FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 17 Dec 2010 16:35:58 +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/Dec/17/t1292603812yc05dpn58qnw2e6.htm/, Retrieved Tue, 07 May 2024 03:11:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=111571, Retrieved Tue, 07 May 2024 03:11:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact157

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Multiple Regressi...] [2010-12-17 13:46:43] [1251ac2db27b84d4a3ba43449388906b]
-   PD  [Multiple Regression] [Multiple Regressi...] [2010-12-17 15:41:32] [1251ac2db27b84d4a3ba43449388906b]
-   P       [Multiple Regression] [MR Paper (monthly...] [2010-12-17 16:35:58] [1638ccfec791c539017705f3e680eb33] [Current]
-   PD        [Multiple Regression] [MR Paper (month)] [2010-12-17 16:45:18] [1251ac2db27b84d4a3ba43449388906b]
-   P           [Multiple Regression] [MR Paper (trend)b] [2010-12-18 12:41:58] [1251ac2db27b84d4a3ba43449388906b]
-   PD            [Multiple Regression] [] [2010-12-18 14:02:17] [1251ac2db27b84d4a3ba43449388906b]
-                   [Multiple Regression] [] [2010-12-24 15:25:05] [dc30d19c3bc2be07fe595ad36c2cf923]
-                   [Multiple Regression] [] [2010-12-24 15:58:02] [dc30d19c3bc2be07fe595ad36c2cf923]
- RMPD            [Classical Decomposition] [CD] [2010-12-18 14:47:46] [1251ac2db27b84d4a3ba43449388906b]
-                   [Classical Decomposition] [CD] [2010-12-18 15:39:32] [1251ac2db27b84d4a3ba43449388906b]
-   P                 [Classical Decomposition] [] [2010-12-24 15:28:04] [dc30d19c3bc2be07fe595ad36c2cf923]
-   P                 [Classical Decomposition] [] [2010-12-24 16:01:09] [dc30d19c3bc2be07fe595ad36c2cf923]
- RM                [Variance Reduction Matrix] [VRM] [2010-12-18 15:58:17] [1251ac2db27b84d4a3ba43449388906b]
-   P                 [Variance Reduction Matrix] [] [2010-12-24 16:05:37] [dc30d19c3bc2be07fe595ad36c2cf923]
- RM                [Spectral Analysis] [Spectral Analysis...] [2010-12-18 18:17:41] [1251ac2db27b84d4a3ba43449388906b]
-   P                 [Spectral Analysis] [] [2010-12-24 15:30:25] [dc30d19c3bc2be07fe595ad36c2cf923]
-   P                 [Spectral Analysis] [] [2010-12-24 16:03:03] [dc30d19c3bc2be07fe595ad36c2cf923]
- RM                [Standard Deviation-Mean Plot] [Mean vs Median Paper] [2010-12-18 18:40:22] [1251ac2db27b84d4a3ba43449388906b]
-   P                 [Standard Deviation-Mean Plot] [] [2010-12-24 16:06:39] [dc30d19c3bc2be07fe595ad36c2cf923]
- RM                [Exponential Smoothing] [Exponential Smoot...] [2010-12-18 18:46:08] [1251ac2db27b84d4a3ba43449388906b]
-   P                 [Exponential Smoothing] [] [2010-12-24 15:31:52] [dc30d19c3bc2be07fe595ad36c2cf923]
-   P                 [Exponential Smoothing] [] [2010-12-24 16:04:11] [dc30d19c3bc2be07fe595ad36c2cf923]
-                 [Multiple Regression] [] [2010-12-24 15:21:29] [dc30d19c3bc2be07fe595ad36c2cf923]
-                 [Multiple Regression] [] [2010-12-24 15:56:42] [dc30d19c3bc2be07fe595ad36c2cf923]
-               [Multiple Regression] [] [2010-12-24 15:18:27] [dc30d19c3bc2be07fe595ad36c2cf923]
-               [Multiple Regression] [] [2010-12-24 15:55:06] [dc30d19c3bc2be07fe595ad36c2cf923]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
15	10	12	16	6	2	0	0
12	9	7	12	6	1	1	2
9	12	11	11	4	1	2	1
10	12	11	12	6	0	0	0
13	9	14	14	6	0	0	0
16	11	16	16	7	1	0	0
14	12	13	13	6	0	0	0
16	11	13	14	7	1	1	0
10	12	5	13	6	0	0	0
8	12	8	13	4	2	0	1
12	11	14	13	5	1	0	0
15	11	15	15	8	0	0	0
14	12	8	14	4	0	1	0
14	6	13	12	6	1	1	2
12	13	12	12	6	1	2	1
12	11	11	12	5	0	0	0
10	12	8	11	4	0	0	0
4	10	4	10	2	0	0	0
14	11	15	15	8	0	1	0
15	12	12	16	7	0	0	0
16	12	14	14	6	0	0	0
12	12	9	13	4	0	1	0
12	11	16	13	4	0	0	0
12	12	10	13	4	0	0	1
12	12	8	13	5	1	0	1
12	12	14	14	4	0	0	0
11	6	6	9	4	3	2	1
11	5	16	14	6	1	0	0
11	12	11	12	6	1	1	0
11	14	7	13	6	1	1	0
11	12	13	11	4	3	1	1
11	9	7	13	2	0	0	0
15	11	14	15	7	0	0	0
15	11	17	16	6	0	0	0
9	11	15	15	7	0	0	0
16	12	8	14	4	0	0	0
13	10	8	8	4	0	2	1
9	12	11	11	4	1	0	0
16	11	16	15	6	0	0	0
12	12	10	15	6	0	0	0
15	9	5	11	3	0	0	2
5	15	8	12	3	0	0	0
11	11	8	12	6	2	2	0
17	11	15	14	5	2	2	0
9	15	6	8	4	0	1	1
13	12	16	16	6	0	0	0
16	9	16	16	6	0	0	0
16	12	16	14	6	0	0	0
14	9	19	12	6	2	0	2
16	11	14	15	6	1	0	0
11	12	15	12	6	0	0	0
11	11	11	14	5	0	0	0
11	6	14	17	6	0	0	0
12	10	12	13	6	0	0	0
12	12	15	13	6	1	1	1
12	13	14	12	5	0	0	0
14	11	13	16	6	0	0	0
10	10	11	12	5	2	0	0
9	11	8	10	4	0	2	0
12	7	11	15	5	0	0	1
10	11	9	12	4	0	0	0
14	11	10	16	6	0	0	0
8	7	4	13	6	0	0	0
16	12	15	15	7	1	0	0
14	14	17	18	6	1	0	0
14	11	12	12	4	0	0	0
12	12	12	13	4	0	0	0
14	11	15	14	6	1	0	0
7	12	13	12	3	1	1	1
19	12	15	15	6	0	0	0
15	12	14	16	4	0	0	0
8	12	8	14	5	0	0	0
10	15	15	15	6	0	0	0
13	11	12	13	7	0	0	0
13	13	14	13	3	0	0	0
10	10	10	11	5	0	0	0
12	12	7	12	3	0	0	0
15	13	16	18	8	0	1	1
7	14	12	12	4	1	0	0
14	11	15	16	6	0	0	0
10	11	7	9	4	0	0	0
6	7	9	11	4	0	3	0
11	11	15	10	5	2	0	0
12	12	7	11	4	0	0	0
14	12	15	13	6	0	0	2
12	10	14	13	7	0	0	0
14	12	14	15	7	0	0	0
11	8	8	13	4	2	2	0
10	7	8	9	5	1	0	1
13	11	14	13	6	0	0	1
8	11	10	12	4	0	0	0
9	11	12	13	5	0	0	0
6	9	15	11	6	0	0	0
12	12	12	14	5	1	0	2
14	13	13	13	5	0	0	0
11	9	12	12	4	0	0	0
8	11	10	15	2	1	0	1
7	12	8	12	3	0	0	0
9	9	6	12	5	0	2	1
14	12	13	13	5	2	1	0
13	12	7	12	5	0	0	0
15	12	13	13	6	0	0	0
5	14	4	5	2	0	0	0
15	11	14	13	5	3	1	0
13	12	13	13	5	0	1	0
12	8	13	13	5	0	0	0
6	12	6	11	2	1	0	0
7	12	7	12	4	0	0	0
13	12	5	12	3	0	0	0
16	11	14	15	8	1	1	0
10	11	13	15	6	0	0	0
16	12	16	16	7	0	0	0
15	10	16	13	6	0	0	0
8	13	7	10	3	0	0	0
11	8	14	15	5	0	0	0
13	12	11	13	6	0	3	1
16	11	17	16	7	1	0	0
11	10	5	13	3	0	0	0
14	13	10	16	8	0	0	0
9	10	11	13	3	2	1	0
8	10	10	14	3	0	0	0
8	7	9	15	4	1	0	1
11	10	12	14	5	2	0	0
12	8	15	13	7	0	0	0
11	12	7	13	6	4	0	0
14	12	13	15	6	0	1	2
11	12	8	16	6	2	1	0
14	11	16	12	5	0	0	0
13	13	15	14	6	2	1	2
12	12	6	14	5	0	0	0
4	8	6	4	4	0	0	0
15	11	12	13	6	2	1	1
10	12	8	16	4	0	0	0
13	13	11	15	6	1	2	1
15	12	13	14	6	1	1	2
12	10	14	14	5	1	2	1
13	12	14	14	6	0	0	0
8	10	10	6	4	0	0	0
10	13	4	13	6	2	0	0
15	11	16	14	6	0	0	0
16	12	12	15	8	0	0	0
16	12	15	16	7	0	0	0
14	10	12	15	6	0	0	0
14	11	14	12	6	1	1	1
12	11	11	14	2	1	1	1
15	11	16	11	5	0	1	2
13	8	14	14	5	1	1	1
16	11	14	14	6	0	0	0
14	12	15	14	6	0	0	0
8	11	9	12	4	0	0	0
16	12	15	14	6	0	1	0
16	12	14	16	8	1	1	1
12	12	15	13	6	0	0	0
11	8	10	14	5	0	3	1
16	12	14	16	8	1	1	1
9	11	9	12	4	0	0	0

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111571&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111571&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111571&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = + 0.100276382934112 + 0.157263405271108FindingFriends[t] + 0.236372152928434KnowingPeople[t] + 0.323351350417035Liked[t] + 0.666471137832579Celebrity[t] -0.0593960178122206B[t] + 0.20312779537524`2B`[t] + 0.500771877991365`3B`[t] -0.104817868555572M1[t] -0.531890927774699M2[t] -0.906326361891386M3[t] -0.42086638214816M4[t] + 0.265856736325493M5[t] -1.09338003746871M6[t] -1.29051116073166M7[t] + 0.50770470226122M8[t] -0.960871026350095M9[t] -0.397549712140203M10[t] -0.768379620810554M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  +  0.100276382934112 +  0.157263405271108FindingFriends[t] +  0.236372152928434KnowingPeople[t] +  0.323351350417035Liked[t] +  0.666471137832579Celebrity[t] -0.0593960178122206B[t] +  0.20312779537524`2B`[t] +  0.500771877991365`3B`[t] -0.104817868555572M1[t] -0.531890927774699M2[t] -0.906326361891386M3[t] -0.42086638214816M4[t] +  0.265856736325493M5[t] -1.09338003746871M6[t] -1.29051116073166M7[t] +  0.50770470226122M8[t] -0.960871026350095M9[t] -0.397549712140203M10[t] -0.768379620810554M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111571&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  +  0.100276382934112 +  0.157263405271108FindingFriends[t] +  0.236372152928434KnowingPeople[t] +  0.323351350417035Liked[t] +  0.666471137832579Celebrity[t] -0.0593960178122206B[t] +  0.20312779537524`2B`[t] +  0.500771877991365`3B`[t] -0.104817868555572M1[t] -0.531890927774699M2[t] -0.906326361891386M3[t] -0.42086638214816M4[t] +  0.265856736325493M5[t] -1.09338003746871M6[t] -1.29051116073166M7[t] +  0.50770470226122M8[t] -0.960871026350095M9[t] -0.397549712140203M10[t] -0.768379620810554M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111571&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111571&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = + 0.100276382934112 + 0.157263405271108FindingFriends[t] + 0.236372152928434KnowingPeople[t] + 0.323351350417035Liked[t] + 0.666471137832579Celebrity[t] -0.0593960178122206B[t] + 0.20312779537524`2B`[t] + 0.500771877991365`3B`[t] -0.104817868555572M1[t] -0.531890927774699M2[t] -0.906326361891386M3[t] -0.42086638214816M4[t] + 0.265856736325493M5[t] -1.09338003746871M6[t] -1.29051116073166M7[t] + 0.50770470226122M8[t] -0.960871026350095M9[t] -0.397549712140203M10[t] -0.768379620810554M11[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.1002763829341121.5754660.06360.9493430.474671
FindingFriends0.1572634052711080.1017281.54590.1244310.062216
KnowingPeople0.2363721529284340.063313.73360.0002760.000138
Liked0.3233513504170350.1019423.17190.0018690.000935
Celebrity0.6664711378325790.1649944.03948.9e-054.4e-05
B-0.05939601781222060.22941-0.25890.7960950.398048
`2B`0.203127795375240.2815290.72150.4718220.235911
`3B`0.5007718779913650.3409411.46880.1441810.072091
M1-0.1048178685555720.842703-0.12440.9011940.450597
M2-0.5318909277746990.837472-0.63510.5264130.263207
M3-0.9063263618913860.841743-1.07670.2834950.141747
M4-0.420866382148160.847106-0.49680.6201060.310053
M50.2658567363254930.8317780.31960.749740.37487
M6-1.093380037468710.836023-1.30780.1931190.09656
M7-1.290511160731660.850436-1.51750.1314520.065726
M80.507704702261220.8535390.59480.5529430.276471
M9-0.9608710263500950.843937-1.13860.2568750.128437
M10-0.3975497121402030.835074-0.47610.6347860.317393
M11-0.7683796208105540.838042-0.91690.3608190.18041

\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) & 0.100276382934112 & 1.575466 & 0.0636 & 0.949343 & 0.474671 \tabularnewline
FindingFriends & 0.157263405271108 & 0.101728 & 1.5459 & 0.124431 & 0.062216 \tabularnewline
KnowingPeople & 0.236372152928434 & 0.06331 & 3.7336 & 0.000276 & 0.000138 \tabularnewline
Liked & 0.323351350417035 & 0.101942 & 3.1719 & 0.001869 & 0.000935 \tabularnewline
Celebrity & 0.666471137832579 & 0.164994 & 4.0394 & 8.9e-05 & 4.4e-05 \tabularnewline
B & -0.0593960178122206 & 0.22941 & -0.2589 & 0.796095 & 0.398048 \tabularnewline
`2B` & 0.20312779537524 & 0.281529 & 0.7215 & 0.471822 & 0.235911 \tabularnewline
`3B` & 0.500771877991365 & 0.340941 & 1.4688 & 0.144181 & 0.072091 \tabularnewline
M1 & -0.104817868555572 & 0.842703 & -0.1244 & 0.901194 & 0.450597 \tabularnewline
