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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, 18 Nov 2012 11:04:18 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/18/t1353254696nvcyn5fvawwi8ef.htm/, Retrieved Mon, 29 Apr 2024 18:03:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=190233, Retrieved Mon, 29 Apr 2024 18:03:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [WS7-1] [2012-11-18 12:56:01] [f4f48270c576c45d216b84daa061a85b]
- R PD  [Multiple Regression] [WS7-4] [2012-11-18 15:59:32] [f4f48270c576c45d216b84daa061a85b]
-   P       [Multiple Regression] [WS7-5] [2012-11-18 16:04:18] [78cbff3691fb0cc454e192ff02249329] [Current]
-   P         [Multiple Regression] [WS7-7] [2012-11-18 17:48:26] [f4f48270c576c45d216b84daa061a85b]
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Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time15 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 15 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190233&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]15 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190233&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190233&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 time15 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
I1[t] = + 11.5560841313819 -0.497696002799604month[t] + 0.365101287131251I2[t] + 0.253315811143267I3[t] + 0.257113786806836E1[t] -0.118527824591692E2[t] + 0.0444575023596896E3[t] -0.212687332769597A[t] + 0.00551734322717097t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
I1[t] =  +  11.5560841313819 -0.497696002799604month[t] +  0.365101287131251I2[t] +  0.253315811143267I3[t] +  0.257113786806836E1[t] -0.118527824591692E2[t] +  0.0444575023596896E3[t] -0.212687332769597A[t] +  0.00551734322717097t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190233&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]I1[t] =  +  11.5560841313819 -0.497696002799604month[t] +  0.365101287131251I2[t] +  0.253315811143267I3[t] +  0.257113786806836E1[t] -0.118527824591692E2[t] +  0.0444575023596896E3[t] -0.212687332769597A[t] +  0.00551734322717097t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190233&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190233&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
I1[t] = + 11.5560841313819 -0.497696002799604month[t] + 0.365101287131251I2[t] + 0.253315811143267I3[t] + 0.257113786806836E1[t] -0.118527824591692E2[t] + 0.0444575023596896E3[t] -0.212687332769597A[t] + 0.00551734322717097t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11.55608413138196.6180931.74610.0827950.041397
month-0.4976960027996040.739787-0.67280.5021170.251059
I20.3651012871312510.0635795.742500
I30.2533158111432670.0508464.9822e-061e-06
E10.2571137868068360.0754363.40840.0008350.000418
E2-0.1185278245916920.059202-2.00210.0470430.023522
E30.04445750235968960.0620720.71620.4749450.237472
A-0.2126873327695970.084646-2.51270.0130180.006509
t0.005517343227170970.0129720.42530.6711920.335596

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 11.5560841313819 & 6.618093 & 1.7461 & 0.082795 & 0.041397 \tabularnewline
month & -0.497696002799604 & 0.739787 & -0.6728 & 0.502117 & 0.251059 \tabularnewline
I2 & 0.365101287131251 & 0.063579 & 5.7425 & 0 & 0 \tabularnewline
I3 & 0.253315811143267 & 0.050846 & 4.982 & 2e-06 & 1e-06 \tabularnewline
E1 & 0.257113786806836 & 0.075436 & 3.4084 & 0.000835 & 0.000418 \tabularnewline
E2 & -0.118527824591692 & 0.059202 & -2.0021 & 0.047043 & 0.023522 \tabularnewline
E3 & 0.0444575023596896 & 0.062072 & 0.7162 & 0.474945 & 0.237472 \tabularnewline
A & -0.212687332769597 & 0.084646 & -2.5127 & 0.013018 & 0.006509 \tabularnewline
t & 0.00551734322717097 & 0.012972 & 0.4253 & 0.671192 & 0.335596 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190233&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]11.5560841313819[/C][C]6.618093[/C][C]1.7461[/C][C]0.082795[/C][C]0.041397[/C][/ROW]
[ROW][C]month[/C][C]-0.497696002799604[/C][C]0.739787[/C][C]-0.6728[/C][C]0.502117[/C][C]0.251059[/C][/ROW]
[ROW][C]I2[/C][C]0.365101287131251[/C][C]0.063579[/C][C]5.7425[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]I3[/C][C]0.253315811143267[/C][C]0.050846[/C][C]4.982[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]E1[/C][C]0.257113786806836[/C][C]0.075436[/C][C]3.4084[/C][C]0.000835[/C][C]0.000418[/C][/ROW]
[ROW][C]E2[/C][C]-0.118527824591692[/C][C]0.059202[/C][C]-2.0021[/C][C]0.047043[/C][C]0.023522[/C][/ROW]
[ROW][C]E3[/C][C]0.0444575023596896[/C][C]0.062072[/C][C]0.7162[/C][C]0.474945[/C][C]0.237472[/C][/ROW]
[ROW][C]A[/C][C]-0.212687332769597[/C][C]0.084646[/C][C]-2.5127[/C][C]0.013018[/C][C]0.006509[/C][/ROW]
[ROW][C]t[/C][C]0.00551734322717097[/C][C]0.012972[/C][C]0.4253[/C][C]0.671192[/C][C]0.335596[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190233&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190233&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11.55608413138196.6180931.74610.0827950.041397
month-0.4976960027996040.739787-0.67280.5021170.251059
I20.3651012871312510.0635795.742500
I30.2533158111432670.0508464.9822e-061e-06
E10.2571137868068360.0754363.40840.0008350.000418
E2-0.1185278245916920.059202-2.00210.0470430.023522
E30.04445750235968960.0620720.71620.4749450.237472
A-0.2126873327695970.084646-2.51270.0130180.006509
t0.005517343227170970.0129720.42530.6711920.335596






Multiple Linear Regression - Regression Statistics
Multiple R0.743360103298425
R-squared0.552584243175845
Adjusted R-squared0.529189955237328
F-TEST (value)23.6204771279247
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.50931737432343
Sum Squared Residuals963.391073817462

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.743360103298425 \tabularnewline
R-squared & 0.552584243175845 \tabularnewline
