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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 computationTue, 23 Nov 2010 16:09:53 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t1290528500xlkxgtv5sduf0wo.htm/, Retrieved Sat, 20 Apr 2024 06:31:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99364, Retrieved Sat, 20 Apr 2024 06:31:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact154

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [Multiple linear r...] [2010-11-19 16:51:37] [97ad38b1c3b35a5feca8b85f7bc7b3ff]
-   PD      [Multiple Regression] [Workshop 7 Link 1] [2010-11-23 16:09:53] [514029464b0621595fe21c9fa38c7009] [Current]
-   P         [Multiple Regression] [Trend wel signifi...] [2010-11-24 17:05:14] [97ad38b1c3b35a5feca8b85f7bc7b3ff]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
14	11	12	11	12	6	6	53	6
18	12	12	8	13	5	3	86	6
11	15	10	12	16	6	0	66	13
12	10	10	10	11	5	4	67	8
16	12	9	7	12	6	7	76	7
18	11	6	6	9	4	0	78	9
14	5	15	8	12	3	3	53	5
14	16	11	16	16	7	10	80	8
15	11	11	8	12	6	3	74	9
15	15	13	16	18	8	6	76	11
17	12	12	7	12	3	1	79	8
19	9	12	11	11	4	3	54	11
10	11	5	16	14	6	5	67	12
18	15	11	16	11	5	6	87	8
14	12	13	12	12	6	6	58	7
14	16	11	13	14	7	7	75	9
17	14	9	19	12	6	2	88	12
14	11	14	7	13	6	2	64	20
16	10	12	8	11	4	0	57	7
18	7	14	12	12	4	6	66	8
14	11	12	13	11	4	1	54	8
12	10	12	11	12	6	5	56	16
17	11	8	8	13	4	4	86	10
9	16	9	16	16	6	7	80	6
16	14	11	15	16	6	7	76	8
14	12	7	11	15	5	2	69	9
11	12	12	12	14	5	2	67	9
16	11	9	7	13	2	3	80	11
13	6	7	9	11	4	3	54	12
17	14	12	15	13	6	3	71	8
15	9	9	6	12	5	8	84	7
14	15	11	14	15	7	7	74	8
16	12	10	14	13	7	6	71	9
9	12	12	7	11	4	6	63	4
15	9	11	15	15	7	5	71	8
17	13	8	14	14	5	10	76	8
13	15	11	17	16	6	5	69	8
15	11	8	14	15	5	5	74	6
16	10	12	5	13	6	5	75	8
16	13	9	14	14	6	2	54	4
12	16	12	8	14	4	6	69	14
11	13	10	8	8	4	4	68	10
15	14	12	13	15	6	2	75	9
17	14	12	14	15	7	8	74	6
13	16	11	16	15	6	10	75	8
16	9	12	11	11	4	5	72	11
14	8	10	10	6	4	10	67	8
11	8	11	10	15	2	7	63	8
12	12	12	10	15	6	6	62	10
12	10	7	8	9	5	7	63	8
15	16	11	14	15	8	4	76	10
16	13	11	14	13	6	4	74	7
15	11	10	12	14	5	3	67	8
12	14	12	13	13	6	4	73	7
12	15	9	5	11	3	3	70	9
8	8	11	10	12	4	3	53	5
13	9	15	6	8	4	0	77	7
11	17	11	15	14	5	15	77	7
14	9	11	12	13	5	0	52	7
15	13	12	16	16	6	4	54	9
10	6	9	15	11	6	5	80	5
11	13	11	12	13	7	6	66	8
12	8	12	8	13	4	3	73	8
15	12	11	14	13	5	9	63	8
15	13	13	14	13	3	5	69	9
14	14	13	13	13	5	0	67	6
16	11	9	12	12	4	2	54	8
15	15	11	15	15	8	0	81	6
15	7	12	8	12	3	0	69	4
13	16	12	16	14	6	10	84	6
17	16	11	14	15	6	1	70	4
13	14	12	13	13	5	6	69	12
15	11	12	15	12	6	11	77	6
13	13	12	7	12	5	3	54	11
15	13	12	5	12	3	9	79	8
16	7	12	7	12	4	2	30	10
15	15	12	13	13	6	8	71	10
16	11	6	14	17	6	8	73	4
15	15	11	14	13	5	9	72	8
14	13	12	13	13	5	9	77	9
15	11	11	11	14	5	8	75	9
7	12	12	15	13	6	6	70	7
17	10	11	13	15	6	6	73	7
13	12	13	14	12	5	5	54	11
15	12	8	13	13	5	4	77	8
14	12	12	9	13	4	2	82	8
13	14	12	8	14	4	6	80	7
16	6	12	6	11	2	3	80	5
12	14	11	13	16	6	8	69	7
14	15	10	16	13	6	8	78	9
17	8	13	7	10	3	5	81	8
15	12	11	11	12	5	6	76	6
17	10	12	8	16	4	2	76	8
12	15	12	13	14	6	4	73	10
16	11	10	5	13	3	3	85	10
11	9	11	8	10	4	5	66	8
15	14	11	10	16	6	5	79	11
9	10	11	9	12	4	7	68	8
16	16	12	16	16	7	7	76	8
10	5	14	4	5	2	6	54	6
10	8	7	4	13	6	1	46	20
15	13	12	11	13	6	5	82	6
11	16	12	14	16	8	14	74	12
13	16	12	15	15	7	7	88	9
14	14	14	17	18	6	1	38	5
18	14	13	10	16	8	8	76	10
16	10	15	15	15	6	10	86	5
14	9	10	11	13	3	6	54	6
14	14	11	15	15	8	6	70	10
14	8	10	10	14	3	2	69	6
14	8	7	9	15	4	2	90	10
12	16	11	14	14	6	8	54	5
14	12	8	15	13	7	3	76	13
15	9	11	9	12	4	0	89	7
15	15	12	12	16	7	8	76	9
13	12	12	10	13	4	4	79	8
17	14	11	16	12	5	3	90	5
17	12	12	15	13	6	0	74	4
19	16	12	14	14	6	0	81	9
15	12	12	12	13	4	6	72	7
13	14	12	15	14	6	9	71	5
9	8	11	9	12	4	9	66	5
15	15	11	12	13	6	5	77	4
15	16	12	15	14	6	8	74	7
16	12	12	6	14	5	0	82	9
11	4	10	4	10	2	4	54	8
14	8	12	8	14	5	3	63	8
11	11	8	10	14	5	5	54	11
15	4	8	6	4	4	0	64	10
13	14	10	12	15	6	4	69	9
16	14	11	14	12	6	10	84	10
14	13	13	11	15	6	8	86	10
15	14	11	15	14	6	6	77	7
16	7	12	13	12	3	3	89	10
16	19	12	15	15	6	5	76	6
11	12	11	16	13	4	3	60	6
13	10	13	4	13	6	2	79	11
16	14	11	15	16	6	7	71	8
12	16	12	12	15	8	0	72	9
9	11	11	15	10	5	8	69	9
13	16	12	15	16	7	8	78	13
13	12	13	14	12	5	5	54	11
14	12	10	14	14	5	9	69	4
19	16	12	14	14	6	0	81	9
13	12	11	11	14	2	5	84	5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99364&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99364&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99364&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 7.38352351231346 -0.0306878871524814Popularity[t] + 0.124924906242383FindingFriends[t] + 0.0643967010784627KnowingPeople[t] + 0.0516317163962406Liked[t] + 0.0364039198046587Celebrity[t] -0.219430414198071WeightedSum[t] + 0.0779073064582762BelongingstoSports[t] -0.043675909023583ParentalCriticism[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  7.38352351231346 -0.0306878871524814Popularity[t] +  0.124924906242383FindingFriends[t] +  0.0643967010784627KnowingPeople[t] +  0.0516317163962406Liked[t] +  0.0364039198046587Celebrity[t] -0.219430414198071WeightedSum[t] +  0.0779073064582762BelongingstoSports[t] -0.043675909023583ParentalCriticism[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99364&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  7.38352351231346 -0.0306878871524814Popularity[t] +  0.124924906242383FindingFriends[t] +  0.0643967010784627KnowingPeople[t] +  0.0516317163962406Liked[t] +  0.0364039198046587Celebrity[t] -0.219430414198071WeightedSum[t] +  0.0779073064582762BelongingstoSports[t] -0.043675909023583ParentalCriticism[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99364&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99364&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 7.38352351231346 -0.0306878871524814Popularity[t] + 0.124924906242383FindingFriends[t] + 0.0643967010784627KnowingPeople[t] + 0.0516317163962406Liked[t] + 0.0364039198046587Celebrity[t] -0.219430414198071WeightedSum[t] + 0.0779073064582762BelongingstoSports[t] -0.043675909023583ParentalCriticism[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.383523512313462.1228563.47810.0006790.000339
Popularity-0.03068788715248140.090767-0.33810.7358110.367906
FindingFriends0.1249249062423830.1056561.18240.2391180.119559
KnowingPeople0.06439670107846270.0745850.86340.3894350.194717
Liked0.05163171639624060.1086470.47520.635390.317695
Celebrity0.03640391980465870.1877940.19390.8465820.423291
WeightedSum-0.2194304141980710.064196-3.41810.0008320.000416
BelongingstoSports0.07790730645827620.0183344.24944e-052e-05
ParentalCriticism-0.0436759090235830.075181-0.58090.5622420.281121

\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) & 7.38352351231346 & 2.122856 & 3.4781 & 0.000679 & 0.000339 \tabularnewline
Popularity & -0.0306878871524814 & 0.090767 & -0.3381 & 0.735811 & 0.367906 \tabularnewline
FindingFriends & 0.124924906242383 & 0.105656 & 1.1824 & 0.239118 & 0.119559 \tabularnewline
KnowingPeople & 0.0643967010784627 & 0.074585 & 0.8634 & 0.389435 & 0.194717 \tabularnewline
Liked & 0.0516317163962406 & 0.108647 & 0.4752 & 0.63539 & 0.317695 \tabularnewline
Celebrity & 0.0364039198046587 & 0.187794 & 0.1939 & 0.846582 & 0.423291 \tabularnewline
WeightedSum & -0.219430414198071 & 0.064196 & -3.4181 & 0.000832 & 0.000416 \tabularnewline
BelongingstoSports & 0.0779073064582762 & 0.018334 & 4.2494 & 4e-05 & 2e-05 \tabularnewline
ParentalCriticism & -0.043675909023583 & 0.075181 & -0.5809 & 0.562242 & 0.281121 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99364&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]7.38352351231346[/C][C]2.122856[/C][C]3.4781[/C][C]0.000679[/C][C]0.000339[/C][/ROW]
[ROW][C]Popularity[/C][C]-0.0306878871524814[/C][C]0.090767[/C][C]-0.3381[/C][C]0.735811[/C][C]0.367906[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.124924906242383[/C][C]0.105656[/C][C]1.1824[/C][C]0.239118[/C][C]0.119559[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.0643967010784627[/C][C]0.074585[/C][C]0.8634[/C][C]0.389435[/C][C]0.194717[/C][/ROW]
[ROW][C]Liked[/C][C]0.0516317163962406[/C][C]0.108647[/C][C]0.4752[/C][C]0.63539[/C][C]0.317695[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.0364039198046587[/C][C]0.187794[/C][C]0.1939[/C][C]0.846582[/C][C]0.423291[/C][/ROW]
[ROW][C]WeightedSum[/C][C]-0.219430414198071[/C][C]0.064196[/C][C]-3.4181[/C][C]0.000832[/C][C]0.000416[/C][/ROW]
[ROW][C]BelongingstoSports[/C][C]0.0779073064582762[/C][C]0.018334[/C][C]4.2494[/C][C]4e-05[/C][C]2e-05[/C][/ROW]
[ROW][C]ParentalCriticism[/C][C]-0.043675909023583[/C][C]0.075181[/C][C]-0.5809[/C][C]0.562242[/C][C]0.281121[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99364&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99364&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)7.383523512313462.1228563.47810.0006790.000339
Popularity-0.03068788715248140.090767-0.33810.7358110.367906
FindingFriends0.1249249062423830.1056561.18240.2391180.119559
KnowingPeople0.06439670107846270.0745850.86340.3894350.194717
Liked0.05163171639624060.1086470.47520.635390.317695
Celebrity0.03640391980465870.1877940.19390.8465820.423291
WeightedSum-0.2194304141980710.064196-3.41810.0008320.000416
BelongingstoSports0.07790730645827620.0183344.24944e-052e-05
ParentalCriticism-0.0436759090235830.075181-0.58090.5622420.281121






Multiple Linear Regression - Regression Statistics
Multiple R0.43021120445241
R-squared0.185081680436393
Adjusted R-squared0.137145308697358
F-TEST (value)3.86098642266822
F-TEST (DF numerator)8
F-TEST (DF denominator)136
p-value0.000398287647880635
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.20667764537581
Sum Squared Residuals662.241967361779

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.43021120445241 \tabularnewline
R-squared & 0.185081680436393 \tabularnewline
Adjusted R-squared & 0.137145308697358 \tabularnewline
