Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 23 Nov 2010 18:22:31 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t1290536452exrwvl6aj8wad8o.htm/, Retrieved Fri, 29 Mar 2024 05:02:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99547, Retrieved Fri, 29 Mar 2024 05:02:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact143
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [multiple regressi...] [2010-11-22 11:59:08] [9f32078fdcdc094ca748857d5ebdb3de]
-   P     [Multiple Regression] [W7] [2010-11-23 18:22:31] [476d588d86fe88306e0383abd6004235] [Current]
Feedback Forum

Post a new message
Dataseries X:
24	0	14	0	11	0	12	0	24	26	0
25	25	11	11	7	7	8	8	25	23	23
17	17	6	6	17	17	8	8	30	25	25
18	0	12	0	10	0	8	0	19	23	0
18	18	8	8	12	12	9	9	22	19	19
16	16	10	10	12	12	7	7	22	29	29
20	20	10	10	11	11	4	4	25	25	25
16	16	11	11	11	11	11	11	23	21	21
18	18	16	16	12	12	7	7	17	22	22
17	17	11	11	13	13	7	7	21	25	25
23	0	13	0	14	0	12	0	19	24	0
30	30	12	12	16	16	10	10	19	18	18
23	23	8	8	11	11	10	10	15	22	22
18	18	12	12	10	10	8	8	16	15	15
15	0	11	0	11	0	8	0	23	22	0
12	0	4	0	15	0	4	0	27	28	0
21	21	9	9	9	9	9	9	22	20	20
15	0	8	0	11	0	8	0	14	12	0
20	0	8	0	17	0	7	0	22	24	0
31	31	14	14	17	17	11	11	23	20	20
27	27	15	15	11	11	9	9	23	21	21
34	0	16	0	18	0	11	0	21	20	0
21	21	9	9	14	14	13	13	19	21	21
31	0	14	0	10	0	8	0	18	23	0
19	0	11	0	11	0	8	0	20	28	0
16	16	8	8	15	15	9	9	23	24	24
20	20	9	9	15	15	6	6	25	24	24
21	0	9	0	13	0	9	0	19	24	0
22	0	9	0	16	0	9	0	24	23	0
17	17	9	9	13	13	6	6	22	23	23
24	0	10	0	9	0	6	0	25	29	0
25	25	16	16	18	18	16	16	26	24	24
26	26	11	11	18	18	5	5	29	18	18
25	25	8	8	12	12	7	7	32	25	25
17	17	9	9	17	17	9	9	25	21	21
32	0	16	0	9	0	6	0	29	26	0
33	0	11	0	9	0	6	0	28	22	0
13	0	16	0	12	0	5	0	17	22	0
32	32	12	12	18	18	12	12	28	22	22
25	0	12	0	12	0	7	0	29	23	0
29	0	14	0	18	0	10	0	26	30	0
22	22	9	9	14	14	9	9	25	23	23
18	0	10	0	15	0	8	0	14	17	0
17	17	9	9	16	16	5	5	25	23	23
20	0	10	0	10	0	8	0	26	23	0
15	0	12	0	11	0	8	0	20	25	0
20	20	14	14	14	14	10	10	18	24	24
33	0	14	0	9	0	6	0	32	24	0
29	29	10	10	12	12	8	8	25	23	23
23	23	14	14	17	17	7	7	25	21	21
26	0	16	0	5	0	4	0	23	24	0
18	0	9	0	12	0	8	0	21	24	0
20	20	10	10	12	12	8	8	20	28	28
11	11	6	6	6	6	4	4	15	16	16
28	0	8	0	24	0	20	0	30	20	0
26	26	13	13	12	12	8	8	24	29	29
22	22	10	10	12	12	8	8	26	27	27
17	0	8	0	14	0	6	0	24	22	0
12	0	7	0	7	0	4	0	22	28	0
14	14	15	15	13	13	8	8	14	16	16
17	0	9	0	12	0	9	0	24	25	0
21	0	10	0	13	0	6	0	24	24	0
19	19	12	12	14	14	7	7	24	28	28
18	0	13	0	8	0	9	0	24	24	0
10	0	10	0	11	0	5	0	19	23	0
29	0	11	0	9	0	5	0	31	30	0
31	0	8	0	11	0	8	0	22	24	0
19	0	9	0	13	0	8	0	27	21	0
9	0	13	0	10	0	6	0	19	25	0
20	20	11	11	11	11	8	8	25	25	25
28	28	8	8	12	12	7	7	20	22	22
19	19	9	9	9	9	7	7	21	23	23
30	30	9	9	15	15	9	9	27	26	26
29	29	15	15	18	18	11	11	23	23	23
26	26	9	9	15	15	6	6	25	25	25
23	23	10	10	12	12	8	8	20	21	21
21	21	12	12	14	14	9	9	22	24	24
19	0	12	0	10	0	8	0	23	29	0
28	28	11	11	13	13	6	6	25	22	22
23	23	14	14	13	13	10	10	25	27	27
18	18	6	6	11	11	8	8	17	26	26
21	0	12	0	13	0	8	0	19	22	0
20	20	8	8	16	16	10	10	25	24	24
23	0	14	0	8	0	5	0	19	27	0
21	0	11	0	16	0	7	0	20	24	0
21	21	10	10	11	11	5	5	26	24	24
15	0	14	0	9	0	8	0	23	29	0
28	28	12	12	16	16	14	14	27	22	22
19	0	10	0	12	0	7	0	17	21	0
26	0	14	0	14	0	8	0	17	24	0
10	10	5	5	8	8	6	6	19	24	24
16	0	11	0	9	0	5	0	17	23	0
22	22	10	10	15	15	6	6	22	20	20
19	0	9	0	11	0	10	0	21	27	0
31	31	10	10	21	21	12	12	32	26	26
31	0	16	0	14	0	9	0	21	25	0
29	29	13	13	18	18	12	12	21	21	21
19	0	9	0	12	0	7	0	18	21	0
22	22	10	10	13	13	8	8	18	19	19
23	23	10	10	15	15	10	10	23	21	21
15	0	7	0	12	0	6	0	19	21	0
20	20	9	9	19	19	10	10	20	16	16
18	18	8	8	15	15	10	10	21	22	22
23	0	14	0	11	0	10	0	20	29	0
25	25	14	14	11	11	5	5	17	15	15
21	21	8	8	10	10	7	7	18	17	17
24	24	9	9	13	13	10	10	19	15	15
25	25	14	14	15	15	11	11	22	21	21
17	0	14	0	12	0	6	0	15	21	0
13	13	8	8	12	12	7	7	14	19	19
28	28	8	8	16	16	12	12	18	24	24
21	0	8	0	9	0	11	0	24	20	0
25	25	7	7	18	18	11	11	35	17	17
9	9	6	6	8	8	11	11	29	23	23
16	16	8	8	13	13	5	5	21	24	24
19	19	6	6	17	17	8	8	25	14	14
17	0	11	0	9	0	6	0	20	19	0
25	0	14	0	15	0	9	0	22	24	0
20	0	11	0	8	0	4	0	13	13	0
29	29	11	11	7	7	4	4	26	22	22
14	14	11	11	12	12	7	7	17	16	16
22	22	14	14	14	14	11	11	25	19	19
15	15	8	8	6	6	6	6	20	25	25
19	0	20	0	8	0	7	0	19	25	0
20	0	11	0	17	0	8	0	21	23	0
15	15	8	8	10	10	4	4	22	24	24
20	20	11	11	11	11	8	8	24	26	26
18	18	10	10	14	14	9	9	21	26	26
33	33	14	14	11	11	8	8	26	25	25
22	22	11	11	13	13	11	11	24	18	18
16	16	9	9	12	12	8	8	16	21	21
17	0	9	0	11	0	5	0	23	26	0
16	16	8	8	9	9	4	4	18	23	23
21	0	10	0	12	0	8	0	16	23	0
26	0	13	0	20	0	10	0	26	22	0
18	18	13	13	12	12	6	6	19	20	20
18	18	12	12	13	13	9	9	21	13	13
17	0	8	0	12	0	9	0	21	24	0
22	22	13	13	12	12	13	13	22	15	15
30	30	14	14	9	9	9	9	23	14	14
30	30	12	12	15	15	10	10	29	22	22
24	24	14	14	24	24	20	20	21	10	10
21	0	15	0	7	0	5	0	21	24	0
21	21	13	13	17	17	11	11	23	22	22
29	29	16	16	11	11	6	6	27	24	24
31	31	9	9	17	17	9	9	25	19	19
20	20	9	9	11	11	7	7	21	20	20
16	16	9	9	12	12	9	9	10	13	13
22	22	8	8	14	14	10	10	20	20	20
20	20	7	7	11	11	9	9	26	22	22
28	28	16	16	16	16	8	8	24	24	24
38	38	11	11	21	21	7	7	29	29	29
22	22	9	9	14	14	6	6	19	12	12
20	20	11	11	20	20	13	13	24	20	20
17	17	9	9	13	13	6	6	19	21	21
28	0	14	0	11	0	8	0	24	24	0
22	22	13	13	15	15	10	10	22	22	22
31	31	16	16	19	19	16	16	17	20	20




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99547&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99547&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99547&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ 72.249.76.132







Multiple Linear Regression - Estimated Regression Equation
O[t] = + 7.32684596059117 + 0.356979436989242CM[t] -0.0592285666748474`CM*G`[t] -0.483865613410507D[t] + 0.175232812441502`D*G`[t] -0.0310127872102974PE[t] + 0.323284473674036`PE*G`[t] + 0.0927245043282856PC[t] -0.123099907387686`PC*G`[t] + 0.509759645731013PS[t] -0.132008906567604`PS*G`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
O[t] =  +  7.32684596059117 +  0.356979436989242CM[t] -0.0592285666748474`CM*G`[t] -0.483865613410507D[t] +  0.175232812441502`D*G`[t] -0.0310127872102974PE[t] +  0.323284473674036`PE*G`[t] +  0.0927245043282856PC[t] -0.123099907387686`PC*G`[t] +  0.509759645731013PS[t] -0.132008906567604`PS*G`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99547&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]O[t] =  +  7.32684596059117 +  0.356979436989242CM[t] -0.0592285666748474`CM*G`[t] -0.483865613410507D[t] +  0.175232812441502`D*G`[t] -0.0310127872102974PE[t] +  0.323284473674036`PE*G`[t] +  0.0927245043282856PC[t] -0.123099907387686`PC*G`[t] +  0.509759645731013PS[t] -0.132008906567604`PS*G`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99547&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99547&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
O[t] = + 7.32684596059117 + 0.356979436989242CM[t] -0.0592285666748474`CM*G`[t] -0.483865613410507D[t] + 0.175232812441502`D*G`[t] -0.0310127872102974PE[t] + 0.323284473674036`PE*G`[t] + 0.0927245043282856PC[t] -0.123099907387686`PC*G`[t] + 0.509759645731013PS[t] -0.132008906567604`PS*G`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.326845960591172.2792843.21450.0016060.000803
CM0.3569794369892420.0862824.13745.9e-052.9e-05
`CM*G`-0.05922856667484740.115217-0.51410.6079810.303991
D-0.4838656134105070.16529-2.92740.0039630.001981
`D*G`0.1752328124415020.2164790.80950.4195530.209777
PE-0.03101278721029740.161508-0.1920.8479920.423996
`PE*G`0.3232844736740360.2031791.59110.1137280.056864
PC0.09272450432828560.2272940.40790.6839040.341952
`PC*G`-0.1230999073876860.276833-0.44470.657210.328605
PS0.5097596457310130.1031614.94142e-061e-06
`PS*G`-0.1320089065676040.106177-1.24330.215740.10787

\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) & 7.32684596059117 & 2.279284 & 3.2145 & 0.001606 & 0.000803 \tabularnewline
CM & 0.356979436989242 & 0.086282 & 4.1374 & 5.9e-05 & 2.9e-05 \tabularnewline
`CM*G` & -0.0592285666748474 & 0.115217 & -0.5141 & 0.607981 & 0.303991 \tabularnewline
D & -0.483865613410507 & 0.16529 & -2.9274 & 0.003963 & 0.001981 \tabularnewline
`D*G` & 0.175232812441502 & 0.216479 & 0.8095 & 0.419553 & 0.209777 \tabularnewline
PE & -0.0310127872102974 & 0.161508 & -0.192 & 0.847992 & 0.423996 \tabularnewline
`PE*G` & 0.323284473674036 & 0.203179 & 1.5911 & 0.113728 & 0.056864 \tabularnewline
PC & 0.0927245043282856 & 0.227294 & 0.4079 & 0.683904 & 0.341952 \tabularnewline
`PC*G` & -0.123099907387686 & 0.276833 & -0.4447 & 0.65721 & 0.328605 \tabularnewline
PS & 0.509759645731013 & 0.103161 & 4.9414 & 2e-06 & 1e-06 \tabularnewline
`PS*G` & -0.132008906567604 & 0.106177 & -1.2433 & 0.21574 & 0.10787 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99547&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]7.32684596059117[/C][C]2.279284[/C][C]3.2145[/C][C]0.001606[/C][C]0.000803[/C][/ROW]
[ROW][C]CM[/C][C]0.356979436989242[/C][C]0.086282[/C][C]4.1374[/C][C]5.9e-05[/C][C]2.9e-05[/C][/ROW]
[ROW][C]`CM*G`[/C][C]-0.0592285666748474[/C][C]0.115217[/C][C]-0.5141[/C][C]0.607981[/C][C]0.303991[/C][/ROW]
[ROW][C]D[/C][C]-0.483865613410507[/C][C]0.16529[/C][C]-2.9274[/C][C]0.003963[/C][C]0.001981[/C][/ROW]
[ROW][C]`D*G`[/C][C]0.175232812441502[/C][C]0.216479[/C][C]0.8095[/C][C]0.419553[/C][C]0.209777[/C][/ROW]
[ROW][C]PE[/C][C]-0.0310127872102974[/C][C]0.161508[/C][C]-0.192[/C][C]0.847992[/C][C]0.423996[/C][/ROW]
[ROW][C]`PE*G`[/C][C]0.323284473674036[/C][C]0.203179[/C][C]1.5911[/C][C]0.113728[/C][C]0.056864[/C][/ROW]
[ROW][C]PC[/C][C]0.0927245043282856[/C][C]0.227294[/C][C]0.4079[/C][C]0.683904[/C][C]0.341952[/C][/ROW]
[ROW][C]`PC*G`[/C][C]-0.123099907387686[/C][C]0.276833[/C][C]-0.4447[/C][C]0.65721[/C][C]0.328605[/C][/ROW]
[ROW][C]PS[/C][C]0.509759645731013[/C][C]0.103161[/C][C]4.9414[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]`PS*G`[/C][C]-0.132008906567604[/C][C]0.106177[/C][C]-1.2433[/C][C]0.21574[/C][C]0.10787[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99547&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99547&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.326845960591172.2792843.21450.0016060.000803
CM0.3569794369892420.0862824.13745.9e-052.9e-05
`CM*G`-0.05922856667484740.115217-0.51410.6079810.303991
D-0.4838656134105070.16529-2.92740.0039630.001981
`D*G`0.1752328124415020.2164790.80950.4195530.209777
PE-0.03101278721029740.161508-0.1920.8479920.423996
`PE*G`0.3232844736740360.2031791.59110.1137280.056864
PC0.09272450432828560.2272940.40790.6839040.341952
`PC*G`-0.1230999073876860.276833-0.44470.657210.328605
PS0.5097596457310130.1031614.94142e-061e-06
`PS*G`-0.1320089065676040.106177-1.24330.215740.10787







Multiple Linear Regression - Regression Statistics
