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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 12 Dec 2012 07:39:16 -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/12/t1355316040oyqsaah55qlue7y.htm/, Retrieved Mon, 29 Apr 2024 06:20:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=198849, Retrieved Mon, 29 Apr 2024 06:20:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact85

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [WS7 3] [2012-11-04 16:26:17] [cb178d3ebce11557293640a297e0ede2]
- R       [Multiple Regression] [Paper 26] [2012-12-12 12:36:12] [cb178d3ebce11557293640a297e0ede2]
-   P         [Multiple Regression] [Paper 27] [2012-12-12 12:39:16] [0eae5e694d1d975eb250a0af7a7338a6] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
09	41	38	13	12	14	12	53	32
09	39	32	16	11	18	11	86	51
09	30	35	19	15	11	14	66	42
09	31	33	15	6	12	12	67	41
09	34	37	14	13	16	21	76	46
09	35	29	13	10	18	12	78	47
09	39	31	19	12	14	22	53	37
09	34	36	15	14	14	11	80	49
09	36	35	14	12	15	10	74	45
09	37	38	15	6	15	13	76	47
09	38	31	16	10	17	10	79	49
09	36	34	16	12	19	8	54	33
09	38	35	16	12	10	15	67	42
09	39	38	16	11	16	14	54	33
09	33	37	17	15	18	10	87	53
09	32	33	15	12	14	14	58	36
09	36	32	15	10	14	14	75	45
09	38	38	20	12	17	11	88	54
09	39	38	18	11	14	10	64	41
09	32	32	16	12	16	13	57	36
09	32	33	16	11	18	7	66	41
09	31	31	16	12	11	14	68	44
09	39	38	19	13	14	12	54	33
09	37	39	16	11	12	14	56	37
09	39	32	17	9	17	11	86	52
09	41	32	17	13	9	9	80	47
09	36	35	16	10	16	11	76	43
09	33	37	15	14	14	15	69	44
09	33	33	16	12	15	14	78	45
09	34	33	14	10	11	13	67	44
09	31	28	15	12	16	9	80	49
09	27	32	12	8	13	15	54	33
09	37	31	14	10	17	10	71	43
09	34	37	16	12	15	11	84	54
09	34	30	14	12	14	13	74	42
09	32	33	7	7	16	8	71	44
09	29	31	10	6	9	20	63	37
09	36	33	14	12	15	12	71	43
09	29	31	16	10	17	10	76	46
09	35	33	16	10	13	10	69	42
09	37	32	16	10	15	9	74	45
09	34	33	14	12	16	14	75	44
09	38	32	20	15	16	8	54	33
09	35	33	14	10	12	14	52	31
09	38	28	14	10	12	11	69	42
09	37	35	11	12	11	13	68	40
09	38	39	14	13	15	9	65	43
09	33	34	15	11	15	11	75	46
09	36	38	16	11	17	15	74	42
09	38	32	14	12	13	11	75	45
09	32	38	16	14	16	10	72	44
09	32	30	14	10	14	14	67	40
09	32	33	12	12	11	18	63	37
09	34	38	16	13	12	14	62	46
09	32	32	9	5	12	11	63	36
09	37	32	14	6	15	12	76	47
09	39	34	16	12	16	13	74	45
09	29	34	16	12	15	9	67	42
09	37	36	15	11	12	10	73	43
09	35	34	16	10	12	15	70	43
09	30	28	12	7	8	20	53	32
09	38	34	16	12	13	12	77	45
09	34	35	16	14	11	12	77	45
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	37	38	20	12	16	10	30	0
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	10	80	47
10	39	36	16	10	16	11	80	47
10	37	35	16	13	12	16	69	43
10	35	31	16	9	14	11	78	46
10	37	34	16	12	17	11	81	48
10	32	36	14	10	15	10	76	46
10	38	36	16	14	17	10	76	45
10	37	35	16	11	12	16	73	45
10	36	37	20	15	16	12	85	52
10	32	28	15	11	11	11	66	42
10	33	39	16	11	15	16	79	47
10	40	32	13	12	9	19	68	41
10	38	35	17	12	16	11	76	47
10	41	39	16	12	15	16	71	43
10	36	35	16	11	10	15	54	33
10	43	42	12	7	10	24	46	30
10	30	34	16	12	15	14	82	49
10	31	33	16	14	11	15	74	44
10	32	41	17	11	13	11	88	55
10	32	33	13	11	14	15	38	11
10	37	34	12	10	18	12	76	47
10	37	32	18	13	16	10	86	53
10	33	40	14	13	14	14	54	33
10	34	40	14	8	14	13	70	44
10	33	35	13	11	14	9	69	42
10	38	36	16	12	14	15	90	55
10	33	37	13	11	12	15	54	33
10	31	27	16	13	14	14	76	46
10	38	39	13	12	15	11	89	54
10	37	38	16	14	15	8	76	47
10	33	31	15	13	15	11	73	45
10	31	33	16	15	13	11	79	47
10	39	32	15	10	17	8	90	55
10	44	39	17	11	17	10	74	44
10	33	36	15	9	19	11	81	53
10	35	33	12	11	15	13	72	44
10	32	33	16	10	13	11	71	42
10	28	32	10	11	9	20	66	40
10	40	37	16	8	15	10	77	46
10	27	30	12	11	15	15	65	40
10	37	38	14	12	15	12	74	46
10	32	29	15	12	16	14	82	53
10	28	22	13	9	11	23	54	33
10	34	35	15	11	14	14	63	42
10	30	35	11	10	11	16	54	35
10	35	34	12	8	15	11	64	40
10	31	35	8	9	13	12	69	41
10	32	34	16	8	15	10	54	33
10	30	34	15	9	16	14	84	51
10	30	35	17	15	14	12	86	53
10	31	23	16	11	15	12	77	46
10	40	31	10	8	16	11	89	55
10	32	27	18	13	16	12	76	47
10	36	36	13	12	11	13	60	38
10	32	31	16	12	12	11	75	46
10	35	32	13	9	9	19	73	46
10	38	39	10	7	16	12	85	53
10	42	37	15	13	13	17	79	47
10	34	38	16	9	16	9	71	41
10	35	39	16	6	12	12	72	44
10	35	34	14	8	9	19	69	43
09	33	31	10	8	13	18	78	51
10	36	32	17	15	13	15	54	33
10	32	37	13	6	14	14	69	43
10	33	36	15	9	19	11	81	53
10	34	32	16	11	13	9	84	51
10	32	35	12	8	12	18	84	50
10	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 time11 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198849&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198849&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198849&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 time11 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Learning[t] = -2.09744119492241 + 0.98345152961758month[t] + 0.105235669801573Connected[t] -0.021934388714044Separate[t] + 0.498650277820933Software[t] + 0.0394190124751529Happiness[t] -0.0739033591796028Depression[t] + 0.0321725689001196Belonging[t] -0.0377946216850454Belonging_Final[t] -0.0129117474811838t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  -2.09744119492241 +  0.98345152961758month[t] +  0.105235669801573Connected[t] -0.021934388714044Separate[t] +  0.498650277820933Software[t] +  0.0394190124751529Happiness[t] -0.0739033591796028Depression[t] +  0.0321725689001196Belonging[t] -0.0377946216850454Belonging_Final[t] -0.0129117474811838t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198849&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  -2.09744119492241 +  0.98345152961758month[t] +  0.105235669801573Connected[t] -0.021934388714044Separate[t] +  0.498650277820933Software[t] +  0.0394190124751529Happiness[t] -0.0739033591796028Depression[t] +  0.0321725689001196Belonging[t] -0.0377946216850454Belonging_Final[t] -0.0129117474811838t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198849&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198849&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] = -2.09744119492241 + 0.98345152961758month[t] + 0.105235669801573Connected[t] -0.021934388714044Separate[t] + 0.498650277820933Software[t] + 0.0394190124751529Happiness[t] -0.0739033591796028Depression[t] + 0.0321725689001196Belonging[t] -0.0377946216850454Belonging_Final[t] -0.0129117474811838t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-2.097441194922415.099384-0.41130.6814230.340711
month0.983451529617580.5485291.79290.0749790.03749
Connected0.1052356698015730.0468862.24450.0262450.013123
Separate-0.0219343887140440.04483-0.48930.6253470.312673
Software0.4986502778209330.071177.006400
Happiness0.03941901247515290.0762180.51720.6057770.302888
Depression-0.07390335917960280.056407-1.31020.1921110.096056
Belonging0.03217256890011960.0447360.71920.4731420.236571
Belonging_Final-0.03779462168504540.06424-0.58830.5571810.27859
t-0.01291174748118380.005824-2.21690.0281140.014057

\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) & -2.09744119492241 & 5.099384 & -0.4113 & 0.681423 & 0.340711 \tabularnewline
month & 0.98345152961758 & 0.548529 & 1.7929 & 0.074979 & 0.03749 \tabularnewline
Connected & 0.105235669801573 & 0.046886 & 2.2445 & 0.026245 & 0.013123 \tabularnewline
Separate & -0.021934388714044 & 0.04483 & -0.4893 & 0.625347 & 0.312673 \tabularnewline
Software & 0.498650277820933 & 0.07117 & 7.0064 & 0 & 0 \tabularnewline
Happiness & 0.0394190124751529 & 0.076218 & 0.5172 & 0.605777 & 0.302888 \tabularnewline
Depression & -0.0739033591796028 & 0.056407 & -1.3102 & 0.192111 & 0.096056 \tabularnewline
Belonging & 0.0321725689001196 & 0.044736 & 0.7192 & 0.473142 & 0.236571 \tabularnewline
Belonging_Final & -0.0377946216850454 & 0.06424 & -0.5883 & 0.557181 & 0.27859 \tabularnewline
t & -0.0129117474811838 & 0.005824 & -2.2169 & 0.028114 & 0.014057 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198849&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]-2.09744119492241[/C][C]5.099384[/C][C]-0.4113[/C][C]0.681423[/C][C]0.340711[/C][/ROW]
[ROW][C]month[/C][C]0.98345152961758[/C][C]0.548529[/C][C]1.7929[/C][C]0.074979[/C][C]0.03749[/C][/ROW]
[ROW][C]Connected[/C][C]0.105235669801573[/C][C]0.046886[/C][C]2.2445[/C][C]0.026245[/C][C]0.013123[/C][/ROW]
[ROW][C]Separate[/C][C]-0.021934388714044[/C][C]0.04483[/C][C]-0.4893[/C][C]0.625347[/C][C]0.312673[/C][/ROW]
[ROW][C]Software[/C][C]0.498650277820933[/C][C]0.07117[/C][C]7.0064[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0394190124751529[/C][C]0.076218[/C][C]0.5172[/C][C]0.605777[/C][C]0.302888[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0739033591796028[/C][C]0.056407[/C][C]-1.3102[/C][C]0.192111[/C][C]0.096056[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0321725689001196[/C][C]0.044736[/C][C]0.7192[/C][C]0.473142[/C][C]0.236571[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0377946216850454[/C][C]0.06424[/C][C]-0.5883[/C][C]0.557181[/C][C]0.27859[/C][/ROW]
[ROW][C]t[/C][C]-0.0129117474811838[/C][C]0.005824[/C][C]-2.2169[/C][C]0.028114[/C][C]0.014057[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198849&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198849&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)-2.097441194922415.099384-0.41130.6814230.340711
month0.983451529617580.5485291.79290.0749790.03749
Connected0.1052356698015730.0468862.24450.0262450.013123
Separate-0.0219343887140440.04483-0.48930.6253470.312673
Software0.4986502778209330.071177.006400
Happiness0.03941901247515290.0762180.51720.6057770.302888
Depression-0.07390335917960280.056407-1.31020.1921110.096056
Belonging0.03217256890011960.0447360.71920.4731420.236571
Belonging_Final-0.03779462168504540.06424-0.58830.5571810.27859
t-0.01291174748118380.005824-2.21690.0281140.014057






Multiple Linear Regression - Regression Statistics
Multiple R0.613915820786412
R-squared0.376892635011853
Adjusted R-squared0.339998119979661
F-TEST (value)10.2154110084653
F-TEST (DF numerator)9
F-TEST (DF denominator)152
p-value3.14848147553448e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.83299774854445
Sum Squared Residuals510.701873417692

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.613915820786412 \tabularnewline
R-squared & 0.376892635011853 \tabularnewline
Adjusted R-squared & 0.339998119979661 \tabularnewline
F-TEST (value) & 10.2154110084653 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 3.14848147553448e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.83299774854445 \tabularnewline
Sum Squared Residuals & 510.701873417692 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198849&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.613915820786412[/C][/ROW]
[ROW][C]R-squared[/C][C]0.376892635011853[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.339998119979661[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.2154110084653[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C]3.14848147553448e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.83299774854445[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]510.701873417692[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198849&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198849&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 R0.613915820786412
