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 computationThu, 31 Oct 2013 12:19:57 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Oct/31/t13832365096dusvjgyb8amr8z.htm/, Retrieved Sun, 28 Apr 2024 20:25:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=221555, Retrieved Sun, 28 Apr 2024 20:25:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact79

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [WS7 month] [2013-10-31 16:19:57] [051b16e407d1738c1ccf7d1d6dccb24d] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
7	41	38	14	12	3
5	39	32	18	11	5
5	30	35	11	14	4
5	31	33	12	12	4
8	34	37	16	21	5
6	35	29	18	12	5
5	39	31	14	22	2
6	34	36	14	11	5
5	36	35	15	10	4
4	37	38	15	13	4
6	38	31	17	10	5
5	36	34	19	8	3
5	38	35	10	15	5
6	39	38	16	14	3
7	33	37	18	10	5
6	32	33	14	14	3
7	36	32	14	14	4
6	38	38	17	11	5
8	39	38	14	10	4
7	32	32	16	13	3
5	32	33	18	7	4
5	31	31	11	14	4
7	39	38	14	12	3
7	37	39	12	14	3
5	39	32	17	11	4
4	41	32	9	9	5
10	36	35	16	11	4
6	33	37	14	15	4
5	33	33	15	14	4
5	34	33	11	13	4
5	31	28	16	9	4
5	27	32	13	15	3
6	37	31	17	10	4
5	34	37	15	11	5
5	34	30	14	13	4
5	32	33	16	8	4
5	29	31	9	20	3
5	36	33	15	12	4
5	29	31	17	10	4
5	35	33	13	10	4
5	37	32	15	9	5
7	34	33	16	14	4
5	38	32	16	8	3
6	35	33	12	14	3
7	38	28	12	11	4
7	37	35	11	13	4
5	38	39	15	9	4
5	33	34	15	11	5
4	36	38	17	15	4
5	38	32	13	11	5
4	32	38	16	10	4
5	32	30	14	14	4
5	32	33	11	18	4
7	34	38	12	14	4
5	32	32	12	11	4
5	37	32	15	12	5
6	39	34	16	13	4
4	29	34	15	9	4
6	37	36	12	10	4
6	35	34	12	15	4
5	30	28	8	20	3
7	38	34	13	12	4
6	34	35	11	12	5
8	31	35	14	14	1
7	34	31	15	13	3
5	35	37	10	11	5
6	36	35	11	17	4
6	30	27	12	12	4
5	39	40	15	13	3
5	35	37	15	14	4
5	38	36	14	13	4
5	31	38	16	15	3
4	34	39	15	13	5
6	38	41	15	10	4
6	34	27	13	11	5
6	39	30	12	19	4
6	37	37	17	13	4
7	34	31	13	17	4
5	28	31	15	13	4
7	37	27	13	9	3
6	33	36	15	11	5
5	37	38	16	10	NA
5	35	37	15	9	5
4	37	33	16	12	4
8	32	34	15	12	4
8	33	31	14	13	5
5	38	39	15	13	4
5	33	34	14	12	4
6	29	32	13	15	3
4	33	33	7	22	4
5	31	36	17	13	4
5	36	32	13	15	3
5	35	41	15	13	5
5	32	28	14	15	5
6	29	30	13	10	5
6	39	36	16	11	4
5	37	35	12	16	4
6	35	31	14	11	4
5	37	34	17	11	4
7	32	36	15	10	4
5	38	36	17	10	4
6	37	35	12	16	4
6	36	37	16	12	5
6	32	28	11	11	4
4	33	39	15	16	4
5	40	32	9	19	3
5	38	35	16	11	5
7	41	39	15	16	4
6	36	35	10	15	3
9	43	42	10	24	2
6	30	34	15	14	5
6	31	33	11	15	4
5	32	41	13	11	5
6	32	33	14	15	1
5	37	34	18	12	5
8	37	32	16	10	5
7	33	40	14	14	3
5	34	40	14	13	4
7	33	35	14	9	5
6	38	36	14	15	5
6	33	37	12	15	3
9	31	27	14	14	4
7	38	39	15	11	5
6	37	38	15	8	4
5	33	31	15	11	4
5	31	33	13	11	4
6	39	32	17	8	5
6	44	39	17	10	4
7	33	36	19	11	5
5	35	33	15	13	4
5	32	33	13	11	4
5	28	32	9	20	4
6	40	37	15	10	4
4	27	30	15	15	3
5	37	38	15	12	4
7	32	29	16	14	5
5	28	22	11	23	3
7	34	35	14	14	4
7	30	35	11	16	3
6	35	34	15	11	4
5	31	35	13	12	3
8	32	34	15	10	3
5	30	34	16	14	5
5	30	35	14	12	5
5	31	23	15	12	5
6	40	31	16	11	5
4	32	27	16	12	5
5	36	36	11	13	4
5	32	31	12	11	4
7	35	32	9	19	4
6	38	39	16	12	5
7	42	37	13	17	5
10	34	38	16	9	4
6	35	39	12	12	4
8	35	34	9	19	4
4	33	31	13	18	5
5	36	32	13	15	3
6	32	37	14	14	4
7	33	36	19	11	5
7	34	32	13	9	5
6	32	35	12	18	5
6	34	36	13	16	5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time12 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 12 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221555&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]12 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221555&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221555&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 time12 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
Y_t [t] = + 4.76674 -0.0702613X_1t[t] + 0.0183558X_2t[t] -0.0125763X_3t[t] + 0.0243342X_4t[t] -0.0637158X_5t[t] -0.22045M1[t] -0.0262015M2[t] + 0.325625M3[t] -0.192451M4[t] + 0.0839012M5[t] + 0.146362M6[t] -0.144542M7[t] -0.203434M8[t] -0.173177M9[t] -0.080801M10[t] + 0.207269M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y_t

[t] =  +  4.76674 -0.0702613X_1t[t] +  0.0183558X_2t[t] -0.0125763X_3t[t] +  0.0243342X_4t[t] -0.0637158X_5t[t] -0.22045M1[t] -0.0262015M2[t] +  0.325625M3[t] -0.192451M4[t] +  0.0839012M5[t] +  0.146362M6[t] -0.144542M7[t] -0.203434M8[t] -0.173177M9[t] -0.080801M10[t] +  0.207269M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221555&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y_t

[t] =  +  4.76674 -0.0702613X_1t[t] +  0.0183558X_2t[t] -0.0125763X_3t[t] +  0.0243342X_4t[t] -0.0637158X_5t[t] -0.22045M1[t] -0.0262015M2[t] +  0.325625M3[t] -0.192451M4[t] +  0.0839012M5[t] +  0.146362M6[t] -0.144542M7[t] -0.203434M8[t] -0.173177M9[t] -0.080801M10[t] +  0.207269M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221555&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221555&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
Y_t [t] = + 4.76674 -0.0702613X_1t[t] + 0.0183558X_2t[t] -0.0125763X_3t[t] + 0.0243342X_4t[t] -0.0637158X_5t[t] -0.22045M1[t] -0.0262015M2[t] + 0.325625M3[t] -0.192451M4[t] + 0.0839012M5[t] + 0.146362M6[t] -0.144542M7[t] -0.203434M8[t] -0.173177M9[t] -0.080801M10[t] + 0.207269M11[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.766741.019764.6746.71368e-063.35684e-06
X_1t-0.07026130.0548355-1.2810.2021440.101072
X_2t0.01835580.02046840.89680.3713280.185664
X_3t-0.01257630.0193426-0.65020.5166080.258304
X_4t0.02433420.03269690.74420.4579460.228973
X_5t-0.06371580.024007-2.6540.008847520.00442376
M1-0.220450.30009-0.73460.4637680.231884
M2-0.02620150.30434-0.086090.9315120.465756
M30.3256250.301261.0810.2815590.14078
M4-0.1924510.303095-0.6350.5264680.263234
M50.08390120.2997010.280.7799180.389959
M60.1463620.3019810.48470.6286450.314322
M7-0.1445420.303981-0.47550.6351550.317577
M8-0.2034340.312604-0.65080.516230.258115
M9-0.1731770.306811-0.56440.5733310.286666
M10-0.0808010.31047-0.26030.795040.39752
M110.2072690.3077950.67340.5017740.250887

\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) & 4.76674 & 1.01976 & 4.674 & 6.71368e-06 & 3.35684e-06 \tabularnewline
X_1t & -0.0702613 & 0.0548355 & -1.281 & 0.202144 & 0.101072 \tabularnewline
X_2t & 0.0183558 & 0.0204684 & 0.8968 & 0.371328 & 0.185664 \tabularnewline
X_3t & -0.0125763 & 0.0193426 & -0.6502 & 0.516608 & 0.258304 \tabularnewline
X_4t & 0.0243342 & 0.0326969 & 0.7442 & 0.457946 & 0.228973 \tabularnewline
X_5t & -0.0637158 & 0.024007 & -2.654 & 0.00884752 & 0.00442376 \tabularnewline
M1 & -0.22045 & 0.30009 & -0.7346 & 0.463768 & 0.231884 \tabularnewline
M2 & -0.0262015 & 0.30434 & -0.08609 & 0.931512 & 0.465756 \tabularnewline
M3 & 0.325625 & 0.30126 & 1.081 & 0.281559 & 0.14078 \tabularnewline
M4 & -0.192451 & 0.303095 & -0.635 & 0.526468 & 0.263234 \tabularnewline
M5 & 0.0839012 & 0.299701 & 0.28 & 0.779918 & 0.389959 \tabularnewline
M6 & 0.146362 & 0.301981 & 0.4847 & 0.628645 & 0.314322 \tabularnewline
M7 & -0.144542 & 0.303981 & -0.4755 & 0.635155 & 0.317577 \tabularnewline
M8 & -0.203434 & 0.312604 & -0.6508 & 0.51623 & 0.258115 \tabularnewline
M9 & -0.173177 & 0.306811 & -0.5644 & 0.573331 & 0.286666 \tabularnewline
M10 & -0.080801 & 0.31047 & -0.2603 & 0.79504 & 0.39752 \tabularnewline
