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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 10 Dec 2012 04:15:01 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/10/t1355131042h5pkmgis5godjq6.htm/, Retrieved Fri, 29 Mar 2024 02:13:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=198063, Retrieved Fri, 29 Mar 2024 02:13:28 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2012-10-31 14:44:12] [83c7ccdb194e46f99f0902896e3c3ab1]
- R     [Multiple Regression] [paper15] [2012-11-29 11:42:11] [5ba6a65192e86ec9d9b86886359e5312]
- R  D      [Multiple Regression] [Multiple regressie] [2012-12-10 09:15:01] [b262fb17dc370e47870f909f9ce3690a] [Current]
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Dataseries X:
2050	2650	13	7	1	1	0	1639
2150	2664	6	5	1	1	0	1193
2150	2921	3	6	1	1	0	1635
1999	2580	4	4	1	1	0	1732
1900	2580	4	4	1	0	0	1534
1800	2774	2	4	1	0	0	1765
1560	1920	1	5	1	1	0	1161
1449	1710	1	3	1	1	0	1010
1375	1837	4	5	1	0	0	1191
1270	1880	8	6	1	0	0	930
1250	2150	15	3	1	0	0	984
1235	1894	14	5	1	1	0	1112
1170	1928	18	8	1	1	0	600
1155	1767	16	4	1	0	0	794
1110	1630	15	3	1	0	1	867
1139	1680	17	4	1	0	1	750
995	1500	15	4	1	0	0	743
900	1400	16	2	1	0	1	731
960	1573	17	6	1	0	0	768
1695	2931	28	3	1	0	1	1142
1553	2200	28	4	1	0	0	1035
1020	1478	53	3	1	0	1	626
1020	1713	30	4	1	0	1	600
850	1190	41	1	1	0	0	600
720	1121	46	4	1	0	0	398
749	1733	43	6	1	0	0	656
2150	2848	4	6	1	1	0	1487
1350	2253	23	4	1	1	0	939
1299	2743	25	5	1	1	1	1232
1250	2180	17	4	1	0	1	1141
1239	1706	14	4	1	0	0	810
1125	1710	16	4	1	1	0	800
1080	2200	26	4	1	0	0	1076
1050	1680	13	4	1	0	0	875
1049	1900	34	3	1	0	0	690
934	1543	20	3	1	0	0	820
875	1173	6	4	1	0	0	456
805	1258	7	4	1	0	1	821
759	997	4	4	1	0	0	461
729	1007	19	6	1	0	0	513
710	1083	22	4	1	0	0	504
975	1500	7	3	0	1	1	700
939	1428	40	2	0	0	0	701
2100	2116	25	3	0	1	0	1209
580	1051	15	2	0	0	0	426
1844	2250	40	6	0	1	0	915
699	1400	45	1	0	1	1	481
1160	1720	5	4	0	0	0	867
1109	1740	4	3	0	0	0	816
1129	1700	6	4	0	0	0	725
1050	1620	6	4	0	0	0	800
1045	1630	6	4	0	0	0	750
1050	1920	8	4	0	0	0	944
1020	1606	5	4	0	0	0	811
1000	1535	7	5	0	0	1	668
1030	1540	6	2	0	0	1	826
975	1739	13	3	0	0	0	880
940	1305	5	3	0	0	0	647
920	1415	7	4	0	0	0	866
945	1580	9	3	0	0	0	810
874	1236	3	4	0	0	0	707
872	1229	6	3	0	0	0	721
870	1273	4	4	0	0	0	638
869	1165	7	4	0	0	0	694
766	1200	7	4	0	0	1	634
739	970	4	4	0	0	1	541




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198063&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198063&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198063&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
PRICE[t] = + 92.7447983263411 + 0.352218356065891SQFT[t] -0.565081670796422AGE[t] + 4.38960663069772FEATS[t] -17.3853378667202NE[t] + 174.94107946041CUST[t] -73.5823425042775COR[t] + 0.498870052680545TAX[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PRICE[t] =  +  92.7447983263411 +  0.352218356065891SQFT[t] -0.565081670796422AGE[t] +  4.38960663069772FEATS[t] -17.3853378667202NE[t] +  174.94107946041CUST[t] -73.5823425042775COR[t] +  0.498870052680545TAX[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198063&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PRICE[t] =  +  92.7447983263411 +  0.352218356065891SQFT[t] -0.565081670796422AGE[t] +  4.38960663069772FEATS[t] -17.3853378667202NE[t] +  174.94107946041CUST[t] -73.5823425042775COR[t] +  0.498870052680545TAX[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198063&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198063&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
PRICE[t] = + 92.7447983263411 + 0.352218356065891SQFT[t] -0.565081670796422AGE[t] + 4.38960663069772FEATS[t] -17.3853378667202NE[t] + 174.94107946041CUST[t] -73.5823425042775COR[t] + 0.498870052680545TAX[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)92.7447983263411101.6070430.91280.3651370.182569
SQFT0.3522183560658910.0957483.67860.0005150.000258
AGE-0.5650816707964222.002529-0.28220.7788070.389403
FEATS4.3896066306977218.5549870.23660.8138220.406911
NE-17.385337866720247.274622-0.36780.7143970.357198
CUST174.9410794604153.7237083.25630.0018870.000944
COR-73.582342504277549.130071-1.49770.1396330.069816
TAX0.4988700526805450.1584853.14770.0025980.001299

\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) & 92.7447983263411 & 101.607043 & 0.9128 & 0.365137 & 0.182569 \tabularnewline
SQFT & 0.352218356065891 & 0.095748 & 3.6786 & 0.000515 & 0.000258 \tabularnewline
AGE & -0.565081670796422 & 2.002529 & -0.2822 & 0.778807 & 0.389403 \tabularnewline
FEATS & 4.38960663069772 & 18.554987 & 0.2366 & 0.813822 & 0.406911 \tabularnewline
