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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, 20 Dec 2012 11:17:53 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/20/t135602030906a11kclhntodeo.htm/, Retrieved Thu, 25 Apr 2024 12:42:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202847, Retrieved Thu, 25 Apr 2024 12:42:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact89

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
-   P     [Multiple Regression] [] [2012-12-20 16:17:53] [e8c322125b0cf2de4bdab96981906a22] [Current]
- R P       [Multiple Regression] [] [2012-12-22 16:49:12] [8c6900b6affe5ced50ee5724b2e31c80]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9	41	38	13	12	14	12	53	32
9	39	32	16	11	18	11	83	51
9	30	35	19	15	11	14	66	42
9	31	33	15	6	12	12	67	41
9	34	37	14	13	16	21	76	46
9	35	29	13	10	18	12	78	47
9	39	31	19	12	14	22	53	37
9	34	36	15	14	14	11	80	49
9	36	35	14	12	15	10	74	45
9	37	38	15	9	15	13	76	47
9	38	31	16	10	17	10	79	49
9	36	34	16	12	19	8	54	33
9	38	35	16	12	10	15	67	42
9	39	38	16	11	16	14	54	33
9	33	37	17	15	18	10	87	53
9	32	33	15	12	14	14	58	36
9	36	32	15	10	14	14	75	45
9	38	38	20	12	17	11	88	54
9	39	38	18	11	14	10	64	41
9	32	32	16	12	16	13	57	36
9	32	33	16	11	18	9.5	66	41
9	31	31	16	12	11	14	68	44
9	39	38	19	13	14	12	54	33
9	37	39	16	11	12	14	56	37
9	39	32	17	12	17	11	86	52
9	41	32	17	13	9	9	80	47
9	36	35	16	10	16	11	76	43
9	33	37	15	14	14	15	69	44
9	33	33	16	12	15	14	78	45
9	34	33	14	10	11	13	67	44
9	31	31	15	12	16	9	80	49
9	27	32	12	8	13	15	54	33
9	37	31	14	10	17	10	71	43
9	34	37	16	12	15	11	84	54
9	34	30	14	12	14	13	74	42
9	32	33	10	7	16	8	71	44
9	29	31	10	9	9	20	63	37
9	36	33	14	12	15	12	71	43
9	29	31	16	10	17	10	76	46
9	35	33	16	10	13	10	69	42
9	37	32	16	10	15	9	74	45
9	34	33	14	12	16	14	75	44
9	38	32	20	15	16	8	54	33
9	35	33	14	10	12	14	52	31
9	38	28	14	10	15	11	69	42
9	37	35	11	12	11	13	68	40
9	38	39	14	13	15	9	65	43
9	33	34	15	11	15	11	75	46
9	36	38	16	11	17	15	74	42
9	38	32	14	12	13	11	75	45
9	32	38	16	14	16	10	72	44
9	32	30	14	10	14	14	67	40
9	32	33	12	12	11	18	63	37
9	34	38	16	13	12	14	62	46
9	32	32	9	5	12	11	63	36
9	37	35	14	6	15	14.5	76	47
9	39	34	16	12	16	13	74	45
9	29	34	16	12	15	9	67	42
9	37	36	15	11	12	10	73	43
9	35	34	16	10	12	15	70	43
9	30	28	12	7	8	20	53	32
9	38	34	16	12	13	12	77	45
9	34	35	16	14	11	12	80	48
9	31	35	14	11	14	14	52	31
9	34	31	16	12	15	13	54	33
10	35	37	17	13	10	11	80	49
10	36	35	18	14	11	17	66	42
10	30	27	18	11	12	12	73	41
10	39	40	12	12	15	13	63	38
10	35	37	16	12	15	14	69	42
10	38	36	10	8	14	13	67	44
10	31	38	14	11	16	15	54	33
10	34	39	18	14	15	13	81	48
10	38	41	18	14	15	10	69	40
10	34	27	16	12	13	11	84	50
10	39	30	17	9	12	19	80	49
10	37	37	16	13	17	13	70	43
10	34	31	16	11	13	17	69	44
10	28	31	13	12	15	13	77	47
10	37	27	16	12	13	9	54	33
10	33	36	16	12	15	11	79	46
10	35	37	16	12	15	9	71	45
10	37	33	15	12	16	12	73	43
10	32	34	15	11	15	12	72	44
10	33	31	16	10	14	13	77	47
10	38	39	14	9	15	13	75	45
10	33	34	16	12	14	12	69	42
10	29	32	16	12	13	15	54	33
10	33	33	15	12	7	22	70	43
10	31	36	12	9	17	13	73	46
10	36	32	17	15	13	15	54	33
10	35	41	16	12	15	13	77	46
10	32	28	15	12	14	15	82	48
10	29	30	13	12	13	12.5	80	47
10	39	36	16	10	16	11	80	47
10	37	35	16	13	12	16	69	43
10	35	31	16	9	14	11	78	46
10	37	34	16	12	17	11	81	48
10	32	36	14	10	15	10	76	46
10	38	36	16	14	17	10	76	45
10	37	35	16	11	12	16	73	45
10	36	37	20	15	16	12	85	52
10	32	28	15	11	11	11	66	42
10	33	39	16	11	15	16	79	47
10	40	32	13	12	9	19	68	41
10	38	35	17	12	16	11	76	47
10	41	39	16	12	15	16	71	43
10	36	35	16	11	10	15	54	33
10	43	42	12	7	10	24	46	30
10	30	34	16	12	15	14	85	52
10	31	33	16	14	11	15	74	44
10	32	41	17	11	13	11	88	55
10	32	33	13	11	14	15	38	11
10	37	34	12	10	18	12	76	47
10	37	32	18	13	16	10	86	53
10	33	40	14	13	14	14	54	33
10	34	40	14	8	14	13	67	44
10	33	35	13	11	14	9	69	42
10	38	36	16	12	14	15	90	55
10	33	37	13	11	12	15	54	33
10	31	27	16	13	14	14	76	46
10	38	39	13	12	15	11	89	54
10	37	38	16	14	15	8	76	47
10	36	31	15	13	15	11	73	45
10	31	33	16	15	13	11	79	47
10	39	32	15	10	17	8	90	55
10	44	39	17	11	17	10	74	44
10	33	36	15	9	19	11	81	53
10	35	33	12	11	15	13	72	44
10	32	33	16	10	13	11	71	42
10	28	32	10	11	9	20	66	40
10	40	37	16	8	15	10	77	46
10	27	30	12	11	15	15	65	40
10	37	38	14	12	15	12	74	46
10	32	29	15	12	16	14	85	53
10	28	22	13	9	11	23	54	33
10	34	35	15	11	14	14	63	42
10	30	35	11	10	11	16	54	35
10	35	34	12	8	15	11	64	40
10	31	35	11	9	13	12	69	41
10	32	34	16	8	15	10	54	33
10	30	37	15	9	16	14	84	51
10	30	35	17	15	14	12	86	53
10	31	23	16	11	15	12	77	46
10	40	31	10	8	16	11	89	55
10	32	27	18	13	16	12	76	47
10	36	36	13	12	11	13	60	38
10	32	31	16	12	12	11	75	46
10	35	32	13	9	9	19	73	46
10	38	39	10	7	16	12	85	53
10	42	37	15	13	13	17	79	47
10	34	38	16	9	16	9	71	41
10	35	39	16	6	12	12	72	44
9	38	34	14	8	9	19	69	43
10	33	31	10	8	13	18	78	51
10	36	32	17	15	13	15	54	33
10	32	37	13	6	14	14	69	43
10	33	36	15	9	19	11	81	53
10	34	32	16	11	13	9	84	51
10	32	38	12	8	12	18	84	50
10	34	36	13	8	13	16	69	46
11	27	26	13	10	10	24	66	43
11	31	26	12	8	14	14	81	47
11	38	33	17	14	16	20	82	50
11	34	39	15	10	10	18	72	43
11	24	30	10	8	11	23	54	33
11	30	33	14	11	14	12	78	48
11	26	25	11	12	12	14	74	44
11	34	38	13	12	9	16	82	50
11	27	37	16	12	9	18	73	41
11	37	31	12	5	11	20	55	34
11	36	37	16	12	16	12	72	44
11	41	35	12	10	9	12	78	47
11	29	25	9	7	13	17	59	35
11	36	28	12	12	16	13	72	44
11	32	35	15	11	13	9	78	44
11	37	33	12	8	9	16	68	43
11	30	30	12	9	12	18	69	41
11	31	31	14	10	16	10	67	41
11	38	37	12	9	11	14	74	42
11	36	36	16	12	14	11	54	33
11	35	30	11	6	13	9	67	41
11	31	36	19	15	15	11	70	44
11	38	32	15	12	14	10	80	48
11	22	28	8	12	16	11	89	55
11	32	36	16	12	13	19	76	44
11	36	34	17	11	14	14	74	43
11	39	31	12	7	15	12	87	52
11	28	28	11	7	13	14	54	30
11	32	36	11	5	11	21	61	39
11	32	36	14	12	11	13	38	11
11	38	40	16	12	14	10	75	44
11	32	33	12	3	15	15	69	42
11	35	37	16	11	11	16	62	41
11	32	32	13	10	15	14	72	44
11	37	38	15	12	12	12	70	44
11	34	31	16	9	14	19	79	48
11	33	37	16	12	14	15	87	53
11	33	33	14	9	8	19	62	37
11	26	32	16	12	13	13	77	44
11	30	30	16	12	9	17	69	44
11	24	30	14	10	15	12	69	40
11	34	31	11	9	17	11	75	42
11	34	32	12	12	13	14	54	35
11	33	34	15	8	15	11	72	43
11	34	36	15	11	15	13	74	45
11	35	37	16	11	14	12	85	55
11	35	36	16	12	16	15	52	31
11	36	33	11	10	13	14	70	44
11	34	33	15	10	16	12	84	50
11	34	33	12	12	9	17	64	40
11	41	44	12	12	16	11	84	53
11	32	39	15	11	11	18	87	54
11	30	32	15	8	10	13	79	49
11	35	35	16	12	11	17	67	40
11	28	25	14	10	15	13	65	41
11	33	35	17	11	17	11	85	52
11	39	34	14	10	14	12	83	52
11	36	35	13	8	8	22	61	36
11	36	39	15	12	15	14	82	52
11	35	33	13	12	11	12	76	46
11	38	36	14	10	16	12	58	31
11	33	32	15	12	10	17	72	44
11	31	32	12	9	15	9	72	44
11	34	36	13	9	9	21	38	11
11	32	36	8	6	16	10	78	46
11	31	32	14	10	19	11	54	33
11	33	34	14	9	12	12	63	34
11	34	33	11	9	8	23	66	42
11	34	35	12	9	11	13	70	43
11	34	30	13	6	14	12	71	43
11	33	38	10	10	9	16	67	44
11	32	34	16	6	15	9	58	36
11	41	33	18	14	13	17	72	46
11	34	32	13	10	16	9	72	44
11	36	31	11	10	11	14	70	43
11	37	30	4	6	12	17	76	50
11	36	27	13	12	13	13	50	33
11	29	31	16	12	10	11	72	43
11	37	30	10	7	11	12	72	44
11	27	32	12	8	12	10	88	53
11	35	35	12	11	8	19	53	34
11	28	28	10	3	12	16	58	35
11	35	33	13	6	12	16	66	40
11	37	31	15	10	15	14	82	53
11	29	35	12	8	11	20	69	42
11	32	35	14	9	13	15	68	43
11	36	32	10	9	14	23	44	29
11	19	21	12	8	10	20	56	36
11	21	20	12	9	12	16	53	30
11	31	34	11	7	15	14	70	42
11	33	32	10	7	13	17	78	47
11	36	34	12	6	13	11	71	44
11	33	32	16	9	13	13	72	45
11	37	33	12	10	12	17	68	44
11	34	33	14	11	12	15	67	43
11	35	37	16	12	9	21	75	43
11	31	32	14	8	9	18	62	40
11	37	34	13	11	15	15	67	41
11	35	30	4	3	10	8	83	52
11	27	30	15	11	14	12	64	38
11	34	38	11	12	15	12	68	41
11	40	36	11	7	7	22	62	39
11	29	32	14	9	14	12	72	43

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time14 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 14 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202847&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]14 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202847&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202847&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 time14 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 12.6656397605175 + 0.391360397352737month[t] + 0.00332056851803813Connected[t] + 0.0113272205869829Separate[t] + 0.0804659748165326Learning[t] -0.0410454512458214Software[t] -0.365596492991835Depression[t] + 0.00760159335154229Belonging[t] + 0.0277171531976943Belonging_Final[t] -0.00816105345875706t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  12.6656397605175 +  0.391360397352737month[t] +  0.00332056851803813Connected[t] +  0.0113272205869829Separate[t] +  0.0804659748165326Learning[t] -0.0410454512458214Software[t] -0.365596492991835Depression[t] +  0.00760159335154229Belonging[t] +  0.0277171531976943Belonging_Final[t] -0.00816105345875706t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202847&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  12.6656397605175 +  0.391360397352737month[t] +  0.00332056851803813Connected[t] +  0.0113272205869829Separate[t] +  0.0804659748165326Learning[t] -0.0410454512458214Software[t] -0.365596492991835Depression[t] +  0.00760159335154229Belonging[t] +  0.0277171531976943Belonging_Final[t] -0.00816105345875706t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202847&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202847&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
Happiness[t] = + 12.6656397605175 + 0.391360397352737month[t] + 0.00332056851803813Connected[t] + 0.0113272205869829Separate[t] + 0.0804659748165326Learning[t] -0.0410454512458214Software[t] -0.365596492991835Depression[t] + 0.00760159335154229Belonging[t] + 0.0277171531976943Belonging_Final[t] -0.00816105345875706t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.66563976051754.1260563.06970.0023750.001188
month0.3913603973527370.4396210.89020.3741890.187094
Connected0.003320568518038130.0374110.08880.9293430.464672
Separate0.01132722058698290.0380050.2980.7659140.382957
Learning0.08046597481653260.0673821.19420.2335220.116761
Software-0.04104545124582140.069708-0.58880.5565060.278253
Depression-0.3655964929918350.039605-9.231200
Belonging0.007601593351542290.0409210.18580.852780.42639
Belonging_Final0.02771715319769430.0607510.45620.6486050.324303
t-0.008161053458757060.004658-1.7520.0809860.040493

\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) & 12.6656397605175 & 4.126056 & 3.0697 & 0.002375 & 0.001188 \tabularnewline
month & 0.391360397352737 & 0.439621 & 0.8902 & 0.374189 & 0.187094 \tabularnewline
Connected & 0.00332056851803813 & 0.037411 & 0.0888 & 0.929343 & 0.464672 \tabularnewline
Separate & 0.0113272205869829 & 0.038005 & 0.298 & 0.765914 & 0.382957 \tabularnewline
Learning & 0.0804659748165326 & 0.067382 & 1.1942 & 0.233522 & 0.116761 \tabularnewline
Software & -0.0410454512458214 & 0.069708 & -0.5888 & 0.556506 & 0.278253 \tabularnewline
Depression & -0.365596492991835 & 0.039605 & -9.2312 & 0 & 0 \tabularnewline
Belonging & 0.00760159335154229 & 0.040921 & 0.1858 & 0.85278 & 0.42639 \tabularnewline
Belonging_Final & 0.0277171531976943 & 0.060751 & 0.4562 & 0.648605 & 0.324303 \tabularnewline
t & -0.00816105345875706 & 0.004658 & -1.752 & 0.080986 & 0.040493 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202847&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]12.6656397605175[/C][C]4.126056[/C][C]3.0697[/C][C]0.002375[/C][C]0.001188[/C][/ROW]
[ROW][C]month[/C][C]0.391360397352737[/C][C]0.439621[/C][C]0.8902[/C][C]0.374189[/C][C]0.187094[/C][/ROW]
[ROW][C]Connected[/C][C]0.00332056851803813[/C][C]0.037411[/C][C]0.0888[/C][C]0.929343[/C][C]0.464672[/C][/ROW]
[ROW][C]Separate[/C][C]0.0113272205869829[/C][C]0.038005[/C][C]0.298[/C][C]0.765914[/C][C]0.382957[/C][/ROW]
[ROW][C]Learning[/C][C]0.0804659748165326[/C][C]0.067382[/C][C]1.1942[/C][C]0.233522[/C][C]0.116761[/C][/ROW]
[ROW][C]Software[/C][C]-0.0410454512458214[/C][C]0.069708[/C][C]-0.5888[/C][C]0.556506[/C][C]0.278253[/C][/ROW]
[ROW][C]Depression[/C][C]-0.365596492991835[/C][C]0.039605[/C][C]-9.2312[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Belonging[/C][C]0.00760159335154229[/C][C]0.040921[/C][C]0.1858[/C][C]0.85278[/C][C]0.42639[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]0.0277171531976943[/C][C]0.060751[/C][C]0.4562[/C][C]0.648605[/C][C]0.324303[/C][/ROW]
[ROW][C]t[/C][C]-0.00816105345875706[/C][C]0.004658[/C][C]-1.752[/C][C]0.080986[/C][C]0.040493[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202847&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202847&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)12.66563976051754.1260563.06970.0023750.001188
month0.3913603973527370.4396210.89020.3741890.187094
Connected0.003320568518038130.0374110.08880.9293430.464672
Separate0.01132722058698290.0380050.2980.7659140.382957
Learning0.08046597481653260.0673821.19420.2335220.116761
Software-0.04104545124582140.069708-0.58880.5565060.278253
Depression-0.3655964929918350.039605-9.231200
Belonging0.007601593351542290.0409210.18580.852780.42639
Belonging_Final0.02771715319769430.0607510.45620.6486050.324303
t-0.008161053458757060.004658-1.7520.0809860.040493






Multiple Linear Regression - Regression Statistics
Multiple R0.615819377213055
R-squared0.379233505351074
Adjusted R-squared0.357237842154853
F-TEST (value)17.2412853373849
F-TEST (DF numerator)9
F-TEST (DF denominator)254
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.00322321150583
Sum Squared Residuals1019.2774217194

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.615819377213055 \tabularnewline
R-squared & 0.379233505351074 \tabularnewline
Adjusted R-squared & 0.357237842154853 \tabularnewline
F-TEST (value) & 17.2412853373849 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 254 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.00322321150583 \tabularnewline
Sum Squared Residuals & 1019.2774217194 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202847&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.615819377213055[/C][/ROW]
[ROW][C]R-squared[/C][C]0.379233505351074[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.357237842154853[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.2412853373849[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]254[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.00322321150583[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1019.2774217194[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202847&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202847&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 R0.615819377213055
R-squared0.379233505351074
Adjusted R-squared0.357237842154853
F-TEST (value)17.2412853373849
F-TEST (DF numerator)9
F-TEST (DF denominator)254
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.00322321150583
Sum Squared Residuals1019.2774217194






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11414.2024876664993-0.202487666499301
21815.52243573247232.47756426752771
31114.1201163988475-3.12011639884748
41214.8512440608166-2.85124406081657
51611.44720113094844.55279886905158
61814.72770203706013.27229796293994
71411.03300634957112.96699365042887
81415.2203340364735-1.22033403647347
91515.4182301472307-0.418230147230724
101514.62482166672790.375178333272081
111715.75513972667461.24486027332545
121915.7899069964813.21009300351897
131013.5888139420518-3.58881394205179
141613.67632197076042.32367802923963
151815.82077607196182.17922392803823
161413.57216618821860.427833811781425
171414.0267325564921-0.0267325564920979
181715.85847950650711.14152049349286
191415.556587784164-1.55658778416397
201613.96665562825342.03334437174661
211815.49745507825122.50254492174879
221113.8754439916875-2.87544399168754
231414.4933724975732-0.493372497573182
241213.7254693192176-1.72546931921757
251715.42467395974391.57532604025609
26915.9291062519614-6.92910625196135
271615.10852642441370.891473575586334
281413.38053035454450.619469645455498
291513.95134528239941.0486547176006
301114.1219255632444-3.12192556324443
311615.77931588690830.220684113091654
321312.85728882424390.142711175756111
331715.18422836643231.81577163356771
341515.3510228838356-0.351022883835589
351413.96282457876350.0371754212365371
361615.72597939829250.274020601707509
37910.9611205605158-1.96112056051579
381514.34947308331910.650526916680881
391715.39078887351931.6092111264807
401315.2611259060912-2.26112590609116
411515.7350346884241-0.73503468842413
421613.63711827306822.36288172693179
431615.71962858065030.280371419349665
441213.1710479985049-1.17104799850488
451514.64711779879170.35288220120832
461113.5972090082519-2.59720900825192
471515.3607625303687-0.360762530368707
481514.86989381581790.130106184182143
491713.4166131469683.58338685303198
