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 computationThu, 19 Nov 2009 09:55:45 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258649862dcfnrhhq832mq4d.htm/, Retrieved Thu, 18 Apr 2024 11:10:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57825, Retrieved Thu, 18 Apr 2024 11:10:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact133

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [] [2009-11-19 16:48:04] [2f674a53c3d7aaa1bcf80e66074d3c9b]
-   P         [Multiple Regression] [] [2009-11-19 16:55:45] [5858ea01c9bd81debbf921a11363ad90] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
50.9	0	52.7	54.8	56	56.6
50.6	0	50.9	52.7	54.8	56
52.1	0	50.6	50.9	52.7	54.8
53.3	0	52.1	50.6	50.9	52.7
53.9	0	53.3	52.1	50.6	50.9
54.3	0	53.9	53.3	52.1	50.6
54.2	0	54.3	53.9	53.3	52.1
54.2	0	54.2	54.3	53.9	53.3
53.5	0	54.2	54.2	54.3	53.9
51.4	0	53.5	54.2	54.2	54.3
50.5	0	51.4	53.5	54.2	54.2
50.3	0	50.5	51.4	53.5	54.2
49.8	0	50.3	50.5	51.4	53.5
50.7	0	49.8	50.3	50.5	51.4
52.8	0	50.7	49.8	50.3	50.5
55.3	0	52.8	50.7	49.8	50.3
57.3	0	55.3	52.8	50.7	49.8
57.5	0	57.3	55.3	52.8	50.7
56.8	0	57.5	57.3	55.3	52.8
56.4	0	56.8	57.5	57.3	55.3
56.3	0	56.4	56.8	57.5	57.3
56.4	0	56.3	56.4	56.8	57.5
57	0	56.4	56.3	56.4	56.8
57.9	0	57	56.4	56.3	56.4
58.9	0	57.9	57	56.4	56.3
58.8	0	58.9	57.9	57	56.4
56.5	1	58.8	58.9	57.9	57
51.9	1	56.5	58.8	58.9	57.9
47.4	1	51.9	56.5	58.8	58.9
44.9	1	47.4	51.9	56.5	58.8
43.9	1	44.9	47.4	51.9	56.5
43.4	1	43.9	44.9	47.4	51.9
42.9	1	43.4	43.9	44.9	47.4
42.6	1	42.9	43.4	43.9	44.9
42.2	1	42.6	42.9	43.4	43.9
41.2	1	42.2	42.6	42.9	43.4
40.2	1	41.2	42.2	42.6	42.9
39.3	1	40.2	41.2	42.2	42.6
38.5	1	39.3	40.2	41.2	42.2
38.3	1	38.5	39.3	40.2	41.2
37.9	1	38.3	38.5	39.3	40.2
37.6	1	37.9	38.3	38.5	39.3
37.3	1	37.6	37.9	38.3	38.5
36	1	37.3	37.6	37.9	38.3
34.5	1	36	37.3	37.6	37.9
33.5	1	34.5	36	37.3	37.6
32.9	1	33.5	34.5	36	37.3
32.9	1	32.9	33.5	34.5	36
32.8	1	32.9	32.9	33.5	34.5
31.9	1	32.8	32.9	32.9	33.5
30.5	1	31.9	32.8	32.9	32.9
29.2	1	30.5	31.9	32.8	32.9
28.7	1	29.2	30.5	31.9	32.8
28.4	1	28.7	29.2	30.5	31.9
28	1	28.4	28.7	29.2	30.5
27.4	1	28	28.4	28.7	29.2
26.9	1	27.4	28	28.4	28.7

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57825&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57825&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57825&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 2.16415698290064 -1.27984653729293X[t] + 2.05394600247998Y1[t] -1.72849331883367Y2[t] + 0.670044338186338Y3[t] -0.0341379716604865Y4[t] + 0.00238579303693468t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  2.16415698290064 -1.27984653729293X[t] +  2.05394600247998Y1[t] -1.72849331883367Y2[t] +  0.670044338186338Y3[t] -0.0341379716604865Y4[t] +  0.00238579303693468t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57825&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  2.16415698290064 -1.27984653729293X[t] +  2.05394600247998Y1[t] -1.72849331883367Y2[t] +  0.670044338186338Y3[t] -0.0341379716604865Y4[t] +  0.00238579303693468t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57825&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57825&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
Y[t] = + 2.16415698290064 -1.27984653729293X[t] + 2.05394600247998Y1[t] -1.72849331883367Y2[t] + 0.670044338186338Y3[t] -0.0341379716604865Y4[t] + 0.00238579303693468t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.164156982900640.9541322.26820.0276690.013835
X-1.279846537292930.360586-3.54930.0008510.000426
Y12.053946002479980.13613715.087400
Y2-1.728493318833670.302339-5.71711e-060
Y30.6700443381863380.2946462.27410.0272880.013644
Y4-0.03413797166048650.126848-0.26910.788940.39447
t0.002385793036934680.0120170.19850.8434350.421718

\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) & 2.16415698290064 & 0.954132 & 2.2682 & 0.027669 & 0.013835 \tabularnewline
X & -1.27984653729293 & 0.360586 & -3.5493 & 0.000851 & 0.000426 \tabularnewline
Y1 & 2.05394600247998 & 0.136137 & 15.0874 & 0 & 0 \tabularnewline
Y2 & -1.72849331883367 & 0.302339 & -5.7171 & 1e-06 & 0 \tabularnewline
Y3 & 0.670044338186338 & 0.294646 & 2.2741 & 0.027288 & 0.013644 \tabularnewline
