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rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 18 Nov 2009 07:31:41 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/18/t1258554808ou9noef5dsgbnm7.htm/, Retrieved Sun, 05 May 2024 15:18:34 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57455, Retrieved Sun, 05 May 2024 15:18:34 +0000

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Estimated Impact

217

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 14:06:21] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-18 14:31:41] [791a4a78a0a7ca497fb8791b982a539e] [Current]

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Dataseries X:

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Histogram

Boxplots

785.8	35
819.3	31.3
849.4	30
880.4	31.3
900.1	33
937.2	31.3
948.9	29
952.6	28.7
947.3	28
974.2	29.7
1000.8	30.7
1032.8	24
1050.7	29
1057.3	33
1075.4	28
1118.4	28.7
1179.8	31.7
1227	34
1257.8	35.3
1251.5	27
1236.3	31.3
1170.6	38.7
1213.1	37.3
1265.5	37.3
1300.8	37.7
1348.4	34.7
1371.9	34.7
1403.3	33.7
1451.8	38.3
1474.2	38
1438.2	38.3
1513.6	42.7
1562.2	41.7
1546.2	39.7
1527.5	39.3
1418.7	39.3
1448.5	37.7
1492.1	38.3
1395.4	37.7
1403.7	37
1316.6	34.3
1274.5	29.7
1264.4	34.7
1323.9	32
1332.1	30.3
1250.2	28.3
1096.7	31.3
1080.8	17.7
1039.2	15.7
792	14.3
746.6	13.3
688.8	11
715.8	2.7
672.9	3.3
629.5	3.7
681.2	1.4
755.4	7.1
760.6	8.1
765.9	12.4
836.8	12.4
904.9	9.2

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 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=57455&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=57455&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57455&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Herdiv[t] = + 105.726577629394 + 27.5215172223384handact[t] -30.9077305549567M1[t] -56.3118969397406M2[t] -35.2907042334095M3[t] -21.4849018494376M4[t] -6.61039049880582M5[t] + 9.71272774076114M6[t] -33.9403029532002M7[t] + 45.1164842309389M8[t] + 22.5052769924888M9[t] -45.7537785237274M10[t] -109.474555417731M11[t] + 8.38280450496339t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Herdiv[t] =  +  105.726577629394 +  27.5215172223384handact[t] -30.9077305549567M1[t] -56.3118969397406M2[t] -35.2907042334095M3[t] -21.4849018494376M4[t] -6.61039049880582M5[t] +  9.71272774076114M6[t] -33.9403029532002M7[t] +  45.1164842309389M8[t] +  22.5052769924888M9[t] -45.7537785237274M10[t] -109.474555417731M11[t] +  8.38280450496339t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57455&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Herdiv[t] =  +  105.726577629394 +  27.5215172223384handact[t] -30.9077305549567M1[t] -56.3118969397406M2[t] -35.2907042334095M3[t] -21.4849018494376M4[t] -6.61039049880582M5[t] +  9.71272774076114M6[t] -33.9403029532002M7[t] +  45.1164842309389M8[t] +  22.5052769924888M9[t] -45.7537785237274M10[t] -109.474555417731M11[t] +  8.38280450496339t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57455&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57455&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
Herdiv[t] = + 105.726577629394 + 27.5215172223384handact[t] -30.9077305549567M1[t] -56.3118969397406M2[t] -35.2907042334095M3[t] -21.4849018494376M4[t] -6.61039049880582M5[t] + 9.71272774076114M6[t] -33.9403029532002M7[t] + 45.1164842309389M8[t] + 22.5052769924888M9[t] -45.7537785237274M10[t] -109.474555417731M11[t] + 8.38280450496339t + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	105.726577629394	81.328204	1.3	0.199941	0.09997
handact	27.5215172223384	1.511728	18.2053	0	0
M1	-30.9077305549567	64.298413	-0.4807	0.632966	0.316483
M2	-56.3118969397406	67.484319	-0.8344	0.408252	0.204126
M3	-35.2907042334095	67.404271	-0.5236	0.603039	0.30152
M4	-21.4849018494376	67.3278	-0.3191	0.751058	0.375529
M5	-6.61039049880582	67.258776	-0.0983	0.922126	0.461063
M6	9.71272774076114	67.211274	0.1445	0.885715	0.442858
M7	-33.9403029532002	67.140684	-0.5055	0.615563	0.307782
M8	45.1164842309389	67.126486	0.6721	0.504803	0.252402
M9	22.5052769924888	67.070132	0.3355	0.738705	0.369352
M10	-45.7537785237274	67.113441	-0.6817	0.498751	0.249375
M11	-109.474555417731	67.261988	-1.6276	0.110299	0.05515
t	8.38280450496339	0.963828	8.6974	0	0

