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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 21 Nov 2010 20:40:05 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/21/t1290371915ojtk691jie3135n.htm/, Retrieved Sat, 27 Apr 2024 19:45:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98407, Retrieved Sat, 27 Apr 2024 19:45:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact193

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   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] [6f3869f9d1e39c73f93153f1f7803f84] [Current]
-    D          [Multiple Regression] [] [2010-11-23 15:44:50] [dc0ae7e1387be9b795f5d6299e383759]
-    D          [Multiple Regression] [workshop 7.3] [2010-11-23 16:16:23] [3df61981e9f4dafed65341be376c4457]
-    D          [Multiple Regression] [workshop 7.3] [2010-11-23 16:16:23] [3df61981e9f4dafed65341be376c4457]
-    D          [Multiple Regression] [workshop 7.3] [2010-11-23 16:16:23] [3df61981e9f4dafed65341be376c4457]
-                 [Multiple Regression] [Workshop 7: model...] [2010-11-24 02:15:45] [b20a509e36241371274681d9edf773da]
- R             [Multiple Regression] [WS 7 Multiple Lin...] [2011-11-22 15:14:13] [74be16979710d4c4e7c6647856088456]
-  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]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ 72.249.76.132
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 & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \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=98407&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[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=98407&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98407&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ 72.249.76.132
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] = -18.3591714482512 + 1.58838471475270T1[t] + 0.79875711851511X1[t] + 0.233450466257558X2[t] + 0.207082749570129X3[t] + 0.571862761699044X4[t] -0.0999815995143383`X5 `[t] + 0.00354783249216934t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
YT[t] =  -18.3591714482512 +  1.58838471475270T1[t] +  0.79875711851511X1[t] +  0.233450466257558X2[t] +  0.207082749570129X3[t] +  0.571862761699044X4[t] -0.0999815995143383`X5
`[t] +  0.00354783249216934t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98407&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]YT[t] =  -18.3591714482512 +  1.58838471475270T1[t] +  0.79875711851511X1[t] +  0.233450466257558X2[t] +  0.207082749570129X3[t] +  0.571862761699044X4[t] -0.0999815995143383`X5
`[t] +  0.00354783249216934t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98407&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98407&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
YT[t] = -18.3591714482512 + 1.58838471475270T1[t] + 0.79875711851511X1[t] + 0.233450466257558X2[t] + 0.207082749570129X3[t] + 0.571862761699044X4[t] -0.0999815995143383`X5 `[t] + 0.00354783249216934t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-18.359171448251219.993412-0.91830.3599470.179973
T11.588384714752702.0021750.79330.4288310.214415
X10.798757118515110.1311486.090500
X20.2334504662575580.1343331.73790.0842760.042138
X30.2070827495701290.1700091.21810.2250970.112548
X40.5718627616990440.0962475.941600
`X5 `-0.09998159951433830.105485-0.94780.3447320.172366
t0.003547832492169340.0084380.42050.6747410.33737

\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) & -18.3591714482512 & 19.993412 & -0.9183 & 0.359947 & 0.179973 \tabularnewline
T1 & 1.58838471475270 & 2.002175 & 0.7933 & 0.428831 & 0.214415 \tabularnewline
X1 & 0.79875711851511 & 0.131148 & 6.0905 & 0 & 0 \tabularnewline
X2 & 0.233450466257558 & 0.134333 & 1.7379 & 0.084276 & 0.042138 \tabularnewline
X3 & 0.207082749570129 & 0.170009 & 1.2181 & 0.225097 & 0.112548 \tabularnewline
X4 & 0.571862761699044 & 0.096247 & 5.9416 & 0 & 0 \tabularnewline
`X5
` & -0.0999815995143383 & 0.105485 & -0.9478 & 0.344732 & 0.172366 \tabularnewline
t & 0.00354783249216934 & 0.008438 & 0.4205 & 0.674741 & 0.33737 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98407&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]-18.3591714482512[/C][C]19.993412[/C][C]-0.9183[/C][C]0.359947[/C][C]0.179973[/C][/ROW]
[ROW][C]T1[/C][C]1.58838471475270[/C][C]2.002175[/C][C]0.7933[/C][C]0.428831[/C][C]0.214415[/C][/ROW]
[ROW][C]X1[/C][C]0.79875711851511[/C][C]0.131148[/C][C]6.0905[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X2[/C][C]0.233450466257558[/C][C]0.134333[/C][C]1.7379[/C][C]0.084276[/C][C]0.042138[/C][/ROW]
[ROW][C]X3[/C][C]0.207082749570129[/C][C]0.170009[/C][C]1.2181[/C][C]0.225097[/C][C]0.112548[/C][/ROW]
[ROW][C]X4[/C][C]0.571862761699044[/C][C]0.096247[/C][C]5.9416[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`X5
`[/C][C]-0.0999815995143383[/C][C]0.105485[/C][C]-0.9478[/C][C]0.344732[/C][C]0.172366[/C][/ROW]
[ROW][C]t[/C][C]0.00354783249216934[/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=98407&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98407&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-18.359171448251219.993412-0.91830.3599470.179973
T11.588384714752702.0021750.79330.4288310.214415
X10.798757118515110.1311486.090500
X20.2334504662575580.1343331.73790.0842760.042138
X30.2070827495701290.1700091.21810.2250970.112548
X40.5718627616990440.0962475.941600
`X5 `-0.09998159951433830.105485-0.94780.3447320.172366
t0.003547832492169340.0084380.42050.6747410.33737






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=98407&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=98407&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98407&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.641530498673369
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
12423.30057129330630.699428706693719
22520.01752246718394.98247753281608
31721.0211399791426-4.02113997914265
41818.0925500792618-0.0925500792618023
51817.69056780293320.309432197066762
61617.877648378172-1.87764837817201
72020.7303968936034-0.7303968936034
81622.2384819662608-6.23848196626085
91822.1097766895970-4.10977668959702
101720.3404956440243-3.34049564402435
112322.16667800377120.833321996228807
123022.02409374820917.97590625179086
132314.97798333049958.02201666950045
141818.8006776299538-0.800677629953757
151521.5420869454813-6.54208694548131
161217.7473672648276-5.74736726482756
172119.31995062782011.68004937217993
181515.0095102272645-0.00951022726447562
192019.58180100715220.41819899284785
203126.17801170877194.8219882912281
212725.06546676357921.93453323642076
223426.87234655364597.12765344635407
232119.52125106772991.47874893227008
243120.777542919189110.2224570808109
251919.2620873882198-0.262087388219842
261620.1257631629215-4.12576316292152
272021.4505453886165-1.45054538861650
282118.17726396710972.82273603289032
292221.84045860638410.15954139361592
301719.3786812679951-2.37868126799510
312420.36288304198333.63711695801675
322530.4026260368561-5.40262603685613
332626.4499559136845-0.449955913684488
342524.0864121807230.913587819277002
351722.8670220283224-5.86702202832238
363227.76056076087404.23943923912604
373323.59838663714899.40161336285112
381321.7984983327297-8.79849833272967
393227.74779011438714.25220988561288
402525.787102563668-0.787102563668005
412926.99465619774862.00534380225137
422221.99154225796620.00845774203379076
431817.12961414405750.87038585594253
441721.6372078571851-4.63720785718515
452022.2319210210565-2.23192102105651
461520.4352937876135-5.43529378761353
472022.1071288311651-2.1071288311651
483328.12117199787564.87882800212443
492922.14115052184136.85884947815874
502326.4998596091402-3.49985960914021
512623.23459751292042.76540248707958
521818.9656042544921-0.965604254492147
532018.79612004574301.20387995425697
541111.7160709940257-0.716070994025735
552829.0105804767345-1.01058047673453
562623.39050434604672.60949565395329
572222.3414695454203-0.341469545420307
581720.1564210484307-3.15642104843071
591215.5692778789805-3.56927787898050
601420.8167935559991-6.81679355599913
611720.8202241820746-3.82022418207459
622121.3347129501434-0.334712950143380
631922.9763818374361-3.97638183743611
641823.1920758880957-5.19207588809565
651017.9120405565538-7.91204055655377
662924.40992651883414.59007348116592
673118.558476918801112.4415230811989
681922.9869414093617-3.98694140936169
69920.0961723263517-11.0961723263517
702022.5809984574057-2.58099845740569
712817.655273641087810.3447263589122
721918.22910835550710.770891644492931
733023.17875625633616.82124374366391
742927.10185744957871.89814255042130
752621.52085974872634.47914025127371
762319.57759138966333.42240861033671
771322.7673890269749-9.7673890269749
782122.6999656986182-1.69996569861817
791921.6345836806373-2.63458368063733
802822.96915701424535.03084298575474
812325.6973992029916-2.69739920299159
821813.95490316162954.04509683837052
832120.76154655918290.238453440817124
842021.9157961866931-1.91579618669312
852320.07774788362762.92225211637243
862120.83860115001720.161398849982816