Multiple R0.623397994765618
R-squared0.388625059877793
Adjusted R-squared0.347034927896691
F-TEST (value)9.34416510277905
F-TEST (DF numerator)10
F-TEST (DF denominator)147
p-value6.79212242005178e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.41759903161865
Sum Squared Residuals1716.95752171535

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.623397994765618 \tabularnewline
R-squared & 0.388625059877793 \tabularnewline
Adjusted R-squared & 0.347034927896691 \tabularnewline
F-TEST (value) & 9.34416510277905 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 147 \tabularnewline
p-value & 6.79212242005178e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.41759903161865 \tabularnewline
Sum Squared Residuals & 1716.95752171535 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99547&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.623397994765618[/C][/ROW]
[ROW][C]R-squared[/C][C]0.388625059877793[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.347034927896691[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.34416510277905[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]10[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]147[/C][/ROW]
[ROW][C]p-value[/C][C]6.79212242005178e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.41759903161865[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1716.95752171535[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99547&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99547&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.623397994765618
R-squared0.388625059877793
Adjusted R-squared0.347034927896691
F-TEST (value)9.34416510277905
F-TEST (DF numerator)10
F-TEST (DF denominator)147
p-value6.79212242005178e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.41759903161865
Sum Squared Residuals1716.95752171535







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12423.14553804221850.854461957781518
22521.86682248932133.13317751067866
33024.70619787461545.29380212538459
41920.1022284798081-1.10222847980808
52220.62844487263331.37155512736673
62223.2539357278194-1.25393572781936
72522.73279077513782.26720922486223
82319.50952371484173.49047628515826
91719.3533854884903-2.35338548849026
102122.0243225269749-1.02432252697485
111922.1598665655467-3.15986656554673
121923.7278847161621-4.72788471616212
131522.9278043521723-7.92780435217232
141617.3287427422355-1.32874274223554
152318.97438334930954.02561665069047
162723.85411304044713.14588695955289
172220.71400036237961.28599963762036
181415.3283837322309-1.32838373223092
192222.9515954383592-0.95159543835922
202324.4257677462697-1.42576774626966
212321.61100289054291.38899710945714
222122.3792292961033-1.37922929610334
231922.4316079216241-3.43160792162414
241823.7752299338472-5.77522993384719
252023.4608589716526-3.46085897165258
262322.79851188721270.201488112787262
272523.77200877667951.22799122332049
281923.1342094194357-4.13420941943571
292422.88839084906311.11160915093695
302221.91646205364540.0835379463545565
312526.1159579815043-1.11595798150433
322623.67339455026572.32660544973434
332923.5819344240985.41806557590197
343225.03995620593336.96004379406672
352522.23892111199542.76107888800462
362924.53932085976224.46067914023782
372825.27658978087992.72341021912010
381715.53191010808341.46808989191663
392826.35818198025321.64181801974678
402922.44633445998396.55366554001613
412626.56693529096-0.566935290959991
422523.60636188250301.39363811749705
431417.8563378961915-3.85633789619151
442522.82365251609612.17634748390394
452621.78391858060764.21608141939244
462020.0197966730921-0.0197966730920678
471821.8150714731331-3.81507147313315
483224.84451223211047.15548776788959
492524.82781720386660.172182796133373
502522.54301313515552.45698686484448
512321.31652708654931.68347291345067
522122.00155939135-1.00155939135
532024.0368130668541-4.03681306685412
541516.4264490613949-1.42644906139486
553024.75672139714475.24327860285527
562425.2751706249969-1.27517062499689
572624.25456406831951.74543593168049
582420.86145169323213.13854830676792
592222.6506184978980-0.650618497897962
601416.4664066566256-2.46640665662557
612422.24706410442011.75293589557994
622422.37217029304041.62782970695965
632423.73671537058860.2632846294114
642420.28287259087743.71712740912255
651917.904937910521.09506208948001
663127.83402469444283.16597530555724
672227.1571704728309-5.15717047283094
682720.79824710393596.2017528960641
691917.23961821629981.76038178370016
702522.30265636193122.69734363806884
712024.7999565993862-4.79995659938623
722121.3125016453599-0.312501645359875
732727.4138927489721-0.413892748972068
742324.9471571086258-1.94715710862584
752525.9362647377293-0.936264737729285
762022.2858105036534-2.28581050365345
772222.7604633484450-0.760463348444953
782323.5177657911834-0.517765791183384
792524.19670528600240.80329471399764
802523.54930461510281.45069538489718
811723.6280693653108-6.6280693653108
821920.5703687834139-1.57036878341388
832524.25141165187470.748588348125348
841922.7422850822930-3.74228508229305
852021.8879908223272-1.88799082232724
862622.62241550322933.37758449677065
872321.15312960361571.84687039638430
882724.52188431994942.47811568005063
891720.2526697734074-3.25266977340741
901722.3760412457908-5.3760412457908
911919.9831294721654-0.983129472165419
921719.6249744934655-2.62497449346553
932222.5478747596857-0.54787475968566
942124.1042795613991-3.10427956139915
953229.06551472792172.93448527207831
962123.7956913539753-2.79569135397530
972124.7785458291776-3.77854582917763
981820.7365353868179-2.73653538681792
991821.5248298414760-3.52482984147597
1002323.1018747569259-0.101874756925861
1011920.1836243613537-1.18362436135368
1022021.7975879969896-1.79758799698959
1032122.6081367464553-1.60813674645531
1042024.1323885337656-4.13238853376561
1051719.1791311281402-2.17913112814022
1061820.2424034384410-2.24240343844095
1071920.8572106203013-1.85721062030133
1082222.4324698906192-0.43246989061923
1091517.5105239414586-2.51052394145862
1101419.2004413271801-5.2004413271801
1111826.572667808271-8.572667808271
1122421.88853660741992.11146339258007
1133523.958711600139811.0412883998602
1142918.847118046421610.1528819535784
1152122.3354701265229-1.33547012652286
1162521.14644148444673.85355851555328
1172018.0356398518591.964360148141
1182222.0807735259196-0.0807735259195528
1191315.8935840669944-2.89358406699437
1202622.80157684365313.19842315634688
1211717.4390415770972-0.439041577097251
1222520.49144411488554.50855588511451
1232020.3391927870663-0.339192787066306
1241917.57710337106761.42289662893241
1252121.0829634567250-0.0829634567249715
1262221.19127959987670.808720400123346
1272422.68040710109461.31959289890544
1282123.2399778177666-2.23997781776660
1292625.24751927311130.752480726888736
1302420.74732009216543.25267990783464
1311620.5101872124217-4.51018721242169
1322322.41693852004820.583061479951772
1331820.8190080445639-2.8190080445639
1341622.0788724431762-6.0788724431762
1352621.83975985313414.16024014686593
1361919.5541578161299-0.554157816129855
1372117.41968092024053.58031907975947
1382122.2211700720996-1.22117007209955
1392218.64377978015463.35622021984542
1402319.58408975538373.41591024461629
1412924.9466159863524.05338401364798
1422120.33652744014640.663472559853618
1432120.04619444492130.953805555078684
1442322.51239332242150.487606677578455
1452723.12225025687113.87774974312893
1462525.6519318180701-0.651931818070076
1472121.0615436711115-0.0615436711115204
1481017.4578058960550-7.45780589605502
1492022.7513670629223-2.75136706292233
1502622.37355994525753.62644005474245
1512424.2251070127566-0.225107012756570
1522932.1262672519407-3.12626725194067
1531919.5422299608837-0.542229960883653
1542422.89247082899081.10752917100924
1551921.1609605753186-2.16096057531862
1562423.18303848140020.81696151859982
1572222.2559762228679-0.255976222867863
1581724.2411685019621-7.24116850196213

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 24 & 23.1455380422185 & 0.854461957781518 \tabularnewline
2 & 25 & 21.8668224893213 & 3.13317751067866 \tabularnewline
3 & 30 & 24.7061978746154 & 5.29380212538459 \tabularnewline
4 & 19 & 20.1022284798081 & -1.10222847980808 \tabularnewline
5 & 22 & 20.6284448726333 & 1.37155512736673 \tabularnewline
6 & 22 & 23.2539357278194 & -1.25393572781936 \tabularnewline
7 & 25 & 22.7327907751378 & 2.26720922486223 \tabularnewline
8 & 23 & 19.5095237148417 & 3.49047628515826 \tabularnewline
9 & 17 & 19.3533854884903 & -2.35338548849026 \tabularnewline
10 & 21 & 22.0243225269749 & -1.02432252697485 \tabularnewline
11 & 19 & 22.1598665655467 & -3.15986656554673 \tabularnewline
12 & 19 & 23.7278847161621 & -4.72788471616212 \tabularnewline
13 & 15 & 22.9278043521723 & -7.92780435217232 \tabularnewline
14 & 16 & 17.3287427422355 & -1.32874274223554 \tabularnewline
15 & 23 & 18.9743833493095 & 4.02561665069047 \tabularnewline
16 & 27 & 23.8541130404471 & 3.14588695955289 \tabularnewline
17 & 22 & 20.7140003623796 & 1.28599963762036 \tabularnewline
18 & 14 & 15.3283837322309 & -1.32838373223092 \tabularnewline
19 & 22 & 22.9515954383592 & -0.95159543835922 \tabularnewline
20 & 23 & 24.4257677462697 & -1.42576774626966 \tabularnewline
21 & 23 & 21.6110028905429 & 1.38899710945714 \tabularnewline
22 & 21 & 22.3792292961033 & -1.37922929610334 \tabularnewline
23 & 19 & 22.4316079216241 & -3.43160792162414 \tabularnewline
24 & 18 & 23.7752299338472 & -5.77522993384719 \tabularnewline
25 & 20 & 23.4608589716526 & -3.46085897165258 \tabularnewline
26 & 23 & 22.7985118872127 & 0.201488112787262 \tabularnewline
27 & 25 & 23.7720087766795 & 1.22799122332049 \tabularnewline
28 & 19 & 23.1342094194357 & -4.13420941943571 \tabularnewline
29 & 24 & 22.8883908490631 & 1.11160915093695 \tabularnewline
30 & 22 & 21.9164620536454 & 0.0835379463545565 \tabularnewline
31 & 25 & 26.1159579815043 & -1.11595798150433 \tabularnewline
32 & 26 & 23.6733945502657 & 2.32660544973434 \tabularnewline
33 & 29 & 23.581934424098 & 5.41806557590197 \tabularnewline
34 & 32 & 25.0399562059333 & 6.96004379406672 \tabularnewline
35 & 25 & 22.2389211119954 & 2.76107888800462 \tabularnewline
36 & 29 & 24.5393208597622 & 4.46067914023782 \tabularnewline
37 & 28 & 25.2765897808799 & 2.72341021912010 \tabularnewline
38 & 17 & 15.5319101080834 & 1.46808989191663 \tabularnewline
39 & 28 & 26.3581819802532 & 1.64181801974678 \tabularnewline
40 & 29 & 22.4463344599839 & 6.55366554001613 \tabularnewline
41 & 26 & 26.56693529096 & -0.566935290959991 \tabularnewline
42 & 25 & 23.6063618825030 & 1.39363811749705 \tabularnewline
43 & 14 & 17.8563378961915 & -3.85633789619151 \tabularnewline
44 & 25 & 22.8236525160961 & 2.17634748390394 \tabularnewline
