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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 11:32:37 -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/t13519567727y1gs8hiz901xz4.htm/, Retrieved Tue, 09 Aug 2022 20:42:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185748, Retrieved Tue, 09 Aug 2022 20:42:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [WS7] [2012-11-03 15:32:37] [4c7c16453d038d093cc11140275f1ca7] [Current]
- RMPD    [Simple Linear Regression] [simple regression] [2012-12-20 19:38:22] [0287c3a79787f56bc35e5faae1b93dfd]
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Dataseries X:
10951	3113	3193	1714	1811	542	577
10840	3085	3167	1700	1798	527	563
10753	3064	3145	1687	1789	516	553
10667	3040	3122	1678	1778	506	543
10585	3017	3100	1668	1768	497	534
10511	2997	3081	1657	1757	490	529
10446	2980	3063	1648	1748	484	523
10396	2967	3049	1640	1740	480	520
10355	2957	3038	1634	1735	477	515
10310	2945	3028	1628	1730	469	510
10263	2935	3018	1622	1724	461	503
10239	2930	3011	1619	1721	458	501
10214	2924	3003	1615	1717	455	500
10192	2917	2996	1612	1714	454	499
10170	2911	2988	1609	1711	452	499
10143	2902	2978	1607	1708	450	498
10131	2897	2969	1607	1706	452	500
10101	2888	2959	1603	1702	449	500
10068	2877	2948	1597	1696	449	501
10022	2862	2933	1588	1688	449	502
9987	2849	2919	1579	1680	453	508
9948	2835	2905	1572	1672	453	511
9928	2826	2896	1567	1668	456	514
9876	2813	2883	1554	1656	455	515
9865	2808	2877	1552	1654	456	517
9859	2803	2873	1551	1655	457	519
9858	2801	2869	1552	1656	458	521
9853	2798	2864	1552	1656	460	523
9858	2795	2860	1554	1659	464	526
9855	2789	2853	1557	1661	466	528
9863	2788	2847	1564	1665	469	531
9855	2781	2838	1564	1664	474	535
9842	2774	2827	1563	1662	477	539
9837	2767	2817	1563	1661	484	545
9823	2759	2807	1559	1656	490	552
9813	2752	2796	1559	1654	494	557
9788	2741	2786	1556	1650	495	560
9757	2728	2773	1549	1643	498	566
9727	2717	2761	1543	1637	500	569
9695	2705	2747	1538	1632	502	572
9651	2687	2729	1533	1627	502	573
9660	2683	2721	1546	1637	501	572
9632	2668	2705	1547	1634	503	574
9606	2657	2690	1547	1632	504	575
9556	2638	2670	1547	1627	502	572
9499	2617	2647	1547	1622	498	568
9428	2595	2623	1540	1613	493	565
9328	2564	2597	1525	1602	483	558
9251	2537	2572	1517	1595	476	554
9190	2514	2550	1512	1591	472	551




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ yule.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 & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185748&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185748&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185748&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 time8 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Totale_bevolking[t] = -15.6001765674043 + 0.976473875861706Vlaams_Gewest_mannen[t] + 1.02996739943033Vlaams_Gewest_vrouwen[t] + 1.0199254341158Waals_Gewest_manen[t] + 0.97401985047526Waals_Gewest_vrouwen[t] + 0.930099163421382Brussel_mannen[t] + 1.07690987605063Brussel_vrouwen[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totale_bevolking[t] =  -15.6001765674043 +  0.976473875861706Vlaams_Gewest_mannen[t] +  1.02996739943033Vlaams_Gewest_vrouwen[t] +  1.0199254341158Waals_Gewest_manen[t] +  0.97401985047526Waals_Gewest_vrouwen[t] +  0.930099163421382Brussel_mannen[t] +  1.07690987605063Brussel_vrouwen[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185748&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totale_bevolking[t] =  -15.6001765674043 +  0.976473875861706Vlaams_Gewest_mannen[t] +  1.02996739943033Vlaams_Gewest_vrouwen[t] +  1.0199254341158Waals_Gewest_manen[t] +  0.97401985047526Waals_Gewest_vrouwen[t] +  0.930099163421382Brussel_mannen[t] +  1.07690987605063Brussel_vrouwen[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185748&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185748&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
