Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 21 Nov 2011 14:24:18 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/21/t1321903520pjmzpy029elzdh7.htm/, Retrieved Thu, 28 Mar 2024 21:47:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145926, Retrieved Thu, 28 Mar 2024 21:47:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact122
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [Multiple linear r...] [2010-11-19 16:51:37] [97ad38b1c3b35a5feca8b85f7bc7b3ff]
-    D    [Multiple Regression] [Multiple linear r...] [2010-11-19 18:08:07] [97ad38b1c3b35a5feca8b85f7bc7b3ff]
- R           [Multiple Regression] [WS 7 - Blog 3] [2011-11-21 19:24:18] [7b8bbc7c94419fec22ed12e329cf3750] [Current]
Feedback Forum

Post a new message
Dataseries X:
15	10	77	5	4	15	11	12	13	6
12	20	63	6	4	9	12	7	11	4
15	16	73	4	10	12	12	13	14	6
12	10	76	6	6	15	11	11	12	5
14	8	90	3	5	17	11	16	12	5
8	14	67	10	8	14	10	10	6	4
11	19	69	8	9	9	11	15	10	5
15	15	70	3	6	12	9	5	11	3
4	23	54	4	8	11	10	4	10	2
13	9	54	3	11	13	12	7	12	5
19	12	76	5	6	16	12	15	15	6
10	14	75	5	8	16	12	5	13	6
15	13	76	6	11	15	13	16	18	8
6	11	80	5	5	10	9	15	11	6
7	11	89	3	10	16	12	13	12	3
14	10	73	4	7	12	12	13	13	6
16	12	74	8	7	15	12	15	14	6
16	18	78	8	13	13	12	15	16	7
14	12	76	8	10	18	13	10	16	8
15	10	69	5	8	13	11	17	16	6
14	15	74	8	6	17	12	14	15	7
12	15	82	2	8	14	12	9	13	4
9	12	77	0	7	13	15	6	8	4
12	9	84	5	5	13	11	11	14	2
14	11	75	2	9	15	12	13	15	6
12	15	54	7	9	13	10	12	13	6
14	16	79	5	11	15	11	10	16	6
10	17	79	2	11	13	13	4	13	6
14	12	69	12	11	14	6	13	12	6
16	11	88	7	9	13	12	15	15	7
10	13	57	0	7	16	12	8	11	4
8	9	69	2	6	14	10	10	14	3
12	11	86	3	6	18	12	8	13	5
11	9	65	0	6	15	12	7	13	6
8	20	66	9	5	9	11	9	12	4
13	8	54	2	4	16	9	14	14	6
11	12	85	3	10	16	10	5	13	3
12	10	79	1	8	17	12	7	12	3
16	11	84	10	6	13	12	16	14	6
16	13	70	1	5	17	11	14	15	6
13	13	54	4	9	15	12	16	16	6
14	13	70	6	10	14	11	15	15	8
5	15	54	6	6	10	14	4	5	2
14	12	69	4	9	13	10	12	15	6
13	13	68	4	10	11	10	8	8	4
16	13	68	7	6	11	11	17	16	7
14	9	71	7	6	16	11	15	16	6
15	9	71	7	6	16	11	16	14	6
15	14	66	0	13	11	10	12	16	6
11	9	67	3	8	15	10	12	14	5
15	9	71	8	10	15	12	13	13	6
16	15	54	8	5	12	11	14	14	6
13	10	76	10	8	17	8	14	14	5
11	13	77	11	6	15	12	15	12	6
12	8	71	6	9	16	10	14	13	7
12	15	69	2	9	14	7	11	15	5
10	13	73	6	7	17	11	13	15	6
8	24	46	1	20	10	7	4	13	6
9	11	66	5	8	11	11	8	10	4
12	13	77	4	8	15	8	13	13	5
14	12	77	6	7	15	11	15	14	6
12	22	70	6	7	7	12	15	13	6
11	11	86	4	10	17	8	8	13	4
14	15	38	1	5	14	14	17	18	6
7	7	66	6	8	18	14	12	12	4
16	14	75	7	9	14	11	13	14	7
16	19	80	7	9	12	12	14	16	8
11	10	64	2	20	14	14	7	13	6
16	9	80	7	6	9	9	16	16	6
13	12	86	8	10	14	13	11	15	6
11	16	54	5	11	11	8	10	14	5
13	13	74	4	7	16	11	14	13	6
14	11	88	2	12	17	9	19	12	6
15	12	85	0	12	16	12	14	16	4
10	11	63	7	8	12	7	8	9	5
15	13	81	0	6	15	11	15	15	8
11	13	81	5	6	15	12	8	16	6
11	10	74	3	9	15	11	8	12	6
6	11	80	3	5	16	12	6	11	2
11	9	80	3	11	16	9	7	13	2
12	13	60	3	6	11	11	16	13	4
13	15	65	7	6	15	13	15	14	6
12	14	62	6	10	12	12	10	15	6
8	14	63	3	8	14	12	8	14	5
9	11	89	0	7	15	11	9	12	4
10	10	76	2	8	17	12	8	16	4
16	11	81	0	9	19	12	14	14	6
15	12	72	9	8	15	11	14	13	5
14	14	84	10	10	16	11	14	12	6
12	14	76	3	13	14	8	15	13	7
12	21	76	7	7	16	9	7	12	6
10	14	78	3	7	15	11	7	9	4
12	13	72	6	7	15	12	12	13	4
8	11	81	5	8	17	13	7	10	3
16	12	72	0	9	12	12	12	15	8
11	12	78	0	9	18	6	6	9	4
12	11	79	4	8	13	12	10	13	4
9	14	52	0	7	14	11	12	13	5
14	13	67	0	6	14	13	13	13	5
15	13	74	7	8	14	11	14	15	7
8	12	73	3	8	12	12	8	13	4
12	14	69	9	4	14	10	14	14	5
10	12	67	4	8	12	10	10	11	5
16	12	76	4	10	15	11	14	15	8
17	12	77	15	7	11	11	15	14	5
8	18	63	7	8	11	11	10	15	2
9	11	84	8	7	15	9	6	12	5
8	15	90	2	10	14	7	9	15	4
11	13	75	8	9	15	11	11	14	5
16	11	76	7	8	16	12	16	16	7
13	11	75	3	8	12	12	14	14	6
5	22	53	3	5	14	15	8	12	3
15	10	87	6	8	18	11	16	11	5
15	11	78	8	9	14	10	16	13	6
12	15	54	5	11	13	13	14	12	5
12	14	58	6	7	14	13	12	12	6
16	11	80	10	8	14	11	16	16	7
12	10	74	0	4	17	12	15	13	6
10	14	56	5	16	12	12	11	12	6
12	14	82	0	9	16	12	6	14	5
4	11	64	0	16	15	8	6	4	4
11	15	67	5	12	10	5	16	14	6
16	11	75	10	8	13	11	16	15	6
7	10	69	0	4	15	12	8	12	3
9	10	72	5	11	16	12	11	11	4
14	16	71	6	11	15	11	12	12	4
11	12	54	1	8	14	12	13	11	4
10	14	68	5	8	11	10	11	12	5
6	15	54	3	12	13	7	9	11	4
14	10	71	3	8	17	12	15	13	6
11	12	53	6	6	14	12	11	12	6
11	15	54	2	8	16	9	12	12	4
9	12	71	5	6	15	11	15	15	7
16	11	69	6	14	12	12	8	14	4
7	10	30	2	10	16	12	7	12	4
8	20	53	3	5	8	11	10	12	4
10	19	68	7	8	9	11	9	12	4
14	17	69	6	12	13	12	13	13	5
9	8	54	3	11	19	12	11	11	4
13	17	66	6	8	11	11	12	13	7
13	11	79	9	8	15	12	5	12	3
12	13	67	2	9	11	12	12	14	5
11	9	74	5	6	15	8	14	15	5
10	10	86	10	5	16	15	15	15	6
12	13	63	9	8	15	11	14	13	5
14	16	69	8	7	12	11	13	16	6
11	12	73	8	4	16	6	14	17	6
13	14	69	5	9	15	13	14	13	3
14	11	71	9	5	13	12	15	14	6
13	13	77	9	9	14	12	13	13	5
16	15	74	14	12	11	12	14	16	8
13	14	82	5	6	15	12	11	13	6
12	14	54	12	4	16	12	14	14	4
9	14	54	6	6	14	10	11	13	3
14	10	80	6	7	13	12	8	14	4
15	8	76	8	9	15	12	12	16	7




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145926&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145926&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145926&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = -1.16892826150712 -0.0807306287138021Depression[t] + 0.0453253788739521Belonging[t] + 0.0964153521063372Weighted_popularity[t] + 0.0772765190141471Parental_criticism[t] -0.0572765637003982Happiness[t] + 0.117596535487944FindingFriends[t] + 0.227711407897117KnowingPeople[t] + 0.34708334464486Liked[t] + 0.520075961070442Celebrity[t] -0.00640747869671336t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  -1.16892826150712 -0.0807306287138021Depression[t] +  0.0453253788739521Belonging[t] +  0.0964153521063372Weighted_popularity[t] +  0.0772765190141471Parental_criticism[t] -0.0572765637003982Happiness[t] +  0.117596535487944FindingFriends[t] +  0.227711407897117KnowingPeople[t] +  0.34708334464486Liked[t] +  0.520075961070442Celebrity[t] -0.00640747869671336t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145926&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  -1.16892826150712 -0.0807306287138021Depression[t] +  0.0453253788739521Belonging[t] +  0.0964153521063372Weighted_popularity[t] +  0.0772765190141471Parental_criticism[t] -0.0572765637003982Happiness[t] +  0.117596535487944FindingFriends[t] +  0.227711407897117KnowingPeople[t] +  0.34708334464486Liked[t] +  0.520075961070442Celebrity[t] -0.00640747869671336t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145926&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145926&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = -1.16892826150712 -0.0807306287138021Depression[t] + 0.0453253788739521Belonging[t] + 0.0964153521063372Weighted_popularity[t] + 0.0772765190141471Parental_criticism[t] -0.0572765637003982Happiness[t] + 0.117596535487944FindingFriends[t] + 0.227711407897117KnowingPeople[t] + 0.34708334464486Liked[t] + 0.520075961070442Celebrity[t] -0.00640747869671336t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.168928261507122.523234-0.46330.6438680.321934
Depression-0.08073062871380210.063126-1.27890.2029840.101492
Belonging0.04532537887395210.0169622.67220.0083990.0042
Weighted_popularity0.09641535210633720.058111.65920.099240.04962
Parental_criticism0.07727651901414710.0646611.19510.2339990.117
Happiness-0.05727656370039820.085478-0.67010.5038760.251938
FindingFriends0.1175965354879440.0936611.25550.2112990.10565
KnowingPeople0.2277114078971170.0642943.54170.0005350.000268
Liked0.347083344644860.0938253.69930.0003060.000153
Celebrity0.5200759610704420.1584053.28320.0012860.000643
t-0.006407478696713360.003735-1.71560.0883660.044183

\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) & -1.16892826150712 & 2.523234 & -0.4633 & 0.643868 & 0.321934 \tabularnewline
Depression & -0.0807306287138021 & 0.063126 & -1.2789 & 0.202984 & 0.101492 \tabularnewline
Belonging & 0.0453253788739521 & 0.016962 & 2.6722 & 0.008399 & 0.0042 \tabularnewline
Weighted_popularity & 0.0964153521063372 & 0.05811 & 1.6592 & 0.09924 & 0.04962 \tabularnewline
Parental_criticism & 0.0772765190141471 & 0.064661 & 1.1951 & 0.233999 & 0.117 \tabularnewline
Happiness & -0.0572765637003982 & 0.085478 & -0.6701 & 0.503876 & 0.251938 \tabularnewline
FindingFriends & 0.117596535487944 & 0.093661 & 1.2555 & 0.211299 & 0.10565 \tabularnewline
KnowingPeople & 0.227711407897117 & 0.064294 & 3.5417 & 0.000535 & 0.000268 \tabularnewline
Liked & 0.34708334464486 & 0.093825 & 3.6993 & 0.000306 & 0.000153 \tabularnewline
Celebrity & 0.520075961070442 & 0.158405 & 3.2832 & 0.001286 & 0.000643 \tabularnewline
t & -0.00640747869671336 & 0.003735 & -1.7156 & 0.088366 & 0.044183 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145926&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]-1.16892826150712[/C][C]2.523234[/C][C]-0.4633[/C][C]0.643868[/C][C]0.321934[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0807306287138021[/C][C]0.063126[/C][C]-1.2789[/C][C]0.202984[/C][C]0.101492[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0453253788739521[/C][C]0.016962[/C][C]2.6722[/C][C]0.008399[/C][C]0.0042[/C][/ROW]
[ROW][C]Weighted_popularity[/C][C]0.0964153521063372[/C][C]0.05811[/C][C]1.6592[/C][C]0.09924[/C][C]0.04962[/C][/ROW]
[ROW][C]Parental_criticism[/C][C]0.0772765190141471[/C][C]0.064661[/C][C]1.1951[/C][C]0.233999[/C][C]0.117[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.0572765637003982[/C][C]0.085478[/C][C]-0.6701[/C][C]0.503876[/C][C]0.251938[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.117596535487944[/C][C]0.093661[/C][C]1.2555[/C][C]0.211299[/C][C]0.10565[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.227711407897117[/C][C]0.064294[/C][C]3.5417[/C][C]0.000535[/C][C]0.000268[/C][/ROW]
[ROW][C]Liked[/C][C]0.34708334464486[/C][C]0.093825[/C][C]3.6993[/C][C]0.000306[/C][C]0.000153[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.520075961070442[/C][C]0.158405[/C][C]3.2832[/C][C]0.001286[/C][C]0.000643[/C][/ROW]
[ROW][C]t[/C][C]-0.00640747869671336[/C][C]0.003735[/C][C]-1.7156[/C][C]0.088366[/C][C]0.044183[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145926&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145926&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.168928261507122.523234-0.46330.6438680.321934
Depression-0.08073062871380210.063126-1.27890.2029840.101492
Belonging0.04532537887395210.0169622.67220.0083990.0042
Weighted_popularity0.09641535210633720.058111.65920.099240.04962
Parental_criticism0.07727651901414710.0646611.19510.2339990.117
Happiness-0.05727656370039820.085478-0.67010.5038760.251938
FindingFriends0.1175965354879440.0936611.25550.2112990.10565
KnowingPeople0.2277114078971170.0642943.54170.0005350.000268
Liked0.347083344644860.0938253.69930.0003060.000153
Celebrity0.5200759610704420.1584053.28320.0012860.000643
