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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 computationMon, 21 Nov 2011 12:47:02 -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/2011/Nov/21/t1321897648jfspyh7iumci1kv.htm/, Retrieved Thu, 31 Oct 2024 23:43:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145858, Retrieved Thu, 31 Oct 2024 23:43:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact145
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] [] [2011-11-21 17:26:01] [86f7284edee3dbb8ea5c7e2dec87d892]
-    D      [Multiple Regression] [] [2011-11-21 17:47:02] [79818163420d1233b8d9d93d595e6c9e] [Current]
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Dataseries X:
1167	333	70
669	223	44
1053	371	35
1939	873	119
678	186	30
321	111	23
2667	1277	46
345	102	39
1367	580	58
1158	420	51
1385	521	65
1155	358	40
1120	435	41
1703	690	76
1189	393	31
3083	1149	82
1357	486	36
1892	767	62
883	338	28
1627	485	38
1412	465	70
1900	816	76
777	265	33
904	307	40
2115	850	126
1858	704	56
1781	693	63
1286	387	46
1035	406	35
1557	573	108
1527	595	34
1220	394	54
1368	521	35
564	172	23
1990	835	46
1557	669	49
2057	749	56
1111	368	38
686	216	19
2011	772	29
2232	1084	26
1032	445	52
1166	451	54
1020	300	45
1735	836	56
3623	1417	596
918	330	57
1579	477	55
2790	1028	99
1496	646	51
1108	342	21
496	218	20
1750	591	58
744	255	21
1101	434	66
1612	654	47
1805	478	55
2460	753	158
1653	689	46
1234	470	45




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145858&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145858&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145858&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 time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
TotalNrPV[t] = + 262.941702139148 + 2.06526024155116TotalNrCC[t] + 0.980975851040243TotalNrPRV[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TotalNrPV[t] =  +  262.941702139148 +  2.06526024155116TotalNrCC[t] +  0.980975851040243TotalNrPRV[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145858&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TotalNrPV[t] =  +  262.941702139148 +  2.06526024155116TotalNrCC[t] +  0.980975851040243TotalNrPRV[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145858&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145858&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
TotalNrPV[t] = + 262.941702139148 + 2.06526024155116TotalNrCC[t] + 0.980975851040243TotalNrPRV[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)262.94170213914851.0977875.14593e-062e-06
TotalNrCC2.065260241551160.09833221.002900
TotalNrPRV0.9809758510402430.3677322.66760.0099270.004964

\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) & 262.941702139148 & 51.097787 & 5.1459 & 3e-06 & 2e-06 \tabularnewline
TotalNrCC & 2.06526024155116 & 0.098332 & 21.0029 & 0 & 0 \tabularnewline
TotalNrPRV & 0.980975851040243 & 0.367732 & 2.6676 & 0.009927 & 0.004964 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145858&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]262.941702139148[/C][C]51.097787[/C][C]5.1459[/C][C]3e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]TotalNrCC[/C][C]2.06526024155116[/C][C]0.098332[/C][C]21.0029[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]TotalNrPRV[/C][C]0.980975851040243[/C][C]0.367732[/C][C]2.6676[/C][C]0.009927[/C][C]0.004964[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145858&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145858&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)262.94170213914851.0977875.14593e-062e-06
TotalNrCC2.065260241551160.09833221.002900
TotalNrPRV0.9809758510402430.3677322.66760.0099270.004964







Multiple Linear Regression - Regression Statistics
Multiple R0.96257369725416
R-squared0.926548122645543
Adjusted R-squared0.923970863791
F-TEST (value)359.509143217231
F-TEST (DF numerator)2
F-TEST (DF denominator)57
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation178.322515087731
Sum Squared Residuals1812538.40507119

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.96257369725416 \tabularnewline
R-squared & 0.926548122645543 \tabularnewline
Adjusted R-squared & 0.923970863791 \tabularnewline
