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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareMultiple Regression
Date of computationSun, 09 Dec 2012 10:00:50 -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/09/t13550654914rhsmcnyg7jj2e7.htm/, Retrieved Thu, 25 Apr 2024 09:33:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197917, Retrieved Thu, 25 Apr 2024 09:33:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact147

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-    D  [Multiple Regression] [WS7 0299787] [2012-11-04 10:59:16] [a2dcdd13e1df6929c5c71aa45a46cc8e]
- R P     [Multiple Regression] [WS7 0299787] [2012-11-04 11:04:11] [a2dcdd13e1df6929c5c71aa45a46cc8e]
-  M          [Multiple Regression] [Paper 0299787] [2012-12-09 15:00:50] [b443327ccd50424d9f9aaa9d8bba1a6f] [Current]
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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
10	34	38	16	13	12	14	62	46
10	32	32	9	5	12	11	63	36
10	37	35	14	6	15	14.5	76	47
10	39	34	16	12	16	13	74	45
10	29	34	16	12	15	9	67	42
10	37	36	15	11	12	10	73	43
10	35	34	16	10	12	15	70	43
10	30	28	12	7	8	20	53	32
10	38	34	16	12	13	12	77	45
10	34	35	16	14	11	12	80	48
10	31	35	14	11	14	14	52	31
10	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
11	43	42	12	7	10	24	46	30
11	30	34	16	12	15	14	85	52
11	31	33	16	14	11	15	74	44
11	32	41	17	11	13	11	88	55
11	32	33	13	11	14	15	38	11
11	37	34	12	10	18	12	76	47
11	37	32	18	13	16	10	86	53
11	33	40	14	13	14	14	54	33
11	34	40	14	8	14	13	67	44
11	33	35	13	11	14	9	69	42
11	38	36	16	12	14	15	90	55
11	33	37	13	11	12	15	54	33
11	31	27	16	13	14	14	76	46
11	38	39	13	12	15	11	89	54
11	37	38	16	14	15	8	76	47
11	36	31	15	13	15	11	73	45
11	31	33	16	15	13	11	79	47
11	39	32	15	10	17	8	90	55
11	44	39	17	11	17	10	74	44
11	33	36	15	9	19	11	81	53
11	35	33	12	11	15	13	72	44
11	32	33	16	10	13	11	71	42
11	28	32	10	11	9	20	66	40
11	40	37	16	8	15	10	77	46
11	27	30	12	11	15	15	65	40
11	37	38	14	12	15	12	74	46
11	32	29	15	12	16	14	85	53
11	28	22	13	9	11	23	54	33
11	34	35	15	11	14	14	63	42
11	30	35	11	10	11	16	54	35
11	35	34	12	8	15	11	64	40
11	31	35	11	9	13	12	69	41
11	32	34	16	8	15	10	54	33
11	30	37	15	9	16	14	84	51
11	30	35	17	15	14	12	86	53
11	31	23	16	11	15	12	77	46
11	40	31	10	8	16	11	89	55
11	32	27	18	13	16	12	76	47
11	36	36	13	12	11	13	60	38
11	32	31	16	12	12	11	75	46
11	35	32	13	9	9	19	73	46
11	38	39	10	7	16	12	85	53
11	42	37	15	13	13	17	79	47
11	34	38	16	9	16	9	71	41
11	35	39	16	6	12	12	72	44
11	38	34	14	8	9	19	69	43
11	33	31	10	8	13	18	78	51
11	36	32	17	15	13	15	54	33
11	32	37	13	6	14	14	69	43
11	33	36	15	9	19	11	81	53
11	34	32	16	11	13	9	84	51
11	32	38	12	8	12	18	84	50
11	34	36	13	8	13	16	69	46

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 10 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197917&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197917&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197917&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 time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.45674050425465 + 0.147563595985744month[t] + 0.0879911037877362Connected[t] -0.0205402382643911Separate[t] + 0.522498727357859Software[t] + 0.0304564703352417Happiness[t] -0.0875673351448767Depression[t] -0.0241643125897779Belonging[t] + 0.06495734898767Belonging_Final[t] -0.00640249915183407t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  5.45674050425465 +  0.147563595985744month[t] +  0.0879911037877362Connected[t] -0.0205402382643911Separate[t] +  0.522498727357859Software[t] +  0.0304564703352417Happiness[t] -0.0875673351448767Depression[t] -0.0241643125897779Belonging[t] +  0.06495734898767Belonging_Final[t] -0.00640249915183407t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197917&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  5.45674050425465 +  0.147563595985744month[t] +  0.0879911037877362Connected[t] -0.0205402382643911Separate[t] +  0.522498727357859Software[t] +  0.0304564703352417Happiness[t] -0.0875673351448767Depression[t] -0.0241643125897779Belonging[t] +  0.06495734898767Belonging_Final[t] -0.00640249915183407t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197917&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197917&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
Learning[t] = + 5.45674050425465 + 0.147563595985744month[t] + 0.0879911037877362Connected[t] -0.0205402382643911Separate[t] + 0.522498727357859Software[t] + 0.0304564703352417Happiness[t] -0.0875673351448767Depression[t] -0.0241643125897779Belonging[t] + 0.06495734898767Belonging_Final[t] -0.00640249915183407t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.456740504254654.9622421.09970.2732340.136617
month0.1475635959857440.5060870.29160.771010.385505
Connected0.08799110378773620.0435692.01960.0451950.022598
Separate-0.02054023826439110.042088-0.4880.6262380.313119
Software0.5224987273578590.0675027.740500
Happiness0.03045647033524170.0717490.42450.6718140.335907
Depression-0.08756733514487670.053313-1.64250.1025610.051281
Belonging-0.02416431258977790.04781-0.50540.6139970.306999
Belonging_Final0.064957348987670.0745130.87180.3847250.192363
t-0.006402499151834070.008885-0.72060.4722870.236143

\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) & 5.45674050425465 & 4.962242 & 1.0997 & 0.273234 & 0.136617 \tabularnewline
month & 0.147563595985744 & 0.506087 & 0.2916 & 0.77101 & 0.385505 \tabularnewline
Connected & 0.0879911037877362 & 0.043569 & 2.0196 & 0.045195 & 0.022598 \tabularnewline
Separate & -0.0205402382643911 & 0.042088 & -0.488 & 0.626238 & 0.313119 \tabularnewline
Software & 0.522498727357859 & 0.067502 & 7.7405 & 0 & 0 \tabularnewline
Happiness & 0.0304564703352417 & 0.071749 & 0.4245 & 0.671814 & 0.335907 \tabularnewline
Depression & -0.0875673351448767 & 0.053313 & -1.6425 & 0.102561 & 0.051281 \tabularnewline
Belonging & -0.0241643125897779 & 0.04781 & -0.5054 & 0.613997 & 0.306999 \tabularnewline
Belonging_Final & 0.06495734898767 & 0.074513 & 0.8718 & 0.384725 & 0.192363 \tabularnewline
t & -0.00640249915183407 & 0.008885 & -0.7206 & 0.472287 & 0.236143 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197917&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]5.45674050425465[/C][C]4.962242[/C][C]1.0997[/C][C]0.273234[/C][C]0.136617[/C][/ROW]
[ROW][C]month[/C][C]0.147563595985744[/C][C]0.506087[/C][C]0.2916[/C][C]0.77101[/C][C]0.385505[/C][/ROW]
[ROW][C]Connected[/C][C]0.0879911037877362[/C][C]0.043569[/C][C]2.0196[/C][C]0.045195[/C][C]0.022598[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0205402382643911[/C][C]0.042088[/C][C]-0.488[/C][C]0.626238[/C][C]0.313119[/C][/ROW]
[ROW][C]Software[/C][C]0.522498727357859[/C][C]0.067502[/C][C]7.7405[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0304564703352417[/C][C]0.071749[/C][C]0.4245[/C][C]0.671814[/C][C]0.335907[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0875673351448767[/C][C]0.053313[/C][C]-1.6425[/C][C]0.102561[/C][C]0.051281[/C][/ROW]
[ROW][C]Belonging[/C][C]-0.0241643125897779[/C][C]0.04781[/C][C]-0.5054[/C][C]0.613997[/C][C]0.306999[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]0.06495734898767[/C][C]0.074513[/C][C]0.8718[/C][C]0.384725[/C][C]0.192363[/C][/ROW]
[ROW][C]t[/C][C]-0.00640249915183407[/C][C]0.008885[/C][C]-0.7206[/C][C]0.472287[/C][C]0.236143[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197917&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197917&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)5.456740504254654.9622421.09970.2732340.136617
month0.1475635959857440.5060870.29160.771010.385505
Connected0.08799110378773620.0435692.01960.0451950.022598
Separate-0.02054023826439110.042088-0.4880.6262380.313119
Software0.5224987273578590.0675027.740500
Happiness0.03045647033524170.0717490.42450.6718140.335907
Depression-0.08756733514487670.053313-1.64250.1025610.051281
Belonging-0.02416431258977790.04781-0.50540.6139970.306999
Belonging_Final0.064957348987670.0745130.87180.3847250.192363
t-0.006402499151834070.008885-0.72060.4722870.236143






Multiple Linear Regression - Regression Statistics
Multiple R0.616084761544292
R-squared0.379560433407088
Adjusted R-squared0.342580591689629
F-TEST (value)10.2639820988713
F-TEST (DF numerator)9
F-TEST (DF denominator)151
p-value2.89035462230913e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.72321311080611
Sum Squared Residuals448.388977213365

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.616084761544292 \tabularnewline
R-squared & 0.379560433407088 \tabularnewline
Adjusted R-squared & 0.342580591689629 \tabularnewline
F-TEST (value) & 10.2639820988713 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 151 \tabularnewline
p-value & 2.89035462230913e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.72321311080611 \tabularnewline
Sum Squared Residuals & 448.388977213365 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197917&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.616084761544292[/C][/ROW]
[ROW][C]R-squared[/C][C]0.379560433407088[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.342580591689629[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.2639820988713[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]151[/C][/ROW]
[ROW][C]p-value[/C][C]2.89035462230913e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.72321311080611[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]448.388977213365[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197917&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197917&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 R0.616084761544292
R-squared0.379560433407088
Adjusted R-squared0.342580591689629
F-TEST (value)10.2639820988713
F-TEST (DF numerator)9
F-TEST (DF denominator)151
p-value2.89035462230913e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.72321311080611
Sum Squared Residuals448.388977213365






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.0490104618212-3.04901046182122
21616.1860219268806-0.186021926880638
31916.76635356363332.23364643636669
41512.30300357762482.69699642237519
51415.5769323249511-1.57693232495106
61315.120994334411-2.120994334411
71915.42750832067513.57249167932494
81517.0137390005-2.01373900049999
91416.1620417775404-2.16204177754041
101514.43339755267070.566602447329285
111615.56230325882630.437696741173666
121616.1641331339235-0.164133133923536
131615.69657310227340.303426897726611
141615.19386834469270.806131655307332
151717.682961316051-0.682961316050965
161515.2276273944881-0.227627394488076
171514.72255492089870.277445079101335
182016.43844214811393.56155785188605
191815.72922791484612.2707720851539
201615.39520222455030.604797775449731
211615.32046730508410.679532694915915
221615.3289480273160.671051972684026
231916.29546503666112.70453496333885
241615.02299579676460.977004203235379
