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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 12 Jan 2012 06:13:51 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Jan/12/t1326366848qlfmet2rnhddgv2.htm/, Retrieved Thu, 02 May 2024 04:31:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=161020, Retrieved Thu, 02 May 2024 04:31:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact141

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Mutiple regression 1] [2011-11-24 15:26:56] [5b1044653d12da6c563533920760fdbb]
- R       [Multiple Regression] [] [2012-01-12 11:13:51] [bbaf0bbad09b34135f8973992e5d67ea] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	2	7	41	38	12	14	12
16	2	5	39	32	11	18	11
19	2	5	30	35	15	11	14
15	1	5	31	33	6	12	12
14	2	8	34	37	13	16	21
13	2	6	35	29	10	18	12
19	2	5	39	31	12	14	22
15	2	6	34	36	14	14	11
14	2	5	36	35	12	15	10
15	2	4	37	38	6	15	13
16	1	6	38	31	10	17	10
16	2	5	36	34	12	19	8
16	1	5	38	35	12	10	15
16	2	6	39	38	11	16	14
17	2	7	33	37	15	18	10
15	1	6	32	33	12	14	14
15	1	7	36	32	10	14	14
20	2	6	38	38	12	17	11
18	1	8	39	38	11	14	10
16	2	7	32	32	12	16	13
16	1	5	32	33	11	18	7
16	2	5	31	31	12	11	14
19	2	7	39	38	13	14	12
16	2	7	37	39	11	12	14
17	1	5	39	32	9	17	11
17	2	4	41	32	13	9	9
16	1	10	36	35	10	16	11
15	2	6	33	37	14	14	15
16	2	5	33	33	12	15	14
14	1	5	34	33	10	11	13
15	2	5	31	28	12	16	9
12	1	5	27	32	8	13	15
14	2	6	37	31	10	17	10
16	2	5	34	37	12	15	11
14	1	5	34	30	12	14	13
7	1	5	32	33	7	16	8
10	1	5	29	31	6	9	20
14	1	5	36	33	12	15	12
16	2	5	29	31	10	17	10
16	1	5	35	33	10	13	10
16	1	5	37	32	10	15	9
14	2	7	34	33	12	16	14
20	1	5	38	32	15	16	8
14	1	6	35	33	10	12	14
14	2	7	38	28	10	12	11
11	2	7	37	35	12	11	13
14	2	5	38	39	13	15	9
15	2	5	33	34	11	15	11
16	2	4	36	38	11	17	15
14	1	5	38	32	12	13	11
16	2	4	32	38	14	16	10
14	1	5	32	30	10	14	14
12	1	5	32	33	12	11	18
16	2	7	34	38	13	12	14
9	1	5	32	32	5	12	11
14	2	5	37	32	6	15	12
16	2	6	39	34	12	16	13
16	2	4	29	34	12	15	9
15	1	6	37	36	11	12	10
16	2	6	35	34	10	12	15
12	1	5	30	28	7	8	20
16	1	7	38	34	12	13	12
16	2	6	34	35	14	11	12
14	2	8	31	35	11	14	14
16	2	7	34	31	12	15	13
17	1	5	35	37	13	10	11
18	2	6	36	35	14	11	17
18	1	6	30	27	11	12	12
12	2	5	39	40	12	15	13
16	1	5	35	37	12	15	14
10	1	5	38	36	8	14	13
14	2	5	31	38	11	16	15
18	2	4	34	39	14	15	13
18	1	6	38	41	14	15	10
16	1	6	34	27	12	13	11
17	2	6	39	30	9	12	19
16	2	6	37	37	13	17	13
16	2	7	34	31	11	13	17
13	1	5	28	31	12	15	13
16	1	7	37	27	12	13	9
16	1	6	33	36	12	15	11
20	1	5	37	38	12	16	10
16	2	5	35	37	12	15	9
15	1	4	37	33	12	16	12
15	2	8	32	34	11	15	12
16	2	8	33	31	10	14	13
14	1	5	38	39	9	15	13
16	2	5	33	34	12	14	12
16	2	6	29	32	12	13	15
15	2	4	33	33	12	7	22
12	2	5	31	36	9	17	13
17	2	5	36	32	15	13	15
16	2	5	35	41	12	15	13
15	2	5	32	28	12	14	15
13	2	6	29	30	12	13	10
16	2	6	39	36	10	16	11
16	2	5	37	35	13	12	16
16	2	6	35	31	9	14	11
16	1	5	37	34	12	17	11
14	1	7	32	36	10	15	10
16	2	5	38	36	14	17	10
16	1	6	37	35	11	12	16
20	2	6	36	37	15	16	12
15	1	6	32	28	11	11	11
16	2	4	33	39	11	15	16
13	1	5	40	32	12	9	19
17	2	5	38	35	12	16	11
16	1	7	41	39	12	15	16
16	1	6	36	35	11	10	15
12	2	9	43	42	7	10	24
16	2	6	30	34	12	15	14
16	2	6	31	33	14	11	15
17	2	5	32	41	11	13	11
13	1	6	32	33	11	14	15
12	2	5	37	34	10	18	12
18	1	8	37	32	13	16	10
14	2	7	33	40	13	14	14
14	2	5	34	40	8	14	13
13	2	7	33	35	11	14	9
16	2	6	38	36	12	14	15
13	2	6	33	37	11	12	15
16	2	9	31	27	13	14	14
13	2	7	38	39	12	15	11
16	2	6	37	38	14	15	8
15	2	5	33	31	13	15	11
16	2	5	31	33	15	13	11
15	1	6	39	32	10	17	8
17	2	6	44	39	11	17	10
15	2	7	33	36	9	19	11
12	2	5	35	33	11	15	13
16	1	5	32	33	10	13	11
10	1	5	28	32	11	9	20
16	2	6	40	37	8	15	10
12	1	4	27	30	11	15	15
14	1	5	37	38	12	15	12
15	2	7	32	29	12	16	14
13	1	5	28	22	9	11	23
15	1	7	34	35	11	14	14
11	2	7	30	35	10	11	16
12	2	6	35	34	8	15	11
8	1	5	31	35	9	13	12
16	2	8	32	34	8	15	10
15	1	5	30	34	9	16	14
17	2	5	30	35	15	14	12
16	1	5	31	23	11	15	12
10	2	6	40	31	8	16	11
18	2	4	32	27	13	16	12
13	1	5	36	36	12	11	13
16	1	5	32	31	12	12	11
13	1	7	35	32	9	9	19
10	2	6	38	39	7	16	12
15	2	7	42	37	13	13	17
16	1	10	34	38	9	16	9
16	2	6	35	39	6	12	12
14	2	8	35	34	8	9	19
10	2	4	33	31	8	13	18
17	2	5	36	32	15	13	15
13	2	6	32	37	6	14	14
15	2	7	33	36	9	19	11
16	2	7	34	32	11	13	9
12	2	6	32	35	8	12	18
13	2	6	34	36	8	13	16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=161020&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=161020&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=161020&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
percieved_competence[t] = + 6.04207610205134 + 0.145036499278661gender[t] + 0.140985144142353age[t] + 0.102250221850415connected[t] -0.0263053859889658seperate[t] + 0.532858482851705software[t] + 0.050866292866091happiness[t] -0.0802171236440452depression[t] -0.00436013736029397t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
percieved_competence[t] =  +  6.04207610205134 +  0.145036499278661gender[t] +  0.140985144142353age[t] +  0.102250221850415connected[t] -0.0263053859889658seperate[t] +  0.532858482851705software[t] +  0.050866292866091happiness[t] -0.0802171236440452depression[t] -0.00436013736029397t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=161020&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]percieved_competence[t] =  +  6.04207610205134 +  0.145036499278661gender[t] +  0.140985144142353age[t] +  0.102250221850415connected[t] -0.0263053859889658seperate[t] +  0.532858482851705software[t] +  0.050866292866091happiness[t] -0.0802171236440452depression[t] -0.00436013736029397t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=161020&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=161020&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
percieved_competence[t] = + 6.04207610205134 + 0.145036499278661gender[t] + 0.140985144142353age[t] + 0.102250221850415connected[t] -0.0263053859889658seperate[t] + 0.532858482851705software[t] + 0.050866292866091happiness[t] -0.0802171236440452depression[t] -0.00436013736029397t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.042076102051342.4739652.44230.0157350.007867
gender0.1450364992786610.3226620.44950.6537070.326854
age0.1409851441423530.1281921.09980.2731480.136574
connected0.1022502218504150.0473822.1580.0324880.016244
seperate-0.02630538598896580.045461-0.57860.5636820.281841
software0.5328584828517050.0704297.565800
happiness0.0508662928660910.0769430.66110.5095460.254773
depression-0.08021712364404520.056287-1.42510.1561520.078076
t-0.004360137360293970.003204-1.36070.1756030.087801

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 6.04207610205134 & 2.473965 & 2.4423 & 0.015735 & 0.007867 \tabularnewline
gender & 0.145036499278661 & 0.322662 & 0.4495 & 0.653707 & 0.326854 \tabularnewline
age & 0.140985144142353 & 0.128192 & 1.0998 & 0.273148 & 0.136574 \tabularnewline
connected & 0.102250221850415 & 0.047382 & 2.158 & 0.032488 & 0.016244 \tabularnewline
seperate & -0.0263053859889658 & 0.045461 & -0.5786 & 0.563682 & 0.281841 \tabularnewline
software & 0.532858482851705 & 0.070429 & 7.5658 & 0 & 0 \tabularnewline
happiness & 0.050866292866091 & 0.076943 & 0.6611 & 0.509546 & 0.254773 \tabularnewline
depression & -0.0802171236440452 & 0.056287 & -1.4251 & 0.156152 & 0.078076 \tabularnewline
t & -0.00436013736029397 & 0.003204 & -1.3607 & 0.175603 & 0.087801 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=161020&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]6.04207610205134[/C][C]2.473965[/C][C]2.4423[/C][C]0.015735[/C][C]0.007867[/C][/ROW]
[ROW][C]gender[/C][C]0.145036499278661[/C][C]0.322662[/C][C]0.4495[/C][C]0.653707[/C][C]0.326854[/C][/ROW]
[ROW][C]age[/C][C]0.140985144142353[/C][C]0.128192[/C][C]1.0998[/C][C]0.273148[/C][C]0.136574[/C][/ROW]
[ROW][C]connected[/C][C]0.102250221850415[/C][C]0.047382[/C][C]2.158[/C][C]0.032488[/C][C]0.016244[/C][/ROW]
[ROW][C]seperate[/C][C]-0.0263053859889658[/C][C]0.045461[/C][C]-0.5786[/C][C]0.563682[/C][C]0.281841[/C][/ROW]
[ROW][C]software[/C][C]0.532858482851705[/C][C]0.070429[/C][C]7.5658[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]happiness[/C][C]0.050866292866091[/C][C]0.076943[/C][C]0.6611[/C][C]0.509546[/C][C]0.254773[/C][/ROW]
[ROW][C]depression[/C][C]-0.0802171236440452[/C][C]0.056287[/C][C]-1.4251[/C][C]0.156152[/C][C]0.078076[/C][/ROW]
[ROW][C]t[/C][C]-0.00436013736029397[/C][C]0.003204[/C][C]-1.3607[/C][C]0.175603[/C][C]0.087801[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=161020&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=161020&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.042076102051342.4739652.44230.0157350.007867
gender0.1450364992786610.3226620.44950.6537070.326854
age0.1409851441423530.1281921.09980.2731480.136574
connected0.1022502218504150.0473822.1580.0324880.016244
seperate-0.02630538598896580.045461-0.57860.5636820.281841
software0.5328584828517050.0704297.565800
happiness0.0508662928660910.0769430.66110.5095460.254773
depression-0.08021712364404520.056287-1.42510.1561520.078076
t-0.004360137360293970.003204-1.36070.1756030.087801






Multiple Linear Regression - Regression Statistics
Multiple R0.605070715580696
R-squared0.366110570853336
Adjusted R-squared0.332966025538478
F-TEST (value)11.045877002549
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value3.02458058598631e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.8427368578023
Sum Squared Residuals519.538906446775