F-TEST (value) & 359.509143217231 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 57 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 178.322515087731 \tabularnewline
Sum Squared Residuals & 1812538.40507119 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145858&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.96257369725416[/C][/ROW]
[ROW][C]R-squared[/C][C]0.926548122645543[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.923970863791[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]359.509143217231[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]57[/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]178.322515087731[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1812538.40507119[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145858&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145858&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 R0.96257369725416
R-squared0.926548122645543
Adjusted R-squared0.923970863791
F-TEST (value)359.509143217231
F-TEST (DF numerator)2
F-TEST (DF denominator)57
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation178.322515087731
Sum Squared Residuals1812538.40507119







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111671019.3416721485147.658327851499
2669766.657673450826-97.6576734508264
310531063.48740654104-10.4874065410364
419392182.6500192871-243.650019287099
5678676.5093825988711.49061740112948
6321514.748033525252-193.748033525252
726672945.40391974783-278.40391974783
8345511.856304967935-166.856304967935
913671517.68924159915-150.689241599154
1011581180.38077199369-22.3807719936871
1113851402.70571830492-17.7057183049175
1211551041.54390265607113.456097343928
1311201201.54991710655-81.549917106552
1417031762.52543348851-59.5254334885062
1511891104.99922845184.0007715489992
1630832716.36573946673366.63426053327
1713571301.9733101704655.0266898295401
1818921907.81681017338-15.8168101733821
19883988.466987612566-105.466987612566
2016271301.87000163099325.129998369011
2114121291.95602403325120.043975966746
2219002022.74822392395-122.748223923952
23777842.607869234533-65.6078692345329
24904936.215630336963-32.2156303369632
2521152142.0158646887-27.0158646887038
2618581771.8195598494286.1804401505824
2717811755.9685281496425.0314718503635
2812861107.3223047673178.677695232702
2910351135.77151499533-100.771514995327
3015571552.281212460314.71878753969173
3115271525.124724797461.87527520254416
3212201129.6269332664890.3730667335224
3313681373.27644277371-5.27644277371027
34564640.728908259873-76.7289082598726
3519902032.55889298222-42.558892982217
3615571692.66862043785-135.668620437845
3720571864.75627071922192.24372928078
3811111060.234553369550.7654466304964
39686727.676455483963-41.6764554839626
4020111885.77090829681125.22909170319
4122322527.18917610765-295.189176107651
4210321232.99325388351-200.993253883506
4311661247.34676703489-81.3467670348937
441020926.66368790130693.3363120986936
4517352044.43391173417-309.433911734171
4636233774.07707163713-151.077071637125
479181000.39320536032-82.3932053603241
4815791302.02450916626276.975490833736
4927902483.14583970672306.854160293276
5014961647.12958658425-151.129586584249
511108989.861197621489118.138802378511
52496732.787951818105-236.787951818105
5317501540.40710425622209.592895743783
54744810.183556606538-66.1835566065383
5511011224.00905314101-123.009053141007
5616121659.7277651125-47.7277651124974
5718051304.08976940782500.910230592185
5824601973.07684849153486.923151508471
5916531731.03089771575-78.0308977157478
6012341277.757928965-43.7579289650035

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1167 & 1019.3416721485 & 147.658327851499 \tabularnewline
2 & 669 & 766.657673450826 & -97.6576734508264 \tabularnewline
3 & 1053 & 1063.48740654104 & -10.4874065410364 \tabularnewline
4 & 1939 & 2182.6500192871 & -243.650019287099 \tabularnewline
5 & 678 & 676.509382598871 & 1.49061740112948 \tabularnewline
6 & 321 & 514.748033525252 & -193.748033525252 \tabularnewline
7 & 2667 & 2945.40391974783 & -278.40391974783 \tabularnewline
8 & 345 & 511.856304967935 & -166.856304967935 \tabularnewline
9 & 1367 & 1517.68924159915 & -150.689241599154 \tabularnewline
10 & 1158 & 1180.38077199369 & -22.3807719936871 \tabularnewline
11 & 1385 & 1402.70571830492 & -17.7057183049175 \tabularnewline