NE & -17.3853378667202 & 47.274622 & -0.3678 & 0.714397 & 0.357198 \tabularnewline
CUST & 174.94107946041 & 53.723708 & 3.2563 & 0.001887 & 0.000944 \tabularnewline
COR & -73.5823425042775 & 49.130071 & -1.4977 & 0.139633 & 0.069816 \tabularnewline
TAX & 0.498870052680545 & 0.158485 & 3.1477 & 0.002598 & 0.001299 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198063&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]92.7447983263411[/C][C]101.607043[/C][C]0.9128[/C][C]0.365137[/C][C]0.182569[/C][/ROW]
[ROW][C]SQFT[/C][C]0.352218356065891[/C][C]0.095748[/C][C]3.6786[/C][C]0.000515[/C][C]0.000258[/C][/ROW]
[ROW][C]AGE[/C][C]-0.565081670796422[/C][C]2.002529[/C][C]-0.2822[/C][C]0.778807[/C][C]0.389403[/C][/ROW]
[ROW][C]FEATS[/C][C]4.38960663069772[/C][C]18.554987[/C][C]0.2366[/C][C]0.813822[/C][C]0.406911[/C][/ROW]
[ROW][C]NE[/C][C]-17.3853378667202[/C][C]47.274622[/C][C]-0.3678[/C][C]0.714397[/C][C]0.357198[/C][/ROW]
[ROW][C]CUST[/C][C]174.94107946041[/C][C]53.723708[/C][C]3.2563[/C][C]0.001887[/C][C]0.000944[/C][/ROW]
[ROW][C]COR[/C][C]-73.5823425042775[/C][C]49.130071[/C][C]-1.4977[/C][C]0.139633[/C][C]0.069816[/C][/ROW]
[ROW][C]TAX[/C][C]0.498870052680545[/C][C]0.158485[/C][C]3.1477[/C][C]0.002598[/C][C]0.001299[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198063&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198063&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)92.7447983263411101.6070430.91280.3651370.182569
SQFT0.3522183560658910.0957483.67860.0005150.000258
AGE-0.5650816707964222.002529-0.28220.7788070.389403
FEATS4.3896066306977218.5549870.23660.8138220.406911
NE-17.385337866720247.274622-0.36780.7143970.357198
CUST174.9410794604153.7237083.25630.0018870.000944
COR-73.582342504277549.130071-1.49770.1396330.069816
TAX0.4988700526805450.1584853.14770.0025980.001299







Multiple Linear Regression - Regression Statistics
Multiple R0.92857453361376
R-squared0.862250664476013
Adjusted R-squared0.845625744671393
F-TEST (value)51.8649518078541
F-TEST (DF numerator)7
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation158.881062924413
Sum Squared Residuals1464105.1450475

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.92857453361376 \tabularnewline
R-squared & 0.862250664476013 \tabularnewline
Adjusted R-squared & 0.845625744671393 \tabularnewline
F-TEST (value) & 51.8649518078541 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 158.881062924413 \tabularnewline
Sum Squared Residuals & 1464105.1450475 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198063&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.92857453361376[/C][/ROW]
[ROW][C]R-squared[/C][C]0.862250664476013[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.845625744671393[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]51.8649518078541[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/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]158.881062924413[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1464105.1450475[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198063&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198063&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 R0.92857453361376
R-squared0.862250664476013
Adjusted R-squared0.845625744671393
F-TEST (value)51.8649518078541
F-TEST (DF numerator)7
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation158.881062924413
Sum Squared Residuals1464105.1450475







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
120502024.7083845325925.2916154674146
221501802.31975645617347.680243543834
321502119.4252888929930.5747111070127
419992038.36492965234-39.3649296523392
519001764.64757976118135.352420238819
618001949.34708634876-149.347086348762
715601527.1308662113532.869133788653
814491369.0564202213579.9435797786479
913751336.2265197654938.7734802345053
1012701223.2961052742246.7038947257821
1112501328.20965266909-78.2096526690898
1212351486.18249465193-251.182494651934
1311701253.64494499464-83.6449449946427
1411551102.3492372464552.6507627535487
1511101013.1059688469396.8940311530749
161139975.608533775701163.391466224299
17995983.42964516094711.570354839053
18900859.29473148572240.7052685142781
199601029.26238639057-69.2623863905733
2016951601.1852528554593.814747144554
2115531368.30648806944184.693511930564
221020817.867992538634202.132007461366
231020905.05516990344114.94483009656
24850775.04259391440374.9574060855974
25720660.31118824249759.6888117575028
267491015.05175402019-266.051754020188
2721502019.31549943266130.68450056734
2813501516.84902369799-166.849023697989
2912991765.2820443905-466.282044390503
3012501346.77590240674-96.7759024067394
