Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 22 Nov 2011 10:14:13 -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/2011/Nov/22/t13219748938js01rpvgg613tb.htm/, Retrieved Fri, 29 Mar 2024 14:23:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146272, Retrieved Fri, 29 Mar 2024 14:23:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact171
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]
-   PD  [Multiple Regression] [Workshop 7 mini-t...] [2010-11-20 16:10:06] [87d60b8864dc39f7ed759c345edfb471]
-   PD    [Multiple Regression] [Workshop 7 mini-t...] [2010-11-21 12:07:24] [87d60b8864dc39f7ed759c345edfb471]
- R PD      [Multiple Regression] [W7-model3] [2010-11-21 20:40:05] [48146708a479232c43a8f6e52fbf83b4]
- R             [Multiple Regression] [WS 7 Multiple Lin...] [2011-11-22 15:14:13] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-  M              [Multiple Regression] [] [2011-11-22 19:58:46] [97a82ed57455ec27012f2e899dc4f1a4]
-                 [Multiple Regression] [WS 7 - Deel 3] [2011-11-22 19:59:51] [95a4a8598e82ac3272c4dca488d0ba38]
-   P               [Multiple Regression] [WS7 Taak 3] [2012-11-19 14:50:50] [d31c851fa7fbee45412c0a7bcdad10e5]
-                     [Multiple Regression] [WS7 taak 2] [2012-11-19 14:58:44] [d31c851fa7fbee45412c0a7bcdad10e5]
-   P                   [Multiple Regression] [Paper regression] [2012-12-19 21:58:06] [69075022316765aefe76d0d287433423]
-   P                 [Multiple Regression] [Paper regression ...] [2012-12-19 22:08:20] [69075022316765aefe76d0d287433423]
Feedback Forum

Post a new message
Dataseries X:
9	24	14	11	12	24	26
9	25	11	7	8	25	23
9	17	6	17	8	30	25
9	18	12	10	8	19	23
9	18	8	12	9	22	19
9	16	10	12	7	22	29
10	20	10	11	4	25	25
10	16	11	11	11	23	21
10	18	16	12	7	17	22
10	17	11	13	7	21	25
10	23	13	14	12	19	24
10	30	12	16	10	19	18
10	23	8	11	10	15	22
10	18	12	10	8	16	15
10	15	11	11	8	23	22
10	12	4	15	4	27	28
10	21	9	9	9	22	20
10	15	8	11	8	14	12
10	20	8	17	7	22	24
10	31	14	17	11	23	20
10	27	15	11	9	23	21
10	34	16	18	11	21	20
10	21	9	14	13	19	21
10	31	14	10	8	18	23
10	19	11	11	8	20	28
10	16	8	15	9	23	24
10	20	9	15	6	25	24
10	21	9	13	9	19	24
10	22	9	16	9	24	23
10	17	9	13	6	22	23
10	24	10	9	6	25	29
10	25	16	18	16	26	24
10	26	11	18	5	29	18
10	25	8	12	7	32	25
10	17	9	17	9	25	21
10	32	16	9	6	29	26
10	33	11	9	6	28	22
10	13	16	12	5	17	22
10	32	12	18	12	28	22
10	25	12	12	7	29	23
10	29	14	18	10	26	30
10	22	9	14	9	25	23
10	18	10	15	8	14	17
10	17	9	16	5	25	23
10	20	10	10	8	26	23
10	15	12	11	8	20	25
10	20	14	14	10	18	24
10	33	14	9	6	32	24
10	29	10	12	8	25	23
10	23	14	17	7	25	21
10	26	16	5	4	23	24
10	18	9	12	8	21	24
10	20	10	12	8	20	28
10	11	6	6	4	15	16
10	28	8	24	20	30	20
10	26	13	12	8	24	29
10	22	10	12	8	26	27
10	17	8	14	6	24	22
10	12	7	7	4	22	28
10	14	15	13	8	14	16
10	17	9	12	9	24	25
10	21	10	13	6	24	24
10	19	12	14	7	24	28
10	18	13	8	9	24	24
10	10	10	11	5	19	23
10	29	11	9	5	31	30
10	31	8	11	8	22	24
10	19	9	13	8	27	21
10	9	13	10	6	19	25
10	20	11	11	8	25	25
10	28	8	12	7	20	22
10	19	9	9	7	21	23
10	30	9	15	9	27	26
10	29	15	18	11	23	23
10	26	9	15	6	25	25
10	23	10	12	8	20	21
10	13	14	13	6	21	25
10	21	12	14	9	22	24
10	19	12	10	8	23	29
10	28	11	13	6	25	22
10	23	14	13	10	25	27
10	18	6	11	8	17	26
10	21	12	13	8	19	22
10	20	8	16	10	25	24
10	23	14	8	5	19	27
10	21	11	16	7	20	24
10	21	10	11	5	26	24
10	15	14	9	8	23	29
10	28	12	16	14	27	22
10	19	10	12	7	17	21
10	26	14	14	8	17	24
10	10	5	8	6	19	24
10	16	11	9	5	17	23
10	22	10	15	6	22	20
10	19	9	11	10	21	27
10	31	10	21	12	32	26
10	31	16	14	9	21	25
10	29	13	18	12	21	21
10	19	9	12	7	18	21
10	22	10	13	8	18	19
10	23	10	15	10	23	21
10	15	7	12	6	19	21
10	20	9	19	10	20	16
10	18	8	15	10	21	22
10	23	14	11	10	20	29
10	25	14	11	5	17	15
10	21	8	10	7	18	17
10	24	9	13	10	19	15
10	25	14	15	11	22	21
10	17	14	12	6	15	21
10	13	8	12	7	14	19
10	28	8	16	12	18	24
10	21	8	9	11	24	20
10	25	7	18	11	35	17
10	9	6	8	11	29	23
10	16	8	13	5	21	24
10	19	6	17	8	25	14
10	17	11	9	6	20	19
10	25	14	15	9	22	24
10	20	11	8	4	13	13
10	29	11	7	4	26	22
10	14	11	12	7	17	16
10	22	14	14	11	25	19
10	15	8	6	6	20	25
10	19	20	8	7	19	25
10	20	11	17	8	21	23
10	15	8	10	4	22	24
10	20	11	11	8	24	26
10	18	10	14	9	21	26
10	33	14	11	8	26	25
10	22	11	13	11	24	18
10	16	9	12	8	16	21
10	17	9	11	5	23	26
10	16	8	9	4	18	23
10	21	10	12	8	16	23
10	26	13	20	10	26	22
10	18	13	12	6	19	20
10	18	12	13	9	21	13
10	17	8	12	9	21	24
10	22	13	12	13	22	15
10	30	14	9	9	23	14
10	30	12	15	10	29	22
10	24	14	24	20	21	10
10	21	15	7	5	21	24
10	21	13	17	11	23	22
10	29	16	11	6	27	24
10	31	9	17	9	25	19
10	20	9	11	7	21	20
10	16	9	12	9	10	13
10	22	8	14	10	20	20
10	20	7	11	9	26	22
10	28	16	16	8	24	24
10	38	11	21	7	29	29
10	22	9	14	6	19	12
10	20	11	20	13	24	20
10	17	9	13	6	19	21
10	28	14	11	8	24	24
10	22	13	15	10	22	22
10	31	16	19	16	17	20




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=146272&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=146272&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146272&T=0

As an alternative you can also use a 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 time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
YT[t] = -18.3591714482501 + 1.58838471475259T1[t] + 0.798757118515112X1[t] + 0.233450466257556X2[t] + 0.207082749570131X3[t] + 0.571862761699043X4[t] -0.0999815995143377`X5 `[t] + 0.00354783249216936t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
YT[t] =  -18.3591714482501 +  1.58838471475259T1[t] +  0.798757118515112X1[t] +  0.233450466257556X2[t] +  0.207082749570131X3[t] +  0.571862761699043X4[t] -0.0999815995143377`X5
`[t] +  0.00354783249216936t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146272&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]YT[t] =  -18.3591714482501 +  1.58838471475259T1[t] +  0.798757118515112X1[t] +  0.233450466257556X2[t] +  0.207082749570131X3[t] +  0.571862761699043X4[t] -0.0999815995143377`X5
`[t] +  0.00354783249216936t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146272&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146272&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
YT[t] = -18.3591714482501 + 1.58838471475259T1[t] + 0.798757118515112X1[t] + 0.233450466257556X2[t] + 0.207082749570131X3[t] + 0.571862761699043X4[t] -0.0999815995143377`X5 `[t] + 0.00354783249216936t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-18.359171448250119.993412-0.91830.3599470.179973
T11.588384714752592.0021750.79330.4288310.214415
X10.7987571185151120.1311486.090500
X20.2334504662575560.1343331.73790.0842760.042138
X30.2070827495701310.1700091.21810.2250970.112548
X40.5718627616990430.0962475.941600
`X5 `-0.09998159951433770.105485-0.94780.3447320.172366
t0.003547832492169360.0084380.42050.6747410.33737

\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) & -18.3591714482501 & 19.993412 & -0.9183 & 0.359947 & 0.179973 \tabularnewline
T1 & 1.58838471475259 & 2.002175 & 0.7933 & 0.428831 & 0.214415 \tabularnewline
X1 & 0.798757118515112 & 0.131148 & 6.0905 & 0 & 0 \tabularnewline
X2 & 0.233450466257556 & 0.134333 & 1.7379 & 0.084276 & 0.042138 \tabularnewline
X3 & 0.207082749570131 & 0.170009 & 1.2181 & 0.225097 & 0.112548 \tabularnewline
X4 & 0.571862761699043 & 0.096247 & 5.9416 & 0 & 0 \tabularnewline
`X5
` & -0.0999815995143377 & 0.105485 & -0.9478 & 0.344732 & 0.172366 \tabularnewline
t & 0.00354783249216936 & 0.008438 & 0.4205 & 0.674741 & 0.33737 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146272&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]-18.3591714482501[/C][C]19.993412[/C][C]-0.9183[/C][C]0.359947[/C][C]0.179973[/C][/ROW]
[ROW][C]T1[/C][C]1.58838471475259[/C][C]2.002175[/C][C]0.7933[/C][C]0.428831[/C][C]0.214415[/C][/ROW]
[ROW][C]X1[/C][C]0.798757118515112[/C][C]0.131148[/C][C]6.0905[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X2[/C][C]0.233450466257556[/C][C]0.134333[/C][C]1.7379[/C][C]0.084276[/C][C]0.042138[/C][/ROW]
[ROW][C]X3[/C][C]0.207082749570131[/C][C]0.170009[/C][C]1.2181[/C][C]0.225097[/C][C]0.112548[/C][/ROW]
[ROW][C]X4[/C][C]0.571862761699043[/C][C]0.096247[/C][C]5.9416[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`X5
`[/C][C]-0.0999815995143377[/C][C]0.105485[/C][C]-0.9478[/C][C]0.344732[/C][C]0.172366[/C][/ROW]
[ROW][C]t[/C][C]0.00354783249216936[/C][C]0.008438[/C][C]0.4205[/C][C]0.674741[/C][C]0.33737[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146272&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146272&T=2

As an alternative you can also use a 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)-18.359171448250119.993412-0.91830.3599470.179973
T11.588384714752592.0021750.79330.4288310.214415
X10.7987571185151120.1311486.090500
X20.2334504662575560.1343331.73790.0842760.042138
X30.2070827495701310.1700091.21810.2250970.112548
X40.5718627616990430.0962475.941600
`X5 `-0.09998159951433770.105485-0.94780.3447320.172366
t0.003547832492169360.0084380.42050.6747410.33737







Multiple Linear Regression - Regression Statistics
Multiple R0.641530498673369
R-squared0.411561380728101
Adjusted R-squared0.384282769238676
F-TEST (value)15.087328799255
F-TEST (DF numerator)7
F-TEST (DF denominator)151
p-value7.32747196252603e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.49056162493448
Sum Squared Residuals3044.93669980746

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.641530498673369 \tabularnewline
R-squared & 0.411561380728101 \tabularnewline
Adjusted R-squared & 0.384282769238676 \tabularnewline
F-TEST (value) & 15.087328799255 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 151 \tabularnewline
p-value & 7.32747196252603e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.49056162493448 \tabularnewline
Sum Squared Residuals & 3044.93669980746 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146272&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.641530498673369[/C][/ROW]
[ROW][C]R-squared[/C][C]0.411561380728101[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.384282769238676[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]15.087328799255[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]151[/C][/ROW]
[ROW][C]p-value[/C][C]7.32747196252603e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.49056162493448[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3044.93669980746[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146272&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146272&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.641530498673369
R-squared0.411561380728101
Adjusted R-squared0.384282769238676
F-TEST (value)15.087328799255
F-TEST (DF numerator)7
F-TEST (DF denominator)151
p-value7.32747196252603e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.49056162493448
Sum Squared Residuals3044.93669980746