251716.52327111462940.476728885370591
261716.9670315886190.0329684113809558
271614.76644515012711.23355484987285
281516.3679090283809-1.36790902838088
291615.36417236873070.635827631269317
301414.5673550619547-0.567355061954664
311515.8962595562111-0.896259556211074
321212.399538615869-0.399538615868987
331415.1370275804905-1.13702758049045
341616.0403222944867-0.0403222944866774
351415.4342652606063-1.43426526060633
361013.2789234544361-3.27892345443609
371012.5692353188849-2.56923531888487
381415.7848933481702-1.78489334817024
391614.46873423929171.53126576070832
401614.73669279717431.26330720282572
411615.14934350369180.850656496308213
421415.4069230426615-1.40692304266148
432017.65884511538882.34115488461121
441414.026619464814-0.0266194648140459
451415.0447004096259-1.04470040962588
461115.448811656535-4.44881165653501
471416.710198242124-2.71019824212402
481515.0996382114152-0.0996382114152192
491614.75002658729911.2499734127009
501415.9644976462787-1.96449764627872
511616.5383768844621-0.538376884462106
521414.0561112677422-0.0561112677421831
531214.4932319603238-2.49323196032376
541616.2196790651626-0.219679065162606
55911.5695101721269-2.56951017212693
561412.64921971767441.35078028232563
571616.0545534287663-0.05455342876633
581615.46233090314680.537669096853166
591515.3373127577086-0.337312757708649
601614.30816606219721.69183393780278
611211.5541532097160.445846790284027
621615.85825481558920.141745184410759
631616.585811286261-0.585811286260983
641414.2364998541117-0.236499854111724
651615.29834022501440.701659774985575
661716.21308390250390.78691609749612
671816.24690310383561.7530968961644
681814.54356531494243.45643468505759
691215.6351315345966-3.6351315345966
701615.36566152035410.63433847964587
711013.789131849363-3.78913184936299
721414.1785908244253-0.178590824425269
731816.44971957529141.55028042470861
741816.78721597937231.21278402062768
751615.810043471270.189956528730027
761713.91518434406733.08481565593275
771616.2085982734379-0.208598273437875
781614.63349287745041.36650712254957
791315.0343823104248-2.03438231042482
801615.83779340206670.162206597933289
811615.42068033586040.579319664139595
821615.873211628040.126788371960041
831515.7144634312669-0.714463431266877
841514.78373163879630.216268361203682
851614.36046890940151.63953109059846
861414.0560716932549-0.0560716932548528
871615.28713574194520.712864258054837
881614.45453937635891.54546062364107
891514.24679537450830.753204625491681
901213.6503435997642-1.65034359976425
911716.61876578749120.381234212508838
921615.29672781633350.703272183666451
931515.096877097657-0.0968770976570464
941314.9572539543321-1.95725395433206
951614.88524402247861.11475597752144
961615.73721122156070.262788778439251
971614.02313540850941.97686459149055
981615.84738175542520.152618244574779
991414.3325070655381-0.332507065538147
1001616.940001690227-0.940001690226981
1011614.69345871870211.30654128129791
1022017.2848044624222.715195537578
1031514.76613821586510.233861784134946
1041614.31642408648051.68357591351953
1051315.0228622261668-2.02286222616685
1061715.88902037135991.11097962864013
1071615.45712925145230.542870748547735
1081614.26693826667961.73306173332035
1091212.0005969501969-0.000596950196859065
1101615.14173483456130.858265165438743
1111615.82561656227760.174383437722378
1121714.96279982258232.03720017741771
1131313.1509986333327-0.150998633332732
1141214.8462612594175-2.84626125941749
1151816.71075811651551.28924188348448
1161415.2509980379671-1.25099803796712
1171413.20805511615590.791944883844143
1181314.9558849040364-1.95588490403643
1191615.70298737450190.297012625498139
1201314.0935310256219-1.09353102562188
1211615.62285709193420.377142908065845
1221315.96209193677-2.96209193677004
1231617.0553726529986-1.05537265299863
1241516.2621382249113-1.2621382249113
1251616.7437130667739-0.743713066773915
1261515.4876652395885-0.487665239588466
1271715.79689881116481.20310118883524
1281514.22802898871150.771971011288529
1291214.8401289874323-2.84012898743225
1301614.05572579379311.94427420620694
1311013.3213728124412-3.32137281244122
1321613.88301501424542.11698498575463
1331213.9063969972042-1.90639699720421
1341415.5730496432251-1.57304964322512
1351515.3557695739857-0.355769573985745
1361312.08324648800970.916753511990259
1371514.62937772375360.370622276246397
1381113.2447853711918-2.2447853711918
1391213.2966873506332-1.29668735063324
1401113.2359344356473-2.23593443564735
1411612.89441805909533.10558194090467
1421513.29740139877321.70259860122683
1431716.66287954271230.337120457287677
1441614.6941899378191.30581006218097
1451014.1605574798599-4.16055747985993
1461815.85179067687392.14820932312605
1471315.052154894862-2.05215489486201
1481615.15927441556090.840725584439128
1491313.0852293404491-0.0852293404490951
1501013.1449173602912-3.1449173602912
1511515.8925878118488-0.892587811848785
1521613.66719983365622.33280016634383
1531611.94693186555174.05306813444826
1541412.65539615556261.34460384443741
1551012.7822320473445-2.78223204734445
1561716.35016693860770.649833061392308
1571311.59174289327241.40825710672763
1581514.03595401415640.964045985843552
1591615.03468923909930.965310760900736
1601212.2780470850852-0.27804708508523
1611312.79693370355860.203066296441365

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.0490104618212 & -3.04901046182122 \tabularnewline
2 & 16 & 16.1860219268806 & -0.186021926880638 \tabularnewline
3 & 19 & 16.7663535636333 & 2.23364643636669 \tabularnewline
4 & 15 & 12.3030035776248 & 2.69699642237519 \tabularnewline
5 & 14 & 15.5769323249511 & -1.57693232495106 \tabularnewline
6 & 13 & 15.120994334411 & -2.120994334411 \tabularnewline
7 & 19 & 15.4275083206751 & 3.57249167932494 \tabularnewline
8 & 15 & 17.0137390005 & -2.01373900049999 \tabularnewline
9 & 14 & 16.1620417775404 & -2.16204177754041 \tabularnewline
10 & 15 & 14.4333975526707 & 0.566602447329285 \tabularnewline
11 & 16 & 15.5623032588263 & 0.437696741173666 \tabularnewline
12 & 16 & 16.1641331339235 & -0.164133133923536 \tabularnewline
13 & 16 & 15.6965731022734 & 0.303426897726611 \tabularnewline
14 & 16 & 15.1938683446927 & 0.806131655307332 \tabularnewline
15 & 17 & 17.682961316051 & -0.682961316050965 \tabularnewline
16 & 15 & 15.2276273944881 & -0.227627394488076 \tabularnewline
17 & 15 & 14.7225549208987 & 0.277445079101335 \tabularnewline
18 & 20 & 16.4384421481139 & 3.56155785188605 \tabularnewline
19 & 18 & 15.7292279148461 & 2.2707720851539 \tabularnewline
20 & 16 & 15.3952022245503 & 0.604797775449731 \tabularnewline
21 & 16 & 15.3204673050841 & 0.679532694915915 \tabularnewline
22 & 16 & 15.328948027316 & 0.671051972684026 \tabularnewline
23 & 19 & 16.2954650366611 & 2.70453496333885 \tabularnewline
24 & 16 & 15.0229957967646 & 0.977004203235379 \tabularnewline
25 & 17 & 16.5232711146294 & 0.476728885370591 \tabularnewline
26 & 17 & 16.967031588619 & 0.0329684113809558 \tabularnewline
27 & 16 & 14.7664451501271 & 1.23355484987285 \tabularnewline
28 & 15 & 16.3679090283809 & -1.36790902838088 \tabularnewline
29 & 16 & 15.3641723687307 & 0.635827631269317 \tabularnewline
30 & 14 & 14.5673550619547 & -0.567355061954664 \tabularnewline
31 & 15 & 15.8962595562111 & -0.896259556211074 \tabularnewline
32 & 12 & 12.399538615869 & -0.399538615868987 \tabularnewline
33 & 14 & 15.1370275804905 & -1.13702758049045 \tabularnewline
34 & 16 & 16.0403222944867 & -0.0403222944866774 \tabularnewline
35 & 14 & 15.4342652606063 & -1.43426526060633 \tabularnewline
36 & 10 & 13.2789234544361 & -3.27892345443609 \tabularnewline
37 & 10 & 12.5692353188849 & -2.56923531888487 \tabularnewline
38 & 14 & 15.7848933481702 & -1.78489334817024 \tabularnewline
39 & 16 & 14.4687342392917 & 1.53126576070832 \tabularnewline
40 & 16 & 14.7366927971743 & 1.26330720282572 \tabularnewline
41 & 16 & 15.1493435036918 & 0.850656496308213 \tabularnewline
42 & 14 & 15.4069230426615 & -1.40692304266148 \tabularnewline
43 & 20 & 17.6588451153888 & 2.34115488461121 \tabularnewline
44 & 14 & 14.026619464814 & -0.0266194648140459 \tabularnewline
45 & 14 & 15.0447004096259 & -1.04470040962588 \tabularnewline
46 & 11 & 15.448811656535 & -4.44881165653501 \tabularnewline
47 & 14 & 16.710198242124 & -2.71019824212402 \tabularnewline
48 & 15 & 15.0996382114152 & -0.0996382114152192 \tabularnewline
49 & 16 & 14.7500265872991 & 1.2499734127009 \tabularnewline
50 & 14 & 15.9644976462787 & -1.96449764627872 \tabularnewline
51 & 16 & 16.5383768844621 & -0.538376884462106 \tabularnewline
52 & 14 & 14.0561112677422 & -0.0561112677421831 \tabularnewline
53 & 12 & 14.4932319603238 & -2.49323196032376 \tabularnewline
54 & 16 & 16.2196790651626 & -0.219679065162606 \tabularnewline
55 & 9 & 11.5695101721269 & -2.56951017212693 \tabularnewline
56 & 14 & 12.6492197176744 & 1.35078028232563 \tabularnewline
57 & 16 & 16.0545534287663 & -0.05455342876633 \tabularnewline
58 & 16 & 15.4623309031468 & 0.537669096853166 \tabularnewline
59 & 15 & 15.3373127577086 & -0.337312757708649 \tabularnewline
60 & 16 & 14.3081660621972 & 1.69183393780278 \tabularnewline
61 & 12 & 11.554153209716 & 0.445846790284027 \tabularnewline
62 & 16 & 15.8582548155892 & 0.141745184410759 \tabularnewline
63 & 16 & 16.585811286261 & -0.585811286260983 \tabularnewline
64 & 14 & 14.2364998541117 & -0.236499854111724 \tabularnewline
65 & 16 & 15.2983402250144 & 0.701659774985575 \tabularnewline
66 & 17 & 16.2130839025039 & 0.78691609749612 \tabularnewline
67 & 18 & 16.2469031038356 & 1.7530968961644 \tabularnewline
68 & 18 & 14.5435653149424 & 3.45643468505759 \tabularnewline
69 & 12 & 15.6351315345966 & -3.6351315345966 \tabularnewline
70 & 16 & 15.3656615203541 & 0.63433847964587 \tabularnewline
71 & 10 & 13.789131849363 & -3.78913184936299 \tabularnewline
72 & 14 & 14.1785908244253 & -0.178590824425269 \tabularnewline
73 & 18 & 16.4497195752914 & 1.55028042470861 \tabularnewline
74 & 18 & 16.7872159793723 & 1.21278402062768 \tabularnewline
75 & 16 & 15.81004347127 & 0.189956528730027 \tabularnewline
76 & 17 & 13.9151843440673 & 3.08481565593275 \tabularnewline
77 & 16 & 16.2085982734379 & -0.208598273437875 \tabularnewline
78 & 16 & 14.6334928774504 & 1.36650712254957 \tabularnewline
79 & 13 & 15.0343823104248 & -2.03438231042482 \tabularnewline
80 & 16 & 15.8377934020667 & 0.162206597933289 \tabularnewline
81 & 16 & 15.4206803358604 & 0.579319664139595 \tabularnewline
82 & 16 & 15.87321162804 & 0.126788371960041 \tabularnewline
83 & 15 & 15.7144634312669 & -0.714463431266877 \tabularnewline
84 & 15 & 14.7837316387963 & 0.216268361203682 \tabularnewline
85 & 16 & 14.3604689094015 & 1.63953109059846 \tabularnewline
86 & 14 & 14.0560716932549 & -0.0560716932548528 \tabularnewline
87 & 16 & 15.2871357419452 & 0.712864258054837 \tabularnewline
88 & 16 & 14.4545393763589 & 1.54546062364107 \tabularnewline