F-TEST (value) & 3.86098642266822 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 136 \tabularnewline
p-value & 0.000398287647880635 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.20667764537581 \tabularnewline
Sum Squared Residuals & 662.241967361779 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99364&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.43021120445241[/C][/ROW]
[ROW][C]R-squared[/C][C]0.185081680436393[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.137145308697358[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.86098642266822[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]136[/C][/ROW]
[ROW][C]p-value[/C][C]0.000398287647880635[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.20667764537581[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]662.241967361779[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99364&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99364&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 R0.43021120445241
R-squared0.185081680436393
Adjusted R-squared0.137145308697358
F-TEST (value)3.86098642266822
F-TEST (DF numerator)8
F-TEST (DF denominator)136
p-value0.000398287647880635
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.20667764537581
Sum Squared Residuals662.241967361779






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11412.64187275894941.35812724105058
21815.66245492087042.33754507912956
31114.5638410704991-3.56384107049906
41213.7124897971026-1.7124897971026
51613.50758507407462.49241492592537
61814.47587424687933.52412575312067
71413.60034008955990.399659910440129
81414.0668465072606-0.0668465072605611
91514.48707470061890.512925299381107
101514.92211626338340.0778837366166308
111715.27977652892751.72022347107254
121913.09662798118345.90337201881662
131013.2407226039100-3.24072260390995
141815.18964077482272.81035922517727
151413.14636710238560.853632897614437
161413.99547177251190.00452822748806751
171716.03262991608300.967370083917037
181413.80900678501970.190993214980283
191613.93946678885892.06053321114112
201813.93150614742374.06849385257634
211413.73393416450220.266065835497767
221212.6889538894390-0.688953889438953
231714.76290521295632.23709478704367
24914.5262358356125-5.52623583561251
251614.37408367744351.6259163225565
261414.0982624030230-0.0982624030230206
271114.5798373060006-3.57983730600061
281614.45893624493581.54106375506424
291312.39159780024840.608402199751598
301714.83229855899812.16770144100193
311513.90267615211711.09732384788292
321414.1079566797044-0.107956679704424
331613.91386458792672.08613541207334
34913.0955833941074-4.09558339410744
351514.56161961271910.438380387280912
361713.16764154959903.83235845040098
371314.3656988756362-1.36569887563615
381514.30933831642120.69066168357881
391614.18385149426031.81614850573967
401613.54515658324292.45484341675712
411213.2228084442785-1.22280844427852
421113.2908891529060-2.29088915290596
431515.2941523206412-0.294152320641214
441714.13149087694842.86850912305162
451313.5892743387683-0.589274338768271
461614.06009866903621.93990133096379
471412.16272058443331.83727941556672
481113.0261851153936-2.02618511539361
491213.2281454419373-1.22814544193733
501212.1357377749987-0.135737774998725
511514.84042674982020.159573250179799
521614.73163225303001.26836774696996
531514.18492087549380.815079124506167
541214.6835652645832-2.68356526458321
551213.4488105346455-1.44881053464549
56813.1737741250945-5.17377412509448
571315.6193869725584-2.61938697255839
581112.5084925652862-1.50849256528621
591413.85294739438860.147052605611409
601513.49474776340541.50525223659462
611014.9930961614974-4.99309616149738
621113.5334475815918-2.53344758159184
631214.6486557476710-2.64865574767095
641512.72810786932292.27189213067711
651514.17595154156420.824048458435787
661415.2260399780871-1.22603997808714
671613.12696404689482.87303595310521
681516.3774731632291-1.37747316322914
691515.1126736514780-0.112673651477972
701314.4510851047861-1.45108510478607
711715.22052176819691.77947823180306
721313.6782917454314-0.678291745431385
731513.67208284727151.32791715272847
741312.80432526446050.195674735539498
751513.36485192603351.63514807396651
761611.34537963579384.65462036420625
771513.48831338065111.51168661934886
781613.55030912552822.44969087447176
791513.33720996598991.66279003401007
801413.80497456872660.195025431273388
811513.72787955231001.27212044768997
82714.2011516932741-7.20115169327412
831714.34579451134712.65420548865291
841312.97185413700850.0281458629915367
851514.47679081092350.523209189076501
861415.5108970724620-1.51089707246204
871314.4468959527896-1.44689595278961
881615.08154571969590.918454280304128
891213.5241846249042-1.52418462490415
901414.0206807256333-0.0206807256332775
911714.70228250711162.29771749288844
921514.24172409429020.75827590570977
931715.19532745612761.80467254387239
941214.5734813667562-2.57348136675622
951614.92468411014081.07531588985916
961113.3539358314833-2.35393583148329
971514.59365519275140.406344807248591
98913.2078618627222-4.20786186272216
991614.53843343026401.46156656973596
1001012.1959425992658-2.19594259926578
1011011.6505048974942-1.65050489749419
1021515.1108710025288-0.110871002528760
1031112.5795127995144-1.57951279951440
1041315.3136167812651-2.31361678126507
1051413.46804779796470.531952202035266
1061814.06797559190003.93202440809997
1071615.19671268368600.803287316313983
1081412.47372598421011.52627401578986
1091414.0598945580578-0.0598945580578271
1101414.5379801403468-0.537980140346823
1111415.6481941562716-1.64819415627158
1121212.3426843400582-0.342684340058196
1131414.6015356153116-0.601535615311623
1141516.4542919939085-1.45429199390852
1151514.04842818988100.95157181011898
1161314.9027110257695-1.90271102576954
1171716.29500126741120.704998732588756
1181715.96039113136641.03960886863360
1191916.15184619816432.84815380183573
1201514.09096836334600.909031636654031
1211313.6983755172766-0.69837551727663
122912.8055899227852-3.80558992278518
1231514.68678230881570.313217691184339
1241514.00280025849740.9971997415026
1251615.80092752480010.199072475199885
1261112.3365984537789-1.33659845377890
1271413.95761831928910.0423816807109123
1281112.2235941214277-1.22359412142766
1291513.54800248801081.45199751198918
1301314.0736011399322-1.07360113993219
1311613.98077550180492.01922449819509
1321414.8176936887082-0.817693688708184
1331514.61183387433100.388166125669049
1341616.0724565893001-0.0724565893000994
1351614.82015007797051.17984992202946
1361113.9907097355359-2.99070973553592
1371315.0832724407808-2.08327244078082
1381613.98454714515212.01545285484788
1391215.4463265938884-3.44632659388842
140913.3114956522893-4.31149565228926
1411314.1920413827861-1.19204138278615
1421312.97185413700850.0281458629915367
1431413.29696215432070.703037845679265
1441916.15184619816432.84815380183573
1451315.1221405425566-2.12214054255660

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 12.6418727589494 & 1.35812724105058 \tabularnewline
2 & 18 & 15.6624549208704 & 2.33754507912956 \tabularnewline
3 & 11 & 14.5638410704991 & -3.56384107049906 \tabularnewline
4 & 12 & 13.7124897971026 & -1.7124897971026 \tabularnewline
5 & 16 & 13.5075850740746 & 2.49241492592537 \tabularnewline
6 & 18 & 14.4758742468793 & 3.52412575312067 \tabularnewline
7 & 14 & 13.6003400895599 & 0.399659910440129 \tabularnewline
8 & 14 & 14.0668465072606 & -0.0668465072605611 \tabularnewline
9 & 15 & 14.4870747006189 & 0.512925299381107 \tabularnewline
10 & 15 & 14.9221162633834 & 0.0778837366166308 \tabularnewline
11 & 17 & 15.2797765289275 & 1.72022347107254 \tabularnewline
12 & 19 & 13.0966279811834 & 5.90337201881662 \tabularnewline
13 & 10 & 13.2407226039100 & -3.24072260390995 \tabularnewline
14 & 18 & 15.1896407748227 & 2.81035922517727 \tabularnewline
15 & 14 & 13.1463671023856 & 0.853632897614437 \tabularnewline
16 & 14 & 13.9954717725119 & 0.00452822748806751 \tabularnewline
17 & 17 & 16.0326299160830 & 0.967370083917037 \tabularnewline
18 & 14 & 13.8090067850197 & 0.190993214980283 \tabularnewline
19 & 16 & 13.9394667888589 & 2.06053321114112 \tabularnewline
20 & 18 & 13.9315061474237 & 4.06849385257634 \tabularnewline
21 & 14 & 13.7339341645022 & 0.266065835497767 \tabularnewline
22 & 12 & 12.6889538894390 & -0.688953889438953 \tabularnewline
23 & 17 & 14.7629052129563 & 2.23709478704367 \tabularnewline
24 & 9 & 14.5262358356125 & -5.52623583561251 \tabularnewline
25 & 16 & 14.3740836774435 & 1.6259163225565 \tabularnewline
26 & 14 & 14.0982624030230 & -0.0982624030230206 \tabularnewline
27 & 11 & 14.5798373060006 & -3.57983730600061 \tabularnewline
28 & 16 & 14.4589362449358 & 1.54106375506424 \tabularnewline
29 & 13 & 12.3915978002484 & 0.608402199751598 \tabularnewline
30 & 17 & 14.8322985589981 & 2.16770144100193 \tabularnewline
31 & 15 & 13.9026761521171 & 1.09732384788292 \tabularnewline
32 & 14 & 14.1079566797044 & -0.107956679704424 \tabularnewline
33 & 16 & 13.9138645879267 & 2.08613541207334 \tabularnewline
34 & 9 & 13.0955833941074 & -4.09558339410744 \tabularnewline
35 & 15 & 14.5616196127191 & 0.438380387280912 \tabularnewline
36 & 17 & 13.1676415495990 & 3.83235845040098 \tabularnewline
37 & 13 & 14.3656988756362 & -1.36569887563615 \tabularnewline
38 & 15 & 14.3093383164212 & 0.69066168357881 \tabularnewline
39 & 16 & 14.1838514942603 & 1.81614850573967 \tabularnewline
40 & 16 & 13.5451565832429 & 2.45484341675712 \tabularnewline
41 & 12 & 13.2228084442785 & -1.22280844427852 \tabularnewline
42 & 11 & 13.2908891529060 & -2.29088915290596 \tabularnewline
43 & 15 & 15.2941523206412 & -0.294152320641214 \tabularnewline
44 & 17 & 14.1314908769484 & 2.86850912305162 \tabularnewline
45 & 13 & 13.5892743387683 & -0.589274338768271 \tabularnewline
46 & 16 & 14.0600986690362 & 1.93990133096379 \tabularnewline
47 & 14 & 12.1627205844333 & 1.83727941556672 \tabularnewline
48 & 11 & 13.0261851153936 & -2.02618511539361 \tabularnewline