Totale_bevolking[t] = -15.6001765674043 + 0.976473875861706Vlaams_Gewest_mannen[t] + 1.02996739943033Vlaams_Gewest_vrouwen[t] + 1.0199254341158Waals_Gewest_manen[t] + 0.97401985047526Waals_Gewest_vrouwen[t] + 0.930099163421382Brussel_mannen[t] + 1.07690987605063Brussel_vrouwen[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-15.600176567404322.712338-0.68690.4958590.24793
Vlaams_Gewest_mannen0.9764738758617060.03001832.5300
Vlaams_Gewest_vrouwen1.029967399430330.02795236.847400
Waals_Gewest_manen1.01992543411580.03543928.779600
Waals_Gewest_vrouwen0.974019850475260.05013919.426300
Brussel_mannen0.9300991634213820.05695316.330900
Brussel_vrouwen1.076909876050630.05123321.019900

\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) & -15.6001765674043 & 22.712338 & -0.6869 & 0.495859 & 0.24793 \tabularnewline
Vlaams_Gewest_mannen & 0.976473875861706 & 0.030018 & 32.53 & 0 & 0 \tabularnewline
Vlaams_Gewest_vrouwen & 1.02996739943033 & 0.027952 & 36.8474 & 0 & 0 \tabularnewline
Waals_Gewest_manen & 1.0199254341158 & 0.035439 & 28.7796 & 0 & 0 \tabularnewline
Waals_Gewest_vrouwen & 0.97401985047526 & 0.050139 & 19.4263 & 0 & 0 \tabularnewline
Brussel_mannen & 0.930099163421382 & 0.056953 & 16.3309 & 0 & 0 \tabularnewline
Brussel_vrouwen & 1.07690987605063 & 0.051233 & 21.0199 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185748&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]-15.6001765674043[/C][C]22.712338[/C][C]-0.6869[/C][C]0.495859[/C][C]0.24793[/C][/ROW]
[ROW][C]Vlaams_Gewest_mannen[/C][C]0.976473875861706[/C][C]0.030018[/C][C]32.53[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Vlaams_Gewest_vrouwen[/C][C]1.02996739943033[/C][C]0.027952[/C][C]36.8474[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Waals_Gewest_manen[/C][C]1.0199254341158[/C][C]0.035439[/C][C]28.7796[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Waals_Gewest_vrouwen[/C][C]0.97401985047526[/C][C]0.050139[/C][C]19.4263[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Brussel_mannen[/C][C]0.930099163421382[/C][C]0.056953[/C][C]16.3309[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Brussel_vrouwen[/C][C]1.07690987605063[/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=185748&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185748&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)-15.600176567404322.712338-0.68690.4958590.24793
Vlaams_Gewest_mannen0.9764738758617060.03001832.5300
Vlaams_Gewest_vrouwen1.029967399430330.02795236.847400
Waals_Gewest_manen1.01992543411580.03543928.779600
Waals_Gewest_vrouwen0.974019850475260.05013919.426300
Brussel_mannen0.9300991634213820.05695316.330900
Brussel_vrouwen1.076909876050630.05123321.019900







Multiple Linear Regression - Regression Statistics
Multiple R0.999998914665498
R-squared0.999997829332175
Adjusted R-squared0.999997526448292
F-TEST (value)3301588.12276667
F-TEST (DF numerator)6
F-TEST (DF denominator)43
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.620267454098993
Sum Squared Residuals16.5434637284212

\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) & 3301588.12276667 \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 & 0.620267454098993 \tabularnewline
