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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 03 Nov 2012 10:15:20 -0400
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/Nov/03/t13519521735efcft0ymrgp3v0.htm/, Retrieved Tue, 09 Aug 2022 20:50:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185733, Retrieved Tue, 09 Aug 2022 20:50:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact91
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [WS 7 multiple reg...] [2012-11-03 14:15:20] [074a00bbc2315ea54a3f557bcf69eecf] [Current]
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Dataseries X:
9190	2514	2550	1512	1591	472	551
9251	2537	2572	1517	1595	476	554
9328	2564	2597	1525	1602	483	558
9428	2595	2623	1540	1613	493	565
9499	2617	2647	1547	1622	498	568
9556	2638	2670	1547	1627	502	572
9606	2657	2690	1547	1632	504	575
9632	2668	2705	1547	1634	503	574
9660	2683	2721	1546	1637	501	572
9651	2687	2729	1533	1627	502	573
9695	2705	2747	1538	1632	502	572
9727	2717	2761	1543	1637	500	569
9757	2728	2773	1549	1643	498	566
9788	2741	2786	1556	1650	495	560
9813	2752	2796	1559	1654	494	557
9823	2759	2807	1559	1656	490	552
9837	2767	2817	1563	1661	484	545
9842	2774	2827	1563	1662	477	539
9855	2781	2838	1564	1664	474	535
9863	2788	2847	1564	1665	469	531
9855	2789	2853	1557	1661	466	528
9858	2795	2860	1554	1659	464	526
9853	2798	2864	1552	1656	460	523
9858	2801	2869	1552	1656	458	521
9859	2803	2873	1551	1655	457	519
9865	2808	2877	1552	1654	456	517
9876	2813	2883	1554	1656	455	515
9928	2826	2896	1567	1668	456	514
9948	2835	2905	1572	1672	453	511
9987	2849	2919	1579	1680	453	508
10022	2862	2933	1588	1688	449	502
10068	2877	2948	1597	1696	449	501
10101	2888	2959	1603	1702	449	500
10131	2897	2969	1607	1706	452	500
10143	2902	2978	1607	1708	450	498
10170	2911	2988	1609	1711	452	499
10192	2917	2996	1612	1714	454	499
10214	2924	3003	1615	1717	455	500
10239	2930	3011	1619	1721	458	501
10263	2935	3018	1622	1724	461	503
10310	2945	3028	1628	1730	469	510
10355	2957	3038	1634	1735	477	515
10396	2967	3049	1640	1740	480	520
10446	2980	3063	1648	1748	484	523
10511	2997	3081	1657	1757	490	529
10585	3017	3100	1668	1768	497	534
10667	3040	3122	1678	1778	506	543
10753	3064	3145	1687	1789	516	553
10840	3085	3167	1700	1798	527	563
10951	3113	3193	1714	1811	542	577




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185733&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185733&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185733&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'Sir Maurice George Kendall' @ kendall.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Totaal[t] = -15.6001765674056 + 0.97647387586164Vlaamsm[t] + 1.02996739943037Vlaamsvr[t] + 1.01992543411572Waalsm[t] + 0.97401985047538Waalsvr[t] + 0.930099163421406Brusselm[t] + 1.0769098760506Brusselvr[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totaal[t] =  -15.6001765674056 +  0.97647387586164Vlaamsm[t] +  1.02996739943037Vlaamsvr[t] +  1.01992543411572Waalsm[t] +  0.97401985047538Waalsvr[t] +  0.930099163421406Brusselm[t] +  1.0769098760506Brusselvr[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185733&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totaal[t] =  -15.6001765674056 +  0.97647387586164Vlaamsm[t] +  1.02996739943037Vlaamsvr[t] +  1.01992543411572Waalsm[t] +  0.97401985047538Waalsvr[t] +  0.930099163421406Brusselm[t] +  1.0769098760506Brusselvr[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185733&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185733&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
