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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 computationThu, 19 Nov 2009 11:33:23 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258655648c2u42r1y94mxdjh.htm/, Retrieved Thu, 28 Mar 2024 20:23:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57881, Retrieved Thu, 28 Mar 2024 20:23:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact78
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [WS7-1] [2009-11-19 18:33:23] [b32ceebc68d054278e6bda97f3d57f91] [Current]
-    D        [Multiple Regression] [WS7-Multipleregre...] [2009-11-19 18:48:17] [408e92805dcb18620260f240a7fb9d53]
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Dataseries X:
3922	8,1
3759	7,7
4138	7,5
4634	7,6
3996	7,8
4308	7,8
4143	7,8
4429	7,5
5219	7,5
4929	7,1
5755	7,5
5592	7,5
4163	7,6
4962	7,7
5208	7,7
4755	7,9
4491	8,1
5732	8,2
5731	8,2
5040	8,2
6102	7,9
4904	7,3
5369	6,9
5578	6,6
4619	6,7
4731	6,9
5011	7
5299	7,1
4146	7,2
4625	7,1
4736	6,9
4219	7
5116	6,8
4205	6,4
4121	6,7
5103	6,6
4300	6,4
4578	6,3
3809	6,2
5526	6,5
4247	6,8
3830	6,8
4394	6,4
4826	6,1
4409	5,8
4569	6,1
4106	7,2
4794	7,3
3914	6,9
3793	6,1
4405	5,8
4022	6,2
4100	7,1
4788	7,7
3163	7,9
3585	7,7
3903	7,4
4178	7,5
3863	8
4187	8,1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57881&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' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57881&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57881&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' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Bouw[t] = + 3917.35496549577 + 90.7009806465004Wman[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Bouw[t] =  +  3917.35496549577 +  90.7009806465004Wman[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57881&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Bouw[t] =  +  3917.35496549577 +  90.7009806465004Wman[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57881&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57881&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
Bouw[t] = + 3917.35496549577 + 90.7009806465004Wman[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3917.35496549577892.6676564.38844.9e-052.4e-05
Wman90.7009806465004123.9278420.73190.4671860.233593

\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) & 3917.35496549577 & 892.667656 & 4.3884 & 4.9e-05 & 2.4e-05 \tabularnewline
Wman & 90.7009806465004 & 123.927842 & 0.7319 & 0.467186 & 0.233593 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57881&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]3917.35496549577[/C][C]892.667656[/C][C]4.3884[/C][C]4.9e-05[/C][C]2.4e-05[/C][/ROW]
[ROW][C]Wman[/C][C]90.7009806465004[/C][C]123.927842[/C][C]0.7319[/C][C]0.467186[/C][C]0.233593[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57881&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57881&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)3917.35496549577892.6676564.38844.9e-052.4e-05
Wman90.7009806465004123.9278420.73190.4671860.233593







Multiple Linear Regression - Regression Statistics
Multiple R0.0956605444102581
R-squared0.00915093975686697
Adjusted R-squared-0.00793266473008347
F-TEST (value)0.535656264101465
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.467185578170894
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation628.221676726639
Sum Squared Residuals22890423.5563353

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0956605444102581 \tabularnewline
R-squared & 0.00915093975686697 \tabularnewline
Adjusted R-squared & -0.00793266473008347 \tabularnewline
F-TEST (value) & 0.535656264101465 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0.467185578170894 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 628.221676726639 \tabularnewline
Sum Squared Residuals & 22890423.5563353 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57881&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0956605444102581[/C][/ROW]
