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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 computationSat, 17 Nov 2012 12:01:32 -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/Nov/17/t1353171724gy9zriw7iyvxqbk.htm/, Retrieved Sat, 27 Apr 2024 13:28:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=190096, Retrieved Sat, 27 Apr 2024 13:28:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact108

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R  D  [Multiple Regression] [Motivation to learn ] [2012-11-17 16:21:22] [9d6050326bbbd058eed49c2dec5f39c1]
-   P       [Multiple Regression] [Motivation to learn] [2012-11-17 17:01:32] [0099dd2d89a142e9b85435bc9194870f] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
26	21	21	23	17	23	4
20	16	15	24	17	20	4
19	19	18	22	18	20	6
19	18	11	20	21	21	8
20	16	8	24	20	24	8
25	23	19	27	28	22	4
25	17	4	28	19	23	4
22	12	20	27	22	20	8
26	19	16	24	16	25	5
22	16	14	23	18	23	4
17	19	10	24	25	27	4
22	20	13	27	17	27	4
19	13	14	27	14	22	4
24	20	8	28	11	24	4
26	27	23	27	27	25	4
21	17	11	23	20	22	8
13	8	9	24	22	28	4
26	25	24	28	22	28	4
20	26	5	27	21	27	4
22	13	15	25	23	25	8
14	19	5	19	17	16	4
21	15	19	24	24	28	7
7	5	6	20	14	21	4
23	16	13	28	17	24	4
17	14	11	26	23	27	5
25	24	17	23	24	14	4
25	24	17	23	24	14	4
19	9	5	20	8	27	4
20	19	9	11	22	20	4
23	19	15	24	23	21	4
22	25	17	25	25	22	4
22	19	17	23	21	21	4
21	18	20	18	24	12	15
15	15	12	20	15	20	10
20	12	7	20	22	24	4
22	21	16	24	21	19	8
18	12	7	23	25	28	4
20	15	14	25	16	23	4
28	28	24	28	28	27	4
22	25	15	26	23	22	4
18	19	15	26	21	27	7
23	20	10	23	21	26	4
20	24	14	22	26	22	6
25	26	18	24	22	21	5
26	25	12	21	21	19	4
15	12	9	20	18	24	16
17	12	9	22	12	19	5
23	15	8	20	25	26	12
21	17	18	25	17	22	6
13	14	10	20	24	28	9
18	16	17	22	15	21	9
19	11	14	23	13	23	4
22	20	16	25	26	28	5
16	11	10	23	16	10	4
24	22	19	23	24	24	4
18	20	10	22	21	21	5
20	19	14	24	20	21	4
24	17	10	25	14	24	4
14	21	4	21	25	24	4
22	23	19	12	25	25	5
24	18	9	17	20	25	4
18	17	12	20	22	23	6
21	27	16	23	20	21	4
23	25	11	23	26	16	4
17	19	18	20	18	17	18
22	22	11	28	22	25	4
24	24	24	24	24	24	6
21	20	17	24	17	23	4
22	19	18	24	24	25	4
16	11	9	24	20	23	5
21	22	19	28	19	28	4
23	22	18	25	20	26	4
22	16	12	21	15	22	5
24	20	23	25	23	19	10
24	24	22	25	26	26	5
16	16	14	18	22	18	8
16	16	14	17	20	18	8
21	22	16	26	24	25	5
26	24	23	28	26	27	4
15	16	7	21	21	12	4
25	27	10	27	25	15	4
18	11	12	22	13	21	5
23	21	12	21	20	23	4
20	20	12	25	22	22	4
17	20	17	22	23	21	8
25	27	21	23	28	24	4
24	20	16	26	22	27	5
17	12	11	19	20	22	14
19	8	14	25	6	28	8
20	21	13	21	21	26	8
15	18	9	13	20	10	4
27	24	19	24	18	19	4
22	16	13	25	23	22	6
23	18	19	26	20	21	4
16	20	13	25	24	24	7
19	20	13	25	22	25	7
25	19	13	22	21	21	4
19	17	14	21	18	20	6
19	16	12	23	21	21	4
26	26	22	25	23	24	7
21	15	11	24	23	23	4
20	22	5	21	15	18	4
24	17	18	21	21	24	8
22	23	19	25	24	24	4
20	21	14	22	23	19	4
18	19	15	20	21	20	10
18	14	12	20	21	18	8
24	17	19	23	20	20	6
24	12	15	28	11	27	4
22	24	17	23	22	23	4
23	18	8	28	27	26	4
22	20	10	24	25	23	5
20	16	12	18	18	17	4
18	20	12	20	20	21	6
25	22	20	28	24	25	4
18	12	12	21	10	23	5
16	16	12	21	27	27	7
20	17	14	25	21	24	8
19	22	6	19	21	20	5
15	12	10	18	18	27	8
19	14	18	21	15	21	10
19	23	18	22	24	24	8
16	15	7	24	22	21	5
17	17	18	15	14	15	12
28	28	9	28	28	25	4
23	20	17	26	18	25	5
25	23	22	23	26	22	4
20	13	11	26	17	24	6
17	18	15	20	19	21	4
23	23	17	22	22	22	4
16	19	15	20	18	23	7
23	23	22	23	24	22	7
11	12	9	22	15	20	10
18	16	13	24	18	23	4
24	23	20	23	26	25	5
23	13	14	22	11	23	8
21	22	14	26	26	22	11
16	18	12	23	21	25	7
24	23	20	27	23	26	4
23	20	20	23	23	22	8
18	10	8	21	15	24	6
20	17	17	26	22	24	7
9	18	9	23	26	25	5
24	15	18	21	16	20	4
25	23	22	27	20	26	8
20	17	10	19	18	21	4
21	17	13	23	22	26	8
25	22	15	25	16	21	6
22	20	18	23	19	22	4
21	20	18	22	20	16	9
21	19	12	22	19	26	5
22	18	12	25	23	28	6
27	22	20	25	24	18	4
24	20	12	28	25	25	4
24	22	16	28	21	23	4
21	18	16	20	21	21	5
18	16	18	25	23	20	6
16	16	16	19	27	25	16
22	16	13	25	23	22	6
20	16	17	22	18	21	6
18	17	13	18	16	16	4
20	18	17	20	16	18	4

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190096&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190096&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190096&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 time9 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
A[t] = + 11.9258699919406 -0.186688640368396I1[t] -0.158780786744706I2[t] + 0.204515893231588I3[t] -0.177385274497646E1[t] + 0.0900592800200716E2[t] -7.10017812565152e-05E3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
A[t] =  +  11.9258699919406 -0.186688640368396I1[t] -0.158780786744706I2[t] +  0.204515893231588I3[t] -0.177385274497646E1[t] +  0.0900592800200716E2[t] -7.10017812565152e-05E3[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190096&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]A[t] =  +  11.9258699919406 -0.186688640368396I1[t] -0.158780786744706I2[t] +  0.204515893231588I3[t] -0.177385274497646E1[t] +  0.0900592800200716E2[t] -7.10017812565152e-05E3[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190096&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190096&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
A[t] = + 11.9258699919406 -0.186688640368396I1[t] -0.158780786744706I2[t] + 0.204515893231588I3[t] -0.177385274497646E1[t] + 0.0900592800200716E2[t] -7.10017812565152e-05E3[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11.92586999194061.6785287.10500
I1-0.1866886403683960.073758-2.53110.0123670.006184
I2-0.1587807867447060.063618-2.49580.0136130.006806
I30.2045158932315880.0480274.25843.6e-051.8e-05
E1-0.1773852744976460.071453-2.48250.0141110.007055
E20.09005928002007160.0553031.62850.1054540.052727
E3-7.10017812565152e-050.057395-0.00120.9990150.499507

\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) & 11.9258699919406 & 1.678528 & 7.105 & 0 & 0 \tabularnewline
I1 & -0.186688640368396 & 0.073758 & -2.5311 & 0.012367 & 0.006184 \tabularnewline
I2 & -0.158780786744706 & 0.063618 & -2.4958 & 0.013613 & 0.006806 \tabularnewline
I3 & 0.204515893231588 & 0.048027 & 4.2584 & 3.6e-05 & 1.8e-05 \tabularnewline
E1 & -0.177385274497646 & 0.071453 & -2.4825 & 0.014111 & 0.007055 \tabularnewline
E2 & 0.0900592800200716 & 0.055303 & 1.6285 & 0.105454 & 0.052727 \tabularnewline
E3 & -7.10017812565152e-05 & 0.057395 & -0.0012 & 0.999015 & 0.499507 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190096&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]11.9258699919406[/C][C]1.678528[/C][C]7.105[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]I1[/C][C]-0.186688640368396[/C][C]0.073758[/C][C]-2.5311[/C][C]0.012367[/C][C]0.006184[/C][/ROW]
[ROW][C]I2[/C][C]-0.158780786744706[/C][C]0.063618[/C][C]-2.4958[/C][C]0.013613[/C][C]0.006806[/C][/ROW]
[ROW][C]I3[/C][C]0.204515893231588[/C][C]0.048027[/C][C]4.2584[/C][C]3.6e-05[/C][C]1.8e-05[/C][/ROW]
[ROW][C]E1[/C][C]-0.177385274497646[/C][C]0.071453[/C][C]-2.4825[/C][C]0.014111[/C][C]0.007055[/C][/ROW]
[ROW][C]E2[/C][C]0.0900592800200716[/C][C]0.055303[/C][C]1.6285[/C][C]0.105454[/C][C]0.052727[/C][/ROW]
[ROW][C]E3[/C][C]-7.10017812565152e-05[/C][C]0.057395[/C][C]-0.0012[/C][C]0.999015[/C][C]0.499507[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190096&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190096&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)11.92586999194061.6785287.10500
I1-0.1866886403683960.073758-2.53110.0123670.006184
I2-0.1587807867447060.063618-2.49580.0136130.006806
I30.2045158932315880.0480274.25843.6e-051.8e-05
E1-0.1773852744976460.071453-2.48250.0141110.007055
E20.09005928002007160.0553031.62850.1054540.052727
E3-7.10017812565152e-050.057395-0.00120.9990150.499507






Multiple Linear Regression - Regression Statistics
Multiple R0.483551387750376
R-squared0.233821944595314
Adjusted R-squared0.20416343922481
F-TEST (value)7.883807416265
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value2.01247018272177e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.34269002911089
Sum Squared Residuals850.670468736813

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.483551387750376 \tabularnewline
R-squared & 0.233821944595314 \tabularnewline
Adjusted R-squared & 0.20416343922481 \tabularnewline
F-TEST (value) & 7.883807416265 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 2.01247018272177e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.34269002911089 \tabularnewline
Sum Squared Residuals & 850.670468736813 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190096&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.483551387750376[/C][/ROW]
[ROW][C]R-squared[/C][C]0.233821944595314[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.20416343922481[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.883807416265[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]2.01247018272177e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.34269002911089[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]850.670468736813[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190096&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190096&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 R0.483551387750376
R-squared0.233821944595314
Adjusted R-squared0.20416343922481
F-TEST (value)7.883807416265
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value2.01247018272177e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.34269002911089
Sum Squared Residuals850.670468736813






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
145.48191598451328-1.48191598451328
245.99168413190375-1.99168413190375
366.76040792074816-0.760407920748157
486.112454842145991.88754515785401
584.829966712217823.17003328778218
645.22319324894052-1.22319324894052
742.120149774475371.87985022552463
887.194150040911140.805849959088861
954.509311533764490.490688466235514
1045.68102250710932-1.68102250710931
1145.77280545430865-1.77280545430865
1244.04149908176322-0.0414990817632202
1345.647723572159-1.647723572159
1441.928014385594182.07198561440582
1545.12917274915581-1.12917274915581
1685.275572242859642.72442775714036
1747.79138393490094-3.79138393490094
1845.02335553593504-1.02335553593504
1942.186301616259361.81369838374064
2086.457264608117571.54273539188243
2145.47752506117738-1.47752506117738
2277.41168679709686-0.41168679709686
2348.76387432794948-4.76387432794948
2444.31276131921977-0.312761319219769
2556.23633617222834-1.23633617222834
2645.00525266789298-1.00525266789298
2745.00525266789298-1.00525266789298
2845.14318991251034-1.14318991251034
2947.04455138084986-3.04455138084986
3045.49556052890361-1.49556052890361
3145.1412585190282-1.14125851902819
3246.08854767019269-2.08854767019269
33158.205308005580316.79469199441969
34106.999782978746113.00021702125389
3546.15023362399578-2.15023362399578
3685.389226932536562.61077306746344
3746.26134891417482-2.26134891417482
3845.67829146555538-1.67829146555538