Adjusted R-squared & 0.529189955237328 \tabularnewline
F-TEST (value) & 23.6204771279247 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.50931737432343 \tabularnewline
Sum Squared Residuals & 963.391073817462 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190233&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.743360103298425[/C][/ROW]
[ROW][C]R-squared[/C][C]0.552584243175845[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.529189955237328[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]23.6204771279247[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.50931737432343[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]963.391073817462[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190233&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190233&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 R0.743360103298425
R-squared0.552584243175845
Adjusted R-squared0.529189955237328
F-TEST (value)23.6204771279247
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.50931737432343
Sum Squared Residuals963.391073817462






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12624.13951381487051.86048618512951
22020.9233711353096-0.923371135309556
31921.7260097096157-2.72600970961572
41918.34248687714050.657513122859483
52018.13820969157351.86179030842651
62524.07086305734130.929136942658667
72519.45435722112375.54564277887626
82220.09060200824761.90939799175243
92622.43874021405733.56125978594269
102220.47192496566421.5280750343358
111720.1647319498158-3.1647319498158
122223.0148619707581-1.01486197075807
131920.8512820771864-1.85128207718638
142422.5942258287741.40577417122601
152626.8460878711547-0.846087871154704
162118.97792039610722.02207960389279
171316.3284470157266-3.32844701572656
182627.3688785545613-1.36887855456135
192022.7434533086229-2.74345330862286
202218.84486447198163.15513552801838
211418.1269474631139-4.12694746311394
222120.56978320572170.430216794278327
23714.1148647132544-7.1148647132544
242321.74440622068691.25559377931312
251719.2083800205107-2.20838002051066
262523.12967571899141.87032428100862
272523.13519306221851.86480693778145
281916.32745272848032.67254727151971
292016.712630045533.28736995447001
302321.50645116187361.49354883812637
312224.273723490158-2.27372349015798
322222.0040593329911-0.00405933299105414
332118.02359223300482.97640776699525
341517.9073539033564-2.90735390335643
352016.1752475633883.82475243661199
362221.8204649200280.179535079971958
371816.77987014592651.22012985407346
382021.0125925116908-1.01259251169079
392827.82442217381580.175577826184152
402224.3109173929062-2.3109173929062
411821.9471081760189-3.94710817601891
422320.87341088618962.12658911381041
432021.8996390377716-1.89963903777158
442524.80519090222470.194809097775326
452622.39667088368243.60332911631761
461514.6644332663050.335566733694969
471718.0126182793631-1.01261827936314
482315.62742556666587.37257443333422
492122.2283891135347-1.22838911353466
501316.6254954158953-3.62549541589527
511820.4042014898089-2.40420148980889
521919.4707860685076-0.470786068507584
532222.2518126511531-0.251812651153068
541617.5350265059378-1.53502650593781
552423.01298674140070.98701325859934
561819.7608690571955-1.7608690571955
572021.2599910888394-1.25999108883945
582420.62369585466713.37630414533289
591418.2374622618237-4.23746226182372
602220.2906654347911.70933456520903
612418.02841362069155.97158637930855
621818.4487731511957-0.448773151195726
632124.4634232807322-3.46342328073218
642321.53870453463191.46129546536809
651718.3705409129728-1.37054091297282
662222.6142331133307-0.614233113330655
672424.9077156113732-0.907715611373193
682122.8902290633938-1.89022906339384
692222.0431811632106-0.0431811632105598
701617.0205548699761-1.02055486997611
712125.1573022994668-4.15730229946679
722323.9307196418191-0.930719641819119
732219.39940102892192.60059897107805
742422.53522082281681.46477917718319
752424.7668832100165-0.766883210016453
761617.5056565405801-1.50565654058009
771617.4911157461838-1.49111574618381
782122.9830497322064-1.98304973220639
792626.0707545896182-0.0707545896181977
801517.2283887374184-2.22838873741835
812523.21191160207241.7880883979276
821818.0732626159408-0.0732626159407618
832320.94458660902072.05541339097927
842021.3319446608009-1.33194466080092
851720.8189650412942-3.81896504129415
862525.042051141019-0.0420511410189786
872422.62847390089121.37152609910881
881714.74738752616272.25261247383726
891918.79738843019510.202611569804868
902020.4006191741632-0.400619174163186
911516.4986162348839-1.49861623488389
922724.6933242376272.30667576237297
932219.63060892233282.36939107766716
942322.87983813044350.120161869556466
951620.8597486046703-4.85974860467028
961921.1467790994405-2.14677909944052
972520.59461360857774.40538639142234
981919.751881707755-0.751881707754952
991919.5141424093018-0.514142409301816
1002625.47631316847470.523686831525268
1012119.01573313982451.98426686017553
1022020.0116583506254-0.0116583506253738
1032420.18960353858843.81039646141164
1042224.162665420277-2.16266542027696
1052021.296300085898-1.29630008589803
1061819.3160922473178-1.31609224731779
1071817.07261538227870.927384617721281
1082421.35080612017332.64919387982671
1092421.1087543177882.89124568221204
1102223.2349137148951-1.23491371489512
1112319.59628335320023.40371664679983
1122219.70117555508382.29882444491625
1132017.98587374198472.01412625801526
1141819.4814235020668-1.48142350206677
1152524.42967357976850.570326420231549
1161818.3156422616838-0.315642261683841
1171617.5190470792768-1.51904707927681
1182019.78985958685060.210140413149402
1191918.51190614461690.4880938553831
1201515.6512840662819-0.651284066281912
1211918.8483356274160.151664372584023
1221921.8888750929243-2.88887509292427
1231617.4430809305523-1.44308093055227
1241717.8439169425446-0.843916942544588
1252823.4148695138854.58513048611505
1262322.98446638874190.0155336112580874
