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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Module--
Title produced by softwareMultiple Regression
Date of computationTue, 22 Nov 2011 06:00:36 -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/22/t1321959652irxscd5ccuuiqih.htm/, Retrieved Sat, 20 Apr 2024 05:04:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146123, Retrieved Sat, 20 Apr 2024 05:04:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact95

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [Multiple linear r...] [2010-11-19 16:51:37] [97ad38b1c3b35a5feca8b85f7bc7b3ff]
- RM        [Multiple Regression] [] [2011-11-22 11:00:36] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
15	10	77	5	4	46	15	11	12	13	6
12	20	63	6	4	37	9	12	7	11	4
15	16	73	4	10	45	12	12	13	14	6
12	10	76	6	6	46	15	11	11	12	5
14	8	90	3	5	55	17	11	16	12	5
8	14	67	10	8	40	14	10	10	6	4
11	19	69	8	9	43	9	11	15	10	5
15	15	70	3	6	43	12	9	5	11	3
4	23	54	4	8	33	11	10	4	10	2
13	9	54	3	11	33	13	12	7	12	5
19	12	76	5	6	47	16	12	15	15	6
10	14	75	5	8	44	16	12	5	13	6
15	13	76	6	11	47	15	13	16	18	8
6	11	80	5	5	49	10	9	15	11	6
7	11	89	3	10	55	16	12	13	12	3
14	10	73	4	7	43	12	12	13	13	6
16	12	74	8	7	46	15	12	15	14	6
16	18	78	8	13	51	13	12	15	16	7
14	12	76	8	10	47	18	13	10	16	8
15	10	69	5	8	42	13	11	17	16	6
14	15	74	8	6	42	17	12	14	15	7
12	15	82	2	8	48	14	12	9	13	4
9	12	77	0	7	45	13	15	6	8	4
12	9	84	5	5	51	13	11	11	14	2
14	11	75	2	9	46	15	12	13	15	6
12	15	54	7	9	33	13	10	12	13	6
14	16	79	5	11	47	15	11	10	16	6
10	17	79	2	11	47	13	13	4	13	6
14	12	69	12	11	42	14	6	13	12	6
16	11	88	7	9	55	13	12	15	15	7
10	13	57	0	7	36	16	12	8	11	4
8	9	69	2	6	42	14	10	10	14	3
12	11	86	3	6	51	18	12	8	13	5
11	9	65	0	6	43	15	12	7	13	6
8	20	66	9	5	40	9	11	9	12	4
13	8	54	2	4	33	16	9	14	14	6
11	12	85	3	10	52	16	10	5	13	3
12	10	79	1	8	49	17	12	7	12	3
16	11	84	10	6	50	13	12	16	14	6
16	13	70	1	5	43	17	11	14	15	6
13	13	54	4	9	33	15	12	16	16	6
14	13	70	6	10	44	14	11	15	15	8
5	15	54	6	6	33	10	14	4	5	2
14	12	69	4	9	41	13	10	12	15	6
13	13	68	4	10	40	11	10	8	8	4
16	13	68	7	6	40	11	11	17	16	7
14	9	71	7	6	41	16	11	15	16	6
15	9	71	7	6	41	16	11	16	14	6
15	14	66	0	13	42	11	10	12	16	6
11	9	67	3	8	42	15	10	12	14	5
15	9	71	8	10	45	15	12	13	13	6
16	15	54	8	5	33	12	11	14	14	6
13	10	76	10	8	46	17	8	14	14	5
11	13	77	11	6	47	15	12	15	12	6
12	8	71	6	9	44	16	10	14	13	7
12	15	69	2	9	44	14	7	11	15	5
10	13	73	6	7	46	17	11	13	15	6
8	24	46	1	20	30	10	7	4	13	6
9	11	66	5	8	42	11	11	8	10	4
12	13	77	4	8	46	15	8	13	13	5
14	12	77	6	7	46	15	11	15	14	6
12	22	70	6	7	43	7	12	15	13	6
11	11	86	4	10	52	17	8	8	13	4
14	15	38	1	5	11	14	14	17	18	6
7	7	66	6	8	41	18	14	12	12	4
16	14	75	7	9	45	14	11	13	14	7
16	19	80	7	9	49	12	12	14	16	8
11	10	64	2	20	41	14	14	7	13	6
16	9	80	7	6	47	9	9	16	16	6
13	12	86	8	10	53	14	13	11	15	6
11	16	54	5	11	35	11	8	10	14	5
13	13	74	4	7	45	16	11	14	13	6
14	11	88	2	12	54	17	9	19	12	6
15	12	85	0	12	53	16	12	14	16	4
10	11	63	7	8	36	12	7	8	9	5
15	13	81	0	6	48	15	11	15	15	8
11	13	81	5	6	48	15	12	8	16	6
11	10	74	3	9	45	15	11	8	12	6
6	11	80	3	5	47	16	12	6	11	2
11	9	80	3	11	49	16	9	7	13	2
12	13	60	3	6	38	11	11	16	13	4
13	15	65	7	6	40	15	13	15	14	6
12	14	62	6	10	46	12	12	10	15	6
8	14	63	3	8	42	14	12	8	14	5
9	11	89	0	7	54	15	11	9	12	4
10	10	76	2	8	45	17	12	8	16	4
16	11	81	0	9	53	19	12	14	14	6
15	12	72	9	8	44	15	11	14	13	5
14	14	84	10	10	51	16	11	14	12	6
12	14	76	3	13	46	14	8	15	13	7
12	21	76	7	7	46	16	9	7	12	6
10	14	78	3	7	45	15	11	7	9	4
12	13	72	6	7	44	15	12	12	13	4
8	11	81	5	8	48	17	13	7	10	3
16	12	72	0	9	44	12	12	12	15	8
11	12	78	0	9	47	18	6	6	9	4
12	11	79	4	8	47	13	12	10	13	4
9	14	52	0	7	31	14	11	12	13	5
14	13	67	0	6	44	14	13	13	13	5
15	13	74	7	8	42	14	11	14	15	7
8	12	73	3	8	41	12	12	8	13	4
12	14	69	9	4	43	14	10	14	14	5
10	12	67	4	8	41	12	10	10	11	5
16	12	76	4	10	47	15	11	14	15	8
17	12	77	15	7	45	11	11	15	14	5
8	18	63	7	8	37	11	11	10	15	2
9	11	84	8	7	54	15	9	6	12	5
8	15	90	2	10	55	14	7	9	15	4
11	13	75	8	9	45	15	11	11	14	5
16	11	76	7	8	47	16	12	16	16	7
13	11	75	3	8	46	12	12	14	14	6
5	22	53	3	5	37	14	15	8	12	3
15	10	87	6	8	53	18	11	16	11	5
15	11	78	8	9	46	14	10	16	13	6
12	15	54	5	11	33	13	13	14	12	5
12	14	58	6	7	36	14	13	12	12	6
16	11	80	10	8	49	14	11	16	16	7
12	10	74	0	4	44	17	12	15	13	6
10	14	56	5	16	37	12	12	11	12	6
12	14	82	0	9	53	16	12	6	14	5
4	11	64	0	16	40	15	8	6	4	4
11	15	67	5	12	42	10	5	16	14	6
16	11	75	10	8	45	13	11	16	15	6
7	10	69	0	4	40	15	12	8	12	3
9	10	72	5	11	44	16	12	11	11	4
14	16	71	6	11	43	15	11	12	12	4
11	12	54	1	8	33	14	12	13	11	4
10	14	68	5	8	44	11	10	11	12	5
6	15	54	3	12	33	13	7	9	11	4
14	10	71	3	8	43	17	12	15	13	6
11	12	53	6	6	32	14	12	11	12	6
11	15	54	2	8	33	16	9	12	12	4
9	12	71	5	6	43	15	11	15	15	7
16	11	69	6	14	42	12	12	8	14	4
7	10	30	2	10	0	16	12	7	12	4
8	20	53	3	5	32	8	11	10	12	4
10	19	68	7	8	41	9	11	9	12	4
14	17	69	6	12	44	13	12	13	13	5
9	8	54	3	11	33	19	12	11	11	4
13	17	66	6	8	42	11	11	12	13	7
13	11	79	9	8	46	15	12	5	12	3
12	13	67	2	9	44	11	12	12	14	5
11	9	74	5	6	45	15	8	14	15	5
10	10	86	10	5	53	16	15	15	15	6
12	13	63	9	8	38	15	11	14	13	5
14	16	69	8	7	43	12	11	13	16	6
11	12	73	8	4	43	16	6	14	17	6
13	14	69	5	9	42	15	13	14	13	3
14	11	71	9	5	42	13	12	15	14	6
13	13	77	9	9	47	14	12	13	13	5
16	15	74	14	12	44	11	12	14	16	8
13	14	82	5	6	49	15	12	11	13	6
12	14	54	12	4	33	16	12	14	14	4
9	14	54	6	6	33	14	10	11	13	3
14	10	80	6	7	47	13	12	8	14	4
15	8	76	8	9	47	15	12	12	16	7

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146123&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146123&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146123&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = -1.40033179694982 -0.0760945554836357Depression[t] + 0.0719081107769416Belonging[t] + 0.0941357682157754Weighted_popularity[t] + 0.0838784648670406Parental_criticism[t] -0.0399931531986961Belonging_final[t] -0.0601894895057667Happiness[t] + 0.118163788663958FindingFriends[t] + 0.23002915116189KnowingPeople[t] + 0.344271299678482Liked[t] + 0.522587758027872Celebrity[t] -0.0063985411849013t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  -1.40033179694982 -0.0760945554836357Depression[t] +  0.0719081107769416Belonging[t] +  0.0941357682157754Weighted_popularity[t] +  0.0838784648670406Parental_criticism[t] -0.0399931531986961Belonging_final[t] -0.0601894895057667Happiness[t] +  0.118163788663958FindingFriends[t] +  0.23002915116189KnowingPeople[t] +  0.344271299678482Liked[t] +  0.522587758027872Celebrity[t] -0.0063985411849013t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146123&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  -1.40033179694982 -0.0760945554836357Depression[t] +  0.0719081107769416Belonging[t] +  0.0941357682157754Weighted_popularity[t] +  0.0838784648670406Parental_criticism[t] -0.0399931531986961Belonging_final[t] -0.0601894895057667Happiness[t] +  0.118163788663958FindingFriends[t] +  0.23002915116189KnowingPeople[t] +  0.344271299678482Liked[t] +  0.522587758027872Celebrity[t] -0.0063985411849013t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146123&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146123&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = -1.40033179694982 -0.0760945554836357Depression[t] + 0.0719081107769416Belonging[t] + 0.0941357682157754Weighted_popularity[t] + 0.0838784648670406Parental_criticism[t] -0.0399931531986961Belonging_final[t] -0.0601894895057667Happiness[t] + 0.118163788663958FindingFriends[t] + 0.23002915116189KnowingPeople[t] + 0.344271299678482Liked[t] + 0.522587758027872Celebrity[t] -0.0063985411849013t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.400331796949822.56448-0.5460.5858770.292939
Depression-0.07609455548363570.063844-1.19190.235270.117635
Belonging0.07190811077694160.0514821.39680.1646320.082316
Weighted_popularity0.09413576821577540.05841.61190.1091690.054585
Parental_criticism0.08387846486704060.0659321.27220.2053530.102677
Belonging_final-0.03999315319869610.073107-0.54710.5851910.292595
Happiness-0.06018948950576670.085851-0.70110.4843740.242187
FindingFriends0.1181637886639580.0938941.25850.2102560.105128
KnowingPeople0.230029151161890.0645893.56145e-040.00025
Liked0.3442712996784820.0941933.6550.000360.00018
Celebrity0.5225877580278720.1588553.28970.0012610.00063
t-0.00639854118490130.003744-1.70910.0895910.044795

\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) & -1.40033179694982 & 2.56448 & -0.546 & 0.585877 & 0.292939 \tabularnewline
Depression & -0.0760945554836357 & 0.063844 & -1.1919 & 0.23527 & 0.117635 \tabularnewline
Belonging & 0.0719081107769416 & 0.051482 & 1.3968 & 0.164632 & 0.082316 \tabularnewline
Weighted_popularity & 0.0941357682157754 & 0.0584 & 1.6119 & 0.109169 & 0.054585 \tabularnewline
Parental_criticism & 0.0838784648670406 & 0.065932 & 1.2722 & 0.205353 & 0.102677 \tabularnewline
Belonging_final & -0.0399931531986961 & 0.073107 & -0.5471 & 0.585191 & 0.292595 \tabularnewline
Happiness & -0.0601894895057667 & 0.085851 & -0.7011 & 0.484374 & 0.242187 \tabularnewline
FindingFriends & 0.118163788663958 & 0.093894 & 1.2585 & 0.210256 & 0.105128 \tabularnewline
KnowingPeople & 0.23002915116189 & 0.064589 & 3.5614 & 5e-04 & 0.00025 \tabularnewline
Liked & 0.344271299678482 & 0.094193 & 3.655 & 0.00036 & 0.00018 \tabularnewline
Celebrity & 0.522587758027872 & 0.158855 & 3.2897 & 0.001261 & 0.00063 \tabularnewline
t & -0.0063985411849013 & 0.003744 & -1.7091 & 0.089591 & 0.044795 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146123&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]-1.40033179694982[/C][C]2.56448[/C][C]-0.546[/C][C]0.585877[/C][C]0.292939[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0760945554836357[/C][C]0.063844[/C][C]-1.1919[/C][C]0.23527[/C][C]0.117635[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0719081107769416[/C][C]0.051482[/C][C]1.3968[/C][C]0.164632[/C][C]0.082316[/C][/ROW]
[ROW][C]Weighted_popularity[/C][C]0.0941357682157754[/C][C]0.0584[/C][C]1.6119[/C][C]0.109169[/C][C]0.054585[/C][/ROW]
[ROW][C]Parental_criticism[/C][C]0.0838784648670406[/C][C]0.065932[/C][C]1.2722[/C][C]0.205353[/C][C]0.102677[/C][/ROW]
[ROW][C]Belonging_final[/C][C]-0.0399931531986961[/C][C]0.073107[/C][C]-0.5471[/C][C]0.585191[/C][C]0.292595[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.0601894895057667[/C][C]0.085851[/C][C]-0.7011[/C][C]0.484374[/C][C]0.242187[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.118163788663958[/C][C]0.093894[/C][C]1.2585[/C][C]0.210256[/C][C]0.105128[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.23002915116189[/C][C]0.064589[/C][C]3.5614[/C][C]5e-04[/C][C]0.00025[/C][/ROW]
[ROW][C]Liked[/C][C]0.344271299678482[/C][C]0.094193[/C][C]3.655[/C][C]0.00036[/C][C]0.00018[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.522587758027872[/C][C]0.158855[/C][C]3.2897[/C][C]0.001261[/C][C]0.00063[/C][/ROW]