3944.71406257686581-0.714062576865812
4044.37472289802723-0.374722898027228
4175.893688611022621.10631138897738
4244.31111198155219-0.311111981552187
4365.682084010327940.317915989672061
4454.534206140628580.465793859371423
4543.721441474650730.278558525349265
46167.132471492220658.86752850777935
4755.86432299127442-0.864322991274419
48125.588377072385766.41162292761424
4966.08223510642525-0.0822351064252516
5097.932874765695031.06712523430497
5196.948674186277612.05132581372239
5246.58469596183767-2.58469596183767
5355.82027982885267-0.820279828852667
5446.59779917323306-2.59779917323306
5545.9178246504014-1.9178246504014
5655.4222954667981-0.422295466798098
5745.580932716717-1.580932716717
5843.615722195844630.384277804155374
5945.32058327137161-1.32058327137161
6058.17364744210642-3.17364744210642
6145.21179239018868-1.21179239018868
6266.75235743894824-0.752357438948243
6344.71041484335178-0.710414843351783
6444.17273035897317-0.172730358973171
65187.4887687558240110.511231244176
6643.587958850975860.412041149024143
6766.44545726857229-0.445457268572289
6845.57869112567594-1.57869112567594
6946.38557212186182-2.38557212186182
7056.57521210242778-1.57521210242778
7145.14038379179298-1.14038379179298
7245.18484772490012-1.18484772490012
7355.35665443136247-0.356654431362471
74106.608974976708093.39102502329191
7556.0390167640891-1.0390167640891
7688.04867285079449-0.0486728507944941
7788.045939565252-0.0459395652519973
7855.33211606653763-0.332116066537632
7945.3379285513097-1.3379285513097
8046.1819611457163-2.1819611457163
8142.477746235285981.52225376471402
8256.53988009380305-1.53988009380305
8344.82628725558965-0.826287255589649
8445.01578242727036-1.01578242727036
8587.220713919827760.779286080172242
8645.70650098285294-1.70650098285294
8754.90935115531920.0906488446808024
88147.525771836047346.47422816395266
8986.075497804029711.92450219597029
9085.680715344602732.31928465539727
9147.60259627821368-3.60259627821368
9244.32281121009501-0.32281121009501
9365.572103466764050.427896533235952
9445.84745649951917-1.84745649951917
9576.147029438453160.852970561546837
9675.406773955526571.59322604447343
9744.88780345065879-0.887803450658794
9866.43729119580886-0.43729119580886
9946.10237648537406-2.10237648537405
10075.078042073365191.92195792663481
10145.68585538013036-1.68585538013036
10243.346019746134940.653980253865063
10386.591805399828421.40819460017158
10445.7776505953982-1.7776505953982
10545.88846153584561-1.88846153584561
106106.95849727047733.0415027295227
10787.138995528068570.861004471931429
10866.35177547116967-0.351775471169671
10944.62965892682917-0.629658926829173
11045.38456101292672-1.38456101292672
11143.873071076210620.126928923789381
11254.680865472846990.319134527153007
11346.5327203845587-2.5327203845587
11466.0960385222365-0.0960385222364962
11545.04865452899511-1.04865452899511
11656.28816473793326-1.28816473793326
11777.55714262490743-0.557142624907427
11885.810955290385072.18904470961493
11954.632208505288130.367791494711869
12087.691544929103760.308455070896237
121107.461448287127862.53855171287214
12286.665356446764741.33464355323526
12355.71131773258843-0.711317732588432
124128.333161585283883.66683841471612
12541.64646618195452.3535338180455
12654.940460572401410.0595394275985908
12746.36616346658572-2.36616346658572
12865.294908363091190.705091636908809
12947.123777135769-3.123777135769
13045.53410943558193-1.53410943558193
13177.0614837058101-0.061483705810104
13276.559422187282370.440577812717626
133107.254561671763762.74543832823624
13446.04587590085367-2.04587590085367
13556.14360731514717-1.14360731514717
13685.517646541332182.48235345866782
137115.103415845458375.89658415454163
13876.344596825864810.655403174135193
13945.16381737531512-1.16381737531512
14086.536673481033241.46332651896676
14166.23788813681542-0.237888136815423
14276.337176975602260.662823024397742
14357.48816602884918-2.48816602884918
14446.4593545803425-2.4593545803425
14585.115982681349682.88401731865032
14645.78723852972815-1.78723852972815
14785.864438582237932.13556141776207
14863.838040653294562.16195934670544
14945.95409321485817-1.95409321485817
15096.408652420431832.59134757956817
15155.24956854995437-0.249568549954365
15265.049599989355510.950400010644491
15345.20793008422005-1.20793008422005
15444.00683687702028-0.00683687702027857
15544.14724375993945-0.147243759939448
15656.76165702756714-1.76165702756714
15767.34157949795809-1.34157949795809
158168.730118750391597.26988124960841
15965.572103466764050.427896533235952
16066.84547474560103-0.845474745601032
16146.77178521352349-2.77178521352349
16246.70277816641054-2.70277816641054

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4 & 5.48191598451328 & -1.48191598451328 \tabularnewline
2 & 4 & 5.99168413190375 & -1.99168413190375 \tabularnewline
3 & 6 & 6.76040792074816 & -0.760407920748157 \tabularnewline
4 & 8 & 6.11245484214599 & 1.88754515785401 \tabularnewline
5 & 8 & 4.82996671221782 & 3.17003328778218 \tabularnewline
6 & 4 & 5.22319324894052 & -1.22319324894052 \tabularnewline
7 & 4 & 2.12014977447537 & 1.87985022552463 \tabularnewline
8 & 8 & 7.19415004091114 & 0.805849959088861 \tabularnewline
9 & 5 & 4.50931153376449 & 0.490688466235514 \tabularnewline
10 & 4 & 5.68102250710932 & -1.68102250710931 \tabularnewline
11 & 4 & 5.77280545430865 & -1.77280545430865 \tabularnewline
12 & 4 & 4.04149908176322 & -0.0414990817632202 \tabularnewline
13 & 4 & 5.647723572159 & -1.647723572159 \tabularnewline
14 & 4 & 1.92801438559418 & 2.07198561440582 \tabularnewline
15 & 4 & 5.12917274915581 & -1.12917274915581 \tabularnewline
16 & 8 & 5.27557224285964 & 2.72442775714036 \tabularnewline
17 & 4 & 7.79138393490094 & -3.79138393490094 \tabularnewline
18 & 4 & 5.02335553593504 & -1.02335553593504 \tabularnewline
19 & 4 & 2.18630161625936 & 1.81369838374064 \tabularnewline
20 & 8 & 6.45726460811757 & 1.54273539188243 \tabularnewline
21 & 4 & 5.47752506117738 & -1.47752506117738 \tabularnewline
22 & 7 & 7.41168679709686 & -0.41168679709686 \tabularnewline
23 & 4 & 8.76387432794948 & -4.76387432794948 \tabularnewline
24 & 4 & 4.31276131921977 & -0.312761319219769 \tabularnewline
25 & 5 & 6.23633617222834 & -1.23633617222834 \tabularnewline
26 & 4 & 5.00525266789298 & -1.00525266789298 \tabularnewline
27 & 4 & 5.00525266789298 & -1.00525266789298 \tabularnewline
28 & 4 & 5.14318991251034 & -1.14318991251034 \tabularnewline
29 & 4 & 7.04455138084986 & -3.04455138084986 \tabularnewline
30 & 4 & 5.49556052890361 & -1.49556052890361 \tabularnewline
31 & 4 & 5.1412585190282 & -1.14125851902819 \tabularnewline
32 & 4 & 6.08854767019269 & -2.08854767019269 \tabularnewline
33 & 15 & 8.20530800558031 & 6.79469199441969 \tabularnewline
34 & 10 & 6.99978297874611 & 3.00021702125389 \tabularnewline
35 & 4 & 6.15023362399578 & -2.15023362399578 \tabularnewline
36 & 8 & 5.38922693253656 & 2.61077306746344 \tabularnewline
37 & 4 & 6.26134891417482 & -2.26134891417482 \tabularnewline
38 & 4 & 5.67829146555538 & -1.67829146555538 \tabularnewline
39 & 4 & 4.71406257686581 & -0.714062576865812 \tabularnewline
40 & 4 & 4.37472289802723 & -0.374722898027228 \tabularnewline
41 & 7 & 5.89368861102262 & 1.10631138897738 \tabularnewline
42 & 4 & 4.31111198155219 & -0.311111981552187 \tabularnewline
43 & 6 & 5.68208401032794 & 0.317915989672061 \tabularnewline
44 & 5 & 4.53420614062858 & 0.465793859371423 \tabularnewline
45 & 4 & 3.72144147465073 & 0.278558525349265 \tabularnewline
46 & 16 & 7.13247149222065 & 8.86752850777935 \tabularnewline
47 & 5 & 5.86432299127442 & -0.864322991274419 \tabularnewline
48 & 12 & 5.58837707238576 & 6.41162292761424 \tabularnewline
49 & 6 & 6.08223510642525 & -0.0822351064252516 \tabularnewline
50 & 9 & 7.93287476569503 & 1.06712523430497 \tabularnewline
51 & 9 & 6.94867418627761 & 2.05132581372239 \tabularnewline
52 & 4 & 6.58469596183767 & -2.58469596183767 \tabularnewline
53 & 5 & 5.82027982885267 & -0.820279828852667 \tabularnewline
54 & 4 & 6.59779917323306 & -2.59779917323306 \tabularnewline
55 & 4 & 5.9178246504014 & -1.9178246504014 \tabularnewline
56 & 5 & 5.4222954667981 & -0.422295466798098 \tabularnewline
57 & 4 & 5.580932716717 & -1.580932716717 \tabularnewline
58 & 4 & 3.61572219584463 & 0.384277804155374 \tabularnewline
59 & 4 & 5.32058327137161 & -1.32058327137161 \tabularnewline
60 & 5 & 8.17364744210642 & -3.17364744210642 \tabularnewline
61 & 4 & 5.21179239018868 & -1.21179239018868 \tabularnewline
62 & 6 & 6.75235743894824 & -0.752357438948243 \tabularnewline
63 & 4 & 4.71041484335178 & -0.710414843351783 \tabularnewline
64 & 4 & 4.17273035897317 & -0.172730358973171 \tabularnewline
65 & 18 & 7.48876875582401 & 10.511231244176 \tabularnewline
66 & 4 & 3.58795885097586 & 0.412041149024143 \tabularnewline
67 & 6 & 6.44545726857229 & -0.445457268572289 \tabularnewline
68 & 4 & 5.57869112567594 & -1.57869112567594 \tabularnewline
69 & 4 & 6.38557212186182 & -2.38557212186182 \tabularnewline
70 & 5 & 6.57521210242778 & -1.57521210242778 \tabularnewline
71 & 4 & 5.14038379179298 & -1.14038379179298 \tabularnewline
72 & 4 & 5.18484772490012 & -1.18484772490012 \tabularnewline
73 & 5 & 5.35665443136247 & -0.356654431362471 \tabularnewline
74 & 10 & 6.60897497670809 & 3.39102502329191 \tabularnewline
75 & 5 & 6.0390167640891 & -1.0390167640891 \tabularnewline
76 & 8 & 8.04867285079449 & -0.0486728507944941 \tabularnewline
77 & 8 & 8.045939565252 & -0.0459395652519973 \tabularnewline
78 & 5 & 5.33211606653763 & -0.332116066537632 \tabularnewline
79 & 4 & 5.3379285513097 & -1.3379285513097 \tabularnewline
80 & 4 & 6.1819611457163 & -2.1819611457163 \tabularnewline
81 & 4 & 2.47774623528598 & 1.52225376471402 \tabularnewline
82 & 5 & 6.53988009380305 & -1.53988009380305 \tabularnewline
83 & 4 & 4.82628725558965 & -0.826287255589649 \tabularnewline
84 & 4 & 5.01578242727036 & -1.01578242727036 \tabularnewline
85 & 8 & 7.22071391982776 & 0.779286080172242 \tabularnewline
86 & 4 & 5.70650098285294 & -1.70650098285294 \tabularnewline
87 & 5 & 4.9093511553192 & 0.0906488446808024 \tabularnewline
88 & 14 & 7.52577183604734 & 6.47422816395266 \tabularnewline
89 & 8 & 6.07549780402971 & 1.92450219597029 \tabularnewline
90 & 8 & 5.68071534460273 & 2.31928465539727 \tabularnewline
91 & 4 & 7.60259627821368 & -3.60259627821368 \tabularnewline
92 & 4 & 4.32281121009501 & -0.32281121009501 \tabularnewline
93 & 6 & 5.57210346676405 & 0.427896533235952 \tabularnewline
94 & 4 & 5.84745649951917 & -1.84745649951917 \tabularnewline
95 & 7 & 6.14702943845316 & 0.852970561546837 \tabularnewline
96 & 7 & 5.40677395552657 & 1.59322604447343 \tabularnewline
97 & 4 & 4.88780345065879 & -0.887803450658794 \tabularnewline
98 & 6 & 6.43729119580886 & -0.43729119580886 \tabularnewline
99 & 4 & 6.10237648537406 & -2.10237648537405 \tabularnewline
100 & 7 & 5.07804207336519 & 1.92195792663481 \tabularnewline