[ROW][C]R-squared[/C][C]0.00915093975686697[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00793266473008347[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.535656264101465[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0.467185578170894[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]628.221676726639[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]22890423.5563353[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57881&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57881&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 R0.0956605444102581
R-squared0.00915093975686697
Adjusted R-squared-0.00793266473008347
F-TEST (value)0.535656264101465
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.467185578170894
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation628.221676726639
Sum Squared Residuals22890423.5563353







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
139224652.03290873243-730.032908732433
237594615.75251647382-856.752516473823
341384597.61232034452-459.612320344523
446344606.6824184091727.3175815908267
539964624.82261453847-628.822614538473
643084624.82261453847-316.822614538473
741434624.82261453847-481.822614538473
844294597.61232034452-168.612320344523
952194597.61232034452621.387679655477
1049294561.33192808592367.668071914077
1157554597.612320344521157.38767965548
1255924597.61232034452994.387679655477
1341634606.68241840917-443.682418409173
1449624615.75251647382346.247483526177
1552084615.75251647382592.247483526177
1647554633.89271260312121.107287396876
1744914652.03290873242-161.032908732424
1857324661.103006797071070.89699320293
1957314661.103006797071069.89699320293
2050404661.10300679707378.896993202926
2161024633.892712603121468.10728739688
2249044579.47212421522324.527875784777
2353694543.19173195662825.808268043377
2455784515.981437762671062.01856223733
2546194525.0515358273293.948464172677
2647314543.19173195662187.808268043377
2750114552.26183002127458.738169978727
2852994561.33192808592737.668071914077
2941464570.40202615057-424.402026150573
3046254561.3319280859263.6680719140768
3147364543.19173195662192.808268043377
3242194552.26183002127-333.261830021273
3351164534.12163389197581.878366108027
3442054497.84124163337-292.841241633373
3541214525.05153582732-404.051535827323
3651034515.98143776267587.018562237327
3743004497.84124163337-197.841241633373
3845784488.7711435687289.2288564312771
3938094479.70104550407-670.701045504073
4055264506.911339698021019.08866030198
4142474534.12163389197-287.121633891973
4238304534.12163389197-704.121633891973
4343944497.84124163337-103.841241633373
4448264470.63094743942355.369052560577
4544094443.42065324547-34.4206532454726
4645694470.6309474394298.3690525605772
4741064570.40202615057-464.402026150573
4847944579.47212421522214.527875784777
4939144543.19173195662-629.191731956623
5037934470.63094743942-677.630947439423
5144054443.42065324547-38.4206532454726
5240224479.70104550407-457.701045504073
5341004561.33192808592-461.331928085923
5447884615.75251647382172.247483526177
5531634633.89271260312-1470.89271260312
5635854615.75251647382-1030.75251647382
5739034588.54222227987-685.542222279873
5841784597.61232034452-419.612320344523
5938634642.96281066777-779.962810667774
6041874652.03290873242-465.032908732423

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3922 & 4652.03290873243 & -730.032908732433 \tabularnewline
2 & 3759 & 4615.75251647382 & -856.752516473823 \tabularnewline
3 & 4138 & 4597.61232034452 & -459.612320344523 \tabularnewline
4 & 4634 & 4606.68241840917 & 27.3175815908267 \tabularnewline
5 & 3996 & 4624.82261453847 & -628.822614538473 \tabularnewline
6 & 4308 & 4624.82261453847 & -316.822614538473 \tabularnewline
7 & 4143 & 4624.82261453847 & -481.822614538473 \tabularnewline
8 & 4429 & 4597.61232034452 & -168.612320344523 \tabularnewline
9 & 5219 & 4597.61232034452 & 621.387679655477 \tabularnewline
10 & 4929 & 4561.33192808592 & 367.668071914077 \tabularnewline
11 & 5755 & 4597.61232034452 & 1157.38767965548 \tabularnewline
12 & 5592 & 4597.61232034452 & 994.387679655477 \tabularnewline
13 & 4163 & 4606.68241840917 & -443.682418409173 \tabularnewline
14 & 4962 & 4615.75251647382 & 346.247483526177 \tabularnewline
15 & 5208 & 4615.75251647382 & 592.247483526177 \tabularnewline
16 & 4755 & 4633.89271260312 & 121.107287396876 \tabularnewline
17 & 4491 & 4652.03290873242 & -161.032908732424 \tabularnewline
18 & 5732 & 4661.10300679707 & 1070.89699320293 \tabularnewline