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.605070715580696 \tabularnewline
R-squared & 0.366110570853336 \tabularnewline
Adjusted R-squared & 0.332966025538478 \tabularnewline
F-TEST (value) & 11.045877002549 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 3.02458058598631e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.8427368578023 \tabularnewline
Sum Squared Residuals & 519.538906446775 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=161020&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.605070715580696[/C][/ROW]
[ROW][C]R-squared[/C][C]0.366110570853336[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.332966025538478[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.045877002549[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]3.02458058598631e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.8427368578023[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]519.538906446775[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=161020&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=161020&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 R0.605070715580696
R-squared0.366110570853336
Adjusted R-squared0.332966025538478
F-TEST (value)11.045877002549
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value3.02458058598631e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.8427368578023
Sum Squared Residuals519.538906446775






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.6511638111484-3.65116381114838
21616.068989069993-0.0689890699930241
31916.60017928842412.39982071157585
41512.02121784010242.97878215989763
51415.9978991946731-1.99789919467307
61315.2493733287637-2.24937332876369
71915.52049872048343.47950127951663
81516.9624510138564-1.96245101385643
91416.1132780128503-2.1132780128503
101512.55346452718882.44653547281119
111615.44624399067890.553756009321129
121616.4904024055108-0.49040240551084
131615.49988432528060.500115674719385
141615.65743629321370.342563706786262
151717.7609003665975-0.760900366597455
161515.3505807933259-0.350580793325935
171514.85679510779520.143204892204789
182016.36212166857213.63787833142792
191815.99170530426232.00829469573769
201615.8274169826710.172583017329034
211615.41992151650310.580078483496913
221615.12623299582970.873767004170278
231916.88359882837282.11640117162724
241615.3205490625990.679450937401008
251714.70708615285812.2929138471419
261716.79621565010110.203784349898871
271615.49961696531680.500383034683249
281516.425824164235-1.42582416423499
291615.45106687749490.548933122505073
301414.2149554491827-0.214955449182659
311515.8213250001047-0.821325000104654
321212.3923703802388-0.392370380238811
331415.3931072461806-1.39310724618065
341615.66694623952010.333053760479855
351415.4903867646498-1.49038676464977
36713.0411358153156-6.04113581531563
371010.9311077677391-0.931107767739136
381415.733974054813-1.73397405481296
391614.40795950307321.59204049692679
401614.61598825409451.38401174590555
411615.02438365580020.975616344199819
421415.8294718948123-1.82947189481232
432017.91328943369872.08671056630133
441414.3677980613534-0.367798061353357
451415.3283885338423-1.32838853384229
461115.892056898258-4.89205689825805
471416.6599472993998-2.65994729939985
481515.0497117697408-0.0497117697408062
491614.88675970098951.11324029901046
501415.8909427740911-1.89094277409109
511616.4178333127765-0.417833312776544
521414.0658298964765-0.0658298964764859
531214.5748031936782-2.57480319367825
541615.97501662793130.0249833720686972
55911.4747650833591-2.4747650833591
561412.73193279233551.26806720766452
571616.1882475371727-0.188247537172711
581615.14941709473360.850582905266352
591515.2817072641108-0.281707264110767
601614.33654985323431.6634501467657
611211.4896230408950.510376959104991
621615.98776351843030.0122364815697093
631616.5161328427869-0.516132842786906
641414.8805815109151-0.88058151091514
651615.81115033828140.188849661718557
661715.76316258508371.23683741491626
671816.30210711882631.69789288117367
681814.60302870152783.39697129847218
691215.7862421359069-3.78624213590694
701615.22654364618920.773456353810819
711013.4531564597402-3.45315645974023
721414.365344283727-0.365344283726967
731816.20858768476371.79141231523628
741816.93820282276531.06179717723467
751615.64545052676930.354549473230739
761713.92728310939923.07271689060076
771616.4013529640169-0.4013529640169
781614.79900898943761.20099101056245
791314.7096002965715-1.70960029657147
801616.2318198969495-0.23181989694948
811615.38202359258860.617976407411428
822015.72415184301984.27584815698021
831615.71598397800420.284016021995754
841515.5455391068136-0.545539106813587
851515.1288747743425-0.128874774342529
861614.64173911743771.35826088256229
871413.88820287972640.111797120273573
881615.27708134167060.722918658329383
891614.76579856923071.23420143076928
901513.99544602229361.00455397770645
911213.4806960203101-1.48069602031009
921716.92606001451550.073939985484493
931615.24629256586930.753707434130738
941515.0658512406601-0.0658512406600965
951315.1933341352671-2.19333413526711
961615.0603086897280.939691310272005
971615.7307930093840.269206990615992
981614.13952338896671.86047661103331
991615.72590022107270.274099778927299
1001414.3524160629756-0.352416062975559
1011617.0577899848507-1.05778998485071
1021614.63922400174281.36077599825718
1032017.28080696728022.71919303271979
1041514.65360964504710.346390354952948
1051614.12758624789661.87241375210336
1061315.0060733649987-2.00607336499873
1071715.86113416446441.13886583553563
1081615.74328502661920.256714973380814
1091614.48493735628221.51506264371779
1101212.7267949574546-0.726794957454556
1111614.9014647065531.09853529344704
1121615.80769484762710.192305152372948
1131714.37818233181632.62181766818366
1141314.3102117255214-1.31021172552138
1151214.7061067261053-2.70610672610529
1161816.68955340398241.31044659601765
1171415.6471995661366-1.64719956613662
1181412.87904407172761.12095592827245
1191315.1053748738777-2.10537487387767
1201615.49653105662560.503468943374428
1211314.3200233554403-1.32002335544035
1221616.0448387417756-0.0448387417755789
1231315.9172544181624-2.91725441816242
1241617.0023326374339-1.00233263743387
1251515.8586143166685-0.858614316668478
1261616.5611273436006-0.56112734360065
1271515.1768471400343-0.17684714003431
1281716.01706114484560.982938855154393
1291514.06364836562470.936351634375277
1301214.7625520885984-2.76255208859841
1311613.83224796511242.16775203488759
1321013.0526315249304-3.05263152493035
1331613.93856230833312.06143769166689
1341213.5595700316117-1.5595700316117
1351415.28176402277-1.28176402277003
1361515.3203400831997-0.320340083199718
1371312.08924894711520.910751052884821
1381514.57866036838360.421339631616382
1391113.4644442341623-2.46444423416226
1401213.3954892718818-1.39548927188183
141813.0207099911854-5.02070999118537
1421613.44220574353872.55779425646125
1431512.92820951191352.07179048808648
1441716.29843304650910.701566953490942
1451614.48638362504741.51361637495262
1461014.0103620078051-4.01036200780513
1471815.59532664192722.40467335807285
1481314.8917604921053-1.89176049210531
1491614.82122693744241.17877306255763
1501312.98637105162330.013628948376688
1511012.9605421828954-2.96054218289535
1521516.2022452493487-1.20224524934874
1531614.29439882068841.70560117931163
1541611.90438745094724.09561254905284
1551412.66512275341321.33487724658678
1561012.254920048858-2.25492004885799
1571716.64265108609640.357348913903601
1581311.57410534637681.42589465362324
1591513.93284424481591.0671557551841
1601615.05690932905680.943090670943157
1611212.2567515916689-0.256751591668855
1621312.64188705217460.358112947825393