872121.8931506037605-0.893150603760456
881523.0305779438395-8.03057794383952
892827.30058354392170.699416456078311
901917.70459000988641.29540999011360
912621.27720519998124.72279480001875
921013.4207961925499-3.4207961925499
931617.1995105289364-1.19951052893642
942221.17134539706720.82865460293282
951918.99893128599510.00106871400488703
963128.94037837692202.05962162307796
973125.29055862881655.70944137118355
982924.85281161756134.14718838243874
991917.50962614549991.49037385450014
1002218.95242751136353.04757248863650
1012322.49639238497760.503607615022413
1021516.2875354180751-1.28753541807506
1032021.4228525089516-1.42285250895161
1041819.6658145225114-1.66581452251145
1052322.25636924276460.743630757235358
1062520.90865743550984.09134256449023
1072116.67227715246434.32772284753566
1082419.56800771168244.4319922883176
1092525.3550235068465-0.355023506846472
1101719.619766860822-2.61976686082202
1111314.6659551691233-1.66595516912329
1122818.42626166372089.57373833627919
1132120.41967645109160.580323548908437
1142528.3159565386191-3.31595653861913
115921.1551764227403-12.1551764227403
1161618.0061106330230-2.00611063302304
1171921.2544613841652-2.25446138416516
1181719.6108037739653-2.61080377396526
1192524.67639153408490.323608465915107
1202015.56713373874454.43286626125549
1212921.87161261143767.12838738856236
1221419.1167857657226-5.11678576572263
1232227.0867941796051-5.08679417960509
1241515.9355784175142-0.935578417514242
1251925.6263325925739-6.62633259257395
1262022.0928920267450-2.09289202674503
1271517.7095654037931-2.70956540379315
1282022.1149283807381-2.11492838073813
1291820.5115649579609-2.51156495796086
1303325.76200252418027.23799747581977
1312224.0135738555549-2.01357385555485
1321616.6900618439135-0.690061843913491
1331719.3420422957593-2.34204229575933
1341615.31348031769890.68651968230106
1352117.29949926087643.70050073912357
1362627.7996968946194-1.79969689461943
1371821.304233865906-3.30423386590599
1381823.2073200148495-5.20732001484945
1391718.6825913123659-1.68259131236590
1402224.9799528930422-2.97995289304222
1413024.92541980820975.0745801917903
1423027.57056272486672.42943727513332
1432429.9683835869881-5.96838358698809
1442122.2960469748642-1.29604697486422
1452125.6227704527493-4.62277045274928
1462927.67396094315831.32603905684171
1473123.4643424664747.53565753352598
1482019.26558935597010.734410644029932
1491614.32613397177091.67386602822906
1502219.22366478822332.77633521177669
1512020.7522347250232-0.752234725023154
1522827.56107748344220.438922516557781
1533826.89041511611.1095848840000
1542219.43627227284222.56372772715775
1552025.9470773992814-5.9470773992814
1561718.31008307594-1.31008307593998
1572824.81405007758513.18594992241494
1582224.4230458313632-2.42304583136319
1593126.33981277238524.66018722761484

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 24 & 23.3005712933063 & 0.699428706693719 \tabularnewline
2 & 25 & 20.0175224671839 & 4.98247753281608 \tabularnewline
3 & 17 & 21.0211399791426 & -4.02113997914265 \tabularnewline
4 & 18 & 18.0925500792618 & -0.0925500792618023 \tabularnewline
5 & 18 & 17.6905678029332 & 0.309432197066762 \tabularnewline
6 & 16 & 17.877648378172 & -1.87764837817201 \tabularnewline
7 & 20 & 20.7303968936034 & -0.7303968936034 \tabularnewline
8 & 16 & 22.2384819662608 & -6.23848196626085 \tabularnewline
9 & 18 & 22.1097766895970 & -4.10977668959702 \tabularnewline
10 & 17 & 20.3404956440243 & -3.34049564402435 \tabularnewline
11 & 23 & 22.1666780037712 & 0.833321996228807 \tabularnewline
12 & 30 & 22.0240937482091 & 7.97590625179086 \tabularnewline
13 & 23 & 14.9779833304995 & 8.02201666950045 \tabularnewline
14 & 18 & 18.8006776299538 & -0.800677629953757 \tabularnewline
15 & 15 & 21.5420869454813 & -6.54208694548131 \tabularnewline
16 & 12 & 17.7473672648276 & -5.74736726482756 \tabularnewline
17 & 21 & 19.3199506278201 & 1.68004937217993 \tabularnewline
18 & 15 & 15.0095102272645 & -0.00951022726447562 \tabularnewline
19 & 20 & 19.5818010071522 & 0.41819899284785 \tabularnewline
20 & 31 & 26.1780117087719 & 4.8219882912281 \tabularnewline
21 & 27 & 25.0654667635792 & 1.93453323642076 \tabularnewline
22 & 34 & 26.8723465536459 & 7.12765344635407 \tabularnewline
23 & 21 & 19.5212510677299 & 1.47874893227008 \tabularnewline
24 & 31 & 20.7775429191891 & 10.2224570808109 \tabularnewline
25 & 19 & 19.2620873882198 & -0.262087388219842 \tabularnewline
26 & 16 & 20.1257631629215 & -4.12576316292152 \tabularnewline
27 & 20 & 21.4505453886165 & -1.45054538861650 \tabularnewline
28 & 21 & 18.1772639671097 & 2.82273603289032 \tabularnewline
29 & 22 & 21.8404586063841 & 0.15954139361592 \tabularnewline
30 & 17 & 19.3786812679951 & -2.37868126799510 \tabularnewline
31 & 24 & 20.3628830419833 & 3.63711695801675 \tabularnewline
32 & 25 & 30.4026260368561 & -5.40262603685613 \tabularnewline
33 & 26 & 26.4499559136845 & -0.449955913684488 \tabularnewline
34 & 25 & 24.086412180723 & 0.913587819277002 \tabularnewline
35 & 17 & 22.8670220283224 & -5.86702202832238 \tabularnewline
36 & 32 & 27.7605607608740 & 4.23943923912604 \tabularnewline
37 & 33 & 23.5983866371489 & 9.40161336285112 \tabularnewline
38 & 13 & 21.7984983327297 & -8.79849833272967 \tabularnewline
39 & 32 & 27.7477901143871 & 4.25220988561288 \tabularnewline
40 & 25 & 25.787102563668 & -0.787102563668005 \tabularnewline
41 & 29 & 26.9946561977486 & 2.00534380225137 \tabularnewline
42 & 22 & 21.9915422579662 & 0.00845774203379076 \tabularnewline
43 & 18 & 17.1296141440575 & 0.87038585594253 \tabularnewline
44 & 17 & 21.6372078571851 & -4.63720785718515 \tabularnewline
45 & 20 & 22.2319210210565 & -2.23192102105651 \tabularnewline
46 & 15 & 20.4352937876135 & -5.43529378761353 \tabularnewline
47 & 20 & 22.1071288311651 & -2.1071288311651 \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 & 2.76540248707958 \tabularnewline
52 & 18 & 18.9656042544921 & -0.965604254492147 \tabularnewline
53 & 20 & 18.7961200457430 & 1.20387995425697 \tabularnewline
54 & 11 & 11.7160709940257 & -0.716070994025735 \tabularnewline
55 & 28 & 29.0105804767345 & -1.01058047673453 \tabularnewline
56 & 26 & 23.3905043460467 & 2.60949565395329 \tabularnewline
57 & 22 & 22.3414695454203 & -0.341469545420307 \tabularnewline
58 & 17 & 20.1564210484307 & -3.15642104843071 \tabularnewline
59 & 12 & 15.5692778789805 & -3.56927787898050 \tabularnewline
60 & 14 & 20.8167935559991 & -6.81679355599913 \tabularnewline
61 & 17 & 20.8202241820746 & -3.82022418207459 \tabularnewline
62 & 21 & 21.3347129501434 & -0.334712950143380 \tabularnewline
63 & 19 & 22.9763818374361 & -3.97638183743611 \tabularnewline
64 & 18 & 23.1920758880957 & -5.19207588809565 \tabularnewline
65 & 10 & 17.9120405565538 & -7.91204055655377 \tabularnewline
66 & 29 & 24.4099265188341 & 4.59007348116592 \tabularnewline
67 & 31 & 18.5584769188011 & 12.4415230811989 \tabularnewline
68 & 19 & 22.9869414093617 & -3.98694140936169 \tabularnewline
69 & 9 & 20.0961723263517 & -11.0961723263517 \tabularnewline
70 & 20 & 22.5809984574057 & -2.58099845740569 \tabularnewline
71 & 28 & 17.6552736410878 & 10.3447263589122 \tabularnewline
72 & 19 & 18.2291083555071 & 0.770891644492931 \tabularnewline
73 & 30 & 23.1787562563361 & 6.82124374366391 \tabularnewline
74 & 29 & 27.1018574495787 & 1.89814255042130 \tabularnewline
75 & 26 & 21.5208597487263 & 4.47914025127371 \tabularnewline
76 & 23 & 19.5775913896633 & 3.42240861033671 \tabularnewline
77 & 13 & 22.7673890269749 & -9.7673890269749 \tabularnewline
78 & 21 & 22.6999656986182 & -1.69996569861817 \tabularnewline
79 & 19 & 21.6345836806373 & -2.63458368063733 \tabularnewline
80 & 28 & 22.9691570142453 & 5.03084298575474 \tabularnewline
81 & 23 & 25.6973992029916 & -2.69739920299159 \tabularnewline
82 & 18 & 13.9549031616295 & 4.04509683837052 \tabularnewline
83 & 21 & 20.7615465591829 & 0.238453440817124 \tabularnewline
84 & 20 & 21.9157961866931 & -1.91579618669312 \tabularnewline
85 & 23 & 20.0777478836276 & 2.92225211637243 \tabularnewline
86 & 21 & 20.8386011500172 & 0.161398849982816 \tabularnewline
87 & 21 & 21.8931506037605 & -0.893150603760456 \tabularnewline
88 & 15 & 23.0305779438395 & -8.03057794383952 \tabularnewline
89 & 28 & 27.3005835439217 & 0.699416456078311 \tabularnewline
90 & 19 & 17.7045900098864 & 1.29540999011360 \tabularnewline
91 & 26 & 21.2772051999812 & 4.72279480001875 \tabularnewline
92 & 10 & 13.4207961925499 & -3.4207961925499 \tabularnewline
93 & 16 & 17.1995105289364 & -1.19951052893642 \tabularnewline
94 & 22 & 21.1713453970672 & 0.82865460293282 \tabularnewline
95 & 19 & 18.9989312859951 & 0.00106871400488703 \tabularnewline
96 & 31 & 28.9403783769220 & 2.05962162307796 \tabularnewline
97 & 31 & 25.2905586288165 & 5.70944137118355 \tabularnewline
98 & 29 & 24.8528116175613 & 4.14718838243874 \tabularnewline
99 & 19 & 17.5096261454999 & 1.49037385450014 \tabularnewline
100 & 22 & 18.9524275113635 & 3.04757248863650 \tabularnewline
101 & 23 & 22.4963923849776 & 0.503607615022413 \tabularnewline