19 & 5731 & 4661.10300679707 & 1069.89699320293 \tabularnewline
20 & 5040 & 4661.10300679707 & 378.896993202926 \tabularnewline
21 & 6102 & 4633.89271260312 & 1468.10728739688 \tabularnewline
22 & 4904 & 4579.47212421522 & 324.527875784777 \tabularnewline
23 & 5369 & 4543.19173195662 & 825.808268043377 \tabularnewline
24 & 5578 & 4515.98143776267 & 1062.01856223733 \tabularnewline
25 & 4619 & 4525.05153582732 & 93.948464172677 \tabularnewline
26 & 4731 & 4543.19173195662 & 187.808268043377 \tabularnewline
27 & 5011 & 4552.26183002127 & 458.738169978727 \tabularnewline
28 & 5299 & 4561.33192808592 & 737.668071914077 \tabularnewline
29 & 4146 & 4570.40202615057 & -424.402026150573 \tabularnewline
30 & 4625 & 4561.33192808592 & 63.6680719140768 \tabularnewline
31 & 4736 & 4543.19173195662 & 192.808268043377 \tabularnewline
32 & 4219 & 4552.26183002127 & -333.261830021273 \tabularnewline
33 & 5116 & 4534.12163389197 & 581.878366108027 \tabularnewline
34 & 4205 & 4497.84124163337 & -292.841241633373 \tabularnewline
35 & 4121 & 4525.05153582732 & -404.051535827323 \tabularnewline
36 & 5103 & 4515.98143776267 & 587.018562237327 \tabularnewline
37 & 4300 & 4497.84124163337 & -197.841241633373 \tabularnewline
38 & 4578 & 4488.77114356872 & 89.2288564312771 \tabularnewline
39 & 3809 & 4479.70104550407 & -670.701045504073 \tabularnewline
40 & 5526 & 4506.91133969802 & 1019.08866030198 \tabularnewline
41 & 4247 & 4534.12163389197 & -287.121633891973 \tabularnewline
42 & 3830 & 4534.12163389197 & -704.121633891973 \tabularnewline
43 & 4394 & 4497.84124163337 & -103.841241633373 \tabularnewline
44 & 4826 & 4470.63094743942 & 355.369052560577 \tabularnewline
45 & 4409 & 4443.42065324547 & -34.4206532454726 \tabularnewline
46 & 4569 & 4470.63094743942 & 98.3690525605772 \tabularnewline
47 & 4106 & 4570.40202615057 & -464.402026150573 \tabularnewline
48 & 4794 & 4579.47212421522 & 214.527875784777 \tabularnewline
49 & 3914 & 4543.19173195662 & -629.191731956623 \tabularnewline
50 & 3793 & 4470.63094743942 & -677.630947439423 \tabularnewline
51 & 4405 & 4443.42065324547 & -38.4206532454726 \tabularnewline
52 & 4022 & 4479.70104550407 & -457.701045504073 \tabularnewline
53 & 4100 & 4561.33192808592 & -461.331928085923 \tabularnewline
54 & 4788 & 4615.75251647382 & 172.247483526177 \tabularnewline
55 & 3163 & 4633.89271260312 & -1470.89271260312 \tabularnewline
56 & 3585 & 4615.75251647382 & -1030.75251647382 \tabularnewline
57 & 3903 & 4588.54222227987 & -685.542222279873 \tabularnewline
58 & 4178 & 4597.61232034452 & -419.612320344523 \tabularnewline
59 & 3863 & 4642.96281066777 & -779.962810667774 \tabularnewline
60 & 4187 & 4652.03290873242 & -465.032908732423 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57881&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]3922[/C][C]4652.03290873243[/C][C]-730.032908732433[/C][/ROW]
[ROW][C]2[/C][C]3759[/C][C]4615.75251647382[/C][C]-856.752516473823[/C][/ROW]
[ROW][C]3[/C][C]4138[/C][C]4597.61232034452[/C][C]-459.612320344523[/C][/ROW]
[ROW][C]4[/C][C]4634[/C][C]4606.68241840917[/C][C]27.3175815908267[/C][/ROW]
[ROW][C]5[/C][C]3996[/C][C]4624.82261453847[/C][C]-628.822614538473[/C][/ROW]
[ROW][C]6[/C][C]4308[/C][C]4624.82261453847[/C][C]-316.822614538473[/C][/ROW]
[ROW][C]7[/C][C]4143[/C][C]4624.82261453847[/C][C]-481.822614538473[/C][/ROW]
[ROW][C]8[/C][C]4429[/C][C]4597.61232034452[/C][C]-168.612320344523[/C][/ROW]
[ROW][C]9[/C][C]5219[/C][C]4597.61232034452[/C][C]621.387679655477[/C][/ROW]
[ROW][C]10[/C][C]4929[/C][C]4561.33192808592[/C][C]367.668071914077[/C][/ROW]
[ROW][C]11[/C][C]5755[/C][C]4597.61232034452[/C][C]1157.38767965548[/C][/ROW]
[ROW][C]12[/C][C]5592[/C][C]4597.61232034452[/C][C]994.387679655477[/C][/ROW]
[ROW][C]13[/C][C]4163[/C][C]4606.68241840917[/C][C]-443.682418409173[/C][/ROW]
[ROW][C]14[/C][C]4962[/C][C]4615.75251647382[/C][C]346.247483526177[/C][/ROW]
[ROW][C]15[/C][C]5208[/C][C]4615.75251647382[/C][C]592.247483526177[/C][/ROW]
[ROW][C]16[/C][C]4755[/C][C]4633.89271260312[/C][C]121.107287396876[/C][/ROW]
[ROW][C]17[/C][C]4491[/C][C]4652.03290873242[/C][C]-161.032908732424[/C][/ROW]
[ROW][C]18[/C][C]5732[/C][C]4661.10300679707[/C][C]1070.89699320293[/C][/ROW]
