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, 17 Dec 2014 11:46:41 +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/2014/Dec/17/t1418816943jbbrrxa333u6x5f.htm/, Retrieved Thu, 16 May 2024 14:51:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270067, Retrieved Thu, 16 May 2024 14:51:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact73

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [MR 2011] [2014-12-17 11:46:41] [6e8ac1d1765f9a3eaeef7407a694f46f] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9	5	4	2	0	1	11	8	7	18	12	20	4	149	68	12.9
8	5	5	1	1	2	19	18	20	23	20	19	4	139	39	12.2
8	4	6	2	0	2	16	12	9	22	14	18	5	148	32	12.8
8	5	5	0	0	0	24	24	19	22	25	24	4	158	62	7.4
8	7	4	0	2	2	15	16	12	19	15	20	4	128	33	6.7
7	3	0	0	1	1	17	19	16	25	20	20	9	224	52	12.6
8	4	5	2	1	0	19	16	17	28	21	24	8	159	62	14.8
9	4	3	2	2	2	19	15	9	16	15	21	11	105	77	13.3
8	7	5	0	1	0	28	28	28	28	28	28	4	159	76	11.1
7	6	2	2	1	1	26	21	20	21	11	10	4	167	41	8.2
9	6	3	3	0	1	15	18	16	22	22	22	6	165	48	11.4
7	2	4	0	1	1	26	22	22	24	22	19	4	159	63	6.4
8	4	6	0	1	1	16	19	17	24	27	27	8	119	30	10.6
8	4	3	2	0	2	24	22	12	26	24	23	4	176	78	12
8	5	4	1	0	0	25	25	18	28	23	24	4	54	19	6.3
8	3	1	2	0	1	22	20	20	24	24	24	11	91	31	11.3
8	4	5	1	1	1	15	16	12	20	21	25	4	163	66	11.9
6	7	4	1	1	2	21	19	16	26	20	24	4	124	35	9.3
9	5	4	1	0	2	22	18	16	21	19	21	6	137	42	9.6
7	2	4	0	0	2	27	26	21	28	25	28	6	121	45	10
7	3	3	2	0	1	26	24	15	27	16	28	4	153	21	6.4
8	6	6	1	0	2	26	20	17	23	24	22	8	148	25	13.8
7	6	5	1	0	2	22	19	17	24	21	26	5	221	44	10.8
8	2	5	1	0	2	21	19	17	24	22	26	4	188	69	13.8
8	7	6	2	2	0	22	23	18	22	25	21	9	149	54	11.7
3	2	4	0	0	0	20	18	15	21	23	26	4	244	74	10.9
9	10	6	1	2	2	21	16	20	25	20	23	7	148	80	16.1
8	4	5	2	0	1	20	18	13	20	21	20	10	92	42	13.4
8	4	6	3	0	2	22	21	21	21	22	24	4	150	61	9.9
6	2	5	2	0	1	21	20	12	26	25	25	4	153	41	11.5
5	4	4	1	0	2	8	15	6	23	23	24	7	94	46	8.3
8	4	4	0	1	2	22	19	13	21	19	20	12	156	39	11.7
8	7	6	2	1	1	20	19	19	27	21	24	7	132	34	9
9	2	4	2	0	1	24	7	12	25	19	25	5	161	51	9.7
7	6	6	3	0	1	17	20	14	23	25	23	8	105	42	10.8
7	3	6	3	1	1	20	20	13	25	16	21	5	97	31	10.3
3	3	3	1	0	0	23	19	12	23	24	23	4	151	39	10.4
7	2	4	0	1	0	20	19	17	19	24	21	9	131	20	12.7
8	5	5	2	1	1	22	20	19	22	18	18	7	166	49	9.3
8	7	6	2	1	2	19	18	10	24	28	24	4	157	53	11.8
7	6	6	2	1	1	15	14	10	19	15	18	4	111	31	5.9
8	4	6	2	1	2	20	17	11	21	17	21	4	145	39	11.4
8	6	6	1	1	2	22	17	11	27	18	23	4	162	54	13
9	4	6	3	0	2	17	8	10	25	26	25	4	163	49	10.8
6	3	5	2	0	2	14	9	7	25	18	22	7	59	34	12.3
9	5	5	2	1	1	24	22	22	23	22	22	4	187	46	11.3
8	2	3	0	0	2	17	20	12	17	19	23	7	109	55	11.8
8	3	5	0	1	2	23	20	18	28	17	24	4	90	42	7.9
8	5	1	0	0	2	25	22	20	25	26	25	4	105	50	12.7
7	7	5	3	1	2	16	22	9	20	21	22	4	83	13	12.3
8	4	6	2	2	1	18	22	16	25	26	24	4	116	37	11.6
7	3	6	0	0	1	20	16	14	21	21	21	8	42	25	6.7
7	2	4	2	2	2	18	14	11	24	12	24	4	148	30	10.9
9	5	6	0	0	1	23	24	20	28	20	25	4	155	28	12.1
7	4	6	0	1	0	24	21	17	20	20	23	4	125	45	13.3
9	6	6	2	2	2	23	20	14	19	24	27	4	116	35	10.1
7	4	5	3	0	2	13	20	8	24	24	27	7	128	28	5.7
6	4	2	0	0	1	20	18	16	21	22	23	12	138	41	14.3
3	2	2	1	0	0	20	14	11	24	21	18	4	49	6	8
9	9	6	2	1	2	19	19	10	23	20	20	4	96	45	13.3
9	8	6	2	2	1	22	24	15	18	23	23	4	164	73	9.3
7	8	5	0	1	2	22	19	15	27	19	24	5	162	17	12.5
6	3	6	3	1	2	15	16	10	25	24	26	15	99	40	7.6
9	2	5	2	0	1	17	16	10	20	21	20	5	202	64	15.9
8	4	4	0	1	2	19	16	18	21	16	23	10	186	37	9.2
8	2	5	3	0	2	20	14	10	23	17	22	9	66	25	9.1
7	2	4	2	1	2	22	22	22	27	23	23	8	183	65	11.1
9	1	5	2	0	2	21	21	16	24	20	17	4	214	100	13
5	4	4	3	1	0	21	15	10	27	19	20	5	188	28	14.5
6	5	6	0	1	1	16	14	7	24	18	22	4	104	35	12.2
8	8	5	1	1	2	20	15	16	23	18	18	9	177	56	12.3
8	4	4	2	0	1	21	14	16	24	21	19	4	126	29	11.4
8	6	5	1	1	1	20	20	16	21	20	19	10	76	43	8.8
8	5	5	2	1	2	23	21	22	23	17	16	4	99	59	14.6
7	3	4	0	0	0	18	14	5	27	25	26	4	139	50	12.6
9	4	2	0	1	0	16	16	10	25	17	25	7	162	59	13
9	6	5	2	1	1	17	13	8	19	17	23	5	108	27	12.6
8	4	6	1	0	1	24	26	16	24	24	18	4	159	61	13.2
4	3	5	0	0	0	13	13	8	25	21	22	4	74	28	9.9
7	8	5	0	2	2	19	18	16	23	22	26	4	110	51	7.7
8	6	3	1	2	2	20	15	14	23	18	25	4	96	35	10.5
6	3	3	0	0	0	22	18	15	25	22	26	4	116	29	13.4
7	5	5	2	1	2	19	21	9	26	20	26	4	87	48	10.9
7	4	6	1	0	2	21	17	21	26	21	24	6	97	25	4.3
3	3	2	2	0	0	15	18	7	16	21	22	10	127	44	10.3
8	7	6	1	0	1	21	20	17	23	20	21	7	106	64	11.8
8	2	4	1	1	1	24	18	18	26	18	22	4	80	32	11.2
8	4	5	3	0	2	22	25	16	25	25	28	4	74	20	11.4
8	6	6	2	1	1	20	20	16	23	23	22	7	91	28	8.6
5	6	5	0	0	2	21	19	14	26	21	26	4	133	34	13.2
6	6	5	2	1	1	19	18	15	22	20	20	8	74	31	12.6
6	4	6	1	1	2	14	12	8	20	21	24	11	114	26	5.6
7	6	5	0	0	2	25	22	22	27	20	21	6	140	58	9.9
7	5	6	1	1	2	11	16	5	20	22	23	14	95	23	8.8
7	5	5	0	0	2	17	18	13	22	15	23	5	98	21	7.7
8	6	4	0	0	2	22	23	22	24	24	23	4	121	21	9
9	8	5	1	1	2	20	20	18	21	22	22	8	126	33	7.3
8	5	5	2	2	1	22	20	15	24	21	23	9	98	16	11.4
8	6	5	2	1	2	15	16	11	26	17	21	4	95	20	13.6
7	4	5	2	1	2	23	22	19	24	23	27	4	110	37	7.9
9	3	4	2	1	2	20	19	19	24	22	23	5	70	35	10.7
7	3	5	3	0	1	22	23	21	27	23	26	4	102	33	10.3
6	2	0	0	0	0	16	6	4	25	16	27	5	86	27	8.3
7	4	5	0	0	1	25	19	17	27	18	27	4	130	41	9.6
8	5	6	0	0	1	18	24	10	19	25	23	4	96	40	14.2
6	3	1	0	1	0	19	19	13	22	18	23	7	102	35	8.5
2	4	1	0	1	0	25	15	15	22	14	23	10	100	28	13.5
4	5	3	3	0	0	21	18	11	25	20	28	4	94	32	4.9
8	3	3	2	0	2	22	18	20	23	19	24	5	52	22	6.4
6	5	6	0	0	1	21	22	13	24	18	20	4	98	44	9.6
8	4	4	2	0	1	22	23	18	24	22	23	4	118	27	11.6
6	4	5	2	0	2	23	18	20	23	21	22	4	99	17	11.1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270067&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270067&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270067&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