\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) & 105.726577629394 & 81.328204 & 1.3 & 0.199941 & 0.09997 \tabularnewline
handact & 27.5215172223384 & 1.511728 & 18.2053 & 0 & 0 \tabularnewline
M1 & -30.9077305549567 & 64.298413 & -0.4807 & 0.632966 & 0.316483 \tabularnewline
M2 & -56.3118969397406 & 67.484319 & -0.8344 & 0.408252 & 0.204126 \tabularnewline
M3 & -35.2907042334095 & 67.404271 & -0.5236 & 0.603039 & 0.30152 \tabularnewline
M4 & -21.4849018494376 & 67.3278 & -0.3191 & 0.751058 & 0.375529 \tabularnewline
M5 & -6.61039049880582 & 67.258776 & -0.0983 & 0.922126 & 0.461063 \tabularnewline
M6 & 9.71272774076114 & 67.211274 & 0.1445 & 0.885715 & 0.442858 \tabularnewline
M7 & -33.9403029532002 & 67.140684 & -0.5055 & 0.615563 & 0.307782 \tabularnewline
M8 & 45.1164842309389 & 67.126486 & 0.6721 & 0.504803 & 0.252402 \tabularnewline
M9 & 22.5052769924888 & 67.070132 & 0.3355 & 0.738705 & 0.369352 \tabularnewline
M10 & -45.7537785237274 & 67.113441 & -0.6817 & 0.498751 & 0.249375 \tabularnewline
M11 & -109.474555417731 & 67.261988 & -1.6276 & 0.110299 & 0.05515 \tabularnewline
t & 8.38280450496339 & 0.963828 & 8.6974 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57455&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]105.726577629394[/C][C]81.328204[/C][C]1.3[/C][C]0.199941[/C][C]0.09997[/C][/ROW]
[ROW][C]handact[/C][C]27.5215172223384[/C][C]1.511728[/C][C]18.2053[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-30.9077305549567[/C][C]64.298413[/C][C]-0.4807[/C][C]0.632966[/C][C]0.316483[/C][/ROW]
[ROW][C]M2[/C][C]-56.3118969397406[/C][C]67.484319[/C][C]-0.8344[/C][C]0.408252[/C][C]0.204126[/C][/ROW]
[ROW][C]M3[/C][C]-35.2907042334095[/C][C]67.404271[/C][C]-0.5236[/C][C]0.603039[/C][C]0.30152[/C][/ROW]
[ROW][C]M4[/C][C]-21.4849018494376[/C][C]67.3278[/C][C]-0.3191[/C][C]0.751058[/C][C]0.375529[/C][/ROW]
[ROW][C]M5[/C][C]-6.61039049880582[/C][C]67.258776[/C][C]-0.0983[/C][C]0.922126[/C][C]0.461063[/C][/ROW]
[ROW][C]M6[/C][C]9.71272774076114[/C][C]67.211274[/C][C]0.1445[/C][C]0.885715[/C][C]0.442858[/C][/ROW]
[ROW][C]M7[/C][C]-33.9403029532002[/C][C]67.140684[/C][C]-0.5055[/C][C]0.615563[/C][C]0.307782[/C][/ROW]
[ROW][C]M8[/C][C]45.1164842309389[/C][C]67.126486[/C][C]0.6721[/C][C]0.504803[/C][C]0.252402[/C][/ROW]
[ROW][C]M9[/C][C]22.5052769924888[/C][C]67.070132[/C][C]0.3355[/C][C]0.738705[/C][C]0.369352[/C][/ROW]
[ROW][C]M10[/C][C]-45.7537785237274[/C][C]67.113441[/C][C]-0.6817[/C][C]0.498751[/C][C]0.249375[/C][/ROW]
[ROW][C]M11[/C][C]-109.474555417731[/C][C]67.261988[/C][C]-1.6276[/C][C]0.110299[/C][C]0.05515[/C][/ROW]
[ROW][C]t[/C][C]8.38280450496339[/C][C]0.963828[/C][C]8.6974[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57455&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57455&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	105.726577629394	81.328204	1.3	0.199941	0.09997
handact	27.5215172223384	1.511728	18.2053	0	0
M1	-30.9077305549567	64.298413	-0.4807	0.632966	0.316483
M2	-56.3118969397406	67.484319	-0.8344	0.408252	0.204126
M3	-35.2907042334095	67.404271	-0.5236	0.603039	0.30152
M4	-21.4849018494376	67.3278	-0.3191	0.751058	0.375529
M5	-6.61039049880582	67.258776	-0.0983	0.922126	0.461063
M6	9.71272774076114	67.211274	0.1445	0.885715	0.442858
M7	-33.9403029532002	67.140684	-0.5055	0.615563	0.307782
M8	45.1164842309389	67.126486	0.6721	0.504803	0.252402
M9	22.5052769924888	67.070132	0.3355	0.738705	0.369352
M10	-45.7537785237274	67.113441	-0.6817	0.498751	0.249375
M11	-109.474555417731	67.261988	-1.6276	0.110299	0.05515
t	8.38280450496339	0.963828	8.6974	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.937311753740401
R-squared	0.878553323699907
Adjusted R-squared	0.844961689829668
F-TEST (value)	26.1539324670445
F-TEST (DF numerator)	13
F-TEST (DF denominator)	47
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	105.977470162103
Sum Squared Residuals	527867.536552091

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.937311753740401 \tabularnewline
R-squared & 0.878553323699907 \tabularnewline
Adjusted R-squared & 0.844961689829668 \tabularnewline
F-TEST (value) & 26.1539324670445 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 105.977470162103 \tabularnewline
Sum Squared Residuals & 527867.536552091 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57455&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.937311753740401[/C][/ROW]
[ROW][C]R-squared[/C][C]0.878553323699907[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.844961689829668[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]26.1539324670445[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/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]105.977470162103[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]527867.536552091[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57455&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57455&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.937311753740401
R-squared	0.878553323699907
Adjusted R-squared	0.844961689829668
F-TEST (value)	26.1539324670445
F-TEST (DF numerator)	13
F-TEST (DF denominator)	47
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	105.977470162103
Sum Squared Residuals	527867.536552091

