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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 20 Nov 2011 09:29:34 -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/20/t1321799516mjf628fseudpe8q.htm/, Retrieved Fri, 09 May 2025 16:53:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145603, Retrieved Fri, 09 May 2025 16:53:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact323

Tree of Dependent Computations

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] [WS7 Tutorial] [2010-11-18 16:04:53] [afe9379cca749d06b3d6872e02cc47ed]
-    D    [Multiple Regression] [WS7 Tutorial Popu...] [2010-11-22 10:41:15] [afe9379cca749d06b3d6872e02cc47ed]
- R  D        [Multiple Regression] [WS 7 Mini-tutorial] [2011-11-20 14:29:34] [6140f0163e532fc168d2f211324acd0a] [Current]
- R  D          [Multiple Regression] [WS 7 Mini-tutorial] [2011-11-20 14:47:41] [f5fdea4413921432bb019d1f20c4f2ec]
-    D            [Multiple Regression] [Ws 7 Mini-tutoria...] [2011-11-20 15:44:07] [f5fdea4413921432bb019d1f20c4f2ec]
-   P               [Multiple Regression] [Ws 7 Mini-tutoria...] [2011-11-20 15:51:02] [f5fdea4413921432bb019d1f20c4f2ec]
- RMP             [Kendall tau Correlation Matrix] [workshop 10 a] [2012-12-07 13:55:33] [dbae308bdff61c0f4902cc85498d0d35]
- R P               [Kendall tau Correlation Matrix] [workshop 10 b] [2012-12-07 14:31:15] [dbae308bdff61c0f4902cc85498d0d35]
- RMP               [Multiple Regression] [workshop 10 c] [2012-12-07 14:38:23] [dbae308bdff61c0f4902cc85498d0d35]
- RMP               [Recursive Partitioning (Regression Trees)] [workshop 10 d] [2012-12-07 14:46:54] [dbae308bdff61c0f4902cc85498d0d35]
-   P                 [Recursive Partitioning (Regression Trees)] [WS 10 recursive p...] [2012-12-10 17:38:57] [8c30f4dd45e15fd207e4faf2fdf6253e]
-                   [Kendall tau Correlation Matrix] [WS 10 Pearson cor...] [2012-12-10 15:35:41] [8c30f4dd45e15fd207e4faf2fdf6253e]
- R P               [Kendall tau Correlation Matrix] [WS 10 Kendall] [2012-12-10 15:49:57] [8c30f4dd45e15fd207e4faf2fdf6253e]
- RMP               [Multiple Regression] [WS 10 multiple re...] [2012-12-10 15:57:07] [8c30f4dd45e15fd207e4faf2fdf6253e]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
26	21	21	23	17	23	4
20	16	15	24	17	20	4
19	19	18	22	18	20	6
19	18	11	20	21	21	8
20	16	8	24	20	24	8
25	23	19	27	28	22	4
25	17	4	28	19	23	4
22	12	20	27	22	20	8
26	19	16	24	16	25	5
22	16	14	23	18	23	4
17	19	10	24	25	27	4
22	20	13	27	17	27	4
19	13	14	27	14	22	4
24	20	8	28	11	24	4
26	27	23	27	27	25	4
21	17	11	23	20	22	8
13	8	9	24	22	28	4
26	25	24	28	22	28	4
20	26	5	27	21	27	4
22	13	15	25	23	25	8
14	19	5	19	17	16	4
21	15	19	24	24	28	7
7	5	6	20	14	21	4
23	16	13	28	17	24	4
17	14	11	26	23	27	5
25	24	17	23	24	14	4
25	24	17	23	24	14	4
19	9	5	20	8	27	4
20	19	9	11	22	20	4
23	19	15	24	23	21	4
22	25	17	25	25	22	4
22	19	17	23	21	21	4
21	18	20	18	24	12	15
15	15	12	20	15	20	10
20	12	7	20	22	24	4
22	21	16	24	21	19	8
18	12	7	23	25	28	4
20	15	14	25	16	23	4
28	28	24	28	28	27	4
22	25	15	26	23	22	4
18	19	15	26	21	27	7
23	20	10	23	21	26	4
20	24	14	22	26	22	6
25	26	18	24	22	21	5
26	25	12	21	21	19	4
15	12	9	20	18	24	16
17	12	9	22	12	19	5
23	15	8	20	25	26	12
21	17	18	25	17	22	6
13	14	10	20	24	28	9
18	16	17	22	15	21	9
19	11	14	23	13	23	4
22	20	16	25	26	28	5
16	11	10	23	16	10	4
24	22	19	23	24	24	4
18	20	10	22	21	21	5
20	19	14	24	20	21	4
24	17	10	25	14	24	4
14	21	4	21	25	24	4
22	23	19	12	25	25	5
24	18	9	17	20	25	4
18	17	12	20	22	23	6
21	27	16	23	20	21	4
23	25	11	23	26	16	4
17	19	18	20	18	17	18
22	22	11	28	22	25	4
24	24	24	24	24	24	6
21	20	17	24	17	23	4
22	19	18	24	24	25	4
16	11	9	24	20	23	5
21	22	19	28	19	28	4
23	22	18	25	20	26	4
22	16	12	21	15	22	5
24	20	23	25	23	19	10
24	24	22	25	26	26	5
16	16	14	18	22	18	8
16	16	14	17	20	18	8
21	22	16	26	24	25	5
26	24	23	28	26	27	4
15	16	7	21	21	12	4
25	27	10	27	25	15	4
18	11	12	22	13	21	5
23	21	12	21	20	23	4
20	20	12	25	22	22	4
17	20	17	22	23	21	8
25	27	21	23	28	24	4
24	20	16	26	22	27	5
17	12	11	19	20	22	14
19	8	14	25	6	28	8
20	21	13	21	21	26	8
15	18	9	13	20	10	4
27	24	19	24	18	19	4
22	16	13	25	23	22	6
23	18	19	26	20	21	4
16	20	13	25	24	24	7
19	20	13	25	22	25	7
25	19	13	22	21	21	4
19	17	14	21	18	20	6
19	16	12	23	21	21	4
26	26	22	25	23	24	7
21	15	11	24	23	23	4
20	22	5	21	15	18	4
24	17	18	21	21	24	8
22	23	19	25	24	24	4
20	21	14	22	23	19	4
18	19	15	20	21	20	10
18	14	12	20	21	18	8
24	17	19	23	20	20	6
24	12	15	28	11	27	4
22	24	17	23	22	23	4
23	18	8	28	27	26	4
22	20	10	24	25	23	5
20	16	12	18	18	17	4
18	20	12	20	20	21	6
25	22	20	28	24	25	4
18	12	12	21	10	23	5
16	16	12	21	27	27	7
20	17	14	25	21	24	8
19	22	6	19	21	20	5
15	12	10	18	18	27	8
19	14	18	21	15	21	10
19	23	18	22	24	24	8
16	15	7	24	22	21	5
17	17	18	15	14	15	12
28	28	9	28	28	25	4
23	20	17	26	18	25	5
25	23	22	23	26	22	4
20	13	11	26	17	24	6
17	18	15	20	19	21	4
23	23	17	22	22	22	4
16	19	15	20	18	23	7
23	23	22	23	24	22	7
11	12	9	22	15	20	10
18	16	13	24	18	23	4
24	23	20	23	26	25	5
23	13	14	22	11	23	8
21	22	14	26	26	22	11
16	18	12	23	21	25	7
24	23	20	27	23	26	4
23	20	20	23	23	22	8
18	10	8	21	15	24	6
20	17	17	26	22	24	7
9	18	9	23	26	25	5
24	15	18	21	16	20	4
25	23	22	27	20	26	8
20	17	10	19	18	21	4
21	17	13	23	22	26	8
25	22	15	25	16	21	6
22	20	18	23	19	22	4
21	20	18	22	20	16	9
21	19	12	22	19	26	5
22	18	12	25	23	28	6
27	22	20	25	24	18	4
24	20	12	28	25	25	4
24	22	16	28	21	23	4
21	18	16	20	21	21	5
18	16	18	25	23	20	6
16	16	16	19	27	25	16
22	16	13	25	23	22	6
20	16	17	22	18	21	6
18	17	13	18	16	16	4
20	18	17	20	16	18	4

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'AstonUniversity' @ aston.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 6 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145603&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145603&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145603&T=0

As an alternative you can also use a QR Code:  
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 time6 seconds
R Server'AstonUniversity' @ aston.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
I1[t] = + 7.0804053213037 + 0.360075596086737I2[t] + 0.251162767910132I3[t] + 0.263436058749965E1[t] -0.11565136288369E2[t] + 0.0385124093880844E3[t] -0.212607823261176`A `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
I1[t] =  +  7.0804053213037 +  0.360075596086737I2[t] +  0.251162767910132I3[t] +  0.263436058749965E1[t] -0.11565136288369E2[t] +  0.0385124093880844E3[t] -0.212607823261176`A
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145603&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]I1[t] =  +  7.0804053213037 +  0.360075596086737I2[t] +  0.251162767910132I3[t] +  0.263436058749965E1[t] -0.11565136288369E2[t] +  0.0385124093880844E3[t] -0.212607823261176`A
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145603&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145603&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
I1[t] = + 7.0804053213037 + 0.360075596086737I2[t] + 0.251162767910132I3[t] + 0.263436058749965E1[t] -0.11565136288369E2[t] + 0.0385124093880844E3[t] -0.212607823261176`A `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.08040532130371.9824663.57150.0004730.000237
I20.3600755960867370.0629125.723400
I30.2511627679101320.0502714.99622e-061e-06
E10.2634360587499650.0748193.5210.0005650.000282
E2-0.115651362883690.05879-1.96720.0509470.025474
E30.03851240938808440.0611710.62960.5298950.264947
`A `-0.2126078232611760.083998-2.53110.0123670.006184

\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) & 7.0804053213037 & 1.982466 & 3.5715 & 0.000473 & 0.000237 \tabularnewline
I2 & 0.360075596086737 & 0.062912 & 5.7234 & 0 & 0 \tabularnewline
I3 & 0.251162767910132 & 0.050271 & 4.9962 & 2e-06 & 1e-06 \tabularnewline
E1 & 0.263436058749965 & 0.074819 & 3.521 & 0.000565 & 0.000282 \tabularnewline
E2 & -0.11565136288369 & 0.05879 & -1.9672 & 0.050947 & 0.025474 \tabularnewline
E3 & 0.0385124093880844 & 0.061171 & 0.6296 & 0.529895 & 0.264947 \tabularnewline
`A
` & -0.212607823261176 & 0.083998 & -2.5311 & 0.012367 & 0.006184 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145603&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]7.0804053213037[/C][C]1.982466[/C][C]3.5715[/C][C]0.000473[/C][C]0.000237[/C][/ROW]
[ROW][C]I2[/C][C]0.360075596086737[/C][C]0.062912[/C][C]5.7234[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]I3[/C][C]0.251162767910132[/C][C]0.050271[/C][C]4.9962[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]E1[/C][C]0.263436058749965[/C][C]0.074819[/C][C]3.521[/C][C]0.000565[/C][C]0.000282[/C][/ROW]
[ROW][C]E2[/C][C]-0.11565136288369[/C][C]0.05879[/C][C]-1.9672[/C][C]0.050947[/C][C]0.025474[/C][/ROW]
[ROW][C]E3[/C][C]0.0385124093880844[/C][C]0.061171[/C][C]0.6296[/C][C]0.529895[/C][C]0.264947[/C][/ROW]
[ROW][C]`A
`[/C][C]-0.212607823261176[/C][C]0.083998[/C][C]-2.5311[/C][C]0.012367[/C][C]0.006184[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145603&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145603&T=2

As an alternative you can also use a QR Code:  
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)7.08040532130371.9824663.57150.0004730.000237
I20.3600755960867370.0629125.723400
I30.2511627679101320.0502714.99622e-061e-06
E10.2634360587499650.0748193.5210.0005650.000282
E2-0.115651362883690.05879-1.96720.0509470.025474
E30.03851240938808440.0611710.62960.5298950.264947
`A `-0.2126078232611760.083998-2.53110.0123670.006184






Multiple Linear Regression - Regression Statistics
Multiple R0.741676532801773
R-squared0.55008407930886
Adjusted R-squared0.532667979153074
F-TEST (value)31.5848022455307
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.5000316295822
Sum Squared Residuals968.774513081274

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.741676532801773 \tabularnewline
R-squared & 0.55008407930886 \tabularnewline
Adjusted R-squared & 0.532667979153074 \tabularnewline
F-TEST (value) & 31.5848022455307 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.5000316295822 \tabularnewline
Sum Squared Residuals & 968.774513081274 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145603&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.741676532801773[/C][/ROW]
[ROW][C]R-squared[/C][C]0.55008407930886[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.532667979153074[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]31.5848022455307[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/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]2.5000316295822[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]968.774513081274[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145603&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145603&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.741676532801773
R-squared0.55008407930886
Adjusted R-squared0.532667979153074
F-TEST (value)31.5848022455307
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.5000316295822
Sum Squared Residuals968.774513081274