Totaal[t] = -15.6001765674056 + 0.97647387586164Vlaamsm[t] + 1.02996739943037Vlaamsvr[t] + 1.01992543411572Waalsm[t] + 0.97401985047538Waalsvr[t] + 0.930099163421406Brusselm[t] + 1.0769098760506Brusselvr[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-15.600176567405622.712338-0.68690.4958590.24793
Vlaamsm0.976473875861640.03001832.5300
Vlaamsvr1.029967399430370.02795236.847400
Waalsm1.019925434115720.03543928.779600
Waalsvr0.974019850475380.05013919.426300
Brusselm0.9300991634214060.05695316.330900
Brusselvr1.07690987605060.05123321.019900

\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) & -15.6001765674056 & 22.712338 & -0.6869 & 0.495859 & 0.24793 \tabularnewline
Vlaamsm & 0.97647387586164 & 0.030018 & 32.53 & 0 & 0 \tabularnewline
Vlaamsvr & 1.02996739943037 & 0.027952 & 36.8474 & 0 & 0 \tabularnewline
Waalsm & 1.01992543411572 & 0.035439 & 28.7796 & 0 & 0 \tabularnewline
Waalsvr & 0.97401985047538 & 0.050139 & 19.4263 & 0 & 0 \tabularnewline
Brusselm & 0.930099163421406 & 0.056953 & 16.3309 & 0 & 0 \tabularnewline
Brusselvr & 1.0769098760506 & 0.051233 & 21.0199 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185733&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]-15.6001765674056[/C][C]22.712338[/C][C]-0.6869[/C][C]0.495859[/C][C]0.24793[/C][/ROW]
[ROW][C]Vlaamsm[/C][C]0.97647387586164[/C][C]0.030018[/C][C]32.53[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Vlaamsvr[/C][C]1.02996739943037[/C][C]0.027952[/C][C]36.8474[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Waalsm[/C][C]1.01992543411572[/C][C]0.035439[/C][C]28.7796[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Waalsvr[/C][C]0.97401985047538[/C][C]0.050139[/C][C]19.4263[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Brusselm[/C][C]0.930099163421406[/C][C]0.056953[/C][C]16.3309[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Brusselvr[/C][C]1.0769098760506[/C][C]0.051233[/C][C]21.0199[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185733&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185733&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)-15.600176567405622.712338-0.68690.4958590.24793
Vlaamsm0.976473875861640.03001832.5300
Vlaamsvr1.029967399430370.02795236.847400
Waalsm1.019925434115720.03543928.779600
Waalsvr0.974019850475380.05013919.426300
Brusselm0.9300991634214060.05695316.330900
Brusselvr1.07690987605060.05123321.019900







Multiple Linear Regression - Regression Statistics
Multiple R0.999998914665498
R-squared0.999997829332175
Adjusted R-squared0.999997526448292
F-TEST (value)3301588.12276727
F-TEST (DF numerator)6
F-TEST (DF denominator)43
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.620267454098936
Sum Squared Residuals16.5434637284182

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999998914665498 \tabularnewline
R-squared & 0.999997829332175 \tabularnewline
Adjusted R-squared & 0.999997526448292 \tabularnewline
F-TEST (value) & 3301588.12276727 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 43 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.620267454098936 \tabularnewline
Sum Squared Residuals & 16.5434637284182 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185733&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999998914665498[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999997829332175[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.999997526448292[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3301588.12276727[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]43[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.620267454098936[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]16.5434637284182[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185733&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185733&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 R0.999998914665498