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12423.30057129330590.699428706694145
22520.0175224671844.98247753281596
31721.0211399791428-4.02113997914275
41818.0925500792619-0.092550079261901
51817.69056780293330.309432197066652
61617.8776483781721-1.87764837817211
72020.7303968936034-0.73039689360339
81622.2384819662609-6.23848196626085
91822.109776689597-4.10977668959702
101720.3404956440243-3.34049564402434
112322.16667800377120.833321996228797
123022.02409374820917.97590625179086
132314.97798333049958.02201666950045
141818.8006776299538-0.800677629953756
151521.5420869454813-6.54208694548131
161217.7473672648275-5.74736726482754
172119.31995062782011.68004937217993
181515.0095102272645-0.00951022726446759
192019.58180100715210.418198992847863
203126.17801170877194.8219882912281
212725.06546676357921.93453323642076
223426.87234655364597.12765344635406
232119.52125106772991.47874893227007
243120.777542919189110.2224570808109
251919.2620873882198-0.262087388219846
261620.1257631629215-4.12576316292152
272021.4505453886165-1.45054538861649
282118.17726396710972.82273603289032
292221.84045860638410.159541393615926
301719.3786812679951-2.37868126799509
312420.36288304198333.63711695801675
322530.4026260368562-5.40262603685615
332626.4499559136845-0.449955913684469
342524.0864121807230.913587819277008
351722.8670220283224-5.86702202832237
363227.7605607608744.23943923912604
373323.59838663714899.40161336285113
381321.7984983327297-8.79849833272967
393227.74779011438714.25220988561288
402525.787102563668-0.787102563668002
412926.99465619774862.00534380225136
422221.99154225796620.00845774203379345
431817.12961414405750.870385855942537
441721.6372078571851-4.63720785718513
452022.2319210210565-2.23192102105651
461520.4352937876135-5.43529378761353
472022.1071288311651-2.10712883116511
483328.12117199787564.87882800212443
492922.14115052184136.85884947815874
502326.4998596091402-3.49985960914021
512623.23459751292042.76540248707957
521818.9656042544921-0.965604254492144
532018.7961200457431.20387995425697
541111.7160709940257-0.716070994025724
552829.0105804767345-1.01058047673454
562623.39050434604672.60949565395329
572222.3414695454203-0.341469545420307
581720.1564210484307-3.1564210484307
591215.5692778789805-3.56927787898049
601420.8167935559991-6.81679355599913
611720.8202241820746-3.82022418207459
622121.3347129501434-0.334712950143374
631922.9763818374361-3.9763818374361
641823.1920758880957-5.19207588809566
651017.9120405565538-7.91204055655376
662924.40992651883414.59007348116592
673118.558476918801112.4415230811989
681922.9869414093617-3.98694140936168
69920.0961723263517-11.0961723263517
702022.5809984574057-2.5809984574057
712817.655273641087710.3447263589123
721918.22910835550710.770891644492931
733023.17875625633616.82124374366391
742927.10185744957871.8981425504213
752621.52085974872634.47914025127372
762319.57759138966333.42240861033671
771322.7673890269749-9.76738902697489
782122.6999656986182-1.69996569861817
791921.6345836806373-2.63458368063734
802822.96915701424535.03084298575475
812325.6973992029916-2.6973992029916
821813.95490316162954.04509683837052
832120.76154655918290.238453440817123
842021.9157961866931-1.91579618669311
852320.07774788362762.92225211637242
862120.83860115001720.161398849982822
872121.8931506037604-0.89315060376045
881523.0305779438395-8.03057794383953
892827.30058354392170.699416456078302
901917.70459000988641.2954099901136
912621.27720519998134.72279480001875
921013.4207961925499-3.42079619254989
931617.1995105289364-1.19951052893641
942221.17134539706720.828654602932829
951918.99893128599510.00106871400488
963128.9403783769222.05962162307796
973125.29055862881655.70944137118354
982924.85281161756134.14718838243874
991917.50962614549991.49037385450014
1002218.95242751136353.0475724886365
1012322.49639238497760.503607615022413
1021516.287535418075-1.28753541807505
1032021.4228525089516-1.4228525089516
1041819.6658145225114-1.66581452251145
1052322.25636924276470.743630757235343
1062520.90865743550984.09134256449023
1072116.67227715246434.32772284753566
1082419.56800771168244.4319922883176
1092525.3550235068465-0.35502350684648
1101719.619766860822-2.61976686082202
1111314.6659551691233-1.66595516912328
1122818.42626166372089.57373833627919
1132120.41967645109160.580323548908432
1142528.3159565386191-3.31595653861912
115921.1551764227403-12.1551764227403
1161618.006110633023-2.00611063302303
1171921.2544613841651-2.25446138416515
1181719.6108037739653-2.61080377396526
1192524.67639153408490.323608465915102
1202015.56713373874454.4328662612555
1212921.87161261143767.12838738856236
1221419.1167857657226-5.11678576572262
1232227.0867941796051-5.0867941796051
1241515.9355784175142-0.935578417514243
1251925.626332592574-6.62633259257396
1262022.092892026745-2.09289202674502
1271517.7095654037931-2.70956540379314
1282022.1149283807381-2.11492838073814
1291820.5115649579609-2.51156495796087
1303325.76200252418027.23799747581976
1312224.0135738555549-2.01357385555486
1321616.6900618439135-0.69006184391349
1331719.3420422957593-2.34204229575932
1341615.31348031769890.686519682301068
1352117.29949926087643.70050073912356
1362627.7996968946194-1.79969689461943
1371821.304233865906-3.30423386590599
1381823.2073200148495-5.20732001484945
1391718.6825913123659-1.6825913123659
1402224.9799528930422-2.97995289304224
1413024.92541980820975.07458019179029
1423027.57056272486672.42943727513332
1432429.9683835869881-5.9683835869881
1442122.2960469748642-1.29604697486422
1452125.6227704527493-4.62277045274928
1462927.67396094315831.32603905684171
1473123.4643424664747.53565753352599
1482019.26558935597010.734410644029935
1491614.32613397177091.67386602822906
1502219.22366478822332.77633521177669
1512020.7522347250232-0.752234725023152
1522827.56107748344220.438922516557777
1533826.89041511611.109584884
1542219.43627227284222.56372772715776
1552025.9470773992814-5.9470773992814
1561718.31008307594-1.31008307593998
1572824.81405007758513.18594992241494
1582224.4230458313632-2.4230458313632
1593126.33981277238524.66018722761482

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 24 & 23.3005712933059 & 0.699428706694145 \tabularnewline
2 & 25 & 20.017522467184 & 4.98247753281596 \tabularnewline
3 & 17 & 21.0211399791428 & -4.02113997914275 \tabularnewline
4 & 18 & 18.0925500792619 & -0.092550079261901 \tabularnewline
5 & 18 & 17.6905678029333 & 0.309432197066652 \tabularnewline
6 & 16 & 17.8776483781721 & -1.87764837817211 \tabularnewline
7 & 20 & 20.7303968936034 & -0.73039689360339 \tabularnewline
8 & 16 & 22.2384819662609 & -6.23848196626085 \tabularnewline
9 & 18 & 22.109776689597 & -4.10977668959702 \tabularnewline
10 & 17 & 20.3404956440243 & -3.34049564402434 \tabularnewline
11 & 23 & 22.1666780037712 & 0.833321996228797 \tabularnewline
12 & 30 & 22.0240937482091 & 7.97590625179086 \tabularnewline
13 & 23 & 14.9779833304995 & 8.02201666950045 \tabularnewline
14 & 18 & 18.8006776299538 & -0.800677629953756 \tabularnewline
15 & 15 & 21.5420869454813 & -6.54208694548131 \tabularnewline
16 & 12 & 17.7473672648275 & -5.74736726482754 \tabularnewline
17 & 21 & 19.3199506278201 & 1.68004937217993 \tabularnewline
18 & 15 & 15.0095102272645 & -0.00951022726446759 \tabularnewline
19 & 20 & 19.5818010071521 & 0.418198992847863 \tabularnewline
20 & 31 & 26.1780117087719 & 4.8219882912281 \tabularnewline
21 & 27 & 25.0654667635792 & 1.93453323642076 \tabularnewline
22 & 34 & 26.8723465536459 & 7.12765344635406 \tabularnewline
23 & 21 & 19.5212510677299 & 1.47874893227007 \tabularnewline
24 & 31 & 20.7775429191891 & 10.2224570808109 \tabularnewline
25 & 19 & 19.2620873882198 & -0.262087388219846 \tabularnewline
26 & 16 & 20.1257631629215 & -4.12576316292152 \tabularnewline
27 & 20 & 21.4505453886165 & -1.45054538861649 \tabularnewline
28 & 21 & 18.1772639671097 & 2.82273603289032 \tabularnewline
29 & 22 & 21.8404586063841 & 0.159541393615926 \tabularnewline
30 & 17 & 19.3786812679951 & -2.37868126799509 \tabularnewline
31 & 24 & 20.3628830419833 & 3.63711695801675 \tabularnewline
32 & 25 & 30.4026260368562 & -5.40262603685615 \tabularnewline
33 & 26 & 26.4499559136845 & -0.449955913684469 \tabularnewline
34 & 25 & 24.086412180723 & 0.913587819277008 \tabularnewline
35 & 17 & 22.8670220283224 & -5.86702202832237 \tabularnewline
36 & 32 & 27.760560760874 & 4.23943923912604 \tabularnewline
37 & 33 & 23.5983866371489 & 9.40161336285113 \tabularnewline
38 & 13 & 21.7984983327297 & -8.79849833272967 \tabularnewline
39 & 32 & 27.7477901143871 & 4.25220988561288 \tabularnewline
40 & 25 & 25.787102563668 & -0.787102563668002 \tabularnewline
41 & 29 & 26.9946561977486 & 2.00534380225136 \tabularnewline
42 & 22 & 21.9915422579662 & 0.00845774203379345 \tabularnewline
43 & 18 & 17.1296141440575 & 0.870385855942537 \tabularnewline
44 & 17 & 21.6372078571851 & -4.63720785718513 \tabularnewline
45 & 20 & 22.2319210210565 & -2.23192102105651 \tabularnewline
46 & 15 & 20.4352937876135 & -5.43529378761353 \tabularnewline
47 & 20 & 22.1071288311651 & -2.10712883116511 \tabularnewline
48 & 33 & 28.1211719978756 & 4.87882800212443 \tabularnewline
49 & 29 & 22.1411505218413 & 6.85884947815874 \tabularnewline
50 & 23 & 26.4998596091402 & -3.49985960914021 \tabularnewline
51 & 26 & 23.2345975129204 & 2.76540248707957 \tabularnewline
52 & 18 & 18.9656042544921 & -0.965604254492144 \tabularnewline
53 & 20 & 18.796120045743 & 1.20387995425697 \tabularnewline
54 & 11 & 11.7160709940257 & -0.716070994025724 \tabularnewline
55 & 28 & 29.0105804767345 & -1.01058047673454 \tabularnewline
56 & 26 & 23.3905043460467 & 2.60949565395329 \tabularnewline
57 & 22 & 22.3414695454203 & -0.341469545420307 \tabularnewline
58 & 17 & 20.1564210484307 & -3.1564210484307 \tabularnewline
59 & 12 & 15.5692778789805 & -3.56927787898049 \tabularnewline
60 & 14 & 20.8167935559991 & -6.81679355599913 \tabularnewline
61 & 17 & 20.8202241820746 & -3.82022418207459 \tabularnewline
62 & 21 & 21.3347129501434 & -0.334712950143374 \tabularnewline
63 & 19 & 22.9763818374361 & -3.9763818374361 \tabularnewline
64 & 18 & 23.1920758880957 & -5.19207588809566 \tabularnewline
65 & 10 & 17.9120405565538 & -7.91204055655376 \tabularnewline
66 & 29 & 24.4099265188341 & 4.59007348116592 \tabularnewline
67 & 31 & 18.5584769188011 & 12.4415230811989 \tabularnewline
68 & 19 & 22.9869414093617 & -3.98694140936168 \tabularnewline
69 & 9 & 20.0961723263517 & -11.0961723263517 \tabularnewline
70 & 20 & 22.5809984574057 & -2.5809984574057 \tabularnewline
71 & 28 & 17.6552736410877 & 10.3447263589123 \tabularnewline
72 & 19 & 18.2291083555071 & 0.770891644492931 \tabularnewline
73 & 30 & 23.1787562563361 & 6.82124374366391 \tabularnewline