89 & 15 & 14.2467953745083 & 0.753204625491681 \tabularnewline
90 & 12 & 13.6503435997642 & -1.65034359976425 \tabularnewline
91 & 17 & 16.6187657874912 & 0.381234212508838 \tabularnewline
92 & 16 & 15.2967278163335 & 0.703272183666451 \tabularnewline
93 & 15 & 15.096877097657 & -0.0968770976570464 \tabularnewline
94 & 13 & 14.9572539543321 & -1.95725395433206 \tabularnewline
95 & 16 & 14.8852440224786 & 1.11475597752144 \tabularnewline
96 & 16 & 15.7372112215607 & 0.262788778439251 \tabularnewline
97 & 16 & 14.0231354085094 & 1.97686459149055 \tabularnewline
98 & 16 & 15.8473817554252 & 0.152618244574779 \tabularnewline
99 & 14 & 14.3325070655381 & -0.332507065538147 \tabularnewline
100 & 16 & 16.940001690227 & -0.940001690226981 \tabularnewline
101 & 16 & 14.6934587187021 & 1.30654128129791 \tabularnewline
102 & 20 & 17.284804462422 & 2.715195537578 \tabularnewline
103 & 15 & 14.7661382158651 & 0.233861784134946 \tabularnewline
104 & 16 & 14.3164240864805 & 1.68357591351953 \tabularnewline
105 & 13 & 15.0228622261668 & -2.02286222616685 \tabularnewline
106 & 17 & 15.8890203713599 & 1.11097962864013 \tabularnewline
107 & 16 & 15.4571292514523 & 0.542870748547735 \tabularnewline
108 & 16 & 14.2669382666796 & 1.73306173332035 \tabularnewline
109 & 12 & 12.0005969501969 & -0.000596950196859065 \tabularnewline
110 & 16 & 15.1417348345613 & 0.858265165438743 \tabularnewline
111 & 16 & 15.8256165622776 & 0.174383437722378 \tabularnewline
112 & 17 & 14.9627998225823 & 2.03720017741771 \tabularnewline
113 & 13 & 13.1509986333327 & -0.150998633332732 \tabularnewline
114 & 12 & 14.8462612594175 & -2.84626125941749 \tabularnewline
115 & 18 & 16.7107581165155 & 1.28924188348448 \tabularnewline
116 & 14 & 15.2509980379671 & -1.25099803796712 \tabularnewline
117 & 14 & 13.2080551161559 & 0.791944883844143 \tabularnewline
118 & 13 & 14.9558849040364 & -1.95588490403643 \tabularnewline
119 & 16 & 15.7029873745019 & 0.297012625498139 \tabularnewline
120 & 13 & 14.0935310256219 & -1.09353102562188 \tabularnewline
121 & 16 & 15.6228570919342 & 0.377142908065845 \tabularnewline
122 & 13 & 15.96209193677 & -2.96209193677004 \tabularnewline
123 & 16 & 17.0553726529986 & -1.05537265299863 \tabularnewline
124 & 15 & 16.2621382249113 & -1.2621382249113 \tabularnewline
125 & 16 & 16.7437130667739 & -0.743713066773915 \tabularnewline
126 & 15 & 15.4876652395885 & -0.487665239588466 \tabularnewline
127 & 17 & 15.7968988111648 & 1.20310118883524 \tabularnewline
128 & 15 & 14.2280289887115 & 0.771971011288529 \tabularnewline
129 & 12 & 14.8401289874323 & -2.84012898743225 \tabularnewline
130 & 16 & 14.0557257937931 & 1.94427420620694 \tabularnewline
131 & 10 & 13.3213728124412 & -3.32137281244122 \tabularnewline
132 & 16 & 13.8830150142454 & 2.11698498575463 \tabularnewline
133 & 12 & 13.9063969972042 & -1.90639699720421 \tabularnewline
134 & 14 & 15.5730496432251 & -1.57304964322512 \tabularnewline
135 & 15 & 15.3557695739857 & -0.355769573985745 \tabularnewline
136 & 13 & 12.0832464880097 & 0.916753511990259 \tabularnewline
137 & 15 & 14.6293777237536 & 0.370622276246397 \tabularnewline
138 & 11 & 13.2447853711918 & -2.2447853711918 \tabularnewline
139 & 12 & 13.2966873506332 & -1.29668735063324 \tabularnewline
140 & 11 & 13.2359344356473 & -2.23593443564735 \tabularnewline
141 & 16 & 12.8944180590953 & 3.10558194090467 \tabularnewline
142 & 15 & 13.2974013987732 & 1.70259860122683 \tabularnewline
143 & 17 & 16.6628795427123 & 0.337120457287677 \tabularnewline
144 & 16 & 14.694189937819 & 1.30581006218097 \tabularnewline
145 & 10 & 14.1605574798599 & -4.16055747985993 \tabularnewline
146 & 18 & 15.8517906768739 & 2.14820932312605 \tabularnewline
147 & 13 & 15.052154894862 & -2.05215489486201 \tabularnewline
148 & 16 & 15.1592744155609 & 0.840725584439128 \tabularnewline
149 & 13 & 13.0852293404491 & -0.0852293404490951 \tabularnewline
150 & 10 & 13.1449173602912 & -3.1449173602912 \tabularnewline
151 & 15 & 15.8925878118488 & -0.892587811848785 \tabularnewline
152 & 16 & 13.6671998336562 & 2.33280016634383 \tabularnewline
153 & 16 & 11.9469318655517 & 4.05306813444826 \tabularnewline
154 & 14 & 12.6553961555626 & 1.34460384443741 \tabularnewline
155 & 10 & 12.7822320473445 & -2.78223204734445 \tabularnewline
156 & 17 & 16.3501669386077 & 0.649833061392308 \tabularnewline
157 & 13 & 11.5917428932724 & 1.40825710672763 \tabularnewline
158 & 15 & 14.0359540141564 & 0.964045985843552 \tabularnewline
159 & 16 & 15.0346892390993 & 0.965310760900736 \tabularnewline
160 & 12 & 12.2780470850852 & -0.27804708508523 \tabularnewline
161 & 13 & 12.7969337035586 & 0.203066296441365 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197917&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]16.0490104618212[/C][C]-3.04901046182122[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.1860219268806[/C][C]-0.186021926880638[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.7663535636333[/C][C]2.23364643636669[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.3030035776248[/C][C]2.69699642237519[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.5769323249511[/C][C]-1.57693232495106[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.120994334411[/C][C]-2.120994334411[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.4275083206751[/C][C]3.57249167932494[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]17.0137390005[/C][C]-2.01373900049999[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.1620417775404[/C][C]-2.16204177754041[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.4333975526707[/C][C]0.566602447329285[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.5623032588263[/C][C]0.437696741173666[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.1641331339235[/C][C]-0.164133133923536[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.6965731022734[/C][C]0.303426897726611[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.1938683446927[/C][C]0.806131655307332[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.682961316051[/C][C]-0.682961316050965[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.2276273944881[/C][C]-0.227627394488076[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.7225549208987[/C][C]0.277445079101335[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.4384421481139[/C][C]3.56155785188605[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.7292279148461[/C][C]2.2707720851539[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.3952022245503[/C][C]0.604797775449731[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.3204673050841[/C][C]0.679532694915915[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.328948027316[/C][C]0.671051972684026[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.2954650366611[/C][C]2.70453496333885[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.0229957967646[/C][C]0.977004203235379[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]16.5232711146294[/C][C]0.476728885370591[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.967031588619[/C][C]0.0329684113809558[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.7664451501271[/C][C]1.23355484987285[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.3679090283809[/C][C]-1.36790902838088[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.3641723687307[/C][C]0.635827631269317[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.5673550619547[/C][C]-0.567355061954664[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.8962595562111[/C][C]-0.896259556211074[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.399538615869[/C][C]-0.399538615868987[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1370275804905[/C][C]-1.13702758049045[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]16.0403222944867[/C][C]-0.0403222944866774[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.4342652606063[/C][C]-1.43426526060633[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]13.2789234544361[/C][C]-3.27892345443609[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]12.5692353188849[/C][C]-2.56923531888487[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.7848933481702[/C][C]-1.78489334817024[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.4687342392917[/C][C]1.53126576070832[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.7366927971743[/C][C]1.26330720282572[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.1493435036918[/C][C]0.850656496308213[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.4069230426615[/C][C]-1.40692304266148[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.6588451153888[/C][C]2.34115488461121[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.026619464814[/C][C]-0.0266194648140459[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.0447004096259[/C][C]-1.04470040962588[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.448811656535[/C][C]-4.44881165653501[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.710198242124[/C][C]-2.71019824212402[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.0996382114152[/C][C]-0.0996382114152192[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]14.7500265872991[/C][C]1.2499734127009[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.9644976462787[/C][C]-1.96449764627872[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.5383768844621[/C][C]-0.538376884462106[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.0561112677422[/C][C]-0.0561112677421831[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.4932319603238[/C][C]-2.49323196032376[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]16.2196790651626[/C][C]-0.219679065162606[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.5695101721269[/C][C]-2.56951017212693[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.6492197176744[/C][C]1.35078028232563[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.0545534287663[/C][C]-0.05455342876633[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.4623309031468[/C][C]0.537669096853166[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.3373127577086[/C][C]-0.337312757708649[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.3081660621972[/C][C]1.69183393780278[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.554153209716[/C][C]0.445846790284027[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.8582548155892[/C][C]0.141745184410759[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.585811286261[/C][C]-0.585811286260983[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.2364998541117[/C][C]-0.236499854111724[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.2983402250144[/C][C]0.701659774985575[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.2130839025039[/C][C]0.78691609749612[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