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.6511638111484 & -3.65116381114838 \tabularnewline
2 & 16 & 16.068989069993 & -0.0689890699930241 \tabularnewline
3 & 19 & 16.6001792884241 & 2.39982071157585 \tabularnewline
4 & 15 & 12.0212178401024 & 2.97878215989763 \tabularnewline
5 & 14 & 15.9978991946731 & -1.99789919467307 \tabularnewline
6 & 13 & 15.2493733287637 & -2.24937332876369 \tabularnewline
7 & 19 & 15.5204987204834 & 3.47950127951663 \tabularnewline
8 & 15 & 16.9624510138564 & -1.96245101385643 \tabularnewline
9 & 14 & 16.1132780128503 & -2.1132780128503 \tabularnewline
10 & 15 & 12.5534645271888 & 2.44653547281119 \tabularnewline
11 & 16 & 15.4462439906789 & 0.553756009321129 \tabularnewline
12 & 16 & 16.4904024055108 & -0.49040240551084 \tabularnewline
13 & 16 & 15.4998843252806 & 0.500115674719385 \tabularnewline
14 & 16 & 15.6574362932137 & 0.342563706786262 \tabularnewline
15 & 17 & 17.7609003665975 & -0.760900366597455 \tabularnewline
16 & 15 & 15.3505807933259 & -0.350580793325935 \tabularnewline
17 & 15 & 14.8567951077952 & 0.143204892204789 \tabularnewline
18 & 20 & 16.3621216685721 & 3.63787833142792 \tabularnewline
19 & 18 & 15.9917053042623 & 2.00829469573769 \tabularnewline
20 & 16 & 15.827416982671 & 0.172583017329034 \tabularnewline
21 & 16 & 15.4199215165031 & 0.580078483496913 \tabularnewline
22 & 16 & 15.1262329958297 & 0.873767004170278 \tabularnewline
23 & 19 & 16.8835988283728 & 2.11640117162724 \tabularnewline
24 & 16 & 15.320549062599 & 0.679450937401008 \tabularnewline
25 & 17 & 14.7070861528581 & 2.2929138471419 \tabularnewline
26 & 17 & 16.7962156501011 & 0.203784349898871 \tabularnewline
27 & 16 & 15.4996169653168 & 0.500383034683249 \tabularnewline
28 & 15 & 16.425824164235 & -1.42582416423499 \tabularnewline
29 & 16 & 15.4510668774949 & 0.548933122505073 \tabularnewline
30 & 14 & 14.2149554491827 & -0.214955449182659 \tabularnewline
31 & 15 & 15.8213250001047 & -0.821325000104654 \tabularnewline
32 & 12 & 12.3923703802388 & -0.392370380238811 \tabularnewline
33 & 14 & 15.3931072461806 & -1.39310724618065 \tabularnewline
34 & 16 & 15.6669462395201 & 0.333053760479855 \tabularnewline
35 & 14 & 15.4903867646498 & -1.49038676464977 \tabularnewline
36 & 7 & 13.0411358153156 & -6.04113581531563 \tabularnewline
37 & 10 & 10.9311077677391 & -0.931107767739136 \tabularnewline
38 & 14 & 15.733974054813 & -1.73397405481296 \tabularnewline
39 & 16 & 14.4079595030732 & 1.59204049692679 \tabularnewline
40 & 16 & 14.6159882540945 & 1.38401174590555 \tabularnewline
41 & 16 & 15.0243836558002 & 0.975616344199819 \tabularnewline
42 & 14 & 15.8294718948123 & -1.82947189481232 \tabularnewline
43 & 20 & 17.9132894336987 & 2.08671056630133 \tabularnewline
44 & 14 & 14.3677980613534 & -0.367798061353357 \tabularnewline
45 & 14 & 15.3283885338423 & -1.32838853384229 \tabularnewline
46 & 11 & 15.892056898258 & -4.89205689825805 \tabularnewline
47 & 14 & 16.6599472993998 & -2.65994729939985 \tabularnewline
48 & 15 & 15.0497117697408 & -0.0497117697408062 \tabularnewline
49 & 16 & 14.8867597009895 & 1.11324029901046 \tabularnewline
50 & 14 & 15.8909427740911 & -1.89094277409109 \tabularnewline
51 & 16 & 16.4178333127765 & -0.417833312776544 \tabularnewline
52 & 14 & 14.0658298964765 & -0.0658298964764859 \tabularnewline
53 & 12 & 14.5748031936782 & -2.57480319367825 \tabularnewline
54 & 16 & 15.9750166279313 & 0.0249833720686972 \tabularnewline
55 & 9 & 11.4747650833591 & -2.4747650833591 \tabularnewline
56 & 14 & 12.7319327923355 & 1.26806720766452 \tabularnewline
57 & 16 & 16.1882475371727 & -0.188247537172711 \tabularnewline
58 & 16 & 15.1494170947336 & 0.850582905266352 \tabularnewline
59 & 15 & 15.2817072641108 & -0.281707264110767 \tabularnewline
60 & 16 & 14.3365498532343 & 1.6634501467657 \tabularnewline
61 & 12 & 11.489623040895 & 0.510376959104991 \tabularnewline
62 & 16 & 15.9877635184303 & 0.0122364815697093 \tabularnewline
63 & 16 & 16.5161328427869 & -0.516132842786906 \tabularnewline
64 & 14 & 14.8805815109151 & -0.88058151091514 \tabularnewline
65 & 16 & 15.8111503382814 & 0.188849661718557 \tabularnewline
66 & 17 & 15.7631625850837 & 1.23683741491626 \tabularnewline
67 & 18 & 16.3021071188263 & 1.69789288117367 \tabularnewline
68 & 18 & 14.6030287015278 & 3.39697129847218 \tabularnewline
69 & 12 & 15.7862421359069 & -3.78624213590694 \tabularnewline
70 & 16 & 15.2265436461892 & 0.773456353810819 \tabularnewline
71 & 10 & 13.4531564597402 & -3.45315645974023 \tabularnewline
72 & 14 & 14.365344283727 & -0.365344283726967 \tabularnewline
73 & 18 & 16.2085876847637 & 1.79141231523628 \tabularnewline
74 & 18 & 16.9382028227653 & 1.06179717723467 \tabularnewline
75 & 16 & 15.6454505267693 & 0.354549473230739 \tabularnewline
76 & 17 & 13.9272831093992 & 3.07271689060076 \tabularnewline
77 & 16 & 16.4013529640169 & -0.4013529640169 \tabularnewline
78 & 16 & 14.7990089894376 & 1.20099101056245 \tabularnewline
79 & 13 & 14.7096002965715 & -1.70960029657147 \tabularnewline
80 & 16 & 16.2318198969495 & -0.23181989694948 \tabularnewline
81 & 16 & 15.3820235925886 & 0.617976407411428 \tabularnewline
82 & 20 & 15.7241518430198 & 4.27584815698021 \tabularnewline
83 & 16 & 15.7159839780042 & 0.284016021995754 \tabularnewline
84 & 15 & 15.5455391068136 & -0.545539106813587 \tabularnewline
85 & 15 & 15.1288747743425 & -0.128874774342529 \tabularnewline
86 & 16 & 14.6417391174377 & 1.35826088256229 \tabularnewline
87 & 14 & 13.8882028797264 & 0.111797120273573 \tabularnewline
88 & 16 & 15.2770813416706 & 0.722918658329383 \tabularnewline
89 & 16 & 14.7657985692307 & 1.23420143076928 \tabularnewline
90 & 15 & 13.9954460222936 & 1.00455397770645 \tabularnewline
91 & 12 & 13.4806960203101 & -1.48069602031009 \tabularnewline
92 & 17 & 16.9260600145155 & 0.073939985484493 \tabularnewline
93 & 16 & 15.2462925658693 & 0.753707434130738 \tabularnewline
94 & 15 & 15.0658512406601 & -0.0658512406600965 \tabularnewline
95 & 13 & 15.1933341352671 & -2.19333413526711 \tabularnewline
96 & 16 & 15.060308689728 & 0.939691310272005 \tabularnewline
97 & 16 & 15.730793009384 & 0.269206990615992 \tabularnewline
98 & 16 & 14.1395233889667 & 1.86047661103331 \tabularnewline
99 & 16 & 15.7259002210727 & 0.274099778927299 \tabularnewline
100 & 14 & 14.3524160629756 & -0.352416062975559 \tabularnewline
101 & 16 & 17.0577899848507 & -1.05778998485071 \tabularnewline
102 & 16 & 14.6392240017428 & 1.36077599825718 \tabularnewline
103 & 20 & 17.2808069672802 & 2.71919303271979 \tabularnewline
104 & 15 & 14.6536096450471 & 0.346390354952948 \tabularnewline
105 & 16 & 14.1275862478966 & 1.87241375210336 \tabularnewline
106 & 13 & 15.0060733649987 & -2.00607336499873 \tabularnewline
107 & 17 & 15.8611341644644 & 1.13886583553563 \tabularnewline
108 & 16 & 15.7432850266192 & 0.256714973380814 \tabularnewline
109 & 16 & 14.4849373562822 & 1.51506264371779 \tabularnewline
110 & 12 & 12.7267949574546 & -0.726794957454556 \tabularnewline
111 & 16 & 14.901464706553 & 1.09853529344704 \tabularnewline
112 & 16 & 15.8076948476271 & 0.192305152372948 \tabularnewline
113 & 17 & 14.3781823318163 & 2.62181766818366 \tabularnewline
114 & 13 & 14.3102117255214 & -1.31021172552138 \tabularnewline
115 & 12 & 14.7061067261053 & -2.70610672610529 \tabularnewline
116 & 18 & 16.6895534039824 & 1.31044659601765 \tabularnewline
117 & 14 & 15.6471995661366 & -1.64719956613662 \tabularnewline
118 & 14 & 12.8790440717276 & 1.12095592827245 \tabularnewline
119 & 13 & 15.1053748738777 & -2.10537487387767 \tabularnewline
120 & 16 & 15.4965310566256 & 0.503468943374428 \tabularnewline
121 & 13 & 14.3200233554403 & -1.32002335544035 \tabularnewline
122 & 16 & 16.0448387417756 & -0.0448387417755789 \tabularnewline
123 & 13 & 15.9172544181624 & -2.91725441816242 \tabularnewline
124 & 16 & 17.0023326374339 & -1.00233263743387 \tabularnewline
125 & 15 & 15.8586143166685 & -0.858614316668478 \tabularnewline
126 & 16 & 16.5611273436006 & -0.56112734360065 \tabularnewline
127 & 15 & 15.1768471400343 & -0.17684714003431 \tabularnewline
128 & 17 & 16.0170611448456 & 0.982938855154393 \tabularnewline
129 & 15 & 14.0636483656247 & 0.936351634375277 \tabularnewline
130 & 12 & 14.7625520885984 & -2.76255208859841 \tabularnewline
131 & 16 & 13.8322479651124 & 2.16775203488759 \tabularnewline
132 & 10 & 13.0526315249304 & -3.05263152493035 \tabularnewline
133 & 16 & 13.9385623083331 & 2.06143769166689 \tabularnewline
134 & 12 & 13.5595700316117 & -1.5595700316117 \tabularnewline
135 & 14 & 15.28176402277 & -1.28176402277003 \tabularnewline
136 & 15 & 15.3203400831997 & -0.320340083199718 \tabularnewline
137 & 13 & 12.0892489471152 & 0.910751052884821 \tabularnewline
138 & 15 & 14.5786603683836 & 0.421339631616382 \tabularnewline
139 & 11 & 13.4644442341623 & -2.46444423416226 \tabularnewline
140 & 12 & 13.3954892718818 & -1.39548927188183 \tabularnewline
141 & 8 & 13.0207099911854 & -5.02070999118537 \tabularnewline
142 & 16 & 13.4422057435387 & 2.55779425646125 \tabularnewline
143 & 15 & 12.9282095119135 & 2.07179048808648 \tabularnewline
144 & 17 & 16.2984330465091 & 0.701566953490942 \tabularnewline
145 & 16 & 14.4863836250474 & 1.51361637495262 \tabularnewline
146 & 10 & 14.0103620078051 & -4.01036200780513 \tabularnewline
147 & 18 & 15.5953266419272 & 2.40467335807285 \tabularnewline
148 & 13 & 14.8917604921053 & -1.89176049210531 \tabularnewline
149 & 16 & 14.8212269374424 & 1.17877306255763 \tabularnewline
150 & 13 & 12.9863710516233 & 0.013628948376688 \tabularnewline
151 & 10 & 12.9605421828954 & -2.96054218289535 \tabularnewline
152 & 15 & 16.2022452493487 & -1.20224524934874 \tabularnewline
153 & 16 & 14.2943988206884 & 1.70560117931163 \tabularnewline
154 & 16 & 11.9043874509472 & 4.09561254905284 \tabularnewline
155 & 14 & 12.6651227534132 & 1.33487724658678 \tabularnewline
156 & 10 & 12.254920048858 & -2.25492004885799 \tabularnewline
157 & 17 & 16.6426510860964 & 0.357348913903601 \tabularnewline
158 & 13 & 11.5741053463768 & 1.42589465362324 \tabularnewline
159 & 15 & 13.9328442448159 & 1.0671557551841 \tabularnewline
160 & 16 & 15.0569093290568 & 0.943090670943157 \tabularnewline
161 & 12 & 12.2567515916689 & -0.256751591668855 \tabularnewline
162 & 13 & 12.6418870521746 & 0.358112947825393 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=161020&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]16.6511638111484[/C][C]-3.65116381114838[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.068989069993[/C][C]-0.0689890699930241[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.6001792884241[/C][C]2.39982071157585[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.0212178401024[/C][C]2.97878215989763[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.9978991946731[/C][C]-1.99789919467307[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.2493733287637[/C][C]-2.24937332876369[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.5204987204834[/C][C]3.47950127951663[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.9624510138564[/C][C]-1.96245101385643[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.1132780128503[/C][C]-2.1132780128503[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.5534645271888[/C][C]2.44653547281119[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.4462439906789[/C][C]0.553756009321129[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.4904024055108[/C][C]-0.49040240551084[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.4998843252806[/C][C]0.500115674719385[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.6574362932137[/C][C]0.342563706786262[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.7609003665975[/C][C]-0.760900366597455[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.3505807933259[/C][C]-0.350580793325935[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.8567951077952[/C][C]0.143204892204789[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.3621216685721[/C][C]3.63787833142792[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.9917053042623[/C][C]2.00829469573769[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.827416982671[/C][C]0.172583017329034[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.4199215165031[/C][C]0.580078483496913[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.1262329958297[/C][C]0.873767004170278[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.8835988283728[/C][C]2.11640117162724[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.320549062599[/C][C]0.679450937401008[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.7070861528581[/C][C]2.2929138471419[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.7962156501011[/C][C]0.203784349898871[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.4996169653168[/C][C]0.500383034683249[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.425824164235[/C][C]-1.42582416423499[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.4510668774949[/C][C]0.548933122505073[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.2149554491827[/C][C]-0.214955449182659[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.8213250001047[/C][C]-0.821325000104654[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.3923703802388[/C][C]-0.392370380238811[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.3931072461806[/C][C]-1.39310724618065[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.6669462395201[/C][C]0.333053760479855[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.4903867646498[/C][C]-1.49038676464977[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]13.0411358153156[/C][C]-6.04113581531563[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]10.9311077677391[/C][C]-0.931107767739136[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.733974054813[/C][C]-1.73397405481296[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.4079595030732[/C][C]1.59204049692679[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.6159882540945[/C][C]1.38401174590555[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.0243836558002[/C][C]0.975616344199819[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.8294718948123[/C][C]-1.82947189481232[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.9132894336987[/C][C]2.08671056630133[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.3677980613534[/C][C]-0.367798061353357[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.3283885338423[/C][C]-1.32838853384229[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.892056898258[/C][C]-4.89205689825805[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.6599472993998[/C][C]-2.65994729939985[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.0497117697408[/C][C]-0.0497117697408062[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]14.8867597009895[/C][C]1.11324029901046[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.8909427740911[/C][C]-1.89094277409109[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.4178333127765[/C][C]-0.417833312776544[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.0658298964765[/C][C]-0.0658298964764859[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.5748031936782[/C][C]-2.57480319367825[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.9750166279313[/C][C]0.0249833720686972[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.4747650833591[/C][C]-2.4747650833591[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.7319327923355[/C][C]1.26806720766452[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.1882475371727[/C][C]-0.188247537172711[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.1494170947336[/C][C]0.850582905266352[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.2817072641108[/C][C]-0.281707264110767[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