74 & 29 & 27.1018574495787 & 1.8981425504213 \tabularnewline
75 & 26 & 21.5208597487263 & 4.47914025127372 \tabularnewline
76 & 23 & 19.5775913896633 & 3.42240861033671 \tabularnewline
77 & 13 & 22.7673890269749 & -9.76738902697489 \tabularnewline
78 & 21 & 22.6999656986182 & -1.69996569861817 \tabularnewline
79 & 19 & 21.6345836806373 & -2.63458368063734 \tabularnewline
80 & 28 & 22.9691570142453 & 5.03084298575475 \tabularnewline
81 & 23 & 25.6973992029916 & -2.6973992029916 \tabularnewline
82 & 18 & 13.9549031616295 & 4.04509683837052 \tabularnewline
83 & 21 & 20.7615465591829 & 0.238453440817123 \tabularnewline
84 & 20 & 21.9157961866931 & -1.91579618669311 \tabularnewline
85 & 23 & 20.0777478836276 & 2.92225211637242 \tabularnewline
86 & 21 & 20.8386011500172 & 0.161398849982822 \tabularnewline
87 & 21 & 21.8931506037604 & -0.89315060376045 \tabularnewline
88 & 15 & 23.0305779438395 & -8.03057794383953 \tabularnewline
89 & 28 & 27.3005835439217 & 0.699416456078302 \tabularnewline
90 & 19 & 17.7045900098864 & 1.2954099901136 \tabularnewline
91 & 26 & 21.2772051999813 & 4.72279480001875 \tabularnewline
92 & 10 & 13.4207961925499 & -3.42079619254989 \tabularnewline
93 & 16 & 17.1995105289364 & -1.19951052893641 \tabularnewline
94 & 22 & 21.1713453970672 & 0.828654602932829 \tabularnewline
95 & 19 & 18.9989312859951 & 0.00106871400488 \tabularnewline
96 & 31 & 28.940378376922 & 2.05962162307796 \tabularnewline
97 & 31 & 25.2905586288165 & 5.70944137118354 \tabularnewline
98 & 29 & 24.8528116175613 & 4.14718838243874 \tabularnewline
99 & 19 & 17.5096261454999 & 1.49037385450014 \tabularnewline
100 & 22 & 18.9524275113635 & 3.0475724886365 \tabularnewline
101 & 23 & 22.4963923849776 & 0.503607615022413 \tabularnewline
102 & 15 & 16.287535418075 & -1.28753541807505 \tabularnewline
103 & 20 & 21.4228525089516 & -1.4228525089516 \tabularnewline
104 & 18 & 19.6658145225114 & -1.66581452251145 \tabularnewline
105 & 23 & 22.2563692427647 & 0.743630757235343 \tabularnewline
106 & 25 & 20.9086574355098 & 4.09134256449023 \tabularnewline
107 & 21 & 16.6722771524643 & 4.32772284753566 \tabularnewline
108 & 24 & 19.5680077116824 & 4.4319922883176 \tabularnewline
109 & 25 & 25.3550235068465 & -0.35502350684648 \tabularnewline
110 & 17 & 19.619766860822 & -2.61976686082202 \tabularnewline
111 & 13 & 14.6659551691233 & -1.66595516912328 \tabularnewline
112 & 28 & 18.4262616637208 & 9.57373833627919 \tabularnewline
113 & 21 & 20.4196764510916 & 0.580323548908432 \tabularnewline
114 & 25 & 28.3159565386191 & -3.31595653861912 \tabularnewline
115 & 9 & 21.1551764227403 & -12.1551764227403 \tabularnewline
116 & 16 & 18.006110633023 & -2.00611063302303 \tabularnewline
117 & 19 & 21.2544613841651 & -2.25446138416515 \tabularnewline
118 & 17 & 19.6108037739653 & -2.61080377396526 \tabularnewline
119 & 25 & 24.6763915340849 & 0.323608465915102 \tabularnewline
120 & 20 & 15.5671337387445 & 4.4328662612555 \tabularnewline
121 & 29 & 21.8716126114376 & 7.12838738856236 \tabularnewline
122 & 14 & 19.1167857657226 & -5.11678576572262 \tabularnewline
123 & 22 & 27.0867941796051 & -5.0867941796051 \tabularnewline
124 & 15 & 15.9355784175142 & -0.935578417514243 \tabularnewline
125 & 19 & 25.626332592574 & -6.62633259257396 \tabularnewline
126 & 20 & 22.092892026745 & -2.09289202674502 \tabularnewline
127 & 15 & 17.7095654037931 & -2.70956540379314 \tabularnewline
128 & 20 & 22.1149283807381 & -2.11492838073814 \tabularnewline
129 & 18 & 20.5115649579609 & -2.51156495796087 \tabularnewline
130 & 33 & 25.7620025241802 & 7.23799747581976 \tabularnewline
131 & 22 & 24.0135738555549 & -2.01357385555486 \tabularnewline
132 & 16 & 16.6900618439135 & -0.69006184391349 \tabularnewline
133 & 17 & 19.3420422957593 & -2.34204229575932 \tabularnewline
134 & 16 & 15.3134803176989 & 0.686519682301068 \tabularnewline
135 & 21 & 17.2994992608764 & 3.70050073912356 \tabularnewline
136 & 26 & 27.7996968946194 & -1.79969689461943 \tabularnewline
137 & 18 & 21.304233865906 & -3.30423386590599 \tabularnewline
138 & 18 & 23.2073200148495 & -5.20732001484945 \tabularnewline
139 & 17 & 18.6825913123659 & -1.6825913123659 \tabularnewline
140 & 22 & 24.9799528930422 & -2.97995289304224 \tabularnewline
141 & 30 & 24.9254198082097 & 5.07458019179029 \tabularnewline
142 & 30 & 27.5705627248667 & 2.42943727513332 \tabularnewline
143 & 24 & 29.9683835869881 & -5.9683835869881 \tabularnewline
144 & 21 & 22.2960469748642 & -1.29604697486422 \tabularnewline
145 & 21 & 25.6227704527493 & -4.62277045274928 \tabularnewline
146 & 29 & 27.6739609431583 & 1.32603905684171 \tabularnewline
147 & 31 & 23.464342466474 & 7.53565753352599 \tabularnewline
148 & 20 & 19.2655893559701 & 0.734410644029935 \tabularnewline
149 & 16 & 14.3261339717709 & 1.67386602822906 \tabularnewline
150 & 22 & 19.2236647882233 & 2.77633521177669 \tabularnewline
151 & 20 & 20.7522347250232 & -0.752234725023152 \tabularnewline
152 & 28 & 27.5610774834422 & 0.438922516557777 \tabularnewline
153 & 38 & 26.890415116 & 11.109584884 \tabularnewline
154 & 22 & 19.4362722728422 & 2.56372772715776 \tabularnewline
155 & 20 & 25.9470773992814 & -5.9470773992814 \tabularnewline
156 & 17 & 18.31008307594 & -1.31008307593998 \tabularnewline
157 & 28 & 24.8140500775851 & 3.18594992241494 \tabularnewline
158 & 22 & 24.4230458313632 & -2.4230458313632 \tabularnewline
159 & 31 & 26.3398127723852 & 4.66018722761482 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146272&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]24[/C][C]23.3005712933059[/C][C]0.699428706694145[/C][/ROW]
[ROW][C]2[/C][C]25[/C][C]20.017522467184[/C][C]4.98247753281596[/C][/ROW]
[ROW][C]3[/C][C]17[/C][C]21.0211399791428[/C][C]-4.02113997914275[/C][/ROW]
[ROW][C]4[/C][C]18[/C][C]18.0925500792619[/C][C]-0.092550079261901[/C][/ROW]
[ROW][C]5[/C][C]18[/C][C]17.6905678029333[/C][C]0.309432197066652[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]17.8776483781721[/C][C]-1.87764837817211[/C][/ROW]
[ROW][C]7[/C][C]20[/C][C]20.7303968936034[/C][C]-0.73039689360339[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]22.2384819662609[/C][C]-6.23848196626085[/C][/ROW]
[ROW][C]9[/C][C]18[/C][C]22.109776689597[/C][C]-4.10977668959702[/C][/ROW]
[ROW][C]10[/C][C]17[/C][C]20.3404956440243[/C][C]-3.34049564402434[/C][/ROW]
[ROW][C]11[/C][C]23[/C][C]22.1666780037712[/C][C]0.833321996228797[/C][/ROW]
[ROW][C]12[/C][C]30[/C][C]22.0240937482091[/C][C]7.97590625179086[/C][/ROW]
[ROW][C]13[/C][C]23[/C][C]14.9779833304995[/C][C]8.02201666950045[/C][/ROW]
[ROW][C]14[/C][C]18[/C][C]18.8006776299538[/C][C]-0.800677629953756[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]21.5420869454813[/C][C]-6.54208694548131[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]17.7473672648275[/C][C]-5.74736726482754[/C][/ROW]
[ROW][C]17[/C][C]21[/C][C]19.3199506278201[/C][C]1.68004937217993[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]15.0095102272645[/C][C]-0.00951022726446759[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]19.5818010071521[/C][C]0.418198992847863[/C][/ROW]
[ROW][C]20[/C][C]31[/C][C]26.1780117087719[/C][C]4.8219882912281[/C][/ROW]
[ROW][C]21[/C][C]27[/C][C]25.0654667635792[/C][C]1.93453323642076[/C][/ROW]
[ROW][C]22[/C][C]34[/C][C]26.8723465536459[/C][C]7.12765344635406[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]19.5212510677299[/C][C]1.47874893227007[/C][/ROW]
[ROW][C]24[/C][C]31[/C][C]20.7775429191891[/C][C]10.2224570808109[/C][/ROW]
[ROW][C]25[/C][C]19[/C][C]19.2620873882198[/C][C]-0.262087388219846[/C][/ROW]
[ROW][C]26[/C][C]16[/C][C]20.1257631629215[/C][C]-4.12576316292152[/C][/ROW]
[ROW][C]27[/C][C]20[/C][C]21.4505453886165[/C][C]-1.45054538861649[/C][/ROW]
[ROW][C]28[/C][C]21[/C][C]18.1772639671097[/C][C]2.82273603289032[/C][/ROW]
[ROW][C]29[/C][C]22[/C][C]21.8404586063841[/C][C]0.159541393615926[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]19.3786812679951[/C][C]-2.37868126799509[/C][/ROW]
[ROW][C]31[/C][C]24[/C][C]20.3628830419833[/C][C]3.63711695801675[/C][/ROW]
[ROW][C]32[/C][C]25[/C][C]30.4026260368562[/C][C]-5.40262603685615[/C][/ROW]
[ROW][C]33[/C][C]26[/C][C]26.4499559136845[/C][C]-0.449955913684469[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]24.086412180723[/C][C]0.913587819277008[/C][/ROW]
[ROW][C]35[/C][C]17[/C][C]22.8670220283224[/C][C]-5.86702202832237[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]27.760560760874[/C][C]4.23943923912604[/C][/ROW]
[ROW][C]37[/C][C]33[/C][C]23.5983866371489[/C][C]9.40161336285113[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]21.7984983327297[/C][C]-8.79849833272967[/C][/ROW]
[ROW][C]39[/C][C]32[/C][C]27.7477901143871[/C][C]4.25220988561288[/C][/ROW]
[ROW][C]40[/C][C]25[/C][C]25.787102563668[/C][C]-0.787102563668002[/C][/ROW]
[ROW][C]41[/C][C]29[/C][C]26.9946561977486[/C][C]2.00534380225136[/C][/ROW]
[ROW][C]42[/C][C]22[/C][C]21.9915422579662[/C][C]0.00845774203379345[/C][/ROW]
[ROW][C]43[/C][C]18[/C][C]17.1296141440575[/C][C]0.870385855942537[/C][/ROW]
[ROW][C]44[/C][C]17[/C][C]21.6372078571851[/C][C]-4.63720785718513[/C][/ROW]
[ROW][C]45[/C][C]20[/C][C]22.2319210210565[/C][C]-2.23192102105651[/C][/ROW]
[ROW][C]46[/C][C]15[/C][C]20.4352937876135[/C][C]-5.43529378761353[/C][/ROW]
[ROW][C]47[/C][C]20[/C][C]22.1071288311651[/C][C]-2.10712883116511[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]28.1211719978756[/C][C]4.87882800212443[/C][/ROW]
[ROW][C]49[/C][C]29[/C][C]22.1411505218413[/C][C]6.85884947815874[/C][/ROW]
[ROW][C]50[/C][C]23[/C][C]26.4998596091402[/C][C]-3.49985960914021[/C][/ROW]
[ROW][C]51[/C][C]26[/C][C]23.2345975129204[/C][C]2.76540248707957[/C][/ROW]
[ROW][C]52[/C][C]18[/C][C]18.9656042544921[/C][C]-0.965604254492144[/C][/ROW]
[ROW][C]53[/C][C]20[/C][C]18.796120045743[/C][C]1.20387995425697[/C][/ROW]
[ROW][C]54[/C][C]11[/C][C]11.7160709940257[/C][C]-0.716070994025724[/C][/ROW]
[ROW][C]55[/C][C]28[/C][C]29.0105804767345[/C][C]-1.01058047673454[/C][/ROW]
[ROW][C]56[/C][C]26[/C][C]23.3905043460467[/C][C]2.60949565395329[/C][/ROW]
[ROW][C]57[/C][C]22[/C][C]22.3414695454203[/C][C]-0.341469545420307[/C][/ROW]
[ROW][C]58[/C][C]17[/C][C]20.1564210484307[/C][C]-3.1564210484307[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]15.5692778789805[/C][C]-3.56927787898049[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]20.8167935559991[/C][C]-6.81679355599913[/C][/ROW]
[ROW][C]61[/C][C]17[/C][C]20.8202241820746[/C][C]-3.82022418207459[/C][/ROW]
[ROW][C]62[/C][C]21[/C][C]21.3347129501434[/C][C]-0.334712950143374[/C][/ROW]
[ROW][C]63[/C][C]19[/C][C]22.9763818374361[/C][C]-3.9763818374361[/C][/ROW]