101 & 4 & 5.68585538013036 & -1.68585538013036 \tabularnewline
102 & 4 & 3.34601974613494 & 0.653980253865063 \tabularnewline
103 & 8 & 6.59180539982842 & 1.40819460017158 \tabularnewline
104 & 4 & 5.7776505953982 & -1.7776505953982 \tabularnewline
105 & 4 & 5.88846153584561 & -1.88846153584561 \tabularnewline
106 & 10 & 6.9584972704773 & 3.0415027295227 \tabularnewline
107 & 8 & 7.13899552806857 & 0.861004471931429 \tabularnewline
108 & 6 & 6.35177547116967 & -0.351775471169671 \tabularnewline
109 & 4 & 4.62965892682917 & -0.629658926829173 \tabularnewline
110 & 4 & 5.38456101292672 & -1.38456101292672 \tabularnewline
111 & 4 & 3.87307107621062 & 0.126928923789381 \tabularnewline
112 & 5 & 4.68086547284699 & 0.319134527153007 \tabularnewline
113 & 4 & 6.5327203845587 & -2.5327203845587 \tabularnewline
114 & 6 & 6.0960385222365 & -0.0960385222364962 \tabularnewline
115 & 4 & 5.04865452899511 & -1.04865452899511 \tabularnewline
116 & 5 & 6.28816473793326 & -1.28816473793326 \tabularnewline
117 & 7 & 7.55714262490743 & -0.557142624907427 \tabularnewline
118 & 8 & 5.81095529038507 & 2.18904470961493 \tabularnewline
119 & 5 & 4.63220850528813 & 0.367791494711869 \tabularnewline
120 & 8 & 7.69154492910376 & 0.308455070896237 \tabularnewline
121 & 10 & 7.46144828712786 & 2.53855171287214 \tabularnewline
122 & 8 & 6.66535644676474 & 1.33464355323526 \tabularnewline
123 & 5 & 5.71131773258843 & -0.711317732588432 \tabularnewline
124 & 12 & 8.33316158528388 & 3.66683841471612 \tabularnewline
125 & 4 & 1.6464661819545 & 2.3535338180455 \tabularnewline
126 & 5 & 4.94046057240141 & 0.0595394275985908 \tabularnewline
127 & 4 & 6.36616346658572 & -2.36616346658572 \tabularnewline
128 & 6 & 5.29490836309119 & 0.705091636908809 \tabularnewline
129 & 4 & 7.123777135769 & -3.123777135769 \tabularnewline
130 & 4 & 5.53410943558193 & -1.53410943558193 \tabularnewline
131 & 7 & 7.0614837058101 & -0.061483705810104 \tabularnewline
132 & 7 & 6.55942218728237 & 0.440577812717626 \tabularnewline
133 & 10 & 7.25456167176376 & 2.74543832823624 \tabularnewline
134 & 4 & 6.04587590085367 & -2.04587590085367 \tabularnewline
135 & 5 & 6.14360731514717 & -1.14360731514717 \tabularnewline
136 & 8 & 5.51764654133218 & 2.48235345866782 \tabularnewline
137 & 11 & 5.10341584545837 & 5.89658415454163 \tabularnewline
138 & 7 & 6.34459682586481 & 0.655403174135193 \tabularnewline
139 & 4 & 5.16381737531512 & -1.16381737531512 \tabularnewline
140 & 8 & 6.53667348103324 & 1.46332651896676 \tabularnewline
141 & 6 & 6.23788813681542 & -0.237888136815423 \tabularnewline
142 & 7 & 6.33717697560226 & 0.662823024397742 \tabularnewline
143 & 5 & 7.48816602884918 & -2.48816602884918 \tabularnewline
144 & 4 & 6.4593545803425 & -2.4593545803425 \tabularnewline
145 & 8 & 5.11598268134968 & 2.88401731865032 \tabularnewline
146 & 4 & 5.78723852972815 & -1.78723852972815 \tabularnewline
147 & 8 & 5.86443858223793 & 2.13556141776207 \tabularnewline
148 & 6 & 3.83804065329456 & 2.16195934670544 \tabularnewline
149 & 4 & 5.95409321485817 & -1.95409321485817 \tabularnewline
150 & 9 & 6.40865242043183 & 2.59134757956817 \tabularnewline
151 & 5 & 5.24956854995437 & -0.249568549954365 \tabularnewline
152 & 6 & 5.04959998935551 & 0.950400010644491 \tabularnewline
153 & 4 & 5.20793008422005 & -1.20793008422005 \tabularnewline
154 & 4 & 4.00683687702028 & -0.00683687702027857 \tabularnewline
155 & 4 & 4.14724375993945 & -0.147243759939448 \tabularnewline
156 & 5 & 6.76165702756714 & -1.76165702756714 \tabularnewline
157 & 6 & 7.34157949795809 & -1.34157949795809 \tabularnewline
158 & 16 & 8.73011875039159 & 7.26988124960841 \tabularnewline
159 & 6 & 5.57210346676405 & 0.427896533235952 \tabularnewline
160 & 6 & 6.84547474560103 & -0.845474745601032 \tabularnewline
161 & 4 & 6.77178521352349 & -2.77178521352349 \tabularnewline
162 & 4 & 6.70277816641054 & -2.70277816641054 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190096&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]4[/C][C]5.48191598451328[/C][C]-1.48191598451328[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]5.99168413190375[/C][C]-1.99168413190375[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]6.76040792074816[/C][C]-0.760407920748157[/C][/ROW]
[ROW][C]4[/C][C]8[/C][C]6.11245484214599[/C][C]1.88754515785401[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]4.82996671221782[/C][C]3.17003328778218[/C][/ROW]
[ROW][C]6[/C][C]4[/C][C]5.22319324894052[/C][C]-1.22319324894052[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]2.12014977447537[/C][C]1.87985022552463[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]7.19415004091114[/C][C]0.805849959088861[/C][/ROW]
[ROW][C]9[/C][C]5[/C][C]4.50931153376449[/C][C]0.490688466235514[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]5.68102250710932[/C][C]-1.68102250710931[/C][/ROW]
[ROW][C]11[/C][C]4[/C][C]5.77280545430865[/C][C]-1.77280545430865[/C][/ROW]
[ROW][C]12[/C][C]4[/C][C]4.04149908176322[/C][C]-0.0414990817632202[/C][/ROW]
[ROW][C]13[/C][C]4[/C][C]5.647723572159[/C][C]-1.647723572159[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]1.92801438559418[/C][C]2.07198561440582[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]5.12917274915581[/C][C]-1.12917274915581[/C][/ROW]
[ROW][C]16[/C][C]8[/C][C]5.27557224285964[/C][C]2.72442775714036[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]7.79138393490094[/C][C]-3.79138393490094[/C][/ROW]
[ROW][C]18[/C][C]4[/C][C]5.02335553593504[/C][C]-1.02335553593504[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]2.18630161625936[/C][C]1.81369838374064[/C][/ROW]
[ROW][C]20[/C][C]8[/C][C]6.45726460811757[/C][C]1.54273539188243[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]5.47752506117738[/C][C]-1.47752506117738[/C][/ROW]
[ROW][C]22[/C][C]7[/C][C]7.41168679709686[/C][C]-0.41168679709686[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]8.76387432794948[/C][C]-4.76387432794948[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]4.31276131921977[/C][C]-0.312761319219769[/C][/ROW]
[ROW][C]25[/C][C]5[/C][C]6.23633617222834[/C][C]-1.23633617222834[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]5.00525266789298[/C][C]-1.00525266789298[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]5.00525266789298[/C][C]-1.00525266789298[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]5.14318991251034[/C][C]-1.14318991251034[/C][/ROW]
[ROW][C]29[/C][C]4[/C][C]7.04455138084986[/C][C]-3.04455138084986[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]5.49556052890361[/C][C]-1.49556052890361[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]5.1412585190282[/C][C]-1.14125851902819[/C][/ROW]
[ROW][C]32[/C][C]4[/C][C]6.08854767019269[/C][C]-2.08854767019269[/C][/ROW]
[ROW][C]33[/C][C]15[/C][C]8.20530800558031[/C][C]6.79469199441969[/C][/ROW]
[ROW][C]34[/C][C]10[/C][C]6.99978297874611[/C][C]3.00021702125389[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]6.15023362399578[/C][C]-2.15023362399578[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]5.38922693253656[/C][C]2.61077306746344[/C][/ROW]
[ROW][C]37[/C][C]4[/C][C]6.26134891417482[/C][C]-2.26134891417482[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]5.67829146555538[/C][C]-1.67829146555538[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]4.71406257686581[/C][C]-0.714062576865812[/C][/ROW]
[ROW][C]40[/C][C]4[/C][C]4.37472289802723[/C][C]-0.374722898027228[/C][/ROW]
[ROW][C]41[/C][C]7[/C][C]5.89368861102262[/C][C]1.10631138897738[/C][/ROW]
[ROW][C]42[/C][C]4[/C][C]4.31111198155219[/C][C]-0.311111981552187[/C][/ROW]
[ROW][C]43[/C][C]6[/C][C]5.68208401032794[/C][C]0.317915989672061[/C][/ROW]
[ROW][C]44[/C][C]5[/C][C]4.53420614062858[/C][C]0.465793859371423[/C][/ROW]
[ROW][C]45[/C][C]4[/C][C]3.72144147465073[/C][C]0.278558525349265[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]7.13247149222065[/C][C]8.86752850777935[/C][/ROW]
[ROW][C]47[/C][C]5[/C][C]5.86432299127442[/C][C]-0.864322991274419[/C][/ROW]
[ROW][C]48[/C][C]12[/C][C]5.58837707238576[/C][C]6.41162292761424[/C][/ROW]
[ROW][C]49[/C][C]6[/C][C]6.08223510642525[/C][C]-0.0822351064252516[/C][/ROW]
[ROW][C]50[/C][C]9[/C][C]7.93287476569503[/C][C]1.06712523430497[/C][/ROW]
[ROW][C]51[/C][C]9[/C][C]6.94867418627761[/C][C]2.05132581372239[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]6.58469596183767[/C][C]-2.58469596183767[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]5.82027982885267[/C][C]-0.820279828852667[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]6.59779917323306[/C][C]-2.59779917323306[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]5.9178246504014[/C][C]-1.9178246504014[/C][/ROW]
[ROW][C]56[/C][C]5[/C][C]5.4222954667981[/C][C]-0.422295466798098[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]5.580932716717[/C][C]-1.580932716717[/C][/ROW]
[ROW][C]58[/C][C]4[/C][C]3.61572219584463[/C][C]0.384277804155374[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]5.32058327137161[/C][C]-1.32058327137161[/C][/ROW]
[ROW][C]60[/C][C]5[/C][C]8.17364744210642[/C][C]-3.17364744210642[/C][/ROW]
[ROW][C]61[/C][C]4[/C][C]5.21179239018868[/C][C]-1.21179239018868[/C][/ROW]
[ROW][C]62[/C][C]6[/C][C]6.75235743894824[/C][C]-0.752357438948243[/C][/ROW]
[ROW][C]63[/C][C]4[/C][C]4.71041484335178[/C][C]-0.710414843351783[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]4.17273035897317[/C][C]-0.172730358973171[/C][/ROW]
[ROW][C]65[/C][C]18[/C][C]7.48876875582401[/C][C]10.511231244176[/C][/ROW]
[ROW][C]66[/C][C]4[/C][C]3.58795885097586[/C][C]0.412041149024143[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]6.44545726857229[/C][C]-0.445457268572289[/C][/ROW]
[ROW][C]68[/C][C]4[/C][C]5.57869112567594[/C][C]-1.57869112567594[/C][/ROW]
[ROW][C]69[/C][C]4[/C][C]6.38557212186182[/C][C]-2.38557212186182[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]6.57521210242778[/C][C]-1.57521210242778[/C][/ROW]
[ROW][C]71[/C][C]4[/C][C]5.14038379179298[/C][C]-1.14038379179298[/C][/ROW]
[ROW][C]72[/C][C]4[/C][C]5.18484772490012[/C][C]-1.18484772490012[/C][/ROW]
[ROW][C]73[/C][C]5[/C][C]5.35665443136247[/C][C]-0.356654431362471[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]6.60897497670809[/C][C]3.39102502329191[/C][/ROW]
[ROW][C]75[/C][C]5[/C][C]6.0390167640891[/C][C]-1.0390167640891[/C][/ROW]
[ROW][C]76[/C][C]8[/C][C]8.04867285079449[/C][C]-0.0486728507944941[/C][/ROW]
[ROW][C]77[/C][C]8[/C][C]8.045939565252[/C][C]-0.0459395652519973[/C][/ROW]
[ROW][C]78[/C][C]5[/C][C]5.33211606653763[/C][C]-0.332116066537632[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]5.3379285513097[/C][C]-1.3379285513097[/C][/ROW]
[ROW][C]80[/C][C]4[/C][C]6.1819611457163[/C][C]-2.1819611457163[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]2.47774623528598[/C][C]1.52225376471402[/C][/ROW]
[ROW][C]82[/C][C]5[/C][C]6.53988009380305[/C][C]-1.53988009380305[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]4.82628725558965[/C][C]-0.826287255589649[/C][/ROW]