TOT [t] = + 6.75518 + 0.294135Calculation[t] + 0.0605373Algebraic_Reasoning[t] -0.0683402Graphical_Interpretation[t] -0.0778445Proportionality_and_Ratio[t] + 0.111142Probability_and_Sampling[t] -0.199339Estimation[t] + 0.0995325AMS.I1[t] -0.0373678AMS.I2[t] -0.153104AMS.I3[t] + 0.100315AMS.E1[t] + 0.108136AMS.E2[t] -0.185215AMS.E3[t] + 0.0346764AMS.A[t] + 0.0110211LFM[t] + 0.026585CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT
[t] =  +  6.75518 +  0.294135Calculation[t] +  0.0605373Algebraic_Reasoning[t] -0.0683402Graphical_Interpretation[t] -0.0778445Proportionality_and_Ratio[t] +  0.111142Probability_and_Sampling[t] -0.199339Estimation[t] +  0.0995325AMS.I1[t] -0.0373678AMS.I2[t] -0.153104AMS.I3[t] +  0.100315AMS.E1[t] +  0.108136AMS.E2[t] -0.185215AMS.E3[t] +  0.0346764AMS.A[t] +  0.0110211LFM[t] +  0.026585CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270067&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT
[t] =  +  6.75518 +  0.294135Calculation[t] +  0.0605373Algebraic_Reasoning[t] -0.0683402Graphical_Interpretation[t] -0.0778445Proportionality_and_Ratio[t] +  0.111142Probability_and_Sampling[t] -0.199339Estimation[t] +  0.0995325AMS.I1[t] -0.0373678AMS.I2[t] -0.153104AMS.I3[t] +  0.100315AMS.E1[t] +  0.108136AMS.E2[t] -0.185215AMS.E3[t] +  0.0346764AMS.A[t] +  0.0110211LFM[t] +  0.026585CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270067&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270067&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
TOT [t] = + 6.75518 + 0.294135Calculation[t] + 0.0605373Algebraic_Reasoning[t] -0.0683402Graphical_Interpretation[t] -0.0778445Proportionality_and_Ratio[t] + 0.111142Probability_and_Sampling[t] -0.199339Estimation[t] + 0.0995325AMS.I1[t] -0.0373678AMS.I2[t] -0.153104AMS.I3[t] + 0.100315AMS.E1[t] + 0.108136AMS.E2[t] -0.185215AMS.E3[t] + 0.0346764AMS.A[t] + 0.0110211LFM[t] + 0.026585CH[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.755183.272552.0640.04169680.0208484
Calculation0.2941350.1896951.5510.1242960.0621478
Algebraic_Reasoning0.06053730.1494770.4050.6863820.343191
Graphical_Interpretation-0.06834020.187228-0.3650.7159070.357953
Proportionality_and_Ratio-0.07784450.232355-0.3350.7383390.369169
Probability_and_Sampling0.1111420.3921160.28340.7774490.388724
Estimation-0.1993390.343213-0.58080.5627350.281367
AMS.I10.09953250.09885861.0070.3165540.158277
AMS.I2-0.03736780.0839359-0.44520.6571810.328591
AMS.I3-0.1531040.0806711-1.8980.06071670.0303584
AMS.E10.1003150.09903181.0130.3136270.156814
AMS.E20.1081360.08984531.2040.2317120.115856
AMS.E3-0.1852150.094059-1.9690.05182090.0259105
AMS.A0.03467640.09885040.35080.726510.363255
LFM0.01102110.007025351.5690.1199950.0599976
CH0.0265850.017091.5560.1230970.0615487

\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) & 6.75518 & 3.27255 & 2.064 & 0.0416968 & 0.0208484 \tabularnewline
Calculation & 0.294135 & 0.189695 & 1.551 & 0.124296 & 0.0621478 \tabularnewline
Algebraic_Reasoning & 0.0605373 & 0.149477 & 0.405 & 0.686382 & 0.343191 \tabularnewline
Graphical_Interpretation & -0.0683402 & 0.187228 & -0.365 & 0.715907 & 0.357953 \tabularnewline
Proportionality_and_Ratio & -0.0778445 & 0.232355 & -0.335 & 0.738339 & 0.369169 \tabularnewline
Probability_and_Sampling & 0.111142 & 0.392116 & 0.2834 & 0.777449 & 0.388724 \tabularnewline
Estimation & -0.199339 & 0.343213 & -0.5808 & 0.562735 & 0.281367 \tabularnewline
AMS.I1 & 0.0995325 & 0.0988586 & 1.007 & 0.316554 & 0.158277 \tabularnewline
AMS.I2 & -0.0373678 & 0.0839359 & -0.4452 & 0.657181 & 0.328591 \tabularnewline
AMS.I3 & -0.153104 & 0.0806711 & -1.898 & 0.0607167 & 0.0303584 \tabularnewline
AMS.E1 & 0.100315 & 0.0990318 & 1.013 & 0.313627 & 0.156814 \tabularnewline
AMS.E2 & 0.108136 & 0.0898453 & 1.204 & 0.231712 & 0.115856 \tabularnewline
AMS.E3 & -0.185215 & 0.094059 & -1.969 & 0.0518209 & 0.0259105 \tabularnewline
AMS.A & 0.0346764 & 0.0988504 & 0.3508 & 0.72651 & 0.363255 \tabularnewline
LFM & 0.0110211 & 0.00702535 & 1.569 & 0.119995 & 0.0599976 \tabularnewline
CH & 0.026585 & 0.01709 & 1.556 & 0.123097 & 0.0615487 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270067&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]6.75518[/C][C]3.27255[/C][C]2.064[/C][C]0.0416968[/C][C]0.0208484[/C][/ROW]
[ROW][C]Calculation[/C][C]0.294135[/C][C]0.189695[/C][C]1.551[/C][C]0.124296[/C][C]0.0621478[/C][/ROW]
[ROW][C]Algebraic_Reasoning[/C][C]0.0605373[/C][C]0.149477[/C][C]0.405[/C][C]0.686382[/C][C]0.343191[/C][/ROW]
[ROW][C]Graphical_Interpretation[/C][C]-0.0683402[/C][C]0.187228[/C][C]-0.365[/C][C]0.715907[/C][C]0.357953[/C][/ROW]
[ROW][C]Proportionality_and_Ratio[/C][C]-0.0778445[/C][C]0.232355[/C][C]-0.335[/C][C]0.738339[/C][C]0.369169[/C][/ROW]
[ROW][C]Probability_and_Sampling[/C][C]0.111142[/C][C]0.392116[/C][C]0.2834[/C][C]0.777449[/C][C]0.388724[/C][/ROW]
[ROW][C]Estimation[/C][C]-0.199339[/C][C]0.343213[/C][C]-0.5808[/C][C]0.562735[/C][C]0.281367[/C][/ROW]
[ROW][C]AMS.I1[/C][C]0.0995325[/C][C]0.0988586[/C][C]1.007[/C][C]0.316554[/C][C]0.158277[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.0373678[/C][C]0.0839359[/C][C]-0.4452[/C][C]0.657181[/C][C]0.328591[/C][/ROW]
[ROW][C]AMS.I3[/C][C]-0.153104[/C][C]0.0806711[/C][C]-1.898[/C][C]0.0607167[/C][C]0.0303584[/C][/ROW]
[ROW][C]AMS.E1[/C][C]0.100315[/C][C]0.0990318[/C][C]1.013[/C][C]0.313627[/C][C]0.156814[/C][/ROW]
[ROW][C]AMS.E2[/C][C]0.108136[/C][C]0.0898453[/C][C]1.204[/C][C]0.231712[/C][C]0.115856[/C][/ROW]
[ROW][C]AMS.E3[/C][C]-0.185215[/C][C]0.094059[/C][C]-1.969[/C][C]0.0518209[/C][C]0.0259105[/C][/ROW]
[ROW][C]AMS.A[/C][C]0.0346764[/C][C]0.0988504[/C][C]0.3508[/C][C]0.72651[/C][C]0.363255[/C][/ROW]