M2 & -0.531890927774699 & 0.837472 & -0.6351 & 0.526413 & 0.263207 \tabularnewline
M3 & -0.906326361891386 & 0.841743 & -1.0767 & 0.283495 & 0.141747 \tabularnewline
M4 & -0.42086638214816 & 0.847106 & -0.4968 & 0.620106 & 0.310053 \tabularnewline
M5 & 0.265856736325493 & 0.831778 & 0.3196 & 0.74974 & 0.37487 \tabularnewline
M6 & -1.09338003746871 & 0.836023 & -1.3078 & 0.193119 & 0.09656 \tabularnewline
M7 & -1.29051116073166 & 0.850436 & -1.5175 & 0.131452 & 0.065726 \tabularnewline
M8 & 0.50770470226122 & 0.853539 & 0.5948 & 0.552943 & 0.276471 \tabularnewline
M9 & -0.960871026350095 & 0.843937 & -1.1386 & 0.256875 & 0.128437 \tabularnewline
M10 & -0.397549712140203 & 0.835074 & -0.4761 & 0.634786 & 0.317393 \tabularnewline
M11 & -0.768379620810554 & 0.838042 & -0.9169 & 0.360819 & 0.18041 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111571&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]0.100276382934112[/C][C]1.575466[/C][C]0.0636[/C][C]0.949343[/C][C]0.474671[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.157263405271108[/C][C]0.101728[/C][C]1.5459[/C][C]0.124431[/C][C]0.062216[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.236372152928434[/C][C]0.06331[/C][C]3.7336[/C][C]0.000276[/C][C]0.000138[/C][/ROW]
[ROW][C]Liked[/C][C]0.323351350417035[/C][C]0.101942[/C][C]3.1719[/C][C]0.001869[/C][C]0.000935[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.666471137832579[/C][C]0.164994[/C][C]4.0394[/C][C]8.9e-05[/C][C]4.4e-05[/C][/ROW]
[ROW][C]B[/C][C]-0.0593960178122206[/C][C]0.22941[/C][C]-0.2589[/C][C]0.796095[/C][C]0.398048[/C][/ROW]
[ROW][C]`2B`[/C][C]0.20312779537524[/C][C]0.281529[/C][C]0.7215[/C][C]0.471822[/C][C]0.235911[/C][/ROW]
[ROW][C]`3B`[/C][C]0.500771877991365[/C][C]0.340941[/C][C]1.4688[/C][C]0.144181[/C][C]0.072091[/C][/ROW]
[ROW][C]M1[/C][C]-0.104817868555572[/C][C]0.842703[/C][C]-0.1244[/C][C]0.901194[/C][C]0.450597[/C][/ROW]
[ROW][C]M2[/C][C]-0.531890927774699[/C][C]0.837472[/C][C]-0.6351[/C][C]0.526413[/C][C]0.263207[/C][/ROW]
[ROW][C]M3[/C][C]-0.906326361891386[/C][C]0.841743[/C][C]-1.0767[/C][C]0.283495[/C][C]0.141747[/C][/ROW]
[ROW][C]M4[/C][C]-0.42086638214816[/C][C]0.847106[/C][C]-0.4968[/C][C]0.620106[/C][C]0.310053[/C][/ROW]
[ROW][C]M5[/C][C]0.265856736325493[/C][C]0.831778[/C][C]0.3196[/C][C]0.74974[/C][C]0.37487[/C][/ROW]
[ROW][C]M6[/C][C]-1.09338003746871[/C][C]0.836023[/C][C]-1.3078[/C][C]0.193119[/C][C]0.09656[/C][/ROW]
[ROW][C]M7[/C][C]-1.29051116073166[/C][C]0.850436[/C][C]-1.5175[/C][C]0.131452[/C][C]0.065726[/C][/ROW]
[ROW][C]M8[/C][C]0.50770470226122[/C][C]0.853539[/C][C]0.5948[/C][C]0.552943[/C][C]0.276471[/C][/ROW]
[ROW][C]M9[/C][C]-0.960871026350095[/C][C]0.843937[/C][C]-1.1386[/C][C]0.256875[/C][C]0.128437[/C][/ROW]
[ROW][C]M10[/C][C]-0.397549712140203[/C][C]0.835074[/C][C]-0.4761[/C][C]0.634786[/C][C]0.317393[/C][/ROW]
[ROW][C]M11[/C][C]-0.768379620810554[/C][C]0.838042[/C][C]-0.9169[/C][C]0.360819[/C][C]0.18041[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111571&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111571&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.1002763829341121.5754660.06360.9493430.474671
FindingFriends0.1572634052711080.1017281.54590.1244310.062216
KnowingPeople0.2363721529284340.063313.73360.0002760.000138
Liked0.3233513504170350.1019423.17190.0018690.000935
Celebrity0.6664711378325790.1649944.03948.9e-054.4e-05
B-0.05939601781222060.22941-0.25890.7960950.398048
`2B`0.203127795375240.2815290.72150.4718220.235911
`3B`0.5007718779913650.3409411.46880.1441810.072091
M1-0.1048178685555720.842703-0.12440.9011940.450597
M2-0.5318909277746990.837472-0.63510.5264130.263207
M3-0.9063263618913860.841743-1.07670.2834950.141747
M4-0.420866382148160.847106-0.49680.6201060.310053
M50.2658567363254930.8317780.31960.749740.37487
M6-1.093380037468710.836023-1.30780.1931190.09656
M7-1.290511160731660.850436-1.51750.1314520.065726
M80.507704702261220.8535390.59480.5529430.276471
M9-0.9608710263500950.843937-1.13860.2568750.128437
M10-0.3975497121402030.835074-0.47610.6347860.317393
M11-0.7683796208105540.838042-0.91690.3608190.18041






Multiple Linear Regression - Regression Statistics
Multiple R0.738146206087162
R-squared0.544859821560871
Adjusted R-squared0.485060382057919
F-TEST (value)9.1114536539089
F-TEST (DF numerator)18
F-TEST (DF denominator)137
p-value6.66133814775094e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.10730300856287
Sum Squared Residuals608.379457876042

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.738146206087162 \tabularnewline
R-squared & 0.544859821560871 \tabularnewline
Adjusted R-squared & 0.485060382057919 \tabularnewline
F-TEST (value) & 9.1114536539089 \tabularnewline
F-TEST (DF numerator) & 18 \tabularnewline
F-TEST (DF denominator) & 137 \tabularnewline
p-value & 6.66133814775094e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.10730300856287 \tabularnewline
Sum Squared Residuals & 608.379457876042 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111571&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.738146206087162[/C][/ROW]
[ROW][C]R-squared[/C][C]0.544859821560871[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.485060382057919[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.1114536539089[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]18[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]137[/C][/ROW]
[ROW][C]p-value[/C][C]6.66133814775094e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.10730300856287[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]608.379457876042[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111571&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111571&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.738146206087162
R-squared0.544859821560871
Adjusted R-squared0.485060382057919
F-TEST (value)9.1114536539089
F-TEST (DF numerator)18
F-TEST (DF denominator)137
p-value6.66133814775094e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.10730300856287
Sum Squared Residuals608.379457876042






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11513.45821480027441.54178519972559
21211.66267973864410.337320261355937
3910.7515854233561-1.75158542335612
41012.0457075782519-2.04570757825191
51313.6164596405316-0.616459640531612
61614.29827180399091.70172819600908
71411.97215845594232.02784154405769
81614.74666517947671.25333482052328
91010.4108213668964-0.410821366896409
10810.7322967065934-2.73229670659337
111211.84753158787590.152468412124053
121515.557795493759-0.557795493758962
131411.16952785360342.83047214639665
141412.60912244040131.39087755959866
151212.8015146076379-0.801514607637853
161211.22197303514820.778026964851774
171010.3670206118581-0.367020611858071
1846.09147478972572-2.09147478972572
191414.4704121284025-0.47041212840254
201515.1705273550904-0.170527355090445
211612.86152209366943.13847790633065
221210.78981681253011.21018318746988
231211.71320077371250.286799226287543
241211.72138276021490.27861723978512
251211.75089570582280.249104294177201
261211.95755991657960.0424400834204132
27117.860649490628793.13935050937121
281112.7140311890182-1.71403118901817
291112.8761624742886-1.87616247428859
301111.2093152497399-0.209315249739901
311110.5182250993730.481774900626972
32119.214466634220951.78553336577905
331513.69408117664791.30591882335215
341514.62339916222750.376600837772497
35914.1229447351158-5.12294473511583
361611.07121792678374.92878207321632
37139.618792613925533.38120738607447
38910.218993388731-1.21899338873096
391613.55489900913092.44510099086915
401212.7793894765746-0.779389476574584
41159.52118655540965.47881344459041
4259.13645426646165-4.13645426646165
431110.5971464907380.402853509261961
441714.03019898723152.96980101276855
4598.873184381254440.126815618745559
461314.5442904145702-1.54429041457018
471613.70167029008652.2983297099135
481614.29513742587631.70486257412369
491414.6636948198169-0.663694819816935
501613.39719411957852.60280588042155
511112.5057362102224-1.50573621022242
521111.8686757359823-0.868675735982295
531114.1147234759694-3.11472347596939
541211.61839061573460.381609384265384
551213.0894064173536-1.08940641735356
561213.1741873888851-1.17418738888512
571413.11458923630390.885410763696123
581010.9692342642606-0.969234264260633
5999.25842508978436-0.258425089784361
601212.4846117254544-0.484611725454421
611010.3988061050514-0.398806105051366
621412.8344528760941.16554712390603
6389.44267685207114-1.44267685207114
641614.56832536123711.43167463876289
651416.3459025095284-2.34590250952838
661410.11936039492353.88063960507647
671210.40284402734871.59715597265128
681414.3498105521258-0.349810552125769
69710.6235374819635-3.62353748196349
701913.98456691122475.01543308877529
711512.36777392437782.6322260756222
72811.7376890646163-3.73768906461626
731014.7490889706227-4.74908897062267
741313.0036142685323-0.00361426853231367
751310.75056539948442.24943460051561
761010.5049861265316-0.504986126531649
77129.78752867151412.21247132848591
781516.6892681443786-1.68926814437861
79710.3346234696617-3.33462346966168
801415.0559092707721-1.05590927077206
81108.099954590148871.90004540985113
8269.76305267609099-3.76305267609099
831111.0544536717411-0.0544536717410568
84129.864791722604142.13520827739586
851414.632139809958-0.632139809958001
861213.3190951691181-1.31909516911807
871413.90588924637770.094110753622328
88119.98541012826011.01458987173991
891010.0418478826802-0.0418478826801826
901312.7491702048540.250829795146043
9189.4494849658037-1.44948496580371
92912.7102676229031-3.71026762290307
93611.6560499795334-5.65604997953335
941213.2277757023603-1.2277757023603
951411.9850822633022.01491773669805
961110.898213621850.101786378149977
97810.7136658937449-2.71366589374489
9879.22615316034233-2.22615316034233
99910.1471529489625-1.14715294896246
1001412.2596678564441.74033214355596
1011311.12047094717931.87952905282075
1021512.16928957920532.83071042079473
10354.906640535462030.0933594645379717
1041513.20795167069851.79204832930148
1051311.83845524786651.16154475213346
1061211.56959514561680.430404854383241
10767.46770165538778-1.46770165538778
108710.1881430730212-3.18814307302118
109138.944109760776164.05589023922384
1101614.93326419061881.06673580938115
1111012.8457825503456-2.84578255034555
1121615.18744488239480.812555117605199
1131513.92311600124261.07688399875745
11487.938852602156930.0611473978430705
1151111.5597085507878-0.559708550787802
1161314.4077852771954-1.40778527719541
1171614.6671529680381.33284703196203
118118.660202457066352.33979754293365
1191414.2454332692655-0.245433269265491
120910.560320846528-1.56032084652795
121810.4581464157102-2.45814641571018