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12624.04472127034571.95527872965433
22020.8852655130369-0.885265513036901
31921.6512414781215-2.65124147812153
41918.27249706337860.727502936621398
52018.08379039352251.91620960647746
62524.00561376059010.99438623940994
72519.42052939950895.57947060049108
82220.06239703702731.93760296297265
92622.31226065577023.68773934422982
102220.37055255165741.62944744834264
111720.0540544243935-3.05405442439352
122222.8831374035301-0.883137403530069
131920.7681630405437-1.76816304054369
142422.46913057186731.53086942813274
152626.6817558076255-0.681755807625479
162118.85689341581352.14310658418646
171316.2275265975684-3.22752659756843
182627.1699974846948-1.1699974846948
192022.5716833852347-2.57168338523468
202218.71669736012023.28330263987977
211417.9826346908936-3.98263469089363
222120.3905572537260.609442746273996
23713.9956913079309-6.9956913079309
242321.59073384976881.40926615023117
251719.0504062318761-2.0504062318761
262522.96412576228682.03587423771324
272522.96412576228682.03587423771324
281916.10981355799842.89018644200164
292016.45559011566833.54440988433168
302321.31009653338311.68990346661695
312224.0435213880944-2.04352138809441
322221.78028873622070.219711263779269
332117.82426932109783.1757306789022
341517.6736131644202-2.67361316442022
352015.95770957354294.04229042645708
362221.58525710741320.414742892586834
371816.55511329869411.44488670130591
382020.7686517988379-0.768651798837936
392827.51780368626480.482196313735207
402224.0359346367915-2.03593463679149
411821.6615223631953-3.66152236319534
422320.5747870038772.42521299612303
432021.5987823026213-1.59878230262134
442524.48715754934310.512842450656877
452622.08103153691443.9189684630856
461514.37134668176380.62865331823617
471717.7382509854984-0.738250985498415
482315.3183072737497.68169272625104
492121.9140746438578-0.91407464385781
501316.2910568649259-3.29105686492587
511820.0674949502068-2.06749495020681
521919.1484313856421-0.148431385642125
532221.89479590993420.105204090065766
541617.2961649033054-1.29616490330543
552423.13142419981440.86857580018561
561819.9061810749254-1.90618107492541
572021.405887854124-1.40588785412399
582420.75396705452633.24603294547365
591418.3613836046921-4.36138360469206
602220.30395637289471.69604362710527
612418.09999564478925.90000435521082
621818.5501733376168-0.55017333761683
632124.5253820998882-3.52538209988819
642321.66294684392151.33705315607851
651718.4575382533232-1.45753825332323
662222.7091174854386-0.709117485438629
672424.9456096437661-0.945609643766136
682122.9434306613684-1.94343066136836
692222.1019831117821-0.101983111782097
701617.1338862413944-1.13388624139443
712125.180910945535-4.180910945535
722323.9467638197151-0.946763819715118
732219.4371887543392.56281124566102
742422.59023857315761.40976142684242
752424.7650500829658-0.765050082965804
761617.5477734663876-1.54777346638765
771617.5156401334051-1.51564013340506
782122.9941486584608-1.9941486584608
792626.0576412597751-0.0576412597750917
801517.3149504668665-2.31495046686651
812523.26281845668031.7371815433197
821818.0930371490346-0.0930371490345515
832320.91043015300352.08956984699652
842021.3342836567611-1.33428365676114
851720.7951942547454-3.79519425474542
862524.97152226453360.0284777354664193
872422.58232501083091.41767498916913
881714.72712426081292.27287573918707
891918.74676700896340.25323299103661
902020.3110474931495-0.311047493149466
911516.468565268968-1.46856526896805
922724.61635758109952.38364241890045
932219.60427703091832.39572296908172
942322.82849821508780.171501784912157
951620.7933450478965-4.79334504789653
961921.063160183052-2.06316018305199
972520.51220160583024.48779839416976
981919.6630031555576-0.663003155557564
991919.4442481084099-0.444248108409862
1002625.32991489849180.670085101508174
1012118.94216789617172.05783210382825
1022019.89806114119730.101938858802682
1032420.0495341295773.95046587042299
1042224.0183719134011-2.01837191340106
1052021.1751880213703-1.1751880213703
1061819.1734956751954-1.17349567519543
1071816.96782021877751.03217978122247
1082421.21438638684082.78561361315923
1092421.46220240670852.53779759329146
1102223.5420401725469-1.5420401725469
1112319.97558239233093.02441760766908
1122220.04747255966671.95252744033325
1132018.31997226575881.68002773424123
1141819.7846780328683-1.78467803286825
1152524.73827967086240.261720329137567
1161818.6136555937986-0.613655593798562
1171617.8167188001528-1.81671880015277
1182020.0986272929363-0.0986272929363412
1191918.79286060982040.207139390179627
1201515.9120371464277-0.912037146427694
1211919.1224626439323-0.122462643932328
1221922.1264696761963-3.12646967619633
1231617.7635355453776-1.76353554537756
1241718.0814343397256-1.08143433972558
1252823.67333734883674.32666265116335
1262323.2190684114996-0.219068411499596
1272523.9366605550881.06333944491203
1282019.05609376166610.943906238333926
1291720.3588821538312-3.35888215383121
1302322.88001610832210.11998389167789
1311620.2738104617943-4.27381046179428
1322323.5301398110718-0.530139811071825
1331116.3667701899296-5.36677018992955
1341820.3828258424972-2.3828258424972
1352423.33726442417080.662735575829212
1362318.98601795178834.01398204821169
1372120.86933622914180.130663770858153
1381619.6806254683521-3.68062546835214
1392424.989082980471-0.98908298047098
1402321.85063102661391.14936897338614
1411816.1365011016931.86349889830698
1422021.2125081157942-1.21250811579419
143918.7740959967257-9.77409599672566
1442420.60402140731433.39597859268565
1452524.98793131189760.0120686881023924
1462018.59520802232751.40479197767246
1472119.28196586341881.71803413658124
1482523.03810327405671.96189672594329
1492222.6613422353731-0.661342235373064
1502120.9881412411050.0118587588949782
1512120.47229578736670.527704212633267
1522220.30433991151011.69566008848987
1532723.67838462889593.32161537110405
1542421.89317497252422.10682502747578
1552424.0035578690968-0.00355786909680716
1562120.16613437271280.83386562728721
1571820.7830660516928-2.78306605169277
1581616.3040025261666-0.304002526166611
1592219.60427703091832.39572296908172
1602020.3583643313393-0.358364331339271
1611819.1240009461349-1.12400094613493
1622021.0926245501383-1.09262455013829