45 & 26 & 21.7839185806076 & 4.21608141939244 \tabularnewline
46 & 20 & 20.0197966730921 & -0.0197966730920678 \tabularnewline
47 & 18 & 21.8150714731331 & -3.81507147313315 \tabularnewline
48 & 32 & 24.8445122321104 & 7.15548776788959 \tabularnewline
49 & 25 & 24.8278172038666 & 0.172182796133373 \tabularnewline
50 & 25 & 22.5430131351555 & 2.45698686484448 \tabularnewline
51 & 23 & 21.3165270865493 & 1.68347291345067 \tabularnewline
52 & 21 & 22.00155939135 & -1.00155939135 \tabularnewline
53 & 20 & 24.0368130668541 & -4.03681306685412 \tabularnewline
54 & 15 & 16.4264490613949 & -1.42644906139486 \tabularnewline
55 & 30 & 24.7567213971447 & 5.24327860285527 \tabularnewline
56 & 24 & 25.2751706249969 & -1.27517062499689 \tabularnewline
57 & 26 & 24.2545640683195 & 1.74543593168049 \tabularnewline
58 & 24 & 20.8614516932321 & 3.13854830676792 \tabularnewline
59 & 22 & 22.6506184978980 & -0.650618497897962 \tabularnewline
60 & 14 & 16.4664066566256 & -2.46640665662557 \tabularnewline
61 & 24 & 22.2470641044201 & 1.75293589557994 \tabularnewline
62 & 24 & 22.3721702930404 & 1.62782970695965 \tabularnewline
63 & 24 & 23.7367153705886 & 0.2632846294114 \tabularnewline
64 & 24 & 20.2828725908774 & 3.71712740912255 \tabularnewline
65 & 19 & 17.90493791052 & 1.09506208948001 \tabularnewline
66 & 31 & 27.8340246944428 & 3.16597530555724 \tabularnewline
67 & 22 & 27.1571704728309 & -5.15717047283094 \tabularnewline
68 & 27 & 20.7982471039359 & 6.2017528960641 \tabularnewline
69 & 19 & 17.2396182162998 & 1.76038178370016 \tabularnewline
70 & 25 & 22.3026563619312 & 2.69734363806884 \tabularnewline
71 & 20 & 24.7999565993862 & -4.79995659938623 \tabularnewline
72 & 21 & 21.3125016453599 & -0.312501645359875 \tabularnewline
73 & 27 & 27.4138927489721 & -0.413892748972068 \tabularnewline
74 & 23 & 24.9471571086258 & -1.94715710862584 \tabularnewline
75 & 25 & 25.9362647377293 & -0.936264737729285 \tabularnewline
76 & 20 & 22.2858105036534 & -2.28581050365345 \tabularnewline
77 & 22 & 22.7604633484450 & -0.760463348444953 \tabularnewline
78 & 23 & 23.5177657911834 & -0.517765791183384 \tabularnewline
79 & 25 & 24.1967052860024 & 0.80329471399764 \tabularnewline
80 & 25 & 23.5493046151028 & 1.45069538489718 \tabularnewline
81 & 17 & 23.6280693653108 & -6.6280693653108 \tabularnewline
82 & 19 & 20.5703687834139 & -1.57036878341388 \tabularnewline
83 & 25 & 24.2514116518747 & 0.748588348125348 \tabularnewline
84 & 19 & 22.7422850822930 & -3.74228508229305 \tabularnewline
85 & 20 & 21.8879908223272 & -1.88799082232724 \tabularnewline
86 & 26 & 22.6224155032293 & 3.37758449677065 \tabularnewline
87 & 23 & 21.1531296036157 & 1.84687039638430 \tabularnewline
88 & 27 & 24.5218843199494 & 2.47811568005063 \tabularnewline
89 & 17 & 20.2526697734074 & -3.25266977340741 \tabularnewline
90 & 17 & 22.3760412457908 & -5.3760412457908 \tabularnewline
91 & 19 & 19.9831294721654 & -0.983129472165419 \tabularnewline
92 & 17 & 19.6249744934655 & -2.62497449346553 \tabularnewline
93 & 22 & 22.5478747596857 & -0.54787475968566 \tabularnewline
94 & 21 & 24.1042795613991 & -3.10427956139915 \tabularnewline
95 & 32 & 29.0655147279217 & 2.93448527207831 \tabularnewline
96 & 21 & 23.7956913539753 & -2.79569135397530 \tabularnewline
97 & 21 & 24.7785458291776 & -3.77854582917763 \tabularnewline
98 & 18 & 20.7365353868179 & -2.73653538681792 \tabularnewline
99 & 18 & 21.5248298414760 & -3.52482984147597 \tabularnewline
100 & 23 & 23.1018747569259 & -0.101874756925861 \tabularnewline
101 & 19 & 20.1836243613537 & -1.18362436135368 \tabularnewline
102 & 20 & 21.7975879969896 & -1.79758799698959 \tabularnewline
103 & 21 & 22.6081367464553 & -1.60813674645531 \tabularnewline
104 & 20 & 24.1323885337656 & -4.13238853376561 \tabularnewline
105 & 17 & 19.1791311281402 & -2.17913112814022 \tabularnewline
106 & 18 & 20.2424034384410 & -2.24240343844095 \tabularnewline
107 & 19 & 20.8572106203013 & -1.85721062030133 \tabularnewline
108 & 22 & 22.4324698906192 & -0.43246989061923 \tabularnewline
109 & 15 & 17.5105239414586 & -2.51052394145862 \tabularnewline
110 & 14 & 19.2004413271801 & -5.2004413271801 \tabularnewline
111 & 18 & 26.572667808271 & -8.572667808271 \tabularnewline
112 & 24 & 21.8885366074199 & 2.11146339258007 \tabularnewline
113 & 35 & 23.9587116001398 & 11.0412883998602 \tabularnewline
114 & 29 & 18.8471180464216 & 10.1528819535784 \tabularnewline
115 & 21 & 22.3354701265229 & -1.33547012652286 \tabularnewline
116 & 25 & 21.1464414844467 & 3.85355851555328 \tabularnewline
117 & 20 & 18.035639851859 & 1.964360148141 \tabularnewline
118 & 22 & 22.0807735259196 & -0.0807735259195528 \tabularnewline
119 & 13 & 15.8935840669944 & -2.89358406699437 \tabularnewline
120 & 26 & 22.8015768436531 & 3.19842315634688 \tabularnewline
121 & 17 & 17.4390415770972 & -0.439041577097251 \tabularnewline
122 & 25 & 20.4914441148855 & 4.50855588511451 \tabularnewline
123 & 20 & 20.3391927870663 & -0.339192787066306 \tabularnewline
124 & 19 & 17.5771033710676 & 1.42289662893241 \tabularnewline
125 & 21 & 21.0829634567250 & -0.0829634567249715 \tabularnewline
126 & 22 & 21.1912795998767 & 0.808720400123346 \tabularnewline
127 & 24 & 22.6804071010946 & 1.31959289890544 \tabularnewline
128 & 21 & 23.2399778177666 & -2.23997781776660 \tabularnewline
129 & 26 & 25.2475192731113 & 0.752480726888736 \tabularnewline
130 & 24 & 20.7473200921654 & 3.25267990783464 \tabularnewline
131 & 16 & 20.5101872124217 & -4.51018721242169 \tabularnewline
132 & 23 & 22.4169385200482 & 0.583061479951772 \tabularnewline
133 & 18 & 20.8190080445639 & -2.8190080445639 \tabularnewline
134 & 16 & 22.0788724431762 & -6.0788724431762 \tabularnewline
135 & 26 & 21.8397598531341 & 4.16024014686593 \tabularnewline
136 & 19 & 19.5541578161299 & -0.554157816129855 \tabularnewline
137 & 21 & 17.4196809202405 & 3.58031907975947 \tabularnewline
138 & 21 & 22.2211700720996 & -1.22117007209955 \tabularnewline
139 & 22 & 18.6437797801546 & 3.35622021984542 \tabularnewline
140 & 23 & 19.5840897553837 & 3.41591024461629 \tabularnewline
141 & 29 & 24.946615986352 & 4.05338401364798 \tabularnewline
142 & 21 & 20.3365274401464 & 0.663472559853618 \tabularnewline
143 & 21 & 20.0461944449213 & 0.953805555078684 \tabularnewline
144 & 23 & 22.5123933224215 & 0.487606677578455 \tabularnewline
145 & 27 & 23.1222502568711 & 3.87774974312893 \tabularnewline
146 & 25 & 25.6519318180701 & -0.651931818070076 \tabularnewline
147 & 21 & 21.0615436711115 & -0.0615436711115204 \tabularnewline
148 & 10 & 17.4578058960550 & -7.45780589605502 \tabularnewline
149 & 20 & 22.7513670629223 & -2.75136706292233 \tabularnewline
150 & 26 & 22.3735599452575 & 3.62644005474245 \tabularnewline
151 & 24 & 24.2251070127566 & -0.225107012756570 \tabularnewline
152 & 29 & 32.1262672519407 & -3.12626725194067 \tabularnewline
153 & 19 & 19.5422299608837 & -0.542229960883653 \tabularnewline
154 & 24 & 22.8924708289908 & 1.10752917100924 \tabularnewline
155 & 19 & 21.1609605753186 & -2.16096057531862 \tabularnewline
156 & 24 & 23.1830384814002 & 0.81696151859982 \tabularnewline
157 & 22 & 22.2559762228679 & -0.255976222867863 \tabularnewline
158 & 17 & 24.2411685019621 & -7.24116850196213 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99547&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]24[/C][C]23.1455380422185[/C][C]0.854461957781518[/C][/ROW]
[ROW][C]2[/C][C]25[/C][C]21.8668224893213[/C][C]3.13317751067866[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]24.7061978746154[/C][C]5.29380212538459[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]20.1022284798081[/C][C]-1.10222847980808[/C][/ROW]
[ROW][C]5[/C][C]22[/C][C]20.6284448726333[/C][C]1.37155512736673[/C][/ROW]
[ROW][C]6[/C][C]22[/C][C]23.2539357278194[/C][C]-1.25393572781936[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]22.7327907751378[/C][C]2.26720922486223[/C][/ROW]
[ROW][C]8[/C][C]23[/C][C]19.5095237148417[/C][C]3.49047628515826[/C][/ROW]
[ROW][C]9[/C][C]17[/C][C]19.3533854884903[/C][C]-2.35338548849026[/C][/ROW]
[ROW][C]10[/C][C]21[/C][C]22.0243225269749[/C][C]-1.02432252697485[/C][/ROW]
[ROW][C]11[/C][C]19[/C][C]22.1598665655467[/C][C]-3.15986656554673[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]23.7278847161621[/C][C]-4.72788471616212[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]22.9278043521723[/C][C]-7.92780435217232[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]17.3287427422355[/C][C]-1.32874274223554[/C][/ROW]
[ROW][C]15[/C][C]23[/C][C]18.9743833493095[/C][C]4.02561665069047[/C][/ROW]
[ROW][C]16[/C][C]27[/C][C]23.8541130404471[/C][C]3.14588695955289[/C][/ROW]
[ROW][C]17[/C][C]22[/C][C]20.7140003623796[/C][C]1.28599963762036[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]15.3283837322309[/C][C]-1.32838373223092[/C][/ROW]
[ROW][C]19[/C][C]22[/C][C]22.9515954383592[/C][C]-0.95159543835922[/C][/ROW]
[ROW][C]20[/C][C]23[/C][C]24.4257677462697[/C][C]-1.42576774626966[/C][/ROW]
[ROW][C]21[/C][C]23[/C][C]21.6110028905429[/C][C]1.38899710945714[/C][/ROW]
[ROW][C]22[/C][C]21[/C][C]22.3792292961033[/C][C]-1.37922929610334[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]22.4316079216241[/C][C]-3.43160792162414[/C][/ROW]
[ROW][C]24[/C][C]18[/C][C]23.7752299338472[/C][C]-5.77522993384719[/C][/ROW]
[ROW][C]25[/C][C]20[/C][C]23.4608589716526[/C][C]-3.46085897165258[/C][/ROW]
[ROW][C]26[/C][C]23[/C][C]22.7985118872127[/C][C]0.201488112787262[/C][/ROW]
[ROW][C]27[/C][C]25[/C][C]23.7720087766795[/C][C]1.22799122332049[/C][/ROW]
[ROW][C]28[/C][C]19[/C][C]23.1342094194357[/C][C]-4.13420941943571[/C][/ROW]
[ROW][C]29[/C][C]24[/C][C]22.8883908490631[/C][C]1.11160915093695[/C][/ROW]
[ROW][C]30[/C][C]22[/C][C]21.9164620536454[/C][C]0.0835379463545565[/C][/ROW]
[ROW][C]31[/C][C]25[/C][C]26.1159579815043[/C][C]-1.11595798150433[/C][/ROW]
[ROW][C]32[/C][C]26[/C][C]23.6733945502657[/C][C]2.32660544973434[/C][/ROW]
[ROW][C]33[/C][C]29[/C][C]23.581934424098[/C][C]5.41806557590197[/C][/ROW]
[ROW][C]34[/C][C]32[/C][C]25.0399562059333[/C][C]6.96004379406672[/C][/ROW]
[ROW][C]35[/C][C]25[/C][C]22.2389211119954[/C][C]2.76107888800462[/C][/ROW]
[ROW][C]36[/C][C]29[/C][C]24.5393208597622[/C][C]4.46067914023782[/C][/ROW]
[ROW][C]37[/C][C]28[/C][C]25.2765897808799[/C][C]2.72341021912010[/C][/ROW]
[ROW][C]38[/C][C]17[/C][C]15.5319101080834[/C][C]1.46808989191663[/C][/ROW]
[ROW][C]39[/C][C]28[/C][C]26.3581819802532[/C][C]1.64181801974678[/C][/ROW]
[ROW][C]40[/C][C]29[/C][C]22.4463344599839[/C][C]6.55366554001613[/C][/ROW]
[ROW][C]41[/C][C]26[/C][C]26.56693529096[/C][C]-0.566935290959991[/C][/ROW]
[ROW][C]42[/C][C]25[/C][C]23.6063618825030[/C][C]1.39363811749705[/C][/ROW]
