]21.9394269892975[/C][/ROW]
[ROW][C]32[/C][C]12193.88[/C][C]12969.1044047922[/C][C]-775.224404792186[/C][/ROW]
[ROW][C]33[/C][C]12656.63[/C][C]12642.9362301894[/C][C]13.6937698105921[/C][/ROW]
[ROW][C]34[/C][C]12812.48[/C][C]12886.1446702486[/C][C]-73.6646702485682[/C][/ROW]
[ROW][C]35[/C][C]12056.67[/C][C]12340.5011832486[/C][C]-283.831183248593[/C][/ROW]
[ROW][C]36[/C][C]11322.38[/C][C]11938.3219296858[/C][C]-615.94192968583[/C][/ROW]
[ROW][C]37[/C][C]11530.75[/C][C]11380.0514826364[/C][C]150.698517363592[/C][/ROW]
[ROW][C]38[/C][C]11114.08[/C][C]12164.0521395884[/C][C]-1049.97213958838[/C][/ROW]
[ROW][C]39[/C][C]9181.73[/C][C]10593.2814057205[/C][C]-1411.55140572055[/C][/ROW]
[ROW][C]40[/C][C]8614.55[/C][C]9410.4667686929[/C][C]-795.916768692905[/C][/ROW]
[ROW][C]41[/C][C]8595.56[/C][C]8174.04321224578[/C][C]421.516787754221[/C][/ROW]
[ROW][C]42[/C][C]8396.2[/C][C]7274.74310189532[/C][C]1121.45689810468[/C][/ROW]
[ROW][C]43[/C][C]7690.5[/C][C]8028.02975046156[/C][C]-337.529750461559[/C][/ROW]
[ROW][C]44[/C][C]7235.47[/C][C]8290.09460984531[/C][C]-1054.62460984531[/C][/ROW]
[ROW][C]45[/C][C]7992.12[/C][C]7896.30402779063[/C][C]95.8159722093657[/C][/ROW]
[ROW][C]46[/C][C]8398.37[/C][C]10020.8744525515[/C][C]-1622.5044525515[/C][/ROW]
[ROW][C]47[/C][C]8593[/C][C]9430.3145697153[/C][C]-837.314569715294[/C][/ROW]
[ROW][C]48[/C][C]8679.75[/C][C]8391.92749340474[/C][C]287.822506595261[/C][/ROW]
[ROW][C]49[/C][C]9374.63[/C][C]8677.5817127196[/C][C]697.048287280401[/C][/ROW]
[ROW][C]50[/C][C]9634.97[/C][C]9520.5760951533[/C][C]114.393904846689[/C][/ROW]
[ROW][C]51[/C][C]9857.34[/C][C]9742.59832944376[/C][C]114.741670556242[/C][/ROW]
[ROW][C]52[/C][C]10238.83[/C][C]10375.3727019946[/C][C]-136.542701994567[/C][/ROW]
[ROW][C]53[/C][C]10433.44[/C][C]9878.14278372166[/C][C]555.29721627834[/C][/ROW]
[ROW][C]54[/C][C]10471.24[/C][C]9867.4113426894[/C][C]603.828657310608[/C][/ROW]
[ROW][C]55[/C][C]10214.51[/C][C]9723.24523136488[/C][C]491.264768635117[/C][/ROW]
[ROW][C]56[/C][C]10677.52[/C][C]9975.25099015872[/C][C]702.269009841283[/C][/ROW]
[ROW][C]57[/C][C]11052.15[/C][C]10176.0831431029[/C][C]876.06685689712[/C][/ROW]
[ROW][C]58[/C][C]10500.19[/C][C]10573.1096162135[/C][C]-72.9196162134961[/C][/ROW]
[ROW][C]59[/C][C]10159.27[/C][C]10893.3075441308[/C][C]-734.03754413081[/C][/ROW]
[ROW][C]60[/C][C]10222.24[/C][C]10178.3864329348[/C][C]43.8535670651953[/C][/ROW]
[ROW][C]61[/C][C]10350.4[/C][C]10348.0160291076[/C][C]2.38397089234955[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114419&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114419&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
110554.2710528.673485796725.5965142033081
210532.5410561.767149073-29.2271490730023
310324.3110493.6990091934-169.389009193449
410695.2510380.5908230387314.65917696132
510827.8110989.0402471199-161.230247119897
610872.4811207.0833375583-334.603337558325
710971.1911069.4769875323-98.2869875323122
811145.6511436.0147278919-290.364727891915
911234.6811023.3500760797211.329923920305
1011333.8811613.3972523385-279.517252338512
1110997.9711955.837659555-957.867659555005
1211036.8911500.2286759272-463.338675927247
1311257.3511861.4299011712-604.079901171181
1411533.5911733.9043614078-200.314361407788
1511963.1211992.6149868671-29.494986867111
1612185.1512171.597660843113.5523391569473
1712377.6212453.7087195467-76.0887195467481
1812512.8912598.0402841931-85.1502841930693
1912631.4812246.4840552193384.995944780666
2012268.5312543.9667949287-275.436794928708
2112754.812793.0621733485-38.2621733485098
2213407.7513278.1457988788129.6042011212
2313480.2113187.6798302622292.530169737786
2413673.2812909.3965343773763.883465622682
2513239.7112631.2063680423608.503631957706
2613557.6912347.91905631411209.77094368588
2713901.2812006.87551828371894.4044817163
2813200.5812588.5994010953611.980598904663
2913406.9712237.82197639131169.14802360868
3012538.1212587.9431892655-49.82318926547
3112419.5712397.630573010721.9394269892975
3212193.8812969.1044047922-775.224404792186
3312656.6312642.936230189413.6937698105921
3412812.4812886.1446702486-73.6646702485682
3512056.6712340.5011832486-283.831183248593
3611322.3811938.3219296858-615.94192968583
3711530.7511380.0514826364150.698517363592
3811114.0812164.0521395884-1049.97213958838
399181.7310593.2814057205-1411.55140572055
408614.559410.4667686929-795.916768692905
418595.568174.04321224578421.516787754221
428396.27274.743101895321121.45689810468
437690.58028.02975046156-337.529750461559
447235.478290.09460984531-1054.62460984531
457992.127896.3040277906395.8159722093657
468398.3710020.8744525515-1622.5044525515
4785939430.3145697153-837.314569715294
488679.758391.92749340474287.822506595261
499374.638677.5817127196697.048287280401
509634.979520.5760951533114.393904846689
519857.349742.59832944376114.741670556242
5210238.8310375.3727019946-136.542701994567
5310433.449878.14278372166555.29721627834
5410471.249867.4113426894603.828657310608
5510214.519723.24523136488491.264768635117
5610677.529975.25099015872702.269009841283
5711052.1510176.0831431029876.06685689712
5810500.1910573.1096162135-72.9196162134961
5910159.2710893.3075441308-734.03754413081
6010222.2410178.386432934843.8535670651953
6110350.410348.01602910762.38397089234955