[ROW][C]19[/C][C]5731[/C][C]4661.10300679707[/C][C]1069.89699320293[/C][/ROW]
[ROW][C]20[/C][C]5040[/C][C]4661.10300679707[/C][C]378.896993202926[/C][/ROW]
[ROW][C]21[/C][C]6102[/C][C]4633.89271260312[/C][C]1468.10728739688[/C][/ROW]
[ROW][C]22[/C][C]4904[/C][C]4579.47212421522[/C][C]324.527875784777[/C][/ROW]
[ROW][C]23[/C][C]5369[/C][C]4543.19173195662[/C][C]825.808268043377[/C][/ROW]
[ROW][C]24[/C][C]5578[/C][C]4515.98143776267[/C][C]1062.01856223733[/C][/ROW]
[ROW][C]25[/C][C]4619[/C][C]4525.05153582732[/C][C]93.948464172677[/C][/ROW]
[ROW][C]26[/C][C]4731[/C][C]4543.19173195662[/C][C]187.808268043377[/C][/ROW]
[ROW][C]27[/C][C]5011[/C][C]4552.26183002127[/C][C]458.738169978727[/C][/ROW]
[ROW][C]28[/C][C]5299[/C][C]4561.33192808592[/C][C]737.668071914077[/C][/ROW]
[ROW][C]29[/C][C]4146[/C][C]4570.40202615057[/C][C]-424.402026150573[/C][/ROW]
[ROW][C]30[/C][C]4625[/C][C]4561.33192808592[/C][C]63.6680719140768[/C][/ROW]
[ROW][C]31[/C][C]4736[/C][C]4543.19173195662[/C][C]192.808268043377[/C][/ROW]
[ROW][C]32[/C][C]4219[/C][C]4552.26183002127[/C][C]-333.261830021273[/C][/ROW]
[ROW][C]33[/C][C]5116[/C][C]4534.12163389197[/C][C]581.878366108027[/C][/ROW]
[ROW][C]34[/C][C]4205[/C][C]4497.84124163337[/C][C]-292.841241633373[/C][/ROW]
[ROW][C]35[/C][C]4121[/C][C]4525.05153582732[/C][C]-404.051535827323[/C][/ROW]
[ROW][C]36[/C][C]5103[/C][C]4515.98143776267[/C][C]587.018562237327[/C][/ROW]
[ROW][C]37[/C][C]4300[/C][C]4497.84124163337[/C][C]-197.841241633373[/C][/ROW]
[ROW][C]38[/C][C]4578[/C][C]4488.77114356872[/C][C]89.2288564312771[/C][/ROW]
[ROW][C]39[/C][C]3809[/C][C]4479.70104550407[/C][C]-670.701045504073[/C][/ROW]
[ROW][C]40[/C][C]5526[/C][C]4506.91133969802[/C][C]1019.08866030198[/C][/ROW]
[ROW][C]41[/C][C]4247[/C][C]4534.12163389197[/C][C]-287.121633891973[/C][/ROW]
[ROW][C]42[/C][C]3830[/C][C]4534.12163389197[/C][C]-704.121633891973[/C][/ROW]
[ROW][C]43[/C][C]4394[/C][C]4497.84124163337[/C][C]-103.841241633373[/C][/ROW]
[ROW][C]44[/C][C]4826[/C][C]4470.63094743942[/C][C]355.369052560577[/C][/ROW]
[ROW][C]45[/C][C]4409[/C][C]4443.42065324547[/C][C]-34.4206532454726[/C][/ROW]
[ROW][C]46[/C][C]4569[/C][C]4470.63094743942[/C][C]98.3690525605772[/C][/ROW]
[ROW][C]47[/C][C]4106[/C][C]4570.40202615057[/C][C]-464.402026150573[/C][/ROW]
[ROW][C]48[/C][C]4794[/C][C]4579.47212421522[/C][C]214.527875784777[/C][/ROW]
[ROW][C]49[/C][C]3914[/C][C]4543.19173195662[/C][C]-629.191731956623[/C][/ROW]
[ROW][C]50[/C][C]3793[/C][C]4470.63094743942[/C][C]-677.630947439423[/C][/ROW]
[ROW][C]51[/C][C]4405[/C][C]4443.42065324547[/C][C]-38.4206532454726[/C][/ROW]
[ROW][C]52[/C][C]4022[/C][C]4479.70104550407[/C][C]-457.701045504073[/C][/ROW]
[ROW][C]53[/C][C]4100[/C][C]4561.33192808592[/C][C]-461.331928085923[/C][/ROW]
[ROW][C]54[/C][C]4788[/C][C]4615.75251647382[/C][C]172.247483526177[/C][/ROW]
[ROW][C]55[/C][C]3163[/C][C]4633.89271260312[/C][C]-1470.89271260312[/C][/ROW]
[ROW][C]56[/C][C]3585[/C][C]4615.75251647382[/C][C]-1030.75251647382[/C][/ROW]
[ROW][C]57[/C][C]3903[/C][C]4588.54222227987[/C][C]-685.542222279873[/C][/ROW]
[ROW][C]58[/C][C]4178[/C][C]4597.61232034452[/C][C]-419.612320344523[/C][/ROW]
[ROW][C]59[/C][C]3863[/C][C]4642.96281066777[/C][C]-779.962810667774[/C][/ROW]
[ROW][C]60[/C][C]4187[/C][C]4652.03290873242[/C][C]-465.032908732423[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57881&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57881&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
139224652.03290873243-730.032908732433
237594615.75251647382-856.752516473823
341384597.61232034452-459.612320344523
446344606.6824184091727.3175815908267
539964624.82261453847-628.822614538473
643084624.82261453847-316.822614538473
741434624.82261453847-481.822614538473
844294597.61232034452-168.612320344523
952194597.61232034452621.387679655477
1049294561.33192808592367.668071914077
1157554597.612320344521157.38767965548
1255924597.61232034452994.387679655477
1341634606.68241840917-443.682418409173
1449624615.75251647382346.247483526177
1552084615.75251647382592.247483526177
1647554633.89271260312121.107287396876
1744914652.03290873242-161.032908732424
1857324661.103006797071070.89699320293
1957314661.103006797071069.89699320293
2050404661.10300679707378.896993202926
2161024633.892712603121468.10728739688