1272523.71161751761571.28838248238435
1282018.78128018788031.2187198121197
1291720.1378309997725-3.13783099977252
1302322.67858800314080.321411996859236
1311620.0833478043604-4.0833478043604
1322323.3381978846261-0.338197884626104
1331116.1171551560373-5.11715515603727
1341820.1644814958978-2.16448149589775
1352423.1698098154560.83019018454404
1362318.79824599955144.2017540004486
1372120.65769320464330.342306795356695
1381619.5015933777544-3.50159337775435
1392424.8330626444964-0.83306264449635
1402321.68624163858531.31375836141472
1411815.94924107015922.05075892984082
1422021.0334965527172-1.03349655271724
143918.6019682030411-9.60196820304113
1442420.45347447843843.54652552156164
1452524.87763246884260.122367531157399
1462018.46135952957171.5386404704283
1472119.15270633580931.8472936641907
1482522.91884341188382.08115658811617
1492222.5541267347884-0.55412673478844
1502120.8538207886110.146179211389038
1512120.38819415711430.611805842885747
1522220.20206794721371.79793205278631
1532723.55678874546263.44321125453739
1542421.76959307762782.23040692237222
1552423.90377253333790.0962274666620831
1562120.09037209609640.909627903903578
1571820.6636869370691-2.66368693706914
1581616.2411928229045-0.241192822904468
1592219.49705757252652.50294242747348
1602020.292678420505-0.292678420505025
1611819.0617214619872-1.06172146198716
1622021.0487459152517-1.04874591525171

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 26 & 24.1395138148705 & 1.86048618512951 \tabularnewline
2 & 20 & 20.9233711353096 & -0.923371135309556 \tabularnewline
3 & 19 & 21.7260097096157 & -2.72600970961572 \tabularnewline
4 & 19 & 18.3424868771405 & 0.657513122859483 \tabularnewline
5 & 20 & 18.1382096915735 & 1.86179030842651 \tabularnewline
6 & 25 & 24.0708630573413 & 0.929136942658667 \tabularnewline
7 & 25 & 19.4543572211237 & 5.54564277887626 \tabularnewline
8 & 22 & 20.0906020082476 & 1.90939799175243 \tabularnewline
9 & 26 & 22.4387402140573 & 3.56125978594269 \tabularnewline
10 & 22 & 20.4719249656642 & 1.5280750343358 \tabularnewline
11 & 17 & 20.1647319498158 & -3.1647319498158 \tabularnewline
12 & 22 & 23.0148619707581 & -1.01486197075807 \tabularnewline
13 & 19 & 20.8512820771864 & -1.85128207718638 \tabularnewline
14 & 24 & 22.594225828774 & 1.40577417122601 \tabularnewline
15 & 26 & 26.8460878711547 & -0.846087871154704 \tabularnewline
16 & 21 & 18.9779203961072 & 2.02207960389279 \tabularnewline
17 & 13 & 16.3284470157266 & -3.32844701572656 \tabularnewline
18 & 26 & 27.3688785545613 & -1.36887855456135 \tabularnewline
19 & 20 & 22.7434533086229 & -2.74345330862286 \tabularnewline
20 & 22 & 18.8448644719816 & 3.15513552801838 \tabularnewline
21 & 14 & 18.1269474631139 & -4.12694746311394 \tabularnewline
22 & 21 & 20.5697832057217 & 0.430216794278327 \tabularnewline
23 & 7 & 14.1148647132544 & -7.1148647132544 \tabularnewline
24 & 23 & 21.7444062206869 & 1.25559377931312 \tabularnewline
25 & 17 & 19.2083800205107 & -2.20838002051066 \tabularnewline
26 & 25 & 23.1296757189914 & 1.87032428100862 \tabularnewline
27 & 25 & 23.1351930622185 & 1.86480693778145 \tabularnewline
28 & 19 & 16.3274527284803 & 2.67254727151971 \tabularnewline
29 & 20 & 16.71263004553 & 3.28736995447001 \tabularnewline
30 & 23 & 21.5064511618736 & 1.49354883812637 \tabularnewline
31 & 22 & 24.273723490158 & -2.27372349015798 \tabularnewline
32 & 22 & 22.0040593329911 & -0.00405933299105414 \tabularnewline
33 & 21 & 18.0235922330048 & 2.97640776699525 \tabularnewline
34 & 15 & 17.9073539033564 & -2.90735390335643 \tabularnewline
35 & 20 & 16.175247563388 & 3.82475243661199 \tabularnewline
36 & 22 & 21.820464920028 & 0.179535079971958 \tabularnewline
37 & 18 & 16.7798701459265 & 1.22012985407346 \tabularnewline
38 & 20 & 21.0125925116908 & -1.01259251169079 \tabularnewline
39 & 28 & 27.8244221738158 & 0.175577826184152 \tabularnewline
40 & 22 & 24.3109173929062 & -2.3109173929062 \tabularnewline
41 & 18 & 21.9471081760189 & -3.94710817601891 \tabularnewline
42 & 23 & 20.8734108861896 & 2.12658911381041 \tabularnewline
43 & 20 & 21.8996390377716 & -1.89963903777158 \tabularnewline
44 & 25 & 24.8051909022247 & 0.194809097775326 \tabularnewline
45 & 26 & 22.3966708836824 & 3.60332911631761 \tabularnewline
46 & 15 & 14.664433266305 & 0.335566733694969 \tabularnewline
47 & 17 & 18.0126182793631 & -1.01261827936314 \tabularnewline
48 & 23 & 15.6274255666658 & 7.37257443333422 \tabularnewline
49 & 21 & 22.2283891135347 & -1.22838911353466 \tabularnewline
50 & 13 & 16.6254954158953 & -3.62549541589527 \tabularnewline
51 & 18 & 20.4042014898089 & -2.40420148980889 \tabularnewline
52 & 19 & 19.4707860685076 & -0.470786068507584 \tabularnewline
53 & 22 & 22.2518126511531 & -0.251812651153068 \tabularnewline
54 & 16 & 17.5350265059378 & -1.53502650593781 \tabularnewline
55 & 24 & 23.0129867414007 & 0.98701325859934 \tabularnewline
56 & 18 & 19.7608690571955 & -1.7608690571955 \tabularnewline
57 & 20 & 21.2599910888394 & -1.25999108883945 \tabularnewline
58 & 24 & 20.6236958546671 & 3.37630414533289 \tabularnewline
59 & 14 & 18.2374622618237 & -4.23746226182372 \tabularnewline
60 & 22 & 20.290665434791 & 1.70933456520903 \tabularnewline
61 & 24 & 18.0284136206915 & 5.97158637930855 \tabularnewline
62 & 18 & 18.4487731511957 & -0.448773151195726 \tabularnewline
63 & 21 & 24.4634232807322 & -3.46342328073218 \tabularnewline
64 & 23 & 21.5387045346319 & 1.46129546536809 \tabularnewline
65 & 17 & 18.3705409129728 & -1.37054091297282 \tabularnewline
66 & 22 & 22.6142331133307 & -0.614233113330655 \tabularnewline
67 & 24 & 24.9077156113732 & -0.907715611373193 \tabularnewline
68 & 21 & 22.8902290633938 & -1.89022906339384 \tabularnewline
69 & 22 & 22.0431811632106 & -0.0431811632105598 \tabularnewline
70 & 16 & 17.0205548699761 & -1.02055486997611 \tabularnewline
71 & 21 & 25.1573022994668 & -4.15730229946679 \tabularnewline