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	785.8	1046.45475436124	-260.654754361239
2	819.3	927.60377875877	-108.30377875877
3	849.4	921.229803581025	-71.8298035810251
4	880.4	979.196382859	-98.7963828590005
5	900.1	1049.24027799257	-149.140277992571
6	937.2	1027.15962145913	-89.9596214591257
7	948.9	928.58990565875	20.3100943412503
8	952.6	1007.77304218115	-55.1730421811506
9	947.3	974.279577392027	-26.9795773920271
10	974.2	961.18990565875	13.0100943412507
11	1000.8	933.373450492048	67.426549507952
12	1032.8	866.836645025075	165.963354974925
13	1050.7	981.919305086773	68.7806949132266
14	1057.3	1074.98401209631	-17.6840120963064
15	1075.4	966.78042319591	108.619576804091
16	1118.4	1008.23409214048	110.165907859519
17	1179.8	1114.05595966309	65.7440403369086
18	1227	1202.061372019	24.9386279810000
19	1257.8	1202.56911821904	55.2308817809581
20	1251.5	1061.58011696274	189.919883037264
21	1236.3	1165.69423828530	70.6057617146957
22	1170.6	1309.47721471936	-138.877214719356
23	1213.1	1215.60911821904	-2.50911821904183
24	1265.5	1333.46647814174	-67.9664781417357
25	1300.8	1321.95015898068	-21.1501589806779
26	1348.4	1222.36424543384	126.035754566158
27	1371.9	1251.76824264514	120.131757354863
28	1403.3	1246.43533231173	156.864667688266
29	1451.8	1396.29162739009	55.5083726099149
30	1474.2	1412.74109496791	61.4589050320859
31	1438.2	1385.72732394562	52.4726760543826
32	1513.6	1594.26159141301	-80.661591413009
33	1562.2	1552.51167145718	9.6883285428162
34	1546.2	1437.59238600125	108.607613998746
35	1527.5	1371.24580672328	156.254193276721
36	1418.7	1489.10316664597	-70.4031666459729
37	1448.5	1422.54381304024	25.9561869597616
38	1492.1	1422.03536149382	70.0646385061792
39	1395.4	1434.92644837171	-39.5264483717124
40	1403.7	1437.84999320501	-34.1499932050108
41	1316.6	1386.79921256029	-70.1992125602924
42	1274.5	1284.90615608207	-10.4061560820662
43	1264.4	1387.24351600476	-122.84351600476
44	1323.9	1400.37501119355	-76.4750111935489
45	1332.1	1339.36002918209	-7.2600291820873
46	1250.2	1224.44074372616	25.7592562738425
47	1096.7	1251.66732300413	-154.967323004133
48	1080.8	995.232048703025	85.5679512969751
49	1039.2	917.664088208355	121.535911791645
50	792	862.11260221726	-70.1126022172607
51	746.6	863.995082206217	-117.395082206217
52	688.8	822.884199483774	-134.084199483774
53	715.8	617.71292239396	98.0870776060394
54	672.9	658.931755471894	13.968244528106
55	629.5	634.670136171831	-5.17013617183116
56	681.2	658.810238249555	22.3897617504446
57	755.4	801.454483683398	-46.0544836833976
58	760.6	769.099749894483	-8.49974989448316
59	765.9	832.104301561498	-66.2043015614982
60	836.8	949.961661484192	-113.161661484192
61	904.9	839.367880322716	65.5321196772839