2249044579.47212421522324.527875784777
2353694543.19173195662825.808268043377
2455784515.981437762671062.01856223733
2546194525.0515358273293.948464172677
2647314543.19173195662187.808268043377
2750114552.26183002127458.738169978727
2852994561.33192808592737.668071914077
2941464570.40202615057-424.402026150573
3046254561.3319280859263.6680719140768
3147364543.19173195662192.808268043377
3242194552.26183002127-333.261830021273
3351164534.12163389197581.878366108027
3442054497.84124163337-292.841241633373
3541214525.05153582732-404.051535827323
3651034515.98143776267587.018562237327
3743004497.84124163337-197.841241633373
3845784488.7711435687289.2288564312771
3938094479.70104550407-670.701045504073
4055264506.911339698021019.08866030198
4142474534.12163389197-287.121633891973
4238304534.12163389197-704.121633891973
4343944497.84124163337-103.841241633373
4448264470.63094743942355.369052560577
4544094443.42065324547-34.4206532454726
4645694470.6309474394298.3690525605772
4741064570.40202615057-464.402026150573
4847944579.47212421522214.527875784777
4939144543.19173195662-629.191731956623
5037934470.63094743942-677.630947439423
5144054443.42065324547-38.4206532454726
5240224479.70104550407-457.701045504073
5341004561.33192808592-461.331928085923
5447884615.75251647382172.247483526177
5531634633.89271260312-1470.89271260312
5635854615.75251647382-1030.75251647382
5739034588.54222227987-685.542222279873
5841784597.61232034452-419.612320344523
5938634642.96281066777-779.962810667774
6041874652.03290873242-465.032908732423







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.1743122531528910.3486245063057820.82568774684711
60.09578098646612720.1915619729322540.904219013533873
70.04156539538463950.0831307907692790.95843460461536
80.01768653890687550.0353730778137510.982313461093125
90.0809919825659710.1619839651319420.919008017434029
100.04339420580095970.08678841160191940.95660579419904
110.2727253798252780.5454507596505570.727274620174722
120.3963865580798870.7927731161597750.603613441920113
130.3491476729134050.698295345826810.650852327086595
140.3224181365069570.6448362730139140.677581863493043
150.3523679297402420.7047358594804840.647632070259758
160.3127158312556110.6254316625112220.68728416874439
170.2617032113072750.5234064226145490.738296788692725
180.5622682873296530.8754634253406940.437731712670347
190.7182552932181670.5634894135636650.281744706781833
200.6764267067716860.6471465864566270.323573293228314
210.9181481289945090.1637037420109830.0818518710054915
220.8999884873019530.2000230253960940.100011512698047
230.922215046470890.1555699070582190.0777849535291097
240.9539734244900710.09205315101985730.0460265755099286
250.9395009977634820.1209980044730360.0604990022365178
260.9215770145890490.1568459708219030.0784229854109514
270.9172427883125190.1655144233749620.0827572116874811
280.9474566067748060.1050867864503880.0525433932251939
290.9413851576710580.1172296846578840.0586148423289421
300.9263350376033020.1473299247933950.0736649623966977
310.9114114633212850.1771770733574300.0885885366787149
320.8932760817675620.2134478364648750.106723918232438
330.915105886477320.1697882270453600.0848941135226798
340.897144014651840.205711970696320.10285598534816
350.8769157547605190.2461684904789620.123084245239481
360.9024323091751040.1951353816497920.097567690824896
370.8698666675538530.2602666648922940.130133332446147
380.8294134698488360.3411730603023290.170586530151164
390.8467864961826120.3064270076347760.153213503817388
400.9701303059776930.05973938804461360.0298696940223068
410.9550804322272580.08983913554548480.0449195677727424
420.952303114502690.095393770994620.04769688549731
430.926875722064630.1462485558707390.0731242779353696
440.9252688683646920.1494622632706160.0747311316353079
450.887398981908690.2252020361826200.112601018091310
460.8594733837607990.2810532324784020.140526616239201
470.8086055314191170.3827889371617660.191394468580883
480.8736210806383760.2527578387232480.126378919361624
490.8252904281048980.3494191437902050.174709571895102
500.7964756017901740.4070487964196520.203524398209826
510.7096944096304770.5806111807390450.290305590369523
520.6088362181637510.7823275636724980.391163781836249
530.4843251204056260.9686502408112510.515674879594374