[ROW][C]LFM[/C][C]0.0110211[/C][C]0.00702535[/C][C]1.569[/C][C]0.119995[/C][C]0.0599976[/C][/ROW]
[ROW][C]CH[/C][C]0.026585[/C][C]0.01709[/C][C]1.556[/C][C]0.123097[/C][C]0.0615487[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270067&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270067&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)6.755183.272552.0640.04169680.0208484
Calculation0.2941350.1896951.5510.1242960.0621478
Algebraic_Reasoning0.06053730.1494770.4050.6863820.343191
Graphical_Interpretation-0.06834020.187228-0.3650.7159070.357953
Proportionality_and_Ratio-0.07784450.232355-0.3350.7383390.369169
Probability_and_Sampling0.1111420.3921160.28340.7774490.388724
Estimation-0.1993390.343213-0.58080.5627350.281367
AMS.I10.09953250.09885861.0070.3165540.158277
AMS.I2-0.03736780.0839359-0.44520.6571810.328591
AMS.I3-0.1531040.0806711-1.8980.06071670.0303584
AMS.E10.1003150.09903181.0130.3136270.156814
AMS.E20.1081360.08984531.2040.2317120.115856
AMS.E3-0.1852150.094059-1.9690.05182090.0259105
AMS.A0.03467640.09885040.35080.726510.363255
LFM0.01102110.007025351.5690.1199950.0599976
CH0.0265850.017091.5560.1230970.0615487






Multiple Linear Regression - Regression Statistics
Multiple R0.454349
R-squared0.206433
Adjusted R-squared0.0824386
F-TEST (value)1.66486
F-TEST (DF numerator)15
F-TEST (DF denominator)96
p-value0.0712788
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.36728
Sum Squared Residuals537.987

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.454349 \tabularnewline
R-squared & 0.206433 \tabularnewline
Adjusted R-squared & 0.0824386 \tabularnewline
F-TEST (value) & 1.66486 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 96 \tabularnewline
p-value & 0.0712788 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.36728 \tabularnewline
Sum Squared Residuals & 537.987 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270067&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.454349[/C][/ROW]
[ROW][C]R-squared[/C][C]0.206433[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0824386[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.66486[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]96[/C][/ROW]
[ROW][C]p-value[/C][C]0.0712788[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.36728[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]537.987[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270067&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270067&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 R0.454349
R-squared0.206433
Adjusted R-squared0.0824386
F-TEST (value)1.66486
F-TEST (DF numerator)15
F-TEST (DF denominator)96
p-value0.0712788
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.36728
Sum Squared Residuals537.987






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.911.78851.11149
212.210.51861.68138
312.811.19441.60564
47.411.6457-4.2457
56.710.3906-3.69056
612.612.56950.0305361
714.811.96722.83282
813.311.98341.31663
911.111.3173-0.21729
108.212.051-3.851
1111.411.31220.0877604
126.411.8045-5.40449
1310.69.844840.755164
141213.416-1.41602
156.39.94774-3.64774
1611.310.01291.28705
1711.911.23710.662944
189.39.99709-0.697088
199.611.1288-1.52883
20109.827930.172071
216.49.46603-3.06603
2213.811.22362.57642
2310.810.8777-0.0776924
2413.811.20452.59547
2511.712.1736-0.473593
2610.911.0801-0.180059
2716.112.1853.91499
2813.411.03152.36851
299.99.95197-0.0519664
3011.511.0460.453954
318.39.88608-1.58608
3211.711.908-0.208008
33910.3854-1.38539
349.712.4207-2.72074
3510.810.43760.362427
3610.39.932010.367991
3710.410.7606-0.360611
3812.710.35022.34984
399.311.5532-2.25315
4011.812.6344-0.83444
415.910.3426-4.44256
4211.410.99740.402612
431312.32110.6789
4410.812.3912-1.59121
4512.39.967422.33258
4611.311.352-0.0519619
4711.810.13931.66071
487.99.8958-1.9958
4912.710.86231.83772
5012.39.36912.9309
5111.610.60250.997497
526.79.52855-2.82855
5310.99.741461.15854
5412.110.35861.74144
5513.310.38042.91959
5610.110.27-0.170004
575.79.80393-4.10393
5814.310.54723.75284
5989.51029-1.51029
6013.311.90281.39722
619.312.26-2.95996
6212.510.5811.91897
637.610.0967-2.49674
6415.913.06382.8362
659.210.2841-1.08405
669.19.96682-0.866819
6711.111.1759-0.0759319
681314-0.999998
6914.512.11942.38061
7012.210.81671.38327
7112.312.5373-0.237348
7211.411.29710.10295
738.810.8351-2.03515
7414.610.74273.85732
7512.612.8215-0.221493
761312.39510.60492
7712.610.72731.87272
7813.212.81240.387567
799.99.757590.142411
807.710.1267-2.42667
8110.510.04940.450571
8213.49.976513.42349
8310.910.38930.510714
844.38.78508-4.48508
8510.39.992810.307186
8611.811.27770.522283
8711.210.10121.09875
8811.48.693582.70642
898.610.3209-1.72087
9013.29.65733.5427
9112.69.802022.79798
925.69.86798-4.26798
939.911.0096-1.10955
948.810.3421-1.54212
957.78.78628-1.08628
9699.53484-0.534837
977.310.6213-3.32129
9811.410.31831.08171
9913.610.17193.42808
1007.99.05809-1.15809
10110.79.641051.05895
10210.38.856441.44356
1038.310.4732-2.1732
1049.610.0064-0.406374
10514.210.77493.42514
1068.510.126-1.62598
10713.58.913784.58622
1084.99.03926-4.13926
1096.48.23352-1.83352
1109.610.5293-0.929257
11111.69.981.62
11211.18.605732.49427

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 11.7885 & 1.11149 \tabularnewline
2 & 12.2 & 10.5186 & 1.68138 \tabularnewline
3 & 12.8 & 11.1944 & 1.60564 \tabularnewline
4 & 7.4 & 11.6457 & -4.2457 \tabularnewline
5 & 6.7 & 10.3906 & -3.69056 \tabularnewline
6 & 12.6 & 12.5695 & 0.0305361 \tabularnewline
7 & 14.8 & 11.9672 & 2.83282 \tabularnewline
8 & 13.3 & 11.9834 & 1.31663 \tabularnewline
9 & 11.1 & 11.3173 & -0.21729 \tabularnewline
10 & 8.2 & 12.051 & -3.851 \tabularnewline