[ROW][C]84[/C][C]4[/C][C]5.01578242727036[/C][C]-1.01578242727036[/C][/ROW]
[ROW][C]85[/C][C]8[/C][C]7.22071391982776[/C][C]0.779286080172242[/C][/ROW]
[ROW][C]86[/C][C]4[/C][C]5.70650098285294[/C][C]-1.70650098285294[/C][/ROW]
[ROW][C]87[/C][C]5[/C][C]4.9093511553192[/C][C]0.0906488446808024[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]7.52577183604734[/C][C]6.47422816395266[/C][/ROW]
[ROW][C]89[/C][C]8[/C][C]6.07549780402971[/C][C]1.92450219597029[/C][/ROW]
[ROW][C]90[/C][C]8[/C][C]5.68071534460273[/C][C]2.31928465539727[/C][/ROW]
[ROW][C]91[/C][C]4[/C][C]7.60259627821368[/C][C]-3.60259627821368[/C][/ROW]
[ROW][C]92[/C][C]4[/C][C]4.32281121009501[/C][C]-0.32281121009501[/C][/ROW]
[ROW][C]93[/C][C]6[/C][C]5.57210346676405[/C][C]0.427896533235952[/C][/ROW]
[ROW][C]94[/C][C]4[/C][C]5.84745649951917[/C][C]-1.84745649951917[/C][/ROW]
[ROW][C]95[/C][C]7[/C][C]6.14702943845316[/C][C]0.852970561546837[/C][/ROW]
[ROW][C]96[/C][C]7[/C][C]5.40677395552657[/C][C]1.59322604447343[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]4.88780345065879[/C][C]-0.887803450658794[/C][/ROW]
[ROW][C]98[/C][C]6[/C][C]6.43729119580886[/C][C]-0.43729119580886[/C][/ROW]
[ROW][C]99[/C][C]4[/C][C]6.10237648537406[/C][C]-2.10237648537405[/C][/ROW]
[ROW][C]100[/C][C]7[/C][C]5.07804207336519[/C][C]1.92195792663481[/C][/ROW]
[ROW][C]101[/C][C]4[/C][C]5.68585538013036[/C][C]-1.68585538013036[/C][/ROW]
[ROW][C]102[/C][C]4[/C][C]3.34601974613494[/C][C]0.653980253865063[/C][/ROW]
[ROW][C]103[/C][C]8[/C][C]6.59180539982842[/C][C]1.40819460017158[/C][/ROW]
[ROW][C]104[/C][C]4[/C][C]5.7776505953982[/C][C]-1.7776505953982[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]5.88846153584561[/C][C]-1.88846153584561[/C][/ROW]
[ROW][C]106[/C][C]10[/C][C]6.9584972704773[/C][C]3.0415027295227[/C][/ROW]
[ROW][C]107[/C][C]8[/C][C]7.13899552806857[/C][C]0.861004471931429[/C][/ROW]
[ROW][C]108[/C][C]6[/C][C]6.35177547116967[/C][C]-0.351775471169671[/C][/ROW]
[ROW][C]109[/C][C]4[/C][C]4.62965892682917[/C][C]-0.629658926829173[/C][/ROW]
[ROW][C]110[/C][C]4[/C][C]5.38456101292672[/C][C]-1.38456101292672[/C][/ROW]
[ROW][C]111[/C][C]4[/C][C]3.87307107621062[/C][C]0.126928923789381[/C][/ROW]
[ROW][C]112[/C][C]5[/C][C]4.68086547284699[/C][C]0.319134527153007[/C][/ROW]
[ROW][C]113[/C][C]4[/C][C]6.5327203845587[/C][C]-2.5327203845587[/C][/ROW]
[ROW][C]114[/C][C]6[/C][C]6.0960385222365[/C][C]-0.0960385222364962[/C][/ROW]
[ROW][C]115[/C][C]4[/C][C]5.04865452899511[/C][C]-1.04865452899511[/C][/ROW]
[ROW][C]116[/C][C]5[/C][C]6.28816473793326[/C][C]-1.28816473793326[/C][/ROW]
[ROW][C]117[/C][C]7[/C][C]7.55714262490743[/C][C]-0.557142624907427[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]5.81095529038507[/C][C]2.18904470961493[/C][/ROW]
[ROW][C]119[/C][C]5[/C][C]4.63220850528813[/C][C]0.367791494711869[/C][/ROW]
[ROW][C]120[/C][C]8[/C][C]7.69154492910376[/C][C]0.308455070896237[/C][/ROW]
[ROW][C]121[/C][C]10[/C][C]7.46144828712786[/C][C]2.53855171287214[/C][/ROW]
[ROW][C]122[/C][C]8[/C][C]6.66535644676474[/C][C]1.33464355323526[/C][/ROW]
[ROW][C]123[/C][C]5[/C][C]5.71131773258843[/C][C]-0.711317732588432[/C][/ROW]
[ROW][C]124[/C][C]12[/C][C]8.33316158528388[/C][C]3.66683841471612[/C][/ROW]
[ROW][C]125[/C][C]4[/C][C]1.6464661819545[/C][C]2.3535338180455[/C][/ROW]
[ROW][C]126[/C][C]5[/C][C]4.94046057240141[/C][C]0.0595394275985908[/C][/ROW]
[ROW][C]127[/C][C]4[/C][C]6.36616346658572[/C][C]-2.36616346658572[/C][/ROW]
[ROW][C]128[/C][C]6[/C][C]5.29490836309119[/C][C]0.705091636908809[/C][/ROW]
[ROW][C]129[/C][C]4[/C][C]7.123777135769[/C][C]-3.123777135769[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]5.53410943558193[/C][C]-1.53410943558193[/C][/ROW]
[ROW][C]131[/C][C]7[/C][C]7.0614837058101[/C][C]-0.061483705810104[/C][/ROW]
[ROW][C]132[/C][C]7[/C][C]6.55942218728237[/C][C]0.440577812717626[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]7.25456167176376[/C][C]2.74543832823624[/C][/ROW]
[ROW][C]134[/C][C]4[/C][C]6.04587590085367[/C][C]-2.04587590085367[/C][/ROW]
[ROW][C]135[/C][C]5[/C][C]6.14360731514717[/C][C]-1.14360731514717[/C][/ROW]
[ROW][C]136[/C][C]8[/C][C]5.51764654133218[/C][C]2.48235345866782[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]5.10341584545837[/C][C]5.89658415454163[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]6.34459682586481[/C][C]0.655403174135193[/C][/ROW]
[ROW][C]139[/C][C]4[/C][C]5.16381737531512[/C][C]-1.16381737531512[/C][/ROW]
[ROW][C]140[/C][C]8[/C][C]6.53667348103324[/C][C]1.46332651896676[/C][/ROW]
[ROW][C]141[/C][C]6[/C][C]6.23788813681542[/C][C]-0.237888136815423[/C][/ROW]
[ROW][C]142[/C][C]7[/C][C]6.33717697560226[/C][C]0.662823024397742[/C][/ROW]
[ROW][C]143[/C][C]5[/C][C]7.48816602884918[/C][C]-2.48816602884918[/C][/ROW]
[ROW][C]144[/C][C]4[/C][C]6.4593545803425[/C][C]-2.4593545803425[/C][/ROW]
[ROW][C]145[/C][C]8[/C][C]5.11598268134968[/C][C]2.88401731865032[/C][/ROW]
[ROW][C]146[/C][C]4[/C][C]5.78723852972815[/C][C]-1.78723852972815[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]5.86443858223793[/C][C]2.13556141776207[/C][/ROW]
[ROW][C]148[/C][C]6[/C][C]3.83804065329456[/C][C]2.16195934670544[/C][/ROW]
[ROW][C]149[/C][C]4[/C][C]5.95409321485817[/C][C]-1.95409321485817[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]6.40865242043183[/C][C]2.59134757956817[/C][/ROW]
[ROW][C]151[/C][C]5[/C][C]5.24956854995437[/C][C]-0.249568549954365[/C][/ROW]
[ROW][C]152[/C][C]6[/C][C]5.04959998935551[/C][C]0.950400010644491[/C][/ROW]
[ROW][C]153[/C][C]4[/C][C]5.20793008422005[/C][C]-1.20793008422005[/C][/ROW]
[ROW][C]154[/C][C]4[/C][C]4.00683687702028[/C][C]-0.00683687702027857[/C][/ROW]
[ROW][C]155[/C][C]4[/C][C]4.14724375993945[/C][C]-0.147243759939448[/C][/ROW]
[ROW][C]156[/C][C]5[/C][C]6.76165702756714[/C][C]-1.76165702756714[/C][/ROW]
[ROW][C]157[/C][C]6[/C][C]7.34157949795809[/C][C]-1.34157949795809[/C][/ROW]
[ROW][C]158[/C][C]16[/C][C]8.73011875039159[/C][C]7.26988124960841[/C][/ROW]
[ROW][C]159[/C][C]6[/C][C]5.57210346676405[/C][C]0.427896533235952[/C][/ROW]
[ROW][C]160[/C][C]6[/C][C]6.84547474560103[/C][C]-0.845474745601032[/C][/ROW]
[ROW][C]161[/C][C]4[/C][C]6.77178521352349[/C][C]-2.77178521352349[/C][/ROW]
[ROW][C]162[/C][C]4[/C][C]6.70277816641054[/C][C]-2.70277816641054[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190096&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190096&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
145.48191598451328-1.48191598451328
245.99168413190375-1.99168413190375
366.76040792074816-0.760407920748157
486.112454842145991.88754515785401
584.829966712217823.17003328778218
645.22319324894052-1.22319324894052
742.120149774475371.87985022552463
887.194150040911140.805849959088861
954.509311533764490.490688466235514
1045.68102250710932-1.68102250710931
1145.77280545430865-1.77280545430865
1244.04149908176322-0.0414990817632202
1345.647723572159-1.647723572159
1441.928014385594182.07198561440582
1545.12917274915581-1.12917274915581
1685.275572242859642.72442775714036
1747.79138393490094-3.79138393490094
1845.02335553593504-1.02335553593504
1942.186301616259361.81369838374064
2086.457264608117571.54273539188243
2145.47752506117738-1.47752506117738
2277.41168679709686-0.41168679709686
2348.76387432794948-4.76387432794948
2444.31276131921977-0.312761319219769
2556.23633617222834-1.23633617222834
2645.00525266789298-1.00525266789298
2745.00525266789298-1.00525266789298
2845.14318991251034-1.14318991251034
2947.04455138084986-3.04455138084986
3045.49556052890361-1.49556052890361
3145.1412585190282-1.14125851902819
3246.08854767019269-2.08854767019269
33158.205308005580316.79469199441969
34106.999782978746113.00021702125389
3546.15023362399578-2.15023362399578
3685.389226932536562.61077306746344
3746.26134891417482-2.26134891417482
3845.67829146555538-1.67829146555538
3944.71406257686581-0.714062576865812
4044.37472289802723-0.374722898027228
4175.893688611022621.10631138897738
4244.31111198155219-0.311111981552187
4365.682084010327940.317915989672061
4454.534206140628580.465793859371423
4543.721441474650730.278558525349265
46167.132471492220658.86752850777935
4755.86432299127442-0.864322991274419
48125.588377072385766.41162292761424
4966.08223510642525-0.0822351064252516
5097.932874765695031.06712523430497
5196.948674186277612.05132581372239
5246.58469596183767-2.58469596183767
5355.82027982885267-0.820279828852667
5446.59779917323306-2.59779917323306
5545.9178246504014-1.9178246504014
5655.4222954667981-0.422295466798098
5745.580932716717-1.580932716717
5843.615722195844630.384277804155374
5945.32058327137161-1.32058327137161
6058.17364744210642-3.17364744210642
6145.21179239018868-1.21179239018868
6266.75235743894824-0.752357438948243
6344.71041484335178-0.710414843351783
6444.17273035897317-0.172730358973171
65187.4887687558240110.511231244176
6643.587958850975860.412041149024143
6766.44545726857229-0.445457268572289
6845.57869112567594-1.57869112567594
6946.38557212186182-2.38557212186182
7056.57521210242778-1.57521210242778
7145.14038379179298-1.14038379179298
7245.18484772490012-1.18484772490012
7355.35665443136247-0.356654431362471
74106.608974976708093.39102502329191
7556.0390167640891-1.0390167640891
7688.04867285079449-0.0486728507944941
7788.045939565252-0.0459395652519973
7855.33211606653763-0.332116066537632
7945.3379285513097-1.3379285513097
8046.1819611457163-2.1819611457163
8142.477746235285981.52225376471402
8256.53988009380305-1.53988009380305
8344.82628725558965-0.826287255589649
8445.01578242727036-1.01578242727036
8587.220713919827760.779286080172242
8645.70650098285294-1.70650098285294
8754.90935115531920.0906488446808024
88147.525771836047346.47422816395266
8986.075497804029711.92450219597029
9085.680715344602732.31928465539727
9147.60259627821368-3.60259627821368
9244.32281121009501-0.32281121009501
9365.572103466764050.427896533235952
9445.84745649951917-1.84745649951917
9576.147029438453160.852970561546837
9675.406773955526571.59322604447343
9744.88780345065879-0.887803450658794
9866.43729119580886-0.43729119580886
9946.10237648537406-2.10237648537405
10075.078042073365191.92195792663481
10145.68585538013036-1.68585538013036
10243.346019746134940.653980253865063
10386.591805399828421.40819460017158
10445.7776505953982-1.7776505953982
10545.88846153584561-1.88846153584561
106106.95849727047733.0415027295227
10787.138995528068570.861004471931429
10866.35177547116967-0.351775471169671
10944.62965892682917-0.629658926829173
11045.38456101292672-1.38456101292672
11143.873071076210620.126928923789381
11254.680865472846990.319134527153007
11346.5327203845587-2.5327203845587
11466.0960385222365-0.0960385222364962
11545.04865452899511-1.04865452899511
11656.28816473793326-1.28816473793326
11777.55714262490743-0.557142624907427
11885.810955290385072.18904470961493
11954.632208505288130.367791494711869