540.691192688602640.6176146227947210.308807311397360
550.883134333813050.2337313323738990.116865666186949

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.174312253152891 & 0.348624506305782 & 0.82568774684711 \tabularnewline
6 & 0.0957809864661272 & 0.191561972932254 & 0.904219013533873 \tabularnewline
7 & 0.0415653953846395 & 0.083130790769279 & 0.95843460461536 \tabularnewline
8 & 0.0176865389068755 & 0.035373077813751 & 0.982313461093125 \tabularnewline
9 & 0.080991982565971 & 0.161983965131942 & 0.919008017434029 \tabularnewline
10 & 0.0433942058009597 & 0.0867884116019194 & 0.95660579419904 \tabularnewline
11 & 0.272725379825278 & 0.545450759650557 & 0.727274620174722 \tabularnewline
12 & 0.396386558079887 & 0.792773116159775 & 0.603613441920113 \tabularnewline
13 & 0.349147672913405 & 0.69829534582681 & 0.650852327086595 \tabularnewline
14 & 0.322418136506957 & 0.644836273013914 & 0.677581863493043 \tabularnewline
15 & 0.352367929740242 & 0.704735859480484 & 0.647632070259758 \tabularnewline
16 & 0.312715831255611 & 0.625431662511222 & 0.68728416874439 \tabularnewline
17 & 0.261703211307275 & 0.523406422614549 & 0.738296788692725 \tabularnewline
18 & 0.562268287329653 & 0.875463425340694 & 0.437731712670347 \tabularnewline
19 & 0.718255293218167 & 0.563489413563665 & 0.281744706781833 \tabularnewline
20 & 0.676426706771686 & 0.647146586456627 & 0.323573293228314 \tabularnewline
21 & 0.918148128994509 & 0.163703742010983 & 0.0818518710054915 \tabularnewline
22 & 0.899988487301953 & 0.200023025396094 & 0.100011512698047 \tabularnewline
23 & 0.92221504647089 & 0.155569907058219 & 0.0777849535291097 \tabularnewline
24 & 0.953973424490071 & 0.0920531510198573 & 0.0460265755099286 \tabularnewline
25 & 0.939500997763482 & 0.120998004473036 & 0.0604990022365178 \tabularnewline
26 & 0.921577014589049 & 0.156845970821903 & 0.0784229854109514 \tabularnewline
27 & 0.917242788312519 & 0.165514423374962 & 0.0827572116874811 \tabularnewline
28 & 0.947456606774806 & 0.105086786450388 & 0.0525433932251939 \tabularnewline
29 & 0.941385157671058 & 0.117229684657884 & 0.0586148423289421 \tabularnewline
30 & 0.926335037603302 & 0.147329924793395 & 0.0736649623966977 \tabularnewline
31 & 0.911411463321285 & 0.177177073357430 & 0.0885885366787149 \tabularnewline
32 & 0.893276081767562 & 0.213447836464875 & 0.106723918232438 \tabularnewline
33 & 0.91510588647732 & 0.169788227045360 & 0.0848941135226798 \tabularnewline
34 & 0.89714401465184 & 0.20571197069632 & 0.10285598534816 \tabularnewline
35 & 0.876915754760519 & 0.246168490478962 & 0.123084245239481 \tabularnewline
36 & 0.902432309175104 & 0.195135381649792 & 0.097567690824896 \tabularnewline
37 & 0.869866667553853 & 0.260266664892294 & 0.130133332446147 \tabularnewline
38 & 0.829413469848836 & 0.341173060302329 & 0.170586530151164 \tabularnewline
39 & 0.846786496182612 & 0.306427007634776 & 0.153213503817388 \tabularnewline
40 & 0.970130305977693 & 0.0597393880446136 & 0.0298696940223068 \tabularnewline
41 & 0.955080432227258 & 0.0898391355454848 & 0.0449195677727424 \tabularnewline
42 & 0.95230311450269 & 0.09539377099462 & 0.04769688549731 \tabularnewline
43 & 0.92687572206463 & 0.146248555870739 & 0.0731242779353696 \tabularnewline
44 & 0.925268868364692 & 0.149462263270616 & 0.0747311316353079 \tabularnewline
45 & 0.88739898190869 & 0.225202036182620 & 0.112601018091310 \tabularnewline
46 & 0.859473383760799 & 0.281053232478402 & 0.140526616239201 \tabularnewline
47 & 0.808605531419117 & 0.382788937161766 & 0.191394468580883 \tabularnewline
48 & 0.873621080638376 & 0.252757838723248 & 0.126378919361624 \tabularnewline
49 & 0.825290428104898 & 0.349419143790205 & 0.174709571895102 \tabularnewline
50 & 0.796475601790174 & 0.407048796419652 & 0.203524398209826 \tabularnewline
51 & 0.709694409630477 & 0.580611180739045 & 0.290305590369523 \tabularnewline
52 & 0.608836218163751 & 0.782327563672498 & 0.391163781836249 \tabularnewline
53 & 0.484325120405626 & 0.968650240811251 & 0.515674879594374 \tabularnewline
54 & 0.69119268860264 & 0.617614622794721 & 0.308807311397360 \tabularnewline