M11 & 0.207269 & 0.307795 & 0.6734 & 0.501774 & 0.250887 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221555&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]4.76674[/C][C]1.01976[/C][C]4.674[/C][C]6.71368e-06[/C][C]3.35684e-06[/C][/ROW]
[ROW][C]X_1t[/C][C]-0.0702613[/C][C]0.0548355[/C][C]-1.281[/C][C]0.202144[/C][C]0.101072[/C][/ROW]
[ROW][C]X_2t[/C][C]0.0183558[/C][C]0.0204684[/C][C]0.8968[/C][C]0.371328[/C][C]0.185664[/C][/ROW]
[ROW][C]X_3t[/C][C]-0.0125763[/C][C]0.0193426[/C][C]-0.6502[/C][C]0.516608[/C][C]0.258304[/C][/ROW]
[ROW][C]X_4t[/C][C]0.0243342[/C][C]0.0326969[/C][C]0.7442[/C][C]0.457946[/C][C]0.228973[/C][/ROW]
[ROW][C]X_5t[/C][C]-0.0637158[/C][C]0.024007[/C][C]-2.654[/C][C]0.00884752[/C][C]0.00442376[/C][/ROW]
[ROW][C]M1[/C][C]-0.22045[/C][C]0.30009[/C][C]-0.7346[/C][C]0.463768[/C][C]0.231884[/C][/ROW]
[ROW][C]M2[/C][C]-0.0262015[/C][C]0.30434[/C][C]-0.08609[/C][C]0.931512[/C][C]0.465756[/C][/ROW]
[ROW][C]M3[/C][C]0.325625[/C][C]0.30126[/C][C]1.081[/C][C]0.281559[/C][C]0.14078[/C][/ROW]
[ROW][C]M4[/C][C]-0.192451[/C][C]0.303095[/C][C]-0.635[/C][C]0.526468[/C][C]0.263234[/C][/ROW]
[ROW][C]M5[/C][C]0.0839012[/C][C]0.299701[/C][C]0.28[/C][C]0.779918[/C][C]0.389959[/C][/ROW]
[ROW][C]M6[/C][C]0.146362[/C][C]0.301981[/C][C]0.4847[/C][C]0.628645[/C][C]0.314322[/C][/ROW]
[ROW][C]M7[/C][C]-0.144542[/C][C]0.303981[/C][C]-0.4755[/C][C]0.635155[/C][C]0.317577[/C][/ROW]
[ROW][C]M8[/C][C]-0.203434[/C][C]0.312604[/C][C]-0.6508[/C][C]0.51623[/C][C]0.258115[/C][/ROW]
[ROW][C]M9[/C][C]-0.173177[/C][C]0.306811[/C][C]-0.5644[/C][C]0.573331[/C][C]0.286666[/C][/ROW]
[ROW][C]M10[/C][C]-0.080801[/C][C]0.31047[/C][C]-0.2603[/C][C]0.79504[/C][C]0.39752[/C][/ROW]
[ROW][C]M11[/C][C]0.207269[/C][C]0.307795[/C][C]0.6734[/C][C]0.501774[/C][C]0.250887[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221555&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221555&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)4.766741.019764.6746.71368e-063.35684e-06
X_1t-0.07026130.0548355-1.2810.2021440.101072
X_2t0.01835580.02046840.89680.3713280.185664
X_3t-0.01257630.0193426-0.65020.5166080.258304
X_4t0.02433420.03269690.74420.4579460.228973
X_5t-0.06371580.024007-2.6540.008847520.00442376
M1-0.220450.30009-0.73460.4637680.231884
M2-0.02620150.30434-0.086090.9315120.465756
M30.3256250.301261.0810.2815590.14078
M4-0.1924510.303095-0.6350.5264680.263234
M50.08390120.2997010.280.7799180.389959
M60.1463620.3019810.48470.6286450.314322
M7-0.1445420.303981-0.47550.6351550.317577
M8-0.2034340.312604-0.65080.516230.258115
M9-0.1731770.306811-0.56440.5733310.286666
M10-0.0808010.31047-0.26030.795040.39752
M110.2072690.3077950.67340.5017740.250887






Multiple Linear Regression - Regression Statistics
Multiple R0.398609
R-squared0.158889
Adjusted R-squared0.0654323
F-TEST (value)1.70013
F-TEST (DF numerator)16
F-TEST (DF denominator)144
p-value0.0526608
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.769519
Sum Squared Residuals85.2709

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.398609 \tabularnewline
R-squared & 0.158889 \tabularnewline
Adjusted R-squared & 0.0654323 \tabularnewline
F-TEST (value) & 1.70013 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 144 \tabularnewline
p-value & 0.0526608 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.769519 \tabularnewline
Sum Squared Residuals & 85.2709 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221555&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.398609[/C][/ROW]
[ROW][C]R-squared[/C][C]0.158889[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0654323[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.70013[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]144[/C][/ROW]
[ROW][C]p-value[/C][C]0.0526608[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.769519[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]85.2709[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221555&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221555&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 R0.398609
R-squared0.158889
Adjusted R-squared0.0654323
F-TEST (value)1.70013
F-TEST (DF numerator)16
F-TEST (DF denominator)144
p-value0.0526608
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.769519
Sum Squared Residuals85.2709






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
133.90524-0.905236
254.439810.560194
344.22721-0.227215
443.904410.0955869
553.498641.50136
654.44270.557302
723.53583-1.53583
853.952891.04711
944.19075-0.190748
1044.14286-0.142865
1154.636620.363383
1234.60127-1.60127
1353.739941.26006
1434.05427-1.05427
1554.541810.458191
1633.77374-0.773744
1744.06583-0.0658347
1854.423960.576039
1944.0016-0.00160342
2033.81746-0.81746
2144.40663-0.406627
2243.889450.11055
2334.29624-1.29624
2433.86359-0.863587
2544.22122-0.221223
2654.45520.544797
2744.29886-0.298861
2843.678080.321921
2944.16305-0.163048
3044.21024-0.210244
3144.30369-0.303688
3233.66577-0.665769
3344.23782-0.237816
3454.157540.842456
3544.38188-0.381881
3644.46742-0.467419
3733.28213-0.282126
3844.23544-0.235444
3944.66003-0.660032
4044.1296-0.129602
4154.567630.432374
4244.12768-0.127676
4334.44559-1.44559
4433.76916-0.76916
4544.03825-0.0382523
4643.872470.127527
4744.62132-0.621315
4854.257720.742283
4943.90610.0939039
5054.299780.700221
5144.67299-0.672993
5243.881730.118265
5343.792490.207508
5443.967460.032542
5544.04697-0.0469701
5654.089140.910857
5744.02132-0.021317
5844.30119-0.301186
5944.43371-0.433709
6043.89630.103698
6133.31388-0.313876
6244.07039-0.0703884
6354.357810.642192
6413.58971-2.58971
6534.12975-1.12975
6654.281390.718608
6743.605770.394225
6843.880270.11973
6933.99179-0.991786
7043.984750.0152479
7144.37985-0.379847
7233.94017-0.940172
7353.935571.06443
7444.22872-0.228716
7554.57080.429197
7643.572720.427283
7744.22829-0.228289
7843.888680.111321
7943.931690.068306
8034.15398-1.15398
8153.989131.01087
82NANA0.4086
8355.3746-0.374597
8443.744410.255589
8542.90671.0933
8655.48481-0.484809
8743.977220.0227831
8844.91956-0.919555
8932.591370.408629
9043.972550.0274515
9144.83097-0.830972
9231.905791.09421
9353.954821.04518
9454.386650.613346
9555.29677-0.296773
9643.706530.293467
9744.21136-0.211361
9844.70543-0.705434
9943.944950.0550485
10044.52063-0.520629
10144.00308-0.00308421
10243.020870.979128
10354.943790.0562132
10443.773340.226658
10544.67483-0.674829
10632.568520.431477
10754.882580.117419
10844.63296-0.632963
10934.08344-1.08344
11021.266870.733133
11154.618670.38133
11243.186560.813439
11358.04884-3.04884
11410.1958860.804114
11554.030120.969875
11655.65308-0.653079
11732.968050.031951
11843.414980.585016
11953.974891.02511
12055.60141-0.601411
12132.786310.213689
12243.471720.528282
12355.20927-0.209271
12444.37935-0.379348
12544.33128-0.331276
12643.418020.581982
12755.23544-0.235439
12843.01620.983798
12955.09877-0.098773
13044.41054-0.410538
13143.471640.528356
13244.12148-0.121483
13344.98708-0.987084
13433.54275-0.542745
13542.802461.19754
13655.53883-0.538829
13733.05386-0.0538552
13844.48909-0.489094
13933.02073-0.0207334
14044.92287-0.922868
14134.01149-1.01149
14232.243110.756895
14354.102020.897976
14454.075180.924821
14554.351810.648191
14654.68390.316099
14754.870410.129586
14844.28799-0.287989
14943.669690.33031
15043.032430.967569