[ROW][C]43[/C][C]14[/C][C]17.8563378961915[/C][C]-3.85633789619151[/C][/ROW]
[ROW][C]44[/C][C]25[/C][C]22.8236525160961[/C][C]2.17634748390394[/C][/ROW]
[ROW][C]45[/C][C]26[/C][C]21.7839185806076[/C][C]4.21608141939244[/C][/ROW]
[ROW][C]46[/C][C]20[/C][C]20.0197966730921[/C][C]-0.0197966730920678[/C][/ROW]
[ROW][C]47[/C][C]18[/C][C]21.8150714731331[/C][C]-3.81507147313315[/C][/ROW]
[ROW][C]48[/C][C]32[/C][C]24.8445122321104[/C][C]7.15548776788959[/C][/ROW]
[ROW][C]49[/C][C]25[/C][C]24.8278172038666[/C][C]0.172182796133373[/C][/ROW]
[ROW][C]50[/C][C]25[/C][C]22.5430131351555[/C][C]2.45698686484448[/C][/ROW]
[ROW][C]51[/C][C]23[/C][C]21.3165270865493[/C][C]1.68347291345067[/C][/ROW]
[ROW][C]52[/C][C]21[/C][C]22.00155939135[/C][C]-1.00155939135[/C][/ROW]
[ROW][C]53[/C][C]20[/C][C]24.0368130668541[/C][C]-4.03681306685412[/C][/ROW]
[ROW][C]54[/C][C]15[/C][C]16.4264490613949[/C][C]-1.42644906139486[/C][/ROW]
[ROW][C]55[/C][C]30[/C][C]24.7567213971447[/C][C]5.24327860285527[/C][/ROW]
[ROW][C]56[/C][C]24[/C][C]25.2751706249969[/C][C]-1.27517062499689[/C][/ROW]
[ROW][C]57[/C][C]26[/C][C]24.2545640683195[/C][C]1.74543593168049[/C][/ROW]
[ROW][C]58[/C][C]24[/C][C]20.8614516932321[/C][C]3.13854830676792[/C][/ROW]
[ROW][C]59[/C][C]22[/C][C]22.6506184978980[/C][C]-0.650618497897962[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]16.4664066566256[/C][C]-2.46640665662557[/C][/ROW]
[ROW][C]61[/C][C]24[/C][C]22.2470641044201[/C][C]1.75293589557994[/C][/ROW]
[ROW][C]62[/C][C]24[/C][C]22.3721702930404[/C][C]1.62782970695965[/C][/ROW]
[ROW][C]63[/C][C]24[/C][C]23.7367153705886[/C][C]0.2632846294114[/C][/ROW]
[ROW][C]64[/C][C]24[/C][C]20.2828725908774[/C][C]3.71712740912255[/C][/ROW]
[ROW][C]65[/C][C]19[/C][C]17.90493791052[/C][C]1.09506208948001[/C][/ROW]
[ROW][C]66[/C][C]31[/C][C]27.8340246944428[/C][C]3.16597530555724[/C][/ROW]
[ROW][C]67[/C][C]22[/C][C]27.1571704728309[/C][C]-5.15717047283094[/C][/ROW]
[ROW][C]68[/C][C]27[/C][C]20.7982471039359[/C][C]6.2017528960641[/C][/ROW]
[ROW][C]69[/C][C]19[/C][C]17.2396182162998[/C][C]1.76038178370016[/C][/ROW]
[ROW][C]70[/C][C]25[/C][C]22.3026563619312[/C][C]2.69734363806884[/C][/ROW]
[ROW][C]71[/C][C]20[/C][C]24.7999565993862[/C][C]-4.79995659938623[/C][/ROW]
[ROW][C]72[/C][C]21[/C][C]21.3125016453599[/C][C]-0.312501645359875[/C][/ROW]
[ROW][C]73[/C][C]27[/C][C]27.4138927489721[/C][C]-0.413892748972068[/C][/ROW]
[ROW][C]74[/C][C]23[/C][C]24.9471571086258[/C][C]-1.94715710862584[/C][/ROW]
[ROW][C]75[/C][C]25[/C][C]25.9362647377293[/C][C]-0.936264737729285[/C][/ROW]
[ROW][C]76[/C][C]20[/C][C]22.2858105036534[/C][C]-2.28581050365345[/C][/ROW]
[ROW][C]77[/C][C]22[/C][C]22.7604633484450[/C][C]-0.760463348444953[/C][/ROW]
[ROW][C]78[/C][C]23[/C][C]23.5177657911834[/C][C]-0.517765791183384[/C][/ROW]
[ROW][C]79[/C][C]25[/C][C]24.1967052860024[/C][C]0.80329471399764[/C][/ROW]
[ROW][C]80[/C][C]25[/C][C]23.5493046151028[/C][C]1.45069538489718[/C][/ROW]
[ROW][C]81[/C][C]17[/C][C]23.6280693653108[/C][C]-6.6280693653108[/C][/ROW]
[ROW][C]82[/C][C]19[/C][C]20.5703687834139[/C][C]-1.57036878341388[/C][/ROW]
[ROW][C]83[/C][C]25[/C][C]24.2514116518747[/C][C]0.748588348125348[/C][/ROW]
[ROW][C]84[/C][C]19[/C][C]22.7422850822930[/C][C]-3.74228508229305[/C][/ROW]
[ROW][C]85[/C][C]20[/C][C]21.8879908223272[/C][C]-1.88799082232724[/C][/ROW]
[ROW][C]86[/C][C]26[/C][C]22.6224155032293[/C][C]3.37758449677065[/C][/ROW]
[ROW][C]87[/C][C]23[/C][C]21.1531296036157[/C][C]1.84687039638430[/C][/ROW]
[ROW][C]88[/C][C]27[/C][C]24.5218843199494[/C][C]2.47811568005063[/C][/ROW]
[ROW][C]89[/C][C]17[/C][C]20.2526697734074[/C][C]-3.25266977340741[/C][/ROW]
[ROW][C]90[/C][C]17[/C][C]22.3760412457908[/C][C]-5.3760412457908[/C][/ROW]
[ROW][C]91[/C][C]19[/C][C]19.9831294721654[/C][C]-0.983129472165419[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]19.6249744934655[/C][C]-2.62497449346553[/C][/ROW]
[ROW][C]93[/C][C]22[/C][C]22.5478747596857[/C][C]-0.54787475968566[/C][/ROW]
[ROW][C]94[/C][C]21[/C][C]24.1042795613991[/C][C]-3.10427956139915[/C][/ROW]
[ROW][C]95[/C][C]32[/C][C]29.0655147279217[/C][C]2.93448527207831[/C][/ROW]
[ROW][C]96[/C][C]21[/C][C]23.7956913539753[/C][C]-2.79569135397530[/C][/ROW]
[ROW][C]97[/C][C]21[/C][C]24.7785458291776[/C][C]-3.77854582917763[/C][/ROW]
[ROW][C]98[/C][C]18[/C][C]20.7365353868179[/C][C]-2.73653538681792[/C][/ROW]
[ROW][C]99[/C][C]18[/C][C]21.5248298414760[/C][C]-3.52482984147597[/C][/ROW]
[ROW][C]100[/C][C]23[/C][C]23.1018747569259[/C][C]-0.101874756925861[/C][/ROW]
[ROW][C]101[/C][C]19[/C][C]20.1836243613537[/C][C]-1.18362436135368[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]21.7975879969896[/C][C]-1.79758799698959[/C][/ROW]
[ROW][C]103[/C][C]21[/C][C]22.6081367464553[/C][C]-1.60813674645531[/C][/ROW]
[ROW][C]104[/C][C]20[/C][C]24.1323885337656[/C][C]-4.13238853376561[/C][/ROW]
[ROW][C]105[/C][C]17[/C][C]19.1791311281402[/C][C]-2.17913112814022[/C][/ROW]
[ROW][C]106[/C][C]18[/C][C]20.2424034384410[/C][C]-2.24240343844095[/C][/ROW]
[ROW][C]107[/C][C]19[/C][C]20.8572106203013[/C][C]-1.85721062030133[/C][/ROW]
[ROW][C]108[/C][C]22[/C][C]22.4324698906192[/C][C]-0.43246989061923[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]17.5105239414586[/C][C]-2.51052394145862[/C][/ROW]
[ROW][C]110[/C][C]14[/C][C]19.2004413271801[/C][C]-5.2004413271801[/C][/ROW]
[ROW][C]111[/C][C]18[/C][C]26.572667808271[/C][C]-8.572667808271[/C][/ROW]
[ROW][C]112[/C][C]24[/C][C]21.8885366074199[/C][C]2.11146339258007[/C][/ROW]
[ROW][C]113[/C][C]35[/C][C]23.9587116001398[/C][C]11.0412883998602[/C][/ROW]
[ROW][C]114[/C][C]29[/C][C]18.8471180464216[/C][C]10.1528819535784[/C][/ROW]
[ROW][C]115[/C][C]21[/C][C]22.3354701265229[/C][C]-1.33547012652286[/C][/ROW]
[ROW][C]116[/C][C]25[/C][C]21.1464414844467[/C][C]3.85355851555328[/C][/ROW]
[ROW][C]117[/C][C]20[/C][C]18.035639851859[/C][C]1.964360148141[/C][/ROW]
[ROW][C]118[/C][C]22[/C][C]22.0807735259196[/C][C]-0.0807735259195528[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]15.8935840669944[/C][C]-2.89358406699437[/C][/ROW]
[ROW][C]120[/C][C]26[/C][C]22.8015768436531[/C][C]3.19842315634688[/C][/ROW]
[ROW][C]121[/C][C]17[/C][C]17.4390415770972[/C][C]-0.439041577097251[/C][/ROW]
[ROW][C]122[/C][C]25[/C][C]20.4914441148855[/C][C]4.50855588511451[/C][/ROW]
[ROW][C]123[/C][C]20[/C][C]20.3391927870663[/C][C]-0.339192787066306[/C][/ROW]
[ROW][C]124[/C][C]19[/C][C]17.5771033710676[/C][C]1.42289662893241[/C][/ROW]
[ROW][C]125[/C][C]21[/C][C]21.0829634567250[/C][C]-0.0829634567249715[/C][/ROW]
[ROW][C]126[/C][C]22[/C][C]21.1912795998767[/C][C]0.808720400123346[/C][/ROW]
[ROW][C]127[/C][C]24[/C][C]22.6804071010946[/C][C]1.31959289890544[/C][/ROW]
[ROW][C]128[/C][C]21[/C][C]23.2399778177666[/C][C]-2.23997781776660[/C][/ROW]
[ROW][C]129[/C][C]26[/C][C]25.2475192731113[/C][C]0.752480726888736[/C][/ROW]
[ROW][C]130[/C][C]24[/C][C]20.7473200921654[/C][C]3.25267990783464[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]20.5101872124217[/C][C]-4.51018721242169[/C][/ROW]
[ROW][C]132[/C][C]23[/C][C]22.4169385200482[/C][C]0.583061479951772[/C][/ROW]
[ROW][C]133[/C][C]18[/C][C]20.8190080445639[/C][C]-2.8190080445639[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]22.0788724431762[/C][C]-6.0788724431762[/C][/ROW]
[ROW][C]135[/C][C]26[/C][C]21.8397598531341[/C][C]4.16024014686593[/C][/ROW]
[ROW][C]136[/C][C]19[/C][C]19.5541578161299[/C][C]-0.554157816129855[/C][/ROW]
[ROW][C]137[/C][C]21[/C][C]17.4196809202405[/C][C]3.58031907975947[/C][/ROW]
[ROW][C]138[/C][C]21[/C][C]22.2211700720996[/C][C]-1.22117007209955[/C][/ROW]
[ROW][C]139[/C][C]22[/C][C]18.6437797801546[/C][C]3.35622021984542[/C][/ROW]
[ROW][C]140[/C][C]23[/C][C]19.5840897553837[/C][C]3.41591024461629[/C][/ROW]
[ROW][C]141[/C][C]29[/C][C]24.946615986352[/C][C]4.05338401364798[/C][/ROW]
[ROW][C]142[/C][C]21[/C][C]20.3365274401464[/C][C]0.663472559853618[/C][/ROW]
[ROW][C]143[/C][C]21[/C][C]20.0461944449213[/C][C]0.953805555078684[/C][/ROW]
[ROW][C]144[/C][C]23[/C][C]22.5123933224215[/C][C]0.487606677578455[/C][/ROW]
[ROW][C]145[/C][C]27[/C][C]23.1222502568711[/C][C]3.87774974312893[/C][/ROW]
[ROW][C]146[/C][C]25[/C][C]25.6519318180701[/C][C]-0.651931818070076[/C][/ROW]
[ROW][C]147[/C][C]21[/C][C]21.0615436711115[/C][C]-0.0615436711115204[/C][/ROW]
[ROW][C]148[/C][C]10[/C][C]17.4578058960550[/C][C]-7.45780589605502[/C][/ROW]
[ROW][C]149[/C][C]20[/C][C]22.7513670629223[/C][C]-2.75136706292233[/C][/ROW]
[ROW][C]150[/C][C]26[/C][C]22.3735599452575[/C][C]3.62644005474245[/C][/ROW]
[ROW][C]151[/C][C]24[/C][C]24.2251070127566[/C][C]-0.225107012756570[/C][/ROW]
[ROW][C]152[/C][C]29[/C][C]32.1262672519407[/C][C]-3.12626725194067[/C][/ROW]
[ROW][C]153[/C][C]19[/C][C]19.5422299608837[/C][C]-0.542229960883653[/C][/ROW]
[ROW][C]154[/C][C]24[/C][C]22.8924708289908[/C][C]1.10752917100924[/C][/ROW]
[ROW][C]155[/C][C]19[/C][C]21.1609605753186[/C][C]-2.16096057531862[/C][/ROW]
[ROW][C]156[/C][C]24[/C][C]23.1830384814002[/C][C]0.81696151859982[/C][/ROW]
[ROW][C]157[/C][C]22[/C][C]22.2559762228679[/C][C]-0.255976222867863[/C][/ROW]
[ROW][C]158[/C][C]17[/C][C]24.2411685019621[/C][C]-7.24116850196213[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99547&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99547&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12423.14553804221850.854461957781518
22521.86682248932133.13317751067866
33024.70619787461545.29380212538459
41920.1022284798081-1.10222847980808
52220.62844487263331.37155512736673
62223.2539357278194-1.25393572781936
72522.73279077513782.26720922486223
82319.50952371484173.49047628515826
91719.3533854884903-2.35338548849026
102122.0243225269749-1.02432252697485
111922.1598665655467-3.15986656554673
121923.7278847161621-4.72788471616212
131522.9278043521723-7.92780435217232
141617.3287427422355-1.32874274223554
152318.97438334930954.02561665069047
162723.85411304044713.14588695955289
172220.71400036237961.28599963762036
181415.3283837322309-1.32838373223092
192222.9515954383592-0.95159543835922
202324.4257677462697-1.42576774626966
212321.61100289054291.38899710945714
222122.3792292961033-1.37922929610334
231922.4316079216241-3.43160792162414
241823.7752299338472-5.77522993384719
252023.4608589716526-3.46085897165258
262322.79851188721270.201488112787262
272523.77200877667951.22799122332049
281923.1342094194357-4.13420941943571
292422.88839084906311.11160915093695
302221.91646205364540.0835379463545565
312526.1159579815043-1.11595798150433
322623.67339455026572.32660544973434
332923.5819344240985.41806557590197
343225.03995620593336.96004379406672
352522.23892111199542.76107888800462
362924.53932085976224.46067914023782
372825.27658978087992.72341021912010
381715.53191010808341.46808989191663