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 785.8 & 1046.45475436124 & -260.654754361239 \tabularnewline
2 & 819.3 & 927.60377875877 & -108.30377875877 \tabularnewline
3 & 849.4 & 921.229803581025 & -71.8298035810251 \tabularnewline
4 & 880.4 & 979.196382859 & -98.7963828590005 \tabularnewline
5 & 900.1 & 1049.24027799257 & -149.140277992571 \tabularnewline
6 & 937.2 & 1027.15962145913 & -89.9596214591257 \tabularnewline
7 & 948.9 & 928.58990565875 & 20.3100943412503 \tabularnewline
8 & 952.6 & 1007.77304218115 & -55.1730421811506 \tabularnewline
9 & 947.3 & 974.279577392027 & -26.9795773920271 \tabularnewline
10 & 974.2 & 961.18990565875 & 13.0100943412507 \tabularnewline
11 & 1000.8 & 933.373450492048 & 67.426549507952 \tabularnewline
12 & 1032.8 & 866.836645025075 & 165.963354974925 \tabularnewline
13 & 1050.7 & 981.919305086773 & 68.7806949132266 \tabularnewline
14 & 1057.3 & 1074.98401209631 & -17.6840120963064 \tabularnewline
15 & 1075.4 & 966.78042319591 & 108.619576804091 \tabularnewline
16 & 1118.4 & 1008.23409214048 & 110.165907859519 \tabularnewline
17 & 1179.8 & 1114.05595966309 & 65.7440403369086 \tabularnewline
18 & 1227 & 1202.061372019 & 24.9386279810000 \tabularnewline
19 & 1257.8 & 1202.56911821904 & 55.2308817809581 \tabularnewline
20 & 1251.5 & 1061.58011696274 & 189.919883037264 \tabularnewline
21 & 1236.3 & 1165.69423828530 & 70.6057617146957 \tabularnewline
22 & 1170.6 & 1309.47721471936 & -138.877214719356 \tabularnewline
23 & 1213.1 & 1215.60911821904 & -2.50911821904183 \tabularnewline
24 & 1265.5 & 1333.46647814174 & -67.9664781417357 \tabularnewline
25 & 1300.8 & 1321.95015898068 & -21.1501589806779 \tabularnewline
26 & 1348.4 & 1222.36424543384 & 126.035754566158 \tabularnewline
27 & 1371.9 & 1251.76824264514 & 120.131757354863 \tabularnewline
28 & 1403.3 & 1246.43533231173 & 156.864667688266 \tabularnewline
29 & 1451.8 & 1396.29162739009 & 55.5083726099149 \tabularnewline
30 & 1474.2 & 1412.74109496791 & 61.4589050320859 \tabularnewline
31 & 1438.2 & 1385.72732394562 & 52.4726760543826 \tabularnewline
32 & 1513.6 & 1594.26159141301 & -80.661591413009 \tabularnewline
33 & 1562.2 & 1552.51167145718 & 9.6883285428162 \tabularnewline
34 & 1546.2 & 1437.59238600125 & 108.607613998746 \tabularnewline
35 & 1527.5 & 1371.24580672328 & 156.254193276721 \tabularnewline
36 & 1418.7 & 1489.10316664597 & -70.4031666459729 \tabularnewline
37 & 1448.5 & 1422.54381304024 & 25.9561869597616 \tabularnewline
38 & 1492.1 & 1422.03536149382 & 70.0646385061792 \tabularnewline
39 & 1395.4 & 1434.92644837171 & -39.5264483717124 \tabularnewline
40 & 1403.7 & 1437.84999320501 & -34.1499932050108 \tabularnewline
41 & 1316.6 & 1386.79921256029 & -70.1992125602924 \tabularnewline
42 & 1274.5 & 1284.90615608207 & -10.4061560820662 \tabularnewline
43 & 1264.4 & 1387.24351600476 & -122.84351600476 \tabularnewline
44 & 1323.9 & 1400.37501119355 & -76.4750111935489 \tabularnewline
45 & 1332.1 & 1339.36002918209 & -7.2600291820873 \tabularnewline
46 & 1250.2 & 1224.44074372616 & 25.7592562738425 \tabularnewline
47 & 1096.7 & 1251.66732300413 & -154.967323004133 \tabularnewline
48 & 1080.8 & 995.232048703025 & 85.5679512969751 \tabularnewline
49 & 1039.2 & 917.664088208355 & 121.535911791645 \tabularnewline
50 & 792 & 862.11260221726 & -70.1126022172607 \tabularnewline
51 & 746.6 & 863.995082206217 & -117.395082206217 \tabularnewline
52 & 688.8 & 822.884199483774 & -134.084199483774 \tabularnewline
53 & 715.8 & 617.71292239396 & 98.0870776060394 \tabularnewline
54 & 672.9 & 658.931755471894 & 13.968244528106 \tabularnewline
55 & 629.5 & 634.670136171831 & -5.17013617183116 \tabularnewline
56 & 681.2 & 658.810238249555 & 22.3897617504446 \tabularnewline
57 & 755.4 & 801.454483683398 & -46.0544836833976 \tabularnewline
58 & 760.6 & 769.099749894483 & -8.49974989448316 \tabularnewline
59 & 765.9 & 832.104301561498 & -66.2043015614982 \tabularnewline