.2469031038356[/C][C]1.7530968961644[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.5435653149424[/C][C]3.45643468505759[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.6351315345966[/C][C]-3.6351315345966[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.3656615203541[/C][C]0.63433847964587[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.789131849363[/C][C]-3.78913184936299[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.1785908244253[/C][C]-0.178590824425269[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.4497195752914[/C][C]1.55028042470861[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]16.7872159793723[/C][C]1.21278402062768[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.81004347127[/C][C]0.189956528730027[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.9151843440673[/C][C]3.08481565593275[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.2085982734379[/C][C]-0.208598273437875[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.6334928774504[/C][C]1.36650712254957[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]15.0343823104248[/C][C]-2.03438231042482[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.8377934020667[/C][C]0.162206597933289[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.4206803358604[/C][C]0.579319664139595[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.87321162804[/C][C]0.126788371960041[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.7144634312669[/C][C]-0.714463431266877[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.7837316387963[/C][C]0.216268361203682[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]14.3604689094015[/C][C]1.63953109059846[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.0560716932549[/C][C]-0.0560716932548528[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]15.2871357419452[/C][C]0.712864258054837[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]14.4545393763589[/C][C]1.54546062364107[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.2467953745083[/C][C]0.753204625491681[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.6503435997642[/C][C]-1.65034359976425[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]16.6187657874912[/C][C]0.381234212508838[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]15.2967278163335[/C][C]0.703272183666451[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]15.096877097657[/C][C]-0.0968770976570464[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]14.9572539543321[/C][C]-1.95725395433206[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.8852440224786[/C][C]1.11475597752144[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.7372112215607[/C][C]0.262788778439251[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.0231354085094[/C][C]1.97686459149055[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.8473817554252[/C][C]0.152618244574779[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]14.3325070655381[/C][C]-0.332507065538147[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]16.940001690227[/C][C]-0.940001690226981[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.6934587187021[/C][C]1.30654128129791[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]17.284804462422[/C][C]2.715195537578[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]14.7661382158651[/C][C]0.233861784134946[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]14.3164240864805[/C][C]1.68357591351953[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]15.0228622261668[/C][C]-2.02286222616685[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]15.8890203713599[/C][C]1.11097962864013[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.4571292514523[/C][C]0.542870748547735[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]14.2669382666796[/C][C]1.73306173332035[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]12.0005969501969[/C][C]-0.000596950196859065[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.1417348345613[/C][C]0.858265165438743[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.8256165622776[/C][C]0.174383437722378[/C][/ROW]
[ROW][C]112[/C][C]17[/C][C]14.9627998225823[/C][C]2.03720017741771[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]13.1509986333327[/C][C]-0.150998633332732[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]14.8462612594175[/C][C]-2.84626125941749[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]16.7107581165155[/C][C]1.28924188348448[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]15.2509980379671[/C][C]-1.25099803796712[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.2080551161559[/C][C]0.791944883844143[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]14.9558849040364[/C][C]-1.95588490403643[/C][/ROW]
[ROW][C]119[/C][C]16[/C][C]15.7029873745019[/C][C]0.297012625498139[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]14.0935310256219[/C][C]-1.09353102562188[/C][/ROW]
[ROW][C]121[/C][C]16[/C][C]15.6228570919342[/C][C]0.377142908065845[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]15.96209193677[/C][C]-2.96209193677004[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]17.0553726529986[/C][C]-1.05537265299863[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]16.2621382249113[/C][C]-1.2621382249113[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]16.7437130667739[/C][C]-0.743713066773915[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]15.4876652395885[/C][C]-0.487665239588466[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.7968988111648[/C][C]1.20310118883524[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]14.2280289887115[/C][C]0.771971011288529[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]14.8401289874323[/C][C]-2.84012898743225[/C][/ROW]
[ROW][C]130[/C][C]16[/C][C]14.0557257937931[/C][C]1.94427420620694[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]13.3213728124412[/C][C]-3.32137281244122[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]13.8830150142454[/C][C]2.11698498575463[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]13.9063969972042[/C][C]-1.90639699720421[/C][/ROW]
[ROW][C]134[/C][C]14[/C][C]15.5730496432251[/C][C]-1.57304964322512[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]15.3557695739857[/C][C]-0.355769573985745[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]12.0832464880097[/C][C]0.916753511990259[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]14.6293777237536[/C][C]0.370622276246397[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]13.2447853711918[/C][C]-2.2447853711918[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]13.2966873506332[/C][C]-1.29668735063324[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.2359344356473[/C][C]-2.23593443564735[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]12.8944180590953[/C][C]3.10558194090467[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]13.2974013987732[/C][C]1.70259860122683[/C][/ROW]
[ROW][C]143[/C][C]17[/C][C]16.6628795427123[/C][C]0.337120457287677[/C][/ROW]
[ROW][C]144[/C][C]16[/C][C]14.694189937819[/C][C]1.30581006218097[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]14.1605574798599[/C][C]-4.16055747985993[/C][/ROW]
[ROW][C]146[/C][C]18[/C][C]15.8517906768739[/C][C]2.14820932312605[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]15.052154894862[/C][C]-2.05215489486201[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]15.1592744155609[/C][C]0.840725584439128[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]13.0852293404491[/C][C]-0.0852293404490951[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]13.1449173602912[/C][C]-3.1449173602912[/C][/ROW]
[ROW][C]151[/C][C]15[/C][C]15.8925878118488[/C][C]-0.892587811848785[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]13.6671998336562[/C][C]2.33280016634383[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]11.9469318655517[/C][C]4.05306813444826[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]12.6553961555626[/C][C]1.34460384443741[/C][/ROW]
[ROW][C]155[/C][C]10[/C][C]12.7822320473445[/C][C]-2.78223204734445[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]16.3501669386077[/C][C]0.649833061392308[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]11.5917428932724[/C][C]1.40825710672763[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]14.0359540141564[/C][C]0.964045985843552[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]15.0346892390993[/C][C]0.965310760900736[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]12.2780470850852[/C][C]-0.27804708508523[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]12.7969337035586[/C][C]0.203066296441365[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197917&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197917&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
11316.0490104618212-3.04901046182122
21616.1860219268806-0.186021926880638
31916.76635356363332.23364643636669
41512.30300357762482.69699642237519
51415.5769323249511-1.57693232495106
61315.120994334411-2.120994334411
71915.42750832067513.57249167932494
81517.0137390005-2.01373900049999
91416.1620417775404-2.16204177754041
101514.43339755267070.566602447329285
111615.56230325882630.437696741173666
121616.1641331339235-0.164133133923536
131615.69657310227340.303426897726611
141615.19386834469270.806131655307332
151717.682961316051-0.682961316050965
161515.2276273944881-0.227627394488076
171514.72255492089870.277445079101335
182016.43844214811393.56155785188605
191815.72922791484612.2707720851539
201615.39520222455030.604797775449731
211615.32046730508410.679532694915915
221615.3289480273160.671051972684026
231916.29546503666112.70453496333885
241615.02299579676460.977004203235379
251716.52327111462940.476728885370591
261716.9670315886190.0329684113809558
271614.76644515012711.23355484987285
281516.3679090283809-1.36790902838088
291615.36417236873070.635827631269317
301414.5673550619547-0.567355061954664
311515.8962595562111-0.896259556211074
321212.399538615869-0.399538615868987
331415.1370275804905-1.13702758049045
341616.0403222944867-0.0403222944866774
351415.4342652606063-1.43426526060633
361013.2789234544361-3.27892345443609
371012.5692353188849-2.56923531888487
381415.7848933481702-1.78489334817024
391614.46873423929171.53126576070832
401614.73669279717431.26330720282572
411615.14934350369180.850656496308213
421415.4069230426615-1.40692304266148
432017.65884511538882.34115488461121
441414.026619464814-0.0266194648140459
451415.0447004096259-1.04470040962588
461115.448811656535-4.44881165653501
471416.710198242124-2.71019824212402
481515.0996382114152-0.0996382114152192
491614.75002658729911.2499734127009
501415.9644976462787-1.96449764627872
511616.5383768844621-0.538376884462106
521414.0561112677422-0.0561112677421831