55 & 0.88313433381305 & 0.233731332373899 & 0.116865666186949 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57881&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]5[/C][C]0.174312253152891[/C][C]0.348624506305782[/C][C]0.82568774684711[/C][/ROW]
[ROW][C]6[/C][C]0.0957809864661272[/C][C]0.191561972932254[/C][C]0.904219013533873[/C][/ROW]
[ROW][C]7[/C][C]0.0415653953846395[/C][C]0.083130790769279[/C][C]0.95843460461536[/C][/ROW]
[ROW][C]8[/C][C]0.0176865389068755[/C][C]0.035373077813751[/C][C]0.982313461093125[/C][/ROW]
[ROW][C]9[/C][C]0.080991982565971[/C][C]0.161983965131942[/C][C]0.919008017434029[/C][/ROW]
[ROW][C]10[/C][C]0.0433942058009597[/C][C]0.0867884116019194[/C][C]0.95660579419904[/C][/ROW]
[ROW][C]11[/C][C]0.272725379825278[/C][C]0.545450759650557[/C][C]0.727274620174722[/C][/ROW]
[ROW][C]12[/C][C]0.396386558079887[/C][C]0.792773116159775[/C][C]0.603613441920113[/C][/ROW]
[ROW][C]13[/C][C]0.349147672913405[/C][C]0.69829534582681[/C][C]0.650852327086595[/C][/ROW]
[ROW][C]14[/C][C]0.322418136506957[/C][C]0.644836273013914[/C][C]0.677581863493043[/C][/ROW]
[ROW][C]15[/C][C]0.352367929740242[/C][C]0.704735859480484[/C][C]0.647632070259758[/C][/ROW]
[ROW][C]16[/C][C]0.312715831255611[/C][C]0.625431662511222[/C][C]0.68728416874439[/C][/ROW]
[ROW][C]17[/C][C]0.261703211307275[/C][C]0.523406422614549[/C][C]0.738296788692725[/C][/ROW]
[ROW][C]18[/C][C]0.562268287329653[/C][C]0.875463425340694[/C][C]0.437731712670347[/C][/ROW]
[ROW][C]19[/C][C]0.718255293218167[/C][C]0.563489413563665[/C][C]0.281744706781833[/C][/ROW]
[ROW][C]20[/C][C]0.676426706771686[/C][C]0.647146586456627[/C][C]0.323573293228314[/C][/ROW]
[ROW][C]21[/C][C]0.918148128994509[/C][C]0.163703742010983[/C][C]0.0818518710054915[/C][/ROW]
[ROW][C]22[/C][C]0.899988487301953[/C][C]0.200023025396094[/C][C]0.100011512698047[/C][/ROW]
[ROW][C]23[/C][C]0.92221504647089[/C][C]0.155569907058219[/C][C]0.0777849535291097[/C][/ROW]
[ROW][C]24[/C][C]0.953973424490071[/C][C]0.0920531510198573[/C][C]0.0460265755099286[/C][/ROW]
[ROW][C]25[/C][C]0.939500997763482[/C][C]0.120998004473036[/C][C]0.0604990022365178[/C][/ROW]
[ROW][C]26[/C][C]0.921577014589049[/C][C]0.156845970821903[/C][C]0.0784229854109514[/C][/ROW]
[ROW][C]27[/C][C]0.917242788312519[/C][C]0.165514423374962[/C][C]0.0827572116874811[/C][/ROW]
[ROW][C]28[/C][C]0.947456606774806[/C][C]0.105086786450388[/C][C]0.0525433932251939[/C][/ROW]
[ROW][C]29[/C][C]0.941385157671058[/C][C]0.117229684657884[/C][C]0.0586148423289421[/C][/ROW]
[ROW][C]30[/C][C]0.926335037603302[/C][C]0.147329924793395[/C][C]0.0736649623966977[/C][/ROW]
[ROW][C]31[/C][C]0.911411463321285[/C][C]0.177177073357430[/C][C]0.0885885366787149[/C][/ROW]
[ROW][C]32[/C][C]0.893276081767562[/C][C]0.213447836464875[/C][C]0.106723918232438[/C][/ROW]
[ROW][C]33[/C][C]0.91510588647732[/C][C]0.169788227045360[/C][C]0.0848941135226798[/C][/ROW]
[ROW][C]34[/C][C]0.89714401465184[/C][C]0.20571197069632[/C][C]0.10285598534816[/C][/ROW]
[ROW][C]35[/C][C]0.876915754760519[/C][C]0.246168490478962[/C][C]0.123084245239481[/C][/ROW]
[ROW][C]36[/C][C]0.902432309175104[/C][C]0.195135381649792[/C][C]0.097567690824896[/C][/ROW]
[ROW][C]37[/C][C]0.869866667553853[/C][C]0.260266664892294[/C][C]0.130133332446147[/C][/ROW]
[ROW][C]38[/C][C]0.829413469848836[/C][C]0.341173060302329[/C][C]0.170586530151164[/C][/ROW]
[ROW][C]39[/C][C]0.846786496182612[/C][C]0.306427007634776[/C][C]0.153213503817388[/C][/ROW]
[ROW][C]40[/C][C]0.970130305977693[/C][C]0.0597393880446136[/C][C]0.0298696940223068[/C][/ROW]
[ROW][C]41[/C][C]0.955080432227258[/C][C]0.0898391355454848[/C][C]0.0449195677727424[/C][/ROW]
[ROW][C]42[/C][C]0.95230311450269[/C][C]0.09539377099462[/C][C]0.04769688549731[/C][/ROW]
[ROW][C]43[/C][C]0.92687572206463[/C][C]0.146248555870739[/C][C]0.0731242779353696[/C][/ROW]
[ROW][C]44[/C][C]0.925268868364692[/C][C]0.149462263270616[/C][C]0.0747311316353079[/C][/ROW]
[ROW][C]45[/C][C]0.88739898190869[/C][C]0.225202036182620[/C][C]0.112601018091310[/C][/ROW]
[ROW][C]46[/C][C]0.859473383760799[/C][C]0.281053232478402[/C][C]0.140526616239201[/C][/ROW]
[ROW][C]47[/C][C]0.808605531419117[/C][C]0.382788937161766[/C][C]0.191394468580883[/C][/ROW]
[ROW][C]48[/C][C]0.873621080638376[/C][C]0.252757838723248[/C][C]0.126378919361624[/C][/ROW]
[ROW][C]49[/C][C]0.825290428104898[/C][C]0.349419143790205[/C][C]0.174709571895102[/C][/ROW]