72 & 23 & 23.9307196418191 & -0.930719641819119 \tabularnewline
73 & 22 & 19.3994010289219 & 2.60059897107805 \tabularnewline
74 & 24 & 22.5352208228168 & 1.46477917718319 \tabularnewline
75 & 24 & 24.7668832100165 & -0.766883210016453 \tabularnewline
76 & 16 & 17.5056565405801 & -1.50565654058009 \tabularnewline
77 & 16 & 17.4911157461838 & -1.49111574618381 \tabularnewline
78 & 21 & 22.9830497322064 & -1.98304973220639 \tabularnewline
79 & 26 & 26.0707545896182 & -0.0707545896181977 \tabularnewline
80 & 15 & 17.2283887374184 & -2.22838873741835 \tabularnewline
81 & 25 & 23.2119116020724 & 1.7880883979276 \tabularnewline
82 & 18 & 18.0732626159408 & -0.0732626159407618 \tabularnewline
83 & 23 & 20.9445866090207 & 2.05541339097927 \tabularnewline
84 & 20 & 21.3319446608009 & -1.33194466080092 \tabularnewline
85 & 17 & 20.8189650412942 & -3.81896504129415 \tabularnewline
86 & 25 & 25.042051141019 & -0.0420511410189786 \tabularnewline
87 & 24 & 22.6284739008912 & 1.37152609910881 \tabularnewline
88 & 17 & 14.7473875261627 & 2.25261247383726 \tabularnewline
89 & 19 & 18.7973884301951 & 0.202611569804868 \tabularnewline
90 & 20 & 20.4006191741632 & -0.400619174163186 \tabularnewline
91 & 15 & 16.4986162348839 & -1.49861623488389 \tabularnewline
92 & 27 & 24.693324237627 & 2.30667576237297 \tabularnewline
93 & 22 & 19.6306089223328 & 2.36939107766716 \tabularnewline
94 & 23 & 22.8798381304435 & 0.120161869556466 \tabularnewline
95 & 16 & 20.8597486046703 & -4.85974860467028 \tabularnewline
96 & 19 & 21.1467790994405 & -2.14677909944052 \tabularnewline
97 & 25 & 20.5946136085777 & 4.40538639142234 \tabularnewline
98 & 19 & 19.751881707755 & -0.751881707754952 \tabularnewline
99 & 19 & 19.5141424093018 & -0.514142409301816 \tabularnewline
100 & 26 & 25.4763131684747 & 0.523686831525268 \tabularnewline
101 & 21 & 19.0157331398245 & 1.98426686017553 \tabularnewline
102 & 20 & 20.0116583506254 & -0.0116583506253738 \tabularnewline
103 & 24 & 20.1896035385884 & 3.81039646141164 \tabularnewline
104 & 22 & 24.162665420277 & -2.16266542027696 \tabularnewline
105 & 20 & 21.296300085898 & -1.29630008589803 \tabularnewline
106 & 18 & 19.3160922473178 & -1.31609224731779 \tabularnewline
107 & 18 & 17.0726153822787 & 0.927384617721281 \tabularnewline
108 & 24 & 21.3508061201733 & 2.64919387982671 \tabularnewline
109 & 24 & 21.108754317788 & 2.89124568221204 \tabularnewline
110 & 22 & 23.2349137148951 & -1.23491371489512 \tabularnewline
111 & 23 & 19.5962833532002 & 3.40371664679983 \tabularnewline
112 & 22 & 19.7011755550838 & 2.29882444491625 \tabularnewline
113 & 20 & 17.9858737419847 & 2.01412625801526 \tabularnewline
114 & 18 & 19.4814235020668 & -1.48142350206677 \tabularnewline
115 & 25 & 24.4296735797685 & 0.570326420231549 \tabularnewline
116 & 18 & 18.3156422616838 & -0.315642261683841 \tabularnewline
117 & 16 & 17.5190470792768 & -1.51904707927681 \tabularnewline
118 & 20 & 19.7898595868506 & 0.210140413149402 \tabularnewline
119 & 19 & 18.5119061446169 & 0.4880938553831 \tabularnewline
120 & 15 & 15.6512840662819 & -0.651284066281912 \tabularnewline
121 & 19 & 18.848335627416 & 0.151664372584023 \tabularnewline
122 & 19 & 21.8888750929243 & -2.88887509292427 \tabularnewline
123 & 16 & 17.4430809305523 & -1.44308093055227 \tabularnewline
124 & 17 & 17.8439169425446 & -0.843916942544588 \tabularnewline
125 & 28 & 23.414869513885 & 4.58513048611505 \tabularnewline
126 & 23 & 22.9844663887419 & 0.0155336112580874 \tabularnewline
127 & 25 & 23.7116175176157 & 1.28838248238435 \tabularnewline
128 & 20 & 18.7812801878803 & 1.2187198121197 \tabularnewline
129 & 17 & 20.1378309997725 & -3.13783099977252 \tabularnewline
130 & 23 & 22.6785880031408 & 0.321411996859236 \tabularnewline
131 & 16 & 20.0833478043604 & -4.0833478043604 \tabularnewline
132 & 23 & 23.3381978846261 & -0.338197884626104 \tabularnewline
133 & 11 & 16.1171551560373 & -5.11715515603727 \tabularnewline
134 & 18 & 20.1644814958978 & -2.16448149589775 \tabularnewline
135 & 24 & 23.169809815456 & 0.83019018454404 \tabularnewline
136 & 23 & 18.7982459995514 & 4.2017540004486 \tabularnewline
137 & 21 & 20.6576932046433 & 0.342306795356695 \tabularnewline
138 & 16 & 19.5015933777544 & -3.50159337775435 \tabularnewline
139 & 24 & 24.8330626444964 & -0.83306264449635 \tabularnewline
140 & 23 & 21.6862416385853 & 1.31375836141472 \tabularnewline
141 & 18 & 15.9492410701592 & 2.05075892984082 \tabularnewline
142 & 20 & 21.0334965527172 & -1.03349655271724 \tabularnewline
143 & 9 & 18.6019682030411 & -9.60196820304113 \tabularnewline
144 & 24 & 20.4534744784384 & 3.54652552156164 \tabularnewline
145 & 25 & 24.8776324688426 & 0.122367531157399 \tabularnewline
146 & 20 & 18.4613595295717 & 1.5386404704283 \tabularnewline
147 & 21 & 19.1527063358093 & 1.8472936641907 \tabularnewline
148 & 25 & 22.9188434118838 & 2.08115658811617 \tabularnewline
149 & 22 & 22.5541267347884 & -0.55412673478844 \tabularnewline
150 & 21 & 20.853820788611 & 0.146179211389038 \tabularnewline
151 & 21 & 20.3881941571143 & 0.611805842885747 \tabularnewline
152 & 22 & 20.2020679472137 & 1.79793205278631 \tabularnewline
153 & 27 & 23.5567887454626 & 3.44321125453739 \tabularnewline
154 & 24 & 21.7695930776278 & 2.23040692237222 \tabularnewline
155 & 24 & 23.9037725333379 & 0.0962274666620831 \tabularnewline
156 & 21 & 20.0903720960964 & 0.909627903903578 \tabularnewline
157 & 18 & 20.6636869370691 & -2.66368693706914 \tabularnewline
158 & 16 & 16.2411928229045 & -0.241192822904468 \tabularnewline
159 & 22 & 19.4970575725265 & 2.50294242747348 \tabularnewline
160 & 20 & 20.292678420505 & -0.292678420505025 \tabularnewline
161 & 18 & 19.0617214619872 & -1.06172146198716 \tabularnewline
162 & 20 & 21.0487459152517 & -1.04874591525171 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190233&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]26[/C][C]24.1395138148705[/C][C]1.86048618512951[/C][/ROW]