12 & 1155 & 1041.54390265607 & 113.456097343928 \tabularnewline
13 & 1120 & 1201.54991710655 & -81.549917106552 \tabularnewline
14 & 1703 & 1762.52543348851 & -59.5254334885062 \tabularnewline
15 & 1189 & 1104.999228451 & 84.0007715489992 \tabularnewline
16 & 3083 & 2716.36573946673 & 366.63426053327 \tabularnewline
17 & 1357 & 1301.97331017046 & 55.0266898295401 \tabularnewline
18 & 1892 & 1907.81681017338 & -15.8168101733821 \tabularnewline
19 & 883 & 988.466987612566 & -105.466987612566 \tabularnewline
20 & 1627 & 1301.87000163099 & 325.129998369011 \tabularnewline
21 & 1412 & 1291.95602403325 & 120.043975966746 \tabularnewline
22 & 1900 & 2022.74822392395 & -122.748223923952 \tabularnewline
23 & 777 & 842.607869234533 & -65.6078692345329 \tabularnewline
24 & 904 & 936.215630336963 & -32.2156303369632 \tabularnewline
25 & 2115 & 2142.0158646887 & -27.0158646887038 \tabularnewline
26 & 1858 & 1771.81955984942 & 86.1804401505824 \tabularnewline
27 & 1781 & 1755.96852814964 & 25.0314718503635 \tabularnewline
28 & 1286 & 1107.3223047673 & 178.677695232702 \tabularnewline
29 & 1035 & 1135.77151499533 & -100.771514995327 \tabularnewline
30 & 1557 & 1552.28121246031 & 4.71878753969173 \tabularnewline
31 & 1527 & 1525.12472479746 & 1.87527520254416 \tabularnewline
32 & 1220 & 1129.62693326648 & 90.3730667335224 \tabularnewline
33 & 1368 & 1373.27644277371 & -5.27644277371027 \tabularnewline
34 & 564 & 640.728908259873 & -76.7289082598726 \tabularnewline
35 & 1990 & 2032.55889298222 & -42.558892982217 \tabularnewline
36 & 1557 & 1692.66862043785 & -135.668620437845 \tabularnewline
37 & 2057 & 1864.75627071922 & 192.24372928078 \tabularnewline
38 & 1111 & 1060.2345533695 & 50.7654466304964 \tabularnewline
39 & 686 & 727.676455483963 & -41.6764554839626 \tabularnewline
40 & 2011 & 1885.77090829681 & 125.22909170319 \tabularnewline
41 & 2232 & 2527.18917610765 & -295.189176107651 \tabularnewline
42 & 1032 & 1232.99325388351 & -200.993253883506 \tabularnewline
43 & 1166 & 1247.34676703489 & -81.3467670348937 \tabularnewline
44 & 1020 & 926.663687901306 & 93.3363120986936 \tabularnewline
45 & 1735 & 2044.43391173417 & -309.433911734171 \tabularnewline
46 & 3623 & 3774.07707163713 & -151.077071637125 \tabularnewline
47 & 918 & 1000.39320536032 & -82.3932053603241 \tabularnewline
48 & 1579 & 1302.02450916626 & 276.975490833736 \tabularnewline
49 & 2790 & 2483.14583970672 & 306.854160293276 \tabularnewline
50 & 1496 & 1647.12958658425 & -151.129586584249 \tabularnewline
51 & 1108 & 989.861197621489 & 118.138802378511 \tabularnewline
52 & 496 & 732.787951818105 & -236.787951818105 \tabularnewline
53 & 1750 & 1540.40710425622 & 209.592895743783 \tabularnewline
54 & 744 & 810.183556606538 & -66.1835566065383 \tabularnewline
55 & 1101 & 1224.00905314101 & -123.009053141007 \tabularnewline
56 & 1612 & 1659.7277651125 & -47.7277651124974 \tabularnewline
57 & 1805 & 1304.08976940782 & 500.910230592185 \tabularnewline
58 & 2460 & 1973.07684849153 & 486.923151508471 \tabularnewline
59 & 1653 & 1731.03089771575 & -78.0308977157478 \tabularnewline
60 & 1234 & 1277.757928965 & -43.7579289650035 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145858&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]1167[/C][C]1019.3416721485[/C][C]147.658327851499[/C][/ROW]
[ROW][C]2[/C][C]669[/C][C]766.657673450826[/C][C]-97.6576734508264[/C][/ROW]
[ROW][C]3[/C][C]1053[/C][C]1063.48740654104[/C][C]-10.4874065410364[/C][/ROW]
[ROW][C]4[/C][C]1939[/C][C]2182.6500192871[/C][C]-243.650019287099[/C][/ROW]
[ROW][C]5[/C][C]678[/C][C]676.509382598871[/C][C]1.49061740112948[/C][/ROW]
[ROW][C]6[/C][C]321[/C][C]514.748033525252[/C][C]-193.748033525252[/C][/ROW]
[ROW][C]7[/C][C]2667[/C][C]2945.40391974783[/C][C]-278.40391974783[/C][/ROW]
[ROW][C]8[/C][C]345[/C][C]511.856304967935[/C][C]-166.856304967935[/C][/ROW]
[ROW][C]9[/C][C]1367[/C][C]1517.68924159915[/C][C]-150.689241599154[/C][/ROW]
[ROW][C]10[/C][C]1158[/C][C]1180.38077199369[/C][C]-22.3807719936871[/C][/ROW]
[ROW][C]11[/C][C]1385[/C][C]1402.70571830492[/C][C]-17.7057183049175[/C][/ROW]
[ROW][C]12[/C][C]1155[/C][C]1041.54390265607[/C][C]113.456097343928[/C][/ROW]
[ROW][C]13[/C][C]1120[/C][C]1201.54991710655[/C][C]-81.549917106552[/C][/ROW]
[ROW][C]14[/C][C]1703[/C][C]1762.52543348851[/C][C]-59.5254334885062[/C][/ROW]