R-squared0.999997829332175
Adjusted R-squared0.999997526448292
F-TEST (value)3301588.12276727
F-TEST (DF numerator)6
F-TEST (DF denominator)43
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.620267454098936
Sum Squared Residuals16.5434637284182







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
191909189.849001224280.15099877571652
292519250.914016010890.085983989109537
393289328.82387171932-0.823871719319845
494289428.72617488975-0.726174889753537
594999498.714699883390.285300116614198
695569555.808036873640.191963126356304
796069604.921415710991.07858428900623
896329631.053169998410.946830001593881
996609660.06787256558-0.0678725655830496
1096519651.22128715569-0.221287155686387
1196959696.23004665785-1.23004665784747
1297279727.24607522817-0.246075228173232
1397579757.21964040837-0.219640408367745
1497889788.00923723273-0.00923723273375928
1598139811.845150774191.15484922580903
1698239822.853202965970.146797034031338
1798379836.795504843120.204495156877387
1898429841.932362418680.0676375813202784
1998559855.96734908405-0.967349084045138
2098639864.08825733912-1.0882573391159
2198559854.187951052430.812048947567841
2298589858.23473202137-0.234732021372657
2398539853.37098654518-0.370986545184052
2498589857.436227090980.563772909023205
2598599858.431180240310.568819759692135
2698659864.395405885460.604594114544728
2798769876.36155131501-0.361551315005284
2899289927.245746030380.754253969618827
2999489948.27839696207-0.27839696207337
3099879988.06948203062-1.06948203062279
311002210021.97281780970.0271821902954123
321006810067.96401477390.0359852261213185
331010110100.92163063360.0783693664132059
341013110130.77564813930.2243518607261
351014310142.86174573550.138254264537816
361017010169.84870323510.151296764928336
371019210191.78931986630.210680133699415
381021410213.82325368660.176746313410042
391023910239.7648246419-0.764824641881989
401026310262.78291891330.217081086658497
411031010309.79016581350.209834186466298
421035510355.6225208629-0.622520862872554
431039610395.88139974280.118600257188467
441044610445.89779227960.102207720395697
451051110511.0248231572-0.0248231571508038
461058510583.95232291831.04767708173438
471066710667.0730390517-0.073039051710533
481075310754.1612999163-1.16129991627919
491084010840.3519329528-0.351932952766044
501095110950.44179371190.558206288088902

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9190 & 9189.84900122428 & 0.15099877571652 \tabularnewline
2 & 9251 & 9250.91401601089 & 0.085983989109537 \tabularnewline
3 & 9328 & 9328.82387171932 & -0.823871719319845 \tabularnewline
4 & 9428 & 9428.72617488975 & -0.726174889753537 \tabularnewline
5 & 9499 & 9498.71469988339 & 0.285300116614198 \tabularnewline
6 & 9556 & 9555.80803687364 & 0.191963126356304 \tabularnewline
7 & 9606 & 9604.92141571099 & 1.07858428900623 \tabularnewline
8 & 9632 & 9631.05316999841 & 0.946830001593881 \tabularnewline
9 & 9660 & 9660.06787256558 & -0.0678725655830496 \tabularnewline
10 & 9651 & 9651.22128715569 & -0.221287155686387 \tabularnewline
11 & 9695 & 9696.23004665785 & -1.23004665784747 \tabularnewline
12 & 9727 & 9727.24607522817 & -0.246075228173232 \tabularnewline
13 & 9757 & 9757.21964040837 & -0.219640408367745 \tabularnewline
14 & 9788 & 9788.00923723273 & -0.00923723273375928 \tabularnewline
15 & 9813 & 9811.84515077419 & 1.15484922580903 \tabularnewline
16 & 9823 & 9822.85320296597 & 0.146797034031338 \tabularnewline