531214.4932319603238-2.49323196032376
541616.2196790651626-0.219679065162606
55911.5695101721269-2.56951017212693
561412.64921971767441.35078028232563
571616.0545534287663-0.05455342876633
581615.46233090314680.537669096853166
591515.3373127577086-0.337312757708649
601614.30816606219721.69183393780278
611211.5541532097160.445846790284027
621615.85825481558920.141745184410759
631616.585811286261-0.585811286260983
641414.2364998541117-0.236499854111724
651615.29834022501440.701659774985575
661716.21308390250390.78691609749612
671816.24690310383561.7530968961644
681814.54356531494243.45643468505759
691215.6351315345966-3.6351315345966
701615.36566152035410.63433847964587
711013.789131849363-3.78913184936299
721414.1785908244253-0.178590824425269
731816.44971957529141.55028042470861
741816.78721597937231.21278402062768
751615.810043471270.189956528730027
761713.91518434406733.08481565593275
771616.2085982734379-0.208598273437875
781614.63349287745041.36650712254957
791315.0343823104248-2.03438231042482
801615.83779340206670.162206597933289
811615.42068033586040.579319664139595
821615.873211628040.126788371960041
831515.7144634312669-0.714463431266877
841514.78373163879630.216268361203682
851614.36046890940151.63953109059846
861414.0560716932549-0.0560716932548528
871615.28713574194520.712864258054837
881614.45453937635891.54546062364107
891514.24679537450830.753204625491681
901213.6503435997642-1.65034359976425
911716.61876578749120.381234212508838
921615.29672781633350.703272183666451
931515.096877097657-0.0968770976570464
941314.9572539543321-1.95725395433206
951614.88524402247861.11475597752144
961615.73721122156070.262788778439251
971614.02313540850941.97686459149055
981615.84738175542520.152618244574779
991414.3325070655381-0.332507065538147
1001616.940001690227-0.940001690226981
1011614.69345871870211.30654128129791
1022017.2848044624222.715195537578
1031514.76613821586510.233861784134946
1041614.31642408648051.68357591351953
1051315.0228622261668-2.02286222616685
1061715.88902037135991.11097962864013
1071615.45712925145230.542870748547735
1081614.26693826667961.73306173332035
1091212.0005969501969-0.000596950196859065
1101615.14173483456130.858265165438743
1111615.82561656227760.174383437722378
1121714.96279982258232.03720017741771
1131313.1509986333327-0.150998633332732
1141214.8462612594175-2.84626125941749
1151816.71075811651551.28924188348448
1161415.2509980379671-1.25099803796712
1171413.20805511615590.791944883844143
1181314.9558849040364-1.95588490403643
1191615.70298737450190.297012625498139
1201314.0935310256219-1.09353102562188
1211615.62285709193420.377142908065845
1221315.96209193677-2.96209193677004
1231617.0553726529986-1.05537265299863
1241516.2621382249113-1.2621382249113
1251616.7437130667739-0.743713066773915
1261515.4876652395885-0.487665239588466
1271715.79689881116481.20310118883524
1281514.22802898871150.771971011288529
1291214.8401289874323-2.84012898743225
1301614.05572579379311.94427420620694
1311013.3213728124412-3.32137281244122
1321613.88301501424542.11698498575463
1331213.9063969972042-1.90639699720421
1341415.5730496432251-1.57304964322512
1351515.3557695739857-0.355769573985745
1361312.08324648800970.916753511990259
1371514.62937772375360.370622276246397
1381113.2447853711918-2.2447853711918
1391213.2966873506332-1.29668735063324
1401113.2359344356473-2.23593443564735
1411612.89441805909533.10558194090467
1421513.29740139877321.70259860122683
1431716.66287954271230.337120457287677
1441614.6941899378191.30581006218097
1451014.1605574798599-4.16055747985993
1461815.85179067687392.14820932312605
1471315.052154894862-2.05215489486201
1481615.15927441556090.840725584439128
1491313.0852293404491-0.0852293404490951
1501013.1449173602912-3.1449173602912
1511515.8925878118488-0.892587811848785
1521613.66719983365622.33280016634383
1531611.94693186555174.05306813444826
1541412.65539615556261.34460384443741
1551012.7822320473445-2.78223204734445
1561716.35016693860770.649833061392308
1571311.59174289327241.40825710672763
1581514.03595401415640.964045985843552
1591615.03468923909930.965310760900736
1601212.2780470850852-0.27804708508523
1611312.79693370355860.203066296441365






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.3731177348982130.7462354697964270.626882265101787
140.3726055975691090.7452111951382180.627394402430891
150.2425795162143760.4851590324287510.757420483785624
160.1946704347152610.3893408694305220.805329565284739
170.2077734523866970.4155469047733940.792226547613303
180.4127016789955220.8254033579910430.587298321004478
190.3639019585879760.7278039171759530.636098041412024
200.3081916690898310.6163833381796620.691808330910169
210.2551571493526860.5103142987053720.744842850647314
220.2968762267375230.5937524534750450.703123773262478
230.455741940546130.911483881092260.54425805945387
240.5985626240796990.8028747518406030.401437375920301
250.5271166892148420.9457666215703160.472883310785158
260.4695934676746550.9391869353493110.530406532325345
270.4602547701443180.9205095402886360.539745229855682
280.5899879780412120.8200240439175760.410012021958788
290.5354196089346990.9291607821306020.464580391065301
300.6485410291029610.7029179417940790.351458970897039
310.6067340044737840.7865319910524320.393265995526216
320.5750393416769320.8499213166461370.424960658323068
330.5575424343093830.8849151313812330.442457565690617
340.5143380009767510.9713239980464980.485661999023249
350.4640315998398110.9280631996796230.535968400160189
360.630165692111530.7396686157769390.36983430788847
370.663160218313780.6736795633724410.33683978168622
380.6438010922563630.7123978154872750.356198907743637
390.6844698086799490.6310603826401020.315530191320051
400.6648158562500080.6703682874999830.335184143749992
410.6238165862398090.7523668275203820.376183413760191
420.5840744217066330.8318511565867350.415925578293367
430.6168572782425560.7662854435148890.383142721757444
440.5628718555309750.8742562889380510.437128144469025
450.5300047047874430.9399905904251150.469995295212557
460.7486969606930780.5026060786138450.251303039306922
470.8228035848220230.3543928303559540.177196415177977
480.7915687837169280.4168624325661430.208431216283072
490.8058654485886530.3882691028226940.194134551411347
500.8024758977556890.3950482044886220.197524102244311
510.7696085020707310.4607829958585390.230391497929269
520.7357021065475720.5285957869048570.264297893452428
530.7654405483679050.469118903264190.234559451632095
540.7254037381957860.5491925236084290.274596261804214
550.7368768218870460.5262463562259080.263123178112954
560.7454058404282760.5091883191434480.254594159571724
570.7068235581102680.5863528837794640.293176441889732
580.6797034952121450.6405930095757090.320296504787855
590.6410870528740640.7178258942518710.358912947125936
600.6496060729005680.7007878541988640.350393927099432
610.6090296315067930.7819407369864140.390970368493207
620.5637383783328350.8725232433343310.436261621667165
630.5209434533791590.9581130932416820.479056546620841
640.4727587966421680.9455175932843370.527241203357832
650.4313778792103270.8627557584206530.568622120789673
660.3999351875487450.799870375097490.600064812451255
670.4005420943871130.8010841887742270.599457905612887
680.5453032987621340.9093934024757320.454696701237866
690.6933066000425840.6133867999148310.306693399957416
700.6569603459388990.6860793081222020.343039654061101
710.8007560887535230.3984878224929540.199243911246477
720.7666533950216580.4666932099566840.233346604978342
730.7638552014549920.4722895970900170.236144798545008
740.7471091518303860.5057816963392280.252890848169614
750.7075630358228770.5848739283542460.292436964177123
760.789410519551140.4211789608977190.21058948044886
770.7541311352226910.4917377295546170.245868864777309
780.7404352452633160.5191295094733690.259564754736684
790.7647577665719340.4704844668561320.235242233428066
800.7266519593939760.5466960812120480.273348040606024
810.6915525365278110.6168949269443780.308447463472189
820.6503118082790460.6993763834419080.349688191720954
830.615793792382320.768412415235360.38420620761768
840.571397688460930.857204623078140.42860231153907
850.5595117219892370.8809765560215260.440488278010763
860.5154791370996340.9690417258007310.484520862900366
870.4729803591152840.9459607182305670.527019640884716
880.4531830305857220.9063660611714450.546816969414278
890.417325546925280.834651093850560.58267445307472
900.4252637969874770.8505275939749530.574736203012523
910.3806056823325620.7612113646651230.619394317667438
920.3452886222981030.6905772445962060.654711377701897
930.3036692422646570.6073384845293140.696330757735343
940.3465504941277360.6931009882554730.653449505872264
950.3167016367678590.6334032735357180.683298363232141
960.2747718478633330.5495436957266650.725228152136667
970.2698566753735480.5397133507470950.730143324626452
980.2327584725851360.4655169451702730.767241527414864
990.2193286281168730.4386572562337460.780671371883127
1000.2149158809748670.4298317619497330.785084119025133
1010.1898285188584830.3796570377169660.810171481141517
1020.2116210381985050.423242076397010.788378961801495
1030.1815003649347130.3630007298694270.818499635065287
1040.1654362993349580.3308725986699160.834563700665042
1050.1897382028643460.3794764057286920.810261797135654
1060.160779321978090.321558643956180.83922067802191
1070.1323867339952570.2647734679905140.867613266004743
1080.1168847014366190.2337694028732390.883115298563381
1090.1193102456090570.2386204912181150.880689754390943
1100.1072264267969240.2144528535938470.892773573203076
1110.09210814774144810.1842162954828960.907891852258552
1120.1179683279205950.2359366558411910.882031672079405
1130.1026432530386150.2052865060772310.897356746961385
1140.1387824688717260.2775649377434530.861217531128274
1150.1512687360315670.3025374720631340.848731263968433
1160.1306864697428840.2613729394857680.869313530257116
1170.1412165368378540.2824330736757080.858783463162146
1180.139077803567320.2781556071346410.86092219643268
1190.1685500778878390.3371001557756780.831449922112161
1200.1397927315747730.2795854631495470.860207268425227
1210.126896401072630.253792802145260.87310359892737
1220.1318665151512690.2637330303025390.868133484848731
1230.1057390894140970.2114781788281930.894260910585903
1240.08579846518801530.1715969303760310.914201534811985
1250.0659382029554880.1318764059109760.934061797044512
1260.04914669094349250.09829338188698490.950853309056508
1270.04747942679901240.09495885359802490.952520573200988
1280.05691120767759990.11382241535520.9430887923224
1290.06182524958470610.1236504991694120.938174750415294
1300.06386773788672830.1277354757734570.936132262113272
1310.08098168106484130.1619633621296830.919018318935159
1320.1455902020412430.2911804040824860.854409797958757
1330.1731083397397880.3462166794795760.826891660260212