11 & 11.4 & 11.3122 & 0.0877604 \tabularnewline
12 & 6.4 & 11.8045 & -5.40449 \tabularnewline
13 & 10.6 & 9.84484 & 0.755164 \tabularnewline
14 & 12 & 13.416 & -1.41602 \tabularnewline
15 & 6.3 & 9.94774 & -3.64774 \tabularnewline
16 & 11.3 & 10.0129 & 1.28705 \tabularnewline
17 & 11.9 & 11.2371 & 0.662944 \tabularnewline
18 & 9.3 & 9.99709 & -0.697088 \tabularnewline
19 & 9.6 & 11.1288 & -1.52883 \tabularnewline
20 & 10 & 9.82793 & 0.172071 \tabularnewline
21 & 6.4 & 9.46603 & -3.06603 \tabularnewline
22 & 13.8 & 11.2236 & 2.57642 \tabularnewline
23 & 10.8 & 10.8777 & -0.0776924 \tabularnewline
24 & 13.8 & 11.2045 & 2.59547 \tabularnewline
25 & 11.7 & 12.1736 & -0.473593 \tabularnewline
26 & 10.9 & 11.0801 & -0.180059 \tabularnewline
27 & 16.1 & 12.185 & 3.91499 \tabularnewline
28 & 13.4 & 11.0315 & 2.36851 \tabularnewline
29 & 9.9 & 9.95197 & -0.0519664 \tabularnewline
30 & 11.5 & 11.046 & 0.453954 \tabularnewline
31 & 8.3 & 9.88608 & -1.58608 \tabularnewline
32 & 11.7 & 11.908 & -0.208008 \tabularnewline
33 & 9 & 10.3854 & -1.38539 \tabularnewline
34 & 9.7 & 12.4207 & -2.72074 \tabularnewline
35 & 10.8 & 10.4376 & 0.362427 \tabularnewline
36 & 10.3 & 9.93201 & 0.367991 \tabularnewline
37 & 10.4 & 10.7606 & -0.360611 \tabularnewline
38 & 12.7 & 10.3502 & 2.34984 \tabularnewline
39 & 9.3 & 11.5532 & -2.25315 \tabularnewline
40 & 11.8 & 12.6344 & -0.83444 \tabularnewline
41 & 5.9 & 10.3426 & -4.44256 \tabularnewline
42 & 11.4 & 10.9974 & 0.402612 \tabularnewline
43 & 13 & 12.3211 & 0.6789 \tabularnewline
44 & 10.8 & 12.3912 & -1.59121 \tabularnewline
45 & 12.3 & 9.96742 & 2.33258 \tabularnewline
46 & 11.3 & 11.352 & -0.0519619 \tabularnewline
47 & 11.8 & 10.1393 & 1.66071 \tabularnewline
48 & 7.9 & 9.8958 & -1.9958 \tabularnewline
49 & 12.7 & 10.8623 & 1.83772 \tabularnewline
50 & 12.3 & 9.3691 & 2.9309 \tabularnewline
51 & 11.6 & 10.6025 & 0.997497 \tabularnewline
52 & 6.7 & 9.52855 & -2.82855 \tabularnewline
53 & 10.9 & 9.74146 & 1.15854 \tabularnewline
54 & 12.1 & 10.3586 & 1.74144 \tabularnewline
55 & 13.3 & 10.3804 & 2.91959 \tabularnewline
56 & 10.1 & 10.27 & -0.170004 \tabularnewline
57 & 5.7 & 9.80393 & -4.10393 \tabularnewline
58 & 14.3 & 10.5472 & 3.75284 \tabularnewline
59 & 8 & 9.51029 & -1.51029 \tabularnewline
60 & 13.3 & 11.9028 & 1.39722 \tabularnewline
61 & 9.3 & 12.26 & -2.95996 \tabularnewline
62 & 12.5 & 10.581 & 1.91897 \tabularnewline
63 & 7.6 & 10.0967 & -2.49674 \tabularnewline
64 & 15.9 & 13.0638 & 2.8362 \tabularnewline
65 & 9.2 & 10.2841 & -1.08405 \tabularnewline
66 & 9.1 & 9.96682 & -0.866819 \tabularnewline
67 & 11.1 & 11.1759 & -0.0759319 \tabularnewline
68 & 13 & 14 & -0.999998 \tabularnewline
69 & 14.5 & 12.1194 & 2.38061 \tabularnewline
70 & 12.2 & 10.8167 & 1.38327 \tabularnewline
71 & 12.3 & 12.5373 & -0.237348 \tabularnewline
72 & 11.4 & 11.2971 & 0.10295 \tabularnewline
73 & 8.8 & 10.8351 & -2.03515 \tabularnewline
74 & 14.6 & 10.7427 & 3.85732 \tabularnewline
75 & 12.6 & 12.8215 & -0.221493 \tabularnewline
76 & 13 & 12.3951 & 0.60492 \tabularnewline
77 & 12.6 & 10.7273 & 1.87272 \tabularnewline
78 & 13.2 & 12.8124 & 0.387567 \tabularnewline
79 & 9.9 & 9.75759 & 0.142411 \tabularnewline
80 & 7.7 & 10.1267 & -2.42667 \tabularnewline
81 & 10.5 & 10.0494 & 0.450571 \tabularnewline
82 & 13.4 & 9.97651 & 3.42349 \tabularnewline
83 & 10.9 & 10.3893 & 0.510714 \tabularnewline
84 & 4.3 & 8.78508 & -4.48508 \tabularnewline
85 & 10.3 & 9.99281 & 0.307186 \tabularnewline
86 & 11.8 & 11.2777 & 0.522283 \tabularnewline
87 & 11.2 & 10.1012 & 1.09875 \tabularnewline
88 & 11.4 & 8.69358 & 2.70642 \tabularnewline
89 & 8.6 & 10.3209 & -1.72087 \tabularnewline
90 & 13.2 & 9.6573 & 3.5427 \tabularnewline
91 & 12.6 & 9.80202 & 2.79798 \tabularnewline
92 & 5.6 & 9.86798 & -4.26798 \tabularnewline
93 & 9.9 & 11.0096 & -1.10955 \tabularnewline
94 & 8.8 & 10.3421 & -1.54212 \tabularnewline
95 & 7.7 & 8.78628 & -1.08628 \tabularnewline
96 & 9 & 9.53484 & -0.534837 \tabularnewline
97 & 7.3 & 10.6213 & -3.32129 \tabularnewline
98 & 11.4 & 10.3183 & 1.08171 \tabularnewline
99 & 13.6 & 10.1719 & 3.42808 \tabularnewline
100 & 7.9 & 9.05809 & -1.15809 \tabularnewline
101 & 10.7 & 9.64105 & 1.05895 \tabularnewline
102 & 10.3 & 8.85644 & 1.44356 \tabularnewline
103 & 8.3 & 10.4732 & -2.1732 \tabularnewline
104 & 9.6 & 10.0064 & -0.406374 \tabularnewline
105 & 14.2 & 10.7749 & 3.42514 \tabularnewline
106 & 8.5 & 10.126 & -1.62598 \tabularnewline
107 & 13.5 & 8.91378 & 4.58622 \tabularnewline
108 & 4.9 & 9.03926 & -4.13926 \tabularnewline
109 & 6.4 & 8.23352 & -1.83352 \tabularnewline
110 & 9.6 & 10.5293 & -0.929257 \tabularnewline
111 & 11.6 & 9.98 & 1.62 \tabularnewline
112 & 11.1 & 8.60573 & 2.49427 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270067&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]12.9[/C][C]11.7885[/C][C]1.11149[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.5186[/C][C]1.68138[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.1944[/C][C]1.60564[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.6457[/C][C]-4.2457[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]10.3906[/C][C]-3.69056[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]12.5695[/C][C]0.0305361[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]11.9672[/C][C]2.83282[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]11.9834[/C][C]1.31663[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]11.3173[/C][C]-0.21729[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]12.051[/C][C]-3.851[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.3122[/C][C]0.0877604[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]11.8045[/C][C]-5.40449[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]9.84484[/C][C]0.755164[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.416[/C][C]-1.41602[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]9.94774[/C][C]-3.64774[/C][/ROW]