122810.7541093361781-2.75410933617805
1231111.343532468272-0.343532468271953
1241213.3519650571308-1.35196505713083
1251111.87270936418-0.872709364179983
1261414.0206638313973-0.0206638313973066
1271111.844687502302-0.844687502302047
1281413.33240488419980.667595115800225
1291314.3410371676024-1.34103716760242
1301210.86739504661921.13260495338081
13145.96752687486148-1.96752687486148
1321513.45414169621661.5458583037834
1331011.6131027590622-1.61310275906218
1341313.9096319400772-0.909631940077212
1351513.82497013874541.17502986125463
1361213.2681602404261-1.26816024042611
1371314.0882498563449-1.08824985634494
13887.549244581293350.450755418706653
139109.883280449233070.116719550766925
1401514.64557872286640.354421277133577
1411614.04507141389471.95492858610533
1421614.97438939947431.02561060052568
1431412.59009373322681.40990626677316
1441413.66293066946870.337069330531349
1451210.82981449163151.17018550836847
1461513.17412945510481.82587054489521
1471312.26504565476540.734954345234571
1481613.24426333260022.75573666739982
1491414.3246220092734-0.324622009273371
15089.41024393613823-1.41024393613823
1511612.97138190759153.02861809240854
1521616.9542464543343-0.954246454334277
1531212.7745428961807-0.774542896180748
1541112.2939853013656-1.29398530136558
1551615.67816213126250.321837868737497
156910.5036239736069-1.50362397360694

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 15 & 13.4582148002744 & 1.54178519972559 \tabularnewline
2 & 12 & 11.6626797386441 & 0.337320261355937 \tabularnewline
3 & 9 & 10.7515854233561 & -1.75158542335612 \tabularnewline
4 & 10 & 12.0457075782519 & -2.04570757825191 \tabularnewline
5 & 13 & 13.6164596405316 & -0.616459640531612 \tabularnewline
6 & 16 & 14.2982718039909 & 1.70172819600908 \tabularnewline
7 & 14 & 11.9721584559423 & 2.02784154405769 \tabularnewline
8 & 16 & 14.7466651794767 & 1.25333482052328 \tabularnewline
9 & 10 & 10.4108213668964 & -0.410821366896409 \tabularnewline
10 & 8 & 10.7322967065934 & -2.73229670659337 \tabularnewline
11 & 12 & 11.8475315878759 & 0.152468412124053 \tabularnewline
12 & 15 & 15.557795493759 & -0.557795493758962 \tabularnewline
13 & 14 & 11.1695278536034 & 2.83047214639665 \tabularnewline
14 & 14 & 12.6091224404013 & 1.39087755959866 \tabularnewline
15 & 12 & 12.8015146076379 & -0.801514607637853 \tabularnewline
16 & 12 & 11.2219730351482 & 0.778026964851774 \tabularnewline
17 & 10 & 10.3670206118581 & -0.367020611858071 \tabularnewline
18 & 4 & 6.09147478972572 & -2.09147478972572 \tabularnewline
19 & 14 & 14.4704121284025 & -0.47041212840254 \tabularnewline
20 & 15 & 15.1705273550904 & -0.170527355090445 \tabularnewline
21 & 16 & 12.8615220936694 & 3.13847790633065 \tabularnewline
22 & 12 & 10.7898168125301 & 1.21018318746988 \tabularnewline
23 & 12 & 11.7132007737125 & 0.286799226287543 \tabularnewline
24 & 12 & 11.7213827602149 & 0.27861723978512 \tabularnewline
25 & 12 & 11.7508957058228 & 0.249104294177201 \tabularnewline
26 & 12 & 11.9575599165796 & 0.0424400834204132 \tabularnewline
27 & 11 & 7.86064949062879 & 3.13935050937121 \tabularnewline
28 & 11 & 12.7140311890182 & -1.71403118901817 \tabularnewline
29 & 11 & 12.8761624742886 & -1.87616247428859 \tabularnewline
30 & 11 & 11.2093152497399 & -0.209315249739901 \tabularnewline
31 & 11 & 10.518225099373 & 0.481774900626972 \tabularnewline
32 & 11 & 9.21446663422095 & 1.78553336577905 \tabularnewline
33 & 15 & 13.6940811766479 & 1.30591882335215 \tabularnewline
34 & 15 & 14.6233991622275 & 0.376600837772497 \tabularnewline
35 & 9 & 14.1229447351158 & -5.12294473511583 \tabularnewline
36 & 16 & 11.0712179267837 & 4.92878207321632 \tabularnewline
37 & 13 & 9.61879261392553 & 3.38120738607447 \tabularnewline
38 & 9 & 10.218993388731 & -1.21899338873096 \tabularnewline
39 & 16 & 13.5548990091309 & 2.44510099086915 \tabularnewline
40 & 12 & 12.7793894765746 & -0.779389476574584 \tabularnewline
41 & 15 & 9.5211865554096 & 5.47881344459041 \tabularnewline
42 & 5 & 9.13645426646165 & -4.13645426646165 \tabularnewline
43 & 11 & 10.597146490738 & 0.402853509261961 \tabularnewline
44 & 17 & 14.0301989872315 & 2.96980101276855 \tabularnewline
45 & 9 & 8.87318438125444 & 0.126815618745559 \tabularnewline
46 & 13 & 14.5442904145702 & -1.54429041457018 \tabularnewline
47 & 16 & 13.7016702900865 & 2.2983297099135 \tabularnewline
48 & 16 & 14.2951374258763 & 1.70486257412369 \tabularnewline
49 & 14 & 14.6636948198169 & -0.663694819816935 \tabularnewline
50 & 16 & 13.3971941195785 & 2.60280588042155 \tabularnewline
51 & 11 & 12.5057362102224 & -1.50573621022242 \tabularnewline
52 & 11 & 11.8686757359823 & -0.868675735982295 \tabularnewline
53 & 11 & 14.1147234759694 & -3.11472347596939 \tabularnewline
54 & 12 & 11.6183906157346 & 0.381609384265384 \tabularnewline
55 & 12 & 13.0894064173536 & -1.08940641735356 \tabularnewline
56 & 12 & 13.1741873888851 & -1.17418738888512 \tabularnewline
57 & 14 & 13.1145892363039 & 0.885410763696123 \tabularnewline
58 & 10 & 10.9692342642606 & -0.969234264260633 \tabularnewline
59 & 9 & 9.25842508978436 & -0.258425089784361 \tabularnewline
60 & 12 & 12.4846117254544 & -0.484611725454421 \tabularnewline
61 & 10 & 10.3988061050514 & -0.398806105051366 \tabularnewline
62 & 14 & 12.834452876094 & 1.16554712390603 \tabularnewline
63 & 8 & 9.44267685207114 & -1.44267685207114 \tabularnewline
64 & 16 & 14.5683253612371 & 1.43167463876289 \tabularnewline
65 & 14 & 16.3459025095284 & -2.34590250952838 \tabularnewline
66 & 14 & 10.1193603949235 & 3.88063960507647 \tabularnewline
67 & 12 & 10.4028440273487 & 1.59715597265128 \tabularnewline
68 & 14 & 14.3498105521258 & -0.349810552125769 \tabularnewline
69 & 7 & 10.6235374819635 & -3.62353748196349 \tabularnewline
70 & 19 & 13.9845669112247 & 5.01543308877529 \tabularnewline
71 & 15 & 12.3677739243778 & 2.6322260756222 \tabularnewline
72 & 8 & 11.7376890646163 & -3.73768906461626 \tabularnewline
73 & 10 & 14.7490889706227 & -4.74908897062267 \tabularnewline
74 & 13 & 13.0036142685323 & -0.00361426853231367 \tabularnewline
75 & 13 & 10.7505653994844 & 2.24943460051561 \tabularnewline
76 & 10 & 10.5049861265316 & -0.504986126531649 \tabularnewline
77 & 12 & 9.7875286715141 & 2.21247132848591 \tabularnewline
78 & 15 & 16.6892681443786 & -1.68926814437861 \tabularnewline
79 & 7 & 10.3346234696617 & -3.33462346966168 \tabularnewline
80 & 14 & 15.0559092707721 & -1.05590927077206 \tabularnewline
81 & 10 & 8.09995459014887 & 1.90004540985113 \tabularnewline
82 & 6 & 9.76305267609099 & -3.76305267609099 \tabularnewline
83 & 11 & 11.0544536717411 & -0.0544536717410568 \tabularnewline
84 & 12 & 9.86479172260414 & 2.13520827739586 \tabularnewline
85 & 14 & 14.632139809958 & -0.632139809958001 \tabularnewline
86 & 12 & 13.3190951691181 & -1.31909516911807 \tabularnewline
87 & 14 & 13.9058892463777 & 0.094110753622328 \tabularnewline
88 & 11 & 9.9854101282601 & 1.01458987173991 \tabularnewline
89 & 10 & 10.0418478826802 & -0.0418478826801826 \tabularnewline
90 & 13 & 12.749170204854 & 0.250829795146043 \tabularnewline
91 & 8 & 9.4494849658037 & -1.44948496580371 \tabularnewline
92 & 9 & 12.7102676229031 & -3.71026762290307 \tabularnewline
93 & 6 & 11.6560499795334 & -5.65604997953335 \tabularnewline
94 & 12 & 13.2277757023603 & -1.2277757023603 \tabularnewline
95 & 14 & 11.985082263302 & 2.01491773669805 \tabularnewline
96 & 11 & 10.89821362185 & 0.101786378149977 \tabularnewline
97 & 8 & 10.7136658937449 & -2.71366589374489 \tabularnewline
98 & 7 & 9.22615316034233 & -2.22615316034233 \tabularnewline
99 & 9 & 10.1471529489625 & -1.14715294896246 \tabularnewline
100 & 14 & 12.259667856444 & 1.74033214355596 \tabularnewline
101 & 13 & 11.1204709471793 & 1.87952905282075 \tabularnewline
102 & 15 & 12.1692895792053 & 2.83071042079473 \tabularnewline
103 & 5 & 4.90664053546203 & 0.0933594645379717 \tabularnewline
104 & 15 & 13.2079516706985 & 1.79204832930148 \tabularnewline
105 & 13 & 11.8384552478665 & 1.16154475213346 \tabularnewline
106 & 12 & 11.5695951456168 & 0.430404854383241 \tabularnewline
107 & 6 & 7.46770165538778 & -1.46770165538778 \tabularnewline
108 & 7 & 10.1881430730212 & -3.18814307302118 \tabularnewline
109 & 13 & 8.94410976077616 & 4.05589023922384 \tabularnewline
110 & 16 & 14.9332641906188 & 1.06673580938115 \tabularnewline
111 & 10 & 12.8457825503456 & -2.84578255034555 \tabularnewline
112 & 16 & 15.1874448823948 & 0.812555117605199 \tabularnewline
113 & 15 & 13.9231160012426 & 1.07688399875745 \tabularnewline
114 & 8 & 7.93885260215693 & 0.0611473978430705 \tabularnewline
115 & 11 & 11.5597085507878 & -0.559708550787802 \tabularnewline
116 & 13 & 14.4077852771954 & -1.40778527719541 \tabularnewline
117 & 16 & 14.667152968038 & 1.33284703196203 \tabularnewline
118 & 11 & 8.66020245706635 & 2.33979754293365 \tabularnewline
119 & 14 & 14.2454332692655 & -0.245433269265491 \tabularnewline
120 & 9 & 10.560320846528 & -1.56032084652795 \tabularnewline
121 & 8 & 10.4581464157102 & -2.45814641571018 \tabularnewline
122 & 8 & 10.7541093361781 & -2.75410933617805 \tabularnewline
123 & 11 & 11.343532468272 & -0.343532468271953 \tabularnewline
124 & 12 & 13.3519650571308 & -1.35196505713083 \tabularnewline
125 & 11 & 11.87270936418 & -0.872709364179983 \tabularnewline
126 & 14 & 14.0206638313973 & -0.0206638313973066 \tabularnewline
127 & 11 & 11.844687502302 & -0.844687502302047 \tabularnewline
128 & 14 & 13.3324048841998 & 0.667595115800225 \tabularnewline
129 & 13 & 14.3410371676024 & -1.34103716760242 \tabularnewline
130 & 12 & 10.8673950466192 & 1.13260495338081 \tabularnewline
131 & 4 & 5.96752687486148 & -1.96752687486148 \tabularnewline
132 & 15 & 13.4541416962166 & 1.5458583037834 \tabularnewline
133 & 10 & 11.6131027590622 & -1.61310275906218 \tabularnewline
134 & 13 & 13.9096319400772 & -0.909631940077212 \tabularnewline
135 & 15 & 13.8249701387454 & 1.17502986125463 \tabularnewline
136 & 12 & 13.2681602404261 & -1.26816024042611 \tabularnewline
137 & 13 & 14.0882498563449 & -1.08824985634494 \tabularnewline
138 & 8 & 7.54924458129335 & 0.450755418706653 \tabularnewline
139 & 10 & 9.88328044923307 & 0.116719550766925 \tabularnewline
140 & 15 & 14.6455787228664 & 0.354421277133577 \tabularnewline
141 & 16 & 14.0450714138947 & 1.95492858610533 \tabularnewline
142 & 16 & 14.9743893994743 & 1.02561060052568 \tabularnewline
143 & 14 & 12.5900937332268 & 1.40990626677316 \tabularnewline
144 & 14 & 13.6629306694687 & 0.337069330531349 \tabularnewline
145 & 12 & 10.8298144916315 & 1.17018550836847 \tabularnewline
146 & 15 & 13.1741294551048 & 1.82587054489521 \tabularnewline
147 & 13 & 12.2650456547654 & 0.734954345234571 \tabularnewline