Y4 & -0.0341379716604865 & 0.126848 & -0.2691 & 0.78894 & 0.39447 \tabularnewline
t & 0.00238579303693468 & 0.012017 & 0.1985 & 0.843435 & 0.421718 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57825&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]2.16415698290064[/C][C]0.954132[/C][C]2.2682[/C][C]0.027669[/C][C]0.013835[/C][/ROW]
[ROW][C]X[/C][C]-1.27984653729293[/C][C]0.360586[/C][C]-3.5493[/C][C]0.000851[/C][C]0.000426[/C][/ROW]
[ROW][C]Y1[/C][C]2.05394600247998[/C][C]0.136137[/C][C]15.0874[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]-1.72849331883367[/C][C]0.302339[/C][C]-5.7171[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]Y3[/C][C]0.670044338186338[/C][C]0.294646[/C][C]2.2741[/C][C]0.027288[/C][C]0.013644[/C][/ROW]
[ROW][C]Y4[/C][C]-0.0341379716604865[/C][C]0.126848[/C][C]-0.2691[/C][C]0.78894[/C][C]0.39447[/C][/ROW]
[ROW][C]t[/C][C]0.00238579303693468[/C][C]0.012017[/C][C]0.1985[/C][C]0.843435[/C][C]0.421718[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57825&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57825&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)2.164156982900640.9541322.26820.0276690.013835
X-1.279846537292930.360586-3.54930.0008510.000426
Y12.053946002479980.13613715.087400
Y2-1.728493318833670.302339-5.71711e-060
Y30.6700443381863380.2946462.27410.0272880.013644
Y4-0.03413797166048650.126848-0.26910.788940.39447
t0.002385793036934680.0120170.19850.8434350.421718






Multiple Linear Regression - Regression Statistics
Multiple R0.99880042950019
R-squared0.997602297969763
Adjusted R-squared0.997314573726135
F-TEST (value)3467.216684243
F-TEST (DF numerator)6
F-TEST (DF denominator)50
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.52899962131653
Sum Squared Residuals13.9920299676516

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.99880042950019 \tabularnewline
R-squared & 0.997602297969763 \tabularnewline
Adjusted R-squared & 0.997314573726135 \tabularnewline
F-TEST (value) & 3467.216684243 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 50 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.52899962131653 \tabularnewline
Sum Squared Residuals & 13.9920299676516 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57825&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.99880042950019[/C][/ROW]
[ROW][C]R-squared[/C][C]0.997602297969763[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.997314573726135[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3467.216684243[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]50[/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]0.52899962131653[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]13.9920299676516[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57825&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57825&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 R0.99880042950019
R-squared0.997602297969763
Adjusted R-squared0.997314573726135
F-TEST (value)3467.216684243
F-TEST (DF numerator)6
F-TEST (DF denominator)50
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.52899962131653
Sum Squared Residuals13.9920299676516






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
150.951.278336976999-0.378336976999010
250.650.42988551229530.170114487704693
352.151.56124793429020.538752065709812
453.354.0287106584488-0.728710658448789
553.953.76352672374420.136473276255827
654.353.93939603444640.360603965553641
754.254.479110485508-0.279110485507947
854.253.94576538768270.254234612317348
953.554.3685354648812-0.868535464881188
1051.452.8524994336993-1.45249943369931
1150.549.75495774187790.745042258122101
1250.351.0695970655031-0.76959706550312
1349.850.8336411149654-1.03364111496538
1450.749.62340240664841.07659759335162
1552.852.23530156819130.564698431808687
1655.354.66713540472480.632864595275185
1757.356.7946591246090.505340875391039
1857.557.9600725612186-0.460072561218556
1956.856.5196820220630.280317977937017
2056.455.99335069681860.406649303181353
2156.356.4498363363635-0.149836336363460
2256.456.4623662256233-0.0623662256233217
235756.59887479567940.401125204320569