12087.691544929103760.308455070896237
121107.461448287127862.53855171287214
12286.665356446764741.33464355323526
12355.71131773258843-0.711317732588432
124128.333161585283883.66683841471612
12541.64646618195452.3535338180455
12654.940460572401410.0595394275985908
12746.36616346658572-2.36616346658572
12865.294908363091190.705091636908809
12947.123777135769-3.123777135769
13045.53410943558193-1.53410943558193
13177.0614837058101-0.061483705810104
13276.559422187282370.440577812717626
133107.254561671763762.74543832823624
13446.04587590085367-2.04587590085367
13556.14360731514717-1.14360731514717
13685.517646541332182.48235345866782
137115.103415845458375.89658415454163
13876.344596825864810.655403174135193
13945.16381737531512-1.16381737531512
14086.536673481033241.46332651896676
14166.23788813681542-0.237888136815423
14276.337176975602260.662823024397742
14357.48816602884918-2.48816602884918
14446.4593545803425-2.4593545803425
14585.115982681349682.88401731865032
14645.78723852972815-1.78723852972815
14785.864438582237932.13556141776207
14863.838040653294562.16195934670544
14945.95409321485817-1.95409321485817
15096.408652420431832.59134757956817
15155.24956854995437-0.249568549954365
15265.049599989355510.950400010644491
15345.20793008422005-1.20793008422005
15444.00683687702028-0.00683687702027857
15544.14724375993945-0.147243759939448
15656.76165702756714-1.76165702756714
15767.34157949795809-1.34157949795809
158168.730118750391597.26988124960841
15965.572103466764050.427896533235952
16066.84547474560103-0.845474745601032
16146.77178521352349-2.77178521352349
16246.70277816641054-2.70277816641054






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.4079054009044030.8158108018088060.592094599095597
110.353869341174380.707738682348760.64613065882562
120.3129767659027660.6259535318055310.687023234097234
130.2156915023375080.4313830046750160.784308497662492
140.1811394464575260.3622788929150520.818860553542474
150.1246252069850360.2492504139700720.875374793014964
160.1137703297778170.2275406595556340.886229670222183
170.1006232589525520.2012465179051050.899376741047448
180.08398202180586080.1679640436117220.916017978194139
190.05479790837271090.1095958167454220.945202091627289
200.0580148982272070.1160297964544140.941985101772793
210.05430322736392580.1086064547278520.945696772636074
220.03716307835151260.07432615670302510.962836921648487
230.03135894301278640.06271788602557280.968641056987214
240.02010535248575720.04021070497151440.979894647514243
250.01228917581308720.02457835162617440.987710824186913
260.01173446384091340.02346892768182680.988265536159087
270.008416190106660290.01683238021332060.99158380989334
280.009203058026401290.01840611605280260.990796941973599
290.01157658409918990.02315316819837980.98842341590081
300.009128647417878110.01825729483575620.990871352582122
310.005696544043018910.01139308808603780.994303455956981
320.004053175746148580.008106351492297160.995946824253851
330.3063529746054830.6127059492109670.693647025394517
340.4587878105303040.9175756210606070.541212189469697
350.4430361729962730.8860723459925450.556963827003727
360.4469320512314080.8938641024628170.553067948768592
370.4132627586555670.8265255173111350.586737241344433
380.3785737571339610.7571475142679220.621426242866039
390.3258303614809140.6516607229618290.674169638519086
400.2769937101653210.5539874203306430.723006289834679
410.2794266231740930.5588532463481860.720573376825907
420.2344165880900920.4688331761801840.765583411909908
430.1978346655958180.3956693311916360.802165334404182
440.1626565746935160.3253131493870320.837343425306484
450.1345108810788610.2690217621577220.865489118921139
460.8272898981224080.3454202037551840.172710101877592
470.7997802109587240.4004395780825520.200219789041276
480.93863765492020.12272469015960.0613623450798001
490.9221382493942730.1557235012114550.0778617506057275
500.9104031920996580.1791936158006840.0895968079003419
510.9080806759145520.1838386481708970.0919193240854483
520.9092888835542630.1814222328914740.0907111164457369
530.8894001634743340.2211996730513330.110599836525666
540.894478132684650.21104373463070.10552186731535
550.886412173964590.2271756520708190.11358782603541
560.8619016972217340.2761966055565320.138098302778266
570.8450013320552580.3099973358894830.154998667944742
580.8149602269840530.3700795460318930.185039773015947
590.7906723565431780.4186552869136440.209327643456822
600.811199401830.3776011963400010.18880059817
610.7888929238958530.4222141522082940.211107076104147
620.7553865277276350.4892269445447310.244613472272365
630.718235435057750.5635291298845010.28176456494225
640.679531343339820.640937313320360.32046865666018
650.9963820726569720.007235854686056130.00361792734302806
660.9949530697844150.01009386043116940.0050469302155847
670.9930151513484790.01396969730304130.00698484865152063
680.9915414034649030.01691719307019470.00845859653509737
690.9916755040610420.01664899187791640.00832449593895819
700.9903119047240380.01937619055192380.00968809527596188
710.9878354457776120.02432910844477560.0121645542223878
720.9849041812626130.03019163747477450.0150958187373873
730.9800045471581870.03999090568362530.0199954528418127
740.9863764201860950.02724715962781110.0136235798139055
750.9829296966664450.03414060666711010.0170703033335551
760.9773732835930620.04525343281387540.0226267164069377
770.9703657415029690.05926851699406170.0296342584970308
780.9621076294299390.07578474114012140.0378923705700607
790.9561302617910510.08773947641789740.0438697382089487
800.9534754256572960.09304914868540850.0465245743427043
810.9527227775129240.09455444497415140.0472772224870757
820.9460937535891170.1078124928217650.0539062464108826
830.9343318545518610.1313362908962770.0656681454481386
840.9207780057117680.1584439885764640.0792219942882319
850.9045059805775940.1909880388448110.0954940194224055
860.8967273960093220.2065452079813560.103272603990678
870.8757757424460610.2484485151078770.124224257553939
880.9735892692244480.05282146155110420.0264107307755521
890.9693430628023740.06131387439525110.0306569371976256
900.9670819289032040.06583614219359140.0329180710967957
910.9735259348585390.0529481302829220.026474065141461
920.9655156303813740.06896873923725240.0344843696186262
930.9558914895918070.08821702081638570.0441085104081928
940.950923118224270.09815376355146040.0490768817757302
950.9390826109512340.1218347780975330.0609173890487664
960.9293970729428880.1412058541142240.0706029270571119
970.9137999633649070.1724000732701870.0862000366350934
980.893647679396850.21270464120630.10635232060315
990.8887635294870560.2224729410258890.111236470512944
1000.8791058362617110.2417883274765790.120894163738289
1010.869469141083760.2610617178324790.130530858916239
1020.8475792085384580.3048415829230840.152420791461542
1030.8255347094947180.3489305810105650.174465290505282
1040.8155287878501410.3689424242997170.184471212149859
1050.8036586468922670.3926827062154660.196341353107733
1060.8254335206648580.3491329586702850.174566479335142
1070.7965357196729680.4069285606540640.203464280327032
1080.7595028292521850.480994341495630.240497170747815
1090.7238903000504240.5522193998991520.276109699949576
1100.697351845955150.60529630808970.30264815404485
1110.6533302150823280.6933395698353440.346669784917672
1120.6051621409700860.7896757180598270.394837859029914
1130.6020815394851330.7958369210297350.397918460514867
1140.5510020813260340.8979958373479320.448997918673966
1150.5178436669813820.9643126660372370.482156333018618
1160.4816364214690490.9632728429380980.518363578530951
1170.4436090677253010.8872181354506020.556390932274699
1180.4228970620398650.8457941240797290.577102937960135
1190.3733148223023990.7466296446047980.626685177697601
1200.3249777334600710.6499554669201430.675022266539929
1210.3281692207860410.6563384415720820.671830779213959
1220.2892563270517190.5785126541034390.710743672948281
1230.251978817344550.50395763468910.74802118265545
1240.413992541621910.8279850832438210.58600745837809
1250.3905127381532670.7810254763065350.609487261846733
1260.3363460682772620.6726921365545240.663653931722738
1270.3404198249419820.6808396498839640.659580175058018
1280.2890500555968050.5781001111936090.710949944403195
1290.30386280753180.60772561506360.6961371924682
1300.2675033524094390.5350067048188780.732496647590561
1310.2192791087551270.4385582175102540.780720891244873
1320.176294540571010.352589081142020.82370545942899
1330.2315819974391130.4631639948782270.768418002560887
1340.2068484530338650.413696906067730.793151546966135
1350.2300048652760460.4600097305520910.769995134723954
1360.3270059552640880.6540119105281760.672994044735912
1370.595924012103290.8081519757934210.40407598789671
1380.5343686376275550.931262724744890.465631362372445
1390.5710916778193890.8578166443612230.428908322180611
1400.4981913630864080.9963827261728150.501808636913592
1410.4995111492981810.9990222985963620.500488850701819
1420.4249528044795670.8499056089591340.575047195520433
1430.561964208390390.8760715832192210.43803579160961
1440.5130266713583470.9739466572833060.486973328641653
1450.4441641029090360.8883282058180720.555835897090964
1460.3699960752151010.7399921504302020.630003924784899
1470.3155029081801550.6310058163603110.684497091819845
1480.6606776609016090.6786446781967820.339322339098391
1490.574579139165210.850841721669580.42542086083479
1500.7894410582516430.4211178834967140.210558941748357
1510.669930357734730.660139284530540.33006964226527
1520.5573750207680010.8852499584639980.442624979231999

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.407905400904403 & 0.815810801808806 & 0.592094599095597 \tabularnewline
11 & 0.35386934117438 & 0.70773868234876 & 0.64613065882562 \tabularnewline
12 & 0.312976765902766 & 0.625953531805531 & 0.687023234097234 \tabularnewline
13 & 0.215691502337508 & 0.431383004675016 & 0.784308497662492 \tabularnewline
14 & 0.181139446457526 & 0.362278892915052 & 0.818860553542474 \tabularnewline
15 & 0.124625206985036 & 0.249250413970072 & 0.875374793014964 \tabularnewline
16 & 0.113770329777817 & 0.227540659555634 & 0.886229670222183 \tabularnewline
17 & 0.100623258952552 & 0.201246517905105 & 0.899376741047448 \tabularnewline
18 & 0.0839820218058608 & 0.167964043611722 & 0.916017978194139 \tabularnewline
19 & 0.0547979083727109 & 0.109595816745422 & 0.945202091627289 \tabularnewline
20 & 0.058014898227207 & 0.116029796454414 & 0.941985101772793 \tabularnewline
21 & 0.0543032273639258 & 0.108606454727852 & 0.945696772636074 \tabularnewline
22 & 0.0371630783515126 & 0.0743261567030251 & 0.962836921648487 \tabularnewline
23 & 0.0313589430127864 & 0.0627178860255728 & 0.968641056987214 \tabularnewline
24 & 0.0201053524857572 & 0.0402107049715144 & 0.979894647514243 \tabularnewline
25 & 0.0122891758130872 & 0.0245783516261744 & 0.987710824186913 \tabularnewline
26 & 0.0117344638409134 & 0.0234689276818268 & 0.988265536159087 \tabularnewline