148 & 16 & 13.2442633326002 & 2.75573666739982 \tabularnewline
149 & 14 & 14.3246220092734 & -0.324622009273371 \tabularnewline
150 & 8 & 9.41024393613823 & -1.41024393613823 \tabularnewline
151 & 16 & 12.9713819075915 & 3.02861809240854 \tabularnewline
152 & 16 & 16.9542464543343 & -0.954246454334277 \tabularnewline
153 & 12 & 12.7745428961807 & -0.774542896180748 \tabularnewline
154 & 11 & 12.2939853013656 & -1.29398530136558 \tabularnewline
155 & 16 & 15.6781621312625 & 0.321837868737497 \tabularnewline
156 & 9 & 10.5036239736069 & -1.50362397360694 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111571&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]15[/C][C]13.4582148002744[/C][C]1.54178519972559[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]11.6626797386441[/C][C]0.337320261355937[/C][/ROW]
[ROW][C]3[/C][C]9[/C][C]10.7515854233561[/C][C]-1.75158542335612[/C][/ROW]
[ROW][C]4[/C][C]10[/C][C]12.0457075782519[/C][C]-2.04570757825191[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]13.6164596405316[/C][C]-0.616459640531612[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]14.2982718039909[/C][C]1.70172819600908[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]11.9721584559423[/C][C]2.02784154405769[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]14.7466651794767[/C][C]1.25333482052328[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]10.4108213668964[/C][C]-0.410821366896409[/C][/ROW]
[ROW][C]10[/C][C]8[/C][C]10.7322967065934[/C][C]-2.73229670659337[/C][/ROW]
[ROW][C]11[/C][C]12[/C][C]11.8475315878759[/C][C]0.152468412124053[/C][/ROW]
[ROW][C]12[/C][C]15[/C][C]15.557795493759[/C][C]-0.557795493758962[/C][/ROW]
[ROW][C]13[/C][C]14[/C][C]11.1695278536034[/C][C]2.83047214639665[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]12.6091224404013[/C][C]1.39087755959866[/C][/ROW]
[ROW][C]15[/C][C]12[/C][C]12.8015146076379[/C][C]-0.801514607637853[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]11.2219730351482[/C][C]0.778026964851774[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]10.3670206118581[/C][C]-0.367020611858071[/C][/ROW]
[ROW][C]18[/C][C]4[/C][C]6.09147478972572[/C][C]-2.09147478972572[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]14.4704121284025[/C][C]-0.47041212840254[/C][/ROW]
[ROW][C]20[/C][C]15[/C][C]15.1705273550904[/C][C]-0.170527355090445[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]12.8615220936694[/C][C]3.13847790633065[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]10.7898168125301[/C][C]1.21018318746988[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]11.7132007737125[/C][C]0.286799226287543[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]11.7213827602149[/C][C]0.27861723978512[/C][/ROW]
[ROW][C]25[/C][C]12[/C][C]11.7508957058228[/C][C]0.249104294177201[/C][/ROW]
[ROW][C]26[/C][C]12[/C][C]11.9575599165796[/C][C]0.0424400834204132[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]7.86064949062879[/C][C]3.13935050937121[/C][/ROW]
[ROW][C]28[/C][C]11[/C][C]12.7140311890182[/C][C]-1.71403118901817[/C][/ROW]
[ROW][C]29[/C][C]11[/C][C]12.8761624742886[/C][C]-1.87616247428859[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]11.2093152497399[/C][C]-0.209315249739901[/C][/ROW]
[ROW][C]31[/C][C]11[/C][C]10.518225099373[/C][C]0.481774900626972[/C][/ROW]
[ROW][C]32[/C][C]11[/C][C]9.21446663422095[/C][C]1.78553336577905[/C][/ROW]
[ROW][C]33[/C][C]15[/C][C]13.6940811766479[/C][C]1.30591882335215[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]14.6233991622275[/C][C]0.376600837772497[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]14.1229447351158[/C][C]-5.12294473511583[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]11.0712179267837[/C][C]4.92878207321632[/C][/ROW]
[ROW][C]37[/C][C]13[/C][C]9.61879261392553[/C][C]3.38120738607447[/C][/ROW]
[ROW][C]38[/C][C]9[/C][C]10.218993388731[/C][C]-1.21899338873096[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]13.5548990091309[/C][C]2.44510099086915[/C][/ROW]
[ROW][C]40[/C][C]12[/C][C]12.7793894765746[/C][C]-0.779389476574584[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]9.5211865554096[/C][C]5.47881344459041[/C][/ROW]
[ROW][C]42[/C][C]5[/C][C]9.13645426646165[/C][C]-4.13645426646165[/C][/ROW]
[ROW][C]43[/C][C]11[/C][C]10.597146490738[/C][C]0.402853509261961[/C][/ROW]
[ROW][C]44[/C][C]17[/C][C]14.0301989872315[/C][C]2.96980101276855[/C][/ROW]
[ROW][C]45[/C][C]9[/C][C]8.87318438125444[/C][C]0.126815618745559[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]14.5442904145702[/C][C]-1.54429041457018[/C][/ROW]
[ROW][C]47[/C][C]16[/C][C]13.7016702900865[/C][C]2.2983297099135[/C][/ROW]
[ROW][C]48[/C][C]16[/C][C]14.2951374258763[/C][C]1.70486257412369[/C][/ROW]
[ROW][C]49[/C][C]14[/C][C]14.6636948198169[/C][C]-0.663694819816935[/C][/ROW]
[ROW][C]50[/C][C]16[/C][C]13.3971941195785[/C][C]2.60280588042155[/C][/ROW]
[ROW][C]51[/C][C]11[/C][C]12.5057362102224[/C][C]-1.50573621022242[/C][/ROW]
[ROW][C]52[/C][C]11[/C][C]11.8686757359823[/C][C]-0.868675735982295[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]14.1147234759694[/C][C]-3.11472347596939[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]11.6183906157346[/C][C]0.381609384265384[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]13.0894064173536[/C][C]-1.08940641735356[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]13.1741873888851[/C][C]-1.17418738888512[/C][/ROW]
[ROW][C]57[/C][C]14[/C][C]13.1145892363039[/C][C]0.885410763696123[/C][/ROW]
[ROW][C]58[/C][C]10[/C][C]10.9692342642606[/C][C]-0.969234264260633[/C][/ROW]
[ROW][C]59[/C][C]9[/C][C]9.25842508978436[/C][C]-0.258425089784361[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]12.4846117254544[/C][C]-0.484611725454421[/C][/ROW]
[ROW][C]61[/C][C]10[/C][C]10.3988061050514[/C][C]-0.398806105051366[/C][/ROW]
[ROW][C]62[/C][C]14[/C][C]12.834452876094[/C][C]1.16554712390603[/C][/ROW]
[ROW][C]63[/C][C]8[/C][C]9.44267685207114[/C][C]-1.44267685207114[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]14.5683253612371[/C][C]1.43167463876289[/C][/ROW]
[ROW][C]65[/C][C]14[/C][C]16.3459025095284[/C][C]-2.34590250952838[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]10.1193603949235[/C][C]3.88063960507647[/C][/ROW]
[ROW][C]67[/C][C]12[/C][C]10.4028440273487[/C][C]1.59715597265128[/C][/ROW]
[ROW][C]68[/C][C]14[/C][C]14.3498105521258[/C][C]-0.349810552125769[/C][/ROW]
[ROW][C]69[/C][C]7[/C][C]10.6235374819635[/C][C]-3.62353748196349[/C][/ROW]
[ROW][C]70[/C][C]19[/C][C]13.9845669112247[/C][C]5.01543308877529[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]12.3677739243778[/C][C]2.6322260756222[/C][/ROW]
[ROW][C]72[/C][C]8[/C][C]11.7376890646163[/C][C]-3.73768906461626[/C][/ROW]
[ROW][C]73[/C][C]10[/C][C]14.7490889706227[/C][C]-4.74908897062267[/C][/ROW]
[ROW][C]74[/C][C]13[/C][C]13.0036142685323[/C][C]-0.00361426853231367[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]10.7505653994844[/C][C]2.24943460051561[/C][/ROW]
[ROW][C]76[/C][C]10[/C][C]10.5049861265316[/C][C]-0.504986126531649[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]9.7875286715141[/C][C]2.21247132848591[/C][/ROW]
[ROW][C]78[/C][C]15[/C][C]16.6892681443786[/C][C]-1.68926814437861[/C][/ROW]
[ROW][C]79[/C][C]7[/C][C]10.3346234696617[/C][C]-3.33462346966168[/C][/ROW]
[ROW][C]80[/C][C]14[/C][C]15.0559092707721[/C][C]-1.05590927077206[/C][/ROW]
[ROW][C]81[/C][C]10[/C][C]8.09995459014887[/C][C]1.90004540985113[/C][/ROW]
[ROW][C]82[/C][C]6[/C][C]9.76305267609099[/C][C]-3.76305267609099[/C][/ROW]
[ROW][C]83[/C][C]11[/C][C]11.0544536717411[/C][C]-0.0544536717410568[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]9.86479172260414[/C][C]2.13520827739586[/C][/ROW]
[ROW][C]85[/C][C]14[/C][C]14.632139809958[/C][C]-0.632139809958001[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]13.3190951691181[/C][C]-1.31909516911807[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]13.9058892463777[/C][C]0.094110753622328[/C][/ROW]
[ROW][C]88[/C][C]11[/C][C]9.9854101282601[/C][C]1.01458987173991[/C][/ROW]
[ROW][C]89[/C][C]10[/C][C]10.0418478826802[/C][C]-0.0418478826801826[/C][/ROW]
[ROW][C]90[/C][C]13[/C][C]12.749170204854[/C][C]0.250829795146043[/C][/ROW]
[ROW][C]91[/C][C]8[/C][C]9.4494849658037[/C][C]-1.44948496580371[/C][/ROW]
[ROW][C]92[/C][C]9[/C][C]12.7102676229031[/C][C]-3.71026762290307[/C][/ROW]
[ROW][C]93[/C][C]6[/C][C]11.6560499795334[/C][C]-5.65604997953335[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]13.2277757023603[/C][C]-1.2277757023603[/C][/ROW]
[ROW][C]95[/C][C]14[/C][C]11.985082263302[/C][C]2.01491773669805[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]10.89821362185[/C][C]0.101786378149977[/C][/ROW]
[ROW][C]97[/C][C]8[/C][C]10.7136658937449[/C][C]-2.71366589374489[/C][/ROW]
[ROW][C]98[/C][C]7[/C][C]9.22615316034233[/C][C]-2.22615316034233[/C][/ROW]
[ROW][C]99[/C][C]9[/C][C]10.1471529489625[/C][C]-1.14715294896246[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]12.259667856444[/C][C]1.74033214355596[/C][/ROW]
[ROW][C]101[/C][C]13[/C][C]11.1204709471793[/C][C]1.87952905282075[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]12.1692895792053[/C][C]2.83071042079473[/C][/ROW]
[ROW][C]103[/C][C]5[/C][C]4.90664053546203[/C][C]0.0933594645379717[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]13.2079516706985[/C][C]1.79204832930148[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]11.8384552478665[/C][C]1.16154475213346[/C][/ROW]
[ROW][C]106[/C][C]12[/C][C]11.5695951456168[/C][C]0.430404854383241[/C][/ROW]
[ROW][C]107[/C][C]6[/C][C]7.46770165538778[/C][C]-1.46770165538778[/C][/ROW]
[ROW][C]108[/C][C]7[/C][C]10.1881430730212[/C][C]-3.18814307302118[/C][/ROW]
[ROW][C]109[/C][C]13[/C][C]8.94410976077616[/C][C]4.05589023922384[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]14.9332641906188[/C][C]1.06673580938115[/C][/ROW]
[ROW][C]111[/C][C]10[/C][C]12.8457825503456[/C][C]-2.84578255034555[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.1874448823948[/C][C]0.812555117605199[/C][/ROW]
[ROW][C]113[/C][C]15[/C][C]13.9231160012426[/C][C]1.07688399875745[/C][/ROW]
[ROW][C]114[/C][C]8[/C][C]7.93885260215693[/C][C]0.0611473978430705[/C][/ROW]
[ROW][C]115[/C][C]11[/C][C]11.5597085507878[/C][C]-0.559708550787802[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]14.4077852771954[/C][C]-1.40778527719541[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]14.667152968038[/C][C]1.33284703196203[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]8.66020245706635[/C][C]2.33979754293365[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]14.2454332692655[/C][C]-0.245433269265491[/C][/ROW]
[ROW][C]120[/C][C]9[/C][C]10.560320846528[/C][C]-1.56032084652795[/C][/ROW]