2457.957.60742961316650.292570386833451
2558.958.49168904811990.408310951880054
2658.859.3909896624323-0.590989662432319
2756.556.7621981204661-0.262198120466067
2851.952.8526776033743-0.952677603374322
2947.447.28130401284170.118695987158349
3044.944.4543138806910.445686119308982
3143.944.0963679814415-0.196367981441466
3243.443.5078762168823-0.107876216882307
3342.942.69029235451930.209707645480736
3442.641.94525239669790.654747603302083
3542.241.8948168509750.305183149024988
3641.241.2762190554071-0.076219055407127
3740.239.73211185787190.467888142128111
3839.339.15126862348610.148731376513872
3938.538.37720718360260.122792816397411
4038.337.656173795080.643826204919994
4137.938.0616631099806-0.161663109980644
4237.637.08285786973770.517142130262305
4337.337.05375869925520.246241300744770
443636.6973185462558-0.697318546255817
4534.534.36076441892720.139235581072815
4633.533.33850061277020.161499387229848
4732.933.0188641334335-0.118864133433515
4832.932.55668849969530.343311500304746
4932.832.9773329033368-0.177332903336784
5031.932.4064354648744-0.506435464874398
5130.530.753601970559-0.253601970559012
5229.229.3691029132556-0.169102913255641
5328.728.52162344223410.178376557765923
5428.428.8367396495484-0.436739649548355
552828.2639238219406-0.263923821940568
5627.427.6726364037011-0.272636403701079
5726.926.9501076071578-0.0501076071578286

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 50.9 & 51.278336976999 & -0.378336976999010 \tabularnewline
2 & 50.6 & 50.4298855122953 & 0.170114487704693 \tabularnewline
3 & 52.1 & 51.5612479342902 & 0.538752065709812 \tabularnewline
4 & 53.3 & 54.0287106584488 & -0.728710658448789 \tabularnewline
5 & 53.9 & 53.7635267237442 & 0.136473276255827 \tabularnewline
6 & 54.3 & 53.9393960344464 & 0.360603965553641 \tabularnewline
7 & 54.2 & 54.479110485508 & -0.279110485507947 \tabularnewline
8 & 54.2 & 53.9457653876827 & 0.254234612317348 \tabularnewline
9 & 53.5 & 54.3685354648812 & -0.868535464881188 \tabularnewline
10 & 51.4 & 52.8524994336993 & -1.45249943369931 \tabularnewline
11 & 50.5 & 49.7549577418779 & 0.745042258122101 \tabularnewline
12 & 50.3 & 51.0695970655031 & -0.76959706550312 \tabularnewline
13 & 49.8 & 50.8336411149654 & -1.03364111496538 \tabularnewline
14 & 50.7 & 49.6234024066484 & 1.07659759335162 \tabularnewline
15 & 52.8 & 52.2353015681913 & 0.564698431808687 \tabularnewline
16 & 55.3 & 54.6671354047248 & 0.632864595275185 \tabularnewline
17 & 57.3 & 56.794659124609 & 0.505340875391039 \tabularnewline
18 & 57.5 & 57.9600725612186 & -0.460072561218556 \tabularnewline
19 & 56.8 & 56.519682022063 & 0.280317977937017 \tabularnewline
20 & 56.4 & 55.9933506968186 & 0.406649303181353 \tabularnewline
21 & 56.3 & 56.4498363363635 & -0.149836336363460 \tabularnewline
22 & 56.4 & 56.4623662256233 & -0.0623662256233217 \tabularnewline
23 & 57 & 56.5988747956794 & 0.401125204320569 \tabularnewline
24 & 57.9 & 57.6074296131665 & 0.292570386833451 \tabularnewline
25 & 58.9 & 58.4916890481199 & 0.408310951880054 \tabularnewline
26 & 58.8 & 59.3909896624323 & -0.590989662432319 \tabularnewline
27 & 56.5 & 56.7621981204661 & -0.262198120466067 \tabularnewline
28 & 51.9 & 52.8526776033743 & -0.952677603374322 \tabularnewline
29 & 47.4 & 47.2813040128417 & 0.118695987158349 \tabularnewline
30 & 44.9 & 44.454313880691 & 0.445686119308982 \tabularnewline
31 & 43.9 & 44.0963679814415 & -0.196367981441466 \tabularnewline
32 & 43.4 & 43.5078762168823 & -0.107876216882307 \tabularnewline
33 & 42.9 & 42.6902923545193 & 0.209707645480736 \tabularnewline
34 & 42.6 & 41.9452523966979 & 0.654747603302083 \tabularnewline
35 & 42.2 & 41.894816850975 & 0.305183149024988 \tabularnewline
36 & 41.2 & 41.2762190554071 & -0.076219055407127 \tabularnewline
37 & 40.2 & 39.7321118578719 & 0.467888142128111 \tabularnewline
38 & 39.3 & 39.1512686234861 & 0.148731376513872 \tabularnewline
39 & 38.5 & 38.3772071836026 & 0.122792816397411 \tabularnewline
40 & 38.3 & 37.65617379508 & 0.643826204919994 \tabularnewline
41 & 37.9 & 38.0616631099806 & -0.161663109980644 \tabularnewline
42 & 37.6 & 37.0828578697377 & 0.517142130262305 \tabularnewline