[ROW][C]64[/C][C]18[/C][C]23.1920758880957[/C][C]-5.19207588809566[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]17.9120405565538[/C][C]-7.91204055655376[/C][/ROW]
[ROW][C]66[/C][C]29[/C][C]24.4099265188341[/C][C]4.59007348116592[/C][/ROW]
[ROW][C]67[/C][C]31[/C][C]18.5584769188011[/C][C]12.4415230811989[/C][/ROW]
[ROW][C]68[/C][C]19[/C][C]22.9869414093617[/C][C]-3.98694140936168[/C][/ROW]
[ROW][C]69[/C][C]9[/C][C]20.0961723263517[/C][C]-11.0961723263517[/C][/ROW]
[ROW][C]70[/C][C]20[/C][C]22.5809984574057[/C][C]-2.5809984574057[/C][/ROW]
[ROW][C]71[/C][C]28[/C][C]17.6552736410877[/C][C]10.3447263589123[/C][/ROW]
[ROW][C]72[/C][C]19[/C][C]18.2291083555071[/C][C]0.770891644492931[/C][/ROW]
[ROW][C]73[/C][C]30[/C][C]23.1787562563361[/C][C]6.82124374366391[/C][/ROW]
[ROW][C]74[/C][C]29[/C][C]27.1018574495787[/C][C]1.8981425504213[/C][/ROW]
[ROW][C]75[/C][C]26[/C][C]21.5208597487263[/C][C]4.47914025127372[/C][/ROW]
[ROW][C]76[/C][C]23[/C][C]19.5775913896633[/C][C]3.42240861033671[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]22.7673890269749[/C][C]-9.76738902697489[/C][/ROW]
[ROW][C]78[/C][C]21[/C][C]22.6999656986182[/C][C]-1.69996569861817[/C][/ROW]
[ROW][C]79[/C][C]19[/C][C]21.6345836806373[/C][C]-2.63458368063734[/C][/ROW]
[ROW][C]80[/C][C]28[/C][C]22.9691570142453[/C][C]5.03084298575475[/C][/ROW]
[ROW][C]81[/C][C]23[/C][C]25.6973992029916[/C][C]-2.6973992029916[/C][/ROW]
[ROW][C]82[/C][C]18[/C][C]13.9549031616295[/C][C]4.04509683837052[/C][/ROW]
[ROW][C]83[/C][C]21[/C][C]20.7615465591829[/C][C]0.238453440817123[/C][/ROW]
[ROW][C]84[/C][C]20[/C][C]21.9157961866931[/C][C]-1.91579618669311[/C][/ROW]
[ROW][C]85[/C][C]23[/C][C]20.0777478836276[/C][C]2.92225211637242[/C][/ROW]
[ROW][C]86[/C][C]21[/C][C]20.8386011500172[/C][C]0.161398849982822[/C][/ROW]
[ROW][C]87[/C][C]21[/C][C]21.8931506037604[/C][C]-0.89315060376045[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]23.0305779438395[/C][C]-8.03057794383953[/C][/ROW]
[ROW][C]89[/C][C]28[/C][C]27.3005835439217[/C][C]0.699416456078302[/C][/ROW]
[ROW][C]90[/C][C]19[/C][C]17.7045900098864[/C][C]1.2954099901136[/C][/ROW]
[ROW][C]91[/C][C]26[/C][C]21.2772051999813[/C][C]4.72279480001875[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.4207961925499[/C][C]-3.42079619254989[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]17.1995105289364[/C][C]-1.19951052893641[/C][/ROW]
[ROW][C]94[/C][C]22[/C][C]21.1713453970672[/C][C]0.828654602932829[/C][/ROW]
[ROW][C]95[/C][C]19[/C][C]18.9989312859951[/C][C]0.00106871400488[/C][/ROW]
[ROW][C]96[/C][C]31[/C][C]28.940378376922[/C][C]2.05962162307796[/C][/ROW]
[ROW][C]97[/C][C]31[/C][C]25.2905586288165[/C][C]5.70944137118354[/C][/ROW]
[ROW][C]98[/C][C]29[/C][C]24.8528116175613[/C][C]4.14718838243874[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]17.5096261454999[/C][C]1.49037385450014[/C][/ROW]
[ROW][C]100[/C][C]22[/C][C]18.9524275113635[/C][C]3.0475724886365[/C][/ROW]
[ROW][C]101[/C][C]23[/C][C]22.4963923849776[/C][C]0.503607615022413[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]16.287535418075[/C][C]-1.28753541807505[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]21.4228525089516[/C][C]-1.4228525089516[/C][/ROW]
[ROW][C]104[/C][C]18[/C][C]19.6658145225114[/C][C]-1.66581452251145[/C][/ROW]
[ROW][C]105[/C][C]23[/C][C]22.2563692427647[/C][C]0.743630757235343[/C][/ROW]
[ROW][C]106[/C][C]25[/C][C]20.9086574355098[/C][C]4.09134256449023[/C][/ROW]
[ROW][C]107[/C][C]21[/C][C]16.6722771524643[/C][C]4.32772284753566[/C][/ROW]
[ROW][C]108[/C][C]24[/C][C]19.5680077116824[/C][C]4.4319922883176[/C][/ROW]
[ROW][C]109[/C][C]25[/C][C]25.3550235068465[/C][C]-0.35502350684648[/C][/ROW]
[ROW][C]110[/C][C]17[/C][C]19.619766860822[/C][C]-2.61976686082202[/C][/ROW]
[ROW][C]111[/C][C]13[/C][C]14.6659551691233[/C][C]-1.66595516912328[/C][/ROW]
[ROW][C]112[/C][C]28[/C][C]18.4262616637208[/C][C]9.57373833627919[/C][/ROW]
[ROW][C]113[/C][C]21[/C][C]20.4196764510916[/C][C]0.580323548908432[/C][/ROW]
[ROW][C]114[/C][C]25[/C][C]28.3159565386191[/C][C]-3.31595653861912[/C][/ROW]
[ROW][C]115[/C][C]9[/C][C]21.1551764227403[/C][C]-12.1551764227403[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]18.006110633023[/C][C]-2.00611063302303[/C][/ROW]
[ROW][C]117[/C][C]19[/C][C]21.2544613841651[/C][C]-2.25446138416515[/C][/ROW]
[ROW][C]118[/C][C]17[/C][C]19.6108037739653[/C][C]-2.61080377396526[/C][/ROW]
[ROW][C]119[/C][C]25[/C][C]24.6763915340849[/C][C]0.323608465915102[/C][/ROW]
[ROW][C]120[/C][C]20[/C][C]15.5671337387445[/C][C]4.4328662612555[/C][/ROW]
[ROW][C]121[/C][C]29[/C][C]21.8716126114376[/C][C]7.12838738856236[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]19.1167857657226[/C][C]-5.11678576572262[/C][/ROW]
[ROW][C]123[/C][C]22[/C][C]27.0867941796051[/C][C]-5.0867941796051[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.9355784175142[/C][C]-0.935578417514243[/C][/ROW]
[ROW][C]125[/C][C]19[/C][C]25.626332592574[/C][C]-6.62633259257396[/C][/ROW]
[ROW][C]126[/C][C]20[/C][C]22.092892026745[/C][C]-2.09289202674502[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]17.7095654037931[/C][C]-2.70956540379314[/C][/ROW]
[ROW][C]128[/C][C]20[/C][C]22.1149283807381[/C][C]-2.11492838073814[/C][/ROW]
[ROW][C]129[/C][C]18[/C][C]20.5115649579609[/C][C]-2.51156495796087[/C][/ROW]
[ROW][C]130[/C][C]33[/C][C]25.7620025241802[/C][C]7.23799747581976[/C][/ROW]
[ROW][C]131[/C][C]22[/C][C]24.0135738555549[/C][C]-2.01357385555486[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]16.6900618439135[/C][C]-0.69006184391349[/C][/ROW]
[ROW][C]133[/C][C]17[/C][C]19.3420422957593[/C][C]-2.34204229575932[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]15.3134803176989[/C][C]0.686519682301068[/C][/ROW]
[ROW][C]135[/C][C]21[/C][C]17.2994992608764[/C][C]3.70050073912356[/C][/ROW]
[ROW][C]136[/C][C]26[/C][C]27.7996968946194[/C][C]-1.79969689461943[/C][/ROW]
[ROW][C]137[/C][C]18[/C][C]21.304233865906[/C][C]-3.30423386590599[/C][/ROW]
[ROW][C]138[/C][C]18[/C][C]23.2073200148495[/C][C]-5.20732001484945[/C][/ROW]
[ROW][C]139[/C][C]17[/C][C]18.6825913123659[/C][C]-1.6825913123659[/C][/ROW]
[ROW][C]140[/C][C]22[/C][C]24.9799528930422[/C][C]-2.97995289304224[/C][/ROW]
[ROW][C]141[/C][C]30[/C][C]24.9254198082097[/C][C]5.07458019179029[/C][/ROW]
[ROW][C]142[/C][C]30[/C][C]27.5705627248667[/C][C]2.42943727513332[/C][/ROW]
[ROW][C]143[/C][C]24[/C][C]29.9683835869881[/C][C]-5.9683835869881[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]22.2960469748642[/C][C]-1.29604697486422[/C][/ROW]
[ROW][C]145[/C][C]21[/C][C]25.6227704527493[/C][C]-4.62277045274928[/C][/ROW]
[ROW][C]146[/C][C]29[/C][C]27.6739609431583[/C][C]1.32603905684171[/C][/ROW]
[ROW][C]147[/C][C]31[/C][C]23.464342466474[/C][C]7.53565753352599[/C][/ROW]
[ROW][C]148[/C][C]20[/C][C]19.2655893559701[/C][C]0.734410644029935[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.3261339717709[/C][C]1.67386602822906[/C][/ROW]
[ROW][C]150[/C][C]22[/C][C]19.2236647882233[/C][C]2.77633521177669[/C][/ROW]
[ROW][C]151[/C][C]20[/C][C]20.7522347250232[/C][C]-0.752234725023152[/C][/ROW]
[ROW][C]152[/C][C]28[/C][C]27.5610774834422[/C][C]0.438922516557777[/C][/ROW]
[ROW][C]153[/C][C]38[/C][C]26.890415116[/C][C]11.109584884[/C][/ROW]
[ROW][C]154[/C][C]22[/C][C]19.4362722728422[/C][C]2.56372772715776[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]25.9470773992814[/C][C]-5.9470773992814[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]18.31008307594[/C][C]-1.31008307593998[/C][/ROW]
[ROW][C]157[/C][C]28[/C][C]24.8140500775851[/C][C]3.18594992241494[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]24.4230458313632[/C][C]-2.4230458313632[/C][/ROW]
[ROW][C]159[/C][C]31[/C][C]26.3398127723852[/C][C]4.66018722761482[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146272&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146272&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12423.30057129330590.699428706694145
22520.0175224671844.98247753281596
31721.0211399791428-4.02113997914275
41818.0925500792619-0.092550079261901
51817.69056780293330.309432197066652
61617.8776483781721-1.87764837817211
72020.7303968936034-0.73039689360339
81622.2384819662609-6.23848196626085
91822.109776689597-4.10977668959702
101720.3404956440243-3.34049564402434
112322.16667800377120.833321996228797
123022.02409374820917.97590625179086
132314.97798333049958.02201666950045
141818.8006776299538-0.800677629953756
151521.5420869454813-6.54208694548131
161217.7473672648275-5.74736726482754
172119.31995062782011.68004937217993
181515.0095102272645-0.00951022726446759
192019.58180100715210.418198992847863
203126.17801170877194.8219882912281
212725.06546676357921.93453323642076
223426.87234655364597.12765344635406
232119.52125106772991.47874893227007
243120.777542919189110.2224570808109
251919.2620873882198-0.262087388219846
261620.1257631629215-4.12576316292152
272021.4505453886165-1.45054538861649
282118.17726396710972.82273603289032
292221.84045860638410.159541393615926
301719.3786812679951-2.37868126799509
312420.36288304198333.63711695801675
322530.4026260368562-5.40262603685615
332626.4499559136845-0.449955913684469
342524.0864121807230.913587819277008
351722.8670220283224-5.86702202832237
363227.7605607608744.23943923912604
373323.59838663714899.40161336285113
381321.7984983327297-8.79849833272967
393227.74779011438714.25220988561288
402525.787102563668-0.787102563668002
412926.99465619774862.00534380225136
422221.99154225796620.00845774203379345
431817.12961414405750.870385855942537
441721.6372078571851-4.63720785718513
452022.2319210210565-2.23192102105651
461520.4352937876135-5.43529378761353
472022.1071288311651-2.10712883116511
483328.12117199787564.87882800212443
492922.14115052184136.85884947815874
502326.4998596091402-3.49985960914021
512623.23459751292042.76540248707957
521818.9656042544921-0.965604254492144
532018.7961200457431.20387995425697
541111.7160709940257-0.716070994025724
552829.0105804767345-1.01058047673454
562623.39050434604672.60949565395329
572222.3414695454203-0.341469545420307
581720.1564210484307-3.1564210484307
591215.5692778789805-3.56927787898049
601420.8167935559991-6.81679355599913
611720.8202241820746-3.82022418207459
622121.3347129501434-0.334712950143374
631922.9763818374361-3.9763818374361
641823.1920758880957-5.19207588809566
651017.9120405565538-7.91204055655376
662924.40992651883414.59007348116592
673118.558476918801112.4415230811989
681922.9869414093617-3.98694140936168
69920.0961723263517-11.0961723263517
702022.5809984574057-2.5809984574057
712817.655273641087710.3447263589123
721918.22910835550710.770891644492931
733023.17875625633616.82124374366391
742927.10185744957871.8981425504213
752621.52085974872634.47914025127372
762319.57759138966333.42240861033671
771322.7673890269749-9.76738902697489