27 & 0.00841619010666029 & 0.0168323802133206 & 0.99158380989334 \tabularnewline
28 & 0.00920305802640129 & 0.0184061160528026 & 0.990796941973599 \tabularnewline
29 & 0.0115765840991899 & 0.0231531681983798 & 0.98842341590081 \tabularnewline
30 & 0.00912864741787811 & 0.0182572948357562 & 0.990871352582122 \tabularnewline
31 & 0.00569654404301891 & 0.0113930880860378 & 0.994303455956981 \tabularnewline
32 & 0.00405317574614858 & 0.00810635149229716 & 0.995946824253851 \tabularnewline
33 & 0.306352974605483 & 0.612705949210967 & 0.693647025394517 \tabularnewline
34 & 0.458787810530304 & 0.917575621060607 & 0.541212189469697 \tabularnewline
35 & 0.443036172996273 & 0.886072345992545 & 0.556963827003727 \tabularnewline
36 & 0.446932051231408 & 0.893864102462817 & 0.553067948768592 \tabularnewline
37 & 0.413262758655567 & 0.826525517311135 & 0.586737241344433 \tabularnewline
38 & 0.378573757133961 & 0.757147514267922 & 0.621426242866039 \tabularnewline
39 & 0.325830361480914 & 0.651660722961829 & 0.674169638519086 \tabularnewline
40 & 0.276993710165321 & 0.553987420330643 & 0.723006289834679 \tabularnewline
41 & 0.279426623174093 & 0.558853246348186 & 0.720573376825907 \tabularnewline
42 & 0.234416588090092 & 0.468833176180184 & 0.765583411909908 \tabularnewline
43 & 0.197834665595818 & 0.395669331191636 & 0.802165334404182 \tabularnewline
44 & 0.162656574693516 & 0.325313149387032 & 0.837343425306484 \tabularnewline
45 & 0.134510881078861 & 0.269021762157722 & 0.865489118921139 \tabularnewline
46 & 0.827289898122408 & 0.345420203755184 & 0.172710101877592 \tabularnewline
47 & 0.799780210958724 & 0.400439578082552 & 0.200219789041276 \tabularnewline
48 & 0.9386376549202 & 0.1227246901596 & 0.0613623450798001 \tabularnewline
49 & 0.922138249394273 & 0.155723501211455 & 0.0778617506057275 \tabularnewline
50 & 0.910403192099658 & 0.179193615800684 & 0.0895968079003419 \tabularnewline
51 & 0.908080675914552 & 0.183838648170897 & 0.0919193240854483 \tabularnewline
52 & 0.909288883554263 & 0.181422232891474 & 0.0907111164457369 \tabularnewline
53 & 0.889400163474334 & 0.221199673051333 & 0.110599836525666 \tabularnewline
54 & 0.89447813268465 & 0.2110437346307 & 0.10552186731535 \tabularnewline
55 & 0.88641217396459 & 0.227175652070819 & 0.11358782603541 \tabularnewline
56 & 0.861901697221734 & 0.276196605556532 & 0.138098302778266 \tabularnewline
57 & 0.845001332055258 & 0.309997335889483 & 0.154998667944742 \tabularnewline
58 & 0.814960226984053 & 0.370079546031893 & 0.185039773015947 \tabularnewline
59 & 0.790672356543178 & 0.418655286913644 & 0.209327643456822 \tabularnewline
60 & 0.81119940183 & 0.377601196340001 & 0.18880059817 \tabularnewline
61 & 0.788892923895853 & 0.422214152208294 & 0.211107076104147 \tabularnewline
62 & 0.755386527727635 & 0.489226944544731 & 0.244613472272365 \tabularnewline
63 & 0.71823543505775 & 0.563529129884501 & 0.28176456494225 \tabularnewline
64 & 0.67953134333982 & 0.64093731332036 & 0.32046865666018 \tabularnewline
65 & 0.996382072656972 & 0.00723585468605613 & 0.00361792734302806 \tabularnewline
66 & 0.994953069784415 & 0.0100938604311694 & 0.0050469302155847 \tabularnewline
67 & 0.993015151348479 & 0.0139696973030413 & 0.00698484865152063 \tabularnewline
68 & 0.991541403464903 & 0.0169171930701947 & 0.00845859653509737 \tabularnewline
69 & 0.991675504061042 & 0.0166489918779164 & 0.00832449593895819 \tabularnewline
70 & 0.990311904724038 & 0.0193761905519238 & 0.00968809527596188 \tabularnewline
71 & 0.987835445777612 & 0.0243291084447756 & 0.0121645542223878 \tabularnewline
72 & 0.984904181262613 & 0.0301916374747745 & 0.0150958187373873 \tabularnewline
73 & 0.980004547158187 & 0.0399909056836253 & 0.0199954528418127 \tabularnewline
74 & 0.986376420186095 & 0.0272471596278111 & 0.0136235798139055 \tabularnewline
75 & 0.982929696666445 & 0.0341406066671101 & 0.0170703033335551 \tabularnewline
76 & 0.977373283593062 & 0.0452534328138754 & 0.0226267164069377 \tabularnewline
77 & 0.970365741502969 & 0.0592685169940617 & 0.0296342584970308 \tabularnewline
78 & 0.962107629429939 & 0.0757847411401214 & 0.0378923705700607 \tabularnewline
79 & 0.956130261791051 & 0.0877394764178974 & 0.0438697382089487 \tabularnewline
80 & 0.953475425657296 & 0.0930491486854085 & 0.0465245743427043 \tabularnewline
81 & 0.952722777512924 & 0.0945544449741514 & 0.0472772224870757 \tabularnewline
82 & 0.946093753589117 & 0.107812492821765 & 0.0539062464108826 \tabularnewline
83 & 0.934331854551861 & 0.131336290896277 & 0.0656681454481386 \tabularnewline
84 & 0.920778005711768 & 0.158443988576464 & 0.0792219942882319 \tabularnewline
85 & 0.904505980577594 & 0.190988038844811 & 0.0954940194224055 \tabularnewline
86 & 0.896727396009322 & 0.206545207981356 & 0.103272603990678 \tabularnewline
87 & 0.875775742446061 & 0.248448515107877 & 0.124224257553939 \tabularnewline
88 & 0.973589269224448 & 0.0528214615511042 & 0.0264107307755521 \tabularnewline
89 & 0.969343062802374 & 0.0613138743952511 & 0.0306569371976256 \tabularnewline
90 & 0.967081928903204 & 0.0658361421935914 & 0.0329180710967957 \tabularnewline
91 & 0.973525934858539 & 0.052948130282922 & 0.026474065141461 \tabularnewline
92 & 0.965515630381374 & 0.0689687392372524 & 0.0344843696186262 \tabularnewline
93 & 0.955891489591807 & 0.0882170208163857 & 0.0441085104081928 \tabularnewline
94 & 0.95092311822427 & 0.0981537635514604 & 0.0490768817757302 \tabularnewline
95 & 0.939082610951234 & 0.121834778097533 & 0.0609173890487664 \tabularnewline
96 & 0.929397072942888 & 0.141205854114224 & 0.0706029270571119 \tabularnewline
97 & 0.913799963364907 & 0.172400073270187 & 0.0862000366350934 \tabularnewline
98 & 0.89364767939685 & 0.2127046412063 & 0.10635232060315 \tabularnewline
99 & 0.888763529487056 & 0.222472941025889 & 0.111236470512944 \tabularnewline
100 & 0.879105836261711 & 0.241788327476579 & 0.120894163738289 \tabularnewline
101 & 0.86946914108376 & 0.261061717832479 & 0.130530858916239 \tabularnewline
102 & 0.847579208538458 & 0.304841582923084 & 0.152420791461542 \tabularnewline
103 & 0.825534709494718 & 0.348930581010565 & 0.174465290505282 \tabularnewline
104 & 0.815528787850141 & 0.368942424299717 & 0.184471212149859 \tabularnewline
105 & 0.803658646892267 & 0.392682706215466 & 0.196341353107733 \tabularnewline
106 & 0.825433520664858 & 0.349132958670285 & 0.174566479335142 \tabularnewline
107 & 0.796535719672968 & 0.406928560654064 & 0.203464280327032 \tabularnewline
108 & 0.759502829252185 & 0.48099434149563 & 0.240497170747815 \tabularnewline
109 & 0.723890300050424 & 0.552219399899152 & 0.276109699949576 \tabularnewline
110 & 0.69735184595515 & 0.6052963080897 & 0.30264815404485 \tabularnewline
111 & 0.653330215082328 & 0.693339569835344 & 0.346669784917672 \tabularnewline
112 & 0.605162140970086 & 0.789675718059827 & 0.394837859029914 \tabularnewline
113 & 0.602081539485133 & 0.795836921029735 & 0.397918460514867 \tabularnewline
114 & 0.551002081326034 & 0.897995837347932 & 0.448997918673966 \tabularnewline
115 & 0.517843666981382 & 0.964312666037237 & 0.482156333018618 \tabularnewline
116 & 0.481636421469049 & 0.963272842938098 & 0.518363578530951 \tabularnewline
117 & 0.443609067725301 & 0.887218135450602 & 0.556390932274699 \tabularnewline
118 & 0.422897062039865 & 0.845794124079729 & 0.577102937960135 \tabularnewline
119 & 0.373314822302399 & 0.746629644604798 & 0.626685177697601 \tabularnewline
120 & 0.324977733460071 & 0.649955466920143 & 0.675022266539929 \tabularnewline
121 & 0.328169220786041 & 0.656338441572082 & 0.671830779213959 \tabularnewline
122 & 0.289256327051719 & 0.578512654103439 & 0.710743672948281 \tabularnewline
123 & 0.25197881734455 & 0.5039576346891 & 0.74802118265545 \tabularnewline
124 & 0.41399254162191 & 0.827985083243821 & 0.58600745837809 \tabularnewline
125 & 0.390512738153267 & 0.781025476306535 & 0.609487261846733 \tabularnewline
126 & 0.336346068277262 & 0.672692136554524 & 0.663653931722738 \tabularnewline
127 & 0.340419824941982 & 0.680839649883964 & 0.659580175058018 \tabularnewline
128 & 0.289050055596805 & 0.578100111193609 & 0.710949944403195 \tabularnewline
129 & 0.3038628075318 & 0.6077256150636 & 0.6961371924682 \tabularnewline
130 & 0.267503352409439 & 0.535006704818878 & 0.732496647590561 \tabularnewline
131 & 0.219279108755127 & 0.438558217510254 & 0.780720891244873 \tabularnewline
132 & 0.17629454057101 & 0.35258908114202 & 0.82370545942899 \tabularnewline
133 & 0.231581997439113 & 0.463163994878227 & 0.768418002560887 \tabularnewline
134 & 0.206848453033865 & 0.41369690606773 & 0.793151546966135 \tabularnewline
135 & 0.230004865276046 & 0.460009730552091 & 0.769995134723954 \tabularnewline
136 & 0.327005955264088 & 0.654011910528176 & 0.672994044735912 \tabularnewline
137 & 0.59592401210329 & 0.808151975793421 & 0.40407598789671 \tabularnewline
138 & 0.534368637627555 & 0.93126272474489 & 0.465631362372445 \tabularnewline
139 & 0.571091677819389 & 0.857816644361223 & 0.428908322180611 \tabularnewline
140 & 0.498191363086408 & 0.996382726172815 & 0.501808636913592 \tabularnewline
141 & 0.499511149298181 & 0.999022298596362 & 0.500488850701819 \tabularnewline
142 & 0.424952804479567 & 0.849905608959134 & 0.575047195520433 \tabularnewline
143 & 0.56196420839039 & 0.876071583219221 & 0.43803579160961 \tabularnewline
144 & 0.513026671358347 & 0.973946657283306 & 0.486973328641653 \tabularnewline
145 & 0.444164102909036 & 0.888328205818072 & 0.555835897090964 \tabularnewline
146 & 0.369996075215101 & 0.739992150430202 & 0.630003924784899 \tabularnewline
147 & 0.315502908180155 & 0.631005816360311 & 0.684497091819845 \tabularnewline
148 & 0.660677660901609 & 0.678644678196782 & 0.339322339098391 \tabularnewline
149 & 0.57457913916521 & 0.85084172166958 & 0.42542086083479 \tabularnewline
150 & 0.789441058251643 & 0.421117883496714 & 0.210558941748357 \tabularnewline
151 & 0.66993035773473 & 0.66013928453054 & 0.33006964226527 \tabularnewline
152 & 0.557375020768001 & 0.885249958463998 & 0.442624979231999 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190096&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]10[/C][C]0.407905400904403[/C][C]0.815810801808806[/C][C]0.592094599095597[/C][/ROW]
[ROW][C]11[/C][C]0.35386934117438[/C][C]0.70773868234876[/C][C]0.64613065882562[/C][/ROW]
[ROW][C]12[/C][C]0.312976765902766[/C][C]0.625953531805531[/C][C]0.687023234097234[/C][/ROW]
[ROW][C]13[/C][C]0.215691502337508[/C][C]0.431383004675016[/C][C]0.784308497662492[/C][/ROW]
[ROW][C]14[/C][C]0.181139446457526[/C][C]0.362278892915052[/C][C]0.818860553542474[/C][/ROW]
[ROW][C]15[/C][C]0.124625206985036[/C][C]0.249250413970072[/C][C]0.875374793014964[/C][/ROW]
[ROW][C]16[/C][C]0.113770329777817[/C][C]0.227540659555634[/C][C]0.886229670222183[/C][/ROW]
[ROW][C]17[/C][C]0.100623258952552[/C][C]0.201246517905105[/C][C]0.899376741047448[/C][/ROW]
