FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 15 Dec 2010 15:12:47 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/15/t1292425923t6vq9kqejuu1kf4.htm/, Retrieved Fri, 03 May 2024 05:41:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=110454, Retrieved Fri, 03 May 2024 05:41:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact120

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Paper: Multiple r...] [2010-12-15 15:12:47] [350231caf55a86a218fd48dc4d2e2f8b] [Current]
Feedback Forum

Post a new message

Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110454&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110454&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110454&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 time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = + 1.42973727530118 + 0.0679559785674146FindingFriends[t] + 0.257687676023935KnowingPeople[t] + 0.366618607059788Liked[t] + 0.675903357645973Celebrity[t] -0.0596577206056682Geslacht[t] -0.128099410571831Happiness[t] -0.647557306757711UsingHands[t] -0.196214101838047Quiet[t] + 0.454124178125426EyeContact[t] + 0.198962264726567CrossArms[t] + 0.194097726936484SmilingTalking[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  +  1.42973727530118 +  0.0679559785674146FindingFriends[t] +  0.257687676023935KnowingPeople[t] +  0.366618607059788Liked[t] +  0.675903357645973Celebrity[t] -0.0596577206056682Geslacht[t] -0.128099410571831Happiness[t] -0.647557306757711UsingHands[t] -0.196214101838047Quiet[t] +  0.454124178125426EyeContact[t] +  0.198962264726567CrossArms[t] +  0.194097726936484SmilingTalking[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110454&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  +  1.42973727530118 +  0.0679559785674146FindingFriends[t] +  0.257687676023935KnowingPeople[t] +  0.366618607059788Liked[t] +  0.675903357645973Celebrity[t] -0.0596577206056682Geslacht[t] -0.128099410571831Happiness[t] -0.647557306757711UsingHands[t] -0.196214101838047Quiet[t] +  0.454124178125426EyeContact[t] +  0.198962264726567CrossArms[t] +  0.194097726936484SmilingTalking[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110454&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110454&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] = + 1.42973727530118 + 0.0679559785674146FindingFriends[t] + 0.257687676023935KnowingPeople[t] + 0.366618607059788Liked[t] + 0.675903357645973Celebrity[t] -0.0596577206056682Geslacht[t] -0.128099410571831Happiness[t] -0.647557306757711UsingHands[t] -0.196214101838047Quiet[t] + 0.454124178125426EyeContact[t] + 0.198962264726567CrossArms[t] + 0.194097726936484SmilingTalking[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.429737275301182.8800680.49640.6206130.310307
FindingFriends0.06795597856741460.114670.59260.5546860.277343
KnowingPeople0.2576876760239350.0803163.20840.0017610.000881
Liked0.3666186070597880.1139763.21660.0017160.000858
Celebrity0.6759033576459730.1813033.7280.000310.000155
Geslacht-0.05965772060566820.50957-0.11710.907020.45351
Happiness-0.1280994105718310.13393-0.95650.3409940.170497
UsingHands-0.6475573067577110.34204-1.89320.061030.030515
Quiet-0.1962141018380470.136818-1.43410.1544540.077227
EyeContact0.4541241781254260.2099672.16280.032780.01639
CrossArms0.1989622647265670.1665681.19450.234930.117465
SmilingTalking0.1940977269364840.3380960.57410.5671120.283556

\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) & 1.42973727530118 & 2.880068 & 0.4964 & 0.620613 & 0.310307 \tabularnewline
FindingFriends & 0.0679559785674146 & 0.11467 & 0.5926 & 0.554686 & 0.277343 \tabularnewline
KnowingPeople & 0.257687676023935 & 0.080316 & 3.2084 & 0.001761 & 0.000881 \tabularnewline
Liked & 0.366618607059788 & 0.113976 & 3.2166 & 0.001716 & 0.000858 \tabularnewline
Celebrity & 0.675903357645973 & 0.181303 & 3.728 & 0.00031 & 0.000155 \tabularnewline
Geslacht & -0.0596577206056682 & 0.50957 & -0.1171 & 0.90702 & 0.45351 \tabularnewline
Happiness & -0.128099410571831 & 0.13393 & -0.9565 & 0.340994 & 0.170497 \tabularnewline
UsingHands & -0.647557306757711 & 0.34204 & -1.8932 & 0.06103 & 0.030515 \tabularnewline
Quiet & -0.196214101838047 & 0.136818 & -1.4341 & 0.154454 & 0.077227 \tabularnewline
EyeContact & 0.454124178125426 & 0.209967 & 2.1628 & 0.03278 & 0.01639 \tabularnewline
CrossArms & 0.198962264726567 & 0.166568 & 1.1945 & 0.23493 & 0.117465 \tabularnewline
SmilingTalking & 0.194097726936484 & 0.338096 & 0.5741 & 0.567112 & 0.283556 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110454&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]1.42973727530118[/C][C]2.880068[/C][C]0.4964[/C][C]0.620613[/C][C]0.310307[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.0679559785674146[/C][C]0.11467[/C][C]0.5926[/C][C]0.554686[/C][C]0.277343[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.257687676023935[/C][C]0.080316[/C][C]3.2084[/C][C]0.001761[/C][C]0.000881[/C][/ROW]
[ROW][C]Liked[/C][C]0.366618607059788[/C][C]0.113976[/C][C]3.2166[/C][C]0.001716[/C][C]0.000858[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.675903357645973[/C][C]0.181303[/C][C]3.728[/C][C]0.00031[/C][C]0.000155[/C][/ROW]
[ROW][C]Geslacht[/C][C]-0.0596577206056682[/C][C]0.50957[/C][C]-0.1171[/C][C]0.90702[/C][C]0.45351[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.128099410571831[/C][C]0.13393[/C][C]-0.9565[/C][C]0.340994[/C][C]0.170497[/C][/ROW]
[ROW][C]UsingHands[/C][C]-0.647557306757711[/C][C]0.34204[/C][C]-1.8932[/C][C]0.06103[/C][C]0.030515[/C][/ROW]
[ROW][C]Quiet[/C][C]-0.196214101838047[/C][C]0.136818[/C][C]-1.4341[/C][C]0.154454[/C][C]0.077227[/C][/ROW]
[ROW][C]EyeContact[/C][C]0.454124178125426[/C][C]0.209967[/C][C]2.1628[/C][C]0.03278[/C][C]0.01639[/C][/ROW]
[ROW][C]CrossArms[/C][C]0.198962264726567[/C][C]0.166568[/C][C]1.1945[/C][C]0.23493[/C][C]0.117465[/C][/ROW]
[ROW][C]SmilingTalking[/C][C]0.194097726936484[/C][C]0.338096[/C][C]0.5741[/C][C]0.567112[/C][C]0.283556[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110454&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110454&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)1.429737275301182.8800680.49640.6206130.310307
FindingFriends0.06795597856741460.114670.59260.5546860.277343
KnowingPeople0.2576876760239350.0803163.20840.0017610.000881
Liked0.3666186070597880.1139763.21660.0017160.000858
Celebrity0.6759033576459730.1813033.7280.000310.000155
Geslacht-0.05965772060566820.50957-0.11710.907020.45351
Happiness-0.1280994105718310.13393-0.95650.3409940.170497
UsingHands-0.6475573067577110.34204-1.89320.061030.030515
Quiet-0.1962141018380470.136818-1.43410.1544540.077227
EyeContact0.4541241781254260.2099672.16280.032780.01639
CrossArms0.1989622647265670.1665681.19450.234930.117465
SmilingTalking0.1940977269364840.3380960.57410.5671120.283556






Multiple Linear Regression - Regression Statistics
Multiple R0.727346569060866
R-squared0.529033031524613
Adjusted R-squared0.480615866541162
F-TEST (value)10.9265594486054
F-TEST (DF numerator)11
F-TEST (DF denominator)107
p-value2.81441536742477e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.05898166248144
Sum Squared Residuals453.616387048529

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.727346569060866 \tabularnewline
R-squared & 0.529033031524613 \tabularnewline
Adjusted R-squared & 0.480615866541162 \tabularnewline
F-TEST (value) & 10.9265594486054 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 107 \tabularnewline
p-value & 2.81441536742477e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.05898166248144 \tabularnewline
Sum Squared Residuals & 453.616387048529 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110454&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.727346569060866[/C][/ROW]
[ROW][C]R-squared[/C][C]0.529033031524613[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.480615866541162[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.9265594486054[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]107[/C][/ROW]
[ROW][C]p-value[/C][C]2.81441536742477e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.05898166248144[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]453.616387048529[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110454&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110454&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 R0.727346569060866
R-squared0.529033031524613
Adjusted R-squared0.480615866541162
F-TEST (value)10.9265594486054
F-TEST (DF numerator)11
F-TEST (DF denominator)107
p-value2.81441536742477e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.05898166248144
Sum Squared Residuals453.616387048529