.3365498532343[/C][C]1.6634501467657[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.489623040895[/C][C]0.510376959104991[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.9877635184303[/C][C]0.0122364815697093[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.5161328427869[/C][C]-0.516132842786906[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.8805815109151[/C][C]-0.88058151091514[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.8111503382814[/C][C]0.188849661718557[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.7631625850837[/C][C]1.23683741491626[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.3021071188263[/C][C]1.69789288117367[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.6030287015278[/C][C]3.39697129847218[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.7862421359069[/C][C]-3.78624213590694[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.2265436461892[/C][C]0.773456353810819[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.4531564597402[/C][C]-3.45315645974023[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.365344283727[/C][C]-0.365344283726967[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.2085876847637[/C][C]1.79141231523628[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]16.9382028227653[/C][C]1.06179717723467[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.6454505267693[/C][C]0.354549473230739[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.9272831093992[/C][C]3.07271689060076[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.4013529640169[/C][C]-0.4013529640169[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.7990089894376[/C][C]1.20099101056245[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.7096002965715[/C][C]-1.70960029657147[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.2318198969495[/C][C]-0.23181989694948[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.3820235925886[/C][C]0.617976407411428[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]15.7241518430198[/C][C]4.27584815698021[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.7159839780042[/C][C]0.284016021995754[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.5455391068136[/C][C]-0.545539106813587[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]15.1288747743425[/C][C]-0.128874774342529[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.6417391174377[/C][C]1.35826088256229[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]13.8882028797264[/C][C]0.111797120273573[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.2770813416706[/C][C]0.722918658329383[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.7657985692307[/C][C]1.23420143076928[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]13.9954460222936[/C][C]1.00455397770645[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.4806960203101[/C][C]-1.48069602031009[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.9260600145155[/C][C]0.073939985484493[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.2462925658693[/C][C]0.753707434130738[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.0658512406601[/C][C]-0.0658512406600965[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.1933341352671[/C][C]-2.19333413526711[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.060308689728[/C][C]0.939691310272005[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.730793009384[/C][C]0.269206990615992[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.1395233889667[/C][C]1.86047661103331[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]15.7259002210727[/C][C]0.274099778927299[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.3524160629756[/C][C]-0.352416062975559[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.0577899848507[/C][C]-1.05778998485071[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.6392240017428[/C][C]1.36077599825718[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.2808069672802[/C][C]2.71919303271979[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.6536096450471[/C][C]0.346390354952948[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.1275862478966[/C][C]1.87241375210336[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.0060733649987[/C][C]-2.00607336499873[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.8611341644644[/C][C]1.13886583553563[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.7432850266192[/C][C]0.256714973380814[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.4849373562822[/C][C]1.51506264371779[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.7267949574546[/C][C]-0.726794957454556[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.901464706553[/C][C]1.09853529344704[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.8076948476271[/C][C]0.192305152372948[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.3781823318163[/C][C]2.62181766818366[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.3102117255214[/C][C]-1.31021172552138[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.7061067261053[/C][C]-2.70610672610529[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.6895534039824[/C][C]1.31044659601765[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.6471995661366[/C][C]-1.64719956613662[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]12.8790440717276[/C][C]1.12095592827245[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]15.1053748738777[/C][C]-2.10537487387767[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.4965310566256[/C][C]0.503468943374428[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.3200233554403[/C][C]-1.32002335544035[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]16.0448387417756[/C][C]-0.0448387417755789[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.9172544181624[/C][C]-2.91725441816242[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]17.0023326374339[/C][C]-1.00233263743387[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.8586143166685[/C][C]-0.858614316668478[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.5611273436006[/C][C]-0.56112734360065[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.1768471400343[/C][C]-0.17684714003431[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.0170611448456[/C][C]0.982938855154393[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]14.0636483656247[/C][C]0.936351634375277[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.7625520885984[/C][C]-2.76255208859841[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]13.8322479651124[/C][C]2.16775203488759[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.0526315249304[/C][C]-3.05263152493035[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]13.9385623083331[/C][C]2.06143769166689[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.5595700316117[/C][C]-1.5595700316117[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.28176402277[/C][C]-1.28176402277003[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.3203400831997[/C][C]-0.320340083199718[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.0892489471152[/C][C]0.910751052884821[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.5786603683836[/C][C]0.421339631616382[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.4644442341623[/C][C]-2.46444423416226[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.3954892718818[/C][C]-1.39548927188183[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.0207099911854[/C][C]-5.02070999118537[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.4422057435387[/C][C]2.55779425646125[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]12.9282095119135[/C][C]2.07179048808648[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.2984330465091[/C][C]0.701566953490942[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.4863836250474[/C][C]1.51361637495262[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.0103620078051[/C][C]-4.01036200780513[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.5953266419272[/C][C]2.40467335807285[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]14.8917604921053[/C][C]-1.89176049210531[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.8212269374424[/C][C]1.17877306255763[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.9863710516233[/C][C]0.013628948376688[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]12.9605421828954[/C][C]-2.96054218289535[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.2022452493487[/C][C]-1.20224524934874[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.2943988206884[/C][C]1.70560117931163[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]11.9043874509472[/C][C]4.09561254905284[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.6651227534132[/C][C]1.33487724658678[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.254920048858[/C][C]-2.25492004885799[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.6426510860964[/C][C]0.357348913903601[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.5741053463768[/C][C]1.42589465362324[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.9328442448159[/C][C]1.0671557551841[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.0569093290568[/C][C]0.943090670943157[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.2567515916689[/C][C]-0.256751591668855[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.6418870521746[/C][C]0.358112947825393[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=161020&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=161020&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.6511638111484-3.65116381114838
21616.068989069993-0.0689890699930241
31916.60017928842412.39982071157585
41512.02121784010242.97878215989763
51415.9978991946731-1.99789919467307
61315.2493733287637-2.24937332876369
71915.52049872048343.47950127951663
81516.9624510138564-1.96245101385643
91416.1132780128503-2.1132780128503
101512.55346452718882.44653547281119
111615.44624399067890.553756009321129
121616.4904024055108-0.49040240551084
131615.49988432528060.500115674719385
141615.65743629321370.342563706786262
151717.7609003665975-0.760900366597455
161515.3505807933259-0.350580793325935
171514.85679510779520.143204892204789
182016.36212166857213.63787833142792
191815.99170530426232.00829469573769
201615.8274169826710.172583017329034
211615.41992151650310.580078483496913
221615.12623299582970.873767004170278
231916.88359882837282.11640117162724
241615.3205490625990.679450937401008
251714.70708615285812.2929138471419
261716.79621565010110.203784349898871
271615.49961696531680.500383034683249
281516.425824164235-1.42582416423499
291615.45106687749490.548933122505073
301414.2149554491827-0.214955449182659
311515.8213250001047-0.821325000104654
321212.3923703802388-0.392370380238811
331415.3931072461806-1.39310724618065
341615.66694623952010.333053760479855
351415.4903867646498-1.49038676464977
36713.0411358153156-6.04113581531563
371010.9311077677391-0.931107767739136
381415.733974054813-1.73397405481296
391614.40795950307321.59204049692679
401614.61598825409451.38401174590555
411615.02438365580020.975616344199819
421415.8294718948123-1.82947189481232
432017.91328943369872.08671056630133
441414.3677980613534-0.367798061353357
451415.3283885338423-1.32838853384229
461115.892056898258-4.89205689825805
471416.6599472993998-2.65994729939985
481515.0497117697408-0.0497117697408062
491614.88675970098951.11324029901046
501415.8909427740911-1.89094277409109
511616.4178333127765-0.417833312776544
521414.0658298964765-0.0658298964764859
531214.5748031936782-2.57480319367825
541615.97501662793130.0249833720686972
55911.4747650833591-2.4747650833591
561412.73193279233551.26806720766452
571616.1882475371727-0.188247537172711
581615.14941709473360.850582905266352
591515.2817072641108-0.281707264110767
601614.33654985323431.6634501467657
611211.4896230408950.510376959104991
621615.98776351843030.0122364815697093
631616.5161328427869-0.516132842786906
641414.8805815109151-0.88058151091514
651615.81115033828140.188849661718557
661715.76316258508371.23683741491626
671816.30210711882631.69789288117367
681814.60302870152783.39697129847218
691215.7862421359069-3.78624213590694
701615.22654364618920.773456353810819
711013.4531564597402-3.45315645974023
721414.365344283727-0.365344283726967
731816.20858768476371.79141231523628
741816.93820282276531.06179717723467
751615.64545052676930.354549473230739
761713.92728310939923.07271689060076
771616.4013529640169-0.4013529640169
781614.79900898943761.20099101056245
791314.7096002965715-1.70960029657147
801616.2318198969495-0.23181989694948
811615.38202359258860.617976407411428
822015.72415184301984.27584815698021
831615.71598397800420.284016021995754
841515.5455391068136-0.545539106813587
851515.1288747743425-0.128874774342529
861614.64173911743771.35826088256229
871413.88820287972640.111797120273573
881615.27708134167060.722918658329383
891614.76579856923071.23420143076928
901513.99544602229361.00455397770645
911213.4806960203101-1.48069602031009
921716.92606001451550.073939985484493
931615.24629256586930.753707434130738
941515.0658512406601-0.0658512406600965
951315.1933341352671-2.19333413526711
961615.0603086897280.939691310272005
971615.7307930093840.269206990615992
981614.13952338896671.86047661103331
991615.72590022107270.274099778927299
1001414.3524160629756-0.352416062975559
1011617.0577899848507-1.05778998485071
1021614.63922400174281.36077599825718
1032017.28080696728022.71919303271979
1041514.65360964504710.346390354952948
1051614.12758624789661.87241375210336
1061315.0060733649987-2.00607336499873
1071715.86113416446441.13886583553563
1081615.74328502661920.256714973380814
1091614.48493735628221.51506264371779
1101212.7267949574546-0.726794957454556
1111614.9014647065531.09853529344704
1121615.80769484762710.192305152372948
1131714.37818233181632.62181766818366
1141314.3102117255214-1.31021172552138
1151214.7061067261053-2.70610672610529
1161816.68955340398241.31044659601765
1171415.6471995661366-1.64719956613662
1181412.87904407172761.12095592827245
1191315.1053748738777-2.10537487387767
1201615.49653105662560.503468943374428
1211314.3200233554403-1.32002335544035
1221616.0448387417756-0.0448387417755789
1231315.9172544181624-2.91725441816242
1241617.0023326374339-1.00233263743387
1251515.8586143166685-0.858614316668478
1261616.5611273436006-0.56112734360065
1271515.1768471400343-0.17684714003431
1281716.01706114484560.982938855154393
1291514.06364836562470.936351634375277
1301214.7625520885984-2.76255208859841
1311613.83224796511242.16775203488759
1321013.0526315249304-3.05263152493035
1331613.93856230833312.06143769166689
1341213.5595700316117-1.5595700316117
1351415.28176402277-1.28176402277003
1361515.3203400831997-0.320340083199718
1371312.08924894711520.910751052884821
1381514.57866036838360.421339631616382
1391113.4644442341623-2.46444423416226
1401213.3954892718818-1.39548927188183
141813.0207099911854-5.02070999118537
1421613.44220574353872.55779425646125
1431512.92820951191352.07179048808648
1441716.29843304650910.701566953490942
1451614.48638362504741.51361637495262
1461014.0103620078051-4.01036200780513
1471815.59532664192722.40467335807285
1481314.8917604921053-1.89176049210531
1491614.82122693744241.17877306255763
1501312.98637105162330.013628948376688
1511012.9605421828954-2.96054218289535
1521516.2022452493487-1.20224524934874
1531614.29439882068841.70560117931163
1541611.90438745094724.09561254905284
1551412.66512275341321.33487724658678
1561012.254920048858-2.25492004885799
1571716.64265108609640.357348913903601
1581311.57410534637681.42589465362324
1591513.93284424481591.0671557551841
1601615.05690932905680.943090670943157
1611212.2567515916689-0.256751591668855
1621312.64188705217460.358112947825393