[ROW][C]18[/C][C]0.0839820218058608[/C][C]0.167964043611722[/C][C]0.916017978194139[/C][/ROW]
[ROW][C]19[/C][C]0.0547979083727109[/C][C]0.109595816745422[/C][C]0.945202091627289[/C][/ROW]
[ROW][C]20[/C][C]0.058014898227207[/C][C]0.116029796454414[/C][C]0.941985101772793[/C][/ROW]
[ROW][C]21[/C][C]0.0543032273639258[/C][C]0.108606454727852[/C][C]0.945696772636074[/C][/ROW]
[ROW][C]22[/C][C]0.0371630783515126[/C][C]0.0743261567030251[/C][C]0.962836921648487[/C][/ROW]
[ROW][C]23[/C][C]0.0313589430127864[/C][C]0.0627178860255728[/C][C]0.968641056987214[/C][/ROW]
[ROW][C]24[/C][C]0.0201053524857572[/C][C]0.0402107049715144[/C][C]0.979894647514243[/C][/ROW]
[ROW][C]25[/C][C]0.0122891758130872[/C][C]0.0245783516261744[/C][C]0.987710824186913[/C][/ROW]
[ROW][C]26[/C][C]0.0117344638409134[/C][C]0.0234689276818268[/C][C]0.988265536159087[/C][/ROW]
[ROW][C]27[/C][C]0.00841619010666029[/C][C]0.0168323802133206[/C][C]0.99158380989334[/C][/ROW]
[ROW][C]28[/C][C]0.00920305802640129[/C][C]0.0184061160528026[/C][C]0.990796941973599[/C][/ROW]
[ROW][C]29[/C][C]0.0115765840991899[/C][C]0.0231531681983798[/C][C]0.98842341590081[/C][/ROW]
[ROW][C]30[/C][C]0.00912864741787811[/C][C]0.0182572948357562[/C][C]0.990871352582122[/C][/ROW]
[ROW][C]31[/C][C]0.00569654404301891[/C][C]0.0113930880860378[/C][C]0.994303455956981[/C][/ROW]
[ROW][C]32[/C][C]0.00405317574614858[/C][C]0.00810635149229716[/C][C]0.995946824253851[/C][/ROW]
[ROW][C]33[/C][C]0.306352974605483[/C][C]0.612705949210967[/C][C]0.693647025394517[/C][/ROW]
[ROW][C]34[/C][C]0.458787810530304[/C][C]0.917575621060607[/C][C]0.541212189469697[/C][/ROW]
[ROW][C]35[/C][C]0.443036172996273[/C][C]0.886072345992545[/C][C]0.556963827003727[/C][/ROW]
[ROW][C]36[/C][C]0.446932051231408[/C][C]0.893864102462817[/C][C]0.553067948768592[/C][/ROW]
[ROW][C]37[/C][C]0.413262758655567[/C][C]0.826525517311135[/C][C]0.586737241344433[/C][/ROW]
[ROW][C]38[/C][C]0.378573757133961[/C][C]0.757147514267922[/C][C]0.621426242866039[/C][/ROW]
[ROW][C]39[/C][C]0.325830361480914[/C][C]0.651660722961829[/C][C]0.674169638519086[/C][/ROW]
[ROW][C]40[/C][C]0.276993710165321[/C][C]0.553987420330643[/C][C]0.723006289834679[/C][/ROW]
[ROW][C]41[/C][C]0.279426623174093[/C][C]0.558853246348186[/C][C]0.720573376825907[/C][/ROW]
[ROW][C]42[/C][C]0.234416588090092[/C][C]0.468833176180184[/C][C]0.765583411909908[/C][/ROW]
[ROW][C]43[/C][C]0.197834665595818[/C][C]0.395669331191636[/C][C]0.802165334404182[/C][/ROW]
[ROW][C]44[/C][C]0.162656574693516[/C][C]0.325313149387032[/C][C]0.837343425306484[/C][/ROW]
[ROW][C]45[/C][C]0.134510881078861[/C][C]0.269021762157722[/C][C]0.865489118921139[/C][/ROW]
[ROW][C]46[/C][C]0.827289898122408[/C][C]0.345420203755184[/C][C]0.172710101877592[/C][/ROW]
[ROW][C]47[/C][C]0.799780210958724[/C][C]0.400439578082552[/C][C]0.200219789041276[/C][/ROW]
[ROW][C]48[/C][C]0.9386376549202[/C][C]0.1227246901596[/C][C]0.0613623450798001[/C][/ROW]
[ROW][C]49[/C][C]0.922138249394273[/C][C]0.155723501211455[/C][C]0.0778617506057275[/C][/ROW]
[ROW][C]50[/C][C]0.910403192099658[/C][C]0.179193615800684[/C][C]0.0895968079003419[/C][/ROW]
[ROW][C]51[/C][C]0.908080675914552[/C][C]0.183838648170897[/C][C]0.0919193240854483[/C][/ROW]
[ROW][C]52[/C][C]0.909288883554263[/C][C]0.181422232891474[/C][C]0.0907111164457369[/C][/ROW]
[ROW][C]53[/C][C]0.889400163474334[/C][C]0.221199673051333[/C][C]0.110599836525666[/C][/ROW]
[ROW][C]54[/C][C]0.89447813268465[/C][C]0.2110437346307[/C][C]0.10552186731535[/C][/ROW]
[ROW][C]55[/C][C]0.88641217396459[/C][C]0.227175652070819[/C][C]0.11358782603541[/C][/ROW]
[ROW][C]56[/C][C]0.861901697221734[/C][C]0.276196605556532[/C][C]0.138098302778266[/C][/ROW]
[ROW][C]57[/C][C]0.845001332055258[/C][C]0.309997335889483[/C][C]0.154998667944742[/C][/ROW]
[ROW][C]58[/C][C]0.814960226984053[/C][C]0.370079546031893[/C][C]0.185039773015947[/C][/ROW]
[ROW][C]59[/C][C]0.790672356543178[/C][C]0.418655286913644[/C][C]0.209327643456822[/C][/ROW]
[ROW][C]60[/C][C]0.81119940183[/C][C]0.377601196340001[/C][C]0.18880059817[/C][/ROW]
[ROW][C]61[/C][C]0.788892923895853[/C][C]0.422214152208294[/C][C]0.211107076104147[/C][/ROW]
[ROW][C]62[/C][C]0.755386527727635[/C][C]0.489226944544731[/C][C]0.244613472272365[/C][/ROW]
[ROW][C]63[/C][C]0.71823543505775[/C][C]0.563529129884501[/C][C]0.28176456494225[/C][/ROW]
[ROW][C]64[/C][C]0.67953134333982[/C][C]0.64093731332036[/C][C]0.32046865666018[/C][/ROW]
[ROW][C]65[/C][C]0.996382072656972[/C][C]0.00723585468605613[/C][C]0.00361792734302806[/C][/ROW]
[ROW][C]66[/C][C]0.994953069784415[/C][C]0.0100938604311694[/C][C]0.0050469302155847[/C][/ROW]
[ROW][C]67[/C][C]0.993015151348479[/C][C]0.0139696973030413[/C][C]0.00698484865152063[/C][/ROW]
[ROW][C]68[/C][C]0.991541403464903[/C][C]0.0169171930701947[/C][C]0.00845859653509737[/C][/ROW]
[ROW][C]69[/C][C]0.991675504061042[/C][C]0.0166489918779164[/C][C]0.00832449593895819[/C][/ROW]
[ROW][C]70[/C][C]0.990311904724038[/C][C]0.0193761905519238[/C][C]0.00968809527596188[/C][/ROW]
[ROW][C]71[/C][C]0.987835445777612[/C][C]0.0243291084447756[/C][C]0.0121645542223878[/C][/ROW]
[ROW][C]72[/C][C]0.984904181262613[/C][C]0.0301916374747745[/C][C]0.0150958187373873[/C][/ROW]
[ROW][C]73[/C][C]0.980004547158187[/C][C]0.0399909056836253[/C][C]0.0199954528418127[/C][/ROW]
[ROW][C]74[/C][C]0.986376420186095[/C][C]0.0272471596278111[/C][C]0.0136235798139055[/C][/ROW]
[ROW][C]75[/C][C]0.982929696666445[/C][C]0.0341406066671101[/C][C]0.0170703033335551[/C][/ROW]
[ROW][C]76[/C][C]0.977373283593062[/C][C]0.0452534328138754[/C][C]0.0226267164069377[/C][/ROW]
[ROW][C]77[/C][C]0.970365741502969[/C][C]0.0592685169940617[/C][C]0.0296342584970308[/C][/ROW]
[ROW][C]78[/C][C]0.962107629429939[/C][C]0.0757847411401214[/C][C]0.0378923705700607[/C][/ROW]
[ROW][C]79[/C][C]0.956130261791051[/C][C]0.0877394764178974[/C][C]0.0438697382089487[/C][/ROW]
[ROW][C]80[/C][C]0.953475425657296[/C][C]0.0930491486854085[/C][C]0.0465245743427043[/C][/ROW]
[ROW][C]81[/C][C]0.952722777512924[/C][C]0.0945544449741514[/C][C]0.0472772224870757[/C][/ROW]
[ROW][C]82[/C][C]0.946093753589117[/C][C]0.107812492821765[/C][C]0.0539062464108826[/C][/ROW]
[ROW][C]83[/C][C]0.934331854551861[/C][C]0.131336290896277[/C][C]0.0656681454481386[/C][/ROW]
[ROW][C]84[/C][C]0.920778005711768[/C][C]0.158443988576464[/C][C]0.0792219942882319[/C][/ROW]
[ROW][C]85[/C][C]0.904505980577594[/C][C]0.190988038844811[/C][C]0.0954940194224055[/C][/ROW]
[ROW][C]86[/C][C]0.896727396009322[/C][C]0.206545207981356[/C][C]0.103272603990678[/C][/ROW]
[ROW][C]87[/C][C]0.875775742446061[/C][C]0.248448515107877[/C][C]0.124224257553939[/C][/ROW]
[ROW][C]88[/C][C]0.973589269224448[/C][C]0.0528214615511042[/C][C]0.0264107307755521[/C][/ROW]
[ROW][C]89[/C][C]0.969343062802374[/C][C]0.0613138743952511[/C][C]0.0306569371976256[/C][/ROW]
[ROW][C]90[/C][C]0.967081928903204[/C][C]0.0658361421935914[/C][C]0.0329180710967957[/C][/ROW]
[ROW][C]91[/C][C]0.973525934858539[/C][C]0.052948130282922[/C][C]0.026474065141461[/C][/ROW]
[ROW][C]92[/C][C]0.965515630381374[/C][C]0.0689687392372524[/C][C]0.0344843696186262[/C][/ROW]
[ROW][C]93[/C][C]0.955891489591807[/C][C]0.0882170208163857[/C][C]0.0441085104081928[/C][/ROW]
[ROW][C]94[/C][C]0.95092311822427[/C][C]0.0981537635514604[/C][C]0.0490768817757302[/C][/ROW]
[ROW][C]95[/C][C]0.939082610951234[/C][C]0.121834778097533[/C][C]0.0609173890487664[/C][/ROW]
[ROW][C]96[/C][C]0.929397072942888[/C][C]0.141205854114224[/C][C]0.0706029270571119[/C][/ROW]
[ROW][C]97[/C][C]0.913799963364907[/C][C]0.172400073270187[/C][C]0.0862000366350934[/C][/ROW]
[ROW][C]98[/C][C]0.89364767939685[/C][C]0.2127046412063[/C][C]0.10635232060315[/C][/ROW]
[ROW][C]99[/C][C]0.888763529487056[/C][C]0.222472941025889[/C][C]0.111236470512944[/C][/ROW]
[ROW][C]100[/C][C]0.879105836261711[/C][C]0.241788327476579[/C][C]0.120894163738289[/C][/ROW]
[ROW][C]101[/C][C]0.86946914108376[/C][C]0.261061717832479[/C][C]0.130530858916239[/C][/ROW]
[ROW][C]102[/C][C]0.847579208538458[/C][C]0.304841582923084[/C][C]0.152420791461542[/C][/ROW]
[ROW][C]103[/C][C]0.825534709494718[/C][C]0.348930581010565[/C][C]0.174465290505282[/C][/ROW]
[ROW][C]104[/C][C]0.815528787850141[/C][C]0.368942424299717[/C][C]0.184471212149859[/C][/ROW]
[ROW][C]105[/C][C]0.803658646892267[/C][C]0.392682706215466[/C][C]0.196341353107733[/C][/ROW]
[ROW][C]106[/C][C]0.825433520664858[/C][C]0.349132958670285[/C][C]0.174566479335142[/C][/ROW]
[ROW][C]107[/C][C]0.796535719672968[/C][C]0.406928560654064[/C][C]0.203464280327032[/C][/ROW]
[ROW][C]108[/C][C]0.759502829252185[/C][C]0.48099434149563[/C][C]0.240497170747815[/C][/ROW]
[ROW][C]109[/C][C]0.723890300050424[/C][C]0.552219399899152[/C][C]0.276109699949576[/C][/ROW]
[ROW][C]110[/C][C]0.69735184595515[/C][C]0.6052963080897[/C][C]0.30264815404485[/C][/ROW]
[ROW][C]111[/C][C]0.653330215082328[/C][C]0.693339569835344[/C][C]0.346669784917672[/C][/ROW]
[ROW][C]112[/C][C]0.605162140970086[/C][C]0.789675718059827[/C][C]0.394837859029914[/C][/ROW]
[ROW][C]113[/C][C]0.602081539485133[/C][C]0.795836921029735[/C][C]0.397918460514867[/C][/ROW]
[ROW][C]114[/C][C]0.551002081326034[/C][C]0.897995837347932[/C][C]0.448997918673966[/C][/ROW]
[ROW][C]115[/C][C]0.517843666981382[/C][C]0.964312666037237[/C][C]0.482156333018618[/C][/ROW]
[ROW][C]116[/C][C]0.481636421469049[/C][C]0.963272842938098[/C][C]0.518363578530951[/C][/ROW]
[ROW][C]117[/C][C]0.443609067725301[/C][C]0.887218135450602[/C][C]0.556390932274699[/C][/ROW]
[ROW][C]118[/C][C]0.422897062039865[/C][C]0.845794124079729[/C][C]0.577102937960135[/C][/ROW]
[ROW][C]119[/C][C]0.373314822302399[/C][C]0.746629644604798[/C][C]0.626685177697601[/C][/ROW]
[ROW][C]120[/C][C]0.324977733460071[/C][C]0.649955466920143[/C][C]0.675022266539929[/C][/ROW]
[ROW][C]121[/C][C]0.328169220786041[/C][C]0.656338441572082[/C][C]0.671830779213959[/C][/ROW]
[ROW][C]122[/C][C]0.289256327051719[/C][C]0.578512654103439[/C][C]0.710743672948281[/C][/ROW]
[ROW][C]123[/C][C]0.25197881734455[/C][C]0.5039576346891[/C][C]0.74802118265545[/C][/ROW]
[ROW][C]124[/C][C]0.41399254162191[/C][C]0.827985083243821[/C][C]0.58600745837809[/C][/ROW]
[ROW][C]125[/C][C]0.390512738153267[/C][C]0.781025476306535[/C][C]0.609487261846733[/C][/ROW]
[ROW][C]126[/C][C]0.336346068277262[/C][C]0.672692136554524[/C][C]0.663653931722738[/C][/ROW]
[ROW][C]127[/C][C]0.340419824941982[/C][C]0.680839649883964[/C][C]0.659580175058018[/C][/ROW]
[ROW][C]128[/C][C]0.289050055596805[/C][C]0.578100111193609[/C][C]0.710949944403195[/C][/ROW]
[ROW][C]129[/C][C]0.3038628075318[/C][C]0.6077256150636[/C][C]0.6961371924682[/C][/ROW]
[ROW][C]130[/C][C]0.267503352409439[/C][C]0.535006704818878[/C][C]0.732496647590561[/C][/ROW]
[ROW][C]131[/C][C]0.219279108755127[/C][C]0.438558217510254[/C][C]0.780720891244873[/C][/ROW]
[ROW][C]132[/C][C]0.17629454057101[/C][C]0.35258908114202[/C][C]0.82370545942899[/C][/ROW]
[ROW][C]133[/C][C]0.231581997439113[/C][C]0.463163994878227[/C][C]0.768418002560887[/C][/ROW]