49 & 12 & 13.2281454419373 & -1.22814544193733 \tabularnewline
50 & 12 & 12.1357377749987 & -0.135737774998725 \tabularnewline
51 & 15 & 14.8404267498202 & 0.159573250179799 \tabularnewline
52 & 16 & 14.7316322530300 & 1.26836774696996 \tabularnewline
53 & 15 & 14.1849208754938 & 0.815079124506167 \tabularnewline
54 & 12 & 14.6835652645832 & -2.68356526458321 \tabularnewline
55 & 12 & 13.4488105346455 & -1.44881053464549 \tabularnewline
56 & 8 & 13.1737741250945 & -5.17377412509448 \tabularnewline
57 & 13 & 15.6193869725584 & -2.61938697255839 \tabularnewline
58 & 11 & 12.5084925652862 & -1.50849256528621 \tabularnewline
59 & 14 & 13.8529473943886 & 0.147052605611409 \tabularnewline
60 & 15 & 13.4947477634054 & 1.50525223659462 \tabularnewline
61 & 10 & 14.9930961614974 & -4.99309616149738 \tabularnewline
62 & 11 & 13.5334475815918 & -2.53344758159184 \tabularnewline
63 & 12 & 14.6486557476710 & -2.64865574767095 \tabularnewline
64 & 15 & 12.7281078693229 & 2.27189213067711 \tabularnewline
65 & 15 & 14.1759515415642 & 0.824048458435787 \tabularnewline
66 & 14 & 15.2260399780871 & -1.22603997808714 \tabularnewline
67 & 16 & 13.1269640468948 & 2.87303595310521 \tabularnewline
68 & 15 & 16.3774731632291 & -1.37747316322914 \tabularnewline
69 & 15 & 15.1126736514780 & -0.112673651477972 \tabularnewline
70 & 13 & 14.4510851047861 & -1.45108510478607 \tabularnewline
71 & 17 & 15.2205217681969 & 1.77947823180306 \tabularnewline
72 & 13 & 13.6782917454314 & -0.678291745431385 \tabularnewline
73 & 15 & 13.6720828472715 & 1.32791715272847 \tabularnewline
74 & 13 & 12.8043252644605 & 0.195674735539498 \tabularnewline
75 & 15 & 13.3648519260335 & 1.63514807396651 \tabularnewline
76 & 16 & 11.3453796357938 & 4.65462036420625 \tabularnewline
77 & 15 & 13.4883133806511 & 1.51168661934886 \tabularnewline
78 & 16 & 13.5503091255282 & 2.44969087447176 \tabularnewline
79 & 15 & 13.3372099659899 & 1.66279003401007 \tabularnewline
80 & 14 & 13.8049745687266 & 0.195025431273388 \tabularnewline
81 & 15 & 13.7278795523100 & 1.27212044768997 \tabularnewline
82 & 7 & 14.2011516932741 & -7.20115169327412 \tabularnewline
83 & 17 & 14.3457945113471 & 2.65420548865291 \tabularnewline
84 & 13 & 12.9718541370085 & 0.0281458629915367 \tabularnewline
85 & 15 & 14.4767908109235 & 0.523209189076501 \tabularnewline
86 & 14 & 15.5108970724620 & -1.51089707246204 \tabularnewline
87 & 13 & 14.4468959527896 & -1.44689595278961 \tabularnewline
88 & 16 & 15.0815457196959 & 0.918454280304128 \tabularnewline
89 & 12 & 13.5241846249042 & -1.52418462490415 \tabularnewline
90 & 14 & 14.0206807256333 & -0.0206807256332775 \tabularnewline
91 & 17 & 14.7022825071116 & 2.29771749288844 \tabularnewline
92 & 15 & 14.2417240942902 & 0.75827590570977 \tabularnewline
93 & 17 & 15.1953274561276 & 1.80467254387239 \tabularnewline
94 & 12 & 14.5734813667562 & -2.57348136675622 \tabularnewline
95 & 16 & 14.9246841101408 & 1.07531588985916 \tabularnewline
96 & 11 & 13.3539358314833 & -2.35393583148329 \tabularnewline
97 & 15 & 14.5936551927514 & 0.406344807248591 \tabularnewline
98 & 9 & 13.2078618627222 & -4.20786186272216 \tabularnewline
99 & 16 & 14.5384334302640 & 1.46156656973596 \tabularnewline
100 & 10 & 12.1959425992658 & -2.19594259926578 \tabularnewline
101 & 10 & 11.6505048974942 & -1.65050489749419 \tabularnewline
102 & 15 & 15.1108710025288 & -0.110871002528760 \tabularnewline
103 & 11 & 12.5795127995144 & -1.57951279951440 \tabularnewline
104 & 13 & 15.3136167812651 & -2.31361678126507 \tabularnewline
105 & 14 & 13.4680477979647 & 0.531952202035266 \tabularnewline
106 & 18 & 14.0679755919000 & 3.93202440809997 \tabularnewline
107 & 16 & 15.1967126836860 & 0.803287316313983 \tabularnewline
108 & 14 & 12.4737259842101 & 1.52627401578986 \tabularnewline
109 & 14 & 14.0598945580578 & -0.0598945580578271 \tabularnewline
110 & 14 & 14.5379801403468 & -0.537980140346823 \tabularnewline
111 & 14 & 15.6481941562716 & -1.64819415627158 \tabularnewline
112 & 12 & 12.3426843400582 & -0.342684340058196 \tabularnewline
113 & 14 & 14.6015356153116 & -0.601535615311623 \tabularnewline
114 & 15 & 16.4542919939085 & -1.45429199390852 \tabularnewline
115 & 15 & 14.0484281898810 & 0.95157181011898 \tabularnewline
116 & 13 & 14.9027110257695 & -1.90271102576954 \tabularnewline
117 & 17 & 16.2950012674112 & 0.704998732588756 \tabularnewline
118 & 17 & 15.9603911313664 & 1.03960886863360 \tabularnewline
119 & 19 & 16.1518461981643 & 2.84815380183573 \tabularnewline
120 & 15 & 14.0909683633460 & 0.909031636654031 \tabularnewline
121 & 13 & 13.6983755172766 & -0.69837551727663 \tabularnewline
122 & 9 & 12.8055899227852 & -3.80558992278518 \tabularnewline
123 & 15 & 14.6867823088157 & 0.313217691184339 \tabularnewline
124 & 15 & 14.0028002584974 & 0.9971997415026 \tabularnewline
125 & 16 & 15.8009275248001 & 0.199072475199885 \tabularnewline
126 & 11 & 12.3365984537789 & -1.33659845377890 \tabularnewline
127 & 14 & 13.9576183192891 & 0.0423816807109123 \tabularnewline
128 & 11 & 12.2235941214277 & -1.22359412142766 \tabularnewline
129 & 15 & 13.5480024880108 & 1.45199751198918 \tabularnewline
130 & 13 & 14.0736011399322 & -1.07360113993219 \tabularnewline
131 & 16 & 13.9807755018049 & 2.01922449819509 \tabularnewline
132 & 14 & 14.8176936887082 & -0.817693688708184 \tabularnewline
133 & 15 & 14.6118338743310 & 0.388166125669049 \tabularnewline
134 & 16 & 16.0724565893001 & -0.0724565893000994 \tabularnewline
135 & 16 & 14.8201500779705 & 1.17984992202946 \tabularnewline
136 & 11 & 13.9907097355359 & -2.99070973553592 \tabularnewline
137 & 13 & 15.0832724407808 & -2.08327244078082 \tabularnewline
138 & 16 & 13.9845471451521 & 2.01545285484788 \tabularnewline
139 & 12 & 15.4463265938884 & -3.44632659388842 \tabularnewline
140 & 9 & 13.3114956522893 & -4.31149565228926 \tabularnewline
141 & 13 & 14.1920413827861 & -1.19204138278615 \tabularnewline
142 & 13 & 12.9718541370085 & 0.0281458629915367 \tabularnewline
143 & 14 & 13.2969621543207 & 0.703037845679265 \tabularnewline
144 & 19 & 16.1518461981643 & 2.84815380183573 \tabularnewline
145 & 13 & 15.1221405425566 & -2.12214054255660 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99364&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]14[/C][C]12.6418727589494[/C][C]1.35812724105058[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]15.6624549208704[/C][C]2.33754507912956[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]14.5638410704991[/C][C]-3.56384107049906[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]13.7124897971026[/C][C]-1.7124897971026[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]13.5075850740746[/C][C]2.49241492592537[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]14.4758742468793[/C][C]3.52412575312067[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]13.6003400895599[/C][C]0.399659910440129[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]14.0668465072606[/C][C]-0.0668465072605611[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]14.4870747006189[/C][C]0.512925299381107[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.9221162633834[/C][C]0.0778837366166308[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]15.2797765289275[/C][C]1.72022347107254[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]13.0966279811834[/C][C]5.90337201881662[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]13.2407226039100[/C][C]-3.24072260390995[/C][/ROW]
[ROW][C]14[/C][C]18[/C][C]15.1896407748227[/C][C]2.81035922517727[/C][/ROW]
[ROW][C]15[/C][C]14[/C][C]13.1463671023856[/C][C]0.853632897614437[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.9954717725119[/C][C]0.00452822748806751[/C][/ROW]
[ROW][C]17[/C][C]17[/C][C]16.0326299160830[/C][C]0.967370083917037[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]13.8090067850197[/C][C]0.190993214980283[/C][/ROW]
[ROW][C]19[/C][C]16[/C][C]13.9394667888589[/C][C]2.06053321114112[/C][/ROW]
[ROW][C]20[/C][C]18[/C][C]13.9315061474237[/C][C]4.06849385257634[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]13.7339341645022[/C][C]0.266065835497767[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]12.6889538894390[/C][C]-0.688953889438953[/C][/ROW]
[ROW][C]23[/C][C]17[/C][C]14.7629052129563[/C][C]2.23709478704367[/C][/ROW]
[ROW][C]24[/C][C]9[/C][C]14.5262358356125[/C][C]-5.52623583561251[/C][/ROW]
[ROW][C]25[/C][C]16[/C][C]14.3740836774435[/C][C]1.6259163225565[/C][/ROW]
[ROW][C]26[/C][C]14[/C][C]14.0982624030230[/C][C]-0.0982624030230206[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]14.5798373060006[/C][C]-3.57983730600061[/C][/ROW]
[ROW][C]28[/C][C]16[/C][C]14.4589362449358[/C][C]1.54106375506424[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]12.3915978002484[/C][C]0.608402199751598[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]14.8322985589981[/C][C]2.16770144100193[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]13.9026761521171[/C][C]1.09732384788292[/C][/ROW]
[ROW][C]32[/C][C]14[/C][C]14.1079566797044[/C][C]-0.107956679704424[/C][/ROW]
[ROW][C]33[/C][C]16[/C][C]13.9138645879267[/C][C]2.08613541207334[/C][/ROW]
[ROW][C]34[/C][C]9[/C][C]13.0955833941074[/C][C]-4.09558339410744[/C][/ROW]
[ROW][C]35[/C][C]15[/C][C]14.5616196127191[/C][C]0.438380387280912[/C][/ROW]
[ROW][C]36[/C][C]17[/C][C]13.1676415495990[/C][C]3.83235845040098[/C][/ROW]
[ROW][C]37[/C][C]13[/C][C]14.3656988756362[/C][C]-1.36569887563615[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]14.3093383164212[/C][C]0.69066168357881[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.1838514942603[/C][C]1.81614850573967[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]13.5451565832429[/C][C]2.45484341675712[/C][/ROW]
[ROW][C]41[/C][C]12[/C][C]13.2228084442785[/C][C]-1.22280844427852[/C][/ROW]