[ROW][C]t[/C][C]-0.0063985411849013[/C][C]0.003744[/C][C]-1.7091[/C][C]0.089591[/C][C]0.044795[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146123&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146123&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.400331796949822.56448-0.5460.5858770.292939
Depression-0.07609455548363570.063844-1.19190.235270.117635
Belonging0.07190811077694160.0514821.39680.1646320.082316
Weighted_popularity0.09413576821577540.05841.61190.1091690.054585
Parental_criticism0.08387846486704060.0659321.27220.2053530.102677
Belonging_final-0.03999315319869610.073107-0.54710.5851910.292595
Happiness-0.06018948950576670.085851-0.70110.4843740.242187
FindingFriends0.1181637886639580.0938941.25850.2102560.105128
KnowingPeople0.230029151161890.0645893.56145e-040.00025
Liked0.3442712996784820.0941933.6550.000360.00018
Celebrity0.5225877580278720.1588553.28970.0012610.00063
t-0.00639854118490130.003744-1.70910.0895910.044795






Multiple Linear Regression - Regression Statistics
Multiple R0.747479529845818
R-squared0.558725647538526
Adjusted R-squared0.52501719005883
F-TEST (value)16.5752362852874
F-TEST (DF numerator)11
F-TEST (DF denominator)144
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.02389408044419
Sum Squared Residuals589.84520383541

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.747479529845818 \tabularnewline
R-squared & 0.558725647538526 \tabularnewline
Adjusted R-squared & 0.52501719005883 \tabularnewline
F-TEST (value) & 16.5752362852874 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 144 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.02389408044419 \tabularnewline
Sum Squared Residuals & 589.84520383541 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146123&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.747479529845818[/C][/ROW]
[ROW][C]R-squared[/C][C]0.558725647538526[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.52501719005883[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]16.5752362852874[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]144[/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]2.02389408044419[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]589.84520383541[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146123&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146123&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.747479529845818
R-squared0.558725647538526
Adjusted R-squared0.52501719005883
F-TEST (value)16.5752362852874
F-TEST (DF numerator)11
F-TEST (DF denominator)144
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.02389408044419
Sum Squared Residuals589.84520383541






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11513.10411888090761.89588111909235
2129.379572235489652.62042776451035
31513.66928290473511.33071709526493
41212.1780196356576-0.178019635657623
51413.63406638481250.365933615187536
689.12171124973132-1.12171124973132
71112.1232135702656-1.12321357026557
8158.352695352270776.64730464772923
946.33029989402401-2.33029989402401
101310.6090666812382.39093331876201
111914.38040437585064.61959562414936
121011.4488108912758-1.44881089127583
131517.291412674971-2.29141267497099
14613.1906313519517-7.19063135195171
15712.1323718286991-5.13237182869911
161413.5267488143510.473251185649045
171614.32039401972771.6796059802723
181615.77987494835390.220125051646118
191415.1842230800641-1.18422308006412
201515.2061068182984-0.206106818298377
211414.6590612639979-0.659061263997861
221211.3650278495430.634972150456989
2399.07843878284137-0.078438782841365
241211.56458655886730.435413441132651
251413.90436367430320.0956363256968043
261212.0395860686947-0.0395860686946695
271413.74491739633450.255082603665456
281011.3237347453513-1.32373474535134
291412.95870637259151.0412936274085
301616.0209419101327-0.0209419101327277
31108.830744427978011.16925557202199
32810.7103914354383-2.71039143543825
331211.74485458927990.255145410720073
341110.89123982907210.10876017092795
35810.3166168319753-2.31661683197532
361312.1237887246380.876211275361967
371110.01556689669140.984433103308587
38129.785784885710042.21421511428996
391615.26963036585610.730369634143929
401612.97847210707623.02152789292375
411313.8822688592405-0.882268859240484
421415.2715261726568-1.27152617265676
4355.55350869007311-0.553508690073111
441413.31750772459150.682492275408455
45139.032165895770663.96783410422934
461615.48302062039740.516979379602637
471414.6731379723986-0.673137972398647
481514.20822598301870.791774016981328
491513.30122948746531.69877051253468
501112.1583384993798-1.15833849937976
511513.60270189935871.39729810064131
521612.61452878680683.38547121319317
531313.3125508280339-0.312550828033872
541113.6932708599582-2.69327085995817
551213.8771452843589-1.87714528435891
561211.53689280320680.463107196793219
571013.3738483993187-3.37384839931873
5889.03838872227876-1.03838872227876
5999.60405812870065-0.604058128700651
601212.0926494029421-0.0926494029421057
611413.94814721482740.0518527851726416
621213.0528342079952-1.05283420799521
631111.0075014050402-0.00750140504019379
641413.90940055219780.0905994478022215
65711.5459981683963-4.54599816839629
661614.04475397274011.95524602725989
671615.53715287144920.462847128550832
681112.264760180326-1.26476018032602
691615.35461214450550.645387855494543
701314.4183599616621-1.41835996166212
711110.82073288780610.17926711219394
721312.80317605040.19682394960001
731414.3362199694722-0.336219969472217
741513.48616893958641.51383106041363
75109.359659407222930.640340592777073
761514.72447441985310.275525580146889
771112.9958102339005-1.99581023390055
781111.4024329141157-0.402432914115737
7968.49918198128773-2.49918198128773
80119.632337418401941.36766258159806
811211.55664370196360.443356298036378
821313.2091406540391-0.209140654039052
831212.3210617317895-0.321061731789543
84810.6490833407533-2.64908334075329
85910.7349212539284-1.73492125392839
861011.6467410654182-1.6467410654182
871613.11587907013892.88412092986112
881512.76522991599382.23477008400617
891413.56960718756760.430392812432437
901213.6433703651392-1.64337036513921
911210.26827476188071.73172523811934
92108.820332062713181.17966793728682
931211.42637461338360.573625386616375
9489.3413456607946-1.3413456607946
951614.05207650692261.94792349307736
96117.750873764631233.24912623536877
971211.4922744805770.507725519422964
98910.2998349775947-1.29983497759473
991411.31071992558732.6892800744127
1001514.44179146272960.558208537270422
101810.7050914338505-2.70509143385053
1021212.2985131903579-0.298513190357941
1031010.4127573387016-0.41275733870163
1041614.78389020264291.21610979735713
1051714.27199666982762.72800333017243
106810.0794170593795-2.07941705937949
107910.5838716669163-1.58387166691634
108811.3755467072374-3.37554670723741
1091112.3744237176035-1.37442371760347
1101615.27596002906290.724039970937074
1111313.4306727557511-0.430672755751127
11257.71119025790908-2.71119025790908
1131512.78467871000662.21532128999338
1141513.9408392167791.05916078322098
1151211.39729190715030.602708092849717
1161211.39560277990660.604397220093366
1171615.72343887247390.276561127526107
1181212.4369449584-0.436944958399992
1191011.6255542363726-1.62555423637262
1201210.56609915655751.43390084344252
12146.22293211352812-2.22293211352812
1221112.9176944639492-1.91769446394925
1231614.6788101160741.32118988392603
12478.79712611731686-1.79712611731686
125910.7125218131455-1.71252181314546
1261410.82810290137853.17189709862155
127119.645373124880181.35462687511982
1281010.7811590578546-0.781159057854594
12968.4773317134976-2.4773317134976
1301412.80235244916451.19764755083546
1311110.82017442731760.179825572682426
132119.118604324233731.88139567576627
133913.8648278690724-4.86482786907244
1341611.3723579274544.62764207254599
13578.44596341492408-1.44596341492408
13688.78090798916262-0.780907989162622
137109.907247113123780.0927528868762196
1381411.91072591833422.0892740816658
13999.55187003377597-0.551870033775968
1401312.24403850580420.755961494195769
141139.584026870191613.41597312980839
1421211.65213641403920.347863585960846
1431112.5351681163983-1.53516811639826
1441014.902001440007-4.90200144000699
1451211.91720443517050.0827955648294775
1461413.24193186753880.758068132461206
1471113.3186321462926-2.3186321462926
1481310.99187776127522.00812223872484
1491413.54288723676160.457112763238449
1501312.56418949320980.435810506790193
1511616.0433459968012-0.0433459968011492
1521312.12901360157780.870986398422214
1531211.16926560519660.830734394803352
15499.09291427494277-0.0929142749427662
1551411.25776782703252.74223217296745
1561514.52799791894940.472002081050606

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 15 & 13.1041188809076 & 1.89588111909235 \tabularnewline
2 & 12 & 9.37957223548965 & 2.62042776451035 \tabularnewline
3 & 15 & 13.6692829047351 & 1.33071709526493 \tabularnewline
4 & 12 & 12.1780196356576 & -0.178019635657623 \tabularnewline
5 & 14 & 13.6340663848125 & 0.365933615187536 \tabularnewline
6 & 8 & 9.12171124973132 & -1.12171124973132 \tabularnewline
7 & 11 & 12.1232135702656 & -1.12321357026557 \tabularnewline
8 & 15 & 8.35269535227077 & 6.64730464772923 \tabularnewline
9 & 4 & 6.33029989402401 & -2.33029989402401 \tabularnewline
10 & 13 & 10.609066681238 & 2.39093331876201 \tabularnewline
11 & 19 & 14.3804043758506 & 4.61959562414936 \tabularnewline
12 & 10 & 11.4488108912758 & -1.44881089127583 \tabularnewline
13 & 15 & 17.291412674971 & -2.29141267497099 \tabularnewline
14 & 6 & 13.1906313519517 & -7.19063135195171 \tabularnewline
15 & 7 & 12.1323718286991 & -5.13237182869911 \tabularnewline
16 & 14 & 13.526748814351 & 0.473251185649045 \tabularnewline
17 & 16 & 14.3203940197277 & 1.6796059802723 \tabularnewline
18 & 16 & 15.7798749483539 & 0.220125051646118 \tabularnewline
19 & 14 & 15.1842230800641 & -1.18422308006412 \tabularnewline
20 & 15 & 15.2061068182984 & -0.206106818298377 \tabularnewline
21 & 14 & 14.6590612639979 & -0.659061263997861 \tabularnewline
22 & 12 & 11.365027849543 & 0.634972150456989 \tabularnewline
23 & 9 & 9.07843878284137 & -0.078438782841365 \tabularnewline
24 & 12 & 11.5645865588673 & 0.435413441132651 \tabularnewline
25 & 14 & 13.9043636743032 & 0.0956363256968043 \tabularnewline
26 & 12 & 12.0395860686947 & -0.0395860686946695 \tabularnewline
27 & 14 & 13.7449173963345 & 0.255082603665456 \tabularnewline
28 & 10 & 11.3237347453513 & -1.32373474535134 \tabularnewline
29 & 14 & 12.9587063725915 & 1.0412936274085 \tabularnewline
30 & 16 & 16.0209419101327 & -0.0209419101327277 \tabularnewline
31 & 10 & 8.83074442797801 & 1.16925557202199 \tabularnewline
32 & 8 & 10.7103914354383 & -2.71039143543825 \tabularnewline
33 & 12 & 11.7448545892799 & 0.255145410720073 \tabularnewline
34 & 11 & 10.8912398290721 & 0.10876017092795 \tabularnewline