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.2001591841433830.4003183682867670.799840815856617
130.2812275516348730.5624551032697450.718772448365127
140.356591190187160.713182380374320.64340880981284
150.3633921700282640.7267843400565290.636607829971736
160.3647896401073460.7295792802146920.635210359892654
170.3391244460283760.6782488920567530.660875553971624
180.7002388810687330.5995222378625350.299761118931267
190.8036402055849030.3927195888301950.196359794415097
200.7364647839159320.5270704321681360.263535216084068
210.6830571690301390.6338856619397210.31694283096986
220.6398644759986450.720271048002710.360135524001355
230.6116348499578990.7767303000842020.388365150042101
240.5622945005579640.8754109988840720.437705499442036
250.5081444284369110.9837111431261780.491855571563089
260.4591278005942550.918255601188510.540872199405745
270.4047645116798480.8095290233596960.595235488320152
280.516923598226110.9661528035477790.48307640177389
290.4630787437388630.9261574874777260.536921256261137
300.5067470133537680.9865059732924630.493252986646232
310.445653772948380.8913075458967590.55434622705162
320.4654913397503070.9309826795006140.534508660249693
330.4367569265408980.8735138530817960.563243073459102
340.3773557587821510.7547115175643010.622644241217849
350.3832355698449760.7664711396899530.616764430155024
360.8724909698409760.2550180603180490.127509030159024
370.8542436372731950.2915127254536110.145756362726805
380.8438681901927520.3122636196144960.156131809807248
390.8698317034727190.2603365930545610.130168296527281
400.8585988602357170.2828022795285660.141401139764283
410.8360486543090750.3279026913818490.163951345690925
420.8182951753119650.3634096493760690.181704824688034
430.8253833365567390.3492333268865210.174616663443261
440.7898716261357390.4202567477285220.210128373864261
450.7540172524213470.4919654951573050.245982747578653
460.8911199191820460.2177601616359080.108880080817954
470.9039626312979580.1920747374040840.096037368702042
480.8856411382975680.2287177234048630.114358861702432
490.8671810946729950.265637810654010.132818905327005
500.8636303011263280.2727393977473450.136369698873672
510.8345249556499840.3309500887000320.165475044350016
520.8019719580838470.3960560838323050.198028041916153
530.821330729125680.3573385417486390.17866927087432
540.8055924417744520.3888151164510970.194407558225548
550.816486645797130.367026708405740.18351335420287
560.813364083862310.3732718322753810.18663591613769
570.7810801422515510.4378397154968970.218919857748449
580.7663771026606770.4672457946786460.233622897339323
590.7328104007222210.5343791985555580.267189599277779
600.744636135836670.5107277283266590.25536386416333
610.711333985389180.577332029221640.28866601461082
620.6759072367422440.6481855265155130.324092763257756
630.6399958701695310.7200082596609380.360004129830469
640.6117066442509290.7765867114981420.388293355749071
650.5794832101428360.8410335797143290.420516789857164
660.5542825460207450.8914349079585090.445717453979255
670.5437062630960330.9125874738079330.456293736903967
680.6698992816437540.6602014367124920.330100718356246
690.7909096769110710.4181806461778590.209090323088929
700.761046483807870.477907032384260.23895351619213
710.8401878380572550.319624323885490.159812161942745
720.8131891373139590.3736217253720810.186810862686041
730.8099536174485690.3800927651028620.190046382551431
740.7894745668928120.4210508662143760.210525433107188
750.7552054033078340.4895891933843320.244794596692166
760.8040622695547070.3918754608905860.195937730445293
770.7719748292673350.4560503414653310.228025170732666
780.7477459857347420.5045080285305150.252254014265258
790.7487804578031740.5024390843936510.251219542196826
800.7132844751086060.5734310497827880.286715524891394
810.6788677172700050.6422645654599890.321132282729995
820.8213085739840180.3573828520319650.178691426015982
830.7896058793621110.4207882412757780.210394120637889
840.7617698805020790.4764602389958420.238230119497921
850.7293346585134930.5413306829730140.270665341486507
860.7072459938473790.5855080123052420.292754006152621
870.6651549683168330.6696900633663340.334845031683167
880.6246385657039510.7507228685920980.375361434296049
890.5926015827373830.8147968345252340.407398417262617
900.5657793307513190.8684413384973620.434220669248681
910.553552243242820.8928955135143590.44644775675718
920.510233005239440.9795339895211210.48976699476056
930.4692766912899870.9385533825799740.530723308710013
940.4233403195431140.8466806390862280.576659680456886
950.4576132044957760.9152264089915520.542386795504224
960.4183041801277990.8366083602555970.581695819872201
970.375931890087520.751863780175040.62406810991248
980.3665172623620620.7330345247241240.633482737637938
990.3232185448397330.6464370896794650.676781455160267
1000.2890216649173430.5780433298346850.710978335082657
1010.2614859561158560.5229719122317120.738514043884144
1020.2445414570278210.4890829140556410.75545854297218
1030.2925823490399250.585164698079850.707417650960075
1040.2517730597024240.5035461194048480.748226940297576
1050.2722733738561850.544546747712370.727726626143815
1060.274160811060880.5483216221217610.72583918893912
1070.2613965139555540.5227930279111080.738603486044446
1080.2361073074569810.4722146149139620.763892692543019
1090.2377558581873440.4755117163746880.762244141812656
1100.2141691765788950.428338353157790.785830823421105
1110.1984665991647180.3969331983294350.801533400835282
1120.1717647990441670.3435295980883330.828235200955833
1130.2528138973588490.5056277947176980.747186102641151
1140.2223813805759040.4447627611518070.777618619424096
1150.2332787916872210.4665575833744430.766721208312779
1160.2172821176320290.4345642352640570.782717882367971
1170.1913195018610850.3826390037221690.808680498138915
1180.2090294449352280.4180588898704560.790970555064772
1190.2073719392093760.4147438784187530.792628060790624
1200.2144881400037570.4289762800075140.785511859996243
1210.1838613219923590.3677226439847190.816138678007641
1220.150980009182140.301960018364280.84901999081786
1230.1603062823454060.3206125646908120.839693717654594
1240.1307343444168440.2614686888336890.869265655583156
1250.1051186621064120.2102373242128240.894881337893588
1260.08170348094936180.1634069618987240.918296519050638
1270.06184653239681130.1236930647936230.938153467603189
1280.06980687492938550.1396137498587710.930193125070614
1290.05885216102818260.1177043220563650.941147838971817
1300.05730868577219680.1146173715443940.942691314227803
1310.07233651759134530.1446730351826910.927663482408655
1320.07676354650552930.1535270930110590.923236453494471
1330.1843921335836870.3687842671673750.815607866416313
1340.1622460019377640.3244920038755270.837753998062236
1350.147166137679360.294332275358720.85283386232064
1360.1136706376473270.2273412752946540.886329362352673
1370.08847745533905590.1769549106781120.911522544660944
1380.07598845736243480.151976914724870.924011542637565
1390.1194016769453590.2388033538907180.880598323054641
1400.08880492966310680.1776098593262140.911195070336893
1410.496694345932880.9933886918657610.50330565406712
1420.430278150359210.860556300718420.56972184964079
1430.4116792810807990.8233585621615980.588320718919201
1440.6564040230082930.6871919539834130.343595976991707
1450.7041499841401610.5917000317196780.295850015859839
1460.6227924595758250.7544150808483510.377207540424175
1470.5426394972927290.9147210054145430.457360502707271
1480.6374238924937470.7251522150125060.362576107506253
1490.5196278160282750.9607443679434490.480372183971725
1500.4163877727677750.832775545535550.583612227232225