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.00457460906761.99542539093237
21210.55043008913781.44956991086223
31513.21186388654731.78813611345268
41211.57594041426630.424059585733664
51011.9464928190928-1.94649281909279
6910.4848107042366-1.48481070423656
71211.9791405952060.020859404794037
8118.922153711065782.07784628893422
91112.1455245684748-1.14552456847483
101112.0181130072561-1.01811300725606
111513.50091713319811.49908286680186
12710.7292190066368-3.72921900663677
131112.0703821396859-1.07038213968595
14119.573334288586061.42666571141394
151011.3067531002422-1.30675310024216
161413.41864709487950.581352905120548
17108.06481950813171.9351804918683
1867.94486080504862-1.94486080504862
19117.794809006139573.20519099386043
201514.30001697085920.699983029140782
211413.2165200536850.783479946314983
22913.7749318041153-4.77493180411528
231311.91756287639821.08243712360175
241610.4926722596685.50732774033198
25137.94759031913415.0524096808659
261212.2790439372672-0.279043937267188
271414.6939977169802-0.693997716980227
28119.736930341557971.26306965844204
29910.1658794665605-1.16587946656046
301614.01138955468931.98861044531072
311212.2770206559694-0.277020655969426
32108.471717002996681.52828299700332
331313.0171515912932-0.0171515912932296
341615.44370607688760.556293923112422
3559.02814785028552-4.02814785028552
36810.4823883456822-2.48238834568223
371112.4819376897124-1.48193768971243
381614.58326163540491.41673836459509
391712.89893704513714.10106295486291
4097.860301892766241.13969810723376
41911.9557965105435-2.95579651054349
421314.244930811429-1.24493081142901
43610.0061701161472-4.00617011614722
441212.1396686008041-0.139668600804067
45811.6243632806534-3.62436328065335
461412.16109953182981.83890046817019
471212.2204693672247-0.220469367224733
481110.52116376755920.478836232440766
491614.436813086781.56318691322003
5089.88974947853361-1.88974947853361
511514.91204265500780.0879573449922338
5279.07804701073926-2.07804701073926
531613.96992984476812.03007015523187
541414.0174721880548-0.0174721880547607
55910.2443822917313-1.24438229173134
561412.03073766976821.96926233023179
571113.8863042732833-2.88630427328328
581511.99947131079153.00052868920854
591512.33430013127292.66569986872715
601312.03073766976820.969262330231787
611111.8085286204357-0.808528620435679
621113.686838705269-2.68683870526898
631212.8274550809735-0.827455080973473
641214.1240710013144-2.12407100131443
651211.98426639152270.0157336084772647
661212.2136697216109-0.213669721610937
671410.43301453906243.56698546093765
6867.46048032476614-1.46048032476614
6979.50174736952451-2.50174736952451
701413.73854087002610.261459129973863
711011.0073354277487-1.00733542774869
72138.188078057030874.81192194296913
731212.7978933403962-0.797893340396246
7499.28824111182083-0.288241111820833
751614.84782091793341.15217908206664
76109.88950902239930.110490977600702
771614.54829351768861.45170648231139
781513.34379209835121.65620790164884
7987.711110498228170.288889501771825
801112.7498386456421-1.74983864564207
811312.19126567536630.808734324633683
821615.97448430596330.025515694036669
831415.05650612315-1.05650612315003
84910.0276436452936-1.02764364529357
8589.95083348958265-1.95083348958265
86810.9682434246756-2.96824342467563
871111.8776732775281-0.877673277528131
881213.6802688662808-1.68026886628082
891413.63843538650250.361564613497518
901514.22471253021560.77528746978441
911613.71774729804292.28225270195712
921614.88891465914871.11108534085131
931112.5885162028708-1.58851620287077
941413.58863498652180.411365013478156
951410.65707572469413.34292427530594
961211.09165031032130.908349689678733
971313.1369529315639-0.136952931563935
981210.787008483611.21299151639001
991615.52982200395760.470177996042445
1001213.216520053685-1.21652005368502
1011110.83283130096120.167168699038784
10245.97962920214694-1.97962920214694
1031615.41599123990940.584008760090567
1041010.5781720296904-0.578172029690445
1051312.72901965567160.270980344328352
1061414.0350926946725-0.0350926946724795
10779.93496221807539-2.93496221807539
1081212.5136356699401-0.513635669940068
1091211.35277164500390.647228354996094
1101313.3254509847209-0.325450984720871
1111512.3225832518332.67741674816699
1121210.57627495827341.4237250417266
113811.163079291537-3.16307929153696
1141014.1591215292839-4.15912152928388
1151614.52850095502471.47149904497526
1161313.0569007304688-0.0569007304688109
11799.49971446490676-0.499714464906765
1181413.50103457448740.498965425512618
1191412.52425779203391.47574220796612