[ROW][C]16[/C][C]11.3[/C][C]10.0129[/C][C]1.28705[/C][/ROW]
[ROW][C]17[/C][C]11.9[/C][C]11.2371[/C][C]0.662944[/C][/ROW]
[ROW][C]18[/C][C]9.3[/C][C]9.99709[/C][C]-0.697088[/C][/ROW]
[ROW][C]19[/C][C]9.6[/C][C]11.1288[/C][C]-1.52883[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]9.82793[/C][C]0.172071[/C][/ROW]
[ROW][C]21[/C][C]6.4[/C][C]9.46603[/C][C]-3.06603[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.2236[/C][C]2.57642[/C][/ROW]
[ROW][C]23[/C][C]10.8[/C][C]10.8777[/C][C]-0.0776924[/C][/ROW]
[ROW][C]24[/C][C]13.8[/C][C]11.2045[/C][C]2.59547[/C][/ROW]
[ROW][C]25[/C][C]11.7[/C][C]12.1736[/C][C]-0.473593[/C][/ROW]
[ROW][C]26[/C][C]10.9[/C][C]11.0801[/C][C]-0.180059[/C][/ROW]
[ROW][C]27[/C][C]16.1[/C][C]12.185[/C][C]3.91499[/C][/ROW]
[ROW][C]28[/C][C]13.4[/C][C]11.0315[/C][C]2.36851[/C][/ROW]
[ROW][C]29[/C][C]9.9[/C][C]9.95197[/C][C]-0.0519664[/C][/ROW]
[ROW][C]30[/C][C]11.5[/C][C]11.046[/C][C]0.453954[/C][/ROW]
[ROW][C]31[/C][C]8.3[/C][C]9.88608[/C][C]-1.58608[/C][/ROW]
[ROW][C]32[/C][C]11.7[/C][C]11.908[/C][C]-0.208008[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]10.3854[/C][C]-1.38539[/C][/ROW]
[ROW][C]34[/C][C]9.7[/C][C]12.4207[/C][C]-2.72074[/C][/ROW]
[ROW][C]35[/C][C]10.8[/C][C]10.4376[/C][C]0.362427[/C][/ROW]
[ROW][C]36[/C][C]10.3[/C][C]9.93201[/C][C]0.367991[/C][/ROW]
[ROW][C]37[/C][C]10.4[/C][C]10.7606[/C][C]-0.360611[/C][/ROW]
[ROW][C]38[/C][C]12.7[/C][C]10.3502[/C][C]2.34984[/C][/ROW]
[ROW][C]39[/C][C]9.3[/C][C]11.5532[/C][C]-2.25315[/C][/ROW]
[ROW][C]40[/C][C]11.8[/C][C]12.6344[/C][C]-0.83444[/C][/ROW]
[ROW][C]41[/C][C]5.9[/C][C]10.3426[/C][C]-4.44256[/C][/ROW]
[ROW][C]42[/C][C]11.4[/C][C]10.9974[/C][C]0.402612[/C][/ROW]
[ROW][C]43[/C][C]13[/C][C]12.3211[/C][C]0.6789[/C][/ROW]
[ROW][C]44[/C][C]10.8[/C][C]12.3912[/C][C]-1.59121[/C][/ROW]
[ROW][C]45[/C][C]12.3[/C][C]9.96742[/C][C]2.33258[/C][/ROW]
[ROW][C]46[/C][C]11.3[/C][C]11.352[/C][C]-0.0519619[/C][/ROW]
[ROW][C]47[/C][C]11.8[/C][C]10.1393[/C][C]1.66071[/C][/ROW]
[ROW][C]48[/C][C]7.9[/C][C]9.8958[/C][C]-1.9958[/C][/ROW]
[ROW][C]49[/C][C]12.7[/C][C]10.8623[/C][C]1.83772[/C][/ROW]
[ROW][C]50[/C][C]12.3[/C][C]9.3691[/C][C]2.9309[/C][/ROW]
[ROW][C]51[/C][C]11.6[/C][C]10.6025[/C][C]0.997497[/C][/ROW]
[ROW][C]52[/C][C]6.7[/C][C]9.52855[/C][C]-2.82855[/C][/ROW]
[ROW][C]53[/C][C]10.9[/C][C]9.74146[/C][C]1.15854[/C][/ROW]
[ROW][C]54[/C][C]12.1[/C][C]10.3586[/C][C]1.74144[/C][/ROW]
[ROW][C]55[/C][C]13.3[/C][C]10.3804[/C][C]2.91959[/C][/ROW]
[ROW][C]56[/C][C]10.1[/C][C]10.27[/C][C]-0.170004[/C][/ROW]
[ROW][C]57[/C][C]5.7[/C][C]9.80393[/C][C]-4.10393[/C][/ROW]
[ROW][C]58[/C][C]14.3[/C][C]10.5472[/C][C]3.75284[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]9.51029[/C][C]-1.51029[/C][/ROW]
[ROW][C]60[/C][C]13.3[/C][C]11.9028[/C][C]1.39722[/C][/ROW]
[ROW][C]61[/C][C]9.3[/C][C]12.26[/C][C]-2.95996[/C][/ROW]
[ROW][C]62[/C][C]12.5[/C][C]10.581[/C][C]1.91897[/C][/ROW]
[ROW][C]63[/C][C]7.6[/C][C]10.0967[/C][C]-2.49674[/C][/ROW]
[ROW][C]64[/C][C]15.9[/C][C]13.0638[/C][C]2.8362[/C][/ROW]
[ROW][C]65[/C][C]9.2[/C][C]10.2841[/C][C]-1.08405[/C][/ROW]
[ROW][C]66[/C][C]9.1[/C][C]9.96682[/C][C]-0.866819[/C][/ROW]
[ROW][C]67[/C][C]11.1[/C][C]11.1759[/C][C]-0.0759319[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]14[/C][C]-0.999998[/C][/ROW]
[ROW][C]69[/C][C]14.5[/C][C]12.1194[/C][C]2.38061[/C][/ROW]
[ROW][C]70[/C][C]12.2[/C][C]10.8167[/C][C]1.38327[/C][/ROW]
[ROW][C]71[/C][C]12.3[/C][C]12.5373[/C][C]-0.237348[/C][/ROW]
[ROW][C]72[/C][C]11.4[/C][C]11.2971[/C][C]0.10295[/C][/ROW]
[ROW][C]73[/C][C]8.8[/C][C]10.8351[/C][C]-2.03515[/C][/ROW]
[ROW][C]74[/C][C]14.6[/C][C]10.7427[/C][C]3.85732[/C][/ROW]
[ROW][C]75[/C][C]12.6[/C][C]12.8215[/C][C]-0.221493[/C][/ROW]
[ROW][C]76[/C][C]13[/C][C]12.3951[/C][C]0.60492[/C][/ROW]
[ROW][C]77[/C][C]12.6[/C][C]10.7273[/C][C]1.87272[/C][/ROW]
[ROW][C]78[/C][C]13.2[/C][C]12.8124[/C][C]0.387567[/C][/ROW]
[ROW][C]79[/C][C]9.9[/C][C]9.75759[/C][C]0.142411[/C][/ROW]
[ROW][C]80[/C][C]7.7[/C][C]10.1267[/C][C]-2.42667[/C][/ROW]
[ROW][C]81[/C][C]10.5[/C][C]10.0494[/C][C]0.450571[/C][/ROW]
[ROW][C]82[/C][C]13.4[/C][C]9.97651[/C][C]3.42349[/C][/ROW]
[ROW][C]83[/C][C]10.9[/C][C]10.3893[/C][C]0.510714[/C][/ROW]
[ROW][C]84[/C][C]4.3[/C][C]8.78508[/C][C]-4.48508[/C][/ROW]
[ROW][C]85[/C][C]10.3[/C][C]9.99281[/C][C]0.307186[/C][/ROW]
[ROW][C]86[/C][C]11.8[/C][C]11.2777[/C][C]0.522283[/C][/ROW]
[ROW][C]87[/C][C]11.2[/C][C]10.1012[/C][C]1.09875[/C][/ROW]
[ROW][C]88[/C][C]11.4[/C][C]8.69358[/C][C]2.70642[/C][/ROW]
[ROW][C]89[/C][C]8.6[/C][C]10.3209[/C][C]-1.72087[/C][/ROW]
[ROW][C]90[/C][C]13.2[/C][C]9.6573[/C][C]3.5427[/C][/ROW]
[ROW][C]91[/C][C]12.6[/C][C]9.80202[/C][C]2.79798[/C][/ROW]
[ROW][C]92[/C][C]5.6[/C][C]9.86798[/C][C]-4.26798[/C][/ROW]
[ROW][C]93[/C][C]9.9[/C][C]11.0096[/C][C]-1.10955[/C][/ROW]
[ROW][C]94[/C][C]8.8[/C][C]10.3421[/C][C]-1.54212[/C][/ROW]
[ROW][C]95[/C][C]7.7[/C][C]8.78628[/C][C]-1.08628[/C][/ROW]
[ROW][C]96[/C][C]9[/C][C]9.53484[/C][C]-0.534837[/C][/ROW]
[ROW][C]97[/C][C]7.3[/C][C]10.6213[/C][C]-3.32129[/C][/ROW]
[ROW][C]98[/C][C]11.4[/C][C]10.3183[/C][C]1.08171[/C][/ROW]
[ROW][C]99[/C][C]13.6[/C][C]10.1719[/C][C]3.42808[/C][/ROW]
[ROW][C]100[/C][C]7.9[/C][C]9.05809[/C][C]-1.15809[/C][/ROW]
[ROW][C]101[/C][C]10.7[/C][C]9.64105[/C][C]1.05895[/C][/ROW]
[ROW][C]102[/C][C]10.3[/C][C]8.85644[/C][C]1.44356[/C][/ROW]
[ROW][C]103[/C][C]8.3[/C][C]10.4732[/C][C]-2.1732[/C][/ROW]
[ROW][C]104[/C][C]9.6[/C][C]10.0064[/C][C]-0.406374[/C][/ROW]