[ROW][C]15[/C][C]1189[/C][C]1104.999228451[/C][C]84.0007715489992[/C][/ROW]
[ROW][C]16[/C][C]3083[/C][C]2716.36573946673[/C][C]366.63426053327[/C][/ROW]
[ROW][C]17[/C][C]1357[/C][C]1301.97331017046[/C][C]55.0266898295401[/C][/ROW]
[ROW][C]18[/C][C]1892[/C][C]1907.81681017338[/C][C]-15.8168101733821[/C][/ROW]
[ROW][C]19[/C][C]883[/C][C]988.466987612566[/C][C]-105.466987612566[/C][/ROW]
[ROW][C]20[/C][C]1627[/C][C]1301.87000163099[/C][C]325.129998369011[/C][/ROW]
[ROW][C]21[/C][C]1412[/C][C]1291.95602403325[/C][C]120.043975966746[/C][/ROW]
[ROW][C]22[/C][C]1900[/C][C]2022.74822392395[/C][C]-122.748223923952[/C][/ROW]
[ROW][C]23[/C][C]777[/C][C]842.607869234533[/C][C]-65.6078692345329[/C][/ROW]
[ROW][C]24[/C][C]904[/C][C]936.215630336963[/C][C]-32.2156303369632[/C][/ROW]
[ROW][C]25[/C][C]2115[/C][C]2142.0158646887[/C][C]-27.0158646887038[/C][/ROW]
[ROW][C]26[/C][C]1858[/C][C]1771.81955984942[/C][C]86.1804401505824[/C][/ROW]
[ROW][C]27[/C][C]1781[/C][C]1755.96852814964[/C][C]25.0314718503635[/C][/ROW]
[ROW][C]28[/C][C]1286[/C][C]1107.3223047673[/C][C]178.677695232702[/C][/ROW]
[ROW][C]29[/C][C]1035[/C][C]1135.77151499533[/C][C]-100.771514995327[/C][/ROW]
[ROW][C]30[/C][C]1557[/C][C]1552.28121246031[/C][C]4.71878753969173[/C][/ROW]
[ROW][C]31[/C][C]1527[/C][C]1525.12472479746[/C][C]1.87527520254416[/C][/ROW]
[ROW][C]32[/C][C]1220[/C][C]1129.62693326648[/C][C]90.3730667335224[/C][/ROW]
[ROW][C]33[/C][C]1368[/C][C]1373.27644277371[/C][C]-5.27644277371027[/C][/ROW]
[ROW][C]34[/C][C]564[/C][C]640.728908259873[/C][C]-76.7289082598726[/C][/ROW]
[ROW][C]35[/C][C]1990[/C][C]2032.55889298222[/C][C]-42.558892982217[/C][/ROW]
[ROW][C]36[/C][C]1557[/C][C]1692.66862043785[/C][C]-135.668620437845[/C][/ROW]
[ROW][C]37[/C][C]2057[/C][C]1864.75627071922[/C][C]192.24372928078[/C][/ROW]
[ROW][C]38[/C][C]1111[/C][C]1060.2345533695[/C][C]50.7654466304964[/C][/ROW]
[ROW][C]39[/C][C]686[/C][C]727.676455483963[/C][C]-41.6764554839626[/C][/ROW]
[ROW][C]40[/C][C]2011[/C][C]1885.77090829681[/C][C]125.22909170319[/C][/ROW]
[ROW][C]41[/C][C]2232[/C][C]2527.18917610765[/C][C]-295.189176107651[/C][/ROW]
[ROW][C]42[/C][C]1032[/C][C]1232.99325388351[/C][C]-200.993253883506[/C][/ROW]
[ROW][C]43[/C][C]1166[/C][C]1247.34676703489[/C][C]-81.3467670348937[/C][/ROW]
[ROW][C]44[/C][C]1020[/C][C]926.663687901306[/C][C]93.3363120986936[/C][/ROW]
[ROW][C]45[/C][C]1735[/C][C]2044.43391173417[/C][C]-309.433911734171[/C][/ROW]
[ROW][C]46[/C][C]3623[/C][C]3774.07707163713[/C][C]-151.077071637125[/C][/ROW]
[ROW][C]47[/C][C]918[/C][C]1000.39320536032[/C][C]-82.3932053603241[/C][/ROW]
[ROW][C]48[/C][C]1579[/C][C]1302.02450916626[/C][C]276.975490833736[/C][/ROW]
[ROW][C]49[/C][C]2790[/C][C]2483.14583970672[/C][C]306.854160293276[/C][/ROW]
[ROW][C]50[/C][C]1496[/C][C]1647.12958658425[/C][C]-151.129586584249[/C][/ROW]
[ROW][C]51[/C][C]1108[/C][C]989.861197621489[/C][C]118.138802378511[/C][/ROW]
[ROW][C]52[/C][C]496[/C][C]732.787951818105[/C][C]-236.787951818105[/C][/ROW]
[ROW][C]53[/C][C]1750[/C][C]1540.40710425622[/C][C]209.592895743783[/C][/ROW]
[ROW][C]54[/C][C]744[/C][C]810.183556606538[/C][C]-66.1835566065383[/C][/ROW]
[ROW][C]55[/C][C]1101[/C][C]1224.00905314101[/C][C]-123.009053141007[/C][/ROW]
[ROW][C]56[/C][C]1612[/C][C]1659.7277651125[/C][C]-47.7277651124974[/C][/ROW]
[ROW][C]57[/C][C]1805[/C][C]1304.08976940782[/C][C]500.910230592185[/C][/ROW]
[ROW][C]58[/C][C]2460[/C][C]1973.07684849153[/C][C]486.923151508471[/C][/ROW]
[ROW][C]59[/C][C]1653[/C][C]1731.03089771575[/C][C]-78.0308977157478[/C][/ROW]
[ROW][C]60[/C][C]1234[/C][C]1277.757928965[/C][C]-43.7579289650035[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145858&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145858&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
111671019.3416721485147.658327851499
2669766.657673450826-97.6576734508264
310531063.48740654104-10.4874065410364
419392182.6500192871-243.650019287099
5678676.5093825988711.49061740112948
6321514.748033525252-193.748033525252
726672945.40391974783-278.40391974783
8345511.856304967935-166.856304967935
913671517.68924159915-150.689241599154
1011581180.38077199369-22.3807719936871
1113851402.70571830492-17.7057183049175
1211551041.54390265607113.456097343928
1311201201.54991710655-81.549917106552