102 & 15 & 16.2875354180751 & -1.28753541807506 \tabularnewline
103 & 20 & 21.4228525089516 & -1.42285250895161 \tabularnewline
104 & 18 & 19.6658145225114 & -1.66581452251145 \tabularnewline
105 & 23 & 22.2563692427646 & 0.743630757235358 \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 & -0.355023506846472 \tabularnewline
110 & 17 & 19.619766860822 & -2.61976686082202 \tabularnewline
111 & 13 & 14.6659551691233 & -1.66595516912329 \tabularnewline
112 & 28 & 18.4262616637208 & 9.57373833627919 \tabularnewline
113 & 21 & 20.4196764510916 & 0.580323548908437 \tabularnewline
114 & 25 & 28.3159565386191 & -3.31595653861913 \tabularnewline
115 & 9 & 21.1551764227403 & -12.1551764227403 \tabularnewline
116 & 16 & 18.0061106330230 & -2.00611063302304 \tabularnewline
117 & 19 & 21.2544613841652 & -2.25446138416516 \tabularnewline
118 & 17 & 19.6108037739653 & -2.61080377396526 \tabularnewline
119 & 25 & 24.6763915340849 & 0.323608465915107 \tabularnewline
120 & 20 & 15.5671337387445 & 4.43286626125549 \tabularnewline
121 & 29 & 21.8716126114376 & 7.12838738856236 \tabularnewline
122 & 14 & 19.1167857657226 & -5.11678576572263 \tabularnewline
123 & 22 & 27.0867941796051 & -5.08679417960509 \tabularnewline
124 & 15 & 15.9355784175142 & -0.935578417514242 \tabularnewline
125 & 19 & 25.6263325925739 & -6.62633259257395 \tabularnewline
126 & 20 & 22.0928920267450 & -2.09289202674503 \tabularnewline
127 & 15 & 17.7095654037931 & -2.70956540379315 \tabularnewline
128 & 20 & 22.1149283807381 & -2.11492838073813 \tabularnewline
129 & 18 & 20.5115649579609 & -2.51156495796086 \tabularnewline
130 & 33 & 25.7620025241802 & 7.23799747581977 \tabularnewline
131 & 22 & 24.0135738555549 & -2.01357385555485 \tabularnewline
132 & 16 & 16.6900618439135 & -0.690061843913491 \tabularnewline
133 & 17 & 19.3420422957593 & -2.34204229575933 \tabularnewline
134 & 16 & 15.3134803176989 & 0.68651968230106 \tabularnewline
135 & 21 & 17.2994992608764 & 3.70050073912357 \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 & -1.68259131236590 \tabularnewline
140 & 22 & 24.9799528930422 & -2.97995289304222 \tabularnewline
141 & 30 & 24.9254198082097 & 5.0745801917903 \tabularnewline
142 & 30 & 27.5705627248667 & 2.42943727513332 \tabularnewline
143 & 24 & 29.9683835869881 & -5.96838358698809 \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 & 7.53565753352598 \tabularnewline
148 & 20 & 19.2655893559701 & 0.734410644029932 \tabularnewline
149 & 16 & 14.3261339717709 & 1.67386602822906 \tabularnewline
150 & 22 & 19.2236647882233 & 2.77633521177669 \tabularnewline
151 & 20 & 20.7522347250232 & -0.752234725023154 \tabularnewline
152 & 28 & 27.5610774834422 & 0.438922516557781 \tabularnewline
153 & 38 & 26.890415116 & 11.1095848840000 \tabularnewline
154 & 22 & 19.4362722728422 & 2.56372772715775 \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 & -2.42304583136319 \tabularnewline
159 & 31 & 26.3398127723852 & 4.66018722761484 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98407&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]23.3005712933063[/C][C]0.699428706693719[/C][/ROW]
[ROW][C]2[/C][C]25[/C][C]20.0175224671839[/C][C]4.98247753281608[/C][/ROW]
[ROW][C]3[/C][C]17[/C][C]21.0211399791426[/C][C]-4.02113997914265[/C][/ROW]
[ROW][C]4[/C][C]18[/C][C]18.0925500792618[/C][C]-0.0925500792618023[/C][/ROW]
[ROW][C]5[/C][C]18[/C][C]17.6905678029332[/C][C]0.309432197066762[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]17.877648378172[/C][C]-1.87764837817201[/C][/ROW]
[ROW][C]7[/C][C]20[/C][C]20.7303968936034[/C][C]-0.7303968936034[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]22.2384819662608[/C][C]-6.23848196626085[/C][/ROW]
[ROW][C]9[/C][C]18[/C][C]22.1097766895970[/C][C]-4.10977668959702[/C][/ROW]
[ROW][C]10[/C][C]17[/C][C]20.3404956440243[/C][C]-3.34049564402435[/C][/ROW]
[ROW][C]11[/C][C]23[/C][C]22.1666780037712[/C][C]0.833321996228807[/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]-0.800677629953757[/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]17.7473672648276[/C][C]-5.74736726482756[/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]-0.00951022726447562[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]19.5818010071522[/C][C]0.41819899284785[/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]7.12765344635407[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]19.5212510677299[/C][C]1.47874893227008[/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]-0.262087388219842[/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]-1.45054538861650[/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]0.15954139361592[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]19.3786812679951[/C][C]-2.37868126799510[/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]30.4026260368561[/C][C]-5.40262603685613[/C][/ROW]
[ROW][C]33[/C][C]26[/C][C]26.4499559136845[/C][C]-0.449955913684488[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]24.086412180723[/C][C]0.913587819277002[/C][/ROW]
[ROW][C]35[/C][C]17[/C][C]22.8670220283224[/C][C]-5.86702202832238[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]27.7605607608740[/C][C]4.23943923912604[/C][/ROW]
[ROW][C]37[/C][C]33[/C][C]23.5983866371489[/C][C]9.40161336285112[/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]-0.787102563668005[/C][/ROW]
[ROW][C]41[/C][C]29[/C][C]26.9946561977486[/C][C]2.00534380225137[/C][/ROW]
[ROW][C]42[/C][C]22[/C][C]21.9915422579662[/C][C]0.00845774203379076[/C][/ROW]
[ROW][C]43[/C][C]18[/C][C]17.1296141440575[/C][C]0.87038585594253[/C][/ROW]
[ROW][C]44[/C][C]17[/C][C]21.6372078571851[/C][C]-4.63720785718515[/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]-2.1071288311651[/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]2.76540248707958[/C][/ROW]
[ROW][C]52[/C][C]18[/C][C]18.9656042544921[/C][C]-0.965604254492147[/C][/ROW]
[ROW][C]53[/C][C]20[/C][C]18.7961200457430[/C][C]1.20387995425697[/C][/ROW]
[ROW][C]54[/C][C]11[/C][C]11.7160709940257[/C][C]-0.716070994025735[/C][/ROW]
[ROW][C]55[/C][C]28[/C][C]29.0105804767345[/C][C]-1.01058047673453[/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]-3.15642104843071[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]15.5692778789805[/C][C]-3.56927787898050[/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]-0.334712950143380[/C][/ROW]
[ROW][C]63[/C][C]19[/C][C]22.9763818374361[/C][C]-3.97638183743611[/C][/ROW]
[ROW][C]64[/C][C]18[/C][C]23.1920758880957[/C][C]-5.19207588809565[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]17.9120405565538[/C][C]-7.91204055655377[/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]-3.98694140936169[/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]-2.58099845740569[/C][/ROW]
[ROW][C]71[/C][C]28[/C][C]17.6552736410878[/C][C]10.3447263589122[/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]1.89814255042130[/C][/ROW]
[ROW][C]75[/C][C]26[/C][C]21.5208597487263[/C][C]4.47914025127371[/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]-9.7673890269749[/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]-2.63458368063733[/C][/ROW]
[ROW][C]80[/C][C]28[/C][C]22.9691570142453[/C][C]5.03084298575474[/C][/ROW]
[ROW][C]81[/C][C]23[/C][C]25.6973992029916[/C][C]-2.69739920299159[/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]0.238453440817124[/C][/ROW]
[ROW][C]84[/C][C]20[/C][C]21.9157961866931[/C][C]-1.91579618669312[/C][/ROW]
[ROW][C]85[/C][C]23[/C][C]20.0777478836276[/C][C]2.92225211637243[/C][/ROW]
[ROW][C]86[/C][C]21[/C][C]20.8386011500172[/C][C]0.161398849982816[/C][/ROW]
[ROW][C]87[/C][C]21[/C][C]21.8931506037605[/C][C]-0.893150603760456[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]23.0305779438395[/C][C]-8.03057794383952[/C][/ROW]
[ROW][C]89[/C][C]28[/C][C]27.3005835439217[/C][C]0.699416456078311[/C][/ROW]
[ROW][C]90[/C][C]19[/C][C]17.7045900098864[/C][C]1.29540999011360[/C][/ROW]
[ROW][C]91[/C][C]26[/C][C]21.2772051999812[/C][C]4.72279480001875[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.4207961925499[/C][C]-3.4207961925499[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]17.1995105289364[/C][C]-1.19951052893642[/C][/ROW]
[ROW][C]94[/C][C]22[/C][C]21.1713453970672[/C][C]0.82865460293282[/C][/ROW]
[ROW][C]95[/C][C]19[/C][C]18.9989312859951[/C][C]0.00106871400488703[/C][/ROW]
[ROW][C]96[/C][C]31[/C][C]28.9403783769220[/C][C]2.05962162307796[/C][/ROW]
[ROW][C]97[/C][C]31[/C][C]25.2905586288165[/C][C]5.70944137118355[/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]3.04757248863650[/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]16.2875354180751[/C][C]-1.28753541807506[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]21.4228525089516[/C][C]-1.42285250895161[/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]22.2563692427646[/C][C]0.743630757235358[/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]-0.355023506846472[/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]-1.66595516912329[/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]0.580323548908437[/C][/ROW]