Sum Squared Residuals & 16.5434637284212 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185748&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]3301588.12276667[/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]0.620267454098993[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]16.5434637284212[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185748&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185748&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)3301588.12276667
F-TEST (DF numerator)6
F-TEST (DF denominator)43
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.620267454098993
Sum Squared Residuals16.5434637284212







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11095110950.44179371190.558206288090932
21084010840.3519329528-0.351932952767379
31075310754.1612999163-1.16129991627984
41066710667.0730390517-0.0730390517110905
51058510583.95232291831.04767708173417
61051110511.0248231572-0.024823157150902
71044610445.89779227960.102207720395682
81039610395.88139974280.118600257188402
91035510355.6225208629-0.622520862872505
101031010309.79016581350.209834186466596
111026310262.78291891330.21708108665879
121023910239.7648246419-0.764824641881756
131021410213.82325368660.176746313410154
141019210191.78931986630.210680133699597
151017010169.84870323510.151296764928427
161014310142.86174573550.138254264537879
171013110130.77564813930.224351860725919
181010110100.92163063360.0783693664129803
191006810067.96401477390.0359852261211054
201002210021.97281780970.0271821902953173
2199879988.06948203062-1.0694820306229
2299489948.27839696207-0.278396962073584
2399289927.245746030380.754253969618753
2498769876.36155131501-0.361551315005513
2598659864.395405885460.604594114544499
2698599858.431180240310.568819759692246
2798589857.436227090980.563772909023296
2898539853.37098654518-0.370986545183947
2998589858.23473202137-0.23473202137228
3098559854.187951052430.812048947568368
3198639864.08825733912-1.08825733911558
3298559855.96734908405-0.96734908404481
3398429841.932362418680.0676375813204484
3498379836.795504843120.204495156877523
3598239822.853202965970.14679703403128
3698139811.845150774191.15484922580873
3797889788.00923723273-0.00923723273400601
3897579757.21964040837-0.219640408368021
3997279727.24607522817-0.246075228173575
4096959696.23004665785-1.23004665784781
4196519651.22128715569-0.221287155686443
4296609660.06787256558-0.0678725655828788
4396329631.053169998410.946830001593992
4496069604.921415710991.07858428900626
4595569555.808036873640.191963126356311
4694999498.714699883390.285300116614157
4794289428.72617488975-0.726174889753597
4893289328.82387171932-0.823871719319023
4992519250.914016010890.0859839891108088
5091909189.849001224290.150998775714803

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 10951 & 10950.4417937119 & 0.558206288090932 \tabularnewline
2 & 10840 & 10840.3519329528 & -0.351932952767379 \tabularnewline
3 & 10753 & 10754.1612999163 & -1.16129991627984 \tabularnewline
4 & 10667 & 10667.0730390517 & -0.0730390517110905 \tabularnewline
5 & 10585 & 10583.9523229183 & 1.04767708173417 \tabularnewline
6 & 10511 & 10511.0248231572 & -0.024823157150902 \tabularnewline
7 & 10446 & 10445.8977922796 & 0.102207720395682 \tabularnewline
8 & 10396 & 10395.8813997428 & 0.118600257188402 \tabularnewline
9 & 10355 & 10355.6225208629 & -0.622520862872505 \tabularnewline
10 & 10310 & 10309.7901658135 & 0.209834186466596 \tabularnewline
11 & 10263 & 10262.7829189133 & 0.21708108665879 \tabularnewline
12 & 10239 & 10239.7648246419 & -0.764824641881756 \tabularnewline
13 & 10214 & 10213.8232536866 & 0.176746313410154 \tabularnewline