501314.6982915310565-1.69829153105652
511615.13208599689240.867914003107633
521413.42529448257210.57470551742786
531111.632148433783-0.632148433783033
541213.6723218257106-1.67232182571062
551214.1818776382948-2.18187763829484
561513.70970718529731.29029281470267
571614.08927653683511.91072346316492
581515.3739331571094-0.373933157109399
591215.0833007897133-3.08330078971331
601213.3165683390931-1.31656833909307
61810.7630153368835-2.76301533688353
621314.4335519740697-1.43355197406972
631114.4473012042819-3.44730120428186
641412.97615364518571.02384635481427
651513.48876589941071.5112341005893
661015.3549785232022-5.35497852320221
671112.8728827834018-1.87288278340178
681214.7307933730987-2.73079337309867
691513.85116611768771.14883388231233
701513.90848670757011.09151329242986
711413.98617370784670.0138262921532562
721613.0412482767362.958751723264
731514.60529699938770.394703000612273
741515.4169057943505-0.41690579435053
751315.1836392707187-2.18363927071868
761212.4467695796265-0.446769579626519
771714.21787025869022.78212974130983
781312.77160466652590.228395333474145
791514.06742700463620.93257299536379
801315.1847492900556-2.18474929005561
811515.0844207871825-0.0844207871824578
821515.7368911773204-0.736891177320396
831614.47257580506541.52742419493456
841514.52030014069550.479699859304548
851414.3585523534151-0.358552353415098
861514.26708791575670.732912084243322
871414.4603189859579-0.460318985957915
881312.95595345922520.0440465407748481
89710.7315575002678-3.73155750026777
901714.0288000773962.97119992260402
911312.91204389955420.0879561004457838
921514.31153026642040.688469733579633
931413.42793695212960.572063047870384
941314.1426075772365-1.14260757723645
951615.1074990989090.892500901091033
961212.9357647294728-0.93576472947277
971415.0193837263294-1.01938372632943
981715.00694820438021.99305179561975
991515.1981519222026-0.198151922202578
1001715.17894727130411.82105272869586
1011213.0628910444722-1.06289104447219
1021614.97937112252191.02062887747809
1031114.5618294279444-3.56182942794436
1041513.17147835887631.82852164112366
105911.4781174402109-2.47811744021091
1061614.97104842067631.02895157932366
1071512.96083293579542.03916706420457
1081012.891003482683-2.89100348268305
109109.393362215344660.60663778465534
1101513.93025709047031.06974290952966
1111113.1610472370106-2.16104723701064
1121315.3241238093833-2.32412380938335
1131411.84146071171962.15853928828041
1141814.20527673931843.79472326068159
1151615.50763257853270.492367421467326
1161412.99496309326091.00503690673908
1171413.96465575628580.035344243714172
1181415.1150905550952-1.11509055509516
1191413.66158953201880.338410467981245
1201212.5643656523482-0.564365652348217
1211413.48875281623730.511247183762699
1221514.85675733437190.143242665628063
1231515.7972042067877-0.797204206787708
1241514.49198295170580.508017048294198
1251314.5892924356604-1.58929243566035
1261716.12327422259550.876725777404484
1271715.17318588944011.82681411055991
1281914.95274491262884.04725508737125
1291513.54469280257711.45530719742292
1301314.5576364803129-1.5576364803129
131910.6172139219704-1.61721392197038
1321515.2173533722711-0.217353372271133
1331512.55622962660162.44377037339844
1341514.12328525973180.876714740268159
1351613.62348696648372.37651303351626
136119.404596604170141.59540339582986
1371413.25069103246140.749308967538551
1381111.9548018583788-0.95480185837882
1391514.15705746869450.842942531305487
1401313.7255585626518-0.725558562651804
1411514.54819804258160.451801957418433
1421613.71043667392282.28956332607724
1431414.3961109005303-0.396110900530313
1441514.07662518616480.923374813835189
1451614.53557751089211.46442248910788
1461614.20788913710081.79211086289916
1471113.2169945547645-2.21699455476448
1481214.4472671605866-2.44726716058661
149911.4021583319191-2.40215833191906
1501614.16835714971061.83164285028945
1511312.26698615150310.733013848496878
1521615.1858918282230.814108171776966
1531214.3094784915759-2.30947849157588
154911.0105624070637-2.01056240706372
1551311.67706140607771.3229385939223
1561312.3815754247350.618424575264993
1571413.22110528732720.778894712672814
1581914.7079133088664.29208669113396
1591315.3547024735452-2.35470247354519
1601211.89105047091940.108949529080622
1611312.4536425610590.546357438940978
162109.587506633382880.412493366617123
1631413.47511022465360.524889775346359
1641611.62271495653144.37728504346862
1651012.133641787957-2.13364178795698
166119.428108415180091.57189158481991
1671414.2923369414318-0.292336941431793
1681213.025364501329-1.02536450132899
169912.8478764932932-3.84787649329321
170911.997480459144-2.997480459144
1711110.85797428810310.142025711896941
1721614.28017229308141.71982770691862
173914.1549476639667-5.1549476639667
1741311.57038743469871.42961256530129
1751613.46628375516432.5337162448357
1761315.3145698795143-2.31456987951428
177912.5191871191037-3.51918711910365
1781211.63372927398430.366270726015655
1791614.66967126524941.33032873475055
1801113.2513733512428-2.25137335124276
1811414.119274718855-0.11927471885502
1821314.9355235318839-1.93552353188387
1831514.63112551885880.368874481141237
1841414.9546530564477-0.954653056447711
1851614.18163012019261.81836987980741
1861311.6125389724631.38746102753703
1871413.50157930302320.49842069697685
1881514.31071830259110.68928169740885
1891312.55976042192270.440239578077348
1901110.48108039102080.518919608979244
1911112.4008541106046-1.40085411060461
1921414.9115717887416-0.91157178874156
1931512.92271561054812.07728438945186
1941112.5168006346407-1.51680063464066
1951513.13204967858131.86795032141872
1961214.0032856376566-2.00328563765661
1971411.72958216510072.27041783489933
1981413.32471199767650.675288002323538
199811.1375462090552-3.13754620905517
2001313.6342324818868-0.634232481886764
201912.0935005425465-3.09350054254652
2021513.70368888300651.2963111169935
2031714.00634862160782.99365137839219
2041312.51640139807340.483598601926646
2051514.38850933158820.611490668411773
2061513.62263144119881.37736855880119
2071414.4359697034974-0.435969703497354
2081612.36258224188473.63741775811527
2091312.86626928848170.133730711518336
2101614.17724920934441.82275079065561
211911.5884134649774-2.58841346497743
2121614.43402963415361.56597036584637
2131112.1131372191025-1.11313721910251
2141013.7707647903946-3.77076479039463
2151111.9264129401552-0.926412940155161
2161513.17779459252081.82220540747917
2171714.6879745989152.31202540108497
2181414.1072575830788-0.107257583078819
21989.83541253751265-1.83541253751265
2201513.39719036653181.60280963346822
2211113.6760939780883-2.67609397808828
2221613.32184719095962.67815280904042
2231011.88891231842-1.88891231842004
2241514.68062050114770.319379498852254
22599.2479178650292-0.2479178650292
2261614.24964767308031.75035232691966
2271913.60311348767265.39688651232735
2281213.3958284640395-1.3958284640395
22989.3612434167882-1.3612434167882
2301113.1702912358422-2.17029123584215
2311413.68229449434590.317705505654149
232911.8907757154562-2.89077571545622
2331514.74998675021140.250013249788616
2341312.05177382745830.948226172541661
2351614.64023114222621.35976885777376
2361112.5955492507236-1.59554925072356
2371211.3231416799810.676858320018971
2381312.54915240258360.450847597416415
2391013.9800537479906-3.98005374799063
2401113.371682089625-2.37168208962497
2411214.5751291489346-2.57512914893464
242810.421327836657-2.42132783665698
2431211.64057851872750.359421481272505
2441212.0299576313429-0.0299576313428628
2451513.21567488957441.78432511042563
2461110.46966279166560.530337208334412
2471312.43944796695350.560552033046486
248148.593473297400415.40652670259959
249109.988269225134960.0117307748650405
2501211.2076549096060.79234509039398
2511512.58593146855272.41406853144726
2521311.58390016996491.41609983003514
2531313.8675490090102-0.8675490090102
2541313.3296251149176-0.329625114917596
2551211.46265470703480.53734529296518
2561212.2602926858436-0.260292685843626
257910.2878813704994-1.28788137049941
258911.1278691011962-2.12786910119618
2591512.12119817039652.87880182960347
2601014.6509465639137-4.6509465639137
2611413.17812668491160.821873315088437
2621513.03447585826221.96552414173776
26379.47180223454386-2.47180223454386
2641413.38396254322130.616037456778746

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 14.2024876664993 & -0.202487666499301 \tabularnewline
2 & 18 & 15.5224357324723 & 2.47756426752771 \tabularnewline
3 & 11 & 14.1201163988475 & -3.12011639884748 \tabularnewline
4 & 12 & 14.8512440608166 & -2.85124406081657 \tabularnewline
5 & 16 & 11.4472011309484 & 4.55279886905158 \tabularnewline
6 & 18 & 14.7277020370601 & 3.27229796293994 \tabularnewline
7 & 14 & 11.0330063495711 & 2.96699365042887 \tabularnewline
8 & 14 & 15.2203340364735 & -1.22033403647347 \tabularnewline
9 & 15 & 15.4182301472307 & -0.418230147230724 \tabularnewline
10 & 15 & 14.6248216667279 & 0.375178333272081 \tabularnewline
11 & 17 & 15.7551397266746 & 1.24486027332545 \tabularnewline
12 & 19 & 15.789906996481 & 3.21009300351897 \tabularnewline
13 & 10 & 13.5888139420518 & -3.58881394205179 \tabularnewline
14 & 16 & 13.6763219707604 & 2.32367802923963 \tabularnewline
15 & 18 & 15.8207760719618 & 2.17922392803823 \tabularnewline
16 & 14 & 13.5721661882186 & 0.427833811781425 \tabularnewline
17 & 14 & 14.0267325564921 & -0.0267325564920979 \tabularnewline
18 & 17 & 15.8584795065071 & 1.14152049349286 \tabularnewline
19 & 14 & 15.556587784164 & -1.55658778416397 \tabularnewline
20 & 16 & 13.9666556282534 & 2.03334437174661 \tabularnewline
21 & 18 & 15.4974550782512 & 2.50254492174879 \tabularnewline
22 & 11 & 13.8754439916875 & -2.87544399168754 \tabularnewline
23 & 14 & 14.4933724975732 & -0.493372497573182 \tabularnewline
24 & 12 & 13.7254693192176 & -1.72546931921757 \tabularnewline
25 & 17 & 15.4246739597439 & 1.57532604025609 \tabularnewline
26 & 9 & 15.9291062519614 & -6.92910625196135 \tabularnewline
27 & 16 & 15.1085264244137 & 0.891473575586334 \tabularnewline
28 & 14 & 13.3805303545445 & 0.619469645455498 \tabularnewline
29 & 15 & 13.9513452823994 & 1.0486547176006 \tabularnewline
30 & 11 & 14.1219255632444 & -3.12192556324443 \tabularnewline
31 & 16 & 15.7793158869083 & 0.220684113091654 \tabularnewline
32 & 13 & 12.8572888242439 & 0.142711175756111 \tabularnewline
33 & 17 & 15.1842283664323 & 1.81577163356771 \tabularnewline
34 & 15 & 15.3510228838356 & -0.351022883835589 \tabularnewline
35 & 14 & 13.9628245787635 & 0.0371754212365371 \tabularnewline
36 & 16 & 15.7259793982925 & 0.274020601707509 \tabularnewline
37 & 9 & 10.9611205605158 & -1.96112056051579 \tabularnewline
38 & 15 & 14.3494730833191 & 0.650526916680881 \tabularnewline
39 & 17 & 15.3907888735193 & 1.6092111264807 \tabularnewline
40 & 13 & 15.2611259060912 & -2.26112590609116 \tabularnewline
41 & 15 & 15.7350346884241 & -0.73503468842413 \tabularnewline
42 & 16 & 13.6371182730682 & 2.36288172693179 \tabularnewline
43 & 16 & 15.7196285806503 & 0.280371419349665 \tabularnewline
44 & 12 & 13.1710479985049 & -1.17104799850488 \tabularnewline
45 & 15 & 14.6471177987917 & 0.35288220120832 \tabularnewline
46 & 11 & 13.5972090082519 & -2.59720900825192 \tabularnewline
47 & 15 & 15.3607625303687 & -0.360762530368707 \tabularnewline
48 & 15 & 14.8698938158179 & 0.130106184182143 \tabularnewline
49 & 17 & 13.416613146968 & 3.58338685303198 \tabularnewline
50 & 13 & 14.6982915310565 & -1.69829153105652 \tabularnewline
51 & 16 & 15.1320859968924 & 0.867914003107633 \tabularnewline
52 & 14 & 13.4252944825721 & 0.57470551742786 \tabularnewline
53 & 11 & 11.632148433783 & -0.632148433783033 \tabularnewline
54 & 12 & 13.6723218257106 & -1.67232182571062 \tabularnewline
55 & 12 & 14.1818776382948 & -2.18187763829484 \tabularnewline
56 & 15 & 13.7097071852973 & 1.29029281470267 \tabularnewline
57 & 16 & 14.0892765368351 & 1.91072346316492 \tabularnewline
58 & 15 & 15.3739331571094 & -0.373933157109399 \tabularnewline
59 & 12 & 15.0833007897133 & -3.08330078971331 \tabularnewline
60 & 12 & 13.3165683390931 & -1.31656833909307 \tabularnewline
61 & 8 & 10.7630153368835 & -2.76301533688353 \tabularnewline
62 & 13 & 14.4335519740697 & -1.43355197406972 \tabularnewline
63 & 11 & 14.4473012042819 & -3.44730120428186 \tabularnewline
64 & 14 & 12.9761536451857 & 1.02384635481427 \tabularnewline
65 & 15 & 13.4887658994107 & 1.5112341005893 \tabularnewline
66 & 10 & 15.3549785232022 & -5.35497852320221 \tabularnewline
67 & 11 & 12.8728827834018 & -1.87288278340178 \tabularnewline
68 & 12 & 14.7307933730987 & -2.73079337309867 \tabularnewline
69 & 15 & 13.8511661176877 & 1.14883388231233 \tabularnewline
70 & 15 & 13.9084867075701 & 1.09151329242986 \tabularnewline
71 & 14 & 13.9861737078467 & 0.0138262921532562 \tabularnewline
72 & 16 & 13.041248276736 & 2.958751723264 \tabularnewline
73 & 15 & 14.6052969993877 & 0.394703000612273 \tabularnewline
74 & 15 & 15.4169057943505 & -0.41690579435053 \tabularnewline
75 & 13 & 15.1836392707187 & -2.18363927071868 \tabularnewline
76 & 12 & 12.4467695796265 & -0.446769579626519 \tabularnewline
77 & 17 & 14.2178702586902 & 2.78212974130983 \tabularnewline
78 & 13 & 12.7716046665259 & 0.228395333474145 \tabularnewline
79 & 15 & 14.0674270046362 & 0.93257299536379 \tabularnewline
80 & 13 & 15.1847492900556 & -2.18474929005561 \tabularnewline
81 & 15 & 15.0844207871825 & -0.0844207871824578 \tabularnewline
82 & 15 & 15.7368911773204 & -0.736891177320396 \tabularnewline
83 & 16 & 14.4725758050654 & 1.52742419493456 \tabularnewline
84 & 15 & 14.5203001406955 & 0.479699859304548 \tabularnewline
85 & 14 & 14.3585523534151 & -0.358552353415098 \tabularnewline
86 & 15 & 14.2670879157567 & 0.732912084243322 \tabularnewline
87 & 14 & 14.4603189859579 & -0.460318985957915 \tabularnewline
88 & 13 & 12.9559534592252 & 0.0440465407748481 \tabularnewline
89 & 7 & 10.7315575002678 & -3.73155750026777 \tabularnewline
90 & 17 & 14.028800077396 & 2.97119992260402 \tabularnewline
91 & 13 & 12.9120438995542 & 0.0879561004457838 \tabularnewline
92 & 15 & 14.3115302664204 & 0.688469733579633 \tabularnewline
93 & 14 & 13.4279369521296 & 0.572063047870384 \tabularnewline
94 & 13 & 14.1426075772365 & -1.14260757723645 \tabularnewline
95 & 16 & 15.107499098909 & 0.892500901091033 \tabularnewline
96 & 12 & 12.9357647294728 & -0.93576472947277 \tabularnewline
97 & 14 & 15.0193837263294 & -1.01938372632943 \tabularnewline
98 & 17 & 15.0069482043802 & 1.99305179561975 \tabularnewline
99 & 15 & 15.1981519222026 & -0.198151922202578 \tabularnewline
100 & 17 & 15.1789472713041 & 1.82105272869586 \tabularnewline
101 & 12 & 13.0628910444722 & -1.06289104447219 \tabularnewline
102 & 16 & 14.9793711225219 & 1.02062887747809 \tabularnewline
103 & 11 & 14.5618294279444 & -3.56182942794436 \tabularnewline
104 & 15 & 13.1714783588763 & 1.82852164112366 \tabularnewline
105 & 9 & 11.4781174402109 & -2.47811744021091 \tabularnewline
106 & 16 & 14.9710484206763 & 1.02895157932366 \tabularnewline
107 & 15 & 12.9608329357954 & 2.03916706420457 \tabularnewline
108 & 10 & 12.891003482683 & -2.89100348268305 \tabularnewline
109 & 10 & 9.39336221534466 & 0.60663778465534 \tabularnewline
110 & 15 & 13.9302570904703 & 1.06974290952966 \tabularnewline
111 & 11 & 13.1610472370106 & -2.16104723701064 \tabularnewline
112 & 13 & 15.3241238093833 & -2.32412380938335 \tabularnewline
113 & 14 & 11.8414607117196 & 2.15853928828041 \tabularnewline
114 & 18 & 14.2052767393184 & 3.79472326068159 \tabularnewline
115 & 16 & 15.5076325785327 & 0.492367421467326 \tabularnewline
116 & 14 & 12.9949630932609 & 1.00503690673908 \tabularnewline
117 & 14 & 13.9646557562858 & 0.035344243714172 \tabularnewline
118 & 14 & 15.1150905550952 & -1.11509055509516 \tabularnewline
119 & 14 & 13.6615895320188 & 0.338410467981245 \tabularnewline
120 & 12 & 12.5643656523482 & -0.564365652348217 \tabularnewline
121 & 14 & 13.4887528162373 & 0.511247183762699 \tabularnewline
122 & 15 & 14.8567573343719 & 0.143242665628063 \tabularnewline
123 & 15 & 15.7972042067877 & -0.797204206787708 \tabularnewline
124 & 15 & 14.4919829517058 & 0.508017048294198 \tabularnewline
125 & 13 & 14.5892924356604 & -1.58929243566035 \tabularnewline
126 & 17 & 16.1232742225955 & 0.876725777404484 \tabularnewline
127 & 17 & 15.1731858894401 & 1.82681411055991 \tabularnewline
128 & 19 & 14.9527449126288 & 4.04725508737125 \tabularnewline
129 & 15 & 13.5446928025771 & 1.45530719742292 \tabularnewline
130 & 13 & 14.5576364803129 & -1.5576364803129 \tabularnewline
131 & 9 & 10.6172139219704 & -1.61721392197038 \tabularnewline
132 & 15 & 15.2173533722711 & -0.217353372271133 \tabularnewline
133 & 15 & 12.5562296266016 & 2.44377037339844 \tabularnewline
134 & 15 & 14.1232852597318 & 0.876714740268159 \tabularnewline