17 & 9837 & 9836.79550484312 & 0.204495156877387 \tabularnewline
18 & 9842 & 9841.93236241868 & 0.0676375813202784 \tabularnewline
19 & 9855 & 9855.96734908405 & -0.967349084045138 \tabularnewline
20 & 9863 & 9864.08825733912 & -1.0882573391159 \tabularnewline
21 & 9855 & 9854.18795105243 & 0.812048947567841 \tabularnewline
22 & 9858 & 9858.23473202137 & -0.234732021372657 \tabularnewline
23 & 9853 & 9853.37098654518 & -0.370986545184052 \tabularnewline
24 & 9858 & 9857.43622709098 & 0.563772909023205 \tabularnewline
25 & 9859 & 9858.43118024031 & 0.568819759692135 \tabularnewline
26 & 9865 & 9864.39540588546 & 0.604594114544728 \tabularnewline
27 & 9876 & 9876.36155131501 & -0.361551315005284 \tabularnewline
28 & 9928 & 9927.24574603038 & 0.754253969618827 \tabularnewline
29 & 9948 & 9948.27839696207 & -0.27839696207337 \tabularnewline
30 & 9987 & 9988.06948203062 & -1.06948203062279 \tabularnewline
31 & 10022 & 10021.9728178097 & 0.0271821902954123 \tabularnewline
32 & 10068 & 10067.9640147739 & 0.0359852261213185 \tabularnewline
33 & 10101 & 10100.9216306336 & 0.0783693664132059 \tabularnewline
34 & 10131 & 10130.7756481393 & 0.2243518607261 \tabularnewline
35 & 10143 & 10142.8617457355 & 0.138254264537816 \tabularnewline
36 & 10170 & 10169.8487032351 & 0.151296764928336 \tabularnewline
37 & 10192 & 10191.7893198663 & 0.210680133699415 \tabularnewline
38 & 10214 & 10213.8232536866 & 0.176746313410042 \tabularnewline
39 & 10239 & 10239.7648246419 & -0.764824641881989 \tabularnewline
40 & 10263 & 10262.7829189133 & 0.217081086658497 \tabularnewline
41 & 10310 & 10309.7901658135 & 0.209834186466298 \tabularnewline
42 & 10355 & 10355.6225208629 & -0.622520862872554 \tabularnewline
43 & 10396 & 10395.8813997428 & 0.118600257188467 \tabularnewline
44 & 10446 & 10445.8977922796 & 0.102207720395697 \tabularnewline
45 & 10511 & 10511.0248231572 & -0.0248231571508038 \tabularnewline
46 & 10585 & 10583.9523229183 & 1.04767708173438 \tabularnewline
47 & 10667 & 10667.0730390517 & -0.073039051710533 \tabularnewline
48 & 10753 & 10754.1612999163 & -1.16129991627919 \tabularnewline
49 & 10840 & 10840.3519329528 & -0.351932952766044 \tabularnewline
50 & 10951 & 10950.4417937119 & 0.558206288088902 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185733&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]9190[/C][C]9189.84900122428[/C][C]0.15099877571652[/C][/ROW]
[ROW][C]2[/C][C]9251[/C][C]9250.91401601089[/C][C]0.085983989109537[/C][/ROW]
[ROW][C]3[/C][C]9328[/C][C]9328.82387171932[/C][C]-0.823871719319845[/C][/ROW]
[ROW][C]4[/C][C]9428[/C][C]9428.72617488975[/C][C]-0.726174889753537[/C][/ROW]
[ROW][C]5[/C][C]9499[/C][C]9498.71469988339[/C][C]0.285300116614198[/C][/ROW]
[ROW][C]6[/C][C]9556[/C][C]9555.80803687364[/C][C]0.191963126356304[/C][/ROW]
[ROW][C]7[/C][C]9606[/C][C]9604.92141571099[/C][C]1.07858428900623[/C][/ROW]
[ROW][C]8[/C][C]9632[/C][C]9631.05316999841[/C][C]0.946830001593881[/C][/ROW]
[ROW][C]9[/C][C]9660[/C][C]9660.06787256558[/C][C]-0.0678725655830496[/C][/ROW]
[ROW][C]10[/C][C]9651[/C][C]9651.22128715569[/C][C]-0.221287155686387[/C][/ROW]
[ROW][C]11[/C][C]9695[/C][C]9696.23004665785[/C][C]-1.23004665784747[/C][/ROW]
[ROW][C]12[/C][C]9727[/C][C]9727.24607522817[/C][C]-0.246075228173232[/C][/ROW]
[ROW][C]13[/C][C]9757[/C][C]9757.21964040837[/C][C]-0.219640408367745[/C][/ROW]
[ROW][C]14[/C][C]9788[/C][C]9788.00923723273[/C][C]-0.00923723273375928[/C][/ROW]
[ROW][C]15[/C][C]9813[/C][C]9811.84515077419[/C][C]1.15484922580903[/C][/ROW]
[ROW][C]16[/C][C]9823[/C][C]9822.85320296597[/C][C]0.146797034031338[/C][/ROW]