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 26 & 24.0447212703457 & 1.95527872965433 \tabularnewline
2 & 20 & 20.8852655130369 & -0.885265513036901 \tabularnewline
3 & 19 & 21.6512414781215 & -2.65124147812153 \tabularnewline
4 & 19 & 18.2724970633786 & 0.727502936621398 \tabularnewline
5 & 20 & 18.0837903935225 & 1.91620960647746 \tabularnewline
6 & 25 & 24.0056137605901 & 0.99438623940994 \tabularnewline
7 & 25 & 19.4205293995089 & 5.57947060049108 \tabularnewline
8 & 22 & 20.0623970370273 & 1.93760296297265 \tabularnewline
9 & 26 & 22.3122606557702 & 3.68773934422982 \tabularnewline
10 & 22 & 20.3705525516574 & 1.62944744834264 \tabularnewline
11 & 17 & 20.0540544243935 & -3.05405442439352 \tabularnewline
12 & 22 & 22.8831374035301 & -0.883137403530069 \tabularnewline
13 & 19 & 20.7681630405437 & -1.76816304054369 \tabularnewline
14 & 24 & 22.4691305718673 & 1.53086942813274 \tabularnewline
15 & 26 & 26.6817558076255 & -0.681755807625479 \tabularnewline
16 & 21 & 18.8568934158135 & 2.14310658418646 \tabularnewline
17 & 13 & 16.2275265975684 & -3.22752659756843 \tabularnewline
18 & 26 & 27.1699974846948 & -1.1699974846948 \tabularnewline
19 & 20 & 22.5716833852347 & -2.57168338523468 \tabularnewline
20 & 22 & 18.7166973601202 & 3.28330263987977 \tabularnewline
21 & 14 & 17.9826346908936 & -3.98263469089363 \tabularnewline
22 & 21 & 20.390557253726 & 0.609442746273996 \tabularnewline
23 & 7 & 13.9956913079309 & -6.9956913079309 \tabularnewline
24 & 23 & 21.5907338497688 & 1.40926615023117 \tabularnewline
25 & 17 & 19.0504062318761 & -2.0504062318761 \tabularnewline
26 & 25 & 22.9641257622868 & 2.03587423771324 \tabularnewline
27 & 25 & 22.9641257622868 & 2.03587423771324 \tabularnewline
28 & 19 & 16.1098135579984 & 2.89018644200164 \tabularnewline
29 & 20 & 16.4555901156683 & 3.54440988433168 \tabularnewline
30 & 23 & 21.3100965333831 & 1.68990346661695 \tabularnewline
31 & 22 & 24.0435213880944 & -2.04352138809441 \tabularnewline
32 & 22 & 21.7802887362207 & 0.219711263779269 \tabularnewline
33 & 21 & 17.8242693210978 & 3.1757306789022 \tabularnewline
34 & 15 & 17.6736131644202 & -2.67361316442022 \tabularnewline
35 & 20 & 15.9577095735429 & 4.04229042645708 \tabularnewline
36 & 22 & 21.5852571074132 & 0.414742892586834 \tabularnewline
37 & 18 & 16.5551132986941 & 1.44488670130591 \tabularnewline
38 & 20 & 20.7686517988379 & -0.768651798837936 \tabularnewline
39 & 28 & 27.5178036862648 & 0.482196313735207 \tabularnewline
40 & 22 & 24.0359346367915 & -2.03593463679149 \tabularnewline
41 & 18 & 21.6615223631953 & -3.66152236319534 \tabularnewline
42 & 23 & 20.574787003877 & 2.42521299612303 \tabularnewline
43 & 20 & 21.5987823026213 & -1.59878230262134 \tabularnewline
44 & 25 & 24.4871575493431 & 0.512842450656877 \tabularnewline
45 & 26 & 22.0810315369144 & 3.9189684630856 \tabularnewline
46 & 15 & 14.3713466817638 & 0.62865331823617 \tabularnewline
47 & 17 & 17.7382509854984 & -0.738250985498415 \tabularnewline
48 & 23 & 15.318307273749 & 7.68169272625104 \tabularnewline
49 & 21 & 21.9140746438578 & -0.91407464385781 \tabularnewline
50 & 13 & 16.2910568649259 & -3.29105686492587 \tabularnewline
51 & 18 & 20.0674949502068 & -2.06749495020681 \tabularnewline
52 & 19 & 19.1484313856421 & -0.148431385642125 \tabularnewline
53 & 22 & 21.8947959099342 & 0.105204090065766 \tabularnewline
54 & 16 & 17.2961649033054 & -1.29616490330543 \tabularnewline
55 & 24 & 23.1314241998144 & 0.86857580018561 \tabularnewline
56 & 18 & 19.9061810749254 & -1.90618107492541 \tabularnewline
57 & 20 & 21.405887854124 & -1.40588785412399 \tabularnewline
58 & 24 & 20.7539670545263 & 3.24603294547365 \tabularnewline
59 & 14 & 18.3613836046921 & -4.36138360469206 \tabularnewline
60 & 22 & 20.3039563728947 & 1.69604362710527 \tabularnewline
61 & 24 & 18.0999956447892 & 5.90000435521082 \tabularnewline
62 & 18 & 18.5501733376168 & -0.55017333761683 \tabularnewline
63 & 21 & 24.5253820998882 & -3.52538209988819 \tabularnewline
64 & 23 & 21.6629468439215 & 1.33705315607851 \tabularnewline
65 & 17 & 18.4575382533232 & -1.45753825332323 \tabularnewline
66 & 22 & 22.7091174854386 & -0.709117485438629 \tabularnewline
67 & 24 & 24.9456096437661 & -0.945609643766136 \tabularnewline
68 & 21 & 22.9434306613684 & -1.94343066136836 \tabularnewline
69 & 22 & 22.1019831117821 & -0.101983111782097 \tabularnewline
70 & 16 & 17.1338862413944 & -1.13388624139443 \tabularnewline
71 & 21 & 25.180910945535 & -4.180910945535 \tabularnewline
72 & 23 & 23.9467638197151 & -0.946763819715118 \tabularnewline
73 & 22 & 19.437188754339 & 2.56281124566102 \tabularnewline
74 & 24 & 22.5902385731576 & 1.40976142684242 \tabularnewline
75 & 24 & 24.7650500829658 & -0.765050082965804 \tabularnewline
76 & 16 & 17.5477734663876 & -1.54777346638765 \tabularnewline
77 & 16 & 17.5156401334051 & -1.51564013340506 \tabularnewline
78 & 21 & 22.9941486584608 & -1.9941486584608 \tabularnewline
79 & 26 & 26.0576412597751 & -0.0576412597750917 \tabularnewline
80 & 15 & 17.3149504668665 & -2.31495046686651 \tabularnewline
81 & 25 & 23.2628184566803 & 1.7371815433197 \tabularnewline
82 & 18 & 18.0930371490346 & -0.0930371490345515 \tabularnewline
83 & 23 & 20.9104301530035 & 2.08956984699652 \tabularnewline
84 & 20 & 21.3342836567611 & -1.33428365676114 \tabularnewline
85 & 17 & 20.7951942547454 & -3.79519425474542 \tabularnewline
86 & 25 & 24.9715222645336 & 0.0284777354664193 \tabularnewline
87 & 24 & 22.5823250108309 & 1.41767498916913 \tabularnewline
88 & 17 & 14.7271242608129 & 2.27287573918707 \tabularnewline
89 & 19 & 18.7467670089634 & 0.25323299103661 \tabularnewline
90 & 20 & 20.3110474931495 & -0.311047493149466 \tabularnewline
91 & 15 & 16.468565268968 & -1.46856526896805 \tabularnewline
92 & 27 & 24.6163575810995 & 2.38364241890045 \tabularnewline
93 & 22 & 19.6042770309183 & 2.39572296908172 \tabularnewline
94 & 23 & 22.8284982150878 & 0.171501784912157 \tabularnewline
95 & 16 & 20.7933450478965 & -4.79334504789653 \tabularnewline
96 & 19 & 21.063160183052 & -2.06316018305199 \tabularnewline
97 & 25 & 20.5122016058302 & 4.48779839416976 \tabularnewline
98 & 19 & 19.6630031555576 & -0.663003155557564 \tabularnewline
99 & 19 & 19.4442481084099 & -0.444248108409862 \tabularnewline
100 & 26 & 25.3299148984918 & 0.670085101508174 \tabularnewline
101 & 21 & 18.9421678961717 & 2.05783210382825 \tabularnewline
102 & 20 & 19.8980611411973 & 0.101938858802682 \tabularnewline
103 & 24 & 20.049534129577 & 3.95046587042299 \tabularnewline
104 & 22 & 24.0183719134011 & -2.01837191340106 \tabularnewline
105 & 20 & 21.1751880213703 & -1.1751880213703 \tabularnewline
106 & 18 & 19.1734956751954 & -1.17349567519543 \tabularnewline
107 & 18 & 16.9678202187775 & 1.03217978122247 \tabularnewline
108 & 24 & 21.2143863868408 & 2.78561361315923 \tabularnewline
109 & 24 & 21.4622024067085 & 2.53779759329146 \tabularnewline
110 & 22 & 23.5420401725469 & -1.5420401725469 \tabularnewline
111 & 23 & 19.9755823923309 & 3.02441760766908 \tabularnewline
112 & 22 & 20.0474725596667 & 1.95252744033325 \tabularnewline
113 & 20 & 18.3199722657588 & 1.68002773424123 \tabularnewline
114 & 18 & 19.7846780328683 & -1.78467803286825 \tabularnewline
115 & 25 & 24.7382796708624 & 0.261720329137567 \tabularnewline
116 & 18 & 18.6136555937986 & -0.613655593798562 \tabularnewline
117 & 16 & 17.8167188001528 & -1.81671880015277 \tabularnewline
118 & 20 & 20.0986272929363 & -0.0986272929363412 \tabularnewline
119 & 19 & 18.7928606098204 & 0.207139390179627 \tabularnewline
120 & 15 & 15.9120371464277 & -0.912037146427694 \tabularnewline
121 & 19 & 19.1224626439323 & -0.122462643932328 \tabularnewline
122 & 19 & 22.1264696761963 & -3.12646967619633 \tabularnewline
123 & 16 & 17.7635355453776 & -1.76353554537756 \tabularnewline
124 & 17 & 18.0814343397256 & -1.08143433972558 \tabularnewline
125 & 28 & 23.6733373488367 & 4.32666265116335 \tabularnewline
126 & 23 & 23.2190684114996 & -0.219068411499596 \tabularnewline
127 & 25 & 23.936660555088 & 1.06333944491203 \tabularnewline
128 & 20 & 19.0560937616661 & 0.943906238333926 \tabularnewline
129 & 17 & 20.3588821538312 & -3.35888215383121 \tabularnewline
130 & 23 & 22.8800161083221 & 0.11998389167789 \tabularnewline
131 & 16 & 20.2738104617943 & -4.27381046179428 \tabularnewline
132 & 23 & 23.5301398110718 & -0.530139811071825 \tabularnewline
133 & 11 & 16.3667701899296 & -5.36677018992955 \tabularnewline
134 & 18 & 20.3828258424972 & -2.3828258424972 \tabularnewline
135 & 24 & 23.3372644241708 & 0.662735575829212 \tabularnewline
136 & 23 & 18.9860179517883 & 4.01398204821169 \tabularnewline
137 & 21 & 20.8693362291418 & 0.130663770858153 \tabularnewline
138 & 16 & 19.6806254683521 & -3.68062546835214 \tabularnewline
139 & 24 & 24.989082980471 & -0.98908298047098 \tabularnewline
140 & 23 & 21.8506310266139 & 1.14936897338614 \tabularnewline
141 & 18 & 16.136501101693 & 1.86349889830698 \tabularnewline
142 & 20 & 21.2125081157942 & -1.21250811579419 \tabularnewline
143 & 9 & 18.7740959967257 & -9.77409599672566 \tabularnewline
144 & 24 & 20.6040214073143 & 3.39597859268565 \tabularnewline
145 & 25 & 24.9879313118976 & 0.0120686881023924 \tabularnewline
146 & 20 & 18.5952080223275 & 1.40479197767246 \tabularnewline
147 & 21 & 19.2819658634188 & 1.71803413658124 \tabularnewline
148 & 25 & 23.0381032740567 & 1.96189672594329 \tabularnewline
149 & 22 & 22.6613422353731 & -0.661342235373064 \tabularnewline
150 & 21 & 20.988141241105 & 0.0118587588949782 \tabularnewline
151 & 21 & 20.4722957873667 & 0.527704212633267 \tabularnewline
152 & 22 & 20.3043399115101 & 1.69566008848987 \tabularnewline
153 & 27 & 23.6783846288959 & 3.32161537110405 \tabularnewline
154 & 24 & 21.8931749725242 & 2.10682502747578 \tabularnewline
155 & 24 & 24.0035578690968 & -0.00355786909680716 \tabularnewline
156 & 21 & 20.1661343727128 & 0.83386562728721 \tabularnewline
157 & 18 & 20.7830660516928 & -2.78306605169277 \tabularnewline
158 & 16 & 16.3040025261666 & -0.304002526166611 \tabularnewline
159 & 22 & 19.6042770309183 & 2.39572296908172 \tabularnewline
160 & 20 & 20.3583643313393 & -0.358364331339271 \tabularnewline
161 & 18 & 19.1240009461349 & -1.12400094613493 \tabularnewline
162 & 20 & 21.0926245501383 & -1.09262455013829 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145603&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]26[/C][C]24.0447212703457[/C][C]1.95527872965433[/C][/ROW]
[ROW][C]2[/C][C]20[/C][C]20.8852655130369[/C][C]-0.885265513036901[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]21.6512414781215[/C][C]-2.65124147812153[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]18.2724970633786[/C][C]0.727502936621398[/C][/ROW]
[ROW][C]5[/C][C]20[/C][C]18.0837903935225[/C][C]1.91620960647746[/C][/ROW]
[ROW][C]6[/C][C]25[/C][C]24.0056137605901[/C][C]0.99438623940994[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]19.4205293995089[/C][C]5.57947060049108[/C][/ROW]
[ROW][C]8[/C][C]22[/C][C]20.0623970370273[/C][C]1.93760296297265[/C][/ROW]
[ROW][C]9[/C][C]26[/C][C]22.3122606557702[/C][C]3.68773934422982[/C][/ROW]
[ROW][C]10[/C][C]22[/C][C]20.3705525516574[/C][C]1.62944744834264[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]20.0540544243935[/C][C]-3.05405442439352[/C][/ROW]
[ROW][C]12[/C][C]22[/C][C]22.8831374035301[/C][C]-0.883137403530069[/C][/ROW]
[ROW][C]13[/C][C]19[/C][C]20.7681630405437[/C][C]-1.76816304054369[/C][/ROW]
[ROW][C]14[/C][C]24[/C][C]22.4691305718673[/C][C]1.53086942813274[/C][/ROW]
[ROW][C]15[/C][C]26[/C][C]26.6817558076255[/C][C]-0.681755807625479[/C][/ROW]
[ROW][C]16[/C][C]21[/C][C]18.8568934158135[/C][C]2.14310658418646[/C][/ROW]
[ROW][C]17[/C][C]13[/C][C]16.2275265975684[/C][C]-3.22752659756843[/C][/ROW]
[ROW][C]18[/C][C]26[/C][C]27.1699974846948[/C][C]-1.1699974846948[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]22.5716833852347[/C][C]-2.57168338523468[/C][/ROW]
[ROW][C]20[/C][C]22[/C][C]18.7166973601202[/C][C]3.28330263987977[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]17.9826346908936[/C][C]-3.98263469089363[/C][/ROW]
[ROW][C]22[/C][C]21[/C][C]20.390557253726[/C][C]0.609442746273996[/C][/ROW]
[ROW][C]23[/C][C]7[/C][C]13.9956913079309[/C][C]-6.9956913079309[/C][/ROW]
[ROW][C]24[/C][C]23[/C][C]21.5907338497688[/C][C]1.40926615023117[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]19.0504062318761[/C][C]-2.0504062318761[/C][/ROW]
[ROW][C]26[/C][C]25[/C][C]22.9641257622868[/C][C]2.03587423771324[/C][/ROW]
[ROW][C]27[/C][C]25[/C][C]22.9641257622868[/C][C]2.03587423771324[/C][/ROW]
[ROW][C]28[/C][C]19[/C][C]16.1098135579984[/C][C]2.89018644200164[/C][/ROW]
[ROW][C]29[/C][C]20[/C][C]16.4555901156683[/C][C]3.54440988433168[/C][/ROW]
[ROW][C]30[/C][C]23[/C][C]21.3100965333831[/C][C]1.68990346661695[/C][/ROW]
[ROW][C]31[/C][C]22[/C][C]24.0435213880944[/C][C]-2.04352138809441[/C][/ROW]
[ROW][C]32[/C][C]22[/C][C]21.7802887362207[/C][C]0.219711263779269[/C][/ROW]
[ROW][C]33[/C][C]21[/C][C]17.8242693210978[/C][C]3.1757306789022[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]17.6736131644202[/C][C]-2.67361316442022[/C][/ROW]
[ROW][C]35[/C][C]20[/C][C]15.9577095735429[/C][C]4.04229042645708[/C][/ROW]
[ROW][C]36[/C][C]22[/C][C]21.5852571074132[/C][C]0.414742892586834[/C][/ROW]
[ROW][C]37[/C][C]18[/C][C]16.5551132986941[/C][C]1.44488670130591[/C][/ROW]
[ROW][C]38[/C][C]20[/C][C]20.7686517988379[/C][C]-0.768651798837936[/C][/ROW]
[ROW][C]39[/C][C]28[/C][C]27.5178036862648[/C][C]0.482196313735207[/C][/ROW]
[ROW][C]40[/C][C]22[/C][C]24.0359346367915[/C][C]-2.03593463679149[/C][/ROW]
[ROW][C]41[/C][C]18[/C][C]21.6615223631953[/C][C]-3.66152236319534[/C][/ROW]
[ROW][C]42[/C][C]23[/C][C]20.574787003877[/C][C]2.42521299612303[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]21.5987823026213[/C][C]-1.59878230262134[/C][/ROW]
[ROW][C]44[/C][C]25[/C][C]24.4871575493431[/C][C]0.512842450656877[/C][/ROW]
[ROW][C]45[/C][C]26[/C][C]22.0810315369144[/C][C]3.9189684630856[/C][/ROW]
[ROW][C]46[/C][C]15[/C][C]14.3713466817638[/C][C]0.62865331823617[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]17.7382509854984[/C][C]-0.738250985498415[/C][/ROW]
[ROW][C]48[/C][C]23[/C][C]15.318307273749[/C][C]7.68169272625104[/C][/ROW]
[ROW][C]49[/C][C]21[/C][C]21.9140746438578[/C][C]-0.91407464385781[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]16.2910568649259[/C][C]-3.29105686492587[/C][/ROW]
[ROW][C]51[/C][C]18[/C][C]20.0674949502068[/C][C]-2.06749495020681[/C][/ROW]
[ROW][C]52[/C][C]19[/C][C]19.1484313856421[/C][C]-0.148431385642125[/C][/ROW]
[ROW][C]53[/C][C]22[/C][C]21.8947959099342[/C][C]0.105204090065766[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]17.2961649033054[/C][C]-1.29616490330543[/C][/ROW]
[ROW][C]55[/C][C]24[/C][C]23.1314241998144[/C][C]0.86857580018561[/C][/ROW]
[ROW][C]56[/C][C]18[/C][C]19.9061810749254[/C][C]-1.90618107492541[/C][/ROW]
[ROW][C]57[/C][C]20[/C][C]21.405887854124[/C][C]-1.40588785412399[/C][/ROW]
[ROW][C]58[/C][C]24[/C][C]20.7539670545263[/C][C]3.24603294547365[/C][/ROW]
[ROW][C]59[/C][C]14[/C][C]18.3613836046921[/C][C]-4.36138360469206[/C][/ROW]
[ROW][C]60[/C][C]22[/C][C]20.3039563728947[/C][C]1.69604362710527[/C][/ROW]
[ROW][C]61[/C][C]24[/C][C]18.0999956447892[/C][C]5.90000435521082[/C][/ROW]
[ROW][C]62[/C][C]18[/C][C]18.5501733376168[/C][C]-0.55017333761683[/C][/ROW]
[ROW][C]63[/C][C]21[/C][C]24.5253820998882[/C][C]-3.52538209988819[/C][/ROW]
[ROW][C]64[/C][C]23[/C][C]21.6629468439215[/C][C]1.33705315607851[/C][/ROW]
[ROW][C]65[/C][C]17[/C][C]18.4575382533232[/C][C]-1.45753825332323[/C][/ROW]
[ROW][C]66[/C][C]22[/C][C]22.7091174854386[/C][C]-0.709117485438629[/C][/ROW]
[ROW][C]67[/C][C]24[/C][C]24.9456096437661[/C][C]-0.945609643766136[/C][/ROW]
[ROW][C]68[/C][C]21[/C][C]22.9434306613684[/C][C]-1.94343066136836[/C][/ROW]
[ROW][C]69[/C][C]22[/C][C]22.1019831117821[/C][C]-0.101983111782097[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]17.1338862413944[/C][C]-1.13388624139443[/C][/ROW]
[ROW][C]71[/C][C]21[/C][C]25.180910945535[/C][C]-4.180910945535[/C][/ROW]
[ROW][C]72[/C][C]23[/C][C]23.9467638197151[/C][C]-0.946763819715118[/C][/ROW]
[ROW][C]73[/C][C]22[/C][C]19.437188754339[/C][C]2.56281124566102[/C][/ROW]
[ROW][C]74[/C][C]24[/C][C]22.5902385731576[/C][C]1.40976142684242[/C][/ROW]
[ROW][C]75[/C][C]24[/C][C]24.7650500829658[/C][C]-0.765050082965804[/C][/ROW]
[ROW][C]76[/C][C]16[/C][C]17.5477734663876[/C][C]-1.54777346638765[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]17.5156401334051[/C][C]-1.51564013340506[/C][/ROW]
[ROW][C]78[/C][C]21[/C][C]22.9941486584608[/C][C]-1.9941486584608[/C][/ROW]
[ROW][C]79[/C][C]26[/C][C]26.0576412597751[/C][C]-0.0576412597750917[/C][/ROW]
[ROW][C]80[/C][C]15[/C][C]17.3149504668665[/C][C]-2.31495046686651[/C][/ROW]
[ROW][C]81[/C][C]25[/C][C]23.2628184566803[/C][C]1.7371815433197[/C][/ROW]
[ROW][C]82[/C][C]18[/C][C]18.0930371490346[/C][C]-0.0930371490345515[/C][/ROW]
[ROW][C]83[/C][C]23[/C][C]20.9104301530035[/C][C]2.08956984699652[/C][/ROW]
[ROW][C]84[/C][C]20[/C][C]21.3342836567611[/C][C]-1.33428365676114[/C][/ROW]
[ROW][C]85[/C][C]17[/C][C]20.7951942547454[/C][C]-3.79519425474542[/C][/ROW]
[ROW][C]86[/C][C]25[/C][C]24.9715222645336[/C][C]0.0284777354664193[/C][/ROW]
[ROW][C]87[/C][C]24[/C][C]22.5823250108309[/C][C]1.41767498916913[/C][/ROW]
[ROW][C]88[/C][C]17[/C][C]14.7271242608129[/C][C]2.27287573918707[/C][/ROW]
[ROW][C]89[/C][C]19[/C][C]18.7467670089634[/C][C]0.25323299103661[/C][/ROW]
[ROW][C]90[/C][C]20[/C][C]20.3110474931495[/C][C]-0.311047493149466[/C][/ROW]
[ROW][C]91[/C][C]15[/C][C]16.468565268968[/C][C]-1.46856526896805[/C][/ROW]
[ROW][C]92[/C][C]27[/C][C]24.6163575810995[/C][C]2.38364241890045[/C][/ROW]
[ROW][C]93[/C][C]22[/C][C]19.6042770309183[/C][C]2.39572296908172[/C][/ROW]
[ROW][C]94[/C][C]23[/C][C]22.8284982150878[/C][C]0.171501784912157[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]20.7933450478965[/C][C]-4.79334504789653[/C][/ROW]
[ROW][C]96[/C][C]19[/C][C]21.063160183052[/C][C]-2.06316018305199[/C][/ROW]
[ROW][C]97[/C][C]25[/C][C]20.5122016058302[/C][C]4.48779839416976[/C][/ROW]
[ROW][C]98[/C][C]19[/C][C]19.6630031555576[/C][C]-0.663003155557564[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]19.4442481084099[/C][C]-0.444248108409862[/C][/ROW]
[ROW][C]100[/C][C]26[/C][C]25.3299148984918[/C][C]0.670085101508174[/C][/ROW]
[ROW][C]101[/C][C]21[/C][C]18.9421678961717[/C][C]2.05783210382825[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]19.8980611411973[/C][C]0.101938858802682[/C][/ROW]
[ROW][C]103[/C][C]24[/C][C]20.049534129577[/C][C]3.95046587042299[/C][/ROW]
[ROW][C]104[/C][C]22[/C][C]24.0183719134011[/C][C]-2.01837191340106[/C][/ROW]
[ROW][C]105[/C][C]20[/C][C]21.1751880213703[/C][C]-1.1751880213703[/C][/ROW]
[ROW][C]106[/C][C]18[/C][C]19.1734956751954[/C][C]-1.17349567519543[/C][/ROW]
[ROW][C]107[/C][C]18[/C][C]16.9678202187775[/C][C]1.03217978122247[/C][/ROW]
[ROW][C]108[/C][C]24[/C][C]21.2143863868408[/C][C]2.78561361315923[/C][/ROW]
[ROW][C]109[/C][C]24[/C][C]21.4622024067085[/C][C]2.53779759329146[/C][/ROW]
[ROW][C]110[/C][C]22[/C][C]23.5420401725469[/C][C]-1.5420401725469[/C][/ROW]
[ROW][C]111[/C][C]23[/C][C]19.9755823923309[/C][C]3.02441760766908[/C][/ROW]
[ROW][C]112[/C][C]22[/C][C]20.0474725596667[/C][C]1.95252744033325[/C][/ROW]
[ROW][C]113[/C][C]20[/C][C]18.3199722657588[/C][C]1.68002773424123[/C][/ROW]
[ROW][C]114[/C][C]18[/C][C]19.7846780328683[/C][C]-1.78467803286825[/C][/ROW]
[ROW][C]115[/C][C]25[/C][C]24.7382796708624[/C][C]0.261720329137567[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]18.6136555937986[/C][C]-0.613655593798562[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]17.8167188001528[/C][C]-1.81671880015277[/C][/ROW]
[ROW][C]118[/C][C]20[/C][C]20.0986272929363[/C][C]-0.0986272929363412[/C][/ROW]
[ROW][C]119[/C][C]19[/C][C]18.7928606098204[/C][C]0.207139390179627[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]15.9120371464277[/C][C]-0.912037146427694[/C][/ROW]
[ROW][C]121[/C][C]19[/C][C]19.1224626439323[/C][C]-0.122462643932328[/C][/ROW]
[ROW][C]122[/C][C]19[/C][C]22.1264696761963[/C][C]-3.12646967619633[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]17.7635355453776[/C][C]-1.76353554537756[/C][/ROW]
[ROW][C]124[/C][C]17[/C][C]18.0814343397256[/C][C]-1.08143433972558[/C][/ROW]
[ROW][C]125[/C][C]28[/C][C]23.6733373488367[/C][C]4.32666265116335[/C][/ROW]
[ROW][C]126[/C][C]23[/C][C]23.2190684114996[/C][C]-0.219068411499596[/C][/ROW]
[ROW][C]127[/C][C]25[/C][C]23.936660555088[/C][C]1.06333944491203[/C][/ROW]
[ROW][C]128[/C][C]20[/C][C]19.0560937616661[/C][C]0.943906238333926[/C][/ROW]
[ROW][C]129[/C][C]17[/C][C]20.3588821538312[/C][C]-3.35888215383121[/C][/ROW]
[ROW][C]130[/C][C]23[/C][C]22.8800161083221[/C][C]0.11998389167789[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]20.2738104617943[/C][C]-4.27381046179428[/C][/ROW]
[ROW][C]132[/C][C]23[/C][C]23.5301398110718[/C][C]-0.530139811071825[/C][/ROW]
[ROW][C]133[/C][C]11[/C][C]16.3667701899296[/C][C]-5.36677018992955[/C][/ROW]
[ROW][C]134[/C][C]18[/C][C]20.3828258424972[/C][C]-2.3828258424972[/C][/ROW]
[ROW][C]135[/C][C]24[/C][C]23.3372644241708[/C][C]0.662735575829212[/C][/ROW]
[ROW][C]136[/C][C]23[/C][C]18.9860179517883[/C][C]4.01398204821169[/C][/ROW]
[ROW][C]137[/C][C]21[/C][C]20.8693362291418[/C][C]0.130663770858153[/C][/ROW]
[ROW][C]138[/C][C]16[/C][C]19.6806254683521[/C][C]-3.68062546835214[/C][/ROW]
[ROW][C]139[/C][C]24[/C][C]24.989082980471[/C][C]-0.98908298047098[/C][/ROW]
[ROW][C]140[/C][C]23[/C][C]21.8506310266139[/C][C]1.14936897338614[/C][/ROW]
[ROW][C]141[/C][C]18[/C][C]16.136501101693[/C][C]1.86349889830698[/C][/ROW]
[ROW][C]142[/C][C]20[/C][C]21.2125081157942[/C][C]-1.21250811579419[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]18.7740959967257[/C][C]-9.77409599672566[/C][/ROW]
[ROW][C]144[/C][C]24[/C][C]20.6040214073143[/C][C]3.39597859268565[/C][/ROW]
[ROW][C]145[/C][C]25[/C][C]24.9879313118976[/C][C]0.0120686881023924[/C][/ROW]
[ROW][C]146[/C][C]20[/C][C]18.5952080223275[/C][C]1.40479197767246[/C][/ROW]
[ROW][C]147[/C][C]21[/C][C]19.2819658634188[/C][C]1.71803413658124[/C][/ROW]
[ROW][C]148[/C][C]25[/C][C]23.0381032740567[/C][C]1.96189672594329[/C][/ROW]
[ROW][C]149[/C][C]22[/C][C]22.6613422353731[/C][C]-0.661342235373064[/C][/ROW]
[ROW][C]150[/C][C]21[/C][C]20.988141241105[/C][C]0.0118587588949782[/C][/ROW]
[ROW][C]151[/C][C]21[/C][C]20.4722957873667[/C][C]0.527704212633267[/C][/ROW]
[ROW][C]152[/C][C]22[/C][C]20.3043399115101[/C][C]1.69566008848987[/C][/ROW]
[ROW][C]153[/C][C]27[/C][C]23.6783846288959[/C][C]3.32161537110405[/C][/ROW]
[ROW][C]154[/C][C]24[/C][C]21.8931749725242[/C][C]2.10682502747578[/C][/ROW]
[ROW][C]155[/C][C]24[/C][C]24.0035578690968[/C][C]-0.00355786909680716[/C][/ROW]
[ROW][C]156[/C][C]21[/C][C]20.1661343727128[/C][C]0.83386562728721[/C][/ROW]
[ROW][C]157[/C][C]18[/C][C]20.7830660516928[/C][C]-2.78306605169277[/C][/ROW]
[ROW][C]158[/C][C]16[/C][C]16.3040025261666[/C][C]-0.304002526166611[/C][/ROW]
[ROW][C]159[/C][C]22[/C][C]19.6042770309183[/C][C]2.39572296908172[/C][/ROW]
[ROW][C]160[/C][C]20[/C][C]20.3583643313393[/C][C]-0.358364331339271[/C][/ROW]
[ROW][C]161[/C][C]18[/C][C]19.1240009461349[/C][C]-1.12400094613493[/C][/ROW]
[ROW][C]162[/C][C]20[/C][C]21.0926245501383[/C][C]-1.09262455013829[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145603&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145603&T=4