[ROW][C]105[/C][C]14.2[/C][C]10.7749[/C][C]3.42514[/C][/ROW]
[ROW][C]106[/C][C]8.5[/C][C]10.126[/C][C]-1.62598[/C][/ROW]
[ROW][C]107[/C][C]13.5[/C][C]8.91378[/C][C]4.58622[/C][/ROW]
[ROW][C]108[/C][C]4.9[/C][C]9.03926[/C][C]-4.13926[/C][/ROW]
[ROW][C]109[/C][C]6.4[/C][C]8.23352[/C][C]-1.83352[/C][/ROW]
[ROW][C]110[/C][C]9.6[/C][C]10.5293[/C][C]-0.929257[/C][/ROW]
[ROW][C]111[/C][C]11.6[/C][C]9.98[/C][C]1.62[/C][/ROW]
[ROW][C]112[/C][C]11.1[/C][C]8.60573[/C][C]2.49427[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270067&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270067&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
112.911.78851.11149
212.210.51861.68138
312.811.19441.60564
47.411.6457-4.2457
56.710.3906-3.69056
612.612.56950.0305361
714.811.96722.83282
813.311.98341.31663
911.111.3173-0.21729
108.212.051-3.851
1111.411.31220.0877604
126.411.8045-5.40449
1310.69.844840.755164
141213.416-1.41602
156.39.94774-3.64774
1611.310.01291.28705
1711.911.23710.662944
189.39.99709-0.697088
199.611.1288-1.52883
20109.827930.172071
216.49.46603-3.06603
2213.811.22362.57642
2310.810.8777-0.0776924
2413.811.20452.59547
2511.712.1736-0.473593
2610.911.0801-0.180059
2716.112.1853.91499
2813.411.03152.36851
299.99.95197-0.0519664
3011.511.0460.453954
318.39.88608-1.58608
3211.711.908-0.208008
33910.3854-1.38539
349.712.4207-2.72074
3510.810.43760.362427
3610.39.932010.367991
3710.410.7606-0.360611
3812.710.35022.34984
399.311.5532-2.25315
4011.812.6344-0.83444
415.910.3426-4.44256
4211.410.99740.402612
431312.32110.6789
4410.812.3912-1.59121
4512.39.967422.33258
4611.311.352-0.0519619
4711.810.13931.66071
487.99.8958-1.9958
4912.710.86231.83772
5012.39.36912.9309
5111.610.60250.997497
526.79.52855-2.82855
5310.99.741461.15854
5412.110.35861.74144
5513.310.38042.91959
5610.110.27-0.170004
575.79.80393-4.10393
5814.310.54723.75284
5989.51029-1.51029
6013.311.90281.39722
619.312.26-2.95996
6212.510.5811.91897
637.610.0967-2.49674
6415.913.06382.8362
659.210.2841-1.08405
669.19.96682-0.866819
6711.111.1759-0.0759319
681314-0.999998
6914.512.11942.38061
7012.210.81671.38327
7112.312.5373-0.237348
7211.411.29710.10295
738.810.8351-2.03515
7414.610.74273.85732
7512.612.8215-0.221493
761312.39510.60492
7712.610.72731.87272
7813.212.81240.387567
799.99.757590.142411
807.710.1267-2.42667
8110.510.04940.450571
8213.49.976513.42349
8310.910.38930.510714
844.38.78508-4.48508
8510.39.992810.307186
8611.811.27770.522283
8711.210.10121.09875
8811.48.693582.70642
898.610.3209-1.72087
9013.29.65733.5427
9112.69.802022.79798
925.69.86798-4.26798
939.911.0096-1.10955
948.810.3421-1.54212
957.78.78628-1.08628
9699.53484-0.534837
977.310.6213-3.32129
9811.410.31831.08171
9913.610.17193.42808
1007.99.05809-1.15809
10110.79.641051.05895
10210.38.856441.44356
1038.310.4732-2.1732
1049.610.0064-0.406374
10514.210.77493.42514
1068.510.126-1.62598
10713.58.913784.58622
1084.99.03926-4.13926
1096.48.23352-1.83352
1109.610.5293-0.929257
11111.69.981.62
11211.18.605732.49427






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.4199610.8399220.580039
200.3655120.7310250.634488
210.2478080.4956170.752192
220.2864160.5728320.713584
230.2439880.4879770.756012
240.2182520.4365040.781748
250.1460140.2920290.853986
260.1102830.2205670.889717
270.09364920.1872980.906351
280.06261350.1252270.937386
290.06304930.1260990.936951
300.04578230.09156470.954218
310.07046160.1409230.929538
320.05193250.1038650.948068
330.09394860.1878970.906051
340.2493320.4986630.750668
350.2178450.435690.782155
360.1736160.3472320.826384
370.2219270.4438550.778073
380.2735480.5470960.726452
390.2967630.5935250.703237
400.2402150.4804310.759785
410.3367550.673510.663245
420.2989020.5978040.701098
430.2587730.5175450.741227
440.2601740.5203480.739826
450.2742740.5485480.725726
460.240260.480520.75974
470.2211840.4423670.778816
480.2028150.405630.797185
490.2562250.5124490.743775
500.3186980.6373950.681302
510.2795560.5591110.720444
520.2828010.5656020.717199
530.2348830.4697670.765117
540.241850.48370.75815
550.3222870.6445750.677713
560.2741590.5483170.725841
570.4418490.8836980.558151
580.548710.9025810.45129
590.6027250.7945510.397275
600.5734840.8530310.426516
610.6123180.7753640.387682
620.56430.87140.4357
630.6267110.7465790.373289
640.6629710.6740570.337029
650.6296460.7407070.370354
660.5683080.8633830.431692
670.51560.9688010.4844
680.460170.9203390.53983
690.4510490.9020980.548951
700.3923380.7846750.607662
710.3342280.6684560.665772
720.2818450.5636890.718155
730.2522820.5045650.747718
740.2944790.5889590.705521
750.2362510.4725020.763749
760.2388790.4777590.761121
770.234330.4686590.76567
780.2300530.4601060.769947
790.1784880.3569770.821512
800.1471140.2942270.852886
810.1133540.2267090.886646
820.1442220.2884440.855778
830.1163670.2327330.883633
840.1336430.2672860.866357
850.09676550.1935310.903234
860.1389010.2778020.861099
870.1046180.2092350.895382
880.07765290.1553060.922347
890.05955740.1191150.940443
900.07404570.1480910.925954
910.05147130.1029430.948529
920.0729380.1458760.927062
930.03673050.0734610.963269

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.419961 & 0.839922 & 0.580039 \tabularnewline
20 & 0.365512 & 0.731025 & 0.634488 \tabularnewline
21 & 0.247808 & 0.495617 & 0.752192 \tabularnewline
22 & 0.286416 & 0.572832 & 0.713584 \tabularnewline
23 & 0.243988 & 0.487977 & 0.756012 \tabularnewline
24 & 0.218252 & 0.436504 & 0.781748 \tabularnewline
25 & 0.146014 & 0.292029 & 0.853986 \tabularnewline
26 & 0.110283 & 0.220567 & 0.889717 \tabularnewline
27 & 0.0936492 & 0.187298 & 0.906351 \tabularnewline
28 & 0.0626135 & 0.125227 & 0.937386 \tabularnewline