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.0045746090676 & 1.99542539093237 \tabularnewline
2 & 12 & 10.5504300891378 & 1.44956991086223 \tabularnewline
3 & 15 & 13.2118638865473 & 1.78813611345268 \tabularnewline
4 & 12 & 11.5759404142663 & 0.424059585733664 \tabularnewline
5 & 10 & 11.9464928190928 & -1.94649281909279 \tabularnewline
6 & 9 & 10.4848107042366 & -1.48481070423656 \tabularnewline
7 & 12 & 11.979140595206 & 0.020859404794037 \tabularnewline
8 & 11 & 8.92215371106578 & 2.07784628893422 \tabularnewline
9 & 11 & 12.1455245684748 & -1.14552456847483 \tabularnewline
10 & 11 & 12.0181130072561 & -1.01811300725606 \tabularnewline
11 & 15 & 13.5009171331981 & 1.49908286680186 \tabularnewline
12 & 7 & 10.7292190066368 & -3.72921900663677 \tabularnewline
13 & 11 & 12.0703821396859 & -1.07038213968595 \tabularnewline
14 & 11 & 9.57333428858606 & 1.42666571141394 \tabularnewline
15 & 10 & 11.3067531002422 & -1.30675310024216 \tabularnewline
16 & 14 & 13.4186470948795 & 0.581352905120548 \tabularnewline
17 & 10 & 8.0648195081317 & 1.9351804918683 \tabularnewline
18 & 6 & 7.94486080504862 & -1.94486080504862 \tabularnewline
19 & 11 & 7.79480900613957 & 3.20519099386043 \tabularnewline
20 & 15 & 14.3000169708592 & 0.699983029140782 \tabularnewline
21 & 14 & 13.216520053685 & 0.783479946314983 \tabularnewline
22 & 9 & 13.7749318041153 & -4.77493180411528 \tabularnewline
23 & 13 & 11.9175628763982 & 1.08243712360175 \tabularnewline
24 & 16 & 10.492672259668 & 5.50732774033198 \tabularnewline
25 & 13 & 7.9475903191341 & 5.0524096808659 \tabularnewline
26 & 12 & 12.2790439372672 & -0.279043937267188 \tabularnewline
27 & 14 & 14.6939977169802 & -0.693997716980227 \tabularnewline
28 & 11 & 9.73693034155797 & 1.26306965844204 \tabularnewline
29 & 9 & 10.1658794665605 & -1.16587946656046 \tabularnewline
30 & 16 & 14.0113895546893 & 1.98861044531072 \tabularnewline
31 & 12 & 12.2770206559694 & -0.277020655969426 \tabularnewline
32 & 10 & 8.47171700299668 & 1.52828299700332 \tabularnewline
33 & 13 & 13.0171515912932 & -0.0171515912932296 \tabularnewline
34 & 16 & 15.4437060768876 & 0.556293923112422 \tabularnewline
35 & 5 & 9.02814785028552 & -4.02814785028552 \tabularnewline
36 & 8 & 10.4823883456822 & -2.48238834568223 \tabularnewline
37 & 11 & 12.4819376897124 & -1.48193768971243 \tabularnewline
38 & 16 & 14.5832616354049 & 1.41673836459509 \tabularnewline
39 & 17 & 12.8989370451371 & 4.10106295486291 \tabularnewline
40 & 9 & 7.86030189276624 & 1.13969810723376 \tabularnewline
41 & 9 & 11.9557965105435 & -2.95579651054349 \tabularnewline
42 & 13 & 14.244930811429 & -1.24493081142901 \tabularnewline
43 & 6 & 10.0061701161472 & -4.00617011614722 \tabularnewline
44 & 12 & 12.1396686008041 & -0.139668600804067 \tabularnewline
45 & 8 & 11.6243632806534 & -3.62436328065335 \tabularnewline
46 & 14 & 12.1610995318298 & 1.83890046817019 \tabularnewline
47 & 12 & 12.2204693672247 & -0.220469367224733 \tabularnewline
48 & 11 & 10.5211637675592 & 0.478836232440766 \tabularnewline
49 & 16 & 14.43681308678 & 1.56318691322003 \tabularnewline
50 & 8 & 9.88974947853361 & -1.88974947853361 \tabularnewline
51 & 15 & 14.9120426550078 & 0.0879573449922338 \tabularnewline
52 & 7 & 9.07804701073926 & -2.07804701073926 \tabularnewline
53 & 16 & 13.9699298447681 & 2.03007015523187 \tabularnewline
54 & 14 & 14.0174721880548 & -0.0174721880547607 \tabularnewline
55 & 9 & 10.2443822917313 & -1.24438229173134 \tabularnewline
56 & 14 & 12.0307376697682 & 1.96926233023179 \tabularnewline
57 & 11 & 13.8863042732833 & -2.88630427328328 \tabularnewline
58 & 15 & 11.9994713107915 & 3.00052868920854 \tabularnewline
59 & 15 & 12.3343001312729 & 2.66569986872715 \tabularnewline
60 & 13 & 12.0307376697682 & 0.969262330231787 \tabularnewline
61 & 11 & 11.8085286204357 & -0.808528620435679 \tabularnewline
62 & 11 & 13.686838705269 & -2.68683870526898 \tabularnewline
63 & 12 & 12.8274550809735 & -0.827455080973473 \tabularnewline
64 & 12 & 14.1240710013144 & -2.12407100131443 \tabularnewline
65 & 12 & 11.9842663915227 & 0.0157336084772647 \tabularnewline
66 & 12 & 12.2136697216109 & -0.213669721610937 \tabularnewline
67 & 14 & 10.4330145390624 & 3.56698546093765 \tabularnewline
68 & 6 & 7.46048032476614 & -1.46048032476614 \tabularnewline
69 & 7 & 9.50174736952451 & -2.50174736952451 \tabularnewline
70 & 14 & 13.7385408700261 & 0.261459129973863 \tabularnewline
71 & 10 & 11.0073354277487 & -1.00733542774869 \tabularnewline
72 & 13 & 8.18807805703087 & 4.81192194296913 \tabularnewline
73 & 12 & 12.7978933403962 & -0.797893340396246 \tabularnewline
74 & 9 & 9.28824111182083 & -0.288241111820833 \tabularnewline
75 & 16 & 14.8478209179334 & 1.15217908206664 \tabularnewline
76 & 10 & 9.8895090223993 & 0.110490977600702 \tabularnewline
77 & 16 & 14.5482935176886 & 1.45170648231139 \tabularnewline
78 & 15 & 13.3437920983512 & 1.65620790164884 \tabularnewline
79 & 8 & 7.71111049822817 & 0.288889501771825 \tabularnewline
80 & 11 & 12.7498386456421 & -1.74983864564207 \tabularnewline
81 & 13 & 12.1912656753663 & 0.808734324633683 \tabularnewline
82 & 16 & 15.9744843059633 & 0.025515694036669 \tabularnewline
83 & 14 & 15.05650612315 & -1.05650612315003 \tabularnewline
84 & 9 & 10.0276436452936 & -1.02764364529357 \tabularnewline
85 & 8 & 9.95083348958265 & -1.95083348958265 \tabularnewline
86 & 8 & 10.9682434246756 & -2.96824342467563 \tabularnewline
87 & 11 & 11.8776732775281 & -0.877673277528131 \tabularnewline
88 & 12 & 13.6802688662808 & -1.68026886628082 \tabularnewline
89 & 14 & 13.6384353865025 & 0.361564613497518 \tabularnewline
90 & 15 & 14.2247125302156 & 0.77528746978441 \tabularnewline
91 & 16 & 13.7177472980429 & 2.28225270195712 \tabularnewline
92 & 16 & 14.8889146591487 & 1.11108534085131 \tabularnewline
93 & 11 & 12.5885162028708 & -1.58851620287077 \tabularnewline
94 & 14 & 13.5886349865218 & 0.411365013478156 \tabularnewline
95 & 14 & 10.6570757246941 & 3.34292427530594 \tabularnewline
96 & 12 & 11.0916503103213 & 0.908349689678733 \tabularnewline
97 & 13 & 13.1369529315639 & -0.136952931563935 \tabularnewline
98 & 12 & 10.78700848361 & 1.21299151639001 \tabularnewline
99 & 16 & 15.5298220039576 & 0.470177996042445 \tabularnewline
100 & 12 & 13.216520053685 & -1.21652005368502 \tabularnewline
101 & 11 & 10.8328313009612 & 0.167168699038784 \tabularnewline
102 & 4 & 5.97962920214694 & -1.97962920214694 \tabularnewline
103 & 16 & 15.4159912399094 & 0.584008760090567 \tabularnewline
104 & 10 & 10.5781720296904 & -0.578172029690445 \tabularnewline
105 & 13 & 12.7290196556716 & 0.270980344328352 \tabularnewline
106 & 14 & 14.0350926946725 & -0.0350926946724795 \tabularnewline
107 & 7 & 9.93496221807539 & -2.93496221807539 \tabularnewline
108 & 12 & 12.5136356699401 & -0.513635669940068 \tabularnewline
109 & 12 & 11.3527716450039 & 0.647228354996094 \tabularnewline
110 & 13 & 13.3254509847209 & -0.325450984720871 \tabularnewline
111 & 15 & 12.322583251833 & 2.67741674816699 \tabularnewline
112 & 12 & 10.5762749582734 & 1.4237250417266 \tabularnewline
113 & 8 & 11.163079291537 & -3.16307929153696 \tabularnewline
114 & 10 & 14.1591215292839 & -4.15912152928388 \tabularnewline
115 & 16 & 14.5285009550247 & 1.47149904497526 \tabularnewline
116 & 13 & 13.0569007304688 & -0.0569007304688109 \tabularnewline
117 & 9 & 9.49971446490676 & -0.499714464906765 \tabularnewline
118 & 14 & 13.5010345744874 & 0.498965425512618 \tabularnewline
119 & 14 & 12.5242577920339 & 1.47574220796612 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110454&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]13[/C][C]11.0045746090676[/C][C]1.99542539093237[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]10.5504300891378[/C][C]1.44956991086223[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.2118638865473[/C][C]1.78813611345268[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]11.5759404142663[/C][C]0.424059585733664[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]11.9464928190928[/C][C]-1.94649281909279[/C][/ROW]
[ROW][C]6[/C][C]9[/C][C]10.4848107042366[/C][C]-1.48481070423656[/C][/ROW]
[ROW][C]7[/C][C]12[/C][C]11.979140595206[/C][C]0.020859404794037[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]8.92215371106578[/C][C]2.07784628893422[/C][/ROW]
[ROW][C]9[/C][C]11[/C][C]12.1455245684748[/C][C]-1.14552456847483[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]12.0181130072561[/C][C]-1.01811300725606[/C][/ROW]
[ROW][C]11[/C][C]15[/C][C]13.5009171331981[/C][C]1.49908286680186[/C][/ROW]
[ROW][C]12[/C][C]7[/C][C]10.7292190066368[/C][C]-3.72921900663677[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]12.0703821396859[/C][C]-1.07038213968595[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]9.57333428858606[/C][C]1.42666571141394[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]11.3067531002422[/C][C]-1.30675310024216[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.4186470948795[/C][C]0.581352905120548[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]8.0648195081317[/C][C]1.9351804918683[/C][/ROW]
[ROW][C]18[/C][C]6[/C][C]7.94486080504862[/C][C]-1.94486080504862[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]7.79480900613957[/C][C]3.20519099386043[/C][/ROW]
[ROW][C]20[/C][C]15[/C][C]14.3000169708592[/C][C]0.699983029140782[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]13.216520053685[/C][C]0.783479946314983[/C][/ROW]
[ROW][C]22[/C][C]9[/C][C]13.7749318041153[/C][C]-4.77493180411528[/C][/ROW]
[ROW][C]23[/C][C]13[/C][C]11.9175628763982[/C][C]1.08243712360175[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]10.492672259668[/C][C]5.50732774033198[/C][/ROW]
[ROW][C]25[/C][C]13[/C][C]7.9475903191341[/C][C]5.0524096808659[/C][/ROW]
[ROW][C]26[/C][C]12[/C][C]12.2790439372672[/C][C]-0.279043937267188[/C][/ROW]
[ROW][C]27[/C][C]14[/C][C]14.6939977169802[/C][C]-0.693997716980227[/C][/ROW]
[ROW][C]28[/C][C]11[/C][C]9.73693034155797[/C][C]1.26306965844204[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.1658794665605[/C][C]-1.16587946656046[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]14.0113895546893[/C][C]1.98861044531072[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]12.2770206559694[/C][C]-0.277020655969426[/C][/ROW]
[ROW][C]32[/C][C]10[/C][C]8.47171700299668[/C][C]1.52828299700332[/C][/ROW]
[ROW][C]33[/C][C]13[/C][C]13.0171515912932[/C][C]-0.0171515912932296[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.4437060768876[/C][C]0.556293923112422[/C][/ROW]
[ROW][C]35[/C][C]5[/C][C]9.02814785028552[/C][C]-4.02814785028552[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]10.4823883456822[/C][C]-2.48238834568223[/C][/ROW]
[ROW][C]37[/C][C]11[/C][C]12.4819376897124[/C][C]-1.48193768971243[/C][/ROW]
[ROW][C]38[/C][C]16[/C][C]14.5832616354049[/C][C]1.41673836459509[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]12.8989370451371[/C][C]4.10106295486291[/C][/ROW]
[ROW][C]40[/C][C]9[/C][C]7.86030189276624[/C][C]1.13969810723376[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]11.9557965105435[/C][C]-2.95579651054349[/C][/ROW]
[ROW][C]42[/C][C]13[/C][C]14.244930811429[/C][C]-1.24493081142901[/C][/ROW]