35 & 8 & 10.3166168319753 & -2.31661683197532 \tabularnewline
36 & 13 & 12.123788724638 & 0.876211275361967 \tabularnewline
37 & 11 & 10.0155668966914 & 0.984433103308587 \tabularnewline
38 & 12 & 9.78578488571004 & 2.21421511428996 \tabularnewline
39 & 16 & 15.2696303658561 & 0.730369634143929 \tabularnewline
40 & 16 & 12.9784721070762 & 3.02152789292375 \tabularnewline
41 & 13 & 13.8822688592405 & -0.882268859240484 \tabularnewline
42 & 14 & 15.2715261726568 & -1.27152617265676 \tabularnewline
43 & 5 & 5.55350869007311 & -0.553508690073111 \tabularnewline
44 & 14 & 13.3175077245915 & 0.682492275408455 \tabularnewline
45 & 13 & 9.03216589577066 & 3.96783410422934 \tabularnewline
46 & 16 & 15.4830206203974 & 0.516979379602637 \tabularnewline
47 & 14 & 14.6731379723986 & -0.673137972398647 \tabularnewline
48 & 15 & 14.2082259830187 & 0.791774016981328 \tabularnewline
49 & 15 & 13.3012294874653 & 1.69877051253468 \tabularnewline
50 & 11 & 12.1583384993798 & -1.15833849937976 \tabularnewline
51 & 15 & 13.6027018993587 & 1.39729810064131 \tabularnewline
52 & 16 & 12.6145287868068 & 3.38547121319317 \tabularnewline
53 & 13 & 13.3125508280339 & -0.312550828033872 \tabularnewline
54 & 11 & 13.6932708599582 & -2.69327085995817 \tabularnewline
55 & 12 & 13.8771452843589 & -1.87714528435891 \tabularnewline
56 & 12 & 11.5368928032068 & 0.463107196793219 \tabularnewline
57 & 10 & 13.3738483993187 & -3.37384839931873 \tabularnewline
58 & 8 & 9.03838872227876 & -1.03838872227876 \tabularnewline
59 & 9 & 9.60405812870065 & -0.604058128700651 \tabularnewline
60 & 12 & 12.0926494029421 & -0.0926494029421057 \tabularnewline
61 & 14 & 13.9481472148274 & 0.0518527851726416 \tabularnewline
62 & 12 & 13.0528342079952 & -1.05283420799521 \tabularnewline
63 & 11 & 11.0075014050402 & -0.00750140504019379 \tabularnewline
64 & 14 & 13.9094005521978 & 0.0905994478022215 \tabularnewline
65 & 7 & 11.5459981683963 & -4.54599816839629 \tabularnewline
66 & 16 & 14.0447539727401 & 1.95524602725989 \tabularnewline
67 & 16 & 15.5371528714492 & 0.462847128550832 \tabularnewline
68 & 11 & 12.264760180326 & -1.26476018032602 \tabularnewline
69 & 16 & 15.3546121445055 & 0.645387855494543 \tabularnewline
70 & 13 & 14.4183599616621 & -1.41835996166212 \tabularnewline
71 & 11 & 10.8207328878061 & 0.17926711219394 \tabularnewline
72 & 13 & 12.8031760504 & 0.19682394960001 \tabularnewline
73 & 14 & 14.3362199694722 & -0.336219969472217 \tabularnewline
74 & 15 & 13.4861689395864 & 1.51383106041363 \tabularnewline
75 & 10 & 9.35965940722293 & 0.640340592777073 \tabularnewline
76 & 15 & 14.7244744198531 & 0.275525580146889 \tabularnewline
77 & 11 & 12.9958102339005 & -1.99581023390055 \tabularnewline
78 & 11 & 11.4024329141157 & -0.402432914115737 \tabularnewline
79 & 6 & 8.49918198128773 & -2.49918198128773 \tabularnewline
80 & 11 & 9.63233741840194 & 1.36766258159806 \tabularnewline
81 & 12 & 11.5566437019636 & 0.443356298036378 \tabularnewline
82 & 13 & 13.2091406540391 & -0.209140654039052 \tabularnewline
83 & 12 & 12.3210617317895 & -0.321061731789543 \tabularnewline
84 & 8 & 10.6490833407533 & -2.64908334075329 \tabularnewline
85 & 9 & 10.7349212539284 & -1.73492125392839 \tabularnewline
86 & 10 & 11.6467410654182 & -1.6467410654182 \tabularnewline
87 & 16 & 13.1158790701389 & 2.88412092986112 \tabularnewline
88 & 15 & 12.7652299159938 & 2.23477008400617 \tabularnewline
89 & 14 & 13.5696071875676 & 0.430392812432437 \tabularnewline
90 & 12 & 13.6433703651392 & -1.64337036513921 \tabularnewline
91 & 12 & 10.2682747618807 & 1.73172523811934 \tabularnewline
92 & 10 & 8.82033206271318 & 1.17966793728682 \tabularnewline
93 & 12 & 11.4263746133836 & 0.573625386616375 \tabularnewline
94 & 8 & 9.3413456607946 & -1.3413456607946 \tabularnewline
95 & 16 & 14.0520765069226 & 1.94792349307736 \tabularnewline
96 & 11 & 7.75087376463123 & 3.24912623536877 \tabularnewline
97 & 12 & 11.492274480577 & 0.507725519422964 \tabularnewline
98 & 9 & 10.2998349775947 & -1.29983497759473 \tabularnewline
99 & 14 & 11.3107199255873 & 2.6892800744127 \tabularnewline
100 & 15 & 14.4417914627296 & 0.558208537270422 \tabularnewline
101 & 8 & 10.7050914338505 & -2.70509143385053 \tabularnewline
102 & 12 & 12.2985131903579 & -0.298513190357941 \tabularnewline
103 & 10 & 10.4127573387016 & -0.41275733870163 \tabularnewline
104 & 16 & 14.7838902026429 & 1.21610979735713 \tabularnewline
105 & 17 & 14.2719966698276 & 2.72800333017243 \tabularnewline
106 & 8 & 10.0794170593795 & -2.07941705937949 \tabularnewline
107 & 9 & 10.5838716669163 & -1.58387166691634 \tabularnewline
108 & 8 & 11.3755467072374 & -3.37554670723741 \tabularnewline
109 & 11 & 12.3744237176035 & -1.37442371760347 \tabularnewline
110 & 16 & 15.2759600290629 & 0.724039970937074 \tabularnewline
111 & 13 & 13.4306727557511 & -0.430672755751127 \tabularnewline
112 & 5 & 7.71119025790908 & -2.71119025790908 \tabularnewline
113 & 15 & 12.7846787100066 & 2.21532128999338 \tabularnewline
114 & 15 & 13.940839216779 & 1.05916078322098 \tabularnewline
115 & 12 & 11.3972919071503 & 0.602708092849717 \tabularnewline
116 & 12 & 11.3956027799066 & 0.604397220093366 \tabularnewline
117 & 16 & 15.7234388724739 & 0.276561127526107 \tabularnewline
118 & 12 & 12.4369449584 & -0.436944958399992 \tabularnewline
119 & 10 & 11.6255542363726 & -1.62555423637262 \tabularnewline
120 & 12 & 10.5660991565575 & 1.43390084344252 \tabularnewline
121 & 4 & 6.22293211352812 & -2.22293211352812 \tabularnewline
122 & 11 & 12.9176944639492 & -1.91769446394925 \tabularnewline
123 & 16 & 14.678810116074 & 1.32118988392603 \tabularnewline
124 & 7 & 8.79712611731686 & -1.79712611731686 \tabularnewline
125 & 9 & 10.7125218131455 & -1.71252181314546 \tabularnewline
126 & 14 & 10.8281029013785 & 3.17189709862155 \tabularnewline
127 & 11 & 9.64537312488018 & 1.35462687511982 \tabularnewline
128 & 10 & 10.7811590578546 & -0.781159057854594 \tabularnewline
129 & 6 & 8.4773317134976 & -2.4773317134976 \tabularnewline
130 & 14 & 12.8023524491645 & 1.19764755083546 \tabularnewline
131 & 11 & 10.8201744273176 & 0.179825572682426 \tabularnewline
132 & 11 & 9.11860432423373 & 1.88139567576627 \tabularnewline
133 & 9 & 13.8648278690724 & -4.86482786907244 \tabularnewline
134 & 16 & 11.372357927454 & 4.62764207254599 \tabularnewline
135 & 7 & 8.44596341492408 & -1.44596341492408 \tabularnewline
136 & 8 & 8.78090798916262 & -0.780907989162622 \tabularnewline
137 & 10 & 9.90724711312378 & 0.0927528868762196 \tabularnewline
138 & 14 & 11.9107259183342 & 2.0892740816658 \tabularnewline
139 & 9 & 9.55187003377597 & -0.551870033775968 \tabularnewline
140 & 13 & 12.2440385058042 & 0.755961494195769 \tabularnewline
141 & 13 & 9.58402687019161 & 3.41597312980839 \tabularnewline
142 & 12 & 11.6521364140392 & 0.347863585960846 \tabularnewline
143 & 11 & 12.5351681163983 & -1.53516811639826 \tabularnewline
144 & 10 & 14.902001440007 & -4.90200144000699 \tabularnewline
145 & 12 & 11.9172044351705 & 0.0827955648294775 \tabularnewline
146 & 14 & 13.2419318675388 & 0.758068132461206 \tabularnewline
147 & 11 & 13.3186321462926 & -2.3186321462926 \tabularnewline
148 & 13 & 10.9918777612752 & 2.00812223872484 \tabularnewline
149 & 14 & 13.5428872367616 & 0.457112763238449 \tabularnewline
150 & 13 & 12.5641894932098 & 0.435810506790193 \tabularnewline
151 & 16 & 16.0433459968012 & -0.0433459968011492 \tabularnewline
152 & 13 & 12.1290136015778 & 0.870986398422214 \tabularnewline
153 & 12 & 11.1692656051966 & 0.830734394803352 \tabularnewline
154 & 9 & 9.09291427494277 & -0.0929142749427662 \tabularnewline
155 & 14 & 11.2577678270325 & 2.74223217296745 \tabularnewline
156 & 15 & 14.5279979189494 & 0.472002081050606 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146123&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]13.1041188809076[/C][C]1.89588111909235[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]9.37957223548965[/C][C]2.62042776451035[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.6692829047351[/C][C]1.33071709526493[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]12.1780196356576[/C][C]-0.178019635657623[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]13.6340663848125[/C][C]0.365933615187536[/C][/ROW]
[ROW][C]6[/C][C]8[/C][C]9.12171124973132[/C][C]-1.12171124973132[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]12.1232135702656[/C][C]-1.12321357026557[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]8.35269535227077[/C][C]6.64730464772923[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]6.33029989402401[/C][C]-2.33029989402401[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]10.609066681238[/C][C]2.39093331876201[/C][/ROW]
[ROW][C]11[/C][C]19[/C][C]14.3804043758506[/C][C]4.61959562414936[/C][/ROW]
[ROW][C]12[/C][C]10[/C][C]11.4488108912758[/C][C]-1.44881089127583[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]17.291412674971[/C][C]-2.29141267497099[/C][/ROW]
[ROW][C]14[/C][C]6[/C][C]13.1906313519517[/C][C]-7.19063135195171[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]12.1323718286991[/C][C]-5.13237182869911[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.526748814351[/C][C]0.473251185649045[/C][/ROW]
[ROW][C]17[/C][C]16[/C][C]14.3203940197277[/C][C]1.6796059802723[/C][/ROW]
[ROW][C]18[/C][C]16[/C][C]15.7798749483539[/C][C]0.220125051646118[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]15.1842230800641[/C][C]-1.18422308006412[/C][/ROW]
[ROW][C]20[/C][C]15[/C][C]15.2061068182984[/C][C]-0.206106818298377[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]14.6590612639979[/C][C]-0.659061263997861[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]11.365027849543[/C][C]0.634972150456989[/C][/ROW]
[ROW][C]23[/C][C]9[/C][C]9.07843878284137[/C][C]-0.078438782841365[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]11.5645865588673[/C][C]0.435413441132651[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]13.9043636743032[/C][C]0.0956363256968043[/C][/ROW]
[ROW][C]26[/C][C]12[/C][C]12.0395860686947[/C][C]-0.0395860686946695[/C][/ROW]
[ROW][C]27[/C][C]14[/C][C]13.7449173963345[/C][C]0.255082603665456[/C][/ROW]
[ROW][C]28[/C][C]10[/C][C]11.3237347453513[/C][C]-1.32373474535134[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.9587063725915[/C][C]1.0412936274085[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]16.0209419101327[/C][C]-0.0209419101327277[/C][/ROW]
[ROW][C]31[/C][C]10[/C][C]8.83074442797801[/C][C]1.16925557202199[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]10.7103914354383[/C][C]-2.71039143543825[/C][/ROW]
[ROW][C]33[/C][C]12[/C][C]11.7448545892799[/C][C]0.255145410720073[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]10.8912398290721[/C][C]0.10876017092795[/C][/ROW]
[ROW][C]35[/C][C]8[/C][C]10.3166168319753[/C][C]-2.31661683197532[/C][/ROW]