135 & 16 & 13.6234869664837 & 2.37651303351626 \tabularnewline
136 & 11 & 9.40459660417014 & 1.59540339582986 \tabularnewline
137 & 14 & 13.2506910324614 & 0.749308967538551 \tabularnewline
138 & 11 & 11.9548018583788 & -0.95480185837882 \tabularnewline
139 & 15 & 14.1570574686945 & 0.842942531305487 \tabularnewline
140 & 13 & 13.7255585626518 & -0.725558562651804 \tabularnewline
141 & 15 & 14.5481980425816 & 0.451801957418433 \tabularnewline
142 & 16 & 13.7104366739228 & 2.28956332607724 \tabularnewline
143 & 14 & 14.3961109005303 & -0.396110900530313 \tabularnewline
144 & 15 & 14.0766251861648 & 0.923374813835189 \tabularnewline
145 & 16 & 14.5355775108921 & 1.46442248910788 \tabularnewline
146 & 16 & 14.2078891371008 & 1.79211086289916 \tabularnewline
147 & 11 & 13.2169945547645 & -2.21699455476448 \tabularnewline
148 & 12 & 14.4472671605866 & -2.44726716058661 \tabularnewline
149 & 9 & 11.4021583319191 & -2.40215833191906 \tabularnewline
150 & 16 & 14.1683571497106 & 1.83164285028945 \tabularnewline
151 & 13 & 12.2669861515031 & 0.733013848496878 \tabularnewline
152 & 16 & 15.185891828223 & 0.814108171776966 \tabularnewline
153 & 12 & 14.3094784915759 & -2.30947849157588 \tabularnewline
154 & 9 & 11.0105624070637 & -2.01056240706372 \tabularnewline
155 & 13 & 11.6770614060777 & 1.3229385939223 \tabularnewline
156 & 13 & 12.381575424735 & 0.618424575264993 \tabularnewline
157 & 14 & 13.2211052873272 & 0.778894712672814 \tabularnewline
158 & 19 & 14.707913308866 & 4.29208669113396 \tabularnewline
159 & 13 & 15.3547024735452 & -2.35470247354519 \tabularnewline
160 & 12 & 11.8910504709194 & 0.108949529080622 \tabularnewline
161 & 13 & 12.453642561059 & 0.546357438940978 \tabularnewline
162 & 10 & 9.58750663338288 & 0.412493366617123 \tabularnewline
163 & 14 & 13.4751102246536 & 0.524889775346359 \tabularnewline
164 & 16 & 11.6227149565314 & 4.37728504346862 \tabularnewline
165 & 10 & 12.133641787957 & -2.13364178795698 \tabularnewline
166 & 11 & 9.42810841518009 & 1.57189158481991 \tabularnewline
167 & 14 & 14.2923369414318 & -0.292336941431793 \tabularnewline
168 & 12 & 13.025364501329 & -1.02536450132899 \tabularnewline
169 & 9 & 12.8478764932932 & -3.84787649329321 \tabularnewline
170 & 9 & 11.997480459144 & -2.997480459144 \tabularnewline
171 & 11 & 10.8579742881031 & 0.142025711896941 \tabularnewline
172 & 16 & 14.2801722930814 & 1.71982770691862 \tabularnewline
173 & 9 & 14.1549476639667 & -5.1549476639667 \tabularnewline
174 & 13 & 11.5703874346987 & 1.42961256530129 \tabularnewline
175 & 16 & 13.4662837551643 & 2.5337162448357 \tabularnewline
176 & 13 & 15.3145698795143 & -2.31456987951428 \tabularnewline
177 & 9 & 12.5191871191037 & -3.51918711910365 \tabularnewline
178 & 12 & 11.6337292739843 & 0.366270726015655 \tabularnewline
179 & 16 & 14.6696712652494 & 1.33032873475055 \tabularnewline
180 & 11 & 13.2513733512428 & -2.25137335124276 \tabularnewline
181 & 14 & 14.119274718855 & -0.11927471885502 \tabularnewline
182 & 13 & 14.9355235318839 & -1.93552353188387 \tabularnewline
183 & 15 & 14.6311255188588 & 0.368874481141237 \tabularnewline
184 & 14 & 14.9546530564477 & -0.954653056447711 \tabularnewline
185 & 16 & 14.1816301201926 & 1.81836987980741 \tabularnewline
186 & 13 & 11.612538972463 & 1.38746102753703 \tabularnewline
187 & 14 & 13.5015793030232 & 0.49842069697685 \tabularnewline
188 & 15 & 14.3107183025911 & 0.68928169740885 \tabularnewline
189 & 13 & 12.5597604219227 & 0.440239578077348 \tabularnewline
190 & 11 & 10.4810803910208 & 0.518919608979244 \tabularnewline
191 & 11 & 12.4008541106046 & -1.40085411060461 \tabularnewline
192 & 14 & 14.9115717887416 & -0.91157178874156 \tabularnewline
193 & 15 & 12.9227156105481 & 2.07728438945186 \tabularnewline
194 & 11 & 12.5168006346407 & -1.51680063464066 \tabularnewline
195 & 15 & 13.1320496785813 & 1.86795032141872 \tabularnewline
196 & 12 & 14.0032856376566 & -2.00328563765661 \tabularnewline
197 & 14 & 11.7295821651007 & 2.27041783489933 \tabularnewline
198 & 14 & 13.3247119976765 & 0.675288002323538 \tabularnewline
199 & 8 & 11.1375462090552 & -3.13754620905517 \tabularnewline
200 & 13 & 13.6342324818868 & -0.634232481886764 \tabularnewline
201 & 9 & 12.0935005425465 & -3.09350054254652 \tabularnewline
202 & 15 & 13.7036888830065 & 1.2963111169935 \tabularnewline
203 & 17 & 14.0063486216078 & 2.99365137839219 \tabularnewline
204 & 13 & 12.5164013980734 & 0.483598601926646 \tabularnewline
205 & 15 & 14.3885093315882 & 0.611490668411773 \tabularnewline
206 & 15 & 13.6226314411988 & 1.37736855880119 \tabularnewline
207 & 14 & 14.4359697034974 & -0.435969703497354 \tabularnewline
208 & 16 & 12.3625822418847 & 3.63741775811527 \tabularnewline
209 & 13 & 12.8662692884817 & 0.133730711518336 \tabularnewline
210 & 16 & 14.1772492093444 & 1.82275079065561 \tabularnewline
211 & 9 & 11.5884134649774 & -2.58841346497743 \tabularnewline
212 & 16 & 14.4340296341536 & 1.56597036584637 \tabularnewline
213 & 11 & 12.1131372191025 & -1.11313721910251 \tabularnewline
214 & 10 & 13.7707647903946 & -3.77076479039463 \tabularnewline
215 & 11 & 11.9264129401552 & -0.926412940155161 \tabularnewline
216 & 15 & 13.1777945925208 & 1.82220540747917 \tabularnewline
217 & 17 & 14.687974598915 & 2.31202540108497 \tabularnewline
218 & 14 & 14.1072575830788 & -0.107257583078819 \tabularnewline
219 & 8 & 9.83541253751265 & -1.83541253751265 \tabularnewline
220 & 15 & 13.3971903665318 & 1.60280963346822 \tabularnewline
221 & 11 & 13.6760939780883 & -2.67609397808828 \tabularnewline
222 & 16 & 13.3218471909596 & 2.67815280904042 \tabularnewline
223 & 10 & 11.88891231842 & -1.88891231842004 \tabularnewline
224 & 15 & 14.6806205011477 & 0.319379498852254 \tabularnewline
225 & 9 & 9.2479178650292 & -0.2479178650292 \tabularnewline
226 & 16 & 14.2496476730803 & 1.75035232691966 \tabularnewline
227 & 19 & 13.6031134876726 & 5.39688651232735 \tabularnewline
228 & 12 & 13.3958284640395 & -1.3958284640395 \tabularnewline
229 & 8 & 9.3612434167882 & -1.3612434167882 \tabularnewline
230 & 11 & 13.1702912358422 & -2.17029123584215 \tabularnewline
231 & 14 & 13.6822944943459 & 0.317705505654149 \tabularnewline
232 & 9 & 11.8907757154562 & -2.89077571545622 \tabularnewline
233 & 15 & 14.7499867502114 & 0.250013249788616 \tabularnewline
234 & 13 & 12.0517738274583 & 0.948226172541661 \tabularnewline
235 & 16 & 14.6402311422262 & 1.35976885777376 \tabularnewline
236 & 11 & 12.5955492507236 & -1.59554925072356 \tabularnewline
237 & 12 & 11.323141679981 & 0.676858320018971 \tabularnewline
238 & 13 & 12.5491524025836 & 0.450847597416415 \tabularnewline
239 & 10 & 13.9800537479906 & -3.98005374799063 \tabularnewline
240 & 11 & 13.371682089625 & -2.37168208962497 \tabularnewline
241 & 12 & 14.5751291489346 & -2.57512914893464 \tabularnewline
242 & 8 & 10.421327836657 & -2.42132783665698 \tabularnewline
243 & 12 & 11.6405785187275 & 0.359421481272505 \tabularnewline
244 & 12 & 12.0299576313429 & -0.0299576313428628 \tabularnewline
245 & 15 & 13.2156748895744 & 1.78432511042563 \tabularnewline
246 & 11 & 10.4696627916656 & 0.530337208334412 \tabularnewline
247 & 13 & 12.4394479669535 & 0.560552033046486 \tabularnewline
248 & 14 & 8.59347329740041 & 5.40652670259959 \tabularnewline
249 & 10 & 9.98826922513496 & 0.0117307748650405 \tabularnewline
250 & 12 & 11.207654909606 & 0.79234509039398 \tabularnewline
251 & 15 & 12.5859314685527 & 2.41406853144726 \tabularnewline
252 & 13 & 11.5839001699649 & 1.41609983003514 \tabularnewline
253 & 13 & 13.8675490090102 & -0.8675490090102 \tabularnewline
254 & 13 & 13.3296251149176 & -0.329625114917596 \tabularnewline
255 & 12 & 11.4626547070348 & 0.53734529296518 \tabularnewline
256 & 12 & 12.2602926858436 & -0.260292685843626 \tabularnewline
257 & 9 & 10.2878813704994 & -1.28788137049941 \tabularnewline
258 & 9 & 11.1278691011962 & -2.12786910119618 \tabularnewline
259 & 15 & 12.1211981703965 & 2.87880182960347 \tabularnewline
260 & 10 & 14.6509465639137 & -4.6509465639137 \tabularnewline
261 & 14 & 13.1781266849116 & 0.821873315088437 \tabularnewline
262 & 15 & 13.0344758582622 & 1.96552414173776 \tabularnewline
263 & 7 & 9.47180223454386 & -2.47180223454386 \tabularnewline
264 & 14 & 13.3839625432213 & 0.616037456778746 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202847&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]14[/C][C]14.2024876664993[/C][C]-0.202487666499301[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]15.5224357324723[/C][C]2.47756426752771[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]14.1201163988475[/C][C]-3.12011639884748[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.8512440608166[/C][C]-2.85124406081657[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]11.4472011309484[/C][C]4.55279886905158[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]14.7277020370601[/C][C]3.27229796293994[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]11.0330063495711[/C][C]2.96699365042887[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]15.2203340364735[/C][C]-1.22033403647347[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]15.4182301472307[/C][C]-0.418230147230724[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.6248216667279[/C][C]0.375178333272081[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]15.7551397266746[/C][C]1.24486027332545[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]15.789906996481[/C][C]3.21009300351897[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]13.5888139420518[/C][C]-3.58881394205179[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]13.6763219707604[/C][C]2.32367802923963[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]15.8207760719618[/C][C]2.17922392803823[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.5721661882186[/C][C]0.427833811781425[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]14.0267325564921[/C][C]-0.0267325564920979[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]15.8584795065071[/C][C]1.14152049349286[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]15.556587784164[/C][C]-1.55658778416397[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]13.9666556282534[/C][C]2.03334437174661[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]15.4974550782512[/C][C]2.50254492174879[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]13.8754439916875[/C][C]-2.87544399168754[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]14.4933724975732[/C][C]-0.493372497573182[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]13.7254693192176[/C][C]-1.72546931921757[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.4246739597439[/C][C]1.57532604025609[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]15.9291062519614[/C][C]-6.92910625196135[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.1085264244137[/C][C]0.891473575586334[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]13.3805303545445[/C][C]0.619469645455498[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]13.9513452823994[/C][C]1.0486547176006[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]14.1219255632444[/C][C]-3.12192556324443[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]15.7793158869083[/C][C]0.220684113091654[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]12.8572888242439[/C][C]0.142711175756111[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]15.1842283664323[/C][C]1.81577163356771[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]15.3510228838356[/C][C]-0.351022883835589[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]13.9628245787635[/C][C]0.0371754212365371[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]15.7259793982925[/C][C]0.274020601707509[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]10.9611205605158[/C][C]-1.96112056051579[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]14.3494730833191[/C][C]0.650526916680881[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]15.3907888735193[/C][C]1.6092111264807[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]15.2611259060912[/C][C]-2.26112590609116[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]15.7350346884241[/C][C]-0.73503468842413[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]13.6371182730682[/C][C]2.36288172693179[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]15.7196285806503[/C][C]0.280371419349665[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]13.1710479985049[/C][C]-1.17104799850488[/C][/ROW]
[ROW][C]45[/C][C]15[/C][C]14.6471177987917[/C][C]0.35288220120832[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]13.5972090082519[/C][C]-2.59720900825192[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]15.3607625303687[/C][C]-0.360762530368707[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.8698938158179[/C][C]0.130106184182143[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]13.416613146968[/C][C]3.58338685303198[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.6982915310565[/C][C]-1.69829153105652[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]15.1320859968924[/C][C]0.867914003107633[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.4252944825721[/C][C]0.57470551742786[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]11.632148433783[/C][C]-0.632148433783033[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]13.6723218257106[/C][C]-1.67232182571062[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]14.1818776382948[/C][C]-2.18187763829484[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]13.7097071852973[/C][C]1.29029281470267[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.0892765368351[/C][C]1.91072346316492[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]15.3739331571094[/C][C]-0.373933157109399[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]15.0833007897133[/C][C]-3.08330078971331[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]13.3165683390931[/C][C]-1.31656833909307[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]10.7630153368835[/C][C]-2.76301533688353[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]14.4335519740697[/C][C]-1.43355197406972[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]14.4473012042819[/C][C]-3.44730120428186[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]12.9761536451857[/C][C]1.02384635481427[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]13.4887658994107[/C][C]1.5112341005893[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]15.3549785232022[/C][C]-5.35497852320221[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]12.8728827834018[/C][C]-1.87288278340178[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]14.7307933730987[/C][C]-2.73079337309867[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]13.8511661176877[/C][C]1.14883388231233[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]13.9084867075701[/C][C]1.09151329242986[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]13.9861737078467[/C][C]0.0138262921532562[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]13.041248276736[/C][C]2.958751723264[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.6052969993877[/C][C]0.394703000612273[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]15.4169057943505[/C][C]-0.41690579435053[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]15.1836392707187[/C][C]-2.18363927071868[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]12.4467695796265[/C][C]-0.446769579626519[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]14.2178702586902[/C][C]2.78212974130983[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]12.7716046665259[/C][C]0.228395333474145[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]14.0674270046362[/C][C]0.93257299536379[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]15.1847492900556[/C][C]-2.18474929005561[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]15.0844207871825[/C][C]-0.0844207871824578[/C][/ROW]
[ROW][C]82[/C][C]15[/C][C]15.7368911773204[/C][C]-0.736891177320396[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]14.4725758050654[/C][C]1.52742419493456[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.5203001406955[/C][C]0.479699859304548[/C][/ROW]
[ROW][C]85[/C][C]14[/C][C]14.3585523534151[/C][C]-0.358552353415098[/C][/ROW]
[ROW][C]86[/C][C]15[/C][C]14.2670879157567[/C][C]0.732912084243322[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.4603189859579[/C][C]-0.460318985957915[/C][/ROW]
[ROW][C]88[/C][C]13[/C][C]12.9559534592252[/C][C]0.0440465407748481[/C][/ROW]
[ROW][C]89[/C][C]7[/C][C]10.7315575002678[/C][C]-3.73155750026777[/C][/ROW]