[ROW][C]42[/C][C]11[/C][C]13.2908891529060[/C][C]-2.29088915290596[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]15.2941523206412[/C][C]-0.294152320641214[/C][/ROW]
[ROW][C]44[/C][C]17[/C][C]14.1314908769484[/C][C]2.86850912305162[/C][/ROW]
[ROW][C]45[/C][C]13[/C][C]13.5892743387683[/C][C]-0.589274338768271[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]14.0600986690362[/C][C]1.93990133096379[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]12.1627205844333[/C][C]1.83727941556672[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]13.0261851153936[/C][C]-2.02618511539361[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]13.2281454419373[/C][C]-1.22814544193733[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]12.1357377749987[/C][C]-0.135737774998725[/C][/ROW]
[ROW][C]51[/C][C]15[/C][C]14.8404267498202[/C][C]0.159573250179799[/C][/ROW]
[ROW][C]52[/C][C]16[/C][C]14.7316322530300[/C][C]1.26836774696996[/C][/ROW]
[ROW][C]53[/C][C]15[/C][C]14.1849208754938[/C][C]0.815079124506167[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]14.6835652645832[/C][C]-2.68356526458321[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]13.4488105346455[/C][C]-1.44881053464549[/C][/ROW]
[ROW][C]56[/C][C]8[/C][C]13.1737741250945[/C][C]-5.17377412509448[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]15.6193869725584[/C][C]-2.61938697255839[/C][/ROW]
[ROW][C]58[/C][C]11[/C][C]12.5084925652862[/C][C]-1.50849256528621[/C][/ROW]
[ROW][C]59[/C][C]14[/C][C]13.8529473943886[/C][C]0.147052605611409[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]13.4947477634054[/C][C]1.50525223659462[/C][/ROW]
[ROW][C]61[/C][C]10[/C][C]14.9930961614974[/C][C]-4.99309616149738[/C][/ROW]
[ROW][C]62[/C][C]11[/C][C]13.5334475815918[/C][C]-2.53344758159184[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]14.6486557476710[/C][C]-2.64865574767095[/C][/ROW]
[ROW][C]64[/C][C]15[/C][C]12.7281078693229[/C][C]2.27189213067711[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]14.1759515415642[/C][C]0.824048458435787[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]15.2260399780871[/C][C]-1.22603997808714[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]13.1269640468948[/C][C]2.87303595310521[/C][/ROW]
[ROW][C]68[/C][C]15[/C][C]16.3774731632291[/C][C]-1.37747316322914[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]15.1126736514780[/C][C]-0.112673651477972[/C][/ROW]
[ROW][C]70[/C][C]13[/C][C]14.4510851047861[/C][C]-1.45108510478607[/C][/ROW]
[ROW][C]71[/C][C]17[/C][C]15.2205217681969[/C][C]1.77947823180306[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]13.6782917454314[/C][C]-0.678291745431385[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]13.6720828472715[/C][C]1.32791715272847[/C][/ROW]
[ROW][C]74[/C][C]13[/C][C]12.8043252644605[/C][C]0.195674735539498[/C][/ROW]
[ROW][C]75[/C][C]15[/C][C]13.3648519260335[/C][C]1.63514807396651[/C][/ROW]
[ROW][C]76[/C][C]16[/C][C]11.3453796357938[/C][C]4.65462036420625[/C][/ROW]
[ROW][C]77[/C][C]15[/C][C]13.4883133806511[/C][C]1.51168661934886[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]13.5503091255282[/C][C]2.44969087447176[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]13.3372099659899[/C][C]1.66279003401007[/C][/ROW]
[ROW][C]80[/C][C]14[/C][C]13.8049745687266[/C][C]0.195025431273388[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]13.7278795523100[/C][C]1.27212044768997[/C][/ROW]
[ROW][C]82[/C][C]7[/C][C]14.2011516932741[/C][C]-7.20115169327412[/C][/ROW]
[ROW][C]83[/C][C]17[/C][C]14.3457945113471[/C][C]2.65420548865291[/C][/ROW]
[ROW][C]84[/C][C]13[/C][C]12.9718541370085[/C][C]0.0281458629915367[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.4767908109235[/C][C]0.523209189076501[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]15.5108970724620[/C][C]-1.51089707246204[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]14.4468959527896[/C][C]-1.44689595278961[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.0815457196959[/C][C]0.918454280304128[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]13.5241846249042[/C][C]-1.52418462490415[/C][/ROW]
[ROW][C]90[/C][C]14[/C][C]14.0206807256333[/C][C]-0.0206807256332775[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]14.7022825071116[/C][C]2.29771749288844[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]14.2417240942902[/C][C]0.75827590570977[/C][/ROW]
[ROW][C]93[/C][C]17[/C][C]15.1953274561276[/C][C]1.80467254387239[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]14.5734813667562[/C][C]-2.57348136675622[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.9246841101408[/C][C]1.07531588985916[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]13.3539358314833[/C][C]-2.35393583148329[/C][/ROW]
[ROW][C]97[/C][C]15[/C][C]14.5936551927514[/C][C]0.406344807248591[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]13.2078618627222[/C][C]-4.20786186272216[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]14.5384334302640[/C][C]1.46156656973596[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]12.1959425992658[/C][C]-2.19594259926578[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]11.6505048974942[/C][C]-1.65050489749419[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]15.1108710025288[/C][C]-0.110871002528760[/C][/ROW]
[ROW][C]103[/C][C]11[/C][C]12.5795127995144[/C][C]-1.57951279951440[/C][/ROW]
[ROW][C]104[/C][C]13[/C][C]15.3136167812651[/C][C]-2.31361678126507[/C][/ROW]
[ROW][C]105[/C][C]14[/C][C]13.4680477979647[/C][C]0.531952202035266[/C][/ROW]
[ROW][C]106[/C][C]18[/C][C]14.0679755919000[/C][C]3.93202440809997[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.1967126836860[/C][C]0.803287316313983[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]12.4737259842101[/C][C]1.52627401578986[/C][/ROW]
[ROW][C]109[/C][C]14[/C][C]14.0598945580578[/C][C]-0.0598945580578271[/C][/ROW]
[ROW][C]110[/C][C]14[/C][C]14.5379801403468[/C][C]-0.537980140346823[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]15.6481941562716[/C][C]-1.64819415627158[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]12.3426843400582[/C][C]-0.342684340058196[/C][/ROW]
[ROW][C]113[/C][C]14[/C][C]14.6015356153116[/C][C]-0.601535615311623[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]16.4542919939085[/C][C]-1.45429199390852[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]14.0484281898810[/C][C]0.95157181011898[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]14.9027110257695[/C][C]-1.90271102576954[/C][/ROW]
[ROW][C]117[/C][C]17[/C][C]16.2950012674112[/C][C]0.704998732588756[/C][/ROW]
[ROW][C]118[/C][C]17[/C][C]15.9603911313664[/C][C]1.03960886863360[/C][/ROW]
[ROW][C]119[/C][C]19[/C][C]16.1518461981643[/C][C]2.84815380183573[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]14.0909683633460[/C][C]0.909031636654031[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]13.6983755172766[/C][C]-0.69837551727663[/C][/ROW]
[ROW][C]122[/C][C]9[/C][C]12.8055899227852[/C][C]-3.80558992278518[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]14.6867823088157[/C][C]0.313217691184339[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]14.0028002584974[/C][C]0.9971997415026[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]15.8009275248001[/C][C]0.199072475199885[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]12.3365984537789[/C][C]-1.33659845377890[/C][/ROW]
[ROW][C]127[/C][C]14[/C][C]13.9576183192891[/C][C]0.0423816807109123[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]12.2235941214277[/C][C]-1.22359412142766[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.5480024880108[/C][C]1.45199751198918[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]14.0736011399322[/C][C]-1.07360113993219[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]13.9807755018049[/C][C]2.01922449819509[/C][/ROW]
[ROW][C]132[/C][C]14[/C][C]14.8176936887082[/C][C]-0.817693688708184[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]14.6118338743310[/C][C]0.388166125669049[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]16.0724565893001[/C][C]-0.0724565893000994[/C][/ROW]
[ROW][C]135[/C][C]16[/C][C]14.8201500779705[/C][C]1.17984992202946[/C][/ROW]
[ROW][C]136[/C][C]11[/C][C]13.9907097355359[/C][C]-2.99070973553592[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]15.0832724407808[/C][C]-2.08327244078082[/C][/ROW]
[ROW][C]138[/C][C]16[/C][C]13.9845471451521[/C][C]2.01545285484788[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]15.4463265938884[/C][C]-3.44632659388842[/C][/ROW]
[ROW][C]140[/C][C]9[/C][C]13.3114956522893[/C][C]-4.31149565228926[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]14.1920413827861[/C][C]-1.19204138278615[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]12.9718541370085[/C][C]0.0281458629915367[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]13.2969621543207[/C][C]0.703037845679265[/C][/ROW]
[ROW][C]144[/C][C]19[/C][C]16.1518461981643[/C][C]2.84815380183573[/C][/ROW]
[ROW][C]145[/C][C]13[/C][C]15.1221405425566[/C][C]-2.12214054255660[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99364&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99364&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
11412.64187275894941.35812724105058
21815.66245492087042.33754507912956
31114.5638410704991-3.56384107049906
41213.7124897971026-1.7124897971026
51613.50758507407462.49241492592537
61814.47587424687933.52412575312067
71413.60034008955990.399659910440129
81414.0668465072606-0.0668465072605611
91514.48707470061890.512925299381107
101514.92211626338340.0778837366166308
111715.27977652892751.72022347107254
121913.09662798118345.90337201881662
131013.2407226039100-3.24072260390995
141815.18964077482272.81035922517727
151413.14636710238560.853632897614437
161413.99547177251190.00452822748806751
171716.03262991608300.967370083917037
181413.80900678501970.190993214980283
191613.93946678885892.06053321114112
201813.93150614742374.06849385257634