[ROW][C]36[/C][C]13[/C][C]12.123788724638[/C][C]0.876211275361967[/C][/ROW]
[ROW][C]37[/C][C]11[/C][C]10.0155668966914[/C][C]0.984433103308587[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]9.78578488571004[/C][C]2.21421511428996[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]15.2696303658561[/C][C]0.730369634143929[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]12.9784721070762[/C][C]3.02152789292375[/C][/ROW]
[ROW][C]41[/C][C]13[/C][C]13.8822688592405[/C][C]-0.882268859240484[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.2715261726568[/C][C]-1.27152617265676[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]5.55350869007311[/C][C]-0.553508690073111[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]13.3175077245915[/C][C]0.682492275408455[/C][/ROW]
[ROW][C]45[/C][C]13[/C][C]9.03216589577066[/C][C]3.96783410422934[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]15.4830206203974[/C][C]0.516979379602637[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]14.6731379723986[/C][C]-0.673137972398647[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.2082259830187[/C][C]0.791774016981328[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]13.3012294874653[/C][C]1.69877051253468[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]12.1583384993798[/C][C]-1.15833849937976[/C][/ROW]
[ROW][C]51[/C][C]15[/C][C]13.6027018993587[/C][C]1.39729810064131[/C][/ROW]
[ROW][C]52[/C][C]16[/C][C]12.6145287868068[/C][C]3.38547121319317[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]13.3125508280339[/C][C]-0.312550828033872[/C][/ROW]
[ROW][C]54[/C][C]11[/C][C]13.6932708599582[/C][C]-2.69327085995817[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]13.8771452843589[/C][C]-1.87714528435891[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]11.5368928032068[/C][C]0.463107196793219[/C][/ROW]
[ROW][C]57[/C][C]10[/C][C]13.3738483993187[/C][C]-3.37384839931873[/C][/ROW]
[ROW][C]58[/C][C]8[/C][C]9.03838872227876[/C][C]-1.03838872227876[/C][/ROW]
[ROW][C]59[/C][C]9[/C][C]9.60405812870065[/C][C]-0.604058128700651[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]12.0926494029421[/C][C]-0.0926494029421057[/C][/ROW]
[ROW][C]61[/C][C]14[/C][C]13.9481472148274[/C][C]0.0518527851726416[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]13.0528342079952[/C][C]-1.05283420799521[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]11.0075014050402[/C][C]-0.00750140504019379[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.9094005521978[/C][C]0.0905994478022215[/C][/ROW]
[ROW][C]65[/C][C]7[/C][C]11.5459981683963[/C][C]-4.54599816839629[/C][/ROW]
[ROW][C]66[/C][C]16[/C][C]14.0447539727401[/C][C]1.95524602725989[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]15.5371528714492[/C][C]0.462847128550832[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]12.264760180326[/C][C]-1.26476018032602[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]15.3546121445055[/C][C]0.645387855494543[/C][/ROW]
[ROW][C]70[/C][C]13[/C][C]14.4183599616621[/C][C]-1.41835996166212[/C][/ROW]
[ROW][C]71[/C][C]11[/C][C]10.8207328878061[/C][C]0.17926711219394[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.8031760504[/C][C]0.19682394960001[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]14.3362199694722[/C][C]-0.336219969472217[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]13.4861689395864[/C][C]1.51383106041363[/C][/ROW]
[ROW][C]75[/C][C]10[/C][C]9.35965940722293[/C][C]0.640340592777073[/C][/ROW]
[ROW][C]76[/C][C]15[/C][C]14.7244744198531[/C][C]0.275525580146889[/C][/ROW]
[ROW][C]77[/C][C]11[/C][C]12.9958102339005[/C][C]-1.99581023390055[/C][/ROW]
[ROW][C]78[/C][C]11[/C][C]11.4024329141157[/C][C]-0.402432914115737[/C][/ROW]
[ROW][C]79[/C][C]6[/C][C]8.49918198128773[/C][C]-2.49918198128773[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]9.63233741840194[/C][C]1.36766258159806[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.5566437019636[/C][C]0.443356298036378[/C][/ROW]
[ROW][C]82[/C][C]13[/C][C]13.2091406540391[/C][C]-0.209140654039052[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]12.3210617317895[/C][C]-0.321061731789543[/C][/ROW]
[ROW][C]84[/C][C]8[/C][C]10.6490833407533[/C][C]-2.64908334075329[/C][/ROW]
[ROW][C]85[/C][C]9[/C][C]10.7349212539284[/C][C]-1.73492125392839[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]11.6467410654182[/C][C]-1.6467410654182[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]13.1158790701389[/C][C]2.88412092986112[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]12.7652299159938[/C][C]2.23477008400617[/C][/ROW]
[ROW][C]89[/C][C]14[/C][C]13.5696071875676[/C][C]0.430392812432437[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.6433703651392[/C][C]-1.64337036513921[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]10.2682747618807[/C][C]1.73172523811934[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]8.82033206271318[/C][C]1.17966793728682[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]11.4263746133836[/C][C]0.573625386616375[/C][/ROW]
[ROW][C]94[/C][C]8[/C][C]9.3413456607946[/C][C]-1.3413456607946[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.0520765069226[/C][C]1.94792349307736[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]7.75087376463123[/C][C]3.24912623536877[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]11.492274480577[/C][C]0.507725519422964[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]10.2998349775947[/C][C]-1.29983497759473[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]11.3107199255873[/C][C]2.6892800744127[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]14.4417914627296[/C][C]0.558208537270422[/C][/ROW]
[ROW][C]101[/C][C]8[/C][C]10.7050914338505[/C][C]-2.70509143385053[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]12.2985131903579[/C][C]-0.298513190357941[/C][/ROW]
[ROW][C]103[/C][C]10[/C][C]10.4127573387016[/C][C]-0.41275733870163[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]14.7838902026429[/C][C]1.21610979735713[/C][/ROW]
[ROW][C]105[/C][C]17[/C][C]14.2719966698276[/C][C]2.72800333017243[/C][/ROW]
[ROW][C]106[/C][C]8[/C][C]10.0794170593795[/C][C]-2.07941705937949[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.5838716669163[/C][C]-1.58387166691634[/C][/ROW]
[ROW][C]108[/C][C]8[/C][C]11.3755467072374[/C][C]-3.37554670723741[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]12.3744237176035[/C][C]-1.37442371760347[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.2759600290629[/C][C]0.724039970937074[/C][/ROW]
[ROW][C]111[/C][C]13[/C][C]13.4306727557511[/C][C]-0.430672755751127[/C][/ROW]
[ROW][C]112[/C][C]5[/C][C]7.71119025790908[/C][C]-2.71119025790908[/C][/ROW]
[ROW][C]113[/C][C]15[/C][C]12.7846787100066[/C][C]2.21532128999338[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]13.940839216779[/C][C]1.05916078322098[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]11.3972919071503[/C][C]0.602708092849717[/C][/ROW]
[ROW][C]116[/C][C]12[/C][C]11.3956027799066[/C][C]0.604397220093366[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]15.7234388724739[/C][C]0.276561127526107[/C][/ROW]
[ROW][C]118[/C][C]12[/C][C]12.4369449584[/C][C]-0.436944958399992[/C][/ROW]
[ROW][C]119[/C][C]10[/C][C]11.6255542363726[/C][C]-1.62555423637262[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]10.5660991565575[/C][C]1.43390084344252[/C][/ROW]
[ROW][C]121[/C][C]4[/C][C]6.22293211352812[/C][C]-2.22293211352812[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]12.9176944639492[/C][C]-1.91769446394925[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]14.678810116074[/C][C]1.32118988392603[/C][/ROW]
[ROW][C]124[/C][C]7[/C][C]8.79712611731686[/C][C]-1.79712611731686[/C][/ROW]
[ROW][C]125[/C][C]9[/C][C]10.7125218131455[/C][C]-1.71252181314546[/C][/ROW]
[ROW][C]126[/C][C]14[/C][C]10.8281029013785[/C][C]3.17189709862155[/C][/ROW]
[ROW][C]127[/C][C]11[/C][C]9.64537312488018[/C][C]1.35462687511982[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]10.7811590578546[/C][C]-0.781159057854594[/C][/ROW]
[ROW][C]129[/C][C]6[/C][C]8.4773317134976[/C][C]-2.4773317134976[/C][/ROW]
[ROW][C]130[/C][C]14[/C][C]12.8023524491645[/C][C]1.19764755083546[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]10.8201744273176[/C][C]0.179825572682426[/C][/ROW]
[ROW][C]132[/C][C]11[/C][C]9.11860432423373[/C][C]1.88139567576627[/C][/ROW]
[ROW][C]133[/C][C]9[/C][C]13.8648278690724[/C][C]-4.86482786907244[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]11.372357927454[/C][C]4.62764207254599[/C][/ROW]
[ROW][C]135[/C][C]7[/C][C]8.44596341492408[/C][C]-1.44596341492408[/C][/ROW]
[ROW][C]136[/C][C]8[/C][C]8.78090798916262[/C][C]-0.780907989162622[/C][/ROW]
[ROW][C]137[/C][C]10[/C][C]9.90724711312378[/C][C]0.0927528868762196[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]11.9107259183342[/C][C]2.0892740816658[/C][/ROW]
[ROW][C]139[/C][C]9[/C][C]9.55187003377597[/C][C]-0.551870033775968[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]12.2440385058042[/C][C]0.755961494195769[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]9.58402687019161[/C][C]3.41597312980839[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]11.6521364140392[/C][C]0.347863585960846[/C][/ROW]
[ROW][C]143[/C][C]11[/C][C]12.5351681163983[/C][C]-1.53516811639826[/C][/ROW]
[ROW][C]144[/C][C]10[/C][C]14.902001440007[/C][C]-4.90200144000699[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]11.9172044351705[/C][C]0.0827955648294775[/C][/ROW]
[ROW][C]146[/C][C]14[/C][C]13.2419318675388[/C][C]0.758068132461206[/C][/ROW]
[ROW][C]147[/C][C]11[/C][C]13.3186321462926[/C][C]-2.3186321462926[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]10.9918777612752[/C][C]2.00812223872484[/C][/ROW]
[ROW][C]149[/C][C]14[/C][C]13.5428872367616[/C][C]0.457112763238449[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.5641894932098[/C][C]0.435810506790193[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]16.0433459968012[/C][C]-0.0433459968011492[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]12.1290136015778[/C][C]0.870986398422214[/C][/ROW]
[ROW][C]153[/C][C]12[/C][C]11.1692656051966[/C][C]0.830734394803352[/C][/ROW]
[ROW][C]154[/C][C]9[/C][C]9.09291427494277[/C][C]-0.0929142749427662[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]11.2577678270325[/C][C]2.74223217296745[/C][/ROW]
[ROW][C]156[/C][C]15[/C][C]14.5279979189494[/C][C]0.472002081050606[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146123&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146123&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11513.10411888090761.89588111909235
2129.379572235489652.62042776451035
31513.66928290473511.33071709526493
41212.1780196356576-0.178019635657623
51413.63406638481250.365933615187536
689.12171124973132-1.12171124973132
71112.1232135702656-1.12321357026557