[ROW][C]90[/C][C]17[/C][C]14.028800077396[/C][C]2.97119992260402[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]12.9120438995542[/C][C]0.0879561004457838[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]14.3115302664204[/C][C]0.688469733579633[/C][/ROW]
[ROW][C]93[/C][C]14[/C][C]13.4279369521296[/C][C]0.572063047870384[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]14.1426075772365[/C][C]-1.14260757723645[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]15.107499098909[/C][C]0.892500901091033[/C][/ROW]
[ROW][C]96[/C][C]12[/C][C]12.9357647294728[/C][C]-0.93576472947277[/C][/ROW]
[ROW][C]97[/C][C]14[/C][C]15.0193837263294[/C][C]-1.01938372632943[/C][/ROW]
[ROW][C]98[/C][C]17[/C][C]15.0069482043802[/C][C]1.99305179561975[/C][/ROW]
[ROW][C]99[/C][C]15[/C][C]15.1981519222026[/C][C]-0.198151922202578[/C][/ROW]
[ROW][C]100[/C][C]17[/C][C]15.1789472713041[/C][C]1.82105272869586[/C][/ROW]
[ROW][C]101[/C][C]12[/C][C]13.0628910444722[/C][C]-1.06289104447219[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.9793711225219[/C][C]1.02062887747809[/C][/ROW]
[ROW][C]103[/C][C]11[/C][C]14.5618294279444[/C][C]-3.56182942794436[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]13.1714783588763[/C][C]1.82852164112366[/C][/ROW]
[ROW][C]105[/C][C]9[/C][C]11.4781174402109[/C][C]-2.47811744021091[/C][/ROW]
[ROW][C]106[/C][C]16[/C][C]14.9710484206763[/C][C]1.02895157932366[/C][/ROW]
[ROW][C]107[/C][C]15[/C][C]12.9608329357954[/C][C]2.03916706420457[/C][/ROW]
[ROW][C]108[/C][C]10[/C][C]12.891003482683[/C][C]-2.89100348268305[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]9.39336221534466[/C][C]0.60663778465534[/C][/ROW]
[ROW][C]110[/C][C]15[/C][C]13.9302570904703[/C][C]1.06974290952966[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]13.1610472370106[/C][C]-2.16104723701064[/C][/ROW]
[ROW][C]112[/C][C]13[/C][C]15.3241238093833[/C][C]-2.32412380938335[/C][/ROW]
[ROW][C]113[/C][C]14[/C][C]11.8414607117196[/C][C]2.15853928828041[/C][/ROW]
[ROW][C]114[/C][C]18[/C][C]14.2052767393184[/C][C]3.79472326068159[/C][/ROW]
[ROW][C]115[/C][C]16[/C][C]15.5076325785327[/C][C]0.492367421467326[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]12.9949630932609[/C][C]1.00503690673908[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.9646557562858[/C][C]0.035344243714172[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]15.1150905550952[/C][C]-1.11509055509516[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]13.6615895320188[/C][C]0.338410467981245[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]12.5643656523482[/C][C]-0.564365652348217[/C][/ROW]
[ROW][C]121[/C][C]14[/C][C]13.4887528162373[/C][C]0.511247183762699[/C][/ROW]
[ROW][C]122[/C][C]15[/C][C]14.8567573343719[/C][C]0.143242665628063[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]15.7972042067877[/C][C]-0.797204206787708[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]14.4919829517058[/C][C]0.508017048294198[/C][/ROW]
[ROW][C]125[/C][C]13[/C][C]14.5892924356604[/C][C]-1.58929243566035[/C][/ROW]
[ROW][C]126[/C][C]17[/C][C]16.1232742225955[/C][C]0.876725777404484[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.1731858894401[/C][C]1.82681411055991[/C][/ROW]
[ROW][C]128[/C][C]19[/C][C]14.9527449126288[/C][C]4.04725508737125[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.5446928025771[/C][C]1.45530719742292[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]14.5576364803129[/C][C]-1.5576364803129[/C][/ROW]
[ROW][C]131[/C][C]9[/C][C]10.6172139219704[/C][C]-1.61721392197038[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]15.2173533722711[/C][C]-0.217353372271133[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]12.5562296266016[/C][C]2.44377037339844[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]14.1232852597318[/C][C]0.876714740268159[/C][/ROW]
[ROW][C]135[/C][C]16[/C][C]13.6234869664837[/C][C]2.37651303351626[/C][/ROW]
[ROW][C]136[/C][C]11[/C][C]9.40459660417014[/C][C]1.59540339582986[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]13.2506910324614[/C][C]0.749308967538551[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]11.9548018583788[/C][C]-0.95480185837882[/C][/ROW]
[ROW][C]139[/C][C]15[/C][C]14.1570574686945[/C][C]0.842942531305487[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]13.7255585626518[/C][C]-0.725558562651804[/C][/ROW]
[ROW][C]141[/C][C]15[/C][C]14.5481980425816[/C][C]0.451801957418433[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.7104366739228[/C][C]2.28956332607724[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]14.3961109005303[/C][C]-0.396110900530313[/C][/ROW]
[ROW][C]144[/C][C]15[/C][C]14.0766251861648[/C][C]0.923374813835189[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.5355775108921[/C][C]1.46442248910788[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]14.2078891371008[/C][C]1.79211086289916[/C][/ROW]
[ROW][C]147[/C][C]11[/C][C]13.2169945547645[/C][C]-2.21699455476448[/C][/ROW]
[ROW][C]148[/C][C]12[/C][C]14.4472671605866[/C][C]-2.44726716058661[/C][/ROW]
[ROW][C]149[/C][C]9[/C][C]11.4021583319191[/C][C]-2.40215833191906[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]14.1683571497106[/C][C]1.83164285028945[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]12.2669861515031[/C][C]0.733013848496878[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.185891828223[/C][C]0.814108171776966[/C][/ROW]
[ROW][C]153[/C][C]12[/C][C]14.3094784915759[/C][C]-2.30947849157588[/C][/ROW]
[ROW][C]154[/C][C]9[/C][C]11.0105624070637[/C][C]-2.01056240706372[/C][/ROW]
[ROW][C]155[/C][C]13[/C][C]11.6770614060777[/C][C]1.3229385939223[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]12.381575424735[/C][C]0.618424575264993[/C][/ROW]
[ROW][C]157[/C][C]14[/C][C]13.2211052873272[/C][C]0.778894712672814[/C][/ROW]
[ROW][C]158[/C][C]19[/C][C]14.707913308866[/C][C]4.29208669113396[/C][/ROW]
[ROW][C]159[/C][C]13[/C][C]15.3547024735452[/C][C]-2.35470247354519[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]11.8910504709194[/C][C]0.108949529080622[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]12.453642561059[/C][C]0.546357438940978[/C][/ROW]
[ROW][C]162[/C][C]10[/C][C]9.58750663338288[/C][C]0.412493366617123[/C][/ROW]
[ROW][C]163[/C][C]14[/C][C]13.4751102246536[/C][C]0.524889775346359[/C][/ROW]
[ROW][C]164[/C][C]16[/C][C]11.6227149565314[/C][C]4.37728504346862[/C][/ROW]
[ROW][C]165[/C][C]10[/C][C]12.133641787957[/C][C]-2.13364178795698[/C][/ROW]
[ROW][C]166[/C][C]11[/C][C]9.42810841518009[/C][C]1.57189158481991[/C][/ROW]
[ROW][C]167[/C][C]14[/C][C]14.2923369414318[/C][C]-0.292336941431793[/C][/ROW]
[ROW][C]168[/C][C]12[/C][C]13.025364501329[/C][C]-1.02536450132899[/C][/ROW]
[ROW][C]169[/C][C]9[/C][C]12.8478764932932[/C][C]-3.84787649329321[/C][/ROW]
[ROW][C]170[/C][C]9[/C][C]11.997480459144[/C][C]-2.997480459144[/C][/ROW]
[ROW][C]171[/C][C]11[/C][C]10.8579742881031[/C][C]0.142025711896941[/C][/ROW]
[ROW][C]172[/C][C]16[/C][C]14.2801722930814[/C][C]1.71982770691862[/C][/ROW]
[ROW][C]173[/C][C]9[/C][C]14.1549476639667[/C][C]-5.1549476639667[/C][/ROW]
[ROW][C]174[/C][C]13[/C][C]11.5703874346987[/C][C]1.42961256530129[/C][/ROW]
[ROW][C]175[/C][C]16[/C][C]13.4662837551643[/C][C]2.5337162448357[/C][/ROW]
[ROW][C]176[/C][C]13[/C][C]15.3145698795143[/C][C]-2.31456987951428[/C][/ROW]
[ROW][C]177[/C][C]9[/C][C]12.5191871191037[/C][C]-3.51918711910365[/C][/ROW]
[ROW][C]178[/C][C]12[/C][C]11.6337292739843[/C][C]0.366270726015655[/C][/ROW]
[ROW][C]179[/C][C]16[/C][C]14.6696712652494[/C][C]1.33032873475055[/C][/ROW]
[ROW][C]180[/C][C]11[/C][C]13.2513733512428[/C][C]-2.25137335124276[/C][/ROW]
[ROW][C]181[/C][C]14[/C][C]14.119274718855[/C][C]-0.11927471885502[/C][/ROW]
[ROW][C]182[/C][C]13[/C][C]14.9355235318839[/C][C]-1.93552353188387[/C][/ROW]
[ROW][C]183[/C][C]15[/C][C]14.6311255188588[/C][C]0.368874481141237[/C][/ROW]
[ROW][C]184[/C][C]14[/C][C]14.9546530564477[/C][C]-0.954653056447711[/C][/ROW]
[ROW][C]185[/C][C]16[/C][C]14.1816301201926[/C][C]1.81836987980741[/C][/ROW]
[ROW][C]186[/C][C]13[/C][C]11.612538972463[/C][C]1.38746102753703[/C][/ROW]
[ROW][C]187[/C][C]14[/C][C]13.5015793030232[/C][C]0.49842069697685[/C][/ROW]
[ROW][C]188[/C][C]15[/C][C]14.3107183025911[/C][C]0.68928169740885[/C][/ROW]
[ROW][C]189[/C][C]13[/C][C]12.5597604219227[/C][C]0.440239578077348[/C][/ROW]
[ROW][C]190[/C][C]11[/C][C]10.4810803910208[/C][C]0.518919608979244[/C][/ROW]
[ROW][C]191[/C][C]11[/C][C]12.4008541106046[/C][C]-1.40085411060461[/C][/ROW]
[ROW][C]192[/C][C]14[/C][C]14.9115717887416[/C][C]-0.91157178874156[/C][/ROW]
[ROW][C]193[/C][C]15[/C][C]12.9227156105481[/C][C]2.07728438945186[/C][/ROW]
[ROW][C]194[/C][C]11[/C][C]12.5168006346407[/C][C]-1.51680063464066[/C][/ROW]
[ROW][C]195[/C][C]15[/C][C]13.1320496785813[/C][C]1.86795032141872[/C][/ROW]
[ROW][C]196[/C][C]12[/C][C]14.0032856376566[/C][C]-2.00328563765661[/C][/ROW]
[ROW][C]197[/C][C]14[/C][C]11.7295821651007[/C][C]2.27041783489933[/C][/ROW]
[ROW][C]198[/C][C]14[/C][C]13.3247119976765[/C][C]0.675288002323538[/C][/ROW]
[ROW][C]199[/C][C]8[/C][C]11.1375462090552[/C][C]-3.13754620905517[/C][/ROW]
[ROW][C]200[/C][C]13[/C][C]13.6342324818868[/C][C]-0.634232481886764[/C][/ROW]
[ROW][C]201[/C][C]9[/C][C]12.0935005425465[/C][C]-3.09350054254652[/C][/ROW]
[ROW][C]202[/C][C]15[/C][C]13.7036888830065[/C][C]1.2963111169935[/C][/ROW]
[ROW][C]203[/C][C]17[/C][C]14.0063486216078[/C][C]2.99365137839219[/C][/ROW]
[ROW][C]204[/C][C]13[/C][C]12.5164013980734[/C][C]0.483598601926646[/C][/ROW]
[ROW][C]205[/C][C]15[/C][C]14.3885093315882[/C][C]0.611490668411773[/C][/ROW]
[ROW][C]206[/C][C]15[/C][C]13.6226314411988[/C][C]1.37736855880119[/C][/ROW]
[ROW][C]207[/C][C]14[/C][C]14.4359697034974[/C][C]-0.435969703497354[/C][/ROW]
[ROW][C]208[/C][C]16[/C][C]12.3625822418847[/C][C]3.63741775811527[/C][/ROW]
[ROW][C]209[/C][C]13[/C][C]12.8662692884817[/C][C]0.133730711518336[/C][/ROW]
[ROW][C]210[/C][C]16[/C][C]14.1772492093444[/C][C]1.82275079065561[/C][/ROW]
[ROW][C]211[/C][C]9[/C][C]11.5884134649774[/C][C]-2.58841346497743[/C][/ROW]
[ROW][C]212[/C][C]16[/C][C]14.4340296341536[/C][C]1.56597036584637[/C][/ROW]
[ROW][C]213[/C][C]11[/C][C]12.1131372191025[/C][C]-1.11313721910251[/C][/ROW]
[ROW][C]214[/C][C]10[/C][C]13.7707647903946[/C][C]-3.77076479039463[/C][/ROW]
[ROW][C]215[/C][C]11[/C][C]11.9264129401552[/C][C]-0.926412940155161[/C][/ROW]
[ROW][C]216[/C][C]15[/C][C]13.1777945925208[/C][C]1.82220540747917[/C][/ROW]
[ROW][C]217[/C][C]17[/C][C]14.687974598915[/C][C]2.31202540108497[/C][/ROW]
[ROW][C]218[/C][C]14[/C][C]14.1072575830788[/C][C]-0.107257583078819[/C][/ROW]
[ROW][C]219[/C][C]8[/C][C]9.83541253751265[/C][C]-1.83541253751265[/C][/ROW]
[ROW][C]220[/C][C]15[/C][C]13.3971903665318[/C][C]1.60280963346822[/C][/ROW]
[ROW][C]221[/C][C]11[/C][C]13.6760939780883[/C][C]-2.67609397808828[/C][/ROW]
[ROW][C]222[/C][C]16[/C][C]13.3218471909596[/C][C]2.67815280904042[/C][/ROW]
[ROW][C]223[/C][C]10[/C][C]11.88891231842[/C][C]-1.88891231842004[/C][/ROW]
[ROW][C]224[/C][C]15[/C][C]14.6806205011477[/C][C]0.319379498852254[/C][/ROW]
[ROW][C]225[/C][C]9[/C][C]9.2479178650292[/C][C]-0.2479178650292[/C][/ROW]
[ROW][C]226[/C][C]16[/C][C]14.2496476730803[/C][C]1.75035232691966[/C][/ROW]
[ROW][C]227[/C][C]19[/C][C]13.6031134876726[/C][C]5.39688651232735[/C][/ROW]
[ROW][C]228[/C][C]12[/C][C]13.3958284640395[/C][C]-1.3958284640395[/C][/ROW]
[ROW][C]229[/C][C]8[/C][C]9.3612434167882[/C][C]-1.3612434167882[/C][/ROW]
[ROW][C]230[/C][C]11[/C][C]13.1702912358422[/C][C]-2.17029123584215[/C][/ROW]
[ROW][C]231[/C][C]14[/C][C]13.6822944943459[/C][C]0.317705505654149[/C][/ROW]
[ROW][C]232[/C][C]9[/C][C]11.8907757154562[/C][C]-2.89077571545622[/C][/ROW]
[ROW][C]233[/C][C]15[/C][C]14.7499867502114[/C][C]0.250013249788616[/C][/ROW]
[ROW][C]234[/C][C]13[/C][C]12.0517738274583[/C][C]0.948226172541661[/C][/ROW]
[ROW][C]235[/C][C]16[/C][C]14.6402311422262[/C][C]1.35976885777376[/C][/ROW]
[ROW][C]236[/C][C]11[/C][C]12.5955492507236[/C][C]-1.59554925072356[/C][/ROW]
[ROW][C]237[/C][C]12[/C][C]11.323141679981[/C][C]0.676858320018971[/C][/ROW]
[ROW][C]238[/C][C]13[/C][C]12.5491524025836[/C][C]0.450847597416415[/C][/ROW]
[ROW][C]239[/C][C]10[/C][C]13.9800537479906[/C][C]-3.98005374799063[/C][/ROW]
[ROW][C]240[/C][C]11[/C][C]13.371682089625[/C][C]-2.37168208962497[/C][/ROW]
[ROW][C]241[/C][C]12[/C][C]14.5751291489346[/C][C]-2.57512914893464[/C][/ROW]
[ROW][C]242[/C][C]8[/C][C]10.421327836657[/C][C]-2.42132783665698[/C][/ROW]
[ROW][C]243[/C][C]12[/C][C]11.6405785187275[/C][C]0.359421481272505[/C][/ROW]
[ROW][C]244[/C][C]12[/C][C]12.0299576313429[/C][C]-0.0299576313428628[/C][/ROW]
[ROW][C]245[/C][C]15[/C][C]13.2156748895744[/C][C]1.78432511042563[/C][/ROW]
[ROW][C]246[/C][C]11[/C][C]10.4696627916656[/C][C]0.530337208334412[/C][/ROW]
[ROW][C]247[/C][C]13[/C][C]12.4394479669535[/C][C]0.560552033046486[/C][/ROW]
[ROW][C]248[/C][C]14[/C][C]8.59347329740041[/C][C]5.40652670259959[/C][/ROW]
[ROW][C]249[/C][C]10[/C][C]9.98826922513496[/C][C]0.0117307748650405[/C][/ROW]
[ROW][C]250[/C][C]12[/C][C]11.207654909606[/C][C]0.79234509039398[/C][/ROW]
[ROW][C]251[/C][C]15[/C][C]12.5859314685527[/C][C]2.41406853144726[/C][/ROW]
[ROW][C]252[/C][C]13[/C][C]11.5839001699649[/C][C]1.41609983003514[/C][/ROW]
[ROW][C]253[/C][C]13[/C][C]13.8675490090102[/C][C]-0.8675490090102[/C][/ROW]
[ROW][C]254[/C][C]13[/C][C]13.3296251149176[/C][C]-0.329625114917596[/C][/ROW]
[ROW][C]255[/C][C]12[/C][C]11.4626547070348[/C][C]0.53734529296518[/C][/ROW]
[ROW][C]256[/C][C]12[/C][C]12.2602926858436[/C][C]-0.260292685843626[/C][/ROW]
[ROW][C]257[/C][C]9[/C][C]10.2878813704994[/C][C]-1.28788137049941[/C][/ROW]
[ROW][C]258[/C][C]9[/C][C]11.1278691011962[/C][C]-2.12786910119618[/C][/ROW]
[ROW][C]259[/C][C]15[/C][C]12.1211981703965[/C][C]2.87880182960347[/C][/ROW]
[ROW][C]260[/C][C]10[/C][C]14.6509465639137[/C][C]-4.6509465639137[/C][/ROW]
[ROW][C]261[/C][C]14[/C][C]13.1781266849116[/C][C]0.821873315088437[/C][/ROW]
[ROW][C]262[/C][C]15[/C][C]13.0344758582622[/C][C]1.96552414173776[/C][/ROW]
[ROW][C]263[/C][C]7[/C][C]9.47180223454386[/C][C]-2.47180223454386[/C][/ROW]
[ROW][C]264[/C][C]14[/C][C]13.3839625432213[/C][C]0.616037456778746[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202847&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202847&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
11414.2024876664993-0.202487666499301
21815.52243573247232.47756426752771
31114.1201163988475-3.12011639884748
41214.8512440608166-2.85124406081657
51611.44720113094844.55279886905158
61814.72770203706013.27229796293994
71411.03300634957112.96699365042887
81415.2203340364735-1.22033403647347
91515.4182301472307-0.418230147230724
101514.62482166672790.375178333272081
111715.75513972667461.24486027332545
121915.7899069964813.21009300351897
131013.5888139420518-3.58881394205179
141613.67632197076042.32367802923963
151815.82077607196182.17922392803823
161413.57216618821860.427833811781425
171414.0267325564921-0.0267325564920979
181715.85847950650711.14152049349286
191415.556587784164-1.55658778416397
201613.96665562825342.03334437174661
211815.49745507825122.50254492174879
221113.8754439916875-2.87544399168754
231414.4933724975732-0.493372497573182
241213.7254693192176-1.72546931921757
251715.42467395974391.57532604025609
26915.9291062519614-6.92910625196135
271615.10852642441370.891473575586334
281413.38053035454450.619469645455498
291513.95134528239941.0486547176006
301114.1219255632444-3.12192556324443
311615.77931588690830.220684113091654
321312.85728882424390.142711175756111
331715.18422836643231.81577163356771
341515.3510228838356-0.351022883835589
351413.96282457876350.0371754212365371
361615.72597939829250.274020601707509
37910.9611205605158-1.96112056051579
381514.34947308331910.650526916680881
391715.39078887351931.6092111264807
401315.2611259060912-2.26112590609116
411515.7350346884241-0.73503468842413
421613.63711827306822.36288172693179
431615.71962858065030.280371419349665
441213.1710479985049-1.17104799850488
451514.64711779879170.35288220120832
461113.5972090082519-2.59720900825192
471515.3607625303687-0.360762530368707
481514.86989381581790.130106184182143
491713.4166131469683.58338685303198
501314.6982915310565-1.69829153105652
511615.13208599689240.867914003107633
521413.42529448257210.57470551742786
531111.632148433783-0.632148433783033
541213.6723218257106-1.67232182571062
551214.1818776382948-2.18187763829484
561513.70970718529731.29029281470267
571614.08927653683511.91072346316492
581515.3739331571094-0.373933157109399
591215.0833007897133-3.08330078971331
601213.3165683390931-1.31656833909307
61810.7630153368835-2.76301533688353
621314.4335519740697-1.43355197406972
631114.4473012042819-3.44730120428186
641412.97615364518571.02384635481427
651513.48876589941071.5112341005893
661015.3549785232022-5.35497852320221
671112.8728827834018-1.87288278340178