t-0.006407478696713360.003735-1.71560.0883660.044183







Multiple Linear Regression - Regression Statistics
Multiple R0.746865837404828
R-squared0.557808579082415
Adjusted R-squared0.527312619019133
F-TEST (value)18.2912286717622
F-TEST (DF numerator)10
F-TEST (DF denominator)145
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.01899776486645
Sum Squared Residuals591.071036307677

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.746865837404828 \tabularnewline
R-squared & 0.557808579082415 \tabularnewline
Adjusted R-squared & 0.527312619019133 \tabularnewline
F-TEST (value) & 18.2912286717622 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 145 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.01899776486645 \tabularnewline
Sum Squared Residuals & 591.071036307677 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145926&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.746865837404828[/C][/ROW]
[ROW][C]R-squared[/C][C]0.557808579082415[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.527312619019133[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]18.2912286717622[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]10[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]145[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.01899776486645[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]591.071036307677[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145926&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145926&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.746865837404828
R-squared0.557808579082415
Adjusted R-squared0.527312619019133
F-TEST (value)18.2912286717622
F-TEST (DF numerator)10
F-TEST (DF denominator)145
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.01899776486645
Sum Squared Residuals591.071036307677







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11513.09808455897341.90191544102664
2129.334611107783792.66538889221621
31513.6510490549111.34895094508902
41212.1896344205315-0.189634420531492
51413.63672484024930.363275159750659
689.09557557624667-1.09557557624667
71112.1115572596557-1.11155725965572
8158.382285938569776.61771406143024
946.33579814388996-2.33579814388996
101310.65320241189012.34679758810994
111914.41939705850164.58060294149841
121011.3894752132707-1.38947521327073
131517.2926358827324-2.29263588273236
14613.187466644766-7.18746664476603
15712.1231023366937-5.12310233669369
161413.47322270244920.526777297550777
171614.36701722296211.6329827770379
181615.84998237932420.150017620675771
191415.2182109966908-1.21821099669079
201515.2172057096217-0.21720570962175
211414.5468136734371-0.546813673437087
221211.25794823023680.742051769763176
2398.988513743333510.0114862566664852
241211.57961856866130.420381431338652
251413.9095348552170.0904651447829506
261212.0679306250819-0.0679306250818924
271413.66451994956330.335480050436709
281011.2303635028932-1.23036350289317
291412.96037591440251.0396240855975
301616.038856051892-0.0388560518920175
31108.922069259730751.07793074026925
32810.7539999726585-2.75399997265848
331211.69681060737380.30318939262623
341111.0749796177072-0.0749796177071755
35810.3105726476984-2.31057264769841
361312.21359081735890.786409182641092
371110.01030467164840.989695328351577
38129.83227841331952.1677215866805
391615.21785582958420.782144170415826
401613.01537483981442.98462516018556
411313.9167692548379-0.916769254837923
421415.3107122591626-1.31071225916263
4355.59431254652437-0.594312546524371
441413.27958921076240.720410789237553
45138.958374404649554.04162559535045
461615.43600075314750.563999246852536
471414.6266103305611-0.626610330561148
481514.15374757047180.846252429528169
491513.33519556290631.66480443709368
501112.2372811627391-1.23728116273909
511513.68470209339671.31529790660325
521612.66602471464453.33397528535547
531313.3258405899626-0.325840589962623
541113.7029888671069-2.70298886710691
551213.9250133183881-1.92501331838815
561211.6098233369160.390176663083983
571013.451342229255-3.45134222925503
5889.04261103534925-1.04261103534925
5999.69310574143097-0.693105741430971
601212.045387994039-0.0453879940389717
611413.9106370572280.0893629427720447
621213.0083713397218-1.00837133972183
631110.97692213531350.0230778646865255
641413.49872552489020.501274475109799
65711.6308647176515-4.63086471765147
661613.99938574768012.00061425231988
671615.49005574093020.509944259069827
681112.2982409395182-1.29824093951819
691615.2870284919230.712971508076961
701314.4142657678109-1.4142657678109
711110.91153041303670.0884695869632988
721312.79854600577340.201453994226599
731414.2807110397621-0.280711039762121
741513.48445667863431.51554332136573
75109.292770560647790.707229439352215
761514.64656239869280.353437601307165
771112.9527797832399-1.95277978323991
781111.4043554773293-0.40435547732932
7968.45757353417261-2.45757353417261
80119.645374917711391.35462508228861
811211.63428523430250.365714765697493
821313.2443154760362-0.244315476036221
831212.6581126741542-0.658112674154231
84810.8160962310738-2.81609623107383
85910.7024135722567-1.70241357225673
861011.6212793989087-1.62127939890874
871613.24291455350252.75708544649748
881512.78266009976772.21733990023233
891413.52538035299060.474619647009403
901213.5699261701497-1.56992617014973
911210.23459942399181.76540057600822
92108.709363374215061.29063662578494
931211.44546726086040.554532739139586
9489.29247098996122-1.29247098996122
951614.03574408224371.96425591775626
96117.722977922127063.27702207787294
971211.44215238202690.557847617973136
98910.3074555378289-1.30745553782888
991411.44728733081412.55271266918589
1001514.31445495535950.685545044640456
101810.5692779610823-2.56927796108232
1021212.373175300765-0.373175300764953
1031010.4270650991506-0.42706509915059
1041614.78831280611371.21168719388628
1051714.20547645726082.79452354273925
106810.0343820261573-2.03438202615729
107910.7078936298132-1.70789362981318
108811.3302451431894-3.33024514318938
1091112.3481588429061-1.34815884290607
1101615.30804175209420.69195824790584
1111313.4300882747453-0.430088274745342
11257.92422944710671-2.92422944710671
1131512.7639954519712.23600454802897
1141513.86478852759561.13521147240442
1151211.40044047143280.599559528567214
1161211.45075099457120.549249005428757
1171615.73069356494480.269306435055161
1181212.4165342860829-0.416534286082865
1191011.7091963037699-1.70919630376992
1201210.66466371609731.33533628390266
12146.22150795113951-2.22150795113951
1221112.9228174382834-1.92281743828336
1231614.65573905637991.34426094362011
12478.76472456379762-1.76472456379762
125910.71615589177-1.71615589177001
1261410.79092939477733.2090706052227
127119.678507834945081.32149216505492
1281010.8792289215278-0.879228921527816
12968.48388602635172-2.48388602635172
1301412.8020205374761.19797946252395
1311110.86688871477920.133111285220822
132119.152723110309561.84727688969044
133913.8708137524157-4.8708137524157
1341611.35724879235754.64275120764247
13577.81813032982012-0.818130329820121
13688.78068323292848-0.78068323292848
137109.867390059925120.132609940074876
1381411.94695515947032.05304484052966
13999.60739523239862-0.60739523239862
1401312.29845509555510.701544904444911
141139.522030607301483.47796939269852
1421211.77003110047150.229968899528461
1431112.56425405171-1.56425405170999
1441014.9395072859872-4.93950728598721
1451211.92877477547560.0712252245243581
1461413.28388009096990.71611990903015
1471113.3075729058824-2.30757290588238
1481310.98743024333922.01256975666079
1491413.52239996856670.477600031433273
1501312.55573089653280.444269103467224
1511615.96681135750490.0331886424950528
1521312.07869782090330.921302179096672
1531211.2563232429470.743676757053048
15499.15504321665876-0.155043216658764
1551411.20378933925442.7962106607456
1561514.57561242141920.424387578580834

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 15 & 13.0980845589734 & 1.90191544102664 \tabularnewline
2 & 12 & 9.33461110778379 & 2.66538889221621 \tabularnewline
3 & 15 & 13.651049054911 & 1.34895094508902 \tabularnewline
4 & 12 & 12.1896344205315 & -0.189634420531492 \tabularnewline
5 & 14 & 13.6367248402493 & 0.363275159750659 \tabularnewline
6 & 8 & 9.09557557624667 & -1.09557557624667 \tabularnewline
7 & 11 & 12.1115572596557 & -1.11155725965572 \tabularnewline
8 & 15 & 8.38228593856977 & 6.61771406143024 \tabularnewline
9 & 4 & 6.33579814388996 & -2.33579814388996 \tabularnewline
10 & 13 & 10.6532024118901 & 2.34679758810994 \tabularnewline
11 & 19 & 14.4193970585016 & 4.58060294149841 \tabularnewline
12 & 10 & 11.3894752132707 & -1.38947521327073 \tabularnewline
13 & 15 & 17.2926358827324 & -2.29263588273236 \tabularnewline
14 & 6 & 13.187466644766 & -7.18746664476603 \tabularnewline
15 & 7 & 12.1231023366937 & -5.12310233669369 \tabularnewline
16 & 14 & 13.4732227024492 & 0.526777297550777 \tabularnewline
17 & 16 & 14.3670172229621 & 1.6329827770379 \tabularnewline
18 & 16 & 15.8499823793242 & 0.150017620675771 \tabularnewline
19 & 14 & 15.2182109966908 & -1.21821099669079 \tabularnewline
20 & 15 & 15.2172057096217 & -0.21720570962175 \tabularnewline
21 & 14 & 14.5468136734371 & -0.546813673437087 \tabularnewline
22 & 12 & 11.2579482302368 & 0.742051769763176 \tabularnewline
23 & 9 & 8.98851374333351 & 0.0114862566664852 \tabularnewline
24 & 12 & 11.5796185686613 & 0.420381431338652 \tabularnewline
25 & 14 & 13.909534855217 & 0.0904651447829506 \tabularnewline
26 & 12 & 12.0679306250819 & -0.0679306250818924 \tabularnewline
27 & 14 & 13.6645199495633 & 0.335480050436709 \tabularnewline
28 & 10 & 11.2303635028932 & -1.23036350289317 \tabularnewline
29 & 14 & 12.9603759144025 & 1.0396240855975 \tabularnewline
30 & 16 & 16.038856051892 & -0.0388560518920175 \tabularnewline
31 & 10 & 8.92206925973075 & 1.07793074026925 \tabularnewline
32 & 8 & 10.7539999726585 & -2.75399997265848 \tabularnewline
33 & 12 & 11.6968106073738 & 0.30318939262623 \tabularnewline
34 & 11 & 11.0749796177072 & -0.0749796177071755 \tabularnewline
35 & 8 & 10.3105726476984 & -2.31057264769841 \tabularnewline
36 & 13 & 12.2135908173589 & 0.786409182641092 \tabularnewline
37 & 11 & 10.0103046716484 & 0.989695328351577 \tabularnewline
38 & 12 & 9.8322784133195 & 2.1677215866805 \tabularnewline
39 & 16 & 15.2178558295842 & 0.782144170415826 \tabularnewline
40 & 16 & 13.0153748398144 & 2.98462516018556 \tabularnewline
41 & 13 & 13.9167692548379 & -0.916769254837923 \tabularnewline
42 & 14 & 15.3107122591626 & -1.31071225916263 \tabularnewline
43 & 5 & 5.59431254652437 & -0.594312546524371 \tabularnewline
44 & 14 & 13.2795892107624 & 0.720410789237553 \tabularnewline
45 & 13 & 8.95837440464955 & 4.04162559535045 \tabularnewline
46 & 16 & 15.4360007531475 & 0.563999246852536 \tabularnewline