1340.1375892385210340.2751784770420670.862410761478966
1350.1124428002612310.2248856005224610.887557199738769
1360.08326603349080360.1665320669816070.916733966509196
1370.1031864103594470.2063728207188930.896813589640553
1380.1085985336255550.217197067251110.891401466374445
1390.08212842316518620.1642568463303720.917871576834814
1400.247364654946410.494729309892820.75263534505359
1410.207582102678570.4151642053571410.79241789732143
1420.1672541064958080.3345082129916160.832745893504192
1430.1251653965402430.2503307930804860.874834603459757
1440.08204348118592140.1640869623718430.917956518814079
1450.1561784195740870.3123568391481740.843821580425913
1460.1709649656668820.3419299313337650.829035034333118
1470.2917664686216360.5835329372432720.708233531378364
1480.1773566151629170.3547132303258340.822643384837083

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.373117734898213 & 0.746235469796427 & 0.626882265101787 \tabularnewline
14 & 0.372605597569109 & 0.745211195138218 & 0.627394402430891 \tabularnewline
15 & 0.242579516214376 & 0.485159032428751 & 0.757420483785624 \tabularnewline
16 & 0.194670434715261 & 0.389340869430522 & 0.805329565284739 \tabularnewline
17 & 0.207773452386697 & 0.415546904773394 & 0.792226547613303 \tabularnewline
18 & 0.412701678995522 & 0.825403357991043 & 0.587298321004478 \tabularnewline
19 & 0.363901958587976 & 0.727803917175953 & 0.636098041412024 \tabularnewline
20 & 0.308191669089831 & 0.616383338179662 & 0.691808330910169 \tabularnewline
21 & 0.255157149352686 & 0.510314298705372 & 0.744842850647314 \tabularnewline
22 & 0.296876226737523 & 0.593752453475045 & 0.703123773262478 \tabularnewline
23 & 0.45574194054613 & 0.91148388109226 & 0.54425805945387 \tabularnewline
24 & 0.598562624079699 & 0.802874751840603 & 0.401437375920301 \tabularnewline
25 & 0.527116689214842 & 0.945766621570316 & 0.472883310785158 \tabularnewline
26 & 0.469593467674655 & 0.939186935349311 & 0.530406532325345 \tabularnewline
27 & 0.460254770144318 & 0.920509540288636 & 0.539745229855682 \tabularnewline
28 & 0.589987978041212 & 0.820024043917576 & 0.410012021958788 \tabularnewline
29 & 0.535419608934699 & 0.929160782130602 & 0.464580391065301 \tabularnewline
30 & 0.648541029102961 & 0.702917941794079 & 0.351458970897039 \tabularnewline
31 & 0.606734004473784 & 0.786531991052432 & 0.393265995526216 \tabularnewline
32 & 0.575039341676932 & 0.849921316646137 & 0.424960658323068 \tabularnewline
33 & 0.557542434309383 & 0.884915131381233 & 0.442457565690617 \tabularnewline
34 & 0.514338000976751 & 0.971323998046498 & 0.485661999023249 \tabularnewline
35 & 0.464031599839811 & 0.928063199679623 & 0.535968400160189 \tabularnewline
36 & 0.63016569211153 & 0.739668615776939 & 0.36983430788847 \tabularnewline
37 & 0.66316021831378 & 0.673679563372441 & 0.33683978168622 \tabularnewline
38 & 0.643801092256363 & 0.712397815487275 & 0.356198907743637 \tabularnewline
39 & 0.684469808679949 & 0.631060382640102 & 0.315530191320051 \tabularnewline
40 & 0.664815856250008 & 0.670368287499983 & 0.335184143749992 \tabularnewline
41 & 0.623816586239809 & 0.752366827520382 & 0.376183413760191 \tabularnewline
42 & 0.584074421706633 & 0.831851156586735 & 0.415925578293367 \tabularnewline
43 & 0.616857278242556 & 0.766285443514889 & 0.383142721757444 \tabularnewline
44 & 0.562871855530975 & 0.874256288938051 & 0.437128144469025 \tabularnewline
45 & 0.530004704787443 & 0.939990590425115 & 0.469995295212557 \tabularnewline
46 & 0.748696960693078 & 0.502606078613845 & 0.251303039306922 \tabularnewline
47 & 0.822803584822023 & 0.354392830355954 & 0.177196415177977 \tabularnewline
48 & 0.791568783716928 & 0.416862432566143 & 0.208431216283072 \tabularnewline
49 & 0.805865448588653 & 0.388269102822694 & 0.194134551411347 \tabularnewline
50 & 0.802475897755689 & 0.395048204488622 & 0.197524102244311 \tabularnewline
51 & 0.769608502070731 & 0.460782995858539 & 0.230391497929269 \tabularnewline
52 & 0.735702106547572 & 0.528595786904857 & 0.264297893452428 \tabularnewline
53 & 0.765440548367905 & 0.46911890326419 & 0.234559451632095 \tabularnewline
54 & 0.725403738195786 & 0.549192523608429 & 0.274596261804214 \tabularnewline
55 & 0.736876821887046 & 0.526246356225908 & 0.263123178112954 \tabularnewline
56 & 0.745405840428276 & 0.509188319143448 & 0.254594159571724 \tabularnewline
57 & 0.706823558110268 & 0.586352883779464 & 0.293176441889732 \tabularnewline
58 & 0.679703495212145 & 0.640593009575709 & 0.320296504787855 \tabularnewline
59 & 0.641087052874064 & 0.717825894251871 & 0.358912947125936 \tabularnewline
60 & 0.649606072900568 & 0.700787854198864 & 0.350393927099432 \tabularnewline
61 & 0.609029631506793 & 0.781940736986414 & 0.390970368493207 \tabularnewline
62 & 0.563738378332835 & 0.872523243334331 & 0.436261621667165 \tabularnewline
63 & 0.520943453379159 & 0.958113093241682 & 0.479056546620841 \tabularnewline
64 & 0.472758796642168 & 0.945517593284337 & 0.527241203357832 \tabularnewline
65 & 0.431377879210327 & 0.862755758420653 & 0.568622120789673 \tabularnewline
66 & 0.399935187548745 & 0.79987037509749 & 0.600064812451255 \tabularnewline
67 & 0.400542094387113 & 0.801084188774227 & 0.599457905612887 \tabularnewline
68 & 0.545303298762134 & 0.909393402475732 & 0.454696701237866 \tabularnewline
69 & 0.693306600042584 & 0.613386799914831 & 0.306693399957416 \tabularnewline
70 & 0.656960345938899 & 0.686079308122202 & 0.343039654061101 \tabularnewline
71 & 0.800756088753523 & 0.398487822492954 & 0.199243911246477 \tabularnewline
72 & 0.766653395021658 & 0.466693209956684 & 0.233346604978342 \tabularnewline
73 & 0.763855201454992 & 0.472289597090017 & 0.236144798545008 \tabularnewline
74 & 0.747109151830386 & 0.505781696339228 & 0.252890848169614 \tabularnewline
75 & 0.707563035822877 & 0.584873928354246 & 0.292436964177123 \tabularnewline
76 & 0.78941051955114 & 0.421178960897719 & 0.21058948044886 \tabularnewline
77 & 0.754131135222691 & 0.491737729554617 & 0.245868864777309 \tabularnewline
78 & 0.740435245263316 & 0.519129509473369 & 0.259564754736684 \tabularnewline
79 & 0.764757766571934 & 0.470484466856132 & 0.235242233428066 \tabularnewline
80 & 0.726651959393976 & 0.546696081212048 & 0.273348040606024 \tabularnewline
81 & 0.691552536527811 & 0.616894926944378 & 0.308447463472189 \tabularnewline
82 & 0.650311808279046 & 0.699376383441908 & 0.349688191720954 \tabularnewline
83 & 0.61579379238232 & 0.76841241523536 & 0.38420620761768 \tabularnewline
84 & 0.57139768846093 & 0.85720462307814 & 0.42860231153907 \tabularnewline
85 & 0.559511721989237 & 0.880976556021526 & 0.440488278010763 \tabularnewline
86 & 0.515479137099634 & 0.969041725800731 & 0.484520862900366 \tabularnewline
87 & 0.472980359115284 & 0.945960718230567 & 0.527019640884716 \tabularnewline
88 & 0.453183030585722 & 0.906366061171445 & 0.546816969414278 \tabularnewline
89 & 0.41732554692528 & 0.83465109385056 & 0.58267445307472 \tabularnewline
90 & 0.425263796987477 & 0.850527593974953 & 0.574736203012523 \tabularnewline
91 & 0.380605682332562 & 0.761211364665123 & 0.619394317667438 \tabularnewline
92 & 0.345288622298103 & 0.690577244596206 & 0.654711377701897 \tabularnewline
93 & 0.303669242264657 & 0.607338484529314 & 0.696330757735343 \tabularnewline
94 & 0.346550494127736 & 0.693100988255473 & 0.653449505872264 \tabularnewline
95 & 0.316701636767859 & 0.633403273535718 & 0.683298363232141 \tabularnewline
96 & 0.274771847863333 & 0.549543695726665 & 0.725228152136667 \tabularnewline
97 & 0.269856675373548 & 0.539713350747095 & 0.730143324626452 \tabularnewline
98 & 0.232758472585136 & 0.465516945170273 & 0.767241527414864 \tabularnewline
99 & 0.219328628116873 & 0.438657256233746 & 0.780671371883127 \tabularnewline
100 & 0.214915880974867 & 0.429831761949733 & 0.785084119025133 \tabularnewline
101 & 0.189828518858483 & 0.379657037716966 & 0.810171481141517 \tabularnewline
102 & 0.211621038198505 & 0.42324207639701 & 0.788378961801495 \tabularnewline
103 & 0.181500364934713 & 0.363000729869427 & 0.818499635065287 \tabularnewline
104 & 0.165436299334958 & 0.330872598669916 & 0.834563700665042 \tabularnewline
105 & 0.189738202864346 & 0.379476405728692 & 0.810261797135654 \tabularnewline
106 & 0.16077932197809 & 0.32155864395618 & 0.83922067802191 \tabularnewline
107 & 0.132386733995257 & 0.264773467990514 & 0.867613266004743 \tabularnewline
108 & 0.116884701436619 & 0.233769402873239 & 0.883115298563381 \tabularnewline
109 & 0.119310245609057 & 0.238620491218115 & 0.880689754390943 \tabularnewline
110 & 0.107226426796924 & 0.214452853593847 & 0.892773573203076 \tabularnewline
111 & 0.0921081477414481 & 0.184216295482896 & 0.907891852258552 \tabularnewline
112 & 0.117968327920595 & 0.235936655841191 & 0.882031672079405 \tabularnewline
113 & 0.102643253038615 & 0.205286506077231 & 0.897356746961385 \tabularnewline
114 & 0.138782468871726 & 0.277564937743453 & 0.861217531128274 \tabularnewline
115 & 0.151268736031567 & 0.302537472063134 & 0.848731263968433 \tabularnewline
116 & 0.130686469742884 & 0.261372939485768 & 0.869313530257116 \tabularnewline
117 & 0.141216536837854 & 0.282433073675708 & 0.858783463162146 \tabularnewline
118 & 0.13907780356732 & 0.278155607134641 & 0.86092219643268 \tabularnewline
119 & 0.168550077887839 & 0.337100155775678 & 0.831449922112161 \tabularnewline
120 & 0.139792731574773 & 0.279585463149547 & 0.860207268425227 \tabularnewline
121 & 0.12689640107263 & 0.25379280214526 & 0.87310359892737 \tabularnewline
122 & 0.131866515151269 & 0.263733030302539 & 0.868133484848731 \tabularnewline
123 & 0.105739089414097 & 0.211478178828193 & 0.894260910585903 \tabularnewline
124 & 0.0857984651880153 & 0.171596930376031 & 0.914201534811985 \tabularnewline
125 & 0.065938202955488 & 0.131876405910976 & 0.934061797044512 \tabularnewline
126 & 0.0491466909434925 & 0.0982933818869849 & 0.950853309056508 \tabularnewline
127 & 0.0474794267990124 & 0.0949588535980249 & 0.952520573200988 \tabularnewline
128 & 0.0569112076775999 & 0.1138224153552 & 0.9430887923224 \tabularnewline
129 & 0.0618252495847061 & 0.123650499169412 & 0.938174750415294 \tabularnewline
130 & 0.0638677378867283 & 0.127735475773457 & 0.936132262113272 \tabularnewline
131 & 0.0809816810648413 & 0.161963362129683 & 0.919018318935159 \tabularnewline
132 & 0.145590202041243 & 0.291180404082486 & 0.854409797958757 \tabularnewline
133 & 0.173108339739788 & 0.346216679479576 & 0.826891660260212 \tabularnewline
134 & 0.137589238521034 & 0.275178477042067 & 0.862410761478966 \tabularnewline
135 & 0.112442800261231 & 0.224885600522461 & 0.887557199738769 \tabularnewline
136 & 0.0832660334908036 & 0.166532066981607 & 0.916733966509196 \tabularnewline
137 & 0.103186410359447 & 0.206372820718893 & 0.896813589640553 \tabularnewline
138 & 0.108598533625555 & 0.21719706725111 & 0.891401466374445 \tabularnewline