15153.610271.38973
15254.853050.14695
15343.943770.0562328
15443.635180.364817
15542.871031.12897
15655.81396-0.813956
15732.889690.110312
15843.5150.484996
15954.047020.952984
16053.721411.27859
16153.959771.04023
1625NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3 & 3.90524 & -0.905236 \tabularnewline
2 & 5 & 4.43981 & 0.560194 \tabularnewline
3 & 4 & 4.22721 & -0.227215 \tabularnewline
4 & 4 & 3.90441 & 0.0955869 \tabularnewline
5 & 5 & 3.49864 & 1.50136 \tabularnewline
6 & 5 & 4.4427 & 0.557302 \tabularnewline
7 & 2 & 3.53583 & -1.53583 \tabularnewline
8 & 5 & 3.95289 & 1.04711 \tabularnewline
9 & 4 & 4.19075 & -0.190748 \tabularnewline
10 & 4 & 4.14286 & -0.142865 \tabularnewline
11 & 5 & 4.63662 & 0.363383 \tabularnewline
12 & 3 & 4.60127 & -1.60127 \tabularnewline
13 & 5 & 3.73994 & 1.26006 \tabularnewline
14 & 3 & 4.05427 & -1.05427 \tabularnewline
15 & 5 & 4.54181 & 0.458191 \tabularnewline
16 & 3 & 3.77374 & -0.773744 \tabularnewline
17 & 4 & 4.06583 & -0.0658347 \tabularnewline
18 & 5 & 4.42396 & 0.576039 \tabularnewline
19 & 4 & 4.0016 & -0.00160342 \tabularnewline
20 & 3 & 3.81746 & -0.81746 \tabularnewline
21 & 4 & 4.40663 & -0.406627 \tabularnewline
22 & 4 & 3.88945 & 0.11055 \tabularnewline
23 & 3 & 4.29624 & -1.29624 \tabularnewline
24 & 3 & 3.86359 & -0.863587 \tabularnewline
25 & 4 & 4.22122 & -0.221223 \tabularnewline
26 & 5 & 4.4552 & 0.544797 \tabularnewline
27 & 4 & 4.29886 & -0.298861 \tabularnewline
28 & 4 & 3.67808 & 0.321921 \tabularnewline
29 & 4 & 4.16305 & -0.163048 \tabularnewline
30 & 4 & 4.21024 & -0.210244 \tabularnewline
31 & 4 & 4.30369 & -0.303688 \tabularnewline
32 & 3 & 3.66577 & -0.665769 \tabularnewline
33 & 4 & 4.23782 & -0.237816 \tabularnewline
34 & 5 & 4.15754 & 0.842456 \tabularnewline
35 & 4 & 4.38188 & -0.381881 \tabularnewline
36 & 4 & 4.46742 & -0.467419 \tabularnewline
37 & 3 & 3.28213 & -0.282126 \tabularnewline
38 & 4 & 4.23544 & -0.235444 \tabularnewline
39 & 4 & 4.66003 & -0.660032 \tabularnewline
40 & 4 & 4.1296 & -0.129602 \tabularnewline
41 & 5 & 4.56763 & 0.432374 \tabularnewline
42 & 4 & 4.12768 & -0.127676 \tabularnewline
43 & 3 & 4.44559 & -1.44559 \tabularnewline
44 & 3 & 3.76916 & -0.76916 \tabularnewline
45 & 4 & 4.03825 & -0.0382523 \tabularnewline
46 & 4 & 3.87247 & 0.127527 \tabularnewline
47 & 4 & 4.62132 & -0.621315 \tabularnewline
48 & 5 & 4.25772 & 0.742283 \tabularnewline
49 & 4 & 3.9061 & 0.0939039 \tabularnewline
50 & 5 & 4.29978 & 0.700221 \tabularnewline
51 & 4 & 4.67299 & -0.672993 \tabularnewline
52 & 4 & 3.88173 & 0.118265 \tabularnewline
53 & 4 & 3.79249 & 0.207508 \tabularnewline
54 & 4 & 3.96746 & 0.032542 \tabularnewline
55 & 4 & 4.04697 & -0.0469701 \tabularnewline
56 & 5 & 4.08914 & 0.910857 \tabularnewline
57 & 4 & 4.02132 & -0.021317 \tabularnewline
58 & 4 & 4.30119 & -0.301186 \tabularnewline
59 & 4 & 4.43371 & -0.433709 \tabularnewline
60 & 4 & 3.8963 & 0.103698 \tabularnewline
61 & 3 & 3.31388 & -0.313876 \tabularnewline
62 & 4 & 4.07039 & -0.0703884 \tabularnewline
63 & 5 & 4.35781 & 0.642192 \tabularnewline
64 & 1 & 3.58971 & -2.58971 \tabularnewline
65 & 3 & 4.12975 & -1.12975 \tabularnewline
66 & 5 & 4.28139 & 0.718608 \tabularnewline
67 & 4 & 3.60577 & 0.394225 \tabularnewline
68 & 4 & 3.88027 & 0.11973 \tabularnewline
69 & 3 & 3.99179 & -0.991786 \tabularnewline
70 & 4 & 3.98475 & 0.0152479 \tabularnewline
71 & 4 & 4.37985 & -0.379847 \tabularnewline
72 & 3 & 3.94017 & -0.940172 \tabularnewline
73 & 5 & 3.93557 & 1.06443 \tabularnewline
74 & 4 & 4.22872 & -0.228716 \tabularnewline
75 & 5 & 4.5708 & 0.429197 \tabularnewline
76 & 4 & 3.57272 & 0.427283 \tabularnewline
77 & 4 & 4.22829 & -0.228289 \tabularnewline
78 & 4 & 3.88868 & 0.111321 \tabularnewline
79 & 4 & 3.93169 & 0.068306 \tabularnewline
80 & 3 & 4.15398 & -1.15398 \tabularnewline
81 & 5 & 3.98913 & 1.01087 \tabularnewline
82 & NA & NA & 0.4086 \tabularnewline
83 & 5 & 5.3746 & -0.374597 \tabularnewline
84 & 4 & 3.74441 & 0.255589 \tabularnewline
85 & 4 & 2.9067 & 1.0933 \tabularnewline
86 & 5 & 5.48481 & -0.484809 \tabularnewline
87 & 4 & 3.97722 & 0.0227831 \tabularnewline
88 & 4 & 4.91956 & -0.919555 \tabularnewline
89 & 3 & 2.59137 & 0.408629 \tabularnewline
90 & 4 & 3.97255 & 0.0274515 \tabularnewline
91 & 4 & 4.83097 & -0.830972 \tabularnewline
92 & 3 & 1.90579 & 1.09421 \tabularnewline
93 & 5 & 3.95482 & 1.04518 \tabularnewline
94 & 5 & 4.38665 & 0.613346 \tabularnewline
95 & 5 & 5.29677 & -0.296773 \tabularnewline
96 & 4 & 3.70653 & 0.293467 \tabularnewline
97 & 4 & 4.21136 & -0.211361 \tabularnewline
98 & 4 & 4.70543 & -0.705434 \tabularnewline
99 & 4 & 3.94495 & 0.0550485 \tabularnewline
100 & 4 & 4.52063 & -0.520629 \tabularnewline
101 & 4 & 4.00308 & -0.00308421 \tabularnewline
102 & 4 & 3.02087 & 0.979128 \tabularnewline
103 & 5 & 4.94379 & 0.0562132 \tabularnewline
104 & 4 & 3.77334 & 0.226658 \tabularnewline
105 & 4 & 4.67483 & -0.674829 \tabularnewline
106 & 3 & 2.56852 & 0.431477 \tabularnewline
107 & 5 & 4.88258 & 0.117419 \tabularnewline
108 & 4 & 4.63296 & -0.632963 \tabularnewline
109 & 3 & 4.08344 & -1.08344 \tabularnewline
110 & 2 & 1.26687 & 0.733133 \tabularnewline
111 & 5 & 4.61867 & 0.38133 \tabularnewline
112 & 4 & 3.18656 & 0.813439 \tabularnewline
113 & 5 & 8.04884 & -3.04884 \tabularnewline
114 & 1 & 0.195886 & 0.804114 \tabularnewline
115 & 5 & 4.03012 & 0.969875 \tabularnewline
116 & 5 & 5.65308 & -0.653079 \tabularnewline
117 & 3 & 2.96805 & 0.031951 \tabularnewline
118 & 4 & 3.41498 & 0.585016 \tabularnewline
119 & 5 & 3.97489 & 1.02511 \tabularnewline
120 & 5 & 5.60141 & -0.601411 \tabularnewline
121 & 3 & 2.78631 & 0.213689 \tabularnewline
122 & 4 & 3.47172 & 0.528282 \tabularnewline
123 & 5 & 5.20927 & -0.209271 \tabularnewline
124 & 4 & 4.37935 & -0.379348 \tabularnewline
125 & 4 & 4.33128 & -0.331276 \tabularnewline
126 & 4 & 3.41802 & 0.581982 \tabularnewline
127 & 5 & 5.23544 & -0.235439 \tabularnewline
128 & 4 & 3.0162 & 0.983798 \tabularnewline
129 & 5 & 5.09877 & -0.098773 \tabularnewline
130 & 4 & 4.41054 & -0.410538 \tabularnewline
131 & 4 & 3.47164 & 0.528356 \tabularnewline
132 & 4 & 4.12148 & -0.121483 \tabularnewline
133 & 4 & 4.98708 & -0.987084 \tabularnewline
134 & 3 & 3.54275 & -0.542745 \tabularnewline
135 & 4 & 2.80246 & 1.19754 \tabularnewline
136 & 5 & 5.53883 & -0.538829 \tabularnewline
137 & 3 & 3.05386 & -0.0538552 \tabularnewline
138 & 4 & 4.48909 & -0.489094 \tabularnewline
139 & 3 & 3.02073 & -0.0207334 \tabularnewline
140 & 4 & 4.92287 & -0.922868 \tabularnewline
141 & 3 & 4.01149 & -1.01149 \tabularnewline
142 & 3 & 2.24311 & 0.756895 \tabularnewline
143 & 5 & 4.10202 & 0.897976 \tabularnewline
144 & 5 & 4.07518 & 0.924821 \tabularnewline
145 & 5 & 4.35181 & 0.648191 \tabularnewline
146 & 5 & 4.6839 & 0.316099 \tabularnewline
147 & 5 & 4.87041 & 0.129586 \tabularnewline
148 & 4 & 4.28799 & -0.287989 \tabularnewline
149 & 4 & 3.66969 & 0.33031 \tabularnewline
150 & 4 & 3.03243 & 0.967569 \tabularnewline
151 & 5 & 3.61027 & 1.38973 \tabularnewline
152 & 5 & 4.85305 & 0.14695 \tabularnewline
153 & 4 & 3.94377 & 0.0562328 \tabularnewline
154 & 4 & 3.63518 & 0.364817 \tabularnewline
155 & 4 & 2.87103 & 1.12897 \tabularnewline