1417031762.52543348851-59.5254334885062
1511891104.99922845184.0007715489992
1630832716.36573946673366.63426053327
1713571301.9733101704655.0266898295401
1818921907.81681017338-15.8168101733821
19883988.466987612566-105.466987612566
2016271301.87000163099325.129998369011
2114121291.95602403325120.043975966746
2219002022.74822392395-122.748223923952
23777842.607869234533-65.6078692345329
24904936.215630336963-32.2156303369632
2521152142.0158646887-27.0158646887038
2618581771.8195598494286.1804401505824
2717811755.9685281496425.0314718503635
2812861107.3223047673178.677695232702
2910351135.77151499533-100.771514995327
3015571552.281212460314.71878753969173
3115271525.124724797461.87527520254416
3212201129.6269332664890.3730667335224
3313681373.27644277371-5.27644277371027
34564640.728908259873-76.7289082598726
3519902032.55889298222-42.558892982217
3615571692.66862043785-135.668620437845
3720571864.75627071922192.24372928078
3811111060.234553369550.7654466304964
39686727.676455483963-41.6764554839626
4020111885.77090829681125.22909170319
4122322527.18917610765-295.189176107651
4210321232.99325388351-200.993253883506
4311661247.34676703489-81.3467670348937
441020926.66368790130693.3363120986936
4517352044.43391173417-309.433911734171
4636233774.07707163713-151.077071637125
479181000.39320536032-82.3932053603241
4815791302.02450916626276.975490833736
4927902483.14583970672306.854160293276
5014961647.12958658425-151.129586584249
511108989.861197621489118.138802378511
52496732.787951818105-236.787951818105
5317501540.40710425622209.592895743783
54744810.183556606538-66.1835566065383
5511011224.00905314101-123.009053141007
5616121659.7277651125-47.7277651124974
5718051304.08976940782500.910230592185
5824601973.07684849153486.923151508471
5916531731.03089771575-78.0308977157478
6012341277.757928965-43.7579289650035







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.5166091584344880.9667816831310230.483390841565512
70.3923722142475110.7847444284950220.607627785752489
80.3432994866030110.6865989732060220.656700513396989
90.2322156056301970.4644312112603940.767784394369803
100.1617277122626050.3234554245252090.838272287737395
110.1127222214563820.2254444429127630.887277778543618
120.1378579626782690.2757159253565380.862142037321731
130.0864659724854890.1729319449709780.913534027514511
140.05477390718107870.1095478143621570.945226092818921
150.05228577113867620.1045715422773520.947714228861324
160.446747612633370.893495225266740.55325238736663
170.3771582661543780.7543165323087560.622841733845622
180.2955883423883090.5911766847766180.704411657611691
190.2387088749435920.4774177498871840.761291125056408
200.4396575812367680.8793151624735370.560342418763232
210.3988720419359630.7977440838719250.601127958064038
220.3521250938585360.7042501877170720.647874906141464
230.2866326642278050.573265328455610.713367335772195
240.2230542407412970.4461084814825940.776945759258703
250.1679887695990110.3359775391980220.832011230400989
260.1337614686186940.2675229372373880.866238531381306
270.09633300895104110.1926660179020820.903666991048959
280.09780971145660370.1956194229132070.902190288543396
290.0762112929204230.1524225858408460.923788707079577
300.05185379222902990.103707584458060.94814620777097
310.03410716722620270.06821433445240540.965892832773797
320.02456166643732750.0491233328746550.975438333562673
330.01524624039674320.03049248079348640.984753759603257
340.01008272416033240.02016544832066490.989917275839668
350.006030220381502410.01206044076300480.993969779618498
360.004689773222090270.009379546444180540.99531022677791
370.005107925826010340.01021585165202070.99489207417399
380.002972004495610020.005944008991220030.99702799550439
390.001660162375410060.003320324750820120.99833983762459
400.001178632138895530.002357264277791070.998821367861104
410.00325312818883210.006506256377664190.996746871811168
420.003702552373726660.007405104747453320.996297447626273
430.0022909747515510.004581949503102010.997709025248449
440.001362973802349910.002725947604699820.99863702619765
450.009177605092285370.01835521018457070.990822394907715
460.06387629801876280.1277525960375260.936123701981237
470.05438281708752820.1087656341750560.945617182912472
480.07667378584496170.1533475716899230.923326214155038