[ROW][C]114[/C][C]25[/C][C]28.3159565386191[/C][C]-3.31595653861913[/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]18.0061106330230[/C][C]-2.00611063302304[/C][/ROW]
[ROW][C]117[/C][C]19[/C][C]21.2544613841652[/C][C]-2.25446138416516[/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]0.323608465915107[/C][/ROW]
[ROW][C]120[/C][C]20[/C][C]15.5671337387445[/C][C]4.43286626125549[/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]-5.11678576572263[/C][/ROW]
[ROW][C]123[/C][C]22[/C][C]27.0867941796051[/C][C]-5.08679417960509[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.9355784175142[/C][C]-0.935578417514242[/C][/ROW]
[ROW][C]125[/C][C]19[/C][C]25.6263325925739[/C][C]-6.62633259257395[/C][/ROW]
[ROW][C]126[/C][C]20[/C][C]22.0928920267450[/C][C]-2.09289202674503[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]17.7095654037931[/C][C]-2.70956540379315[/C][/ROW]
[ROW][C]128[/C][C]20[/C][C]22.1149283807381[/C][C]-2.11492838073813[/C][/ROW]
[ROW][C]129[/C][C]18[/C][C]20.5115649579609[/C][C]-2.51156495796086[/C][/ROW]
[ROW][C]130[/C][C]33[/C][C]25.7620025241802[/C][C]7.23799747581977[/C][/ROW]
[ROW][C]131[/C][C]22[/C][C]24.0135738555549[/C][C]-2.01357385555485[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]16.6900618439135[/C][C]-0.690061843913491[/C][/ROW]
[ROW][C]133[/C][C]17[/C][C]19.3420422957593[/C][C]-2.34204229575933[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]15.3134803176989[/C][C]0.68651968230106[/C][/ROW]
[ROW][C]135[/C][C]21[/C][C]17.2994992608764[/C][C]3.70050073912357[/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]-1.68259131236590[/C][/ROW]
[ROW][C]140[/C][C]22[/C][C]24.9799528930422[/C][C]-2.97995289304222[/C][/ROW]
[ROW][C]141[/C][C]30[/C][C]24.9254198082097[/C][C]5.0745801917903[/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]-5.96838358698809[/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]7.53565753352598[/C][/ROW]
[ROW][C]148[/C][C]20[/C][C]19.2655893559701[/C][C]0.734410644029932[/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]-0.752234725023154[/C][/ROW]
[ROW][C]152[/C][C]28[/C][C]27.5610774834422[/C][C]0.438922516557781[/C][/ROW]
[ROW][C]153[/C][C]38[/C][C]26.890415116[/C][C]11.1095848840000[/C][/ROW]
[ROW][C]154[/C][C]22[/C][C]19.4362722728422[/C][C]2.56372772715775[/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]-2.42304583136319[/C][/ROW]
[ROW][C]159[/C][C]31[/C][C]26.3398127723852[/C][C]4.66018722761484[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98407&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98407&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12423.30057129330630.699428706693719
22520.01752246718394.98247753281608
31721.0211399791426-4.02113997914265
41818.0925500792618-0.0925500792618023
51817.69056780293320.309432197066762
61617.877648378172-1.87764837817201
72020.7303968936034-0.7303968936034
81622.2384819662608-6.23848196626085
91822.1097766895970-4.10977668959702
101720.3404956440243-3.34049564402435
112322.16667800377120.833321996228807
123022.02409374820917.97590625179086
132314.97798333049958.02201666950045
141818.8006776299538-0.800677629953757
151521.5420869454813-6.54208694548131
161217.7473672648276-5.74736726482756
172119.31995062782011.68004937217993
181515.0095102272645-0.00951022726447562
192019.58180100715220.41819899284785
203126.17801170877194.8219882912281
212725.06546676357921.93453323642076
223426.87234655364597.12765344635407
232119.52125106772991.47874893227008
243120.777542919189110.2224570808109
251919.2620873882198-0.262087388219842
261620.1257631629215-4.12576316292152
272021.4505453886165-1.45054538861650
282118.17726396710972.82273603289032
292221.84045860638410.15954139361592
301719.3786812679951-2.37868126799510
312420.36288304198333.63711695801675
322530.4026260368561-5.40262603685613
332626.4499559136845-0.449955913684488
342524.0864121807230.913587819277002
351722.8670220283224-5.86702202832238
363227.76056076087404.23943923912604
373323.59838663714899.40161336285112
381321.7984983327297-8.79849833272967
393227.74779011438714.25220988561288
402525.787102563668-0.787102563668005
412926.99465619774862.00534380225137
422221.99154225796620.00845774203379076
431817.12961414405750.87038585594253
441721.6372078571851-4.63720785718515
452022.2319210210565-2.23192102105651
461520.4352937876135-5.43529378761353
472022.1071288311651-2.1071288311651
483328.12117199787564.87882800212443
492922.14115052184136.85884947815874
502326.4998596091402-3.49985960914021
512623.23459751292042.76540248707958
521818.9656042544921-0.965604254492147
532018.79612004574301.20387995425697
541111.7160709940257-0.716070994025735
552829.0105804767345-1.01058047673453
562623.39050434604672.60949565395329
572222.3414695454203-0.341469545420307
581720.1564210484307-3.15642104843071
591215.5692778789805-3.56927787898050
601420.8167935559991-6.81679355599913
611720.8202241820746-3.82022418207459
622121.3347129501434-0.334712950143380
631922.9763818374361-3.97638183743611
641823.1920758880957-5.19207588809565
651017.9120405565538-7.91204055655377
662924.40992651883414.59007348116592
673118.558476918801112.4415230811989
681922.9869414093617-3.98694140936169
69920.0961723263517-11.0961723263517
702022.5809984574057-2.58099845740569
712817.655273641087810.3447263589122
721918.22910835550710.770891644492931
733023.17875625633616.82124374366391
742927.10185744957871.89814255042130
752621.52085974872634.47914025127371
762319.57759138966333.42240861033671
771322.7673890269749-9.7673890269749
782122.6999656986182-1.69996569861817
791921.6345836806373-2.63458368063733
802822.96915701424535.03084298575474
812325.6973992029916-2.69739920299159
821813.95490316162954.04509683837052
832120.76154655918290.238453440817124
842021.9157961866931-1.91579618669312
852320.07774788362762.92225211637243
862120.83860115001720.161398849982816
872121.8931506037605-0.893150603760456
881523.0305779438395-8.03057794383952
892827.30058354392170.699416456078311
901917.70459000988641.29540999011360
912621.27720519998124.72279480001875
921013.4207961925499-3.4207961925499
931617.1995105289364-1.19951052893642
942221.17134539706720.82865460293282
951918.99893128599510.00106871400488703
963128.94037837692202.05962162307796
973125.29055862881655.70944137118355
982924.85281161756134.14718838243874
991917.50962614549991.49037385450014
1002218.95242751136353.04757248863650
1012322.49639238497760.503607615022413
1021516.2875354180751-1.28753541807506
1032021.4228525089516-1.42285250895161
1041819.6658145225114-1.66581452251145
1052322.25636924276460.743630757235358
1062520.90865743550984.09134256449023
1072116.67227715246434.32772284753566
1082419.56800771168244.4319922883176
1092525.3550235068465-0.355023506846472
1101719.619766860822-2.61976686082202
1111314.6659551691233-1.66595516912329
1122818.42626166372089.57373833627919
1132120.41967645109160.580323548908437
1142528.3159565386191-3.31595653861913
115921.1551764227403-12.1551764227403
1161618.0061106330230-2.00611063302304
1171921.2544613841652-2.25446138416516
1181719.6108037739653-2.61080377396526
1192524.67639153408490.323608465915107
1202015.56713373874454.43286626125549
1212921.87161261143767.12838738856236
1221419.1167857657226-5.11678576572263
1232227.0867941796051-5.08679417960509
1241515.9355784175142-0.935578417514242
1251925.6263325925739-6.62633259257395
1262022.0928920267450-2.09289202674503
1271517.7095654037931-2.70956540379315
1282022.1149283807381-2.11492838073813
1291820.5115649579609-2.51156495796086
1303325.76200252418027.23799747581977
1312224.0135738555549-2.01357385555485
1321616.6900618439135-0.690061843913491
1331719.3420422957593-2.34204229575933
1341615.31348031769890.68651968230106
1352117.29949926087643.70050073912357
1362627.7996968946194-1.79969689461943
1371821.304233865906-3.30423386590599
1381823.2073200148495-5.20732001484945
1391718.6825913123659-1.68259131236590
1402224.9799528930422-2.97995289304222
1413024.92541980820975.0745801917903
1423027.57056272486672.42943727513332
1432429.9683835869881-5.96838358698809
1442122.2960469748642-1.29604697486422
1452125.6227704527493-4.62277045274928
1462927.67396094315831.32603905684171
1473123.4643424664747.53565753352598
1482019.26558935597010.734410644029932
1491614.32613397177091.67386602822906
1502219.22366478822332.77633521177669
1512020.7522347250232-0.752234725023154
1522827.56107748344220.438922516557781
1533826.89041511611.1095848840000
1542219.43627227284222.56372772715775
1552025.9470773992814-5.9470773992814
1561718.31008307594-1.31008307593998
1572824.81405007758513.18594992241494
1582224.4230458313632-2.42304583136319
1593126.33981277238524.66018722761484






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.4829193016149590.9658386032299180.517080698385041
120.5540732393436450.891853521312710.445926760656355
130.7785712074528930.4428575850942130.221428792547107
140.8044013778971670.3911972442056660.195598622102833
150.7447735642805760.5104528714388470.255226435719424
160.664232544631890.671534910736220.33576745536811