3112391089.97600171091149.023998289086
3211251260.20709072719-135.207090727189
3310801389.89032357093-309.890323570932
3410501113.80995954823-63.8099595482321
3510491082.7507164194-33.7507164194048
369341029.7730135435-95.7730135435024
37875730.164272645252144.835727354748
38805868.042977964178-63.0429779641776
39759671.79835558265187.2016444173493
40729701.56477008214727.4352299178529
41710713.369076395245-3.36907639524523
429751080.85335445421-105.85335445421
43939931.5964641470347.40353585296587
4421001615.15559103514484.844408964861
45580675.747921192954-95.747921192954
4618441520.37765009003323.622349909965
47699906.126660558923-207.126660558923
4811601145.8137246025214.186275397485
4911091123.59119407722-14.5911940772238
5011291067.3647283297661.6352716702365
5110501076.60251379553-26.602513795533
5210451055.18119472216-10.1811947221647
5310501252.97514485971-202.975144859706
5410201077.72410906089-57.7241090608929
551000911.05528903172488.9447109682759
561030979.03411091428350.9658890857171
579751150.08092405554-175.080924055545
58940885.50208861475354.4979113852474
599201036.75809260814-116.758092608145
609451061.41762843662-116.417628436616
61874896.650995179329-22.6509951793293
62872895.084795781309-23.0847957813087
63870874.695959048013-4.69595904801333
64869862.8978545306186.10214546938173
65766771.710951327814-5.71095132781431
66739646.00105954575892.998940454242

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2050 & 2024.70838453259 & 25.2916154674146 \tabularnewline
2 & 2150 & 1802.31975645617 & 347.680243543834 \tabularnewline
3 & 2150 & 2119.42528889299 & 30.5747111070127 \tabularnewline
4 & 1999 & 2038.36492965234 & -39.3649296523392 \tabularnewline
5 & 1900 & 1764.64757976118 & 135.352420238819 \tabularnewline
6 & 1800 & 1949.34708634876 & -149.347086348762 \tabularnewline
7 & 1560 & 1527.13086621135 & 32.869133788653 \tabularnewline
8 & 1449 & 1369.05642022135 & 79.9435797786479 \tabularnewline
9 & 1375 & 1336.22651976549 & 38.7734802345053 \tabularnewline
10 & 1270 & 1223.29610527422 & 46.7038947257821 \tabularnewline
11 & 1250 & 1328.20965266909 & -78.2096526690898 \tabularnewline
12 & 1235 & 1486.18249465193 & -251.182494651934 \tabularnewline
13 & 1170 & 1253.64494499464 & -83.6449449946427 \tabularnewline
14 & 1155 & 1102.34923724645 & 52.6507627535487 \tabularnewline
15 & 1110 & 1013.10596884693 & 96.8940311530749 \tabularnewline
16 & 1139 & 975.608533775701 & 163.391466224299 \tabularnewline
17 & 995 & 983.429645160947 & 11.570354839053 \tabularnewline
18 & 900 & 859.294731485722 & 40.7052685142781 \tabularnewline
19 & 960 & 1029.26238639057 & -69.2623863905733 \tabularnewline
20 & 1695 & 1601.18525285545 & 93.814747144554 \tabularnewline
21 & 1553 & 1368.30648806944 & 184.693511930564 \tabularnewline
22 & 1020 & 817.867992538634 & 202.132007461366 \tabularnewline
23 & 1020 & 905.05516990344 & 114.94483009656 \tabularnewline
24 & 850 & 775.042593914403 & 74.9574060855974 \tabularnewline
25 & 720 & 660.311188242497 & 59.6888117575028 \tabularnewline
26 & 749 & 1015.05175402019 & -266.051754020188 \tabularnewline
27 & 2150 & 2019.31549943266 & 130.68450056734 \tabularnewline
28 & 1350 & 1516.84902369799 & -166.849023697989 \tabularnewline
29 & 1299 & 1765.2820443905 & -466.282044390503 \tabularnewline
30 & 1250 & 1346.77590240674 & -96.7759024067394 \tabularnewline
31 & 1239 & 1089.97600171091 & 149.023998289086 \tabularnewline
32 & 1125 & 1260.20709072719 & -135.207090727189 \tabularnewline
33 & 1080 & 1389.89032357093 & -309.890323570932 \tabularnewline
34 & 1050 & 1113.80995954823 & -63.8099595482321 \tabularnewline
35 & 1049 & 1082.7507164194 & -33.7507164194048 \tabularnewline
36 & 934 & 1029.7730135435 & -95.7730135435024 \tabularnewline
37 & 875 & 730.164272645252 & 144.835727354748 \tabularnewline
38 & 805 & 868.042977964178 & -63.0429779641776 \tabularnewline
39 & 759 & 671.798355582651 & 87.2016444173493 \tabularnewline
40 & 729 & 701.564770082147 & 27.4352299178529 \tabularnewline
41 & 710 & 713.369076395245 & -3.36907639524523 \tabularnewline
42 & 975 & 1080.85335445421 & -105.85335445421 \tabularnewline
43 & 939 & 931.596464147034 & 7.40353585296587 \tabularnewline
44 & 2100 & 1615.15559103514 & 484.844408964861 \tabularnewline
45 & 580 & 675.747921192954 & -95.747921192954 \tabularnewline
46 & 1844 & 1520.37765009003 & 323.622349909965 \tabularnewline
47 & 699 & 906.126660558923 & -207.126660558923 \tabularnewline
48 & 1160 & 1145.81372460252 & 14.186275397485 \tabularnewline
49 & 1109 & 1123.59119407722 & -14.5911940772238 \tabularnewline
50 & 1129 & 1067.36472832976 & 61.6352716702365 \tabularnewline