47 & 14 & 14.6266103305611 & -0.626610330561148 \tabularnewline
48 & 15 & 14.1537475704718 & 0.846252429528169 \tabularnewline
49 & 15 & 13.3351955629063 & 1.66480443709368 \tabularnewline
50 & 11 & 12.2372811627391 & -1.23728116273909 \tabularnewline
51 & 15 & 13.6847020933967 & 1.31529790660325 \tabularnewline
52 & 16 & 12.6660247146445 & 3.33397528535547 \tabularnewline
53 & 13 & 13.3258405899626 & -0.325840589962623 \tabularnewline
54 & 11 & 13.7029888671069 & -2.70298886710691 \tabularnewline
55 & 12 & 13.9250133183881 & -1.92501331838815 \tabularnewline
56 & 12 & 11.609823336916 & 0.390176663083983 \tabularnewline
57 & 10 & 13.451342229255 & -3.45134222925503 \tabularnewline
58 & 8 & 9.04261103534925 & -1.04261103534925 \tabularnewline
59 & 9 & 9.69310574143097 & -0.693105741430971 \tabularnewline
60 & 12 & 12.045387994039 & -0.0453879940389717 \tabularnewline
61 & 14 & 13.910637057228 & 0.0893629427720447 \tabularnewline
62 & 12 & 13.0083713397218 & -1.00837133972183 \tabularnewline
63 & 11 & 10.9769221353135 & 0.0230778646865255 \tabularnewline
64 & 14 & 13.4987255248902 & 0.501274475109799 \tabularnewline
65 & 7 & 11.6308647176515 & -4.63086471765147 \tabularnewline
66 & 16 & 13.9993857476801 & 2.00061425231988 \tabularnewline
67 & 16 & 15.4900557409302 & 0.509944259069827 \tabularnewline
68 & 11 & 12.2982409395182 & -1.29824093951819 \tabularnewline
69 & 16 & 15.287028491923 & 0.712971508076961 \tabularnewline
70 & 13 & 14.4142657678109 & -1.4142657678109 \tabularnewline
71 & 11 & 10.9115304130367 & 0.0884695869632988 \tabularnewline
72 & 13 & 12.7985460057734 & 0.201453994226599 \tabularnewline
73 & 14 & 14.2807110397621 & -0.280711039762121 \tabularnewline
74 & 15 & 13.4844566786343 & 1.51554332136573 \tabularnewline
75 & 10 & 9.29277056064779 & 0.707229439352215 \tabularnewline
76 & 15 & 14.6465623986928 & 0.353437601307165 \tabularnewline
77 & 11 & 12.9527797832399 & -1.95277978323991 \tabularnewline
78 & 11 & 11.4043554773293 & -0.40435547732932 \tabularnewline
79 & 6 & 8.45757353417261 & -2.45757353417261 \tabularnewline
80 & 11 & 9.64537491771139 & 1.35462508228861 \tabularnewline
81 & 12 & 11.6342852343025 & 0.365714765697493 \tabularnewline
82 & 13 & 13.2443154760362 & -0.244315476036221 \tabularnewline
83 & 12 & 12.6581126741542 & -0.658112674154231 \tabularnewline
84 & 8 & 10.8160962310738 & -2.81609623107383 \tabularnewline
85 & 9 & 10.7024135722567 & -1.70241357225673 \tabularnewline
86 & 10 & 11.6212793989087 & -1.62127939890874 \tabularnewline
87 & 16 & 13.2429145535025 & 2.75708544649748 \tabularnewline
88 & 15 & 12.7826600997677 & 2.21733990023233 \tabularnewline
89 & 14 & 13.5253803529906 & 0.474619647009403 \tabularnewline
90 & 12 & 13.5699261701497 & -1.56992617014973 \tabularnewline
91 & 12 & 10.2345994239918 & 1.76540057600822 \tabularnewline
92 & 10 & 8.70936337421506 & 1.29063662578494 \tabularnewline
93 & 12 & 11.4454672608604 & 0.554532739139586 \tabularnewline
94 & 8 & 9.29247098996122 & -1.29247098996122 \tabularnewline
95 & 16 & 14.0357440822437 & 1.96425591775626 \tabularnewline
96 & 11 & 7.72297792212706 & 3.27702207787294 \tabularnewline
97 & 12 & 11.4421523820269 & 0.557847617973136 \tabularnewline
98 & 9 & 10.3074555378289 & -1.30745553782888 \tabularnewline
99 & 14 & 11.4472873308141 & 2.55271266918589 \tabularnewline
100 & 15 & 14.3144549553595 & 0.685545044640456 \tabularnewline
101 & 8 & 10.5692779610823 & -2.56927796108232 \tabularnewline
102 & 12 & 12.373175300765 & -0.373175300764953 \tabularnewline
103 & 10 & 10.4270650991506 & -0.42706509915059 \tabularnewline
104 & 16 & 14.7883128061137 & 1.21168719388628 \tabularnewline
105 & 17 & 14.2054764572608 & 2.79452354273925 \tabularnewline
106 & 8 & 10.0343820261573 & -2.03438202615729 \tabularnewline
107 & 9 & 10.7078936298132 & -1.70789362981318 \tabularnewline
108 & 8 & 11.3302451431894 & -3.33024514318938 \tabularnewline
109 & 11 & 12.3481588429061 & -1.34815884290607 \tabularnewline
110 & 16 & 15.3080417520942 & 0.69195824790584 \tabularnewline
111 & 13 & 13.4300882747453 & -0.430088274745342 \tabularnewline
112 & 5 & 7.92422944710671 & -2.92422944710671 \tabularnewline
113 & 15 & 12.763995451971 & 2.23600454802897 \tabularnewline
114 & 15 & 13.8647885275956 & 1.13521147240442 \tabularnewline
115 & 12 & 11.4004404714328 & 0.599559528567214 \tabularnewline
116 & 12 & 11.4507509945712 & 0.549249005428757 \tabularnewline
117 & 16 & 15.7306935649448 & 0.269306435055161 \tabularnewline
118 & 12 & 12.4165342860829 & -0.416534286082865 \tabularnewline
119 & 10 & 11.7091963037699 & -1.70919630376992 \tabularnewline
120 & 12 & 10.6646637160973 & 1.33533628390266 \tabularnewline
121 & 4 & 6.22150795113951 & -2.22150795113951 \tabularnewline
122 & 11 & 12.9228174382834 & -1.92281743828336 \tabularnewline
123 & 16 & 14.6557390563799 & 1.34426094362011 \tabularnewline
124 & 7 & 8.76472456379762 & -1.76472456379762 \tabularnewline
125 & 9 & 10.71615589177 & -1.71615589177001 \tabularnewline
126 & 14 & 10.7909293947773 & 3.2090706052227 \tabularnewline
127 & 11 & 9.67850783494508 & 1.32149216505492 \tabularnewline
128 & 10 & 10.8792289215278 & -0.879228921527816 \tabularnewline
129 & 6 & 8.48388602635172 & -2.48388602635172 \tabularnewline
130 & 14 & 12.802020537476 & 1.19797946252395 \tabularnewline
131 & 11 & 10.8668887147792 & 0.133111285220822 \tabularnewline
132 & 11 & 9.15272311030956 & 1.84727688969044 \tabularnewline
133 & 9 & 13.8708137524157 & -4.8708137524157 \tabularnewline
134 & 16 & 11.3572487923575 & 4.64275120764247 \tabularnewline
135 & 7 & 7.81813032982012 & -0.818130329820121 \tabularnewline
136 & 8 & 8.78068323292848 & -0.78068323292848 \tabularnewline
137 & 10 & 9.86739005992512 & 0.132609940074876 \tabularnewline
138 & 14 & 11.9469551594703 & 2.05304484052966 \tabularnewline
139 & 9 & 9.60739523239862 & -0.60739523239862 \tabularnewline
140 & 13 & 12.2984550955551 & 0.701544904444911 \tabularnewline
141 & 13 & 9.52203060730148 & 3.47796939269852 \tabularnewline
142 & 12 & 11.7700311004715 & 0.229968899528461 \tabularnewline
143 & 11 & 12.56425405171 & -1.56425405170999 \tabularnewline
144 & 10 & 14.9395072859872 & -4.93950728598721 \tabularnewline
145 & 12 & 11.9287747754756 & 0.0712252245243581 \tabularnewline
146 & 14 & 13.2838800909699 & 0.71611990903015 \tabularnewline
147 & 11 & 13.3075729058824 & -2.30757290588238 \tabularnewline
148 & 13 & 10.9874302433392 & 2.01256975666079 \tabularnewline
149 & 14 & 13.5223999685667 & 0.477600031433273 \tabularnewline
150 & 13 & 12.5557308965328 & 0.444269103467224 \tabularnewline
151 & 16 & 15.9668113575049 & 0.0331886424950528 \tabularnewline
152 & 13 & 12.0786978209033 & 0.921302179096672 \tabularnewline
153 & 12 & 11.256323242947 & 0.743676757053048 \tabularnewline
154 & 9 & 9.15504321665876 & -0.155043216658764 \tabularnewline
155 & 14 & 11.2037893392544 & 2.7962106607456 \tabularnewline
156 & 15 & 14.5756124214192 & 0.424387578580834 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145926&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]13.0980845589734[/C][C]1.90191544102664[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]9.33461110778379[/C][C]2.66538889221621[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.651049054911[/C][C]1.34895094508902[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]12.1896344205315[/C][C]-0.189634420531492[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]13.6367248402493[/C][C]0.363275159750659[/C][/ROW]
[ROW][C]6[/C][C]8[/C][C]9.09557557624667[/C][C]-1.09557557624667[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]12.1115572596557[/C][C]-1.11155725965572[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]8.38228593856977[/C][C]6.61771406143024[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]6.33579814388996[/C][C]-2.33579814388996[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]10.6532024118901[/C][C]2.34679758810994[/C][/ROW]
[ROW][C]11[/C][C]19[/C][C]14.4193970585016[/C][C]4.58060294149841[/C][/ROW]
[ROW][C]12[/C][C]10[/C][C]11.3894752132707[/C][C]-1.38947521327073[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]17.2926358827324[/C][C]-2.29263588273236[/C][/ROW]
[ROW][C]14[/C][C]6[/C][C]13.187466644766[/C][C]-7.18746664476603[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]12.1231023366937[/C][C]-5.12310233669369[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.4732227024492[/C][C]0.526777297550777[/C][/ROW]
[ROW][C]17[/C][C]16[/C][C]14.3670172229621[/C][C]1.6329827770379[/C][/ROW]
[ROW][C]18[/C][C]16[/C][C]15.8499823793242[/C][C]0.150017620675771[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]15.2182109966908[/C][C]-1.21821099669079[/C][/ROW]
[ROW][C]20[/C][C]15[/C][C]15.2172057096217[/C][C]-0.21720570962175[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]14.5468136734371[/C][C]-0.546813673437087[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]11.2579482302368[/C][C]0.742051769763176[/C][/ROW]
[ROW][C]23[/C][C]9[/C][C]8.98851374333351[/C][C]0.0114862566664852[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]11.5796185686613[/C][C]0.420381431338652[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]13.909534855217[/C][C]0.0904651447829506[/C][/ROW]
[ROW][C]26[/C][C]12[/C][C]12.0679306250819[/C][C]-0.0679306250818924[/C][/ROW]
[ROW][C]27[/C][C]14[/C][C]13.6645199495633[/C][C]0.335480050436709[/C][/ROW]
[ROW][C]28[/C][C]10[/C][C]11.2303635028932[/C][C]-1.23036350289317[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.9603759144025[/C][C]1.0396240855975[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]16.038856051892[/C][C]-0.0388560518920175[/C][/ROW]
[ROW][C]31[/C][C]10[/C][C]8.92206925973075[/C][C]1.07793074026925[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]10.7539999726585[/C][C]-2.75399997265848[/C][/ROW]
[ROW][C]33[/C][C]12[/C][C]11.6968106073738[/C][C]0.30318939262623[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]11.0749796177072[/C][C]-0.0749796177071755[/C][/ROW]
[ROW][C]35[/C][C]8[/C][C]10.3105726476984[/C][C]-2.31057264769841[/C][/ROW]
[ROW][C]36[/C][C]13[/C][C]12.2135908173589[/C][C]0.786409182641092[/C][/ROW]
[ROW][C]37[/C][C]11[/C][C]10.0103046716484[/C][C]0.989695328351577[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]9.8322784133195[/C][C]2.1677215866805[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]15.2178558295842[/C][C]0.782144170415826[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]13.0153748398144[/C][C]2.98462516018556[/C][/ROW]
[ROW][C]41[/C][C]13[/C][C]13.9167692548379[/C][C]-0.916769254837923[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.3107122591626[/C][C]-1.31071225916263[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]5.59431254652437[/C][C]-0.594312546524371[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]13.2795892107624[/C][C]0.720410789237553[/C][/ROW]