R-squared0.376892635011853
Adjusted R-squared0.339998119979661
F-TEST (value)10.2154110084653
F-TEST (DF numerator)9
F-TEST (DF denominator)152
p-value3.14848147553448e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.83299774854445
Sum Squared Residuals510.701873417692






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.3664139710184-3.36641397101843
21616.3511633091657-0.351163309165727
31916.51898553091012.48101446908987
41512.42451865168972.57548134831027
51415.7232541324637-1.72325413246366
61315.2656211047155-2.26562110471552
71915.30400616730523.69599383269477
81516.8806055339461-1.88060553394615
91416.1742644041345-2.1742644041345
101512.97592931027852.0240706897215
111615.53787163070030.462128369299686
121616.273030400883-0.273030400882967
131615.65465277829340.345347221706565
141615.41484889014510.585151109854867
151717.467312425524-0.467312425524046
161515.1971663135579-0.197166313557947
171514.83661315449190.163386845508144
182016.31792588547183.68207411452817
191815.58643428002822.41356571997185
201615.2880225275770.711977472422955
211615.37736430526470.622635694735344
221614.95944061439271.04055938560727
231916.36491241186522.63508758813475
241614.80881628817561.19118371182445
251714.97967892729642.02032107270356
261717.0002319442846-0.00023194428462091
271615.05000242829720.949997571702789
281516.0346619396124-1.03466193961235
291615.47726806141630.522731938583733
301414.1724151011575-0.17241510115751
311515.6727476298542-0.672747629854199
321212.3631045000814-0.363104500081357
331415.117964695222-1.11796469522201
341615.50480133472990.495198665270138
351415.590014348632-1.59001434863198
36713.0838245770787-6.08382457707872
371011.144832722913-1.14483272291303
381415.6749573229188-1.67495732291879
391614.24608783136781.75391216863221
401614.58901577980671.41098422019331
411615.00873012421810.991269875781934
421415.3953469414223-1.39534694142229
432017.50480014203482.49519985796515
441414.0711435079219-0.0711435079218569
451414.836513623721-0.836513623720953
461115.4183169847174-4.41831698471737
471416.164941544866-2.16494154486604
481514.79875794189220.201242058107797
491614.91604615503151.08395384496848
501415.8005884479215-1.80058844792146
511616.1553942165782-0.15539421657819
521413.93922064809650.0607793519035076
531214.4286294054258-2.42862940542583
541614.97787561692651.02212438307346
55911.5287255028484-2.52872550284839
561412.58749861760791.41250138239208
571615.70985085809280.290149141907209
581614.78895271959341.21104728040656
591515.0384876703864-0.0384876703864245
601613.89428858031092.10571141968912
611211.33246830407810.667531695921863
621615.59222047933980.407779520660198
631616.0548941946298-0.0548941946298443
641414.9090369340526-0.909036934052623
651615.90029829473820.0997017052618297
661716.54215066302110.457849336978946
671816.58713888518161.41286111481836
681815.29427580749962.70572419250038
691216.2780001670708-4.27800016707075
701615.87790247400630.122097525993657
711014.1025809788943-4.10258097889431
721414.7336303482619-0.733630348261873
731816.92056979584581.07943020415424
741817.42272817436050.577271825639482
751616.2505555665492-0.250555566549225
761714.48052662864362.51947337135644
771616.6437611904071-0.643761190407118
781614.92619153295941.07380846704057
791315.2989641923043-2.29896419230428
801616.3268420585494-0.326842058549413
811615.9395935806240.0604064193759571
822016.5791748279823.42082517201795
831616.0305278253941-0.0305278253940706
841516.2734682884789-1.27346828847888
851515.1044073223945-0.104407322394541
861614.69804074082681.30195925917317
871414.5878450728652-0.587845072865234
881615.60921055176810.390789448231925
891614.81565887415831.18434112584166
901514.58473271361270.415267286387273
911213.8420498259366-1.84204982593664
921717.0095241537886-0.00952415378859874
931615.67330015072330.326699849276682
941515.5278763174161-0.527876317416138
951315.4589360504098-2.45893605040981
961615.41382779126410.586172208735916
971615.97841730939680.0215826906031617
981614.47269474177921.52730525822079
991616.2395875019776-0.239587501977568
1001414.5691198055174-0.569119805517357
1011617.2988558347647-1.29885583476469
1021614.96965904857941.03034095142056
1032017.37704192677752.62295807322253
1041514.78947159178470.210528408215322
1051614.65794677952951.34205322047049
1061315.4485210392978-2.44852103929781
1071716.05710756792490.942892432075079
1081615.85354510885870.146454891141304
1091614.61136313175721.38863686824285
1101212.3778323218427-0.37783232184269
1111615.04182668831830.958173311681685
1121615.85339870313870.14660129686133
1131714.68342327001882.3165767299812
1141314.6441271171427-1.64412711714273
1151214.8781464171168-2.87814641711676
1161816.56898093282631.43101906717371
1171415.3115701636548-1.31157016365482
1181413.08356631991660.916433680083436
1191314.9100717908552-1.91007179085521
1201615.64092799140810.359072008591917
1211314.1756844001004-1.17568440010045
1221615.53815357382540.461846426174627
1231315.8770440848009-2.87704408480089
1241616.8461606455066-0.846160645506614
1251515.8245581211274-0.824558121127439
1261616.5932149573372-0.593214957337209
1271515.3817989797384-0.381798979738357
1281715.9933481558621.00665184413796
1291513.781337703971.21866229603004
1301214.7871167246804-2.78711672468043
1311614.07223305785251.92776694214753
1321013.2508834140524-3.25088341405243
1331613.99785512159512.00214487840492
1341213.7375513285654-1.73755132856539
1351415.3846669147381-1.38466691473812
1361514.92741681019710.0725831898029422
1371312.14398748055050.856012519449538
1381514.20743204921560.792567950784441
1391112.9838728206167-1.98387282061673
1401213.1817186815902-1.18171868159018
141813.1949069826952-5.19490698269524
1421612.85692819919683.14307180080318
1431513.16087484236291.83912515763706
1441716.17565496093240.824345039067645
1451614.55101868070691.44898131929306
1461013.9730436215555-3.97304362155554
1471815.50944567822182.49055432177818
1481314.7758089049077-1.77580890490772
1491614.81908371264631.18091628735374
1501312.83016470373040.169835296269571
1511012.8968837656032-2.8968837656032
1521515.8866436250681-0.886643625068071
1531613.69418210894812.30581789105187
1541611.80802338549724.19197661450276
1551412.20778050053011.79221949946985
1561011.2855246056713-1.2855246056713
1571716.17026056751170.829739432488345
1581311.35684638517171.64315361482834
1591513.39398527953441.60601472046556
1601614.65474690593161.34525309406839
1611212.2028551958358-0.20285519583582
1621312.23429608331650.765703916683517

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.3664139710184 & -3.36641397101843 \tabularnewline
2 & 16 & 16.3511633091657 & -0.351163309165727 \tabularnewline
3 & 19 & 16.5189855309101 & 2.48101446908987 \tabularnewline
4 & 15 & 12.4245186516897 & 2.57548134831027 \tabularnewline
5 & 14 & 15.7232541324637 & -1.72325413246366 \tabularnewline
6 & 13 & 15.2656211047155 & -2.26562110471552 \tabularnewline
7 & 19 & 15.3040061673052 & 3.69599383269477 \tabularnewline
8 & 15 & 16.8806055339461 & -1.88060553394615 \tabularnewline
9 & 14 & 16.1742644041345 & -2.1742644041345 \tabularnewline
10 & 15 & 12.9759293102785 & 2.0240706897215 \tabularnewline
11 & 16 & 15.5378716307003 & 0.462128369299686 \tabularnewline
12 & 16 & 16.273030400883 & -0.273030400882967 \tabularnewline
13 & 16 & 15.6546527782934 & 0.345347221706565 \tabularnewline
14 & 16 & 15.4148488901451 & 0.585151109854867 \tabularnewline
15 & 17 & 17.467312425524 & -0.467312425524046 \tabularnewline
16 & 15 & 15.1971663135579 & -0.197166313557947 \tabularnewline
17 & 15 & 14.8366131544919 & 0.163386845508144 \tabularnewline
18 & 20 & 16.3179258854718 & 3.68207411452817 \tabularnewline
19 & 18 & 15.5864342800282 & 2.41356571997185 \tabularnewline
20 & 16 & 15.288022527577 & 0.711977472422955 \tabularnewline
21 & 16 & 15.3773643052647 & 0.622635694735344 \tabularnewline
22 & 16 & 14.9594406143927 & 1.04055938560727 \tabularnewline
23 & 19 & 16.3649124118652 & 2.63508758813475 \tabularnewline
24 & 16 & 14.8088162881756 & 1.19118371182445 \tabularnewline
25 & 17 & 14.9796789272964 & 2.02032107270356 \tabularnewline
26 & 17 & 17.0002319442846 & -0.00023194428462091 \tabularnewline
27 & 16 & 15.0500024282972 & 0.949997571702789 \tabularnewline
28 & 15 & 16.0346619396124 & -1.03466193961235 \tabularnewline
29 & 16 & 15.4772680614163 & 0.522731938583733 \tabularnewline
30 & 14 & 14.1724151011575 & -0.17241510115751 \tabularnewline
31 & 15 & 15.6727476298542 & -0.672747629854199 \tabularnewline
32 & 12 & 12.3631045000814 & -0.363104500081357 \tabularnewline
33 & 14 & 15.117964695222 & -1.11796469522201 \tabularnewline
34 & 16 & 15.5048013347299 & 0.495198665270138 \tabularnewline
35 & 14 & 15.590014348632 & -1.59001434863198 \tabularnewline
36 & 7 & 13.0838245770787 & -6.08382457707872 \tabularnewline
37 & 10 & 11.144832722913 & -1.14483272291303 \tabularnewline
38 & 14 & 15.6749573229188 & -1.67495732291879 \tabularnewline
39 & 16 & 14.2460878313678 & 1.75391216863221 \tabularnewline
40 & 16 & 14.5890157798067 & 1.41098422019331 \tabularnewline
41 & 16 & 15.0087301242181 & 0.991269875781934 \tabularnewline
42 & 14 & 15.3953469414223 & -1.39534694142229 \tabularnewline
43 & 20 & 17.5048001420348 & 2.49519985796515 \tabularnewline
44 & 14 & 14.0711435079219 & -0.0711435079218569 \tabularnewline
45 & 14 & 14.836513623721 & -0.836513623720953 \tabularnewline
46 & 11 & 15.4183169847174 & -4.41831698471737 \tabularnewline
47 & 14 & 16.164941544866 & -2.16494154486604 \tabularnewline
48 & 15 & 14.7987579418922 & 0.201242058107797 \tabularnewline
49 & 16 & 14.9160461550315 & 1.08395384496848 \tabularnewline
50 & 14 & 15.8005884479215 & -1.80058844792146 \tabularnewline
51 & 16 & 16.1553942165782 & -0.15539421657819 \tabularnewline
52 & 14 & 13.9392206480965 & 0.0607793519035076 \tabularnewline
53 & 12 & 14.4286294054258 & -2.42862940542583 \tabularnewline
54 & 16 & 14.9778756169265 & 1.02212438307346 \tabularnewline
55 & 9 & 11.5287255028484 & -2.52872550284839 \tabularnewline
56 & 14 & 12.5874986176079 & 1.41250138239208 \tabularnewline
57 & 16 & 15.7098508580928 & 0.290149141907209 \tabularnewline
58 & 16 & 14.7889527195934 & 1.21104728040656 \tabularnewline
59 & 15 & 15.0384876703864 & -0.0384876703864245 \tabularnewline
60 & 16 & 13.8942885803109 & 2.10571141968912 \tabularnewline
61 & 12 & 11.3324683040781 & 0.667531695921863 \tabularnewline
62 & 16 & 15.5922204793398 & 0.407779520660198 \tabularnewline
63 & 16 & 16.0548941946298 & -0.0548941946298443 \tabularnewline
64 & 14 & 14.9090369340526 & -0.909036934052623 \tabularnewline
65 & 16 & 15.9002982947382 & 0.0997017052618297 \tabularnewline
66 & 17 & 16.5421506630211 & 0.457849336978946 \tabularnewline
67 & 18 & 16.5871388851816 & 1.41286111481836 \tabularnewline
68 & 18 & 15.2942758074996 & 2.70572419250038 \tabularnewline
69 & 12 & 16.2780001670708 & -4.27800016707075 \tabularnewline
70 & 16 & 15.8779024740063 & 0.122097525993657 \tabularnewline
71 & 10 & 14.1025809788943 & -4.10258097889431 \tabularnewline