8158.352695352270776.64730464772923
946.33029989402401-2.33029989402401
101310.6090666812382.39093331876201
111914.38040437585064.61959562414936
121011.4488108912758-1.44881089127583
131517.291412674971-2.29141267497099
14613.1906313519517-7.19063135195171
15712.1323718286991-5.13237182869911
161413.5267488143510.473251185649045
171614.32039401972771.6796059802723
181615.77987494835390.220125051646118
191415.1842230800641-1.18422308006412
201515.2061068182984-0.206106818298377
211414.6590612639979-0.659061263997861
221211.3650278495430.634972150456989
2399.07843878284137-0.078438782841365
241211.56458655886730.435413441132651
251413.90436367430320.0956363256968043
261212.0395860686947-0.0395860686946695
271413.74491739633450.255082603665456
281011.3237347453513-1.32373474535134
291412.95870637259151.0412936274085
301616.0209419101327-0.0209419101327277
31108.830744427978011.16925557202199
32810.7103914354383-2.71039143543825
331211.74485458927990.255145410720073
341110.89123982907210.10876017092795
35810.3166168319753-2.31661683197532
361312.1237887246380.876211275361967
371110.01556689669140.984433103308587
38129.785784885710042.21421511428996
391615.26963036585610.730369634143929
401612.97847210707623.02152789292375
411313.8822688592405-0.882268859240484
421415.2715261726568-1.27152617265676
4355.55350869007311-0.553508690073111
441413.31750772459150.682492275408455
45139.032165895770663.96783410422934
461615.48302062039740.516979379602637
471414.6731379723986-0.673137972398647
481514.20822598301870.791774016981328
491513.30122948746531.69877051253468
501112.1583384993798-1.15833849937976
511513.60270189935871.39729810064131
521612.61452878680683.38547121319317
531313.3125508280339-0.312550828033872
541113.6932708599582-2.69327085995817
551213.8771452843589-1.87714528435891
561211.53689280320680.463107196793219
571013.3738483993187-3.37384839931873
5889.03838872227876-1.03838872227876
5999.60405812870065-0.604058128700651
601212.0926494029421-0.0926494029421057
611413.94814721482740.0518527851726416
621213.0528342079952-1.05283420799521
631111.0075014050402-0.00750140504019379
641413.90940055219780.0905994478022215
65711.5459981683963-4.54599816839629
661614.04475397274011.95524602725989
671615.53715287144920.462847128550832
681112.264760180326-1.26476018032602
691615.35461214450550.645387855494543
701314.4183599616621-1.41835996166212
711110.82073288780610.17926711219394
721312.80317605040.19682394960001
731414.3362199694722-0.336219969472217
741513.48616893958641.51383106041363
75109.359659407222930.640340592777073
761514.72447441985310.275525580146889
771112.9958102339005-1.99581023390055
781111.4024329141157-0.402432914115737
7968.49918198128773-2.49918198128773
80119.632337418401941.36766258159806
811211.55664370196360.443356298036378
821313.2091406540391-0.209140654039052
831212.3210617317895-0.321061731789543
84810.6490833407533-2.64908334075329
85910.7349212539284-1.73492125392839
861011.6467410654182-1.6467410654182
871613.11587907013892.88412092986112
881512.76522991599382.23477008400617
891413.56960718756760.430392812432437
901213.6433703651392-1.64337036513921
911210.26827476188071.73172523811934
92108.820332062713181.17966793728682
931211.42637461338360.573625386616375
9489.3413456607946-1.3413456607946
951614.05207650692261.94792349307736
96117.750873764631233.24912623536877
971211.4922744805770.507725519422964
98910.2998349775947-1.29983497759473
991411.31071992558732.6892800744127
1001514.44179146272960.558208537270422
101810.7050914338505-2.70509143385053
1021212.2985131903579-0.298513190357941
1031010.4127573387016-0.41275733870163
1041614.78389020264291.21610979735713
1051714.27199666982762.72800333017243
106810.0794170593795-2.07941705937949
107910.5838716669163-1.58387166691634
108811.3755467072374-3.37554670723741
1091112.3744237176035-1.37442371760347
1101615.27596002906290.724039970937074
1111313.4306727557511-0.430672755751127
11257.71119025790908-2.71119025790908
1131512.78467871000662.21532128999338
1141513.9408392167791.05916078322098
1151211.39729190715030.602708092849717
1161211.39560277990660.604397220093366
1171615.72343887247390.276561127526107
1181212.4369449584-0.436944958399992
1191011.6255542363726-1.62555423637262
1201210.56609915655751.43390084344252
12146.22293211352812-2.22293211352812
1221112.9176944639492-1.91769446394925
1231614.6788101160741.32118988392603
12478.79712611731686-1.79712611731686
125910.7125218131455-1.71252181314546
1261410.82810290137853.17189709862155
127119.645373124880181.35462687511982
1281010.7811590578546-0.781159057854594
12968.4773317134976-2.4773317134976
1301412.80235244916451.19764755083546
1311110.82017442731760.179825572682426
132119.118604324233731.88139567576627
133913.8648278690724-4.86482786907244
1341611.3723579274544.62764207254599
13578.44596341492408-1.44596341492408
13688.78090798916262-0.780907989162622
137109.907247113123780.0927528868762196
1381411.91072591833422.0892740816658
13999.55187003377597-0.551870033775968
1401312.24403850580420.755961494195769
141139.584026870191613.41597312980839
1421211.65213641403920.347863585960846
1431112.5351681163983-1.53516811639826
1441014.902001440007-4.90200144000699
1451211.91720443517050.0827955648294775
1461413.24193186753880.758068132461206
1471113.3186321462926-2.3186321462926
1481310.99187776127522.00812223872484
1491413.54288723676160.457112763238449
1501312.56418949320980.435810506790193
1511616.0433459968012-0.0433459968011492
1521312.12901360157780.870986398422214
1531211.16926560519660.830734394803352
15499.09291427494277-0.0929142749427662
1551411.25776782703252.74223217296745
1561514.52799791894940.472002081050606






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.9336492573955650.1327014852088710.0663507426044354
160.9999306218741460.0001387562517079226.93781258539608e-05
170.999825330422720.0003493391545604180.000174669577280209
180.9995788242493360.0008423515013271790.000421175750663589
190.9994424629633410.001115074073318620.000557537036659308
200.9989417082623730.00211658347525390.00105829173762695
210.9990347553211670.001930489357665040.000965244678832521
220.9995672504713650.0008654990572709790.00043274952863549
230.9991578278316070.001684344336786130.000842172168393065
240.9984453260016660.003109347996668590.00155467399833429
250.9972355174606210.005528965078757310.00276448253937865
260.9952678823144680.009464235371065080.00473211768553254
270.9959426887683370.008114622463326860.00405731123166343
280.9935169324214010.01296613515719710.00648306757859857
290.9965824577184790.00683508456304210.00341754228152105
300.9952171762680080.009565647463983370.00478282373199169
310.9929213369923820.01415732601523650.00707866300761827
320.9962843345489040.007431330902191570.00371566545109579
330.9943308617574320.01133827648513560.00566913824256782
340.9922774370725640.01544512585487130.00772256292743563
350.9912793561000530.01744128779989330.00872064389994667
360.9877578153960070.02448436920798570.0122421846039929
370.9848936426377310.03021271472453720.0151063573622686
380.9860230592496970.02795388150060690.0139769407503034
390.9852617601434230.02947647971315330.0147382398565766
400.9898363558670450.02032728826591010.010163644132955
410.986022983535750.02795403292850070.0139770164642503
420.980778542402160.03844291519568050.0192214575978403
430.9734890365830660.05302192683386860.0265109634169343
440.9676904142696490.06461917146070140.0323095857303507
450.9912925549644440.01741489007111140.00870744503555572
460.9880136781771970.02397264364560570.0119863218228028
470.9839868942327420.03202621153451680.0160131057672584
480.9790217195390530.04195656092189410.0209782804609471
490.9765208322078370.04695833558432590.023479167792163
500.9720102590318650.0559794819362690.0279897409681345
510.9694319506334640.06113609873307220.0305680493665361
520.9821389886582160.03572202268356770.0178610113417838
530.9760673762037820.0478652475924350.0239326237962175
540.9767379353712320.04652412925753510.0232620646287676
550.9733760106219360.05324797875612820.0266239893780641
560.9663856462991970.0672287074016060.033614353700803
570.9750728129680490.04985437406390260.0249271870319513
580.9686046853847950.06279062923041050.0313953146152052
590.9587840317144120.08243193657117530.0412159682855877
600.9467447109821430.1065105780357140.0532552890178571
610.932843091196050.13431381760790.06715690880395
620.9198991157672450.160201768465510.0801008842327552
630.9002519497902520.1994961004194960.099748050209748
640.8846227746413640.2307544507172730.115377225358636
650.9361582272630740.1276835454738520.0638417727369262
660.9413024341610880.1173951316778230.0586975658389116
670.9281614025785530.1436771948428930.0718385974214467
680.9155660044640050.168867991071990.0844339955359949
690.8984462267925830.2031075464148350.101553773207417
700.8854602364057880.2290795271884240.114539763594212
710.8627780799441780.2744438401116440.137221920055822
720.8368683374662710.3262633250674580.163131662533729
730.8207122115727180.3585755768545640.179287788427282
740.8151383342403230.3697233315193530.184861665759677
750.7902475510344580.4195048979310830.209752448965542
760.7561521080818770.4876957838362470.243847891918123
770.7466585749393580.5066828501212840.253341425060642
780.7057336850527970.5885326298944070.294266314947203
790.7113377505204560.5773244989590880.288662249479544
800.6972040707100980.6055918585798040.302795929289902
810.6618986882728770.6762026234542470.338101311727123
820.6199648520953050.760070295809390.380035147904695
830.5726544565941390.8546910868117220.427345543405861
840.5814236588777470.8371526822445050.418576341122253
850.5630902037335110.8738195925329770.436909796266489
860.5395088497290230.9209823005419540.460491150270977
870.5917909278401390.8164181443197210.408209072159861
880.6207437251626070.7585125496747860.379256274837393
890.5858614756210710.8282770487578570.414138524378929
900.569916826068620.8601663478627610.43008317393138
910.5582096358566140.8835807282867720.441790364143386
920.5264658416113370.9470683167773250.473534158388663
930.4829474754425660.9658949508851310.517052524557434
940.4654655263681090.9309310527362180.534534473631891
950.4793073149375680.9586146298751370.520692685062432
960.6190566541635230.7618866916729540.380943345836477
970.5739056003687620.8521887992624770.426094399631238
980.5379113310184910.9241773379630180.462088668981509
990.6049847962496110.7900304075007770.395015203750389
1000.5705912134037510.8588175731924980.429408786596249
1010.5908218220078050.8183563559843910.409178177992195
1020.5444237587305190.9111524825389630.455576241269481
1030.494212308851230.988424617702460.50578769114877
1040.5002203976503560.9995592046992880.499779602349644
1050.5519343664408130.8961312671183740.448065633559187
1060.5548921509762650.890215698047470.445107849023735