60 & 836.8 & 949.961661484192 & -113.161661484192 \tabularnewline
61 & 904.9 & 839.367880322716 & 65.5321196772839 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57455&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]785.8[/C][C]1046.45475436124[/C][C]-260.654754361239[/C][/ROW]
[ROW][C]2[/C][C]819.3[/C][C]927.60377875877[/C][C]-108.30377875877[/C][/ROW]
[ROW][C]3[/C][C]849.4[/C][C]921.229803581025[/C][C]-71.8298035810251[/C][/ROW]
[ROW][C]4[/C][C]880.4[/C][C]979.196382859[/C][C]-98.7963828590005[/C][/ROW]
[ROW][C]5[/C][C]900.1[/C][C]1049.24027799257[/C][C]-149.140277992571[/C][/ROW]
[ROW][C]6[/C][C]937.2[/C][C]1027.15962145913[/C][C]-89.9596214591257[/C][/ROW]
[ROW][C]7[/C][C]948.9[/C][C]928.58990565875[/C][C]20.3100943412503[/C][/ROW]
[ROW][C]8[/C][C]952.6[/C][C]1007.77304218115[/C][C]-55.1730421811506[/C][/ROW]
[ROW][C]9[/C][C]947.3[/C][C]974.279577392027[/C][C]-26.9795773920271[/C][/ROW]
[ROW][C]10[/C][C]974.2[/C][C]961.18990565875[/C][C]13.0100943412507[/C][/ROW]
[ROW][C]11[/C][C]1000.8[/C][C]933.373450492048[/C][C]67.426549507952[/C][/ROW]
[ROW][C]12[/C][C]1032.8[/C][C]866.836645025075[/C][C]165.963354974925[/C][/ROW]
[ROW][C]13[/C][C]1050.7[/C][C]981.919305086773[/C][C]68.7806949132266[/C][/ROW]
[ROW][C]14[/C][C]1057.3[/C][C]1074.98401209631[/C][C]-17.6840120963064[/C][/ROW]
[ROW][C]15[/C][C]1075.4[/C][C]966.78042319591[/C][C]108.619576804091[/C][/ROW]
[ROW][C]16[/C][C]1118.4[/C][C]1008.23409214048[/C][C]110.165907859519[/C][/ROW]
[ROW][C]17[/C][C]1179.8[/C][C]1114.05595966309[/C][C]65.7440403369086[/C][/ROW]
[ROW][C]18[/C][C]1227[/C][C]1202.061372019[/C][C]24.9386279810000[/C][/ROW]
[ROW][C]19[/C][C]1257.8[/C][C]1202.56911821904[/C][C]55.2308817809581[/C][/ROW]
[ROW][C]20[/C][C]1251.5[/C][C]1061.58011696274[/C][C]189.919883037264[/C][/ROW]
[ROW][C]21[/C][C]1236.3[/C][C]1165.69423828530[/C][C]70.6057617146957[/C][/ROW]
[ROW][C]22[/C][C]1170.6[/C][C]1309.47721471936[/C][C]-138.877214719356[/C][/ROW]
[ROW][C]23[/C][C]1213.1[/C][C]1215.60911821904[/C][C]-2.50911821904183[/C][/ROW]
[ROW][C]24[/C][C]1265.5[/C][C]1333.46647814174[/C][C]-67.9664781417357[/C][/ROW]
[ROW][C]25[/C][C]1300.8[/C][C]1321.95015898068[/C][C]-21.1501589806779[/C][/ROW]
[ROW][C]26[/C][C]1348.4[/C][C]1222.36424543384[/C][C]126.035754566158[/C][/ROW]
[ROW][C]27[/C][C]1371.9[/C][C]1251.76824264514[/C][C]120.131757354863[/C][/ROW]
[ROW][C]28[/C][C]1403.3[/C][C]1246.43533231173[/C][C]156.864667688266[/C][/ROW]
[ROW][C]29[/C][C]1451.8[/C][C]1396.29162739009[/C][C]55.5083726099149[/C][/ROW]
[ROW][C]30[/C][C]1474.2[/C][C]1412.74109496791[/C][C]61.4589050320859[/C][/ROW]
[ROW][C]31[/C][C]1438.2[/C][C]1385.72732394562[/C][C]52.4726760543826[/C][/ROW]
[ROW][C]32[/C][C]1513.6[/C][C]1594.26159141301[/C][C]-80.661591413009[/C][/ROW]
[ROW][C]33[/C][C]1562.2[/C][C]1552.51167145718[/C][C]9.6883285428162[/C][/ROW]
[ROW][C]34[/C][C]1546.2[/C][C]1437.59238600125[/C][C]108.607613998746[/C][/ROW]
[ROW][C]35[/C][C]1527.5[/C][C]1371.24580672328[/C][C]156.254193276721[/C][/ROW]
[ROW][C]36[/C][C]1418.7[/C][C]1489.10316664597[/C][C]-70.4031666459729[/C][/ROW]
[ROW][C]37[/C][C]1448.5[/C][C]1422.54381304024[/C][C]25.9561869597616[/C][/ROW]
[ROW][C]38[/C][C]1492.1[/C][C]1422.03536149382[/C][C]70.0646385061792[/C][/ROW]
[ROW][C]39[/C][C]1395.4[/C][C]1434.92644837171[/C][C]-39.5264483717124[/C][/ROW]
[ROW][C]40[/C][C]1403.7[/C][C]1437.84999320501[/C][C]-34.1499932050108[/C][/ROW]
[ROW][C]41[/C][C]1316.6[/C][C]1386.79921256029[/C][C]-70.1992125602924[/C][/ROW]
[ROW][C]42[/C][C]1274.5[/C][C]1284.90615608207[/C][C]-10.4061560820662[/C][/ROW]
[ROW][C]43[/C][C]1264.4[/C][C]1387.24351600476[/C][C]-122.84351600476[/C][/ROW]
[ROW][C]44[/C][C]1323.9[/C][C]1400.37501119355[/C][C]-76.4750111935489[/C][/ROW]
[ROW][C]45[/C][C]1332.1[/C][C]1339.36002918209[/C][C]-7.2600291820873[/C][/ROW]
[ROW][C]46[/C][C]1250.2[/C][C]1224.44074372616[/C][C]25.7592562738425[/C][/ROW]
[ROW][C]47[/C][C]1096.7[/C][C]1251.66732300413[/C][C]-154.967323004133[/C][/ROW]