[ROW][C]2[/C][C]20[/C][C]20.9233711353096[/C][C]-0.923371135309556[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]21.7260097096157[/C][C]-2.72600970961572[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]18.3424868771405[/C][C]0.657513122859483[/C][/ROW]
[ROW][C]5[/C][C]20[/C][C]18.1382096915735[/C][C]1.86179030842651[/C][/ROW]
[ROW][C]6[/C][C]25[/C][C]24.0708630573413[/C][C]0.929136942658667[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]19.4543572211237[/C][C]5.54564277887626[/C][/ROW]
[ROW][C]8[/C][C]22[/C][C]20.0906020082476[/C][C]1.90939799175243[/C][/ROW]
[ROW][C]9[/C][C]26[/C][C]22.4387402140573[/C][C]3.56125978594269[/C][/ROW]
[ROW][C]10[/C][C]22[/C][C]20.4719249656642[/C][C]1.5280750343358[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]20.1647319498158[/C][C]-3.1647319498158[/C][/ROW]
[ROW][C]12[/C][C]22[/C][C]23.0148619707581[/C][C]-1.01486197075807[/C][/ROW]
[ROW][C]13[/C][C]19[/C][C]20.8512820771864[/C][C]-1.85128207718638[/C][/ROW]
[ROW][C]14[/C][C]24[/C][C]22.594225828774[/C][C]1.40577417122601[/C][/ROW]
[ROW][C]15[/C][C]26[/C][C]26.8460878711547[/C][C]-0.846087871154704[/C][/ROW]
[ROW][C]16[/C][C]21[/C][C]18.9779203961072[/C][C]2.02207960389279[/C][/ROW]
[ROW][C]17[/C][C]13[/C][C]16.3284470157266[/C][C]-3.32844701572656[/C][/ROW]
[ROW][C]18[/C][C]26[/C][C]27.3688785545613[/C][C]-1.36887855456135[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]22.7434533086229[/C][C]-2.74345330862286[/C][/ROW]
[ROW][C]20[/C][C]22[/C][C]18.8448644719816[/C][C]3.15513552801838[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]18.1269474631139[/C][C]-4.12694746311394[/C][/ROW]
[ROW][C]22[/C][C]21[/C][C]20.5697832057217[/C][C]0.430216794278327[/C][/ROW]
[ROW][C]23[/C][C]7[/C][C]14.1148647132544[/C][C]-7.1148647132544[/C][/ROW]
[ROW][C]24[/C][C]23[/C][C]21.7444062206869[/C][C]1.25559377931312[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]19.2083800205107[/C][C]-2.20838002051066[/C][/ROW]
[ROW][C]26[/C][C]25[/C][C]23.1296757189914[/C][C]1.87032428100862[/C][/ROW]
[ROW][C]27[/C][C]25[/C][C]23.1351930622185[/C][C]1.86480693778145[/C][/ROW]
[ROW][C]28[/C][C]19[/C][C]16.3274527284803[/C][C]2.67254727151971[/C][/ROW]
[ROW][C]29[/C][C]20[/C][C]16.71263004553[/C][C]3.28736995447001[/C][/ROW]
[ROW][C]30[/C][C]23[/C][C]21.5064511618736[/C][C]1.49354883812637[/C][/ROW]
[ROW][C]31[/C][C]22[/C][C]24.273723490158[/C][C]-2.27372349015798[/C][/ROW]
[ROW][C]32[/C][C]22[/C][C]22.0040593329911[/C][C]-0.00405933299105414[/C][/ROW]
[ROW][C]33[/C][C]21[/C][C]18.0235922330048[/C][C]2.97640776699525[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]17.9073539033564[/C][C]-2.90735390335643[/C][/ROW]
[ROW][C]35[/C][C]20[/C][C]16.175247563388[/C][C]3.82475243661199[/C][/ROW]
[ROW][C]36[/C][C]22[/C][C]21.820464920028[/C][C]0.179535079971958[/C][/ROW]
[ROW][C]37[/C][C]18[/C][C]16.7798701459265[/C][C]1.22012985407346[/C][/ROW]
[ROW][C]38[/C][C]20[/C][C]21.0125925116908[/C][C]-1.01259251169079[/C][/ROW]
[ROW][C]39[/C][C]28[/C][C]27.8244221738158[/C][C]0.175577826184152[/C][/ROW]
[ROW][C]40[/C][C]22[/C][C]24.3109173929062[/C][C]-2.3109173929062[/C][/ROW]
[ROW][C]41[/C][C]18[/C][C]21.9471081760189[/C][C]-3.94710817601891[/C][/ROW]
[ROW][C]42[/C][C]23[/C][C]20.8734108861896[/C][C]2.12658911381041[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]21.8996390377716[/C][C]-1.89963903777158[/C][/ROW]
[ROW][C]44[/C][C]25[/C][C]24.8051909022247[/C][C]0.194809097775326[/C][/ROW]
[ROW][C]45[/C][C]26[/C][C]22.3966708836824[/C][C]3.60332911631761[/C][/ROW]
[ROW][C]46[/C][C]15[/C][C]14.664433266305[/C][C]0.335566733694969[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]18.0126182793631[/C][C]-1.01261827936314[/C][/ROW]
[ROW][C]48[/C][C]23[/C][C]15.6274255666658[/C][C]7.37257443333422[/C][/ROW]
[ROW][C]49[/C][C]21[/C][C]22.2283891135347[/C][C]-1.22838911353466[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]16.6254954158953[/C][C]-3.62549541589527[/C][/ROW]
[ROW][C]51[/C][C]18[/C][C]20.4042014898089[/C][C]-2.40420148980889[/C][/ROW]
[ROW][C]52[/C][C]19[/C][C]19.4707860685076[/C][C]-0.470786068507584[/C][/ROW]
[ROW][C]53[/C][C]22[/C][C]22.2518126511531[/C][C]-0.251812651153068[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]17.5350265059378[/C][C]-1.53502650593781[/C][/ROW]
[ROW][C]55[/C][C]24[/C][C]23.0129867414007[/C][C]0.98701325859934[/C][/ROW]
[ROW][C]56[/C][C]18[/C][C]19.7608690571955[/C][C]-1.7608690571955[/C][/ROW]
[ROW][C]57[/C][C]20[/C][C]21.2599910888394[/C][C]-1.25999108883945[/C][/ROW]
[ROW][C]58[/C][C]24[/C][C]20.6236958546671[/C][C]3.37630414533289[/C][/ROW]
[ROW][C]59[/C][C]14[/C][C]18.2374622618237[/C][C]-4.23746226182372[/C][/ROW]
[ROW][C]60[/C][C]22[/C][C]20.290665434791[/C][C]1.70933456520903[/C][/ROW]
[ROW][C]61[/C][C]24[/C][C]18.0284136206915[/C][C]5.97158637930855[/C][/ROW]
[ROW][C]62[/C][C]18[/C][C]18.4487731511957[/C][C]-0.448773151195726[/C][/ROW]
[ROW][C]63[/C][C]21[/C][C]24.4634232807322[/C][C]-3.46342328073218[/C][/ROW]
[ROW][C]64[/C][C]23[/C][C]21.5387045346319[/C][C]1.46129546536809[/C][/ROW]
[ROW][C]65[/C][C]17[/C][C]18.3705409129728[/C][C]-1.37054091297282[/C][/ROW]
[ROW][C]66[/C][C]22[/C][C]22.6142331133307[/C][C]-0.614233113330655[/C][/ROW]
[ROW][C]67[/C][C]24[/C][C]24.9077156113732[/C][C]-0.907715611373193[/C][/ROW]
[ROW][C]68[/C][C]21[/C][C]22.8902290633938[/C][C]-1.89022906339384[/C][/ROW]
[ROW][C]69[/C][C]22[/C][C]22.0431811632106[/C][C]-0.0431811632105598[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]17.0205548699761[/C][C]-1.02055486997611[/C][/ROW]
[ROW][C]71[/C][C]21[/C][C]25.1573022994668[/C][C]-4.15730229946679[/C][/ROW]
[ROW][C]72[/C][C]23[/C][C]23.9307196418191[/C][C]-0.930719641819119[/C][/ROW]
[ROW][C]73[/C][C]22[/C][C]19.3994010289219[/C][C]2.60059897107805[/C][/ROW]
[ROW][C]74[/C][C]24[/C][C]22.5352208228168[/C][C]1.46477917718319[/C][/ROW]