392826.35818198025321.64181801974678
402922.44633445998396.55366554001613
412626.56693529096-0.566935290959991
422523.60636188250301.39363811749705
431417.8563378961915-3.85633789619151
442522.82365251609612.17634748390394
452621.78391858060764.21608141939244
462020.0197966730921-0.0197966730920678
471821.8150714731331-3.81507147313315
483224.84451223211047.15548776788959
492524.82781720386660.172182796133373
502522.54301313515552.45698686484448
512321.31652708654931.68347291345067
522122.00155939135-1.00155939135
532024.0368130668541-4.03681306685412
541516.4264490613949-1.42644906139486
553024.75672139714475.24327860285527
562425.2751706249969-1.27517062499689
572624.25456406831951.74543593168049
582420.86145169323213.13854830676792
592222.6506184978980-0.650618497897962
601416.4664066566256-2.46640665662557
612422.24706410442011.75293589557994
622422.37217029304041.62782970695965
632423.73671537058860.2632846294114
642420.28287259087743.71712740912255
651917.904937910521.09506208948001
663127.83402469444283.16597530555724
672227.1571704728309-5.15717047283094
682720.79824710393596.2017528960641
691917.23961821629981.76038178370016
702522.30265636193122.69734363806884
712024.7999565993862-4.79995659938623
722121.3125016453599-0.312501645359875
732727.4138927489721-0.413892748972068
742324.9471571086258-1.94715710862584
752525.9362647377293-0.936264737729285
762022.2858105036534-2.28581050365345
772222.7604633484450-0.760463348444953
782323.5177657911834-0.517765791183384
792524.19670528600240.80329471399764
802523.54930461510281.45069538489718
811723.6280693653108-6.6280693653108
821920.5703687834139-1.57036878341388
832524.25141165187470.748588348125348
841922.7422850822930-3.74228508229305
852021.8879908223272-1.88799082232724
862622.62241550322933.37758449677065
872321.15312960361571.84687039638430
882724.52188431994942.47811568005063
891720.2526697734074-3.25266977340741
901722.3760412457908-5.3760412457908
911919.9831294721654-0.983129472165419
921719.6249744934655-2.62497449346553
932222.5478747596857-0.54787475968566
942124.1042795613991-3.10427956139915
953229.06551472792172.93448527207831
962123.7956913539753-2.79569135397530
972124.7785458291776-3.77854582917763
981820.7365353868179-2.73653538681792
991821.5248298414760-3.52482984147597
1002323.1018747569259-0.101874756925861
1011920.1836243613537-1.18362436135368
1022021.7975879969896-1.79758799698959
1032122.6081367464553-1.60813674645531
1042024.1323885337656-4.13238853376561
1051719.1791311281402-2.17913112814022
1061820.2424034384410-2.24240343844095
1071920.8572106203013-1.85721062030133
1082222.4324698906192-0.43246989061923
1091517.5105239414586-2.51052394145862
1101419.2004413271801-5.2004413271801
1111826.572667808271-8.572667808271
1122421.88853660741992.11146339258007
1133523.958711600139811.0412883998602
1142918.847118046421610.1528819535784
1152122.3354701265229-1.33547012652286
1162521.14644148444673.85355851555328
1172018.0356398518591.964360148141
1182222.0807735259196-0.0807735259195528
1191315.8935840669944-2.89358406699437
1202622.80157684365313.19842315634688
1211717.4390415770972-0.439041577097251
1222520.49144411488554.50855588511451
1232020.3391927870663-0.339192787066306
1241917.57710337106761.42289662893241
1252121.0829634567250-0.0829634567249715
1262221.19127959987670.808720400123346
1272422.68040710109461.31959289890544
1282123.2399778177666-2.23997781776660
1292625.24751927311130.752480726888736
1302420.74732009216543.25267990783464
1311620.5101872124217-4.51018721242169
1322322.41693852004820.583061479951772
1331820.8190080445639-2.8190080445639
1341622.0788724431762-6.0788724431762
1352621.83975985313414.16024014686593
1361919.5541578161299-0.554157816129855
1372117.41968092024053.58031907975947
1382122.2211700720996-1.22117007209955
1392218.64377978015463.35622021984542
1402319.58408975538373.41591024461629
1412924.9466159863524.05338401364798
1422120.33652744014640.663472559853618
1432120.04619444492130.953805555078684
1442322.51239332242150.487606677578455
1452723.12225025687113.87774974312893
1462525.6519318180701-0.651931818070076
1472121.0615436711115-0.0615436711115204
1481017.4578058960550-7.45780589605502
1492022.7513670629223-2.75136706292233
1502622.37355994525753.62644005474245
1512424.2251070127566-0.225107012756570
1522932.1262672519407-3.12626725194067
1531919.5422299608837-0.542229960883653
1542422.89247082899081.10752917100924
1551921.1609605753186-2.16096057531862
1562423.18303848140020.81696151859982
1572222.2559762228679-0.255976222867863
1581724.2411685019621-7.24116850196213







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
140.9735634859758580.05287302804828460.0264365140241423
150.9455744315490520.1088511369018970.0544255684509485
160.9035338384119310.1929323231761380.0964661615880688
170.8474225921095320.3051548157809370.152577407890468
180.7733501550761460.4532996898477080.226649844923854
190.7938385536594890.4123228926810220.206161446340511
200.7608726143158120.4782547713683760.239127385684188
210.7425996778345740.5148006443308520.257400322165426
220.7076721162836520.5846557674326960.292327883716348
230.6643096335636260.6713807328727470.335690366436374
240.6555445059779780.6889109880440440.344455494022022
250.6442597690537140.7114804618925710.355740230946286
260.5682026588479860.8635946823040280.431797341152014
270.4945763640017450.989152728003490.505423635998255
280.4416392864603240.8832785729206470.558360713539676
290.4100660486581410.8201320973162830.589933951341859
300.3436475986349960.6872951972699910.656352401365004
310.3153217517159430.6306435034318860.684678248284057
320.3106333334258460.6212666668516920.689366666574154
330.400563252989660.801126505979320.59943674701034
340.5466588165776250.906682366844750.453341183422375
350.5084821565198240.9830356869603520.491517843480176
360.564293879884450.87141224023110.43570612011555
370.6184214318917030.7631571362165940.381578568108297
380.5816259816523180.8367480366953640.418374018347682
390.5305438244955990.9389123510088030.469456175504401
400.6523682098279930.6952635803440150.347631790172007
410.5983594174135050.803281165172990.401640582586495
420.5447325271948890.9105349456102220.455267472805111
430.5453382361682030.9093235276635950.454661763831797
440.5046489129565510.9907021740868980.495351087043449
450.5436714639969910.9126570720060190.456328536003009
460.4880714354488530.9761428708977050.511928564551148
470.4917515019958670.9835030039917350.508248498004133
480.6003264532235060.7993470935529880.399673546776494
490.5496507669871070.9006984660257850.450349233012893
500.5188812952754540.962237409449090.481118704724546
510.5093311384253580.9813377231492830.490668861574642
520.4595875864231060.9191751728462130.540412413576894
530.4919380010218540.9838760020437070.508061998978147
540.4561417927010010.9122835854020020.543858207298999
550.6185270995401890.7629458009196230.381472900459811
560.5755107353730650.848978529253870.424489264626935
570.5355498462079160.9289003075841690.464450153792084
580.5195855313840920.9608289372318160.480414468615908
590.4717392410606110.9434784821212220.528260758939389
600.4359640218174840.8719280436349680.564035978182516
610.3981574827270490.7963149654540980.601842517272951
620.3623657095105870.7247314190211750.637634290489413
630.317853579835260.635707159670520.68214642016474
640.3266479621709270.6532959243418540.673352037829073
650.2860117960680720.5720235921361440.713988203931928
660.3264786302283360.6529572604566730.673521369771664
670.3857437699770700.7714875399541410.61425623002293
680.4990263875300350.998052775060070.500973612469965
690.451545364878520.903090729757040.54845463512148
700.4360221613942650.872044322788530.563977838605735
710.5105016422196560.9789967155606880.489498357780344
720.4623603290781220.9247206581562440.537639670921878
730.41750624656480.83501249312960.5824937534352
740.3840469850040670.7680939700081330.615953014995933
750.3496293139267970.6992586278535950.650370686073203
760.3248141810752460.6496283621504930.675185818924754
770.2836908203962310.5673816407924620.716309179603769
780.2493707048458170.4987414096916330.750629295154183
790.2145199058510150.4290398117020310.785480094148985
800.1892744401198740.3785488802397480.810725559880126
810.3000387047432110.6000774094864230.699961295256789
820.2710835706479630.5421671412959250.728916429352037
830.2342848305491410.4685696610982830.765715169450859
840.2326053809486090.4652107618972180.767394619051391
850.2081258566275940.4162517132551890.791874143372406
860.2076259006572620.4152518013145230.792374099342738
870.1839739323444880.3679478646889770.816026067655512
880.1713032226199380.3426064452398760.828696777380062
890.1640569014126490.3281138028252990.83594309858735
900.1995838368330280.3991676736660560.800416163166972
910.1698731530501960.3397463061003920.830126846949804
920.1511803368886090.3023606737772170.848819663111391
930.1273550901845680.2547101803691360.872644909815432
940.1172709580655420.2345419161310850.882729041934458
950.1138121052844650.2276242105689310.886187894715535
960.1020335092590480.2040670185180960.897966490740952
970.1040427029230850.2080854058461700.895957297076915
980.09253416721773320.1850683344354660.907465832782267
990.09146912481511880.1829382496302380.908530875184881
1000.07243667932974730.1448733586594950.927563320670253
1010.05723829503347350.1144765900669470.942761704966527
1020.04632572532386120.09265145064772250.953674274676139
1030.0368349835854440.0736699671708880.963165016414556
1040.0389300929345070.0778601858690140.961069907065493
1050.03194431543909780.06388863087819550.968055684560902
1060.02760401680589540.05520803361179080.972395983194105
1070.02354690392575230.04709380785150450.976453096074248
1080.01735871295398820.03471742590797640.982641287046012
1090.01590832805488110.03181665610976210.984091671945119
1100.02190827843321250.04381655686642510.978091721566787
1110.1157082289444000.2314164578888010.8842917710556
1120.1062591126995600.2125182253991210.89374088730044
1130.4570947327532970.9141894655065930.542905267246703
1140.7856502963521160.4286994072957670.214349703647884
1150.7443418695194730.5113162609610540.255658130480527
1160.8219565434877910.3560869130244170.178043456512209
1170.799922502786950.40015499442610.20007749721305
1180.7551673350943040.4896653298113910.244832664905696
1190.764233893069380.4715322138612420.235766106930621
1200.735721300832810.5285573983343810.264278699167190
1210.681943379315360.6361132413692810.318056620684641
1220.7026859069316840.5946281861366330.297314093068316
1230.6476136839698240.7047726320603520.352386316030176
1240.5883769984607260.8232460030785480.411623001539274