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12624.04472127034571.95527872965433
22020.8852655130369-0.885265513036901
31921.6512414781215-2.65124147812153
41918.27249706337860.727502936621398
52018.08379039352251.91620960647746
62524.00561376059010.99438623940994
72519.42052939950895.57947060049108
82220.06239703702731.93760296297265
92622.31226065577023.68773934422982
102220.37055255165741.62944744834264
111720.0540544243935-3.05405442439352
122222.8831374035301-0.883137403530069
131920.7681630405437-1.76816304054369
142422.46913057186731.53086942813274
152626.6817558076255-0.681755807625479
162118.85689341581352.14310658418646
171316.2275265975684-3.22752659756843
182627.1699974846948-1.1699974846948
192022.5716833852347-2.57168338523468
202218.71669736012023.28330263987977
211417.9826346908936-3.98263469089363
222120.3905572537260.609442746273996
23713.9956913079309-6.9956913079309
242321.59073384976881.40926615023117
251719.0504062318761-2.0504062318761
262522.96412576228682.03587423771324
272522.96412576228682.03587423771324
281916.10981355799842.89018644200164
292016.45559011566833.54440988433168
302321.31009653338311.68990346661695
312224.0435213880944-2.04352138809441
322221.78028873622070.219711263779269
332117.82426932109783.1757306789022
341517.6736131644202-2.67361316442022
352015.95770957354294.04229042645708
362221.58525710741320.414742892586834
371816.55511329869411.44488670130591
382020.7686517988379-0.768651798837936
392827.51780368626480.482196313735207
402224.0359346367915-2.03593463679149
411821.6615223631953-3.66152236319534
422320.5747870038772.42521299612303
432021.5987823026213-1.59878230262134
442524.48715754934310.512842450656877
452622.08103153691443.9189684630856
461514.37134668176380.62865331823617
471717.7382509854984-0.738250985498415
482315.3183072737497.68169272625104
492121.9140746438578-0.91407464385781
501316.2910568649259-3.29105686492587
511820.0674949502068-2.06749495020681
521919.1484313856421-0.148431385642125
532221.89479590993420.105204090065766
541617.2961649033054-1.29616490330543
552423.13142419981440.86857580018561
561819.9061810749254-1.90618107492541
572021.405887854124-1.40588785412399
582420.75396705452633.24603294547365
591418.3613836046921-4.36138360469206
602220.30395637289471.69604362710527
612418.09999564478925.90000435521082
621818.5501733376168-0.55017333761683
632124.5253820998882-3.52538209988819
642321.66294684392151.33705315607851
651718.4575382533232-1.45753825332323
662222.7091174854386-0.709117485438629
672424.9456096437661-0.945609643766136
682122.9434306613684-1.94343066136836
692222.1019831117821-0.101983111782097
701617.1338862413944-1.13388624139443
712125.180910945535-4.180910945535
722323.9467638197151-0.946763819715118
732219.4371887543392.56281124566102
742422.59023857315761.40976142684242
752424.7650500829658-0.765050082965804
761617.5477734663876-1.54777346638765
771617.5156401334051-1.51564013340506
782122.9941486584608-1.9941486584608
792626.0576412597751-0.0576412597750917
801517.3149504668665-2.31495046686651
812523.26281845668031.7371815433197
821818.0930371490346-0.0930371490345515
832320.91043015300352.08956984699652
842021.3342836567611-1.33428365676114
851720.7951942547454-3.79519425474542
862524.97152226453360.0284777354664193
872422.58232501083091.41767498916913
881714.72712426081292.27287573918707
891918.74676700896340.25323299103661
902020.3110474931495-0.311047493149466
911516.468565268968-1.46856526896805
922724.61635758109952.38364241890045
932219.60427703091832.39572296908172
942322.82849821508780.171501784912157
951620.7933450478965-4.79334504789653
961921.063160183052-2.06316018305199
972520.51220160583024.48779839416976
981919.6630031555576-0.663003155557564
991919.4442481084099-0.444248108409862
1002625.32991489849180.670085101508174
1012118.94216789617172.05783210382825
1022019.89806114119730.101938858802682
1032420.0495341295773.95046587042299
1042224.0183719134011-2.01837191340106
1052021.1751880213703-1.1751880213703
1061819.1734956751954-1.17349567519543
1071816.96782021877751.03217978122247
1082421.21438638684082.78561361315923
1092421.46220240670852.53779759329146
1102223.5420401725469-1.5420401725469
1112319.97558239233093.02441760766908
1122220.04747255966671.95252744033325
1132018.31997226575881.68002773424123
1141819.7846780328683-1.78467803286825
1152524.73827967086240.261720329137567
1161818.6136555937986-0.613655593798562
1171617.8167188001528-1.81671880015277
1182020.0986272929363-0.0986272929363412
1191918.79286060982040.207139390179627
1201515.9120371464277-0.912037146427694
1211919.1224626439323-0.122462643932328
1221922.1264696761963-3.12646967619633
1231617.7635355453776-1.76353554537756
1241718.0814343397256-1.08143433972558
1252823.67333734883674.32666265116335
1262323.2190684114996-0.219068411499596
1272523.9366605550881.06333944491203
1282019.05609376166610.943906238333926
1291720.3588821538312-3.35888215383121
1302322.88001610832210.11998389167789
1311620.2738104617943-4.27381046179428
1322323.5301398110718-0.530139811071825
1331116.3667701899296-5.36677018992955
1341820.3828258424972-2.3828258424972
1352423.33726442417080.662735575829212
1362318.98601795178834.01398204821169
1372120.86933622914180.130663770858153
1381619.6806254683521-3.68062546835214
1392424.989082980471-0.98908298047098
1402321.85063102661391.14936897338614
1411816.1365011016931.86349889830698
1422021.2125081157942-1.21250811579419
143918.7740959967257-9.77409599672566
1442420.60402140731433.39597859268565
1452524.98793131189760.0120686881023924
1462018.59520802232751.40479197767246
1472119.28196586341881.71803413658124
1482523.03810327405671.96189672594329
1492222.6613422353731-0.661342235373064
1502120.9881412411050.0118587588949782
1512120.47229578736670.527704212633267
1522220.30433991151011.69566008848987
1532723.67838462889593.32161537110405
1542421.89317497252422.10682502747578
1552424.0035578690968-0.00355786909680716
1562120.16613437271280.83386562728721
1571820.7830660516928-2.78306605169277
1581616.3040025261666-0.304002526166611
1592219.60427703091832.39572296908172
1602020.3583643313393-0.358364331339271
1611819.1240009461349-1.12400094613493
1622021.0926245501383-1.09262455013829