[ROW][C]75[/C][C]24[/C][C]24.7668832100165[/C][C]-0.766883210016453[/C][/ROW]
[ROW][C]76[/C][C]16[/C][C]17.5056565405801[/C][C]-1.50565654058009[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]17.4911157461838[/C][C]-1.49111574618381[/C][/ROW]
[ROW][C]78[/C][C]21[/C][C]22.9830497322064[/C][C]-1.98304973220639[/C][/ROW]
[ROW][C]79[/C][C]26[/C][C]26.0707545896182[/C][C]-0.0707545896181977[/C][/ROW]
[ROW][C]80[/C][C]15[/C][C]17.2283887374184[/C][C]-2.22838873741835[/C][/ROW]
[ROW][C]81[/C][C]25[/C][C]23.2119116020724[/C][C]1.7880883979276[/C][/ROW]
[ROW][C]82[/C][C]18[/C][C]18.0732626159408[/C][C]-0.0732626159407618[/C][/ROW]
[ROW][C]83[/C][C]23[/C][C]20.9445866090207[/C][C]2.05541339097927[/C][/ROW]
[ROW][C]84[/C][C]20[/C][C]21.3319446608009[/C][C]-1.33194466080092[/C][/ROW]
[ROW][C]85[/C][C]17[/C][C]20.8189650412942[/C][C]-3.81896504129415[/C][/ROW]
[ROW][C]86[/C][C]25[/C][C]25.042051141019[/C][C]-0.0420511410189786[/C][/ROW]
[ROW][C]87[/C][C]24[/C][C]22.6284739008912[/C][C]1.37152609910881[/C][/ROW]
[ROW][C]88[/C][C]17[/C][C]14.7473875261627[/C][C]2.25261247383726[/C][/ROW]
[ROW][C]89[/C][C]19[/C][C]18.7973884301951[/C][C]0.202611569804868[/C][/ROW]
[ROW][C]90[/C][C]20[/C][C]20.4006191741632[/C][C]-0.400619174163186[/C][/ROW]
[ROW][C]91[/C][C]15[/C][C]16.4986162348839[/C][C]-1.49861623488389[/C][/ROW]
[ROW][C]92[/C][C]27[/C][C]24.693324237627[/C][C]2.30667576237297[/C][/ROW]
[ROW][C]93[/C][C]22[/C][C]19.6306089223328[/C][C]2.36939107766716[/C][/ROW]
[ROW][C]94[/C][C]23[/C][C]22.8798381304435[/C][C]0.120161869556466[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]20.8597486046703[/C][C]-4.85974860467028[/C][/ROW]
[ROW][C]96[/C][C]19[/C][C]21.1467790994405[/C][C]-2.14677909944052[/C][/ROW]
[ROW][C]97[/C][C]25[/C][C]20.5946136085777[/C][C]4.40538639142234[/C][/ROW]
[ROW][C]98[/C][C]19[/C][C]19.751881707755[/C][C]-0.751881707754952[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]19.5141424093018[/C][C]-0.514142409301816[/C][/ROW]
[ROW][C]100[/C][C]26[/C][C]25.4763131684747[/C][C]0.523686831525268[/C][/ROW]
[ROW][C]101[/C][C]21[/C][C]19.0157331398245[/C][C]1.98426686017553[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]20.0116583506254[/C][C]-0.0116583506253738[/C][/ROW]
[ROW][C]103[/C][C]24[/C][C]20.1896035385884[/C][C]3.81039646141164[/C][/ROW]
[ROW][C]104[/C][C]22[/C][C]24.162665420277[/C][C]-2.16266542027696[/C][/ROW]
[ROW][C]105[/C][C]20[/C][C]21.296300085898[/C][C]-1.29630008589803[/C][/ROW]
[ROW][C]106[/C][C]18[/C][C]19.3160922473178[/C][C]-1.31609224731779[/C][/ROW]
[ROW][C]107[/C][C]18[/C][C]17.0726153822787[/C][C]0.927384617721281[/C][/ROW]
[ROW][C]108[/C][C]24[/C][C]21.3508061201733[/C][C]2.64919387982671[/C][/ROW]
[ROW][C]109[/C][C]24[/C][C]21.108754317788[/C][C]2.89124568221204[/C][/ROW]
[ROW][C]110[/C][C]22[/C][C]23.2349137148951[/C][C]-1.23491371489512[/C][/ROW]
[ROW][C]111[/C][C]23[/C][C]19.5962833532002[/C][C]3.40371664679983[/C][/ROW]
[ROW][C]112[/C][C]22[/C][C]19.7011755550838[/C][C]2.29882444491625[/C][/ROW]
[ROW][C]113[/C][C]20[/C][C]17.9858737419847[/C][C]2.01412625801526[/C][/ROW]
[ROW][C]114[/C][C]18[/C][C]19.4814235020668[/C][C]-1.48142350206677[/C][/ROW]
[ROW][C]115[/C][C]25[/C][C]24.4296735797685[/C][C]0.570326420231549[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]18.3156422616838[/C][C]-0.315642261683841[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]17.5190470792768[/C][C]-1.51904707927681[/C][/ROW]
[ROW][C]118[/C][C]20[/C][C]19.7898595868506[/C][C]0.210140413149402[/C][/ROW]
[ROW][C]119[/C][C]19[/C][C]18.5119061446169[/C][C]0.4880938553831[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]15.6512840662819[/C][C]-0.651284066281912[/C][/ROW]
[ROW][C]121[/C][C]19[/C][C]18.848335627416[/C][C]0.151664372584023[/C][/ROW]
[ROW][C]122[/C][C]19[/C][C]21.8888750929243[/C][C]-2.88887509292427[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]17.4430809305523[/C][C]-1.44308093055227[/C][/ROW]
[ROW][C]124[/C][C]17[/C][C]17.8439169425446[/C][C]-0.843916942544588[/C][/ROW]
[ROW][C]125[/C][C]28[/C][C]23.414869513885[/C][C]4.58513048611505[/C][/ROW]
[ROW][C]126[/C][C]23[/C][C]22.9844663887419[/C][C]0.0155336112580874[/C][/ROW]
[ROW][C]127[/C][C]25[/C][C]23.7116175176157[/C][C]1.28838248238435[/C][/ROW]
[ROW][C]128[/C][C]20[/C][C]18.7812801878803[/C][C]1.2187198121197[/C][/ROW]
[ROW][C]129[/C][C]17[/C][C]20.1378309997725[/C][C]-3.13783099977252[/C][/ROW]
[ROW][C]130[/C][C]23[/C][C]22.6785880031408[/C][C]0.321411996859236[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]20.0833478043604[/C][C]-4.0833478043604[/C][/ROW]
[ROW][C]132[/C][C]23[/C][C]23.3381978846261[/C][C]-0.338197884626104[/C][/ROW]
[ROW][C]133[/C][C]11[/C][C]16.1171551560373[/C][C]-5.11715515603727[/C][/ROW]
[ROW][C]134[/C][C]18[/C][C]20.1644814958978[/C][C]-2.16448149589775[/C][/ROW]
[ROW][C]135[/C][C]24[/C][C]23.169809815456[/C][C]0.83019018454404[/C][/ROW]
[ROW][C]136[/C][C]23[/C][C]18.7982459995514[/C][C]4.2017540004486[/C][/ROW]
[ROW][C]137[/C][C]21[/C][C]20.6576932046433[/C][C]0.342306795356695[/C][/ROW]
[ROW][C]138[/C][C]16[/C][C]19.5015933777544[/C][C]-3.50159337775435[/C][/ROW]
[ROW][C]139[/C][C]24[/C][C]24.8330626444964[/C][C]-0.83306264449635[/C][/ROW]
[ROW][C]140[/C][C]23[/C][C]21.6862416385853[/C][C]1.31375836141472[/C][/ROW]
[ROW][C]141[/C][C]18[/C][C]15.9492410701592[/C][C]2.05075892984082[/C][/ROW]
[ROW][C]142[/C][C]20[/C][C]21.0334965527172[/C][C]-1.03349655271724[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]18.6019682030411[/C][C]-9.60196820304113[/C][/ROW]
[ROW][C]144[/C][C]24[/C][C]20.4534744784384[/C][C]3.54652552156164[/C][/ROW]
[ROW][C]145[/C][C]25[/C][C]24.8776324688426[/C][C]0.122367531157399[/C][/ROW]
[ROW][C]146[/C][C]20[/C][C]18.4613595295717[/C][C]1.5386404704283[/C][/ROW]
[ROW][C]147[/C][C]21[/C][C]19.1527063358093[/C][C]1.8472936641907[/C][/ROW]