[ROW][C]45[/C][C]13[/C][C]8.95837440464955[/C][C]4.04162559535045[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]15.4360007531475[/C][C]0.563999246852536[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]14.6266103305611[/C][C]-0.626610330561148[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.1537475704718[/C][C]0.846252429528169[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]13.3351955629063[/C][C]1.66480443709368[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]12.2372811627391[/C][C]-1.23728116273909[/C][/ROW]
[ROW][C]51[/C][C]15[/C][C]13.6847020933967[/C][C]1.31529790660325[/C][/ROW]
[ROW][C]52[/C][C]16[/C][C]12.6660247146445[/C][C]3.33397528535547[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]13.3258405899626[/C][C]-0.325840589962623[/C][/ROW]
[ROW][C]54[/C][C]11[/C][C]13.7029888671069[/C][C]-2.70298886710691[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]13.9250133183881[/C][C]-1.92501331838815[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]11.609823336916[/C][C]0.390176663083983[/C][/ROW]
[ROW][C]57[/C][C]10[/C][C]13.451342229255[/C][C]-3.45134222925503[/C][/ROW]
[ROW][C]58[/C][C]8[/C][C]9.04261103534925[/C][C]-1.04261103534925[/C][/ROW]
[ROW][C]59[/C][C]9[/C][C]9.69310574143097[/C][C]-0.693105741430971[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]12.045387994039[/C][C]-0.0453879940389717[/C][/ROW]
[ROW][C]61[/C][C]14[/C][C]13.910637057228[/C][C]0.0893629427720447[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]13.0083713397218[/C][C]-1.00837133972183[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]10.9769221353135[/C][C]0.0230778646865255[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.4987255248902[/C][C]0.501274475109799[/C][/ROW]
[ROW][C]65[/C][C]7[/C][C]11.6308647176515[/C][C]-4.63086471765147[/C][/ROW]
[ROW][C]66[/C][C]16[/C][C]13.9993857476801[/C][C]2.00061425231988[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]15.4900557409302[/C][C]0.509944259069827[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]12.2982409395182[/C][C]-1.29824093951819[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]15.287028491923[/C][C]0.712971508076961[/C][/ROW]
[ROW][C]70[/C][C]13[/C][C]14.4142657678109[/C][C]-1.4142657678109[/C][/ROW]
[ROW][C]71[/C][C]11[/C][C]10.9115304130367[/C][C]0.0884695869632988[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.7985460057734[/C][C]0.201453994226599[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]14.2807110397621[/C][C]-0.280711039762121[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]13.4844566786343[/C][C]1.51554332136573[/C][/ROW]
[ROW][C]75[/C][C]10[/C][C]9.29277056064779[/C][C]0.707229439352215[/C][/ROW]
[ROW][C]76[/C][C]15[/C][C]14.6465623986928[/C][C]0.353437601307165[/C][/ROW]
[ROW][C]77[/C][C]11[/C][C]12.9527797832399[/C][C]-1.95277978323991[/C][/ROW]
[ROW][C]78[/C][C]11[/C][C]11.4043554773293[/C][C]-0.40435547732932[/C][/ROW]
[ROW][C]79[/C][C]6[/C][C]8.45757353417261[/C][C]-2.45757353417261[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]9.64537491771139[/C][C]1.35462508228861[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.6342852343025[/C][C]0.365714765697493[/C][/ROW]
[ROW][C]82[/C][C]13[/C][C]13.2443154760362[/C][C]-0.244315476036221[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]12.6581126741542[/C][C]-0.658112674154231[/C][/ROW]
[ROW][C]84[/C][C]8[/C][C]10.8160962310738[/C][C]-2.81609623107383[/C][/ROW]
[ROW][C]85[/C][C]9[/C][C]10.7024135722567[/C][C]-1.70241357225673[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]11.6212793989087[/C][C]-1.62127939890874[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]13.2429145535025[/C][C]2.75708544649748[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]12.7826600997677[/C][C]2.21733990023233[/C][/ROW]
[ROW][C]89[/C][C]14[/C][C]13.5253803529906[/C][C]0.474619647009403[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.5699261701497[/C][C]-1.56992617014973[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]10.2345994239918[/C][C]1.76540057600822[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]8.70936337421506[/C][C]1.29063662578494[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]11.4454672608604[/C][C]0.554532739139586[/C][/ROW]
[ROW][C]94[/C][C]8[/C][C]9.29247098996122[/C][C]-1.29247098996122[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.0357440822437[/C][C]1.96425591775626[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]7.72297792212706[/C][C]3.27702207787294[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]11.4421523820269[/C][C]0.557847617973136[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]10.3074555378289[/C][C]-1.30745553782888[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]11.4472873308141[/C][C]2.55271266918589[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]14.3144549553595[/C][C]0.685545044640456[/C][/ROW]
[ROW][C]101[/C][C]8[/C][C]10.5692779610823[/C][C]-2.56927796108232[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]12.373175300765[/C][C]-0.373175300764953[/C][/ROW]
[ROW][C]103[/C][C]10[/C][C]10.4270650991506[/C][C]-0.42706509915059[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]14.7883128061137[/C][C]1.21168719388628[/C][/ROW]
[ROW][C]105[/C][C]17[/C][C]14.2054764572608[/C][C]2.79452354273925[/C][/ROW]
[ROW][C]106[/C][C]8[/C][C]10.0343820261573[/C][C]-2.03438202615729[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.7078936298132[/C][C]-1.70789362981318[/C][/ROW]
[ROW][C]108[/C][C]8[/C][C]11.3302451431894[/C][C]-3.33024514318938[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]12.3481588429061[/C][C]-1.34815884290607[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.3080417520942[/C][C]0.69195824790584[/C][/ROW]
[ROW][C]111[/C][C]13[/C][C]13.4300882747453[/C][C]-0.430088274745342[/C][/ROW]
[ROW][C]112[/C][C]5[/C][C]7.92422944710671[/C][C]-2.92422944710671[/C][/ROW]
[ROW][C]113[/C][C]15[/C][C]12.763995451971[/C][C]2.23600454802897[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]13.8647885275956[/C][C]1.13521147240442[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]11.4004404714328[/C][C]0.599559528567214[/C][/ROW]
[ROW][C]116[/C][C]12[/C][C]11.4507509945712[/C][C]0.549249005428757[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]15.7306935649448[/C][C]0.269306435055161[/C][/ROW]
[ROW][C]118[/C][C]12[/C][C]12.4165342860829[/C][C]-0.416534286082865[/C][/ROW]
[ROW][C]119[/C][C]10[/C][C]11.7091963037699[/C][C]-1.70919630376992[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]10.6646637160973[/C][C]1.33533628390266[/C][/ROW]
[ROW][C]121[/C][C]4[/C][C]6.22150795113951[/C][C]-2.22150795113951[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]12.9228174382834[/C][C]-1.92281743828336[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]14.6557390563799[/C][C]1.34426094362011[/C][/ROW]
[ROW][C]124[/C][C]7[/C][C]8.76472456379762[/C][C]-1.76472456379762[/C][/ROW]
[ROW][C]125[/C][C]9[/C][C]10.71615589177[/C][C]-1.71615589177001[/C][/ROW]
[ROW][C]126[/C][C]14[/C][C]10.7909293947773[/C][C]3.2090706052227[/C][/ROW]
[ROW][C]127[/C][C]11[/C][C]9.67850783494508[/C][C]1.32149216505492[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]10.8792289215278[/C][C]-0.879228921527816[/C][/ROW]
[ROW][C]129[/C][C]6[/C][C]8.48388602635172[/C][C]-2.48388602635172[/C][/ROW]
[ROW][C]130[/C][C]14[/C][C]12.802020537476[/C][C]1.19797946252395[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]10.8668887147792[/C][C]0.133111285220822[/C][/ROW]
[ROW][C]132[/C][C]11[/C][C]9.15272311030956[/C][C]1.84727688969044[/C][/ROW]
[ROW][C]133[/C][C]9[/C][C]13.8708137524157[/C][C]-4.8708137524157[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]11.3572487923575[/C][C]4.64275120764247[/C][/ROW]
[ROW][C]135[/C][C]7[/C][C]7.81813032982012[/C][C]-0.818130329820121[/C][/ROW]
[ROW][C]136[/C][C]8[/C][C]8.78068323292848[/C][C]-0.78068323292848[/C][/ROW]
[ROW][C]137[/C][C]10[/C][C]9.86739005992512[/C][C]0.132609940074876[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]11.9469551594703[/C][C]2.05304484052966[/C][/ROW]
[ROW][C]139[/C][C]9[/C][C]9.60739523239862[/C][C]-0.60739523239862[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]12.2984550955551[/C][C]0.701544904444911[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]9.52203060730148[/C][C]3.47796939269852[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]11.7700311004715[/C][C]0.229968899528461[/C][/ROW]
[ROW][C]143[/C][C]11[/C][C]12.56425405171[/C][C]-1.56425405170999[/C][/ROW]
[ROW][C]144[/C][C]10[/C][C]14.9395072859872[/C][C]-4.93950728598721[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]11.9287747754756[/C][C]0.0712252245243581[/C][/ROW]
[ROW][C]146[/C][C]14[/C][C]13.2838800909699[/C][C]0.71611990903015[/C][/ROW]
[ROW][C]147[/C][C]11[/C][C]13.3075729058824[/C][C]-2.30757290588238[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]10.9874302433392[/C][C]2.01256975666079[/C][/ROW]
[ROW][C]149[/C][C]14[/C][C]13.5223999685667[/C][C]0.477600031433273[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.5557308965328[/C][C]0.444269103467224[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]15.9668113575049[/C][C]0.0331886424950528[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]12.0786978209033[/C][C]0.921302179096672[/C][/ROW]
[ROW][C]153[/C][C]12[/C][C]11.256323242947[/C][C]0.743676757053048[/C][/ROW]
[ROW][C]154[/C][C]9[/C][C]9.15504321665876[/C][C]-0.155043216658764[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]11.2037893392544[/C][C]2.7962106607456[/C][/ROW]
[ROW][C]156[/C][C]15[/C][C]14.5756124214192[/C][C]0.424387578580834[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145926&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145926&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11513.09808455897341.90191544102664
2129.334611107783792.66538889221621
31513.6510490549111.34895094508902
41212.1896344205315-0.189634420531492
51413.63672484024930.363275159750659
689.09557557624667-1.09557557624667
71112.1115572596557-1.11155725965572
8158.382285938569776.61771406143024
946.33579814388996-2.33579814388996
101310.65320241189012.34679758810994
111914.41939705850164.58060294149841
121011.3894752132707-1.38947521327073
131517.2926358827324-2.29263588273236
14613.187466644766-7.18746664476603
15712.1231023366937-5.12310233669369
161413.47322270244920.526777297550777
171614.36701722296211.6329827770379
181615.84998237932420.150017620675771
191415.2182109966908-1.21821099669079
201515.2172057096217-0.21720570962175
211414.5468136734371-0.546813673437087
221211.25794823023680.742051769763176
2398.988513743333510.0114862566664852
241211.57961856866130.420381431338652
251413.9095348552170.0904651447829506