51 & 1050 & 1076.60251379553 & -26.602513795533 \tabularnewline
52 & 1045 & 1055.18119472216 & -10.1811947221647 \tabularnewline
53 & 1050 & 1252.97514485971 & -202.975144859706 \tabularnewline
54 & 1020 & 1077.72410906089 & -57.7241090608929 \tabularnewline
55 & 1000 & 911.055289031724 & 88.9447109682759 \tabularnewline
56 & 1030 & 979.034110914283 & 50.9658890857171 \tabularnewline
57 & 975 & 1150.08092405554 & -175.080924055545 \tabularnewline
58 & 940 & 885.502088614753 & 54.4979113852474 \tabularnewline
59 & 920 & 1036.75809260814 & -116.758092608145 \tabularnewline
60 & 945 & 1061.41762843662 & -116.417628436616 \tabularnewline
61 & 874 & 896.650995179329 & -22.6509951793293 \tabularnewline
62 & 872 & 895.084795781309 & -23.0847957813087 \tabularnewline
63 & 870 & 874.695959048013 & -4.69595904801333 \tabularnewline
64 & 869 & 862.897854530618 & 6.10214546938173 \tabularnewline
65 & 766 & 771.710951327814 & -5.71095132781431 \tabularnewline
66 & 739 & 646.001059545758 & 92.998940454242 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198063&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]2050[/C][C]2024.70838453259[/C][C]25.2916154674146[/C][/ROW]
[ROW][C]2[/C][C]2150[/C][C]1802.31975645617[/C][C]347.680243543834[/C][/ROW]
[ROW][C]3[/C][C]2150[/C][C]2119.42528889299[/C][C]30.5747111070127[/C][/ROW]
[ROW][C]4[/C][C]1999[/C][C]2038.36492965234[/C][C]-39.3649296523392[/C][/ROW]
[ROW][C]5[/C][C]1900[/C][C]1764.64757976118[/C][C]135.352420238819[/C][/ROW]
[ROW][C]6[/C][C]1800[/C][C]1949.34708634876[/C][C]-149.347086348762[/C][/ROW]
[ROW][C]7[/C][C]1560[/C][C]1527.13086621135[/C][C]32.869133788653[/C][/ROW]
[ROW][C]8[/C][C]1449[/C][C]1369.05642022135[/C][C]79.9435797786479[/C][/ROW]
[ROW][C]9[/C][C]1375[/C][C]1336.22651976549[/C][C]38.7734802345053[/C][/ROW]
[ROW][C]10[/C][C]1270[/C][C]1223.29610527422[/C][C]46.7038947257821[/C][/ROW]
[ROW][C]11[/C][C]1250[/C][C]1328.20965266909[/C][C]-78.2096526690898[/C][/ROW]
[ROW][C]12[/C][C]1235[/C][C]1486.18249465193[/C][C]-251.182494651934[/C][/ROW]
[ROW][C]13[/C][C]1170[/C][C]1253.64494499464[/C][C]-83.6449449946427[/C][/ROW]
[ROW][C]14[/C][C]1155[/C][C]1102.34923724645[/C][C]52.6507627535487[/C][/ROW]
[ROW][C]15[/C][C]1110[/C][C]1013.10596884693[/C][C]96.8940311530749[/C][/ROW]
[ROW][C]16[/C][C]1139[/C][C]975.608533775701[/C][C]163.391466224299[/C][/ROW]
[ROW][C]17[/C][C]995[/C][C]983.429645160947[/C][C]11.570354839053[/C][/ROW]
[ROW][C]18[/C][C]900[/C][C]859.294731485722[/C][C]40.7052685142781[/C][/ROW]
[ROW][C]19[/C][C]960[/C][C]1029.26238639057[/C][C]-69.2623863905733[/C][/ROW]
[ROW][C]20[/C][C]1695[/C][C]1601.18525285545[/C][C]93.814747144554[/C][/ROW]
[ROW][C]21[/C][C]1553[/C][C]1368.30648806944[/C][C]184.693511930564[/C][/ROW]
[ROW][C]22[/C][C]1020[/C][C]817.867992538634[/C][C]202.132007461366[/C][/ROW]
[ROW][C]23[/C][C]1020[/C][C]905.05516990344[/C][C]114.94483009656[/C][/ROW]
[ROW][C]24[/C][C]850[/C][C]775.042593914403[/C][C]74.9574060855974[/C][/ROW]
[ROW][C]25[/C][C]720[/C][C]660.311188242497[/C][C]59.6888117575028[/C][/ROW]
[ROW][C]26[/C][C]749[/C][C]1015.05175402019[/C][C]-266.051754020188[/C][/ROW]
[ROW][C]27[/C][C]2150[/C][C]2019.31549943266[/C][C]130.68450056734[/C][/ROW]
[ROW][C]28[/C][C]1350[/C][C]1516.84902369799[/C][C]-166.849023697989[/C][/ROW]
[ROW][C]29[/C][C]1299[/C][C]1765.2820443905[/C][C]-466.282044390503[/C][/ROW]
[ROW][C]30[/C][C]1250[/C][C]1346.77590240674[/C][C]-96.7759024067394[/C][/ROW]
[ROW][C]31[/C][C]1239[/C][C]1089.97600171091[/C][C]149.023998289086[/C][/ROW]
[ROW][C]32[/C][C]1125[/C][C]1260.20709072719[/C][C]-135.207090727189[/C][/ROW]
[ROW][C]33[/C][C]1080[/C][C]1389.89032357093[/C][C]-309.890323570932[/C][/ROW]
[ROW][C]34[/C][C]1050[/C][C]1113.80995954823[/C][C]-63.8099595482321[/C][/ROW]
[ROW][C]35[/C][C]1049[/C][C]1082.7507164194[/C][C]-33.7507164194048[/C][/ROW]
[ROW][C]36[/C][C]934[/C][C]1029.7730135435[/C][C]-95.7730135435024[/C][/ROW]
[ROW][C]37[/C][C]875[/C][C]730.164272645252[/C][C]144.835727354748[/C][/ROW]
[ROW][C]38[/C][C]805[/C][C]868.042977964178[/C][C]-63.0429779641776[/C][/ROW]
[ROW][C]39[/C][C]759[/C][C]671.798355582651[/C][C]87.2016444173493[/C][/ROW]
[ROW][C]40[/C][C]729[/C][C]701.564770082147[/C][C]27.4352299178529[/C][/ROW]
[ROW][C]41[/C][C]710[/C][C]713.369076395245[/C][C]-3.36907639524523[/C][/ROW]
[ROW][C]42[/C][C]975[/C][C]1080.85335445421[/C][C]-105.85335445421[/C][/ROW]
[ROW][C]43[/C][C]939[/C][C]931.596464147034[/C][C]7.40353585296587[/C][/ROW]
[ROW][C]44[/C][C]2100[/C][C]1615.15559103514[/C][C]484.844408964861[/C][/ROW]
[ROW][C]45[/C][C]580[/C][C]675.747921192954[/C][C]-95.747921192954[/C][/ROW]
[ROW][C]46[/C][C]1844[/C][C]1520.37765009003[/C][C]323.622349909965[/C][/ROW]