43 & 37.3 & 37.0537586992552 & 0.246241300744770 \tabularnewline
44 & 36 & 36.6973185462558 & -0.697318546255817 \tabularnewline
45 & 34.5 & 34.3607644189272 & 0.139235581072815 \tabularnewline
46 & 33.5 & 33.3385006127702 & 0.161499387229848 \tabularnewline
47 & 32.9 & 33.0188641334335 & -0.118864133433515 \tabularnewline
48 & 32.9 & 32.5566884996953 & 0.343311500304746 \tabularnewline
49 & 32.8 & 32.9773329033368 & -0.177332903336784 \tabularnewline
50 & 31.9 & 32.4064354648744 & -0.506435464874398 \tabularnewline
51 & 30.5 & 30.753601970559 & -0.253601970559012 \tabularnewline
52 & 29.2 & 29.3691029132556 & -0.169102913255641 \tabularnewline
53 & 28.7 & 28.5216234422341 & 0.178376557765923 \tabularnewline
54 & 28.4 & 28.8367396495484 & -0.436739649548355 \tabularnewline
55 & 28 & 28.2639238219406 & -0.263923821940568 \tabularnewline
56 & 27.4 & 27.6726364037011 & -0.272636403701079 \tabularnewline
57 & 26.9 & 26.9501076071578 & -0.0501076071578286 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57825&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]50.9[/C][C]51.278336976999[/C][C]-0.378336976999010[/C][/ROW]
[ROW][C]2[/C][C]50.6[/C][C]50.4298855122953[/C][C]0.170114487704693[/C][/ROW]
[ROW][C]3[/C][C]52.1[/C][C]51.5612479342902[/C][C]0.538752065709812[/C][/ROW]
[ROW][C]4[/C][C]53.3[/C][C]54.0287106584488[/C][C]-0.728710658448789[/C][/ROW]
[ROW][C]5[/C][C]53.9[/C][C]53.7635267237442[/C][C]0.136473276255827[/C][/ROW]
[ROW][C]6[/C][C]54.3[/C][C]53.9393960344464[/C][C]0.360603965553641[/C][/ROW]
[ROW][C]7[/C][C]54.2[/C][C]54.479110485508[/C][C]-0.279110485507947[/C][/ROW]
[ROW][C]8[/C][C]54.2[/C][C]53.9457653876827[/C][C]0.254234612317348[/C][/ROW]
[ROW][C]9[/C][C]53.5[/C][C]54.3685354648812[/C][C]-0.868535464881188[/C][/ROW]
[ROW][C]10[/C][C]51.4[/C][C]52.8524994336993[/C][C]-1.45249943369931[/C][/ROW]
[ROW][C]11[/C][C]50.5[/C][C]49.7549577418779[/C][C]0.745042258122101[/C][/ROW]
[ROW][C]12[/C][C]50.3[/C][C]51.0695970655031[/C][C]-0.76959706550312[/C][/ROW]
[ROW][C]13[/C][C]49.8[/C][C]50.8336411149654[/C][C]-1.03364111496538[/C][/ROW]
[ROW][C]14[/C][C]50.7[/C][C]49.6234024066484[/C][C]1.07659759335162[/C][/ROW]
[ROW][C]15[/C][C]52.8[/C][C]52.2353015681913[/C][C]0.564698431808687[/C][/ROW]
[ROW][C]16[/C][C]55.3[/C][C]54.6671354047248[/C][C]0.632864595275185[/C][/ROW]
[ROW][C]17[/C][C]57.3[/C][C]56.794659124609[/C][C]0.505340875391039[/C][/ROW]
[ROW][C]18[/C][C]57.5[/C][C]57.9600725612186[/C][C]-0.460072561218556[/C][/ROW]
[ROW][C]19[/C][C]56.8[/C][C]56.519682022063[/C][C]0.280317977937017[/C][/ROW]
[ROW][C]20[/C][C]56.4[/C][C]55.9933506968186[/C][C]0.406649303181353[/C][/ROW]
[ROW][C]21[/C][C]56.3[/C][C]56.4498363363635[/C][C]-0.149836336363460[/C][/ROW]
[ROW][C]22[/C][C]56.4[/C][C]56.4623662256233[/C][C]-0.0623662256233217[/C][/ROW]
[ROW][C]23[/C][C]57[/C][C]56.5988747956794[/C][C]0.401125204320569[/C][/ROW]
[ROW][C]24[/C][C]57.9[/C][C]57.6074296131665[/C][C]0.292570386833451[/C][/ROW]
[ROW][C]25[/C][C]58.9[/C][C]58.4916890481199[/C][C]0.408310951880054[/C][/ROW]
[ROW][C]26[/C][C]58.8[/C][C]59.3909896624323[/C][C]-0.590989662432319[/C][/ROW]
[ROW][C]27[/C][C]56.5[/C][C]56.7621981204661[/C][C]-0.262198120466067[/C][/ROW]
[ROW][C]28[/C][C]51.9[/C][C]52.8526776033743[/C][C]-0.952677603374322[/C][/ROW]
[ROW][C]29[/C][C]47.4[/C][C]47.2813040128417[/C][C]0.118695987158349[/C][/ROW]
[ROW][C]30[/C][C]44.9[/C][C]44.454313880691[/C][C]0.445686119308982[/C][/ROW]
[ROW][C]31[/C][C]43.9[/C][C]44.0963679814415[/C][C]-0.196367981441466[/C][/ROW]
[ROW][C]32[/C][C]43.4[/C][C]43.5078762168823[/C][C]-0.107876216882307[/C][/ROW]
[ROW][C]33[/C][C]42.9[/C][C]42.6902923545193[/C][C]0.209707645480736[/C][/ROW]
[ROW][C]34[/C][C]42.6[/C][C]41.9452523966979[/C][C]0.654747603302083[/C][/ROW]
[ROW][C]35[/C][C]42.2[/C][C]41.894816850975[/C][C]0.305183149024988[/C][/ROW]
[ROW][C]36[/C][C]41.2[/C][C]41.2762190554071[/C][C]-0.076219055407127[/C][/ROW]
[ROW][C]37[/C][C]40.2[/C][C]39.7321118578719[/C][C]0.467888142128111[/C][/ROW]
[ROW][C]38[/C][C]39.3[/C][C]39.1512686234861[/C][C]0.148731376513872[/C][/ROW]
[ROW][C]39[/C][C]38.5[/C][C]38.3772071836026[/C][C]0.122792816397411[/C][/ROW]