156 & 5 & 5.81396 & -0.813956 \tabularnewline
157 & 3 & 2.88969 & 0.110312 \tabularnewline
158 & 4 & 3.515 & 0.484996 \tabularnewline
159 & 5 & 4.04702 & 0.952984 \tabularnewline
160 & 5 & 3.72141 & 1.27859 \tabularnewline
161 & 5 & 3.95977 & 1.04023 \tabularnewline
162 & 5 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221555&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]3[/C][C]3.90524[/C][C]-0.905236[/C][/ROW]
[ROW][C]2[/C][C]5[/C][C]4.43981[/C][C]0.560194[/C][/ROW]
[ROW][C]3[/C][C]4[/C][C]4.22721[/C][C]-0.227215[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]3.90441[/C][C]0.0955869[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]3.49864[/C][C]1.50136[/C][/ROW]
[ROW][C]6[/C][C]5[/C][C]4.4427[/C][C]0.557302[/C][/ROW]
[ROW][C]7[/C][C]2[/C][C]3.53583[/C][C]-1.53583[/C][/ROW]
[ROW][C]8[/C][C]5[/C][C]3.95289[/C][C]1.04711[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]4.19075[/C][C]-0.190748[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]4.14286[/C][C]-0.142865[/C][/ROW]
[ROW][C]11[/C][C]5[/C][C]4.63662[/C][C]0.363383[/C][/ROW]
[ROW][C]12[/C][C]3[/C][C]4.60127[/C][C]-1.60127[/C][/ROW]
[ROW][C]13[/C][C]5[/C][C]3.73994[/C][C]1.26006[/C][/ROW]
[ROW][C]14[/C][C]3[/C][C]4.05427[/C][C]-1.05427[/C][/ROW]
[ROW][C]15[/C][C]5[/C][C]4.54181[/C][C]0.458191[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]3.77374[/C][C]-0.773744[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]4.06583[/C][C]-0.0658347[/C][/ROW]
[ROW][C]18[/C][C]5[/C][C]4.42396[/C][C]0.576039[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]4.0016[/C][C]-0.00160342[/C][/ROW]
[ROW][C]20[/C][C]3[/C][C]3.81746[/C][C]-0.81746[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]4.40663[/C][C]-0.406627[/C][/ROW]
[ROW][C]22[/C][C]4[/C][C]3.88945[/C][C]0.11055[/C][/ROW]
[ROW][C]23[/C][C]3[/C][C]4.29624[/C][C]-1.29624[/C][/ROW]
[ROW][C]24[/C][C]3[/C][C]3.86359[/C][C]-0.863587[/C][/ROW]
[ROW][C]25[/C][C]4[/C][C]4.22122[/C][C]-0.221223[/C][/ROW]
[ROW][C]26[/C][C]5[/C][C]4.4552[/C][C]0.544797[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]4.29886[/C][C]-0.298861[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]3.67808[/C][C]0.321921[/C][/ROW]
[ROW][C]29[/C][C]4[/C][C]4.16305[/C][C]-0.163048[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]4.21024[/C][C]-0.210244[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]4.30369[/C][C]-0.303688[/C][/ROW]
[ROW][C]32[/C][C]3[/C][C]3.66577[/C][C]-0.665769[/C][/ROW]
[ROW][C]33[/C][C]4[/C][C]4.23782[/C][C]-0.237816[/C][/ROW]
[ROW][C]34[/C][C]5[/C][C]4.15754[/C][C]0.842456[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]4.38188[/C][C]-0.381881[/C][/ROW]
[ROW][C]36[/C][C]4[/C][C]4.46742[/C][C]-0.467419[/C][/ROW]
[ROW][C]37[/C][C]3[/C][C]3.28213[/C][C]-0.282126[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]4.23544[/C][C]-0.235444[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]4.66003[/C][C]-0.660032[/C][/ROW]
[ROW][C]40[/C][C]4[/C][C]4.1296[/C][C]-0.129602[/C][/ROW]
[ROW][C]41[/C][C]5[/C][C]4.56763[/C][C]0.432374[/C][/ROW]
[ROW][C]42[/C][C]4[/C][C]4.12768[/C][C]-0.127676[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]4.44559[/C][C]-1.44559[/C][/ROW]
[ROW][C]44[/C][C]3[/C][C]3.76916[/C][C]-0.76916[/C][/ROW]
[ROW][C]45[/C][C]4[/C][C]4.03825[/C][C]-0.0382523[/C][/ROW]
[ROW][C]46[/C][C]4[/C][C]3.87247[/C][C]0.127527[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]4.62132[/C][C]-0.621315[/C][/ROW]
[ROW][C]48[/C][C]5[/C][C]4.25772[/C][C]0.742283[/C][/ROW]
[ROW][C]49[/C][C]4[/C][C]3.9061[/C][C]0.0939039[/C][/ROW]
[ROW][C]50[/C][C]5[/C][C]4.29978[/C][C]0.700221[/C][/ROW]
[ROW][C]51[/C][C]4[/C][C]4.67299[/C][C]-0.672993[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]3.88173[/C][C]0.118265[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]3.79249[/C][C]0.207508[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]3.96746[/C][C]0.032542[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]4.04697[/C][C]-0.0469701[/C][/ROW]
[ROW][C]56[/C][C]5[/C][C]4.08914[/C][C]0.910857[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]4.02132[/C][C]-0.021317[/C][/ROW]
[ROW][C]58[/C][C]4[/C][C]4.30119[/C][C]-0.301186[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]4.43371[/C][C]-0.433709[/C][/ROW]
[ROW][C]60[/C][C]4[/C][C]3.8963[/C][C]0.103698[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]3.31388[/C][C]-0.313876[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]4.07039[/C][C]-0.0703884[/C][/ROW]
[ROW][C]63[/C][C]5[/C][C]4.35781[/C][C]0.642192[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]3.58971[/C][C]-2.58971[/C][/ROW]
[ROW][C]65[/C][C]3[/C][C]4.12975[/C][C]-1.12975[/C][/ROW]
[ROW][C]66[/C][C]5[/C][C]4.28139[/C][C]0.718608[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]3.60577[/C][C]0.394225[/C][/ROW]
[ROW][C]68[/C][C]4[/C][C]3.88027[/C][C]0.11973[/C][/ROW]
[ROW][C]69[/C][C]3[/C][C]3.99179[/C][C]-0.991786[/C][/ROW]
[ROW][C]70[/C][C]4[/C][C]3.98475[/C][C]0.0152479[/C][/ROW]
[ROW][C]71[/C][C]4[/C][C]4.37985[/C][C]-0.379847[/C][/ROW]
[ROW][C]72[/C][C]3[/C][C]3.94017[/C][C]-0.940172[/C][/ROW]
[ROW][C]73[/C][C]5[/C][C]3.93557[/C][C]1.06443[/C][/ROW]
[ROW][C]74[/C][C]4[/C][C]4.22872[/C][C]-0.228716[/C][/ROW]
[ROW][C]75[/C][C]5[/C][C]4.5708[/C][C]0.429197[/C][/ROW]
[ROW][C]76[/C][C]4[/C][C]3.57272[/C][C]0.427283[/C][/ROW]
[ROW][C]77[/C][C]4[/C][C]4.22829[/C][C]-0.228289[/C][/ROW]
[ROW][C]78[/C][C]4[/C][C]3.88868[/C][C]0.111321[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]3.93169[/C][C]0.068306[/C][/ROW]
[ROW][C]80[/C][C]3[/C][C]4.15398[/C][C]-1.15398[/C][/ROW]
[ROW][C]81[/C][C]5[/C][C]3.98913[/C][C]1.01087[/C][/ROW]
[ROW][C]82[/C][C]NA[/C][C]NA[/C][C]0.4086[/C][/ROW]
[ROW][C]83[/C][C]5[/C][C]5.3746[/C][C]-0.374597[/C][/ROW]
[ROW][C]84[/C][C]4[/C][C]3.74441[/C][C]0.255589[/C][/ROW]
[ROW][C]85[/C][C]4[/C][C]2.9067[/C][C]1.0933[/C][/ROW]
[ROW][C]86[/C][C]5[/C][C]5.48481[/C][C]-0.484809[/C][/ROW]
[ROW][C]87[/C][C]4[/C][C]3.97722[/C][C]0.0227831[/C][/ROW]
[ROW][C]88[/C][C]4[/C][C]4.91956[/C][C]-0.919555[/C][/ROW]
[ROW][C]89[/C][C]3[/C][C]2.59137[/C][C]0.408629[/C][/ROW]
[ROW][C]90[/C][C]4[/C][C]3.97255[/C][C]0.0274515[/C][/ROW]
[ROW][C]91[/C][C]4[/C][C]4.83097[/C][C]-0.830972[/C][/ROW]
[ROW][C]92[/C][C]3[/C][C]1.90579[/C][C]1.09421[/C][/ROW]
[ROW][C]93[/C][C]5[/C][C]3.95482[/C][C]1.04518[/C][/ROW]
[ROW][C]94[/C][C]5[/C][C]4.38665[/C][C]0.613346[/C][/ROW]
[ROW][C]95[/C][C]5[/C][C]5.29677[/C][C]-0.296773[/C][/ROW]
[ROW][C]96[/C][C]4[/C][C]3.70653[/C][C]0.293467[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]4.21136[/C][C]-0.211361[/C][/ROW]
[ROW][C]98[/C][C]4[/C][C]4.70543[/C][C]-0.705434[/C][/ROW]
[ROW][C]99[/C][C]4[/C][C]3.94495[/C][C]0.0550485[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]4.52063[/C][C]-0.520629[/C][/ROW]
[ROW][C]101[/C][C]4[/C][C]4.00308[/C][C]-0.00308421[/C][/ROW]
[ROW][C]102[/C][C]4[/C][C]3.02087[/C][C]0.979128[/C][/ROW]
[ROW][C]103[/C][C]5[/C][C]4.94379[/C][C]0.0562132[/C][/ROW]
[ROW][C]104[/C][C]4[/C][C]3.77334[/C][C]0.226658[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]4.67483[/C][C]-0.674829[/C][/ROW]
[ROW][C]106[/C][C]3[/C][C]2.56852[/C][C]0.431477[/C][/ROW]
[ROW][C]107[/C][C]5[/C][C]4.88258[/C][C]0.117419[/C][/ROW]