[ROW][C]47[/C][C]699[/C][C]906.126660558923[/C][C]-207.126660558923[/C][/ROW]
[ROW][C]48[/C][C]1160[/C][C]1145.81372460252[/C][C]14.186275397485[/C][/ROW]
[ROW][C]49[/C][C]1109[/C][C]1123.59119407722[/C][C]-14.5911940772238[/C][/ROW]
[ROW][C]50[/C][C]1129[/C][C]1067.36472832976[/C][C]61.6352716702365[/C][/ROW]
[ROW][C]51[/C][C]1050[/C][C]1076.60251379553[/C][C]-26.602513795533[/C][/ROW]
[ROW][C]52[/C][C]1045[/C][C]1055.18119472216[/C][C]-10.1811947221647[/C][/ROW]
[ROW][C]53[/C][C]1050[/C][C]1252.97514485971[/C][C]-202.975144859706[/C][/ROW]
[ROW][C]54[/C][C]1020[/C][C]1077.72410906089[/C][C]-57.7241090608929[/C][/ROW]
[ROW][C]55[/C][C]1000[/C][C]911.055289031724[/C][C]88.9447109682759[/C][/ROW]
[ROW][C]56[/C][C]1030[/C][C]979.034110914283[/C][C]50.9658890857171[/C][/ROW]
[ROW][C]57[/C][C]975[/C][C]1150.08092405554[/C][C]-175.080924055545[/C][/ROW]
[ROW][C]58[/C][C]940[/C][C]885.502088614753[/C][C]54.4979113852474[/C][/ROW]
[ROW][C]59[/C][C]920[/C][C]1036.75809260814[/C][C]-116.758092608145[/C][/ROW]
[ROW][C]60[/C][C]945[/C][C]1061.41762843662[/C][C]-116.417628436616[/C][/ROW]
[ROW][C]61[/C][C]874[/C][C]896.650995179329[/C][C]-22.6509951793293[/C][/ROW]
[ROW][C]62[/C][C]872[/C][C]895.084795781309[/C][C]-23.0847957813087[/C][/ROW]
[ROW][C]63[/C][C]870[/C][C]874.695959048013[/C][C]-4.69595904801333[/C][/ROW]
[ROW][C]64[/C][C]869[/C][C]862.897854530618[/C][C]6.10214546938173[/C][/ROW]
[ROW][C]65[/C][C]766[/C][C]771.710951327814[/C][C]-5.71095132781431[/C][/ROW]
[ROW][C]66[/C][C]739[/C][C]646.001059545758[/C][C]92.998940454242[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198063&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198063&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 IndexActualsInterpolationForecastResidualsPrediction Error
120502024.7083845325925.2916154674146
221501802.31975645617347.680243543834
321502119.4252888929930.5747111070127
419992038.36492965234-39.3649296523392
519001764.64757976118135.352420238819
618001949.34708634876-149.347086348762
715601527.1308662113532.869133788653
814491369.0564202213579.9435797786479
913751336.2265197654938.7734802345053
1012701223.2961052742246.7038947257821
1112501328.20965266909-78.2096526690898
1212351486.18249465193-251.182494651934
1311701253.64494499464-83.6449449946427
1411551102.3492372464552.6507627535487
1511101013.1059688469396.8940311530749
161139975.608533775701163.391466224299
17995983.42964516094711.570354839053
18900859.29473148572240.7052685142781
199601029.26238639057-69.2623863905733
2016951601.1852528554593.814747144554
2115531368.30648806944184.693511930564
221020817.867992538634202.132007461366
231020905.05516990344114.94483009656
24850775.04259391440374.9574060855974
25720660.31118824249759.6888117575028
267491015.05175402019-266.051754020188
2721502019.31549943266130.68450056734
2813501516.84902369799-166.849023697989
2912991765.2820443905-466.282044390503
3012501346.77590240674-96.7759024067394
3112391089.97600171091149.023998289086
3211251260.20709072719-135.207090727189
3310801389.89032357093-309.890323570932
3410501113.80995954823-63.8099595482321
3510491082.7507164194-33.7507164194048
369341029.7730135435-95.7730135435024
37875730.164272645252144.835727354748
38805868.042977964178-63.0429779641776
39759671.79835558265187.2016444173493
40729701.56477008214727.4352299178529
41710713.369076395245-3.36907639524523
429751080.85335445421-105.85335445421
43939931.5964641470347.40353585296587
4421001615.15559103514484.844408964861
45580675.747921192954-95.747921192954
4618441520.37765009003323.622349909965
47699906.126660558923-207.126660558923
4811601145.8137246025214.186275397485
4911091123.59119407722-14.5911940772238
5011291067.3647283297661.6352716702365
5110501076.60251379553-26.602513795533
5210451055.18119472216-10.1811947221647
5310501252.97514485971-202.975144859706
5410201077.72410906089-57.7241090608929
551000911.05528903172488.9447109682759
561030979.03411091428350.9658890857171
579751150.08092405554-175.080924055545
58940885.50208861475354.4979113852474
599201036.75809260814-116.758092608145
609451061.41762843662-116.417628436616
61874896.650995179329-22.6509951793293
62872895.084795781309-23.0847957813087
63870874.695959048013-4.69595904801333
64869862.8978545306186.10214546938173
65766771.710951327814-5.71095132781431
66739646.00105954575892.998940454242







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.5797434238169980.8405131523660030.420256576183002
120.5802880615493780.8394238769012430.419711938450622
130.5565598620306610.8868802759386780.443440137969339
140.5057366656443510.9885266687112990.494263334355649