[ROW][C]134[/C][C]0.206848453033865[/C][C]0.41369690606773[/C][C]0.793151546966135[/C][/ROW]
[ROW][C]135[/C][C]0.230004865276046[/C][C]0.460009730552091[/C][C]0.769995134723954[/C][/ROW]
[ROW][C]136[/C][C]0.327005955264088[/C][C]0.654011910528176[/C][C]0.672994044735912[/C][/ROW]
[ROW][C]137[/C][C]0.59592401210329[/C][C]0.808151975793421[/C][C]0.40407598789671[/C][/ROW]
[ROW][C]138[/C][C]0.534368637627555[/C][C]0.93126272474489[/C][C]0.465631362372445[/C][/ROW]
[ROW][C]139[/C][C]0.571091677819389[/C][C]0.857816644361223[/C][C]0.428908322180611[/C][/ROW]
[ROW][C]140[/C][C]0.498191363086408[/C][C]0.996382726172815[/C][C]0.501808636913592[/C][/ROW]
[ROW][C]141[/C][C]0.499511149298181[/C][C]0.999022298596362[/C][C]0.500488850701819[/C][/ROW]
[ROW][C]142[/C][C]0.424952804479567[/C][C]0.849905608959134[/C][C]0.575047195520433[/C][/ROW]
[ROW][C]143[/C][C]0.56196420839039[/C][C]0.876071583219221[/C][C]0.43803579160961[/C][/ROW]
[ROW][C]144[/C][C]0.513026671358347[/C][C]0.973946657283306[/C][C]0.486973328641653[/C][/ROW]
[ROW][C]145[/C][C]0.444164102909036[/C][C]0.888328205818072[/C][C]0.555835897090964[/C][/ROW]
[ROW][C]146[/C][C]0.369996075215101[/C][C]0.739992150430202[/C][C]0.630003924784899[/C][/ROW]
[ROW][C]147[/C][C]0.315502908180155[/C][C]0.631005816360311[/C][C]0.684497091819845[/C][/ROW]
[ROW][C]148[/C][C]0.660677660901609[/C][C]0.678644678196782[/C][C]0.339322339098391[/C][/ROW]
[ROW][C]149[/C][C]0.57457913916521[/C][C]0.85084172166958[/C][C]0.42542086083479[/C][/ROW]
[ROW][C]150[/C][C]0.789441058251643[/C][C]0.421117883496714[/C][C]0.210558941748357[/C][/ROW]
[ROW][C]151[/C][C]0.66993035773473[/C][C]0.66013928453054[/C][C]0.33006964226527[/C][/ROW]
[ROW][C]152[/C][C]0.557375020768001[/C][C]0.885249958463998[/C][C]0.442624979231999[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190096&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190096&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
100.4079054009044030.8158108018088060.592094599095597
110.353869341174380.707738682348760.64613065882562
120.3129767659027660.6259535318055310.687023234097234
130.2156915023375080.4313830046750160.784308497662492
140.1811394464575260.3622788929150520.818860553542474
150.1246252069850360.2492504139700720.875374793014964
160.1137703297778170.2275406595556340.886229670222183
170.1006232589525520.2012465179051050.899376741047448
180.08398202180586080.1679640436117220.916017978194139
190.05479790837271090.1095958167454220.945202091627289
200.0580148982272070.1160297964544140.941985101772793
210.05430322736392580.1086064547278520.945696772636074
220.03716307835151260.07432615670302510.962836921648487
230.03135894301278640.06271788602557280.968641056987214
240.02010535248575720.04021070497151440.979894647514243
250.01228917581308720.02457835162617440.987710824186913
260.01173446384091340.02346892768182680.988265536159087
270.008416190106660290.01683238021332060.99158380989334
280.009203058026401290.01840611605280260.990796941973599
290.01157658409918990.02315316819837980.98842341590081
300.009128647417878110.01825729483575620.990871352582122
310.005696544043018910.01139308808603780.994303455956981
320.004053175746148580.008106351492297160.995946824253851
330.3063529746054830.6127059492109670.693647025394517
340.4587878105303040.9175756210606070.541212189469697
350.4430361729962730.8860723459925450.556963827003727
360.4469320512314080.8938641024628170.553067948768592
370.4132627586555670.8265255173111350.586737241344433
380.3785737571339610.7571475142679220.621426242866039
390.3258303614809140.6516607229618290.674169638519086
400.2769937101653210.5539874203306430.723006289834679
410.2794266231740930.5588532463481860.720573376825907
420.2344165880900920.4688331761801840.765583411909908
430.1978346655958180.3956693311916360.802165334404182
440.1626565746935160.3253131493870320.837343425306484
450.1345108810788610.2690217621577220.865489118921139
460.8272898981224080.3454202037551840.172710101877592
470.7997802109587240.4004395780825520.200219789041276
480.93863765492020.12272469015960.0613623450798001
490.9221382493942730.1557235012114550.0778617506057275
500.9104031920996580.1791936158006840.0895968079003419
510.9080806759145520.1838386481708970.0919193240854483
520.9092888835542630.1814222328914740.0907111164457369
530.8894001634743340.2211996730513330.110599836525666
540.894478132684650.21104373463070.10552186731535
550.886412173964590.2271756520708190.11358782603541
560.8619016972217340.2761966055565320.138098302778266
570.8450013320552580.3099973358894830.154998667944742
580.8149602269840530.3700795460318930.185039773015947
590.7906723565431780.4186552869136440.209327643456822
600.811199401830.3776011963400010.18880059817
610.7888929238958530.4222141522082940.211107076104147
620.7553865277276350.4892269445447310.244613472272365
630.718235435057750.5635291298845010.28176456494225
640.679531343339820.640937313320360.32046865666018
650.9963820726569720.007235854686056130.00361792734302806
660.9949530697844150.01009386043116940.0050469302155847
670.9930151513484790.01396969730304130.00698484865152063
680.9915414034649030.01691719307019470.00845859653509737
690.9916755040610420.01664899187791640.00832449593895819
700.9903119047240380.01937619055192380.00968809527596188
710.9878354457776120.02432910844477560.0121645542223878
720.9849041812626130.03019163747477450.0150958187373873
730.9800045471581870.03999090568362530.0199954528418127
740.9863764201860950.02724715962781110.0136235798139055
750.9829296966664450.03414060666711010.0170703033335551
760.9773732835930620.04525343281387540.0226267164069377
770.9703657415029690.05926851699406170.0296342584970308
780.9621076294299390.07578474114012140.0378923705700607
790.9561302617910510.08773947641789740.0438697382089487
800.9534754256572960.09304914868540850.0465245743427043
810.9527227775129240.09455444497415140.0472772224870757
820.9460937535891170.1078124928217650.0539062464108826
830.9343318545518610.1313362908962770.0656681454481386
840.9207780057117680.1584439885764640.0792219942882319
850.9045059805775940.1909880388448110.0954940194224055
860.8967273960093220.2065452079813560.103272603990678
870.8757757424460610.2484485151078770.124224257553939
880.9735892692244480.05282146155110420.0264107307755521
890.9693430628023740.06131387439525110.0306569371976256
900.9670819289032040.06583614219359140.0329180710967957
910.9735259348585390.0529481302829220.026474065141461
920.9655156303813740.06896873923725240.0344843696186262
930.9558914895918070.08821702081638570.0441085104081928
940.950923118224270.09815376355146040.0490768817757302
950.9390826109512340.1218347780975330.0609173890487664
960.9293970729428880.1412058541142240.0706029270571119
970.9137999633649070.1724000732701870.0862000366350934
980.893647679396850.21270464120630.10635232060315
990.8887635294870560.2224729410258890.111236470512944
1000.8791058362617110.2417883274765790.120894163738289
1010.869469141083760.2610617178324790.130530858916239
1020.8475792085384580.3048415829230840.152420791461542
1030.8255347094947180.3489305810105650.174465290505282
1040.8155287878501410.3689424242997170.184471212149859
1050.8036586468922670.3926827062154660.196341353107733
1060.8254335206648580.3491329586702850.174566479335142
1070.7965357196729680.4069285606540640.203464280327032
1080.7595028292521850.480994341495630.240497170747815
1090.7238903000504240.5522193998991520.276109699949576
1100.697351845955150.60529630808970.30264815404485
1110.6533302150823280.6933395698353440.346669784917672
1120.6051621409700860.7896757180598270.394837859029914
1130.6020815394851330.7958369210297350.397918460514867
1140.5510020813260340.8979958373479320.448997918673966
1150.5178436669813820.9643126660372370.482156333018618
1160.4816364214690490.9632728429380980.518363578530951
1170.4436090677253010.8872181354506020.556390932274699
1180.4228970620398650.8457941240797290.577102937960135
1190.3733148223023990.7466296446047980.626685177697601
1200.3249777334600710.6499554669201430.675022266539929
1210.3281692207860410.6563384415720820.671830779213959
1220.2892563270517190.5785126541034390.710743672948281
1230.251978817344550.50395763468910.74802118265545
1240.413992541621910.8279850832438210.58600745837809
1250.3905127381532670.7810254763065350.609487261846733
1260.3363460682772620.6726921365545240.663653931722738
1270.3404198249419820.6808396498839640.659580175058018
1280.2890500555968050.5781001111936090.710949944403195
1290.30386280753180.60772561506360.6961371924682
1300.2675033524094390.5350067048188780.732496647590561
1310.2192791087551270.4385582175102540.780720891244873
1320.176294540571010.352589081142020.82370545942899
1330.2315819974391130.4631639948782270.768418002560887
1340.2068484530338650.413696906067730.793151546966135
1350.2300048652760460.4600097305520910.769995134723954
1360.3270059552640880.6540119105281760.672994044735912
1370.595924012103290.8081519757934210.40407598789671
1380.5343686376275550.931262724744890.465631362372445
1390.5710916778193890.8578166443612230.428908322180611
1400.4981913630864080.9963827261728150.501808636913592
1410.4995111492981810.9990222985963620.500488850701819
1420.4249528044795670.8499056089591340.575047195520433
1430.561964208390390.8760715832192210.43803579160961
1440.5130266713583470.9739466572833060.486973328641653
1450.4441641029090360.8883282058180720.555835897090964
1460.3699960752151010.7399921504302020.630003924784899
1470.3155029081801550.6310058163603110.684497091819845
1480.6606776609016090.6786446781967820.339322339098391
1490.574579139165210.850841721669580.42542086083479
1500.7894410582516430.4211178834967140.210558941748357
1510.669930357734730.660139284530540.33006964226527
1520.5573750207680010.8852499584639980.442624979231999






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.013986013986014NOK
5% type I error level210.146853146853147NOK
10% type I error level350.244755244755245NOK

\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 & 2 & 0.013986013986014 & NOK \tabularnewline
5% type I error level & 21 & 0.146853146853147 & NOK \tabularnewline
10% type I error level & 35 & 0.244755244755245 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190096&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]2[/C][C]0.013986013986014[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]21[/C][C]0.146853146853147[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]35[/C][C]0.244755244755245[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190096&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190096&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 level20.013986013986014NOK
5% type I error level210.146853146853147NOK
10% type I error level350.244755244755245NOK


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 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}