[ROW][C]17[/C][C]9837[/C][C]9836.79550484312[/C][C]0.204495156877387[/C][/ROW]
[ROW][C]18[/C][C]9842[/C][C]9841.93236241868[/C][C]0.0676375813202784[/C][/ROW]
[ROW][C]19[/C][C]9855[/C][C]9855.96734908405[/C][C]-0.967349084045138[/C][/ROW]
[ROW][C]20[/C][C]9863[/C][C]9864.08825733912[/C][C]-1.0882573391159[/C][/ROW]
[ROW][C]21[/C][C]9855[/C][C]9854.18795105243[/C][C]0.812048947567841[/C][/ROW]
[ROW][C]22[/C][C]9858[/C][C]9858.23473202137[/C][C]-0.234732021372657[/C][/ROW]
[ROW][C]23[/C][C]9853[/C][C]9853.37098654518[/C][C]-0.370986545184052[/C][/ROW]
[ROW][C]24[/C][C]9858[/C][C]9857.43622709098[/C][C]0.563772909023205[/C][/ROW]
[ROW][C]25[/C][C]9859[/C][C]9858.43118024031[/C][C]0.568819759692135[/C][/ROW]
[ROW][C]26[/C][C]9865[/C][C]9864.39540588546[/C][C]0.604594114544728[/C][/ROW]
[ROW][C]27[/C][C]9876[/C][C]9876.36155131501[/C][C]-0.361551315005284[/C][/ROW]
[ROW][C]28[/C][C]9928[/C][C]9927.24574603038[/C][C]0.754253969618827[/C][/ROW]
[ROW][C]29[/C][C]9948[/C][C]9948.27839696207[/C][C]-0.27839696207337[/C][/ROW]
[ROW][C]30[/C][C]9987[/C][C]9988.06948203062[/C][C]-1.06948203062279[/C][/ROW]
[ROW][C]31[/C][C]10022[/C][C]10021.9728178097[/C][C]0.0271821902954123[/C][/ROW]
[ROW][C]32[/C][C]10068[/C][C]10067.9640147739[/C][C]0.0359852261213185[/C][/ROW]
[ROW][C]33[/C][C]10101[/C][C]10100.9216306336[/C][C]0.0783693664132059[/C][/ROW]
[ROW][C]34[/C][C]10131[/C][C]10130.7756481393[/C][C]0.2243518607261[/C][/ROW]
[ROW][C]35[/C][C]10143[/C][C]10142.8617457355[/C][C]0.138254264537816[/C][/ROW]
[ROW][C]36[/C][C]10170[/C][C]10169.8487032351[/C][C]0.151296764928336[/C][/ROW]
[ROW][C]37[/C][C]10192[/C][C]10191.7893198663[/C][C]0.210680133699415[/C][/ROW]
[ROW][C]38[/C][C]10214[/C][C]10213.8232536866[/C][C]0.176746313410042[/C][/ROW]
[ROW][C]39[/C][C]10239[/C][C]10239.7648246419[/C][C]-0.764824641881989[/C][/ROW]
[ROW][C]40[/C][C]10263[/C][C]10262.7829189133[/C][C]0.217081086658497[/C][/ROW]
[ROW][C]41[/C][C]10310[/C][C]10309.7901658135[/C][C]0.209834186466298[/C][/ROW]
[ROW][C]42[/C][C]10355[/C][C]10355.6225208629[/C][C]-0.622520862872554[/C][/ROW]
[ROW][C]43[/C][C]10396[/C][C]10395.8813997428[/C][C]0.118600257188467[/C][/ROW]
[ROW][C]44[/C][C]10446[/C][C]10445.8977922796[/C][C]0.102207720395697[/C][/ROW]
[ROW][C]45[/C][C]10511[/C][C]10511.0248231572[/C][C]-0.0248231571508038[/C][/ROW]
[ROW][C]46[/C][C]10585[/C][C]10583.9523229183[/C][C]1.04767708173438[/C][/ROW]
[ROW][C]47[/C][C]10667[/C][C]10667.0730390517[/C][C]-0.073039051710533[/C][/ROW]
[ROW][C]48[/C][C]10753[/C][C]10754.1612999163[/C][C]-1.16129991627919[/C][/ROW]
[ROW][C]49[/C][C]10840[/C][C]10840.3519329528[/C][C]-0.351932952766044[/C][/ROW]
[ROW][C]50[/C][C]10951[/C][C]10950.4417937119[/C][C]0.558206288088902[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185733&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185733&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
191909189.849001224280.15099877571652
292519250.914016010890.085983989109537
393289328.82387171932-0.823871719319845
494289428.72617488975-0.726174889753537
594999498.714699883390.285300116614198
695569555.808036873640.191963126356304
796069604.921415710991.07858428900623
896329631.053169998410.946830001593881
996609660.06787256558-0.0678725655830496
1096519651.22128715569-0.221287155686387
1196959696.23004665785-1.23004665784747
1297279727.24607522817-0.246075228173232
1397579757.21964040837-0.219640408367745
1497889788.00923723273-0.00923723273375928
1598139811.845150774191.15484922580903
1698239822.853202965970.146797034031338
1798379836.795504843120.204495156877387
1898429841.932362418680.0676375813202784
1998559855.96734908405-0.967349084045138