14 & 10192 & 10191.7893198663 & 0.210680133699597 \tabularnewline
15 & 10170 & 10169.8487032351 & 0.151296764928427 \tabularnewline
16 & 10143 & 10142.8617457355 & 0.138254264537879 \tabularnewline
17 & 10131 & 10130.7756481393 & 0.224351860725919 \tabularnewline
18 & 10101 & 10100.9216306336 & 0.0783693664129803 \tabularnewline
19 & 10068 & 10067.9640147739 & 0.0359852261211054 \tabularnewline
20 & 10022 & 10021.9728178097 & 0.0271821902953173 \tabularnewline
21 & 9987 & 9988.06948203062 & -1.0694820306229 \tabularnewline
22 & 9948 & 9948.27839696207 & -0.278396962073584 \tabularnewline
23 & 9928 & 9927.24574603038 & 0.754253969618753 \tabularnewline
24 & 9876 & 9876.36155131501 & -0.361551315005513 \tabularnewline
25 & 9865 & 9864.39540588546 & 0.604594114544499 \tabularnewline
26 & 9859 & 9858.43118024031 & 0.568819759692246 \tabularnewline
27 & 9858 & 9857.43622709098 & 0.563772909023296 \tabularnewline
28 & 9853 & 9853.37098654518 & -0.370986545183947 \tabularnewline
29 & 9858 & 9858.23473202137 & -0.23473202137228 \tabularnewline
30 & 9855 & 9854.18795105243 & 0.812048947568368 \tabularnewline
31 & 9863 & 9864.08825733912 & -1.08825733911558 \tabularnewline
32 & 9855 & 9855.96734908405 & -0.96734908404481 \tabularnewline
33 & 9842 & 9841.93236241868 & 0.0676375813204484 \tabularnewline
34 & 9837 & 9836.79550484312 & 0.204495156877523 \tabularnewline
35 & 9823 & 9822.85320296597 & 0.14679703403128 \tabularnewline
36 & 9813 & 9811.84515077419 & 1.15484922580873 \tabularnewline
37 & 9788 & 9788.00923723273 & -0.00923723273400601 \tabularnewline
38 & 9757 & 9757.21964040837 & -0.219640408368021 \tabularnewline
39 & 9727 & 9727.24607522817 & -0.246075228173575 \tabularnewline
40 & 9695 & 9696.23004665785 & -1.23004665784781 \tabularnewline
41 & 9651 & 9651.22128715569 & -0.221287155686443 \tabularnewline
42 & 9660 & 9660.06787256558 & -0.0678725655828788 \tabularnewline
43 & 9632 & 9631.05316999841 & 0.946830001593992 \tabularnewline
44 & 9606 & 9604.92141571099 & 1.07858428900626 \tabularnewline
45 & 9556 & 9555.80803687364 & 0.191963126356311 \tabularnewline
46 & 9499 & 9498.71469988339 & 0.285300116614157 \tabularnewline
47 & 9428 & 9428.72617488975 & -0.726174889753597 \tabularnewline
48 & 9328 & 9328.82387171932 & -0.823871719319023 \tabularnewline
49 & 9251 & 9250.91401601089 & 0.0859839891108088 \tabularnewline
50 & 9190 & 9189.84900122429 & 0.150998775714803 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185748&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]10951[/C][C]10950.4417937119[/C][C]0.558206288090932[/C][/ROW]
[ROW][C]2[/C][C]10840[/C][C]10840.3519329528[/C][C]-0.351932952767379[/C][/ROW]
[ROW][C]3[/C][C]10753[/C][C]10754.1612999163[/C][C]-1.16129991627984[/C][/ROW]
[ROW][C]4[/C][C]10667[/C][C]10667.0730390517[/C][C]-0.0730390517110905[/C][/ROW]
[ROW][C]5[/C][C]10585[/C][C]10583.9523229183[/C][C]1.04767708173417[/C][/ROW]
[ROW][C]6[/C][C]10511[/C][C]10511.0248231572[/C][C]-0.024823157150902[/C][/ROW]
[ROW][C]7[/C][C]10446[/C][C]10445.8977922796[/C][C]0.102207720395682[/C][/ROW]
[ROW][C]8[/C][C]10396[/C][C]10395.8813997428[/C][C]0.118600257188402[/C][/ROW]
[ROW][C]9[/C][C]10355[/C][C]10355.6225208629[/C][C]-0.622520862872505[/C][/ROW]
[ROW][C]10[/C][C]10310[/C][C]10309.7901658135[/C][C]0.209834186466596[/C][/ROW]
[ROW][C]11[/C][C]10263[/C][C]10262.7829189133[/C][C]0.21708108665879[/C][/ROW]
[ROW][C]12[/C][C]10239[/C][C]10239.7648246419[/C][C]-0.764824641881756[/C][/ROW]
[ROW][C]13[/C][C]10214[/C][C]10213.8232536866[/C][C]0.176746313410154[/C][/ROW]
[ROW][C]14[/C][C]10192[/C][C]10191.7893198663[/C][C]0.210680133699597[/C][/ROW]