782122.6999656986182-1.69996569861817
791921.6345836806373-2.63458368063734
802822.96915701424535.03084298575475
812325.6973992029916-2.6973992029916
821813.95490316162954.04509683837052
832120.76154655918290.238453440817123
842021.9157961866931-1.91579618669311
852320.07774788362762.92225211637242
862120.83860115001720.161398849982822
872121.8931506037604-0.89315060376045
881523.0305779438395-8.03057794383953
892827.30058354392170.699416456078302
901917.70459000988641.2954099901136
912621.27720519998134.72279480001875
921013.4207961925499-3.42079619254989
931617.1995105289364-1.19951052893641
942221.17134539706720.828654602932829
951918.99893128599510.00106871400488
963128.9403783769222.05962162307796
973125.29055862881655.70944137118354
982924.85281161756134.14718838243874
991917.50962614549991.49037385450014
1002218.95242751136353.0475724886365
1012322.49639238497760.503607615022413
1021516.287535418075-1.28753541807505
1032021.4228525089516-1.4228525089516
1041819.6658145225114-1.66581452251145
1052322.25636924276470.743630757235343
1062520.90865743550984.09134256449023
1072116.67227715246434.32772284753566
1082419.56800771168244.4319922883176
1092525.3550235068465-0.35502350684648
1101719.619766860822-2.61976686082202
1111314.6659551691233-1.66595516912328
1122818.42626166372089.57373833627919
1132120.41967645109160.580323548908432
1142528.3159565386191-3.31595653861912
115921.1551764227403-12.1551764227403
1161618.006110633023-2.00611063302303
1171921.2544613841651-2.25446138416515
1181719.6108037739653-2.61080377396526
1192524.67639153408490.323608465915102
1202015.56713373874454.4328662612555
1212921.87161261143767.12838738856236
1221419.1167857657226-5.11678576572262
1232227.0867941796051-5.0867941796051
1241515.9355784175142-0.935578417514243
1251925.626332592574-6.62633259257396
1262022.092892026745-2.09289202674502
1271517.7095654037931-2.70956540379314
1282022.1149283807381-2.11492838073814
1291820.5115649579609-2.51156495796087
1303325.76200252418027.23799747581976
1312224.0135738555549-2.01357385555486
1321616.6900618439135-0.69006184391349
1331719.3420422957593-2.34204229575932
1341615.31348031769890.686519682301068
1352117.29949926087643.70050073912356
1362627.7996968946194-1.79969689461943
1371821.304233865906-3.30423386590599
1381823.2073200148495-5.20732001484945
1391718.6825913123659-1.6825913123659
1402224.9799528930422-2.97995289304224
1413024.92541980820975.07458019179029
1423027.57056272486672.42943727513332
1432429.9683835869881-5.9683835869881
1442122.2960469748642-1.29604697486422
1452125.6227704527493-4.62277045274928
1462927.67396094315831.32603905684171
1473123.4643424664747.53565753352599
1482019.26558935597010.734410644029935
1491614.32613397177091.67386602822906
1502219.22366478822332.77633521177669
1512020.7522347250232-0.752234725023152
1522827.56107748344220.438922516557777
1533826.89041511611.109584884
1542219.43627227284222.56372772715776
1552025.9470773992814-5.9470773992814
1561718.31008307594-1.31008307593998
1572824.81405007758513.18594992241494
1582224.4230458313632-2.4230458313632
1593126.33981277238524.66018722761482







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.4829193016149580.9658386032299160.517080698385042
120.5540732393436440.8918535213127110.445926760656356
130.7785712074528930.4428575850942150.221428792547107
140.8044013778971670.3911972442056670.195598622102833
150.7447735642805760.5104528714388480.255226435719424
160.664232544631890.6715349107362210.33576745536811
170.623527767746130.7529444645077390.37647223225387
180.5814159093338990.8371681813322010.418584090666101
190.5415638152832810.9168723694334380.458436184716719
200.5621504795336020.8756990409327970.437849520466398
210.4853487611294020.9706975222588040.514651238870598
220.4559249064379620.9118498128759240.544075093562038
230.4180293134236050.8360586268472090.581970686576396
240.5251318710594420.9497362578811150.474868128940558
250.4965945694728410.9931891389456820.503405430527159
260.5284303120479990.9431393759040030.471569687952001
270.4624564691639470.9249129383278940.537543530836053
280.3994328697227670.7988657394455330.600567130277233
290.3374991199397940.6749982398795880.662500880060206
300.3034494262526350.6068988525052690.696550573747365
310.2920688108971480.5841376217942950.707931189102852
320.4278905143310570.8557810286621140.572109485668943
330.3677709725805480.7355419451610970.632229027419452
340.3389216242501930.6778432485003860.661078375749807
350.3899119760466980.7798239520933950.610088023953302
360.354184460665440.708368921330880.64581553933456
370.4654268922738750.9308537845477510.534573107726125
380.7615144023517240.4769711952965530.238485597648277
390.7480429223637580.5039141552724850.251957077636242
400.7114107689347480.5771784621305040.288589231065252
410.6764471981337640.6471056037324720.323552801866236
420.628165076844510.743669846310980.37183492315549
430.5764227236989060.8471545526021890.423577276301094
440.5640438355426730.8719123289146550.435956164457327
450.5471458311909280.9057083376181440.452854168809072
460.5791926855220730.8416146289558540.420807314477927
470.5354765355173420.9290469289653150.464523464482658
480.525984911848510.9480301763029810.47401508815149
490.5862103539832980.8275792920334050.413789646016702
500.5573213315437010.8853573369125990.442678668456299
510.5264197363296410.9471605273407180.473580263670359
520.4766790272664280.9533580545328550.523320972733572
530.4330087212924440.8660174425848880.566991278707556
540.3882146891237590.7764293782475180.611785310876241
550.3473362447661950.694672489532390.652663755233805
560.3187228020099790.6374456040199590.681277197990021
570.2758557123679040.5517114247358080.724144287632096
580.2478299770059350.495659954011870.752170022994065
590.2295442247609730.4590884495219450.770455775239027
600.2646070039174290.5292140078348580.735392996082571
610.248127640276520.4962552805530410.75187235972348
620.21413018103550.4282603620710.7858698189645
630.1969075329268410.3938150658536820.803092467073159
640.2179466817221940.4358933634443890.782053318277806
650.2669794120658070.5339588241316140.733020587934193
660.2759291272540510.5518582545081010.724070872745949
670.637769420135870.7244611597282610.36223057986413
680.6219181725308810.7561636549382390.378081827469119
690.7951952892038080.4096094215923850.204804710796192
700.7678147506359340.4643704987281330.232185249364067
710.9127531564915340.1744936870169310.0872468435084657
720.8937271218045190.2125457563909630.106272878195482
730.9242926095928780.1514147808142450.0757073904071225
740.9124970207299570.1750059585400850.0875029792700426
750.9163309680112960.1673380639774070.0836690319887037
760.9106624721733070.1786750556533860.0893375278266929
770.9620940858164280.07581182836714470.0379059141835724
780.9526852434423340.09462951311533260.0473147565576663
790.943473929806760.113052140386480.0565260701932399
800.9479428525491880.1041142949016250.0520571474508123
810.9382652388085330.1234695223829350.0617347611914673
820.9375827240267030.1248345519465940.062417275973297
830.9220533362512840.1558933274974310.0779466637487156
840.9065742500025280.1868514999949440.0934257499974718
850.8967632782064960.2064734435870070.103236721793504
860.8780800500150440.2438398999699110.121919949984956
870.8533755756665250.2932488486669510.146624424333475
880.9053301920169020.1893396159661950.0946698079830975
890.8861555888715220.2276888222569560.113844411128478
900.8636477117300990.2727045765398030.136352288269901
910.8658274788426220.2683450423147570.134172521157378
920.8543847808816980.2912304382366030.145615219118302
930.8298317007731770.3403365984536470.170168299226823
940.799553747405970.4008925051880610.200446252594031
950.7636884466440350.4726231067119290.236311553355965
960.7310555749981380.5378888500037240.268944425001862
970.7517419273315290.4965161453369410.248258072668471
980.7493465068530560.5013069862938890.250653493146944
990.7122346964319530.5755306071360940.287765303568047
1000.6897448649238670.6205102701522650.310255135076133
1010.6494349222362850.701130155527430.350565077763715
1020.6066348457904760.7867303084190470.393365154209524
1030.5632442167487310.8735115665025390.436755783251269
1040.5186541566685340.9626916866629320.481345843331466
1050.4758806504744060.9517613009488130.524119349525594
1060.463612141119950.9272242822398990.53638785888005
1070.470779049273710.941558098547420.52922095072629
1080.5095328074496360.9809343851007280.490467192550364
1090.4709717567628780.9419435135257560.529028243237122
1100.4286116910002730.8572233820005450.571388308999727
1110.3814515054168460.7629030108336910.618548494583154
1120.7112886403632380.5774227192735250.288711359636762
1130.7473975444481890.5052049111036220.252602455551811
1140.7200322666398490.5599354667203020.279967733360151
1150.8546521660591680.2906956678816640.145347833940832
1160.8227162001041920.3545675997916160.177283799895808
1170.7895983733747360.4208032532505280.210401626625264
1180.7524050165307830.4951899669384340.247594983469217
1190.7217768440500780.5564463118998430.278223155949921
1200.7738925738290680.4522148523418640.226107426170932
1210.860942680640340.278114638719320.13905731935966
1220.8384937134451110.3230125731097790.161506286554889
1230.81442814843470.3711437031306010.1855718515653
1240.7715492958540480.4569014082919030.228450704145952
1250.7746982776176890.4506034447646220.225301722382311
1260.7259122102917470.5481755794165070.274087789708253
1270.6906768862708890.6186462274582230.309323113729111
1280.6397780036985880.7204439926028240.360221996301412
1290.5907368861560870.8185262276878260.409263113843913
1300.7185032158087570.5629935683824870.281496784191243
1310.6603237787138160.6793524425723680.339676221286184
1320.5951269309861740.8097461380276510.404873069013826
1330.5537187639610460.8925624720779080.446281236038954
1340.4814457747577620.9628915495155250.518554225242238
1350.504899838699720.990200322600560.49510016130028
1360.4324005633879530.8648011267759060.567599436612047
1370.383851090629990.7677021812599810.61614890937001
1380.396248754615260.792497509230520.60375124538474
1390.3237631621555350.647526324311070.676236837844465
1400.2544030009655450.508806001931090.745596999034455
1410.3440471799139950.688094359827990.655952820086005
1420.2945759068292850.589151813658570.705424093170715
1430.2264455868972520.4528911737945030.773554413102748
1440.1596988583803380.3193977167606760.840301141619662
1450.2399300775530420.4798601551060830.760069922446958
1460.1693483072714130.3386966145428260.830651692728587
1470.1605212839060140.3210425678120290.839478716093986
1480.0873080906605050.174616181321010.912691909339495