[ROW][C]148[/C][C]25[/C][C]22.9188434118838[/C][C]2.08115658811617[/C][/ROW]
[ROW][C]149[/C][C]22[/C][C]22.5541267347884[/C][C]-0.55412673478844[/C][/ROW]
[ROW][C]150[/C][C]21[/C][C]20.853820788611[/C][C]0.146179211389038[/C][/ROW]
[ROW][C]151[/C][C]21[/C][C]20.3881941571143[/C][C]0.611805842885747[/C][/ROW]
[ROW][C]152[/C][C]22[/C][C]20.2020679472137[/C][C]1.79793205278631[/C][/ROW]
[ROW][C]153[/C][C]27[/C][C]23.5567887454626[/C][C]3.44321125453739[/C][/ROW]
[ROW][C]154[/C][C]24[/C][C]21.7695930776278[/C][C]2.23040692237222[/C][/ROW]
[ROW][C]155[/C][C]24[/C][C]23.9037725333379[/C][C]0.0962274666620831[/C][/ROW]
[ROW][C]156[/C][C]21[/C][C]20.0903720960964[/C][C]0.909627903903578[/C][/ROW]
[ROW][C]157[/C][C]18[/C][C]20.6636869370691[/C][C]-2.66368693706914[/C][/ROW]
[ROW][C]158[/C][C]16[/C][C]16.2411928229045[/C][C]-0.241192822904468[/C][/ROW]
[ROW][C]159[/C][C]22[/C][C]19.4970575725265[/C][C]2.50294242747348[/C][/ROW]
[ROW][C]160[/C][C]20[/C][C]20.292678420505[/C][C]-0.292678420505025[/C][/ROW]
[ROW][C]161[/C][C]18[/C][C]19.0617214619872[/C][C]-1.06172146198716[/C][/ROW]
[ROW][C]162[/C][C]20[/C][C]21.0487459152517[/C][C]-1.04874591525171[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190233&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190233&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
12624.13951381487051.86048618512951
22020.9233711353096-0.923371135309556
31921.7260097096157-2.72600970961572
41918.34248687714050.657513122859483
52018.13820969157351.86179030842651
62524.07086305734130.929136942658667
72519.45435722112375.54564277887626
82220.09060200824761.90939799175243
92622.43874021405733.56125978594269
102220.47192496566421.5280750343358
111720.1647319498158-3.1647319498158
122223.0148619707581-1.01486197075807
131920.8512820771864-1.85128207718638
142422.5942258287741.40577417122601
152626.8460878711547-0.846087871154704
162118.97792039610722.02207960389279
171316.3284470157266-3.32844701572656
182627.3688785545613-1.36887855456135
192022.7434533086229-2.74345330862286
202218.84486447198163.15513552801838
211418.1269474631139-4.12694746311394
222120.56978320572170.430216794278327
23714.1148647132544-7.1148647132544
242321.74440622068691.25559377931312
251719.2083800205107-2.20838002051066
262523.12967571899141.87032428100862
272523.13519306221851.86480693778145
281916.32745272848032.67254727151971
292016.712630045533.28736995447001
302321.50645116187361.49354883812637
312224.273723490158-2.27372349015798
322222.0040593329911-0.00405933299105414
332118.02359223300482.97640776699525
341517.9073539033564-2.90735390335643
352016.1752475633883.82475243661199
362221.8204649200280.179535079971958
371816.77987014592651.22012985407346
382021.0125925116908-1.01259251169079
392827.82442217381580.175577826184152
402224.3109173929062-2.3109173929062
411821.9471081760189-3.94710817601891
422320.87341088618962.12658911381041
432021.8996390377716-1.89963903777158
442524.80519090222470.194809097775326
452622.39667088368243.60332911631761
461514.6644332663050.335566733694969
471718.0126182793631-1.01261827936314
482315.62742556666587.37257443333422
492122.2283891135347-1.22838911353466
501316.6254954158953-3.62549541589527
511820.4042014898089-2.40420148980889
521919.4707860685076-0.470786068507584
532222.2518126511531-0.251812651153068
541617.5350265059378-1.53502650593781
552423.01298674140070.98701325859934
561819.7608690571955-1.7608690571955
572021.2599910888394-1.25999108883945
582420.62369585466713.37630414533289
591418.2374622618237-4.23746226182372
602220.2906654347911.70933456520903
612418.02841362069155.97158637930855
621818.4487731511957-0.448773151195726
632124.4634232807322-3.46342328073218
642321.53870453463191.46129546536809
651718.3705409129728-1.37054091297282
662222.6142331133307-0.614233113330655
672424.9077156113732-0.907715611373193
682122.8902290633938-1.89022906339384
692222.0431811632106-0.0431811632105598
701617.0205548699761-1.02055486997611
712125.1573022994668-4.15730229946679
722323.9307196418191-0.930719641819119
732219.39940102892192.60059897107805
742422.53522082281681.46477917718319
752424.7668832100165-0.766883210016453
761617.5056565405801-1.50565654058009
771617.4911157461838-1.49111574618381
782122.9830497322064-1.98304973220639
792626.0707545896182-0.0707545896181977
801517.2283887374184-2.22838873741835
812523.21191160207241.7880883979276
821818.0732626159408-0.0732626159407618
832320.94458660902072.05541339097927
842021.3319446608009-1.33194466080092
851720.8189650412942-3.81896504129415
862525.042051141019-0.0420511410189786
872422.62847390089121.37152609910881
881714.74738752616272.25261247383726
891918.79738843019510.202611569804868
902020.4006191741632-0.400619174163186
911516.4986162348839-1.49861623488389
922724.6933242376272.30667576237297
932219.63060892233282.36939107766716
942322.87983813044350.120161869556466
951620.8597486046703-4.85974860467028
961921.1467790994405-2.14677909944052
972520.59461360857774.40538639142234
981919.751881707755-0.751881707754952
991919.5141424093018-0.514142409301816
1002625.47631316847470.523686831525268
1012119.01573313982451.98426686017553
1022020.0116583506254-0.0116583506253738
1032420.18960353858843.81039646141164
1042224.162665420277-2.16266542027696
1052021.296300085898-1.29630008589803
1061819.3160922473178-1.31609224731779
1071817.07261538227870.927384617721281
1082421.35080612017332.64919387982671
1092421.1087543177882.89124568221204
1102223.2349137148951-1.23491371489512
1112319.59628335320023.40371664679983
1122219.70117555508382.29882444491625
1132017.98587374198472.01412625801526
1141819.4814235020668-1.48142350206677
1152524.42967357976850.570326420231549
1161818.3156422616838-0.315642261683841
1171617.5190470792768-1.51904707927681
1182019.78985958685060.210140413149402
1191918.51190614461690.4880938553831
1201515.6512840662819-0.651284066281912
1211918.8483356274160.151664372584023