150.385939694862150.7718793897242990.61406030513785
160.2961537672433410.5923075344866820.703846232756659
170.2341612127958740.4683224255917480.765838787204126
180.165835797275240.3316715945504810.83416420272476
190.1100189656819580.2200379313639170.889981034318042
200.08390088147100790.1678017629420160.916099118528992
210.2319153457855240.4638306915710480.768084654214476
220.2839860570436350.5679721140872690.716013942956365
230.2782061122656970.5564122245313940.721793887734303
240.2249960206850630.4499920413701260.775003979314937
250.1744600683324220.3489201366648440.825539931667578
260.3014684819000350.602936963800070.698531518099965
270.2770777033841650.554155406768330.722922296615835
280.3313554354507110.6627108709014220.668644564549289
290.8497728454730470.3004543090539060.150227154526953
300.8132321845193890.3735356309612210.186767815480611
310.8158227084713530.3683545830572940.184177291528647
320.8499915178206890.3000169643586210.15000848217931
330.9447578856595140.1104842286809730.0552421143404863
340.9311750750843220.1376498498313570.0688249249156785
350.9009814160622360.1980371678755290.0990185839377643
360.8828541130611050.2342917738777910.117145886938895
370.8817612115317510.2364775769364980.118238788468249
380.8700132913270560.2599734173458870.129986708672944
390.8581029305301620.2837941389396750.141897069469838
400.8141192984780480.3717614030439050.185880701521952
410.7507553736898870.4984892526202270.249244626310113
420.7885937250791920.4228125498416160.211406274920808
430.9727982445744460.05440351085110780.0272017554255539
440.9933552686610930.0132894626778140.00664473133890701
450.9963194965672040.007361006865592050.00368050343279602
460.9999857136130092.85727739826483e-051.42863869913241e-05
470.9999579879872828.40240254368873e-054.20120127184437e-05
480.9999542401649569.15196700884637e-054.57598350442319e-05
490.9998351406435980.0003297187128034210.00016485935640171
500.9996456015268030.0007087969463939620.000354398473196981
510.9989688167224750.002062366555049780.00103118327752489
520.9967383742107210.006523251578558830.00326162578927941
530.9942057584396060.01158848312078870.00579424156039433
540.9799794973338980.0400410053322030.0200205026661015
550.9818080980821540.03638380383569270.0181919019178463

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.579743423816998 & 0.840513152366003 & 0.420256576183002 \tabularnewline
12 & 0.580288061549378 & 0.839423876901243 & 0.419711938450622 \tabularnewline
13 & 0.556559862030661 & 0.886880275938678 & 0.443440137969339 \tabularnewline
14 & 0.505736665644351 & 0.988526668711299 & 0.494263334355649 \tabularnewline
15 & 0.38593969486215 & 0.771879389724299 & 0.61406030513785 \tabularnewline
16 & 0.296153767243341 & 0.592307534486682 & 0.703846232756659 \tabularnewline
17 & 0.234161212795874 & 0.468322425591748 & 0.765838787204126 \tabularnewline
18 & 0.16583579727524 & 0.331671594550481 & 0.83416420272476 \tabularnewline
19 & 0.110018965681958 & 0.220037931363917 & 0.889981034318042 \tabularnewline
20 & 0.0839008814710079 & 0.167801762942016 & 0.916099118528992 \tabularnewline
21 & 0.231915345785524 & 0.463830691571048 & 0.768084654214476 \tabularnewline
22 & 0.283986057043635 & 0.567972114087269 & 0.716013942956365 \tabularnewline
23 & 0.278206112265697 & 0.556412224531394 & 0.721793887734303 \tabularnewline
24 & 0.224996020685063 & 0.449992041370126 & 0.775003979314937 \tabularnewline
25 & 0.174460068332422 & 0.348920136664844 & 0.825539931667578 \tabularnewline
26 & 0.301468481900035 & 0.60293696380007 & 0.698531518099965 \tabularnewline
27 & 0.277077703384165 & 0.55415540676833 & 0.722922296615835 \tabularnewline
28 & 0.331355435450711 & 0.662710870901422 & 0.668644564549289 \tabularnewline
29 & 0.849772845473047 & 0.300454309053906 & 0.150227154526953 \tabularnewline
30 & 0.813232184519389 & 0.373535630961221 & 0.186767815480611 \tabularnewline
31 & 0.815822708471353 & 0.368354583057294 & 0.184177291528647 \tabularnewline
32 & 0.849991517820689 & 0.300016964358621 & 0.15000848217931 \tabularnewline
33 & 0.944757885659514 & 0.110484228680973 & 0.0552421143404863 \tabularnewline
34 & 0.931175075084322 & 0.137649849831357 & 0.0688249249156785 \tabularnewline
35 & 0.900981416062236 & 0.198037167875529 & 0.0990185839377643 \tabularnewline
36 & 0.882854113061105 & 0.234291773877791 & 0.117145886938895 \tabularnewline
37 & 0.881761211531751 & 0.236477576936498 & 0.118238788468249 \tabularnewline
38 & 0.870013291327056 & 0.259973417345887 & 0.129986708672944 \tabularnewline
39 & 0.858102930530162 & 0.283794138939675 & 0.141897069469838 \tabularnewline