170.6235277677461310.7529444645077390.376472232253869
180.5814159093338980.8371681813322030.418584090666102
190.5415638152832810.9168723694334380.458436184716719
200.5621504795336010.8756990409327970.437849520466399
210.4853487611294020.9706975222588040.514651238870598
220.4559249064379630.9118498128759260.544075093562037
230.4180293134236040.8360586268472080.581970686576396
240.5251318710594430.9497362578811140.474868128940557
250.4965945694728410.9931891389456820.503405430527159
260.5284303120479990.9431393759040010.471569687952001
270.4624564691639470.9249129383278940.537543530836053
280.3994328697227680.7988657394455360.600567130277232
290.3374991199397940.6749982398795890.662500880060206
300.3034494262526350.6068988525052690.696550573747365
310.2920688108971490.5841376217942970.707931189102851
320.4278905143310570.8557810286621140.572109485668943
330.3677709725805480.7355419451610960.632229027419452
340.3389216242501930.6778432485003860.661078375749807
350.3899119760466970.7798239520933940.610088023953303
360.354184460665440.708368921330880.64581553933456
370.4654268922738750.930853784547750.534573107726125
380.7615144023517240.4769711952965510.238485597648276
390.7480429223637580.5039141552724830.251957077636242
400.7114107689347490.5771784621305030.288589231065251
410.6764471981337660.6471056037324690.323552801866234
420.628165076844510.743669846310980.37183492315549
430.5764227236989070.8471545526021860.423577276301093
440.5640438355426710.8719123289146590.435956164457329
450.5471458311909280.9057083376181430.452854168809072
460.5791926855220740.8416146289558520.420807314477926
470.5354765355173430.9290469289653130.464523464482657
480.5259849118485100.9480301763029790.474015088151489
490.58621035398330.82757929203340.4137896460167
500.55732133154370.88535733691260.4426786684563
510.5264197363296410.9471605273407180.473580263670359
520.4766790272664290.9533580545328570.523320972733571
530.4330087212924440.8660174425848890.566991278707556
540.3882146891237590.7764293782475180.611785310876241
550.3473362447661960.6946724895323920.652663755233804
560.3187228020099800.6374456040199590.68127719799002
570.2758557123679040.5517114247358090.724144287632096
580.2478299770059350.4956599540118690.752170022994066
590.2295442247609740.4590884495219470.770455775239026
600.2646070039174270.5292140078348550.735392996082573
610.248127640276520.496255280553040.75187235972348
620.2141301810354990.4282603620709980.785869818964501
630.1969075329268410.3938150658536820.803092467073159
640.2179466817221930.4358933634443860.782053318277807
650.2669794120658070.5339588241316140.733020587934193
660.2759291272540540.5518582545081080.724070872745946
670.637769420135870.724461159728260.36223057986413
680.6219181725308830.7561636549382340.378081827469117
690.7951952892038090.4096094215923830.204804710796191
700.7678147506359330.4643704987281340.232185249364067
710.9127531564915340.1744936870169320.0872468435084658
720.8937271218045160.2125457563909670.106272878195484
730.9242926095928780.1514147808142450.0757073904071223
740.9124970207299570.1750059585400860.0875029792700431
750.9163309680112960.1673380639774080.0836690319887039
760.9106624721733080.1786750556533840.089337527826692
770.9620940858164270.07581182836714570.0379059141835728
780.9526852434423330.09462951311533320.0473147565576666
790.943473929806760.1130521403864800.0565260701932398
800.9479428525491880.1041142949016250.0520571474508123
810.9382652388085330.1234695223829350.0617347611914673
820.9375827240267030.1248345519465940.0624172759732972
830.9220533362512840.1558933274974330.0779466637487164
840.9065742500025290.1868514999949420.0934257499974709
850.8967632782064960.2064734435870070.103236721793503
860.8780800500150440.2438398999699110.121919949984956
870.8533755756665240.2932488486669530.146624424333476
880.9053301920169020.1893396159661960.0946698079830979
890.8861555888715220.2276888222569560.113844411128478
900.8636477117300980.2727045765398040.136352288269902
910.8658274788426220.2683450423147560.134172521157378
920.8543847808816980.2912304382366030.145615219118302
930.8298317007731760.3403365984536480.170168299226824
940.799553747405970.4008925051880610.200446252594031
950.7636884466440350.472623106711930.236311553355965
960.7310555749981380.5378888500037230.268944425001862
970.751741927331530.496516145336940.24825807266847
980.7493465068530560.5013069862938890.250653493146944
990.7122346964319530.5755306071360930.287765303568047
1000.6897448649238680.6205102701522650.310255135076132
1010.6494349222362850.701130155527430.350565077763715
1020.6066348457904770.7867303084190460.393365154209523
1030.563244216748730.8735115665025390.436755783251269
1040.5186541566685340.9626916866629320.481345843331466
1050.4758806504744080.9517613009488160.524119349525592
1060.4636121411199460.9272242822398920.536387858880054
1070.4707790492737110.9415580985474220.529220950726289
1080.5095328074496350.980934385100730.490467192550365
1090.470971756762880.941943513525760.52902824323712
1100.4286116910002730.8572233820005460.571388308999727
1110.3814515054168480.7629030108336960.618548494583152
1120.7112886403632380.5774227192735250.288711359636762
1130.7473975444481890.5052049111036220.252602455551811
1140.7200322666398490.5599354667203020.279967733360151
1150.8546521660591680.2906956678816640.145347833940832
1160.8227162001041930.3545675997916150.177283799895807
1170.7895983733747360.4208032532505280.210401626625264
1180.7524050165307840.4951899669384320.247594983469216
1190.7217768440500780.5564463118998440.278223155949922
1200.7738925738290690.4522148523418620.226107426170931
1210.860942680640340.2781146387193210.139057319359660
1220.838493713445110.3230125731097790.161506286554890
1230.81442814843470.37114370313060.1855718515653
1240.771549295854050.4569014082919010.228450704145950
1250.774698277617690.4506034447646210.225301722382310
1260.7259122102917470.5481755794165060.274087789708253
1270.690676886270890.6186462274582210.309323113729111
1280.6397780036985880.7204439926028230.360221996301412
1290.5907368861560870.8185262276878270.409263113843913
1300.7185032158087570.5629935683824870.281496784191244
1310.6603237787138160.6793524425723690.339676221286184
1320.5951269309861740.8097461380276510.404873069013826
1330.5537187639610460.8925624720779090.446281236038954
1340.4814457747577620.9628915495155250.518554225242238
1350.5048998386997190.9902003226005620.495100161300281
1360.4324005633879520.8648011267759040.567599436612048
1370.3838510906299900.7677021812599790.61614890937001
1380.3962487546152590.7924975092305190.603751245384741
1390.3237631621555350.647526324311070.676236837844465
1400.2544030009655450.508806001931090.745596999034455
1410.3440471799139950.688094359827990.655952820086005
1420.2945759068292850.589151813658570.705424093170715
1430.2264455868972510.4528911737945030.773554413102749
1440.1596988583803390.3193977167606770.840301141619661
1450.2399300775530420.4798601551060830.760069922446958
1460.1693483072714130.3386966145428270.830651692728587
1470.1605212839060140.3210425678120280.839478716093986
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 & 0.482919301614959 & 0.965838603229918 & 0.517080698385041 \tabularnewline
12 & 0.554073239343645 & 0.89185352131271 & 0.445926760656355 \tabularnewline
13 & 0.778571207452893 & 0.442857585094213 & 0.221428792547107 \tabularnewline
14 & 0.804401377897167 & 0.391197244205666 & 0.195598622102833 \tabularnewline
15 & 0.744773564280576 & 0.510452871438847 & 0.255226435719424 \tabularnewline
16 & 0.66423254463189 & 0.67153491073622 & 0.33576745536811 \tabularnewline
17 & 0.623527767746131 & 0.752944464507739 & 0.376472232253869 \tabularnewline
18 & 0.581415909333898 & 0.837168181332203 & 0.418584090666102 \tabularnewline
19 & 0.541563815283281 & 0.916872369433438 & 0.458436184716719 \tabularnewline
20 & 0.562150479533601 & 0.875699040932797 & 0.437849520466399 \tabularnewline
21 & 0.485348761129402 & 0.970697522258804 & 0.514651238870598 \tabularnewline
22 & 0.455924906437963 & 0.911849812875926 & 0.544075093562037 \tabularnewline
23 & 0.418029313423604 & 0.836058626847208 & 0.581970686576396 \tabularnewline
24 & 0.525131871059443 & 0.949736257881114 & 0.474868128940557 \tabularnewline
25 & 0.496594569472841 & 0.993189138945682 & 0.503405430527159 \tabularnewline
26 & 0.528430312047999 & 0.943139375904001 & 0.471569687952001 \tabularnewline
27 & 0.462456469163947 & 0.924912938327894 & 0.537543530836053 \tabularnewline
28 & 0.399432869722768 & 0.798865739445536 & 0.600567130277232 \tabularnewline
29 & 0.337499119939794 & 0.674998239879589 & 0.662500880060206 \tabularnewline
30 & 0.303449426252635 & 0.606898852505269 & 0.696550573747365 \tabularnewline
31 & 0.292068810897149 & 0.584137621794297 & 0.707931189102851 \tabularnewline
32 & 0.427890514331057 & 0.855781028662114 & 0.572109485668943 \tabularnewline
33 & 0.367770972580548 & 0.735541945161096 & 0.632229027419452 \tabularnewline
34 & 0.338921624250193 & 0.677843248500386 & 0.661078375749807 \tabularnewline