72 & 14 & 14.7336303482619 & -0.733630348261873 \tabularnewline
73 & 18 & 16.9205697958458 & 1.07943020415424 \tabularnewline
74 & 18 & 17.4227281743605 & 0.577271825639482 \tabularnewline
75 & 16 & 16.2505555665492 & -0.250555566549225 \tabularnewline
76 & 17 & 14.4805266286436 & 2.51947337135644 \tabularnewline
77 & 16 & 16.6437611904071 & -0.643761190407118 \tabularnewline
78 & 16 & 14.9261915329594 & 1.07380846704057 \tabularnewline
79 & 13 & 15.2989641923043 & -2.29896419230428 \tabularnewline
80 & 16 & 16.3268420585494 & -0.326842058549413 \tabularnewline
81 & 16 & 15.939593580624 & 0.0604064193759571 \tabularnewline
82 & 20 & 16.579174827982 & 3.42082517201795 \tabularnewline
83 & 16 & 16.0305278253941 & -0.0305278253940706 \tabularnewline
84 & 15 & 16.2734682884789 & -1.27346828847888 \tabularnewline
85 & 15 & 15.1044073223945 & -0.104407322394541 \tabularnewline
86 & 16 & 14.6980407408268 & 1.30195925917317 \tabularnewline
87 & 14 & 14.5878450728652 & -0.587845072865234 \tabularnewline
88 & 16 & 15.6092105517681 & 0.390789448231925 \tabularnewline
89 & 16 & 14.8156588741583 & 1.18434112584166 \tabularnewline
90 & 15 & 14.5847327136127 & 0.415267286387273 \tabularnewline
91 & 12 & 13.8420498259366 & -1.84204982593664 \tabularnewline
92 & 17 & 17.0095241537886 & -0.00952415378859874 \tabularnewline
93 & 16 & 15.6733001507233 & 0.326699849276682 \tabularnewline
94 & 15 & 15.5278763174161 & -0.527876317416138 \tabularnewline
95 & 13 & 15.4589360504098 & -2.45893605040981 \tabularnewline
96 & 16 & 15.4138277912641 & 0.586172208735916 \tabularnewline
97 & 16 & 15.9784173093968 & 0.0215826906031617 \tabularnewline
98 & 16 & 14.4726947417792 & 1.52730525822079 \tabularnewline
99 & 16 & 16.2395875019776 & -0.239587501977568 \tabularnewline
100 & 14 & 14.5691198055174 & -0.569119805517357 \tabularnewline
101 & 16 & 17.2988558347647 & -1.29885583476469 \tabularnewline
102 & 16 & 14.9696590485794 & 1.03034095142056 \tabularnewline
103 & 20 & 17.3770419267775 & 2.62295807322253 \tabularnewline
104 & 15 & 14.7894715917847 & 0.210528408215322 \tabularnewline
105 & 16 & 14.6579467795295 & 1.34205322047049 \tabularnewline
106 & 13 & 15.4485210392978 & -2.44852103929781 \tabularnewline
107 & 17 & 16.0571075679249 & 0.942892432075079 \tabularnewline
108 & 16 & 15.8535451088587 & 0.146454891141304 \tabularnewline
109 & 16 & 14.6113631317572 & 1.38863686824285 \tabularnewline
110 & 12 & 12.3778323218427 & -0.37783232184269 \tabularnewline
111 & 16 & 15.0418266883183 & 0.958173311681685 \tabularnewline
112 & 16 & 15.8533987031387 & 0.14660129686133 \tabularnewline
113 & 17 & 14.6834232700188 & 2.3165767299812 \tabularnewline
114 & 13 & 14.6441271171427 & -1.64412711714273 \tabularnewline
115 & 12 & 14.8781464171168 & -2.87814641711676 \tabularnewline
116 & 18 & 16.5689809328263 & 1.43101906717371 \tabularnewline
117 & 14 & 15.3115701636548 & -1.31157016365482 \tabularnewline
118 & 14 & 13.0835663199166 & 0.916433680083436 \tabularnewline
119 & 13 & 14.9100717908552 & -1.91007179085521 \tabularnewline
120 & 16 & 15.6409279914081 & 0.359072008591917 \tabularnewline
121 & 13 & 14.1756844001004 & -1.17568440010045 \tabularnewline
122 & 16 & 15.5381535738254 & 0.461846426174627 \tabularnewline
123 & 13 & 15.8770440848009 & -2.87704408480089 \tabularnewline
124 & 16 & 16.8461606455066 & -0.846160645506614 \tabularnewline
125 & 15 & 15.8245581211274 & -0.824558121127439 \tabularnewline
126 & 16 & 16.5932149573372 & -0.593214957337209 \tabularnewline
127 & 15 & 15.3817989797384 & -0.381798979738357 \tabularnewline
128 & 17 & 15.993348155862 & 1.00665184413796 \tabularnewline
129 & 15 & 13.78133770397 & 1.21866229603004 \tabularnewline
130 & 12 & 14.7871167246804 & -2.78711672468043 \tabularnewline
131 & 16 & 14.0722330578525 & 1.92776694214753 \tabularnewline
132 & 10 & 13.2508834140524 & -3.25088341405243 \tabularnewline
133 & 16 & 13.9978551215951 & 2.00214487840492 \tabularnewline
134 & 12 & 13.7375513285654 & -1.73755132856539 \tabularnewline
135 & 14 & 15.3846669147381 & -1.38466691473812 \tabularnewline
136 & 15 & 14.9274168101971 & 0.0725831898029422 \tabularnewline
137 & 13 & 12.1439874805505 & 0.856012519449538 \tabularnewline
138 & 15 & 14.2074320492156 & 0.792567950784441 \tabularnewline
139 & 11 & 12.9838728206167 & -1.98387282061673 \tabularnewline
140 & 12 & 13.1817186815902 & -1.18171868159018 \tabularnewline
141 & 8 & 13.1949069826952 & -5.19490698269524 \tabularnewline
142 & 16 & 12.8569281991968 & 3.14307180080318 \tabularnewline
143 & 15 & 13.1608748423629 & 1.83912515763706 \tabularnewline
144 & 17 & 16.1756549609324 & 0.824345039067645 \tabularnewline
145 & 16 & 14.5510186807069 & 1.44898131929306 \tabularnewline
146 & 10 & 13.9730436215555 & -3.97304362155554 \tabularnewline
147 & 18 & 15.5094456782218 & 2.49055432177818 \tabularnewline
148 & 13 & 14.7758089049077 & -1.77580890490772 \tabularnewline
149 & 16 & 14.8190837126463 & 1.18091628735374 \tabularnewline
150 & 13 & 12.8301647037304 & 0.169835296269571 \tabularnewline
151 & 10 & 12.8968837656032 & -2.8968837656032 \tabularnewline
152 & 15 & 15.8866436250681 & -0.886643625068071 \tabularnewline
153 & 16 & 13.6941821089481 & 2.30581789105187 \tabularnewline
154 & 16 & 11.8080233854972 & 4.19197661450276 \tabularnewline
155 & 14 & 12.2077805005301 & 1.79221949946985 \tabularnewline
156 & 10 & 11.2855246056713 & -1.2855246056713 \tabularnewline
157 & 17 & 16.1702605675117 & 0.829739432488345 \tabularnewline
158 & 13 & 11.3568463851717 & 1.64315361482834 \tabularnewline
159 & 15 & 13.3939852795344 & 1.60601472046556 \tabularnewline
160 & 16 & 14.6547469059316 & 1.34525309406839 \tabularnewline
161 & 12 & 12.2028551958358 & -0.20285519583582 \tabularnewline
162 & 13 & 12.2342960833165 & 0.765703916683517 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198849&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]16.3664139710184[/C][C]-3.36641397101843[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.3511633091657[/C][C]-0.351163309165727[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.5189855309101[/C][C]2.48101446908987[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.4245186516897[/C][C]2.57548134831027[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.7232541324637[/C][C]-1.72325413246366[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.2656211047155[/C][C]-2.26562110471552[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.3040061673052[/C][C]3.69599383269477[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.8806055339461[/C][C]-1.88060553394615[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.1742644041345[/C][C]-2.1742644041345[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.9759293102785[/C][C]2.0240706897215[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.5378716307003[/C][C]0.462128369299686[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.273030400883[/C][C]-0.273030400882967[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.6546527782934[/C][C]0.345347221706565[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.4148488901451[/C][C]0.585151109854867[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.467312425524[/C][C]-0.467312425524046[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.1971663135579[/C][C]-0.197166313557947[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.8366131544919[/C][C]0.163386845508144[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.3179258854718[/C][C]3.68207411452817[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.5864342800282[/C][C]2.41356571997185[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.288022527577[/C][C]0.711977472422955[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.3773643052647[/C][C]0.622635694735344[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.9594406143927[/C][C]1.04055938560727[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.3649124118652[/C][C]2.63508758813475[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.8088162881756[/C][C]1.19118371182445[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.9796789272964[/C][C]2.02032107270356[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]17.0002319442846[/C][C]-0.00023194428462091[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.0500024282972[/C][C]0.949997571702789[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.0346619396124[/C][C]-1.03466193961235[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.4772680614163[/C][C]0.522731938583733[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.1724151011575[/C][C]-0.17241510115751[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.6727476298542[/C][C]-0.672747629854199[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.3631045000814[/C][C]-0.363104500081357[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.117964695222[/C][C]-1.11796469522201[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.5048013347299[/C][C]0.495198665270138[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.590014348632[/C][C]-1.59001434863198[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]13.0838245770787[/C][C]-6.08382457707872[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]11.144832722913[/C][C]-1.14483272291303[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.6749573229188[/C][C]-1.67495732291879[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.2460878313678[/C][C]1.75391216863221[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.5890157798067[/C][C]1.41098422019331[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.0087301242181[/C][C]0.991269875781934[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.3953469414223[/C][C]-1.39534694142229[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.5048001420348[/C][C]2.49519985796515[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.0711435079219[/C][C]-0.0711435079218569[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.836513623721[/C][C]-0.836513623720953[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.4183169847174[/C][C]-4.41831698471737[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.164941544866[/C][C]-2.16494154486604[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.7987579418922[/C][C]0.201242058107797[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]14.9160461550315[/C][C]1.08395384496848[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.8005884479215[/C][C]-1.80058844792146[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.1553942165782[/C][C]-0.15539421657819[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.9392206480965[/C][C]0.0607793519035076[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.4286294054258[/C][C]-2.42862940542583[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]14.9778756169265[/C][C]1.02212438307346[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.5287255028484[/C][C]-2.52872550284839[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.5874986176079[/C][C]1.41250138239208[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.7098508580928[/C][C]0.290149141907209[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