[ROW][C]40[/C][C]38.3[/C][C]37.65617379508[/C][C]0.643826204919994[/C][/ROW]
[ROW][C]41[/C][C]37.9[/C][C]38.0616631099806[/C][C]-0.161663109980644[/C][/ROW]
[ROW][C]42[/C][C]37.6[/C][C]37.0828578697377[/C][C]0.517142130262305[/C][/ROW]
[ROW][C]43[/C][C]37.3[/C][C]37.0537586992552[/C][C]0.246241300744770[/C][/ROW]
[ROW][C]44[/C][C]36[/C][C]36.6973185462558[/C][C]-0.697318546255817[/C][/ROW]
[ROW][C]45[/C][C]34.5[/C][C]34.3607644189272[/C][C]0.139235581072815[/C][/ROW]
[ROW][C]46[/C][C]33.5[/C][C]33.3385006127702[/C][C]0.161499387229848[/C][/ROW]
[ROW][C]47[/C][C]32.9[/C][C]33.0188641334335[/C][C]-0.118864133433515[/C][/ROW]
[ROW][C]48[/C][C]32.9[/C][C]32.5566884996953[/C][C]0.343311500304746[/C][/ROW]
[ROW][C]49[/C][C]32.8[/C][C]32.9773329033368[/C][C]-0.177332903336784[/C][/ROW]
[ROW][C]50[/C][C]31.9[/C][C]32.4064354648744[/C][C]-0.506435464874398[/C][/ROW]
[ROW][C]51[/C][C]30.5[/C][C]30.753601970559[/C][C]-0.253601970559012[/C][/ROW]
[ROW][C]52[/C][C]29.2[/C][C]29.3691029132556[/C][C]-0.169102913255641[/C][/ROW]
[ROW][C]53[/C][C]28.7[/C][C]28.5216234422341[/C][C]0.178376557765923[/C][/ROW]
[ROW][C]54[/C][C]28.4[/C][C]28.8367396495484[/C][C]-0.436739649548355[/C][/ROW]
[ROW][C]55[/C][C]28[/C][C]28.2639238219406[/C][C]-0.263923821940568[/C][/ROW]
[ROW][C]56[/C][C]27.4[/C][C]27.6726364037011[/C][C]-0.272636403701079[/C][/ROW]
[ROW][C]57[/C][C]26.9[/C][C]26.9501076071578[/C][C]-0.0501076071578286[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57825&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57825&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
150.951.278336976999-0.378336976999010
250.650.42988551229530.170114487704693
352.151.56124793429020.538752065709812
453.354.0287106584488-0.728710658448789
553.953.76352672374420.136473276255827
654.353.93939603444640.360603965553641
754.254.479110485508-0.279110485507947
854.253.94576538768270.254234612317348
953.554.3685354648812-0.868535464881188
1051.452.8524994336993-1.45249943369931
1150.549.75495774187790.745042258122101
1250.351.0695970655031-0.76959706550312
1349.850.8336411149654-1.03364111496538
1450.749.62340240664841.07659759335162
1552.852.23530156819130.564698431808687
1655.354.66713540472480.632864595275185
1757.356.7946591246090.505340875391039
1857.557.9600725612186-0.460072561218556
1956.856.5196820220630.280317977937017
2056.455.99335069681860.406649303181353
2156.356.4498363363635-0.149836336363460
2256.456.4623662256233-0.0623662256233217
235756.59887479567940.401125204320569
2457.957.60742961316650.292570386833451
2558.958.49168904811990.408310951880054
2658.859.3909896624323-0.590989662432319
2756.556.7621981204661-0.262198120466067
2851.952.8526776033743-0.952677603374322
2947.447.28130401284170.118695987158349
3044.944.4543138806910.445686119308982
3143.944.0963679814415-0.196367981441466
3243.443.5078762168823-0.107876216882307
3342.942.69029235451930.209707645480736
3442.641.94525239669790.654747603302083
3542.241.8948168509750.305183149024988
3641.241.2762190554071-0.076219055407127
3740.239.73211185787190.467888142128111
3839.339.15126862348610.148731376513872
3938.538.37720718360260.122792816397411
4038.337.656173795080.643826204919994
4137.938.0616631099806-0.161663109980644
4237.637.08285786973770.517142130262305
4337.337.05375869925520.246241300744770
443636.6973185462558-0.697318546255817
4534.534.36076441892720.139235581072815
4633.533.33850061277020.161499387229848
4732.933.0188641334335-0.118864133433515
4832.932.55668849969530.343311500304746
4932.832.9773329033368-0.177332903336784
5031.932.4064354648744-0.506435464874398
5130.530.753601970559-0.253601970559012
5229.229.3691029132556-0.169102913255641
5328.728.52162344223410.178376557765923
5428.428.8367396495484-0.436739649548355
552828.2639238219406-0.263923821940568
5627.427.6726364037011-0.272636403701079
5726.926.9501076071578-0.0501076071578286






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.9015246988113630.1969506023772740.0984753011886369
110.8461125511544150.3077748976911710.153887448845586
120.9292025316308040.1415949367383920.0707974683691961