[ROW][C]15[/C][C]10170[/C][C]10169.8487032351[/C][C]0.151296764928427[/C][/ROW]
[ROW][C]16[/C][C]10143[/C][C]10142.8617457355[/C][C]0.138254264537879[/C][/ROW]
[ROW][C]17[/C][C]10131[/C][C]10130.7756481393[/C][C]0.224351860725919[/C][/ROW]
[ROW][C]18[/C][C]10101[/C][C]10100.9216306336[/C][C]0.0783693664129803[/C][/ROW]
[ROW][C]19[/C][C]10068[/C][C]10067.9640147739[/C][C]0.0359852261211054[/C][/ROW]
[ROW][C]20[/C][C]10022[/C][C]10021.9728178097[/C][C]0.0271821902953173[/C][/ROW]
[ROW][C]21[/C][C]9987[/C][C]9988.06948203062[/C][C]-1.0694820306229[/C][/ROW]
[ROW][C]22[/C][C]9948[/C][C]9948.27839696207[/C][C]-0.278396962073584[/C][/ROW]
[ROW][C]23[/C][C]9928[/C][C]9927.24574603038[/C][C]0.754253969618753[/C][/ROW]
[ROW][C]24[/C][C]9876[/C][C]9876.36155131501[/C][C]-0.361551315005513[/C][/ROW]
[ROW][C]25[/C][C]9865[/C][C]9864.39540588546[/C][C]0.604594114544499[/C][/ROW]
[ROW][C]26[/C][C]9859[/C][C]9858.43118024031[/C][C]0.568819759692246[/C][/ROW]
[ROW][C]27[/C][C]9858[/C][C]9857.43622709098[/C][C]0.563772909023296[/C][/ROW]
[ROW][C]28[/C][C]9853[/C][C]9853.37098654518[/C][C]-0.370986545183947[/C][/ROW]
[ROW][C]29[/C][C]9858[/C][C]9858.23473202137[/C][C]-0.23473202137228[/C][/ROW]
[ROW][C]30[/C][C]9855[/C][C]9854.18795105243[/C][C]0.812048947568368[/C][/ROW]
[ROW][C]31[/C][C]9863[/C][C]9864.08825733912[/C][C]-1.08825733911558[/C][/ROW]
[ROW][C]32[/C][C]9855[/C][C]9855.96734908405[/C][C]-0.96734908404481[/C][/ROW]
[ROW][C]33[/C][C]9842[/C][C]9841.93236241868[/C][C]0.0676375813204484[/C][/ROW]
[ROW][C]34[/C][C]9837[/C][C]9836.79550484312[/C][C]0.204495156877523[/C][/ROW]
[ROW][C]35[/C][C]9823[/C][C]9822.85320296597[/C][C]0.14679703403128[/C][/ROW]
[ROW][C]36[/C][C]9813[/C][C]9811.84515077419[/C][C]1.15484922580873[/C][/ROW]
[ROW][C]37[/C][C]9788[/C][C]9788.00923723273[/C][C]-0.00923723273400601[/C][/ROW]
[ROW][C]38[/C][C]9757[/C][C]9757.21964040837[/C][C]-0.219640408368021[/C][/ROW]
[ROW][C]39[/C][C]9727[/C][C]9727.24607522817[/C][C]-0.246075228173575[/C][/ROW]
[ROW][C]40[/C][C]9695[/C][C]9696.23004665785[/C][C]-1.23004665784781[/C][/ROW]
[ROW][C]41[/C][C]9651[/C][C]9651.22128715569[/C][C]-0.221287155686443[/C][/ROW]
[ROW][C]42[/C][C]9660[/C][C]9660.06787256558[/C][C]-0.0678725655828788[/C][/ROW]
[ROW][C]43[/C][C]9632[/C][C]9631.05316999841[/C][C]0.946830001593992[/C][/ROW]
[ROW][C]44[/C][C]9606[/C][C]9604.92141571099[/C][C]1.07858428900626[/C][/ROW]
[ROW][C]45[/C][C]9556[/C][C]9555.80803687364[/C][C]0.191963126356311[/C][/ROW]
[ROW][C]46[/C][C]9499[/C][C]9498.71469988339[/C][C]0.285300116614157[/C][/ROW]
[ROW][C]47[/C][C]9428[/C][C]9428.72617488975[/C][C]-0.726174889753597[/C][/ROW]
[ROW][C]48[/C][C]9328[/C][C]9328.82387171932[/C][C]-0.823871719319023[/C][/ROW]
[ROW][C]49[/C][C]9251[/C][C]9250.91401601089[/C][C]0.0859839891108088[/C][/ROW]
[ROW][C]50[/C][C]9190[/C][C]9189.84900122429[/C][C]0.150998775714803[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185748&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185748&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
11095110950.44179371190.558206288090932
21084010840.3519329528-0.351932952767379
31075310754.1612999163-1.16129991627984
41066710667.0730390517-0.0730390517110905
51058510583.95232291831.04767708173417
61051110511.0248231572-0.024823157150902
71044610445.89779227960.102207720395682
81039610395.88139974280.118600257188402
91035510355.6225208629-0.622520862872505
101031010309.79016581350.209834186466596
111026310262.78291891330.21708108665879
121023910239.7648246419-0.764824641881756
131021410213.82325368660.176746313410154
141019210191.78931986630.210680133699597