490.07967679168828440.1593535833765690.920323208311716
500.07619339440073230.1523867888014650.923806605599268
510.07357665662149480.147153313242990.926423343378505
520.06529163273392530.1305832654678510.934708367266075
530.05066039897669970.1013207979533990.9493396010233
540.02359801724434510.04719603448869010.976401982755655

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.516609158434488 & 0.966781683131023 & 0.483390841565512 \tabularnewline
7 & 0.392372214247511 & 0.784744428495022 & 0.607627785752489 \tabularnewline
8 & 0.343299486603011 & 0.686598973206022 & 0.656700513396989 \tabularnewline
9 & 0.232215605630197 & 0.464431211260394 & 0.767784394369803 \tabularnewline
10 & 0.161727712262605 & 0.323455424525209 & 0.838272287737395 \tabularnewline
11 & 0.112722221456382 & 0.225444442912763 & 0.887277778543618 \tabularnewline
12 & 0.137857962678269 & 0.275715925356538 & 0.862142037321731 \tabularnewline
13 & 0.086465972485489 & 0.172931944970978 & 0.913534027514511 \tabularnewline
14 & 0.0547739071810787 & 0.109547814362157 & 0.945226092818921 \tabularnewline
15 & 0.0522857711386762 & 0.104571542277352 & 0.947714228861324 \tabularnewline
16 & 0.44674761263337 & 0.89349522526674 & 0.55325238736663 \tabularnewline
17 & 0.377158266154378 & 0.754316532308756 & 0.622841733845622 \tabularnewline
18 & 0.295588342388309 & 0.591176684776618 & 0.704411657611691 \tabularnewline
19 & 0.238708874943592 & 0.477417749887184 & 0.761291125056408 \tabularnewline
20 & 0.439657581236768 & 0.879315162473537 & 0.560342418763232 \tabularnewline
21 & 0.398872041935963 & 0.797744083871925 & 0.601127958064038 \tabularnewline
22 & 0.352125093858536 & 0.704250187717072 & 0.647874906141464 \tabularnewline
23 & 0.286632664227805 & 0.57326532845561 & 0.713367335772195 \tabularnewline
24 & 0.223054240741297 & 0.446108481482594 & 0.776945759258703 \tabularnewline
25 & 0.167988769599011 & 0.335977539198022 & 0.832011230400989 \tabularnewline
26 & 0.133761468618694 & 0.267522937237388 & 0.866238531381306 \tabularnewline
27 & 0.0963330089510411 & 0.192666017902082 & 0.903666991048959 \tabularnewline
28 & 0.0978097114566037 & 0.195619422913207 & 0.902190288543396 \tabularnewline
29 & 0.076211292920423 & 0.152422585840846 & 0.923788707079577 \tabularnewline
30 & 0.0518537922290299 & 0.10370758445806 & 0.94814620777097 \tabularnewline
31 & 0.0341071672262027 & 0.0682143344524054 & 0.965892832773797 \tabularnewline
32 & 0.0245616664373275 & 0.049123332874655 & 0.975438333562673 \tabularnewline
33 & 0.0152462403967432 & 0.0304924807934864 & 0.984753759603257 \tabularnewline
34 & 0.0100827241603324 & 0.0201654483206649 & 0.989917275839668 \tabularnewline
35 & 0.00603022038150241 & 0.0120604407630048 & 0.993969779618498 \tabularnewline
36 & 0.00468977322209027 & 0.00937954644418054 & 0.99531022677791 \tabularnewline
37 & 0.00510792582601034 & 0.0102158516520207 & 0.99489207417399 \tabularnewline
38 & 0.00297200449561002 & 0.00594400899122003 & 0.99702799550439 \tabularnewline
39 & 0.00166016237541006 & 0.00332032475082012 & 0.99833983762459 \tabularnewline
40 & 0.00117863213889553 & 0.00235726427779107 & 0.998821367861104 \tabularnewline
41 & 0.0032531281888321 & 0.00650625637766419 & 0.996746871811168 \tabularnewline
42 & 0.00370255237372666 & 0.00740510474745332 & 0.996297447626273 \tabularnewline
43 & 0.002290974751551 & 0.00458194950310201 & 0.997709025248449 \tabularnewline
44 & 0.00136297380234991 & 0.00272594760469982 & 0.99863702619765 \tabularnewline
45 & 0.00917760509228537 & 0.0183552101845707 & 0.990822394907715 \tabularnewline
46 & 0.0638762980187628 & 0.127752596037526 & 0.936123701981237 \tabularnewline
47 & 0.0543828170875282 & 0.108765634175056 & 0.945617182912472 \tabularnewline
48 & 0.0766737858449617 & 0.153347571689923 & 0.923326214155038 \tabularnewline
49 & 0.0796767916882844 & 0.159353583376569 & 0.920323208311716 \tabularnewline
50 & 0.0761933944007323 & 0.152386788801465 & 0.923806605599268 \tabularnewline
51 & 0.0735766566214948 & 0.14715331324299 & 0.926423343378505 \tabularnewline
52 & 0.0652916327339253 & 0.130583265467851 & 0.934708367266075 \tabularnewline
53 & 0.0506603989766997 & 0.101320797953399 & 0.9493396010233 \tabularnewline