211413.73393416450220.266065835497767
221212.6889538894390-0.688953889438953
231714.76290521295632.23709478704367
24914.5262358356125-5.52623583561251
251614.37408367744351.6259163225565
261414.0982624030230-0.0982624030230206
271114.5798373060006-3.57983730600061
281614.45893624493581.54106375506424
291312.39159780024840.608402199751598
301714.83229855899812.16770144100193
311513.90267615211711.09732384788292
321414.1079566797044-0.107956679704424
331613.91386458792672.08613541207334
34913.0955833941074-4.09558339410744
351514.56161961271910.438380387280912
361713.16764154959903.83235845040098
371314.3656988756362-1.36569887563615
381514.30933831642120.69066168357881
391614.18385149426031.81614850573967
401613.54515658324292.45484341675712
411213.2228084442785-1.22280844427852
421113.2908891529060-2.29088915290596
431515.2941523206412-0.294152320641214
441714.13149087694842.86850912305162
451313.5892743387683-0.589274338768271
461614.06009866903621.93990133096379
471412.16272058443331.83727941556672
481113.0261851153936-2.02618511539361
491213.2281454419373-1.22814544193733
501212.1357377749987-0.135737774998725
511514.84042674982020.159573250179799
521614.73163225303001.26836774696996
531514.18492087549380.815079124506167
541214.6835652645832-2.68356526458321
551213.4488105346455-1.44881053464549
56813.1737741250945-5.17377412509448
571315.6193869725584-2.61938697255839
581112.5084925652862-1.50849256528621
591413.85294739438860.147052605611409
601513.49474776340541.50525223659462
611014.9930961614974-4.99309616149738
621113.5334475815918-2.53344758159184
631214.6486557476710-2.64865574767095
641512.72810786932292.27189213067711
651514.17595154156420.824048458435787
661415.2260399780871-1.22603997808714
671613.12696404689482.87303595310521
681516.3774731632291-1.37747316322914
691515.1126736514780-0.112673651477972
701314.4510851047861-1.45108510478607
711715.22052176819691.77947823180306
721313.6782917454314-0.678291745431385
731513.67208284727151.32791715272847
741312.80432526446050.195674735539498
751513.36485192603351.63514807396651
761611.34537963579384.65462036420625
771513.48831338065111.51168661934886
781613.55030912552822.44969087447176
791513.33720996598991.66279003401007
801413.80497456872660.195025431273388
811513.72787955231001.27212044768997
82714.2011516932741-7.20115169327412
831714.34579451134712.65420548865291
841312.97185413700850.0281458629915367
851514.47679081092350.523209189076501
861415.5108970724620-1.51089707246204
871314.4468959527896-1.44689595278961
881615.08154571969590.918454280304128
891213.5241846249042-1.52418462490415
901414.0206807256333-0.0206807256332775
911714.70228250711162.29771749288844
921514.24172409429020.75827590570977
931715.19532745612761.80467254387239
941214.5734813667562-2.57348136675622
951614.92468411014081.07531588985916
961113.3539358314833-2.35393583148329
971514.59365519275140.406344807248591
98913.2078618627222-4.20786186272216
991614.53843343026401.46156656973596
1001012.1959425992658-2.19594259926578
1011011.6505048974942-1.65050489749419
1021515.1108710025288-0.110871002528760
1031112.5795127995144-1.57951279951440
1041315.3136167812651-2.31361678126507
1051413.46804779796470.531952202035266
1061814.06797559190003.93202440809997
1071615.19671268368600.803287316313983
1081412.47372598421011.52627401578986
1091414.0598945580578-0.0598945580578271
1101414.5379801403468-0.537980140346823
1111415.6481941562716-1.64819415627158
1121212.3426843400582-0.342684340058196
1131414.6015356153116-0.601535615311623
1141516.4542919939085-1.45429199390852
1151514.04842818988100.95157181011898
1161314.9027110257695-1.90271102576954
1171716.29500126741120.704998732588756
1181715.96039113136641.03960886863360
1191916.15184619816432.84815380183573
1201514.09096836334600.909031636654031
1211313.6983755172766-0.69837551727663
122912.8055899227852-3.80558992278518
1231514.68678230881570.313217691184339
1241514.00280025849740.9971997415026
1251615.80092752480010.199072475199885
1261112.3365984537789-1.33659845377890
1271413.95761831928910.0423816807109123
1281112.2235941214277-1.22359412142766
1291513.54800248801081.45199751198918
1301314.0736011399322-1.07360113993219
1311613.98077550180492.01922449819509
1321414.8176936887082-0.817693688708184
1331514.61183387433100.388166125669049
1341616.0724565893001-0.0724565893000994
1351614.82015007797051.17984992202946
1361113.9907097355359-2.99070973553592
1371315.0832724407808-2.08327244078082
1381613.98454714515212.01545285484788
1391215.4463265938884-3.44632659388842
140913.3114956522893-4.31149565228926
1411314.1920413827861-1.19204138278615
1421312.97185413700850.0281458629915367
1431413.29696215432070.703037845679265
1441916.15184619816432.84815380183573
1451315.1221405425566-2.12214054255660






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.9016546517740510.1966906964518980.098345348225949
130.8288105118343580.3423789763312830.171189488165641
140.7604271501811570.4791456996376860.239572849818843
150.669988534263130.6600229314737380.330011465736869
160.5922388013682680.8155223972634630.407761198631732
170.4868853888405760.9737707776811520.513114611159424
180.5952623821774170.8094752356451670.404737617822583
190.5164294325710080.9671411348579830.483570567428992
200.4942032620116340.9884065240232680.505796737988366
210.4236565332984510.8473130665969020.576343466701549
220.3836220234355560.7672440468711120.616377976564444
230.3223442174158240.6446884348316490.677655782584176
240.5720493855524540.8559012288950910.427950614447546
250.6093507278783220.7812985442433560.390649272121678
260.5993650908607380.8012698182785250.400634909139262
270.6913079071991170.6173841856017660.308692092800883
280.6335615497213880.7328769005572230.366438450278612
290.5669310295976170.8661379408047670.433068970402383
300.5655280457522090.8689439084955830.434471954247791
310.5347607772449040.9304784455101920.465239222755096
320.4718228128030870.9436456256061740.528177187196913
330.4554115142311530.9108230284623060.544588485768847
340.7212986509929080.5574026980141830.278701349007092
350.6699093752592090.6601812494815810.330090624740791
360.7688352275232830.4623295449534330.231164772476717
370.7269113391326380.5461773217347240.273088660867362
380.685127776774240.6297444464515190.314872223225759
390.6497650178537580.7004699642924830.350234982146242
400.7595087141432870.4809825717134260.240491285856713
410.7176969364656440.5646061270687130.282303063534356
420.7761298381173360.4477403237653290.223870161882664
430.7321307546965270.5357384906069460.267869245303473
440.7541773057620170.4916453884759660.245822694237983
450.7105791791658870.5788416416682250.289420820834113
460.6824259460111110.6351481079777770.317574053988889
470.6811191618793440.6377616762413120.318880838120656
480.681277755532350.63744448893530.31872224446765
490.6401314819411070.7197370361177860.359868518058893
500.598103850684560.803792298630880.40189614931544
510.5460151541027030.9079696917945940.453984845897297
520.5050158427454630.9899683145090750.494984157254537
530.4575340709305210.9150681418610420.542465929069479
540.5141397906720020.9717204186559970.485860209327998
550.4732139086211090.9464278172422180.526786091378891
560.7210567126407310.5578865747185380.278943287359269
570.8195325780364190.3609348439271630.180467421963581
580.7987998204641020.4024003590717970.201200179535898
590.7619177059557360.4761645880885290.238082294044264
600.7571683148312560.4856633703374880.242831685168744
610.9219090777474190.1561818445051620.0780909222525811
620.9248170972861050.1503658054277900.0751829027138952
630.9335014875014040.1329970249971930.0664985124985964
640.9334957543492730.1330084913014540.066504245650727
650.9180407516219320.1639184967561370.0819592483780685
660.9029395109373780.1941209781252430.0970604890626215
670.9186012964222140.1627974071555720.081398703577786
680.906304007206110.1873919855877800.0936959927938902
690.8835563729721620.2328872540556760.116443627027838
700.8685438502230810.2629122995538370.131456149776919
710.8614079464784930.2771841070430140.138592053521507
720.834525508642870.3309489827142600.165474491357130
730.8148637455382860.3702725089234280.185136254461714
740.7797076419659230.4405847160681550.220292358034077
750.7651026131062720.4697947737874560.234897386893728
760.88890386062940.2221922787411990.111096139370599
770.8801577781406830.2396844437186340.119842221859317
780.8817634662486810.2364730675026390.118236533751319
790.8765342496258040.2469315007483920.123465750374196
800.8510571067481520.2978857865036950.148942893251848
810.835450525538320.3290989489233590.164549474461680
820.988316502608440.02336699478312030.0116834973915602
830.9900024506665650.01999509866686980.0099975493334349
840.9864153452352330.0271693095295340.013584654764767
850.9819839817244180.03603203655116490.0180160182755825
860.978726888085560.0425462238288780.021273111914439
870.974021902697960.05195619460407920.0259780973020396
880.967778458751080.0644430824978390.0322215412489195
890.9614808327512860.07703833449742870.0385191672487143
900.9491935791330.1016128417340000.0508064208669998
910.9591188445247560.08176231095048750.0408811554752438
920.949271924191180.1014561516176410.0507280758088206
930.9474828661260380.1050342677479230.0525171338739616
940.9533443564678950.09331128706420910.0466556435321046
950.9517201486383860.09655970272322870.0482798513616144
960.9481248187451860.1037503625096280.0518751812548138
970.9338068977431240.1323862045137530.0661931022568764
980.9616016634943860.07679667301122750.0383983365056138
990.953900036717550.09219992656489850.0460999632824493
1000.9466944130698140.1066111738603710.0533055869301855
1010.9342256375302830.1315487249394350.0657743624697175
1020.9134970997661530.1730058004676950.0865029002338474
1030.898356595563050.2032868088739000.101643404436950
1040.9116182391809480.1767635216381050.0883817608190525