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.0086953359219630.0173906718439260.991304664078037
120.002509473332932140.005018946665864280.997490526667068
130.0005704009373347470.001140801874669490.999429599062665
140.0001316016005831730.0002632032011663470.999868398399417
152.61510263608374e-055.23020527216747e-050.999973848973639
164.60673042409094e-059.21346084818188e-050.99995393269576
172.38337896278801e-054.76675792557602e-050.999976166210372
182.40889505652024e-054.81779011304049e-050.999975911049435
192.49538186387101e-054.99076372774203e-050.999975046181361
203.77314338137755e-057.5462867627551e-050.999962268566186
217.84173641128795e-050.0001568347282257590.999921582635887
220.0005213395213629670.001042679042725930.999478660478637
230.001546103563624280.003092207127248550.998453896436376
240.002937895803587490.005875791607174970.997062104196413
250.003098318612833020.006196637225666040.996901681387167
260.003083564652338210.006167129304676420.996916435347662
270.008214432417240020.016428864834480.99178556758276
280.01341153585758210.02682307171516410.986588464142418
290.02903977081731990.05807954163463980.97096022918268
300.02889195570131290.05778391140262580.971108044298687
310.02498254323775080.04996508647550170.97501745676225
320.02035455695224860.04070911390449710.979645443047751
330.01622011046096350.0324402209219270.983779889539037
340.0789673566848020.1579347133696040.921032643315198
350.109397235845150.21879447169030.89060276415485
360.186916858148460.3738337162969190.81308314185154
370.1782265767193670.3564531534387340.821773423280633
380.51114064824050.9777187035190.4888593517595
390.8329499638049070.3341000723901860.167050036195093
400.7914775779995910.4170448440008170.208522422000409
410.7385713748744370.5228572502511260.261428625125563
420.9432107733805580.1135784532388850.0567892266194424
430.9857287290211360.02854254195772820.0142712709788641
440.9749714073927740.05005718521445220.0250285926072261
450.9978777073892020.004244585221596170.00212229261079808
460.9996273695689260.0007452608621477770.000372630431073889
470.9996610698806920.0006778602386162650.000338930119308133
480.9987465796980730.002506840603853770.00125342030192689
490.9994271851588870.001145629682225980.000572814841112992
500.9959729167976430.008054166404713740.00402708320235687