681214.7307933730987-2.73079337309867
691513.85116611768771.14883388231233
701513.90848670757011.09151329242986
711413.98617370784670.0138262921532562
721613.0412482767362.958751723264
731514.60529699938770.394703000612273
741515.4169057943505-0.41690579435053
751315.1836392707187-2.18363927071868
761212.4467695796265-0.446769579626519
771714.21787025869022.78212974130983
781312.77160466652590.228395333474145
791514.06742700463620.93257299536379
801315.1847492900556-2.18474929005561
811515.0844207871825-0.0844207871824578
821515.7368911773204-0.736891177320396
831614.47257580506541.52742419493456
841514.52030014069550.479699859304548
851414.3585523534151-0.358552353415098
861514.26708791575670.732912084243322
871414.4603189859579-0.460318985957915
881312.95595345922520.0440465407748481
89710.7315575002678-3.73155750026777
901714.0288000773962.97119992260402
911312.91204389955420.0879561004457838
921514.31153026642040.688469733579633
931413.42793695212960.572063047870384
941314.1426075772365-1.14260757723645
951615.1074990989090.892500901091033
961212.9357647294728-0.93576472947277
971415.0193837263294-1.01938372632943
981715.00694820438021.99305179561975
991515.1981519222026-0.198151922202578
1001715.17894727130411.82105272869586
1011213.0628910444722-1.06289104447219
1021614.97937112252191.02062887747809
1031114.5618294279444-3.56182942794436
1041513.17147835887631.82852164112366
105911.4781174402109-2.47811744021091
1061614.97104842067631.02895157932366
1071512.96083293579542.03916706420457
1081012.891003482683-2.89100348268305
109109.393362215344660.60663778465534
1101513.93025709047031.06974290952966
1111113.1610472370106-2.16104723701064
1121315.3241238093833-2.32412380938335
1131411.84146071171962.15853928828041
1141814.20527673931843.79472326068159
1151615.50763257853270.492367421467326
1161412.99496309326091.00503690673908
1171413.96465575628580.035344243714172
1181415.1150905550952-1.11509055509516
1191413.66158953201880.338410467981245
1201212.5643656523482-0.564365652348217
1211413.48875281623730.511247183762699
1221514.85675733437190.143242665628063
1231515.7972042067877-0.797204206787708
1241514.49198295170580.508017048294198
1251314.5892924356604-1.58929243566035
1261716.12327422259550.876725777404484
1271715.17318588944011.82681411055991
1281914.95274491262884.04725508737125
1291513.54469280257711.45530719742292
1301314.5576364803129-1.5576364803129
131910.6172139219704-1.61721392197038
1321515.2173533722711-0.217353372271133
1331512.55622962660162.44377037339844
1341514.12328525973180.876714740268159
1351613.62348696648372.37651303351626
136119.404596604170141.59540339582986
1371413.25069103246140.749308967538551
1381111.9548018583788-0.95480185837882
1391514.15705746869450.842942531305487
1401313.7255585626518-0.725558562651804
1411514.54819804258160.451801957418433
1421613.71043667392282.28956332607724
1431414.3961109005303-0.396110900530313
1441514.07662518616480.923374813835189
1451614.53557751089211.46442248910788
1461614.20788913710081.79211086289916
1471113.2169945547645-2.21699455476448
1481214.4472671605866-2.44726716058661
149911.4021583319191-2.40215833191906
1501614.16835714971061.83164285028945
1511312.26698615150310.733013848496878
1521615.1858918282230.814108171776966
1531214.3094784915759-2.30947849157588
154911.0105624070637-2.01056240706372
1551311.67706140607771.3229385939223
1561312.3815754247350.618424575264993
1571413.22110528732720.778894712672814
1581914.7079133088664.29208669113396
1591315.3547024735452-2.35470247354519
1601211.89105047091940.108949529080622
1611312.4536425610590.546357438940978
162109.587506633382880.412493366617123
1631413.47511022465360.524889775346359
1641611.62271495653144.37728504346862
1651012.133641787957-2.13364178795698
166119.428108415180091.57189158481991
1671414.2923369414318-0.292336941431793
1681213.025364501329-1.02536450132899
169912.8478764932932-3.84787649329321
170911.997480459144-2.997480459144
1711110.85797428810310.142025711896941
1721614.28017229308141.71982770691862
173914.1549476639667-5.1549476639667
1741311.57038743469871.42961256530129
1751613.46628375516432.5337162448357
1761315.3145698795143-2.31456987951428
177912.5191871191037-3.51918711910365
1781211.63372927398430.366270726015655
1791614.66967126524941.33032873475055
1801113.2513733512428-2.25137335124276
1811414.119274718855-0.11927471885502
1821314.9355235318839-1.93552353188387
1831514.63112551885880.368874481141237
1841414.9546530564477-0.954653056447711
1851614.18163012019261.81836987980741
1861311.6125389724631.38746102753703
1871413.50157930302320.49842069697685
1881514.31071830259110.68928169740885
1891312.55976042192270.440239578077348
1901110.48108039102080.518919608979244
1911112.4008541106046-1.40085411060461
1921414.9115717887416-0.91157178874156
1931512.92271561054812.07728438945186
1941112.5168006346407-1.51680063464066
1951513.13204967858131.86795032141872
1961214.0032856376566-2.00328563765661
1971411.72958216510072.27041783489933
1981413.32471199767650.675288002323538
199811.1375462090552-3.13754620905517
2001313.6342324818868-0.634232481886764
201912.0935005425465-3.09350054254652
2021513.70368888300651.2963111169935
2031714.00634862160782.99365137839219
2041312.51640139807340.483598601926646
2051514.38850933158820.611490668411773
2061513.62263144119881.37736855880119
2071414.4359697034974-0.435969703497354
2081612.36258224188473.63741775811527
2091312.86626928848170.133730711518336
2101614.17724920934441.82275079065561
211911.5884134649774-2.58841346497743
2121614.43402963415361.56597036584637
2131112.1131372191025-1.11313721910251
2141013.7707647903946-3.77076479039463
2151111.9264129401552-0.926412940155161
2161513.17779459252081.82220540747917
2171714.6879745989152.31202540108497
2181414.1072575830788-0.107257583078819
21989.83541253751265-1.83541253751265
2201513.39719036653181.60280963346822
2211113.6760939780883-2.67609397808828
2221613.32184719095962.67815280904042
2231011.88891231842-1.88891231842004
2241514.68062050114770.319379498852254
22599.2479178650292-0.2479178650292
2261614.24964767308031.75035232691966
2271913.60311348767265.39688651232735
2281213.3958284640395-1.3958284640395
22989.3612434167882-1.3612434167882
2301113.1702912358422-2.17029123584215
2311413.68229449434590.317705505654149
232911.8907757154562-2.89077571545622
2331514.74998675021140.250013249788616
2341312.05177382745830.948226172541661
2351614.64023114222621.35976885777376
2361112.5955492507236-1.59554925072356
2371211.3231416799810.676858320018971
2381312.54915240258360.450847597416415
2391013.9800537479906-3.98005374799063
2401113.371682089625-2.37168208962497
2411214.5751291489346-2.57512914893464
242810.421327836657-2.42132783665698
2431211.64057851872750.359421481272505
2441212.0299576313429-0.0299576313428628
2451513.21567488957441.78432511042563
2461110.46966279166560.530337208334412
2471312.43944796695350.560552033046486
248148.593473297400415.40652670259959
249109.988269225134960.0117307748650405
2501211.2076549096060.79234509039398
2511512.58593146855272.41406853144726
2521311.58390016996491.41609983003514
2531313.8675490090102-0.8675490090102
2541313.3296251149176-0.329625114917596
2551211.46265470703480.53734529296518
2561212.2602926858436-0.260292685843626
257910.2878813704994-1.28788137049941
258911.1278691011962-2.12786910119618
2591512.12119817039652.87880182960347
2601014.6509465639137-4.6509465639137
2611413.17812668491160.821873315088437
2621513.03447585826221.96552414173776
26379.47180223454386-2.47180223454386
2641413.38396254322130.616037456778746






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.9920918281302120.01581634373957550.00790817186978775
140.9819717629608250.03605647407835020.0180282370391751
150.9775870322863080.0448259354273850.0224129677136925
160.9584253062970520.08314938740589510.0415746937029476
170.980876279345860.03824744130828090.0191237206541405
180.9670679865328130.06586402693437480.0329320134671874
190.9471640215529080.1056719568941840.0528359784470919
200.9383650115563740.1232699768872530.0616349884436265
210.9434823957191440.1130352085617110.0565176042808556
220.9523320969528320.09533580609433570.0476679030471678
230.954679693393030.09064061321394050.0453203066069703
240.9355076803513090.1289846392973820.0644923196486912
250.9203040702900330.1593918594199330.0796959297099667
260.9993759758604240.001248048279152840.000624024139576421
270.9990407452501750.001918509499649990.000959254749824994
280.9984829510892870.003034097821425760.00151704891071288
290.9976136581498110.004772683700378770.00238634185018938
300.9974812400907850.00503751981843090.00251875990921545
310.9964761959132080.007047608173583130.00352380408679156
320.9945952123198730.01080957536025310.00540478768012655
330.9946168022858910.01076639542821830.00538319771410914
340.9925407767156040.0149184465687910.00745922328439549
350.9896340095436110.02073198091277880.0103659904563894
360.985827688135660.02834462372867950.0141723118643398
370.9879681644120750.02406367117585070.0120318355879253
380.9842267799899110.03154644002017870.0157732200100893
390.9836093785941640.03278124281167190.0163906214058359
400.9811280399731270.03774392005374510.0188719600268726
410.9744268780658690.05114624386826120.0255731219341306
420.9756283899049440.04874322019011280.0243716100950564
430.9691930947204240.06161381055915290.0308069052795765
440.9609810730826520.07803785383469560.0390189269173478
450.9500937949858970.09981241002820630.0499062050141032
460.9511654686307520.09766906273849550.0488345313692477
470.9423140906346970.1153718187306060.0576859093653028
480.928693752969160.1426124940616790.0713062470308397
490.9506706055347320.09865878893053510.0493293944652675
500.9435342429471740.1129315141056510.0564657570528255
510.9332400663045810.1335198673908370.0667599336954187
520.9182708005326030.1634583989347950.0817291994673973
530.9020978798415640.1958042403168720.0979021201584361
540.8846319217243910.2307361565512170.115368078275609
550.8747973860649920.2504052278700160.125202613935008
560.8640546993263650.271890601347270.135945300673635
570.8599329458408550.280134108318290.140067054159145
580.8340344970186810.3319310059626370.165965502981319
590.8586427508493870.2827144983012260.141357249150613
600.8426754673968720.3146490652062560.157324532603128
610.8515831761037250.2968336477925490.148416823896275
620.8362393177296130.3275213645407740.163760682270387
630.8710005563732910.2579988872534170.128999443626709
640.864853842312060.2702923153758810.13514615768794
650.86550208384440.26899583231120.1344979161556
660.8835770092003250.232845981599350.116422990799675
670.8862637234571330.2274725530857330.113736276542867
680.8766366986470640.2467266027058720.123363301352936
690.9148922676361170.1702154647277660.085107732363883
700.9221165321621710.1557669356756580.0778834678378291
710.9154324451048710.1691351097902580.0845675548951292
720.9399914954161280.1200170091677430.0600085045838715
730.9285454925390180.1429090149219630.0714545074609816
740.9139600700181930.1720798599636130.0860399299818065
750.9055688887480930.1888622225038150.0944311112519074
760.8886073268390560.2227853463218880.111392673160944
770.9123825947218520.1752348105562970.0876174052781484
780.8984116451957160.2031767096085680.101588354804284
790.8894692055790280.2210615888419430.110530794420972
800.8830277218392980.2339445563214030.116972278160702
810.862757347376060.2744853052478810.13724265262394
820.8419261006528080.3161477986943850.158073899347192
830.8370875759225370.3258248481549260.162912424077463
840.8163460592135090.3673078815729810.183653940786491
850.7907565597036430.4184868805927140.209243440296357
860.7649861193542210.4700277612915570.235013880645779
870.7353185027296590.5293629945406810.264681497270341
880.703887503102710.592224993794580.29611249689729
890.7746807960787790.4506384078424430.225319203921221
900.8121944812800460.3756110374399080.187805518719954
910.788751520365440.4224969592691210.21124847963456
920.7626010305926730.4747979388146540.237398969407327
930.7403458345116230.5193083309767530.259654165488377
940.7158204679647120.5683590640705770.284179532035288
950.6908051628376830.6183896743246340.309194837162317
960.6619033286453610.6761933427092780.338096671354639
970.6338905843624540.7322188312750920.366109415637546
980.6404679743322570.7190640513354860.359532025667743
990.6049702246791420.7900595506417170.395029775320858
1000.6011342043309190.7977315913381620.398865795669081
1010.5729987627312990.8540024745374020.427001237268701
1020.5490000396856280.9019999206287430.450999960314372
1030.6007719387957550.798456122408490.399228061204245
1040.5887511603726110.8224976792547790.411248839627389
1050.6018680866796310.7962638266407390.398131913320369
1060.5814421366637730.8371157266724530.418557863336227
1070.5765564128293530.8468871743412940.423443587170647
1080.6137365900013430.7725268199973150.386263409998658
1090.5790651876165190.8418696247669610.420934812383481
1100.5565440561971010.8869118876057980.443455943802899
1110.5577914795060280.8844170409879440.442208520493972
1120.5725783940220930.8548432119558140.427421605977907
1130.5594927893179630.8810144213640750.440507210682037
1140.6656019985169050.6687960029661890.334398001483095
1150.6414182239173910.7171635521652180.358581776082609
1160.6147516109346820.7704967781306360.385248389065318
1170.5795659758987550.8408680482024910.420434024101245
1180.5533321603322680.8933356793354650.446667839667732
1190.5182388873125270.9635222253749460.481761112687473
1200.4845351292637670.9690702585275340.515464870736233
1210.4639681089507290.9279362179014590.536031891049271
1220.4287287357525780.8574574715051560.571271264247422
1230.3988617778699920.7977235557399840.601138222130008
1240.3730855000606570.7461710001213140.626914499939343
1250.3594986263758060.7189972527516130.640501373624194
1260.3355715289053240.6711430578106470.664428471094676
1270.3231296216652430.6462592433304870.676870378334757
1280.4362148316538620.8724296633077230.563785168346138
1290.4193549514888280.8387099029776560.580645048511172
1300.4061947350959630.8123894701919250.593805264904037
1310.391650740748040.7833014814960810.60834925925196
1320.3592173011085890.7184346022171790.640782698891411
1330.3840533997102880.7681067994205770.615946600289712
1340.3550086478680870.7100172957361730.644991352131913
1350.3719113702521040.7438227405042080.628088629747896
1360.3647304308544260.7294608617088520.635269569145574
1370.3353136330609870.6706272661219750.664686366939013
1380.3112565414307380.6225130828614760.688743458569262
1390.2837312497250570.5674624994501140.716268750274943
1400.2591108393772410.5182216787544820.740889160622759
1410.231929126219490.463858252438980.76807087378051
1420.2372209638516430.4744419277032850.762779036148357
1430.211058034323430.4221160686468590.78894196567657
1440.1918905575819260.3837811151638520.808109442418074
1450.1790539676447270.3581079352894540.820946032355273
1460.1736939123406780.3473878246813550.826306087659322
1470.1811157967545080.3622315935090160.818884203245492
1480.1963352076277480.3926704152554950.803664792372252
1490.2131545578303210.4263091156606420.786845442169679
1500.2084824895892580.4169649791785160.791517510410742
1510.1866759157838020.3733518315676040.813324084216198
1520.1666105959177030.3332211918354060.833389404082297
1530.1766949128816010.3533898257632010.823305087118399
1540.1887194005151440.3774388010302880.811280599484856
1550.1703274090095570.3406548180191130.829672590990443
1560.1541766283448990.3083532566897970.845823371655101
1570.1341821756485490.2683643512970980.865817824351451
1580.2065194651140120.4130389302280240.793480534885988
1590.223514728228820.4470294564576390.77648527177118
1600.1979070729804340.3958141459608670.802092927019566
1610.1734166903708820.3468333807417640.826583309629118
1620.1510124658253280.3020249316506560.848987534174672
1630.1312285594687920.2624571189375840.868771440531208
1640.2194948465823160.4389896931646320.780505153417684
1650.2216913620541820.4433827241083630.778308637945818
1660.2119574672455340.4239149344910690.788042532754466
1670.1869599150515050.3739198301030090.813040084948495
1680.1689282312678020.3378564625356030.831071768732198
1690.2262451689227510.4524903378455020.773754831077249
1700.2533547309644370.5067094619288740.746645269035563
1710.2249831636251860.4499663272503730.775016836374813
1720.2176752106916860.4353504213833720.782324789308314
1730.382935308599290.765870617198580.61706469140071
1740.3625557365078180.7251114730156350.637444263492182
1750.3794077668404530.7588155336809060.620592233159547
1760.3890013555683570.7780027111367140.610998644431643
1770.4582140265964180.9164280531928370.541785973403582
1780.4211895238518970.8423790477037930.578810476148103