1050.8863147218635890.2273705562728220.113685278136411
1060.946552793920040.106894412159920.05344720607996
1070.93601371369360.1279725726128000.0639862863064002
1080.9467287160107230.1065425679785550.0532712839892774
1090.9268655213641670.1462689572716670.0731344786358333
1100.9067185797479390.1865628405041220.093281420252061
1110.8834001388746810.2331997222506370.116599861125319
1120.8473591292795530.3052817414408940.152640870720447
1130.8174896732684720.3650206534630570.182510326731528
1140.7953938695176130.4092122609647740.204606130482387
1150.7663180448529140.4673639102941720.233681955147086
1160.7417572390041530.5164855219916940.258242760995847
1170.690336242117010.6193275157659810.309663757882990
1180.6339996799156410.7320006401687180.366000320084359
1190.6305837616704610.7388324766590770.369416238329539
1200.5962647352224570.8074705295550860.403735264777543
1210.5199382043538420.9601235912923150.480061795646158
1220.5592996421076790.8814007157846420.440700357892321
1230.4768360617211240.9536721234422470.523163938278876
1240.4123222036906540.8246444073813090.587677796309346
1250.3351164981999060.6702329963998120.664883501800094
1260.2621632942234290.5243265884468570.737836705776571
1270.2436903890073210.4873807780146430.756309610992679
1280.1757840640716380.3515681281432760.824215935928362
1290.2077410628827570.4154821257655140.792258937117243
1300.141935306288260.283870612576520.85806469371174
1310.1659338003145460.3318676006290920.834066199685454
1320.1867444924217880.3734889848435770.813255507578212
1330.1025189348593980.2050378697187960.897481065140602

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.901654651774051 & 0.196690696451898 & 0.098345348225949 \tabularnewline
13 & 0.828810511834358 & 0.342378976331283 & 0.171189488165641 \tabularnewline
14 & 0.760427150181157 & 0.479145699637686 & 0.239572849818843 \tabularnewline
15 & 0.66998853426313 & 0.660022931473738 & 0.330011465736869 \tabularnewline
16 & 0.592238801368268 & 0.815522397263463 & 0.407761198631732 \tabularnewline
17 & 0.486885388840576 & 0.973770777681152 & 0.513114611159424 \tabularnewline
18 & 0.595262382177417 & 0.809475235645167 & 0.404737617822583 \tabularnewline
19 & 0.516429432571008 & 0.967141134857983 & 0.483570567428992 \tabularnewline
20 & 0.494203262011634 & 0.988406524023268 & 0.505796737988366 \tabularnewline
21 & 0.423656533298451 & 0.847313066596902 & 0.576343466701549 \tabularnewline
22 & 0.383622023435556 & 0.767244046871112 & 0.616377976564444 \tabularnewline
23 & 0.322344217415824 & 0.644688434831649 & 0.677655782584176 \tabularnewline
24 & 0.572049385552454 & 0.855901228895091 & 0.427950614447546 \tabularnewline
25 & 0.609350727878322 & 0.781298544243356 & 0.390649272121678 \tabularnewline
26 & 0.599365090860738 & 0.801269818278525 & 0.400634909139262 \tabularnewline
27 & 0.691307907199117 & 0.617384185601766 & 0.308692092800883 \tabularnewline
28 & 0.633561549721388 & 0.732876900557223 & 0.366438450278612 \tabularnewline
29 & 0.566931029597617 & 0.866137940804767 & 0.433068970402383 \tabularnewline
30 & 0.565528045752209 & 0.868943908495583 & 0.434471954247791 \tabularnewline
31 & 0.534760777244904 & 0.930478445510192 & 0.465239222755096 \tabularnewline
32 & 0.471822812803087 & 0.943645625606174 & 0.528177187196913 \tabularnewline
33 & 0.455411514231153 & 0.910823028462306 & 0.544588485768847 \tabularnewline
34 & 0.721298650992908 & 0.557402698014183 & 0.278701349007092 \tabularnewline
35 & 0.669909375259209 & 0.660181249481581 & 0.330090624740791 \tabularnewline
36 & 0.768835227523283 & 0.462329544953433 & 0.231164772476717 \tabularnewline
37 & 0.726911339132638 & 0.546177321734724 & 0.273088660867362 \tabularnewline
38 & 0.68512777677424 & 0.629744446451519 & 0.314872223225759 \tabularnewline
39 & 0.649765017853758 & 0.700469964292483 & 0.350234982146242 \tabularnewline
40 & 0.759508714143287 & 0.480982571713426 & 0.240491285856713 \tabularnewline
41 & 0.717696936465644 & 0.564606127068713 & 0.282303063534356 \tabularnewline
42 & 0.776129838117336 & 0.447740323765329 & 0.223870161882664 \tabularnewline
43 & 0.732130754696527 & 0.535738490606946 & 0.267869245303473 \tabularnewline
44 & 0.754177305762017 & 0.491645388475966 & 0.245822694237983 \tabularnewline
45 & 0.710579179165887 & 0.578841641668225 & 0.289420820834113 \tabularnewline
46 & 0.682425946011111 & 0.635148107977777 & 0.317574053988889 \tabularnewline
47 & 0.681119161879344 & 0.637761676241312 & 0.318880838120656 \tabularnewline
48 & 0.68127775553235 & 0.6374444889353 & 0.31872224446765 \tabularnewline
49 & 0.640131481941107 & 0.719737036117786 & 0.359868518058893 \tabularnewline
50 & 0.59810385068456 & 0.80379229863088 & 0.40189614931544 \tabularnewline
51 & 0.546015154102703 & 0.907969691794594 & 0.453984845897297 \tabularnewline
52 & 0.505015842745463 & 0.989968314509075 & 0.494984157254537 \tabularnewline
53 & 0.457534070930521 & 0.915068141861042 & 0.542465929069479 \tabularnewline
54 & 0.514139790672002 & 0.971720418655997 & 0.485860209327998 \tabularnewline
55 & 0.473213908621109 & 0.946427817242218 & 0.526786091378891 \tabularnewline
56 & 0.721056712640731 & 0.557886574718538 & 0.278943287359269 \tabularnewline
57 & 0.819532578036419 & 0.360934843927163 & 0.180467421963581 \tabularnewline
58 & 0.798799820464102 & 0.402400359071797 & 0.201200179535898 \tabularnewline
59 & 0.761917705955736 & 0.476164588088529 & 0.238082294044264 \tabularnewline
60 & 0.757168314831256 & 0.485663370337488 & 0.242831685168744 \tabularnewline
61 & 0.921909077747419 & 0.156181844505162 & 0.0780909222525811 \tabularnewline
62 & 0.924817097286105 & 0.150365805427790 & 0.0751829027138952 \tabularnewline
63 & 0.933501487501404 & 0.132997024997193 & 0.0664985124985964 \tabularnewline
64 & 0.933495754349273 & 0.133008491301454 & 0.066504245650727 \tabularnewline
65 & 0.918040751621932 & 0.163918496756137 & 0.0819592483780685 \tabularnewline
66 & 0.902939510937378 & 0.194120978125243 & 0.0970604890626215 \tabularnewline
67 & 0.918601296422214 & 0.162797407155572 & 0.081398703577786 \tabularnewline
68 & 0.90630400720611 & 0.187391985587780 & 0.0936959927938902 \tabularnewline
69 & 0.883556372972162 & 0.232887254055676 & 0.116443627027838 \tabularnewline
70 & 0.868543850223081 & 0.262912299553837 & 0.131456149776919 \tabularnewline
71 & 0.861407946478493 & 0.277184107043014 & 0.138592053521507 \tabularnewline
72 & 0.83452550864287 & 0.330948982714260 & 0.165474491357130 \tabularnewline
73 & 0.814863745538286 & 0.370272508923428 & 0.185136254461714 \tabularnewline
74 & 0.779707641965923 & 0.440584716068155 & 0.220292358034077 \tabularnewline
75 & 0.765102613106272 & 0.469794773787456 & 0.234897386893728 \tabularnewline
76 & 0.8889038606294 & 0.222192278741199 & 0.111096139370599 \tabularnewline
77 & 0.880157778140683 & 0.239684443718634 & 0.119842221859317 \tabularnewline
78 & 0.881763466248681 & 0.236473067502639 & 0.118236533751319 \tabularnewline
79 & 0.876534249625804 & 0.246931500748392 & 0.123465750374196 \tabularnewline
80 & 0.851057106748152 & 0.297885786503695 & 0.148942893251848 \tabularnewline
81 & 0.83545052553832 & 0.329098948923359 & 0.164549474461680 \tabularnewline
82 & 0.98831650260844 & 0.0233669947831203 & 0.0116834973915602 \tabularnewline
83 & 0.990002450666565 & 0.0199950986668698 & 0.0099975493334349 \tabularnewline
84 & 0.986415345235233 & 0.027169309529534 & 0.013584654764767 \tabularnewline
85 & 0.981983981724418 & 0.0360320365511649 & 0.0180160182755825 \tabularnewline
86 & 0.97872688808556 & 0.042546223828878 & 0.021273111914439 \tabularnewline
87 & 0.97402190269796 & 0.0519561946040792 & 0.0259780973020396 \tabularnewline
88 & 0.96777845875108 & 0.064443082497839 & 0.0322215412489195 \tabularnewline
89 & 0.961480832751286 & 0.0770383344974287 & 0.0385191672487143 \tabularnewline
90 & 0.949193579133 & 0.101612841734000 & 0.0508064208669998 \tabularnewline
91 & 0.959118844524756 & 0.0817623109504875 & 0.0408811554752438 \tabularnewline
92 & 0.94927192419118 & 0.101456151617641 & 0.0507280758088206 \tabularnewline
93 & 0.947482866126038 & 0.105034267747923 & 0.0525171338739616 \tabularnewline
94 & 0.953344356467895 & 0.0933112870642091 & 0.0466556435321046 \tabularnewline
95 & 0.951720148638386 & 0.0965597027232287 & 0.0482798513616144 \tabularnewline
96 & 0.948124818745186 & 0.103750362509628 & 0.0518751812548138 \tabularnewline
97 & 0.933806897743124 & 0.132386204513753 & 0.0661931022568764 \tabularnewline
98 & 0.961601663494386 & 0.0767966730112275 & 0.0383983365056138 \tabularnewline
99 & 0.95390003671755 & 0.0921999265648985 & 0.0460999632824493 \tabularnewline
100 & 0.946694413069814 & 0.106611173860371 & 0.0533055869301855 \tabularnewline
101 & 0.934225637530283 & 0.131548724939435 & 0.0657743624697175 \tabularnewline
102 & 0.913497099766153 & 0.173005800467695 & 0.0865029002338474 \tabularnewline
103 & 0.89835659556305 & 0.203286808873900 & 0.101643404436950 \tabularnewline
104 & 0.911618239180948 & 0.176763521638105 & 0.0883817608190525 \tabularnewline
105 & 0.886314721863589 & 0.227370556272822 & 0.113685278136411 \tabularnewline
106 & 0.94655279392004 & 0.10689441215992 & 0.05344720607996 \tabularnewline
107 & 0.9360137136936 & 0.127972572612800 & 0.0639862863064002 \tabularnewline
108 & 0.946728716010723 & 0.106542567978555 & 0.0532712839892774 \tabularnewline
109 & 0.926865521364167 & 0.146268957271667 & 0.0731344786358333 \tabularnewline
110 & 0.906718579747939 & 0.186562840504122 & 0.093281420252061 \tabularnewline
111 & 0.883400138874681 & 0.233199722250637 & 0.116599861125319 \tabularnewline
112 & 0.847359129279553 & 0.305281741440894 & 0.152640870720447 \tabularnewline
113 & 0.817489673268472 & 0.365020653463057 & 0.182510326731528 \tabularnewline
114 & 0.795393869517613 & 0.409212260964774 & 0.204606130482387 \tabularnewline
115 & 0.766318044852914 & 0.467363910294172 & 0.233681955147086 \tabularnewline