151017010169.84870323510.151296764928427
161014310142.86174573550.138254264537879
171013110130.77564813930.224351860725919
181010110100.92163063360.0783693664129803
191006810067.96401477390.0359852261211054
201002210021.97281780970.0271821902953173
2199879988.06948203062-1.0694820306229
2299489948.27839696207-0.278396962073584
2399289927.245746030380.754253969618753
2498769876.36155131501-0.361551315005513
2598659864.395405885460.604594114544499
2698599858.431180240310.568819759692246
2798589857.436227090980.563772909023296
2898539853.37098654518-0.370986545183947
2998589858.23473202137-0.23473202137228
3098559854.187951052430.812048947568368
3198639864.08825733912-1.08825733911558
3298559855.96734908405-0.96734908404481
3398429841.932362418680.0676375813204484
3498379836.795504843120.204495156877523
3598239822.853202965970.14679703403128
3698139811.845150774191.15484922580873
3797889788.00923723273-0.00923723273400601
3897579757.21964040837-0.219640408368021
3997279727.24607522817-0.246075228173575
4096959696.23004665785-1.23004665784781
4196519651.22128715569-0.221287155686443
4296609660.06787256558-0.0678725655828788
4396329631.053169998410.946830001593992
4496069604.921415710991.07858428900626
4595569555.808036873640.191963126356311
4694999498.714699883390.285300116614157
4794289428.72617488975-0.726174889753597
4893289328.82387171932-0.823871719319023
4992519250.914016010890.0859839891108088
5091909189.849001224290.150998775714803







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.4850908170329480.9701816340658970.514909182967052
110.4056224201903610.8112448403807210.594377579809639
120.5385848864476180.9228302271047640.461415113552382
130.4306658120326930.8613316240653850.569334187967308
140.3101482082975440.6202964165950870.689851791702456
150.2403179137218910.4806358274437820.759682086278109
160.237047989951060.474095979902120.76295201004894
170.1862673421823120.3725346843646230.813732657817688
180.1369024359189110.2738048718378210.863097564081089
190.08596360741636340.1719272148327270.914036392583637
200.05883437361445430.1176687472289090.941165626385546
210.06083334721024220.1216666944204840.939166652789758
220.04597137932466490.09194275864932990.954028620675335
230.1336698525295730.2673397050591470.866330147470427
240.09516926572600070.1903385314520010.904830734273999
250.123918932669290.2478378653385810.87608106733071
260.1270881533694960.2541763067389910.872911846630504
270.177483235959030.354966471918060.82251676404097
280.190478022508770.3809560450175390.80952197749123
290.1540377876560150.3080755753120290.845962212343985
300.4969863659364510.9939727318729010.503013634063549
310.5472455845460760.9055088309078480.452754415453924
320.607799390800870.7844012183982610.39220060919913
330.5801127145071020.8397745709857960.419887285492898
340.5966117469536390.8067765060927210.403388253046361
350.6259468463490670.7481063073018660.374053153650933
360.818756822830590.3624863543388210.18124317716941
370.8140175461631650.371964907673670.185982453836835
380.7340910205670530.5318179588658940.265908979432947
390.897248244694760.205503510610480.10275175530524
400.8123271558724580.3753456882550850.187672844127542

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.485090817032948 & 0.970181634065897 & 0.514909182967052 \tabularnewline
11 & 0.405622420190361 & 0.811244840380721 & 0.594377579809639 \tabularnewline
12 & 0.538584886447618 & 0.922830227104764 & 0.461415113552382 \tabularnewline
13 & 0.430665812032693 & 0.861331624065385 & 0.569334187967308 \tabularnewline
14 & 0.310148208297544 & 0.620296416595087 & 0.689851791702456 \tabularnewline