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.3620873920066080.7241747840132170.637912607993392
110.6117916167014730.7764167665970540.388208383298527
120.801000999922440.3979980001551210.19899900007756
130.8354548834393980.3290902331212040.164545116560602
140.799206429109240.4015871417815210.200793570890761
150.7292938026606240.5414123946787520.270706197339376
160.6476397914459770.7047204171080460.352360208554023
170.5726604668310880.8546790663378240.427339533168912
180.4874535930099820.9749071860199650.512546406990018
190.5860615393494550.827876921301090.413938460650545
200.5792052313670350.841589537265930.420794768632965
210.5884305250040420.8231389499919160.411569474995958
220.5104013763455060.9791972473089890.489598623654494
230.6492497429940440.7015005140119110.350750257005956
240.5853155271828240.8293689456343530.414684472817176
250.542144594376320.9157108112473610.45785540562368
260.5278253891158850.944349221768230.472174610884115
270.4844846383705530.9689692767411060.515515361629447
280.7327106884647070.5345786230705870.267289311535293
290.8754194773993630.2491610452012750.124580522600637
300.8597941817482090.2804116365035820.140205818251791
310.8513180220074710.2973639559850570.148681977992529
320.8133141951428670.3733716097142660.186685804857133
330.81206091531080.3758781693784010.187939084689201
340.8710792102034990.2578415795930030.128920789796501
350.9195750639685480.1608498720629040.080424936031452
360.8980726114165930.2038547771668140.101927388583407
370.8799149210373880.2401701579252230.120085078962612
380.85143357877950.2971328424410010.148566421220501
390.8173420820004610.3653158359990780.182657917999539
400.808496033911480.3830079321770410.191503966088521
410.8534311964369330.2931376071261340.146568803563067
420.84620071854470.3075985629105990.1537992814553
430.8344859169363310.3310281661273370.165514083063669
440.7998778190981680.4002443618036640.200122180901832
450.8309092093750080.3381815812499830.169090790624992
460.7981557979818820.4036884040362350.201844202018118
470.7635744211435180.4728511577129640.236425578856482
480.9322359249709550.1355281500580890.0677640750290446
490.9163290182007320.1673419635985350.0836709817992677
500.9405363348125060.1189273303749870.0594636651874937
510.9365771476086520.1268457047826960.0634228523913479
520.9209724774808670.1580550450382650.0790275225191327
530.9010686198593590.1978627602812820.098931380140641
540.8845665280570840.2308669438858320.115433471942916
550.8610295418661910.2779409162676180.138970458133809
560.8538957954690010.2922084090619970.146104204530999
570.8339095180735990.3321809638528030.166090481926401
580.8544844060764120.2910311878471750.145515593923588
590.9054991404458590.1890017191082820.094500859554141
600.8927176469846790.2145647060306420.107282353015321
610.9620521972220470.07589560555590640.0379478027779532
620.9525533287540810.09489334249183750.0474466712459187
630.963503997298640.07299200540272110.0364960027013606
640.9558855187433980.08822896251320380.0441144812566019
650.9525899947524450.09482001049510920.0474100052475546
660.9409681079788180.1180637840423640.0590318920211819
670.9282981427980530.1434037144038940.0717018572019472
680.9218777843239410.1562444313521180.0781222156760591
690.9032750010946110.1934499978107780.0967249989053889
700.8867496592497840.2265006815004310.113250340750216
710.9229590680369330.1540818639261340.077040931963067
720.9077916350290780.1844167299418430.0922083649709217
730.9079914710439360.1840170579121290.0920085289560643
740.8925659164513130.2148681670973740.107434083548687
750.8714607054609620.2570785890780760.128539294539038
760.8582237955581560.2835524088836880.141776204441844
770.8422662239586840.3154675520826330.157733776041316
780.8326704180429630.3346591639140730.167329581957037
790.8037253349335750.392549330132850.196274665066425
800.8008613327341520.3982773345316960.199138667265848
810.779594155279470.440811689441060.22040584472053
820.744891863992430.5102162720151410.255108136007571
830.736865713613670.526268572772660.26313428638633
840.7111926586534230.5776146826931540.288807341346577
850.7594544486877560.4810911026244880.240545551312244
860.7221153441882190.5557693116235620.277884655811781
870.6942983989307060.6114032021385870.305701601069294
880.7047482545872040.5905034908255920.295251745412796
890.6652516288000370.6694967423999270.334748371199963
900.626928294348130.7461434113037390.37307170565187
910.5934961488972830.8130077022054350.406503851102717
920.5818805778497420.8362388443005160.418119422150258
930.5727454321701670.8545091356596670.427254567829833
940.5310544159963350.937891168007330.468945584003665
950.6504045582634130.6991908834731740.349595441736587
960.636158934939480.7276821301210390.36384106506052
970.7314276221587860.5371447556824270.268572377841214
980.6929384684709050.6141230630581910.307061531529095
990.6515759734239960.6968480531520090.348424026576004
1000.6085570841864790.7828858316270420.391442915813521
1010.5906726894345730.8186546211308540.409327310565427
1020.5433312045286510.9133375909426980.456668795471349
1030.6365147723008080.7269704553983850.363485227699193
1040.623825180178390.752349639643220.37617481982161
1050.586810281381670.826379437236660.41318971861833
1060.5452253676017520.9095492647964960.454774632398248
1070.5116897901054560.9766204197890880.488310209894544
1080.5190813437596980.9618373124806040.480918656240302
1090.4966600616164830.9933201232329660.503339938383517
1100.4678201545461970.9356403090923940.532179845453803
1110.4884015504124280.9768031008248550.511598449587572
1120.4784040564986050.956808112997210.521595943501395
1130.4722157033573510.9444314067147020.527784296642649
1140.4370634284785540.8741268569571070.562936571521446
1150.3883521271927790.7767042543855570.611647872807221
1160.342547871264690.6850957425293790.65745212873531
1170.3068386842901970.6136773685803940.693161315709803
1180.2618498136014960.5236996272029920.738150186398504
1190.2296470288927960.4592940577855910.770352971107204
1200.1987644709626010.3975289419252010.8012355290374
1210.1632298827086130.3264597654172250.836770117291387
1220.1712590709234290.3425181418468580.828740929076571
1230.1440614565559650.288122913111930.855938543444035
1240.116652052235690.2333041044713790.88334794776431
1250.2125283751923730.4250567503847470.787471624807627
1260.1798854208944420.3597708417888840.820114579105558
1270.1496668251272630.2993336502545260.850333174872737
1280.1200227931238840.2400455862477680.879977206876116
1290.1271950218658190.2543900437316390.87280497813418
1300.0998569652210140.1997139304420280.900143034778986
1310.14698697473490.29397394946980.8530130252651
1320.1201908828815130.2403817657630260.879809117118487
1330.2600792290229810.5201584580459620.739920770977019
1340.2683847277312340.5367694554624680.731615272268766
1350.2489371496840240.4978742993680470.751062850315977
1360.2207491021574950.441498204314990.779250897842505
1370.1766675285945330.3533350571890650.823332471405467
1380.2081340761212810.4162681522425620.791865923878719
1390.1674549856649030.3349099713298060.832545014335097
1400.1323495660905460.2646991321810930.867650433909454
1410.09906075573952490.198121511479050.900939244260475
1420.08483620269748790.1696724053949760.915163797302512
1430.9070931737391620.1858136525216770.0929068262608385
1440.988647682885090.02270463422981950.0113523171149097
1450.9775948491037650.04481030179246890.0224051508962345
1460.9584137360635470.08317252787290660.0415862639364533
1470.939987958953870.1200240820922590.0600120410461293
1480.9319275408087020.1361449183825970.0680724591912985
1490.8810784310771430.2378431378457140.118921568922857
1500.8056271249356820.3887457501286360.194372875064318
1510.6828821113426020.6342357773147950.317117888657398
1520.5158347700267210.9683304599465580.484165229973279