54 & 0.0235980172443451 & 0.0471960344886901 & 0.976401982755655 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145858&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]6[/C][C]0.516609158434488[/C][C]0.966781683131023[/C][C]0.483390841565512[/C][/ROW]
[ROW][C]7[/C][C]0.392372214247511[/C][C]0.784744428495022[/C][C]0.607627785752489[/C][/ROW]
[ROW][C]8[/C][C]0.343299486603011[/C][C]0.686598973206022[/C][C]0.656700513396989[/C][/ROW]
[ROW][C]9[/C][C]0.232215605630197[/C][C]0.464431211260394[/C][C]0.767784394369803[/C][/ROW]
[ROW][C]10[/C][C]0.161727712262605[/C][C]0.323455424525209[/C][C]0.838272287737395[/C][/ROW]
[ROW][C]11[/C][C]0.112722221456382[/C][C]0.225444442912763[/C][C]0.887277778543618[/C][/ROW]
[ROW][C]12[/C][C]0.137857962678269[/C][C]0.275715925356538[/C][C]0.862142037321731[/C][/ROW]
[ROW][C]13[/C][C]0.086465972485489[/C][C]0.172931944970978[/C][C]0.913534027514511[/C][/ROW]
[ROW][C]14[/C][C]0.0547739071810787[/C][C]0.109547814362157[/C][C]0.945226092818921[/C][/ROW]
[ROW][C]15[/C][C]0.0522857711386762[/C][C]0.104571542277352[/C][C]0.947714228861324[/C][/ROW]
[ROW][C]16[/C][C]0.44674761263337[/C][C]0.89349522526674[/C][C]0.55325238736663[/C][/ROW]
[ROW][C]17[/C][C]0.377158266154378[/C][C]0.754316532308756[/C][C]0.622841733845622[/C][/ROW]
[ROW][C]18[/C][C]0.295588342388309[/C][C]0.591176684776618[/C][C]0.704411657611691[/C][/ROW]
[ROW][C]19[/C][C]0.238708874943592[/C][C]0.477417749887184[/C][C]0.761291125056408[/C][/ROW]
[ROW][C]20[/C][C]0.439657581236768[/C][C]0.879315162473537[/C][C]0.560342418763232[/C][/ROW]
[ROW][C]21[/C][C]0.398872041935963[/C][C]0.797744083871925[/C][C]0.601127958064038[/C][/ROW]
[ROW][C]22[/C][C]0.352125093858536[/C][C]0.704250187717072[/C][C]0.647874906141464[/C][/ROW]
[ROW][C]23[/C][C]0.286632664227805[/C][C]0.57326532845561[/C][C]0.713367335772195[/C][/ROW]
[ROW][C]24[/C][C]0.223054240741297[/C][C]0.446108481482594[/C][C]0.776945759258703[/C][/ROW]
[ROW][C]25[/C][C]0.167988769599011[/C][C]0.335977539198022[/C][C]0.832011230400989[/C][/ROW]
[ROW][C]26[/C][C]0.133761468618694[/C][C]0.267522937237388[/C][C]0.866238531381306[/C][/ROW]
[ROW][C]27[/C][C]0.0963330089510411[/C][C]0.192666017902082[/C][C]0.903666991048959[/C][/ROW]
[ROW][C]28[/C][C]0.0978097114566037[/C][C]0.195619422913207[/C][C]0.902190288543396[/C][/ROW]
[ROW][C]29[/C][C]0.076211292920423[/C][C]0.152422585840846[/C][C]0.923788707079577[/C][/ROW]
[ROW][C]30[/C][C]0.0518537922290299[/C][C]0.10370758445806[/C][C]0.94814620777097[/C][/ROW]
[ROW][C]31[/C][C]0.0341071672262027[/C][C]0.0682143344524054[/C][C]0.965892832773797[/C][/ROW]
[ROW][C]32[/C][C]0.0245616664373275[/C][C]0.049123332874655[/C][C]0.975438333562673[/C][/ROW]
[ROW][C]33[/C][C]0.0152462403967432[/C][C]0.0304924807934864[/C][C]0.984753759603257[/C][/ROW]
[ROW][C]34[/C][C]0.0100827241603324[/C][C]0.0201654483206649[/C][C]0.989917275839668[/C][/ROW]
[ROW][C]35[/C][C]0.00603022038150241[/C][C]0.0120604407630048[/C][C]0.993969779618498[/C][/ROW]
[ROW][C]36[/C][C]0.00468977322209027[/C][C]0.00937954644418054[/C][C]0.99531022677791[/C][/ROW]
[ROW][C]37[/C][C]0.00510792582601034[/C][C]0.0102158516520207[/C][C]0.99489207417399[/C][/ROW]
[ROW][C]38[/C][C]0.00297200449561002[/C][C]0.00594400899122003[/C][C]0.99702799550439[/C][/ROW]
[ROW][C]39[/C][C]0.00166016237541006[/C][C]0.00332032475082012[/C][C]0.99833983762459[/C][/ROW]
[ROW][C]40[/C][C]0.00117863213889553[/C][C]0.00235726427779107[/C][C]0.998821367861104[/C][/ROW]
[ROW][C]41[/C][C]0.0032531281888321[/C][C]0.00650625637766419[/C][C]0.996746871811168[/C][/ROW]
[ROW][C]42[/C][C]0.00370255237372666[/C][C]0.00740510474745332[/C][C]0.996297447626273[/C][/ROW]
[ROW][C]43[/C][C]0.002290974751551[/C][C]0.00458194950310201[/C][C]0.997709025248449[/C][/ROW]
[ROW][C]44[/C][C]0.00136297380234991[/C][C]0.00272594760469982[/C][C]0.99863702619765[/C][/ROW]
[ROW][C]45[/C][C]0.00917760509228537[/C][C]0.0183552101845707[/C][C]0.990822394907715[/C][/ROW]
[ROW][C]46[/C][C]0.0638762980187628[/C][C]0.127752596037526[/C][C]0.936123701981237[/C][/ROW]
[ROW][C]47[/C][C]0.0543828170875282[/C][C]0.108765634175056[/C][C]0.945617182912472[/C][/ROW]
[ROW][C]48[/C][C]0.0766737858449617[/C][C]0.153347571689923[/C][C]0.923326214155038[/C][/ROW]
[ROW][C]49[/C][C]0.0796767916882844[/C][C]0.159353583376569[/C][C]0.920323208311716[/C][/ROW]
[ROW][C]50[/C][C]0.0761933944007323[/C][C]0.152386788801465[/C][C]0.923806605599268[/C][/ROW]