15 & 0.240317913721891 & 0.480635827443782 & 0.759682086278109 \tabularnewline
16 & 0.23704798995106 & 0.47409597990212 & 0.76295201004894 \tabularnewline
17 & 0.186267342182312 & 0.372534684364623 & 0.813732657817688 \tabularnewline
18 & 0.136902435918911 & 0.273804871837821 & 0.863097564081089 \tabularnewline
19 & 0.0859636074163634 & 0.171927214832727 & 0.914036392583637 \tabularnewline
20 & 0.0588343736144543 & 0.117668747228909 & 0.941165626385546 \tabularnewline
21 & 0.0608333472102422 & 0.121666694420484 & 0.939166652789758 \tabularnewline
22 & 0.0459713793246649 & 0.0919427586493299 & 0.954028620675335 \tabularnewline
23 & 0.133669852529573 & 0.267339705059147 & 0.866330147470427 \tabularnewline
24 & 0.0951692657260007 & 0.190338531452001 & 0.904830734273999 \tabularnewline
25 & 0.12391893266929 & 0.247837865338581 & 0.87608106733071 \tabularnewline
26 & 0.127088153369496 & 0.254176306738991 & 0.872911846630504 \tabularnewline
27 & 0.17748323595903 & 0.35496647191806 & 0.82251676404097 \tabularnewline
28 & 0.19047802250877 & 0.380956045017539 & 0.80952197749123 \tabularnewline
29 & 0.154037787656015 & 0.308075575312029 & 0.845962212343985 \tabularnewline
30 & 0.496986365936451 & 0.993972731872901 & 0.503013634063549 \tabularnewline
31 & 0.547245584546076 & 0.905508830907848 & 0.452754415453924 \tabularnewline
32 & 0.60779939080087 & 0.784401218398261 & 0.39220060919913 \tabularnewline
33 & 0.580112714507102 & 0.839774570985796 & 0.419887285492898 \tabularnewline
34 & 0.596611746953639 & 0.806776506092721 & 0.403388253046361 \tabularnewline
35 & 0.625946846349067 & 0.748106307301866 & 0.374053153650933 \tabularnewline
36 & 0.81875682283059 & 0.362486354338821 & 0.18124317716941 \tabularnewline
37 & 0.814017546163165 & 0.37196490767367 & 0.185982453836835 \tabularnewline
38 & 0.734091020567053 & 0.531817958865894 & 0.265908979432947 \tabularnewline
39 & 0.89724824469476 & 0.20550351061048 & 0.10275175530524 \tabularnewline
40 & 0.812327155872458 & 0.375345688255085 & 0.187672844127542 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185748&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]0.485090817032948[/C][C]0.970181634065897[/C][C]0.514909182967052[/C][/ROW]
[ROW][C]11[/C][C]0.405622420190361[/C][C]0.811244840380721[/C][C]0.594377579809639[/C][/ROW]
[ROW][C]12[/C][C]0.538584886447618[/C][C]0.922830227104764[/C][C]0.461415113552382[/C][/ROW]
[ROW][C]13[/C][C]0.430665812032693[/C][C]0.861331624065385[/C][C]0.569334187967308[/C][/ROW]
[ROW][C]14[/C][C]0.310148208297544[/C][C]0.620296416595087[/C][C]0.689851791702456[/C][/ROW]
[ROW][C]15[/C][C]0.240317913721891[/C][C]0.480635827443782[/C][C]0.759682086278109[/C][/ROW]
[ROW][C]16[/C][C]0.23704798995106[/C][C]0.47409597990212[/C][C]0.76295201004894[/C][/ROW]
[ROW][C]17[/C][C]0.186267342182312[/C][C]0.372534684364623[/C][C]0.813732657817688[/C][/ROW]
[ROW][C]18[/C][C]0.136902435918911[/C][C]0.273804871837821[/C][C]0.863097564081089[/C][/ROW]
[ROW][C]19[/C][C]0.0859636074163634[/C][C]0.171927214832727[/C][C]0.914036392583637[/C][/ROW]
[ROW][C]20[/C][C]0.0588343736144543[/C][C]0.117668747228909[/C][C]0.941165626385546[/C][/ROW]
[ROW][C]21[/C][C]0.0608333472102422[/C][C]0.121666694420484[/C][C]0.939166652789758[/C][/ROW]
[ROW][C]22[/C][C]0.0459713793246649[/C][C]0.0919427586493299[/C][C]0.954028620675335[/C][/ROW]
[ROW][C]23[/C][C]0.133669852529573[/C][C]0.267339705059147[/C][C]0.866330147470427[/C][/ROW]
[ROW][C]24[/C][C]0.0951692657260007[/C][C]0.190338531452001[/C][C]0.904830734273999[/C][/ROW]
[ROW][C]25[/C][C]0.12391893266929[/C][C]0.247837865338581[/C][C]0.87608106733071[/C][/ROW]