[ROW][C]121[/C][C]8[/C][C]10.4581464157102[/C][C]-2.45814641571018[/C][/ROW]
[ROW][C]122[/C][C]8[/C][C]10.7541093361781[/C][C]-2.75410933617805[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]11.343532468272[/C][C]-0.343532468271953[/C][/ROW]
[ROW][C]124[/C][C]12[/C][C]13.3519650571308[/C][C]-1.35196505713083[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]11.87270936418[/C][C]-0.872709364179983[/C][/ROW]
[ROW][C]126[/C][C]14[/C][C]14.0206638313973[/C][C]-0.0206638313973066[/C][/ROW]
[ROW][C]127[/C][C]11[/C][C]11.844687502302[/C][C]-0.844687502302047[/C][/ROW]
[ROW][C]128[/C][C]14[/C][C]13.3324048841998[/C][C]0.667595115800225[/C][/ROW]
[ROW][C]129[/C][C]13[/C][C]14.3410371676024[/C][C]-1.34103716760242[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]10.8673950466192[/C][C]1.13260495338081[/C][/ROW]
[ROW][C]131[/C][C]4[/C][C]5.96752687486148[/C][C]-1.96752687486148[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]13.4541416962166[/C][C]1.5458583037834[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.6131027590622[/C][C]-1.61310275906218[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]13.9096319400772[/C][C]-0.909631940077212[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]13.8249701387454[/C][C]1.17502986125463[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]13.2681602404261[/C][C]-1.26816024042611[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]14.0882498563449[/C][C]-1.08824985634494[/C][/ROW]
[ROW][C]138[/C][C]8[/C][C]7.54924458129335[/C][C]0.450755418706653[/C][/ROW]
[ROW][C]139[/C][C]10[/C][C]9.88328044923307[/C][C]0.116719550766925[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]14.6455787228664[/C][C]0.354421277133577[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]14.0450714138947[/C][C]1.95492858610533[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]14.9743893994743[/C][C]1.02561060052568[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]12.5900937332268[/C][C]1.40990626677316[/C][/ROW]
[ROW][C]144[/C][C]14[/C][C]13.6629306694687[/C][C]0.337069330531349[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]10.8298144916315[/C][C]1.17018550836847[/C][/ROW]
[ROW][C]146[/C][C]15[/C][C]13.1741294551048[/C][C]1.82587054489521[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]12.2650456547654[/C][C]0.734954345234571[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]13.2442633326002[/C][C]2.75573666739982[/C][/ROW]
[ROW][C]149[/C][C]14[/C][C]14.3246220092734[/C][C]-0.324622009273371[/C][/ROW]
[ROW][C]150[/C][C]8[/C][C]9.41024393613823[/C][C]-1.41024393613823[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]12.9713819075915[/C][C]3.02861809240854[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]16.9542464543343[/C][C]-0.954246454334277[/C][/ROW]
[ROW][C]153[/C][C]12[/C][C]12.7745428961807[/C][C]-0.774542896180748[/C][/ROW]
[ROW][C]154[/C][C]11[/C][C]12.2939853013656[/C][C]-1.29398530136558[/C][/ROW]
[ROW][C]155[/C][C]16[/C][C]15.6781621312625[/C][C]0.321837868737497[/C][/ROW]
[ROW][C]156[/C][C]9[/C][C]10.5036239736069[/C][C]-1.50362397360694[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111571&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111571&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11513.45821480027441.54178519972559
21211.66267973864410.337320261355937
3910.7515854233561-1.75158542335612
41012.0457075782519-2.04570757825191
51313.6164596405316-0.616459640531612
61614.29827180399091.70172819600908
71411.97215845594232.02784154405769
81614.74666517947671.25333482052328
91010.4108213668964-0.410821366896409
10810.7322967065934-2.73229670659337
111211.84753158787590.152468412124053
121515.557795493759-0.557795493758962
131411.16952785360342.83047214639665
141412.60912244040131.39087755959866
151212.8015146076379-0.801514607637853
161211.22197303514820.778026964851774
171010.3670206118581-0.367020611858071
1846.09147478972572-2.09147478972572
191414.4704121284025-0.47041212840254
201515.1705273550904-0.170527355090445
211612.86152209366943.13847790633065
221210.78981681253011.21018318746988
231211.71320077371250.286799226287543
241211.72138276021490.27861723978512
251211.75089570582280.249104294177201
261211.95755991657960.0424400834204132
27117.860649490628793.13935050937121
281112.7140311890182-1.71403118901817
291112.8761624742886-1.87616247428859
301111.2093152497399-0.209315249739901
311110.5182250993730.481774900626972
32119.214466634220951.78553336577905
331513.69408117664791.30591882335215
341514.62339916222750.376600837772497
35914.1229447351158-5.12294473511583
361611.07121792678374.92878207321632
37139.618792613925533.38120738607447
38910.218993388731-1.21899338873096
391613.55489900913092.44510099086915
401212.7793894765746-0.779389476574584
41159.52118655540965.47881344459041
4259.13645426646165-4.13645426646165
431110.5971464907380.402853509261961
441714.03019898723152.96980101276855
4598.873184381254440.126815618745559
461314.5442904145702-1.54429041457018
471613.70167029008652.2983297099135
481614.29513742587631.70486257412369
491414.6636948198169-0.663694819816935
501613.39719411957852.60280588042155
511112.5057362102224-1.50573621022242
521111.8686757359823-0.868675735982295
531114.1147234759694-3.11472347596939
541211.61839061573460.381609384265384
551213.0894064173536-1.08940641735356
561213.1741873888851-1.17418738888512
571413.11458923630390.885410763696123
581010.9692342642606-0.969234264260633
5999.25842508978436-0.258425089784361
601212.4846117254544-0.484611725454421
611010.3988061050514-0.398806105051366
621412.8344528760941.16554712390603
6389.44267685207114-1.44267685207114
641614.56832536123711.43167463876289
651416.3459025095284-2.34590250952838
661410.11936039492353.88063960507647
671210.40284402734871.59715597265128
681414.3498105521258-0.349810552125769
69710.6235374819635-3.62353748196349
701913.98456691122475.01543308877529
711512.36777392437782.6322260756222
72811.7376890646163-3.73768906461626
731014.7490889706227-4.74908897062267
741313.0036142685323-0.00361426853231367
751310.75056539948442.24943460051561
761010.5049861265316-0.504986126531649
77129.78752867151412.21247132848591
781516.6892681443786-1.68926814437861
79710.3346234696617-3.33462346966168
801415.0559092707721-1.05590927077206
81108.099954590148871.90004540985113
8269.76305267609099-3.76305267609099
831111.0544536717411-0.0544536717410568
84129.864791722604142.13520827739586
851414.632139809958-0.632139809958001
861213.3190951691181-1.31909516911807
871413.90588924637770.094110753622328
88119.98541012826011.01458987173991
891010.0418478826802-0.0418478826801826
901312.7491702048540.250829795146043
9189.4494849658037-1.44948496580371
92912.7102676229031-3.71026762290307
93611.6560499795334-5.65604997953335
941213.2277757023603-1.2277757023603
951411.9850822633022.01491773669805
961110.898213621850.101786378149977
97810.7136658937449-2.71366589374489
9879.22615316034233-2.22615316034233
99910.1471529489625-1.14715294896246
1001412.2596678564441.74033214355596
1011311.12047094717931.87952905282075
1021512.16928957920532.83071042079473
10354.906640535462030.0933594645379717
1041513.20795167069851.79204832930148
1051311.83845524786651.16154475213346
1061211.56959514561680.430404854383241
10767.46770165538778-1.46770165538778
108710.1881430730212-3.18814307302118
109138.944109760776164.05589023922384
1101614.93326419061881.06673580938115
1111012.8457825503456-2.84578255034555
1121615.18744488239480.812555117605199
1131513.92311600124261.07688399875745
11487.938852602156930.0611473978430705
1151111.5597085507878-0.559708550787802
1161314.4077852771954-1.40778527719541
1171614.6671529680381.33284703196203
118118.660202457066352.33979754293365
1191414.2454332692655-0.245433269265491
120910.560320846528-1.56032084652795
121810.4581464157102-2.45814641571018
122810.7541093361781-2.75410933617805
1231111.343532468272-0.343532468271953
1241213.3519650571308-1.35196505713083
1251111.87270936418-0.872709364179983
1261414.0206638313973-0.0206638313973066
1271111.844687502302-0.844687502302047
1281413.33240488419980.667595115800225
1291314.3410371676024-1.34103716760242
1301210.86739504661921.13260495338081
13145.96752687486148-1.96752687486148
1321513.45414169621661.5458583037834
1331011.6131027590622-1.61310275906218
1341313.9096319400772-0.909631940077212
1351513.82497013874541.17502986125463
1361213.2681602404261-1.26816024042611
1371314.0882498563449-1.08824985634494
13887.549244581293350.450755418706653
139109.883280449233070.116719550766925
1401514.64557872286640.354421277133577
1411614.04507141389471.95492858610533
1421614.97438939947431.02561060052568
1431412.59009373322681.40990626677316
1441413.66293066946870.337069330531349
1451210.82981449163151.17018550836847
1461513.17412945510481.82587054489521
1471312.26504565476540.734954345234571
1481613.24426333260022.75573666739982
1491414.3246220092734-0.324622009273371
15089.41024393613823-1.41024393613823
1511612.97138190759153.02861809240854
1521616.9542464543343-0.954246454334277
1531212.7745428961807-0.774542896180748
1541112.2939853013656-1.29398530136558
1551615.67816213126250.321837868737497
156910.5036239736069-1.50362397360694






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
220.3237862089701210.6475724179402410.67621379102988
230.1981773974693490.3963547949386990.80182260253065
240.1498630513901410.2997261027802830.850136948609859
250.09611102601490670.1922220520298130.903888973985093
260.1959610377244330.3919220754488660.804038962275567
270.3364837679544780.6729675359089550.663516232045522
280.3796815031579960.7593630063159910.620318496842004
290.3807179515464430.7614359030928860.619282048453557
300.2968311712997680.5936623425995370.703168828700232
310.2291996441177990.4583992882355970.770800355882201
320.1785523508608570.3571047017217150.821447649139143
330.130651026053070.261302052106140.86934897394693
340.09153504251223980.183070085024480.90846495748776
350.200531744053980.4010634881079610.79946825594602
360.3949000358111420.7898000716222830.605099964188858
370.3618690186883020.7237380373766040.638130981311698
380.3420669497243910.6841338994487820.657933050275609
390.386070050024740.772140100049480.61392994997526
400.3333971012710010.6667942025420010.666602898728999
410.6213303334199530.7573393331600950.378669666580047
420.7403399157610340.5193201684779320.259660084238966
430.6874059788088930.6251880423822150.312594021191107
440.6800950017376810.6398099965246380.319904998262319
450.6227810267958240.7544379464083510.377218973204175
460.591417488579880.817165022840240.40858251142012
470.5940845445907650.8118309108184690.405915455409235
480.5487378360296690.9025243279406620.451262163970331