[ROW][C]108[/C][C]4[/C][C]4.63296[/C][C]-0.632963[/C][/ROW]
[ROW][C]109[/C][C]3[/C][C]4.08344[/C][C]-1.08344[/C][/ROW]
[ROW][C]110[/C][C]2[/C][C]1.26687[/C][C]0.733133[/C][/ROW]
[ROW][C]111[/C][C]5[/C][C]4.61867[/C][C]0.38133[/C][/ROW]
[ROW][C]112[/C][C]4[/C][C]3.18656[/C][C]0.813439[/C][/ROW]
[ROW][C]113[/C][C]5[/C][C]8.04884[/C][C]-3.04884[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0.195886[/C][C]0.804114[/C][/ROW]
[ROW][C]115[/C][C]5[/C][C]4.03012[/C][C]0.969875[/C][/ROW]
[ROW][C]116[/C][C]5[/C][C]5.65308[/C][C]-0.653079[/C][/ROW]
[ROW][C]117[/C][C]3[/C][C]2.96805[/C][C]0.031951[/C][/ROW]
[ROW][C]118[/C][C]4[/C][C]3.41498[/C][C]0.585016[/C][/ROW]
[ROW][C]119[/C][C]5[/C][C]3.97489[/C][C]1.02511[/C][/ROW]
[ROW][C]120[/C][C]5[/C][C]5.60141[/C][C]-0.601411[/C][/ROW]
[ROW][C]121[/C][C]3[/C][C]2.78631[/C][C]0.213689[/C][/ROW]
[ROW][C]122[/C][C]4[/C][C]3.47172[/C][C]0.528282[/C][/ROW]
[ROW][C]123[/C][C]5[/C][C]5.20927[/C][C]-0.209271[/C][/ROW]
[ROW][C]124[/C][C]4[/C][C]4.37935[/C][C]-0.379348[/C][/ROW]
[ROW][C]125[/C][C]4[/C][C]4.33128[/C][C]-0.331276[/C][/ROW]
[ROW][C]126[/C][C]4[/C][C]3.41802[/C][C]0.581982[/C][/ROW]
[ROW][C]127[/C][C]5[/C][C]5.23544[/C][C]-0.235439[/C][/ROW]
[ROW][C]128[/C][C]4[/C][C]3.0162[/C][C]0.983798[/C][/ROW]
[ROW][C]129[/C][C]5[/C][C]5.09877[/C][C]-0.098773[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]4.41054[/C][C]-0.410538[/C][/ROW]
[ROW][C]131[/C][C]4[/C][C]3.47164[/C][C]0.528356[/C][/ROW]
[ROW][C]132[/C][C]4[/C][C]4.12148[/C][C]-0.121483[/C][/ROW]
[ROW][C]133[/C][C]4[/C][C]4.98708[/C][C]-0.987084[/C][/ROW]
[ROW][C]134[/C][C]3[/C][C]3.54275[/C][C]-0.542745[/C][/ROW]
[ROW][C]135[/C][C]4[/C][C]2.80246[/C][C]1.19754[/C][/ROW]
[ROW][C]136[/C][C]5[/C][C]5.53883[/C][C]-0.538829[/C][/ROW]
[ROW][C]137[/C][C]3[/C][C]3.05386[/C][C]-0.0538552[/C][/ROW]
[ROW][C]138[/C][C]4[/C][C]4.48909[/C][C]-0.489094[/C][/ROW]
[ROW][C]139[/C][C]3[/C][C]3.02073[/C][C]-0.0207334[/C][/ROW]
[ROW][C]140[/C][C]4[/C][C]4.92287[/C][C]-0.922868[/C][/ROW]
[ROW][C]141[/C][C]3[/C][C]4.01149[/C][C]-1.01149[/C][/ROW]
[ROW][C]142[/C][C]3[/C][C]2.24311[/C][C]0.756895[/C][/ROW]
[ROW][C]143[/C][C]5[/C][C]4.10202[/C][C]0.897976[/C][/ROW]
[ROW][C]144[/C][C]5[/C][C]4.07518[/C][C]0.924821[/C][/ROW]
[ROW][C]145[/C][C]5[/C][C]4.35181[/C][C]0.648191[/C][/ROW]
[ROW][C]146[/C][C]5[/C][C]4.6839[/C][C]0.316099[/C][/ROW]
[ROW][C]147[/C][C]5[/C][C]4.87041[/C][C]0.129586[/C][/ROW]
[ROW][C]148[/C][C]4[/C][C]4.28799[/C][C]-0.287989[/C][/ROW]
[ROW][C]149[/C][C]4[/C][C]3.66969[/C][C]0.33031[/C][/ROW]
[ROW][C]150[/C][C]4[/C][C]3.03243[/C][C]0.967569[/C][/ROW]
[ROW][C]151[/C][C]5[/C][C]3.61027[/C][C]1.38973[/C][/ROW]
[ROW][C]152[/C][C]5[/C][C]4.85305[/C][C]0.14695[/C][/ROW]
[ROW][C]153[/C][C]4[/C][C]3.94377[/C][C]0.0562328[/C][/ROW]
[ROW][C]154[/C][C]4[/C][C]3.63518[/C][C]0.364817[/C][/ROW]
[ROW][C]155[/C][C]4[/C][C]2.87103[/C][C]1.12897[/C][/ROW]
[ROW][C]156[/C][C]5[/C][C]5.81396[/C][C]-0.813956[/C][/ROW]
[ROW][C]157[/C][C]3[/C][C]2.88969[/C][C]0.110312[/C][/ROW]
[ROW][C]158[/C][C]4[/C][C]3.515[/C][C]0.484996[/C][/ROW]
[ROW][C]159[/C][C]5[/C][C]4.04702[/C][C]0.952984[/C][/ROW]
[ROW][C]160[/C][C]5[/C][C]3.72141[/C][C]1.27859[/C][/ROW]
[ROW][C]161[/C][C]5[/C][C]3.95977[/C][C]1.04023[/C][/ROW]
[ROW][C]162[/C][C]5[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221555&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221555&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
133.90524-0.905236
254.439810.560194
344.22721-0.227215
443.904410.0955869
553.498641.50136
654.44270.557302
723.53583-1.53583
853.952891.04711
944.19075-0.190748
1044.14286-0.142865
1154.636620.363383
1234.60127-1.60127
1353.739941.26006
1434.05427-1.05427
1554.541810.458191
1633.77374-0.773744
1744.06583-0.0658347
1854.423960.576039
1944.0016-0.00160342
2033.81746-0.81746
2144.40663-0.406627
2243.889450.11055
2334.29624-1.29624
2433.86359-0.863587
2544.22122-0.221223
2654.45520.544797
2744.29886-0.298861
2843.678080.321921
2944.16305-0.163048
3044.21024-0.210244
3144.30369-0.303688
3233.66577-0.665769
3344.23782-0.237816
3454.157540.842456
3544.38188-0.381881
3644.46742-0.467419
3733.28213-0.282126
3844.23544-0.235444
3944.66003-0.660032
4044.1296-0.129602
4154.567630.432374
4244.12768-0.127676
4334.44559-1.44559
4433.76916-0.76916
4544.03825-0.0382523
4643.872470.127527
4744.62132-0.621315
4854.257720.742283
4943.90610.0939039
5054.299780.700221
5144.67299-0.672993
5243.881730.118265
5343.792490.207508
5443.967460.032542
5544.04697-0.0469701
5654.089140.910857
5744.02132-0.021317
5844.30119-0.301186
5944.43371-0.433709
6043.89630.103698
6133.31388-0.313876
6244.07039-0.0703884
6354.357810.642192
6413.58971-2.58971
6534.12975-1.12975
6654.281390.718608
6743.605770.394225
6843.880270.11973
6933.99179-0.991786
7043.984750.0152479
7144.37985-0.379847
7233.94017-0.940172
7353.935571.06443
7444.22872-0.228716
7554.57080.429197
7643.572720.427283
7744.22829-0.228289
7843.888680.111321
7943.931690.068306
8034.15398-1.15398
8153.989131.01087
82NANA0.4086
8355.3746-0.374597
8443.744410.255589
8542.90671.0933
8655.48481-0.484809
8743.977220.0227831
8844.91956-0.919555
8932.591370.408629
9043.972550.0274515
9144.83097-0.830972
9231.905791.09421
9353.954821.04518
9454.386650.613346
9555.29677-0.296773
9643.706530.293467
9744.21136-0.211361
9844.70543-0.705434
9943.944950.0550485
10044.52063-0.520629
10144.00308-0.00308421
10243.020870.979128
10354.943790.0562132
10443.773340.226658
10544.67483-0.674829
10632.568520.431477
10754.882580.117419
10844.63296-0.632963
10934.08344-1.08344
11021.266870.733133
11154.618670.38133
11243.186560.813439
11358.04884-3.04884
11410.1958860.804114
11554.030120.969875
11655.65308-0.653079
11732.968050.031951
11843.414980.585016
11953.974891.02511
12055.60141-0.601411
12132.786310.213689
12243.471720.528282
12355.20927-0.209271
12444.37935-0.379348
12544.33128-0.331276
12643.418020.581982
12755.23544-0.235439
12843.01620.983798
12955.09877-0.098773
13044.41054-0.410538
13143.471640.528356
13244.12148-0.121483
13344.98708-0.987084
13433.54275-0.542745
13542.802461.19754
13655.53883-0.538829
13733.05386-0.0538552
13844.48909-0.489094
13933.02073-0.0207334
14044.92287-0.922868
14134.01149-1.01149
14232.243110.756895
14354.102020.897976
14454.075180.924821
14554.351810.648191
14654.68390.316099
14754.870410.129586
14844.28799-0.287989
14943.669690.33031
15043.032430.967569
15153.610271.38973
15254.853050.14695
15343.943770.0562328
15443.635180.364817
15542.871031.12897
15655.81396-0.813956
15732.889690.110312
15843.5150.484996
15954.047020.952984
16053.721411.27859
16153.959771.04023
1625NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.5829580.8340840.417042
210.9659050.06819050.0340952
220.9463590.1072820.0536412
230.9485370.1029260.0514632
240.9696240.06075260.0303763
250.9507650.09846910.0492346
260.9255470.1489070.0744533
270.896090.207820.10391
280.879230.241540.12077
290.8852160.2295670.114784
300.8813920.2372170.118608
310.8408030.3183940.159197
320.817950.36410.18205
330.767360.465280.23264
340.7461280.5077440.253872
350.6925580.6148850.307442
360.6586310.6827390.341369
370.5941440.8117110.405856
380.5308680.9382640.469132
390.4964030.9928060.503597