[ROW][C]48[/C][C]1080.8[/C][C]995.232048703025[/C][C]85.5679512969751[/C][/ROW]
[ROW][C]49[/C][C]1039.2[/C][C]917.664088208355[/C][C]121.535911791645[/C][/ROW]
[ROW][C]50[/C][C]792[/C][C]862.11260221726[/C][C]-70.1126022172607[/C][/ROW]
[ROW][C]51[/C][C]746.6[/C][C]863.995082206217[/C][C]-117.395082206217[/C][/ROW]
[ROW][C]52[/C][C]688.8[/C][C]822.884199483774[/C][C]-134.084199483774[/C][/ROW]
[ROW][C]53[/C][C]715.8[/C][C]617.71292239396[/C][C]98.0870776060394[/C][/ROW]
[ROW][C]54[/C][C]672.9[/C][C]658.931755471894[/C][C]13.968244528106[/C][/ROW]
[ROW][C]55[/C][C]629.5[/C][C]634.670136171831[/C][C]-5.17013617183116[/C][/ROW]
[ROW][C]56[/C][C]681.2[/C][C]658.810238249555[/C][C]22.3897617504446[/C][/ROW]
[ROW][C]57[/C][C]755.4[/C][C]801.454483683398[/C][C]-46.0544836833976[/C][/ROW]
[ROW][C]58[/C][C]760.6[/C][C]769.099749894483[/C][C]-8.49974989448316[/C][/ROW]
[ROW][C]59[/C][C]765.9[/C][C]832.104301561498[/C][C]-66.2043015614982[/C][/ROW]
[ROW][C]60[/C][C]836.8[/C][C]949.961661484192[/C][C]-113.161661484192[/C][/ROW]
[ROW][C]61[/C][C]904.9[/C][C]839.367880322716[/C][C]65.5321196772839[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57455&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57455&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	785.8	1046.45475436124	-260.654754361239
2	819.3	927.60377875877	-108.30377875877
3	849.4	921.229803581025	-71.8298035810251
4	880.4	979.196382859	-98.7963828590005
5	900.1	1049.24027799257	-149.140277992571
6	937.2	1027.15962145913	-89.9596214591257
7	948.9	928.58990565875	20.3100943412503
8	952.6	1007.77304218115	-55.1730421811506
9	947.3	974.279577392027	-26.9795773920271
10	974.2	961.18990565875	13.0100943412507
11	1000.8	933.373450492048	67.426549507952
12	1032.8	866.836645025075	165.963354974925
13	1050.7	981.919305086773	68.7806949132266
14	1057.3	1074.98401209631	-17.6840120963064
15	1075.4	966.78042319591	108.619576804091
16	1118.4	1008.23409214048	110.165907859519
17	1179.8	1114.05595966309	65.7440403369086
18	1227	1202.061372019	24.9386279810000
19	1257.8	1202.56911821904	55.2308817809581
20	1251.5	1061.58011696274	189.919883037264
21	1236.3	1165.69423828530	70.6057617146957
22	1170.6	1309.47721471936	-138.877214719356
23	1213.1	1215.60911821904	-2.50911821904183
24	1265.5	1333.46647814174	-67.9664781417357
25	1300.8	1321.95015898068	-21.1501589806779
26	1348.4	1222.36424543384	126.035754566158
27	1371.9	1251.76824264514	120.131757354863
28	1403.3	1246.43533231173	156.864667688266
29	1451.8	1396.29162739009	55.5083726099149
30	1474.2	1412.74109496791	61.4589050320859
31	1438.2	1385.72732394562	52.4726760543826
32	1513.6	1594.26159141301	-80.661591413009
33	1562.2	1552.51167145718	9.6883285428162
34	1546.2	1437.59238600125	108.607613998746
35	1527.5	1371.24580672328	156.254193276721
36	1418.7	1489.10316664597	-70.4031666459729
37	1448.5	1422.54381304024	25.9561869597616
38	1492.1	1422.03536149382	70.0646385061792
39	1395.4	1434.92644837171	-39.5264483717124
40	1403.7	1437.84999320501	-34.1499932050108
41	1316.6	1386.79921256029	-70.1992125602924
42	1274.5	1284.90615608207	-10.4061560820662
43	1264.4	1387.24351600476	-122.84351600476
44	1323.9	1400.37501119355	-76.4750111935489
45	1332.1	1339.36002918209	-7.2600291820873
46	1250.2	1224.44074372616	25.7592562738425
47	1096.7	1251.66732300413	-154.967323004133
48	1080.8	995.232048703025	85.5679512969751
49	1039.2	917.664088208355	121.535911791645
50	792	862.11260221726	-70.1126022172607
51	746.6	863.995082206217	-117.395082206217
52	688.8	822.884199483774	-134.084199483774
53	715.8	617.71292239396	98.0870776060394
54	672.9	658.931755471894	13.968244528106
55	629.5	634.670136171831	-5.17013617183116
56	681.2	658.810238249555	22.3897617504446
57	755.4	801.454483683398	-46.0544836833976
58	760.6	769.099749894483	-8.49974989448316
59	765.9	832.104301561498	-66.2043015614982
60	836.8	949.961661484192	-113.161661484192
61	904.9	839.367880322716	65.5321196772839