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.008695335921963 & 0.017390671843926 & 0.991304664078037 \tabularnewline
12 & 0.00250947333293214 & 0.00501894666586428 & 0.997490526667068 \tabularnewline
13 & 0.000570400937334747 & 0.00114080187466949 & 0.999429599062665 \tabularnewline
14 & 0.000131601600583173 & 0.000263203201166347 & 0.999868398399417 \tabularnewline
15 & 2.61510263608374e-05 & 5.23020527216747e-05 & 0.999973848973639 \tabularnewline
16 & 4.60673042409094e-05 & 9.21346084818188e-05 & 0.99995393269576 \tabularnewline
17 & 2.38337896278801e-05 & 4.76675792557602e-05 & 0.999976166210372 \tabularnewline
18 & 2.40889505652024e-05 & 4.81779011304049e-05 & 0.999975911049435 \tabularnewline
19 & 2.49538186387101e-05 & 4.99076372774203e-05 & 0.999975046181361 \tabularnewline
20 & 3.77314338137755e-05 & 7.5462867627551e-05 & 0.999962268566186 \tabularnewline
21 & 7.84173641128795e-05 & 0.000156834728225759 & 0.999921582635887 \tabularnewline
22 & 0.000521339521362967 & 0.00104267904272593 & 0.999478660478637 \tabularnewline
23 & 0.00154610356362428 & 0.00309220712724855 & 0.998453896436376 \tabularnewline
24 & 0.00293789580358749 & 0.00587579160717497 & 0.997062104196413 \tabularnewline
25 & 0.00309831861283302 & 0.00619663722566604 & 0.996901681387167 \tabularnewline
26 & 0.00308356465233821 & 0.00616712930467642 & 0.996916435347662 \tabularnewline
27 & 0.00821443241724002 & 0.01642886483448 & 0.99178556758276 \tabularnewline
28 & 0.0134115358575821 & 0.0268230717151641 & 0.986588464142418 \tabularnewline
29 & 0.0290397708173199 & 0.0580795416346398 & 0.97096022918268 \tabularnewline
30 & 0.0288919557013129 & 0.0577839114026258 & 0.971108044298687 \tabularnewline
31 & 0.0249825432377508 & 0.0499650864755017 & 0.97501745676225 \tabularnewline
32 & 0.0203545569522486 & 0.0407091139044971 & 0.979645443047751 \tabularnewline
33 & 0.0162201104609635 & 0.032440220921927 & 0.983779889539037 \tabularnewline
34 & 0.078967356684802 & 0.157934713369604 & 0.921032643315198 \tabularnewline
35 & 0.10939723584515 & 0.2187944716903 & 0.89060276415485 \tabularnewline
36 & 0.18691685814846 & 0.373833716296919 & 0.81308314185154 \tabularnewline
37 & 0.178226576719367 & 0.356453153438734 & 0.821773423280633 \tabularnewline
38 & 0.5111406482405 & 0.977718703519 & 0.4888593517595 \tabularnewline
39 & 0.832949963804907 & 0.334100072390186 & 0.167050036195093 \tabularnewline
40 & 0.791477577999591 & 0.417044844000817 & 0.208522422000409 \tabularnewline
41 & 0.738571374874437 & 0.522857250251126 & 0.261428625125563 \tabularnewline
42 & 0.943210773380558 & 0.113578453238885 & 0.0567892266194424 \tabularnewline
43 & 0.985728729021136 & 0.0285425419577282 & 0.0142712709788641 \tabularnewline
44 & 0.974971407392774 & 0.0500571852144522 & 0.0250285926072261 \tabularnewline
45 & 0.997877707389202 & 0.00424458522159617 & 0.00212229261079808 \tabularnewline
46 & 0.999627369568926 & 0.000745260862147777 & 0.000372630431073889 \tabularnewline
47 & 0.999661069880692 & 0.000677860238616265 & 0.000338930119308133 \tabularnewline
48 & 0.998746579698073 & 0.00250684060385377 & 0.00125342030192689 \tabularnewline
49 & 0.999427185158887 & 0.00114562968222598 & 0.000572814841112992 \tabularnewline
50 & 0.995972916797643 & 0.00805416640471374 & 0.00402708320235687 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114419&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.008695335921963[/C][C]0.017390671843926[/C][C]0.991304664078037[/C][/ROW]
[ROW][C]12[/C][C]0.00250947333293214[/C][C]0.00501894666586428[/C][C]0.997490526667068[/C][/ROW]
[ROW][C]13[/C][C]0.000570400937334747[/C][C]0.00114080187466949[/C][C]0.999429599062665[/C][/ROW]