261212.0679306250819-0.0679306250818924
271413.66451994956330.335480050436709
281011.2303635028932-1.23036350289317
291412.96037591440251.0396240855975
301616.038856051892-0.0388560518920175
31108.922069259730751.07793074026925
32810.7539999726585-2.75399997265848
331211.69681060737380.30318939262623
341111.0749796177072-0.0749796177071755
35810.3105726476984-2.31057264769841
361312.21359081735890.786409182641092
371110.01030467164840.989695328351577
38129.83227841331952.1677215866805
391615.21785582958420.782144170415826
401613.01537483981442.98462516018556
411313.9167692548379-0.916769254837923
421415.3107122591626-1.31071225916263
4355.59431254652437-0.594312546524371
441413.27958921076240.720410789237553
45138.958374404649554.04162559535045
461615.43600075314750.563999246852536
471414.6266103305611-0.626610330561148
481514.15374757047180.846252429528169
491513.33519556290631.66480443709368
501112.2372811627391-1.23728116273909
511513.68470209339671.31529790660325
521612.66602471464453.33397528535547
531313.3258405899626-0.325840589962623
541113.7029888671069-2.70298886710691
551213.9250133183881-1.92501331838815
561211.6098233369160.390176663083983
571013.451342229255-3.45134222925503
5889.04261103534925-1.04261103534925
5999.69310574143097-0.693105741430971
601212.045387994039-0.0453879940389717
611413.9106370572280.0893629427720447
621213.0083713397218-1.00837133972183
631110.97692213531350.0230778646865255
641413.49872552489020.501274475109799
65711.6308647176515-4.63086471765147
661613.99938574768012.00061425231988
671615.49005574093020.509944259069827
681112.2982409395182-1.29824093951819
691615.2870284919230.712971508076961
701314.4142657678109-1.4142657678109
711110.91153041303670.0884695869632988
721312.79854600577340.201453994226599
731414.2807110397621-0.280711039762121
741513.48445667863431.51554332136573
75109.292770560647790.707229439352215
761514.64656239869280.353437601307165
771112.9527797832399-1.95277978323991
781111.4043554773293-0.40435547732932
7968.45757353417261-2.45757353417261
80119.645374917711391.35462508228861
811211.63428523430250.365714765697493
821313.2443154760362-0.244315476036221
831212.6581126741542-0.658112674154231
84810.8160962310738-2.81609623107383
85910.7024135722567-1.70241357225673
861011.6212793989087-1.62127939890874
871613.24291455350252.75708544649748
881512.78266009976772.21733990023233
891413.52538035299060.474619647009403
901213.5699261701497-1.56992617014973
911210.23459942399181.76540057600822
92108.709363374215061.29063662578494
931211.44546726086040.554532739139586
9489.29247098996122-1.29247098996122
951614.03574408224371.96425591775626
96117.722977922127063.27702207787294
971211.44215238202690.557847617973136
98910.3074555378289-1.30745553782888
991411.44728733081412.55271266918589
1001514.31445495535950.685545044640456
101810.5692779610823-2.56927796108232
1021212.373175300765-0.373175300764953
1031010.4270650991506-0.42706509915059
1041614.78831280611371.21168719388628
1051714.20547645726082.79452354273925
106810.0343820261573-2.03438202615729
107910.7078936298132-1.70789362981318
108811.3302451431894-3.33024514318938
1091112.3481588429061-1.34815884290607
1101615.30804175209420.69195824790584
1111313.4300882747453-0.430088274745342
11257.92422944710671-2.92422944710671
1131512.7639954519712.23600454802897
1141513.86478852759561.13521147240442
1151211.40044047143280.599559528567214
1161211.45075099457120.549249005428757
1171615.73069356494480.269306435055161
1181212.4165342860829-0.416534286082865
1191011.7091963037699-1.70919630376992
1201210.66466371609731.33533628390266
12146.22150795113951-2.22150795113951
1221112.9228174382834-1.92281743828336
1231614.65573905637991.34426094362011
12478.76472456379762-1.76472456379762
125910.71615589177-1.71615589177001
1261410.79092939477733.2090706052227
127119.678507834945081.32149216505492
1281010.8792289215278-0.879228921527816
12968.48388602635172-2.48388602635172
1301412.8020205374761.19797946252395
1311110.86688871477920.133111285220822
132119.152723110309561.84727688969044
133913.8708137524157-4.8708137524157
1341611.35724879235754.64275120764247
13577.81813032982012-0.818130329820121
13688.78068323292848-0.78068323292848
137109.867390059925120.132609940074876
1381411.94695515947032.05304484052966
13999.60739523239862-0.60739523239862
1401312.29845509555510.701544904444911
141139.522030607301483.47796939269852
1421211.77003110047150.229968899528461
1431112.56425405171-1.56425405170999
1441014.9395072859872-4.93950728598721
1451211.92877477547560.0712252245243581
1461413.28388009096990.71611990903015
1471113.3075729058824-2.30757290588238
1481310.98743024333922.01256975666079
1491413.52239996856670.477600031433273
1501312.55573089653280.444269103467224
1511615.96681135750490.0331886424950528
1521312.07869782090330.921302179096672
1531211.2563232429470.743676757053048
15499.15504321665876-0.155043216658764
1551411.20378933925442.7962106607456
1561514.57561242141920.424387578580834







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
140.9997492612568940.0005014774862120820.000250738743106041
150.999897136847380.000205726305240520.00010286315262026
160.9998951477071390.0002097045857227910.000104852292861396
170.9998315700222490.0003368599555028130.000168429977751407
180.9998796455022720.0002407089954564890.000120354497728244
190.9997378545297440.0005242909405124810.00026214547025624
200.9995654370285150.000869125942969170.000434562971484585
210.999137460356580.001725079286839470.000862539643419733
220.999452924095780.00109415180844060.000547075904220301
230.9992083203828090.001583359234381850.000791679617190925
240.9985830162137010.002833967572597910.00141698378629896
250.9976450218883550.00470995622328940.0023549781116447
260.9960943352579950.007811329484010860.00390566474200543
270.995320812576780.00935837484643940.0046791874232197
280.9926112195267690.01477756094646260.00738878047323132
290.9954354374067310.009129125186537410.0045645625932687
300.9942476637566470.01150467248670660.0057523362433533
310.9914964421529550.01700711569409030.00850355784704514
320.9954311589920260.009137682015948550.00456884100797427
330.9932089017751560.01358219644968720.00679109822484358
340.9899418917989390.02011621640212190.0100581082010609
350.9885356329761390.02292873404772260.0114643670238613
360.9839023346807050.03219533063858920.0160976653192946
370.9805971214720980.03880575705580380.0194028785279019
380.9824818204152470.03503635916950610.017518179584753
390.9812120621090440.03757587578191130.0187879378909557
400.9867396987244780.02652060255104320.0132603012755216
410.9823696646598140.03526067068037190.0176303353401859
420.976205184821190.047589630357620.02379481517881
430.9678150684793230.06436986304135360.0321849315206768
440.9602667505273680.0794664989452640.039733249472632
450.989203529823550.02159294035290010.01079647017645
460.9854827407696040.0290345184607930.0145172592303965
470.9805813563355850.03883728732883090.0194186436644155
480.9751229755244260.04975404895114770.0248770244755738
490.9720529445409580.05589411091808390.0279470554590419
500.9675090998195370.06498180036092510.0324909001804625
510.9635855328752970.0728289342494060.036414467124703
520.9775419898325130.04491602033497370.0224580101674869
530.9702407884678590.05951842306428280.0297592115321414
540.9712801644903890.05743967101922290.0287198355096115
550.9679156955536210.06416860889275880.0320843044463794
560.9593089862514420.08138202749711650.0406910137485582
570.9718415897918080.05631682041638310.0281584102081915
580.9645182704996930.07096345900061430.0354817295003071
590.9540984762727360.09180304745452730.0459015237272636
600.9415396066020830.1169207867958340.0584603933979171
610.9272512266280620.1454975467438760.072748773371938
620.9124334765069090.1751330469861820.087566523493091
630.8918937967409010.2162124065181970.108106203259099
640.8761863779879960.2476272440240080.123813622012004
650.93509498119810.12981003760380.0649050188019
660.9414434120754380.1171131758491230.0585565879245615
670.928401953629120.143196092741760.0715980463708799
680.9163987282966980.1672025434066040.0836012717033019
690.9000105078924780.1999789842150440.0999894921075221
700.8873821522202740.2252356955594520.112617847779726
710.8649266577201260.2701466845597480.135073342279874
720.8392516056700320.3214967886599350.160748394329968
730.8238246260399710.3523507479200570.176175373960029
740.8172288642915420.3655422714169150.182771135708458
750.7936048529359530.4127902941280940.206395147064047
760.7609771100858490.4780457798283010.239022889914151
770.7510137525773920.4979724948452160.248986247422608
780.7107362792671430.5785274414657150.289263720732857
790.7152341394659130.5695317210681740.284765860534087
800.7011333650869890.5977332698260230.298866634913011
810.6644571489291570.6710857021416850.335542851070843
820.6217935672504750.7564128654990490.378206432749525
830.5746544625480010.8506910749039970.425345537451999
840.5968753401092130.8062493197815730.403124659890787
850.5780599061909940.8438801876180130.421940093809006
860.5545419791813780.8909160416372430.445458020818622
870.5971582603009790.8056834793980420.402841739699021
880.6252310135678020.7495379728643960.374768986432198
890.5912221789544480.8175556420911040.408777821045552
900.5718954488561890.8562091022876220.428104551143811
910.5641121564393510.8717756871212980.435887843560649
920.5372589310704550.925482137859090.462741068929545
930.4938645032188870.9877290064377740.506135496781113
940.4751315789452450.950263157890490.524868421054755
950.4901708993316510.9803417986633020.509829100668349
960.63433053850410.73133892299180.3656694614959
970.590805468357350.81838906328530.40919453164265
980.5557193654086350.8885612691827310.444280634591365
990.6135686294146040.7728627411707920.386431370585396
1000.5845495580907640.8309008838184720.415450441909236
1010.5983612438312650.8032775123374690.401638756168735
1020.5514689781239760.8970620437520470.448531021876024
1030.5014317908163590.9971364183672820.498568209183641
1040.509095427668290.981809144663420.49090457233171
1050.5666181835178960.8667636329642080.433381816482104
1060.5669412906700470.8661174186599070.433058709329953
1070.5265533044676240.9468933910647510.473446695532376
1080.6113309046650180.7773381906699650.388669095334982
1090.592419206520890.815161586958220.40758079347911
1100.5493061972848160.9013876054303680.450693802715184
1110.4939868535680090.9879737071360180.506013146431991
1120.6451668519738170.7096662960523660.354833148026183
1130.6595099309755260.6809801380489480.340490069024474
1140.6518289861665060.6963420276669880.348171013833494
1150.598986485998670.8020270280026590.40101351400133
1160.5614663665922060.8770672668155890.438533633407794
1170.5184822119699760.9630355760600470.481517788030024