130.988808776928130.02238244614374100.0111912230718705
140.9855060834425670.02898783311486630.0144939165574331
150.9801191502035830.03976169959283380.0198808497964169
160.9979337218847260.004132556230547550.00206627811527377
170.9988074877612170.002385024477566270.00119251223878314
180.9993913872102640.001217225579472490.000608612789736243
190.9995992165583430.000801566883314780.00040078344165739
200.9997622525235540.0004754949528914210.000237747476445710
210.999889974880390.0002200502392223440.000110025119611172
220.9999254855173580.0001490289652829457.45144826414727e-05
230.9998842119159450.000231576168110340.00011578808405517
240.9997649348068810.000470130386237540.00023506519311877
250.9998313668259120.0003372663481762780.000168633174088139
260.9997158808515080.0005682382969841480.000284119148492074
270.999981067879763.78642404808122e-051.89321202404061e-05
280.999969802237436.03955251384981e-053.01977625692491e-05
290.9999319623097830.0001360753804336376.80376902168184e-05
300.9999613845640237.72308719550925e-053.86154359775462e-05
310.9999715291283225.69417433561704e-052.84708716780852e-05
320.999960354556227.92908875581408e-053.96454437790704e-05
330.999902225586330.0001955488273391569.77744136695782e-05
340.9997406953664810.000518609267037440.00025930463351872
350.9994072823373770.001185435325245250.000592717662622624
360.9994071941634580.001185611673083590.000592805836541794
370.9985958561406620.002808287718676690.00140414385933835
380.9978680952576540.004263809484691670.00213190474234584
390.9981017870638140.003796425872371550.00189821293618577
400.9954399258975250.009120148204950520.00456007410247526
410.9989284399913660.002143120017267080.00107156000863354
420.9968413202649630.00631735947007320.0031586797350366
430.9958462583800530.008307483239893340.00415374161994667
440.9909416469057870.01811670618842560.0090583530942128
450.9817327953889510.03653440922209740.0182672046110487
460.9487320007432580.1025359985134850.0512679992567423
470.8728588603822250.254282279235550.127141139617775

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.901524698811363 & 0.196950602377274 & 0.0984753011886369 \tabularnewline
11 & 0.846112551154415 & 0.307774897691171 & 0.153887448845586 \tabularnewline
12 & 0.929202531630804 & 0.141594936738392 & 0.0707974683691961 \tabularnewline
13 & 0.98880877692813 & 0.0223824461437410 & 0.0111912230718705 \tabularnewline
14 & 0.985506083442567 & 0.0289878331148663 & 0.0144939165574331 \tabularnewline
15 & 0.980119150203583 & 0.0397616995928338 & 0.0198808497964169 \tabularnewline
16 & 0.997933721884726 & 0.00413255623054755 & 0.00206627811527377 \tabularnewline
17 & 0.998807487761217 & 0.00238502447756627 & 0.00119251223878314 \tabularnewline
18 & 0.999391387210264 & 0.00121722557947249 & 0.000608612789736243 \tabularnewline
19 & 0.999599216558343 & 0.00080156688331478 & 0.00040078344165739 \tabularnewline
20 & 0.999762252523554 & 0.000475494952891421 & 0.000237747476445710 \tabularnewline
21 & 0.99988997488039 & 0.000220050239222344 & 0.000110025119611172 \tabularnewline
22 & 0.999925485517358 & 0.000149028965282945 & 7.45144826414727e-05 \tabularnewline
23 & 0.999884211915945 & 0.00023157616811034 & 0.00011578808405517 \tabularnewline
24 & 0.999764934806881 & 0.00047013038623754 & 0.00023506519311877 \tabularnewline
25 & 0.999831366825912 & 0.000337266348176278 & 0.000168633174088139 \tabularnewline
26 & 0.999715880851508 & 0.000568238296984148 & 0.000284119148492074 \tabularnewline
27 & 0.99998106787976 & 3.78642404808122e-05 & 1.89321202404061e-05 \tabularnewline
28 & 0.99996980223743 & 6.03955251384981e-05 & 3.01977625692491e-05 \tabularnewline
29 & 0.999931962309783 & 0.000136075380433637 & 6.80376902168184e-05 \tabularnewline
30 & 0.999961384564023 & 7.72308719550925e-05 & 3.86154359775462e-05 \tabularnewline
31 & 0.999971529128322 & 5.69417433561704e-05 & 2.84708716780852e-05 \tabularnewline
32 & 0.99996035455622 & 7.92908875581408e-05 & 3.96454437790704e-05 \tabularnewline
33 & 0.99990222558633 & 0.000195548827339156 & 9.77744136695782e-05 \tabularnewline
34 & 0.999740695366481 & 0.00051860926703744 & 0.00025930463351872 \tabularnewline