116 & 0.741757239004153 & 0.516485521991694 & 0.258242760995847 \tabularnewline
117 & 0.69033624211701 & 0.619327515765981 & 0.309663757882990 \tabularnewline
118 & 0.633999679915641 & 0.732000640168718 & 0.366000320084359 \tabularnewline
119 & 0.630583761670461 & 0.738832476659077 & 0.369416238329539 \tabularnewline
120 & 0.596264735222457 & 0.807470529555086 & 0.403735264777543 \tabularnewline
121 & 0.519938204353842 & 0.960123591292315 & 0.480061795646158 \tabularnewline
122 & 0.559299642107679 & 0.881400715784642 & 0.440700357892321 \tabularnewline
123 & 0.476836061721124 & 0.953672123442247 & 0.523163938278876 \tabularnewline
124 & 0.412322203690654 & 0.824644407381309 & 0.587677796309346 \tabularnewline
125 & 0.335116498199906 & 0.670232996399812 & 0.664883501800094 \tabularnewline
126 & 0.262163294223429 & 0.524326588446857 & 0.737836705776571 \tabularnewline
127 & 0.243690389007321 & 0.487380778014643 & 0.756309610992679 \tabularnewline
128 & 0.175784064071638 & 0.351568128143276 & 0.824215935928362 \tabularnewline
129 & 0.207741062882757 & 0.415482125765514 & 0.792258937117243 \tabularnewline
130 & 0.14193530628826 & 0.28387061257652 & 0.85806469371174 \tabularnewline
131 & 0.165933800314546 & 0.331867600629092 & 0.834066199685454 \tabularnewline
132 & 0.186744492421788 & 0.373488984843577 & 0.813255507578212 \tabularnewline
133 & 0.102518934859398 & 0.205037869718796 & 0.897481065140602 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99364&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]12[/C][C]0.901654651774051[/C][C]0.196690696451898[/C][C]0.098345348225949[/C][/ROW]
[ROW][C]13[/C][C]0.828810511834358[/C][C]0.342378976331283[/C][C]0.171189488165641[/C][/ROW]
[ROW][C]14[/C][C]0.760427150181157[/C][C]0.479145699637686[/C][C]0.239572849818843[/C][/ROW]
[ROW][C]15[/C][C]0.66998853426313[/C][C]0.660022931473738[/C][C]0.330011465736869[/C][/ROW]
[ROW][C]16[/C][C]0.592238801368268[/C][C]0.815522397263463[/C][C]0.407761198631732[/C][/ROW]
[ROW][C]17[/C][C]0.486885388840576[/C][C]0.973770777681152[/C][C]0.513114611159424[/C][/ROW]
[ROW][C]18[/C][C]0.595262382177417[/C][C]0.809475235645167[/C][C]0.404737617822583[/C][/ROW]
[ROW][C]19[/C][C]0.516429432571008[/C][C]0.967141134857983[/C][C]0.483570567428992[/C][/ROW]
[ROW][C]20[/C][C]0.494203262011634[/C][C]0.988406524023268[/C][C]0.505796737988366[/C][/ROW]
[ROW][C]21[/C][C]0.423656533298451[/C][C]0.847313066596902[/C][C]0.576343466701549[/C][/ROW]
[ROW][C]22[/C][C]0.383622023435556[/C][C]0.767244046871112[/C][C]0.616377976564444[/C][/ROW]
[ROW][C]23[/C][C]0.322344217415824[/C][C]0.644688434831649[/C][C]0.677655782584176[/C][/ROW]
[ROW][C]24[/C][C]0.572049385552454[/C][C]0.855901228895091[/C][C]0.427950614447546[/C][/ROW]
[ROW][C]25[/C][C]0.609350727878322[/C][C]0.781298544243356[/C][C]0.390649272121678[/C][/ROW]
[ROW][C]26[/C][C]0.599365090860738[/C][C]0.801269818278525[/C][C]0.400634909139262[/C][/ROW]
[ROW][C]27[/C][C]0.691307907199117[/C][C]0.617384185601766[/C][C]0.308692092800883[/C][/ROW]
[ROW][C]28[/C][C]0.633561549721388[/C][C]0.732876900557223[/C][C]0.366438450278612[/C][/ROW]
[ROW][C]29[/C][C]0.566931029597617[/C][C]0.866137940804767[/C][C]0.433068970402383[/C][/ROW]
[ROW][C]30[/C][C]0.565528045752209[/C][C]0.868943908495583[/C][C]0.434471954247791[/C][/ROW]
[ROW][C]31[/C][C]0.534760777244904[/C][C]0.930478445510192[/C][C]0.465239222755096[/C][/ROW]
[ROW][C]32[/C][C]0.471822812803087[/C][C]0.943645625606174[/C][C]0.528177187196913[/C][/ROW]
[ROW][C]33[/C][C]0.455411514231153[/C][C]0.910823028462306[/C][C]0.544588485768847[/C][/ROW]
[ROW][C]34[/C][C]0.721298650992908[/C][C]0.557402698014183[/C][C]0.278701349007092[/C][/ROW]
[ROW][C]35[/C][C]0.669909375259209[/C][C]0.660181249481581[/C][C]0.330090624740791[/C][/ROW]
[ROW][C]36[/C][C]0.768835227523283[/C][C]0.462329544953433[/C][C]0.231164772476717[/C][/ROW]
[ROW][C]37[/C][C]0.726911339132638[/C][C]0.546177321734724[/C][C]0.273088660867362[/C][/ROW]
[ROW][C]38[/C][C]0.68512777677424[/C][C]0.629744446451519[/C][C]0.314872223225759[/C][/ROW]
[ROW][C]39[/C][C]0.649765017853758[/C][C]0.700469964292483[/C][C]0.350234982146242[/C][/ROW]
[ROW][C]40[/C][C]0.759508714143287[/C][C]0.480982571713426[/C][C]0.240491285856713[/C][/ROW]
[ROW][C]41[/C][C]0.717696936465644[/C][C]0.564606127068713[/C][C]0.282303063534356[/C][/ROW]
[ROW][C]42[/C][C]0.776129838117336[/C][C]0.447740323765329[/C][C]0.223870161882664[/C][/ROW]
[ROW][C]43[/C][C]0.732130754696527[/C][C]0.535738490606946[/C][C]0.267869245303473[/C][/ROW]
[ROW][C]44[/C][C]0.754177305762017[/C][C]0.491645388475966[/C][C]0.245822694237983[/C][/ROW]
[ROW][C]45[/C][C]0.710579179165887[/C][C]0.578841641668225[/C][C]0.289420820834113[/C][/ROW]
[ROW][C]46[/C][C]0.682425946011111[/C][C]0.635148107977777[/C][C]0.317574053988889[/C][/ROW]
[ROW][C]47[/C][C]0.681119161879344[/C][C]0.637761676241312[/C][C]0.318880838120656[/C][/ROW]
[ROW][C]48[/C][C]0.68127775553235[/C][C]0.6374444889353[/C][C]0.31872224446765[/C][/ROW]
[ROW][C]49[/C][C]0.640131481941107[/C][C]0.719737036117786[/C][C]0.359868518058893[/C][/ROW]
[ROW][C]50[/C][C]0.59810385068456[/C][C]0.80379229863088[/C][C]0.40189614931544[/C][/ROW]
[ROW][C]51[/C][C]0.546015154102703[/C][C]0.907969691794594[/C][C]0.453984845897297[/C][/ROW]
[ROW][C]52[/C][C]0.505015842745463[/C][C]0.989968314509075[/C][C]0.494984157254537[/C][/ROW]
[ROW][C]53[/C][C]0.457534070930521[/C][C]0.915068141861042[/C][C]0.542465929069479[/C][/ROW]
[ROW][C]54[/C][C]0.514139790672002[/C][C]0.971720418655997[/C][C]0.485860209327998[/C][/ROW]
[ROW][C]55[/C][C]0.473213908621109[/C][C]0.946427817242218[/C][C]0.526786091378891[/C][/ROW]
[ROW][C]56[/C][C]0.721056712640731[/C][C]0.557886574718538[/C][C]0.278943287359269[/C][/ROW]
[ROW][C]57[/C][C]0.819532578036419[/C][C]0.360934843927163[/C][C]0.180467421963581[/C][/ROW]
[ROW][C]58[/C][C]0.798799820464102[/C][C]0.402400359071797[/C][C]0.201200179535898[/C][/ROW]
[ROW][C]59[/C][C]0.761917705955736[/C][C]0.476164588088529[/C][C]0.238082294044264[/C][/ROW]
[ROW][C]60[/C][C]0.757168314831256[/C][C]0.485663370337488[/C][C]0.242831685168744[/C][/ROW]
[ROW][C]61[/C][C]0.921909077747419[/C][C]0.156181844505162[/C][C]0.0780909222525811[/C][/ROW]
[ROW][C]62[/C][C]0.924817097286105[/C][C]0.150365805427790[/C][C]0.0751829027138952[/C][/ROW]
[ROW][C]63[/C][C]0.933501487501404[/C][C]0.132997024997193[/C][C]0.0664985124985964[/C][/ROW]
[ROW][C]64[/C][C]0.933495754349273[/C][C]0.133008491301454[/C][C]0.066504245650727[/C][/ROW]
[ROW][C]65[/C][C]0.918040751621932[/C][C]0.163918496756137[/C][C]0.0819592483780685[/C][/ROW]
[ROW][C]66[/C][C]0.902939510937378[/C][C]0.194120978125243[/C][C]0.0970604890626215[/C][/ROW]
[ROW][C]67[/C][C]0.918601296422214[/C][C]0.162797407155572[/C][C]0.081398703577786[/C][/ROW]
[ROW][C]68[/C][C]0.90630400720611[/C][C]0.187391985587780[/C][C]0.0936959927938902[/C][/ROW]
[ROW][C]69[/C][C]0.883556372972162[/C][C]0.232887254055676[/C][C]0.116443627027838[/C][/ROW]
[ROW][C]70[/C][C]0.868543850223081[/C][C]0.262912299553837[/C][C]0.131456149776919[/C][/ROW]
[ROW][C]71[/C][C]0.861407946478493[/C][C]0.277184107043014[/C][C]0.138592053521507[/C][/ROW]
[ROW][C]72[/C][C]0.83452550864287[/C][C]0.330948982714260[/C][C]0.165474491357130[/C][/ROW]
[ROW][C]73[/C][C]0.814863745538286[/C][C]0.370272508923428[/C][C]0.185136254461714[/C][/ROW]
[ROW][C]74[/C][C]0.779707641965923[/C][C]0.440584716068155[/C][C]0.220292358034077[/C][/ROW]
[ROW][C]75[/C][C]0.765102613106272[/C][C]0.469794773787456[/C][C]0.234897386893728[/C][/ROW]
[ROW][C]76[/C][C]0.8889038606294[/C][C]0.222192278741199[/C][C]0.111096139370599[/C][/ROW]
[ROW][C]77[/C][C]0.880157778140683[/C][C]0.239684443718634[/C][C]0.119842221859317[/C][/ROW]
[ROW][C]78[/C][C]0.881763466248681[/C][C]0.236473067502639[/C][C]0.118236533751319[/C][/ROW]
[ROW][C]79[/C][C]0.876534249625804[/C][C]0.246931500748392[/C][C]0.123465750374196[/C][/ROW]
[ROW][C]80[/C][C]0.851057106748152[/C][C]0.297885786503695[/C][C]0.148942893251848[/C][/ROW]
[ROW][C]81[/C][C]0.83545052553832[/C][C]0.329098948923359[/C][C]0.164549474461680[/C][/ROW]
[ROW][C]82[/C][C]0.98831650260844[/C][C]0.0233669947831203[/C][C]0.0116834973915602[/C][/ROW]
[ROW][C]83[/C][C]0.990002450666565[/C][C]0.0199950986668698[/C][C]0.0099975493334349[/C][/ROW]
[ROW][C]84[/C][C]0.986415345235233[/C][C]0.027169309529534[/C][C]0.013584654764767[/C][/ROW]
[ROW][C]85[/C][C]0.981983981724418[/C][C]0.0360320365511649[/C][C]0.0180160182755825[/C][/ROW]
[ROW][C]86[/C][C]0.97872688808556[/C][C]0.042546223828878[/C][C]0.021273111914439[/C][/ROW]
[ROW][C]87[/C][C]0.97402190269796[/C][C]0.0519561946040792[/C][C]0.0259780973020396[/C][/ROW]
[ROW][C]88[/C][C]0.96777845875108[/C][C]0.064443082497839[/C][C]0.0322215412489195[/C][/ROW]
[ROW][C]89[/C][C]0.961480832751286[/C][C]0.0770383344974287[/C][C]0.0385191672487143[/C][/ROW]
[ROW][C]90[/C][C]0.949193579133[/C][C]0.101612841734000[/C][C]0.0508064208669998[/C][/ROW]
[ROW][C]91[/C][C]0.959118844524756[/C][C]0.0817623109504875[/C][C]0.0408811554752438[/C][/ROW]
[ROW][C]92[/C][C]0.94927192419118[/C][C]0.101456151617641[/C][C]0.0507280758088206[/C][/ROW]
[ROW][C]93[/C][C]0.947482866126038[/C][C]0.105034267747923[/C][C]0.0525171338739616[/C][/ROW]
[ROW][C]94[/C][C]0.953344356467895[/C][C]0.0933112870642091[/C][C]0.0466556435321046[/C][/ROW]
[ROW][C]95[/C][C]0.951720148638386[/C][C]0.0965597027232287[/C][C]0.0482798513616144[/C][/ROW]
[ROW][C]96[/C][C]0.948124818745186[/C][C]0.103750362509628[/C][C]0.0518751812548138[/C][/ROW]
[ROW][C]97[/C][C]0.933806897743124[/C][C]0.132386204513753[/C][C]0.0661931022568764[/C][/ROW]
[ROW][C]98[/C][C]0.961601663494386[/C][C]0.0767966730112275[/C][C]0.0383983365056138[/C][/ROW]
[ROW][C]99[/C][C]0.95390003671755[/C][C]0.0921999265648985[/C][C]0.0460999632824493[/C][/ROW]
[ROW][C]100[/C][C]0.946694413069814[/C][C]0.106611173860371[/C][C]0.0533055869301855[/C][/ROW]
[ROW][C]101[/C][C]0.934225637530283[/C][C]0.131548724939435[/C][C]0.0657743624697175[/C][/ROW]