400.4321210.8642420.567879
410.3785960.7571930.621404
420.3271170.6542340.672883
430.3903410.7806810.609659
440.3630820.7261630.636918
450.3123550.6247090.687645
460.2651060.5302110.734894
470.2331990.4663980.766801
480.3531920.7063850.646808
490.303580.607160.69642
500.2883440.5766880.711656
510.2598090.5196180.740191
520.2210580.4421160.778942
530.1852950.370590.814705
540.1534490.3068970.846551
550.1352630.2705260.864737
560.1761960.3523920.823804
570.1473010.2946020.852699
580.1282180.2564350.871782
590.1071350.2142710.892865
600.09448410.1889680.905516
610.07635290.1527060.923647
620.05957490.119150.940425
630.05673410.1134680.943266
640.3420880.6841770.657912
650.4241850.8483710.575815
660.4143420.8286850.585658
670.4092680.8185350.590732
680.362440.7248790.63756
690.3916740.7833480.608326
700.3435720.6871450.656428
710.3134190.6268390.686581
720.3314540.6629090.668546
730.3928210.7856410.607179
740.3463020.6926050.653698
750.3116120.6232230.688388
760.2828120.5656240.717188
770.2488880.4977760.751112
780.2110460.4220920.788954
790.2026090.4052180.797391
800.2643310.5286620.735669
810.3184550.6369090.681545
820.2995060.5990110.700494
830.2842870.5685740.715713
840.254490.508980.74551
850.3168420.6336830.683158
860.2877430.5754850.712257
870.2545890.5091780.745411
880.2819290.5638580.718071
890.2745050.5490090.725495
900.2535980.5071950.746402
910.274560.549120.72544
920.3329720.6659430.667028
930.3752430.7504850.624757
940.3534140.7068280.646586
950.3631680.7263370.636832
960.3334720.6669450.666528
970.2928970.5857940.707103
980.2986390.5972780.701361
990.2717250.543450.728275
1000.2699560.5399120.730044
1010.2373360.4746720.762664
1020.2541090.5082170.745891
1030.2146270.4292540.785373
1040.1897710.3795420.810229
1050.1681380.3362760.831862
1060.1453650.2907290.854635
1070.1676870.3353730.832313
1080.147240.2944790.85276
1090.201230.4024590.79877
1100.1975120.3950230.802488
1110.1677120.3354230.832288
1120.1857630.3715270.814237
1130.9494970.1010050.0505027
1140.9386640.1226720.0613359
1150.9362340.1275330.0637663
1160.9283080.1433850.0716924
1170.9134620.1730770.0865383
1180.8978660.2042690.102134
1190.8980090.2039810.101991
1200.8726020.2547970.127398
1210.8351770.3296460.164823
1220.8024830.3950350.197517
1230.8073170.3853670.192683
1240.7848410.4303180.215159
1250.7304840.5390320.269516
1260.6725820.6548370.327418
1270.7648030.4703940.235197
1280.735410.529180.26459
1290.6661170.6677660.333883
1300.6212460.7575080.378754
1310.5520820.8958360.447918
1320.5168010.9663980.483199
1330.4717550.9435110.528245
1340.4623970.9247940.537603
1350.3942930.7885850.605707
1360.4514650.9029310.548535
1370.4447580.8895170.555242
1380.3611790.7223580.638821
1390.3218820.6437640.678118
1400.2438610.4877210.756139
1410.7456610.5086790.254339
1420.7316060.5367870.268394

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.582958 & 0.834084 & 0.417042 \tabularnewline
21 & 0.965905 & 0.0681905 & 0.0340952 \tabularnewline
22 & 0.946359 & 0.107282 & 0.0536412 \tabularnewline
23 & 0.948537 & 0.102926 & 0.0514632 \tabularnewline
24 & 0.969624 & 0.0607526 & 0.0303763 \tabularnewline
25 & 0.950765 & 0.0984691 & 0.0492346 \tabularnewline
26 & 0.925547 & 0.148907 & 0.0744533 \tabularnewline
27 & 0.89609 & 0.20782 & 0.10391 \tabularnewline
28 & 0.87923 & 0.24154 & 0.12077 \tabularnewline
29 & 0.885216 & 0.229567 & 0.114784 \tabularnewline
30 & 0.881392 & 0.237217 & 0.118608 \tabularnewline
31 & 0.840803 & 0.318394 & 0.159197 \tabularnewline
32 & 0.81795 & 0.3641 & 0.18205 \tabularnewline
33 & 0.76736 & 0.46528 & 0.23264 \tabularnewline
34 & 0.746128 & 0.507744 & 0.253872 \tabularnewline
35 & 0.692558 & 0.614885 & 0.307442 \tabularnewline
36 & 0.658631 & 0.682739 & 0.341369 \tabularnewline
37 & 0.594144 & 0.811711 & 0.405856 \tabularnewline
38 & 0.530868 & 0.938264 & 0.469132 \tabularnewline
39 & 0.496403 & 0.992806 & 0.503597 \tabularnewline
40 & 0.432121 & 0.864242 & 0.567879 \tabularnewline
41 & 0.378596 & 0.757193 & 0.621404 \tabularnewline
42 & 0.327117 & 0.654234 & 0.672883 \tabularnewline
43 & 0.390341 & 0.780681 & 0.609659 \tabularnewline
44 & 0.363082 & 0.726163 & 0.636918 \tabularnewline
45 & 0.312355 & 0.624709 & 0.687645 \tabularnewline
46 & 0.265106 & 0.530211 & 0.734894 \tabularnewline
47 & 0.233199 & 0.466398 & 0.766801 \tabularnewline
48 & 0.353192 & 0.706385 & 0.646808 \tabularnewline
49 & 0.30358 & 0.60716 & 0.69642 \tabularnewline
50 & 0.288344 & 0.576688 & 0.711656 \tabularnewline
51 & 0.259809 & 0.519618 & 0.740191 \tabularnewline
52 & 0.221058 & 0.442116 & 0.778942 \tabularnewline
53 & 0.185295 & 0.37059 & 0.814705 \tabularnewline
54 & 0.153449 & 0.306897 & 0.846551 \tabularnewline
55 & 0.135263 & 0.270526 & 0.864737 \tabularnewline
56 & 0.176196 & 0.352392 & 0.823804 \tabularnewline
57 & 0.147301 & 0.294602 & 0.852699 \tabularnewline
58 & 0.128218 & 0.256435 & 0.871782 \tabularnewline
59 & 0.107135 & 0.214271 & 0.892865 \tabularnewline
60 & 0.0944841 & 0.188968 & 0.905516 \tabularnewline
61 & 0.0763529 & 0.152706 & 0.923647 \tabularnewline
62 & 0.0595749 & 0.11915 & 0.940425 \tabularnewline
63 & 0.0567341 & 0.113468 & 0.943266 \tabularnewline
64 & 0.342088 & 0.684177 & 0.657912 \tabularnewline
65 & 0.424185 & 0.848371 & 0.575815 \tabularnewline
66 & 0.414342 & 0.828685 & 0.585658 \tabularnewline
67 & 0.409268 & 0.818535 & 0.590732 \tabularnewline
68 & 0.36244 & 0.724879 & 0.63756 \tabularnewline
69 & 0.391674 & 0.783348 & 0.608326 \tabularnewline
70 & 0.343572 & 0.687145 & 0.656428 \tabularnewline
71 & 0.313419 & 0.626839 & 0.686581 \tabularnewline
72 & 0.331454 & 0.662909 & 0.668546 \tabularnewline
73 & 0.392821 & 0.785641 & 0.607179 \tabularnewline
74 & 0.346302 & 0.692605 & 0.653698 \tabularnewline
75 & 0.311612 & 0.623223 & 0.688388 \tabularnewline
76 & 0.282812 & 0.565624 & 0.717188 \tabularnewline
77 & 0.248888 & 0.497776 & 0.751112 \tabularnewline
78 & 0.211046 & 0.422092 & 0.788954 \tabularnewline
79 & 0.202609 & 0.405218 & 0.797391 \tabularnewline
80 & 0.264331 & 0.528662 & 0.735669 \tabularnewline
81 & 0.318455 & 0.636909 & 0.681545 \tabularnewline
82 & 0.299506 & 0.599011 & 0.700494 \tabularnewline
83 & 0.284287 & 0.568574 & 0.715713 \tabularnewline
84 & 0.25449 & 0.50898 & 0.74551 \tabularnewline
85 & 0.316842 & 0.633683 & 0.683158 \tabularnewline
86 & 0.287743 & 0.575485 & 0.712257 \tabularnewline
87 & 0.254589 & 0.509178 & 0.745411 \tabularnewline
88 & 0.281929 & 0.563858 & 0.718071 \tabularnewline
89 & 0.274505 & 0.549009 & 0.725495 \tabularnewline
90 & 0.253598 & 0.507195 & 0.746402 \tabularnewline
91 & 0.27456 & 0.54912 & 0.72544 \tabularnewline
92 & 0.332972 & 0.665943 & 0.667028 \tabularnewline
93 & 0.375243 & 0.750485 & 0.624757 \tabularnewline
94 & 0.353414 & 0.706828 & 0.646586 \tabularnewline
95 & 0.363168 & 0.726337 & 0.636832 \tabularnewline
96 & 0.333472 & 0.666945 & 0.666528 \tabularnewline
97 & 0.292897 & 0.585794 & 0.707103 \tabularnewline
98 & 0.298639 & 0.597278 & 0.701361 \tabularnewline
99 & 0.271725 & 0.54345 & 0.728275 \tabularnewline
100 & 0.269956 & 0.539912 & 0.730044 \tabularnewline
101 & 0.237336 & 0.474672 & 0.762664 \tabularnewline
102 & 0.254109 & 0.508217 & 0.745891 \tabularnewline
103 & 0.214627 & 0.429254 & 0.785373 \tabularnewline