2098639864.08825733912-1.0882573391159
2198559854.187951052430.812048947567841
2298589858.23473202137-0.234732021372657
2398539853.37098654518-0.370986545184052
2498589857.436227090980.563772909023205
2598599858.431180240310.568819759692135
2698659864.395405885460.604594114544728
2798769876.36155131501-0.361551315005284
2899289927.245746030380.754253969618827
2999489948.27839696207-0.27839696207337
3099879988.06948203062-1.06948203062279
311002210021.97281780970.0271821902954123
321006810067.96401477390.0359852261213185
331010110100.92163063360.0783693664132059
341013110130.77564813930.2243518607261
351014310142.86174573550.138254264537816
361017010169.84870323510.151296764928336
371019210191.78931986630.210680133699415
381021410213.82325368660.176746313410042
391023910239.7648246419-0.764824641881989
401026310262.78291891330.217081086658497
411031010309.79016581350.209834186466298
421035510355.6225208629-0.622520862872554
431039610395.88139974280.118600257188467
441044610445.89779227960.102207720395697
451051110511.0248231572-0.0248231571508038
461058510583.95232291831.04767708173438
471066710667.0730390517-0.073039051710533
481075310754.1612999163-1.16129991627919
491084010840.3519329528-0.351932952766044
501095110950.44179371190.558206288088902







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.1876728441271130.3753456882542270.812327155872887
110.1027517553050320.2055035106100630.897248244694968
120.2659089794326840.5318179588653690.734091020567316
130.1859824538366690.3719649076733370.814017546163331
140.1812431771697990.3624863543395970.818756822830201
150.3740531536505890.7481063073011770.625946846349411
160.4033882530461290.8067765060922580.596611746953871
170.4198872854932060.8397745709864110.580112714506794
180.3922006091993380.7844012183986750.607799390800662
190.4527544154539390.9055088309078780.547245584546061
200.5030136340639520.9939727318720960.496986365936048
210.8459622123446240.3080755753107510.154037787655376
220.8095219774905140.3809560450189720.190478022509486
230.8225167640412480.3549664719175030.177483235958752
240.8729118466300960.2541763067398080.127088153369904
250.8760810673306220.2478378653387560.123918932669378
260.904830734273890.190338531452220.0951692657261099
270.8663301474704930.2673397050590140.133669852529507
280.9540286206756270.09194275864874620.0459713793243731
290.9391666527899480.1216666944201030.0608333472100516
300.9411656263858810.1176687472282380.0588343736141189
310.9140363925838540.1719272148322920.0859636074161462
320.8630975640804760.2738048718390480.136902435919524
330.8137326578172290.3725346843655420.186267342182771
340.7629520100497760.4740959799004480.237047989950224
350.7596820862775830.4806358274448330.240317913722416
360.6898517917016630.6202964165966740.310148208298337
370.5693341879677050.8613316240645910.430665812032295
380.4614151135507080.9228302271014160.538584886449292
390.5943775798090520.8112448403818970.405622420190948
400.5149091829654430.9701816340691140.485090817034557

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.187672844127113 & 0.375345688254227 & 0.812327155872887 \tabularnewline
11 & 0.102751755305032 & 0.205503510610063 & 0.897248244694968 \tabularnewline
12 & 0.265908979432684 & 0.531817958865369 & 0.734091020567316 \tabularnewline
13 & 0.185982453836669 & 0.371964907673337 & 0.814017546163331 \tabularnewline
14 & 0.181243177169799 & 0.362486354339597 & 0.818756822830201 \tabularnewline
15 & 0.374053153650589 & 0.748106307301177 & 0.625946846349411 \tabularnewline
16 & 0.403388253046129 & 0.806776506092258 & 0.596611746953871 \tabularnewline