1180.5057864211651750.988427157669650.494213578834825
1190.5265866491714050.9468267016571890.473413350828595
1200.4719280996890090.9438561993780180.528071900310991
1210.446656039848590.893312079697180.55334396015141
1220.4062971632009450.8125943264018910.593702836799055
1230.4513333974960030.9026667949920060.548666602503997
1240.4109431067698680.8218862135397360.589056893230132
1250.4445165454474130.8890330908948270.555483454552587
1260.4640486629798170.9280973259596340.535951337020183
1270.4522297360553760.9044594721107520.547770263944624
1280.3826198011587810.7652396023175620.617380198841219
1290.6069338593179050.7861322813641910.393066140682095
1300.7167583950213690.5664832099572610.283241604978631
1310.7712682568877770.4574634862244460.228731743112223
1320.8290645162296210.3418709675407580.170935483770379
1330.8068867699170460.3862264601659080.193113230082954
1340.8620868893475240.2758262213049510.137913110652476
1350.8029249787245340.3941500425509320.197075021275466
1360.7477576402932160.5044847194135680.252242359706784
1370.8073846023370440.3852307953259110.192615397662956
1380.748551388071170.5028972238576610.25144861192883
1390.644129329121650.71174134175670.35587067087835
1400.5136510207734950.9726979584530090.486348979226505
1410.4818939207945280.9637878415890550.518106079205472
1420.3364232238923890.6728464477847780.663576776107611

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
14 & 0.999749261256894 & 0.000501477486212082 & 0.000250738743106041 \tabularnewline
15 & 0.99989713684738 & 0.00020572630524052 & 0.00010286315262026 \tabularnewline
16 & 0.999895147707139 & 0.000209704585722791 & 0.000104852292861396 \tabularnewline
17 & 0.999831570022249 & 0.000336859955502813 & 0.000168429977751407 \tabularnewline
18 & 0.999879645502272 & 0.000240708995456489 & 0.000120354497728244 \tabularnewline
19 & 0.999737854529744 & 0.000524290940512481 & 0.00026214547025624 \tabularnewline
20 & 0.999565437028515 & 0.00086912594296917 & 0.000434562971484585 \tabularnewline
21 & 0.99913746035658 & 0.00172507928683947 & 0.000862539643419733 \tabularnewline
22 & 0.99945292409578 & 0.0010941518084406 & 0.000547075904220301 \tabularnewline
23 & 0.999208320382809 & 0.00158335923438185 & 0.000791679617190925 \tabularnewline
24 & 0.998583016213701 & 0.00283396757259791 & 0.00141698378629896 \tabularnewline
25 & 0.997645021888355 & 0.0047099562232894 & 0.0023549781116447 \tabularnewline
26 & 0.996094335257995 & 0.00781132948401086 & 0.00390566474200543 \tabularnewline
27 & 0.99532081257678 & 0.0093583748464394 & 0.0046791874232197 \tabularnewline
28 & 0.992611219526769 & 0.0147775609464626 & 0.00738878047323132 \tabularnewline
29 & 0.995435437406731 & 0.00912912518653741 & 0.0045645625932687 \tabularnewline
30 & 0.994247663756647 & 0.0115046724867066 & 0.0057523362433533 \tabularnewline
31 & 0.991496442152955 & 0.0170071156940903 & 0.00850355784704514 \tabularnewline
32 & 0.995431158992026 & 0.00913768201594855 & 0.00456884100797427 \tabularnewline
33 & 0.993208901775156 & 0.0135821964496872 & 0.00679109822484358 \tabularnewline
34 & 0.989941891798939 & 0.0201162164021219 & 0.0100581082010609 \tabularnewline
35 & 0.988535632976139 & 0.0229287340477226 & 0.0114643670238613 \tabularnewline
36 & 0.983902334680705 & 0.0321953306385892 & 0.0160976653192946 \tabularnewline
37 & 0.980597121472098 & 0.0388057570558038 & 0.0194028785279019 \tabularnewline
38 & 0.982481820415247 & 0.0350363591695061 & 0.017518179584753 \tabularnewline
39 & 0.981212062109044 & 0.0375758757819113 & 0.0187879378909557 \tabularnewline
40 & 0.986739698724478 & 0.0265206025510432 & 0.0132603012755216 \tabularnewline
41 & 0.982369664659814 & 0.0352606706803719 & 0.0176303353401859 \tabularnewline
42 & 0.97620518482119 & 0.04758963035762 & 0.02379481517881 \tabularnewline
43 & 0.967815068479323 & 0.0643698630413536 & 0.0321849315206768 \tabularnewline
44 & 0.960266750527368 & 0.079466498945264 & 0.039733249472632 \tabularnewline
45 & 0.98920352982355 & 0.0215929403529001 & 0.01079647017645 \tabularnewline
46 & 0.985482740769604 & 0.029034518460793 & 0.0145172592303965 \tabularnewline
47 & 0.980581356335585 & 0.0388372873288309 & 0.0194186436644155 \tabularnewline
48 & 0.975122975524426 & 0.0497540489511477 & 0.0248770244755738 \tabularnewline
49 & 0.972052944540958 & 0.0558941109180839 & 0.0279470554590419 \tabularnewline
50 & 0.967509099819537 & 0.0649818003609251 & 0.0324909001804625 \tabularnewline
51 & 0.963585532875297 & 0.072828934249406 & 0.036414467124703 \tabularnewline
52 & 0.977541989832513 & 0.0449160203349737 & 0.0224580101674869 \tabularnewline
53 & 0.970240788467859 & 0.0595184230642828 & 0.0297592115321414 \tabularnewline
54 & 0.971280164490389 & 0.0574396710192229 & 0.0287198355096115 \tabularnewline
55 & 0.967915695553621 & 0.0641686088927588 & 0.0320843044463794 \tabularnewline
56 & 0.959308986251442 & 0.0813820274971165 & 0.0406910137485582 \tabularnewline
57 & 0.971841589791808 & 0.0563168204163831 & 0.0281584102081915 \tabularnewline
58 & 0.964518270499693 & 0.0709634590006143 & 0.0354817295003071 \tabularnewline
59 & 0.954098476272736 & 0.0918030474545273 & 0.0459015237272636 \tabularnewline
60 & 0.941539606602083 & 0.116920786795834 & 0.0584603933979171 \tabularnewline
61 & 0.927251226628062 & 0.145497546743876 & 0.072748773371938 \tabularnewline
62 & 0.912433476506909 & 0.175133046986182 & 0.087566523493091 \tabularnewline
63 & 0.891893796740901 & 0.216212406518197 & 0.108106203259099 \tabularnewline
64 & 0.876186377987996 & 0.247627244024008 & 0.123813622012004 \tabularnewline
65 & 0.9350949811981 & 0.1298100376038 & 0.0649050188019 \tabularnewline
66 & 0.941443412075438 & 0.117113175849123 & 0.0585565879245615 \tabularnewline
67 & 0.92840195362912 & 0.14319609274176 & 0.0715980463708799 \tabularnewline
68 & 0.916398728296698 & 0.167202543406604 & 0.0836012717033019 \tabularnewline
69 & 0.900010507892478 & 0.199978984215044 & 0.0999894921075221 \tabularnewline
70 & 0.887382152220274 & 0.225235695559452 & 0.112617847779726 \tabularnewline
71 & 0.864926657720126 & 0.270146684559748 & 0.135073342279874 \tabularnewline
72 & 0.839251605670032 & 0.321496788659935 & 0.160748394329968 \tabularnewline
73 & 0.823824626039971 & 0.352350747920057 & 0.176175373960029 \tabularnewline
74 & 0.817228864291542 & 0.365542271416915 & 0.182771135708458 \tabularnewline
75 & 0.793604852935953 & 0.412790294128094 & 0.206395147064047 \tabularnewline
76 & 0.760977110085849 & 0.478045779828301 & 0.239022889914151 \tabularnewline
77 & 0.751013752577392 & 0.497972494845216 & 0.248986247422608 \tabularnewline
78 & 0.710736279267143 & 0.578527441465715 & 0.289263720732857 \tabularnewline
79 & 0.715234139465913 & 0.569531721068174 & 0.284765860534087 \tabularnewline
80 & 0.701133365086989 & 0.597733269826023 & 0.298866634913011 \tabularnewline
81 & 0.664457148929157 & 0.671085702141685 & 0.335542851070843 \tabularnewline
82 & 0.621793567250475 & 0.756412865499049 & 0.378206432749525 \tabularnewline
83 & 0.574654462548001 & 0.850691074903997 & 0.425345537451999 \tabularnewline
84 & 0.596875340109213 & 0.806249319781573 & 0.403124659890787 \tabularnewline
85 & 0.578059906190994 & 0.843880187618013 & 0.421940093809006 \tabularnewline
86 & 0.554541979181378 & 0.890916041637243 & 0.445458020818622 \tabularnewline
87 & 0.597158260300979 & 0.805683479398042 & 0.402841739699021 \tabularnewline
88 & 0.625231013567802 & 0.749537972864396 & 0.374768986432198 \tabularnewline
89 & 0.591222178954448 & 0.817555642091104 & 0.408777821045552 \tabularnewline
90 & 0.571895448856189 & 0.856209102287622 & 0.428104551143811 \tabularnewline
91 & 0.564112156439351 & 0.871775687121298 & 0.435887843560649 \tabularnewline
92 & 0.537258931070455 & 0.92548213785909 & 0.462741068929545 \tabularnewline
93 & 0.493864503218887 & 0.987729006437774 & 0.506135496781113 \tabularnewline
94 & 0.475131578945245 & 0.95026315789049 & 0.524868421054755 \tabularnewline
95 & 0.490170899331651 & 0.980341798663302 & 0.509829100668349 \tabularnewline
96 & 0.6343305385041 & 0.7313389229918 & 0.3656694614959 \tabularnewline
97 & 0.59080546835735 & 0.8183890632853 & 0.40919453164265 \tabularnewline
98 & 0.555719365408635 & 0.888561269182731 & 0.444280634591365 \tabularnewline
99 & 0.613568629414604 & 0.772862741170792 & 0.386431370585396 \tabularnewline
100 & 0.584549558090764 & 0.830900883818472 & 0.415450441909236 \tabularnewline
101 & 0.598361243831265 & 0.803277512337469 & 0.401638756168735 \tabularnewline
102 & 0.551468978123976 & 0.897062043752047 & 0.448531021876024 \tabularnewline
103 & 0.501431790816359 & 0.997136418367282 & 0.498568209183641 \tabularnewline
104 & 0.50909542766829 & 0.98180914466342 & 0.49090457233171 \tabularnewline
105 & 0.566618183517896 & 0.866763632964208 & 0.433381816482104 \tabularnewline
106 & 0.566941290670047 & 0.866117418659907 & 0.433058709329953 \tabularnewline
107 & 0.526553304467624 & 0.946893391064751 & 0.473446695532376 \tabularnewline
108 & 0.611330904665018 & 0.777338190669965 & 0.388669095334982 \tabularnewline
109 & 0.59241920652089 & 0.81516158695822 & 0.40758079347911 \tabularnewline
110 & 0.549306197284816 & 0.901387605430368 & 0.450693802715184 \tabularnewline
111 & 0.493986853568009 & 0.987973707136018 & 0.506013146431991 \tabularnewline
112 & 0.645166851973817 & 0.709666296052366 & 0.354833148026183 \tabularnewline
113 & 0.659509930975526 & 0.680980138048948 & 0.340490069024474 \tabularnewline
114 & 0.651828986166506 & 0.696342027666988 & 0.348171013833494 \tabularnewline
115 & 0.59898648599867 & 0.802027028002659 & 0.40101351400133 \tabularnewline
116 & 0.561466366592206 & 0.877067266815589 & 0.438533633407794 \tabularnewline
117 & 0.518482211969976 & 0.963035576060047 & 0.481517788030024 \tabularnewline
118 & 0.505786421165175 & 0.98842715766965 & 0.494213578834825 \tabularnewline
119 & 0.526586649171405 & 0.946826701657189 & 0.473413350828595 \tabularnewline
120 & 0.471928099689009 & 0.943856199378018 & 0.528071900310991 \tabularnewline
121 & 0.44665603984859 & 0.89331207969718 & 0.55334396015141 \tabularnewline
122 & 0.406297163200945 & 0.812594326401891 & 0.593702836799055 \tabularnewline
123 & 0.451333397496003 & 0.902666794992006 & 0.548666602503997 \tabularnewline
124 & 0.410943106769868 & 0.821886213539736 & 0.589056893230132 \tabularnewline
125 & 0.444516545447413 & 0.889033090894827 & 0.555483454552587 \tabularnewline
126 & 0.464048662979817 & 0.928097325959634 & 0.535951337020183 \tabularnewline
127 & 0.452229736055376 & 0.904459472110752 & 0.547770263944624 \tabularnewline
128 & 0.382619801158781 & 0.765239602317562 & 0.617380198841219 \tabularnewline
129 & 0.606933859317905 & 0.786132281364191 & 0.393066140682095 \tabularnewline