35 & 0.999407282337377 & 0.00118543532524525 & 0.000592717662622624 \tabularnewline
36 & 0.999407194163458 & 0.00118561167308359 & 0.000592805836541794 \tabularnewline
37 & 0.998595856140662 & 0.00280828771867669 & 0.00140414385933835 \tabularnewline
38 & 0.997868095257654 & 0.00426380948469167 & 0.00213190474234584 \tabularnewline
39 & 0.998101787063814 & 0.00379642587237155 & 0.00189821293618577 \tabularnewline
40 & 0.995439925897525 & 0.00912014820495052 & 0.00456007410247526 \tabularnewline
41 & 0.998928439991366 & 0.00214312001726708 & 0.00107156000863354 \tabularnewline
42 & 0.996841320264963 & 0.0063173594700732 & 0.0031586797350366 \tabularnewline
43 & 0.995846258380053 & 0.00830748323989334 & 0.00415374161994667 \tabularnewline
44 & 0.990941646905787 & 0.0181167061884256 & 0.0090583530942128 \tabularnewline
45 & 0.981732795388951 & 0.0365344092220974 & 0.0182672046110487 \tabularnewline
46 & 0.948732000743258 & 0.102535998513485 & 0.0512679992567423 \tabularnewline
47 & 0.872858860382225 & 0.25428227923555 & 0.127141139617775 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57825&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.901524698811363[/C][C]0.196950602377274[/C][C]0.0984753011886369[/C][/ROW]
[ROW][C]11[/C][C]0.846112551154415[/C][C]0.307774897691171[/C][C]0.153887448845586[/C][/ROW]
[ROW][C]12[/C][C]0.929202531630804[/C][C]0.141594936738392[/C][C]0.0707974683691961[/C][/ROW]
[ROW][C]13[/C][C]0.98880877692813[/C][C]0.0223824461437410[/C][C]0.0111912230718705[/C][/ROW]
[ROW][C]14[/C][C]0.985506083442567[/C][C]0.0289878331148663[/C][C]0.0144939165574331[/C][/ROW]
[ROW][C]15[/C][C]0.980119150203583[/C][C]0.0397616995928338[/C][C]0.0198808497964169[/C][/ROW]
[ROW][C]16[/C][C]0.997933721884726[/C][C]0.00413255623054755[/C][C]0.00206627811527377[/C][/ROW]
[ROW][C]17[/C][C]0.998807487761217[/C][C]0.00238502447756627[/C][C]0.00119251223878314[/C][/ROW]
[ROW][C]18[/C][C]0.999391387210264[/C][C]0.00121722557947249[/C][C]0.000608612789736243[/C][/ROW]
[ROW][C]19[/C][C]0.999599216558343[/C][C]0.00080156688331478[/C][C]0.00040078344165739[/C][/ROW]
[ROW][C]20[/C][C]0.999762252523554[/C][C]0.000475494952891421[/C][C]0.000237747476445710[/C][/ROW]
[ROW][C]21[/C][C]0.99988997488039[/C][C]0.000220050239222344[/C][C]0.000110025119611172[/C][/ROW]
[ROW][C]22[/C][C]0.999925485517358[/C][C]0.000149028965282945[/C][C]7.45144826414727e-05[/C][/ROW]
[ROW][C]23[/C][C]0.999884211915945[/C][C]0.00023157616811034[/C][C]0.00011578808405517[/C][/ROW]
[ROW][C]24[/C][C]0.999764934806881[/C][C]0.00047013038623754[/C][C]0.00023506519311877[/C][/ROW]
[ROW][C]25[/C][C]0.999831366825912[/C][C]0.000337266348176278[/C][C]0.000168633174088139[/C][/ROW]
[ROW][C]26[/C][C]0.999715880851508[/C][C]0.000568238296984148[/C][C]0.000284119148492074[/C][/ROW]
[ROW][C]27[/C][C]0.99998106787976[/C][C]3.78642404808122e-05[/C][C]1.89321202404061e-05[/C][/ROW]
[ROW][C]28[/C][C]0.99996980223743[/C][C]6.03955251384981e-05[/C][C]3.01977625692491e-05[/C][/ROW]
[ROW][C]29[/C][C]0.999931962309783[/C][C]0.000136075380433637[/C][C]6.80376902168184e-05[/C][/ROW]
[ROW][C]30[/C][C]0.999961384564023[/C][C]7.72308719550925e-05[/C][C]3.86154359775462e-05[/C][/ROW]
[ROW][C]31[/C][C]0.999971529128322[/C][C]5.69417433561704e-05[/C][C]2.84708716780852e-05[/C][/ROW]
[ROW][C]32[/C][C]0.99996035455622[/C][C]7.92908875581408e-05[/C][C]3.96454437790704e-05[/C][/ROW]
[ROW][C]33[/C][C]0.99990222558633[/C][C]0.000195548827339156[/C][C]9.77744136695782e-05[/C][/ROW]
[ROW][C]34[/C][C]0.999740695366481[/C][C]0.00051860926703744[/C][C]0.00025930463351872[/C][/ROW]
[ROW][C]35[/C][C]0.999407282337377[/C][C]0.00118543532524525[/C][C]0.000592717662622624[/C][/ROW]
[ROW][C]36[/C][C]0.999407194163458[/C][C]0.00118561167308359[/C][C]0.000592805836541794[/C][/ROW]
[ROW][C]37[/C][C]0.998595856140662[/C][C]0.00280828771867669[/C][C]0.00140414385933835[/C][/ROW]
[ROW][C]38[/C][C]0.997868095257654[/C][C]0.00426380948469167[/C][C]0.00213190474234584[/C][/ROW]
[ROW][C]39[/C][C]0.998101787063814[/C][C]0.00379642587237155[/C][C]0.00189821293618577[/C][/ROW]
[ROW][C]40[/C][C]0.995439925897525[/C][C]0.00912014820495052[/C][C]0.00456007410247526[/C][/ROW]