1070.5119420381920780.9761159236158440.488057961807922
1080.6097245627127630.7805508745744750.390275437287237
1090.5930667409027040.8138665181945910.406933259097296
1100.5505772612457470.8988454775085060.449422738754253
1110.4948265996685140.9896531993370270.505173400331486
1120.6168093432778450.7663813134443110.383190656722155
1130.6261543908035790.7476912183928420.373845609196421
1140.6152354880973110.7695290238053780.384764511902689
1150.5599876296091180.8800247407817650.440012370390882
1160.5211388776550420.9577222446899160.478861122344958
1170.4770161818485170.9540323636970330.522983818151483
1180.4638834165828040.9277668331656080.536116583417196
1190.494739372148820.989478744297640.50526062785118
1200.441530858356720.883061716713440.55846914164328
1210.4134829522043020.8269659044086040.586517047795698
1220.3685878329170540.7371756658341070.631412167082946
1230.4260640319806940.8521280639613880.573935968019306
1240.3873901037021710.7747802074043420.612609896297829
1250.4151331684270880.8302663368541760.584866831572912
1260.4448625504681390.8897251009362780.555137449531861
1270.4320321230372650.864064246074530.567967876962735
1280.3616135946336760.7232271892673510.638386405366324
1290.5980661188481590.8038677623036820.401933881151841
1300.7384688316503320.5230623366993360.261531168349668
1310.7418200110498450.516359977900310.258179988950155
1320.7892672181567960.4214655636864070.210732781843204
1330.7626227260879260.4747545478241490.237377273912074
1340.8089580224469450.3820839551061090.191041977553055
1350.7326375291746150.534724941650770.267362470825385
1360.6681748812628150.6636502374743690.331825118737185
1370.7582451793360050.4835096413279910.241754820663995
1380.7042372239546890.5915255520906210.295762776045311
1390.6469509147001220.7060981705997560.353049085299878
1400.5030939508278410.9938120983443180.496906049172159
1410.402127666541330.8042553330826610.59787233345867

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.933649257395565 & 0.132701485208871 & 0.0663507426044354 \tabularnewline
16 & 0.999930621874146 & 0.000138756251707922 & 6.93781258539608e-05 \tabularnewline
17 & 0.99982533042272 & 0.000349339154560418 & 0.000174669577280209 \tabularnewline
18 & 0.999578824249336 & 0.000842351501327179 & 0.000421175750663589 \tabularnewline
19 & 0.999442462963341 & 0.00111507407331862 & 0.000557537036659308 \tabularnewline
20 & 0.998941708262373 & 0.0021165834752539 & 0.00105829173762695 \tabularnewline
21 & 0.999034755321167 & 0.00193048935766504 & 0.000965244678832521 \tabularnewline
22 & 0.999567250471365 & 0.000865499057270979 & 0.00043274952863549 \tabularnewline
23 & 0.999157827831607 & 0.00168434433678613 & 0.000842172168393065 \tabularnewline
24 & 0.998445326001666 & 0.00310934799666859 & 0.00155467399833429 \tabularnewline
25 & 0.997235517460621 & 0.00552896507875731 & 0.00276448253937865 \tabularnewline
26 & 0.995267882314468 & 0.00946423537106508 & 0.00473211768553254 \tabularnewline
27 & 0.995942688768337 & 0.00811462246332686 & 0.00405731123166343 \tabularnewline
28 & 0.993516932421401 & 0.0129661351571971 & 0.00648306757859857 \tabularnewline
29 & 0.996582457718479 & 0.0068350845630421 & 0.00341754228152105 \tabularnewline
30 & 0.995217176268008 & 0.00956564746398337 & 0.00478282373199169 \tabularnewline
31 & 0.992921336992382 & 0.0141573260152365 & 0.00707866300761827 \tabularnewline
32 & 0.996284334548904 & 0.00743133090219157 & 0.00371566545109579 \tabularnewline
33 & 0.994330861757432 & 0.0113382764851356 & 0.00566913824256782 \tabularnewline
34 & 0.992277437072564 & 0.0154451258548713 & 0.00772256292743563 \tabularnewline
35 & 0.991279356100053 & 0.0174412877998933 & 0.00872064389994667 \tabularnewline
36 & 0.987757815396007 & 0.0244843692079857 & 0.0122421846039929 \tabularnewline
37 & 0.984893642637731 & 0.0302127147245372 & 0.0151063573622686 \tabularnewline
38 & 0.986023059249697 & 0.0279538815006069 & 0.0139769407503034 \tabularnewline
39 & 0.985261760143423 & 0.0294764797131533 & 0.0147382398565766 \tabularnewline
40 & 0.989836355867045 & 0.0203272882659101 & 0.010163644132955 \tabularnewline
41 & 0.98602298353575 & 0.0279540329285007 & 0.0139770164642503 \tabularnewline
42 & 0.98077854240216 & 0.0384429151956805 & 0.0192214575978403 \tabularnewline
43 & 0.973489036583066 & 0.0530219268338686 & 0.0265109634169343 \tabularnewline
44 & 0.967690414269649 & 0.0646191714607014 & 0.0323095857303507 \tabularnewline
45 & 0.991292554964444 & 0.0174148900711114 & 0.00870744503555572 \tabularnewline
46 & 0.988013678177197 & 0.0239726436456057 & 0.0119863218228028 \tabularnewline
47 & 0.983986894232742 & 0.0320262115345168 & 0.0160131057672584 \tabularnewline
48 & 0.979021719539053 & 0.0419565609218941 & 0.0209782804609471 \tabularnewline
49 & 0.976520832207837 & 0.0469583355843259 & 0.023479167792163 \tabularnewline
50 & 0.972010259031865 & 0.055979481936269 & 0.0279897409681345 \tabularnewline
51 & 0.969431950633464 & 0.0611360987330722 & 0.0305680493665361 \tabularnewline
52 & 0.982138988658216 & 0.0357220226835677 & 0.0178610113417838 \tabularnewline
53 & 0.976067376203782 & 0.047865247592435 & 0.0239326237962175 \tabularnewline
54 & 0.976737935371232 & 0.0465241292575351 & 0.0232620646287676 \tabularnewline
55 & 0.973376010621936 & 0.0532479787561282 & 0.0266239893780641 \tabularnewline
56 & 0.966385646299197 & 0.067228707401606 & 0.033614353700803 \tabularnewline
57 & 0.975072812968049 & 0.0498543740639026 & 0.0249271870319513 \tabularnewline
58 & 0.968604685384795 & 0.0627906292304105 & 0.0313953146152052 \tabularnewline
59 & 0.958784031714412 & 0.0824319365711753 & 0.0412159682855877 \tabularnewline
60 & 0.946744710982143 & 0.106510578035714 & 0.0532552890178571 \tabularnewline
61 & 0.93284309119605 & 0.1343138176079 & 0.06715690880395 \tabularnewline
62 & 0.919899115767245 & 0.16020176846551 & 0.0801008842327552 \tabularnewline
63 & 0.900251949790252 & 0.199496100419496 & 0.099748050209748 \tabularnewline
64 & 0.884622774641364 & 0.230754450717273 & 0.115377225358636 \tabularnewline
65 & 0.936158227263074 & 0.127683545473852 & 0.0638417727369262 \tabularnewline
66 & 0.941302434161088 & 0.117395131677823 & 0.0586975658389116 \tabularnewline
67 & 0.928161402578553 & 0.143677194842893 & 0.0718385974214467 \tabularnewline
68 & 0.915566004464005 & 0.16886799107199 & 0.0844339955359949 \tabularnewline
69 & 0.898446226792583 & 0.203107546414835 & 0.101553773207417 \tabularnewline
70 & 0.885460236405788 & 0.229079527188424 & 0.114539763594212 \tabularnewline
71 & 0.862778079944178 & 0.274443840111644 & 0.137221920055822 \tabularnewline
72 & 0.836868337466271 & 0.326263325067458 & 0.163131662533729 \tabularnewline
73 & 0.820712211572718 & 0.358575576854564 & 0.179287788427282 \tabularnewline
74 & 0.815138334240323 & 0.369723331519353 & 0.184861665759677 \tabularnewline
75 & 0.790247551034458 & 0.419504897931083 & 0.209752448965542 \tabularnewline
76 & 0.756152108081877 & 0.487695783836247 & 0.243847891918123 \tabularnewline
77 & 0.746658574939358 & 0.506682850121284 & 0.253341425060642 \tabularnewline
78 & 0.705733685052797 & 0.588532629894407 & 0.294266314947203 \tabularnewline
79 & 0.711337750520456 & 0.577324498959088 & 0.288662249479544 \tabularnewline
80 & 0.697204070710098 & 0.605591858579804 & 0.302795929289902 \tabularnewline
81 & 0.661898688272877 & 0.676202623454247 & 0.338101311727123 \tabularnewline
82 & 0.619964852095305 & 0.76007029580939 & 0.380035147904695 \tabularnewline
83 & 0.572654456594139 & 0.854691086811722 & 0.427345543405861 \tabularnewline
84 & 0.581423658877747 & 0.837152682244505 & 0.418576341122253 \tabularnewline
85 & 0.563090203733511 & 0.873819592532977 & 0.436909796266489 \tabularnewline
86 & 0.539508849729023 & 0.920982300541954 & 0.460491150270977 \tabularnewline
87 & 0.591790927840139 & 0.816418144319721 & 0.408209072159861 \tabularnewline
88 & 0.620743725162607 & 0.758512549674786 & 0.379256274837393 \tabularnewline
89 & 0.585861475621071 & 0.828277048757857 & 0.414138524378929 \tabularnewline
90 & 0.56991682606862 & 0.860166347862761 & 0.43008317393138 \tabularnewline
91 & 0.558209635856614 & 0.883580728286772 & 0.441790364143386 \tabularnewline
92 & 0.526465841611337 & 0.947068316777325 & 0.473534158388663 \tabularnewline
93 & 0.482947475442566 & 0.965894950885131 & 0.517052524557434 \tabularnewline
94 & 0.465465526368109 & 0.930931052736218 & 0.534534473631891 \tabularnewline
95 & 0.479307314937568 & 0.958614629875137 & 0.520692685062432 \tabularnewline
96 & 0.619056654163523 & 0.761886691672954 & 0.380943345836477 \tabularnewline
97 & 0.573905600368762 & 0.852188799262477 & 0.426094399631238 \tabularnewline
98 & 0.537911331018491 & 0.924177337963018 & 0.462088668981509 \tabularnewline
99 & 0.604984796249611 & 0.790030407500777 & 0.395015203750389 \tabularnewline
100 & 0.570591213403751 & 0.858817573192498 & 0.429408786596249 \tabularnewline
101 & 0.590821822007805 & 0.818356355984391 & 0.409178177992195 \tabularnewline
102 & 0.544423758730519 & 0.911152482538963 & 0.455576241269481 \tabularnewline
103 & 0.49421230885123 & 0.98842461770246 & 0.50578769114877 \tabularnewline
104 & 0.500220397650356 & 0.999559204699288 & 0.499779602349644 \tabularnewline
105 & 0.551934366440813 & 0.896131267118374 & 0.448065633559187 \tabularnewline
106 & 0.554892150976265 & 0.89021569804747 & 0.445107849023735 \tabularnewline
107 & 0.511942038192078 & 0.976115923615844 & 0.488057961807922 \tabularnewline
108 & 0.609724562712763 & 0.780550874574475 & 0.390275437287237 \tabularnewline
109 & 0.593066740902704 & 0.813866518194591 & 0.406933259097296 \tabularnewline
110 & 0.550577261245747 & 0.898845477508506 & 0.449422738754253 \tabularnewline
111 & 0.494826599668514 & 0.989653199337027 & 0.505173400331486 \tabularnewline
112 & 0.616809343277845 & 0.766381313444311 & 0.383190656722155 \tabularnewline
113 & 0.626154390803579 & 0.747691218392842 & 0.373845609196421 \tabularnewline
114 & 0.615235488097311 & 0.769529023805378 & 0.384764511902689 \tabularnewline
115 & 0.559987629609118 & 0.880024740781765 & 0.440012370390882 \tabularnewline
116 & 0.521138877655042 & 0.957722244689916 & 0.478861122344958 \tabularnewline
117 & 0.477016181848517 & 0.954032363697033 & 0.522983818151483 \tabularnewline
118 & 0.463883416582804 & 0.927766833165608 & 0.536116583417196 \tabularnewline
119 & 0.49473937214882 & 0.98947874429764 & 0.50526062785118 \tabularnewline
120 & 0.44153085835672 & 0.88306171671344 & 0.55846914164328 \tabularnewline
121 & 0.413482952204302 & 0.826965904408604 & 0.586517047795698 \tabularnewline
122 & 0.368587832917054 & 0.737175665834107 & 0.631412167082946 \tabularnewline
123 & 0.426064031980694 & 0.852128063961388 & 0.573935968019306 \tabularnewline
124 & 0.387390103702171 & 0.774780207404342 & 0.612609896297829 \tabularnewline