[ROW][C]26[/C][C]0.127088153369496[/C][C]0.254176306738991[/C][C]0.872911846630504[/C][/ROW]
[ROW][C]27[/C][C]0.17748323595903[/C][C]0.35496647191806[/C][C]0.82251676404097[/C][/ROW]
[ROW][C]28[/C][C]0.19047802250877[/C][C]0.380956045017539[/C][C]0.80952197749123[/C][/ROW]
[ROW][C]29[/C][C]0.154037787656015[/C][C]0.308075575312029[/C][C]0.845962212343985[/C][/ROW]
[ROW][C]30[/C][C]0.496986365936451[/C][C]0.993972731872901[/C][C]0.503013634063549[/C][/ROW]
[ROW][C]31[/C][C]0.547245584546076[/C][C]0.905508830907848[/C][C]0.452754415453924[/C][/ROW]
[ROW][C]32[/C][C]0.60779939080087[/C][C]0.784401218398261[/C][C]0.39220060919913[/C][/ROW]
[ROW][C]33[/C][C]0.580112714507102[/C][C]0.839774570985796[/C][C]0.419887285492898[/C][/ROW]
[ROW][C]34[/C][C]0.596611746953639[/C][C]0.806776506092721[/C][C]0.403388253046361[/C][/ROW]
[ROW][C]35[/C][C]0.625946846349067[/C][C]0.748106307301866[/C][C]0.374053153650933[/C][/ROW]
[ROW][C]36[/C][C]0.81875682283059[/C][C]0.362486354338821[/C][C]0.18124317716941[/C][/ROW]
[ROW][C]37[/C][C]0.814017546163165[/C][C]0.37196490767367[/C][C]0.185982453836835[/C][/ROW]
[ROW][C]38[/C][C]0.734091020567053[/C][C]0.531817958865894[/C][C]0.265908979432947[/C][/ROW]
[ROW][C]39[/C][C]0.89724824469476[/C][C]0.20550351061048[/C][C]0.10275175530524[/C][/ROW]
[ROW][C]40[/C][C]0.812327155872458[/C][C]0.375345688255085[/C][C]0.187672844127542[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185748&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185748&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
100.4850908170329480.9701816340658970.514909182967052
110.4056224201903610.8112448403807210.594377579809639
120.5385848864476180.9228302271047640.461415113552382
130.4306658120326930.8613316240653850.569334187967308
140.3101482082975440.6202964165950870.689851791702456
150.2403179137218910.4806358274437820.759682086278109
160.237047989951060.474095979902120.76295201004894
170.1862673421823120.3725346843646230.813732657817688
180.1369024359189110.2738048718378210.863097564081089
190.08596360741636340.1719272148327270.914036392583637
200.05883437361445430.1176687472289090.941165626385546
210.06083334721024220.1216666944204840.939166652789758
220.04597137932466490.09194275864932990.954028620675335
230.1336698525295730.2673397050591470.866330147470427
240.09516926572600070.1903385314520010.904830734273999
250.123918932669290.2478378653385810.87608106733071
260.1270881533694960.2541763067389910.872911846630504
270.177483235959030.354966471918060.82251676404097
280.190478022508770.3809560450175390.80952197749123
290.1540377876560150.3080755753120290.845962212343985
300.4969863659364510.9939727318729010.503013634063549
310.5472455845460760.9055088309078480.452754415453924
320.607799390800870.7844012183982610.39220060919913
330.5801127145071020.8397745709857960.419887285492898
340.5966117469536390.8067765060927210.403388253046361
350.6259468463490670.7481063073018660.374053153650933
360.818756822830590.3624863543388210.18124317716941
370.8140175461631650.371964907673670.185982453836835
380.7340910205670530.5318179588658940.265908979432947
390.897248244694760.205503510610480.10275175530524
400.8123271558724580.3753456882550850.187672844127542







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=185748&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=185748&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185748&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 level00OK
5% type I error level00OK
10% type I error level10.032258064516129OK



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')
}