.7889527195934[/C][C]1.21104728040656[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.0384876703864[/C][C]-0.0384876703864245[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]13.8942885803109[/C][C]2.10571141968912[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.3324683040781[/C][C]0.667531695921863[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.5922204793398[/C][C]0.407779520660198[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.0548941946298[/C][C]-0.0548941946298443[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.9090369340526[/C][C]-0.909036934052623[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.9002982947382[/C][C]0.0997017052618297[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.5421506630211[/C][C]0.457849336978946[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.5871388851816[/C][C]1.41286111481836[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]15.2942758074996[/C][C]2.70572419250038[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]16.2780001670708[/C][C]-4.27800016707075[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.8779024740063[/C][C]0.122097525993657[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]14.1025809788943[/C][C]-4.10258097889431[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.7336303482619[/C][C]-0.733630348261873[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.9205697958458[/C][C]1.07943020415424[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.4227281743605[/C][C]0.577271825639482[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]16.2505555665492[/C][C]-0.250555566549225[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]14.4805266286436[/C][C]2.51947337135644[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.6437611904071[/C][C]-0.643761190407118[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.9261915329594[/C][C]1.07380846704057[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]15.2989641923043[/C][C]-2.29896419230428[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.3268420585494[/C][C]-0.326842058549413[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.939593580624[/C][C]0.0604064193759571[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]16.579174827982[/C][C]3.42082517201795[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]16.0305278253941[/C][C]-0.0305278253940706[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]16.2734682884789[/C][C]-1.27346828847888[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]15.1044073223945[/C][C]-0.104407322394541[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.6980407408268[/C][C]1.30195925917317[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.5878450728652[/C][C]-0.587845072865234[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.6092105517681[/C][C]0.390789448231925[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.8156588741583[/C][C]1.18434112584166[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.5847327136127[/C][C]0.415267286387273[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.8420498259366[/C][C]-1.84204982593664[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]17.0095241537886[/C][C]-0.00952415378859874[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.6733001507233[/C][C]0.326699849276682[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.5278763174161[/C][C]-0.527876317416138[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.4589360504098[/C][C]-2.45893605040981[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.4138277912641[/C][C]0.586172208735916[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.9784173093968[/C][C]0.0215826906031617[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.4726947417792[/C][C]1.52730525822079[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.2395875019776[/C][C]-0.239587501977568[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.5691198055174[/C][C]-0.569119805517357[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.2988558347647[/C][C]-1.29885583476469[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.9696590485794[/C][C]1.03034095142056[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.3770419267775[/C][C]2.62295807322253[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.7894715917847[/C][C]0.210528408215322[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.6579467795295[/C][C]1.34205322047049[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.4485210392978[/C][C]-2.44852103929781[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]16.0571075679249[/C][C]0.942892432075079[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.8535451088587[/C][C]0.146454891141304[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.6113631317572[/C][C]1.38863686824285[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.3778323218427[/C][C]-0.37783232184269[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.0418266883183[/C][C]0.958173311681685[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.8533987031387[/C][C]0.14660129686133[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.6834232700188[/C][C]2.3165767299812[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.6441271171427[/C][C]-1.64412711714273[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.8781464171168[/C][C]-2.87814641711676[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.5689809328263[/C][C]1.43101906717371[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.3115701636548[/C][C]-1.31157016365482[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.0835663199166[/C][C]0.916433680083436[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.9100717908552[/C][C]-1.91007179085521[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.6409279914081[/C][C]0.359072008591917[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.1756844001004[/C][C]-1.17568440010045[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.5381535738254[/C][C]0.461846426174627[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.8770440848009[/C][C]-2.87704408480089[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]16.8461606455066[/C][C]-0.846160645506614[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.8245581211274[/C][C]-0.824558121127439[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.5932149573372[/C][C]-0.593214957337209[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.3817989797384[/C][C]-0.381798979738357[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]15.993348155862[/C][C]1.00665184413796[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.78133770397[/C][C]1.21866229603004[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.7871167246804[/C][C]-2.78711672468043[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.0722330578525[/C][C]1.92776694214753[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.2508834140524[/C][C]-3.25088341405243[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]13.9978551215951[/C][C]2.00214487840492[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.7375513285654[/C][C]-1.73755132856539[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.3846669147381[/C][C]-1.38466691473812[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]14.9274168101971[/C][C]0.0725831898029422[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.1439874805505[/C][C]0.856012519449538[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.2074320492156[/C][C]0.792567950784441[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]12.9838728206167[/C][C]-1.98387282061673[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.1817186815902[/C][C]-1.18171868159018[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.1949069826952[/C][C]-5.19490698269524[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]12.8569281991968[/C][C]3.14307180080318[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.1608748423629[/C][C]1.83912515763706[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.1756549609324[/C][C]0.824345039067645[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.5510186807069[/C][C]1.44898131929306[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]13.9730436215555[/C][C]-3.97304362155554[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.5094456782218[/C][C]2.49055432177818[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]14.7758089049077[/C][C]-1.77580890490772[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.8190837126463[/C][C]1.18091628735374[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.8301647037304[/C][C]0.169835296269571[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]12.8968837656032[/C][C]-2.8968837656032[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]15.8866436250681[/C][C]-0.886643625068071[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]13.6941821089481[/C][C]2.30581789105187[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]11.8080233854972[/C][C]4.19197661450276[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.2077805005301[/C][C]1.79221949946985[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]11.2855246056713[/C][C]-1.2855246056713[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.1702605675117[/C][C]0.829739432488345[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.3568463851717[/C][C]1.64315361482834[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.3939852795344[/C][C]1.60601472046556[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]14.6547469059316[/C][C]1.34525309406839[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.2028551958358[/C][C]-0.20285519583582[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.2342960833165[/C][C]0.765703916683517[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198849&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198849&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
11316.3664139710184-3.36641397101843
21616.3511633091657-0.351163309165727
31916.51898553091012.48101446908987
41512.42451865168972.57548134831027
51415.7232541324637-1.72325413246366
61315.2656211047155-2.26562110471552
71915.30400616730523.69599383269477
81516.8806055339461-1.88060553394615
91416.1742644041345-2.1742644041345
101512.97592931027852.0240706897215
111615.53787163070030.462128369299686
121616.273030400883-0.273030400882967
131615.65465277829340.345347221706565
141615.41484889014510.585151109854867
151717.467312425524-0.467312425524046
161515.1971663135579-0.197166313557947
171514.83661315449190.163386845508144
182016.31792588547183.68207411452817
191815.58643428002822.41356571997185
201615.2880225275770.711977472422955
211615.37736430526470.622635694735344
221614.95944061439271.04055938560727
231916.36491241186522.63508758813475
241614.80881628817561.19118371182445
251714.97967892729642.02032107270356
261717.0002319442846-0.00023194428462091
271615.05000242829720.949997571702789
281516.0346619396124-1.03466193961235
291615.47726806141630.522731938583733
301414.1724151011575-0.17241510115751
311515.6727476298542-0.672747629854199
321212.3631045000814-0.363104500081357
331415.117964695222-1.11796469522201