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.0092996600793034	0.0185993201586068	0.990700339920697
18	0.00453680010147977	0.00907360020295954	0.99546319989852
19	0.00131235039447105	0.0026247007889421	0.99868764960553
20	0.00070103072915786	0.00140206145831572	0.999298969270842
21	0.000142430463551736	0.000284860927103471	0.999857569536448
22	0.00133101692370086	0.00266203384740172	0.998668983076299
23	0.000798203034892902	0.00159640606978580	0.999201796965107
24	0.000447844287574643	0.000895688575149286	0.999552155712425
25	0.00103040876373838	0.00206081752747675	0.998969591236262
26	0.000501923346387768	0.00100384669277554	0.999498076653612
27	0.000218107831960038	0.000436215663920076	0.99978189216804
28	9.0415740734423e-05	0.000180831481468846	0.999909584259266
29	3.94271370946234e-05	7.88542741892467e-05	0.999960572862905
30	1.19423373520128e-05	2.38846747040255e-05	0.999988057662648
31	1.63354133621231e-05	3.26708267242461e-05	0.999983664586638
32	1.62294634663001e-05	3.24589269326002e-05	0.999983770536534
33	6.13657403089166e-05	0.000122731480617833	0.99993863425969
34	0.000105028850707121	0.000210057701414243	0.999894971149293
35	0.000297342587900748	0.000594685175801497	0.9997026574121
36	0.00501346777590459	0.0100269355518092	0.994986532224095
37	0.0388596352086286	0.0777192704172572	0.961140364791371
38	0.119813055390055	0.239626110780111	0.880186944609945
39	0.404856934662577	0.809713869325154	0.595143065337423
40	0.817380914315745	0.365238171368510	0.182619085684255
41	0.906191930285913	0.187616139428174	0.0938080697140869
42	0.898791373615794	0.202417252768413	0.101208626384206
43	0.86492287502317	0.270154249953659	0.135077124976830
44	0.760793923324338	0.478412153351324	0.239206076675662