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.200159184143383 & 0.400318368286767 & 0.799840815856617 \tabularnewline
13 & 0.281227551634873 & 0.562455103269745 & 0.718772448365127 \tabularnewline
14 & 0.35659119018716 & 0.71318238037432 & 0.64340880981284 \tabularnewline
15 & 0.363392170028264 & 0.726784340056529 & 0.636607829971736 \tabularnewline
16 & 0.364789640107346 & 0.729579280214692 & 0.635210359892654 \tabularnewline
17 & 0.339124446028376 & 0.678248892056753 & 0.660875553971624 \tabularnewline
18 & 0.700238881068733 & 0.599522237862535 & 0.299761118931267 \tabularnewline
19 & 0.803640205584903 & 0.392719588830195 & 0.196359794415097 \tabularnewline
20 & 0.736464783915932 & 0.527070432168136 & 0.263535216084068 \tabularnewline
21 & 0.683057169030139 & 0.633885661939721 & 0.31694283096986 \tabularnewline
22 & 0.639864475998645 & 0.72027104800271 & 0.360135524001355 \tabularnewline
23 & 0.611634849957899 & 0.776730300084202 & 0.388365150042101 \tabularnewline
24 & 0.562294500557964 & 0.875410998884072 & 0.437705499442036 \tabularnewline
25 & 0.508144428436911 & 0.983711143126178 & 0.491855571563089 \tabularnewline
26 & 0.459127800594255 & 0.91825560118851 & 0.540872199405745 \tabularnewline
27 & 0.404764511679848 & 0.809529023359696 & 0.595235488320152 \tabularnewline
28 & 0.51692359822611 & 0.966152803547779 & 0.48307640177389 \tabularnewline
29 & 0.463078743738863 & 0.926157487477726 & 0.536921256261137 \tabularnewline
30 & 0.506747013353768 & 0.986505973292463 & 0.493252986646232 \tabularnewline
31 & 0.44565377294838 & 0.891307545896759 & 0.55434622705162 \tabularnewline
32 & 0.465491339750307 & 0.930982679500614 & 0.534508660249693 \tabularnewline
33 & 0.436756926540898 & 0.873513853081796 & 0.563243073459102 \tabularnewline
34 & 0.377355758782151 & 0.754711517564301 & 0.622644241217849 \tabularnewline
35 & 0.383235569844976 & 0.766471139689953 & 0.616764430155024 \tabularnewline
36 & 0.872490969840976 & 0.255018060318049 & 0.127509030159024 \tabularnewline
37 & 0.854243637273195 & 0.291512725453611 & 0.145756362726805 \tabularnewline
38 & 0.843868190192752 & 0.312263619614496 & 0.156131809807248 \tabularnewline
39 & 0.869831703472719 & 0.260336593054561 & 0.130168296527281 \tabularnewline
40 & 0.858598860235717 & 0.282802279528566 & 0.141401139764283 \tabularnewline
41 & 0.836048654309075 & 0.327902691381849 & 0.163951345690925 \tabularnewline
42 & 0.818295175311965 & 0.363409649376069 & 0.181704824688034 \tabularnewline
43 & 0.825383336556739 & 0.349233326886521 & 0.174616663443261 \tabularnewline
44 & 0.789871626135739 & 0.420256747728522 & 0.210128373864261 \tabularnewline
45 & 0.754017252421347 & 0.491965495157305 & 0.245982747578653 \tabularnewline
46 & 0.891119919182046 & 0.217760161635908 & 0.108880080817954 \tabularnewline
47 & 0.903962631297958 & 0.192074737404084 & 0.096037368702042 \tabularnewline
48 & 0.885641138297568 & 0.228717723404863 & 0.114358861702432 \tabularnewline
49 & 0.867181094672995 & 0.26563781065401 & 0.132818905327005 \tabularnewline
50 & 0.863630301126328 & 0.272739397747345 & 0.136369698873672 \tabularnewline
51 & 0.834524955649984 & 0.330950088700032 & 0.165475044350016 \tabularnewline
52 & 0.801971958083847 & 0.396056083832305 & 0.198028041916153 \tabularnewline
53 & 0.82133072912568 & 0.357338541748639 & 0.17866927087432 \tabularnewline
54 & 0.805592441774452 & 0.388815116451097 & 0.194407558225548 \tabularnewline
55 & 0.81648664579713 & 0.36702670840574 & 0.18351335420287 \tabularnewline
56 & 0.81336408386231 & 0.373271832275381 & 0.18663591613769 \tabularnewline
57 & 0.781080142251551 & 0.437839715496897 & 0.218919857748449 \tabularnewline
58 & 0.766377102660677 & 0.467245794678646 & 0.233622897339323 \tabularnewline
59 & 0.732810400722221 & 0.534379198555558 & 0.267189599277779 \tabularnewline
60 & 0.74463613583667 & 0.510727728326659 & 0.25536386416333 \tabularnewline
61 & 0.71133398538918 & 0.57733202922164 & 0.28866601461082 \tabularnewline
62 & 0.675907236742244 & 0.648185526515513 & 0.324092763257756 \tabularnewline
63 & 0.639995870169531 & 0.720008259660938 & 0.360004129830469 \tabularnewline
64 & 0.611706644250929 & 0.776586711498142 & 0.388293355749071 \tabularnewline
65 & 0.579483210142836 & 0.841033579714329 & 0.420516789857164 \tabularnewline
66 & 0.554282546020745 & 0.891434907958509 & 0.445717453979255 \tabularnewline
67 & 0.543706263096033 & 0.912587473807933 & 0.456293736903967 \tabularnewline
68 & 0.669899281643754 & 0.660201436712492 & 0.330100718356246 \tabularnewline
69 & 0.790909676911071 & 0.418180646177859 & 0.209090323088929 \tabularnewline
70 & 0.76104648380787 & 0.47790703238426 & 0.23895351619213 \tabularnewline
71 & 0.840187838057255 & 0.31962432388549 & 0.159812161942745 \tabularnewline
72 & 0.813189137313959 & 0.373621725372081 & 0.186810862686041 \tabularnewline
73 & 0.809953617448569 & 0.380092765102862 & 0.190046382551431 \tabularnewline
74 & 0.789474566892812 & 0.421050866214376 & 0.210525433107188 \tabularnewline
75 & 0.755205403307834 & 0.489589193384332 & 0.244794596692166 \tabularnewline
76 & 0.804062269554707 & 0.391875460890586 & 0.195937730445293 \tabularnewline
77 & 0.771974829267335 & 0.456050341465331 & 0.228025170732666 \tabularnewline
78 & 0.747745985734742 & 0.504508028530515 & 0.252254014265258 \tabularnewline
79 & 0.748780457803174 & 0.502439084393651 & 0.251219542196826 \tabularnewline
80 & 0.713284475108606 & 0.573431049782788 & 0.286715524891394 \tabularnewline
81 & 0.678867717270005 & 0.642264565459989 & 0.321132282729995 \tabularnewline
82 & 0.821308573984018 & 0.357382852031965 & 0.178691426015982 \tabularnewline
83 & 0.789605879362111 & 0.420788241275778 & 0.210394120637889 \tabularnewline
84 & 0.761769880502079 & 0.476460238995842 & 0.238230119497921 \tabularnewline
85 & 0.729334658513493 & 0.541330682973014 & 0.270665341486507 \tabularnewline
86 & 0.707245993847379 & 0.585508012305242 & 0.292754006152621 \tabularnewline
87 & 0.665154968316833 & 0.669690063366334 & 0.334845031683167 \tabularnewline
88 & 0.624638565703951 & 0.750722868592098 & 0.375361434296049 \tabularnewline
89 & 0.592601582737383 & 0.814796834525234 & 0.407398417262617 \tabularnewline
90 & 0.565779330751319 & 0.868441338497362 & 0.434220669248681 \tabularnewline
91 & 0.55355224324282 & 0.892895513514359 & 0.44644775675718 \tabularnewline
92 & 0.51023300523944 & 0.979533989521121 & 0.48976699476056 \tabularnewline
93 & 0.469276691289987 & 0.938553382579974 & 0.530723308710013 \tabularnewline
94 & 0.423340319543114 & 0.846680639086228 & 0.576659680456886 \tabularnewline
95 & 0.457613204495776 & 0.915226408991552 & 0.542386795504224 \tabularnewline
96 & 0.418304180127799 & 0.836608360255597 & 0.581695819872201 \tabularnewline
97 & 0.37593189008752 & 0.75186378017504 & 0.62406810991248 \tabularnewline
98 & 0.366517262362062 & 0.733034524724124 & 0.633482737637938 \tabularnewline
99 & 0.323218544839733 & 0.646437089679465 & 0.676781455160267 \tabularnewline
100 & 0.289021664917343 & 0.578043329834685 & 0.710978335082657 \tabularnewline
101 & 0.261485956115856 & 0.522971912231712 & 0.738514043884144 \tabularnewline
102 & 0.244541457027821 & 0.489082914055641 & 0.75545854297218 \tabularnewline
103 & 0.292582349039925 & 0.58516469807985 & 0.707417650960075 \tabularnewline
104 & 0.251773059702424 & 0.503546119404848 & 0.748226940297576 \tabularnewline
105 & 0.272273373856185 & 0.54454674771237 & 0.727726626143815 \tabularnewline
106 & 0.27416081106088 & 0.548321622121761 & 0.72583918893912 \tabularnewline
107 & 0.261396513955554 & 0.522793027911108 & 0.738603486044446 \tabularnewline
108 & 0.236107307456981 & 0.472214614913962 & 0.763892692543019 \tabularnewline
109 & 0.237755858187344 & 0.475511716374688 & 0.762244141812656 \tabularnewline
110 & 0.214169176578895 & 0.42833835315779 & 0.785830823421105 \tabularnewline
111 & 0.198466599164718 & 0.396933198329435 & 0.801533400835282 \tabularnewline
112 & 0.171764799044167 & 0.343529598088333 & 0.828235200955833 \tabularnewline
113 & 0.252813897358849 & 0.505627794717698 & 0.747186102641151 \tabularnewline
114 & 0.222381380575904 & 0.444762761151807 & 0.777618619424096 \tabularnewline
115 & 0.233278791687221 & 0.466557583374443 & 0.766721208312779 \tabularnewline
116 & 0.217282117632029 & 0.434564235264057 & 0.782717882367971 \tabularnewline
117 & 0.191319501861085 & 0.382639003722169 & 0.808680498138915 \tabularnewline
118 & 0.209029444935228 & 0.418058889870456 & 0.790970555064772 \tabularnewline
119 & 0.207371939209376 & 0.414743878418753 & 0.792628060790624 \tabularnewline
120 & 0.214488140003757 & 0.428976280007514 & 0.785511859996243 \tabularnewline
121 & 0.183861321992359 & 0.367722643984719 & 0.816138678007641 \tabularnewline
122 & 0.15098000918214 & 0.30196001836428 & 0.84901999081786 \tabularnewline
123 & 0.160306282345406 & 0.320612564690812 & 0.839693717654594 \tabularnewline
124 & 0.130734344416844 & 0.261468688833689 & 0.869265655583156 \tabularnewline
125 & 0.105118662106412 & 0.210237324212824 & 0.894881337893588 \tabularnewline
126 & 0.0817034809493618 & 0.163406961898724 & 0.918296519050638 \tabularnewline
127 & 0.0618465323968113 & 0.123693064793623 & 0.938153467603189 \tabularnewline
128 & 0.0698068749293855 & 0.139613749858771 & 0.930193125070614 \tabularnewline
129 & 0.0588521610281826 & 0.117704322056365 & 0.941147838971817 \tabularnewline
130 & 0.0573086857721968 & 0.114617371544394 & 0.942691314227803 \tabularnewline
131 & 0.0723365175913453 & 0.144673035182691 & 0.927663482408655 \tabularnewline
132 & 0.0767635465055293 & 0.153527093011059 & 0.923236453494471 \tabularnewline
133 & 0.184392133583687 & 0.368784267167375 & 0.815607866416313 \tabularnewline
134 & 0.162246001937764 & 0.324492003875527 & 0.837753998062236 \tabularnewline
135 & 0.14716613767936 & 0.29433227535872 & 0.85283386232064 \tabularnewline
136 & 0.113670637647327 & 0.227341275294654 & 0.886329362352673 \tabularnewline
137 & 0.0884774553390559 & 0.176954910678112 & 0.911522544660944 \tabularnewline
138 & 0.0759884573624348 & 0.15197691472487 & 0.924011542637565 \tabularnewline
139 & 0.119401676945359 & 0.238803353890718 & 0.880598323054641 \tabularnewline
140 & 0.0888049296631068 & 0.177609859326214 & 0.911195070336893 \tabularnewline
141 & 0.49669434593288 & 0.993388691865761 & 0.50330565406712 \tabularnewline
142 & 0.43027815035921 & 0.86055630071842 & 0.56972184964079 \tabularnewline
143 & 0.411679281080799 & 0.823358562161598 & 0.588320718919201 \tabularnewline
144 & 0.656404023008293 & 0.687191953983413 & 0.343595976991707 \tabularnewline
145 & 0.704149984140161 & 0.591700031719678 & 0.295850015859839 \tabularnewline
146 & 0.622792459575825 & 0.754415080848351 & 0.377207540424175 \tabularnewline