[ROW][C]14[/C][C]0.000131601600583173[/C][C]0.000263203201166347[/C][C]0.999868398399417[/C][/ROW]
[ROW][C]15[/C][C]2.61510263608374e-05[/C][C]5.23020527216747e-05[/C][C]0.999973848973639[/C][/ROW]
[ROW][C]16[/C][C]4.60673042409094e-05[/C][C]9.21346084818188e-05[/C][C]0.99995393269576[/C][/ROW]
[ROW][C]17[/C][C]2.38337896278801e-05[/C][C]4.76675792557602e-05[/C][C]0.999976166210372[/C][/ROW]
[ROW][C]18[/C][C]2.40889505652024e-05[/C][C]4.81779011304049e-05[/C][C]0.999975911049435[/C][/ROW]
[ROW][C]19[/C][C]2.49538186387101e-05[/C][C]4.99076372774203e-05[/C][C]0.999975046181361[/C][/ROW]
[ROW][C]20[/C][C]3.77314338137755e-05[/C][C]7.5462867627551e-05[/C][C]0.999962268566186[/C][/ROW]
[ROW][C]21[/C][C]7.84173641128795e-05[/C][C]0.000156834728225759[/C][C]0.999921582635887[/C][/ROW]
[ROW][C]22[/C][C]0.000521339521362967[/C][C]0.00104267904272593[/C][C]0.999478660478637[/C][/ROW]
[ROW][C]23[/C][C]0.00154610356362428[/C][C]0.00309220712724855[/C][C]0.998453896436376[/C][/ROW]
[ROW][C]24[/C][C]0.00293789580358749[/C][C]0.00587579160717497[/C][C]0.997062104196413[/C][/ROW]
[ROW][C]25[/C][C]0.00309831861283302[/C][C]0.00619663722566604[/C][C]0.996901681387167[/C][/ROW]
[ROW][C]26[/C][C]0.00308356465233821[/C][C]0.00616712930467642[/C][C]0.996916435347662[/C][/ROW]
[ROW][C]27[/C][C]0.00821443241724002[/C][C]0.01642886483448[/C][C]0.99178556758276[/C][/ROW]
[ROW][C]28[/C][C]0.0134115358575821[/C][C]0.0268230717151641[/C][C]0.986588464142418[/C][/ROW]
[ROW][C]29[/C][C]0.0290397708173199[/C][C]0.0580795416346398[/C][C]0.97096022918268[/C][/ROW]
[ROW][C]30[/C][C]0.0288919557013129[/C][C]0.0577839114026258[/C][C]0.971108044298687[/C][/ROW]
[ROW][C]31[/C][C]0.0249825432377508[/C][C]0.0499650864755017[/C][C]0.97501745676225[/C][/ROW]
[ROW][C]32[/C][C]0.0203545569522486[/C][C]0.0407091139044971[/C][C]0.979645443047751[/C][/ROW]
[ROW][C]33[/C][C]0.0162201104609635[/C][C]0.032440220921927[/C][C]0.983779889539037[/C][/ROW]
[ROW][C]34[/C][C]0.078967356684802[/C][C]0.157934713369604[/C][C]0.921032643315198[/C][/ROW]
[ROW][C]35[/C][C]0.10939723584515[/C][C]0.2187944716903[/C][C]0.89060276415485[/C][/ROW]
[ROW][C]36[/C][C]0.18691685814846[/C][C]0.373833716296919[/C][C]0.81308314185154[/C][/ROW]
[ROW][C]37[/C][C]0.178226576719367[/C][C]0.356453153438734[/C][C]0.821773423280633[/C][/ROW]
[ROW][C]38[/C][C]0.5111406482405[/C][C]0.977718703519[/C][C]0.4888593517595[/C][/ROW]
[ROW][C]39[/C][C]0.832949963804907[/C][C]0.334100072390186[/C][C]0.167050036195093[/C][/ROW]
[ROW][C]40[/C][C]0.791477577999591[/C][C]0.417044844000817[/C][C]0.208522422000409[/C][/ROW]
[ROW][C]41[/C][C]0.738571374874437[/C][C]0.522857250251126[/C][C]0.261428625125563[/C][/ROW]
[ROW][C]42[/C][C]0.943210773380558[/C][C]0.113578453238885[/C][C]0.0567892266194424[/C][/ROW]
[ROW][C]43[/C][C]0.985728729021136[/C][C]0.0285425419577282[/C][C]0.0142712709788641[/C][/ROW]
[ROW][C]44[/C][C]0.974971407392774[/C][C]0.0500571852144522[/C][C]0.0250285926072261[/C][/ROW]
[ROW][C]45[/C][C]0.997877707389202[/C][C]0.00424458522159617[/C][C]0.00212229261079808[/C][/ROW]
[ROW][C]46[/C][C]0.999627369568926[/C][C]0.000745260862147777[/C][C]0.000372630431073889[/C][/ROW]
[ROW][C]47[/C][C]0.999661069880692[/C][C]0.000677860238616265[/C][C]0.000338930119308133[/C][/ROW]
[ROW][C]48[/C][C]0.998746579698073[/C][C]0.00250684060385377[/C][C]0.00125342030192689[/C][/ROW]
[ROW][C]49[/C][C]0.999427185158887[/C][C]0.00114562968222598[/C][C]0.000572814841112992[/C][/ROW]
[ROW][C]50[/C][C]0.995972916797643[/C][C]0.00805416640471374[/C][C]0.00402708320235687[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114419&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114419&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.0086953359219630.0173906718439260.991304664078037