104 & 0.189771 & 0.379542 & 0.810229 \tabularnewline
105 & 0.168138 & 0.336276 & 0.831862 \tabularnewline
106 & 0.145365 & 0.290729 & 0.854635 \tabularnewline
107 & 0.167687 & 0.335373 & 0.832313 \tabularnewline
108 & 0.14724 & 0.294479 & 0.85276 \tabularnewline
109 & 0.20123 & 0.402459 & 0.79877 \tabularnewline
110 & 0.197512 & 0.395023 & 0.802488 \tabularnewline
111 & 0.167712 & 0.335423 & 0.832288 \tabularnewline
112 & 0.185763 & 0.371527 & 0.814237 \tabularnewline
113 & 0.949497 & 0.101005 & 0.0505027 \tabularnewline
114 & 0.938664 & 0.122672 & 0.0613359 \tabularnewline
115 & 0.936234 & 0.127533 & 0.0637663 \tabularnewline
116 & 0.928308 & 0.143385 & 0.0716924 \tabularnewline
117 & 0.913462 & 0.173077 & 0.0865383 \tabularnewline
118 & 0.897866 & 0.204269 & 0.102134 \tabularnewline
119 & 0.898009 & 0.203981 & 0.101991 \tabularnewline
120 & 0.872602 & 0.254797 & 0.127398 \tabularnewline
121 & 0.835177 & 0.329646 & 0.164823 \tabularnewline
122 & 0.802483 & 0.395035 & 0.197517 \tabularnewline
123 & 0.807317 & 0.385367 & 0.192683 \tabularnewline
124 & 0.784841 & 0.430318 & 0.215159 \tabularnewline
125 & 0.730484 & 0.539032 & 0.269516 \tabularnewline
126 & 0.672582 & 0.654837 & 0.327418 \tabularnewline
127 & 0.764803 & 0.470394 & 0.235197 \tabularnewline
128 & 0.73541 & 0.52918 & 0.26459 \tabularnewline
129 & 0.666117 & 0.667766 & 0.333883 \tabularnewline
130 & 0.621246 & 0.757508 & 0.378754 \tabularnewline
131 & 0.552082 & 0.895836 & 0.447918 \tabularnewline
132 & 0.516801 & 0.966398 & 0.483199 \tabularnewline
133 & 0.471755 & 0.943511 & 0.528245 \tabularnewline
134 & 0.462397 & 0.924794 & 0.537603 \tabularnewline
135 & 0.394293 & 0.788585 & 0.605707 \tabularnewline
136 & 0.451465 & 0.902931 & 0.548535 \tabularnewline
137 & 0.444758 & 0.889517 & 0.555242 \tabularnewline
138 & 0.361179 & 0.722358 & 0.638821 \tabularnewline
139 & 0.321882 & 0.643764 & 0.678118 \tabularnewline
140 & 0.243861 & 0.487721 & 0.756139 \tabularnewline
141 & 0.745661 & 0.508679 & 0.254339 \tabularnewline
142 & 0.731606 & 0.536787 & 0.268394 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221555&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]20[/C][C]0.582958[/C][C]0.834084[/C][C]0.417042[/C][/ROW]
[ROW][C]21[/C][C]0.965905[/C][C]0.0681905[/C][C]0.0340952[/C][/ROW]
[ROW][C]22[/C][C]0.946359[/C][C]0.107282[/C][C]0.0536412[/C][/ROW]
[ROW][C]23[/C][C]0.948537[/C][C]0.102926[/C][C]0.0514632[/C][/ROW]
[ROW][C]24[/C][C]0.969624[/C][C]0.0607526[/C][C]0.0303763[/C][/ROW]
[ROW][C]25[/C][C]0.950765[/C][C]0.0984691[/C][C]0.0492346[/C][/ROW]
[ROW][C]26[/C][C]0.925547[/C][C]0.148907[/C][C]0.0744533[/C][/ROW]
[ROW][C]27[/C][C]0.89609[/C][C]0.20782[/C][C]0.10391[/C][/ROW]
[ROW][C]28[/C][C]0.87923[/C][C]0.24154[/C][C]0.12077[/C][/ROW]
[ROW][C]29[/C][C]0.885216[/C][C]0.229567[/C][C]0.114784[/C][/ROW]
[ROW][C]30[/C][C]0.881392[/C][C]0.237217[/C][C]0.118608[/C][/ROW]
[ROW][C]31[/C][C]0.840803[/C][C]0.318394[/C][C]0.159197[/C][/ROW]
[ROW][C]32[/C][C]0.81795[/C][C]0.3641[/C][C]0.18205[/C][/ROW]
[ROW][C]33[/C][C]0.76736[/C][C]0.46528[/C][C]0.23264[/C][/ROW]
[ROW][C]34[/C][C]0.746128[/C][C]0.507744[/C][C]0.253872[/C][/ROW]
[ROW][C]35[/C][C]0.692558[/C][C]0.614885[/C][C]0.307442[/C][/ROW]
[ROW][C]36[/C][C]0.658631[/C][C]0.682739[/C][C]0.341369[/C][/ROW]
[ROW][C]37[/C][C]0.594144[/C][C]0.811711[/C][C]0.405856[/C][/ROW]
[ROW][C]38[/C][C]0.530868[/C][C]0.938264[/C][C]0.469132[/C][/ROW]
[ROW][C]39[/C][C]0.496403[/C][C]0.992806[/C][C]0.503597[/C][/ROW]
[ROW][C]40[/C][C]0.432121[/C][C]0.864242[/C][C]0.567879[/C][/ROW]
[ROW][C]41[/C][C]0.378596[/C][C]0.757193[/C][C]0.621404[/C][/ROW]
[ROW][C]42[/C][C]0.327117[/C][C]0.654234[/C][C]0.672883[/C][/ROW]
[ROW][C]43[/C][C]0.390341[/C][C]0.780681[/C][C]0.609659[/C][/ROW]
[ROW][C]44[/C][C]0.363082[/C][C]0.726163[/C][C]0.636918[/C][/ROW]
[ROW][C]45[/C][C]0.312355[/C][C]0.624709[/C][C]0.687645[/C][/ROW]
[ROW][C]46[/C][C]0.265106[/C][C]0.530211[/C][C]0.734894[/C][/ROW]
[ROW][C]47[/C][C]0.233199[/C][C]0.466398[/C][C]0.766801[/C][/ROW]
[ROW][C]48[/C][C]0.353192[/C][C]0.706385[/C][C]0.646808[/C][/ROW]
[ROW][C]49[/C][C]0.30358[/C][C]0.60716[/C][C]0.69642[/C][/ROW]
[ROW][C]50[/C][C]0.288344[/C][C]0.576688[/C][C]0.711656[/C][/ROW]
[ROW][C]51[/C][C]0.259809[/C][C]0.519618[/C][C]0.740191[/C][/ROW]
[ROW][C]52[/C][C]0.221058[/C][C]0.442116[/C][C]0.778942[/C][/ROW]
[ROW][C]53[/C][C]0.185295[/C][C]0.37059[/C][C]0.814705[/C][/ROW]
[ROW][C]54[/C][C]0.153449[/C][C]0.306897[/C][C]0.846551[/C][/ROW]
[ROW][C]55[/C][C]0.135263[/C][C]0.270526[/C][C]0.864737[/C][/ROW]
[ROW][C]56[/C][C]0.176196[/C][C]0.352392[/C][C]0.823804[/C][/ROW]
[ROW][C]57[/C][C]0.147301[/C][C]0.294602[/C][C]0.852699[/C][/ROW]
[ROW][C]58[/C][C]0.128218[/C][C]0.256435[/C][C]0.871782[/C][/ROW]
[ROW][C]59[/C][C]0.107135[/C][C]0.214271[/C][C]0.892865[/C][/ROW]
[ROW][C]60[/C][C]0.0944841[/C][C]0.188968[/C][C]0.905516[/C][/ROW]
[ROW][C]61[/C][C]0.0763529[/C][C]0.152706[/C][C]0.923647[/C][/ROW]
[ROW][C]62[/C][C]0.0595749[/C][C]0.11915[/C][C]0.940425[/C][/ROW]
[ROW][C]63[/C][C]0.0567341[/C][C]0.113468[/C][C]0.943266[/C][/ROW]
[ROW][C]64[/C][C]0.342088[/C][C]0.684177[/C][C]0.657912[/C][/ROW]
[ROW][C]65[/C][C]0.424185[/C][C]0.848371[/C][C]0.575815[/C][/ROW]
[ROW][C]66[/C][C]0.414342[/C][C]0.828685[/C][C]0.585658[/C][/ROW]
[ROW][C]67[/C][C]0.409268[/C][C]0.818535[/C][C]0.590732[/C][/ROW]
[ROW][C]68[/C][C]0.36244[/C][C]0.724879[/C][C]0.63756[/C][/ROW]
[ROW][C]69[/C][C]0.391674[/C][C]0.783348[/C][C]0.608326[/C][/ROW]
[ROW][C]70[/C][C]0.343572[/C][C]0.687145[/C][C]0.656428[/C][/ROW]
[ROW][C]71[/C][C]0.313419[/C][C]0.626839[/C][C]0.686581[/C][/ROW]
[ROW][C]72[/C][C]0.331454[/C][C]0.662909[/C][C]0.668546[/C][/ROW]
[ROW][C]73[/C][C]0.392821[/C][C]0.785641[/C][C]0.607179[/C][/ROW]
[ROW][C]74[/C][C]0.346302[/C][C]0.692605[/C][C]0.653698[/C][/ROW]
[ROW][C]75[/C][C]0.311612[/C][C]0.623223[/C][C]0.688388[/C][/ROW]
[ROW][C]76[/C][C]0.282812[/C][C]0.565624[/C][C]0.717188[/C][/ROW]
[ROW][C]77[/C][C]0.248888[/C][C]0.497776[/C][C]0.751112[/C][/ROW]
[ROW][C]78[/C][C]0.211046[/C][C]0.422092[/C][C]0.788954[/C][/ROW]
[ROW][C]79[/C][C]0.202609[/C][C]0.405218[/C][C]0.797391[/C][/ROW]
[ROW][C]80[/C][C]0.264331[/C][C]0.528662[/C][C]0.735669[/C][/ROW]
[ROW][C]81[/C][C]0.318455[/C][C]0.636909[/C][C]0.681545[/C][/ROW]
[ROW][C]82[/C][C]0.299506[/C][C]0.599011[/C][C]0.700494[/C][/ROW]
[ROW][C]83[/C][C]0.284287[/C][C]0.568574[/C][C]0.715713[/C][/ROW]
[ROW][C]84[/C][C]0.25449[/C][C]0.50898[/C][C]0.74551[/C][/ROW]
[ROW][C]85[/C][C]0.316842[/C][C]0.633683[/C][C]0.683158[/C][/ROW]
[ROW][C]86[/C][C]0.287743[/C][C]0.575485[/C][C]0.712257[/C][/ROW]
[ROW][C]87[/C][C]0.254589[/C][C]0.509178[/C][C]0.745411[/C][/ROW]
[ROW][C]88[/C][C]0.281929[/C][C]0.563858[/C][C]0.718071[/C][/ROW]
[ROW][C]89[/C][C]0.274505[/C][C]0.549009[/C][C]0.725495[/C][/ROW]
[ROW][C]90[/C][C]0.253598[/C][C]0.507195[/C][C]0.746402[/C][/ROW]
[ROW][C]91[/C][C]0.27456[/C][C]0.54912[/C][C]0.72544[/C][/ROW]
[ROW][C]92[/C][C]0.332972[/C][C]0.665943[/C][C]0.667028[/C][/ROW]
[ROW][C]93[/C][C]0.375243[/C][C]0.750485[/C][C]0.624757[/C][/ROW]
[ROW][C]94[/C][C]0.353414[/C][C]0.706828[/C][C]0.646586[/C][/ROW]
[ROW][C]95[/C][C]0.363168[/C][C]0.726337[/C][C]0.636832[/C][/ROW]