[ROW][C]41[/C][C]0.998928439991366[/C][C]0.00214312001726708[/C][C]0.00107156000863354[/C][/ROW]
[ROW][C]42[/C][C]0.996841320264963[/C][C]0.0063173594700732[/C][C]0.0031586797350366[/C][/ROW]
[ROW][C]43[/C][C]0.995846258380053[/C][C]0.00830748323989334[/C][C]0.00415374161994667[/C][/ROW]
[ROW][C]44[/C][C]0.990941646905787[/C][C]0.0181167061884256[/C][C]0.0090583530942128[/C][/ROW]
[ROW][C]45[/C][C]0.981732795388951[/C][C]0.0365344092220974[/C][C]0.0182672046110487[/C][/ROW]
[ROW][C]46[/C][C]0.948732000743258[/C][C]0.102535998513485[/C][C]0.0512679992567423[/C][/ROW]
[ROW][C]47[/C][C]0.872858860382225[/C][C]0.25428227923555[/C][C]0.127141139617775[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57825&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57825&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.9015246988113630.1969506023772740.0984753011886369
110.8461125511544150.3077748976911710.153887448845586
120.9292025316308040.1415949367383920.0707974683691961
130.988808776928130.02238244614374100.0111912230718705
140.9855060834425670.02898783311486630.0144939165574331
150.9801191502035830.03976169959283380.0198808497964169
160.9979337218847260.004132556230547550.00206627811527377
170.9988074877612170.002385024477566270.00119251223878314
180.9993913872102640.001217225579472490.000608612789736243
190.9995992165583430.000801566883314780.00040078344165739
200.9997622525235540.0004754949528914210.000237747476445710
210.999889974880390.0002200502392223440.000110025119611172
220.9999254855173580.0001490289652829457.45144826414727e-05
230.9998842119159450.000231576168110340.00011578808405517
240.9997649348068810.000470130386237540.00023506519311877
250.9998313668259120.0003372663481762780.000168633174088139
260.9997158808515080.0005682382969841480.000284119148492074
270.999981067879763.78642404808122e-051.89321202404061e-05
280.999969802237436.03955251384981e-053.01977625692491e-05
290.9999319623097830.0001360753804336376.80376902168184e-05
300.9999613845640237.72308719550925e-053.86154359775462e-05
310.9999715291283225.69417433561704e-052.84708716780852e-05
320.999960354556227.92908875581408e-053.96454437790704e-05
330.999902225586330.0001955488273391569.77744136695782e-05
340.9997406953664810.000518609267037440.00025930463351872
350.9994072823373770.001185435325245250.000592717662622624
360.9994071941634580.001185611673083590.000592805836541794
370.9985958561406620.002808287718676690.00140414385933835
380.9978680952576540.004263809484691670.00213190474234584
390.9981017870638140.003796425872371550.00189821293618577
400.9954399258975250.009120148204950520.00456007410247526
410.9989284399913660.002143120017267080.00107156000863354
420.9968413202649630.00631735947007320.0031586797350366
430.9958462583800530.008307483239893340.00415374161994667
440.9909416469057870.01811670618842560.0090583530942128
450.9817327953889510.03653440922209740.0182672046110487
460.9487320007432580.1025359985134850.0512679992567423
470.8728588603822250.254282279235550.127141139617775






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level280.736842105263158NOK
5% type I error level330.868421052631579NOK
10% type I error level330.868421052631579NOK

\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 & 28 & 0.736842105263158 & NOK \tabularnewline
5% type I error level & 33 & 0.868421052631579 & NOK \tabularnewline
10% type I error level & 33 & 0.868421052631579 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57825&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]28[/C][C]0.736842105263158[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]33[/C][C]0.868421052631579[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]33[/C][C]0.868421052631579[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57825&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57825&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 level280.736842105263158NOK
5% type I error level330.868421052631579NOK
10% type I error level330.868421052631579NOK


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 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}