1790.3963335696173010.7926671392346030.603666430382699
1800.400586748023310.8011734960466190.59941325197669
1810.3660834775798650.732166955159730.633916522420135
1820.3724964742712570.7449929485425140.627503525728743
1830.3367613590261240.6735227180522480.663238640973876
1840.3144613253984770.6289226507969550.685538674601523
1850.3129966754445350.6259933508890690.687003324555465
1860.3007832602516780.6015665205033550.699216739748322
1870.2679457763322940.5358915526645870.732054223667706
1880.2389510367550150.4779020735100310.761048963244985
1890.2097767482223890.4195534964447780.790223251777611
1900.1848832499340870.3697664998681740.815116750065913
1910.1915568763501760.3831137527003510.808443123649824
1920.1762194953573230.3524389907146470.823780504642677
1930.181864779457930.3637295589158590.81813522054207
1940.172424752412710.344849504825420.82757524758729
1950.1684762658752770.3369525317505550.831523734124723
1960.179790377703630.359580755407260.82020962229637
1970.2144674642158370.4289349284316740.785532535784163
1980.1970596526463380.3941193052926750.802940347353662
1990.2287791726779280.4575583453558570.771220827322072
2000.1990394207259030.3980788414518050.800960579274097
2010.2381273755052170.4762547510104330.761872624494783
2020.2149766593593610.4299533187187220.785023340640639
2030.2618469762305190.5236939524610380.738153023769481
2040.233321581731240.466643163462480.76667841826876
2050.2022392949510530.4044785899021050.797760705048947
2060.1828192589275570.3656385178551130.817180741072443
2070.1553232164605380.3106464329210750.844676783539462
2080.1744103437119340.3488206874238690.825589656288066
2090.1468541797115140.2937083594230280.853145820288486
2100.1611266852267130.3222533704534260.838873314773287
2110.1820488355162440.3640976710324880.817951164483756
2120.1683660137055860.3367320274111720.831633986294414
2130.1438535629118040.2877071258236080.856146437088196
2140.1846808445932350.3693616891864690.815319155406765
2150.1624174856012590.3248349712025180.837582514398741
2160.1488616248418960.2977232496837930.851138375158104
2170.1747252115393680.3494504230787350.825274788460632
2180.1487326025065570.2974652050131150.851267397493443
2190.1355460492941260.2710920985882530.864453950705874
2200.1428110293812340.2856220587624680.857188970618766
2210.1440195781499310.2880391562998610.85598042185007
2220.1478571692872130.2957143385744260.852142830712787
2230.1321368527512190.2642737055024370.867863147248781
2240.1071937158882240.2143874317764470.892806284111776
2250.09519161579029060.1903832315805810.904808384209709
2260.1179866782530150.235973356506030.882013321746985
2270.3097432499818380.6194864999636770.690256750018162
2280.2720114072749350.5440228145498690.727988592725066
2290.2363021593909810.4726043187819620.763697840609019
2300.2134263212752320.4268526425504630.786573678724768
2310.180174944285550.36034988857110.81982505571445
2320.2028853719715110.4057707439430220.797114628028489
2330.1657472096942780.3314944193885550.834252790305722
2340.1344489015704640.2688978031409280.865551098429536
2350.1392076582876220.2784153165752440.860792341712378
2360.1148342042951390.2296684085902780.885165795704861
2370.09518069342746530.1903613868549310.904819306572535
2380.06970211122667560.1394042224533510.930297888773324
2390.1448456871781470.2896913743562950.855154312821853
2400.1608328571562010.3216657143124020.839167142843799
2410.1607025518295750.3214051036591510.839297448170425
2420.7636308121363010.4727383757273970.236369187863699
2430.6895432366326330.6209135267347350.310456763367367
2440.6374343753676390.7251312492647230.362565624632361
2450.5887640968991930.8224718062016130.411235903100807
2460.5032107353976260.9935785292047470.496789264602374
2470.4758807098641980.9517614197283960.524119290135802
2480.4582829620508130.9165659241016250.541717037949187
2490.3388451882860260.6776903765720530.661154811713974
2500.3592750919985210.7185501839970420.640724908001479
2510.244076182877310.488152365754620.75592381712269

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.992091828130212 & 0.0158163437395755 & 0.00790817186978775 \tabularnewline
14 & 0.981971762960825 & 0.0360564740783502 & 0.0180282370391751 \tabularnewline
15 & 0.977587032286308 & 0.044825935427385 & 0.0224129677136925 \tabularnewline
16 & 0.958425306297052 & 0.0831493874058951 & 0.0415746937029476 \tabularnewline
17 & 0.98087627934586 & 0.0382474413082809 & 0.0191237206541405 \tabularnewline
18 & 0.967067986532813 & 0.0658640269343748 & 0.0329320134671874 \tabularnewline
19 & 0.947164021552908 & 0.105671956894184 & 0.0528359784470919 \tabularnewline
20 & 0.938365011556374 & 0.123269976887253 & 0.0616349884436265 \tabularnewline
21 & 0.943482395719144 & 0.113035208561711 & 0.0565176042808556 \tabularnewline
22 & 0.952332096952832 & 0.0953358060943357 & 0.0476679030471678 \tabularnewline
23 & 0.95467969339303 & 0.0906406132139405 & 0.0453203066069703 \tabularnewline
24 & 0.935507680351309 & 0.128984639297382 & 0.0644923196486912 \tabularnewline
25 & 0.920304070290033 & 0.159391859419933 & 0.0796959297099667 \tabularnewline
26 & 0.999375975860424 & 0.00124804827915284 & 0.000624024139576421 \tabularnewline
27 & 0.999040745250175 & 0.00191850949964999 & 0.000959254749824994 \tabularnewline
28 & 0.998482951089287 & 0.00303409782142576 & 0.00151704891071288 \tabularnewline
29 & 0.997613658149811 & 0.00477268370037877 & 0.00238634185018938 \tabularnewline
30 & 0.997481240090785 & 0.0050375198184309 & 0.00251875990921545 \tabularnewline
31 & 0.996476195913208 & 0.00704760817358313 & 0.00352380408679156 \tabularnewline
32 & 0.994595212319873 & 0.0108095753602531 & 0.00540478768012655 \tabularnewline
33 & 0.994616802285891 & 0.0107663954282183 & 0.00538319771410914 \tabularnewline
34 & 0.992540776715604 & 0.014918446568791 & 0.00745922328439549 \tabularnewline
35 & 0.989634009543611 & 0.0207319809127788 & 0.0103659904563894 \tabularnewline
36 & 0.98582768813566 & 0.0283446237286795 & 0.0141723118643398 \tabularnewline
37 & 0.987968164412075 & 0.0240636711758507 & 0.0120318355879253 \tabularnewline
38 & 0.984226779989911 & 0.0315464400201787 & 0.0157732200100893 \tabularnewline
39 & 0.983609378594164 & 0.0327812428116719 & 0.0163906214058359 \tabularnewline
40 & 0.981128039973127 & 0.0377439200537451 & 0.0188719600268726 \tabularnewline
41 & 0.974426878065869 & 0.0511462438682612 & 0.0255731219341306 \tabularnewline
42 & 0.975628389904944 & 0.0487432201901128 & 0.0243716100950564 \tabularnewline
43 & 0.969193094720424 & 0.0616138105591529 & 0.0308069052795765 \tabularnewline
44 & 0.960981073082652 & 0.0780378538346956 & 0.0390189269173478 \tabularnewline
45 & 0.950093794985897 & 0.0998124100282063 & 0.0499062050141032 \tabularnewline
46 & 0.951165468630752 & 0.0976690627384955 & 0.0488345313692477 \tabularnewline
47 & 0.942314090634697 & 0.115371818730606 & 0.0576859093653028 \tabularnewline
48 & 0.92869375296916 & 0.142612494061679 & 0.0713062470308397 \tabularnewline
49 & 0.950670605534732 & 0.0986587889305351 & 0.0493293944652675 \tabularnewline
50 & 0.943534242947174 & 0.112931514105651 & 0.0564657570528255 \tabularnewline
51 & 0.933240066304581 & 0.133519867390837 & 0.0667599336954187 \tabularnewline
52 & 0.918270800532603 & 0.163458398934795 & 0.0817291994673973 \tabularnewline
53 & 0.902097879841564 & 0.195804240316872 & 0.0979021201584361 \tabularnewline
54 & 0.884631921724391 & 0.230736156551217 & 0.115368078275609 \tabularnewline
55 & 0.874797386064992 & 0.250405227870016 & 0.125202613935008 \tabularnewline
56 & 0.864054699326365 & 0.27189060134727 & 0.135945300673635 \tabularnewline
57 & 0.859932945840855 & 0.28013410831829 & 0.140067054159145 \tabularnewline
58 & 0.834034497018681 & 0.331931005962637 & 0.165965502981319 \tabularnewline
59 & 0.858642750849387 & 0.282714498301226 & 0.141357249150613 \tabularnewline
60 & 0.842675467396872 & 0.314649065206256 & 0.157324532603128 \tabularnewline
61 & 0.851583176103725 & 0.296833647792549 & 0.148416823896275 \tabularnewline
62 & 0.836239317729613 & 0.327521364540774 & 0.163760682270387 \tabularnewline
63 & 0.871000556373291 & 0.257998887253417 & 0.128999443626709 \tabularnewline
64 & 0.86485384231206 & 0.270292315375881 & 0.13514615768794 \tabularnewline
65 & 0.8655020838444 & 0.2689958323112 & 0.1344979161556 \tabularnewline
66 & 0.883577009200325 & 0.23284598159935 & 0.116422990799675 \tabularnewline
67 & 0.886263723457133 & 0.227472553085733 & 0.113736276542867 \tabularnewline
68 & 0.876636698647064 & 0.246726602705872 & 0.123363301352936 \tabularnewline
69 & 0.914892267636117 & 0.170215464727766 & 0.085107732363883 \tabularnewline
70 & 0.922116532162171 & 0.155766935675658 & 0.0778834678378291 \tabularnewline
71 & 0.915432445104871 & 0.169135109790258 & 0.0845675548951292 \tabularnewline
72 & 0.939991495416128 & 0.120017009167743 & 0.0600085045838715 \tabularnewline
73 & 0.928545492539018 & 0.142909014921963 & 0.0714545074609816 \tabularnewline
74 & 0.913960070018193 & 0.172079859963613 & 0.0860399299818065 \tabularnewline
75 & 0.905568888748093 & 0.188862222503815 & 0.0944311112519074 \tabularnewline
76 & 0.888607326839056 & 0.222785346321888 & 0.111392673160944 \tabularnewline
77 & 0.912382594721852 & 0.175234810556297 & 0.0876174052781484 \tabularnewline
78 & 0.898411645195716 & 0.203176709608568 & 0.101588354804284 \tabularnewline
79 & 0.889469205579028 & 0.221061588841943 & 0.110530794420972 \tabularnewline
80 & 0.883027721839298 & 0.233944556321403 & 0.116972278160702 \tabularnewline
81 & 0.86275734737606 & 0.274485305247881 & 0.13724265262394 \tabularnewline
82 & 0.841926100652808 & 0.316147798694385 & 0.158073899347192 \tabularnewline
83 & 0.837087575922537 & 0.325824848154926 & 0.162912424077463 \tabularnewline
84 & 0.816346059213509 & 0.367307881572981 & 0.183653940786491 \tabularnewline
85 & 0.790756559703643 & 0.418486880592714 & 0.209243440296357 \tabularnewline
86 & 0.764986119354221 & 0.470027761291557 & 0.235013880645779 \tabularnewline
87 & 0.735318502729659 & 0.529362994540681 & 0.264681497270341 \tabularnewline
88 & 0.70388750310271 & 0.59222499379458 & 0.29611249689729 \tabularnewline
89 & 0.774680796078779 & 0.450638407842443 & 0.225319203921221 \tabularnewline
90 & 0.812194481280046 & 0.375611037439908 & 0.187805518719954 \tabularnewline
91 & 0.78875152036544 & 0.422496959269121 & 0.21124847963456 \tabularnewline
92 & 0.762601030592673 & 0.474797938814654 & 0.237398969407327 \tabularnewline
93 & 0.740345834511623 & 0.519308330976753 & 0.259654165488377 \tabularnewline
94 & 0.715820467964712 & 0.568359064070577 & 0.284179532035288 \tabularnewline
95 & 0.690805162837683 & 0.618389674324634 & 0.309194837162317 \tabularnewline
96 & 0.661903328645361 & 0.676193342709278 & 0.338096671354639 \tabularnewline
97 & 0.633890584362454 & 0.732218831275092 & 0.366109415637546 \tabularnewline
98 & 0.640467974332257 & 0.719064051335486 & 0.359532025667743 \tabularnewline
99 & 0.604970224679142 & 0.790059550641717 & 0.395029775320858 \tabularnewline
100 & 0.601134204330919 & 0.797731591338162 & 0.398865795669081 \tabularnewline
101 & 0.572998762731299 & 0.854002474537402 & 0.427001237268701 \tabularnewline
102 & 0.549000039685628 & 0.901999920628743 & 0.450999960314372 \tabularnewline
103 & 0.600771938795755 & 0.79845612240849 & 0.399228061204245 \tabularnewline
104 & 0.588751160372611 & 0.822497679254779 & 0.411248839627389 \tabularnewline
105 & 0.601868086679631 & 0.796263826640739 & 0.398131913320369 \tabularnewline
106 & 0.581442136663773 & 0.837115726672453 & 0.418557863336227 \tabularnewline
107 & 0.576556412829353 & 0.846887174341294 & 0.423443587170647 \tabularnewline
108 & 0.613736590001343 & 0.772526819997315 & 0.386263409998658 \tabularnewline
109 & 0.579065187616519 & 0.841869624766961 & 0.420934812383481 \tabularnewline
110 & 0.556544056197101 & 0.886911887605798 & 0.443455943802899 \tabularnewline
111 & 0.557791479506028 & 0.884417040987944 & 0.442208520493972 \tabularnewline
112 & 0.572578394022093 & 0.854843211955814 & 0.427421605977907 \tabularnewline
113 & 0.559492789317963 & 0.881014421364075 & 0.440507210682037 \tabularnewline
114 & 0.665601998516905 & 0.668796002966189 & 0.334398001483095 \tabularnewline
115 & 0.641418223917391 & 0.717163552165218 & 0.358581776082609 \tabularnewline
116 & 0.614751610934682 & 0.770496778130636 & 0.385248389065318 \tabularnewline
117 & 0.579565975898755 & 0.840868048202491 & 0.420434024101245 \tabularnewline
118 & 0.553332160332268 & 0.893335679335465 & 0.446667839667732 \tabularnewline
119 & 0.518238887312527 & 0.963522225374946 & 0.481761112687473 \tabularnewline
120 & 0.484535129263767 & 0.969070258527534 & 0.515464870736233 \tabularnewline
121 & 0.463968108950729 & 0.927936217901459 & 0.536031891049271 \tabularnewline
122 & 0.428728735752578 & 0.857457471505156 & 0.571271264247422 \tabularnewline
123 & 0.398861777869992 & 0.797723555739984 & 0.601138222130008 \tabularnewline
124 & 0.373085500060657 & 0.746171000121314 & 0.626914499939343 \tabularnewline
125 & 0.359498626375806 & 0.718997252751613 & 0.640501373624194 \tabularnewline
126 & 0.335571528905324 & 0.671143057810647 & 0.664428471094676 \tabularnewline
127 & 0.323129621665243 & 0.646259243330487 & 0.676870378334757 \tabularnewline
128 & 0.436214831653862 & 0.872429663307723 & 0.563785168346138 \tabularnewline
129 & 0.419354951488828 & 0.838709902977656 & 0.580645048511172 \tabularnewline
130 & 0.406194735095963 & 0.812389470191925 & 0.593805264904037 \tabularnewline
131 & 0.39165074074804 & 0.783301481496081 & 0.60834925925196 \tabularnewline
132 & 0.359217301108589 & 0.718434602217179 & 0.640782698891411 \tabularnewline
133 & 0.384053399710288 & 0.768106799420577 & 0.615946600289712 \tabularnewline
134 & 0.355008647868087 & 0.710017295736173 & 0.644991352131913 \tabularnewline
135 & 0.371911370252104 & 0.743822740504208 & 0.628088629747896 \tabularnewline
136 & 0.364730430854426 & 0.729460861708852 & 0.635269569145574 \tabularnewline
137 & 0.335313633060987 & 0.670627266121975 & 0.664686366939013 \tabularnewline
138 & 0.311256541430738 & 0.622513082861476 & 0.688743458569262 \tabularnewline
139 & 0.283731249725057 & 0.567462499450114 & 0.716268750274943 \tabularnewline
140 & 0.259110839377241 & 0.518221678754482 & 0.740889160622759 \tabularnewline
141 & 0.23192912621949 & 0.46385825243898 & 0.76807087378051 \tabularnewline
142 & 0.237220963851643 & 0.474441927703285 & 0.762779036148357 \tabularnewline
143 & 0.21105803432343 & 0.422116068646859 & 0.78894196567657 \tabularnewline
144 & 0.191890557581926 & 0.383781115163852 & 0.808109442418074 \tabularnewline
145 & 0.179053967644727 & 0.358107935289454 & 0.820946032355273 \tabularnewline
146 & 0.173693912340678 & 0.347387824681355 & 0.826306087659322 \tabularnewline
147 & 0.181115796754508 & 0.362231593509016 & 0.818884203245492 \tabularnewline
148 & 0.196335207627748 & 0.392670415255495 & 0.803664792372252 \tabularnewline
149 & 0.213154557830321 & 0.426309115660642 & 0.786845442169679 \tabularnewline
150 & 0.208482489589258 & 0.416964979178516 & 0.791517510410742 \tabularnewline
151 & 0.186675915783802 & 0.373351831567604 & 0.813324084216198 \tabularnewline
152 & 0.166610595917703 & 0.333221191835406 & 0.833389404082297 \tabularnewline
153 & 0.176694912881601 & 0.353389825763201 & 0.823305087118399 \tabularnewline
154 & 0.188719400515144 & 0.377438801030288 & 0.811280599484856 \tabularnewline
155 & 0.170327409009557 & 0.340654818019113 & 0.829672590990443 \tabularnewline
156 & 0.154176628344899 & 0.308353256689797 & 0.845823371655101 \tabularnewline
157 & 0.134182175648549 & 0.268364351297098 & 0.865817824351451 \tabularnewline
158 & 0.206519465114012 & 0.413038930228024 & 0.793480534885988 \tabularnewline
159 & 0.22351472822882 & 0.447029456457639 & 0.77648527177118 \tabularnewline
160 & 0.197907072980434 & 0.395814145960867 & 0.802092927019566 \tabularnewline
161 & 0.173416690370882 & 0.346833380741764 & 0.826583309629118 \tabularnewline
162 & 0.151012465825328 & 0.302024931650656 & 0.848987534174672 \tabularnewline
163 & 0.131228559468792 & 0.262457118937584 & 0.868771440531208 \tabularnewline
164 & 0.219494846582316 & 0.438989693164632 & 0.780505153417684 \tabularnewline
165 & 0.221691362054182 & 0.443382724108363 & 0.778308637945818 \tabularnewline
166 & 0.211957467245534 & 0.423914934491069 & 0.788042532754466 \tabularnewline
167 & 0.186959915051505 & 0.373919830103009 & 0.813040084948495 \tabularnewline
168 & 0.168928231267802 & 0.337856462535603 & 0.831071768732198 \tabularnewline
169 & 0.226245168922751 & 0.452490337845502 & 0.773754831077249 \tabularnewline
170 & 0.253354730964437 & 0.506709461928874 & 0.746645269035563 \tabularnewline
171 & 0.224983163625186 & 0.449966327250373 & 0.775016836374813 \tabularnewline