17 & 0.419887285493206 & 0.839774570986411 & 0.580112714506794 \tabularnewline
18 & 0.392200609199338 & 0.784401218398675 & 0.607799390800662 \tabularnewline
19 & 0.452754415453939 & 0.905508830907878 & 0.547245584546061 \tabularnewline
20 & 0.503013634063952 & 0.993972731872096 & 0.496986365936048 \tabularnewline
21 & 0.845962212344624 & 0.308075575310751 & 0.154037787655376 \tabularnewline
22 & 0.809521977490514 & 0.380956045018972 & 0.190478022509486 \tabularnewline
23 & 0.822516764041248 & 0.354966471917503 & 0.177483235958752 \tabularnewline
24 & 0.872911846630096 & 0.254176306739808 & 0.127088153369904 \tabularnewline
25 & 0.876081067330622 & 0.247837865338756 & 0.123918932669378 \tabularnewline
26 & 0.90483073427389 & 0.19033853145222 & 0.0951692657261099 \tabularnewline
27 & 0.866330147470493 & 0.267339705059014 & 0.133669852529507 \tabularnewline
28 & 0.954028620675627 & 0.0919427586487462 & 0.0459713793243731 \tabularnewline
29 & 0.939166652789948 & 0.121666694420103 & 0.0608333472100516 \tabularnewline
30 & 0.941165626385881 & 0.117668747228238 & 0.0588343736141189 \tabularnewline
31 & 0.914036392583854 & 0.171927214832292 & 0.0859636074161462 \tabularnewline
32 & 0.863097564080476 & 0.273804871839048 & 0.136902435919524 \tabularnewline
33 & 0.813732657817229 & 0.372534684365542 & 0.186267342182771 \tabularnewline
34 & 0.762952010049776 & 0.474095979900448 & 0.237047989950224 \tabularnewline
35 & 0.759682086277583 & 0.480635827444833 & 0.240317913722416 \tabularnewline
36 & 0.689851791701663 & 0.620296416596674 & 0.310148208298337 \tabularnewline
37 & 0.569334187967705 & 0.861331624064591 & 0.430665812032295 \tabularnewline
38 & 0.461415113550708 & 0.922830227101416 & 0.538584886449292 \tabularnewline
39 & 0.594377579809052 & 0.811244840381897 & 0.405622420190948 \tabularnewline
40 & 0.514909182965443 & 0.970181634069114 & 0.485090817034557 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185733&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.187672844127113[/C][C]0.375345688254227[/C][C]0.812327155872887[/C][/ROW]
[ROW][C]11[/C][C]0.102751755305032[/C][C]0.205503510610063[/C][C]0.897248244694968[/C][/ROW]
[ROW][C]12[/C][C]0.265908979432684[/C][C]0.531817958865369[/C][C]0.734091020567316[/C][/ROW]
[ROW][C]13[/C][C]0.185982453836669[/C][C]0.371964907673337[/C][C]0.814017546163331[/C][/ROW]
[ROW][C]14[/C][C]0.181243177169799[/C][C]0.362486354339597[/C][C]0.818756822830201[/C][/ROW]
[ROW][C]15[/C][C]0.374053153650589[/C][C]0.748106307301177[/C][C]0.625946846349411[/C][/ROW]
[ROW][C]16[/C][C]0.403388253046129[/C][C]0.806776506092258[/C][C]0.596611746953871[/C][/ROW]
[ROW][C]17[/C][C]0.419887285493206[/C][C]0.839774570986411[/C][C]0.580112714506794[/C][/ROW]
[ROW][C]18[/C][C]0.392200609199338[/C][C]0.784401218398675[/C][C]0.607799390800662[/C][/ROW]
[ROW][C]19[/C][C]0.452754415453939[/C][C]0.905508830907878[/C][C]0.547245584546061[/C][/ROW]
[ROW][C]20[/C][C]0.503013634063952[/C][C]0.993972731872096[/C][C]0.496986365936048[/C][/ROW]
[ROW][C]21[/C][C]0.845962212344624[/C][C]0.308075575310751[/C][C]0.154037787655376[/C][/ROW]
[ROW][C]22[/C][C]0.809521977490514[/C][C]0.380956045018972[/C][C]0.190478022509486[/C][/ROW]
[ROW][C]23[/C][C]0.822516764041248[/C][C]0.354966471917503[/C][C]0.177483235958752[/C][/ROW]
[ROW][C]24[/C][C]0.872911846630096[/C][C]0.254176306739808[/C][C]0.127088153369904[/C][/ROW]
[ROW][C]25[/C][C]0.876081067330622[/C][C]0.247837865338756[/C][C]0.123918932669378[/C][/ROW]
[ROW][C]26[/C][C]0.90483073427389[/C][C]0.19033853145222[/C][C]0.0951692657261099[/C][/ROW]
[ROW][C]27[/C][C]0.866330147470493[/C][C]0.267339705059014[/C][C]0.133669852529507[/C][/ROW]