1250.547545669816720.904908660366560.45245433018328
1260.5007400884780520.9985198230438960.499259911521948
1270.4447796631440270.8895593262880540.555220336855973
1280.3843992775395220.7687985550790450.615600722460478
1290.3286866656599670.6573733313199340.671313334340033
1300.3096518361904700.6193036723809410.69034816380953
1310.3131151280606020.6262302561212030.686884871939399
1320.3821631470583390.7643262941166780.617836852941661
1330.352283373971770.704566747943540.64771662602823
1340.3022388934269280.6044777868538560.697761106573072
1350.2438292185900090.4876584371800170.756170781409991
1360.1862251455357200.3724502910714390.81377485446428
1370.1797805220026770.3595610440053530.820219477997324
1380.1261461965906670.2522923931813340.873853803409333
1390.09699337880382160.1939867576076430.903006621196178
1400.0729144525093620.1458289050187240.927085547490638
1410.08441511266564360.1688302253312870.915584887334356
1420.1434369215272090.2868738430544190.85656307847279
1430.08226356211768830.1645271242353770.917736437882312
1440.04441999580495240.08883999160990470.955580004195048

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
14 & 0.973563485975858 & 0.0528730280482846 & 0.0264365140241423 \tabularnewline
15 & 0.945574431549052 & 0.108851136901897 & 0.0544255684509485 \tabularnewline
16 & 0.903533838411931 & 0.192932323176138 & 0.0964661615880688 \tabularnewline
17 & 0.847422592109532 & 0.305154815780937 & 0.152577407890468 \tabularnewline
18 & 0.773350155076146 & 0.453299689847708 & 0.226649844923854 \tabularnewline
19 & 0.793838553659489 & 0.412322892681022 & 0.206161446340511 \tabularnewline
20 & 0.760872614315812 & 0.478254771368376 & 0.239127385684188 \tabularnewline
21 & 0.742599677834574 & 0.514800644330852 & 0.257400322165426 \tabularnewline
22 & 0.707672116283652 & 0.584655767432696 & 0.292327883716348 \tabularnewline
23 & 0.664309633563626 & 0.671380732872747 & 0.335690366436374 \tabularnewline
24 & 0.655544505977978 & 0.688910988044044 & 0.344455494022022 \tabularnewline
25 & 0.644259769053714 & 0.711480461892571 & 0.355740230946286 \tabularnewline
26 & 0.568202658847986 & 0.863594682304028 & 0.431797341152014 \tabularnewline
27 & 0.494576364001745 & 0.98915272800349 & 0.505423635998255 \tabularnewline
28 & 0.441639286460324 & 0.883278572920647 & 0.558360713539676 \tabularnewline
29 & 0.410066048658141 & 0.820132097316283 & 0.589933951341859 \tabularnewline
30 & 0.343647598634996 & 0.687295197269991 & 0.656352401365004 \tabularnewline
31 & 0.315321751715943 & 0.630643503431886 & 0.684678248284057 \tabularnewline
32 & 0.310633333425846 & 0.621266666851692 & 0.689366666574154 \tabularnewline
33 & 0.40056325298966 & 0.80112650597932 & 0.59943674701034 \tabularnewline
34 & 0.546658816577625 & 0.90668236684475 & 0.453341183422375 \tabularnewline
35 & 0.508482156519824 & 0.983035686960352 & 0.491517843480176 \tabularnewline
36 & 0.56429387988445 & 0.8714122402311 & 0.43570612011555 \tabularnewline
37 & 0.618421431891703 & 0.763157136216594 & 0.381578568108297 \tabularnewline
38 & 0.581625981652318 & 0.836748036695364 & 0.418374018347682 \tabularnewline
39 & 0.530543824495599 & 0.938912351008803 & 0.469456175504401 \tabularnewline
40 & 0.652368209827993 & 0.695263580344015 & 0.347631790172007 \tabularnewline
41 & 0.598359417413505 & 0.80328116517299 & 0.401640582586495 \tabularnewline
42 & 0.544732527194889 & 0.910534945610222 & 0.455267472805111 \tabularnewline
43 & 0.545338236168203 & 0.909323527663595 & 0.454661763831797 \tabularnewline
44 & 0.504648912956551 & 0.990702174086898 & 0.495351087043449 \tabularnewline
45 & 0.543671463996991 & 0.912657072006019 & 0.456328536003009 \tabularnewline
46 & 0.488071435448853 & 0.976142870897705 & 0.511928564551148 \tabularnewline
47 & 0.491751501995867 & 0.983503003991735 & 0.508248498004133 \tabularnewline
48 & 0.600326453223506 & 0.799347093552988 & 0.399673546776494 \tabularnewline
49 & 0.549650766987107 & 0.900698466025785 & 0.450349233012893 \tabularnewline
50 & 0.518881295275454 & 0.96223740944909 & 0.481118704724546 \tabularnewline
51 & 0.509331138425358 & 0.981337723149283 & 0.490668861574642 \tabularnewline
52 & 0.459587586423106 & 0.919175172846213 & 0.540412413576894 \tabularnewline
53 & 0.491938001021854 & 0.983876002043707 & 0.508061998978147 \tabularnewline
54 & 0.456141792701001 & 0.912283585402002 & 0.543858207298999 \tabularnewline
55 & 0.618527099540189 & 0.762945800919623 & 0.381472900459811 \tabularnewline
56 & 0.575510735373065 & 0.84897852925387 & 0.424489264626935 \tabularnewline
57 & 0.535549846207916 & 0.928900307584169 & 0.464450153792084 \tabularnewline
58 & 0.519585531384092 & 0.960828937231816 & 0.480414468615908 \tabularnewline
59 & 0.471739241060611 & 0.943478482121222 & 0.528260758939389 \tabularnewline
60 & 0.435964021817484 & 0.871928043634968 & 0.564035978182516 \tabularnewline
61 & 0.398157482727049 & 0.796314965454098 & 0.601842517272951 \tabularnewline
62 & 0.362365709510587 & 0.724731419021175 & 0.637634290489413 \tabularnewline
63 & 0.31785357983526 & 0.63570715967052 & 0.68214642016474 \tabularnewline
64 & 0.326647962170927 & 0.653295924341854 & 0.673352037829073 \tabularnewline
65 & 0.286011796068072 & 0.572023592136144 & 0.713988203931928 \tabularnewline
66 & 0.326478630228336 & 0.652957260456673 & 0.673521369771664 \tabularnewline
67 & 0.385743769977070 & 0.771487539954141 & 0.61425623002293 \tabularnewline
68 & 0.499026387530035 & 0.99805277506007 & 0.500973612469965 \tabularnewline
69 & 0.45154536487852 & 0.90309072975704 & 0.54845463512148 \tabularnewline
70 & 0.436022161394265 & 0.87204432278853 & 0.563977838605735 \tabularnewline
71 & 0.510501642219656 & 0.978996715560688 & 0.489498357780344 \tabularnewline
72 & 0.462360329078122 & 0.924720658156244 & 0.537639670921878 \tabularnewline
73 & 0.4175062465648 & 0.8350124931296 & 0.5824937534352 \tabularnewline
74 & 0.384046985004067 & 0.768093970008133 & 0.615953014995933 \tabularnewline
75 & 0.349629313926797 & 0.699258627853595 & 0.650370686073203 \tabularnewline
76 & 0.324814181075246 & 0.649628362150493 & 0.675185818924754 \tabularnewline
77 & 0.283690820396231 & 0.567381640792462 & 0.716309179603769 \tabularnewline
78 & 0.249370704845817 & 0.498741409691633 & 0.750629295154183 \tabularnewline
79 & 0.214519905851015 & 0.429039811702031 & 0.785480094148985 \tabularnewline
80 & 0.189274440119874 & 0.378548880239748 & 0.810725559880126 \tabularnewline
81 & 0.300038704743211 & 0.600077409486423 & 0.699961295256789 \tabularnewline
82 & 0.271083570647963 & 0.542167141295925 & 0.728916429352037 \tabularnewline
83 & 0.234284830549141 & 0.468569661098283 & 0.765715169450859 \tabularnewline
84 & 0.232605380948609 & 0.465210761897218 & 0.767394619051391 \tabularnewline
85 & 0.208125856627594 & 0.416251713255189 & 0.791874143372406 \tabularnewline
86 & 0.207625900657262 & 0.415251801314523 & 0.792374099342738 \tabularnewline
87 & 0.183973932344488 & 0.367947864688977 & 0.816026067655512 \tabularnewline
88 & 0.171303222619938 & 0.342606445239876 & 0.828696777380062 \tabularnewline
89 & 0.164056901412649 & 0.328113802825299 & 0.83594309858735 \tabularnewline
90 & 0.199583836833028 & 0.399167673666056 & 0.800416163166972 \tabularnewline
91 & 0.169873153050196 & 0.339746306100392 & 0.830126846949804 \tabularnewline
92 & 0.151180336888609 & 0.302360673777217 & 0.848819663111391 \tabularnewline
93 & 0.127355090184568 & 0.254710180369136 & 0.872644909815432 \tabularnewline
94 & 0.117270958065542 & 0.234541916131085 & 0.882729041934458 \tabularnewline
95 & 0.113812105284465 & 0.227624210568931 & 0.886187894715535 \tabularnewline
96 & 0.102033509259048 & 0.204067018518096 & 0.897966490740952 \tabularnewline
97 & 0.104042702923085 & 0.208085405846170 & 0.895957297076915 \tabularnewline
98 & 0.0925341672177332 & 0.185068334435466 & 0.907465832782267 \tabularnewline
99 & 0.0914691248151188 & 0.182938249630238 & 0.908530875184881 \tabularnewline
100 & 0.0724366793297473 & 0.144873358659495 & 0.927563320670253 \tabularnewline
101 & 0.0572382950334735 & 0.114476590066947 & 0.942761704966527 \tabularnewline
102 & 0.0463257253238612 & 0.0926514506477225 & 0.953674274676139 \tabularnewline
103 & 0.036834983585444 & 0.073669967170888 & 0.963165016414556 \tabularnewline
104 & 0.038930092934507 & 0.077860185869014 & 0.961069907065493 \tabularnewline
105 & 0.0319443154390978 & 0.0638886308781955 & 0.968055684560902 \tabularnewline
106 & 0.0276040168058954 & 0.0552080336117908 & 0.972395983194105 \tabularnewline
107 & 0.0235469039257523 & 0.0470938078515045 & 0.976453096074248 \tabularnewline
108 & 0.0173587129539882 & 0.0347174259079764 & 0.982641287046012 \tabularnewline
109 & 0.0159083280548811 & 0.0318166561097621 & 0.984091671945119 \tabularnewline
110 & 0.0219082784332125 & 0.0438165568664251 & 0.978091721566787 \tabularnewline
111 & 0.115708228944400 & 0.231416457888801 & 0.8842917710556 \tabularnewline
112 & 0.106259112699560 & 0.212518225399121 & 0.89374088730044 \tabularnewline
113 & 0.457094732753297 & 0.914189465506593 & 0.542905267246703 \tabularnewline
114 & 0.785650296352116 & 0.428699407295767 & 0.214349703647884 \tabularnewline
115 & 0.744341869519473 & 0.511316260961054 & 0.255658130480527 \tabularnewline
116 & 0.821956543487791 & 0.356086913024417 & 0.178043456512209 \tabularnewline
117 & 0.79992250278695 & 0.4001549944261 & 0.20007749721305 \tabularnewline
118 & 0.755167335094304 & 0.489665329811391 & 0.244832664905696 \tabularnewline
119 & 0.76423389306938 & 0.471532213861242 & 0.235766106930621 \tabularnewline
120 & 0.73572130083281 & 0.528557398334381 & 0.264278699167190 \tabularnewline
121 & 0.68194337931536 & 0.636113241369281 & 0.318056620684641 \tabularnewline
122 & 0.702685906931684 & 0.594628186136633 & 0.297314093068316 \tabularnewline
123 & 0.647613683969824 & 0.704772632060352 & 0.352386316030176 \tabularnewline
124 & 0.588376998460726 & 0.823246003078548 & 0.411623001539274 \tabularnewline
125 & 0.54754566981672 & 0.90490866036656 & 0.45245433018328 \tabularnewline
126 & 0.500740088478052 & 0.998519823043896 & 0.499259911521948 \tabularnewline
127 & 0.444779663144027 & 0.889559326288054 & 0.555220336855973 \tabularnewline
128 & 0.384399277539522 & 0.768798555079045 & 0.615600722460478 \tabularnewline
129 & 0.328686665659967 & 0.657373331319934 & 0.671313334340033 \tabularnewline
130 & 0.309651836190470 & 0.619303672380941 & 0.69034816380953 \tabularnewline
131 & 0.313115128060602 & 0.626230256121203 & 0.686884871939399 \tabularnewline
132 & 0.382163147058339 & 0.764326294116678 & 0.617836852941661 \tabularnewline
133 & 0.35228337397177 & 0.70456674794354 & 0.64771662602823 \tabularnewline