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.482919301614958 & 0.965838603229916 & 0.517080698385042 \tabularnewline
12 & 0.554073239343644 & 0.891853521312711 & 0.445926760656356 \tabularnewline
13 & 0.778571207452893 & 0.442857585094215 & 0.221428792547107 \tabularnewline
14 & 0.804401377897167 & 0.391197244205667 & 0.195598622102833 \tabularnewline
15 & 0.744773564280576 & 0.510452871438848 & 0.255226435719424 \tabularnewline
16 & 0.66423254463189 & 0.671534910736221 & 0.33576745536811 \tabularnewline
17 & 0.62352776774613 & 0.752944464507739 & 0.37647223225387 \tabularnewline
18 & 0.581415909333899 & 0.837168181332201 & 0.418584090666101 \tabularnewline
19 & 0.541563815283281 & 0.916872369433438 & 0.458436184716719 \tabularnewline
20 & 0.562150479533602 & 0.875699040932797 & 0.437849520466398 \tabularnewline
21 & 0.485348761129402 & 0.970697522258804 & 0.514651238870598 \tabularnewline
22 & 0.455924906437962 & 0.911849812875924 & 0.544075093562038 \tabularnewline
23 & 0.418029313423605 & 0.836058626847209 & 0.581970686576396 \tabularnewline
24 & 0.525131871059442 & 0.949736257881115 & 0.474868128940558 \tabularnewline
25 & 0.496594569472841 & 0.993189138945682 & 0.503405430527159 \tabularnewline
26 & 0.528430312047999 & 0.943139375904003 & 0.471569687952001 \tabularnewline
27 & 0.462456469163947 & 0.924912938327894 & 0.537543530836053 \tabularnewline
28 & 0.399432869722767 & 0.798865739445533 & 0.600567130277233 \tabularnewline
29 & 0.337499119939794 & 0.674998239879588 & 0.662500880060206 \tabularnewline
30 & 0.303449426252635 & 0.606898852505269 & 0.696550573747365 \tabularnewline
31 & 0.292068810897148 & 0.584137621794295 & 0.707931189102852 \tabularnewline
32 & 0.427890514331057 & 0.855781028662114 & 0.572109485668943 \tabularnewline
33 & 0.367770972580548 & 0.735541945161097 & 0.632229027419452 \tabularnewline
34 & 0.338921624250193 & 0.677843248500386 & 0.661078375749807 \tabularnewline
35 & 0.389911976046698 & 0.779823952093395 & 0.610088023953302 \tabularnewline
36 & 0.35418446066544 & 0.70836892133088 & 0.64581553933456 \tabularnewline
37 & 0.465426892273875 & 0.930853784547751 & 0.534573107726125 \tabularnewline
38 & 0.761514402351724 & 0.476971195296553 & 0.238485597648277 \tabularnewline
39 & 0.748042922363758 & 0.503914155272485 & 0.251957077636242 \tabularnewline
40 & 0.711410768934748 & 0.577178462130504 & 0.288589231065252 \tabularnewline
41 & 0.676447198133764 & 0.647105603732472 & 0.323552801866236 \tabularnewline
42 & 0.62816507684451 & 0.74366984631098 & 0.37183492315549 \tabularnewline
43 & 0.576422723698906 & 0.847154552602189 & 0.423577276301094 \tabularnewline
44 & 0.564043835542673 & 0.871912328914655 & 0.435956164457327 \tabularnewline
45 & 0.547145831190928 & 0.905708337618144 & 0.452854168809072 \tabularnewline
46 & 0.579192685522073 & 0.841614628955854 & 0.420807314477927 \tabularnewline
47 & 0.535476535517342 & 0.929046928965315 & 0.464523464482658 \tabularnewline
48 & 0.52598491184851 & 0.948030176302981 & 0.47401508815149 \tabularnewline
49 & 0.586210353983298 & 0.827579292033405 & 0.413789646016702 \tabularnewline
50 & 0.557321331543701 & 0.885357336912599 & 0.442678668456299 \tabularnewline
51 & 0.526419736329641 & 0.947160527340718 & 0.473580263670359 \tabularnewline
52 & 0.476679027266428 & 0.953358054532855 & 0.523320972733572 \tabularnewline
53 & 0.433008721292444 & 0.866017442584888 & 0.566991278707556 \tabularnewline
54 & 0.388214689123759 & 0.776429378247518 & 0.611785310876241 \tabularnewline
55 & 0.347336244766195 & 0.69467248953239 & 0.652663755233805 \tabularnewline
56 & 0.318722802009979 & 0.637445604019959 & 0.681277197990021 \tabularnewline
57 & 0.275855712367904 & 0.551711424735808 & 0.724144287632096 \tabularnewline
58 & 0.247829977005935 & 0.49565995401187 & 0.752170022994065 \tabularnewline
59 & 0.229544224760973 & 0.459088449521945 & 0.770455775239027 \tabularnewline
60 & 0.264607003917429 & 0.529214007834858 & 0.735392996082571 \tabularnewline
61 & 0.24812764027652 & 0.496255280553041 & 0.75187235972348 \tabularnewline
62 & 0.2141301810355 & 0.428260362071 & 0.7858698189645 \tabularnewline
63 & 0.196907532926841 & 0.393815065853682 & 0.803092467073159 \tabularnewline
64 & 0.217946681722194 & 0.435893363444389 & 0.782053318277806 \tabularnewline
65 & 0.266979412065807 & 0.533958824131614 & 0.733020587934193 \tabularnewline
66 & 0.275929127254051 & 0.551858254508101 & 0.724070872745949 \tabularnewline
67 & 0.63776942013587 & 0.724461159728261 & 0.36223057986413 \tabularnewline
68 & 0.621918172530881 & 0.756163654938239 & 0.378081827469119 \tabularnewline
69 & 0.795195289203808 & 0.409609421592385 & 0.204804710796192 \tabularnewline
70 & 0.767814750635934 & 0.464370498728133 & 0.232185249364067 \tabularnewline
71 & 0.912753156491534 & 0.174493687016931 & 0.0872468435084657 \tabularnewline
72 & 0.893727121804519 & 0.212545756390963 & 0.106272878195482 \tabularnewline
73 & 0.924292609592878 & 0.151414780814245 & 0.0757073904071225 \tabularnewline
74 & 0.912497020729957 & 0.175005958540085 & 0.0875029792700426 \tabularnewline
75 & 0.916330968011296 & 0.167338063977407 & 0.0836690319887037 \tabularnewline
76 & 0.910662472173307 & 0.178675055653386 & 0.0893375278266929 \tabularnewline
77 & 0.962094085816428 & 0.0758118283671447 & 0.0379059141835724 \tabularnewline
78 & 0.952685243442334 & 0.0946295131153326 & 0.0473147565576663 \tabularnewline
79 & 0.94347392980676 & 0.11305214038648 & 0.0565260701932399 \tabularnewline
80 & 0.947942852549188 & 0.104114294901625 & 0.0520571474508123 \tabularnewline
81 & 0.938265238808533 & 0.123469522382935 & 0.0617347611914673 \tabularnewline
82 & 0.937582724026703 & 0.124834551946594 & 0.062417275973297 \tabularnewline
83 & 0.922053336251284 & 0.155893327497431 & 0.0779466637487156 \tabularnewline
84 & 0.906574250002528 & 0.186851499994944 & 0.0934257499974718 \tabularnewline
85 & 0.896763278206496 & 0.206473443587007 & 0.103236721793504 \tabularnewline
86 & 0.878080050015044 & 0.243839899969911 & 0.121919949984956 \tabularnewline
87 & 0.853375575666525 & 0.293248848666951 & 0.146624424333475 \tabularnewline
88 & 0.905330192016902 & 0.189339615966195 & 0.0946698079830975 \tabularnewline
89 & 0.886155588871522 & 0.227688822256956 & 0.113844411128478 \tabularnewline
90 & 0.863647711730099 & 0.272704576539803 & 0.136352288269901 \tabularnewline
91 & 0.865827478842622 & 0.268345042314757 & 0.134172521157378 \tabularnewline
92 & 0.854384780881698 & 0.291230438236603 & 0.145615219118302 \tabularnewline
93 & 0.829831700773177 & 0.340336598453647 & 0.170168299226823 \tabularnewline
94 & 0.79955374740597 & 0.400892505188061 & 0.200446252594031 \tabularnewline
95 & 0.763688446644035 & 0.472623106711929 & 0.236311553355965 \tabularnewline
96 & 0.731055574998138 & 0.537888850003724 & 0.268944425001862 \tabularnewline
97 & 0.751741927331529 & 0.496516145336941 & 0.248258072668471 \tabularnewline
98 & 0.749346506853056 & 0.501306986293889 & 0.250653493146944 \tabularnewline
99 & 0.712234696431953 & 0.575530607136094 & 0.287765303568047 \tabularnewline
100 & 0.689744864923867 & 0.620510270152265 & 0.310255135076133 \tabularnewline
101 & 0.649434922236285 & 0.70113015552743 & 0.350565077763715 \tabularnewline
102 & 0.606634845790476 & 0.786730308419047 & 0.393365154209524 \tabularnewline
103 & 0.563244216748731 & 0.873511566502539 & 0.436755783251269 \tabularnewline
104 & 0.518654156668534 & 0.962691686662932 & 0.481345843331466 \tabularnewline
105 & 0.475880650474406 & 0.951761300948813 & 0.524119349525594 \tabularnewline
106 & 0.46361214111995 & 0.927224282239899 & 0.53638785888005 \tabularnewline
107 & 0.47077904927371 & 0.94155809854742 & 0.52922095072629 \tabularnewline
108 & 0.509532807449636 & 0.980934385100728 & 0.490467192550364 \tabularnewline
109 & 0.470971756762878 & 0.941943513525756 & 0.529028243237122 \tabularnewline
110 & 0.428611691000273 & 0.857223382000545 & 0.571388308999727 \tabularnewline
111 & 0.381451505416846 & 0.762903010833691 & 0.618548494583154 \tabularnewline
112 & 0.711288640363238 & 0.577422719273525 & 0.288711359636762 \tabularnewline
113 & 0.747397544448189 & 0.505204911103622 & 0.252602455551811 \tabularnewline
114 & 0.720032266639849 & 0.559935466720302 & 0.279967733360151 \tabularnewline
115 & 0.854652166059168 & 0.290695667881664 & 0.145347833940832 \tabularnewline
116 & 0.822716200104192 & 0.354567599791616 & 0.177283799895808 \tabularnewline
117 & 0.789598373374736 & 0.420803253250528 & 0.210401626625264 \tabularnewline
118 & 0.752405016530783 & 0.495189966938434 & 0.247594983469217 \tabularnewline
119 & 0.721776844050078 & 0.556446311899843 & 0.278223155949921 \tabularnewline
120 & 0.773892573829068 & 0.452214852341864 & 0.226107426170932 \tabularnewline
121 & 0.86094268064034 & 0.27811463871932 & 0.13905731935966 \tabularnewline
122 & 0.838493713445111 & 0.323012573109779 & 0.161506286554889 \tabularnewline
123 & 0.8144281484347 & 0.371143703130601 & 0.1855718515653 \tabularnewline
124 & 0.771549295854048 & 0.456901408291903 & 0.228450704145952 \tabularnewline
125 & 0.774698277617689 & 0.450603444764622 & 0.225301722382311 \tabularnewline
126 & 0.725912210291747 & 0.548175579416507 & 0.274087789708253 \tabularnewline
127 & 0.690676886270889 & 0.618646227458223 & 0.309323113729111 \tabularnewline
128 & 0.639778003698588 & 0.720443992602824 & 0.360221996301412 \tabularnewline
129 & 0.590736886156087 & 0.818526227687826 & 0.409263113843913 \tabularnewline
130 & 0.718503215808757 & 0.562993568382487 & 0.281496784191243 \tabularnewline
131 & 0.660323778713816 & 0.679352442572368 & 0.339676221286184 \tabularnewline
132 & 0.595126930986174 & 0.809746138027651 & 0.404873069013826 \tabularnewline
133 & 0.553718763961046 & 0.892562472077908 & 0.446281236038954 \tabularnewline
134 & 0.481445774757762 & 0.962891549515525 & 0.518554225242238 \tabularnewline
135 & 0.50489983869972 & 0.99020032260056 & 0.49510016130028 \tabularnewline
136 & 0.432400563387953 & 0.864801126775906 & 0.567599436612047 \tabularnewline
137 & 0.38385109062999 & 0.767702181259981 & 0.61614890937001 \tabularnewline
138 & 0.39624875461526 & 0.79249750923052 & 0.60375124538474 \tabularnewline
139 & 0.323763162155535 & 0.64752632431107 & 0.676236837844465 \tabularnewline
140 & 0.254403000965545 & 0.50880600193109 & 0.745596999034455 \tabularnewline
141 & 0.344047179913995 & 0.68809435982799 & 0.655952820086005 \tabularnewline
142 & 0.294575906829285 & 0.58915181365857 & 0.705424093170715 \tabularnewline
143 & 0.226445586897252 & 0.452891173794503 & 0.773554413102748 \tabularnewline
144 & 0.159698858380338 & 0.319397716760676 & 0.840301141619662 \tabularnewline
145 & 0.239930077553042 & 0.479860155106083 & 0.760069922446958 \tabularnewline