341615.50480133472990.495198665270138
351415.590014348632-1.59001434863198
36713.0838245770787-6.08382457707872
371011.144832722913-1.14483272291303
381415.6749573229188-1.67495732291879
391614.24608783136781.75391216863221
401614.58901577980671.41098422019331
411615.00873012421810.991269875781934
421415.3953469414223-1.39534694142229
432017.50480014203482.49519985796515
441414.0711435079219-0.0711435079218569
451414.836513623721-0.836513623720953
461115.4183169847174-4.41831698471737
471416.164941544866-2.16494154486604
481514.79875794189220.201242058107797
491614.91604615503151.08395384496848
501415.8005884479215-1.80058844792146
511616.1553942165782-0.15539421657819
521413.93922064809650.0607793519035076
531214.4286294054258-2.42862940542583
541614.97787561692651.02212438307346
55911.5287255028484-2.52872550284839
561412.58749861760791.41250138239208
571615.70985085809280.290149141907209
581614.78895271959341.21104728040656
591515.0384876703864-0.0384876703864245
601613.89428858031092.10571141968912
611211.33246830407810.667531695921863
621615.59222047933980.407779520660198
631616.0548941946298-0.0548941946298443
641414.9090369340526-0.909036934052623
651615.90029829473820.0997017052618297
661716.54215066302110.457849336978946
671816.58713888518161.41286111481836
681815.29427580749962.70572419250038
691216.2780001670708-4.27800016707075
701615.87790247400630.122097525993657
711014.1025809788943-4.10258097889431
721414.7336303482619-0.733630348261873
731816.92056979584581.07943020415424
741817.42272817436050.577271825639482
751616.2505555665492-0.250555566549225
761714.48052662864362.51947337135644
771616.6437611904071-0.643761190407118
781614.92619153295941.07380846704057
791315.2989641923043-2.29896419230428
801616.3268420585494-0.326842058549413
811615.9395935806240.0604064193759571
822016.5791748279823.42082517201795
831616.0305278253941-0.0305278253940706
841516.2734682884789-1.27346828847888
851515.1044073223945-0.104407322394541
861614.69804074082681.30195925917317
871414.5878450728652-0.587845072865234
881615.60921055176810.390789448231925
891614.81565887415831.18434112584166
901514.58473271361270.415267286387273
911213.8420498259366-1.84204982593664
921717.0095241537886-0.00952415378859874
931615.67330015072330.326699849276682
941515.5278763174161-0.527876317416138
951315.4589360504098-2.45893605040981
961615.41382779126410.586172208735916
971615.97841730939680.0215826906031617
981614.47269474177921.52730525822079
991616.2395875019776-0.239587501977568
1001414.5691198055174-0.569119805517357
1011617.2988558347647-1.29885583476469
1021614.96965904857941.03034095142056
1032017.37704192677752.62295807322253
1041514.78947159178470.210528408215322
1051614.65794677952951.34205322047049
1061315.4485210392978-2.44852103929781
1071716.05710756792490.942892432075079
1081615.85354510885870.146454891141304
1091614.61136313175721.38863686824285
1101212.3778323218427-0.37783232184269
1111615.04182668831830.958173311681685
1121615.85339870313870.14660129686133
1131714.68342327001882.3165767299812
1141314.6441271171427-1.64412711714273
1151214.8781464171168-2.87814641711676
1161816.56898093282631.43101906717371
1171415.3115701636548-1.31157016365482
1181413.08356631991660.916433680083436
1191314.9100717908552-1.91007179085521
1201615.64092799140810.359072008591917
1211314.1756844001004-1.17568440010045
1221615.53815357382540.461846426174627
1231315.8770440848009-2.87704408480089
1241616.8461606455066-0.846160645506614
1251515.8245581211274-0.824558121127439
1261616.5932149573372-0.593214957337209
1271515.3817989797384-0.381798979738357
1281715.9933481558621.00665184413796
1291513.781337703971.21866229603004
1301214.7871167246804-2.78711672468043
1311614.07223305785251.92776694214753
1321013.2508834140524-3.25088341405243
1331613.99785512159512.00214487840492
1341213.7375513285654-1.73755132856539
1351415.3846669147381-1.38466691473812
1361514.92741681019710.0725831898029422
1371312.14398748055050.856012519449538
1381514.20743204921560.792567950784441
1391112.9838728206167-1.98387282061673
1401213.1817186815902-1.18171868159018
141813.1949069826952-5.19490698269524
1421612.85692819919683.14307180080318
1431513.16087484236291.83912515763706
1441716.17565496093240.824345039067645
1451614.55101868070691.44898131929306
1461013.9730436215555-3.97304362155554
1471815.50944567822182.49055432177818
1481314.7758089049077-1.77580890490772
1491614.81908371264631.18091628735374
1501312.83016470373040.169835296269571
1511012.8968837656032-2.8968837656032
1521515.8866436250681-0.886643625068071
1531613.69418210894812.30581789105187
1541611.80802338549724.19197661450276
1551412.20778050053011.79221949946985
1561011.2855246056713-1.2855246056713
1571716.17026056751170.829739432488345
1581311.35684638517171.64315361482834
1591513.39398527953441.60601472046556
1601614.65474690593161.34525309406839
1611212.2028551958358-0.20285519583582
1621312.23429608331650.765703916683517






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.7422449725981170.5155100548037650.257755027401883
140.7180231641958270.5639536716083470.281976835804173
150.5973968674673010.8052062650653970.402603132532699
160.4848571098640610.9697142197281230.515142890135939
170.4065323046363610.8130646092727210.593467695363639
180.5644983795686440.8710032408627110.435501620431356
190.4794924332672670.9589848665345350.520507566732733
200.39081623405030.7816324681005990.6091837659497
210.319691519638360.6393830392767190.68030848036164
220.3123308864274540.6246617728549070.687669113572546
230.4440897648876170.8881795297752330.555910235112383
240.5188743574703940.9622512850592120.481125642529606
250.465188004322190.9303760086443810.53481199567781
260.3964570922546080.7929141845092160.603542907745392
270.3682531617103310.7365063234206630.631746838289669
280.4736559979546590.9473119959093190.526344002045341
290.4163868134615140.8327736269230290.583613186538486
300.5280024083078410.9439951833843190.471997591692159
310.4769577254391780.9539154508783560.523042274560822
320.4395677641938120.8791355283876240.560432235806188
330.4272714294218790.8545428588437580.572728570578121
340.3962695579648550.792539115929710.603730442035145
350.3450002417804730.6900004835609460.654999758219527
360.8800912845654510.2398174308690970.119908715434549
370.855229317940770.289541364118460.14477068205923
380.8367695887455050.3264608225089890.163230411254495
390.8665338580168850.2669322839662290.133466141983115
400.8566656919758210.2866686160483580.143334308024179
410.8328875827087820.3342248345824370.167112417291218
420.8045111767048150.3909776465903690.195488823295185
430.8386270424202410.3227459151595190.161372957579759
440.8034084726282040.3931830547435910.196591527371796
450.7759670590221960.4480658819556070.224032940977804
460.8930019496304930.2139961007390130.106998050369507
470.9111296393700060.1777407212599880.0888703606299939
480.8911523351347340.2176953297305330.108847664865266
490.8903181763299910.2193636473400180.109681823670009
500.8799359343253180.2401281313493640.120064065674682
510.8532028474889530.2935943050220940.146797152511047
520.82432834558640.35134330882720.1756716544136
530.8277587669627440.3444824660745120.172241233037256
540.8030395801333440.3939208397333130.196960419866656
550.8152872232128980.3694255535742040.184712776787102
560.7968232375467590.4063535249064830.203176762453241
570.7607841243146340.4784317513707310.239215875685366
580.7501132858560970.4997734282878060.249886714143903
590.7165252241122080.5669495517755830.283474775887792
600.7340145181991580.5319709636016850.265985481800842
610.704006878391510.591986243216980.29599312160849
620.6715350851173730.6569298297652530.328464914882627
630.6343869267388540.7312261465222910.365613073261146
640.5891484560148920.8217030879702160.410851543985108
650.5447936611007450.910412677798510.455206338899255
660.5005210200425180.9989579599149650.499478979957482
670.4796945891154640.9593891782309280.520305410884536
680.5501164360893330.8997671278213340.449883563910667
690.7298948394074980.5402103211850030.270105160592502
700.6900687500282750.6198624999434490.309931249971725
710.8255759968439990.3488480063120030.174424003156001
720.7950451480647090.4099097038705810.204954851935291
730.7832958158824270.4334083682351450.216704184117572
740.7622681494813010.4754637010373990.237731850518699
750.7249166020861130.5501667958277740.275083397913887
760.7526420405385740.4947159189228530.247357959461426
770.7154458422340850.569108315531830.284554157765915
780.6920280634497370.6159438731005260.307971936550263
790.7086077431327090.5827845137345820.291392256867291
800.6665418455169130.6669163089661740.333458154483087
810.6260054811693250.7479890376613510.373994518830675
820.7631559378437920.4736881243124170.236844062156208
830.7262540471425950.547491905714810.273745952857405
840.6980801679406980.6038396641186030.301919832059302
850.6559180590181020.6881638819637960.344081940981898
860.6384785440619430.7230429118761130.361521455938057
870.5940518089062470.8118963821875060.405948191093753
880.5512684834136180.8974630331727650.448731516586382
890.5287193095926120.9425613808147760.471280690407388
900.4886592050757390.9773184101514790.511340794924261
910.4783163206009760.9566326412019520.521683679399024
920.4364143005339750.872828601067950.563585699466025
930.3942973599103350.788594719820670.605702640089665
940.350645344145050.7012906882900990.64935465585495
950.3780685243725490.7561370487450990.621931475627451
960.3413597496773880.6827194993547760.658640250322612
970.299669197419930.599338394839860.70033080258007
980.2919501909993640.5839003819987270.708049809000636
990.2513881328002240.5027762656004490.748611867199776
1000.2179813114794940.4359626229589870.782018688520506
1010.1930271059734210.3860542119468420.806972894026579
1020.1754613482443610.3509226964887210.824538651755639
1030.2238764055061810.4477528110123630.776123594493819
1040.1895250570747980.3790501141495960.810474942925202
1050.1842904418085440.3685808836170890.815709558191456
1060.1936991113406320.3873982226812630.806300888659368
1070.175835044419960.3516700888399210.82416495558004
1080.155346037024850.3106920740497010.844653962975149
1090.1540367303641630.3080734607283260.845963269635837
1100.1460330257656090.2920660515312190.85396697423439
1110.1333813942209330.2667627884418660.866618605779067
1120.1142658378773530.2285316757547060.885734162122647
1130.1587606326873420.3175212653746840.841239367312658
1140.13915560071430.27831120142860.8608443992857
1150.1536448795880150.307289759176030.846355120411985
1160.1636412465072760.3272824930145510.836358753492724
1170.1363839593914720.2727679187829440.863616040608528
1180.1387749206575010.2775498413150010.861225079342499
1190.1233404156905630.2466808313811250.876659584309437
1200.1362099740039570.2724199480079130.863790025996043
1210.1099319357524340.2198638715048680.890068064247566
1220.09690424522423770.1938084904484750.903095754775762
1230.09053932641297810.1810786528259560.909460673587022
1240.06965362872404310.1393072574480860.930346371275957
1250.05251662192706230.1050332438541250.947483378072938