[ROW][C]96[/C][C]0.333472[/C][C]0.666945[/C][C]0.666528[/C][/ROW]
[ROW][C]97[/C][C]0.292897[/C][C]0.585794[/C][C]0.707103[/C][/ROW]
[ROW][C]98[/C][C]0.298639[/C][C]0.597278[/C][C]0.701361[/C][/ROW]
[ROW][C]99[/C][C]0.271725[/C][C]0.54345[/C][C]0.728275[/C][/ROW]
[ROW][C]100[/C][C]0.269956[/C][C]0.539912[/C][C]0.730044[/C][/ROW]
[ROW][C]101[/C][C]0.237336[/C][C]0.474672[/C][C]0.762664[/C][/ROW]
[ROW][C]102[/C][C]0.254109[/C][C]0.508217[/C][C]0.745891[/C][/ROW]
[ROW][C]103[/C][C]0.214627[/C][C]0.429254[/C][C]0.785373[/C][/ROW]
[ROW][C]104[/C][C]0.189771[/C][C]0.379542[/C][C]0.810229[/C][/ROW]
[ROW][C]105[/C][C]0.168138[/C][C]0.336276[/C][C]0.831862[/C][/ROW]
[ROW][C]106[/C][C]0.145365[/C][C]0.290729[/C][C]0.854635[/C][/ROW]
[ROW][C]107[/C][C]0.167687[/C][C]0.335373[/C][C]0.832313[/C][/ROW]
[ROW][C]108[/C][C]0.14724[/C][C]0.294479[/C][C]0.85276[/C][/ROW]
[ROW][C]109[/C][C]0.20123[/C][C]0.402459[/C][C]0.79877[/C][/ROW]
[ROW][C]110[/C][C]0.197512[/C][C]0.395023[/C][C]0.802488[/C][/ROW]
[ROW][C]111[/C][C]0.167712[/C][C]0.335423[/C][C]0.832288[/C][/ROW]
[ROW][C]112[/C][C]0.185763[/C][C]0.371527[/C][C]0.814237[/C][/ROW]
[ROW][C]113[/C][C]0.949497[/C][C]0.101005[/C][C]0.0505027[/C][/ROW]
[ROW][C]114[/C][C]0.938664[/C][C]0.122672[/C][C]0.0613359[/C][/ROW]
[ROW][C]115[/C][C]0.936234[/C][C]0.127533[/C][C]0.0637663[/C][/ROW]
[ROW][C]116[/C][C]0.928308[/C][C]0.143385[/C][C]0.0716924[/C][/ROW]
[ROW][C]117[/C][C]0.913462[/C][C]0.173077[/C][C]0.0865383[/C][/ROW]
[ROW][C]118[/C][C]0.897866[/C][C]0.204269[/C][C]0.102134[/C][/ROW]
[ROW][C]119[/C][C]0.898009[/C][C]0.203981[/C][C]0.101991[/C][/ROW]
[ROW][C]120[/C][C]0.872602[/C][C]0.254797[/C][C]0.127398[/C][/ROW]
[ROW][C]121[/C][C]0.835177[/C][C]0.329646[/C][C]0.164823[/C][/ROW]
[ROW][C]122[/C][C]0.802483[/C][C]0.395035[/C][C]0.197517[/C][/ROW]
[ROW][C]123[/C][C]0.807317[/C][C]0.385367[/C][C]0.192683[/C][/ROW]
[ROW][C]124[/C][C]0.784841[/C][C]0.430318[/C][C]0.215159[/C][/ROW]
[ROW][C]125[/C][C]0.730484[/C][C]0.539032[/C][C]0.269516[/C][/ROW]
[ROW][C]126[/C][C]0.672582[/C][C]0.654837[/C][C]0.327418[/C][/ROW]
[ROW][C]127[/C][C]0.764803[/C][C]0.470394[/C][C]0.235197[/C][/ROW]
[ROW][C]128[/C][C]0.73541[/C][C]0.52918[/C][C]0.26459[/C][/ROW]
[ROW][C]129[/C][C]0.666117[/C][C]0.667766[/C][C]0.333883[/C][/ROW]
[ROW][C]130[/C][C]0.621246[/C][C]0.757508[/C][C]0.378754[/C][/ROW]
[ROW][C]131[/C][C]0.552082[/C][C]0.895836[/C][C]0.447918[/C][/ROW]
[ROW][C]132[/C][C]0.516801[/C][C]0.966398[/C][C]0.483199[/C][/ROW]
[ROW][C]133[/C][C]0.471755[/C][C]0.943511[/C][C]0.528245[/C][/ROW]
[ROW][C]134[/C][C]0.462397[/C][C]0.924794[/C][C]0.537603[/C][/ROW]
[ROW][C]135[/C][C]0.394293[/C][C]0.788585[/C][C]0.605707[/C][/ROW]
[ROW][C]136[/C][C]0.451465[/C][C]0.902931[/C][C]0.548535[/C][/ROW]
[ROW][C]137[/C][C]0.444758[/C][C]0.889517[/C][C]0.555242[/C][/ROW]
[ROW][C]138[/C][C]0.361179[/C][C]0.722358[/C][C]0.638821[/C][/ROW]
[ROW][C]139[/C][C]0.321882[/C][C]0.643764[/C][C]0.678118[/C][/ROW]
[ROW][C]140[/C][C]0.243861[/C][C]0.487721[/C][C]0.756139[/C][/ROW]
[ROW][C]141[/C][C]0.745661[/C][C]0.508679[/C][C]0.254339[/C][/ROW]
[ROW][C]142[/C][C]0.731606[/C][C]0.536787[/C][C]0.268394[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221555&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221555&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
200.5829580.8340840.417042
210.9659050.06819050.0340952
220.9463590.1072820.0536412
230.9485370.1029260.0514632
240.9696240.06075260.0303763
250.9507650.09846910.0492346
260.9255470.1489070.0744533
270.896090.207820.10391
280.879230.241540.12077
290.8852160.2295670.114784
300.8813920.2372170.118608
310.8408030.3183940.159197
320.817950.36410.18205
330.767360.465280.23264
340.7461280.5077440.253872
350.6925580.6148850.307442
360.6586310.6827390.341369
370.5941440.8117110.405856
380.5308680.9382640.469132
390.4964030.9928060.503597
400.4321210.8642420.567879
410.3785960.7571930.621404
420.3271170.6542340.672883
430.3903410.7806810.609659
440.3630820.7261630.636918
450.3123550.6247090.687645
460.2651060.5302110.734894
470.2331990.4663980.766801
480.3531920.7063850.646808
490.303580.607160.69642
500.2883440.5766880.711656
510.2598090.5196180.740191
520.2210580.4421160.778942
530.1852950.370590.814705
540.1534490.3068970.846551
550.1352630.2705260.864737
560.1761960.3523920.823804
570.1473010.2946020.852699
580.1282180.2564350.871782
590.1071350.2142710.892865
600.09448410.1889680.905516
610.07635290.1527060.923647
620.05957490.119150.940425
630.05673410.1134680.943266
640.3420880.6841770.657912
650.4241850.8483710.575815
660.4143420.8286850.585658
670.4092680.8185350.590732
680.362440.7248790.63756
690.3916740.7833480.608326
700.3435720.6871450.656428
710.3134190.6268390.686581
720.3314540.6629090.668546
730.3928210.7856410.607179
740.3463020.6926050.653698
750.3116120.6232230.688388
760.2828120.5656240.717188
770.2488880.4977760.751112
780.2110460.4220920.788954
790.2026090.4052180.797391
800.2643310.5286620.735669
810.3184550.6369090.681545
820.2995060.5990110.700494
830.2842870.5685740.715713
840.254490.508980.74551
850.3168420.6336830.683158
860.2877430.5754850.712257
870.2545890.5091780.745411
880.2819290.5638580.718071
890.2745050.5490090.725495
900.2535980.5071950.746402
910.274560.549120.72544
920.3329720.6659430.667028
930.3752430.7504850.624757
940.3534140.7068280.646586
950.3631680.7263370.636832
960.3334720.6669450.666528
970.2928970.5857940.707103
980.2986390.5972780.701361
990.2717250.543450.728275
1000.2699560.5399120.730044
1010.2373360.4746720.762664
1020.2541090.5082170.745891
1030.2146270.4292540.785373
1040.1897710.3795420.810229
1050.1681380.3362760.831862
1060.1453650.2907290.854635
1070.1676870.3353730.832313
1080.147240.2944790.85276
1090.201230.4024590.79877
1100.1975120.3950230.802488
1110.1677120.3354230.832288
1120.1857630.3715270.814237
1130.9494970.1010050.0505027
1140.9386640.1226720.0613359
1150.9362340.1275330.0637663
1160.9283080.1433850.0716924
1170.9134620.1730770.0865383
1180.8978660.2042690.102134
1190.8980090.2039810.101991
1200.8726020.2547970.127398
1210.8351770.3296460.164823
1220.8024830.3950350.197517
1230.8073170.3853670.192683
1240.7848410.4303180.215159
1250.7304840.5390320.269516
1260.6725820.6548370.327418
1270.7648030.4703940.235197
1280.735410.529180.26459
1290.6661170.6677660.333883
1300.6212460.7575080.378754
1310.5520820.8958360.447918
1320.5168010.9663980.483199
1330.4717550.9435110.528245
1340.4623970.9247940.537603
1350.3942930.7885850.605707
1360.4514650.9029310.548535
1370.4447580.8895170.555242
1380.3611790.7223580.638821
1390.3218820.6437640.678118
1400.2438610.4877210.756139
1410.7456610.5086790.254339
1420.7316060.5367870.268394






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level30.0243902OK

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 3 & 0.0243902 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221555&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0243902[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221555&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221555&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 level00OK
10% type I error level30.0243902OK


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