139 & 0.0821284231651862 & 0.164256846330372 & 0.917871576834814 \tabularnewline
140 & 0.24736465494641 & 0.49472930989282 & 0.75263534505359 \tabularnewline
141 & 0.20758210267857 & 0.415164205357141 & 0.79241789732143 \tabularnewline
142 & 0.167254106495808 & 0.334508212991616 & 0.832745893504192 \tabularnewline
143 & 0.125165396540243 & 0.250330793080486 & 0.874834603459757 \tabularnewline
144 & 0.0820434811859214 & 0.164086962371843 & 0.917956518814079 \tabularnewline
145 & 0.156178419574087 & 0.312356839148174 & 0.843821580425913 \tabularnewline
146 & 0.170964965666882 & 0.341929931333765 & 0.829035034333118 \tabularnewline
147 & 0.291766468621636 & 0.583532937243272 & 0.708233531378364 \tabularnewline
148 & 0.177356615162917 & 0.354713230325834 & 0.822643384837083 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197917&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]0.373117734898213[/C][C]0.746235469796427[/C][C]0.626882265101787[/C][/ROW]
[ROW][C]14[/C][C]0.372605597569109[/C][C]0.745211195138218[/C][C]0.627394402430891[/C][/ROW]
[ROW][C]15[/C][C]0.242579516214376[/C][C]0.485159032428751[/C][C]0.757420483785624[/C][/ROW]
[ROW][C]16[/C][C]0.194670434715261[/C][C]0.389340869430522[/C][C]0.805329565284739[/C][/ROW]
[ROW][C]17[/C][C]0.207773452386697[/C][C]0.415546904773394[/C][C]0.792226547613303[/C][/ROW]
[ROW][C]18[/C][C]0.412701678995522[/C][C]0.825403357991043[/C][C]0.587298321004478[/C][/ROW]
[ROW][C]19[/C][C]0.363901958587976[/C][C]0.727803917175953[/C][C]0.636098041412024[/C][/ROW]
[ROW][C]20[/C][C]0.308191669089831[/C][C]0.616383338179662[/C][C]0.691808330910169[/C][/ROW]
[ROW][C]21[/C][C]0.255157149352686[/C][C]0.510314298705372[/C][C]0.744842850647314[/C][/ROW]
[ROW][C]22[/C][C]0.296876226737523[/C][C]0.593752453475045[/C][C]0.703123773262478[/C][/ROW]
[ROW][C]23[/C][C]0.45574194054613[/C][C]0.91148388109226[/C][C]0.54425805945387[/C][/ROW]
[ROW][C]24[/C][C]0.598562624079699[/C][C]0.802874751840603[/C][C]0.401437375920301[/C][/ROW]
[ROW][C]25[/C][C]0.527116689214842[/C][C]0.945766621570316[/C][C]0.472883310785158[/C][/ROW]
[ROW][C]26[/C][C]0.469593467674655[/C][C]0.939186935349311[/C][C]0.530406532325345[/C][/ROW]
[ROW][C]27[/C][C]0.460254770144318[/C][C]0.920509540288636[/C][C]0.539745229855682[/C][/ROW]
[ROW][C]28[/C][C]0.589987978041212[/C][C]0.820024043917576[/C][C]0.410012021958788[/C][/ROW]
[ROW][C]29[/C][C]0.535419608934699[/C][C]0.929160782130602[/C][C]0.464580391065301[/C][/ROW]
[ROW][C]30[/C][C]0.648541029102961[/C][C]0.702917941794079[/C][C]0.351458970897039[/C][/ROW]
[ROW][C]31[/C][C]0.606734004473784[/C][C]0.786531991052432[/C][C]0.393265995526216[/C][/ROW]
[ROW][C]32[/C][C]0.575039341676932[/C][C]0.849921316646137[/C][C]0.424960658323068[/C][/ROW]
[ROW][C]33[/C][C]0.557542434309383[/C][C]0.884915131381233[/C][C]0.442457565690617[/C][/ROW]
[ROW][C]34[/C][C]0.514338000976751[/C][C]0.971323998046498[/C][C]0.485661999023249[/C][/ROW]
[ROW][C]35[/C][C]0.464031599839811[/C][C]0.928063199679623[/C][C]0.535968400160189[/C][/ROW]
[ROW][C]36[/C][C]0.63016569211153[/C][C]0.739668615776939[/C][C]0.36983430788847[/C][/ROW]
[ROW][C]37[/C][C]0.66316021831378[/C][C]0.673679563372441[/C][C]0.33683978168622[/C][/ROW]
[ROW][C]38[/C][C]0.643801092256363[/C][C]0.712397815487275[/C][C]0.356198907743637[/C][/ROW]
[ROW][C]39[/C][C]0.684469808679949[/C][C]0.631060382640102[/C][C]0.315530191320051[/C][/ROW]
[ROW][C]40[/C][C]0.664815856250008[/C][C]0.670368287499983[/C][C]0.335184143749992[/C][/ROW]
[ROW][C]41[/C][C]0.623816586239809[/C][C]0.752366827520382[/C][C]0.376183413760191[/C][/ROW]
[ROW][C]42[/C][C]0.584074421706633[/C][C]0.831851156586735[/C][C]0.415925578293367[/C][/ROW]
[ROW][C]43[/C][C]0.616857278242556[/C][C]0.766285443514889[/C][C]0.383142721757444[/C][/ROW]
[ROW][C]44[/C][C]0.562871855530975[/C][C]0.874256288938051[/C][C]0.437128144469025[/C][/ROW]
[ROW][C]45[/C][C]0.530004704787443[/C][C]0.939990590425115[/C][C]0.469995295212557[/C][/ROW]
[ROW][C]46[/C][C]0.748696960693078[/C][C]0.502606078613845[/C][C]0.251303039306922[/C][/ROW]
[ROW][C]47[/C][C]0.822803584822023[/C][C]0.354392830355954[/C][C]0.177196415177977[/C][/ROW]
[ROW][C]48[/C][C]0.791568783716928[/C][C]0.416862432566143[/C][C]0.208431216283072[/C][/ROW]
[ROW][C]49[/C][C]0.805865448588653[/C][C]0.388269102822694[/C][C]0.194134551411347[/C][/ROW]
[ROW][C]50[/C][C]0.802475897755689[/C][C]0.395048204488622[/C][C]0.197524102244311[/C][/ROW]
[ROW][C]51[/C][C]0.769608502070731[/C][C]0.460782995858539[/C][C]0.230391497929269[/C][/ROW]
[ROW][C]52[/C][C]0.735702106547572[/C][C]0.528595786904857[/C][C]0.264297893452428[/C][/ROW]
[ROW][C]53[/C][C]0.765440548367905[/C][C]0.46911890326419[/C][C]0.234559451632095[/C][/ROW]
[ROW][C]54[/C][C]0.725403738195786[/C][C]0.549192523608429[/C][C]0.274596261804214[/C][/ROW]
[ROW][C]55[/C][C]0.736876821887046[/C][C]0.526246356225908[/C][C]0.263123178112954[/C][/ROW]
[ROW][C]56[/C][C]0.745405840428276[/C][C]0.509188319143448[/C][C]0.254594159571724[/C][/ROW]
[ROW][C]57[/C][C]0.706823558110268[/C][C]0.586352883779464[/C][C]0.293176441889732[/C][/ROW]
[ROW][C]58[/C][C]0.679703495212145[/C][C]0.640593009575709[/C][C]0.320296504787855[/C][/ROW]
[ROW][C]59[/C][C]0.641087052874064[/C][C]0.717825894251871[/C][C]0.358912947125936[/C][/ROW]
[ROW][C]60[/C][C]0.649606072900568[/C][C]0.700787854198864[/C][C]0.350393927099432[/C][/ROW]
[ROW][C]61[/C][C]0.609029631506793[/C][C]0.781940736986414[/C][C]0.390970368493207[/C][/ROW]
[ROW][C]62[/C][C]0.563738378332835[/C][C]0.872523243334331[/C][C]0.436261621667165[/C][/ROW]
[ROW][C]63[/C][C]0.520943453379159[/C][C]0.958113093241682[/C][C]0.479056546620841[/C][/ROW]
[ROW][C]64[/C][C]0.472758796642168[/C][C]0.945517593284337[/C][C]0.527241203357832[/C][/ROW]
[ROW][C]65[/C][C]0.431377879210327[/C][C]0.862755758420653[/C][C]0.568622120789673[/C][/ROW]
[ROW][C]66[/C][C]0.399935187548745[/C][C]0.79987037509749[/C][C]0.600064812451255[/C][/ROW]
[ROW][C]67[/C][C]0.400542094387113[/C][C]0.801084188774227[/C][C]0.599457905612887[/C][/ROW]
[ROW][C]68[/C][C]0.545303298762134[/C][C]0.909393402475732[/C][C]0.454696701237866[/C][/ROW]
[ROW][C]69[/C][C]0.693306600042584[/C][C]0.613386799914831[/C][C]0.306693399957416[/C][/ROW]
[ROW][C]70[/C][C]0.656960345938899[/C][C]0.686079308122202[/C][C]0.343039654061101[/C][/ROW]
[ROW][C]71[/C][C]0.800756088753523[/C][C]0.398487822492954[/C][C]0.199243911246477[/C][/ROW]
[ROW][C]72[/C][C]0.766653395021658[/C][C]0.466693209956684[/C][C]0.233346604978342[/C][/ROW]
[ROW][C]73[/C][C]0.763855201454992[/C][C]0.472289597090017[/C][C]0.236144798545008[/C][/ROW]
[ROW][C]74[/C][C]0.747109151830386[/C][C]0.505781696339228[/C][C]0.252890848169614[/C][/ROW]
[ROW][C]75[/C][C]0.707563035822877[/C][C]0.584873928354246[/C][C]0.292436964177123[/C][/ROW]
[ROW][C]76[/C][C]0.78941051955114[/C][C]0.421178960897719[/C][C]0.21058948044886[/C][/ROW]
[ROW][C]77[/C][C]0.754131135222691[/C][C]0.491737729554617[/C][C]0.245868864777309[/C][/ROW]
[ROW][C]78[/C][C]0.740435245263316[/C][C]0.519129509473369[/C][C]0.259564754736684[/C][/ROW]
[ROW][C]79[/C][C]0.764757766571934[/C][C]0.470484466856132[/C][C]0.235242233428066[/C][/ROW]
[ROW][C]80[/C][C]0.726651959393976[/C][C]0.546696081212048[/C][C]0.273348040606024[/C][/ROW]
[ROW][C]81[/C][C]0.691552536527811[/C][C]0.616894926944378[/C][C]0.308447463472189[/C][/ROW]
[ROW][C]82[/C][C]0.650311808279046[/C][C]0.699376383441908[/C][C]0.349688191720954[/C][/ROW]
[ROW][C]83[/C][C]0.61579379238232[/C][C]0.76841241523536[/C][C]0.38420620761768[/C][/ROW]
[ROW][C]84[/C][C]0.57139768846093[/C][C]0.85720462307814[/C][C]0.42860231153907[/C][/ROW]
[ROW][C]85[/C][C]0.559511721989237[/C][C]0.880976556021526[/C][C]0.440488278010763[/C][/ROW]
[ROW][C]86[/C][C]0.515479137099634[/C][C]0.969041725800731[/C][C]0.484520862900366[/C][/ROW]
[ROW][C]87[/C][C]0.472980359115284[/C][C]0.945960718230567[/C][C]0.527019640884716[/C][/ROW]
[ROW][C]88[/C][C]0.453183030585722[/C][C]0.906366061171445[/C][C]0.546816969414278[/C][/ROW]
[ROW][C]89[/C][C]0.41732554692528[/C][C]0.83465109385056[/C][C]0.58267445307472[/C][/ROW]
[ROW][C]90[/C][C]0.425263796987477[/C][C]0.850527593974953[/C][C]0.574736203012523[/C][/ROW]
[ROW][C]91[/C][C]0.380605682332562[/C][C]0.761211364665123[/C][C]0.619394317667438[/C][/ROW]
[ROW][C]92[/C][C]0.345288622298103[/C][C]0.690577244596206[/C][C]0.654711377701897[/C][/ROW]
[ROW][C]93[/C][C]0.303669242264657[/C][C]0.607338484529314[/C][C]0.696330757735343[/C][/ROW]
[ROW][C]94[/C][C]0.346550494127736[/C][C]0.693100988255473[/C][C]0.653449505872264[/C][/ROW]
[ROW][C]95[/C][C]0.316701636767859[/C][C]0.633403273535718[/C][C]0.683298363232141[/C][/ROW]
[ROW][C]96[/C][C]0.274771847863333[/C][C]0.549543695726665[/C][C]0.725228152136667[/C][/ROW]
[ROW][C]97[/C][C]0.269856675373548[/C][C]0.539713350747095[/C][C]0.730143324626452[/C][/ROW]
[ROW][C]98[/C][C]0.232758472585136[/C][C]0.465516945170273[/C][C]0.767241527414864[/C][/ROW]
[ROW][C]99[/C][C]0.219328628116873[/C][C]0.438657256233746[/C][C]0.780671371883127[/C][/ROW]
[ROW][C]100[/C][C]0.214915880974867[/C][C]0.429831761949733[/C][C]0.785084119025133[/C][/ROW]
[ROW][C]101[/C][C]0.189828518858483[/C][C]0.379657037716966[/C][C]0.810171481141517[/C][/ROW]
[ROW][C]102[/C][C]0.211621038198505[/C][C]0.42324207639701[/C][C]0.788378961801495[/C][/ROW]
[ROW][C]103[/C][C]0.181500364934713[/C][C]0.363000729869427[/C][C]0.818499635065287[/C][/ROW]
[ROW][C]104[/C][C]0.165436299334958[/C][C]0.330872598669916[/C][C]0.834563700665042[/C][/ROW]
[ROW][C]105[/C][C]0.189738202864346[/C][C]0.379476405728692[/C][C]0.810261797135654[/C][/ROW]
[ROW][C]106[/C][C]0.16077932197809[/C][C]0.32155864395618[/C][C]0.83922067802191[/C][/ROW]
[ROW][C]107[/C][C]0.132386733995257[/C][C]0.264773467990514[/C][C]0.867613266004743[/C][/ROW]
[ROW][C]108[/C][C]0.116884701436619[/C][C]0.233769402873239[/C][C]0.883115298563381[/C][/ROW]
[ROW][C]109[/C][C]0.119310245609057[/C][C]0.238620491218115[/C][C]0.880689754390943[/C][/ROW]
[ROW][C]110[/C][C]0.107226426796924[/C][C]0.214452853593847[/C][C]0.892773573203076[/C][/ROW]
[ROW][C]111[/C][C]0.0921081477414481[/C][C]0.184216295482896[/C][C]0.907891852258552[/C][/ROW]
[ROW][C]112[/C][C]0.117968327920595[/C][C]0.235936655841191[/C][C]0.882031672079405[/C][/ROW]
[ROW][C]113[/C][C]0.102643253038615[/C][C]0.205286506077231[/C][C]0.897356746961385[/C][/ROW]
[ROW][C]114[/C][C]0.138782468871726[/C][C]0.277564937743453[/C][C]0.861217531128274[/C][/ROW]
[ROW][C]115[/C][C]0.151268736031567[/C][C]0.302537472063134[/C][C]0.848731263968433[/C][/ROW]
[ROW][C]116[/C][C]0.130686469742884[/C][C]0.261372939485768[/C][C]0.869313530257116[/C][/ROW]
[ROW][C]117[/C][C]0.141216536837854[/C][C]0.282433073675708[/C][C]0.858783463162146[/C][/ROW]
[ROW][C]118[/C][C]0.13907780356732[/C][C]0.278155607134641[/C][C]0.86092219643268[/C][/ROW]
[ROW][C]119[/C][C]0.168550077887839[/C][C]0.337100155775678[/C][C]0.831449922112161[/C][/ROW]