[ROW][C]102[/C][C]0.913497099766153[/C][C]0.173005800467695[/C][C]0.0865029002338474[/C][/ROW]
[ROW][C]103[/C][C]0.89835659556305[/C][C]0.203286808873900[/C][C]0.101643404436950[/C][/ROW]
[ROW][C]104[/C][C]0.911618239180948[/C][C]0.176763521638105[/C][C]0.0883817608190525[/C][/ROW]
[ROW][C]105[/C][C]0.886314721863589[/C][C]0.227370556272822[/C][C]0.113685278136411[/C][/ROW]
[ROW][C]106[/C][C]0.94655279392004[/C][C]0.10689441215992[/C][C]0.05344720607996[/C][/ROW]
[ROW][C]107[/C][C]0.9360137136936[/C][C]0.127972572612800[/C][C]0.0639862863064002[/C][/ROW]
[ROW][C]108[/C][C]0.946728716010723[/C][C]0.106542567978555[/C][C]0.0532712839892774[/C][/ROW]
[ROW][C]109[/C][C]0.926865521364167[/C][C]0.146268957271667[/C][C]0.0731344786358333[/C][/ROW]
[ROW][C]110[/C][C]0.906718579747939[/C][C]0.186562840504122[/C][C]0.093281420252061[/C][/ROW]
[ROW][C]111[/C][C]0.883400138874681[/C][C]0.233199722250637[/C][C]0.116599861125319[/C][/ROW]
[ROW][C]112[/C][C]0.847359129279553[/C][C]0.305281741440894[/C][C]0.152640870720447[/C][/ROW]
[ROW][C]113[/C][C]0.817489673268472[/C][C]0.365020653463057[/C][C]0.182510326731528[/C][/ROW]
[ROW][C]114[/C][C]0.795393869517613[/C][C]0.409212260964774[/C][C]0.204606130482387[/C][/ROW]
[ROW][C]115[/C][C]0.766318044852914[/C][C]0.467363910294172[/C][C]0.233681955147086[/C][/ROW]
[ROW][C]116[/C][C]0.741757239004153[/C][C]0.516485521991694[/C][C]0.258242760995847[/C][/ROW]
[ROW][C]117[/C][C]0.69033624211701[/C][C]0.619327515765981[/C][C]0.309663757882990[/C][/ROW]
[ROW][C]118[/C][C]0.633999679915641[/C][C]0.732000640168718[/C][C]0.366000320084359[/C][/ROW]
[ROW][C]119[/C][C]0.630583761670461[/C][C]0.738832476659077[/C][C]0.369416238329539[/C][/ROW]
[ROW][C]120[/C][C]0.596264735222457[/C][C]0.807470529555086[/C][C]0.403735264777543[/C][/ROW]
[ROW][C]121[/C][C]0.519938204353842[/C][C]0.960123591292315[/C][C]0.480061795646158[/C][/ROW]
[ROW][C]122[/C][C]0.559299642107679[/C][C]0.881400715784642[/C][C]0.440700357892321[/C][/ROW]
[ROW][C]123[/C][C]0.476836061721124[/C][C]0.953672123442247[/C][C]0.523163938278876[/C][/ROW]
[ROW][C]124[/C][C]0.412322203690654[/C][C]0.824644407381309[/C][C]0.587677796309346[/C][/ROW]
[ROW][C]125[/C][C]0.335116498199906[/C][C]0.670232996399812[/C][C]0.664883501800094[/C][/ROW]
[ROW][C]126[/C][C]0.262163294223429[/C][C]0.524326588446857[/C][C]0.737836705776571[/C][/ROW]
[ROW][C]127[/C][C]0.243690389007321[/C][C]0.487380778014643[/C][C]0.756309610992679[/C][/ROW]
[ROW][C]128[/C][C]0.175784064071638[/C][C]0.351568128143276[/C][C]0.824215935928362[/C][/ROW]
[ROW][C]129[/C][C]0.207741062882757[/C][C]0.415482125765514[/C][C]0.792258937117243[/C][/ROW]
[ROW][C]130[/C][C]0.14193530628826[/C][C]0.28387061257652[/C][C]0.85806469371174[/C][/ROW]
[ROW][C]131[/C][C]0.165933800314546[/C][C]0.331867600629092[/C][C]0.834066199685454[/C][/ROW]
[ROW][C]132[/C][C]0.186744492421788[/C][C]0.373488984843577[/C][C]0.813255507578212[/C][/ROW]
[ROW][C]133[/C][C]0.102518934859398[/C][C]0.205037869718796[/C][C]0.897481065140602[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99364&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99364&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
120.9016546517740510.1966906964518980.098345348225949
130.8288105118343580.3423789763312830.171189488165641
140.7604271501811570.4791456996376860.239572849818843
150.669988534263130.6600229314737380.330011465736869
160.5922388013682680.8155223972634630.407761198631732
170.4868853888405760.9737707776811520.513114611159424
180.5952623821774170.8094752356451670.404737617822583
190.5164294325710080.9671411348579830.483570567428992
200.4942032620116340.9884065240232680.505796737988366
210.4236565332984510.8473130665969020.576343466701549
220.3836220234355560.7672440468711120.616377976564444
230.3223442174158240.6446884348316490.677655782584176
240.5720493855524540.8559012288950910.427950614447546
250.6093507278783220.7812985442433560.390649272121678
260.5993650908607380.8012698182785250.400634909139262
270.6913079071991170.6173841856017660.308692092800883
280.6335615497213880.7328769005572230.366438450278612
290.5669310295976170.8661379408047670.433068970402383
300.5655280457522090.8689439084955830.434471954247791
310.5347607772449040.9304784455101920.465239222755096
320.4718228128030870.9436456256061740.528177187196913
330.4554115142311530.9108230284623060.544588485768847
340.7212986509929080.5574026980141830.278701349007092
350.6699093752592090.6601812494815810.330090624740791
360.7688352275232830.4623295449534330.231164772476717
370.7269113391326380.5461773217347240.273088660867362
380.685127776774240.6297444464515190.314872223225759
390.6497650178537580.7004699642924830.350234982146242
400.7595087141432870.4809825717134260.240491285856713
410.7176969364656440.5646061270687130.282303063534356
420.7761298381173360.4477403237653290.223870161882664
430.7321307546965270.5357384906069460.267869245303473
440.7541773057620170.4916453884759660.245822694237983
450.7105791791658870.5788416416682250.289420820834113
460.6824259460111110.6351481079777770.317574053988889
470.6811191618793440.6377616762413120.318880838120656
480.681277755532350.63744448893530.31872224446765
490.6401314819411070.7197370361177860.359868518058893
500.598103850684560.803792298630880.40189614931544
510.5460151541027030.9079696917945940.453984845897297
520.5050158427454630.9899683145090750.494984157254537
530.4575340709305210.9150681418610420.542465929069479
540.5141397906720020.9717204186559970.485860209327998
550.4732139086211090.9464278172422180.526786091378891
560.7210567126407310.5578865747185380.278943287359269
570.8195325780364190.3609348439271630.180467421963581
580.7987998204641020.4024003590717970.201200179535898
590.7619177059557360.4761645880885290.238082294044264
600.7571683148312560.4856633703374880.242831685168744
610.9219090777474190.1561818445051620.0780909222525811
620.9248170972861050.1503658054277900.0751829027138952
630.9335014875014040.1329970249971930.0664985124985964
640.9334957543492730.1330084913014540.066504245650727
650.9180407516219320.1639184967561370.0819592483780685
660.9029395109373780.1941209781252430.0970604890626215
670.9186012964222140.1627974071555720.081398703577786
680.906304007206110.1873919855877800.0936959927938902
690.8835563729721620.2328872540556760.116443627027838
700.8685438502230810.2629122995538370.131456149776919
710.8614079464784930.2771841070430140.138592053521507
720.834525508642870.3309489827142600.165474491357130
730.8148637455382860.3702725089234280.185136254461714
740.7797076419659230.4405847160681550.220292358034077
750.7651026131062720.4697947737874560.234897386893728
760.88890386062940.2221922787411990.111096139370599
770.8801577781406830.2396844437186340.119842221859317
780.8817634662486810.2364730675026390.118236533751319
790.8765342496258040.2469315007483920.123465750374196
800.8510571067481520.2978857865036950.148942893251848
810.835450525538320.3290989489233590.164549474461680
820.988316502608440.02336699478312030.0116834973915602
830.9900024506665650.01999509866686980.0099975493334349
840.9864153452352330.0271693095295340.013584654764767
850.9819839817244180.03603203655116490.0180160182755825
860.978726888085560.0425462238288780.021273111914439
870.974021902697960.05195619460407920.0259780973020396
880.967778458751080.0644430824978390.0322215412489195
890.9614808327512860.07703833449742870.0385191672487143
900.9491935791330.1016128417340000.0508064208669998
910.9591188445247560.08176231095048750.0408811554752438
920.949271924191180.1014561516176410.0507280758088206
930.9474828661260380.1050342677479230.0525171338739616
940.9533443564678950.09331128706420910.0466556435321046
950.9517201486383860.09655970272322870.0482798513616144
960.9481248187451860.1037503625096280.0518751812548138
970.9338068977431240.1323862045137530.0661931022568764
980.9616016634943860.07679667301122750.0383983365056138
990.953900036717550.09219992656489850.0460999632824493
1000.9466944130698140.1066111738603710.0533055869301855
1010.9342256375302830.1315487249394350.0657743624697175
1020.9134970997661530.1730058004676950.0865029002338474
1030.898356595563050.2032868088739000.101643404436950
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1080.9467287160107230.1065425679785550.0532712839892774
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1100.9067185797479390.1865628405041220.093281420252061
1110.8834001388746810.2331997222506370.116599861125319
1120.8473591292795530.3052817414408940.152640870720447
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1240.4123222036906540.8246444073813090.587677796309346
1250.3351164981999060.6702329963998120.664883501800094
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1280.1757840640716380.3515681281432760.824215935928362
1290.2077410628827570.4154821257655140.792258937117243
1300.141935306288260.283870612576520.85806469371174
1310.1659338003145460.3318676006290920.834066199685454
1320.1867444924217880.3734889848435770.813255507578212
1330.1025189348593980.2050378697187960.897481065140602






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level50.0409836065573770OK
10% type I error level130.106557377049180NOK

\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 & 5 & 0.0409836065573770 & OK \tabularnewline
10% type I error level & 13 & 0.106557377049180 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99364&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]5[/C][C]0.0409836065573770[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.106557377049180[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99364&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99364&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level50.0409836065573770OK
10% type I error level130.106557377049180NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

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