147 & 0.542639497292729 & 0.914721005414543 & 0.457360502707271 \tabularnewline
148 & 0.637423892493747 & 0.725152215012506 & 0.362576107506253 \tabularnewline
149 & 0.519627816028275 & 0.960744367943449 & 0.480372183971725 \tabularnewline
150 & 0.416387772767775 & 0.83277554553555 & 0.583612227232225 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=161020&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]0.200159184143383[/C][C]0.400318368286767[/C][C]0.799840815856617[/C][/ROW]
[ROW][C]13[/C][C]0.281227551634873[/C][C]0.562455103269745[/C][C]0.718772448365127[/C][/ROW]
[ROW][C]14[/C][C]0.35659119018716[/C][C]0.71318238037432[/C][C]0.64340880981284[/C][/ROW]
[ROW][C]15[/C][C]0.363392170028264[/C][C]0.726784340056529[/C][C]0.636607829971736[/C][/ROW]
[ROW][C]16[/C][C]0.364789640107346[/C][C]0.729579280214692[/C][C]0.635210359892654[/C][/ROW]
[ROW][C]17[/C][C]0.339124446028376[/C][C]0.678248892056753[/C][C]0.660875553971624[/C][/ROW]
[ROW][C]18[/C][C]0.700238881068733[/C][C]0.599522237862535[/C][C]0.299761118931267[/C][/ROW]
[ROW][C]19[/C][C]0.803640205584903[/C][C]0.392719588830195[/C][C]0.196359794415097[/C][/ROW]
[ROW][C]20[/C][C]0.736464783915932[/C][C]0.527070432168136[/C][C]0.263535216084068[/C][/ROW]
[ROW][C]21[/C][C]0.683057169030139[/C][C]0.633885661939721[/C][C]0.31694283096986[/C][/ROW]
[ROW][C]22[/C][C]0.639864475998645[/C][C]0.72027104800271[/C][C]0.360135524001355[/C][/ROW]
[ROW][C]23[/C][C]0.611634849957899[/C][C]0.776730300084202[/C][C]0.388365150042101[/C][/ROW]
[ROW][C]24[/C][C]0.562294500557964[/C][C]0.875410998884072[/C][C]0.437705499442036[/C][/ROW]
[ROW][C]25[/C][C]0.508144428436911[/C][C]0.983711143126178[/C][C]0.491855571563089[/C][/ROW]
[ROW][C]26[/C][C]0.459127800594255[/C][C]0.91825560118851[/C][C]0.540872199405745[/C][/ROW]
[ROW][C]27[/C][C]0.404764511679848[/C][C]0.809529023359696[/C][C]0.595235488320152[/C][/ROW]
[ROW][C]28[/C][C]0.51692359822611[/C][C]0.966152803547779[/C][C]0.48307640177389[/C][/ROW]
[ROW][C]29[/C][C]0.463078743738863[/C][C]0.926157487477726[/C][C]0.536921256261137[/C][/ROW]
[ROW][C]30[/C][C]0.506747013353768[/C][C]0.986505973292463[/C][C]0.493252986646232[/C][/ROW]
[ROW][C]31[/C][C]0.44565377294838[/C][C]0.891307545896759[/C][C]0.55434622705162[/C][/ROW]
[ROW][C]32[/C][C]0.465491339750307[/C][C]0.930982679500614[/C][C]0.534508660249693[/C][/ROW]
[ROW][C]33[/C][C]0.436756926540898[/C][C]0.873513853081796[/C][C]0.563243073459102[/C][/ROW]
[ROW][C]34[/C][C]0.377355758782151[/C][C]0.754711517564301[/C][C]0.622644241217849[/C][/ROW]
[ROW][C]35[/C][C]0.383235569844976[/C][C]0.766471139689953[/C][C]0.616764430155024[/C][/ROW]
[ROW][C]36[/C][C]0.872490969840976[/C][C]0.255018060318049[/C][C]0.127509030159024[/C][/ROW]
[ROW][C]37[/C][C]0.854243637273195[/C][C]0.291512725453611[/C][C]0.145756362726805[/C][/ROW]
[ROW][C]38[/C][C]0.843868190192752[/C][C]0.312263619614496[/C][C]0.156131809807248[/C][/ROW]
[ROW][C]39[/C][C]0.869831703472719[/C][C]0.260336593054561[/C][C]0.130168296527281[/C][/ROW]
[ROW][C]40[/C][C]0.858598860235717[/C][C]0.282802279528566[/C][C]0.141401139764283[/C][/ROW]
[ROW][C]41[/C][C]0.836048654309075[/C][C]0.327902691381849[/C][C]0.163951345690925[/C][/ROW]
[ROW][C]42[/C][C]0.818295175311965[/C][C]0.363409649376069[/C][C]0.181704824688034[/C][/ROW]
[ROW][C]43[/C][C]0.825383336556739[/C][C]0.349233326886521[/C][C]0.174616663443261[/C][/ROW]
[ROW][C]44[/C][C]0.789871626135739[/C][C]0.420256747728522[/C][C]0.210128373864261[/C][/ROW]
[ROW][C]45[/C][C]0.754017252421347[/C][C]0.491965495157305[/C][C]0.245982747578653[/C][/ROW]
[ROW][C]46[/C][C]0.891119919182046[/C][C]0.217760161635908[/C][C]0.108880080817954[/C][/ROW]
[ROW][C]47[/C][C]0.903962631297958[/C][C]0.192074737404084[/C][C]0.096037368702042[/C][/ROW]
[ROW][C]48[/C][C]0.885641138297568[/C][C]0.228717723404863[/C][C]0.114358861702432[/C][/ROW]
[ROW][C]49[/C][C]0.867181094672995[/C][C]0.26563781065401[/C][C]0.132818905327005[/C][/ROW]
[ROW][C]50[/C][C]0.863630301126328[/C][C]0.272739397747345[/C][C]0.136369698873672[/C][/ROW]
[ROW][C]51[/C][C]0.834524955649984[/C][C]0.330950088700032[/C][C]0.165475044350016[/C][/ROW]
[ROW][C]52[/C][C]0.801971958083847[/C][C]0.396056083832305[/C][C]0.198028041916153[/C][/ROW]
[ROW][C]53[/C][C]0.82133072912568[/C][C]0.357338541748639[/C][C]0.17866927087432[/C][/ROW]
[ROW][C]54[/C][C]0.805592441774452[/C][C]0.388815116451097[/C][C]0.194407558225548[/C][/ROW]
[ROW][C]55[/C][C]0.81648664579713[/C][C]0.36702670840574[/C][C]0.18351335420287[/C][/ROW]
[ROW][C]56[/C][C]0.81336408386231[/C][C]0.373271832275381[/C][C]0.18663591613769[/C][/ROW]
[ROW][C]57[/C][C]0.781080142251551[/C][C]0.437839715496897[/C][C]0.218919857748449[/C][/ROW]
[ROW][C]58[/C][C]0.766377102660677[/C][C]0.467245794678646[/C][C]0.233622897339323[/C][/ROW]
[ROW][C]59[/C][C]0.732810400722221[/C][C]0.534379198555558[/C][C]0.267189599277779[/C][/ROW]
[ROW][C]60[/C][C]0.74463613583667[/C][C]0.510727728326659[/C][C]0.25536386416333[/C][/ROW]
[ROW][C]61[/C][C]0.71133398538918[/C][C]0.57733202922164[/C][C]0.28866601461082[/C][/ROW]
[ROW][C]62[/C][C]0.675907236742244[/C][C]0.648185526515513[/C][C]0.324092763257756[/C][/ROW]
[ROW][C]63[/C][C]0.639995870169531[/C][C]0.720008259660938[/C][C]0.360004129830469[/C][/ROW]
[ROW][C]64[/C][C]0.611706644250929[/C][C]0.776586711498142[/C][C]0.388293355749071[/C][/ROW]
[ROW][C]65[/C][C]0.579483210142836[/C][C]0.841033579714329[/C][C]0.420516789857164[/C][/ROW]
[ROW][C]66[/C][C]0.554282546020745[/C][C]0.891434907958509[/C][C]0.445717453979255[/C][/ROW]
[ROW][C]67[/C][C]0.543706263096033[/C][C]0.912587473807933[/C][C]0.456293736903967[/C][/ROW]
[ROW][C]68[/C][C]0.669899281643754[/C][C]0.660201436712492[/C][C]0.330100718356246[/C][/ROW]
[ROW][C]69[/C][C]0.790909676911071[/C][C]0.418180646177859[/C][C]0.209090323088929[/C][/ROW]
[ROW][C]70[/C][C]0.76104648380787[/C][C]0.47790703238426[/C][C]0.23895351619213[/C][/ROW]
[ROW][C]71[/C][C]0.840187838057255[/C][C]0.31962432388549[/C][C]0.159812161942745[/C][/ROW]
[ROW][C]72[/C][C]0.813189137313959[/C][C]0.373621725372081[/C][C]0.186810862686041[/C][/ROW]
[ROW][C]73[/C][C]0.809953617448569[/C][C]0.380092765102862[/C][C]0.190046382551431[/C][/ROW]
[ROW][C]74[/C][C]0.789474566892812[/C][C]0.421050866214376[/C][C]0.210525433107188[/C][/ROW]
[ROW][C]75[/C][C]0.755205403307834[/C][C]0.489589193384332[/C][C]0.244794596692166[/C][/ROW]
[ROW][C]76[/C][C]0.804062269554707[/C][C]0.391875460890586[/C][C]0.195937730445293[/C][/ROW]
[ROW][C]77[/C][C]0.771974829267335[/C][C]0.456050341465331[/C][C]0.228025170732666[/C][/ROW]
[ROW][C]78[/C][C]0.747745985734742[/C][C]0.504508028530515[/C][C]0.252254014265258[/C][/ROW]
[ROW][C]79[/C][C]0.748780457803174[/C][C]0.502439084393651[/C][C]0.251219542196826[/C][/ROW]
[ROW][C]80[/C][C]0.713284475108606[/C][C]0.573431049782788[/C][C]0.286715524891394[/C][/ROW]
[ROW][C]81[/C][C]0.678867717270005[/C][C]0.642264565459989[/C][C]0.321132282729995[/C][/ROW]
[ROW][C]82[/C][C]0.821308573984018[/C][C]0.357382852031965[/C][C]0.178691426015982[/C][/ROW]
[ROW][C]83[/C][C]0.789605879362111[/C][C]0.420788241275778[/C][C]0.210394120637889[/C][/ROW]
[ROW][C]84[/C][C]0.761769880502079[/C][C]0.476460238995842[/C][C]0.238230119497921[/C][/ROW]
[ROW][C]85[/C][C]0.729334658513493[/C][C]0.541330682973014[/C][C]0.270665341486507[/C][/ROW]
[ROW][C]86[/C][C]0.707245993847379[/C][C]0.585508012305242[/C][C]0.292754006152621[/C][/ROW]
[ROW][C]87[/C][C]0.665154968316833[/C][C]0.669690063366334[/C][C]0.334845031683167[/C][/ROW]
[ROW][C]88[/C][C]0.624638565703951[/C][C]0.750722868592098[/C][C]0.375361434296049[/C][/ROW]
[ROW][C]89[/C][C]0.592601582737383[/C][C]0.814796834525234[/C][C]0.407398417262617[/C][/ROW]
[ROW][C]90[/C][C]0.565779330751319[/C][C]0.868441338497362[/C][C]0.434220669248681[/C][/ROW]
[ROW][C]91[/C][C]0.55355224324282[/C][C]0.892895513514359[/C][C]0.44644775675718[/C][/ROW]
[ROW][C]92[/C][C]0.51023300523944[/C][C]0.979533989521121[/C][C]0.48976699476056[/C][/ROW]
[ROW][C]93[/C][C]0.469276691289987[/C][C]0.938553382579974[/C][C]0.530723308710013[/C][/ROW]
[ROW][C]94[/C][C]0.423340319543114[/C][C]0.846680639086228[/C][C]0.576659680456886[/C][/ROW]
[ROW][C]95[/C][C]0.457613204495776[/C][C]0.915226408991552[/C][C]0.542386795504224[/C][/ROW]
[ROW][C]96[/C][C]0.418304180127799[/C][C]0.836608360255597[/C][C]0.581695819872201[/C][/ROW]
[ROW][C]97[/C][C]0.37593189008752[/C][C]0.75186378017504[/C][C]0.62406810991248[/C][/ROW]
[ROW][C]98[/C][C]0.366517262362062[/C][C]0.733034524724124[/C][C]0.633482737637938[/C][/ROW]
[ROW][C]99[/C][C]0.323218544839733[/C][C]0.646437089679465[/C][C]0.676781455160267[/C][/ROW]
[ROW][C]100[/C][C]0.289021664917343[/C][C]0.578043329834685[/C][C]0.710978335082657[/C][/ROW]
[ROW][C]101[/C][C]0.261485956115856[/C][C]0.522971912231712[/C][C]0.738514043884144[/C][/ROW]
[ROW][C]102[/C][C]0.244541457027821[/C][C]0.489082914055641[/C][C]0.75545854297218[/C][/ROW]
[ROW][C]103[/C][C]0.292582349039925[/C][C]0.58516469807985[/C][C]0.707417650960075[/C][/ROW]
[ROW][C]104[/C][C]0.251773059702424[/C][C]0.503546119404848[/C][C]0.748226940297576[/C][/ROW]
[ROW][C]105[/C][C]0.272273373856185[/C][C]0.54454674771237[/C][C]0.727726626143815[/C][/ROW]
[ROW][C]106[/C][C]0.27416081106088[/C][C]0.548321622121761[/C][C]0.72583918893912[/C][/ROW]
[ROW][C]107[/C][C]0.261396513955554[/C][C]0.522793027911108[/C][C]0.738603486044446[/C][/ROW]
[ROW][C]108[/C][C]0.236107307456981[/C][C]0.472214614913962[/C][C]0.763892692543019[/C][/ROW]
[ROW][C]109[/C][C]0.237755858187344[/C][C]0.475511716374688[/C][C]0.762244141812656[/C][/ROW]
[ROW][C]110[/C][C]0.214169176578895[/C][C]0.42833835315779[/C][C]0.785830823421105[/C][/ROW]
[ROW][C]111[/C][C]0.198466599164718[/C][C]0.396933198329435[/C][C]0.801533400835282[/C][/ROW]
[ROW][C]112[/C][C]0.171764799044167[/C][C]0.343529598088333[/C][C]0.828235200955833[/C][/ROW]
[ROW][C]113[/C][C]0.252813897358849[/C][C]0.505627794717698[/C][C]0.747186102641151[/C][/ROW]
[ROW][C]114[/C][C]0.222381380575904[/C][C]0.444762761151807[/C][C]0.777618619424096[/C][/ROW]
[ROW][C]115[/C][C]0.233278791687221[/C][C]0.466557583374443[/C][C]0.766721208312779[/C][/ROW]
[ROW][C]116[/C][C]0.217282117632029[/C][C]0.434564235264057[/C][C]0.782717882367971[/C][/ROW]
[ROW][C]117[/C][C]0.191319501861085[/C][C]0.382639003722169[/C][C]0.808680498138915[/C][/ROW]
[ROW][C]118[/C][C]0.209029444935228[/C][C]0.418058889870456[/C][C]0.790970555064772[/C][/ROW]
[ROW][C]119[/C][C]0.207371939209376[/C][C]0.414743878418753[/C][C]0.792628060790624[/C][/ROW]
[ROW][C]120[/C][C]0.214488140003757[/C][C]0.428976280007514[/C][C]0.785511859996243[/C][/ROW]
[ROW][C]121[/C][C]0.183861321992359[/C][C]0.367722643984719[/C][C]0.816138678007641[/C][/ROW]
[ROW][C]122[/C][C]0.15098000918214[/C][C]0.30196001836428[/C][C]0.84901999081786[/C][/ROW]
[ROW][C]123[/C][C]0.160306282345406[/C][C]0.320612564690812[/C][C]0.839693717654594[/C][/ROW]