172 & 0.217675210691686 & 0.435350421383372 & 0.782324789308314 \tabularnewline
173 & 0.38293530859929 & 0.76587061719858 & 0.61706469140071 \tabularnewline
174 & 0.362555736507818 & 0.725111473015635 & 0.637444263492182 \tabularnewline
175 & 0.379407766840453 & 0.758815533680906 & 0.620592233159547 \tabularnewline
176 & 0.389001355568357 & 0.778002711136714 & 0.610998644431643 \tabularnewline
177 & 0.458214026596418 & 0.916428053192837 & 0.541785973403582 \tabularnewline
178 & 0.421189523851897 & 0.842379047703793 & 0.578810476148103 \tabularnewline
179 & 0.396333569617301 & 0.792667139234603 & 0.603666430382699 \tabularnewline
180 & 0.40058674802331 & 0.801173496046619 & 0.59941325197669 \tabularnewline
181 & 0.366083477579865 & 0.73216695515973 & 0.633916522420135 \tabularnewline
182 & 0.372496474271257 & 0.744992948542514 & 0.627503525728743 \tabularnewline
183 & 0.336761359026124 & 0.673522718052248 & 0.663238640973876 \tabularnewline
184 & 0.314461325398477 & 0.628922650796955 & 0.685538674601523 \tabularnewline
185 & 0.312996675444535 & 0.625993350889069 & 0.687003324555465 \tabularnewline
186 & 0.300783260251678 & 0.601566520503355 & 0.699216739748322 \tabularnewline
187 & 0.267945776332294 & 0.535891552664587 & 0.732054223667706 \tabularnewline
188 & 0.238951036755015 & 0.477902073510031 & 0.761048963244985 \tabularnewline
189 & 0.209776748222389 & 0.419553496444778 & 0.790223251777611 \tabularnewline
190 & 0.184883249934087 & 0.369766499868174 & 0.815116750065913 \tabularnewline
191 & 0.191556876350176 & 0.383113752700351 & 0.808443123649824 \tabularnewline
192 & 0.176219495357323 & 0.352438990714647 & 0.823780504642677 \tabularnewline
193 & 0.18186477945793 & 0.363729558915859 & 0.81813522054207 \tabularnewline
194 & 0.17242475241271 & 0.34484950482542 & 0.82757524758729 \tabularnewline
195 & 0.168476265875277 & 0.336952531750555 & 0.831523734124723 \tabularnewline
196 & 0.17979037770363 & 0.35958075540726 & 0.82020962229637 \tabularnewline
197 & 0.214467464215837 & 0.428934928431674 & 0.785532535784163 \tabularnewline
198 & 0.197059652646338 & 0.394119305292675 & 0.802940347353662 \tabularnewline
199 & 0.228779172677928 & 0.457558345355857 & 0.771220827322072 \tabularnewline
200 & 0.199039420725903 & 0.398078841451805 & 0.800960579274097 \tabularnewline
201 & 0.238127375505217 & 0.476254751010433 & 0.761872624494783 \tabularnewline
202 & 0.214976659359361 & 0.429953318718722 & 0.785023340640639 \tabularnewline
203 & 0.261846976230519 & 0.523693952461038 & 0.738153023769481 \tabularnewline
204 & 0.23332158173124 & 0.46664316346248 & 0.76667841826876 \tabularnewline
205 & 0.202239294951053 & 0.404478589902105 & 0.797760705048947 \tabularnewline
206 & 0.182819258927557 & 0.365638517855113 & 0.817180741072443 \tabularnewline
207 & 0.155323216460538 & 0.310646432921075 & 0.844676783539462 \tabularnewline
208 & 0.174410343711934 & 0.348820687423869 & 0.825589656288066 \tabularnewline
209 & 0.146854179711514 & 0.293708359423028 & 0.853145820288486 \tabularnewline
210 & 0.161126685226713 & 0.322253370453426 & 0.838873314773287 \tabularnewline
211 & 0.182048835516244 & 0.364097671032488 & 0.817951164483756 \tabularnewline
212 & 0.168366013705586 & 0.336732027411172 & 0.831633986294414 \tabularnewline
213 & 0.143853562911804 & 0.287707125823608 & 0.856146437088196 \tabularnewline
214 & 0.184680844593235 & 0.369361689186469 & 0.815319155406765 \tabularnewline
215 & 0.162417485601259 & 0.324834971202518 & 0.837582514398741 \tabularnewline
216 & 0.148861624841896 & 0.297723249683793 & 0.851138375158104 \tabularnewline
217 & 0.174725211539368 & 0.349450423078735 & 0.825274788460632 \tabularnewline
218 & 0.148732602506557 & 0.297465205013115 & 0.851267397493443 \tabularnewline
219 & 0.135546049294126 & 0.271092098588253 & 0.864453950705874 \tabularnewline
220 & 0.142811029381234 & 0.285622058762468 & 0.857188970618766 \tabularnewline
221 & 0.144019578149931 & 0.288039156299861 & 0.85598042185007 \tabularnewline
222 & 0.147857169287213 & 0.295714338574426 & 0.852142830712787 \tabularnewline
223 & 0.132136852751219 & 0.264273705502437 & 0.867863147248781 \tabularnewline
224 & 0.107193715888224 & 0.214387431776447 & 0.892806284111776 \tabularnewline
225 & 0.0951916157902906 & 0.190383231580581 & 0.904808384209709 \tabularnewline
226 & 0.117986678253015 & 0.23597335650603 & 0.882013321746985 \tabularnewline
227 & 0.309743249981838 & 0.619486499963677 & 0.690256750018162 \tabularnewline
228 & 0.272011407274935 & 0.544022814549869 & 0.727988592725066 \tabularnewline
229 & 0.236302159390981 & 0.472604318781962 & 0.763697840609019 \tabularnewline
230 & 0.213426321275232 & 0.426852642550463 & 0.786573678724768 \tabularnewline
231 & 0.18017494428555 & 0.3603498885711 & 0.81982505571445 \tabularnewline
232 & 0.202885371971511 & 0.405770743943022 & 0.797114628028489 \tabularnewline
233 & 0.165747209694278 & 0.331494419388555 & 0.834252790305722 \tabularnewline
234 & 0.134448901570464 & 0.268897803140928 & 0.865551098429536 \tabularnewline
235 & 0.139207658287622 & 0.278415316575244 & 0.860792341712378 \tabularnewline
236 & 0.114834204295139 & 0.229668408590278 & 0.885165795704861 \tabularnewline
237 & 0.0951806934274653 & 0.190361386854931 & 0.904819306572535 \tabularnewline
238 & 0.0697021112266756 & 0.139404222453351 & 0.930297888773324 \tabularnewline
239 & 0.144845687178147 & 0.289691374356295 & 0.855154312821853 \tabularnewline
240 & 0.160832857156201 & 0.321665714312402 & 0.839167142843799 \tabularnewline
241 & 0.160702551829575 & 0.321405103659151 & 0.839297448170425 \tabularnewline
242 & 0.763630812136301 & 0.472738375727397 & 0.236369187863699 \tabularnewline
243 & 0.689543236632633 & 0.620913526734735 & 0.310456763367367 \tabularnewline
244 & 0.637434375367639 & 0.725131249264723 & 0.362565624632361 \tabularnewline
245 & 0.588764096899193 & 0.822471806201613 & 0.411235903100807 \tabularnewline
246 & 0.503210735397626 & 0.993578529204747 & 0.496789264602374 \tabularnewline
247 & 0.475880709864198 & 0.951761419728396 & 0.524119290135802 \tabularnewline
248 & 0.458282962050813 & 0.916565924101625 & 0.541717037949187 \tabularnewline
249 & 0.338845188286026 & 0.677690376572053 & 0.661154811713974 \tabularnewline
250 & 0.359275091998521 & 0.718550183997042 & 0.640724908001479 \tabularnewline
251 & 0.24407618287731 & 0.48815236575462 & 0.75592381712269 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202847&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]13[/C][C]0.992091828130212[/C][C]0.0158163437395755[/C][C]0.00790817186978775[/C][/ROW]
[ROW][C]14[/C][C]0.981971762960825[/C][C]0.0360564740783502[/C][C]0.0180282370391751[/C][/ROW]
[ROW][C]15[/C][C]0.977587032286308[/C][C]0.044825935427385[/C][C]0.0224129677136925[/C][/ROW]
[ROW][C]16[/C][C]0.958425306297052[/C][C]0.0831493874058951[/C][C]0.0415746937029476[/C][/ROW]
[ROW][C]17[/C][C]0.98087627934586[/C][C]0.0382474413082809[/C][C]0.0191237206541405[/C][/ROW]
[ROW][C]18[/C][C]0.967067986532813[/C][C]0.0658640269343748[/C][C]0.0329320134671874[/C][/ROW]
[ROW][C]19[/C][C]0.947164021552908[/C][C]0.105671956894184[/C][C]0.0528359784470919[/C][/ROW]
[ROW][C]20[/C][C]0.938365011556374[/C][C]0.123269976887253[/C][C]0.0616349884436265[/C][/ROW]
[ROW][C]21[/C][C]0.943482395719144[/C][C]0.113035208561711[/C][C]0.0565176042808556[/C][/ROW]
[ROW][C]22[/C][C]0.952332096952832[/C][C]0.0953358060943357[/C][C]0.0476679030471678[/C][/ROW]
[ROW][C]23[/C][C]0.95467969339303[/C][C]0.0906406132139405[/C][C]0.0453203066069703[/C][/ROW]
[ROW][C]24[/C][C]0.935507680351309[/C][C]0.128984639297382[/C][C]0.0644923196486912[/C][/ROW]
[ROW][C]25[/C][C]0.920304070290033[/C][C]0.159391859419933[/C][C]0.0796959297099667[/C][/ROW]
[ROW][C]26[/C][C]0.999375975860424[/C][C]0.00124804827915284[/C][C]0.000624024139576421[/C][/ROW]
[ROW][C]27[/C][C]0.999040745250175[/C][C]0.00191850949964999[/C][C]0.000959254749824994[/C][/ROW]
[ROW][C]28[/C][C]0.998482951089287[/C][C]0.00303409782142576[/C][C]0.00151704891071288[/C][/ROW]
[ROW][C]29[/C][C]0.997613658149811[/C][C]0.00477268370037877[/C][C]0.00238634185018938[/C][/ROW]
[ROW][C]30[/C][C]0.997481240090785[/C][C]0.0050375198184309[/C][C]0.00251875990921545[/C][/ROW]
[ROW][C]31[/C][C]0.996476195913208[/C][C]0.00704760817358313[/C][C]0.00352380408679156[/C][/ROW]
[ROW][C]32[/C][C]0.994595212319873[/C][C]0.0108095753602531[/C][C]0.00540478768012655[/C][/ROW]
[ROW][C]33[/C][C]0.994616802285891[/C][C]0.0107663954282183[/C][C]0.00538319771410914[/C][/ROW]
[ROW][C]34[/C][C]0.992540776715604[/C][C]0.014918446568791[/C][C]0.00745922328439549[/C][/ROW]
[ROW][C]35[/C][C]0.989634009543611[/C][C]0.0207319809127788[/C][C]0.0103659904563894[/C][/ROW]
[ROW][C]36[/C][C]0.98582768813566[/C][C]0.0283446237286795[/C][C]0.0141723118643398[/C][/ROW]
[ROW][C]37[/C][C]0.987968164412075[/C][C]0.0240636711758507[/C][C]0.0120318355879253[/C][/ROW]
[ROW][C]38[/C][C]0.984226779989911[/C][C]0.0315464400201787[/C][C]0.0157732200100893[/C][/ROW]
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[ROW][C]40[/C][C]0.981128039973127[/C][C]0.0377439200537451[/C][C]0.0188719600268726[/C][/ROW]
[ROW][C]41[/C][C]0.974426878065869[/C][C]0.0511462438682612[/C][C]0.0255731219341306[/C][/ROW]
[ROW][C]42[/C][C]0.975628389904944[/C][C]0.0487432201901128[/C][C]0.0243716100950564[/C][/ROW]
[ROW][C]43[/C][C]0.969193094720424[/C][C]0.0616138105591529[/C][C]0.0308069052795765[/C][/ROW]
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[ROW][C]112[/C][C]0.572578394022093[/C][C]0.854843211955814[/C][C]0.427421605977907[/C][/ROW]
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[ROW][C]116[/C][C]0.614751610934682[/C][C]0.770496778130636[/C][C]0.385248389065318[/C][/ROW]
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[ROW][C]118[/C][C]0.553332160332268[/C][C]0.893335679335465[/C][C]0.446667839667732[/C][/ROW]
[ROW][C]119[/C][C]0.518238887312527[/C][C]0.963522225374946[/C][C]0.481761112687473[/C][/ROW]
[ROW][C]120[/C][C]0.484535129263767[/C][C]0.969070258527534[/C][C]0.515464870736233[/C][/ROW]
[ROW][C]121[/C][C]0.463968108950729[/C][C]0.927936217901459[/C][C]0.536031891049271[/C][/ROW]
[ROW][C]122[/C][C]0.428728735752578[/C][C]0.857457471505156[/C][C]0.571271264247422[/C][/ROW]
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[ROW][C]126[/C][C]0.335571528905324[/C][C]0.671143057810647[/C][C]0.664428471094676[/C][/ROW]
[ROW][C]127[/C][C]0.323129621665243[/C][C]0.646259243330487[/C][C]0.676870378334757[/C][/ROW]
[ROW][C]128[/C][C]0.436214831653862[/C][C]0.872429663307723[/C][C]0.563785168346138[/C][/ROW]
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[ROW][C]130[/C][C]0.406194735095963[/C][C]0.812389470191925[/C][C]0.593805264904037[/C][/ROW]
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[ROW][C]132[/C][C]0.359217301108589[/C][C]0.718434602217179[/C][C]0.640782698891411[/C][/ROW]
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[ROW][C]166[/C][C]0.211957467245534[/C][C]0.423914934491069[/C][C]0.788042532754466[/C][/ROW]
[ROW][C]167[/C][C]0.186959915051505[/C][C]0.373919830103009[/C][C]0.813040084948495[/C][/ROW]
[ROW][C]168[/C][C]0.168928231267802[/C][C]0.337856462535603[/C][C]0.831071768732198[/C][/ROW]
[ROW][C]169[/C][C]0.226245168922751[/C][C]0.452490337845502[/C][C]0.773754831077249[/C][/ROW]
[ROW][C]170[/C][C]0.253354730964437[/C][C]0.506709461928874[/C][C]0.746645269035563[/C][/ROW]
[ROW][C]171[/C][C]0.224983163625186[/C][C]0.449966327250373[/C][C]0.775016836374813[/C][/ROW]
[ROW][C]172[/C][C]0.217675210691686[/C][C]0.435350421383372[/C][C]0.782324789308314[/C][/ROW]
[ROW][C]173[/C][C]0.38293530859929[/C][C]0.76587061719858[/C][C]0.61706469140071[/C][/ROW]
[ROW][C]174[/C][C]0.362555736507818[/C][C]0.725111473015635[/C][C]0.637444263492182[/C][/ROW]
[ROW][C]175[/C][C]0.379407766840453[/C][C]0.758815533680906[/C][C]0.620592233159547[/C][/ROW]
[ROW][C]176[/C][C]0.389001355568357[/C][C]0.778002711136714[/C][C]0.610998644431643[/C][/ROW]
[ROW][C]177[/C][C]0.458214026596418[/C][C]0.916428053192837[/C][C]0.541785973403582[/C][/ROW]
[ROW][C]178[/C][C]0.421189523851897[/C][C]0.842379047703793[/C][C]0.578810476148103[/C][/ROW]
[ROW][C]179[/C][C]0.396333569617301[/C][C]0.792667139234603[/C][C]0.603666430382699[/C][/ROW]
[ROW][C]180[/C][C]0.40058674802331[/C][C]0.801173496046619[/C][C]0.59941325197669[/C][/ROW]
[ROW][C]181[/C][C]0.366083477579865[/C][C]0.73216695515973[/C][C]0.633916522420135[/C][/ROW]
[ROW][C]182[/C][C]0.372496474271257[/C][C]0.744992948542514[/C][C]0.627503525728743[/C][/ROW]
[ROW][C]183[/C][C]0.336761359026124[/C][C]0.673522718052248[/C][C]0.663238640973876[/C][/ROW]
[ROW][C]184[/C][C]0.314461325398477[/C][C]0.628922650796955[/C][C]0.685538674601523[/C][/ROW]
[ROW][C]185[/C][C]0.312996675444535[/C][C]0.625993350889069[/C][C]0.687003324555465[/C][/ROW]
[ROW][C]186[/C][C]0.300783260251678[/C][C]0.601566520503355[/C][C]0.699216739748322[/C][/ROW]
[ROW][C]187[/C][C]0.267945776332294[/C][C]0.535891552664587[/C][C]0.732054223667706[/C][/ROW]
[ROW][C]188[/C][C]0.238951036755015[/C][C]0.477902073510031[/C][C]0.761048963244985[/C][/ROW]
[ROW][C]189[/C][C]0.209776748222389[/C][C]0.419553496444778[/C][C]0.790223251777611[/C][/ROW]
[ROW][C]190[/C][C]0.184883249934087[/C][C]0.369766499868174[/C][C]0.815116750065913[/C][/ROW]
[ROW][C]191[/C][C]0.191556876350176[/C][C]0.383113752700351[/C][C]0.808443123649824[/C][/ROW]
[ROW][C]192[/C][C]0.176219495357323[/C][C]0.352438990714647[/C][C]0.823780504642677[/C][/ROW]
[ROW][C]193[/C][C]0.18186477945793[/C][C]0.363729558915859[/C][C]0.81813522054207[/C][/ROW]
[ROW][C]194[/C][C]0.17242475241271[/C][C]0.34484950482542[/C][C]0.82757524758729[/C][/ROW]
[ROW][C]195[/C][C]0.168476265875277[/C][C]0.336952531750555[/C][C]0.831523734124723[/C][/ROW]
[ROW][C]196[/C][C]0.17979037770363[/C][C]0.35958075540726[/C][C]0.82020962229637[/C][/ROW]
[ROW][C]197[/C][C]0.214467464215837[/C][C]0.428934928431674[/C][C]0.785532535784163[/C][/ROW]
[ROW][C]198[/C][C]0.197059652646338[/C][C]0.394119305292675[/C][C]0.802940347353662[/C][/ROW]
[ROW][C]199[/C][C]0.228779172677928[/C][C]0.457558345355857[/C][C]0.771220827322072[/C][/ROW]
[ROW][C]200[/C][C]0.199039420725903[/C][C]0.398078841451805[/C][C]0.800960579274097[/C][/ROW]
[ROW][C]201[/C][C]0.238127375505217[/C][C]0.476254751010433[/C][C]0.761872624494783[/C][/ROW]
[ROW][C]202[/C][C]0.214976659359361[/C][C]0.429953318718722[/C][C]0.785023340640639[/C][/ROW]
[ROW][C]203[/C][C]0.261846976230519[/C][C]0.523693952461038[/C][C]0.738153023769481[/C][/ROW]
[ROW][C]204[/C][C]0.23332158173124[/C][C]0.46664316346248[/C][C]0.76667841826876[/C][/ROW]
[ROW][C]205[/C][C]0.202239294951053[/C][C]0.404478589902105[/C][C]0.797760705048947[/C][/ROW]
[ROW][C]206[/C][C]0.182819258927557[/C][C]0.365638517855113[/C][C]0.817180741072443[/C][/ROW]
[ROW][C]207[/C][C]0.155323216460538[/C][C]0.310646432921075[/C][C]0.844676783539462[/C][/ROW]
[ROW][C]208[/C][C]0.174410343711934[/C][C]0.348820687423869[/C][C]0.825589656288066[/C][/ROW]
[ROW][C]209[/C][C]0.146854179711514[/C][C]0.293708359423028[/C][C]0.853145820288486[/C][/ROW]
[ROW][C]210[/C][C]0.161126685226713[/C][C]0.322253370453426[/C][C]0.838873314773287[/C][/ROW]
[ROW][C]211[/C][C]0.182048835516244[/C][C]0.364097671032488[/C][C]0.817951164483756[/C][/ROW]
[ROW][C]212[/C][C]0.168366013705586[/C][C]0.336732027411172[/C][C]0.831633986294414[/C][/ROW]
[ROW][C]213[/C][C]0.143853562911804[/C][C]0.287707125823608[/C][C]0.856146437088196[/C][/ROW]
[ROW][C]214[/C][C]0.184680844593235[/C][C]0.369361689186469[/C][C]0.815319155406765[/C][/ROW]
[ROW][C]215[/C][C]0.162417485601259[/C][C]0.324834971202518[/C][C]0.837582514398741[/C][/ROW]
[ROW][C]216[/C][C]0.148861624841896[/C][C]0.297723249683793[/C][C]0.851138375158104[/C][/ROW]
[ROW][C]217[/C][C]0.174725211539368[/C][C]0.349450423078735[/C][C]0.825274788460632[/C][/ROW]
[ROW][C]218[/C][C]0.148732602506557[/C][C]0.297465205013115[/C][C]0.851267397493443[/C][/ROW]
[ROW][C]219[/C][C]0.135546049294126[/C][C]0.271092098588253[/C][C]0.864453950705874[/C][/ROW]
[ROW][C]220[/C][C]0.142811029381234[/C][C]0.285622058762468[/C][C]0.857188970618766[/C][/ROW]
[ROW][C]221[/C][C]0.144019578149931[/C][C]0.288039156299861[/C][C]0.85598042185007[/C][/ROW]
[ROW][C]222[/C][C]0.147857169287213[/C][C]0.295714338574426[/C][C]0.852142830712787[/C][/ROW]
[ROW][C]223[/C][C]0.132136852751219[/C][C]0.264273705502437[/C][C]0.867863147248781[/C][/ROW]
[ROW][C]224[/C][C]0.107193715888224[/C][C]0.214387431776447[/C][C]0.892806284111776[/C][/ROW]
[ROW][C]225[/C][C]0.0951916157902906[/C][C]0.190383231580581[/C][C]0.904808384209709[/C][/ROW]
[ROW][C]226[/C][C]0.117986678253015[/C][C]0.23597335650603[/C][C]0.882013321746985[/C][/ROW]
[ROW][C]227[/C][C]0.309743249981838[/C][C]0.619486499963677[/C][C]0.690256750018162[/C][/ROW]