146 & 0.169348307271413 & 0.338696614542826 & 0.830651692728587 \tabularnewline
147 & 0.160521283906014 & 0.321042567812029 & 0.839478716093986 \tabularnewline
148 & 0.087308090660505 & 0.17461618132101 & 0.912691909339495 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146272&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]11[/C][C]0.482919301614958[/C][C]0.965838603229916[/C][C]0.517080698385042[/C][/ROW]
[ROW][C]12[/C][C]0.554073239343644[/C][C]0.891853521312711[/C][C]0.445926760656356[/C][/ROW]
[ROW][C]13[/C][C]0.778571207452893[/C][C]0.442857585094215[/C][C]0.221428792547107[/C][/ROW]
[ROW][C]14[/C][C]0.804401377897167[/C][C]0.391197244205667[/C][C]0.195598622102833[/C][/ROW]
[ROW][C]15[/C][C]0.744773564280576[/C][C]0.510452871438848[/C][C]0.255226435719424[/C][/ROW]
[ROW][C]16[/C][C]0.66423254463189[/C][C]0.671534910736221[/C][C]0.33576745536811[/C][/ROW]
[ROW][C]17[/C][C]0.62352776774613[/C][C]0.752944464507739[/C][C]0.37647223225387[/C][/ROW]
[ROW][C]18[/C][C]0.581415909333899[/C][C]0.837168181332201[/C][C]0.418584090666101[/C][/ROW]
[ROW][C]19[/C][C]0.541563815283281[/C][C]0.916872369433438[/C][C]0.458436184716719[/C][/ROW]
[ROW][C]20[/C][C]0.562150479533602[/C][C]0.875699040932797[/C][C]0.437849520466398[/C][/ROW]
[ROW][C]21[/C][C]0.485348761129402[/C][C]0.970697522258804[/C][C]0.514651238870598[/C][/ROW]
[ROW][C]22[/C][C]0.455924906437962[/C][C]0.911849812875924[/C][C]0.544075093562038[/C][/ROW]
[ROW][C]23[/C][C]0.418029313423605[/C][C]0.836058626847209[/C][C]0.581970686576396[/C][/ROW]
[ROW][C]24[/C][C]0.525131871059442[/C][C]0.949736257881115[/C][C]0.474868128940558[/C][/ROW]
[ROW][C]25[/C][C]0.496594569472841[/C][C]0.993189138945682[/C][C]0.503405430527159[/C][/ROW]
[ROW][C]26[/C][C]0.528430312047999[/C][C]0.943139375904003[/C][C]0.471569687952001[/C][/ROW]
[ROW][C]27[/C][C]0.462456469163947[/C][C]0.924912938327894[/C][C]0.537543530836053[/C][/ROW]
[ROW][C]28[/C][C]0.399432869722767[/C][C]0.798865739445533[/C][C]0.600567130277233[/C][/ROW]
[ROW][C]29[/C][C]0.337499119939794[/C][C]0.674998239879588[/C][C]0.662500880060206[/C][/ROW]
[ROW][C]30[/C][C]0.303449426252635[/C][C]0.606898852505269[/C][C]0.696550573747365[/C][/ROW]
[ROW][C]31[/C][C]0.292068810897148[/C][C]0.584137621794295[/C][C]0.707931189102852[/C][/ROW]
[ROW][C]32[/C][C]0.427890514331057[/C][C]0.855781028662114[/C][C]0.572109485668943[/C][/ROW]
[ROW][C]33[/C][C]0.367770972580548[/C][C]0.735541945161097[/C][C]0.632229027419452[/C][/ROW]
[ROW][C]34[/C][C]0.338921624250193[/C][C]0.677843248500386[/C][C]0.661078375749807[/C][/ROW]
[ROW][C]35[/C][C]0.389911976046698[/C][C]0.779823952093395[/C][C]0.610088023953302[/C][/ROW]
[ROW][C]36[/C][C]0.35418446066544[/C][C]0.70836892133088[/C][C]0.64581553933456[/C][/ROW]
[ROW][C]37[/C][C]0.465426892273875[/C][C]0.930853784547751[/C][C]0.534573107726125[/C][/ROW]
[ROW][C]38[/C][C]0.761514402351724[/C][C]0.476971195296553[/C][C]0.238485597648277[/C][/ROW]
[ROW][C]39[/C][C]0.748042922363758[/C][C]0.503914155272485[/C][C]0.251957077636242[/C][/ROW]
[ROW][C]40[/C][C]0.711410768934748[/C][C]0.577178462130504[/C][C]0.288589231065252[/C][/ROW]
[ROW][C]41[/C][C]0.676447198133764[/C][C]0.647105603732472[/C][C]0.323552801866236[/C][/ROW]
[ROW][C]42[/C][C]0.62816507684451[/C][C]0.74366984631098[/C][C]0.37183492315549[/C][/ROW]
[ROW][C]43[/C][C]0.576422723698906[/C][C]0.847154552602189[/C][C]0.423577276301094[/C][/ROW]
[ROW][C]44[/C][C]0.564043835542673[/C][C]0.871912328914655[/C][C]0.435956164457327[/C][/ROW]
[ROW][C]45[/C][C]0.547145831190928[/C][C]0.905708337618144[/C][C]0.452854168809072[/C][/ROW]
[ROW][C]46[/C][C]0.579192685522073[/C][C]0.841614628955854[/C][C]0.420807314477927[/C][/ROW]
[ROW][C]47[/C][C]0.535476535517342[/C][C]0.929046928965315[/C][C]0.464523464482658[/C][/ROW]
[ROW][C]48[/C][C]0.52598491184851[/C][C]0.948030176302981[/C][C]0.47401508815149[/C][/ROW]
[ROW][C]49[/C][C]0.586210353983298[/C][C]0.827579292033405[/C][C]0.413789646016702[/C][/ROW]
[ROW][C]50[/C][C]0.557321331543701[/C][C]0.885357336912599[/C][C]0.442678668456299[/C][/ROW]
[ROW][C]51[/C][C]0.526419736329641[/C][C]0.947160527340718[/C][C]0.473580263670359[/C][/ROW]
[ROW][C]52[/C][C]0.476679027266428[/C][C]0.953358054532855[/C][C]0.523320972733572[/C][/ROW]
[ROW][C]53[/C][C]0.433008721292444[/C][C]0.866017442584888[/C][C]0.566991278707556[/C][/ROW]
[ROW][C]54[/C][C]0.388214689123759[/C][C]0.776429378247518[/C][C]0.611785310876241[/C][/ROW]
[ROW][C]55[/C][C]0.347336244766195[/C][C]0.69467248953239[/C][C]0.652663755233805[/C][/ROW]
[ROW][C]56[/C][C]0.318722802009979[/C][C]0.637445604019959[/C][C]0.681277197990021[/C][/ROW]
[ROW][C]57[/C][C]0.275855712367904[/C][C]0.551711424735808[/C][C]0.724144287632096[/C][/ROW]
[ROW][C]58[/C][C]0.247829977005935[/C][C]0.49565995401187[/C][C]0.752170022994065[/C][/ROW]
[ROW][C]59[/C][C]0.229544224760973[/C][C]0.459088449521945[/C][C]0.770455775239027[/C][/ROW]
[ROW][C]60[/C][C]0.264607003917429[/C][C]0.529214007834858[/C][C]0.735392996082571[/C][/ROW]
[ROW][C]61[/C][C]0.24812764027652[/C][C]0.496255280553041[/C][C]0.75187235972348[/C][/ROW]
[ROW][C]62[/C][C]0.2141301810355[/C][C]0.428260362071[/C][C]0.7858698189645[/C][/ROW]
[ROW][C]63[/C][C]0.196907532926841[/C][C]0.393815065853682[/C][C]0.803092467073159[/C][/ROW]
[ROW][C]64[/C][C]0.217946681722194[/C][C]0.435893363444389[/C][C]0.782053318277806[/C][/ROW]
[ROW][C]65[/C][C]0.266979412065807[/C][C]0.533958824131614[/C][C]0.733020587934193[/C][/ROW]
[ROW][C]66[/C][C]0.275929127254051[/C][C]0.551858254508101[/C][C]0.724070872745949[/C][/ROW]
[ROW][C]67[/C][C]0.63776942013587[/C][C]0.724461159728261[/C][C]0.36223057986413[/C][/ROW]
[ROW][C]68[/C][C]0.621918172530881[/C][C]0.756163654938239[/C][C]0.378081827469119[/C][/ROW]
[ROW][C]69[/C][C]0.795195289203808[/C][C]0.409609421592385[/C][C]0.204804710796192[/C][/ROW]
[ROW][C]70[/C][C]0.767814750635934[/C][C]0.464370498728133[/C][C]0.232185249364067[/C][/ROW]
[ROW][C]71[/C][C]0.912753156491534[/C][C]0.174493687016931[/C][C]0.0872468435084657[/C][/ROW]
[ROW][C]72[/C][C]0.893727121804519[/C][C]0.212545756390963[/C][C]0.106272878195482[/C][/ROW]
[ROW][C]73[/C][C]0.924292609592878[/C][C]0.151414780814245[/C][C]0.0757073904071225[/C][/ROW]
[ROW][C]74[/C][C]0.912497020729957[/C][C]0.175005958540085[/C][C]0.0875029792700426[/C][/ROW]
[ROW][C]75[/C][C]0.916330968011296[/C][C]0.167338063977407[/C][C]0.0836690319887037[/C][/ROW]
[ROW][C]76[/C][C]0.910662472173307[/C][C]0.178675055653386[/C][C]0.0893375278266929[/C][/ROW]
[ROW][C]77[/C][C]0.962094085816428[/C][C]0.0758118283671447[/C][C]0.0379059141835724[/C][/ROW]
[ROW][C]78[/C][C]0.952685243442334[/C][C]0.0946295131153326[/C][C]0.0473147565576663[/C][/ROW]
[ROW][C]79[/C][C]0.94347392980676[/C][C]0.11305214038648[/C][C]0.0565260701932399[/C][/ROW]
[ROW][C]80[/C][C]0.947942852549188[/C][C]0.104114294901625[/C][C]0.0520571474508123[/C][/ROW]
[ROW][C]81[/C][C]0.938265238808533[/C][C]0.123469522382935[/C][C]0.0617347611914673[/C][/ROW]
[ROW][C]82[/C][C]0.937582724026703[/C][C]0.124834551946594[/C][C]0.062417275973297[/C][/ROW]
[ROW][C]83[/C][C]0.922053336251284[/C][C]0.155893327497431[/C][C]0.0779466637487156[/C][/ROW]
[ROW][C]84[/C][C]0.906574250002528[/C][C]0.186851499994944[/C][C]0.0934257499974718[/C][/ROW]
[ROW][C]85[/C][C]0.896763278206496[/C][C]0.206473443587007[/C][C]0.103236721793504[/C][/ROW]
[ROW][C]86[/C][C]0.878080050015044[/C][C]0.243839899969911[/C][C]0.121919949984956[/C][/ROW]
[ROW][C]87[/C][C]0.853375575666525[/C][C]0.293248848666951[/C][C]0.146624424333475[/C][/ROW]
[ROW][C]88[/C][C]0.905330192016902[/C][C]0.189339615966195[/C][C]0.0946698079830975[/C][/ROW]
[ROW][C]89[/C][C]0.886155588871522[/C][C]0.227688822256956[/C][C]0.113844411128478[/C][/ROW]
[ROW][C]90[/C][C]0.863647711730099[/C][C]0.272704576539803[/C][C]0.136352288269901[/C][/ROW]
[ROW][C]91[/C][C]0.865827478842622[/C][C]0.268345042314757[/C][C]0.134172521157378[/C][/ROW]
[ROW][C]92[/C][C]0.854384780881698[/C][C]0.291230438236603[/C][C]0.145615219118302[/C][/ROW]
[ROW][C]93[/C][C]0.829831700773177[/C][C]0.340336598453647[/C][C]0.170168299226823[/C][/ROW]
[ROW][C]94[/C][C]0.79955374740597[/C][C]0.400892505188061[/C][C]0.200446252594031[/C][/ROW]
[ROW][C]95[/C][C]0.763688446644035[/C][C]0.472623106711929[/C][C]0.236311553355965[/C][/ROW]
[ROW][C]96[/C][C]0.731055574998138[/C][C]0.537888850003724[/C][C]0.268944425001862[/C][/ROW]
[ROW][C]97[/C][C]0.751741927331529[/C][C]0.496516145336941[/C][C]0.248258072668471[/C][/ROW]
[ROW][C]98[/C][C]0.749346506853056[/C][C]0.501306986293889[/C][C]0.250653493146944[/C][/ROW]
[ROW][C]99[/C][C]0.712234696431953[/C][C]0.575530607136094[/C][C]0.287765303568047[/C][/ROW]
[ROW][C]100[/C][C]0.689744864923867[/C][C]0.620510270152265[/C][C]0.310255135076133[/C][/ROW]
[ROW][C]101[/C][C]0.649434922236285[/C][C]0.70113015552743[/C][C]0.350565077763715[/C][/ROW]
[ROW][C]102[/C][C]0.606634845790476[/C][C]0.786730308419047[/C][C]0.393365154209524[/C][/ROW]
[ROW][C]103[/C][C]0.563244216748731[/C][C]0.873511566502539[/C][C]0.436755783251269[/C][/ROW]
[ROW][C]104[/C][C]0.518654156668534[/C][C]0.962691686662932[/C][C]0.481345843331466[/C][/ROW]
[ROW][C]105[/C][C]0.475880650474406[/C][C]0.951761300948813[/C][C]0.524119349525594[/C][/ROW]
[ROW][C]106[/C][C]0.46361214111995[/C][C]0.927224282239899[/C][C]0.53638785888005[/C][/ROW]
[ROW][C]107[/C][C]0.47077904927371[/C][C]0.94155809854742[/C][C]0.52922095072629[/C][/ROW]
[ROW][C]108[/C][C]0.509532807449636[/C][C]0.980934385100728[/C][C]0.490467192550364[/C][/ROW]
[ROW][C]109[/C][C]0.470971756762878[/C][C]0.941943513525756[/C][C]0.529028243237122[/C][/ROW]
[ROW][C]110[/C][C]0.428611691000273[/C][C]0.857223382000545[/C][C]0.571388308999727[/C][/ROW]
[ROW][C]111[/C][C]0.381451505416846[/C][C]0.762903010833691[/C][C]0.618548494583154[/C][/ROW]
[ROW][C]112[/C][C]0.711288640363238[/C][C]0.577422719273525[/C][C]0.288711359636762[/C][/ROW]
[ROW][C]113[/C][C]0.747397544448189[/C][C]0.505204911103622[/C][C]0.252602455551811[/C][/ROW]
[ROW][C]114[/C][C]0.720032266639849[/C][C]0.559935466720302[/C][C]0.279967733360151[/C][/ROW]
[ROW][C]115[/C][C]0.854652166059168[/C][C]0.290695667881664[/C][C]0.145347833940832[/C][/ROW]
[ROW][C]116[/C][C]0.822716200104192[/C][C]0.354567599791616[/C][C]0.177283799895808[/C][/ROW]
[ROW][C]117[/C][C]0.789598373374736[/C][C]0.420803253250528[/C][C]0.210401626625264[/C][/ROW]
[ROW][C]118[/C][C]0.752405016530783[/C][C]0.495189966938434[/C][C]0.247594983469217[/C][/ROW]
[ROW][C]119[/C][C]0.721776844050078[/C][C]0.556446311899843[/C][C]0.278223155949921[/C][/ROW]
[ROW][C]120[/C][C]0.773892573829068[/C][C]0.452214852341864[/C][C]0.226107426170932[/C][/ROW]
[ROW][C]121[/C][C]0.86094268064034[/C][C]0.27811463871932[/C][C]0.13905731935966[/C][/ROW]