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.362087392006608 & 0.724174784013217 & 0.637912607993392 \tabularnewline
11 & 0.611791616701473 & 0.776416766597054 & 0.388208383298527 \tabularnewline
12 & 0.80100099992244 & 0.397998000155121 & 0.19899900007756 \tabularnewline
13 & 0.835454883439398 & 0.329090233121204 & 0.164545116560602 \tabularnewline
14 & 0.79920642910924 & 0.401587141781521 & 0.200793570890761 \tabularnewline
15 & 0.729293802660624 & 0.541412394678752 & 0.270706197339376 \tabularnewline
16 & 0.647639791445977 & 0.704720417108046 & 0.352360208554023 \tabularnewline
17 & 0.572660466831088 & 0.854679066337824 & 0.427339533168912 \tabularnewline
18 & 0.487453593009982 & 0.974907186019965 & 0.512546406990018 \tabularnewline
19 & 0.586061539349455 & 0.82787692130109 & 0.413938460650545 \tabularnewline
20 & 0.579205231367035 & 0.84158953726593 & 0.420794768632965 \tabularnewline
21 & 0.588430525004042 & 0.823138949991916 & 0.411569474995958 \tabularnewline
22 & 0.510401376345506 & 0.979197247308989 & 0.489598623654494 \tabularnewline
23 & 0.649249742994044 & 0.701500514011911 & 0.350750257005956 \tabularnewline
24 & 0.585315527182824 & 0.829368945634353 & 0.414684472817176 \tabularnewline
25 & 0.54214459437632 & 0.915710811247361 & 0.45785540562368 \tabularnewline
26 & 0.527825389115885 & 0.94434922176823 & 0.472174610884115 \tabularnewline
27 & 0.484484638370553 & 0.968969276741106 & 0.515515361629447 \tabularnewline
28 & 0.732710688464707 & 0.534578623070587 & 0.267289311535293 \tabularnewline
29 & 0.875419477399363 & 0.249161045201275 & 0.124580522600637 \tabularnewline
30 & 0.859794181748209 & 0.280411636503582 & 0.140205818251791 \tabularnewline
31 & 0.851318022007471 & 0.297363955985057 & 0.148681977992529 \tabularnewline
32 & 0.813314195142867 & 0.373371609714266 & 0.186685804857133 \tabularnewline
33 & 0.8120609153108 & 0.375878169378401 & 0.187939084689201 \tabularnewline
34 & 0.871079210203499 & 0.257841579593003 & 0.128920789796501 \tabularnewline
35 & 0.919575063968548 & 0.160849872062904 & 0.080424936031452 \tabularnewline
36 & 0.898072611416593 & 0.203854777166814 & 0.101927388583407 \tabularnewline
37 & 0.879914921037388 & 0.240170157925223 & 0.120085078962612 \tabularnewline
38 & 0.8514335787795 & 0.297132842441001 & 0.148566421220501 \tabularnewline
39 & 0.817342082000461 & 0.365315835999078 & 0.182657917999539 \tabularnewline
40 & 0.80849603391148 & 0.383007932177041 & 0.191503966088521 \tabularnewline
41 & 0.853431196436933 & 0.293137607126134 & 0.146568803563067 \tabularnewline
42 & 0.8462007185447 & 0.307598562910599 & 0.1537992814553 \tabularnewline
43 & 0.834485916936331 & 0.331028166127337 & 0.165514083063669 \tabularnewline
44 & 0.799877819098168 & 0.400244361803664 & 0.200122180901832 \tabularnewline
45 & 0.830909209375008 & 0.338181581249983 & 0.169090790624992 \tabularnewline
46 & 0.798155797981882 & 0.403688404036235 & 0.201844202018118 \tabularnewline
47 & 0.763574421143518 & 0.472851157712964 & 0.236425578856482 \tabularnewline
48 & 0.932235924970955 & 0.135528150058089 & 0.0677640750290446 \tabularnewline
49 & 0.916329018200732 & 0.167341963598535 & 0.0836709817992677 \tabularnewline
50 & 0.940536334812506 & 0.118927330374987 & 0.0594636651874937 \tabularnewline
51 & 0.936577147608652 & 0.126845704782696 & 0.0634228523913479 \tabularnewline
52 & 0.920972477480867 & 0.158055045038265 & 0.0790275225191327 \tabularnewline
53 & 0.901068619859359 & 0.197862760281282 & 0.098931380140641 \tabularnewline
54 & 0.884566528057084 & 0.230866943885832 & 0.115433471942916 \tabularnewline
55 & 0.861029541866191 & 0.277940916267618 & 0.138970458133809 \tabularnewline
56 & 0.853895795469001 & 0.292208409061997 & 0.146104204530999 \tabularnewline
57 & 0.833909518073599 & 0.332180963852803 & 0.166090481926401 \tabularnewline
58 & 0.854484406076412 & 0.291031187847175 & 0.145515593923588 \tabularnewline
59 & 0.905499140445859 & 0.189001719108282 & 0.094500859554141 \tabularnewline
60 & 0.892717646984679 & 0.214564706030642 & 0.107282353015321 \tabularnewline
61 & 0.962052197222047 & 0.0758956055559064 & 0.0379478027779532 \tabularnewline
62 & 0.952553328754081 & 0.0948933424918375 & 0.0474466712459187 \tabularnewline
63 & 0.96350399729864 & 0.0729920054027211 & 0.0364960027013606 \tabularnewline
64 & 0.955885518743398 & 0.0882289625132038 & 0.0441144812566019 \tabularnewline
65 & 0.952589994752445 & 0.0948200104951092 & 0.0474100052475546 \tabularnewline
66 & 0.940968107978818 & 0.118063784042364 & 0.0590318920211819 \tabularnewline
67 & 0.928298142798053 & 0.143403714403894 & 0.0717018572019472 \tabularnewline
68 & 0.921877784323941 & 0.156244431352118 & 0.0781222156760591 \tabularnewline
69 & 0.903275001094611 & 0.193449997810778 & 0.0967249989053889 \tabularnewline
70 & 0.886749659249784 & 0.226500681500431 & 0.113250340750216 \tabularnewline
71 & 0.922959068036933 & 0.154081863926134 & 0.077040931963067 \tabularnewline
72 & 0.907791635029078 & 0.184416729941843 & 0.0922083649709217 \tabularnewline
73 & 0.907991471043936 & 0.184017057912129 & 0.0920085289560643 \tabularnewline
74 & 0.892565916451313 & 0.214868167097374 & 0.107434083548687 \tabularnewline
75 & 0.871460705460962 & 0.257078589078076 & 0.128539294539038 \tabularnewline
76 & 0.858223795558156 & 0.283552408883688 & 0.141776204441844 \tabularnewline
77 & 0.842266223958684 & 0.315467552082633 & 0.157733776041316 \tabularnewline
78 & 0.832670418042963 & 0.334659163914073 & 0.167329581957037 \tabularnewline
79 & 0.803725334933575 & 0.39254933013285 & 0.196274665066425 \tabularnewline
80 & 0.800861332734152 & 0.398277334531696 & 0.199138667265848 \tabularnewline
81 & 0.77959415527947 & 0.44081168944106 & 0.22040584472053 \tabularnewline
82 & 0.74489186399243 & 0.510216272015141 & 0.255108136007571 \tabularnewline
83 & 0.73686571361367 & 0.52626857277266 & 0.26313428638633 \tabularnewline
84 & 0.711192658653423 & 0.577614682693154 & 0.288807341346577 \tabularnewline
85 & 0.759454448687756 & 0.481091102624488 & 0.240545551312244 \tabularnewline
86 & 0.722115344188219 & 0.555769311623562 & 0.277884655811781 \tabularnewline
87 & 0.694298398930706 & 0.611403202138587 & 0.305701601069294 \tabularnewline
88 & 0.704748254587204 & 0.590503490825592 & 0.295251745412796 \tabularnewline
89 & 0.665251628800037 & 0.669496742399927 & 0.334748371199963 \tabularnewline
90 & 0.62692829434813 & 0.746143411303739 & 0.37307170565187 \tabularnewline
91 & 0.593496148897283 & 0.813007702205435 & 0.406503851102717 \tabularnewline
92 & 0.581880577849742 & 0.836238844300516 & 0.418119422150258 \tabularnewline
93 & 0.572745432170167 & 0.854509135659667 & 0.427254567829833 \tabularnewline
94 & 0.531054415996335 & 0.93789116800733 & 0.468945584003665 \tabularnewline
95 & 0.650404558263413 & 0.699190883473174 & 0.349595441736587 \tabularnewline
96 & 0.63615893493948 & 0.727682130121039 & 0.36384106506052 \tabularnewline
97 & 0.731427622158786 & 0.537144755682427 & 0.268572377841214 \tabularnewline
98 & 0.692938468470905 & 0.614123063058191 & 0.307061531529095 \tabularnewline
99 & 0.651575973423996 & 0.696848053152009 & 0.348424026576004 \tabularnewline
100 & 0.608557084186479 & 0.782885831627042 & 0.391442915813521 \tabularnewline
101 & 0.590672689434573 & 0.818654621130854 & 0.409327310565427 \tabularnewline
102 & 0.543331204528651 & 0.913337590942698 & 0.456668795471349 \tabularnewline
103 & 0.636514772300808 & 0.726970455398385 & 0.363485227699193 \tabularnewline
104 & 0.62382518017839 & 0.75234963964322 & 0.37617481982161 \tabularnewline
105 & 0.58681028138167 & 0.82637943723666 & 0.41318971861833 \tabularnewline
106 & 0.545225367601752 & 0.909549264796496 & 0.454774632398248 \tabularnewline
107 & 0.511689790105456 & 0.976620419789088 & 0.488310209894544 \tabularnewline
108 & 0.519081343759698 & 0.961837312480604 & 0.480918656240302 \tabularnewline
109 & 0.496660061616483 & 0.993320123232966 & 0.503339938383517 \tabularnewline
110 & 0.467820154546197 & 0.935640309092394 & 0.532179845453803 \tabularnewline
111 & 0.488401550412428 & 0.976803100824855 & 0.511598449587572 \tabularnewline
112 & 0.478404056498605 & 0.95680811299721 & 0.521595943501395 \tabularnewline
113 & 0.472215703357351 & 0.944431406714702 & 0.527784296642649 \tabularnewline
114 & 0.437063428478554 & 0.874126856957107 & 0.562936571521446 \tabularnewline
115 & 0.388352127192779 & 0.776704254385557 & 0.611647872807221 \tabularnewline
116 & 0.34254787126469 & 0.685095742529379 & 0.65745212873531 \tabularnewline
117 & 0.306838684290197 & 0.613677368580394 & 0.693161315709803 \tabularnewline
118 & 0.261849813601496 & 0.523699627202992 & 0.738150186398504 \tabularnewline
119 & 0.229647028892796 & 0.459294057785591 & 0.770352971107204 \tabularnewline
120 & 0.198764470962601 & 0.397528941925201 & 0.8012355290374 \tabularnewline
121 & 0.163229882708613 & 0.326459765417225 & 0.836770117291387 \tabularnewline
122 & 0.171259070923429 & 0.342518141846858 & 0.828740929076571 \tabularnewline
123 & 0.144061456555965 & 0.28812291311193 & 0.855938543444035 \tabularnewline
124 & 0.11665205223569 & 0.233304104471379 & 0.88334794776431 \tabularnewline
125 & 0.212528375192373 & 0.425056750384747 & 0.787471624807627 \tabularnewline
126 & 0.179885420894442 & 0.359770841788884 & 0.820114579105558 \tabularnewline
127 & 0.149666825127263 & 0.299333650254526 & 0.850333174872737 \tabularnewline
128 & 0.120022793123884 & 0.240045586247768 & 0.879977206876116 \tabularnewline
129 & 0.127195021865819 & 0.254390043731639 & 0.87280497813418 \tabularnewline
130 & 0.099856965221014 & 0.199713930442028 & 0.900143034778986 \tabularnewline
131 & 0.1469869747349 & 0.2939739494698 & 0.8530130252651 \tabularnewline
132 & 0.120190882881513 & 0.240381765763026 & 0.879809117118487 \tabularnewline
133 & 0.260079229022981 & 0.520158458045962 & 0.739920770977019 \tabularnewline
134 & 0.268384727731234 & 0.536769455462468 & 0.731615272268766 \tabularnewline
135 & 0.248937149684024 & 0.497874299368047 & 0.751062850315977 \tabularnewline
136 & 0.220749102157495 & 0.44149820431499 & 0.779250897842505 \tabularnewline
137 & 0.176667528594533 & 0.353335057189065 & 0.823332471405467 \tabularnewline
138 & 0.208134076121281 & 0.416268152242562 & 0.791865923878719 \tabularnewline
139 & 0.167454985664903 & 0.334909971329806 & 0.832545014335097 \tabularnewline
140 & 0.132349566090546 & 0.264699132181093 & 0.867650433909454 \tabularnewline
141 & 0.0990607557395249 & 0.19812151147905 & 0.900939244260475 \tabularnewline
142 & 0.0848362026974879 & 0.169672405394976 & 0.915163797302512 \tabularnewline
143 & 0.907093173739162 & 0.185813652521677 & 0.0929068262608385 \tabularnewline
144 & 0.98864768288509 & 0.0227046342298195 & 0.0113523171149097 \tabularnewline
145 & 0.977594849103765 & 0.0448103017924689 & 0.0224051508962345 \tabularnewline
146 & 0.958413736063547 & 0.0831725278729066 & 0.0415862639364533 \tabularnewline
147 & 0.93998795895387 & 0.120024082092259 & 0.0600120410461293 \tabularnewline
148 & 0.931927540808702 & 0.136144918382597 & 0.0680724591912985 \tabularnewline
149 & 0.881078431077143 & 0.237843137845714 & 0.118921568922857 \tabularnewline
150 & 0.805627124935682 & 0.388745750128636 & 0.194372875064318 \tabularnewline
151 & 0.682882111342602 & 0.634235777314795 & 0.317117888657398 \tabularnewline
152 & 0.515834770026721 & 0.968330459946558 & 0.484165229973279 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145603&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.362087392006608[/C][C]0.724174784013217[/C][C]0.637912607993392[/C][/ROW]
[ROW][C]11[/C][C]0.611791616701473[/C][C]0.776416766597054[/C][C]0.388208383298527[/C][/ROW]
[ROW][C]12[/C][C]0.80100099992244[/C][C]0.397998000155121[/C][C]0.19899900007756[/C][/ROW]
[ROW][C]13[/C][C]0.835454883439398[/C][C]0.329090233121204[/C][C]0.164545116560602[/C][/ROW]
[ROW][C]14[/C][C]0.79920642910924[/C][C]0.401587141781521[/C][C]0.200793570890761[/C][/ROW]
[ROW][C]15[/C][C]0.729293802660624[/C][C]0.541412394678752[/C][C]0.270706197339376[/C][/ROW]
[ROW][C]16[/C][C]0.647639791445977[/C][C]0.704720417108046[/C][C]0.352360208554023[/C][/ROW]
[ROW][C]17[/C][C]0.572660466831088[/C][C]0.854679066337824[/C][C]0.427339533168912[/C][/ROW]
[ROW][C]18[/C][C]0.487453593009982[/C][C]0.974907186019965[/C][C]0.512546406990018[/C][/ROW]
[ROW][C]19[/C][C]0.586061539349455[/C][C]0.82787692130109[/C][C]0.413938460650545[/C][/ROW]
[ROW][C]20[/C][C]0.579205231367035[/C][C]0.84158953726593[/C][C]0.420794768632965[/C][/ROW]