[ROW][C]120[/C][C]0.139792731574773[/C][C]0.279585463149547[/C][C]0.860207268425227[/C][/ROW]
[ROW][C]121[/C][C]0.12689640107263[/C][C]0.25379280214526[/C][C]0.87310359892737[/C][/ROW]
[ROW][C]122[/C][C]0.131866515151269[/C][C]0.263733030302539[/C][C]0.868133484848731[/C][/ROW]
[ROW][C]123[/C][C]0.105739089414097[/C][C]0.211478178828193[/C][C]0.894260910585903[/C][/ROW]
[ROW][C]124[/C][C]0.0857984651880153[/C][C]0.171596930376031[/C][C]0.914201534811985[/C][/ROW]
[ROW][C]125[/C][C]0.065938202955488[/C][C]0.131876405910976[/C][C]0.934061797044512[/C][/ROW]
[ROW][C]126[/C][C]0.0491466909434925[/C][C]0.0982933818869849[/C][C]0.950853309056508[/C][/ROW]
[ROW][C]127[/C][C]0.0474794267990124[/C][C]0.0949588535980249[/C][C]0.952520573200988[/C][/ROW]
[ROW][C]128[/C][C]0.0569112076775999[/C][C]0.1138224153552[/C][C]0.9430887923224[/C][/ROW]
[ROW][C]129[/C][C]0.0618252495847061[/C][C]0.123650499169412[/C][C]0.938174750415294[/C][/ROW]
[ROW][C]130[/C][C]0.0638677378867283[/C][C]0.127735475773457[/C][C]0.936132262113272[/C][/ROW]
[ROW][C]131[/C][C]0.0809816810648413[/C][C]0.161963362129683[/C][C]0.919018318935159[/C][/ROW]
[ROW][C]132[/C][C]0.145590202041243[/C][C]0.291180404082486[/C][C]0.854409797958757[/C][/ROW]
[ROW][C]133[/C][C]0.173108339739788[/C][C]0.346216679479576[/C][C]0.826891660260212[/C][/ROW]
[ROW][C]134[/C][C]0.137589238521034[/C][C]0.275178477042067[/C][C]0.862410761478966[/C][/ROW]
[ROW][C]135[/C][C]0.112442800261231[/C][C]0.224885600522461[/C][C]0.887557199738769[/C][/ROW]
[ROW][C]136[/C][C]0.0832660334908036[/C][C]0.166532066981607[/C][C]0.916733966509196[/C][/ROW]
[ROW][C]137[/C][C]0.103186410359447[/C][C]0.206372820718893[/C][C]0.896813589640553[/C][/ROW]
[ROW][C]138[/C][C]0.108598533625555[/C][C]0.21719706725111[/C][C]0.891401466374445[/C][/ROW]
[ROW][C]139[/C][C]0.0821284231651862[/C][C]0.164256846330372[/C][C]0.917871576834814[/C][/ROW]
[ROW][C]140[/C][C]0.24736465494641[/C][C]0.49472930989282[/C][C]0.75263534505359[/C][/ROW]
[ROW][C]141[/C][C]0.20758210267857[/C][C]0.415164205357141[/C][C]0.79241789732143[/C][/ROW]
[ROW][C]142[/C][C]0.167254106495808[/C][C]0.334508212991616[/C][C]0.832745893504192[/C][/ROW]
[ROW][C]143[/C][C]0.125165396540243[/C][C]0.250330793080486[/C][C]0.874834603459757[/C][/ROW]
[ROW][C]144[/C][C]0.0820434811859214[/C][C]0.164086962371843[/C][C]0.917956518814079[/C][/ROW]
[ROW][C]145[/C][C]0.156178419574087[/C][C]0.312356839148174[/C][C]0.843821580425913[/C][/ROW]
[ROW][C]146[/C][C]0.170964965666882[/C][C]0.341929931333765[/C][C]0.829035034333118[/C][/ROW]
[ROW][C]147[/C][C]0.291766468621636[/C][C]0.583532937243272[/C][C]0.708233531378364[/C][/ROW]
[ROW][C]148[/C][C]0.177356615162917[/C][C]0.354713230325834[/C][C]0.822643384837083[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197917&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197917&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
130.3731177348982130.7462354697964270.626882265101787
140.3726055975691090.7452111951382180.627394402430891
150.2425795162143760.4851590324287510.757420483785624
160.1946704347152610.3893408694305220.805329565284739
170.2077734523866970.4155469047733940.792226547613303
180.4127016789955220.8254033579910430.587298321004478
190.3639019585879760.7278039171759530.636098041412024
200.3081916690898310.6163833381796620.691808330910169
210.2551571493526860.5103142987053720.744842850647314
220.2968762267375230.5937524534750450.703123773262478
230.455741940546130.911483881092260.54425805945387
240.5985626240796990.8028747518406030.401437375920301
250.5271166892148420.9457666215703160.472883310785158
260.4695934676746550.9391869353493110.530406532325345
270.4602547701443180.9205095402886360.539745229855682
280.5899879780412120.8200240439175760.410012021958788
290.5354196089346990.9291607821306020.464580391065301
300.6485410291029610.7029179417940790.351458970897039
310.6067340044737840.7865319910524320.393265995526216
320.5750393416769320.8499213166461370.424960658323068
330.5575424343093830.8849151313812330.442457565690617
340.5143380009767510.9713239980464980.485661999023249
350.4640315998398110.9280631996796230.535968400160189
360.630165692111530.7396686157769390.36983430788847
370.663160218313780.6736795633724410.33683978168622
380.6438010922563630.7123978154872750.356198907743637
390.6844698086799490.6310603826401020.315530191320051
400.6648158562500080.6703682874999830.335184143749992
410.6238165862398090.7523668275203820.376183413760191
420.5840744217066330.8318511565867350.415925578293367
430.6168572782425560.7662854435148890.383142721757444
440.5628718555309750.8742562889380510.437128144469025
450.5300047047874430.9399905904251150.469995295212557
460.7486969606930780.5026060786138450.251303039306922
470.8228035848220230.3543928303559540.177196415177977
480.7915687837169280.4168624325661430.208431216283072
490.8058654485886530.3882691028226940.194134551411347
500.8024758977556890.3950482044886220.197524102244311
510.7696085020707310.4607829958585390.230391497929269
520.7357021065475720.5285957869048570.264297893452428
530.7654405483679050.469118903264190.234559451632095
540.7254037381957860.5491925236084290.274596261804214
550.7368768218870460.5262463562259080.263123178112954
560.7454058404282760.5091883191434480.254594159571724
570.7068235581102680.5863528837794640.293176441889732
580.6797034952121450.6405930095757090.320296504787855
590.6410870528740640.7178258942518710.358912947125936
600.6496060729005680.7007878541988640.350393927099432
610.6090296315067930.7819407369864140.390970368493207
620.5637383783328350.8725232433343310.436261621667165
630.5209434533791590.9581130932416820.479056546620841
640.4727587966421680.9455175932843370.527241203357832
650.4313778792103270.8627557584206530.568622120789673
660.3999351875487450.799870375097490.600064812451255
670.4005420943871130.8010841887742270.599457905612887
680.5453032987621340.9093934024757320.454696701237866
690.6933066000425840.6133867999148310.306693399957416
700.6569603459388990.6860793081222020.343039654061101
710.8007560887535230.3984878224929540.199243911246477
720.7666533950216580.4666932099566840.233346604978342
730.7638552014549920.4722895970900170.236144798545008
740.7471091518303860.5057816963392280.252890848169614
750.7075630358228770.5848739283542460.292436964177123
760.789410519551140.4211789608977190.21058948044886
770.7541311352226910.4917377295546170.245868864777309
780.7404352452633160.5191295094733690.259564754736684
790.7647577665719340.4704844668561320.235242233428066
800.7266519593939760.5466960812120480.273348040606024
810.6915525365278110.6168949269443780.308447463472189
820.6503118082790460.6993763834419080.349688191720954
830.615793792382320.768412415235360.38420620761768
840.571397688460930.857204623078140.42860231153907
850.5595117219892370.8809765560215260.440488278010763
860.5154791370996340.9690417258007310.484520862900366
870.4729803591152840.9459607182305670.527019640884716
880.4531830305857220.9063660611714450.546816969414278
890.417325546925280.834651093850560.58267445307472
900.4252637969874770.8505275939749530.574736203012523
910.3806056823325620.7612113646651230.619394317667438
920.3452886222981030.6905772445962060.654711377701897
930.3036692422646570.6073384845293140.696330757735343
940.3465504941277360.6931009882554730.653449505872264
950.3167016367678590.6334032735357180.683298363232141
960.2747718478633330.5495436957266650.725228152136667
970.2698566753735480.5397133507470950.730143324626452
980.2327584725851360.4655169451702730.767241527414864
990.2193286281168730.4386572562337460.780671371883127
1000.2149158809748670.4298317619497330.785084119025133
1010.1898285188584830.3796570377169660.810171481141517
1020.2116210381985050.423242076397010.788378961801495
1030.1815003649347130.3630007298694270.818499635065287
1040.1654362993349580.3308725986699160.834563700665042
1050.1897382028643460.3794764057286920.810261797135654
1060.160779321978090.321558643956180.83922067802191
1070.1323867339952570.2647734679905140.867613266004743
1080.1168847014366190.2337694028732390.883115298563381
1090.1193102456090570.2386204912181150.880689754390943
1100.1072264267969240.2144528535938470.892773573203076
1110.09210814774144810.1842162954828960.907891852258552
1120.1179683279205950.2359366558411910.882031672079405
1130.1026432530386150.2052865060772310.897356746961385
1140.1387824688717260.2775649377434530.861217531128274
1150.1512687360315670.3025374720631340.848731263968433
1160.1306864697428840.2613729394857680.869313530257116
1170.1412165368378540.2824330736757080.858783463162146
1180.139077803567320.2781556071346410.86092219643268
1190.1685500778878390.3371001557756780.831449922112161
1200.1397927315747730.2795854631495470.860207268425227
1210.126896401072630.253792802145260.87310359892737
1220.1318665151512690.2637330303025390.868133484848731
1230.1057390894140970.2114781788281930.894260910585903
1240.08579846518801530.1715969303760310.914201534811985
1250.0659382029554880.1318764059109760.934061797044512
1260.04914669094349250.09829338188698490.950853309056508
1270.04747942679901240.09495885359802490.952520573200988
1280.05691120767759990.11382241535520.9430887923224
1290.06182524958470610.1236504991694120.938174750415294
1300.06386773788672830.1277354757734570.936132262113272
1310.08098168106484130.1619633621296830.919018318935159
1320.1455902020412430.2911804040824860.854409797958757
1330.1731083397397880.3462166794795760.826891660260212
1340.1375892385210340.2751784770420670.862410761478966
1350.1124428002612310.2248856005224610.887557199738769
1360.08326603349080360.1665320669816070.916733966509196
1370.1031864103594470.2063728207188930.896813589640553
1380.1085985336255550.217197067251110.891401466374445
1390.08212842316518620.1642568463303720.917871576834814
1400.247364654946410.494729309892820.75263534505359
1410.207582102678570.4151642053571410.79241789732143
1420.1672541064958080.3345082129916160.832745893504192
1430.1251653965402430.2503307930804860.874834603459757
1440.08204348118592140.1640869623718430.917956518814079
1450.1561784195740870.3123568391481740.843821580425913
1460.1709649656668820.3419299313337650.829035034333118
1470.2917664686216360.5835329372432720.708233531378364
1480.1773566151629170.3547132303258340.822643384837083






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.0147058823529412OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 2 & 0.0147058823529412 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197917&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0147058823529412[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197917&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197917&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.0147058823529412OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}