490.5191808999194370.9616382001611270.480819100080563
500.5613127396828930.8773745206342150.438687260317108
510.5158941475602520.9682117048794960.484105852439748
520.4695869529504720.9391739059009450.530413047049528
530.7104433740869390.5791132518261230.289556625913061
540.679234998279720.6415300034405610.320765001720281
550.6488800442454570.7022399115090860.351119955754543
560.6086768558084460.7826462883831070.391323144191554
570.5706154056819390.8587691886361220.429384594318061
580.5265091140581410.9469817718837180.473490885941859
590.4741024074398480.9482048148796960.525897592560152
600.5104218938278650.979156212344270.489578106172135
610.4804460354852450.960892070970490.519553964514755
620.4394034517224820.8788069034449640.560596548277518
630.4019209338845590.8038418677691180.598079066115441
640.3990327090600420.7980654181200830.600967290939958
650.4290503610962520.8581007221925030.570949638903748
660.55492833653340.8901433269332010.445071663466601
670.5236098402807770.9527803194384460.476390159719223
680.4729870526194860.9459741052389730.527012947380514
690.6546077736201450.690784452759710.345392226379855
700.8239086309166310.3521827381667380.176091369083369
710.8358861320411560.3282277359176880.164113867958844
720.8965281244005980.2069437511988050.103471875599402
730.9672678221485190.06546435570296260.0327321778514813
740.9565316340559440.08693673188811150.0434683659440557
750.9537910262134460.09241794757310860.0462089737865543
760.9418799999452650.116240000109470.0581200000547348
770.9441988058980180.1116023882039640.0558011941019821
780.9417815135926340.1164369728147320.0582184864073662
790.9657425784953430.06851484300931320.0342574215046566
800.9574937991510660.08501240169786760.0425062008489338
810.96173642610340.07652714779319810.0382635738965991
820.9846558226734080.03068835465318380.0153441773265919
830.9795987678996150.04080246420077090.0204012321003854
840.9822303324289890.03553933514202230.0177696675710111
850.9770789838046920.04584203239061680.0229210161953084
860.9723387435762070.0553225128475870.0276612564237935
870.963074191463940.07385161707211860.0369258085360593
880.9571324157646480.08573516847070410.042867584235352
890.952296605479260.09540678904148130.0477033945207406
900.9377102503212730.1245794993574550.0622897496787274
910.9282529209398120.1434941581203760.0717470790601879
920.9496664564066070.1006670871867850.0503335435933926
930.9930986677976130.01380266440477350.00690133220238673
940.9925371203714630.01492575925707370.00746287962853684
950.9913800263971470.01723994720570610.00861997360285305
960.9889406704027950.02211865919440980.0110593295972049
970.9922698109181660.01546037816366820.00773018908183411
980.9916102914858680.01677941702826340.00838970851413171
990.9889668817841740.02206623643165260.0110331182158263
1000.9857056477433160.02858870451336740.0142943522566837
1010.9907270925383870.01854581492322530.00927290746161265
1020.9915722598872470.01685548022550690.00842774011275347
1030.9886042910022270.02279141799554690.0113957089977735
1040.9870231361918830.02595372761623360.0129768638081168
1050.9841844374982180.03163112500356460.0158155625017823
1060.9782834149543850.04343317009123010.021716585045615
1070.9709615461611850.05807690767762910.0290384538388145
1080.9770993544995930.04580129100081360.0229006455004068
1090.9960532210470570.007893557905886930.00394677895294347
1100.9948506778562180.01029864428756370.00514932214378185
1110.9980320136933460.00393597261330820.0019679863066541
1120.996744251316620.006511497366758710.00325574868337935
1130.9959983549456180.008003290108763540.00400164505438177
1140.9932372437501930.01352551249961370.00676275624980684
1150.98996514890140.02006970219720180.0100348510986009
1160.9843858315657070.03122833686858550.0156141684342927
1170.9799676847274940.04006463054501160.0200323152725058
1180.9866322851178110.02673542976437710.0133677148821886
1190.979580920757630.04083815848473930.0204190792423697
1200.9694420576978380.0611158846043240.030557942302162
1210.9690290554925850.06194188901482930.0309709445074146
1220.956712673764440.08657465247112140.0432873262355607
1230.9430338132670050.1139323734659890.0569661867329947
1240.9678090261461560.06438194770768850.0321909738538443
1250.956422408613240.08715518277352140.0435775913867607
1260.9288276848924960.1423446302150080.0711723151075038
1270.9037796959153070.1924406081693860.096220304084693
1280.8628795975841090.2742408048317830.137120402415891
1290.8278217511345480.3443564977309050.172178248865452
1300.8516723761482460.2966552477035080.148327623851754
1310.7857881462755780.4284237074488440.214211853724422
1320.7500351877486580.4999296245026840.249964812251342
1330.9827694135270770.03446117294584580.0172305864729229
1340.9733794126862580.05324117462748470.0266205873137423

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
22 & 0.323786208970121 & 0.647572417940241 & 0.67621379102988 \tabularnewline
23 & 0.198177397469349 & 0.396354794938699 & 0.80182260253065 \tabularnewline
24 & 0.149863051390141 & 0.299726102780283 & 0.850136948609859 \tabularnewline
25 & 0.0961110260149067 & 0.192222052029813 & 0.903888973985093 \tabularnewline
26 & 0.195961037724433 & 0.391922075448866 & 0.804038962275567 \tabularnewline
27 & 0.336483767954478 & 0.672967535908955 & 0.663516232045522 \tabularnewline
28 & 0.379681503157996 & 0.759363006315991 & 0.620318496842004 \tabularnewline
29 & 0.380717951546443 & 0.761435903092886 & 0.619282048453557 \tabularnewline
30 & 0.296831171299768 & 0.593662342599537 & 0.703168828700232 \tabularnewline
31 & 0.229199644117799 & 0.458399288235597 & 0.770800355882201 \tabularnewline
32 & 0.178552350860857 & 0.357104701721715 & 0.821447649139143 \tabularnewline
33 & 0.13065102605307 & 0.26130205210614 & 0.86934897394693 \tabularnewline
34 & 0.0915350425122398 & 0.18307008502448 & 0.90846495748776 \tabularnewline
35 & 0.20053174405398 & 0.401063488107961 & 0.79946825594602 \tabularnewline
36 & 0.394900035811142 & 0.789800071622283 & 0.605099964188858 \tabularnewline
37 & 0.361869018688302 & 0.723738037376604 & 0.638130981311698 \tabularnewline
38 & 0.342066949724391 & 0.684133899448782 & 0.657933050275609 \tabularnewline
39 & 0.38607005002474 & 0.77214010004948 & 0.61392994997526 \tabularnewline
40 & 0.333397101271001 & 0.666794202542001 & 0.666602898728999 \tabularnewline
41 & 0.621330333419953 & 0.757339333160095 & 0.378669666580047 \tabularnewline
42 & 0.740339915761034 & 0.519320168477932 & 0.259660084238966 \tabularnewline
43 & 0.687405978808893 & 0.625188042382215 & 0.312594021191107 \tabularnewline
44 & 0.680095001737681 & 0.639809996524638 & 0.319904998262319 \tabularnewline
45 & 0.622781026795824 & 0.754437946408351 & 0.377218973204175 \tabularnewline
46 & 0.59141748857988 & 0.81716502284024 & 0.40858251142012 \tabularnewline
47 & 0.594084544590765 & 0.811830910818469 & 0.405915455409235 \tabularnewline
48 & 0.548737836029669 & 0.902524327940662 & 0.451262163970331 \tabularnewline
49 & 0.519180899919437 & 0.961638200161127 & 0.480819100080563 \tabularnewline
50 & 0.561312739682893 & 0.877374520634215 & 0.438687260317108 \tabularnewline
51 & 0.515894147560252 & 0.968211704879496 & 0.484105852439748 \tabularnewline
52 & 0.469586952950472 & 0.939173905900945 & 0.530413047049528 \tabularnewline
53 & 0.710443374086939 & 0.579113251826123 & 0.289556625913061 \tabularnewline
54 & 0.67923499827972 & 0.641530003440561 & 0.320765001720281 \tabularnewline
55 & 0.648880044245457 & 0.702239911509086 & 0.351119955754543 \tabularnewline
56 & 0.608676855808446 & 0.782646288383107 & 0.391323144191554 \tabularnewline
57 & 0.570615405681939 & 0.858769188636122 & 0.429384594318061 \tabularnewline
58 & 0.526509114058141 & 0.946981771883718 & 0.473490885941859 \tabularnewline
59 & 0.474102407439848 & 0.948204814879696 & 0.525897592560152 \tabularnewline
60 & 0.510421893827865 & 0.97915621234427 & 0.489578106172135 \tabularnewline
61 & 0.480446035485245 & 0.96089207097049 & 0.519553964514755 \tabularnewline
62 & 0.439403451722482 & 0.878806903444964 & 0.560596548277518 \tabularnewline
63 & 0.401920933884559 & 0.803841867769118 & 0.598079066115441 \tabularnewline
64 & 0.399032709060042 & 0.798065418120083 & 0.600967290939958 \tabularnewline
65 & 0.429050361096252 & 0.858100722192503 & 0.570949638903748 \tabularnewline
66 & 0.5549283365334 & 0.890143326933201 & 0.445071663466601 \tabularnewline
67 & 0.523609840280777 & 0.952780319438446 & 0.476390159719223 \tabularnewline
68 & 0.472987052619486 & 0.945974105238973 & 0.527012947380514 \tabularnewline
69 & 0.654607773620145 & 0.69078445275971 & 0.345392226379855 \tabularnewline
70 & 0.823908630916631 & 0.352182738166738 & 0.176091369083369 \tabularnewline
71 & 0.835886132041156 & 0.328227735917688 & 0.164113867958844 \tabularnewline
72 & 0.896528124400598 & 0.206943751198805 & 0.103471875599402 \tabularnewline
73 & 0.967267822148519 & 0.0654643557029626 & 0.0327321778514813 \tabularnewline
74 & 0.956531634055944 & 0.0869367318881115 & 0.0434683659440557 \tabularnewline
75 & 0.953791026213446 & 0.0924179475731086 & 0.0462089737865543 \tabularnewline
76 & 0.941879999945265 & 0.11624000010947 & 0.0581200000547348 \tabularnewline
77 & 0.944198805898018 & 0.111602388203964 & 0.0558011941019821 \tabularnewline
78 & 0.941781513592634 & 0.116436972814732 & 0.0582184864073662 \tabularnewline
79 & 0.965742578495343 & 0.0685148430093132 & 0.0342574215046566 \tabularnewline
80 & 0.957493799151066 & 0.0850124016978676 & 0.0425062008489338 \tabularnewline
81 & 0.9617364261034 & 0.0765271477931981 & 0.0382635738965991 \tabularnewline
82 & 0.984655822673408 & 0.0306883546531838 & 0.0153441773265919 \tabularnewline
83 & 0.979598767899615 & 0.0408024642007709 & 0.0204012321003854 \tabularnewline
84 & 0.982230332428989 & 0.0355393351420223 & 0.0177696675710111 \tabularnewline
85 & 0.977078983804692 & 0.0458420323906168 & 0.0229210161953084 \tabularnewline
86 & 0.972338743576207 & 0.055322512847587 & 0.0276612564237935 \tabularnewline
87 & 0.96307419146394 & 0.0738516170721186 & 0.0369258085360593 \tabularnewline
88 & 0.957132415764648 & 0.0857351684707041 & 0.042867584235352 \tabularnewline
89 & 0.95229660547926 & 0.0954067890414813 & 0.0477033945207406 \tabularnewline
90 & 0.937710250321273 & 0.124579499357455 & 0.0622897496787274 \tabularnewline
91 & 0.928252920939812 & 0.143494158120376 & 0.0717470790601879 \tabularnewline
92 & 0.949666456406607 & 0.100667087186785 & 0.0503335435933926 \tabularnewline
93 & 0.993098667797613 & 0.0138026644047735 & 0.00690133220238673 \tabularnewline
94 & 0.992537120371463 & 0.0149257592570737 & 0.00746287962853684 \tabularnewline
95 & 0.991380026397147 & 0.0172399472057061 & 0.00861997360285305 \tabularnewline