[ROW][C]43[/C][C]6[/C][C]10.0061701161472[/C][C]-4.00617011614722[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]12.1396686008041[/C][C]-0.139668600804067[/C][/ROW]
[ROW][C]45[/C][C]8[/C][C]11.6243632806534[/C][C]-3.62436328065335[/C][/ROW]
[ROW][C]46[/C][C]14[/C][C]12.1610995318298[/C][C]1.83890046817019[/C][/ROW]
[ROW][C]47[/C][C]12[/C][C]12.2204693672247[/C][C]-0.220469367224733[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]10.5211637675592[/C][C]0.478836232440766[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]14.43681308678[/C][C]1.56318691322003[/C][/ROW]
[ROW][C]50[/C][C]8[/C][C]9.88974947853361[/C][C]-1.88974947853361[/C][/ROW]
[ROW][C]51[/C][C]15[/C][C]14.9120426550078[/C][C]0.0879573449922338[/C][/ROW]
[ROW][C]52[/C][C]7[/C][C]9.07804701073926[/C][C]-2.07804701073926[/C][/ROW]
[ROW][C]53[/C][C]16[/C][C]13.9699298447681[/C][C]2.03007015523187[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]14.0174721880548[/C][C]-0.0174721880547607[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]10.2443822917313[/C][C]-1.24438229173134[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.0307376697682[/C][C]1.96926233023179[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]13.8863042732833[/C][C]-2.88630427328328[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]11.9994713107915[/C][C]3.00052868920854[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]12.3343001312729[/C][C]2.66569986872715[/C][/ROW]
[ROW][C]60[/C][C]13[/C][C]12.0307376697682[/C][C]0.969262330231787[/C][/ROW]
[ROW][C]61[/C][C]11[/C][C]11.8085286204357[/C][C]-0.808528620435679[/C][/ROW]
[ROW][C]62[/C][C]11[/C][C]13.686838705269[/C][C]-2.68683870526898[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]12.8274550809735[/C][C]-0.827455080973473[/C][/ROW]
[ROW][C]64[/C][C]12[/C][C]14.1240710013144[/C][C]-2.12407100131443[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]11.9842663915227[/C][C]0.0157336084772647[/C][/ROW]
[ROW][C]66[/C][C]12[/C][C]12.2136697216109[/C][C]-0.213669721610937[/C][/ROW]
[ROW][C]67[/C][C]14[/C][C]10.4330145390624[/C][C]3.56698546093765[/C][/ROW]
[ROW][C]68[/C][C]6[/C][C]7.46048032476614[/C][C]-1.46048032476614[/C][/ROW]
[ROW][C]69[/C][C]7[/C][C]9.50174736952451[/C][C]-2.50174736952451[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]13.7385408700261[/C][C]0.261459129973863[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]11.0073354277487[/C][C]-1.00733542774869[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]8.18807805703087[/C][C]4.81192194296913[/C][/ROW]
[ROW][C]73[/C][C]12[/C][C]12.7978933403962[/C][C]-0.797893340396246[/C][/ROW]
[ROW][C]74[/C][C]9[/C][C]9.28824111182083[/C][C]-0.288241111820833[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]14.8478209179334[/C][C]1.15217908206664[/C][/ROW]
[ROW][C]76[/C][C]10[/C][C]9.8895090223993[/C][C]0.110490977600702[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]14.5482935176886[/C][C]1.45170648231139[/C][/ROW]
[ROW][C]78[/C][C]15[/C][C]13.3437920983512[/C][C]1.65620790164884[/C][/ROW]
[ROW][C]79[/C][C]8[/C][C]7.71111049822817[/C][C]0.288889501771825[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]12.7498386456421[/C][C]-1.74983864564207[/C][/ROW]
[ROW][C]81[/C][C]13[/C][C]12.1912656753663[/C][C]0.808734324633683[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.9744843059633[/C][C]0.025515694036669[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]15.05650612315[/C][C]-1.05650612315003[/C][/ROW]
[ROW][C]84[/C][C]9[/C][C]10.0276436452936[/C][C]-1.02764364529357[/C][/ROW]
[ROW][C]85[/C][C]8[/C][C]9.95083348958265[/C][C]-1.95083348958265[/C][/ROW]
[ROW][C]86[/C][C]8[/C][C]10.9682434246756[/C][C]-2.96824342467563[/C][/ROW]
[ROW][C]87[/C][C]11[/C][C]11.8776732775281[/C][C]-0.877673277528131[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]13.6802688662808[/C][C]-1.68026886628082[/C][/ROW]
[ROW][C]89[/C][C]14[/C][C]13.6384353865025[/C][C]0.361564613497518[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.2247125302156[/C][C]0.77528746978441[/C][/ROW]
[ROW][C]91[/C][C]16[/C][C]13.7177472980429[/C][C]2.28225270195712[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]14.8889146591487[/C][C]1.11108534085131[/C][/ROW]
[ROW][C]93[/C][C]11[/C][C]12.5885162028708[/C][C]-1.58851620287077[/C][/ROW]
[ROW][C]94[/C][C]14[/C][C]13.5886349865218[/C][C]0.411365013478156[/C][/ROW]
[ROW][C]95[/C][C]14[/C][C]10.6570757246941[/C][C]3.34292427530594[/C][/ROW]
[ROW][C]96[/C][C]12[/C][C]11.0916503103213[/C][C]0.908349689678733[/C][/ROW]
[ROW][C]97[/C][C]13[/C][C]13.1369529315639[/C][C]-0.136952931563935[/C][/ROW]
[ROW][C]98[/C][C]12[/C][C]10.78700848361[/C][C]1.21299151639001[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]15.5298220039576[/C][C]0.470177996042445[/C][/ROW]
[ROW][C]100[/C][C]12[/C][C]13.216520053685[/C][C]-1.21652005368502[/C][/ROW]
[ROW][C]101[/C][C]11[/C][C]10.8328313009612[/C][C]0.167168699038784[/C][/ROW]
[ROW][C]102[/C][C]4[/C][C]5.97962920214694[/C][C]-1.97962920214694[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]15.4159912399094[/C][C]0.584008760090567[/C][/ROW]
[ROW][C]104[/C][C]10[/C][C]10.5781720296904[/C][C]-0.578172029690445[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]12.7290196556716[/C][C]0.270980344328352[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]14.0350926946725[/C][C]-0.0350926946724795[/C][/ROW]
[ROW][C]107[/C][C]7[/C][C]9.93496221807539[/C][C]-2.93496221807539[/C][/ROW]
[ROW][C]108[/C][C]12[/C][C]12.5136356699401[/C][C]-0.513635669940068[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]11.3527716450039[/C][C]0.647228354996094[/C][/ROW]
[ROW][C]110[/C][C]13[/C][C]13.3254509847209[/C][C]-0.325450984720871[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]12.322583251833[/C][C]2.67741674816699[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]10.5762749582734[/C][C]1.4237250417266[/C][/ROW]
[ROW][C]113[/C][C]8[/C][C]11.163079291537[/C][C]-3.16307929153696[/C][/ROW]
[ROW][C]114[/C][C]10[/C][C]14.1591215292839[/C][C]-4.15912152928388[/C][/ROW]
[ROW][C]115[/C][C]16[/C][C]14.5285009550247[/C][C]1.47149904497526[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]13.0569007304688[/C][C]-0.0569007304688109[/C][/ROW]
[ROW][C]117[/C][C]9[/C][C]9.49971446490676[/C][C]-0.499714464906765[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.5010345744874[/C][C]0.498965425512618[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]12.5242577920339[/C][C]1.47574220796612[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110454&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110454&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
11311.00457460906761.99542539093237
21210.55043008913781.44956991086223
31513.21186388654731.78813611345268
41211.57594041426630.424059585733664
51011.9464928190928-1.94649281909279
6910.4848107042366-1.48481070423656
71211.9791405952060.020859404794037
8118.922153711065782.07784628893422
91112.1455245684748-1.14552456847483
101112.0181130072561-1.01811300725606
111513.50091713319811.49908286680186
12710.7292190066368-3.72921900663677
131112.0703821396859-1.07038213968595
14119.573334288586061.42666571141394
151011.3067531002422-1.30675310024216
161413.41864709487950.581352905120548
17108.06481950813171.9351804918683
1867.94486080504862-1.94486080504862
19117.794809006139573.20519099386043
201514.30001697085920.699983029140782
211413.2165200536850.783479946314983
22913.7749318041153-4.77493180411528
231311.91756287639821.08243712360175
241610.4926722596685.50732774033198
25137.94759031913415.0524096808659
261212.2790439372672-0.279043937267188
271414.6939977169802-0.693997716980227
28119.736930341557971.26306965844204
29910.1658794665605-1.16587946656046
301614.01138955468931.98861044531072
311212.2770206559694-0.277020655969426
32108.471717002996681.52828299700332
331313.0171515912932-0.0171515912932296
341615.44370607688760.556293923112422
3559.02814785028552-4.02814785028552
36810.4823883456822-2.48238834568223
371112.4819376897124-1.48193768971243
381614.58326163540491.41673836459509
391712.89893704513714.10106295486291
4097.860301892766241.13969810723376
41911.9557965105435-2.95579651054349
421314.244930811429-1.24493081142901
43610.0061701161472-4.00617011614722
441212.1396686008041-0.139668600804067
45811.6243632806534-3.62436328065335
461412.16109953182981.83890046817019
471212.2204693672247-0.220469367224733
481110.52116376755920.478836232440766
491614.436813086781.56318691322003
5089.88974947853361-1.88974947853361
511514.91204265500780.0879573449922338
5279.07804701073926-2.07804701073926
531613.96992984476812.03007015523187
541414.0174721880548-0.0174721880547607
55910.2443822917313-1.24438229173134
561412.03073766976821.96926233023179
571113.8863042732833-2.88630427328328
581511.99947131079153.00052868920854
591512.33430013127292.66569986872715
601312.03073766976820.969262330231787
611111.8085286204357-0.808528620435679
621113.686838705269-2.68683870526898
631212.8274550809735-0.827455080973473
641214.1240710013144-2.12407100131443
651211.98426639152270.0157336084772647
661212.2136697216109-0.213669721610937
671410.43301453906243.56698546093765
6867.46048032476614-1.46048032476614
6979.50174736952451-2.50174736952451
701413.73854087002610.261459129973863
711011.0073354277487-1.00733542774869
72138.188078057030874.81192194296913
731212.7978933403962-0.797893340396246
7499.28824111182083-0.288241111820833
751614.84782091793341.15217908206664
76109.88950902239930.110490977600702
771614.54829351768861.45170648231139
781513.34379209835121.65620790164884
7987.711110498228170.288889501771825
801112.7498386456421-1.74983864564207
811312.19126567536630.808734324633683
821615.97448430596330.025515694036669
831415.05650612315-1.05650612315003
84910.0276436452936-1.02764364529357
8589.95083348958265-1.95083348958265
86810.9682434246756-2.96824342467563
871111.8776732775281-0.877673277528131
881213.6802688662808-1.68026886628082
891413.63843538650250.361564613497518
901514.22471253021560.77528746978441
911613.71774729804292.28225270195712
921614.88891465914871.11108534085131
931112.5885162028708-1.58851620287077
941413.58863498652180.411365013478156
951410.65707572469413.34292427530594
961211.09165031032130.908349689678733
971313.1369529315639-0.136952931563935
981210.787008483611.21299151639001
991615.52982200395760.470177996042445
1001213.216520053685-1.21652005368502
1011110.83283130096120.167168699038784
10245.97962920214694-1.97962920214694
1031615.41599123990940.584008760090567
1041010.5781720296904-0.578172029690445
1051312.72901965567160.270980344328352
1061414.0350926946725-0.0350926946724795
10779.93496221807539-2.93496221807539
1081212.5136356699401-0.513635669940068
1091211.35277164500390.647228354996094
1101313.3254509847209-0.325450984720871
1111512.3225832518332.67741674816699
1121210.57627495827341.4237250417266
113811.163079291537-3.16307929153696
1141014.1591215292839-4.15912152928388
1151614.52850095502471.47149904497526
1161313.0569007304688-0.0569007304688109
11799.49971446490676-0.499714464906765
1181413.50103457448740.498965425512618
1191412.52425779203391.47574220796612