[ROW][C]124[/C][C]0.130734344416844[/C][C]0.261468688833689[/C][C]0.869265655583156[/C][/ROW]
[ROW][C]125[/C][C]0.105118662106412[/C][C]0.210237324212824[/C][C]0.894881337893588[/C][/ROW]
[ROW][C]126[/C][C]0.0817034809493618[/C][C]0.163406961898724[/C][C]0.918296519050638[/C][/ROW]
[ROW][C]127[/C][C]0.0618465323968113[/C][C]0.123693064793623[/C][C]0.938153467603189[/C][/ROW]
[ROW][C]128[/C][C]0.0698068749293855[/C][C]0.139613749858771[/C][C]0.930193125070614[/C][/ROW]
[ROW][C]129[/C][C]0.0588521610281826[/C][C]0.117704322056365[/C][C]0.941147838971817[/C][/ROW]
[ROW][C]130[/C][C]0.0573086857721968[/C][C]0.114617371544394[/C][C]0.942691314227803[/C][/ROW]
[ROW][C]131[/C][C]0.0723365175913453[/C][C]0.144673035182691[/C][C]0.927663482408655[/C][/ROW]
[ROW][C]132[/C][C]0.0767635465055293[/C][C]0.153527093011059[/C][C]0.923236453494471[/C][/ROW]
[ROW][C]133[/C][C]0.184392133583687[/C][C]0.368784267167375[/C][C]0.815607866416313[/C][/ROW]
[ROW][C]134[/C][C]0.162246001937764[/C][C]0.324492003875527[/C][C]0.837753998062236[/C][/ROW]
[ROW][C]135[/C][C]0.14716613767936[/C][C]0.29433227535872[/C][C]0.85283386232064[/C][/ROW]
[ROW][C]136[/C][C]0.113670637647327[/C][C]0.227341275294654[/C][C]0.886329362352673[/C][/ROW]
[ROW][C]137[/C][C]0.0884774553390559[/C][C]0.176954910678112[/C][C]0.911522544660944[/C][/ROW]
[ROW][C]138[/C][C]0.0759884573624348[/C][C]0.15197691472487[/C][C]0.924011542637565[/C][/ROW]
[ROW][C]139[/C][C]0.119401676945359[/C][C]0.238803353890718[/C][C]0.880598323054641[/C][/ROW]
[ROW][C]140[/C][C]0.0888049296631068[/C][C]0.177609859326214[/C][C]0.911195070336893[/C][/ROW]
[ROW][C]141[/C][C]0.49669434593288[/C][C]0.993388691865761[/C][C]0.50330565406712[/C][/ROW]
[ROW][C]142[/C][C]0.43027815035921[/C][C]0.86055630071842[/C][C]0.56972184964079[/C][/ROW]
[ROW][C]143[/C][C]0.411679281080799[/C][C]0.823358562161598[/C][C]0.588320718919201[/C][/ROW]
[ROW][C]144[/C][C]0.656404023008293[/C][C]0.687191953983413[/C][C]0.343595976991707[/C][/ROW]
[ROW][C]145[/C][C]0.704149984140161[/C][C]0.591700031719678[/C][C]0.295850015859839[/C][/ROW]
[ROW][C]146[/C][C]0.622792459575825[/C][C]0.754415080848351[/C][C]0.377207540424175[/C][/ROW]
[ROW][C]147[/C][C]0.542639497292729[/C][C]0.914721005414543[/C][C]0.457360502707271[/C][/ROW]
[ROW][C]148[/C][C]0.637423892493747[/C][C]0.725152215012506[/C][C]0.362576107506253[/C][/ROW]
[ROW][C]149[/C][C]0.519627816028275[/C][C]0.960744367943449[/C][C]0.480372183971725[/C][/ROW]
[ROW][C]150[/C][C]0.416387772767775[/C][C]0.83277554553555[/C][C]0.583612227232225[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=161020&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=161020&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
120.2001591841433830.4003183682867670.799840815856617
130.2812275516348730.5624551032697450.718772448365127
140.356591190187160.713182380374320.64340880981284
150.3633921700282640.7267843400565290.636607829971736
160.3647896401073460.7295792802146920.635210359892654
170.3391244460283760.6782488920567530.660875553971624
180.7002388810687330.5995222378625350.299761118931267
190.8036402055849030.3927195888301950.196359794415097
200.7364647839159320.5270704321681360.263535216084068
210.6830571690301390.6338856619397210.31694283096986
220.6398644759986450.720271048002710.360135524001355
230.6116348499578990.7767303000842020.388365150042101
240.5622945005579640.8754109988840720.437705499442036
250.5081444284369110.9837111431261780.491855571563089
260.4591278005942550.918255601188510.540872199405745
270.4047645116798480.8095290233596960.595235488320152
280.516923598226110.9661528035477790.48307640177389
290.4630787437388630.9261574874777260.536921256261137
300.5067470133537680.9865059732924630.493252986646232
310.445653772948380.8913075458967590.55434622705162
320.4654913397503070.9309826795006140.534508660249693
330.4367569265408980.8735138530817960.563243073459102
340.3773557587821510.7547115175643010.622644241217849
350.3832355698449760.7664711396899530.616764430155024
360.8724909698409760.2550180603180490.127509030159024
370.8542436372731950.2915127254536110.145756362726805
380.8438681901927520.3122636196144960.156131809807248
390.8698317034727190.2603365930545610.130168296527281
400.8585988602357170.2828022795285660.141401139764283
410.8360486543090750.3279026913818490.163951345690925
420.8182951753119650.3634096493760690.181704824688034
430.8253833365567390.3492333268865210.174616663443261
440.7898716261357390.4202567477285220.210128373864261
450.7540172524213470.4919654951573050.245982747578653
460.8911199191820460.2177601616359080.108880080817954
470.9039626312979580.1920747374040840.096037368702042
480.8856411382975680.2287177234048630.114358861702432
490.8671810946729950.265637810654010.132818905327005
500.8636303011263280.2727393977473450.136369698873672
510.8345249556499840.3309500887000320.165475044350016
520.8019719580838470.3960560838323050.198028041916153
530.821330729125680.3573385417486390.17866927087432
540.8055924417744520.3888151164510970.194407558225548
550.816486645797130.367026708405740.18351335420287
560.813364083862310.3732718322753810.18663591613769
570.7810801422515510.4378397154968970.218919857748449
580.7663771026606770.4672457946786460.233622897339323
590.7328104007222210.5343791985555580.267189599277779
600.744636135836670.5107277283266590.25536386416333
610.711333985389180.577332029221640.28866601461082
620.6759072367422440.6481855265155130.324092763257756
630.6399958701695310.7200082596609380.360004129830469
640.6117066442509290.7765867114981420.388293355749071
650.5794832101428360.8410335797143290.420516789857164
660.5542825460207450.8914349079585090.445717453979255
670.5437062630960330.9125874738079330.456293736903967
680.6698992816437540.6602014367124920.330100718356246
690.7909096769110710.4181806461778590.209090323088929
700.761046483807870.477907032384260.23895351619213
710.8401878380572550.319624323885490.159812161942745
720.8131891373139590.3736217253720810.186810862686041
730.8099536174485690.3800927651028620.190046382551431
740.7894745668928120.4210508662143760.210525433107188
750.7552054033078340.4895891933843320.244794596692166
760.8040622695547070.3918754608905860.195937730445293
770.7719748292673350.4560503414653310.228025170732666
780.7477459857347420.5045080285305150.252254014265258
790.7487804578031740.5024390843936510.251219542196826
800.7132844751086060.5734310497827880.286715524891394
810.6788677172700050.6422645654599890.321132282729995
820.8213085739840180.3573828520319650.178691426015982
830.7896058793621110.4207882412757780.210394120637889
840.7617698805020790.4764602389958420.238230119497921
850.7293346585134930.5413306829730140.270665341486507
860.7072459938473790.5855080123052420.292754006152621
870.6651549683168330.6696900633663340.334845031683167
880.6246385657039510.7507228685920980.375361434296049
890.5926015827373830.8147968345252340.407398417262617
900.5657793307513190.8684413384973620.434220669248681
910.553552243242820.8928955135143590.44644775675718
920.510233005239440.9795339895211210.48976699476056
930.4692766912899870.9385533825799740.530723308710013
940.4233403195431140.8466806390862280.576659680456886
950.4576132044957760.9152264089915520.542386795504224
960.4183041801277990.8366083602555970.581695819872201
970.375931890087520.751863780175040.62406810991248
980.3665172623620620.7330345247241240.633482737637938
990.3232185448397330.6464370896794650.676781455160267
1000.2890216649173430.5780433298346850.710978335082657
1010.2614859561158560.5229719122317120.738514043884144
1020.2445414570278210.4890829140556410.75545854297218
1030.2925823490399250.585164698079850.707417650960075
1040.2517730597024240.5035461194048480.748226940297576
1050.2722733738561850.544546747712370.727726626143815
1060.274160811060880.5483216221217610.72583918893912
1070.2613965139555540.5227930279111080.738603486044446
1080.2361073074569810.4722146149139620.763892692543019
1090.2377558581873440.4755117163746880.762244141812656
1100.2141691765788950.428338353157790.785830823421105
1110.1984665991647180.3969331983294350.801533400835282
1120.1717647990441670.3435295980883330.828235200955833
1130.2528138973588490.5056277947176980.747186102641151
1140.2223813805759040.4447627611518070.777618619424096
1150.2332787916872210.4665575833744430.766721208312779
1160.2172821176320290.4345642352640570.782717882367971
1170.1913195018610850.3826390037221690.808680498138915
1180.2090294449352280.4180588898704560.790970555064772
1190.2073719392093760.4147438784187530.792628060790624
1200.2144881400037570.4289762800075140.785511859996243
1210.1838613219923590.3677226439847190.816138678007641
1220.150980009182140.301960018364280.84901999081786
1230.1603062823454060.3206125646908120.839693717654594
1240.1307343444168440.2614686888336890.869265655583156
1250.1051186621064120.2102373242128240.894881337893588
1260.08170348094936180.1634069618987240.918296519050638
1270.06184653239681130.1236930647936230.938153467603189
1280.06980687492938550.1396137498587710.930193125070614
1290.05885216102818260.1177043220563650.941147838971817
1300.05730868577219680.1146173715443940.942691314227803
1310.07233651759134530.1446730351826910.927663482408655
1320.07676354650552930.1535270930110590.923236453494471
1330.1843921335836870.3687842671673750.815607866416313
1340.1622460019377640.3244920038755270.837753998062236
1350.147166137679360.294332275358720.85283386232064
1360.1136706376473270.2273412752946540.886329362352673
1370.08847745533905590.1769549106781120.911522544660944
1380.07598845736243480.151976914724870.924011542637565
1390.1194016769453590.2388033538907180.880598323054641
1400.08880492966310680.1776098593262140.911195070336893
1410.496694345932880.9933886918657610.50330565406712
1420.430278150359210.860556300718420.56972184964079
1430.4116792810807990.8233585621615980.588320718919201
1440.6564040230082930.6871919539834130.343595976991707
1450.7041499841401610.5917000317196780.295850015859839
1460.6227924595758250.7544150808483510.377207540424175
1470.5426394972927290.9147210054145430.457360502707271
1480.6374238924937470.7251522150125060.362576107506253
1490.5196278160282750.9607443679434490.480372183971725
1500.4163877727677750.832775545535550.583612227232225






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=161020&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=161020&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=161020&T=6

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


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

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


Figures (Output of Computation)

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

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