[ROW][C]51[/C][C]0.0735766566214948[/C][C]0.14715331324299[/C][C]0.926423343378505[/C][/ROW]
[ROW][C]52[/C][C]0.0652916327339253[/C][C]0.130583265467851[/C][C]0.934708367266075[/C][/ROW]
[ROW][C]53[/C][C]0.0506603989766997[/C][C]0.101320797953399[/C][C]0.9493396010233[/C][/ROW]
[ROW][C]54[/C][C]0.0235980172443451[/C][C]0.0471960344886901[/C][C]0.976401982755655[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145858&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145858&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
60.5166091584344880.9667816831310230.483390841565512
70.3923722142475110.7847444284950220.607627785752489
80.3432994866030110.6865989732060220.656700513396989
90.2322156056301970.4644312112603940.767784394369803
100.1617277122626050.3234554245252090.838272287737395
110.1127222214563820.2254444429127630.887277778543618
120.1378579626782690.2757159253565380.862142037321731
130.0864659724854890.1729319449709780.913534027514511
140.05477390718107870.1095478143621570.945226092818921
150.05228577113867620.1045715422773520.947714228861324
160.446747612633370.893495225266740.55325238736663
170.3771582661543780.7543165323087560.622841733845622
180.2955883423883090.5911766847766180.704411657611691
190.2387088749435920.4774177498871840.761291125056408
200.4396575812367680.8793151624735370.560342418763232
210.3988720419359630.7977440838719250.601127958064038
220.3521250938585360.7042501877170720.647874906141464
230.2866326642278050.573265328455610.713367335772195
240.2230542407412970.4461084814825940.776945759258703
250.1679887695990110.3359775391980220.832011230400989
260.1337614686186940.2675229372373880.866238531381306
270.09633300895104110.1926660179020820.903666991048959
280.09780971145660370.1956194229132070.902190288543396
290.0762112929204230.1524225858408460.923788707079577
300.05185379222902990.103707584458060.94814620777097
310.03410716722620270.06821433445240540.965892832773797
320.02456166643732750.0491233328746550.975438333562673
330.01524624039674320.03049248079348640.984753759603257
340.01008272416033240.02016544832066490.989917275839668
350.006030220381502410.01206044076300480.993969779618498
360.004689773222090270.009379546444180540.99531022677791
370.005107925826010340.01021585165202070.99489207417399
380.002972004495610020.005944008991220030.99702799550439
390.001660162375410060.003320324750820120.99833983762459
400.001178632138895530.002357264277791070.998821367861104
410.00325312818883210.006506256377664190.996746871811168
420.003702552373726660.007405104747453320.996297447626273
430.0022909747515510.004581949503102010.997709025248449
440.001362973802349910.002725947604699820.99863702619765
450.009177605092285370.01835521018457070.990822394907715
460.06387629801876280.1277525960375260.936123701981237
470.05438281708752820.1087656341750560.945617182912472
480.07667378584496170.1533475716899230.923326214155038
490.07967679168828440.1593535833765690.920323208311716
500.07619339440073230.1523867888014650.923806605599268
510.07357665662149480.147153313242990.926423343378505
520.06529163273392530.1305832654678510.934708367266075
530.05066039897669970.1013207979533990.9493396010233
540.02359801724434510.04719603448869010.976401982755655







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.163265306122449NOK
5% type I error level150.306122448979592NOK
10% type I error level160.326530612244898NOK

\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 & 0.163265306122449 & NOK \tabularnewline
5% type I error level & 15 & 0.306122448979592 & NOK \tabularnewline
10% type I error level & 16 & 0.326530612244898 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145858&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]0.163265306122449[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]15[/C][C]0.306122448979592[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.326530612244898[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145858&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145858&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 level80.163265306122449NOK
5% type I error level150.306122448979592NOK
10% type I error level160.326530612244898NOK



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')
}