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.4817838182706710.9635676365413420.518216181729329
160.448354354720380.8967087094407610.55164564527962
170.3131467632976560.6262935265953110.686853236702344
180.4502288487318320.9004576974636640.549771151268168
190.4070970007906260.8141940015812530.592902999209374
200.3520445759176780.7040891518353560.647955424082322
210.2569887979699830.5139775959399660.743011202030017
220.4606333577347870.9212667154695740.539366642265213
230.3760603551619370.7521207103238740.623939644838063
240.5688259183005190.8623481633989630.431174081699481
250.8356425691315070.3287148617369860.164357430868493
260.8846969713461130.2306060573077740.115303028653887
270.8455982970737650.3088034058524710.154401702926235
280.807133631626290.3857327367474180.192866368373709
290.8211403654607810.3577192690784380.178859634539219
300.795357237495630.409285525008740.20464276250437
310.7596328192578560.4807343614842880.240367180742144
320.7302373400453080.5395253199093840.269762659954692
330.6932363519590750.613527296081850.306763648040925
340.6641942329592590.6716115340814820.335805767040741
350.885893914972570.228212170054860.11410608502743
360.8899504245382610.2200991509234770.110049575461739
370.8628413347219060.2743173305561890.137158665278094
380.8437821299766910.3124357400466180.156217870023309
390.8813640143756280.2372719712487440.118635985624372
400.9109939680009050.1780120639981890.0890060319990945
410.9389296755158280.1221406489683430.0610703244841716
420.9346696607121870.1306606785756260.0653303392878131
430.9604860704034270.0790278591931450.0395139295965725
440.946290204164750.1074195916704990.0537097958352495
450.9801672237235320.03966555255293510.0198327762764676
460.9778031287234930.04439374255301430.0221968712765072
470.9688127574422120.06237448511557530.0311872425577877
480.9588812166258860.08223756674822780.0411187833741139
490.9580224426542510.08395511469149770.0419775573457488
500.9598665858108920.08026682837821620.0401334141891081
510.945601461628080.1087970767438390.0543985383719195
520.9397020314806330.1205959370387340.060297968519367
530.949526919028540.1009461619429210.0504730809714605
540.9338677622447410.1322644755105170.0661322377552587
550.9225930836131560.1548138327736890.0774069163868445
560.9210484410190650.1579031179618690.0789515589809345
570.942511004379340.1149779912413180.0574889956206589
580.965788710395410.06842257920918040.0342112896045902
590.9823025304005470.03539493919890610.017697469599453
600.9766684192607340.04666316147853290.0233315807392664
610.9691946293864450.06161074122711020.0308053706135551
620.9748071552930250.05038568941395040.0251928447069752
630.9684993528038230.06300129439235420.0315006471961771
640.9706570811616480.05868583767670510.0293429188383525
650.959703731216060.08059253756788250.0402962687839413
660.9460063322671040.1079873354657910.0539936677328956
670.9742457231194820.05150855376103670.0257542768805184
680.9690433720098270.06191325598034580.0309566279901729
690.9673861595604180.06522768087916330.0326138404395817
700.9548783671145640.09024326577087280.0451216328854364
710.9437377680009550.112524463998090.0562622319990448
720.9910794378559650.01784112428806960.00892056214403479
730.9877350127527990.02452997449440260.0122649872472013
740.9818625684924060.03627486301518860.0181374315075943
750.9753380526768350.04932389464633030.0246619473231652
760.9659518391120720.06809632177585640.0340481608879282
770.9631379705992010.07372405880159690.0368620294007985
780.9596730715166480.0806538569667040.040326928483352
790.9435202188660270.1129595622679460.0564797811339731
800.932203897091330.1355922058173380.0677961029086692
810.9158278420885730.1683443158228530.0841721579114266
820.917592154686350.1648156906273010.0824078453136506
830.889458755619020.221082488761960.11054124438098
840.8557424907288310.2885150185423370.144257509271169
850.8403083749490210.3193832501019580.159691625050979
860.9372310395072120.1255379209855770.0627689604927883
870.913042799365020.1739144012699610.0869572006349803
880.9434050262581430.1131899474837140.0565949737418568
890.9199211413286260.1601577173427480.0800788586713739
900.8916376308136830.2167247383726330.108362369186317
910.8888827858705380.2222344282589230.111117214129462
920.84567904045110.30864191909780.1543209595489
930.7956264284452810.4087471431094380.204373571554719
940.7349381876441090.5301236247117820.265061812355891
950.8089198209293890.3821603581412230.191080179070611
960.7563919593231860.4872160813536280.243608040676814
970.6731705548356990.6536588903286020.326829445164301
980.6705649098149130.6588701803701750.329435090185087
990.7148186590806860.5703626818386280.285181340919314
1000.6741456034341910.6517087931316180.325854396565809
1010.6674175349563490.6651649300873010.332582465043651
1020.5441428814449770.9117142371100460.455857118555023
1030.4332238013343560.8664476026687120.566776198665644
1040.282017433956290.564034867912580.71798256604371