[ROW][C]228[/C][C]0.272011407274935[/C][C]0.544022814549869[/C][C]0.727988592725066[/C][/ROW]
[ROW][C]229[/C][C]0.236302159390981[/C][C]0.472604318781962[/C][C]0.763697840609019[/C][/ROW]
[ROW][C]230[/C][C]0.213426321275232[/C][C]0.426852642550463[/C][C]0.786573678724768[/C][/ROW]
[ROW][C]231[/C][C]0.18017494428555[/C][C]0.3603498885711[/C][C]0.81982505571445[/C][/ROW]
[ROW][C]232[/C][C]0.202885371971511[/C][C]0.405770743943022[/C][C]0.797114628028489[/C][/ROW]
[ROW][C]233[/C][C]0.165747209694278[/C][C]0.331494419388555[/C][C]0.834252790305722[/C][/ROW]
[ROW][C]234[/C][C]0.134448901570464[/C][C]0.268897803140928[/C][C]0.865551098429536[/C][/ROW]
[ROW][C]235[/C][C]0.139207658287622[/C][C]0.278415316575244[/C][C]0.860792341712378[/C][/ROW]
[ROW][C]236[/C][C]0.114834204295139[/C][C]0.229668408590278[/C][C]0.885165795704861[/C][/ROW]
[ROW][C]237[/C][C]0.0951806934274653[/C][C]0.190361386854931[/C][C]0.904819306572535[/C][/ROW]
[ROW][C]238[/C][C]0.0697021112266756[/C][C]0.139404222453351[/C][C]0.930297888773324[/C][/ROW]
[ROW][C]239[/C][C]0.144845687178147[/C][C]0.289691374356295[/C][C]0.855154312821853[/C][/ROW]
[ROW][C]240[/C][C]0.160832857156201[/C][C]0.321665714312402[/C][C]0.839167142843799[/C][/ROW]
[ROW][C]241[/C][C]0.160702551829575[/C][C]0.321405103659151[/C][C]0.839297448170425[/C][/ROW]
[ROW][C]242[/C][C]0.763630812136301[/C][C]0.472738375727397[/C][C]0.236369187863699[/C][/ROW]
[ROW][C]243[/C][C]0.689543236632633[/C][C]0.620913526734735[/C][C]0.310456763367367[/C][/ROW]
[ROW][C]244[/C][C]0.637434375367639[/C][C]0.725131249264723[/C][C]0.362565624632361[/C][/ROW]
[ROW][C]245[/C][C]0.588764096899193[/C][C]0.822471806201613[/C][C]0.411235903100807[/C][/ROW]
[ROW][C]246[/C][C]0.503210735397626[/C][C]0.993578529204747[/C][C]0.496789264602374[/C][/ROW]
[ROW][C]247[/C][C]0.475880709864198[/C][C]0.951761419728396[/C][C]0.524119290135802[/C][/ROW]
[ROW][C]248[/C][C]0.458282962050813[/C][C]0.916565924101625[/C][C]0.541717037949187[/C][/ROW]
[ROW][C]249[/C][C]0.338845188286026[/C][C]0.677690376572053[/C][C]0.661154811713974[/C][/ROW]
[ROW][C]250[/C][C]0.359275091998521[/C][C]0.718550183997042[/C][C]0.640724908001479[/C][/ROW]
[ROW][C]251[/C][C]0.24407618287731[/C][C]0.48815236575462[/C][C]0.75592381712269[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202847&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202847&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
130.9920918281302120.01581634373957550.00790817186978775
140.9819717629608250.03605647407835020.0180282370391751
150.9775870322863080.0448259354273850.0224129677136925
160.9584253062970520.08314938740589510.0415746937029476
170.980876279345860.03824744130828090.0191237206541405
180.9670679865328130.06586402693437480.0329320134671874
190.9471640215529080.1056719568941840.0528359784470919
200.9383650115563740.1232699768872530.0616349884436265
210.9434823957191440.1130352085617110.0565176042808556
220.9523320969528320.09533580609433570.0476679030471678
230.954679693393030.09064061321394050.0453203066069703
240.9355076803513090.1289846392973820.0644923196486912
250.9203040702900330.1593918594199330.0796959297099667
260.9993759758604240.001248048279152840.000624024139576421
270.9990407452501750.001918509499649990.000959254749824994
280.9984829510892870.003034097821425760.00151704891071288
290.9976136581498110.004772683700378770.00238634185018938
300.9974812400907850.00503751981843090.00251875990921545
310.9964761959132080.007047608173583130.00352380408679156
320.9945952123198730.01080957536025310.00540478768012655
330.9946168022858910.01076639542821830.00538319771410914
340.9925407767156040.0149184465687910.00745922328439549
350.9896340095436110.02073198091277880.0103659904563894
360.985827688135660.02834462372867950.0141723118643398
370.9879681644120750.02406367117585070.0120318355879253
380.9842267799899110.03154644002017870.0157732200100893
390.9836093785941640.03278124281167190.0163906214058359
400.9811280399731270.03774392005374510.0188719600268726
410.9744268780658690.05114624386826120.0255731219341306
420.9756283899049440.04874322019011280.0243716100950564
430.9691930947204240.06161381055915290.0308069052795765
440.9609810730826520.07803785383469560.0390189269173478
450.9500937949858970.09981241002820630.0499062050141032
460.9511654686307520.09766906273849550.0488345313692477
470.9423140906346970.1153718187306060.0576859093653028
480.928693752969160.1426124940616790.0713062470308397
490.9506706055347320.09865878893053510.0493293944652675
500.9435342429471740.1129315141056510.0564657570528255
510.9332400663045810.1335198673908370.0667599336954187
520.9182708005326030.1634583989347950.0817291994673973
530.9020978798415640.1958042403168720.0979021201584361
540.8846319217243910.2307361565512170.115368078275609
550.8747973860649920.2504052278700160.125202613935008
560.8640546993263650.271890601347270.135945300673635
570.8599329458408550.280134108318290.140067054159145
580.8340344970186810.3319310059626370.165965502981319
590.8586427508493870.2827144983012260.141357249150613
600.8426754673968720.3146490652062560.157324532603128
610.8515831761037250.2968336477925490.148416823896275
620.8362393177296130.3275213645407740.163760682270387
630.8710005563732910.2579988872534170.128999443626709
640.864853842312060.2702923153758810.13514615768794
650.86550208384440.26899583231120.1344979161556
660.8835770092003250.232845981599350.116422990799675
670.8862637234571330.2274725530857330.113736276542867
680.8766366986470640.2467266027058720.123363301352936
690.9148922676361170.1702154647277660.085107732363883
700.9221165321621710.1557669356756580.0778834678378291
710.9154324451048710.1691351097902580.0845675548951292
720.9399914954161280.1200170091677430.0600085045838715
730.9285454925390180.1429090149219630.0714545074609816
740.9139600700181930.1720798599636130.0860399299818065
750.9055688887480930.1888622225038150.0944311112519074
760.8886073268390560.2227853463218880.111392673160944
770.9123825947218520.1752348105562970.0876174052781484
780.8984116451957160.2031767096085680.101588354804284
790.8894692055790280.2210615888419430.110530794420972
800.8830277218392980.2339445563214030.116972278160702
810.862757347376060.2744853052478810.13724265262394
820.8419261006528080.3161477986943850.158073899347192
830.8370875759225370.3258248481549260.162912424077463
840.8163460592135090.3673078815729810.183653940786491
850.7907565597036430.4184868805927140.209243440296357
860.7649861193542210.4700277612915570.235013880645779
870.7353185027296590.5293629945406810.264681497270341
880.703887503102710.592224993794580.29611249689729
890.7746807960787790.4506384078424430.225319203921221
900.8121944812800460.3756110374399080.187805518719954
910.788751520365440.4224969592691210.21124847963456
920.7626010305926730.4747979388146540.237398969407327
930.7403458345116230.5193083309767530.259654165488377
940.7158204679647120.5683590640705770.284179532035288
950.6908051628376830.6183896743246340.309194837162317
960.6619033286453610.6761933427092780.338096671354639
970.6338905843624540.7322188312750920.366109415637546
980.6404679743322570.7190640513354860.359532025667743
990.6049702246791420.7900595506417170.395029775320858
1000.6011342043309190.7977315913381620.398865795669081
1010.5729987627312990.8540024745374020.427001237268701
1020.5490000396856280.9019999206287430.450999960314372
1030.6007719387957550.798456122408490.399228061204245
1040.5887511603726110.8224976792547790.411248839627389
1050.6018680866796310.7962638266407390.398131913320369
1060.5814421366637730.8371157266724530.418557863336227
1070.5765564128293530.8468871743412940.423443587170647
1080.6137365900013430.7725268199973150.386263409998658
1090.5790651876165190.8418696247669610.420934812383481
1100.5565440561971010.8869118876057980.443455943802899
1110.5577914795060280.8844170409879440.442208520493972
1120.5725783940220930.8548432119558140.427421605977907
1130.5594927893179630.8810144213640750.440507210682037
1140.6656019985169050.6687960029661890.334398001483095
1150.6414182239173910.7171635521652180.358581776082609
1160.6147516109346820.7704967781306360.385248389065318
1170.5795659758987550.8408680482024910.420434024101245
1180.5533321603322680.8933356793354650.446667839667732
1190.5182388873125270.9635222253749460.481761112687473
1200.4845351292637670.9690702585275340.515464870736233
1210.4639681089507290.9279362179014590.536031891049271
1220.4287287357525780.8574574715051560.571271264247422
1230.3988617778699920.7977235557399840.601138222130008
1240.3730855000606570.7461710001213140.626914499939343
1250.3594986263758060.7189972527516130.640501373624194
1260.3355715289053240.6711430578106470.664428471094676
1270.3231296216652430.6462592433304870.676870378334757
1280.4362148316538620.8724296633077230.563785168346138
1290.4193549514888280.8387099029776560.580645048511172
1300.4061947350959630.8123894701919250.593805264904037
1310.391650740748040.7833014814960810.60834925925196
1320.3592173011085890.7184346022171790.640782698891411
1330.3840533997102880.7681067994205770.615946600289712
1340.3550086478680870.7100172957361730.644991352131913
1350.3719113702521040.7438227405042080.628088629747896
1360.3647304308544260.7294608617088520.635269569145574
1370.3353136330609870.6706272661219750.664686366939013
1380.3112565414307380.6225130828614760.688743458569262
1390.2837312497250570.5674624994501140.716268750274943
1400.2591108393772410.5182216787544820.740889160622759
1410.231929126219490.463858252438980.76807087378051
1420.2372209638516430.4744419277032850.762779036148357
1430.211058034323430.4221160686468590.78894196567657
1440.1918905575819260.3837811151638520.808109442418074
1450.1790539676447270.3581079352894540.820946032355273
1460.1736939123406780.3473878246813550.826306087659322
1470.1811157967545080.3622315935090160.818884203245492
1480.1963352076277480.3926704152554950.803664792372252
1490.2131545578303210.4263091156606420.786845442169679
1500.2084824895892580.4169649791785160.791517510410742
1510.1866759157838020.3733518315676040.813324084216198
1520.1666105959177030.3332211918354060.833389404082297
1530.1766949128816010.3533898257632010.823305087118399
1540.1887194005151440.3774388010302880.811280599484856
1550.1703274090095570.3406548180191130.829672590990443
1560.1541766283448990.3083532566897970.845823371655101
1570.1341821756485490.2683643512970980.865817824351451
1580.2065194651140120.4130389302280240.793480534885988
1590.223514728228820.4470294564576390.77648527177118
1600.1979070729804340.3958141459608670.802092927019566
1610.1734166903708820.3468333807417640.826583309629118
1620.1510124658253280.3020249316506560.848987534174672
1630.1312285594687920.2624571189375840.868771440531208
1640.2194948465823160.4389896931646320.780505153417684
1650.2216913620541820.4433827241083630.778308637945818
1660.2119574672455340.4239149344910690.788042532754466
1670.1869599150515050.3739198301030090.813040084948495
1680.1689282312678020.3378564625356030.831071768732198
1690.2262451689227510.4524903378455020.773754831077249
1700.2533547309644370.5067094619288740.746645269035563
1710.2249831636251860.4499663272503730.775016836374813
1720.2176752106916860.4353504213833720.782324789308314
1730.382935308599290.765870617198580.61706469140071
1740.3625557365078180.7251114730156350.637444263492182
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1770.4582140265964180.9164280531928370.541785973403582
1780.4211895238518970.8423790477037930.578810476148103
1790.3963335696173010.7926671392346030.603666430382699
1800.400586748023310.8011734960466190.59941325197669
1810.3660834775798650.732166955159730.633916522420135
1820.3724964742712570.7449929485425140.627503525728743
1830.3367613590261240.6735227180522480.663238640973876
1840.3144613253984770.6289226507969550.685538674601523
1850.3129966754445350.6259933508890690.687003324555465
1860.3007832602516780.6015665205033550.699216739748322
1870.2679457763322940.5358915526645870.732054223667706
1880.2389510367550150.4779020735100310.761048963244985
1890.2097767482223890.4195534964447780.790223251777611
1900.1848832499340870.3697664998681740.815116750065913
1910.1915568763501760.3831137527003510.808443123649824
1920.1762194953573230.3524389907146470.823780504642677
1930.181864779457930.3637295589158590.81813522054207
1940.172424752412710.344849504825420.82757524758729
1950.1684762658752770.3369525317505550.831523734124723
1960.179790377703630.359580755407260.82020962229637
1970.2144674642158370.4289349284316740.785532535784163
1980.1970596526463380.3941193052926750.802940347353662
1990.2287791726779280.4575583453558570.771220827322072
2000.1990394207259030.3980788414518050.800960579274097
2010.2381273755052170.4762547510104330.761872624494783
2020.2149766593593610.4299533187187220.785023340640639
2030.2618469762305190.5236939524610380.738153023769481
2040.233321581731240.466643163462480.76667841826876
2050.2022392949510530.4044785899021050.797760705048947
2060.1828192589275570.3656385178551130.817180741072443
2070.1553232164605380.3106464329210750.844676783539462
2080.1744103437119340.3488206874238690.825589656288066
2090.1468541797115140.2937083594230280.853145820288486
2100.1611266852267130.3222533704534260.838873314773287
2110.1820488355162440.3640976710324880.817951164483756
2120.1683660137055860.3367320274111720.831633986294414
2130.1438535629118040.2877071258236080.856146437088196
2140.1846808445932350.3693616891864690.815319155406765
2150.1624174856012590.3248349712025180.837582514398741
2160.1488616248418960.2977232496837930.851138375158104
2170.1747252115393680.3494504230787350.825274788460632
2180.1487326025065570.2974652050131150.851267397493443
2190.1355460492941260.2710920985882530.864453950705874
2200.1428110293812340.2856220587624680.857188970618766
2210.1440195781499310.2880391562998610.85598042185007
2220.1478571692872130.2957143385744260.852142830712787
2230.1321368527512190.2642737055024370.867863147248781
2240.1071937158882240.2143874317764470.892806284111776
2250.09519161579029060.1903832315805810.904808384209709
2260.1179866782530150.235973356506030.882013321746985
2270.3097432499818380.6194864999636770.690256750018162
2280.2720114072749350.5440228145498690.727988592725066
2290.2363021593909810.4726043187819620.763697840609019
2300.2134263212752320.4268526425504630.786573678724768
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2320.2028853719715110.4057707439430220.797114628028489
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2340.1344489015704640.2688978031409280.865551098429536
2350.1392076582876220.2784153165752440.860792341712378
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2380.06970211122667560.1394042224533510.930297888773324
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2400.1608328571562010.3216657143124020.839167142843799
2410.1607025518295750.3214051036591510.839297448170425
2420.7636308121363010.4727383757273970.236369187863699
2430.6895432366326330.6209135267347350.310456763367367
2440.6374343753676390.7251312492647230.362565624632361
2450.5887640968991930.8224718062016130.411235903100807
2460.5032107353976260.9935785292047470.496789264602374
2470.4758807098641980.9517614197283960.524119290135802
2480.4582829620508130.9165659241016250.541717037949187
2490.3388451882860260.6776903765720530.661154811713974
2500.3592750919985210.7185501839970420.640724908001479
2510.244076182877310.488152365754620.75592381712269






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level60.0251046025104603NOK
5% type I error level200.0836820083682008NOK
10% type I error level300.125523012552301NOK

\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 & 6 & 0.0251046025104603 & NOK \tabularnewline
5% type I error level & 20 & 0.0836820083682008 & NOK \tabularnewline
10% type I error level & 30 & 0.125523012552301 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202847&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]6[/C][C]0.0251046025104603[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]20[/C][C]0.0836820083682008[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]30[/C][C]0.125523012552301[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202847&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202847&T=6

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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 level60.0251046025104603NOK
5% type I error level200.0836820083682008NOK
10% type I error level300.125523012552301NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = monthly births ; par2 = Bron: Algemene Directie Statistiek en Economische Informatie - FOD Economie ; par3 = monthly births ;
Parameters (R input):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}