40 & 0.814119298478048 & 0.371761403043905 & 0.185880701521952 \tabularnewline
41 & 0.750755373689887 & 0.498489252620227 & 0.249244626310113 \tabularnewline
42 & 0.788593725079192 & 0.422812549841616 & 0.211406274920808 \tabularnewline
43 & 0.972798244574446 & 0.0544035108511078 & 0.0272017554255539 \tabularnewline
44 & 0.993355268661093 & 0.013289462677814 & 0.00664473133890701 \tabularnewline
45 & 0.996319496567204 & 0.00736100686559205 & 0.00368050343279602 \tabularnewline
46 & 0.999985713613009 & 2.85727739826483e-05 & 1.42863869913241e-05 \tabularnewline
47 & 0.999957987987282 & 8.40240254368873e-05 & 4.20120127184437e-05 \tabularnewline
48 & 0.999954240164956 & 9.15196700884637e-05 & 4.57598350442319e-05 \tabularnewline
49 & 0.999835140643598 & 0.000329718712803421 & 0.00016485935640171 \tabularnewline
50 & 0.999645601526803 & 0.000708796946393962 & 0.000354398473196981 \tabularnewline
51 & 0.998968816722475 & 0.00206236655504978 & 0.00103118327752489 \tabularnewline
52 & 0.996738374210721 & 0.00652325157855883 & 0.00326162578927941 \tabularnewline
53 & 0.994205758439606 & 0.0115884831207887 & 0.00579424156039433 \tabularnewline
54 & 0.979979497333898 & 0.040041005332203 & 0.0200205026661015 \tabularnewline
55 & 0.981808098082154 & 0.0363838038356927 & 0.0181919019178463 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198063&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.579743423816998[/C][C]0.840513152366003[/C][C]0.420256576183002[/C][/ROW]
[ROW][C]12[/C][C]0.580288061549378[/C][C]0.839423876901243[/C][C]0.419711938450622[/C][/ROW]
[ROW][C]13[/C][C]0.556559862030661[/C][C]0.886880275938678[/C][C]0.443440137969339[/C][/ROW]
[ROW][C]14[/C][C]0.505736665644351[/C][C]0.988526668711299[/C][C]0.494263334355649[/C][/ROW]
[ROW][C]15[/C][C]0.38593969486215[/C][C]0.771879389724299[/C][C]0.61406030513785[/C][/ROW]
[ROW][C]16[/C][C]0.296153767243341[/C][C]0.592307534486682[/C][C]0.703846232756659[/C][/ROW]
[ROW][C]17[/C][C]0.234161212795874[/C][C]0.468322425591748[/C][C]0.765838787204126[/C][/ROW]
[ROW][C]18[/C][C]0.16583579727524[/C][C]0.331671594550481[/C][C]0.83416420272476[/C][/ROW]
[ROW][C]19[/C][C]0.110018965681958[/C][C]0.220037931363917[/C][C]0.889981034318042[/C][/ROW]
[ROW][C]20[/C][C]0.0839008814710079[/C][C]0.167801762942016[/C][C]0.916099118528992[/C][/ROW]
[ROW][C]21[/C][C]0.231915345785524[/C][C]0.463830691571048[/C][C]0.768084654214476[/C][/ROW]
[ROW][C]22[/C][C]0.283986057043635[/C][C]0.567972114087269[/C][C]0.716013942956365[/C][/ROW]
[ROW][C]23[/C][C]0.278206112265697[/C][C]0.556412224531394[/C][C]0.721793887734303[/C][/ROW]
[ROW][C]24[/C][C]0.224996020685063[/C][C]0.449992041370126[/C][C]0.775003979314937[/C][/ROW]
[ROW][C]25[/C][C]0.174460068332422[/C][C]0.348920136664844[/C][C]0.825539931667578[/C][/ROW]
[ROW][C]26[/C][C]0.301468481900035[/C][C]0.60293696380007[/C][C]0.698531518099965[/C][/ROW]
[ROW][C]27[/C][C]0.277077703384165[/C][C]0.55415540676833[/C][C]0.722922296615835[/C][/ROW]
[ROW][C]28[/C][C]0.331355435450711[/C][C]0.662710870901422[/C][C]0.668644564549289[/C][/ROW]
[ROW][C]29[/C][C]0.849772845473047[/C][C]0.300454309053906[/C][C]0.150227154526953[/C][/ROW]
[ROW][C]30[/C][C]0.813232184519389[/C][C]0.373535630961221[/C][C]0.186767815480611[/C][/ROW]
[ROW][C]31[/C][C]0.815822708471353[/C][C]0.368354583057294[/C][C]0.184177291528647[/C][/ROW]
[ROW][C]32[/C][C]0.849991517820689[/C][C]0.300016964358621[/C][C]0.15000848217931[/C][/ROW]
[ROW][C]33[/C][C]0.944757885659514[/C][C]0.110484228680973[/C][C]0.0552421143404863[/C][/ROW]
[ROW][C]34[/C][C]0.931175075084322[/C][C]0.137649849831357[/C][C]0.0688249249156785[/C][/ROW]
[ROW][C]35[/C][C]0.900981416062236[/C][C]0.198037167875529[/C][C]0.0990185839377643[/C][/ROW]
[ROW][C]36[/C][C]0.882854113061105[/C][C]0.234291773877791[/C][C]0.117145886938895[/C][/ROW]
[ROW][C]37[/C][C]0.881761211531751[/C][C]0.236477576936498[/C][C]0.118238788468249[/C][/ROW]
[ROW][C]38[/C][C]0.870013291327056[/C][C]0.259973417345887[/C][C]0.129986708672944[/C][/ROW]
[ROW][C]39[/C][C]0.858102930530162[/C][C]0.283794138939675[/C][C]0.141897069469838[/C][/ROW]
[ROW][C]40[/C][C]0.814119298478048[/C][C]0.371761403043905[/C][C]0.185880701521952[/C][/ROW]
[ROW][C]41[/C][C]0.750755373689887[/C][C]0.498489252620227[/C][C]0.249244626310113[/C][/ROW]
[ROW][C]42[/C][C]0.788593725079192[/C][C]0.422812549841616[/C][C]0.211406274920808[/C][/ROW]
[ROW][C]43[/C][C]0.972798244574446[/C][C]0.0544035108511078[/C][C]0.0272017554255539[/C][/ROW]
[ROW][C]44[/C][C]0.993355268661093[/C][C]0.013289462677814[/C][C]0.00664473133890701[/C][/ROW]
[ROW][C]45[/C][C]0.996319496567204[/C][C]0.00736100686559205[/C][C]0.00368050343279602[/C][/ROW]