134 & 0.302238893426928 & 0.604477786853856 & 0.697761106573072 \tabularnewline
135 & 0.243829218590009 & 0.487658437180017 & 0.756170781409991 \tabularnewline
136 & 0.186225145535720 & 0.372450291071439 & 0.81377485446428 \tabularnewline
137 & 0.179780522002677 & 0.359561044005353 & 0.820219477997324 \tabularnewline
138 & 0.126146196590667 & 0.252292393181334 & 0.873853803409333 \tabularnewline
139 & 0.0969933788038216 & 0.193986757607643 & 0.903006621196178 \tabularnewline
140 & 0.072914452509362 & 0.145828905018724 & 0.927085547490638 \tabularnewline
141 & 0.0844151126656436 & 0.168830225331287 & 0.915584887334356 \tabularnewline
142 & 0.143436921527209 & 0.286873843054419 & 0.85656307847279 \tabularnewline
143 & 0.0822635621176883 & 0.164527124235377 & 0.917736437882312 \tabularnewline
144 & 0.0444199958049524 & 0.0888399916099047 & 0.955580004195048 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99547&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]14[/C][C]0.973563485975858[/C][C]0.0528730280482846[/C][C]0.0264365140241423[/C][/ROW]
[ROW][C]15[/C][C]0.945574431549052[/C][C]0.108851136901897[/C][C]0.0544255684509485[/C][/ROW]
[ROW][C]16[/C][C]0.903533838411931[/C][C]0.192932323176138[/C][C]0.0964661615880688[/C][/ROW]
[ROW][C]17[/C][C]0.847422592109532[/C][C]0.305154815780937[/C][C]0.152577407890468[/C][/ROW]
[ROW][C]18[/C][C]0.773350155076146[/C][C]0.453299689847708[/C][C]0.226649844923854[/C][/ROW]
[ROW][C]19[/C][C]0.793838553659489[/C][C]0.412322892681022[/C][C]0.206161446340511[/C][/ROW]
[ROW][C]20[/C][C]0.760872614315812[/C][C]0.478254771368376[/C][C]0.239127385684188[/C][/ROW]
[ROW][C]21[/C][C]0.742599677834574[/C][C]0.514800644330852[/C][C]0.257400322165426[/C][/ROW]
[ROW][C]22[/C][C]0.707672116283652[/C][C]0.584655767432696[/C][C]0.292327883716348[/C][/ROW]
[ROW][C]23[/C][C]0.664309633563626[/C][C]0.671380732872747[/C][C]0.335690366436374[/C][/ROW]
[ROW][C]24[/C][C]0.655544505977978[/C][C]0.688910988044044[/C][C]0.344455494022022[/C][/ROW]
[ROW][C]25[/C][C]0.644259769053714[/C][C]0.711480461892571[/C][C]0.355740230946286[/C][/ROW]
[ROW][C]26[/C][C]0.568202658847986[/C][C]0.863594682304028[/C][C]0.431797341152014[/C][/ROW]
[ROW][C]27[/C][C]0.494576364001745[/C][C]0.98915272800349[/C][C]0.505423635998255[/C][/ROW]
[ROW][C]28[/C][C]0.441639286460324[/C][C]0.883278572920647[/C][C]0.558360713539676[/C][/ROW]
[ROW][C]29[/C][C]0.410066048658141[/C][C]0.820132097316283[/C][C]0.589933951341859[/C][/ROW]
[ROW][C]30[/C][C]0.343647598634996[/C][C]0.687295197269991[/C][C]0.656352401365004[/C][/ROW]
[ROW][C]31[/C][C]0.315321751715943[/C][C]0.630643503431886[/C][C]0.684678248284057[/C][/ROW]
[ROW][C]32[/C][C]0.310633333425846[/C][C]0.621266666851692[/C][C]0.689366666574154[/C][/ROW]
[ROW][C]33[/C][C]0.40056325298966[/C][C]0.80112650597932[/C][C]0.59943674701034[/C][/ROW]
[ROW][C]34[/C][C]0.546658816577625[/C][C]0.90668236684475[/C][C]0.453341183422375[/C][/ROW]
[ROW][C]35[/C][C]0.508482156519824[/C][C]0.983035686960352[/C][C]0.491517843480176[/C][/ROW]
[ROW][C]36[/C][C]0.56429387988445[/C][C]0.8714122402311[/C][C]0.43570612011555[/C][/ROW]
[ROW][C]37[/C][C]0.618421431891703[/C][C]0.763157136216594[/C][C]0.381578568108297[/C][/ROW]
[ROW][C]38[/C][C]0.581625981652318[/C][C]0.836748036695364[/C][C]0.418374018347682[/C][/ROW]
[ROW][C]39[/C][C]0.530543824495599[/C][C]0.938912351008803[/C][C]0.469456175504401[/C][/ROW]
[ROW][C]40[/C][C]0.652368209827993[/C][C]0.695263580344015[/C][C]0.347631790172007[/C][/ROW]
[ROW][C]41[/C][C]0.598359417413505[/C][C]0.80328116517299[/C][C]0.401640582586495[/C][/ROW]
[ROW][C]42[/C][C]0.544732527194889[/C][C]0.910534945610222[/C][C]0.455267472805111[/C][/ROW]
[ROW][C]43[/C][C]0.545338236168203[/C][C]0.909323527663595[/C][C]0.454661763831797[/C][/ROW]
[ROW][C]44[/C][C]0.504648912956551[/C][C]0.990702174086898[/C][C]0.495351087043449[/C][/ROW]
[ROW][C]45[/C][C]0.543671463996991[/C][C]0.912657072006019[/C][C]0.456328536003009[/C][/ROW]
[ROW][C]46[/C][C]0.488071435448853[/C][C]0.976142870897705[/C][C]0.511928564551148[/C][/ROW]
[ROW][C]47[/C][C]0.491751501995867[/C][C]0.983503003991735[/C][C]0.508248498004133[/C][/ROW]
[ROW][C]48[/C][C]0.600326453223506[/C][C]0.799347093552988[/C][C]0.399673546776494[/C][/ROW]
[ROW][C]49[/C][C]0.549650766987107[/C][C]0.900698466025785[/C][C]0.450349233012893[/C][/ROW]
[ROW][C]50[/C][C]0.518881295275454[/C][C]0.96223740944909[/C][C]0.481118704724546[/C][/ROW]
[ROW][C]51[/C][C]0.509331138425358[/C][C]0.981337723149283[/C][C]0.490668861574642[/C][/ROW]
[ROW][C]52[/C][C]0.459587586423106[/C][C]0.919175172846213[/C][C]0.540412413576894[/C][/ROW]
[ROW][C]53[/C][C]0.491938001021854[/C][C]0.983876002043707[/C][C]0.508061998978147[/C][/ROW]
[ROW][C]54[/C][C]0.456141792701001[/C][C]0.912283585402002[/C][C]0.543858207298999[/C][/ROW]
[ROW][C]55[/C][C]0.618527099540189[/C][C]0.762945800919623[/C][C]0.381472900459811[/C][/ROW]
[ROW][C]56[/C][C]0.575510735373065[/C][C]0.84897852925387[/C][C]0.424489264626935[/C][/ROW]
[ROW][C]57[/C][C]0.535549846207916[/C][C]0.928900307584169[/C][C]0.464450153792084[/C][/ROW]
[ROW][C]58[/C][C]0.519585531384092[/C][C]0.960828937231816[/C][C]0.480414468615908[/C][/ROW]
[ROW][C]59[/C][C]0.471739241060611[/C][C]0.943478482121222[/C][C]0.528260758939389[/C][/ROW]
[ROW][C]60[/C][C]0.435964021817484[/C][C]0.871928043634968[/C][C]0.564035978182516[/C][/ROW]
[ROW][C]61[/C][C]0.398157482727049[/C][C]0.796314965454098[/C][C]0.601842517272951[/C][/ROW]
[ROW][C]62[/C][C]0.362365709510587[/C][C]0.724731419021175[/C][C]0.637634290489413[/C][/ROW]
[ROW][C]63[/C][C]0.31785357983526[/C][C]0.63570715967052[/C][C]0.68214642016474[/C][/ROW]
[ROW][C]64[/C][C]0.326647962170927[/C][C]0.653295924341854[/C][C]0.673352037829073[/C][/ROW]
[ROW][C]65[/C][C]0.286011796068072[/C][C]0.572023592136144[/C][C]0.713988203931928[/C][/ROW]
[ROW][C]66[/C][C]0.326478630228336[/C][C]0.652957260456673[/C][C]0.673521369771664[/C][/ROW]
[ROW][C]67[/C][C]0.385743769977070[/C][C]0.771487539954141[/C][C]0.61425623002293[/C][/ROW]
[ROW][C]68[/C][C]0.499026387530035[/C][C]0.99805277506007[/C][C]0.500973612469965[/C][/ROW]
[ROW][C]69[/C][C]0.45154536487852[/C][C]0.90309072975704[/C][C]0.54845463512148[/C][/ROW]
[ROW][C]70[/C][C]0.436022161394265[/C][C]0.87204432278853[/C][C]0.563977838605735[/C][/ROW]
[ROW][C]71[/C][C]0.510501642219656[/C][C]0.978996715560688[/C][C]0.489498357780344[/C][/ROW]
[ROW][C]72[/C][C]0.462360329078122[/C][C]0.924720658156244[/C][C]0.537639670921878[/C][/ROW]
[ROW][C]73[/C][C]0.4175062465648[/C][C]0.8350124931296[/C][C]0.5824937534352[/C][/ROW]
[ROW][C]74[/C][C]0.384046985004067[/C][C]0.768093970008133[/C][C]0.615953014995933[/C][/ROW]
[ROW][C]75[/C][C]0.349629313926797[/C][C]0.699258627853595[/C][C]0.650370686073203[/C][/ROW]
[ROW][C]76[/C][C]0.324814181075246[/C][C]0.649628362150493[/C][C]0.675185818924754[/C][/ROW]
[ROW][C]77[/C][C]0.283690820396231[/C][C]0.567381640792462[/C][C]0.716309179603769[/C][/ROW]
[ROW][C]78[/C][C]0.249370704845817[/C][C]0.498741409691633[/C][C]0.750629295154183[/C][/ROW]
[ROW][C]79[/C][C]0.214519905851015[/C][C]0.429039811702031[/C][C]0.785480094148985[/C][/ROW]
[ROW][C]80[/C][C]0.189274440119874[/C][C]0.378548880239748[/C][C]0.810725559880126[/C][/ROW]
[ROW][C]81[/C][C]0.300038704743211[/C][C]0.600077409486423[/C][C]0.699961295256789[/C][/ROW]
[ROW][C]82[/C][C]0.271083570647963[/C][C]0.542167141295925[/C][C]0.728916429352037[/C][/ROW]
[ROW][C]83[/C][C]0.234284830549141[/C][C]0.468569661098283[/C][C]0.765715169450859[/C][/ROW]
[ROW][C]84[/C][C]0.232605380948609[/C][C]0.465210761897218[/C][C]0.767394619051391[/C][/ROW]
[ROW][C]85[/C][C]0.208125856627594[/C][C]0.416251713255189[/C][C]0.791874143372406[/C][/ROW]
[ROW][C]86[/C][C]0.207625900657262[/C][C]0.415251801314523[/C][C]0.792374099342738[/C][/ROW]
[ROW][C]87[/C][C]0.183973932344488[/C][C]0.367947864688977[/C][C]0.816026067655512[/C][/ROW]
[ROW][C]88[/C][C]0.171303222619938[/C][C]0.342606445239876[/C][C]0.828696777380062[/C][/ROW]
[ROW][C]89[/C][C]0.164056901412649[/C][C]0.328113802825299[/C][C]0.83594309858735[/C][/ROW]
[ROW][C]90[/C][C]0.199583836833028[/C][C]0.399167673666056[/C][C]0.800416163166972[/C][/ROW]
[ROW][C]91[/C][C]0.169873153050196[/C][C]0.339746306100392[/C][C]0.830126846949804[/C][/ROW]
[ROW][C]92[/C][C]0.151180336888609[/C][C]0.302360673777217[/C][C]0.848819663111391[/C][/ROW]
[ROW][C]93[/C][C]0.127355090184568[/C][C]0.254710180369136[/C][C]0.872644909815432[/C][/ROW]
[ROW][C]94[/C][C]0.117270958065542[/C][C]0.234541916131085[/C][C]0.882729041934458[/C][/ROW]
[ROW][C]95[/C][C]0.113812105284465[/C][C]0.227624210568931[/C][C]0.886187894715535[/C][/ROW]
[ROW][C]96[/C][C]0.102033509259048[/C][C]0.204067018518096[/C][C]0.897966490740952[/C][/ROW]
[ROW][C]97[/C][C]0.104042702923085[/C][C]0.208085405846170[/C][C]0.895957297076915[/C][/ROW]
[ROW][C]98[/C][C]0.0925341672177332[/C][C]0.185068334435466[/C][C]0.907465832782267[/C][/ROW]
[ROW][C]99[/C][C]0.0914691248151188[/C][C]0.182938249630238[/C][C]0.908530875184881[/C][/ROW]
[ROW][C]100[/C][C]0.0724366793297473[/C][C]0.144873358659495[/C][C]0.927563320670253[/C][/ROW]
[ROW][C]101[/C][C]0.0572382950334735[/C][C]0.114476590066947[/C][C]0.942761704966527[/C][/ROW]
[ROW][C]102[/C][C]0.0463257253238612[/C][C]0.0926514506477225[/C][C]0.953674274676139[/C][/ROW]
[ROW][C]103[/C][C]0.036834983585444[/C][C]0.073669967170888[/C][C]0.963165016414556[/C][/ROW]
[ROW][C]104[/C][C]0.038930092934507[/C][C]0.077860185869014[/C][C]0.961069907065493[/C][/ROW]
[ROW][C]105[/C][C]0.0319443154390978[/C][C]0.0638886308781955[/C][C]0.968055684560902[/C][/ROW]
[ROW][C]106[/C][C]0.0276040168058954[/C][C]0.0552080336117908[/C][C]0.972395983194105[/C][/ROW]
[ROW][C]107[/C][C]0.0235469039257523[/C][C]0.0470938078515045[/C][C]0.976453096074248[/C][/ROW]
[ROW][C]108[/C][C]0.0173587129539882[/C][C]0.0347174259079764[/C][C]0.982641287046012[/C][/ROW]
[ROW][C]109[/C][C]0.0159083280548811[/C][C]0.0318166561097621[/C][C]0.984091671945119[/C][/ROW]
[ROW][C]110[/C][C]0.0219082784332125[/C][C]0.0438165568664251[/C][C]0.978091721566787[/C][/ROW]
[ROW][C]111[/C][C]0.115708228944400[/C][C]0.231416457888801[/C][C]0.8842917710556[/C][/ROW]
[ROW][C]112[/C][C]0.106259112699560[/C][C]0.212518225399121[/C][C]0.89374088730044[/C][/ROW]
[ROW][C]113[/C][C]0.457094732753297[/C][C]0.914189465506593[/C][C]0.542905267246703[/C][/ROW]
[ROW][C]114[/C][C]0.785650296352116[/C][C]0.428699407295767[/C][C]0.214349703647884[/C][/ROW]
[ROW][C]115[/C][C]0.744341869519473[/C][C]0.511316260961054[/C][C]0.255658130480527[/C][/ROW]
[ROW][C]116[/C][C]0.821956543487791[/C][C]0.356086913024417[/C][C]0.178043456512209[/C][/ROW]
[ROW][C]117[/C][C]0.79992250278695[/C][C]0.4001549944261[/C][C]0.20007749721305[/C][/ROW]