130 & 0.716758395021369 & 0.566483209957261 & 0.283241604978631 \tabularnewline
131 & 0.771268256887777 & 0.457463486224446 & 0.228731743112223 \tabularnewline
132 & 0.829064516229621 & 0.341870967540758 & 0.170935483770379 \tabularnewline
133 & 0.806886769917046 & 0.386226460165908 & 0.193113230082954 \tabularnewline
134 & 0.862086889347524 & 0.275826221304951 & 0.137913110652476 \tabularnewline
135 & 0.802924978724534 & 0.394150042550932 & 0.197075021275466 \tabularnewline
136 & 0.747757640293216 & 0.504484719413568 & 0.252242359706784 \tabularnewline
137 & 0.807384602337044 & 0.385230795325911 & 0.192615397662956 \tabularnewline
138 & 0.74855138807117 & 0.502897223857661 & 0.25144861192883 \tabularnewline
139 & 0.64412932912165 & 0.7117413417567 & 0.35587067087835 \tabularnewline
140 & 0.513651020773495 & 0.972697958453009 & 0.486348979226505 \tabularnewline
141 & 0.481893920794528 & 0.963787841589055 & 0.518106079205472 \tabularnewline
142 & 0.336423223892389 & 0.672846447784778 & 0.663576776107611 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145926&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]14[/C][C]0.999749261256894[/C][C]0.000501477486212082[/C][C]0.000250738743106041[/C][/ROW]
[ROW][C]15[/C][C]0.99989713684738[/C][C]0.00020572630524052[/C][C]0.00010286315262026[/C][/ROW]
[ROW][C]16[/C][C]0.999895147707139[/C][C]0.000209704585722791[/C][C]0.000104852292861396[/C][/ROW]
[ROW][C]17[/C][C]0.999831570022249[/C][C]0.000336859955502813[/C][C]0.000168429977751407[/C][/ROW]
[ROW][C]18[/C][C]0.999879645502272[/C][C]0.000240708995456489[/C][C]0.000120354497728244[/C][/ROW]
[ROW][C]19[/C][C]0.999737854529744[/C][C]0.000524290940512481[/C][C]0.00026214547025624[/C][/ROW]
[ROW][C]20[/C][C]0.999565437028515[/C][C]0.00086912594296917[/C][C]0.000434562971484585[/C][/ROW]
[ROW][C]21[/C][C]0.99913746035658[/C][C]0.00172507928683947[/C][C]0.000862539643419733[/C][/ROW]
[ROW][C]22[/C][C]0.99945292409578[/C][C]0.0010941518084406[/C][C]0.000547075904220301[/C][/ROW]
[ROW][C]23[/C][C]0.999208320382809[/C][C]0.00158335923438185[/C][C]0.000791679617190925[/C][/ROW]
[ROW][C]24[/C][C]0.998583016213701[/C][C]0.00283396757259791[/C][C]0.00141698378629896[/C][/ROW]
[ROW][C]25[/C][C]0.997645021888355[/C][C]0.0047099562232894[/C][C]0.0023549781116447[/C][/ROW]
[ROW][C]26[/C][C]0.996094335257995[/C][C]0.00781132948401086[/C][C]0.00390566474200543[/C][/ROW]
[ROW][C]27[/C][C]0.99532081257678[/C][C]0.0093583748464394[/C][C]0.0046791874232197[/C][/ROW]
[ROW][C]28[/C][C]0.992611219526769[/C][C]0.0147775609464626[/C][C]0.00738878047323132[/C][/ROW]
[ROW][C]29[/C][C]0.995435437406731[/C][C]0.00912912518653741[/C][C]0.0045645625932687[/C][/ROW]
[ROW][C]30[/C][C]0.994247663756647[/C][C]0.0115046724867066[/C][C]0.0057523362433533[/C][/ROW]
[ROW][C]31[/C][C]0.991496442152955[/C][C]0.0170071156940903[/C][C]0.00850355784704514[/C][/ROW]
[ROW][C]32[/C][C]0.995431158992026[/C][C]0.00913768201594855[/C][C]0.00456884100797427[/C][/ROW]
[ROW][C]33[/C][C]0.993208901775156[/C][C]0.0135821964496872[/C][C]0.00679109822484358[/C][/ROW]
[ROW][C]34[/C][C]0.989941891798939[/C][C]0.0201162164021219[/C][C]0.0100581082010609[/C][/ROW]
[ROW][C]35[/C][C]0.988535632976139[/C][C]0.0229287340477226[/C][C]0.0114643670238613[/C][/ROW]
[ROW][C]36[/C][C]0.983902334680705[/C][C]0.0321953306385892[/C][C]0.0160976653192946[/C][/ROW]
[ROW][C]37[/C][C]0.980597121472098[/C][C]0.0388057570558038[/C][C]0.0194028785279019[/C][/ROW]
[ROW][C]38[/C][C]0.982481820415247[/C][C]0.0350363591695061[/C][C]0.017518179584753[/C][/ROW]
[ROW][C]39[/C][C]0.981212062109044[/C][C]0.0375758757819113[/C][C]0.0187879378909557[/C][/ROW]
[ROW][C]40[/C][C]0.986739698724478[/C][C]0.0265206025510432[/C][C]0.0132603012755216[/C][/ROW]
[ROW][C]41[/C][C]0.982369664659814[/C][C]0.0352606706803719[/C][C]0.0176303353401859[/C][/ROW]
[ROW][C]42[/C][C]0.97620518482119[/C][C]0.04758963035762[/C][C]0.02379481517881[/C][/ROW]
[ROW][C]43[/C][C]0.967815068479323[/C][C]0.0643698630413536[/C][C]0.0321849315206768[/C][/ROW]
[ROW][C]44[/C][C]0.960266750527368[/C][C]0.079466498945264[/C][C]0.039733249472632[/C][/ROW]
[ROW][C]45[/C][C]0.98920352982355[/C][C]0.0215929403529001[/C][C]0.01079647017645[/C][/ROW]
[ROW][C]46[/C][C]0.985482740769604[/C][C]0.029034518460793[/C][C]0.0145172592303965[/C][/ROW]
[ROW][C]47[/C][C]0.980581356335585[/C][C]0.0388372873288309[/C][C]0.0194186436644155[/C][/ROW]
[ROW][C]48[/C][C]0.975122975524426[/C][C]0.0497540489511477[/C][C]0.0248770244755738[/C][/ROW]
[ROW][C]49[/C][C]0.972052944540958[/C][C]0.0558941109180839[/C][C]0.0279470554590419[/C][/ROW]
[ROW][C]50[/C][C]0.967509099819537[/C][C]0.0649818003609251[/C][C]0.0324909001804625[/C][/ROW]
[ROW][C]51[/C][C]0.963585532875297[/C][C]0.072828934249406[/C][C]0.036414467124703[/C][/ROW]
[ROW][C]52[/C][C]0.977541989832513[/C][C]0.0449160203349737[/C][C]0.0224580101674869[/C][/ROW]
[ROW][C]53[/C][C]0.970240788467859[/C][C]0.0595184230642828[/C][C]0.0297592115321414[/C][/ROW]
[ROW][C]54[/C][C]0.971280164490389[/C][C]0.0574396710192229[/C][C]0.0287198355096115[/C][/ROW]
[ROW][C]55[/C][C]0.967915695553621[/C][C]0.0641686088927588[/C][C]0.0320843044463794[/C][/ROW]
[ROW][C]56[/C][C]0.959308986251442[/C][C]0.0813820274971165[/C][C]0.0406910137485582[/C][/ROW]
[ROW][C]57[/C][C]0.971841589791808[/C][C]0.0563168204163831[/C][C]0.0281584102081915[/C][/ROW]
[ROW][C]58[/C][C]0.964518270499693[/C][C]0.0709634590006143[/C][C]0.0354817295003071[/C][/ROW]
[ROW][C]59[/C][C]0.954098476272736[/C][C]0.0918030474545273[/C][C]0.0459015237272636[/C][/ROW]
[ROW][C]60[/C][C]0.941539606602083[/C][C]0.116920786795834[/C][C]0.0584603933979171[/C][/ROW]
[ROW][C]61[/C][C]0.927251226628062[/C][C]0.145497546743876[/C][C]0.072748773371938[/C][/ROW]
[ROW][C]62[/C][C]0.912433476506909[/C][C]0.175133046986182[/C][C]0.087566523493091[/C][/ROW]
[ROW][C]63[/C][C]0.891893796740901[/C][C]0.216212406518197[/C][C]0.108106203259099[/C][/ROW]
[ROW][C]64[/C][C]0.876186377987996[/C][C]0.247627244024008[/C][C]0.123813622012004[/C][/ROW]
[ROW][C]65[/C][C]0.9350949811981[/C][C]0.1298100376038[/C][C]0.0649050188019[/C][/ROW]
[ROW][C]66[/C][C]0.941443412075438[/C][C]0.117113175849123[/C][C]0.0585565879245615[/C][/ROW]
[ROW][C]67[/C][C]0.92840195362912[/C][C]0.14319609274176[/C][C]0.0715980463708799[/C][/ROW]
[ROW][C]68[/C][C]0.916398728296698[/C][C]0.167202543406604[/C][C]0.0836012717033019[/C][/ROW]
[ROW][C]69[/C][C]0.900010507892478[/C][C]0.199978984215044[/C][C]0.0999894921075221[/C][/ROW]
[ROW][C]70[/C][C]0.887382152220274[/C][C]0.225235695559452[/C][C]0.112617847779726[/C][/ROW]
[ROW][C]71[/C][C]0.864926657720126[/C][C]0.270146684559748[/C][C]0.135073342279874[/C][/ROW]
[ROW][C]72[/C][C]0.839251605670032[/C][C]0.321496788659935[/C][C]0.160748394329968[/C][/ROW]
[ROW][C]73[/C][C]0.823824626039971[/C][C]0.352350747920057[/C][C]0.176175373960029[/C][/ROW]
[ROW][C]74[/C][C]0.817228864291542[/C][C]0.365542271416915[/C][C]0.182771135708458[/C][/ROW]
[ROW][C]75[/C][C]0.793604852935953[/C][C]0.412790294128094[/C][C]0.206395147064047[/C][/ROW]
[ROW][C]76[/C][C]0.760977110085849[/C][C]0.478045779828301[/C][C]0.239022889914151[/C][/ROW]
[ROW][C]77[/C][C]0.751013752577392[/C][C]0.497972494845216[/C][C]0.248986247422608[/C][/ROW]
[ROW][C]78[/C][C]0.710736279267143[/C][C]0.578527441465715[/C][C]0.289263720732857[/C][/ROW]
[ROW][C]79[/C][C]0.715234139465913[/C][C]0.569531721068174[/C][C]0.284765860534087[/C][/ROW]
[ROW][C]80[/C][C]0.701133365086989[/C][C]0.597733269826023[/C][C]0.298866634913011[/C][/ROW]
[ROW][C]81[/C][C]0.664457148929157[/C][C]0.671085702141685[/C][C]0.335542851070843[/C][/ROW]
[ROW][C]82[/C][C]0.621793567250475[/C][C]0.756412865499049[/C][C]0.378206432749525[/C][/ROW]
[ROW][C]83[/C][C]0.574654462548001[/C][C]0.850691074903997[/C][C]0.425345537451999[/C][/ROW]
[ROW][C]84[/C][C]0.596875340109213[/C][C]0.806249319781573[/C][C]0.403124659890787[/C][/ROW]
[ROW][C]85[/C][C]0.578059906190994[/C][C]0.843880187618013[/C][C]0.421940093809006[/C][/ROW]
[ROW][C]86[/C][C]0.554541979181378[/C][C]0.890916041637243[/C][C]0.445458020818622[/C][/ROW]
[ROW][C]87[/C][C]0.597158260300979[/C][C]0.805683479398042[/C][C]0.402841739699021[/C][/ROW]
[ROW][C]88[/C][C]0.625231013567802[/C][C]0.749537972864396[/C][C]0.374768986432198[/C][/ROW]
[ROW][C]89[/C][C]0.591222178954448[/C][C]0.817555642091104[/C][C]0.408777821045552[/C][/ROW]
[ROW][C]90[/C][C]0.571895448856189[/C][C]0.856209102287622[/C][C]0.428104551143811[/C][/ROW]
[ROW][C]91[/C][C]0.564112156439351[/C][C]0.871775687121298[/C][C]0.435887843560649[/C][/ROW]
[ROW][C]92[/C][C]0.537258931070455[/C][C]0.92548213785909[/C][C]0.462741068929545[/C][/ROW]
[ROW][C]93[/C][C]0.493864503218887[/C][C]0.987729006437774[/C][C]0.506135496781113[/C][/ROW]
[ROW][C]94[/C][C]0.475131578945245[/C][C]0.95026315789049[/C][C]0.524868421054755[/C][/ROW]
[ROW][C]95[/C][C]0.490170899331651[/C][C]0.980341798663302[/C][C]0.509829100668349[/C][/ROW]
[ROW][C]96[/C][C]0.6343305385041[/C][C]0.7313389229918[/C][C]0.3656694614959[/C][/ROW]
[ROW][C]97[/C][C]0.59080546835735[/C][C]0.8183890632853[/C][C]0.40919453164265[/C][/ROW]
[ROW][C]98[/C][C]0.555719365408635[/C][C]0.888561269182731[/C][C]0.444280634591365[/C][/ROW]
[ROW][C]99[/C][C]0.613568629414604[/C][C]0.772862741170792[/C][C]0.386431370585396[/C][/ROW]
[ROW][C]100[/C][C]0.584549558090764[/C][C]0.830900883818472[/C][C]0.415450441909236[/C][/ROW]
[ROW][C]101[/C][C]0.598361243831265[/C][C]0.803277512337469[/C][C]0.401638756168735[/C][/ROW]
[ROW][C]102[/C][C]0.551468978123976[/C][C]0.897062043752047[/C][C]0.448531021876024[/C][/ROW]
[ROW][C]103[/C][C]0.501431790816359[/C][C]0.997136418367282[/C][C]0.498568209183641[/C][/ROW]
[ROW][C]104[/C][C]0.50909542766829[/C][C]0.98180914466342[/C][C]0.49090457233171[/C][/ROW]
[ROW][C]105[/C][C]0.566618183517896[/C][C]0.866763632964208[/C][C]0.433381816482104[/C][/ROW]
[ROW][C]106[/C][C]0.566941290670047[/C][C]0.866117418659907[/C][C]0.433058709329953[/C][/ROW]
[ROW][C]107[/C][C]0.526553304467624[/C][C]0.946893391064751[/C][C]0.473446695532376[/C][/ROW]
[ROW][C]108[/C][C]0.611330904665018[/C][C]0.777338190669965[/C][C]0.388669095334982[/C][/ROW]
[ROW][C]109[/C][C]0.59241920652089[/C][C]0.81516158695822[/C][C]0.40758079347911[/C][/ROW]
[ROW][C]110[/C][C]0.549306197284816[/C][C]0.901387605430368[/C][C]0.450693802715184[/C][/ROW]
[ROW][C]111[/C][C]0.493986853568009[/C][C]0.987973707136018[/C][C]0.506013146431991[/C][/ROW]
[ROW][C]112[/C][C]0.645166851973817[/C][C]0.709666296052366[/C][C]0.354833148026183[/C][/ROW]
[ROW][C]113[/C][C]0.659509930975526[/C][C]0.680980138048948[/C][C]0.340490069024474[/C][/ROW]
[ROW][C]114[/C][C]0.651828986166506[/C][C]0.696342027666988[/C][C]0.348171013833494[/C][/ROW]
[ROW][C]115[/C][C]0.59898648599867[/C][C]0.802027028002659[/C][C]0.40101351400133[/C][/ROW]
[ROW][C]116[/C][C]0.561466366592206[/C][C]0.877067266815589[/C][C]0.438533633407794[/C][/ROW]