29 & 0.0630493 & 0.126099 & 0.936951 \tabularnewline
30 & 0.0457823 & 0.0915647 & 0.954218 \tabularnewline
31 & 0.0704616 & 0.140923 & 0.929538 \tabularnewline
32 & 0.0519325 & 0.103865 & 0.948068 \tabularnewline
33 & 0.0939486 & 0.187897 & 0.906051 \tabularnewline
34 & 0.249332 & 0.498663 & 0.750668 \tabularnewline
35 & 0.217845 & 0.43569 & 0.782155 \tabularnewline
36 & 0.173616 & 0.347232 & 0.826384 \tabularnewline
37 & 0.221927 & 0.443855 & 0.778073 \tabularnewline
38 & 0.273548 & 0.547096 & 0.726452 \tabularnewline
39 & 0.296763 & 0.593525 & 0.703237 \tabularnewline
40 & 0.240215 & 0.480431 & 0.759785 \tabularnewline
41 & 0.336755 & 0.67351 & 0.663245 \tabularnewline
42 & 0.298902 & 0.597804 & 0.701098 \tabularnewline
43 & 0.258773 & 0.517545 & 0.741227 \tabularnewline
44 & 0.260174 & 0.520348 & 0.739826 \tabularnewline
45 & 0.274274 & 0.548548 & 0.725726 \tabularnewline
46 & 0.24026 & 0.48052 & 0.75974 \tabularnewline
47 & 0.221184 & 0.442367 & 0.778816 \tabularnewline
48 & 0.202815 & 0.40563 & 0.797185 \tabularnewline
49 & 0.256225 & 0.512449 & 0.743775 \tabularnewline
50 & 0.318698 & 0.637395 & 0.681302 \tabularnewline
51 & 0.279556 & 0.559111 & 0.720444 \tabularnewline
52 & 0.282801 & 0.565602 & 0.717199 \tabularnewline
53 & 0.234883 & 0.469767 & 0.765117 \tabularnewline
54 & 0.24185 & 0.4837 & 0.75815 \tabularnewline
55 & 0.322287 & 0.644575 & 0.677713 \tabularnewline
56 & 0.274159 & 0.548317 & 0.725841 \tabularnewline
57 & 0.441849 & 0.883698 & 0.558151 \tabularnewline
58 & 0.54871 & 0.902581 & 0.45129 \tabularnewline
59 & 0.602725 & 0.794551 & 0.397275 \tabularnewline
60 & 0.573484 & 0.853031 & 0.426516 \tabularnewline
61 & 0.612318 & 0.775364 & 0.387682 \tabularnewline
62 & 0.5643 & 0.8714 & 0.4357 \tabularnewline
63 & 0.626711 & 0.746579 & 0.373289 \tabularnewline
64 & 0.662971 & 0.674057 & 0.337029 \tabularnewline
65 & 0.629646 & 0.740707 & 0.370354 \tabularnewline
66 & 0.568308 & 0.863383 & 0.431692 \tabularnewline
67 & 0.5156 & 0.968801 & 0.4844 \tabularnewline
68 & 0.46017 & 0.920339 & 0.53983 \tabularnewline
69 & 0.451049 & 0.902098 & 0.548951 \tabularnewline
70 & 0.392338 & 0.784675 & 0.607662 \tabularnewline
71 & 0.334228 & 0.668456 & 0.665772 \tabularnewline
72 & 0.281845 & 0.563689 & 0.718155 \tabularnewline
73 & 0.252282 & 0.504565 & 0.747718 \tabularnewline
74 & 0.294479 & 0.588959 & 0.705521 \tabularnewline
75 & 0.236251 & 0.472502 & 0.763749 \tabularnewline
76 & 0.238879 & 0.477759 & 0.761121 \tabularnewline
77 & 0.23433 & 0.468659 & 0.76567 \tabularnewline
78 & 0.230053 & 0.460106 & 0.769947 \tabularnewline
79 & 0.178488 & 0.356977 & 0.821512 \tabularnewline
80 & 0.147114 & 0.294227 & 0.852886 \tabularnewline
81 & 0.113354 & 0.226709 & 0.886646 \tabularnewline
82 & 0.144222 & 0.288444 & 0.855778 \tabularnewline
83 & 0.116367 & 0.232733 & 0.883633 \tabularnewline
84 & 0.133643 & 0.267286 & 0.866357 \tabularnewline
85 & 0.0967655 & 0.193531 & 0.903234 \tabularnewline
86 & 0.138901 & 0.277802 & 0.861099 \tabularnewline
87 & 0.104618 & 0.209235 & 0.895382 \tabularnewline
88 & 0.0776529 & 0.155306 & 0.922347 \tabularnewline
89 & 0.0595574 & 0.119115 & 0.940443 \tabularnewline
90 & 0.0740457 & 0.148091 & 0.925954 \tabularnewline
91 & 0.0514713 & 0.102943 & 0.948529 \tabularnewline
92 & 0.072938 & 0.145876 & 0.927062 \tabularnewline
93 & 0.0367305 & 0.073461 & 0.963269 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270067&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]19[/C][C]0.419961[/C][C]0.839922[/C][C]0.580039[/C][/ROW]
[ROW][C]20[/C][C]0.365512[/C][C]0.731025[/C][C]0.634488[/C][/ROW]
[ROW][C]21[/C][C]0.247808[/C][C]0.495617[/C][C]0.752192[/C][/ROW]
[ROW][C]22[/C][C]0.286416[/C][C]0.572832[/C][C]0.713584[/C][/ROW]
[ROW][C]23[/C][C]0.243988[/C][C]0.487977[/C][C]0.756012[/C][/ROW]
[ROW][C]24[/C][C]0.218252[/C][C]0.436504[/C][C]0.781748[/C][/ROW]
[ROW][C]25[/C][C]0.146014[/C][C]0.292029[/C][C]0.853986[/C][/ROW]
[ROW][C]26[/C][C]0.110283[/C][C]0.220567[/C][C]0.889717[/C][/ROW]
[ROW][C]27[/C][C]0.0936492[/C][C]0.187298[/C][C]0.906351[/C][/ROW]
[ROW][C]28[/C][C]0.0626135[/C][C]0.125227[/C][C]0.937386[/C][/ROW]
[ROW][C]29[/C][C]0.0630493[/C][C]0.126099[/C][C]0.936951[/C][/ROW]
[ROW][C]30[/C][C]0.0457823[/C][C]0.0915647[/C][C]0.954218[/C][/ROW]
[ROW][C]31[/C][C]0.0704616[/C][C]0.140923[/C][C]0.929538[/C][/ROW]
[ROW][C]32[/C][C]0.0519325[/C][C]0.103865[/C][C]0.948068[/C][/ROW]
[ROW][C]33[/C][C]0.0939486[/C][C]0.187897[/C][C]0.906051[/C][/ROW]
[ROW][C]34[/C][C]0.249332[/C][C]0.498663[/C][C]0.750668[/C][/ROW]
[ROW][C]35[/C][C]0.217845[/C][C]0.43569[/C][C]0.782155[/C][/ROW]
[ROW][C]36[/C][C]0.173616[/C][C]0.347232[/C][C]0.826384[/C][/ROW]
[ROW][C]37[/C][C]0.221927[/C][C]0.443855[/C][C]0.778073[/C][/ROW]
[ROW][C]38[/C][C]0.273548[/C][C]0.547096[/C][C]0.726452[/C][/ROW]
[ROW][C]39[/C][C]0.296763[/C][C]0.593525[/C][C]0.703237[/C][/ROW]
[ROW][C]40[/C][C]0.240215[/C][C]0.480431[/C][C]0.759785[/C][/ROW]
[ROW][C]41[/C][C]0.336755[/C][C]0.67351[/C][C]0.663245[/C][/ROW]
[ROW][C]42[/C][C]0.298902[/C][C]0.597804[/C][C]0.701098[/C][/ROW]
[ROW][C]43[/C][C]0.258773[/C][C]0.517545[/C][C]0.741227[/C][/ROW]
[ROW][C]44[/C][C]0.260174[/C][C]0.520348[/C][C]0.739826[/C][/ROW]
[ROW][C]45[/C][C]0.274274[/C][C]0.548548[/C][C]0.725726[/C][/ROW]
[ROW][C]46[/C][C]0.24026[/C][C]0.48052[/C][C]0.75974[/C][/ROW]
[ROW][C]47[/C][C]0.221184[/C][C]0.442367[/C][C]0.778816[/C][/ROW]
[ROW][C]48[/C][C]0.202815[/C][C]0.40563[/C][C]0.797185[/C][/ROW]
[ROW][C]49[/C][C]0.256225[/C][C]0.512449[/C][C]0.743775[/C][/ROW]
[ROW][C]50[/C][C]0.318698[/C][C]0.637395[/C][C]0.681302[/C][/ROW]
[ROW][C]51[/C][C]0.279556[/C][C]0.559111[/C][C]0.720444[/C][/ROW]