1260.03933549893800340.07867099787600690.960664501061997
1270.02837266705736250.05674533411472490.971627332942637
1280.02970952481651160.05941904963302330.970290475183488
1290.03183281789821390.06366563579642780.968167182101786
1300.03001581989676370.06003163979352740.969984180103236
1310.03634419815782490.07268839631564990.963655801842175
1320.03693665020532760.07387330041065510.963063349794672
1330.09293726638228980.185874532764580.90706273361771
1340.09484935240972680.1896987048194540.905150647590273
1350.07456199846630590.1491239969326120.925438001533694
1360.05767681169855910.1153536233971180.942323188301441
1370.04103509716029390.08207019432058770.958964902839706
1380.04557188768336940.09114377536673880.954428112316631
1390.04038235230341230.08076470460682460.959617647696588
1400.02661872538170340.05323745076340680.973381274618297
1410.5425805938276330.9148388123447350.457419406172367
1420.4832961798937770.9665923597875550.516703820106223
1430.4047549598130830.8095099196261650.595245040186917
1440.3126933542082370.6253867084164750.687306645791763
1450.229694300836640.459388601673280.77030569916336
1460.3673944814045760.7347889628091530.632605518595424
1470.2920704873616060.5841409747232120.707929512638394
1480.4499192212675470.8998384425350930.550080778732453
1490.4624887954856660.9249775909713310.537511204514334

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.742244972598117 & 0.515510054803765 & 0.257755027401883 \tabularnewline
14 & 0.718023164195827 & 0.563953671608347 & 0.281976835804173 \tabularnewline
15 & 0.597396867467301 & 0.805206265065397 & 0.402603132532699 \tabularnewline
16 & 0.484857109864061 & 0.969714219728123 & 0.515142890135939 \tabularnewline
17 & 0.406532304636361 & 0.813064609272721 & 0.593467695363639 \tabularnewline
18 & 0.564498379568644 & 0.871003240862711 & 0.435501620431356 \tabularnewline
19 & 0.479492433267267 & 0.958984866534535 & 0.520507566732733 \tabularnewline
20 & 0.3908162340503 & 0.781632468100599 & 0.6091837659497 \tabularnewline
21 & 0.31969151963836 & 0.639383039276719 & 0.68030848036164 \tabularnewline
22 & 0.312330886427454 & 0.624661772854907 & 0.687669113572546 \tabularnewline
23 & 0.444089764887617 & 0.888179529775233 & 0.555910235112383 \tabularnewline
24 & 0.518874357470394 & 0.962251285059212 & 0.481125642529606 \tabularnewline
25 & 0.46518800432219 & 0.930376008644381 & 0.53481199567781 \tabularnewline
26 & 0.396457092254608 & 0.792914184509216 & 0.603542907745392 \tabularnewline
27 & 0.368253161710331 & 0.736506323420663 & 0.631746838289669 \tabularnewline
28 & 0.473655997954659 & 0.947311995909319 & 0.526344002045341 \tabularnewline
29 & 0.416386813461514 & 0.832773626923029 & 0.583613186538486 \tabularnewline
30 & 0.528002408307841 & 0.943995183384319 & 0.471997591692159 \tabularnewline
31 & 0.476957725439178 & 0.953915450878356 & 0.523042274560822 \tabularnewline
32 & 0.439567764193812 & 0.879135528387624 & 0.560432235806188 \tabularnewline
33 & 0.427271429421879 & 0.854542858843758 & 0.572728570578121 \tabularnewline
34 & 0.396269557964855 & 0.79253911592971 & 0.603730442035145 \tabularnewline
35 & 0.345000241780473 & 0.690000483560946 & 0.654999758219527 \tabularnewline
36 & 0.880091284565451 & 0.239817430869097 & 0.119908715434549 \tabularnewline
37 & 0.85522931794077 & 0.28954136411846 & 0.14477068205923 \tabularnewline
38 & 0.836769588745505 & 0.326460822508989 & 0.163230411254495 \tabularnewline
39 & 0.866533858016885 & 0.266932283966229 & 0.133466141983115 \tabularnewline
40 & 0.856665691975821 & 0.286668616048358 & 0.143334308024179 \tabularnewline
41 & 0.832887582708782 & 0.334224834582437 & 0.167112417291218 \tabularnewline
42 & 0.804511176704815 & 0.390977646590369 & 0.195488823295185 \tabularnewline
43 & 0.838627042420241 & 0.322745915159519 & 0.161372957579759 \tabularnewline
44 & 0.803408472628204 & 0.393183054743591 & 0.196591527371796 \tabularnewline
45 & 0.775967059022196 & 0.448065881955607 & 0.224032940977804 \tabularnewline
46 & 0.893001949630493 & 0.213996100739013 & 0.106998050369507 \tabularnewline
47 & 0.911129639370006 & 0.177740721259988 & 0.0888703606299939 \tabularnewline
48 & 0.891152335134734 & 0.217695329730533 & 0.108847664865266 \tabularnewline
49 & 0.890318176329991 & 0.219363647340018 & 0.109681823670009 \tabularnewline
50 & 0.879935934325318 & 0.240128131349364 & 0.120064065674682 \tabularnewline
51 & 0.853202847488953 & 0.293594305022094 & 0.146797152511047 \tabularnewline
52 & 0.8243283455864 & 0.3513433088272 & 0.1756716544136 \tabularnewline
53 & 0.827758766962744 & 0.344482466074512 & 0.172241233037256 \tabularnewline
54 & 0.803039580133344 & 0.393920839733313 & 0.196960419866656 \tabularnewline
55 & 0.815287223212898 & 0.369425553574204 & 0.184712776787102 \tabularnewline
56 & 0.796823237546759 & 0.406353524906483 & 0.203176762453241 \tabularnewline
57 & 0.760784124314634 & 0.478431751370731 & 0.239215875685366 \tabularnewline
58 & 0.750113285856097 & 0.499773428287806 & 0.249886714143903 \tabularnewline
59 & 0.716525224112208 & 0.566949551775583 & 0.283474775887792 \tabularnewline
60 & 0.734014518199158 & 0.531970963601685 & 0.265985481800842 \tabularnewline
61 & 0.70400687839151 & 0.59198624321698 & 0.29599312160849 \tabularnewline
62 & 0.671535085117373 & 0.656929829765253 & 0.328464914882627 \tabularnewline
63 & 0.634386926738854 & 0.731226146522291 & 0.365613073261146 \tabularnewline
64 & 0.589148456014892 & 0.821703087970216 & 0.410851543985108 \tabularnewline
65 & 0.544793661100745 & 0.91041267779851 & 0.455206338899255 \tabularnewline
66 & 0.500521020042518 & 0.998957959914965 & 0.499478979957482 \tabularnewline
67 & 0.479694589115464 & 0.959389178230928 & 0.520305410884536 \tabularnewline
68 & 0.550116436089333 & 0.899767127821334 & 0.449883563910667 \tabularnewline
69 & 0.729894839407498 & 0.540210321185003 & 0.270105160592502 \tabularnewline
70 & 0.690068750028275 & 0.619862499943449 & 0.309931249971725 \tabularnewline
71 & 0.825575996843999 & 0.348848006312003 & 0.174424003156001 \tabularnewline
72 & 0.795045148064709 & 0.409909703870581 & 0.204954851935291 \tabularnewline
73 & 0.783295815882427 & 0.433408368235145 & 0.216704184117572 \tabularnewline
74 & 0.762268149481301 & 0.475463701037399 & 0.237731850518699 \tabularnewline
75 & 0.724916602086113 & 0.550166795827774 & 0.275083397913887 \tabularnewline
76 & 0.752642040538574 & 0.494715918922853 & 0.247357959461426 \tabularnewline
77 & 0.715445842234085 & 0.56910831553183 & 0.284554157765915 \tabularnewline
78 & 0.692028063449737 & 0.615943873100526 & 0.307971936550263 \tabularnewline
79 & 0.708607743132709 & 0.582784513734582 & 0.291392256867291 \tabularnewline
80 & 0.666541845516913 & 0.666916308966174 & 0.333458154483087 \tabularnewline
81 & 0.626005481169325 & 0.747989037661351 & 0.373994518830675 \tabularnewline
82 & 0.763155937843792 & 0.473688124312417 & 0.236844062156208 \tabularnewline
83 & 0.726254047142595 & 0.54749190571481 & 0.273745952857405 \tabularnewline
84 & 0.698080167940698 & 0.603839664118603 & 0.301919832059302 \tabularnewline
85 & 0.655918059018102 & 0.688163881963796 & 0.344081940981898 \tabularnewline
86 & 0.638478544061943 & 0.723042911876113 & 0.361521455938057 \tabularnewline
87 & 0.594051808906247 & 0.811896382187506 & 0.405948191093753 \tabularnewline
88 & 0.551268483413618 & 0.897463033172765 & 0.448731516586382 \tabularnewline
89 & 0.528719309592612 & 0.942561380814776 & 0.471280690407388 \tabularnewline
90 & 0.488659205075739 & 0.977318410151479 & 0.511340794924261 \tabularnewline
91 & 0.478316320600976 & 0.956632641201952 & 0.521683679399024 \tabularnewline
92 & 0.436414300533975 & 0.87282860106795 & 0.563585699466025 \tabularnewline
93 & 0.394297359910335 & 0.78859471982067 & 0.605702640089665 \tabularnewline
94 & 0.35064534414505 & 0.701290688290099 & 0.64935465585495 \tabularnewline
95 & 0.378068524372549 & 0.756137048745099 & 0.621931475627451 \tabularnewline
96 & 0.341359749677388 & 0.682719499354776 & 0.658640250322612 \tabularnewline
97 & 0.29966919741993 & 0.59933839483986 & 0.70033080258007 \tabularnewline
98 & 0.291950190999364 & 0.583900381998727 & 0.708049809000636 \tabularnewline
99 & 0.251388132800224 & 0.502776265600449 & 0.748611867199776 \tabularnewline
100 & 0.217981311479494 & 0.435962622958987 & 0.782018688520506 \tabularnewline
101 & 0.193027105973421 & 0.386054211946842 & 0.806972894026579 \tabularnewline
102 & 0.175461348244361 & 0.350922696488721 & 0.824538651755639 \tabularnewline
103 & 0.223876405506181 & 0.447752811012363 & 0.776123594493819 \tabularnewline
104 & 0.189525057074798 & 0.379050114149596 & 0.810474942925202 \tabularnewline
105 & 0.184290441808544 & 0.368580883617089 & 0.815709558191456 \tabularnewline
106 & 0.193699111340632 & 0.387398222681263 & 0.806300888659368 \tabularnewline
107 & 0.17583504441996 & 0.351670088839921 & 0.82416495558004 \tabularnewline
108 & 0.15534603702485 & 0.310692074049701 & 0.844653962975149 \tabularnewline
109 & 0.154036730364163 & 0.308073460728326 & 0.845963269635837 \tabularnewline
110 & 0.146033025765609 & 0.292066051531219 & 0.85396697423439 \tabularnewline
111 & 0.133381394220933 & 0.266762788441866 & 0.866618605779067 \tabularnewline
112 & 0.114265837877353 & 0.228531675754706 & 0.885734162122647 \tabularnewline
113 & 0.158760632687342 & 0.317521265374684 & 0.841239367312658 \tabularnewline
114 & 0.1391556007143 & 0.2783112014286 & 0.8608443992857 \tabularnewline
115 & 0.153644879588015 & 0.30728975917603 & 0.846355120411985 \tabularnewline
116 & 0.163641246507276 & 0.327282493014551 & 0.836358753492724 \tabularnewline
117 & 0.136383959391472 & 0.272767918782944 & 0.863616040608528 \tabularnewline
118 & 0.138774920657501 & 0.277549841315001 & 0.861225079342499 \tabularnewline
119 & 0.123340415690563 & 0.246680831381125 & 0.876659584309437 \tabularnewline
120 & 0.136209974003957 & 0.272419948007913 & 0.863790025996043 \tabularnewline
121 & 0.109931935752434 & 0.219863871504868 & 0.890068064247566 \tabularnewline
122 & 0.0969042452242377 & 0.193808490448475 & 0.903095754775762 \tabularnewline
123 & 0.0905393264129781 & 0.181078652825956 & 0.909460673587022 \tabularnewline
124 & 0.0696536287240431 & 0.139307257448086 & 0.930346371275957 \tabularnewline
125 & 0.0525166219270623 & 0.105033243854125 & 0.947483378072938 \tabularnewline
126 & 0.0393354989380034 & 0.0786709978760069 & 0.960664501061997 \tabularnewline
127 & 0.0283726670573625 & 0.0567453341147249 & 0.971627332942637 \tabularnewline
128 & 0.0297095248165116 & 0.0594190496330233 & 0.970290475183488 \tabularnewline
129 & 0.0318328178982139 & 0.0636656357964278 & 0.968167182101786 \tabularnewline
130 & 0.0300158198967637 & 0.0600316397935274 & 0.969984180103236 \tabularnewline
131 & 0.0363441981578249 & 0.0726883963156499 & 0.963655801842175 \tabularnewline
132 & 0.0369366502053276 & 0.0738733004106551 & 0.963063349794672 \tabularnewline
133 & 0.0929372663822898 & 0.18587453276458 & 0.90706273361771 \tabularnewline
134 & 0.0948493524097268 & 0.189698704819454 & 0.905150647590273 \tabularnewline
135 & 0.0745619984663059 & 0.149123996932612 & 0.925438001533694 \tabularnewline