[ROW][C]50[/C][C]0.796475601790174[/C][C]0.407048796419652[/C][C]0.203524398209826[/C][/ROW]
[ROW][C]51[/C][C]0.709694409630477[/C][C]0.580611180739045[/C][C]0.290305590369523[/C][/ROW]
[ROW][C]52[/C][C]0.608836218163751[/C][C]0.782327563672498[/C][C]0.391163781836249[/C][/ROW]
[ROW][C]53[/C][C]0.484325120405626[/C][C]0.968650240811251[/C][C]0.515674879594374[/C][/ROW]
[ROW][C]54[/C][C]0.69119268860264[/C][C]0.617614622794721[/C][C]0.308807311397360[/C][/ROW]
[ROW][C]55[/C][C]0.88313433381305[/C][C]0.233731332373899[/C][C]0.116865666186949[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57881&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57881&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
50.1743122531528910.3486245063057820.82568774684711
60.09578098646612720.1915619729322540.904219013533873
70.04156539538463950.0831307907692790.95843460461536
80.01768653890687550.0353730778137510.982313461093125
90.0809919825659710.1619839651319420.919008017434029
100.04339420580095970.08678841160191940.95660579419904
110.2727253798252780.5454507596505570.727274620174722
120.3963865580798870.7927731161597750.603613441920113
130.3491476729134050.698295345826810.650852327086595
140.3224181365069570.6448362730139140.677581863493043
150.3523679297402420.7047358594804840.647632070259758
160.3127158312556110.6254316625112220.68728416874439
170.2617032113072750.5234064226145490.738296788692725
180.5622682873296530.8754634253406940.437731712670347
190.7182552932181670.5634894135636650.281744706781833
200.6764267067716860.6471465864566270.323573293228314
210.9181481289945090.1637037420109830.0818518710054915
220.8999884873019530.2000230253960940.100011512698047
230.922215046470890.1555699070582190.0777849535291097
240.9539734244900710.09205315101985730.0460265755099286
250.9395009977634820.1209980044730360.0604990022365178
260.9215770145890490.1568459708219030.0784229854109514
270.9172427883125190.1655144233749620.0827572116874811
280.9474566067748060.1050867864503880.0525433932251939
290.9413851576710580.1172296846578840.0586148423289421
300.9263350376033020.1473299247933950.0736649623966977
310.9114114633212850.1771770733574300.0885885366787149
320.8932760817675620.2134478364648750.106723918232438
330.915105886477320.1697882270453600.0848941135226798
340.897144014651840.205711970696320.10285598534816
350.8769157547605190.2461684904789620.123084245239481
360.9024323091751040.1951353816497920.097567690824896
370.8698666675538530.2602666648922940.130133332446147
380.8294134698488360.3411730603023290.170586530151164
390.8467864961826120.3064270076347760.153213503817388
400.9701303059776930.05973938804461360.0298696940223068
410.9550804322272580.08983913554548480.0449195677727424
420.952303114502690.095393770994620.04769688549731
430.926875722064630.1462485558707390.0731242779353696
440.9252688683646920.1494622632706160.0747311316353079
450.887398981908690.2252020361826200.112601018091310
460.8594733837607990.2810532324784020.140526616239201
470.8086055314191170.3827889371617660.191394468580883
480.8736210806383760.2527578387232480.126378919361624
490.8252904281048980.3494191437902050.174709571895102
500.7964756017901740.4070487964196520.203524398209826
510.7096944096304770.5806111807390450.290305590369523
520.6088362181637510.7823275636724980.391163781836249
530.4843251204056260.9686502408112510.515674879594374
540.691192688602640.6176146227947210.308807311397360
550.883134333813050.2337313323738990.116865666186949







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0196078431372549OK
10% type I error level70.137254901960784NOK

\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 & 1 & 0.0196078431372549 & OK \tabularnewline
10% type I error level & 7 & 0.137254901960784 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57881&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]1[/C][C]0.0196078431372549[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.137254901960784[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57881&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57881&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 level10.0196078431372549OK
10% type I error level70.137254901960784NOK



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')
}