35 & 0.389911976046697 & 0.779823952093394 & 0.610088023953303 \tabularnewline
36 & 0.35418446066544 & 0.70836892133088 & 0.64581553933456 \tabularnewline
37 & 0.465426892273875 & 0.93085378454775 & 0.534573107726125 \tabularnewline
38 & 0.761514402351724 & 0.476971195296551 & 0.238485597648276 \tabularnewline
39 & 0.748042922363758 & 0.503914155272483 & 0.251957077636242 \tabularnewline
40 & 0.711410768934749 & 0.577178462130503 & 0.288589231065251 \tabularnewline
41 & 0.676447198133766 & 0.647105603732469 & 0.323552801866234 \tabularnewline
42 & 0.62816507684451 & 0.74366984631098 & 0.37183492315549 \tabularnewline
43 & 0.576422723698907 & 0.847154552602186 & 0.423577276301093 \tabularnewline
44 & 0.564043835542671 & 0.871912328914659 & 0.435956164457329 \tabularnewline
45 & 0.547145831190928 & 0.905708337618143 & 0.452854168809072 \tabularnewline
46 & 0.579192685522074 & 0.841614628955852 & 0.420807314477926 \tabularnewline
47 & 0.535476535517343 & 0.929046928965313 & 0.464523464482657 \tabularnewline
48 & 0.525984911848510 & 0.948030176302979 & 0.474015088151489 \tabularnewline
49 & 0.5862103539833 & 0.8275792920334 & 0.4137896460167 \tabularnewline
50 & 0.5573213315437 & 0.8853573369126 & 0.4426786684563 \tabularnewline
51 & 0.526419736329641 & 0.947160527340718 & 0.473580263670359 \tabularnewline
52 & 0.476679027266429 & 0.953358054532857 & 0.523320972733571 \tabularnewline
53 & 0.433008721292444 & 0.866017442584889 & 0.566991278707556 \tabularnewline
54 & 0.388214689123759 & 0.776429378247518 & 0.611785310876241 \tabularnewline
55 & 0.347336244766196 & 0.694672489532392 & 0.652663755233804 \tabularnewline
56 & 0.318722802009980 & 0.637445604019959 & 0.68127719799002 \tabularnewline
57 & 0.275855712367904 & 0.551711424735809 & 0.724144287632096 \tabularnewline
58 & 0.247829977005935 & 0.495659954011869 & 0.752170022994066 \tabularnewline
59 & 0.229544224760974 & 0.459088449521947 & 0.770455775239026 \tabularnewline
60 & 0.264607003917427 & 0.529214007834855 & 0.735392996082573 \tabularnewline
61 & 0.24812764027652 & 0.49625528055304 & 0.75187235972348 \tabularnewline
62 & 0.214130181035499 & 0.428260362070998 & 0.785869818964501 \tabularnewline
63 & 0.196907532926841 & 0.393815065853682 & 0.803092467073159 \tabularnewline
64 & 0.217946681722193 & 0.435893363444386 & 0.782053318277807 \tabularnewline
65 & 0.266979412065807 & 0.533958824131614 & 0.733020587934193 \tabularnewline
66 & 0.275929127254054 & 0.551858254508108 & 0.724070872745946 \tabularnewline
67 & 0.63776942013587 & 0.72446115972826 & 0.36223057986413 \tabularnewline
68 & 0.621918172530883 & 0.756163654938234 & 0.378081827469117 \tabularnewline
69 & 0.795195289203809 & 0.409609421592383 & 0.204804710796191 \tabularnewline
70 & 0.767814750635933 & 0.464370498728134 & 0.232185249364067 \tabularnewline
71 & 0.912753156491534 & 0.174493687016932 & 0.0872468435084658 \tabularnewline
72 & 0.893727121804516 & 0.212545756390967 & 0.106272878195484 \tabularnewline
73 & 0.924292609592878 & 0.151414780814245 & 0.0757073904071223 \tabularnewline
74 & 0.912497020729957 & 0.175005958540086 & 0.0875029792700431 \tabularnewline
75 & 0.916330968011296 & 0.167338063977408 & 0.0836690319887039 \tabularnewline
76 & 0.910662472173308 & 0.178675055653384 & 0.089337527826692 \tabularnewline
77 & 0.962094085816427 & 0.0758118283671457 & 0.0379059141835728 \tabularnewline
78 & 0.952685243442333 & 0.0946295131153332 & 0.0473147565576666 \tabularnewline
79 & 0.94347392980676 & 0.113052140386480 & 0.0565260701932398 \tabularnewline
80 & 0.947942852549188 & 0.104114294901625 & 0.0520571474508123 \tabularnewline
81 & 0.938265238808533 & 0.123469522382935 & 0.0617347611914673 \tabularnewline
82 & 0.937582724026703 & 0.124834551946594 & 0.0624172759732972 \tabularnewline
83 & 0.922053336251284 & 0.155893327497433 & 0.0779466637487164 \tabularnewline
84 & 0.906574250002529 & 0.186851499994942 & 0.0934257499974709 \tabularnewline
85 & 0.896763278206496 & 0.206473443587007 & 0.103236721793503 \tabularnewline
86 & 0.878080050015044 & 0.243839899969911 & 0.121919949984956 \tabularnewline
87 & 0.853375575666524 & 0.293248848666953 & 0.146624424333476 \tabularnewline
88 & 0.905330192016902 & 0.189339615966196 & 0.0946698079830979 \tabularnewline
89 & 0.886155588871522 & 0.227688822256956 & 0.113844411128478 \tabularnewline
90 & 0.863647711730098 & 0.272704576539804 & 0.136352288269902 \tabularnewline
91 & 0.865827478842622 & 0.268345042314756 & 0.134172521157378 \tabularnewline
92 & 0.854384780881698 & 0.291230438236603 & 0.145615219118302 \tabularnewline
93 & 0.829831700773176 & 0.340336598453648 & 0.170168299226824 \tabularnewline
94 & 0.79955374740597 & 0.400892505188061 & 0.200446252594031 \tabularnewline
95 & 0.763688446644035 & 0.47262310671193 & 0.236311553355965 \tabularnewline
96 & 0.731055574998138 & 0.537888850003723 & 0.268944425001862 \tabularnewline
97 & 0.75174192733153 & 0.49651614533694 & 0.24825807266847 \tabularnewline
98 & 0.749346506853056 & 0.501306986293889 & 0.250653493146944 \tabularnewline
99 & 0.712234696431953 & 0.575530607136093 & 0.287765303568047 \tabularnewline
100 & 0.689744864923868 & 0.620510270152265 & 0.310255135076132 \tabularnewline
101 & 0.649434922236285 & 0.70113015552743 & 0.350565077763715 \tabularnewline
102 & 0.606634845790477 & 0.786730308419046 & 0.393365154209523 \tabularnewline
103 & 0.56324421674873 & 0.873511566502539 & 0.436755783251269 \tabularnewline
104 & 0.518654156668534 & 0.962691686662932 & 0.481345843331466 \tabularnewline
105 & 0.475880650474408 & 0.951761300948816 & 0.524119349525592 \tabularnewline
106 & 0.463612141119946 & 0.927224282239892 & 0.536387858880054 \tabularnewline
107 & 0.470779049273711 & 0.941558098547422 & 0.529220950726289 \tabularnewline
108 & 0.509532807449635 & 0.98093438510073 & 0.490467192550365 \tabularnewline
109 & 0.47097175676288 & 0.94194351352576 & 0.52902824323712 \tabularnewline
110 & 0.428611691000273 & 0.857223382000546 & 0.571388308999727 \tabularnewline
111 & 0.381451505416848 & 0.762903010833696 & 0.618548494583152 \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 & 0.822716200104193 & 0.354567599791615 & 0.177283799895807 \tabularnewline
117 & 0.789598373374736 & 0.420803253250528 & 0.210401626625264 \tabularnewline
118 & 0.752405016530784 & 0.495189966938432 & 0.247594983469216 \tabularnewline
119 & 0.721776844050078 & 0.556446311899844 & 0.278223155949922 \tabularnewline
120 & 0.773892573829069 & 0.452214852341862 & 0.226107426170931 \tabularnewline
121 & 0.86094268064034 & 0.278114638719321 & 0.139057319359660 \tabularnewline
122 & 0.83849371344511 & 0.323012573109779 & 0.161506286554890 \tabularnewline
123 & 0.8144281484347 & 0.3711437031306 & 0.1855718515653 \tabularnewline
124 & 0.77154929585405 & 0.456901408291901 & 0.228450704145950 \tabularnewline
125 & 0.77469827761769 & 0.450603444764621 & 0.225301722382310 \tabularnewline
126 & 0.725912210291747 & 0.548175579416506 & 0.274087789708253 \tabularnewline
127 & 0.69067688627089 & 0.618646227458221 & 0.309323113729111 \tabularnewline
128 & 0.639778003698588 & 0.720443992602823 & 0.360221996301412 \tabularnewline
129 & 0.590736886156087 & 0.818526227687827 & 0.409263113843913 \tabularnewline
130 & 0.718503215808757 & 0.562993568382487 & 0.281496784191244 \tabularnewline
131 & 0.660323778713816 & 0.679352442572369 & 0.339676221286184 \tabularnewline
132 & 0.595126930986174 & 0.809746138027651 & 0.404873069013826 \tabularnewline
133 & 0.553718763961046 & 0.892562472077909 & 0.446281236038954 \tabularnewline
134 & 0.481445774757762 & 0.962891549515525 & 0.518554225242238 \tabularnewline
135 & 0.504899838699719 & 0.990200322600562 & 0.495100161300281 \tabularnewline
136 & 0.432400563387952 & 0.864801126775904 & 0.567599436612048 \tabularnewline
137 & 0.383851090629990 & 0.767702181259979 & 0.61614890937001 \tabularnewline
138 & 0.396248754615259 & 0.792497509230519 & 0.603751245384741 \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 & 0.226445586897251 & 0.452891173794503 & 0.773554413102749 \tabularnewline
144 & 0.159698858380339 & 0.319397716760677 & 0.840301141619661 \tabularnewline
145 & 0.239930077553042 & 0.479860155106083 & 0.760069922446958 \tabularnewline
146 & 0.169348307271413 & 0.338696614542827 & 0.830651692728587 \tabularnewline
147 & 0.160521283906014 & 0.321042567812028 & 0.839478716093986 \tabularnewline
148 & 0.087308090660505 & 0.17461618132101 & 0.912691909339495 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98407&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]0.482919301614959[/C][C]0.965838603229918[/C][C]0.517080698385041[/C][/ROW]
[ROW][C]12[/C][C]0.554073239343645[/C][C]0.89185352131271[/C][C]0.445926760656355[/C][/ROW]
[ROW][C]13[/C][C]0.778571207452893[/C][C]0.442857585094213[/C][C]0.221428792547107[/C][/ROW]
[ROW][C]14[/C][C]0.804401377897167[/C][C]0.391197244205666[/C][C]0.195598622102833[/C][/ROW]
[ROW][C]15[/C][C]0.744773564280576[/C][C]0.510452871438847[/C][C]0.255226435719424[/C][/ROW]