136 & 0.0576768116985591 & 0.115353623397118 & 0.942323188301441 \tabularnewline
137 & 0.0410350971602939 & 0.0820701943205877 & 0.958964902839706 \tabularnewline
138 & 0.0455718876833694 & 0.0911437753667388 & 0.954428112316631 \tabularnewline
139 & 0.0403823523034123 & 0.0807647046068246 & 0.959617647696588 \tabularnewline
140 & 0.0266187253817034 & 0.0532374507634068 & 0.973381274618297 \tabularnewline
141 & 0.542580593827633 & 0.914838812344735 & 0.457419406172367 \tabularnewline
142 & 0.483296179893777 & 0.966592359787555 & 0.516703820106223 \tabularnewline
143 & 0.404754959813083 & 0.809509919626165 & 0.595245040186917 \tabularnewline
144 & 0.312693354208237 & 0.625386708416475 & 0.687306645791763 \tabularnewline
145 & 0.22969430083664 & 0.45938860167328 & 0.77030569916336 \tabularnewline
146 & 0.367394481404576 & 0.734788962809153 & 0.632605518595424 \tabularnewline
147 & 0.292070487361606 & 0.584140974723212 & 0.707929512638394 \tabularnewline
148 & 0.449919221267547 & 0.899838442535093 & 0.550080778732453 \tabularnewline
149 & 0.462488795485666 & 0.924977590971331 & 0.537511204514334 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198849&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]0.742244972598117[/C][C]0.515510054803765[/C][C]0.257755027401883[/C][/ROW]
[ROW][C]14[/C][C]0.718023164195827[/C][C]0.563953671608347[/C][C]0.281976835804173[/C][/ROW]
[ROW][C]15[/C][C]0.597396867467301[/C][C]0.805206265065397[/C][C]0.402603132532699[/C][/ROW]
[ROW][C]16[/C][C]0.484857109864061[/C][C]0.969714219728123[/C][C]0.515142890135939[/C][/ROW]
[ROW][C]17[/C][C]0.406532304636361[/C][C]0.813064609272721[/C][C]0.593467695363639[/C][/ROW]
[ROW][C]18[/C][C]0.564498379568644[/C][C]0.871003240862711[/C][C]0.435501620431356[/C][/ROW]
[ROW][C]19[/C][C]0.479492433267267[/C][C]0.958984866534535[/C][C]0.520507566732733[/C][/ROW]
[ROW][C]20[/C][C]0.3908162340503[/C][C]0.781632468100599[/C][C]0.6091837659497[/C][/ROW]
[ROW][C]21[/C][C]0.31969151963836[/C][C]0.639383039276719[/C][C]0.68030848036164[/C][/ROW]
[ROW][C]22[/C][C]0.312330886427454[/C][C]0.624661772854907[/C][C]0.687669113572546[/C][/ROW]
[ROW][C]23[/C][C]0.444089764887617[/C][C]0.888179529775233[/C][C]0.555910235112383[/C][/ROW]
[ROW][C]24[/C][C]0.518874357470394[/C][C]0.962251285059212[/C][C]0.481125642529606[/C][/ROW]
[ROW][C]25[/C][C]0.46518800432219[/C][C]0.930376008644381[/C][C]0.53481199567781[/C][/ROW]
[ROW][C]26[/C][C]0.396457092254608[/C][C]0.792914184509216[/C][C]0.603542907745392[/C][/ROW]
[ROW][C]27[/C][C]0.368253161710331[/C][C]0.736506323420663[/C][C]0.631746838289669[/C][/ROW]
[ROW][C]28[/C][C]0.473655997954659[/C][C]0.947311995909319[/C][C]0.526344002045341[/C][/ROW]
[ROW][C]29[/C][C]0.416386813461514[/C][C]0.832773626923029[/C][C]0.583613186538486[/C][/ROW]
[ROW][C]30[/C][C]0.528002408307841[/C][C]0.943995183384319[/C][C]0.471997591692159[/C][/ROW]
[ROW][C]31[/C][C]0.476957725439178[/C][C]0.953915450878356[/C][C]0.523042274560822[/C][/ROW]
[ROW][C]32[/C][C]0.439567764193812[/C][C]0.879135528387624[/C][C]0.560432235806188[/C][/ROW]
[ROW][C]33[/C][C]0.427271429421879[/C][C]0.854542858843758[/C][C]0.572728570578121[/C][/ROW]
[ROW][C]34[/C][C]0.396269557964855[/C][C]0.79253911592971[/C][C]0.603730442035145[/C][/ROW]
[ROW][C]35[/C][C]0.345000241780473[/C][C]0.690000483560946[/C][C]0.654999758219527[/C][/ROW]
[ROW][C]36[/C][C]0.880091284565451[/C][C]0.239817430869097[/C][C]0.119908715434549[/C][/ROW]
[ROW][C]37[/C][C]0.85522931794077[/C][C]0.28954136411846[/C][C]0.14477068205923[/C][/ROW]
[ROW][C]38[/C][C]0.836769588745505[/C][C]0.326460822508989[/C][C]0.163230411254495[/C][/ROW]
[ROW][C]39[/C][C]0.866533858016885[/C][C]0.266932283966229[/C][C]0.133466141983115[/C][/ROW]
[ROW][C]40[/C][C]0.856665691975821[/C][C]0.286668616048358[/C][C]0.143334308024179[/C][/ROW]
[ROW][C]41[/C][C]0.832887582708782[/C][C]0.334224834582437[/C][C]0.167112417291218[/C][/ROW]
[ROW][C]42[/C][C]0.804511176704815[/C][C]0.390977646590369[/C][C]0.195488823295185[/C][/ROW]
[ROW][C]43[/C][C]0.838627042420241[/C][C]0.322745915159519[/C][C]0.161372957579759[/C][/ROW]
[ROW][C]44[/C][C]0.803408472628204[/C][C]0.393183054743591[/C][C]0.196591527371796[/C][/ROW]
[ROW][C]45[/C][C]0.775967059022196[/C][C]0.448065881955607[/C][C]0.224032940977804[/C][/ROW]
[ROW][C]46[/C][C]0.893001949630493[/C][C]0.213996100739013[/C][C]0.106998050369507[/C][/ROW]
[ROW][C]47[/C][C]0.911129639370006[/C][C]0.177740721259988[/C][C]0.0888703606299939[/C][/ROW]
[ROW][C]48[/C][C]0.891152335134734[/C][C]0.217695329730533[/C][C]0.108847664865266[/C][/ROW]
[ROW][C]49[/C][C]0.890318176329991[/C][C]0.219363647340018[/C][C]0.109681823670009[/C][/ROW]
[ROW][C]50[/C][C]0.879935934325318[/C][C]0.240128131349364[/C][C]0.120064065674682[/C][/ROW]
[ROW][C]51[/C][C]0.853202847488953[/C][C]0.293594305022094[/C][C]0.146797152511047[/C][/ROW]
[ROW][C]52[/C][C]0.8243283455864[/C][C]0.3513433088272[/C][C]0.1756716544136[/C][/ROW]
[ROW][C]53[/C][C]0.827758766962744[/C][C]0.344482466074512[/C][C]0.172241233037256[/C][/ROW]
[ROW][C]54[/C][C]0.803039580133344[/C][C]0.393920839733313[/C][C]0.196960419866656[/C][/ROW]
[ROW][C]55[/C][C]0.815287223212898[/C][C]0.369425553574204[/C][C]0.184712776787102[/C][/ROW]
[ROW][C]56[/C][C]0.796823237546759[/C][C]0.406353524906483[/C][C]0.203176762453241[/C][/ROW]
[ROW][C]57[/C][C]0.760784124314634[/C][C]0.478431751370731[/C][C]0.239215875685366[/C][/ROW]
[ROW][C]58[/C][C]0.750113285856097[/C][C]0.499773428287806[/C][C]0.249886714143903[/C][/ROW]
[ROW][C]59[/C][C]0.716525224112208[/C][C]0.566949551775583[/C][C]0.283474775887792[/C][/ROW]
[ROW][C]60[/C][C]0.734014518199158[/C][C]0.531970963601685[/C][C]0.265985481800842[/C][/ROW]
[ROW][C]61[/C][C]0.70400687839151[/C][C]0.59198624321698[/C][C]0.29599312160849[/C][/ROW]
[ROW][C]62[/C][C]0.671535085117373[/C][C]0.656929829765253[/C][C]0.328464914882627[/C][/ROW]
[ROW][C]63[/C][C]0.634386926738854[/C][C]0.731226146522291[/C][C]0.365613073261146[/C][/ROW]
[ROW][C]64[/C][C]0.589148456014892[/C][C]0.821703087970216[/C][C]0.410851543985108[/C][/ROW]
[ROW][C]65[/C][C]0.544793661100745[/C][C]0.91041267779851[/C][C]0.455206338899255[/C][/ROW]
[ROW][C]66[/C][C]0.500521020042518[/C][C]0.998957959914965[/C][C]0.499478979957482[/C][/ROW]
[ROW][C]67[/C][C]0.479694589115464[/C][C]0.959389178230928[/C][C]0.520305410884536[/C][/ROW]
[ROW][C]68[/C][C]0.550116436089333[/C][C]0.899767127821334[/C][C]0.449883563910667[/C][/ROW]
[ROW][C]69[/C][C]0.729894839407498[/C][C]0.540210321185003[/C][C]0.270105160592502[/C][/ROW]
[ROW][C]70[/C][C]0.690068750028275[/C][C]0.619862499943449[/C][C]0.309931249971725[/C][/ROW]
[ROW][C]71[/C][C]0.825575996843999[/C][C]0.348848006312003[/C][C]0.174424003156001[/C][/ROW]
[ROW][C]72[/C][C]0.795045148064709[/C][C]0.409909703870581[/C][C]0.204954851935291[/C][/ROW]
[ROW][C]73[/C][C]0.783295815882427[/C][C]0.433408368235145[/C][C]0.216704184117572[/C][/ROW]
[ROW][C]74[/C][C]0.762268149481301[/C][C]0.475463701037399[/C][C]0.237731850518699[/C][/ROW]
[ROW][C]75[/C][C]0.724916602086113[/C][C]0.550166795827774[/C][C]0.275083397913887[/C][/ROW]
[ROW][C]76[/C][C]0.752642040538574[/C][C]0.494715918922853[/C][C]0.247357959461426[/C][/ROW]
[ROW][C]77[/C][C]0.715445842234085[/C][C]0.56910831553183[/C][C]0.284554157765915[/C][/ROW]
[ROW][C]78[/C][C]0.692028063449737[/C][C]0.615943873100526[/C][C]0.307971936550263[/C][/ROW]
[ROW][C]79[/C][C]0.708607743132709[/C][C]0.582784513734582[/C][C]0.291392256867291[/C][/ROW]
[ROW][C]80[/C][C]0.666541845516913[/C][C]0.666916308966174[/C][C]0.333458154483087[/C][/ROW]
[ROW][C]81[/C][C]0.626005481169325[/C][C]0.747989037661351[/C][C]0.373994518830675[/C][/ROW]
[ROW][C]82[/C][C]0.763155937843792[/C][C]0.473688124312417[/C][C]0.236844062156208[/C][/ROW]
[ROW][C]83[/C][C]0.726254047142595[/C][C]0.54749190571481[/C][C]0.273745952857405[/C][/ROW]
[ROW][C]84[/C][C]0.698080167940698[/C][C]0.603839664118603[/C][C]0.301919832059302[/C][/ROW]
[ROW][C]85[/C][C]0.655918059018102[/C][C]0.688163881963796[/C][C]0.344081940981898[/C][/ROW]
[ROW][C]86[/C][C]0.638478544061943[/C][C]0.723042911876113[/C][C]0.361521455938057[/C][/ROW]
[ROW][C]87[/C][C]0.594051808906247[/C][C]0.811896382187506[/C][C]0.405948191093753[/C][/ROW]
[ROW][C]88[/C][C]0.551268483413618[/C][C]0.897463033172765[/C][C]0.448731516586382[/C][/ROW]
[ROW][C]89[/C][C]0.528719309592612[/C][C]0.942561380814776[/C][C]0.471280690407388[/C][/ROW]
[ROW][C]90[/C][C]0.488659205075739[/C][C]0.977318410151479[/C][C]0.511340794924261[/C][/ROW]
[ROW][C]91[/C][C]0.478316320600976[/C][C]0.956632641201952[/C][C]0.521683679399024[/C][/ROW]
[ROW][C]92[/C][C]0.436414300533975[/C][C]0.87282860106795[/C][C]0.563585699466025[/C][/ROW]
[ROW][C]93[/C][C]0.394297359910335[/C][C]0.78859471982067[/C][C]0.605702640089665[/C][/ROW]
[ROW][C]94[/C][C]0.35064534414505[/C][C]0.701290688290099[/C][C]0.64935465585495[/C][/ROW]
[ROW][C]95[/C][C]0.378068524372549[/C][C]0.756137048745099[/C][C]0.621931475627451[/C][/ROW]
[ROW][C]96[/C][C]0.341359749677388[/C][C]0.682719499354776[/C][C]0.658640250322612[/C][/ROW]
[ROW][C]97[/C][C]0.29966919741993[/C][C]0.59933839483986[/C][C]0.70033080258007[/C][/ROW]
[ROW][C]98[/C][C]0.291950190999364[/C][C]0.583900381998727[/C][C]0.708049809000636[/C][/ROW]
[ROW][C]99[/C][C]0.251388132800224[/C][C]0.502776265600449[/C][C]0.748611867199776[/C][/ROW]
[ROW][C]100[/C][C]0.217981311479494[/C][C]0.435962622958987[/C][C]0.782018688520506[/C][/ROW]
[ROW][C]101[/C][C]0.193027105973421[/C][C]0.386054211946842[/C][C]0.806972894026579[/C][/ROW]
[ROW][C]102[/C][C]0.175461348244361[/C][C]0.350922696488721[/C][C]0.824538651755639[/C][/ROW]
[ROW][C]103[/C][C]0.223876405506181[/C][C]0.447752811012363[/C][C]0.776123594493819[/C][/ROW]
[ROW][C]104[/C][C]0.189525057074798[/C][C]0.379050114149596[/C][C]0.810474942925202[/C][/ROW]
[ROW][C]105[/C][C]0.184290441808544[/C][C]0.368580883617089[/C][C]0.815709558191456[/C][/ROW]
[ROW][C]106[/C][C]0.193699111340632[/C][C]0.387398222681263[/C][C]0.806300888659368[/C][/ROW]
[ROW][C]107[/C][C]0.17583504441996[/C][C]0.351670088839921[/C][C]0.82416495558004[/C][/ROW]
[ROW][C]108[/C][C]0.15534603702485[/C][C]0.310692074049701[/C][C]0.844653962975149[/C][/ROW]
[ROW][C]109[/C][C]0.154036730364163[/C][C]0.308073460728326[/C][C]0.845963269635837[/C][/ROW]
[ROW][C]110[/C][C]0.146033025765609[/C][C]0.292066051531219[/C][C]0.85396697423439[/C][/ROW]
[ROW][C]111[/C][C]0.133381394220933[/C][C]0.266762788441866[/C][C]0.866618605779067[/C][/ROW]
[ROW][C]112[/C][C]0.114265837877353[/C][C]0.228531675754706[/C][C]0.885734162122647[/C][/ROW]
[ROW][C]113[/C][C]0.158760632687342[/C][C]0.317521265374684[/C][C]0.841239367312658[/C][/ROW]
[ROW][C]114[/C][C]0.1391556007143[/C][C]0.2783112014286[/C][C]0.8608443992857[/C][/ROW]
[ROW][C]115[/C][C]0.153644879588015[/C][C]0.30728975917603[/C][C]0.846355120411985[/C][/ROW]
[ROW][C]116[/C][C]0.163641246507276[/C][C]0.327282493014551[/C][C]0.836358753492724[/C][/ROW]
[ROW][C]117[/C][C]0.136383959391472[/C][C]0.272767918782944[/C][C]0.863616040608528[/C][/ROW]
[ROW][C]118[/C][C]0.138774920657501[/C][C]0.277549841315001[/C][C]0.861225079342499[/C][/ROW]
[ROW][C]119[/C][C]0.123340415690563[/C][C]0.246680831381125[/C][C]0.876659584309437[/C][/ROW]