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.481783818270671 & 0.963567636541342 & 0.518216181729329 \tabularnewline
16 & 0.44835435472038 & 0.896708709440761 & 0.55164564527962 \tabularnewline
17 & 0.313146763297656 & 0.626293526595311 & 0.686853236702344 \tabularnewline
18 & 0.450228848731832 & 0.900457697463664 & 0.549771151268168 \tabularnewline
19 & 0.407097000790626 & 0.814194001581253 & 0.592902999209374 \tabularnewline
20 & 0.352044575917678 & 0.704089151835356 & 0.647955424082322 \tabularnewline
21 & 0.256988797969983 & 0.513977595939966 & 0.743011202030017 \tabularnewline
22 & 0.460633357734787 & 0.921266715469574 & 0.539366642265213 \tabularnewline
23 & 0.376060355161937 & 0.752120710323874 & 0.623939644838063 \tabularnewline
24 & 0.568825918300519 & 0.862348163398963 & 0.431174081699481 \tabularnewline
25 & 0.835642569131507 & 0.328714861736986 & 0.164357430868493 \tabularnewline
26 & 0.884696971346113 & 0.230606057307774 & 0.115303028653887 \tabularnewline
27 & 0.845598297073765 & 0.308803405852471 & 0.154401702926235 \tabularnewline
28 & 0.80713363162629 & 0.385732736747418 & 0.192866368373709 \tabularnewline
29 & 0.821140365460781 & 0.357719269078438 & 0.178859634539219 \tabularnewline
30 & 0.79535723749563 & 0.40928552500874 & 0.20464276250437 \tabularnewline
31 & 0.759632819257856 & 0.480734361484288 & 0.240367180742144 \tabularnewline
32 & 0.730237340045308 & 0.539525319909384 & 0.269762659954692 \tabularnewline
33 & 0.693236351959075 & 0.61352729608185 & 0.306763648040925 \tabularnewline
34 & 0.664194232959259 & 0.671611534081482 & 0.335805767040741 \tabularnewline
35 & 0.88589391497257 & 0.22821217005486 & 0.11410608502743 \tabularnewline
36 & 0.889950424538261 & 0.220099150923477 & 0.110049575461739 \tabularnewline
37 & 0.862841334721906 & 0.274317330556189 & 0.137158665278094 \tabularnewline
38 & 0.843782129976691 & 0.312435740046618 & 0.156217870023309 \tabularnewline
39 & 0.881364014375628 & 0.237271971248744 & 0.118635985624372 \tabularnewline
40 & 0.910993968000905 & 0.178012063998189 & 0.0890060319990945 \tabularnewline
41 & 0.938929675515828 & 0.122140648968343 & 0.0610703244841716 \tabularnewline
42 & 0.934669660712187 & 0.130660678575626 & 0.0653303392878131 \tabularnewline
43 & 0.960486070403427 & 0.079027859193145 & 0.0395139295965725 \tabularnewline
44 & 0.94629020416475 & 0.107419591670499 & 0.0537097958352495 \tabularnewline
45 & 0.980167223723532 & 0.0396655525529351 & 0.0198327762764676 \tabularnewline
46 & 0.977803128723493 & 0.0443937425530143 & 0.0221968712765072 \tabularnewline
47 & 0.968812757442212 & 0.0623744851155753 & 0.0311872425577877 \tabularnewline
48 & 0.958881216625886 & 0.0822375667482278 & 0.0411187833741139 \tabularnewline
49 & 0.958022442654251 & 0.0839551146914977 & 0.0419775573457488 \tabularnewline
50 & 0.959866585810892 & 0.0802668283782162 & 0.0401334141891081 \tabularnewline
51 & 0.94560146162808 & 0.108797076743839 & 0.0543985383719195 \tabularnewline
52 & 0.939702031480633 & 0.120595937038734 & 0.060297968519367 \tabularnewline
53 & 0.94952691902854 & 0.100946161942921 & 0.0504730809714605 \tabularnewline
54 & 0.933867762244741 & 0.132264475510517 & 0.0661322377552587 \tabularnewline
55 & 0.922593083613156 & 0.154813832773689 & 0.0774069163868445 \tabularnewline
56 & 0.921048441019065 & 0.157903117961869 & 0.0789515589809345 \tabularnewline
57 & 0.94251100437934 & 0.114977991241318 & 0.0574889956206589 \tabularnewline
58 & 0.96578871039541 & 0.0684225792091804 & 0.0342112896045902 \tabularnewline
59 & 0.982302530400547 & 0.0353949391989061 & 0.017697469599453 \tabularnewline
60 & 0.976668419260734 & 0.0466631614785329 & 0.0233315807392664 \tabularnewline
61 & 0.969194629386445 & 0.0616107412271102 & 0.0308053706135551 \tabularnewline
62 & 0.974807155293025 & 0.0503856894139504 & 0.0251928447069752 \tabularnewline
63 & 0.968499352803823 & 0.0630012943923542 & 0.0315006471961771 \tabularnewline
64 & 0.970657081161648 & 0.0586858376767051 & 0.0293429188383525 \tabularnewline
65 & 0.95970373121606 & 0.0805925375678825 & 0.0402962687839413 \tabularnewline
66 & 0.946006332267104 & 0.107987335465791 & 0.0539936677328956 \tabularnewline
67 & 0.974245723119482 & 0.0515085537610367 & 0.0257542768805184 \tabularnewline
68 & 0.969043372009827 & 0.0619132559803458 & 0.0309566279901729 \tabularnewline
69 & 0.967386159560418 & 0.0652276808791633 & 0.0326138404395817 \tabularnewline
70 & 0.954878367114564 & 0.0902432657708728 & 0.0451216328854364 \tabularnewline
71 & 0.943737768000955 & 0.11252446399809 & 0.0562622319990448 \tabularnewline
72 & 0.991079437855965 & 0.0178411242880696 & 0.00892056214403479 \tabularnewline
73 & 0.987735012752799 & 0.0245299744944026 & 0.0122649872472013 \tabularnewline
74 & 0.981862568492406 & 0.0362748630151886 & 0.0181374315075943 \tabularnewline
75 & 0.975338052676835 & 0.0493238946463303 & 0.0246619473231652 \tabularnewline
76 & 0.965951839112072 & 0.0680963217758564 & 0.0340481608879282 \tabularnewline
77 & 0.963137970599201 & 0.0737240588015969 & 0.0368620294007985 \tabularnewline
78 & 0.959673071516648 & 0.080653856966704 & 0.040326928483352 \tabularnewline
79 & 0.943520218866027 & 0.112959562267946 & 0.0564797811339731 \tabularnewline
80 & 0.93220389709133 & 0.135592205817338 & 0.0677961029086692 \tabularnewline
81 & 0.915827842088573 & 0.168344315822853 & 0.0841721579114266 \tabularnewline
82 & 0.91759215468635 & 0.164815690627301 & 0.0824078453136506 \tabularnewline
83 & 0.88945875561902 & 0.22108248876196 & 0.11054124438098 \tabularnewline
84 & 0.855742490728831 & 0.288515018542337 & 0.144257509271169 \tabularnewline
85 & 0.840308374949021 & 0.319383250101958 & 0.159691625050979 \tabularnewline
86 & 0.937231039507212 & 0.125537920985577 & 0.0627689604927883 \tabularnewline
87 & 0.91304279936502 & 0.173914401269961 & 0.0869572006349803 \tabularnewline
88 & 0.943405026258143 & 0.113189947483714 & 0.0565949737418568 \tabularnewline
89 & 0.919921141328626 & 0.160157717342748 & 0.0800788586713739 \tabularnewline
90 & 0.891637630813683 & 0.216724738372633 & 0.108362369186317 \tabularnewline
91 & 0.888882785870538 & 0.222234428258923 & 0.111117214129462 \tabularnewline
92 & 0.8456790404511 & 0.3086419190978 & 0.1543209595489 \tabularnewline
93 & 0.795626428445281 & 0.408747143109438 & 0.204373571554719 \tabularnewline
94 & 0.734938187644109 & 0.530123624711782 & 0.265061812355891 \tabularnewline
95 & 0.808919820929389 & 0.382160358141223 & 0.191080179070611 \tabularnewline
96 & 0.756391959323186 & 0.487216081353628 & 0.243608040676814 \tabularnewline
97 & 0.673170554835699 & 0.653658890328602 & 0.326829445164301 \tabularnewline
98 & 0.670564909814913 & 0.658870180370175 & 0.329435090185087 \tabularnewline
99 & 0.714818659080686 & 0.570362681838628 & 0.285181340919314 \tabularnewline
100 & 0.674145603434191 & 0.651708793131618 & 0.325854396565809 \tabularnewline
101 & 0.667417534956349 & 0.665164930087301 & 0.332582465043651 \tabularnewline
102 & 0.544142881444977 & 0.911714237110046 & 0.455857118555023 \tabularnewline
103 & 0.433223801334356 & 0.866447602668712 & 0.566776198665644 \tabularnewline
104 & 0.28201743395629 & 0.56403486791258 & 0.71798256604371 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110454&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]0.481783818270671[/C][C]0.963567636541342[/C][C]0.518216181729329[/C][/ROW]