[ROW][C]16[/C][C]0.66423254463189[/C][C]0.67153491073622[/C][C]0.33576745536811[/C][/ROW]
[ROW][C]17[/C][C]0.623527767746131[/C][C]0.752944464507739[/C][C]0.376472232253869[/C][/ROW]
[ROW][C]18[/C][C]0.581415909333898[/C][C]0.837168181332203[/C][C]0.418584090666102[/C][/ROW]
[ROW][C]19[/C][C]0.541563815283281[/C][C]0.916872369433438[/C][C]0.458436184716719[/C][/ROW]
[ROW][C]20[/C][C]0.562150479533601[/C][C]0.875699040932797[/C][C]0.437849520466399[/C][/ROW]
[ROW][C]21[/C][C]0.485348761129402[/C][C]0.970697522258804[/C][C]0.514651238870598[/C][/ROW]
[ROW][C]22[/C][C]0.455924906437963[/C][C]0.911849812875926[/C][C]0.544075093562037[/C][/ROW]
[ROW][C]23[/C][C]0.418029313423604[/C][C]0.836058626847208[/C][C]0.581970686576396[/C][/ROW]
[ROW][C]24[/C][C]0.525131871059443[/C][C]0.949736257881114[/C][C]0.474868128940557[/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]0.943139375904001[/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]0.399432869722768[/C][C]0.798865739445536[/C][C]0.600567130277232[/C][/ROW]
[ROW][C]29[/C][C]0.337499119939794[/C][C]0.674998239879589[/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]0.292068810897149[/C][C]0.584137621794297[/C][C]0.707931189102851[/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]0.735541945161096[/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]0.389911976046697[/C][C]0.779823952093394[/C][C]0.610088023953303[/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]0.93085378454775[/C][C]0.534573107726125[/C][/ROW]
[ROW][C]38[/C][C]0.761514402351724[/C][C]0.476971195296551[/C][C]0.238485597648276[/C][/ROW]
[ROW][C]39[/C][C]0.748042922363758[/C][C]0.503914155272483[/C][C]0.251957077636242[/C][/ROW]
[ROW][C]40[/C][C]0.711410768934749[/C][C]0.577178462130503[/C][C]0.288589231065251[/C][/ROW]
[ROW][C]41[/C][C]0.676447198133766[/C][C]0.647105603732469[/C][C]0.323552801866234[/C][/ROW]
[ROW][C]42[/C][C]0.62816507684451[/C][C]0.74366984631098[/C][C]0.37183492315549[/C][/ROW]
[ROW][C]43[/C][C]0.576422723698907[/C][C]0.847154552602186[/C][C]0.423577276301093[/C][/ROW]
[ROW][C]44[/C][C]0.564043835542671[/C][C]0.871912328914659[/C][C]0.435956164457329[/C][/ROW]
[ROW][C]45[/C][C]0.547145831190928[/C][C]0.905708337618143[/C][C]0.452854168809072[/C][/ROW]
[ROW][C]46[/C][C]0.579192685522074[/C][C]0.841614628955852[/C][C]0.420807314477926[/C][/ROW]
[ROW][C]47[/C][C]0.535476535517343[/C][C]0.929046928965313[/C][C]0.464523464482657[/C][/ROW]
[ROW][C]48[/C][C]0.525984911848510[/C][C]0.948030176302979[/C][C]0.474015088151489[/C][/ROW]
[ROW][C]49[/C][C]0.5862103539833[/C][C]0.8275792920334[/C][C]0.4137896460167[/C][/ROW]
[ROW][C]50[/C][C]0.5573213315437[/C][C]0.8853573369126[/C][C]0.4426786684563[/C][/ROW]
[ROW][C]51[/C][C]0.526419736329641[/C][C]0.947160527340718[/C][C]0.473580263670359[/C][/ROW]
[ROW][C]52[/C][C]0.476679027266429[/C][C]0.953358054532857[/C][C]0.523320972733571[/C][/ROW]
[ROW][C]53[/C][C]0.433008721292444[/C][C]0.866017442584889[/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]0.347336244766196[/C][C]0.694672489532392[/C][C]0.652663755233804[/C][/ROW]
[ROW][C]56[/C][C]0.318722802009980[/C][C]0.637445604019959[/C][C]0.68127719799002[/C][/ROW]
[ROW][C]57[/C][C]0.275855712367904[/C][C]0.551711424735809[/C][C]0.724144287632096[/C][/ROW]
[ROW][C]58[/C][C]0.247829977005935[/C][C]0.495659954011869[/C][C]0.752170022994066[/C][/ROW]
[ROW][C]59[/C][C]0.229544224760974[/C][C]0.459088449521947[/C][C]0.770455775239026[/C][/ROW]
[ROW][C]60[/C][C]0.264607003917427[/C][C]0.529214007834855[/C][C]0.735392996082573[/C][/ROW]
[ROW][C]61[/C][C]0.24812764027652[/C][C]0.49625528055304[/C][C]0.75187235972348[/C][/ROW]
[ROW][C]62[/C][C]0.214130181035499[/C][C]0.428260362070998[/C][C]0.785869818964501[/C][/ROW]
[ROW][C]63[/C][C]0.196907532926841[/C][C]0.393815065853682[/C][C]0.803092467073159[/C][/ROW]
[ROW][C]64[/C][C]0.217946681722193[/C][C]0.435893363444386[/C][C]0.782053318277807[/C][/ROW]
[ROW][C]65[/C][C]0.266979412065807[/C][C]0.533958824131614[/C][C]0.733020587934193[/C][/ROW]
[ROW][C]66[/C][C]0.275929127254054[/C][C]0.551858254508108[/C][C]0.724070872745946[/C][/ROW]
[ROW][C]67[/C][C]0.63776942013587[/C][C]0.72446115972826[/C][C]0.36223057986413[/C][/ROW]
[ROW][C]68[/C][C]0.621918172530883[/C][C]0.756163654938234[/C][C]0.378081827469117[/C][/ROW]
[ROW][C]69[/C][C]0.795195289203809[/C][C]0.409609421592383[/C][C]0.204804710796191[/C][/ROW]
[ROW][C]70[/C][C]0.767814750635933[/C][C]0.464370498728134[/C][C]0.232185249364067[/C][/ROW]
[ROW][C]71[/C][C]0.912753156491534[/C][C]0.174493687016932[/C][C]0.0872468435084658[/C][/ROW]
[ROW][C]72[/C][C]0.893727121804516[/C][C]0.212545756390967[/C][C]0.106272878195484[/C][/ROW]
[ROW][C]73[/C][C]0.924292609592878[/C][C]0.151414780814245[/C][C]0.0757073904071223[/C][/ROW]
[ROW][C]74[/C][C]0.912497020729957[/C][C]0.175005958540086[/C][C]0.0875029792700431[/C][/ROW]
[ROW][C]75[/C][C]0.916330968011296[/C][C]0.167338063977408[/C][C]0.0836690319887039[/C][/ROW]
[ROW][C]76[/C][C]0.910662472173308[/C][C]0.178675055653384[/C][C]0.089337527826692[/C][/ROW]
[ROW][C]77[/C][C]0.962094085816427[/C][C]0.0758118283671457[/C][C]0.0379059141835728[/C][/ROW]
[ROW][C]78[/C][C]0.952685243442333[/C][C]0.0946295131153332[/C][C]0.0473147565576666[/C][/ROW]
[ROW][C]79[/C][C]0.94347392980676[/C][C]0.113052140386480[/C][C]0.0565260701932398[/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]0.0624172759732972[/C][/ROW]
[ROW][C]83[/C][C]0.922053336251284[/C][C]0.155893327497433[/C][C]0.0779466637487164[/C][/ROW]
[ROW][C]84[/C][C]0.906574250002529[/C][C]0.186851499994942[/C][C]0.0934257499974709[/C][/ROW]
[ROW][C]85[/C][C]0.896763278206496[/C][C]0.206473443587007[/C][C]0.103236721793503[/C][/ROW]
[ROW][C]86[/C][C]0.878080050015044[/C][C]0.243839899969911[/C][C]0.121919949984956[/C][/ROW]
[ROW][C]87[/C][C]0.853375575666524[/C][C]0.293248848666953[/C][C]0.146624424333476[/C][/ROW]
[ROW][C]88[/C][C]0.905330192016902[/C][C]0.189339615966196[/C][C]0.0946698079830979[/C][/ROW]
[ROW][C]89[/C][C]0.886155588871522[/C][C]0.227688822256956[/C][C]0.113844411128478[/C][/ROW]
[ROW][C]90[/C][C]0.863647711730098[/C][C]0.272704576539804[/C][C]0.136352288269902[/C][/ROW]
[ROW][C]91[/C][C]0.865827478842622[/C][C]0.268345042314756[/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]0.829831700773176[/C][C]0.340336598453648[/C][C]0.170168299226824[/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]0.47262310671193[/C][C]0.236311553355965[/C][/ROW]
[ROW][C]96[/C][C]0.731055574998138[/C][C]0.537888850003723[/C][C]0.268944425001862[/C][/ROW]
[ROW][C]97[/C][C]0.75174192733153[/C][C]0.49651614533694[/C][C]0.24825807266847[/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]0.575530607136093[/C][C]0.287765303568047[/C][/ROW]
[ROW][C]100[/C][C]0.689744864923868[/C][C]0.620510270152265[/C][C]0.310255135076132[/C][/ROW]
[ROW][C]101[/C][C]0.649434922236285[/C][C]0.70113015552743[/C][C]0.350565077763715[/C][/ROW]
[ROW][C]102[/C][C]0.606634845790477[/C][C]0.786730308419046[/C][C]0.393365154209523[/C][/ROW]
[ROW][C]103[/C][C]0.56324421674873[/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]0.475880650474408[/C][C]0.951761300948816[/C][C]0.524119349525592[/C][/ROW]
[ROW][C]106[/C][C]0.463612141119946[/C][C]0.927224282239892[/C][C]0.536387858880054[/C][/ROW]
[ROW][C]107[/C][C]0.470779049273711[/C][C]0.941558098547422[/C][C]0.529220950726289[/C][/ROW]
[ROW][C]108[/C][C]0.509532807449635[/C][C]0.98093438510073[/C][C]0.490467192550365[/C][/ROW]
[ROW][C]109[/C][C]0.47097175676288[/C][C]0.94194351352576[/C][C]0.52902824323712[/C][/ROW]
[ROW][C]110[/C][C]0.428611691000273[/C][C]0.857223382000546[/C][C]0.571388308999727[/C][/ROW]
[ROW][C]111[/C][C]0.381451505416848[/C][C]0.762903010833696[/C][C]0.618548494583152[/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]0.822716200104193[/C][C]0.354567599791615[/C][C]0.177283799895807[/C][/ROW]
[ROW][C]117[/C][C]0.789598373374736[/C][C]0.420803253250528[/C][C]0.210401626625264[/C][/ROW]
[ROW][C]118[/C][C]0.752405016530784[/C][C]0.495189966938432[/C][C]0.247594983469216[/C][/ROW]
[ROW][C]119[/C][C]0.721776844050078[/C][C]0.556446311899844[/C][C]0.278223155949922[/C][/ROW]
[ROW][C]120[/C][C]0.773892573829069[/C][C]0.452214852341862[/C][C]0.226107426170931[/C][/ROW]
[ROW][C]121[/C][C]0.86094268064034[/C][C]0.278114638719321[/C][C]0.139057319359660[/C][/ROW]
[ROW][C]122[/C][C]0.83849371344511[/C][C]0.323012573109779[/C][C]0.161506286554890[/C][/ROW]
[ROW][C]123[/C][C]0.8144281484347[/C][C]0.3711437031306[/C][C]0.1855718515653[/C][/ROW]
[ROW][C]124[/C][C]0.77154929585405[/C][C]0.456901408291901[/C][C]0.228450704145950[/C][/ROW]
[ROW][C]125[/C][C]0.77469827761769[/C][C]0.450603444764621[/C][C]0.225301722382310[/C][/ROW]
[ROW][C]126[/C][C]0.725912210291747[/C][C]0.548175579416506[/C][C]0.274087789708253[/C][/ROW]
[ROW][C]127[/C][C]0.69067688627089[/C][C]0.618646227458221[/C][C]0.309323113729111[/C][/ROW]
[ROW][C]128[/C][C]0.639778003698588[/C][C]0.720443992602823[/C][C]0.360221996301412[/C][/ROW]
[ROW][C]129[/C][C]0.590736886156087[/C][C]0.818526227687827[/C][C]0.409263113843913[/C][/ROW]