[ROW][C]21[/C][C]0.588430525004042[/C][C]0.823138949991916[/C][C]0.411569474995958[/C][/ROW]
[ROW][C]22[/C][C]0.510401376345506[/C][C]0.979197247308989[/C][C]0.489598623654494[/C][/ROW]
[ROW][C]23[/C][C]0.649249742994044[/C][C]0.701500514011911[/C][C]0.350750257005956[/C][/ROW]
[ROW][C]24[/C][C]0.585315527182824[/C][C]0.829368945634353[/C][C]0.414684472817176[/C][/ROW]
[ROW][C]25[/C][C]0.54214459437632[/C][C]0.915710811247361[/C][C]0.45785540562368[/C][/ROW]
[ROW][C]26[/C][C]0.527825389115885[/C][C]0.94434922176823[/C][C]0.472174610884115[/C][/ROW]
[ROW][C]27[/C][C]0.484484638370553[/C][C]0.968969276741106[/C][C]0.515515361629447[/C][/ROW]
[ROW][C]28[/C][C]0.732710688464707[/C][C]0.534578623070587[/C][C]0.267289311535293[/C][/ROW]
[ROW][C]29[/C][C]0.875419477399363[/C][C]0.249161045201275[/C][C]0.124580522600637[/C][/ROW]
[ROW][C]30[/C][C]0.859794181748209[/C][C]0.280411636503582[/C][C]0.140205818251791[/C][/ROW]
[ROW][C]31[/C][C]0.851318022007471[/C][C]0.297363955985057[/C][C]0.148681977992529[/C][/ROW]
[ROW][C]32[/C][C]0.813314195142867[/C][C]0.373371609714266[/C][C]0.186685804857133[/C][/ROW]
[ROW][C]33[/C][C]0.8120609153108[/C][C]0.375878169378401[/C][C]0.187939084689201[/C][/ROW]
[ROW][C]34[/C][C]0.871079210203499[/C][C]0.257841579593003[/C][C]0.128920789796501[/C][/ROW]
[ROW][C]35[/C][C]0.919575063968548[/C][C]0.160849872062904[/C][C]0.080424936031452[/C][/ROW]
[ROW][C]36[/C][C]0.898072611416593[/C][C]0.203854777166814[/C][C]0.101927388583407[/C][/ROW]
[ROW][C]37[/C][C]0.879914921037388[/C][C]0.240170157925223[/C][C]0.120085078962612[/C][/ROW]
[ROW][C]38[/C][C]0.8514335787795[/C][C]0.297132842441001[/C][C]0.148566421220501[/C][/ROW]
[ROW][C]39[/C][C]0.817342082000461[/C][C]0.365315835999078[/C][C]0.182657917999539[/C][/ROW]
[ROW][C]40[/C][C]0.80849603391148[/C][C]0.383007932177041[/C][C]0.191503966088521[/C][/ROW]
[ROW][C]41[/C][C]0.853431196436933[/C][C]0.293137607126134[/C][C]0.146568803563067[/C][/ROW]
[ROW][C]42[/C][C]0.8462007185447[/C][C]0.307598562910599[/C][C]0.1537992814553[/C][/ROW]
[ROW][C]43[/C][C]0.834485916936331[/C][C]0.331028166127337[/C][C]0.165514083063669[/C][/ROW]
[ROW][C]44[/C][C]0.799877819098168[/C][C]0.400244361803664[/C][C]0.200122180901832[/C][/ROW]
[ROW][C]45[/C][C]0.830909209375008[/C][C]0.338181581249983[/C][C]0.169090790624992[/C][/ROW]
[ROW][C]46[/C][C]0.798155797981882[/C][C]0.403688404036235[/C][C]0.201844202018118[/C][/ROW]
[ROW][C]47[/C][C]0.763574421143518[/C][C]0.472851157712964[/C][C]0.236425578856482[/C][/ROW]
[ROW][C]48[/C][C]0.932235924970955[/C][C]0.135528150058089[/C][C]0.0677640750290446[/C][/ROW]
[ROW][C]49[/C][C]0.916329018200732[/C][C]0.167341963598535[/C][C]0.0836709817992677[/C][/ROW]
[ROW][C]50[/C][C]0.940536334812506[/C][C]0.118927330374987[/C][C]0.0594636651874937[/C][/ROW]
[ROW][C]51[/C][C]0.936577147608652[/C][C]0.126845704782696[/C][C]0.0634228523913479[/C][/ROW]
[ROW][C]52[/C][C]0.920972477480867[/C][C]0.158055045038265[/C][C]0.0790275225191327[/C][/ROW]
[ROW][C]53[/C][C]0.901068619859359[/C][C]0.197862760281282[/C][C]0.098931380140641[/C][/ROW]
[ROW][C]54[/C][C]0.884566528057084[/C][C]0.230866943885832[/C][C]0.115433471942916[/C][/ROW]
[ROW][C]55[/C][C]0.861029541866191[/C][C]0.277940916267618[/C][C]0.138970458133809[/C][/ROW]
[ROW][C]56[/C][C]0.853895795469001[/C][C]0.292208409061997[/C][C]0.146104204530999[/C][/ROW]
[ROW][C]57[/C][C]0.833909518073599[/C][C]0.332180963852803[/C][C]0.166090481926401[/C][/ROW]
[ROW][C]58[/C][C]0.854484406076412[/C][C]0.291031187847175[/C][C]0.145515593923588[/C][/ROW]
[ROW][C]59[/C][C]0.905499140445859[/C][C]0.189001719108282[/C][C]0.094500859554141[/C][/ROW]
[ROW][C]60[/C][C]0.892717646984679[/C][C]0.214564706030642[/C][C]0.107282353015321[/C][/ROW]
[ROW][C]61[/C][C]0.962052197222047[/C][C]0.0758956055559064[/C][C]0.0379478027779532[/C][/ROW]
[ROW][C]62[/C][C]0.952553328754081[/C][C]0.0948933424918375[/C][C]0.0474466712459187[/C][/ROW]
[ROW][C]63[/C][C]0.96350399729864[/C][C]0.0729920054027211[/C][C]0.0364960027013606[/C][/ROW]
[ROW][C]64[/C][C]0.955885518743398[/C][C]0.0882289625132038[/C][C]0.0441144812566019[/C][/ROW]
[ROW][C]65[/C][C]0.952589994752445[/C][C]0.0948200104951092[/C][C]0.0474100052475546[/C][/ROW]
[ROW][C]66[/C][C]0.940968107978818[/C][C]0.118063784042364[/C][C]0.0590318920211819[/C][/ROW]
[ROW][C]67[/C][C]0.928298142798053[/C][C]0.143403714403894[/C][C]0.0717018572019472[/C][/ROW]
[ROW][C]68[/C][C]0.921877784323941[/C][C]0.156244431352118[/C][C]0.0781222156760591[/C][/ROW]
[ROW][C]69[/C][C]0.903275001094611[/C][C]0.193449997810778[/C][C]0.0967249989053889[/C][/ROW]
[ROW][C]70[/C][C]0.886749659249784[/C][C]0.226500681500431[/C][C]0.113250340750216[/C][/ROW]
[ROW][C]71[/C][C]0.922959068036933[/C][C]0.154081863926134[/C][C]0.077040931963067[/C][/ROW]
[ROW][C]72[/C][C]0.907791635029078[/C][C]0.184416729941843[/C][C]0.0922083649709217[/C][/ROW]
[ROW][C]73[/C][C]0.907991471043936[/C][C]0.184017057912129[/C][C]0.0920085289560643[/C][/ROW]
[ROW][C]74[/C][C]0.892565916451313[/C][C]0.214868167097374[/C][C]0.107434083548687[/C][/ROW]
[ROW][C]75[/C][C]0.871460705460962[/C][C]0.257078589078076[/C][C]0.128539294539038[/C][/ROW]
[ROW][C]76[/C][C]0.858223795558156[/C][C]0.283552408883688[/C][C]0.141776204441844[/C][/ROW]
[ROW][C]77[/C][C]0.842266223958684[/C][C]0.315467552082633[/C][C]0.157733776041316[/C][/ROW]
[ROW][C]78[/C][C]0.832670418042963[/C][C]0.334659163914073[/C][C]0.167329581957037[/C][/ROW]
[ROW][C]79[/C][C]0.803725334933575[/C][C]0.39254933013285[/C][C]0.196274665066425[/C][/ROW]
[ROW][C]80[/C][C]0.800861332734152[/C][C]0.398277334531696[/C][C]0.199138667265848[/C][/ROW]
[ROW][C]81[/C][C]0.77959415527947[/C][C]0.44081168944106[/C][C]0.22040584472053[/C][/ROW]
[ROW][C]82[/C][C]0.74489186399243[/C][C]0.510216272015141[/C][C]0.255108136007571[/C][/ROW]
[ROW][C]83[/C][C]0.73686571361367[/C][C]0.52626857277266[/C][C]0.26313428638633[/C][/ROW]
[ROW][C]84[/C][C]0.711192658653423[/C][C]0.577614682693154[/C][C]0.288807341346577[/C][/ROW]
[ROW][C]85[/C][C]0.759454448687756[/C][C]0.481091102624488[/C][C]0.240545551312244[/C][/ROW]
[ROW][C]86[/C][C]0.722115344188219[/C][C]0.555769311623562[/C][C]0.277884655811781[/C][/ROW]
[ROW][C]87[/C][C]0.694298398930706[/C][C]0.611403202138587[/C][C]0.305701601069294[/C][/ROW]
[ROW][C]88[/C][C]0.704748254587204[/C][C]0.590503490825592[/C][C]0.295251745412796[/C][/ROW]
[ROW][C]89[/C][C]0.665251628800037[/C][C]0.669496742399927[/C][C]0.334748371199963[/C][/ROW]
[ROW][C]90[/C][C]0.62692829434813[/C][C]0.746143411303739[/C][C]0.37307170565187[/C][/ROW]
[ROW][C]91[/C][C]0.593496148897283[/C][C]0.813007702205435[/C][C]0.406503851102717[/C][/ROW]
[ROW][C]92[/C][C]0.581880577849742[/C][C]0.836238844300516[/C][C]0.418119422150258[/C][/ROW]
[ROW][C]93[/C][C]0.572745432170167[/C][C]0.854509135659667[/C][C]0.427254567829833[/C][/ROW]
[ROW][C]94[/C][C]0.531054415996335[/C][C]0.93789116800733[/C][C]0.468945584003665[/C][/ROW]
[ROW][C]95[/C][C]0.650404558263413[/C][C]0.699190883473174[/C][C]0.349595441736587[/C][/ROW]
[ROW][C]96[/C][C]0.63615893493948[/C][C]0.727682130121039[/C][C]0.36384106506052[/C][/ROW]
[ROW][C]97[/C][C]0.731427622158786[/C][C]0.537144755682427[/C][C]0.268572377841214[/C][/ROW]
[ROW][C]98[/C][C]0.692938468470905[/C][C]0.614123063058191[/C][C]0.307061531529095[/C][/ROW]
[ROW][C]99[/C][C]0.651575973423996[/C][C]0.696848053152009[/C][C]0.348424026576004[/C][/ROW]
[ROW][C]100[/C][C]0.608557084186479[/C][C]0.782885831627042[/C][C]0.391442915813521[/C][/ROW]
[ROW][C]101[/C][C]0.590672689434573[/C][C]0.818654621130854[/C][C]0.409327310565427[/C][/ROW]
[ROW][C]102[/C][C]0.543331204528651[/C][C]0.913337590942698[/C][C]0.456668795471349[/C][/ROW]
[ROW][C]103[/C][C]0.636514772300808[/C][C]0.726970455398385[/C][C]0.363485227699193[/C][/ROW]
[ROW][C]104[/C][C]0.62382518017839[/C][C]0.75234963964322[/C][C]0.37617481982161[/C][/ROW]
[ROW][C]105[/C][C]0.58681028138167[/C][C]0.82637943723666[/C][C]0.41318971861833[/C][/ROW]
[ROW][C]106[/C][C]0.545225367601752[/C][C]0.909549264796496[/C][C]0.454774632398248[/C][/ROW]
[ROW][C]107[/C][C]0.511689790105456[/C][C]0.976620419789088[/C][C]0.488310209894544[/C][/ROW]
[ROW][C]108[/C][C]0.519081343759698[/C][C]0.961837312480604[/C][C]0.480918656240302[/C][/ROW]
[ROW][C]109[/C][C]0.496660061616483[/C][C]0.993320123232966[/C][C]0.503339938383517[/C][/ROW]
[ROW][C]110[/C][C]0.467820154546197[/C][C]0.935640309092394[/C][C]0.532179845453803[/C][/ROW]
[ROW][C]111[/C][C]0.488401550412428[/C][C]0.976803100824855[/C][C]0.511598449587572[/C][/ROW]
[ROW][C]112[/C][C]0.478404056498605[/C][C]0.95680811299721[/C][C]0.521595943501395[/C][/ROW]
[ROW][C]113[/C][C]0.472215703357351[/C][C]0.944431406714702[/C][C]0.527784296642649[/C][/ROW]
[ROW][C]114[/C][C]0.437063428478554[/C][C]0.874126856957107[/C][C]0.562936571521446[/C][/ROW]
[ROW][C]115[/C][C]0.388352127192779[/C][C]0.776704254385557[/C][C]0.611647872807221[/C][/ROW]
[ROW][C]116[/C][C]0.34254787126469[/C][C]0.685095742529379[/C][C]0.65745212873531[/C][/ROW]
[ROW][C]117[/C][C]0.306838684290197[/C][C]0.613677368580394[/C][C]0.693161315709803[/C][/ROW]
[ROW][C]118[/C][C]0.261849813601496[/C][C]0.523699627202992[/C][C]0.738150186398504[/C][/ROW]
[ROW][C]119[/C][C]0.229647028892796[/C][C]0.459294057785591[/C][C]0.770352971107204[/C][/ROW]
[ROW][C]120[/C][C]0.198764470962601[/C][C]0.397528941925201[/C][C]0.8012355290374[/C][/ROW]
[ROW][C]121[/C][C]0.163229882708613[/C][C]0.326459765417225[/C][C]0.836770117291387[/C][/ROW]
[ROW][C]122[/C][C]0.171259070923429[/C][C]0.342518141846858[/C][C]0.828740929076571[/C][/ROW]
[ROW][C]123[/C][C]0.144061456555965[/C][C]0.28812291311193[/C][C]0.855938543444035[/C][/ROW]
[ROW][C]124[/C][C]0.11665205223569[/C][C]0.233304104471379[/C][C]0.88334794776431[/C][/ROW]
[ROW][C]125[/C][C]0.212528375192373[/C][C]0.425056750384747[/C][C]0.787471624807627[/C][/ROW]
[ROW][C]126[/C][C]0.179885420894442[/C][C]0.359770841788884[/C][C]0.820114579105558[/C][/ROW]
[ROW][C]127[/C][C]0.149666825127263[/C][C]0.299333650254526[/C][C]0.850333174872737[/C][/ROW]
[ROW][C]128[/C][C]0.120022793123884[/C][C]0.240045586247768[/C][C]0.879977206876116[/C][/ROW]
[ROW][C]129[/C][C]0.127195021865819[/C][C]0.254390043731639[/C][C]0.87280497813418[/C][/ROW]
[ROW][C]130[/C][C]0.099856965221014[/C][C]0.199713930442028[/C][C]0.900143034778986[/C][/ROW]
[ROW][C]131[/C][C]0.1469869747349[/C][C]0.2939739494698[/C][C]0.8530130252651[/C][/ROW]
[ROW][C]132[/C][C]0.120190882881513[/C][C]0.240381765763026[/C][C]0.879809117118487[/C][/ROW]
[ROW][C]133[/C][C]0.260079229022981[/C][C]0.520158458045962[/C][C]0.739920770977019[/C][/ROW]
[ROW][C]134[/C][C]0.268384727731234[/C][C]0.536769455462468[/C][C]0.731615272268766[/C][/ROW]
[ROW][C]135[/C][C]0.248937149684024[/C][C]0.497874299368047[/C][C]0.751062850315977[/C][/ROW]
[ROW][C]136[/C][C]0.220749102157495[/C][C]0.44149820431499[/C][C]0.779250897842505[/C][/ROW]
[ROW][C]137[/C][C]0.176667528594533[/C][C]0.353335057189065[/C][C]0.823332471405467[/C][/ROW]
[ROW][C]138[/C][C]0.208134076121281[/C][C]0.416268152242562[/C][C]0.791865923878719[/C][/ROW]
[ROW][C]139[/C][C]0.167454985664903[/C][C]0.334909971329806[/C][C]0.832545014335097[/C][/ROW]
[ROW][C]140[/C][C]0.132349566090546[/C][C]0.264699132181093[/C][C]0.867650433909454[/C][/ROW]
[ROW][C]141[/C][C]0.0990607557395249[/C][C]0.19812151147905[/C][C]0.900939244260475[/C][/ROW]
[ROW][C]142[/C][C]0.0848362026974879[/C][C]0.169672405394976[/C][C]0.915163797302512[/C][/ROW]
[ROW][C]143[/C][C]0.907093173739162[/C][C]0.185813652521677[/C][C]0.0929068262608385[/C][/ROW]
[ROW][C]144[/C][C]0.98864768288509[/C][C]0.0227046342298195[/C][C]0.0113523171149097[/C][/ROW]
[ROW][C]145[/C][C]0.977594849103765[/C][C]0.0448103017924689[/C][C]0.0224051508962345[/C][/ROW]
[ROW][C]146[/C][C]0.958413736063547[/C][C]0.0831725278729066[/C][C]0.0415862639364533[/C][/ROW]
[ROW][C]147[/C][C]0.93998795895387[/C][C]0.120024082092259[/C][C]0.0600120410461293[/C][/ROW]
[ROW][C]148[/C][C]0.931927540808702[/C][C]0.136144918382597[/C][C]0.0680724591912985[/C][/ROW]
[ROW][C]149[/C][C]0.881078431077143[/C][C]0.237843137845714[/C][C]0.118921568922857[/C][/ROW]
[ROW][C]150[/C][C]0.805627124935682[/C][C]0.388745750128636[/C][C]0.194372875064318[/C][/ROW]
[ROW][C]151[/C][C]0.682882111342602[/C][C]0.634235777314795[/C][C]0.317117888657398[/C][/ROW]
[ROW][C]152[/C][C]0.515834770026721[/C][C]0.968330459946558[/C][C]0.484165229973279[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145603&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145603&T=5