125 & 0.415133168427088 & 0.830266336854176 & 0.584866831572912 \tabularnewline
126 & 0.444862550468139 & 0.889725100936278 & 0.555137449531861 \tabularnewline
127 & 0.432032123037265 & 0.86406424607453 & 0.567967876962735 \tabularnewline
128 & 0.361613594633676 & 0.723227189267351 & 0.638386405366324 \tabularnewline
129 & 0.598066118848159 & 0.803867762303682 & 0.401933881151841 \tabularnewline
130 & 0.738468831650332 & 0.523062336699336 & 0.261531168349668 \tabularnewline
131 & 0.741820011049845 & 0.51635997790031 & 0.258179988950155 \tabularnewline
132 & 0.789267218156796 & 0.421465563686407 & 0.210732781843204 \tabularnewline
133 & 0.762622726087926 & 0.474754547824149 & 0.237377273912074 \tabularnewline
134 & 0.808958022446945 & 0.382083955106109 & 0.191041977553055 \tabularnewline
135 & 0.732637529174615 & 0.53472494165077 & 0.267362470825385 \tabularnewline
136 & 0.668174881262815 & 0.663650237474369 & 0.331825118737185 \tabularnewline
137 & 0.758245179336005 & 0.483509641327991 & 0.241754820663995 \tabularnewline
138 & 0.704237223954689 & 0.591525552090621 & 0.295762776045311 \tabularnewline
139 & 0.646950914700122 & 0.706098170599756 & 0.353049085299878 \tabularnewline
140 & 0.503093950827841 & 0.993812098344318 & 0.496906049172159 \tabularnewline
141 & 0.40212766654133 & 0.804255333082661 & 0.59787233345867 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146123&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]15[/C][C]0.933649257395565[/C][C]0.132701485208871[/C][C]0.0663507426044354[/C][/ROW]
[ROW][C]16[/C][C]0.999930621874146[/C][C]0.000138756251707922[/C][C]6.93781258539608e-05[/C][/ROW]
[ROW][C]17[/C][C]0.99982533042272[/C][C]0.000349339154560418[/C][C]0.000174669577280209[/C][/ROW]
[ROW][C]18[/C][C]0.999578824249336[/C][C]0.000842351501327179[/C][C]0.000421175750663589[/C][/ROW]
[ROW][C]19[/C][C]0.999442462963341[/C][C]0.00111507407331862[/C][C]0.000557537036659308[/C][/ROW]
[ROW][C]20[/C][C]0.998941708262373[/C][C]0.0021165834752539[/C][C]0.00105829173762695[/C][/ROW]
[ROW][C]21[/C][C]0.999034755321167[/C][C]0.00193048935766504[/C][C]0.000965244678832521[/C][/ROW]
[ROW][C]22[/C][C]0.999567250471365[/C][C]0.000865499057270979[/C][C]0.00043274952863549[/C][/ROW]
[ROW][C]23[/C][C]0.999157827831607[/C][C]0.00168434433678613[/C][C]0.000842172168393065[/C][/ROW]
[ROW][C]24[/C][C]0.998445326001666[/C][C]0.00310934799666859[/C][C]0.00155467399833429[/C][/ROW]
[ROW][C]25[/C][C]0.997235517460621[/C][C]0.00552896507875731[/C][C]0.00276448253937865[/C][/ROW]
[ROW][C]26[/C][C]0.995267882314468[/C][C]0.00946423537106508[/C][C]0.00473211768553254[/C][/ROW]
[ROW][C]27[/C][C]0.995942688768337[/C][C]0.00811462246332686[/C][C]0.00405731123166343[/C][/ROW]
[ROW][C]28[/C][C]0.993516932421401[/C][C]0.0129661351571971[/C][C]0.00648306757859857[/C][/ROW]
[ROW][C]29[/C][C]0.996582457718479[/C][C]0.0068350845630421[/C][C]0.00341754228152105[/C][/ROW]
[ROW][C]30[/C][C]0.995217176268008[/C][C]0.00956564746398337[/C][C]0.00478282373199169[/C][/ROW]
[ROW][C]31[/C][C]0.992921336992382[/C][C]0.0141573260152365[/C][C]0.00707866300761827[/C][/ROW]
[ROW][C]32[/C][C]0.996284334548904[/C][C]0.00743133090219157[/C][C]0.00371566545109579[/C][/ROW]
[ROW][C]33[/C][C]0.994330861757432[/C][C]0.0113382764851356[/C][C]0.00566913824256782[/C][/ROW]
[ROW][C]34[/C][C]0.992277437072564[/C][C]0.0154451258548713[/C][C]0.00772256292743563[/C][/ROW]
[ROW][C]35[/C][C]0.991279356100053[/C][C]0.0174412877998933[/C][C]0.00872064389994667[/C][/ROW]
[ROW][C]36[/C][C]0.987757815396007[/C][C]0.0244843692079857[/C][C]0.0122421846039929[/C][/ROW]
[ROW][C]37[/C][C]0.984893642637731[/C][C]0.0302127147245372[/C][C]0.0151063573622686[/C][/ROW]
[ROW][C]38[/C][C]0.986023059249697[/C][C]0.0279538815006069[/C][C]0.0139769407503034[/C][/ROW]
[ROW][C]39[/C][C]0.985261760143423[/C][C]0.0294764797131533[/C][C]0.0147382398565766[/C][/ROW]
[ROW][C]40[/C][C]0.989836355867045[/C][C]0.0203272882659101[/C][C]0.010163644132955[/C][/ROW]
[ROW][C]41[/C][C]0.98602298353575[/C][C]0.0279540329285007[/C][C]0.0139770164642503[/C][/ROW]
[ROW][C]42[/C][C]0.98077854240216[/C][C]0.0384429151956805[/C][C]0.0192214575978403[/C][/ROW]
[ROW][C]43[/C][C]0.973489036583066[/C][C]0.0530219268338686[/C][C]0.0265109634169343[/C][/ROW]
[ROW][C]44[/C][C]0.967690414269649[/C][C]0.0646191714607014[/C][C]0.0323095857303507[/C][/ROW]
[ROW][C]45[/C][C]0.991292554964444[/C][C]0.0174148900711114[/C][C]0.00870744503555572[/C][/ROW]
[ROW][C]46[/C][C]0.988013678177197[/C][C]0.0239726436456057[/C][C]0.0119863218228028[/C][/ROW]
[ROW][C]47[/C][C]0.983986894232742[/C][C]0.0320262115345168[/C][C]0.0160131057672584[/C][/ROW]
[ROW][C]48[/C][C]0.979021719539053[/C][C]0.0419565609218941[/C][C]0.0209782804609471[/C][/ROW]
[ROW][C]49[/C][C]0.976520832207837[/C][C]0.0469583355843259[/C][C]0.023479167792163[/C][/ROW]
[ROW][C]50[/C][C]0.972010259031865[/C][C]0.055979481936269[/C][C]0.0279897409681345[/C][/ROW]
[ROW][C]51[/C][C]0.969431950633464[/C][C]0.0611360987330722[/C][C]0.0305680493665361[/C][/ROW]
[ROW][C]52[/C][C]0.982138988658216[/C][C]0.0357220226835677[/C][C]0.0178610113417838[/C][/ROW]
[ROW][C]53[/C][C]0.976067376203782[/C][C]0.047865247592435[/C][C]0.0239326237962175[/C][/ROW]
[ROW][C]54[/C][C]0.976737935371232[/C][C]0.0465241292575351[/C][C]0.0232620646287676[/C][/ROW]
[ROW][C]55[/C][C]0.973376010621936[/C][C]0.0532479787561282[/C][C]0.0266239893780641[/C][/ROW]
[ROW][C]56[/C][C]0.966385646299197[/C][C]0.067228707401606[/C][C]0.033614353700803[/C][/ROW]
[ROW][C]57[/C][C]0.975072812968049[/C][C]0.0498543740639026[/C][C]0.0249271870319513[/C][/ROW]
[ROW][C]58[/C][C]0.968604685384795[/C][C]0.0627906292304105[/C][C]0.0313953146152052[/C][/ROW]
[ROW][C]59[/C][C]0.958784031714412[/C][C]0.0824319365711753[/C][C]0.0412159682855877[/C][/ROW]
[ROW][C]60[/C][C]0.946744710982143[/C][C]0.106510578035714[/C][C]0.0532552890178571[/C][/ROW]
[ROW][C]61[/C][C]0.93284309119605[/C][C]0.1343138176079[/C][C]0.06715690880395[/C][/ROW]
[ROW][C]62[/C][C]0.919899115767245[/C][C]0.16020176846551[/C][C]0.0801008842327552[/C][/ROW]
[ROW][C]63[/C][C]0.900251949790252[/C][C]0.199496100419496[/C][C]0.099748050209748[/C][/ROW]
[ROW][C]64[/C][C]0.884622774641364[/C][C]0.230754450717273[/C][C]0.115377225358636[/C][/ROW]
[ROW][C]65[/C][C]0.936158227263074[/C][C]0.127683545473852[/C][C]0.0638417727369262[/C][/ROW]
[ROW][C]66[/C][C]0.941302434161088[/C][C]0.117395131677823[/C][C]0.0586975658389116[/C][/ROW]
[ROW][C]67[/C][C]0.928161402578553[/C][C]0.143677194842893[/C][C]0.0718385974214467[/C][/ROW]
[ROW][C]68[/C][C]0.915566004464005[/C][C]0.16886799107199[/C][C]0.0844339955359949[/C][/ROW]
[ROW][C]69[/C][C]0.898446226792583[/C][C]0.203107546414835[/C][C]0.101553773207417[/C][/ROW]
[ROW][C]70[/C][C]0.885460236405788[/C][C]0.229079527188424[/C][C]0.114539763594212[/C][/ROW]
[ROW][C]71[/C][C]0.862778079944178[/C][C]0.274443840111644[/C][C]0.137221920055822[/C][/ROW]
[ROW][C]72[/C][C]0.836868337466271[/C][C]0.326263325067458[/C][C]0.163131662533729[/C][/ROW]
[ROW][C]73[/C][C]0.820712211572718[/C][C]0.358575576854564[/C][C]0.179287788427282[/C][/ROW]
[ROW][C]74[/C][C]0.815138334240323[/C][C]0.369723331519353[/C][C]0.184861665759677[/C][/ROW]
[ROW][C]75[/C][C]0.790247551034458[/C][C]0.419504897931083[/C][C]0.209752448965542[/C][/ROW]
[ROW][C]76[/C][C]0.756152108081877[/C][C]0.487695783836247[/C][C]0.243847891918123[/C][/ROW]
[ROW][C]77[/C][C]0.746658574939358[/C][C]0.506682850121284[/C][C]0.253341425060642[/C][/ROW]
[ROW][C]78[/C][C]0.705733685052797[/C][C]0.588532629894407[/C][C]0.294266314947203[/C][/ROW]
[ROW][C]79[/C][C]0.711337750520456[/C][C]0.577324498959088[/C][C]0.288662249479544[/C][/ROW]
[ROW][C]80[/C][C]0.697204070710098[/C][C]0.605591858579804[/C][C]0.302795929289902[/C][/ROW]
[ROW][C]81[/C][C]0.661898688272877[/C][C]0.676202623454247[/C][C]0.338101311727123[/C][/ROW]
[ROW][C]82[/C][C]0.619964852095305[/C][C]0.76007029580939[/C][C]0.380035147904695[/C][/ROW]
[ROW][C]83[/C][C]0.572654456594139[/C][C]0.854691086811722[/C][C]0.427345543405861[/C][/ROW]
[ROW][C]84[/C][C]0.581423658877747[/C][C]0.837152682244505[/C][C]0.418576341122253[/C][/ROW]
[ROW][C]85[/C][C]0.563090203733511[/C][C]0.873819592532977[/C][C]0.436909796266489[/C][/ROW]
[ROW][C]86[/C][C]0.539508849729023[/C][C]0.920982300541954[/C][C]0.460491150270977[/C][/ROW]
[ROW][C]87[/C][C]0.591790927840139[/C][C]0.816418144319721[/C][C]0.408209072159861[/C][/ROW]
[ROW][C]88[/C][C]0.620743725162607[/C][C]0.758512549674786[/C][C]0.379256274837393[/C][/ROW]
[ROW][C]89[/C][C]0.585861475621071[/C][C]0.828277048757857[/C][C]0.414138524378929[/C][/ROW]
[ROW][C]90[/C][C]0.56991682606862[/C][C]0.860166347862761[/C][C]0.43008317393138[/C][/ROW]
[ROW][C]91[/C][C]0.558209635856614[/C][C]0.883580728286772[/C][C]0.441790364143386[/C][/ROW]
[ROW][C]92[/C][C]0.526465841611337[/C][C]0.947068316777325[/C][C]0.473534158388663[/C][/ROW]
[ROW][C]93[/C][C]0.482947475442566[/C][C]0.965894950885131[/C][C]0.517052524557434[/C][/ROW]
[ROW][C]94[/C][C]0.465465526368109[/C][C]0.930931052736218[/C][C]0.534534473631891[/C][/ROW]
[ROW][C]95[/C][C]0.479307314937568[/C][C]0.958614629875137[/C][C]0.520692685062432[/C][/ROW]
[ROW][C]96[/C][C]0.619056654163523[/C][C]0.761886691672954[/C][C]0.380943345836477[/C][/ROW]
[ROW][C]97[/C][C]0.573905600368762[/C][C]0.852188799262477[/C][C]0.426094399631238[/C][/ROW]
[ROW][C]98[/C][C]0.537911331018491[/C][C]0.924177337963018[/C][C]0.462088668981509[/C][/ROW]
[ROW][C]99[/C][C]0.604984796249611[/C][C]0.790030407500777[/C][C]0.395015203750389[/C][/ROW]
[ROW][C]100[/C][C]0.570591213403751[/C][C]0.858817573192498[/C][C]0.429408786596249[/C][/ROW]
[ROW][C]101[/C][C]0.590821822007805[/C][C]0.818356355984391[/C][C]0.409178177992195[/C][/ROW]
[ROW][C]102[/C][C]0.544423758730519[/C][C]0.911152482538963[/C][C]0.455576241269481[/C][/ROW]
[ROW][C]103[/C][C]0.49421230885123[/C][C]0.98842461770246[/C][C]0.50578769114877[/C][/ROW]
[ROW][C]104[/C][C]0.500220397650356[/C][C]0.999559204699288[/C][C]0.499779602349644[/C][/ROW]
[ROW][C]105[/C][C]0.551934366440813[/C][C]0.896131267118374[/C][C]0.448065633559187[/C][/ROW]
[ROW][C]106[/C][C]0.554892150976265[/C][C]0.89021569804747[/C][C]0.445107849023735[/C][/ROW]
[ROW][C]107[/C][C]0.511942038192078[/C][C]0.976115923615844[/C][C]0.488057961807922[/C][/ROW]
[ROW][C]108[/C][C]0.609724562712763[/C][C]0.780550874574475[/C][C]0.390275437287237[/C][/ROW]
[ROW][C]109[/C][C]0.593066740902704[/C][C]0.813866518194591[/C][C]0.406933259097296[/C][/ROW]
[ROW][C]110[/C][C]0.550577261245747[/C][C]0.898845477508506[/C][C]0.449422738754253[/C][/ROW]
[ROW][C]111[/C][C]0.494826599668514[/C][C]0.989653199337027[/C][C]0.505173400331486[/C][/ROW]
[ROW][C]112[/C][C]0.616809343277845[/C][C]0.766381313444311[/C][C]0.383190656722155[/C][/ROW]
[ROW][C]113[/C][C]0.626154390803579[/C][C]0.747691218392842[/C][C]0.373845609196421[/C][/ROW]