[ROW][C]130[/C][C]0.718503215808757[/C][C]0.562993568382487[/C][C]0.281496784191244[/C][/ROW]
[ROW][C]131[/C][C]0.660323778713816[/C][C]0.679352442572369[/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]0.892562472077909[/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]0.504899838699719[/C][C]0.990200322600562[/C][C]0.495100161300281[/C][/ROW]
[ROW][C]136[/C][C]0.432400563387952[/C][C]0.864801126775904[/C][C]0.567599436612048[/C][/ROW]
[ROW][C]137[/C][C]0.383851090629990[/C][C]0.767702181259979[/C][C]0.61614890937001[/C][/ROW]
[ROW][C]138[/C][C]0.396248754615259[/C][C]0.792497509230519[/C][C]0.603751245384741[/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]0.226445586897251[/C][C]0.452891173794503[/C][C]0.773554413102749[/C][/ROW]
[ROW][C]144[/C][C]0.159698858380339[/C][C]0.319397716760677[/C][C]0.840301141619661[/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]0.338696614542827[/C][C]0.830651692728587[/C][/ROW]
[ROW][C]147[/C][C]0.160521283906014[/C][C]0.321042567812028[/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=98407&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98407&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.4829193016149590.9658386032299180.517080698385041
120.5540732393436450.891853521312710.445926760656355
130.7785712074528930.4428575850942130.221428792547107
140.8044013778971670.3911972442056660.195598622102833
150.7447735642805760.5104528714388470.255226435719424
160.664232544631890.671534910736220.33576745536811
170.6235277677461310.7529444645077390.376472232253869
180.5814159093338980.8371681813322030.418584090666102
190.5415638152832810.9168723694334380.458436184716719
200.5621504795336010.8756990409327970.437849520466399
210.4853487611294020.9706975222588040.514651238870598
220.4559249064379630.9118498128759260.544075093562037
230.4180293134236040.8360586268472080.581970686576396
240.5251318710594430.9497362578811140.474868128940557
250.4965945694728410.9931891389456820.503405430527159
260.5284303120479990.9431393759040010.471569687952001
270.4624564691639470.9249129383278940.537543530836053
280.3994328697227680.7988657394455360.600567130277232
290.3374991199397940.6749982398795890.662500880060206
300.3034494262526350.6068988525052690.696550573747365
310.2920688108971490.5841376217942970.707931189102851
320.4278905143310570.8557810286621140.572109485668943
330.3677709725805480.7355419451610960.632229027419452
340.3389216242501930.6778432485003860.661078375749807
350.3899119760466970.7798239520933940.610088023953303
360.354184460665440.708368921330880.64581553933456
370.4654268922738750.930853784547750.534573107726125
380.7615144023517240.4769711952965510.238485597648276
390.7480429223637580.5039141552724830.251957077636242
400.7114107689347490.5771784621305030.288589231065251
410.6764471981337660.6471056037324690.323552801866234
420.628165076844510.743669846310980.37183492315549
430.5764227236989070.8471545526021860.423577276301093
440.5640438355426710.8719123289146590.435956164457329
450.5471458311909280.9057083376181430.452854168809072
460.5791926855220740.8416146289558520.420807314477926
470.5354765355173430.9290469289653130.464523464482657
480.5259849118485100.9480301763029790.474015088151489
490.58621035398330.82757929203340.4137896460167
500.55732133154370.88535733691260.4426786684563
510.5264197363296410.9471605273407180.473580263670359
520.4766790272664290.9533580545328570.523320972733571
530.4330087212924440.8660174425848890.566991278707556
540.3882146891237590.7764293782475180.611785310876241
550.3473362447661960.6946724895323920.652663755233804
560.3187228020099800.6374456040199590.68127719799002
570.2758557123679040.5517114247358090.724144287632096
580.2478299770059350.4956599540118690.752170022994066
590.2295442247609740.4590884495219470.770455775239026
600.2646070039174270.5292140078348550.735392996082573
610.248127640276520.496255280553040.75187235972348
620.2141301810354990.4282603620709980.785869818964501
630.1969075329268410.3938150658536820.803092467073159
640.2179466817221930.4358933634443860.782053318277807
650.2669794120658070.5339588241316140.733020587934193
660.2759291272540540.5518582545081080.724070872745946
670.637769420135870.724461159728260.36223057986413
680.6219181725308830.7561636549382340.378081827469117
690.7951952892038090.4096094215923830.204804710796191
700.7678147506359330.4643704987281340.232185249364067
710.9127531564915340.1744936870169320.0872468435084658
720.8937271218045160.2125457563909670.106272878195484
730.9242926095928780.1514147808142450.0757073904071223
740.9124970207299570.1750059585400860.0875029792700431
750.9163309680112960.1673380639774080.0836690319887039
760.9106624721733080.1786750556533840.089337527826692
770.9620940858164270.07581182836714570.0379059141835728
780.9526852434423330.09462951311533320.0473147565576666
790.943473929806760.1130521403864800.0565260701932398
800.9479428525491880.1041142949016250.0520571474508123
810.9382652388085330.1234695223829350.0617347611914673
820.9375827240267030.1248345519465940.0624172759732972
830.9220533362512840.1558933274974330.0779466637487164
840.9065742500025290.1868514999949420.0934257499974709
850.8967632782064960.2064734435870070.103236721793503
860.8780800500150440.2438398999699110.121919949984956
870.8533755756665240.2932488486669530.146624424333476
880.9053301920169020.1893396159661960.0946698079830979
890.8861555888715220.2276888222569560.113844411128478
900.8636477117300980.2727045765398040.136352288269902
910.8658274788426220.2683450423147560.134172521157378
920.8543847808816980.2912304382366030.145615219118302
930.8298317007731760.3403365984536480.170168299226824
940.799553747405970.4008925051880610.200446252594031
950.7636884466440350.472623106711930.236311553355965
960.7310555749981380.5378888500037230.268944425001862
970.751741927331530.496516145336940.24825807266847
980.7493465068530560.5013069862938890.250653493146944
990.7122346964319530.5755306071360930.287765303568047
1000.6897448649238680.6205102701522650.310255135076132
1010.6494349222362850.701130155527430.350565077763715
1020.6066348457904770.7867303084190460.393365154209523
1030.563244216748730.8735115665025390.436755783251269
1040.5186541566685340.9626916866629320.481345843331466
1050.4758806504744080.9517613009488160.524119349525592
1060.4636121411199460.9272242822398920.536387858880054
1070.4707790492737110.9415580985474220.529220950726289
1080.5095328074496350.980934385100730.490467192550365
1090.470971756762880.941943513525760.52902824323712
1100.4286116910002730.8572233820005460.571388308999727
1110.3814515054168480.7629030108336960.618548494583152
1120.7112886403632380.5774227192735250.288711359636762
1130.7473975444481890.5052049111036220.252602455551811
1140.7200322666398490.5599354667203020.279967733360151
1150.8546521660591680.2906956678816640.145347833940832
1160.8227162001041930.3545675997916150.177283799895807
1170.7895983733747360.4208032532505280.210401626625264
1180.7524050165307840.4951899669384320.247594983469216
1190.7217768440500780.5564463118998440.278223155949922
1200.7738925738290690.4522148523418620.226107426170931
1210.860942680640340.2781146387193210.139057319359660
1220.838493713445110.3230125731097790.161506286554890
1230.81442814843470.37114370313060.1855718515653
1240.771549295854050.4569014082919010.228450704145950
1250.774698277617690.4506034447646210.225301722382310
1260.7259122102917470.5481755794165060.274087789708253
1270.690676886270890.6186462274582210.309323113729111
1280.6397780036985880.7204439926028230.360221996301412
1290.5907368861560870.8185262276878270.409263113843913
1300.7185032158087570.5629935683824870.281496784191244
1310.6603237787138160.6793524425723690.339676221286184
1320.5951269309861740.8097461380276510.404873069013826
1330.5537187639610460.8925624720779090.446281236038954
1340.4814457747577620.9628915495155250.518554225242238
1350.5048998386997190.9902003226005620.495100161300281
1360.4324005633879520.8648011267759040.567599436612048
1370.3838510906299900.7677021812599790.61614890937001
1380.3962487546152590.7924975092305190.603751245384741
1390.3237631621555350.647526324311070.676236837844465
1400.2544030009655450.508806001931090.745596999034455
1410.3440471799139950.688094359827990.655952820086005
1420.2945759068292850.589151813658570.705424093170715
1430.2264455868972510.4528911737945030.773554413102749
1440.1596988583803390.3193977167606770.840301141619661
1450.2399300775530420.4798601551060830.760069922446958
1460.1693483072714130.3386966145428270.830651692728587
1470.1605212839060140.3210425678120280.839478716093986
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=98407&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=98407&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98407&T=6

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


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

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


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 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')
}