96 & 0.988940670402795 & 0.0221186591944098 & 0.0110593295972049 \tabularnewline
97 & 0.992269810918166 & 0.0154603781636682 & 0.00773018908183411 \tabularnewline
98 & 0.991610291485868 & 0.0167794170282634 & 0.00838970851413171 \tabularnewline
99 & 0.988966881784174 & 0.0220662364316526 & 0.0110331182158263 \tabularnewline
100 & 0.985705647743316 & 0.0285887045133674 & 0.0142943522566837 \tabularnewline
101 & 0.990727092538387 & 0.0185458149232253 & 0.00927290746161265 \tabularnewline
102 & 0.991572259887247 & 0.0168554802255069 & 0.00842774011275347 \tabularnewline
103 & 0.988604291002227 & 0.0227914179955469 & 0.0113957089977735 \tabularnewline
104 & 0.987023136191883 & 0.0259537276162336 & 0.0129768638081168 \tabularnewline
105 & 0.984184437498218 & 0.0316311250035646 & 0.0158155625017823 \tabularnewline
106 & 0.978283414954385 & 0.0434331700912301 & 0.021716585045615 \tabularnewline
107 & 0.970961546161185 & 0.0580769076776291 & 0.0290384538388145 \tabularnewline
108 & 0.977099354499593 & 0.0458012910008136 & 0.0229006455004068 \tabularnewline
109 & 0.996053221047057 & 0.00789355790588693 & 0.00394677895294347 \tabularnewline
110 & 0.994850677856218 & 0.0102986442875637 & 0.00514932214378185 \tabularnewline
111 & 0.998032013693346 & 0.0039359726133082 & 0.0019679863066541 \tabularnewline
112 & 0.99674425131662 & 0.00651149736675871 & 0.00325574868337935 \tabularnewline
113 & 0.995998354945618 & 0.00800329010876354 & 0.00400164505438177 \tabularnewline
114 & 0.993237243750193 & 0.0135255124996137 & 0.00676275624980684 \tabularnewline
115 & 0.9899651489014 & 0.0200697021972018 & 0.0100348510986009 \tabularnewline
116 & 0.984385831565707 & 0.0312283368685855 & 0.0156141684342927 \tabularnewline
117 & 0.979967684727494 & 0.0400646305450116 & 0.0200323152725058 \tabularnewline
118 & 0.986632285117811 & 0.0267354297643771 & 0.0133677148821886 \tabularnewline
119 & 0.97958092075763 & 0.0408381584847393 & 0.0204190792423697 \tabularnewline
120 & 0.969442057697838 & 0.061115884604324 & 0.030557942302162 \tabularnewline
121 & 0.969029055492585 & 0.0619418890148293 & 0.0309709445074146 \tabularnewline
122 & 0.95671267376444 & 0.0865746524711214 & 0.0432873262355607 \tabularnewline
123 & 0.943033813267005 & 0.113932373465989 & 0.0569661867329947 \tabularnewline
124 & 0.967809026146156 & 0.0643819477076885 & 0.0321909738538443 \tabularnewline
125 & 0.95642240861324 & 0.0871551827735214 & 0.0435775913867607 \tabularnewline
126 & 0.928827684892496 & 0.142344630215008 & 0.0711723151075038 \tabularnewline
127 & 0.903779695915307 & 0.192440608169386 & 0.096220304084693 \tabularnewline
128 & 0.862879597584109 & 0.274240804831783 & 0.137120402415891 \tabularnewline
129 & 0.827821751134548 & 0.344356497730905 & 0.172178248865452 \tabularnewline
130 & 0.851672376148246 & 0.296655247703508 & 0.148327623851754 \tabularnewline
131 & 0.785788146275578 & 0.428423707448844 & 0.214211853724422 \tabularnewline
132 & 0.750035187748658 & 0.499929624502684 & 0.249964812251342 \tabularnewline
133 & 0.982769413527077 & 0.0344611729458458 & 0.0172305864729229 \tabularnewline
134 & 0.973379412686258 & 0.0532411746274847 & 0.0266205873137423 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111571&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]22[/C][C]0.323786208970121[/C][C]0.647572417940241[/C][C]0.67621379102988[/C][/ROW]
[ROW][C]23[/C][C]0.198177397469349[/C][C]0.396354794938699[/C][C]0.80182260253065[/C][/ROW]
[ROW][C]24[/C][C]0.149863051390141[/C][C]0.299726102780283[/C][C]0.850136948609859[/C][/ROW]
[ROW][C]25[/C][C]0.0961110260149067[/C][C]0.192222052029813[/C][C]0.903888973985093[/C][/ROW]
[ROW][C]26[/C][C]0.195961037724433[/C][C]0.391922075448866[/C][C]0.804038962275567[/C][/ROW]
[ROW][C]27[/C][C]0.336483767954478[/C][C]0.672967535908955[/C][C]0.663516232045522[/C][/ROW]
[ROW][C]28[/C][C]0.379681503157996[/C][C]0.759363006315991[/C][C]0.620318496842004[/C][/ROW]
[ROW][C]29[/C][C]0.380717951546443[/C][C]0.761435903092886[/C][C]0.619282048453557[/C][/ROW]
[ROW][C]30[/C][C]0.296831171299768[/C][C]0.593662342599537[/C][C]0.703168828700232[/C][/ROW]
[ROW][C]31[/C][C]0.229199644117799[/C][C]0.458399288235597[/C][C]0.770800355882201[/C][/ROW]
[ROW][C]32[/C][C]0.178552350860857[/C][C]0.357104701721715[/C][C]0.821447649139143[/C][/ROW]
[ROW][C]33[/C][C]0.13065102605307[/C][C]0.26130205210614[/C][C]0.86934897394693[/C][/ROW]
[ROW][C]34[/C][C]0.0915350425122398[/C][C]0.18307008502448[/C][C]0.90846495748776[/C][/ROW]
[ROW][C]35[/C][C]0.20053174405398[/C][C]0.401063488107961[/C][C]0.79946825594602[/C][/ROW]
[ROW][C]36[/C][C]0.394900035811142[/C][C]0.789800071622283[/C][C]0.605099964188858[/C][/ROW]
[ROW][C]37[/C][C]0.361869018688302[/C][C]0.723738037376604[/C][C]0.638130981311698[/C][/ROW]
[ROW][C]38[/C][C]0.342066949724391[/C][C]0.684133899448782[/C][C]0.657933050275609[/C][/ROW]
[ROW][C]39[/C][C]0.38607005002474[/C][C]0.77214010004948[/C][C]0.61392994997526[/C][/ROW]
[ROW][C]40[/C][C]0.333397101271001[/C][C]0.666794202542001[/C][C]0.666602898728999[/C][/ROW]
[ROW][C]41[/C][C]0.621330333419953[/C][C]0.757339333160095[/C][C]0.378669666580047[/C][/ROW]
[ROW][C]42[/C][C]0.740339915761034[/C][C]0.519320168477932[/C][C]0.259660084238966[/C][/ROW]
[ROW][C]43[/C][C]0.687405978808893[/C][C]0.625188042382215[/C][C]0.312594021191107[/C][/ROW]
[ROW][C]44[/C][C]0.680095001737681[/C][C]0.639809996524638[/C][C]0.319904998262319[/C][/ROW]
[ROW][C]45[/C][C]0.622781026795824[/C][C]0.754437946408351[/C][C]0.377218973204175[/C][/ROW]
[ROW][C]46[/C][C]0.59141748857988[/C][C]0.81716502284024[/C][C]0.40858251142012[/C][/ROW]
[ROW][C]47[/C][C]0.594084544590765[/C][C]0.811830910818469[/C][C]0.405915455409235[/C][/ROW]
[ROW][C]48[/C][C]0.548737836029669[/C][C]0.902524327940662[/C][C]0.451262163970331[/C][/ROW]
[ROW][C]49[/C][C]0.519180899919437[/C][C]0.961638200161127[/C][C]0.480819100080563[/C][/ROW]
[ROW][C]50[/C][C]0.561312739682893[/C][C]0.877374520634215[/C][C]0.438687260317108[/C][/ROW]
[ROW][C]51[/C][C]0.515894147560252[/C][C]0.968211704879496[/C][C]0.484105852439748[/C][/ROW]
[ROW][C]52[/C][C]0.469586952950472[/C][C]0.939173905900945[/C][C]0.530413047049528[/C][/ROW]
[ROW][C]53[/C][C]0.710443374086939[/C][C]0.579113251826123[/C][C]0.289556625913061[/C][/ROW]
[ROW][C]54[/C][C]0.67923499827972[/C][C]0.641530003440561[/C][C]0.320765001720281[/C][/ROW]
[ROW][C]55[/C][C]0.648880044245457[/C][C]0.702239911509086[/C][C]0.351119955754543[/C][/ROW]
[ROW][C]56[/C][C]0.608676855808446[/C][C]0.782646288383107[/C][C]0.391323144191554[/C][/ROW]
[ROW][C]57[/C][C]0.570615405681939[/C][C]0.858769188636122[/C][C]0.429384594318061[/C][/ROW]
[ROW][C]58[/C][C]0.526509114058141[/C][C]0.946981771883718[/C][C]0.473490885941859[/C][/ROW]
[ROW][C]59[/C][C]0.474102407439848[/C][C]0.948204814879696[/C][C]0.525897592560152[/C][/ROW]
[ROW][C]60[/C][C]0.510421893827865[/C][C]0.97915621234427[/C][C]0.489578106172135[/C][/ROW]
[ROW][C]61[/C][C]0.480446035485245[/C][C]0.96089207097049[/C][C]0.519553964514755[/C][/ROW]
[ROW][C]62[/C][C]0.439403451722482[/C][C]0.878806903444964[/C][C]0.560596548277518[/C][/ROW]
[ROW][C]63[/C][C]0.401920933884559[/C][C]0.803841867769118[/C][C]0.598079066115441[/C][/ROW]
[ROW][C]64[/C][C]0.399032709060042[/C][C]0.798065418120083[/C][C]0.600967290939958[/C][/ROW]
[ROW][C]65[/C][C]0.429050361096252[/C][C]0.858100722192503[/C][C]0.570949638903748[/C][/ROW]
[ROW][C]66[/C][C]0.5549283365334[/C][C]0.890143326933201[/C][C]0.445071663466601[/C][/ROW]
[ROW][C]67[/C][C]0.523609840280777[/C][C]0.952780319438446[/C][C]0.476390159719223[/C][/ROW]
[ROW][C]68[/C][C]0.472987052619486[/C][C]0.945974105238973[/C][C]0.527012947380514[/C][/ROW]
[ROW][C]69[/C][C]0.654607773620145[/C][C]0.69078445275971[/C][C]0.345392226379855[/C][/ROW]
[ROW][C]70[/C][C]0.823908630916631[/C][C]0.352182738166738[/C][C]0.176091369083369[/C][/ROW]
[ROW][C]71[/C][C]0.835886132041156[/C][C]0.328227735917688[/C][C]0.164113867958844[/C][/ROW]
[ROW][C]72[/C][C]0.896528124400598[/C][C]0.206943751198805[/C][C]0.103471875599402[/C][/ROW]
[ROW][C]73[/C][C]0.967267822148519[/C][C]0.0654643557029626[/C][C]0.0327321778514813[/C][/ROW]
[ROW][C]74[/C][C]0.956531634055944[/C][C]0.0869367318881115[/C][C]0.0434683659440557[/C][/ROW]
[ROW][C]75[/C][C]0.953791026213446[/C][C]0.0924179475731086[/C][C]0.0462089737865543[/C][/ROW]
[ROW][C]76[/C][C]0.941879999945265[/C][C]0.11624000010947[/C][C]0.0581200000547348[/C][/ROW]
[ROW][C]77[/C][C]0.944198805898018[/C][C]0.111602388203964[/C][C]0.0558011941019821[/C][/ROW]
[ROW][C]78[/C][C]0.941781513592634[/C][C]0.116436972814732[/C][C]0.0582184864073662[/C][/ROW]
[ROW][C]79[/C][C]0.965742578495343[/C][C]0.0685148430093132[/C][C]0.0342574215046566[/C][/ROW]
[ROW][C]80[/C][C]0.957493799151066[/C][C]0.0850124016978676[/C][C]0.0425062008489338[/C][/ROW]
[ROW][C]81[/C][C]0.9617364261034[/C][C]0.0765271477931981[/C][C]0.0382635738965991[/C][/ROW]
[ROW][C]82[/C][C]0.984655822673408[/C][C]0.0306883546531838[/C][C]0.0153441773265919[/C][/ROW]
[ROW][C]83[/C][C]0.979598767899615[/C][C]0.0408024642007709[/C][C]0.0204012321003854[/C][/ROW]
[ROW][C]84[/C][C]0.982230332428989[/C][C]0.0355393351420223[/C][C]0.0177696675710111[/C][/ROW]
[ROW][C]85[/C][C]0.977078983804692[/C][C]0.0458420323906168[/C][C]0.0229210161953084[/C][/ROW]
[ROW][C]86[/C][C]0.972338743576207[/C][C]0.055322512847587[/C][C]0.0276612564237935[/C][/ROW]
[ROW][C]87[/C][C]0.96307419146394[/C][C]0.0738516170721186[/C][C]0.0369258085360593[/C][/ROW]
[ROW][C]88[/C][C]0.957132415764648[/C][C]0.0857351684707041[/C][C]0.042867584235352[/C][/ROW]
[ROW][C]89[/C][C]0.95229660547926[/C][C]0.0954067890414813[/C][C]0.0477033945207406[/C][/ROW]
[ROW][C]90[/C][C]0.937710250321273[/C][C]0.124579499357455[/C][C]0.0622897496787274[/C][/ROW]
[ROW][C]91[/C][C]0.928252920939812[/C][C]0.143494158120376[/C][C]0.0717470790601879[/C][/ROW]
[ROW][C]92[/C][C]0.949666456406607[/C][C]0.100667087186785[/C][C]0.0503335435933926[/C][/ROW]
[ROW][C]93[/C][C]0.993098667797613[/C][C]0.0138026644047735[/C][C]0.00690133220238673[/C][/ROW]
[ROW][C]94[/C][C]0.992537120371463[/C][C]0.0149257592570737[/C][C]0.00746287962853684[/C][/ROW]
[ROW][C]95[/C][C]0.991380026397147[/C][C]0.0172399472057061[/C][C]0.00861997360285305[/C][/ROW]
[ROW][C]96[/C][C]0.988940670402795[/C][C]0.0221186591944098[/C][C]0.0110593295972049[/C][/ROW]
[ROW][C]97[/C][C]0.992269810918166[/C][C]0.0154603781636682[/C][C]0.00773018908183411[/C][/ROW]
[ROW][C]98[/C][C]0.991610291485868[/C][C]0.0167794170282634[/C][C]0.00838970851413171[/C][/ROW]
[ROW][C]99[/C][C]0.988966881784174[/C][C]0.0220662364316526[/C][C]0.0110331182158263[/C][/ROW]
[ROW][C]100[/C][C]0.985705647743316[/C][C]0.0285887045133674[/C][C]0.0142943522566837[/C][/ROW]