120.002509473332932140.005018946665864280.997490526667068
130.0005704009373347470.001140801874669490.999429599062665
140.0001316016005831730.0002632032011663470.999868398399417
152.61510263608374e-055.23020527216747e-050.999973848973639
164.60673042409094e-059.21346084818188e-050.99995393269576
172.38337896278801e-054.76675792557602e-050.999976166210372
182.40889505652024e-054.81779011304049e-050.999975911049435
192.49538186387101e-054.99076372774203e-050.999975046181361
203.77314338137755e-057.5462867627551e-050.999962268566186
217.84173641128795e-050.0001568347282257590.999921582635887
220.0005213395213629670.001042679042725930.999478660478637
230.001546103563624280.003092207127248550.998453896436376
240.002937895803587490.005875791607174970.997062104196413
250.003098318612833020.006196637225666040.996901681387167
260.003083564652338210.006167129304676420.996916435347662
270.008214432417240020.016428864834480.99178556758276
280.01341153585758210.02682307171516410.986588464142418
290.02903977081731990.05807954163463980.97096022918268
300.02889195570131290.05778391140262580.971108044298687
310.02498254323775080.04996508647550170.97501745676225
320.02035455695224860.04070911390449710.979645443047751
330.01622011046096350.0324402209219270.983779889539037
340.0789673566848020.1579347133696040.921032643315198
350.109397235845150.21879447169030.89060276415485
360.186916858148460.3738337162969190.81308314185154
370.1782265767193670.3564531534387340.821773423280633
380.51114064824050.9777187035190.4888593517595
390.8329499638049070.3341000723901860.167050036195093
400.7914775779995910.4170448440008170.208522422000409
410.7385713748744370.5228572502511260.261428625125563
420.9432107733805580.1135784532388850.0567892266194424
430.9857287290211360.02854254195772820.0142712709788641
440.9749714073927740.05005718521445220.0250285926072261
450.9978777073892020.004244585221596170.00212229261079808
460.9996273695689260.0007452608621477770.000372630431073889
470.9996610698806920.0006778602386162650.000338930119308133
480.9987465796980730.002506840603853770.00125342030192689
490.9994271851588870.001145629682225980.000572814841112992
500.9959729167976430.008054166404713740.00402708320235687






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level210.525NOK
5% type I error level280.7NOK
10% type I error level310.775NOK

\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 & 21 & 0.525 & NOK \tabularnewline
5% type I error level & 28 & 0.7 & NOK \tabularnewline
10% type I error level & 31 & 0.775 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114419&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]21[/C][C]0.525[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]28[/C][C]0.7[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]31[/C][C]0.775[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114419&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114419&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 level210.525NOK
5% type I error level280.7NOK
10% type I error level310.775NOK


Figures (Output of Computation)

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


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


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


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


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


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


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


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


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


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


Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}