[ROW][C]117[/C][C]0.518482211969976[/C][C]0.963035576060047[/C][C]0.481517788030024[/C][/ROW]
[ROW][C]118[/C][C]0.505786421165175[/C][C]0.98842715766965[/C][C]0.494213578834825[/C][/ROW]
[ROW][C]119[/C][C]0.526586649171405[/C][C]0.946826701657189[/C][C]0.473413350828595[/C][/ROW]
[ROW][C]120[/C][C]0.471928099689009[/C][C]0.943856199378018[/C][C]0.528071900310991[/C][/ROW]
[ROW][C]121[/C][C]0.44665603984859[/C][C]0.89331207969718[/C][C]0.55334396015141[/C][/ROW]
[ROW][C]122[/C][C]0.406297163200945[/C][C]0.812594326401891[/C][C]0.593702836799055[/C][/ROW]
[ROW][C]123[/C][C]0.451333397496003[/C][C]0.902666794992006[/C][C]0.548666602503997[/C][/ROW]
[ROW][C]124[/C][C]0.410943106769868[/C][C]0.821886213539736[/C][C]0.589056893230132[/C][/ROW]
[ROW][C]125[/C][C]0.444516545447413[/C][C]0.889033090894827[/C][C]0.555483454552587[/C][/ROW]
[ROW][C]126[/C][C]0.464048662979817[/C][C]0.928097325959634[/C][C]0.535951337020183[/C][/ROW]
[ROW][C]127[/C][C]0.452229736055376[/C][C]0.904459472110752[/C][C]0.547770263944624[/C][/ROW]
[ROW][C]128[/C][C]0.382619801158781[/C][C]0.765239602317562[/C][C]0.617380198841219[/C][/ROW]
[ROW][C]129[/C][C]0.606933859317905[/C][C]0.786132281364191[/C][C]0.393066140682095[/C][/ROW]
[ROW][C]130[/C][C]0.716758395021369[/C][C]0.566483209957261[/C][C]0.283241604978631[/C][/ROW]
[ROW][C]131[/C][C]0.771268256887777[/C][C]0.457463486224446[/C][C]0.228731743112223[/C][/ROW]
[ROW][C]132[/C][C]0.829064516229621[/C][C]0.341870967540758[/C][C]0.170935483770379[/C][/ROW]
[ROW][C]133[/C][C]0.806886769917046[/C][C]0.386226460165908[/C][C]0.193113230082954[/C][/ROW]
[ROW][C]134[/C][C]0.862086889347524[/C][C]0.275826221304951[/C][C]0.137913110652476[/C][/ROW]
[ROW][C]135[/C][C]0.802924978724534[/C][C]0.394150042550932[/C][C]0.197075021275466[/C][/ROW]
[ROW][C]136[/C][C]0.747757640293216[/C][C]0.504484719413568[/C][C]0.252242359706784[/C][/ROW]
[ROW][C]137[/C][C]0.807384602337044[/C][C]0.385230795325911[/C][C]0.192615397662956[/C][/ROW]
[ROW][C]138[/C][C]0.74855138807117[/C][C]0.502897223857661[/C][C]0.25144861192883[/C][/ROW]
[ROW][C]139[/C][C]0.64412932912165[/C][C]0.7117413417567[/C][C]0.35587067087835[/C][/ROW]
[ROW][C]140[/C][C]0.513651020773495[/C][C]0.972697958453009[/C][C]0.486348979226505[/C][/ROW]
[ROW][C]141[/C][C]0.481893920794528[/C][C]0.963787841589055[/C][C]0.518106079205472[/C][/ROW]
[ROW][C]142[/C][C]0.336423223892389[/C][C]0.672846447784778[/C][C]0.663576776107611[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145926&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145926&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
140.9997492612568940.0005014774862120820.000250738743106041
150.999897136847380.000205726305240520.00010286315262026
160.9998951477071390.0002097045857227910.000104852292861396
170.9998315700222490.0003368599555028130.000168429977751407
180.9998796455022720.0002407089954564890.000120354497728244
190.9997378545297440.0005242909405124810.00026214547025624
200.9995654370285150.000869125942969170.000434562971484585
210.999137460356580.001725079286839470.000862539643419733
220.999452924095780.00109415180844060.000547075904220301
230.9992083203828090.001583359234381850.000791679617190925
240.9985830162137010.002833967572597910.00141698378629896
250.9976450218883550.00470995622328940.0023549781116447
260.9960943352579950.007811329484010860.00390566474200543
270.995320812576780.00935837484643940.0046791874232197
280.9926112195267690.01477756094646260.00738878047323132
290.9954354374067310.009129125186537410.0045645625932687
300.9942476637566470.01150467248670660.0057523362433533
310.9914964421529550.01700711569409030.00850355784704514
320.9954311589920260.009137682015948550.00456884100797427
330.9932089017751560.01358219644968720.00679109822484358
340.9899418917989390.02011621640212190.0100581082010609
350.9885356329761390.02292873404772260.0114643670238613
360.9839023346807050.03219533063858920.0160976653192946
370.9805971214720980.03880575705580380.0194028785279019
380.9824818204152470.03503635916950610.017518179584753
390.9812120621090440.03757587578191130.0187879378909557
400.9867396987244780.02652060255104320.0132603012755216
410.9823696646598140.03526067068037190.0176303353401859
420.976205184821190.047589630357620.02379481517881
430.9678150684793230.06436986304135360.0321849315206768
440.9602667505273680.0794664989452640.039733249472632
450.989203529823550.02159294035290010.01079647017645
460.9854827407696040.0290345184607930.0145172592303965
470.9805813563355850.03883728732883090.0194186436644155
480.9751229755244260.04975404895114770.0248770244755738
490.9720529445409580.05589411091808390.0279470554590419
500.9675090998195370.06498180036092510.0324909001804625
510.9635855328752970.0728289342494060.036414467124703
520.9775419898325130.04491602033497370.0224580101674869
530.9702407884678590.05951842306428280.0297592115321414
540.9712801644903890.05743967101922290.0287198355096115
550.9679156955536210.06416860889275880.0320843044463794
560.9593089862514420.08138202749711650.0406910137485582
570.9718415897918080.05631682041638310.0281584102081915
580.9645182704996930.07096345900061430.0354817295003071
590.9540984762727360.09180304745452730.0459015237272636
600.9415396066020830.1169207867958340.0584603933979171
610.9272512266280620.1454975467438760.072748773371938
620.9124334765069090.1751330469861820.087566523493091
630.8918937967409010.2162124065181970.108106203259099
640.8761863779879960.2476272440240080.123813622012004
650.93509498119810.12981003760380.0649050188019
660.9414434120754380.1171131758491230.0585565879245615
670.928401953629120.143196092741760.0715980463708799
680.9163987282966980.1672025434066040.0836012717033019
690.9000105078924780.1999789842150440.0999894921075221
700.8873821522202740.2252356955594520.112617847779726
710.8649266577201260.2701466845597480.135073342279874
720.8392516056700320.3214967886599350.160748394329968
730.8238246260399710.3523507479200570.176175373960029
740.8172288642915420.3655422714169150.182771135708458
750.7936048529359530.4127902941280940.206395147064047
760.7609771100858490.4780457798283010.239022889914151
770.7510137525773920.4979724948452160.248986247422608
780.7107362792671430.5785274414657150.289263720732857
790.7152341394659130.5695317210681740.284765860534087
800.7011333650869890.5977332698260230.298866634913011
810.6644571489291570.6710857021416850.335542851070843
820.6217935672504750.7564128654990490.378206432749525
830.5746544625480010.8506910749039970.425345537451999
840.5968753401092130.8062493197815730.403124659890787
850.5780599061909940.8438801876180130.421940093809006
860.5545419791813780.8909160416372430.445458020818622
870.5971582603009790.8056834793980420.402841739699021
880.6252310135678020.7495379728643960.374768986432198
890.5912221789544480.8175556420911040.408777821045552
900.5718954488561890.8562091022876220.428104551143811
910.5641121564393510.8717756871212980.435887843560649
920.5372589310704550.925482137859090.462741068929545
930.4938645032188870.9877290064377740.506135496781113
940.4751315789452450.950263157890490.524868421054755
950.4901708993316510.9803417986633020.509829100668349
960.63433053850410.73133892299180.3656694614959
970.590805468357350.81838906328530.40919453164265
980.5557193654086350.8885612691827310.444280634591365
990.6135686294146040.7728627411707920.386431370585396
1000.5845495580907640.8309008838184720.415450441909236
1010.5983612438312650.8032775123374690.401638756168735
1020.5514689781239760.8970620437520470.448531021876024
1030.5014317908163590.9971364183672820.498568209183641
1040.509095427668290.981809144663420.49090457233171
1050.5666181835178960.8667636329642080.433381816482104
1060.5669412906700470.8661174186599070.433058709329953
1070.5265533044676240.9468933910647510.473446695532376
1080.6113309046650180.7773381906699650.388669095334982
1090.592419206520890.815161586958220.40758079347911
1100.5493061972848160.9013876054303680.450693802715184
1110.4939868535680090.9879737071360180.506013146431991
1120.6451668519738170.7096662960523660.354833148026183
1130.6595099309755260.6809801380489480.340490069024474
1140.6518289861665060.6963420276669880.348171013833494
1150.598986485998670.8020270280026590.40101351400133
1160.5614663665922060.8770672668155890.438533633407794
1170.5184822119699760.9630355760600470.481517788030024
1180.5057864211651750.988427157669650.494213578834825
1190.5265866491714050.9468267016571890.473413350828595
1200.4719280996890090.9438561993780180.528071900310991
1210.446656039848590.893312079697180.55334396015141
1220.4062971632009450.8125943264018910.593702836799055
1230.4513333974960030.9026667949920060.548666602503997
1240.4109431067698680.8218862135397360.589056893230132
1250.4445165454474130.8890330908948270.555483454552587
1260.4640486629798170.9280973259596340.535951337020183
1270.4522297360553760.9044594721107520.547770263944624
1280.3826198011587810.7652396023175620.617380198841219
1290.6069338593179050.7861322813641910.393066140682095
1300.7167583950213690.5664832099572610.283241604978631
1310.7712682568877770.4574634862244460.228731743112223
1320.8290645162296210.3418709675407580.170935483770379
1330.8068867699170460.3862264601659080.193113230082954
1340.8620868893475240.2758262213049510.137913110652476
1350.8029249787245340.3941500425509320.197075021275466
1360.7477576402932160.5044847194135680.252242359706784
1370.8073846023370440.3852307953259110.192615397662956
1380.748551388071170.5028972238576610.25144861192883
1390.644129329121650.71174134175670.35587067087835
1400.5136510207734950.9726979584530090.486348979226505
1410.4818939207945280.9637878415890550.518106079205472
1420.3364232238923890.6728464477847780.663576776107611







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level160.124031007751938NOK
5% type I error level340.263565891472868NOK
10% type I error level460.356589147286822NOK

\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 & 16 & 0.124031007751938 & NOK \tabularnewline
5% type I error level & 34 & 0.263565891472868 & NOK \tabularnewline
10% type I error level & 46 & 0.356589147286822 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145926&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]16[/C][C]0.124031007751938[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]34[/C][C]0.263565891472868[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]46[/C][C]0.356589147286822[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145926&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145926&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level160.124031007751938NOK
5% type I error level340.263565891472868NOK
10% type I error level460.356589147286822NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}