[ROW][C]101[/C][C]0.990727092538387[/C][C]0.0185458149232253[/C][C]0.00927290746161265[/C][/ROW]
[ROW][C]102[/C][C]0.991572259887247[/C][C]0.0168554802255069[/C][C]0.00842774011275347[/C][/ROW]
[ROW][C]103[/C][C]0.988604291002227[/C][C]0.0227914179955469[/C][C]0.0113957089977735[/C][/ROW]
[ROW][C]104[/C][C]0.987023136191883[/C][C]0.0259537276162336[/C][C]0.0129768638081168[/C][/ROW]
[ROW][C]105[/C][C]0.984184437498218[/C][C]0.0316311250035646[/C][C]0.0158155625017823[/C][/ROW]
[ROW][C]106[/C][C]0.978283414954385[/C][C]0.0434331700912301[/C][C]0.021716585045615[/C][/ROW]
[ROW][C]107[/C][C]0.970961546161185[/C][C]0.0580769076776291[/C][C]0.0290384538388145[/C][/ROW]
[ROW][C]108[/C][C]0.977099354499593[/C][C]0.0458012910008136[/C][C]0.0229006455004068[/C][/ROW]
[ROW][C]109[/C][C]0.996053221047057[/C][C]0.00789355790588693[/C][C]0.00394677895294347[/C][/ROW]
[ROW][C]110[/C][C]0.994850677856218[/C][C]0.0102986442875637[/C][C]0.00514932214378185[/C][/ROW]
[ROW][C]111[/C][C]0.998032013693346[/C][C]0.0039359726133082[/C][C]0.0019679863066541[/C][/ROW]
[ROW][C]112[/C][C]0.99674425131662[/C][C]0.00651149736675871[/C][C]0.00325574868337935[/C][/ROW]
[ROW][C]113[/C][C]0.995998354945618[/C][C]0.00800329010876354[/C][C]0.00400164505438177[/C][/ROW]
[ROW][C]114[/C][C]0.993237243750193[/C][C]0.0135255124996137[/C][C]0.00676275624980684[/C][/ROW]
[ROW][C]115[/C][C]0.9899651489014[/C][C]0.0200697021972018[/C][C]0.0100348510986009[/C][/ROW]
[ROW][C]116[/C][C]0.984385831565707[/C][C]0.0312283368685855[/C][C]0.0156141684342927[/C][/ROW]
[ROW][C]117[/C][C]0.979967684727494[/C][C]0.0400646305450116[/C][C]0.0200323152725058[/C][/ROW]
[ROW][C]118[/C][C]0.986632285117811[/C][C]0.0267354297643771[/C][C]0.0133677148821886[/C][/ROW]
[ROW][C]119[/C][C]0.97958092075763[/C][C]0.0408381584847393[/C][C]0.0204190792423697[/C][/ROW]
[ROW][C]120[/C][C]0.969442057697838[/C][C]0.061115884604324[/C][C]0.030557942302162[/C][/ROW]
[ROW][C]121[/C][C]0.969029055492585[/C][C]0.0619418890148293[/C][C]0.0309709445074146[/C][/ROW]
[ROW][C]122[/C][C]0.95671267376444[/C][C]0.0865746524711214[/C][C]0.0432873262355607[/C][/ROW]
[ROW][C]123[/C][C]0.943033813267005[/C][C]0.113932373465989[/C][C]0.0569661867329947[/C][/ROW]
[ROW][C]124[/C][C]0.967809026146156[/C][C]0.0643819477076885[/C][C]0.0321909738538443[/C][/ROW]
[ROW][C]125[/C][C]0.95642240861324[/C][C]0.0871551827735214[/C][C]0.0435775913867607[/C][/ROW]
[ROW][C]126[/C][C]0.928827684892496[/C][C]0.142344630215008[/C][C]0.0711723151075038[/C][/ROW]
[ROW][C]127[/C][C]0.903779695915307[/C][C]0.192440608169386[/C][C]0.096220304084693[/C][/ROW]
[ROW][C]128[/C][C]0.862879597584109[/C][C]0.274240804831783[/C][C]0.137120402415891[/C][/ROW]
[ROW][C]129[/C][C]0.827821751134548[/C][C]0.344356497730905[/C][C]0.172178248865452[/C][/ROW]
[ROW][C]130[/C][C]0.851672376148246[/C][C]0.296655247703508[/C][C]0.148327623851754[/C][/ROW]
[ROW][C]131[/C][C]0.785788146275578[/C][C]0.428423707448844[/C][C]0.214211853724422[/C][/ROW]
[ROW][C]132[/C][C]0.750035187748658[/C][C]0.499929624502684[/C][C]0.249964812251342[/C][/ROW]
[ROW][C]133[/C][C]0.982769413527077[/C][C]0.0344611729458458[/C][C]0.0172305864729229[/C][/ROW]
[ROW][C]134[/C][C]0.973379412686258[/C][C]0.0532411746274847[/C][C]0.0266205873137423[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111571&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111571&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
220.3237862089701210.6475724179402410.67621379102988
230.1981773974693490.3963547949386990.80182260253065
240.1498630513901410.2997261027802830.850136948609859
250.09611102601490670.1922220520298130.903888973985093
260.1959610377244330.3919220754488660.804038962275567
270.3364837679544780.6729675359089550.663516232045522
280.3796815031579960.7593630063159910.620318496842004
290.3807179515464430.7614359030928860.619282048453557
300.2968311712997680.5936623425995370.703168828700232
310.2291996441177990.4583992882355970.770800355882201
320.1785523508608570.3571047017217150.821447649139143
330.130651026053070.261302052106140.86934897394693
340.09153504251223980.183070085024480.90846495748776
350.200531744053980.4010634881079610.79946825594602
360.3949000358111420.7898000716222830.605099964188858
370.3618690186883020.7237380373766040.638130981311698
380.3420669497243910.6841338994487820.657933050275609
390.386070050024740.772140100049480.61392994997526
400.3333971012710010.6667942025420010.666602898728999
410.6213303334199530.7573393331600950.378669666580047
420.7403399157610340.5193201684779320.259660084238966
430.6874059788088930.6251880423822150.312594021191107
440.6800950017376810.6398099965246380.319904998262319
450.6227810267958240.7544379464083510.377218973204175
460.591417488579880.817165022840240.40858251142012
470.5940845445907650.8118309108184690.405915455409235
480.5487378360296690.9025243279406620.451262163970331
490.5191808999194370.9616382001611270.480819100080563
500.5613127396828930.8773745206342150.438687260317108
510.5158941475602520.9682117048794960.484105852439748
520.4695869529504720.9391739059009450.530413047049528
530.7104433740869390.5791132518261230.289556625913061
540.679234998279720.6415300034405610.320765001720281
550.6488800442454570.7022399115090860.351119955754543
560.6086768558084460.7826462883831070.391323144191554
570.5706154056819390.8587691886361220.429384594318061
580.5265091140581410.9469817718837180.473490885941859
590.4741024074398480.9482048148796960.525897592560152
600.5104218938278650.979156212344270.489578106172135
610.4804460354852450.960892070970490.519553964514755
620.4394034517224820.8788069034449640.560596548277518
630.4019209338845590.8038418677691180.598079066115441
640.3990327090600420.7980654181200830.600967290939958
650.4290503610962520.8581007221925030.570949638903748
660.55492833653340.8901433269332010.445071663466601
670.5236098402807770.9527803194384460.476390159719223
680.4729870526194860.9459741052389730.527012947380514
690.6546077736201450.690784452759710.345392226379855
700.8239086309166310.3521827381667380.176091369083369
710.8358861320411560.3282277359176880.164113867958844
720.8965281244005980.2069437511988050.103471875599402
730.9672678221485190.06546435570296260.0327321778514813
740.9565316340559440.08693673188811150.0434683659440557
750.9537910262134460.09241794757310860.0462089737865543
760.9418799999452650.116240000109470.0581200000547348
770.9441988058980180.1116023882039640.0558011941019821
780.9417815135926340.1164369728147320.0582184864073662
790.9657425784953430.06851484300931320.0342574215046566
800.9574937991510660.08501240169786760.0425062008489338
810.96173642610340.07652714779319810.0382635738965991
820.9846558226734080.03068835465318380.0153441773265919
830.9795987678996150.04080246420077090.0204012321003854
840.9822303324289890.03553933514202230.0177696675710111
850.9770789838046920.04584203239061680.0229210161953084
860.9723387435762070.0553225128475870.0276612564237935
870.963074191463940.07385161707211860.0369258085360593
880.9571324157646480.08573516847070410.042867584235352
890.952296605479260.09540678904148130.0477033945207406
900.9377102503212730.1245794993574550.0622897496787274
910.9282529209398120.1434941581203760.0717470790601879
920.9496664564066070.1006670871867850.0503335435933926
930.9930986677976130.01380266440477350.00690133220238673
940.9925371203714630.01492575925707370.00746287962853684
950.9913800263971470.01723994720570610.00861997360285305
960.9889406704027950.02211865919440980.0110593295972049
970.9922698109181660.01546037816366820.00773018908183411
980.9916102914858680.01677941702826340.00838970851413171
990.9889668817841740.02206623643165260.0110331182158263
1000.9857056477433160.02858870451336740.0142943522566837
1010.9907270925383870.01854581492322530.00927290746161265
1020.9915722598872470.01685548022550690.00842774011275347
1030.9886042910022270.02279141799554690.0113957089977735
1040.9870231361918830.02595372761623360.0129768638081168
1050.9841844374982180.03163112500356460.0158155625017823
1060.9782834149543850.04343317009123010.021716585045615
1070.9709615461611850.05807690767762910.0290384538388145
1080.9770993544995930.04580129100081360.0229006455004068
1090.9960532210470570.007893557905886930.00394677895294347
1100.9948506778562180.01029864428756370.00514932214378185
1110.9980320136933460.00393597261330820.0019679863066541
1120.996744251316620.006511497366758710.00325574868337935
1130.9959983549456180.008003290108763540.00400164505438177
1140.9932372437501930.01352551249961370.00676275624980684
1150.98996514890140.02006970219720180.0100348510986009
1160.9843858315657070.03122833686858550.0156141684342927
1170.9799676847274940.04006463054501160.0200323152725058
1180.9866322851178110.02673542976437710.0133677148821886
1190.979580920757630.04083815848473930.0204190792423697
1200.9694420576978380.0611158846043240.030557942302162
1210.9690290554925850.06194188901482930.0309709445074146
1220.956712673764440.08657465247112140.0432873262355607
1230.9430338132670050.1139323734659890.0569661867329947
1240.9678090261461560.06438194770768850.0321909738538443
1250.956422408613240.08715518277352140.0435775913867607
1260.9288276848924960.1423446302150080.0711723151075038
1270.9037796959153070.1924406081693860.096220304084693
1280.8628795975841090.2742408048317830.137120402415891
1290.8278217511345480.3443564977309050.172178248865452
1300.8516723761482460.2966552477035080.148327623851754
1310.7857881462755780.4284237074488440.214211853724422
1320.7500351877486580.4999296245026840.249964812251342
1330.9827694135270770.03446117294584580.0172305864729229
1340.9733794126862580.05324117462748470.0266205873137423






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.0353982300884956NOK
5% type I error level310.274336283185841NOK
10% type I error level480.424778761061947NOK

\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 & 4 & 0.0353982300884956 & NOK \tabularnewline
5% type I error level & 31 & 0.274336283185841 & NOK \tabularnewline
10% type I error level & 48 & 0.424778761061947 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111571&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]4[/C][C]0.0353982300884956[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]31[/C][C]0.274336283185841[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]48[/C][C]0.424778761061947[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111571&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111571&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.0353982300884956NOK
5% type I error level310.274336283185841NOK
10% type I error level480.424778761061947NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}