[ROW][C]16[/C][C]0.44835435472038[/C][C]0.896708709440761[/C][C]0.55164564527962[/C][/ROW]
[ROW][C]17[/C][C]0.313146763297656[/C][C]0.626293526595311[/C][C]0.686853236702344[/C][/ROW]
[ROW][C]18[/C][C]0.450228848731832[/C][C]0.900457697463664[/C][C]0.549771151268168[/C][/ROW]
[ROW][C]19[/C][C]0.407097000790626[/C][C]0.814194001581253[/C][C]0.592902999209374[/C][/ROW]
[ROW][C]20[/C][C]0.352044575917678[/C][C]0.704089151835356[/C][C]0.647955424082322[/C][/ROW]
[ROW][C]21[/C][C]0.256988797969983[/C][C]0.513977595939966[/C][C]0.743011202030017[/C][/ROW]
[ROW][C]22[/C][C]0.460633357734787[/C][C]0.921266715469574[/C][C]0.539366642265213[/C][/ROW]
[ROW][C]23[/C][C]0.376060355161937[/C][C]0.752120710323874[/C][C]0.623939644838063[/C][/ROW]
[ROW][C]24[/C][C]0.568825918300519[/C][C]0.862348163398963[/C][C]0.431174081699481[/C][/ROW]
[ROW][C]25[/C][C]0.835642569131507[/C][C]0.328714861736986[/C][C]0.164357430868493[/C][/ROW]
[ROW][C]26[/C][C]0.884696971346113[/C][C]0.230606057307774[/C][C]0.115303028653887[/C][/ROW]
[ROW][C]27[/C][C]0.845598297073765[/C][C]0.308803405852471[/C][C]0.154401702926235[/C][/ROW]
[ROW][C]28[/C][C]0.80713363162629[/C][C]0.385732736747418[/C][C]0.192866368373709[/C][/ROW]
[ROW][C]29[/C][C]0.821140365460781[/C][C]0.357719269078438[/C][C]0.178859634539219[/C][/ROW]
[ROW][C]30[/C][C]0.79535723749563[/C][C]0.40928552500874[/C][C]0.20464276250437[/C][/ROW]
[ROW][C]31[/C][C]0.759632819257856[/C][C]0.480734361484288[/C][C]0.240367180742144[/C][/ROW]
[ROW][C]32[/C][C]0.730237340045308[/C][C]0.539525319909384[/C][C]0.269762659954692[/C][/ROW]
[ROW][C]33[/C][C]0.693236351959075[/C][C]0.61352729608185[/C][C]0.306763648040925[/C][/ROW]
[ROW][C]34[/C][C]0.664194232959259[/C][C]0.671611534081482[/C][C]0.335805767040741[/C][/ROW]
[ROW][C]35[/C][C]0.88589391497257[/C][C]0.22821217005486[/C][C]0.11410608502743[/C][/ROW]
[ROW][C]36[/C][C]0.889950424538261[/C][C]0.220099150923477[/C][C]0.110049575461739[/C][/ROW]
[ROW][C]37[/C][C]0.862841334721906[/C][C]0.274317330556189[/C][C]0.137158665278094[/C][/ROW]
[ROW][C]38[/C][C]0.843782129976691[/C][C]0.312435740046618[/C][C]0.156217870023309[/C][/ROW]
[ROW][C]39[/C][C]0.881364014375628[/C][C]0.237271971248744[/C][C]0.118635985624372[/C][/ROW]
[ROW][C]40[/C][C]0.910993968000905[/C][C]0.178012063998189[/C][C]0.0890060319990945[/C][/ROW]
[ROW][C]41[/C][C]0.938929675515828[/C][C]0.122140648968343[/C][C]0.0610703244841716[/C][/ROW]
[ROW][C]42[/C][C]0.934669660712187[/C][C]0.130660678575626[/C][C]0.0653303392878131[/C][/ROW]
[ROW][C]43[/C][C]0.960486070403427[/C][C]0.079027859193145[/C][C]0.0395139295965725[/C][/ROW]
[ROW][C]44[/C][C]0.94629020416475[/C][C]0.107419591670499[/C][C]0.0537097958352495[/C][/ROW]
[ROW][C]45[/C][C]0.980167223723532[/C][C]0.0396655525529351[/C][C]0.0198327762764676[/C][/ROW]
[ROW][C]46[/C][C]0.977803128723493[/C][C]0.0443937425530143[/C][C]0.0221968712765072[/C][/ROW]
[ROW][C]47[/C][C]0.968812757442212[/C][C]0.0623744851155753[/C][C]0.0311872425577877[/C][/ROW]
[ROW][C]48[/C][C]0.958881216625886[/C][C]0.0822375667482278[/C][C]0.0411187833741139[/C][/ROW]
[ROW][C]49[/C][C]0.958022442654251[/C][C]0.0839551146914977[/C][C]0.0419775573457488[/C][/ROW]
[ROW][C]50[/C][C]0.959866585810892[/C][C]0.0802668283782162[/C][C]0.0401334141891081[/C][/ROW]
[ROW][C]51[/C][C]0.94560146162808[/C][C]0.108797076743839[/C][C]0.0543985383719195[/C][/ROW]
[ROW][C]52[/C][C]0.939702031480633[/C][C]0.120595937038734[/C][C]0.060297968519367[/C][/ROW]
[ROW][C]53[/C][C]0.94952691902854[/C][C]0.100946161942921[/C][C]0.0504730809714605[/C][/ROW]
[ROW][C]54[/C][C]0.933867762244741[/C][C]0.132264475510517[/C][C]0.0661322377552587[/C][/ROW]
[ROW][C]55[/C][C]0.922593083613156[/C][C]0.154813832773689[/C][C]0.0774069163868445[/C][/ROW]
[ROW][C]56[/C][C]0.921048441019065[/C][C]0.157903117961869[/C][C]0.0789515589809345[/C][/ROW]
[ROW][C]57[/C][C]0.94251100437934[/C][C]0.114977991241318[/C][C]0.0574889956206589[/C][/ROW]
[ROW][C]58[/C][C]0.96578871039541[/C][C]0.0684225792091804[/C][C]0.0342112896045902[/C][/ROW]
[ROW][C]59[/C][C]0.982302530400547[/C][C]0.0353949391989061[/C][C]0.017697469599453[/C][/ROW]
[ROW][C]60[/C][C]0.976668419260734[/C][C]0.0466631614785329[/C][C]0.0233315807392664[/C][/ROW]
[ROW][C]61[/C][C]0.969194629386445[/C][C]0.0616107412271102[/C][C]0.0308053706135551[/C][/ROW]
[ROW][C]62[/C][C]0.974807155293025[/C][C]0.0503856894139504[/C][C]0.0251928447069752[/C][/ROW]
[ROW][C]63[/C][C]0.968499352803823[/C][C]0.0630012943923542[/C][C]0.0315006471961771[/C][/ROW]
[ROW][C]64[/C][C]0.970657081161648[/C][C]0.0586858376767051[/C][C]0.0293429188383525[/C][/ROW]
[ROW][C]65[/C][C]0.95970373121606[/C][C]0.0805925375678825[/C][C]0.0402962687839413[/C][/ROW]
[ROW][C]66[/C][C]0.946006332267104[/C][C]0.107987335465791[/C][C]0.0539936677328956[/C][/ROW]
[ROW][C]67[/C][C]0.974245723119482[/C][C]0.0515085537610367[/C][C]0.0257542768805184[/C][/ROW]
[ROW][C]68[/C][C]0.969043372009827[/C][C]0.0619132559803458[/C][C]0.0309566279901729[/C][/ROW]
[ROW][C]69[/C][C]0.967386159560418[/C][C]0.0652276808791633[/C][C]0.0326138404395817[/C][/ROW]
[ROW][C]70[/C][C]0.954878367114564[/C][C]0.0902432657708728[/C][C]0.0451216328854364[/C][/ROW]
[ROW][C]71[/C][C]0.943737768000955[/C][C]0.11252446399809[/C][C]0.0562622319990448[/C][/ROW]
[ROW][C]72[/C][C]0.991079437855965[/C][C]0.0178411242880696[/C][C]0.00892056214403479[/C][/ROW]
[ROW][C]73[/C][C]0.987735012752799[/C][C]0.0245299744944026[/C][C]0.0122649872472013[/C][/ROW]
[ROW][C]74[/C][C]0.981862568492406[/C][C]0.0362748630151886[/C][C]0.0181374315075943[/C][/ROW]
[ROW][C]75[/C][C]0.975338052676835[/C][C]0.0493238946463303[/C][C]0.0246619473231652[/C][/ROW]
[ROW][C]76[/C][C]0.965951839112072[/C][C]0.0680963217758564[/C][C]0.0340481608879282[/C][/ROW]
[ROW][C]77[/C][C]0.963137970599201[/C][C]0.0737240588015969[/C][C]0.0368620294007985[/C][/ROW]
[ROW][C]78[/C][C]0.959673071516648[/C][C]0.080653856966704[/C][C]0.040326928483352[/C][/ROW]
[ROW][C]79[/C][C]0.943520218866027[/C][C]0.112959562267946[/C][C]0.0564797811339731[/C][/ROW]
[ROW][C]80[/C][C]0.93220389709133[/C][C]0.135592205817338[/C][C]0.0677961029086692[/C][/ROW]
[ROW][C]81[/C][C]0.915827842088573[/C][C]0.168344315822853[/C][C]0.0841721579114266[/C][/ROW]
[ROW][C]82[/C][C]0.91759215468635[/C][C]0.164815690627301[/C][C]0.0824078453136506[/C][/ROW]
[ROW][C]83[/C][C]0.88945875561902[/C][C]0.22108248876196[/C][C]0.11054124438098[/C][/ROW]
[ROW][C]84[/C][C]0.855742490728831[/C][C]0.288515018542337[/C][C]0.144257509271169[/C][/ROW]
[ROW][C]85[/C][C]0.840308374949021[/C][C]0.319383250101958[/C][C]0.159691625050979[/C][/ROW]
[ROW][C]86[/C][C]0.937231039507212[/C][C]0.125537920985577[/C][C]0.0627689604927883[/C][/ROW]
[ROW][C]87[/C][C]0.91304279936502[/C][C]0.173914401269961[/C][C]0.0869572006349803[/C][/ROW]
[ROW][C]88[/C][C]0.943405026258143[/C][C]0.113189947483714[/C][C]0.0565949737418568[/C][/ROW]
[ROW][C]89[/C][C]0.919921141328626[/C][C]0.160157717342748[/C][C]0.0800788586713739[/C][/ROW]
[ROW][C]90[/C][C]0.891637630813683[/C][C]0.216724738372633[/C][C]0.108362369186317[/C][/ROW]
[ROW][C]91[/C][C]0.888882785870538[/C][C]0.222234428258923[/C][C]0.111117214129462[/C][/ROW]
[ROW][C]92[/C][C]0.8456790404511[/C][C]0.3086419190978[/C][C]0.1543209595489[/C][/ROW]
[ROW][C]93[/C][C]0.795626428445281[/C][C]0.408747143109438[/C][C]0.204373571554719[/C][/ROW]
[ROW][C]94[/C][C]0.734938187644109[/C][C]0.530123624711782[/C][C]0.265061812355891[/C][/ROW]