[ROW][C]120[/C][C]0.136209974003957[/C][C]0.272419948007913[/C][C]0.863790025996043[/C][/ROW]
[ROW][C]121[/C][C]0.109931935752434[/C][C]0.219863871504868[/C][C]0.890068064247566[/C][/ROW]
[ROW][C]122[/C][C]0.0969042452242377[/C][C]0.193808490448475[/C][C]0.903095754775762[/C][/ROW]
[ROW][C]123[/C][C]0.0905393264129781[/C][C]0.181078652825956[/C][C]0.909460673587022[/C][/ROW]
[ROW][C]124[/C][C]0.0696536287240431[/C][C]0.139307257448086[/C][C]0.930346371275957[/C][/ROW]
[ROW][C]125[/C][C]0.0525166219270623[/C][C]0.105033243854125[/C][C]0.947483378072938[/C][/ROW]
[ROW][C]126[/C][C]0.0393354989380034[/C][C]0.0786709978760069[/C][C]0.960664501061997[/C][/ROW]
[ROW][C]127[/C][C]0.0283726670573625[/C][C]0.0567453341147249[/C][C]0.971627332942637[/C][/ROW]
[ROW][C]128[/C][C]0.0297095248165116[/C][C]0.0594190496330233[/C][C]0.970290475183488[/C][/ROW]
[ROW][C]129[/C][C]0.0318328178982139[/C][C]0.0636656357964278[/C][C]0.968167182101786[/C][/ROW]
[ROW][C]130[/C][C]0.0300158198967637[/C][C]0.0600316397935274[/C][C]0.969984180103236[/C][/ROW]
[ROW][C]131[/C][C]0.0363441981578249[/C][C]0.0726883963156499[/C][C]0.963655801842175[/C][/ROW]
[ROW][C]132[/C][C]0.0369366502053276[/C][C]0.0738733004106551[/C][C]0.963063349794672[/C][/ROW]
[ROW][C]133[/C][C]0.0929372663822898[/C][C]0.18587453276458[/C][C]0.90706273361771[/C][/ROW]
[ROW][C]134[/C][C]0.0948493524097268[/C][C]0.189698704819454[/C][C]0.905150647590273[/C][/ROW]
[ROW][C]135[/C][C]0.0745619984663059[/C][C]0.149123996932612[/C][C]0.925438001533694[/C][/ROW]
[ROW][C]136[/C][C]0.0576768116985591[/C][C]0.115353623397118[/C][C]0.942323188301441[/C][/ROW]
[ROW][C]137[/C][C]0.0410350971602939[/C][C]0.0820701943205877[/C][C]0.958964902839706[/C][/ROW]
[ROW][C]138[/C][C]0.0455718876833694[/C][C]0.0911437753667388[/C][C]0.954428112316631[/C][/ROW]
[ROW][C]139[/C][C]0.0403823523034123[/C][C]0.0807647046068246[/C][C]0.959617647696588[/C][/ROW]
[ROW][C]140[/C][C]0.0266187253817034[/C][C]0.0532374507634068[/C][C]0.973381274618297[/C][/ROW]
[ROW][C]141[/C][C]0.542580593827633[/C][C]0.914838812344735[/C][C]0.457419406172367[/C][/ROW]
[ROW][C]142[/C][C]0.483296179893777[/C][C]0.966592359787555[/C][C]0.516703820106223[/C][/ROW]
[ROW][C]143[/C][C]0.404754959813083[/C][C]0.809509919626165[/C][C]0.595245040186917[/C][/ROW]
[ROW][C]144[/C][C]0.312693354208237[/C][C]0.625386708416475[/C][C]0.687306645791763[/C][/ROW]
[ROW][C]145[/C][C]0.22969430083664[/C][C]0.45938860167328[/C][C]0.77030569916336[/C][/ROW]
[ROW][C]146[/C][C]0.367394481404576[/C][C]0.734788962809153[/C][C]0.632605518595424[/C][/ROW]
[ROW][C]147[/C][C]0.292070487361606[/C][C]0.584140974723212[/C][C]0.707929512638394[/C][/ROW]
[ROW][C]148[/C][C]0.449919221267547[/C][C]0.899838442535093[/C][C]0.550080778732453[/C][/ROW]
[ROW][C]149[/C][C]0.462488795485666[/C][C]0.924977590971331[/C][C]0.537511204514334[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198849&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198849&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
130.7422449725981170.5155100548037650.257755027401883
140.7180231641958270.5639536716083470.281976835804173
150.5973968674673010.8052062650653970.402603132532699
160.4848571098640610.9697142197281230.515142890135939
170.4065323046363610.8130646092727210.593467695363639
180.5644983795686440.8710032408627110.435501620431356
190.4794924332672670.9589848665345350.520507566732733
200.39081623405030.7816324681005990.6091837659497
210.319691519638360.6393830392767190.68030848036164
220.3123308864274540.6246617728549070.687669113572546
230.4440897648876170.8881795297752330.555910235112383
240.5188743574703940.9622512850592120.481125642529606
250.465188004322190.9303760086443810.53481199567781
260.3964570922546080.7929141845092160.603542907745392
270.3682531617103310.7365063234206630.631746838289669
280.4736559979546590.9473119959093190.526344002045341
290.4163868134615140.8327736269230290.583613186538486
300.5280024083078410.9439951833843190.471997591692159
310.4769577254391780.9539154508783560.523042274560822
320.4395677641938120.8791355283876240.560432235806188
330.4272714294218790.8545428588437580.572728570578121
340.3962695579648550.792539115929710.603730442035145
350.3450002417804730.6900004835609460.654999758219527
360.8800912845654510.2398174308690970.119908715434549
370.855229317940770.289541364118460.14477068205923
380.8367695887455050.3264608225089890.163230411254495
390.8665338580168850.2669322839662290.133466141983115
400.8566656919758210.2866686160483580.143334308024179
410.8328875827087820.3342248345824370.167112417291218
420.8045111767048150.3909776465903690.195488823295185
430.8386270424202410.3227459151595190.161372957579759
440.8034084726282040.3931830547435910.196591527371796
450.7759670590221960.4480658819556070.224032940977804
460.8930019496304930.2139961007390130.106998050369507
470.9111296393700060.1777407212599880.0888703606299939
480.8911523351347340.2176953297305330.108847664865266
490.8903181763299910.2193636473400180.109681823670009
500.8799359343253180.2401281313493640.120064065674682
510.8532028474889530.2935943050220940.146797152511047
520.82432834558640.35134330882720.1756716544136
530.8277587669627440.3444824660745120.172241233037256
540.8030395801333440.3939208397333130.196960419866656
550.8152872232128980.3694255535742040.184712776787102
560.7968232375467590.4063535249064830.203176762453241
570.7607841243146340.4784317513707310.239215875685366
580.7501132858560970.4997734282878060.249886714143903
590.7165252241122080.5669495517755830.283474775887792
600.7340145181991580.5319709636016850.265985481800842
610.704006878391510.591986243216980.29599312160849
620.6715350851173730.6569298297652530.328464914882627
630.6343869267388540.7312261465222910.365613073261146
640.5891484560148920.8217030879702160.410851543985108
650.5447936611007450.910412677798510.455206338899255
660.5005210200425180.9989579599149650.499478979957482
670.4796945891154640.9593891782309280.520305410884536
680.5501164360893330.8997671278213340.449883563910667
690.7298948394074980.5402103211850030.270105160592502
700.6900687500282750.6198624999434490.309931249971725
710.8255759968439990.3488480063120030.174424003156001
720.7950451480647090.4099097038705810.204954851935291
730.7832958158824270.4334083682351450.216704184117572
740.7622681494813010.4754637010373990.237731850518699
750.7249166020861130.5501667958277740.275083397913887
760.7526420405385740.4947159189228530.247357959461426
770.7154458422340850.569108315531830.284554157765915
780.6920280634497370.6159438731005260.307971936550263
790.7086077431327090.5827845137345820.291392256867291
800.6665418455169130.6669163089661740.333458154483087
810.6260054811693250.7479890376613510.373994518830675
820.7631559378437920.4736881243124170.236844062156208
830.7262540471425950.547491905714810.273745952857405
840.6980801679406980.6038396641186030.301919832059302
850.6559180590181020.6881638819637960.344081940981898
860.6384785440619430.7230429118761130.361521455938057
870.5940518089062470.8118963821875060.405948191093753
880.5512684834136180.8974630331727650.448731516586382
890.5287193095926120.9425613808147760.471280690407388
900.4886592050757390.9773184101514790.511340794924261
910.4783163206009760.9566326412019520.521683679399024
920.4364143005339750.872828601067950.563585699466025
930.3942973599103350.788594719820670.605702640089665
940.350645344145050.7012906882900990.64935465585495
950.3780685243725490.7561370487450990.621931475627451
960.3413597496773880.6827194993547760.658640250322612
970.299669197419930.599338394839860.70033080258007
980.2919501909993640.5839003819987270.708049809000636
990.2513881328002240.5027762656004490.748611867199776
1000.2179813114794940.4359626229589870.782018688520506
1010.1930271059734210.3860542119468420.806972894026579
1020.1754613482443610.3509226964887210.824538651755639
1030.2238764055061810.4477528110123630.776123594493819
1040.1895250570747980.3790501141495960.810474942925202
1050.1842904418085440.3685808836170890.815709558191456
1060.1936991113406320.3873982226812630.806300888659368
1070.175835044419960.3516700888399210.82416495558004
1080.155346037024850.3106920740497010.844653962975149
1090.1540367303641630.3080734607283260.845963269635837
1100.1460330257656090.2920660515312190.85396697423439
1110.1333813942209330.2667627884418660.866618605779067
1120.1142658378773530.2285316757547060.885734162122647
1130.1587606326873420.3175212653746840.841239367312658
1140.13915560071430.27831120142860.8608443992857
1150.1536448795880150.307289759176030.846355120411985
1160.1636412465072760.3272824930145510.836358753492724
1170.1363839593914720.2727679187829440.863616040608528
1180.1387749206575010.2775498413150010.861225079342499
1190.1233404156905630.2466808313811250.876659584309437
1200.1362099740039570.2724199480079130.863790025996043
1210.1099319357524340.2198638715048680.890068064247566
1220.09690424522423770.1938084904484750.903095754775762
1230.09053932641297810.1810786528259560.909460673587022
1240.06965362872404310.1393072574480860.930346371275957
1250.05251662192706230.1050332438541250.947483378072938
1260.03933549893800340.07867099787600690.960664501061997
1270.02837266705736250.05674533411472490.971627332942637
1280.02970952481651160.05941904963302330.970290475183488
1290.03183281789821390.06366563579642780.968167182101786
1300.03001581989676370.06003163979352740.969984180103236
1310.03634419815782490.07268839631564990.963655801842175
1320.03693665020532760.07387330041065510.963063349794672
1330.09293726638228980.185874532764580.90706273361771
1340.09484935240972680.1896987048194540.905150647590273
1350.07456199846630590.1491239969326120.925438001533694
1360.05767681169855910.1153536233971180.942323188301441
1370.04103509716029390.08207019432058770.958964902839706
1380.04557188768336940.09114377536673880.954428112316631
1390.04038235230341230.08076470460682460.959617647696588
1400.02661872538170340.05323745076340680.973381274618297
1410.5425805938276330.9148388123447350.457419406172367
1420.4832961798937770.9665923597875550.516703820106223
1430.4047549598130830.8095099196261650.595245040186917
1440.3126933542082370.6253867084164750.687306645791763
1450.229694300836640.459388601673280.77030569916336
1460.3673944814045760.7347889628091530.632605518595424
1470.2920704873616060.5841409747232120.707929512638394
1480.4499192212675470.8998384425350930.550080778732453
1490.4624887954856660.9249775909713310.537511204514334






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 level110.0802919708029197OK

\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 & 11 & 0.0802919708029197 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198849&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]11[/C][C]0.0802919708029197[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198849&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198849&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 level110.0802919708029197OK


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 ;
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')
}