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.0092996600793034 & 0.0185993201586068 & 0.990700339920697 \tabularnewline
18 & 0.00453680010147977 & 0.00907360020295954 & 0.99546319989852 \tabularnewline
19 & 0.00131235039447105 & 0.0026247007889421 & 0.99868764960553 \tabularnewline
20 & 0.00070103072915786 & 0.00140206145831572 & 0.999298969270842 \tabularnewline
21 & 0.000142430463551736 & 0.000284860927103471 & 0.999857569536448 \tabularnewline
22 & 0.00133101692370086 & 0.00266203384740172 & 0.998668983076299 \tabularnewline
23 & 0.000798203034892902 & 0.00159640606978580 & 0.999201796965107 \tabularnewline
24 & 0.000447844287574643 & 0.000895688575149286 & 0.999552155712425 \tabularnewline
25 & 0.00103040876373838 & 0.00206081752747675 & 0.998969591236262 \tabularnewline
26 & 0.000501923346387768 & 0.00100384669277554 & 0.999498076653612 \tabularnewline
27 & 0.000218107831960038 & 0.000436215663920076 & 0.99978189216804 \tabularnewline
28 & 9.0415740734423e-05 & 0.000180831481468846 & 0.999909584259266 \tabularnewline
29 & 3.94271370946234e-05 & 7.88542741892467e-05 & 0.999960572862905 \tabularnewline
30 & 1.19423373520128e-05 & 2.38846747040255e-05 & 0.999988057662648 \tabularnewline
31 & 1.63354133621231e-05 & 3.26708267242461e-05 & 0.999983664586638 \tabularnewline
32 & 1.62294634663001e-05 & 3.24589269326002e-05 & 0.999983770536534 \tabularnewline
33 & 6.13657403089166e-05 & 0.000122731480617833 & 0.99993863425969 \tabularnewline
34 & 0.000105028850707121 & 0.000210057701414243 & 0.999894971149293 \tabularnewline
35 & 0.000297342587900748 & 0.000594685175801497 & 0.9997026574121 \tabularnewline
36 & 0.00501346777590459 & 0.0100269355518092 & 0.994986532224095 \tabularnewline
37 & 0.0388596352086286 & 0.0777192704172572 & 0.961140364791371 \tabularnewline
38 & 0.119813055390055 & 0.239626110780111 & 0.880186944609945 \tabularnewline
39 & 0.404856934662577 & 0.809713869325154 & 0.595143065337423 \tabularnewline
40 & 0.817380914315745 & 0.365238171368510 & 0.182619085684255 \tabularnewline
41 & 0.906191930285913 & 0.187616139428174 & 0.0938080697140869 \tabularnewline
42 & 0.898791373615794 & 0.202417252768413 & 0.101208626384206 \tabularnewline
43 & 0.86492287502317 & 0.270154249953659 & 0.135077124976830 \tabularnewline
44 & 0.760793923324338 & 0.478412153351324 & 0.239206076675662 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57455&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]17[/C][C]0.0092996600793034[/C][C]0.0185993201586068[/C][C]0.990700339920697[/C][/ROW]
[ROW][C]18[/C][C]0.00453680010147977[/C][C]0.00907360020295954[/C][C]0.99546319989852[/C][/ROW]
[ROW][C]19[/C][C]0.00131235039447105[/C][C]0.0026247007889421[/C][C]0.99868764960553[/C][/ROW]
[ROW][C]20[/C][C]0.00070103072915786[/C][C]0.00140206145831572[/C][C]0.999298969270842[/C][/ROW]
[ROW][C]21[/C][C]0.000142430463551736[/C][C]0.000284860927103471[/C][C]0.999857569536448[/C][/ROW]
[ROW][C]22[/C][C]0.00133101692370086[/C][C]0.00266203384740172[/C][C]0.998668983076299[/C][/ROW]
[ROW][C]23[/C][C]0.000798203034892902[/C][C]0.00159640606978580[/C][C]0.999201796965107[/C][/ROW]
[ROW][C]24[/C][C]0.000447844287574643[/C][C]0.000895688575149286[/C][C]0.999552155712425[/C][/ROW]
[ROW][C]25[/C][C]0.00103040876373838[/C][C]0.00206081752747675[/C][C]0.998969591236262[/C][/ROW]
[ROW][C]26[/C][C]0.000501923346387768[/C][C]0.00100384669277554[/C][C]0.999498076653612[/C][/ROW]
[ROW][C]27[/C][C]0.000218107831960038[/C][C]0.000436215663920076[/C][C]0.99978189216804[/C][/ROW]
[ROW][C]28[/C][C]9.0415740734423e-05[/C][C]0.000180831481468846[/C][C]0.999909584259266[/C][/ROW]
[ROW][C]29[/C][C]3.94271370946234e-05[/C][C]7.88542741892467e-05[/C][C]0.999960572862905[/C][/ROW]
[ROW][C]30[/C][C]1.19423373520128e-05[/C][C]2.38846747040255e-05[/C][C]0.999988057662648[/C][/ROW]
[ROW][C]31[/C][C]1.63354133621231e-05[/C][C]3.26708267242461e-05[/C][C]0.999983664586638[/C][/ROW]
[ROW][C]32[/C][C]1.62294634663001e-05[/C][C]3.24589269326002e-05[/C][C]0.999983770536534[/C][/ROW]
[ROW][C]33[/C][C]6.13657403089166e-05[/C][C]0.000122731480617833[/C][C]0.99993863425969[/C][/ROW]
[ROW][C]34[/C][C]0.000105028850707121[/C][C]0.000210057701414243[/C][C]0.999894971149293[/C][/ROW]
[ROW][C]35[/C][C]0.000297342587900748[/C][C]0.000594685175801497[/C][C]0.9997026574121[/C][/ROW]
[ROW][C]36[/C][C]0.00501346777590459[/C][C]0.0100269355518092[/C][C]0.994986532224095[/C][/ROW]
[ROW][C]37[/C][C]0.0388596352086286[/C][C]0.0777192704172572[/C][C]0.961140364791371[/C][/ROW]
[ROW][C]38[/C][C]0.119813055390055[/C][C]0.239626110780111[/C][C]0.880186944609945[/C][/ROW]
[ROW][C]39[/C][C]0.404856934662577[/C][C]0.809713869325154[/C][C]0.595143065337423[/C][/ROW]
[ROW][C]40[/C][C]0.817380914315745[/C][C]0.365238171368510[/C][C]0.182619085684255[/C][/ROW]
[ROW][C]41[/C][C]0.906191930285913[/C][C]0.187616139428174[/C][C]0.0938080697140869[/C][/ROW]
[ROW][C]42[/C][C]0.898791373615794[/C][C]0.202417252768413[/C][C]0.101208626384206[/C][/ROW]
[ROW][C]43[/C][C]0.86492287502317[/C][C]0.270154249953659[/C][C]0.135077124976830[/C][/ROW]
[ROW][C]44[/C][C]0.760793923324338[/C][C]0.478412153351324[/C][C]0.239206076675662[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57455&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57455&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.0092996600793034	0.0185993201586068	0.990700339920697
18	0.00453680010147977	0.00907360020295954	0.99546319989852
19	0.00131235039447105	0.0026247007889421	0.99868764960553
20	0.00070103072915786	0.00140206145831572	0.999298969270842
21	0.000142430463551736	0.000284860927103471	0.999857569536448
22	0.00133101692370086	0.00266203384740172	0.998668983076299
23	0.000798203034892902	0.00159640606978580	0.999201796965107
24	0.000447844287574643	0.000895688575149286	0.999552155712425
25	0.00103040876373838	0.00206081752747675	0.998969591236262
26	0.000501923346387768	0.00100384669277554	0.999498076653612
27	0.000218107831960038	0.000436215663920076	0.99978189216804
28	9.0415740734423e-05	0.000180831481468846	0.999909584259266
29	3.94271370946234e-05	7.88542741892467e-05	0.999960572862905
30	1.19423373520128e-05	2.38846747040255e-05	0.999988057662648
31	1.63354133621231e-05	3.26708267242461e-05	0.999983664586638
32	1.62294634663001e-05	3.24589269326002e-05	0.999983770536534
33	6.13657403089166e-05	0.000122731480617833	0.99993863425969
34	0.000105028850707121	0.000210057701414243	0.999894971149293
35	0.000297342587900748	0.000594685175801497	0.9997026574121
36	0.00501346777590459	0.0100269355518092	0.994986532224095
37	0.0388596352086286	0.0777192704172572	0.961140364791371
38	0.119813055390055	0.239626110780111	0.880186944609945
39	0.404856934662577	0.809713869325154	0.595143065337423
40	0.817380914315745	0.365238171368510	0.182619085684255
41	0.906191930285913	0.187616139428174	0.0938080697140869
42	0.898791373615794	0.202417252768413	0.101208626384206
43	0.86492287502317	0.270154249953659	0.135077124976830
44	0.760793923324338	0.478412153351324	0.239206076675662

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	18	0.642857142857143	NOK
5% type I error level	20	0.714285714285714	NOK
10% type I error level	21	0.75	NOK

\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 & 18 & 0.642857142857143 & NOK \tabularnewline
5% type I error level & 20 & 0.714285714285714 & NOK \tabularnewline
10% type I error level & 21 & 0.75 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57455&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]18[/C][C]0.642857142857143[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]20[/C][C]0.714285714285714[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]21[/C][C]0.75[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57455&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57455&T=6

As an alternative you can also use a 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 tests	OK/NOK
1% type I error level	18	0.642857142857143	NOK
5% type I error level	20	0.714285714285714	NOK
10% type I error level	21	0.75	NOK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code