[ROW][C]118[/C][C]0.755167335094304[/C][C]0.489665329811391[/C][C]0.244832664905696[/C][/ROW]
[ROW][C]119[/C][C]0.76423389306938[/C][C]0.471532213861242[/C][C]0.235766106930621[/C][/ROW]
[ROW][C]120[/C][C]0.73572130083281[/C][C]0.528557398334381[/C][C]0.264278699167190[/C][/ROW]
[ROW][C]121[/C][C]0.68194337931536[/C][C]0.636113241369281[/C][C]0.318056620684641[/C][/ROW]
[ROW][C]122[/C][C]0.702685906931684[/C][C]0.594628186136633[/C][C]0.297314093068316[/C][/ROW]
[ROW][C]123[/C][C]0.647613683969824[/C][C]0.704772632060352[/C][C]0.352386316030176[/C][/ROW]
[ROW][C]124[/C][C]0.588376998460726[/C][C]0.823246003078548[/C][C]0.411623001539274[/C][/ROW]
[ROW][C]125[/C][C]0.54754566981672[/C][C]0.90490866036656[/C][C]0.45245433018328[/C][/ROW]
[ROW][C]126[/C][C]0.500740088478052[/C][C]0.998519823043896[/C][C]0.499259911521948[/C][/ROW]
[ROW][C]127[/C][C]0.444779663144027[/C][C]0.889559326288054[/C][C]0.555220336855973[/C][/ROW]
[ROW][C]128[/C][C]0.384399277539522[/C][C]0.768798555079045[/C][C]0.615600722460478[/C][/ROW]
[ROW][C]129[/C][C]0.328686665659967[/C][C]0.657373331319934[/C][C]0.671313334340033[/C][/ROW]
[ROW][C]130[/C][C]0.309651836190470[/C][C]0.619303672380941[/C][C]0.69034816380953[/C][/ROW]
[ROW][C]131[/C][C]0.313115128060602[/C][C]0.626230256121203[/C][C]0.686884871939399[/C][/ROW]
[ROW][C]132[/C][C]0.382163147058339[/C][C]0.764326294116678[/C][C]0.617836852941661[/C][/ROW]
[ROW][C]133[/C][C]0.35228337397177[/C][C]0.70456674794354[/C][C]0.64771662602823[/C][/ROW]
[ROW][C]134[/C][C]0.302238893426928[/C][C]0.604477786853856[/C][C]0.697761106573072[/C][/ROW]
[ROW][C]135[/C][C]0.243829218590009[/C][C]0.487658437180017[/C][C]0.756170781409991[/C][/ROW]
[ROW][C]136[/C][C]0.186225145535720[/C][C]0.372450291071439[/C][C]0.81377485446428[/C][/ROW]
[ROW][C]137[/C][C]0.179780522002677[/C][C]0.359561044005353[/C][C]0.820219477997324[/C][/ROW]
[ROW][C]138[/C][C]0.126146196590667[/C][C]0.252292393181334[/C][C]0.873853803409333[/C][/ROW]
[ROW][C]139[/C][C]0.0969933788038216[/C][C]0.193986757607643[/C][C]0.903006621196178[/C][/ROW]
[ROW][C]140[/C][C]0.072914452509362[/C][C]0.145828905018724[/C][C]0.927085547490638[/C][/ROW]
[ROW][C]141[/C][C]0.0844151126656436[/C][C]0.168830225331287[/C][C]0.915584887334356[/C][/ROW]
[ROW][C]142[/C][C]0.143436921527209[/C][C]0.286873843054419[/C][C]0.85656307847279[/C][/ROW]
[ROW][C]143[/C][C]0.0822635621176883[/C][C]0.164527124235377[/C][C]0.917736437882312[/C][/ROW]
[ROW][C]144[/C][C]0.0444199958049524[/C][C]0.0888399916099047[/C][C]0.955580004195048[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99547&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99547&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
140.9735634859758580.05287302804828460.0264365140241423
150.9455744315490520.1088511369018970.0544255684509485
160.9035338384119310.1929323231761380.0964661615880688
170.8474225921095320.3051548157809370.152577407890468
180.7733501550761460.4532996898477080.226649844923854
190.7938385536594890.4123228926810220.206161446340511
200.7608726143158120.4782547713683760.239127385684188
210.7425996778345740.5148006443308520.257400322165426
220.7076721162836520.5846557674326960.292327883716348
230.6643096335636260.6713807328727470.335690366436374
240.6555445059779780.6889109880440440.344455494022022
250.6442597690537140.7114804618925710.355740230946286
260.5682026588479860.8635946823040280.431797341152014
270.4945763640017450.989152728003490.505423635998255
280.4416392864603240.8832785729206470.558360713539676
290.4100660486581410.8201320973162830.589933951341859
300.3436475986349960.6872951972699910.656352401365004
310.3153217517159430.6306435034318860.684678248284057
320.3106333334258460.6212666668516920.689366666574154
330.400563252989660.801126505979320.59943674701034
340.5466588165776250.906682366844750.453341183422375
350.5084821565198240.9830356869603520.491517843480176
360.564293879884450.87141224023110.43570612011555
370.6184214318917030.7631571362165940.381578568108297
380.5816259816523180.8367480366953640.418374018347682
390.5305438244955990.9389123510088030.469456175504401
400.6523682098279930.6952635803440150.347631790172007
410.5983594174135050.803281165172990.401640582586495
420.5447325271948890.9105349456102220.455267472805111
430.5453382361682030.9093235276635950.454661763831797
440.5046489129565510.9907021740868980.495351087043449
450.5436714639969910.9126570720060190.456328536003009
460.4880714354488530.9761428708977050.511928564551148
470.4917515019958670.9835030039917350.508248498004133
480.6003264532235060.7993470935529880.399673546776494
490.5496507669871070.9006984660257850.450349233012893
500.5188812952754540.962237409449090.481118704724546
510.5093311384253580.9813377231492830.490668861574642
520.4595875864231060.9191751728462130.540412413576894
530.4919380010218540.9838760020437070.508061998978147
540.4561417927010010.9122835854020020.543858207298999
550.6185270995401890.7629458009196230.381472900459811
560.5755107353730650.848978529253870.424489264626935
570.5355498462079160.9289003075841690.464450153792084
580.5195855313840920.9608289372318160.480414468615908
590.4717392410606110.9434784821212220.528260758939389
600.4359640218174840.8719280436349680.564035978182516
610.3981574827270490.7963149654540980.601842517272951
620.3623657095105870.7247314190211750.637634290489413
630.317853579835260.635707159670520.68214642016474
640.3266479621709270.6532959243418540.673352037829073
650.2860117960680720.5720235921361440.713988203931928
660.3264786302283360.6529572604566730.673521369771664
670.3857437699770700.7714875399541410.61425623002293
680.4990263875300350.998052775060070.500973612469965
690.451545364878520.903090729757040.54845463512148
700.4360221613942650.872044322788530.563977838605735
710.5105016422196560.9789967155606880.489498357780344
720.4623603290781220.9247206581562440.537639670921878
730.41750624656480.83501249312960.5824937534352
740.3840469850040670.7680939700081330.615953014995933
750.3496293139267970.6992586278535950.650370686073203
760.3248141810752460.6496283621504930.675185818924754
770.2836908203962310.5673816407924620.716309179603769
780.2493707048458170.4987414096916330.750629295154183
790.2145199058510150.4290398117020310.785480094148985
800.1892744401198740.3785488802397480.810725559880126
810.3000387047432110.6000774094864230.699961295256789
820.2710835706479630.5421671412959250.728916429352037
830.2342848305491410.4685696610982830.765715169450859
840.2326053809486090.4652107618972180.767394619051391
850.2081258566275940.4162517132551890.791874143372406
860.2076259006572620.4152518013145230.792374099342738
870.1839739323444880.3679478646889770.816026067655512
880.1713032226199380.3426064452398760.828696777380062
890.1640569014126490.3281138028252990.83594309858735
900.1995838368330280.3991676736660560.800416163166972
910.1698731530501960.3397463061003920.830126846949804
920.1511803368886090.3023606737772170.848819663111391
930.1273550901845680.2547101803691360.872644909815432
940.1172709580655420.2345419161310850.882729041934458
950.1138121052844650.2276242105689310.886187894715535
960.1020335092590480.2040670185180960.897966490740952
970.1040427029230850.2080854058461700.895957297076915
980.09253416721773320.1850683344354660.907465832782267
990.09146912481511880.1829382496302380.908530875184881
1000.07243667932974730.1448733586594950.927563320670253
1010.05723829503347350.1144765900669470.942761704966527
1020.04632572532386120.09265145064772250.953674274676139
1030.0368349835854440.0736699671708880.963165016414556
1040.0389300929345070.0778601858690140.961069907065493
1050.03194431543909780.06388863087819550.968055684560902
1060.02760401680589540.05520803361179080.972395983194105
1070.02354690392575230.04709380785150450.976453096074248
1080.01735871295398820.03471742590797640.982641287046012
1090.01590832805488110.03181665610976210.984091671945119
1100.02190827843321250.04381655686642510.978091721566787
1110.1157082289444000.2314164578888010.8842917710556
1120.1062591126995600.2125182253991210.89374088730044
1130.4570947327532970.9141894655065930.542905267246703
1140.7856502963521160.4286994072957670.214349703647884
1150.7443418695194730.5113162609610540.255658130480527
1160.8219565434877910.3560869130244170.178043456512209
1170.799922502786950.40015499442610.20007749721305
1180.7551673350943040.4896653298113910.244832664905696
1190.764233893069380.4715322138612420.235766106930621
1200.735721300832810.5285573983343810.264278699167190
1210.681943379315360.6361132413692810.318056620684641
1220.7026859069316840.5946281861366330.297314093068316
1230.6476136839698240.7047726320603520.352386316030176
1240.5883769984607260.8232460030785480.411623001539274
1250.547545669816720.904908660366560.45245433018328
1260.5007400884780520.9985198230438960.499259911521948
1270.4447796631440270.8895593262880540.555220336855973
1280.3843992775395220.7687985550790450.615600722460478
1290.3286866656599670.6573733313199340.671313334340033
1300.3096518361904700.6193036723809410.69034816380953
1310.3131151280606020.6262302561212030.686884871939399
1320.3821631470583390.7643262941166780.617836852941661
1330.352283373971770.704566747943540.64771662602823
1340.3022388934269280.6044777868538560.697761106573072
1350.2438292185900090.4876584371800170.756170781409991
1360.1862251455357200.3724502910714390.81377485446428
1370.1797805220026770.3595610440053530.820219477997324
1380.1261461965906670.2522923931813340.873853803409333
1390.09699337880382160.1939867576076430.903006621196178
1400.0729144525093620.1458289050187240.927085547490638
1410.08441511266564360.1688302253312870.915584887334356
1420.1434369215272090.2868738430544190.85656307847279
1430.08226356211768830.1645271242353770.917736437882312
1440.04441999580495240.08883999160990470.955580004195048







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level40.0305343511450382OK
10% type I error level110.083969465648855OK

\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 & 4 & 0.0305343511450382 & OK \tabularnewline
10% type I error level & 11 & 0.083969465648855 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99547&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]4[/C][C]0.0305343511450382[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]11[/C][C]0.083969465648855[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99547&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99547&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level40.0305343511450382OK
10% type I error level110.083969465648855OK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 9 ; 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')
}