[ROW][C]52[/C][C]0.282801[/C][C]0.565602[/C][C]0.717199[/C][/ROW]
[ROW][C]53[/C][C]0.234883[/C][C]0.469767[/C][C]0.765117[/C][/ROW]
[ROW][C]54[/C][C]0.24185[/C][C]0.4837[/C][C]0.75815[/C][/ROW]
[ROW][C]55[/C][C]0.322287[/C][C]0.644575[/C][C]0.677713[/C][/ROW]
[ROW][C]56[/C][C]0.274159[/C][C]0.548317[/C][C]0.725841[/C][/ROW]
[ROW][C]57[/C][C]0.441849[/C][C]0.883698[/C][C]0.558151[/C][/ROW]
[ROW][C]58[/C][C]0.54871[/C][C]0.902581[/C][C]0.45129[/C][/ROW]
[ROW][C]59[/C][C]0.602725[/C][C]0.794551[/C][C]0.397275[/C][/ROW]
[ROW][C]60[/C][C]0.573484[/C][C]0.853031[/C][C]0.426516[/C][/ROW]
[ROW][C]61[/C][C]0.612318[/C][C]0.775364[/C][C]0.387682[/C][/ROW]
[ROW][C]62[/C][C]0.5643[/C][C]0.8714[/C][C]0.4357[/C][/ROW]
[ROW][C]63[/C][C]0.626711[/C][C]0.746579[/C][C]0.373289[/C][/ROW]
[ROW][C]64[/C][C]0.662971[/C][C]0.674057[/C][C]0.337029[/C][/ROW]
[ROW][C]65[/C][C]0.629646[/C][C]0.740707[/C][C]0.370354[/C][/ROW]
[ROW][C]66[/C][C]0.568308[/C][C]0.863383[/C][C]0.431692[/C][/ROW]
[ROW][C]67[/C][C]0.5156[/C][C]0.968801[/C][C]0.4844[/C][/ROW]
[ROW][C]68[/C][C]0.46017[/C][C]0.920339[/C][C]0.53983[/C][/ROW]
[ROW][C]69[/C][C]0.451049[/C][C]0.902098[/C][C]0.548951[/C][/ROW]
[ROW][C]70[/C][C]0.392338[/C][C]0.784675[/C][C]0.607662[/C][/ROW]
[ROW][C]71[/C][C]0.334228[/C][C]0.668456[/C][C]0.665772[/C][/ROW]
[ROW][C]72[/C][C]0.281845[/C][C]0.563689[/C][C]0.718155[/C][/ROW]
[ROW][C]73[/C][C]0.252282[/C][C]0.504565[/C][C]0.747718[/C][/ROW]
[ROW][C]74[/C][C]0.294479[/C][C]0.588959[/C][C]0.705521[/C][/ROW]
[ROW][C]75[/C][C]0.236251[/C][C]0.472502[/C][C]0.763749[/C][/ROW]
[ROW][C]76[/C][C]0.238879[/C][C]0.477759[/C][C]0.761121[/C][/ROW]
[ROW][C]77[/C][C]0.23433[/C][C]0.468659[/C][C]0.76567[/C][/ROW]
[ROW][C]78[/C][C]0.230053[/C][C]0.460106[/C][C]0.769947[/C][/ROW]
[ROW][C]79[/C][C]0.178488[/C][C]0.356977[/C][C]0.821512[/C][/ROW]
[ROW][C]80[/C][C]0.147114[/C][C]0.294227[/C][C]0.852886[/C][/ROW]
[ROW][C]81[/C][C]0.113354[/C][C]0.226709[/C][C]0.886646[/C][/ROW]
[ROW][C]82[/C][C]0.144222[/C][C]0.288444[/C][C]0.855778[/C][/ROW]
[ROW][C]83[/C][C]0.116367[/C][C]0.232733[/C][C]0.883633[/C][/ROW]
[ROW][C]84[/C][C]0.133643[/C][C]0.267286[/C][C]0.866357[/C][/ROW]
[ROW][C]85[/C][C]0.0967655[/C][C]0.193531[/C][C]0.903234[/C][/ROW]
[ROW][C]86[/C][C]0.138901[/C][C]0.277802[/C][C]0.861099[/C][/ROW]
[ROW][C]87[/C][C]0.104618[/C][C]0.209235[/C][C]0.895382[/C][/ROW]
[ROW][C]88[/C][C]0.0776529[/C][C]0.155306[/C][C]0.922347[/C][/ROW]
[ROW][C]89[/C][C]0.0595574[/C][C]0.119115[/C][C]0.940443[/C][/ROW]
[ROW][C]90[/C][C]0.0740457[/C][C]0.148091[/C][C]0.925954[/C][/ROW]
[ROW][C]91[/C][C]0.0514713[/C][C]0.102943[/C][C]0.948529[/C][/ROW]
[ROW][C]92[/C][C]0.072938[/C][C]0.145876[/C][C]0.927062[/C][/ROW]
[ROW][C]93[/C][C]0.0367305[/C][C]0.073461[/C][C]0.963269[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270067&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270067&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
190.4199610.8399220.580039
200.3655120.7310250.634488
210.2478080.4956170.752192
220.2864160.5728320.713584
230.2439880.4879770.756012
240.2182520.4365040.781748
250.1460140.2920290.853986
260.1102830.2205670.889717
270.09364920.1872980.906351
280.06261350.1252270.937386
290.06304930.1260990.936951
300.04578230.09156470.954218
310.07046160.1409230.929538
320.05193250.1038650.948068
330.09394860.1878970.906051
340.2493320.4986630.750668
350.2178450.435690.782155
360.1736160.3472320.826384
370.2219270.4438550.778073
380.2735480.5470960.726452
390.2967630.5935250.703237
400.2402150.4804310.759785
410.3367550.673510.663245
420.2989020.5978040.701098
430.2587730.5175450.741227
440.2601740.5203480.739826
450.2742740.5485480.725726
460.240260.480520.75974
470.2211840.4423670.778816
480.2028150.405630.797185
490.2562250.5124490.743775
500.3186980.6373950.681302
510.2795560.5591110.720444
520.2828010.5656020.717199
530.2348830.4697670.765117
540.241850.48370.75815
550.3222870.6445750.677713
560.2741590.5483170.725841
570.4418490.8836980.558151
580.548710.9025810.45129
590.6027250.7945510.397275
600.5734840.8530310.426516
610.6123180.7753640.387682
620.56430.87140.4357
630.6267110.7465790.373289
640.6629710.6740570.337029
650.6296460.7407070.370354
660.5683080.8633830.431692
670.51560.9688010.4844
680.460170.9203390.53983
690.4510490.9020980.548951
700.3923380.7846750.607662
710.3342280.6684560.665772
720.2818450.5636890.718155
730.2522820.5045650.747718
740.2944790.5889590.705521
750.2362510.4725020.763749
760.2388790.4777590.761121
770.234330.4686590.76567
780.2300530.4601060.769947
790.1784880.3569770.821512
800.1471140.2942270.852886
810.1133540.2267090.886646
820.1442220.2884440.855778
830.1163670.2327330.883633
840.1336430.2672860.866357
850.09676550.1935310.903234
860.1389010.2778020.861099
870.1046180.2092350.895382
880.07765290.1553060.922347
890.05955740.1191150.940443
900.07404570.1480910.925954
910.05147130.1029430.948529
920.0729380.1458760.927062
930.03673050.0734610.963269






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 level20.0266667OK

\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 & 2 & 0.0266667 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270067&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]2[/C][C]0.0266667[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270067&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270067&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 level20.0266667OK


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 = 16 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 16 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '16'
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')
}