[ROW][C]46[/C][C]0.999985713613009[/C][C]2.85727739826483e-05[/C][C]1.42863869913241e-05[/C][/ROW]
[ROW][C]47[/C][C]0.999957987987282[/C][C]8.40240254368873e-05[/C][C]4.20120127184437e-05[/C][/ROW]
[ROW][C]48[/C][C]0.999954240164956[/C][C]9.15196700884637e-05[/C][C]4.57598350442319e-05[/C][/ROW]
[ROW][C]49[/C][C]0.999835140643598[/C][C]0.000329718712803421[/C][C]0.00016485935640171[/C][/ROW]
[ROW][C]50[/C][C]0.999645601526803[/C][C]0.000708796946393962[/C][C]0.000354398473196981[/C][/ROW]
[ROW][C]51[/C][C]0.998968816722475[/C][C]0.00206236655504978[/C][C]0.00103118327752489[/C][/ROW]
[ROW][C]52[/C][C]0.996738374210721[/C][C]0.00652325157855883[/C][C]0.00326162578927941[/C][/ROW]
[ROW][C]53[/C][C]0.994205758439606[/C][C]0.0115884831207887[/C][C]0.00579424156039433[/C][/ROW]
[ROW][C]54[/C][C]0.979979497333898[/C][C]0.040041005332203[/C][C]0.0200205026661015[/C][/ROW]
[ROW][C]55[/C][C]0.981808098082154[/C][C]0.0363838038356927[/C][C]0.0181919019178463[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198063&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198063&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.5797434238169980.8405131523660030.420256576183002
120.5802880615493780.8394238769012430.419711938450622
130.5565598620306610.8868802759386780.443440137969339
140.5057366656443510.9885266687112990.494263334355649
150.385939694862150.7718793897242990.61406030513785
160.2961537672433410.5923075344866820.703846232756659
170.2341612127958740.4683224255917480.765838787204126
180.165835797275240.3316715945504810.83416420272476
190.1100189656819580.2200379313639170.889981034318042
200.08390088147100790.1678017629420160.916099118528992
210.2319153457855240.4638306915710480.768084654214476
220.2839860570436350.5679721140872690.716013942956365
230.2782061122656970.5564122245313940.721793887734303
240.2249960206850630.4499920413701260.775003979314937
250.1744600683324220.3489201366648440.825539931667578
260.3014684819000350.602936963800070.698531518099965
270.2770777033841650.554155406768330.722922296615835
280.3313554354507110.6627108709014220.668644564549289
290.8497728454730470.3004543090539060.150227154526953
300.8132321845193890.3735356309612210.186767815480611
310.8158227084713530.3683545830572940.184177291528647
320.8499915178206890.3000169643586210.15000848217931
330.9447578856595140.1104842286809730.0552421143404863
340.9311750750843220.1376498498313570.0688249249156785
350.9009814160622360.1980371678755290.0990185839377643
360.8828541130611050.2342917738777910.117145886938895
370.8817612115317510.2364775769364980.118238788468249
380.8700132913270560.2599734173458870.129986708672944
390.8581029305301620.2837941389396750.141897069469838
400.8141192984780480.3717614030439050.185880701521952
410.7507553736898870.4984892526202270.249244626310113
420.7885937250791920.4228125498416160.211406274920808
430.9727982445744460.05440351085110780.0272017554255539
440.9933552686610930.0132894626778140.00664473133890701
450.9963194965672040.007361006865592050.00368050343279602
460.9999857136130092.85727739826483e-051.42863869913241e-05
470.9999579879872828.40240254368873e-054.20120127184437e-05
480.9999542401649569.15196700884637e-054.57598350442319e-05
490.9998351406435980.0003297187128034210.00016485935640171
500.9996456015268030.0007087969463939620.000354398473196981
510.9989688167224750.002062366555049780.00103118327752489
520.9967383742107210.006523251578558830.00326162578927941
530.9942057584396060.01158848312078870.00579424156039433
540.9799794973338980.0400410053322030.0200205026661015
550.9818080980821540.03638380383569270.0181919019178463







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.177777777777778NOK
5% type I error level120.266666666666667NOK
10% type I error level130.288888888888889NOK

\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 & 8 & 0.177777777777778 & NOK \tabularnewline
5% type I error level & 12 & 0.266666666666667 & NOK \tabularnewline
10% type I error level & 13 & 0.288888888888889 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198063&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]8[/C][C]0.177777777777778[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]12[/C][C]0.266666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.288888888888889[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198063&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198063&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 testsOK/NOK
1% type I error level80.177777777777778NOK
5% type I error level120.266666666666667NOK
10% type I error level130.288888888888889NOK



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