[ROW][C]114[/C][C]0.615235488097311[/C][C]0.769529023805378[/C][C]0.384764511902689[/C][/ROW]
[ROW][C]115[/C][C]0.559987629609118[/C][C]0.880024740781765[/C][C]0.440012370390882[/C][/ROW]
[ROW][C]116[/C][C]0.521138877655042[/C][C]0.957722244689916[/C][C]0.478861122344958[/C][/ROW]
[ROW][C]117[/C][C]0.477016181848517[/C][C]0.954032363697033[/C][C]0.522983818151483[/C][/ROW]
[ROW][C]118[/C][C]0.463883416582804[/C][C]0.927766833165608[/C][C]0.536116583417196[/C][/ROW]
[ROW][C]119[/C][C]0.49473937214882[/C][C]0.98947874429764[/C][C]0.50526062785118[/C][/ROW]
[ROW][C]120[/C][C]0.44153085835672[/C][C]0.88306171671344[/C][C]0.55846914164328[/C][/ROW]
[ROW][C]121[/C][C]0.413482952204302[/C][C]0.826965904408604[/C][C]0.586517047795698[/C][/ROW]
[ROW][C]122[/C][C]0.368587832917054[/C][C]0.737175665834107[/C][C]0.631412167082946[/C][/ROW]
[ROW][C]123[/C][C]0.426064031980694[/C][C]0.852128063961388[/C][C]0.573935968019306[/C][/ROW]
[ROW][C]124[/C][C]0.387390103702171[/C][C]0.774780207404342[/C][C]0.612609896297829[/C][/ROW]
[ROW][C]125[/C][C]0.415133168427088[/C][C]0.830266336854176[/C][C]0.584866831572912[/C][/ROW]
[ROW][C]126[/C][C]0.444862550468139[/C][C]0.889725100936278[/C][C]0.555137449531861[/C][/ROW]
[ROW][C]127[/C][C]0.432032123037265[/C][C]0.86406424607453[/C][C]0.567967876962735[/C][/ROW]
[ROW][C]128[/C][C]0.361613594633676[/C][C]0.723227189267351[/C][C]0.638386405366324[/C][/ROW]
[ROW][C]129[/C][C]0.598066118848159[/C][C]0.803867762303682[/C][C]0.401933881151841[/C][/ROW]
[ROW][C]130[/C][C]0.738468831650332[/C][C]0.523062336699336[/C][C]0.261531168349668[/C][/ROW]
[ROW][C]131[/C][C]0.741820011049845[/C][C]0.51635997790031[/C][C]0.258179988950155[/C][/ROW]
[ROW][C]132[/C][C]0.789267218156796[/C][C]0.421465563686407[/C][C]0.210732781843204[/C][/ROW]
[ROW][C]133[/C][C]0.762622726087926[/C][C]0.474754547824149[/C][C]0.237377273912074[/C][/ROW]
[ROW][C]134[/C][C]0.808958022446945[/C][C]0.382083955106109[/C][C]0.191041977553055[/C][/ROW]
[ROW][C]135[/C][C]0.732637529174615[/C][C]0.53472494165077[/C][C]0.267362470825385[/C][/ROW]
[ROW][C]136[/C][C]0.668174881262815[/C][C]0.663650237474369[/C][C]0.331825118737185[/C][/ROW]
[ROW][C]137[/C][C]0.758245179336005[/C][C]0.483509641327991[/C][C]0.241754820663995[/C][/ROW]
[ROW][C]138[/C][C]0.704237223954689[/C][C]0.591525552090621[/C][C]0.295762776045311[/C][/ROW]
[ROW][C]139[/C][C]0.646950914700122[/C][C]0.706098170599756[/C][C]0.353049085299878[/C][/ROW]
[ROW][C]140[/C][C]0.503093950827841[/C][C]0.993812098344318[/C][C]0.496906049172159[/C][/ROW]
[ROW][C]141[/C][C]0.40212766654133[/C][C]0.804255333082661[/C][C]0.59787233345867[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146123&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146123&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.9336492573955650.1327014852088710.0663507426044354
160.9999306218741460.0001387562517079226.93781258539608e-05
170.999825330422720.0003493391545604180.000174669577280209
180.9995788242493360.0008423515013271790.000421175750663589
190.9994424629633410.001115074073318620.000557537036659308
200.9989417082623730.00211658347525390.00105829173762695
210.9990347553211670.001930489357665040.000965244678832521
220.9995672504713650.0008654990572709790.00043274952863549
230.9991578278316070.001684344336786130.000842172168393065
240.9984453260016660.003109347996668590.00155467399833429
250.9972355174606210.005528965078757310.00276448253937865
260.9952678823144680.009464235371065080.00473211768553254
270.9959426887683370.008114622463326860.00405731123166343
280.9935169324214010.01296613515719710.00648306757859857
290.9965824577184790.00683508456304210.00341754228152105
300.9952171762680080.009565647463983370.00478282373199169
310.9929213369923820.01415732601523650.00707866300761827
320.9962843345489040.007431330902191570.00371566545109579
330.9943308617574320.01133827648513560.00566913824256782
340.9922774370725640.01544512585487130.00772256292743563
350.9912793561000530.01744128779989330.00872064389994667
360.9877578153960070.02448436920798570.0122421846039929
370.9848936426377310.03021271472453720.0151063573622686
380.9860230592496970.02795388150060690.0139769407503034
390.9852617601434230.02947647971315330.0147382398565766
400.9898363558670450.02032728826591010.010163644132955
410.986022983535750.02795403292850070.0139770164642503
420.980778542402160.03844291519568050.0192214575978403
430.9734890365830660.05302192683386860.0265109634169343
440.9676904142696490.06461917146070140.0323095857303507
450.9912925549644440.01741489007111140.00870744503555572
460.9880136781771970.02397264364560570.0119863218228028
470.9839868942327420.03202621153451680.0160131057672584
480.9790217195390530.04195656092189410.0209782804609471
490.9765208322078370.04695833558432590.023479167792163
500.9720102590318650.0559794819362690.0279897409681345
510.9694319506334640.06113609873307220.0305680493665361
520.9821389886582160.03572202268356770.0178610113417838
530.9760673762037820.0478652475924350.0239326237962175
540.9767379353712320.04652412925753510.0232620646287676
550.9733760106219360.05324797875612820.0266239893780641
560.9663856462991970.0672287074016060.033614353700803
570.9750728129680490.04985437406390260.0249271870319513
580.9686046853847950.06279062923041050.0313953146152052
590.9587840317144120.08243193657117530.0412159682855877
600.9467447109821430.1065105780357140.0532552890178571
610.932843091196050.13431381760790.06715690880395
620.9198991157672450.160201768465510.0801008842327552
630.9002519497902520.1994961004194960.099748050209748
640.8846227746413640.2307544507172730.115377225358636
650.9361582272630740.1276835454738520.0638417727369262
660.9413024341610880.1173951316778230.0586975658389116
670.9281614025785530.1436771948428930.0718385974214467
680.9155660044640050.168867991071990.0844339955359949
690.8984462267925830.2031075464148350.101553773207417
700.8854602364057880.2290795271884240.114539763594212
710.8627780799441780.2744438401116440.137221920055822
720.8368683374662710.3262633250674580.163131662533729
730.8207122115727180.3585755768545640.179287788427282
740.8151383342403230.3697233315193530.184861665759677
750.7902475510344580.4195048979310830.209752448965542
760.7561521080818770.4876957838362470.243847891918123
770.7466585749393580.5066828501212840.253341425060642
780.7057336850527970.5885326298944070.294266314947203
790.7113377505204560.5773244989590880.288662249479544
800.6972040707100980.6055918585798040.302795929289902
810.6618986882728770.6762026234542470.338101311727123
820.6199648520953050.760070295809390.380035147904695
830.5726544565941390.8546910868117220.427345543405861
840.5814236588777470.8371526822445050.418576341122253
850.5630902037335110.8738195925329770.436909796266489
860.5395088497290230.9209823005419540.460491150270977
870.5917909278401390.8164181443197210.408209072159861
880.6207437251626070.7585125496747860.379256274837393
890.5858614756210710.8282770487578570.414138524378929
900.569916826068620.8601663478627610.43008317393138
910.5582096358566140.8835807282867720.441790364143386
920.5264658416113370.9470683167773250.473534158388663
930.4829474754425660.9658949508851310.517052524557434
940.4654655263681090.9309310527362180.534534473631891
950.4793073149375680.9586146298751370.520692685062432
960.6190566541635230.7618866916729540.380943345836477
970.5739056003687620.8521887992624770.426094399631238
980.5379113310184910.9241773379630180.462088668981509
990.6049847962496110.7900304075007770.395015203750389
1000.5705912134037510.8588175731924980.429408786596249
1010.5908218220078050.8183563559843910.409178177992195
1020.5444237587305190.9111524825389630.455576241269481
1030.494212308851230.988424617702460.50578769114877
1040.5002203976503560.9995592046992880.499779602349644
1050.5519343664408130.8961312671183740.448065633559187
1060.5548921509762650.890215698047470.445107849023735
1070.5119420381920780.9761159236158440.488057961807922
1080.6097245627127630.7805508745744750.390275437287237
1090.5930667409027040.8138665181945910.406933259097296
1100.5505772612457470.8988454775085060.449422738754253
1110.4948265996685140.9896531993370270.505173400331486
1120.6168093432778450.7663813134443110.383190656722155
1130.6261543908035790.7476912183928420.373845609196421
1140.6152354880973110.7695290238053780.384764511902689
1150.5599876296091180.8800247407817650.440012370390882
1160.5211388776550420.9577222446899160.478861122344958
1170.4770161818485170.9540323636970330.522983818151483
1180.4638834165828040.9277668331656080.536116583417196
1190.494739372148820.989478744297640.50526062785118
1200.441530858356720.883061716713440.55846914164328
1210.4134829522043020.8269659044086040.586517047795698
1220.3685878329170540.7371756658341070.631412167082946
1230.4260640319806940.8521280639613880.573935968019306
1240.3873901037021710.7747802074043420.612609896297829
1250.4151331684270880.8302663368541760.584866831572912
1260.4448625504681390.8897251009362780.555137449531861
1270.4320321230372650.864064246074530.567967876962735
1280.3616135946336760.7232271892673510.638386405366324
1290.5980661188481590.8038677623036820.401933881151841
1300.7384688316503320.5230623366993360.261531168349668
1310.7418200110498450.516359977900310.258179988950155
1320.7892672181567960.4214655636864070.210732781843204
1330.7626227260879260.4747545478241490.237377273912074
1340.8089580224469450.3820839551061090.191041977553055
1350.7326375291746150.534724941650770.267362470825385
1360.6681748812628150.6636502374743690.331825118737185
1370.7582451793360050.4835096413279910.241754820663995
1380.7042372239546890.5915255520906210.295762776045311
1390.6469509147001220.7060981705997560.353049085299878
1400.5030939508278410.9938120983443180.496906049172159
1410.402127666541330.8042553330826610.59787233345867






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level150.118110236220472NOK
5% type I error level360.283464566929134NOK
10% type I error level440.346456692913386NOK

\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 & 15 & 0.118110236220472 & NOK \tabularnewline
5% type I error level & 36 & 0.283464566929134 & NOK \tabularnewline
10% type I error level & 44 & 0.346456692913386 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146123&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]15[/C][C]0.118110236220472[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]36[/C][C]0.283464566929134[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]44[/C][C]0.346456692913386[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146123&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146123&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level150.118110236220472NOK
5% type I error level360.283464566929134NOK
10% type I error level440.346456692913386NOK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}