[ROW][C]122[/C][C]0.838493713445111[/C][C]0.323012573109779[/C][C]0.161506286554889[/C][/ROW]
[ROW][C]123[/C][C]0.8144281484347[/C][C]0.371143703130601[/C][C]0.1855718515653[/C][/ROW]
[ROW][C]124[/C][C]0.771549295854048[/C][C]0.456901408291903[/C][C]0.228450704145952[/C][/ROW]
[ROW][C]125[/C][C]0.774698277617689[/C][C]0.450603444764622[/C][C]0.225301722382311[/C][/ROW]
[ROW][C]126[/C][C]0.725912210291747[/C][C]0.548175579416507[/C][C]0.274087789708253[/C][/ROW]
[ROW][C]127[/C][C]0.690676886270889[/C][C]0.618646227458223[/C][C]0.309323113729111[/C][/ROW]
[ROW][C]128[/C][C]0.639778003698588[/C][C]0.720443992602824[/C][C]0.360221996301412[/C][/ROW]
[ROW][C]129[/C][C]0.590736886156087[/C][C]0.818526227687826[/C][C]0.409263113843913[/C][/ROW]
[ROW][C]130[/C][C]0.718503215808757[/C][C]0.562993568382487[/C][C]0.281496784191243[/C][/ROW]
[ROW][C]131[/C][C]0.660323778713816[/C][C]0.679352442572368[/C][C]0.339676221286184[/C][/ROW]
[ROW][C]132[/C][C]0.595126930986174[/C][C]0.809746138027651[/C][C]0.404873069013826[/C][/ROW]
[ROW][C]133[/C][C]0.553718763961046[/C][C]0.892562472077908[/C][C]0.446281236038954[/C][/ROW]
[ROW][C]134[/C][C]0.481445774757762[/C][C]0.962891549515525[/C][C]0.518554225242238[/C][/ROW]
[ROW][C]135[/C][C]0.50489983869972[/C][C]0.99020032260056[/C][C]0.49510016130028[/C][/ROW]
[ROW][C]136[/C][C]0.432400563387953[/C][C]0.864801126775906[/C][C]0.567599436612047[/C][/ROW]
[ROW][C]137[/C][C]0.38385109062999[/C][C]0.767702181259981[/C][C]0.61614890937001[/C][/ROW]
[ROW][C]138[/C][C]0.39624875461526[/C][C]0.79249750923052[/C][C]0.60375124538474[/C][/ROW]
[ROW][C]139[/C][C]0.323763162155535[/C][C]0.64752632431107[/C][C]0.676236837844465[/C][/ROW]
[ROW][C]140[/C][C]0.254403000965545[/C][C]0.50880600193109[/C][C]0.745596999034455[/C][/ROW]
[ROW][C]141[/C][C]0.344047179913995[/C][C]0.68809435982799[/C][C]0.655952820086005[/C][/ROW]
[ROW][C]142[/C][C]0.294575906829285[/C][C]0.58915181365857[/C][C]0.705424093170715[/C][/ROW]
[ROW][C]143[/C][C]0.226445586897252[/C][C]0.452891173794503[/C][C]0.773554413102748[/C][/ROW]
[ROW][C]144[/C][C]0.159698858380338[/C][C]0.319397716760676[/C][C]0.840301141619662[/C][/ROW]
[ROW][C]145[/C][C]0.239930077553042[/C][C]0.479860155106083[/C][C]0.760069922446958[/C][/ROW]
[ROW][C]146[/C][C]0.169348307271413[/C][C]0.338696614542826[/C][C]0.830651692728587[/C][/ROW]
[ROW][C]147[/C][C]0.160521283906014[/C][C]0.321042567812029[/C][C]0.839478716093986[/C][/ROW]
[ROW][C]148[/C][C]0.087308090660505[/C][C]0.17461618132101[/C][C]0.912691909339495[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146272&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146272&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.4829193016149580.9658386032299160.517080698385042
120.5540732393436440.8918535213127110.445926760656356
130.7785712074528930.4428575850942150.221428792547107
140.8044013778971670.3911972442056670.195598622102833
150.7447735642805760.5104528714388480.255226435719424
160.664232544631890.6715349107362210.33576745536811
170.623527767746130.7529444645077390.37647223225387
180.5814159093338990.8371681813322010.418584090666101
190.5415638152832810.9168723694334380.458436184716719
200.5621504795336020.8756990409327970.437849520466398
210.4853487611294020.9706975222588040.514651238870598
220.4559249064379620.9118498128759240.544075093562038
230.4180293134236050.8360586268472090.581970686576396
240.5251318710594420.9497362578811150.474868128940558
250.4965945694728410.9931891389456820.503405430527159
260.5284303120479990.9431393759040030.471569687952001
270.4624564691639470.9249129383278940.537543530836053
280.3994328697227670.7988657394455330.600567130277233
290.3374991199397940.6749982398795880.662500880060206
300.3034494262526350.6068988525052690.696550573747365
310.2920688108971480.5841376217942950.707931189102852
320.4278905143310570.8557810286621140.572109485668943
330.3677709725805480.7355419451610970.632229027419452
340.3389216242501930.6778432485003860.661078375749807
350.3899119760466980.7798239520933950.610088023953302
360.354184460665440.708368921330880.64581553933456
370.4654268922738750.9308537845477510.534573107726125
380.7615144023517240.4769711952965530.238485597648277
390.7480429223637580.5039141552724850.251957077636242
400.7114107689347480.5771784621305040.288589231065252
410.6764471981337640.6471056037324720.323552801866236
420.628165076844510.743669846310980.37183492315549
430.5764227236989060.8471545526021890.423577276301094
440.5640438355426730.8719123289146550.435956164457327
450.5471458311909280.9057083376181440.452854168809072
460.5791926855220730.8416146289558540.420807314477927
470.5354765355173420.9290469289653150.464523464482658
480.525984911848510.9480301763029810.47401508815149
490.5862103539832980.8275792920334050.413789646016702
500.5573213315437010.8853573369125990.442678668456299
510.5264197363296410.9471605273407180.473580263670359
520.4766790272664280.9533580545328550.523320972733572
530.4330087212924440.8660174425848880.566991278707556
540.3882146891237590.7764293782475180.611785310876241
550.3473362447661950.694672489532390.652663755233805
560.3187228020099790.6374456040199590.681277197990021
570.2758557123679040.5517114247358080.724144287632096
580.2478299770059350.495659954011870.752170022994065
590.2295442247609730.4590884495219450.770455775239027
600.2646070039174290.5292140078348580.735392996082571
610.248127640276520.4962552805530410.75187235972348
620.21413018103550.4282603620710.7858698189645
630.1969075329268410.3938150658536820.803092467073159
640.2179466817221940.4358933634443890.782053318277806
650.2669794120658070.5339588241316140.733020587934193
660.2759291272540510.5518582545081010.724070872745949
670.637769420135870.7244611597282610.36223057986413
680.6219181725308810.7561636549382390.378081827469119
690.7951952892038080.4096094215923850.204804710796192
700.7678147506359340.4643704987281330.232185249364067
710.9127531564915340.1744936870169310.0872468435084657
720.8937271218045190.2125457563909630.106272878195482
730.9242926095928780.1514147808142450.0757073904071225
740.9124970207299570.1750059585400850.0875029792700426
750.9163309680112960.1673380639774070.0836690319887037
760.9106624721733070.1786750556533860.0893375278266929
770.9620940858164280.07581182836714470.0379059141835724
780.9526852434423340.09462951311533260.0473147565576663
790.943473929806760.113052140386480.0565260701932399
800.9479428525491880.1041142949016250.0520571474508123
810.9382652388085330.1234695223829350.0617347611914673
820.9375827240267030.1248345519465940.062417275973297
830.9220533362512840.1558933274974310.0779466637487156
840.9065742500025280.1868514999949440.0934257499974718
850.8967632782064960.2064734435870070.103236721793504
860.8780800500150440.2438398999699110.121919949984956
870.8533755756665250.2932488486669510.146624424333475
880.9053301920169020.1893396159661950.0946698079830975
890.8861555888715220.2276888222569560.113844411128478
900.8636477117300990.2727045765398030.136352288269901
910.8658274788426220.2683450423147570.134172521157378
920.8543847808816980.2912304382366030.145615219118302
930.8298317007731770.3403365984536470.170168299226823
940.799553747405970.4008925051880610.200446252594031
950.7636884466440350.4726231067119290.236311553355965
960.7310555749981380.5378888500037240.268944425001862
970.7517419273315290.4965161453369410.248258072668471
980.7493465068530560.5013069862938890.250653493146944
990.7122346964319530.5755306071360940.287765303568047
1000.6897448649238670.6205102701522650.310255135076133
1010.6494349222362850.701130155527430.350565077763715
1020.6066348457904760.7867303084190470.393365154209524
1030.5632442167487310.8735115665025390.436755783251269
1040.5186541566685340.9626916866629320.481345843331466
1050.4758806504744060.9517613009488130.524119349525594
1060.463612141119950.9272242822398990.53638785888005
1070.470779049273710.941558098547420.52922095072629
1080.5095328074496360.9809343851007280.490467192550364
1090.4709717567628780.9419435135257560.529028243237122
1100.4286116910002730.8572233820005450.571388308999727
1110.3814515054168460.7629030108336910.618548494583154
1120.7112886403632380.5774227192735250.288711359636762
1130.7473975444481890.5052049111036220.252602455551811
1140.7200322666398490.5599354667203020.279967733360151
1150.8546521660591680.2906956678816640.145347833940832
1160.8227162001041920.3545675997916160.177283799895808
1170.7895983733747360.4208032532505280.210401626625264
1180.7524050165307830.4951899669384340.247594983469217
1190.7217768440500780.5564463118998430.278223155949921
1200.7738925738290680.4522148523418640.226107426170932
1210.860942680640340.278114638719320.13905731935966
1220.8384937134451110.3230125731097790.161506286554889
1230.81442814843470.3711437031306010.1855718515653
1240.7715492958540480.4569014082919030.228450704145952
1250.7746982776176890.4506034447646220.225301722382311
1260.7259122102917470.5481755794165070.274087789708253
1270.6906768862708890.6186462274582230.309323113729111
1280.6397780036985880.7204439926028240.360221996301412
1290.5907368861560870.8185262276878260.409263113843913
1300.7185032158087570.5629935683824870.281496784191243
1310.6603237787138160.6793524425723680.339676221286184
1320.5951269309861740.8097461380276510.404873069013826
1330.5537187639610460.8925624720779080.446281236038954
1340.4814457747577620.9628915495155250.518554225242238
1350.504899838699720.990200322600560.49510016130028
1360.4324005633879530.8648011267759060.567599436612047
1370.383851090629990.7677021812599810.61614890937001
1380.396248754615260.792497509230520.60375124538474
1390.3237631621555350.647526324311070.676236837844465
1400.2544030009655450.508806001931090.745596999034455
1410.3440471799139950.688094359827990.655952820086005
1420.2945759068292850.589151813658570.705424093170715
1430.2264455868972520.4528911737945030.773554413102748
1440.1596988583803380.3193977167606760.840301141619662
1450.2399300775530420.4798601551060830.760069922446958
1460.1693483072714130.3386966145428260.830651692728587
1470.1605212839060140.3210425678120290.839478716093986
1480.0873080906605050.174616181321010.912691909339495







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.0144927536231884OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 2 & 0.0144927536231884 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146272&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0144927536231884[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146272&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146272&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.0144927536231884OK



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