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.3620873920066080.7241747840132170.637912607993392
110.6117916167014730.7764167665970540.388208383298527
120.801000999922440.3979980001551210.19899900007756
130.8354548834393980.3290902331212040.164545116560602
140.799206429109240.4015871417815210.200793570890761
150.7292938026606240.5414123946787520.270706197339376
160.6476397914459770.7047204171080460.352360208554023
170.5726604668310880.8546790663378240.427339533168912
180.4874535930099820.9749071860199650.512546406990018
190.5860615393494550.827876921301090.413938460650545
200.5792052313670350.841589537265930.420794768632965
210.5884305250040420.8231389499919160.411569474995958
220.5104013763455060.9791972473089890.489598623654494
230.6492497429940440.7015005140119110.350750257005956
240.5853155271828240.8293689456343530.414684472817176
250.542144594376320.9157108112473610.45785540562368
260.5278253891158850.944349221768230.472174610884115
270.4844846383705530.9689692767411060.515515361629447
280.7327106884647070.5345786230705870.267289311535293
290.8754194773993630.2491610452012750.124580522600637
300.8597941817482090.2804116365035820.140205818251791
310.8513180220074710.2973639559850570.148681977992529
320.8133141951428670.3733716097142660.186685804857133
330.81206091531080.3758781693784010.187939084689201
340.8710792102034990.2578415795930030.128920789796501
350.9195750639685480.1608498720629040.080424936031452
360.8980726114165930.2038547771668140.101927388583407
370.8799149210373880.2401701579252230.120085078962612
380.85143357877950.2971328424410010.148566421220501
390.8173420820004610.3653158359990780.182657917999539
400.808496033911480.3830079321770410.191503966088521
410.8534311964369330.2931376071261340.146568803563067
420.84620071854470.3075985629105990.1537992814553
430.8344859169363310.3310281661273370.165514083063669
440.7998778190981680.4002443618036640.200122180901832
450.8309092093750080.3381815812499830.169090790624992
460.7981557979818820.4036884040362350.201844202018118
470.7635744211435180.4728511577129640.236425578856482
480.9322359249709550.1355281500580890.0677640750290446
490.9163290182007320.1673419635985350.0836709817992677
500.9405363348125060.1189273303749870.0594636651874937
510.9365771476086520.1268457047826960.0634228523913479
520.9209724774808670.1580550450382650.0790275225191327
530.9010686198593590.1978627602812820.098931380140641
540.8845665280570840.2308669438858320.115433471942916
550.8610295418661910.2779409162676180.138970458133809
560.8538957954690010.2922084090619970.146104204530999
570.8339095180735990.3321809638528030.166090481926401
580.8544844060764120.2910311878471750.145515593923588
590.9054991404458590.1890017191082820.094500859554141
600.8927176469846790.2145647060306420.107282353015321
610.9620521972220470.07589560555590640.0379478027779532
620.9525533287540810.09489334249183750.0474466712459187
630.963503997298640.07299200540272110.0364960027013606
640.9558855187433980.08822896251320380.0441144812566019
650.9525899947524450.09482001049510920.0474100052475546
660.9409681079788180.1180637840423640.0590318920211819
670.9282981427980530.1434037144038940.0717018572019472
680.9218777843239410.1562444313521180.0781222156760591
690.9032750010946110.1934499978107780.0967249989053889
700.8867496592497840.2265006815004310.113250340750216
710.9229590680369330.1540818639261340.077040931963067
720.9077916350290780.1844167299418430.0922083649709217
730.9079914710439360.1840170579121290.0920085289560643
740.8925659164513130.2148681670973740.107434083548687
750.8714607054609620.2570785890780760.128539294539038
760.8582237955581560.2835524088836880.141776204441844
770.8422662239586840.3154675520826330.157733776041316
780.8326704180429630.3346591639140730.167329581957037
790.8037253349335750.392549330132850.196274665066425
800.8008613327341520.3982773345316960.199138667265848
810.779594155279470.440811689441060.22040584472053
820.744891863992430.5102162720151410.255108136007571
830.736865713613670.526268572772660.26313428638633
840.7111926586534230.5776146826931540.288807341346577
850.7594544486877560.4810911026244880.240545551312244
860.7221153441882190.5557693116235620.277884655811781
870.6942983989307060.6114032021385870.305701601069294
880.7047482545872040.5905034908255920.295251745412796
890.6652516288000370.6694967423999270.334748371199963
900.626928294348130.7461434113037390.37307170565187
910.5934961488972830.8130077022054350.406503851102717
920.5818805778497420.8362388443005160.418119422150258
930.5727454321701670.8545091356596670.427254567829833
940.5310544159963350.937891168007330.468945584003665
950.6504045582634130.6991908834731740.349595441736587
960.636158934939480.7276821301210390.36384106506052
970.7314276221587860.5371447556824270.268572377841214
980.6929384684709050.6141230630581910.307061531529095
990.6515759734239960.6968480531520090.348424026576004
1000.6085570841864790.7828858316270420.391442915813521
1010.5906726894345730.8186546211308540.409327310565427
1020.5433312045286510.9133375909426980.456668795471349
1030.6365147723008080.7269704553983850.363485227699193
1040.623825180178390.752349639643220.37617481982161
1050.586810281381670.826379437236660.41318971861833
1060.5452253676017520.9095492647964960.454774632398248
1070.5116897901054560.9766204197890880.488310209894544
1080.5190813437596980.9618373124806040.480918656240302
1090.4966600616164830.9933201232329660.503339938383517
1100.4678201545461970.9356403090923940.532179845453803
1110.4884015504124280.9768031008248550.511598449587572
1120.4784040564986050.956808112997210.521595943501395
1130.4722157033573510.9444314067147020.527784296642649
1140.4370634284785540.8741268569571070.562936571521446
1150.3883521271927790.7767042543855570.611647872807221
1160.342547871264690.6850957425293790.65745212873531
1170.3068386842901970.6136773685803940.693161315709803
1180.2618498136014960.5236996272029920.738150186398504
1190.2296470288927960.4592940577855910.770352971107204
1200.1987644709626010.3975289419252010.8012355290374
1210.1632298827086130.3264597654172250.836770117291387
1220.1712590709234290.3425181418468580.828740929076571
1230.1440614565559650.288122913111930.855938543444035
1240.116652052235690.2333041044713790.88334794776431
1250.2125283751923730.4250567503847470.787471624807627
1260.1798854208944420.3597708417888840.820114579105558
1270.1496668251272630.2993336502545260.850333174872737
1280.1200227931238840.2400455862477680.879977206876116
1290.1271950218658190.2543900437316390.87280497813418
1300.0998569652210140.1997139304420280.900143034778986
1310.14698697473490.29397394946980.8530130252651
1320.1201908828815130.2403817657630260.879809117118487
1330.2600792290229810.5201584580459620.739920770977019
1340.2683847277312340.5367694554624680.731615272268766
1350.2489371496840240.4978742993680470.751062850315977
1360.2207491021574950.441498204314990.779250897842505
1370.1766675285945330.3533350571890650.823332471405467
1380.2081340761212810.4162681522425620.791865923878719
1390.1674549856649030.3349099713298060.832545014335097
1400.1323495660905460.2646991321810930.867650433909454
1410.09906075573952490.198121511479050.900939244260475
1420.08483620269748790.1696724053949760.915163797302512
1430.9070931737391620.1858136525216770.0929068262608385
1440.988647682885090.02270463422981950.0113523171149097
1450.9775948491037650.04481030179246890.0224051508962345
1460.9584137360635470.08317252787290660.0415862639364533
1470.939987958953870.1200240820922590.0600120410461293
1480.9319275408087020.1361449183825970.0680724591912985
1490.8810784310771430.2378431378457140.118921568922857
1500.8056271249356820.3887457501286360.194372875064318
1510.6828821113426020.6342357773147950.317117888657398
1520.5158347700267210.9683304599465580.484165229973279






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.013986013986014OK
10% type I error level80.0559440559440559OK

\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 & 2 & 0.013986013986014 & OK \tabularnewline
10% type I error level & 8 & 0.0559440559440559 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145603&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]2[/C][C]0.013986013986014[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]8[/C][C]0.0559440559440559[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145603&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145603&T=6

As an alternative you can also use a QR Code:  
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 level20.013986013986014OK
10% type I error level80.0559440559440559OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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')
}