[ROW][C]95[/C][C]0.808919820929389[/C][C]0.382160358141223[/C][C]0.191080179070611[/C][/ROW]
[ROW][C]96[/C][C]0.756391959323186[/C][C]0.487216081353628[/C][C]0.243608040676814[/C][/ROW]
[ROW][C]97[/C][C]0.673170554835699[/C][C]0.653658890328602[/C][C]0.326829445164301[/C][/ROW]
[ROW][C]98[/C][C]0.670564909814913[/C][C]0.658870180370175[/C][C]0.329435090185087[/C][/ROW]
[ROW][C]99[/C][C]0.714818659080686[/C][C]0.570362681838628[/C][C]0.285181340919314[/C][/ROW]
[ROW][C]100[/C][C]0.674145603434191[/C][C]0.651708793131618[/C][C]0.325854396565809[/C][/ROW]
[ROW][C]101[/C][C]0.667417534956349[/C][C]0.665164930087301[/C][C]0.332582465043651[/C][/ROW]
[ROW][C]102[/C][C]0.544142881444977[/C][C]0.911714237110046[/C][C]0.455857118555023[/C][/ROW]
[ROW][C]103[/C][C]0.433223801334356[/C][C]0.866447602668712[/C][C]0.566776198665644[/C][/ROW]
[ROW][C]104[/C][C]0.28201743395629[/C][C]0.56403486791258[/C][C]0.71798256604371[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110454&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110454&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
150.4817838182706710.9635676365413420.518216181729329
160.448354354720380.8967087094407610.55164564527962
170.3131467632976560.6262935265953110.686853236702344
180.4502288487318320.9004576974636640.549771151268168
190.4070970007906260.8141940015812530.592902999209374
200.3520445759176780.7040891518353560.647955424082322
210.2569887979699830.5139775959399660.743011202030017
220.4606333577347870.9212667154695740.539366642265213
230.3760603551619370.7521207103238740.623939644838063
240.5688259183005190.8623481633989630.431174081699481
250.8356425691315070.3287148617369860.164357430868493
260.8846969713461130.2306060573077740.115303028653887
270.8455982970737650.3088034058524710.154401702926235
280.807133631626290.3857327367474180.192866368373709
290.8211403654607810.3577192690784380.178859634539219
300.795357237495630.409285525008740.20464276250437
310.7596328192578560.4807343614842880.240367180742144
320.7302373400453080.5395253199093840.269762659954692
330.6932363519590750.613527296081850.306763648040925
340.6641942329592590.6716115340814820.335805767040741
350.885893914972570.228212170054860.11410608502743
360.8899504245382610.2200991509234770.110049575461739
370.8628413347219060.2743173305561890.137158665278094
380.8437821299766910.3124357400466180.156217870023309
390.8813640143756280.2372719712487440.118635985624372
400.9109939680009050.1780120639981890.0890060319990945
410.9389296755158280.1221406489683430.0610703244841716
420.9346696607121870.1306606785756260.0653303392878131
430.9604860704034270.0790278591931450.0395139295965725
440.946290204164750.1074195916704990.0537097958352495
450.9801672237235320.03966555255293510.0198327762764676
460.9778031287234930.04439374255301430.0221968712765072
470.9688127574422120.06237448511557530.0311872425577877
480.9588812166258860.08223756674822780.0411187833741139
490.9580224426542510.08395511469149770.0419775573457488
500.9598665858108920.08026682837821620.0401334141891081
510.945601461628080.1087970767438390.0543985383719195
520.9397020314806330.1205959370387340.060297968519367
530.949526919028540.1009461619429210.0504730809714605
540.9338677622447410.1322644755105170.0661322377552587
550.9225930836131560.1548138327736890.0774069163868445
560.9210484410190650.1579031179618690.0789515589809345
570.942511004379340.1149779912413180.0574889956206589
580.965788710395410.06842257920918040.0342112896045902
590.9823025304005470.03539493919890610.017697469599453
600.9766684192607340.04666316147853290.0233315807392664
610.9691946293864450.06161074122711020.0308053706135551
620.9748071552930250.05038568941395040.0251928447069752
630.9684993528038230.06300129439235420.0315006471961771
640.9706570811616480.05868583767670510.0293429188383525
650.959703731216060.08059253756788250.0402962687839413
660.9460063322671040.1079873354657910.0539936677328956
670.9742457231194820.05150855376103670.0257542768805184
680.9690433720098270.06191325598034580.0309566279901729
690.9673861595604180.06522768087916330.0326138404395817
700.9548783671145640.09024326577087280.0451216328854364
710.9437377680009550.112524463998090.0562622319990448
720.9910794378559650.01784112428806960.00892056214403479
730.9877350127527990.02452997449440260.0122649872472013
740.9818625684924060.03627486301518860.0181374315075943
750.9753380526768350.04932389464633030.0246619473231652
760.9659518391120720.06809632177585640.0340481608879282
770.9631379705992010.07372405880159690.0368620294007985
780.9596730715166480.0806538569667040.040326928483352
790.9435202188660270.1129595622679460.0564797811339731
800.932203897091330.1355922058173380.0677961029086692
810.9158278420885730.1683443158228530.0841721579114266
820.917592154686350.1648156906273010.0824078453136506
830.889458755619020.221082488761960.11054124438098
840.8557424907288310.2885150185423370.144257509271169
850.8403083749490210.3193832501019580.159691625050979
860.9372310395072120.1255379209855770.0627689604927883
870.913042799365020.1739144012699610.0869572006349803
880.9434050262581430.1131899474837140.0565949737418568
890.9199211413286260.1601577173427480.0800788586713739
900.8916376308136830.2167247383726330.108362369186317
910.8888827858705380.2222344282589230.111117214129462
920.84567904045110.30864191909780.1543209595489
930.7956264284452810.4087471431094380.204373571554719
940.7349381876441090.5301236247117820.265061812355891
950.8089198209293890.3821603581412230.191080179070611
960.7563919593231860.4872160813536280.243608040676814
970.6731705548356990.6536588903286020.326829445164301
980.6705649098149130.6588701803701750.329435090185087
990.7148186590806860.5703626818386280.285181340919314
1000.6741456034341910.6517087931316180.325854396565809
1010.6674175349563490.6651649300873010.332582465043651
1020.5441428814449770.9117142371100460.455857118555023
1030.4332238013343560.8664476026687120.566776198665644
1040.282017433956290.564034867912580.71798256604371






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level80.0888888888888889NOK
10% type I error level260.288888888888889NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 8 & 0.0888888888888889 & NOK \tabularnewline
10% type I error level & 26 & 0.288888888888889 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110454&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.0888888888888889[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]26[/C][C]0.288888888888889[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110454&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110454&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level80.0888888888888889NOK
10% type I error level260.288888888888889NOK


Figures (Output of Computation)

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


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


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


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


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


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


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


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


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


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


Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}