[ROW][C]28[/C][C]0.954028620675627[/C][C]0.0919427586487462[/C][C]0.0459713793243731[/C][/ROW]
[ROW][C]29[/C][C]0.939166652789948[/C][C]0.121666694420103[/C][C]0.0608333472100516[/C][/ROW]
[ROW][C]30[/C][C]0.941165626385881[/C][C]0.117668747228238[/C][C]0.0588343736141189[/C][/ROW]
[ROW][C]31[/C][C]0.914036392583854[/C][C]0.171927214832292[/C][C]0.0859636074161462[/C][/ROW]
[ROW][C]32[/C][C]0.863097564080476[/C][C]0.273804871839048[/C][C]0.136902435919524[/C][/ROW]
[ROW][C]33[/C][C]0.813732657817229[/C][C]0.372534684365542[/C][C]0.186267342182771[/C][/ROW]
[ROW][C]34[/C][C]0.762952010049776[/C][C]0.474095979900448[/C][C]0.237047989950224[/C][/ROW]
[ROW][C]35[/C][C]0.759682086277583[/C][C]0.480635827444833[/C][C]0.240317913722416[/C][/ROW]
[ROW][C]36[/C][C]0.689851791701663[/C][C]0.620296416596674[/C][C]0.310148208298337[/C][/ROW]
[ROW][C]37[/C][C]0.569334187967705[/C][C]0.861331624064591[/C][C]0.430665812032295[/C][/ROW]
[ROW][C]38[/C][C]0.461415113550708[/C][C]0.922830227101416[/C][C]0.538584886449292[/C][/ROW]
[ROW][C]39[/C][C]0.594377579809052[/C][C]0.811244840381897[/C][C]0.405622420190948[/C][/ROW]
[ROW][C]40[/C][C]0.514909182965443[/C][C]0.970181634069114[/C][C]0.485090817034557[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185733&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185733&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
100.1876728441271130.3753456882542270.812327155872887
110.1027517553050320.2055035106100630.897248244694968
120.2659089794326840.5318179588653690.734091020567316
130.1859824538366690.3719649076733370.814017546163331
140.1812431771697990.3624863543395970.818756822830201
150.3740531536505890.7481063073011770.625946846349411
160.4033882530461290.8067765060922580.596611746953871
170.4198872854932060.8397745709864110.580112714506794
180.3922006091993380.7844012183986750.607799390800662
190.4527544154539390.9055088309078780.547245584546061
200.5030136340639520.9939727318720960.496986365936048
210.8459622123446240.3080755753107510.154037787655376
220.8095219774905140.3809560450189720.190478022509486
230.8225167640412480.3549664719175030.177483235958752
240.8729118466300960.2541763067398080.127088153369904
250.8760810673306220.2478378653387560.123918932669378
260.904830734273890.190338531452220.0951692657261099
270.8663301474704930.2673397050590140.133669852529507
280.9540286206756270.09194275864874620.0459713793243731
290.9391666527899480.1216666944201030.0608333472100516
300.9411656263858810.1176687472282380.0588343736141189
310.9140363925838540.1719272148322920.0859636074161462
320.8630975640804760.2738048718390480.136902435919524
330.8137326578172290.3725346843655420.186267342182771
340.7629520100497760.4740959799004480.237047989950224
350.7596820862775830.4806358274448330.240317913722416
360.6898517917016630.6202964165966740.310148208298337
370.5693341879677050.8613316240645910.430665812032295
380.4614151135507080.9228302271014160.538584886449292
390.5943775798090520.8112448403818970.405622420190948
400.5149091829654430.9701816340691140.485090817034557







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.032258064516129OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.032258064516129 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185733&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.032258064516129[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185733&T=6

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Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.032258064516129OK



Parameters (Session):
par1 = 2 ; 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')
}