1221921.8888750929243-2.88887509292427
1231617.4430809305523-1.44308093055227
1241717.8439169425446-0.843916942544588
1252823.4148695138854.58513048611505
1262322.98446638874190.0155336112580874
1272523.71161751761571.28838248238435
1282018.78128018788031.2187198121197
1291720.1378309997725-3.13783099977252
1302322.67858800314080.321411996859236
1311620.0833478043604-4.0833478043604
1322323.3381978846261-0.338197884626104
1331116.1171551560373-5.11715515603727
1341820.1644814958978-2.16448149589775
1352423.1698098154560.83019018454404
1362318.79824599955144.2017540004486
1372120.65769320464330.342306795356695
1381619.5015933777544-3.50159337775435
1392424.8330626444964-0.83306264449635
1402321.68624163858531.31375836141472
1411815.94924107015922.05075892984082
1422021.0334965527172-1.03349655271724
143918.6019682030411-9.60196820304113
1442420.45347447843843.54652552156164
1452524.87763246884260.122367531157399
1462018.46135952957171.5386404704283
1472119.15270633580931.8472936641907
1482522.91884341188382.08115658811617
1492222.5541267347884-0.55412673478844
1502120.8538207886110.146179211389038
1512120.38819415711430.611805842885747
1522220.20206794721371.79793205278631
1532723.55678874546263.44321125453739
1542421.76959307762782.23040692237222
1552423.90377253333790.0962274666620831
1562120.09037209609640.909627903903578
1571820.6636869370691-2.66368693706914
1581616.2411928229045-0.241192822904468
1592219.49705757252652.50294242747348
1602020.292678420505-0.292678420505025
1611819.0617214619872-1.06172146198716
1622021.0487459152517-1.04874591525171






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.9338477604792110.1323044790415780.066152239520789
130.921915652511550.1561686949768990.0780843474884495
140.8666710296414330.2666579407171350.133328970358567
150.7958301225268780.4083397549462450.204169877473122
160.7684252504548160.4631494990903690.231574749545184
170.6898985434498990.6202029131002020.310101456550101
180.5993953573210460.8012092853579080.400604642678954
190.6212702325284210.7574595349431580.378729767471579
200.6640958342972030.6718083314055940.335904165702797
210.5984709895474080.8030580209051830.401529010452592
220.5315713136353390.9368573727293210.468428686364661
230.5841074738542470.8317850522915060.415892526145753
240.5610228158868420.8779543682263150.438977184113158
250.4953817493973610.9907634987947210.504618250602639
260.5870497948513230.8259004102973530.412950205148677
270.5634799109718410.8730401780563190.436520089028159
280.8108941969654420.3782116060691160.189105803034558
290.9147019045413540.1705961909172920.0852980954586458
300.8969838672398860.2060322655202280.103016132760114
310.8956973674873030.2086052650253940.104302632512697
320.8642054806682220.2715890386635560.135794519331778
330.864932220171740.270135559656520.13506777982826
340.9040536474371430.1918927051257140.0959463525628568
350.9445320223551490.1109359552897030.0554679776448514
360.9272281351157230.1455437297685540.0727718648842771
370.9129752895131460.1740494209737070.0870247104868537
380.8892436991865670.2215126016268650.110756300813433
390.860137934209160.2797241315816790.13986206579084
400.8489382844321520.3021234311356970.151061715567848
410.877233239643170.245533520713660.12276676035683
420.8752935989729910.2494128020540180.124706401027009
430.8611902570577930.2776194858844140.138809742942207
440.8302515175662760.3394969648674480.169748482433724
450.8599231067839720.2801537864320560.140076893216028
460.829089875575180.341820248849640.17091012442482
470.7951745533857030.4096508932285930.204825446614297
480.9460073443290410.1079853113419170.0539926556709586
490.931274638683770.137450722632460.06872536131623
500.9487164967852710.1025670064294580.0512835032147289
510.9423403093937230.1153193812125540.0576596906062771
520.9298840258097870.1402319483804250.0701159741902127
530.9114447925607460.1771104148785080.0885552074392538
540.8939078467103130.2121843065793740.106092153289687
550.8712388025314450.2575223949371090.128761197468554
560.8594089170371630.2811821659256730.140591082962837
570.8329665396529210.3340669206941580.167033460347079
580.8657089302925290.2685821394149430.134291069707471
590.9062215904562740.1875568190874520.093778409543726
600.895390194448420.209219611103160.10460980555158
610.9670800278276140.06583994434477230.0329199721723861
620.9584824433547280.08303511329054350.0415175566452718
630.9645112395221880.07097752095562480.0354887604778124
640.9592052702776430.08158945944471460.0407947297223573
650.9532841697827870.09343166043442570.0467158302172128
660.9409422164824170.1181155670351660.0590577835175829
670.9259067834034420.1481864331931170.0740932165965583
680.9142438623520860.1715122752958280.085756137647914
690.8948935478609210.2102129042781590.105106452139079
700.8736771859818260.2526456280363480.126322814018174
710.902634998508450.1947300029831010.0973650014915504
720.884134677328430.231730645343140.11586532267157
730.8926983297412360.2146033405175290.107301670258764
740.881301521665150.2373969566697010.11869847833485
750.857038510351930.285922979296140.14296148964807
760.8362418317401530.3275163365196930.163758168259847
770.8123585527770930.3752828944458140.187641447222907
780.7957662725137460.4084674549725080.204233727486254
790.7674533752076550.4650932495846890.232546624792345
800.7539406527029640.4921186945940730.246059347297036
810.7410667601235470.5178664797529070.258933239876453
820.7083962964638130.5832074070723750.291603703536187
830.7037590199102820.5924819601794350.296240980089718
840.6750327651621550.649934469675690.324967234837845
850.7198438054772410.5603123890455180.280156194522759
860.67923974381410.64152051237180.3207602561859
870.6561059107577560.6877881784844870.343894089242244
880.6656656556678080.6686686886643840.334334344332192
890.6320924931464370.7358150137071250.367907506853563
900.5874700090124350.825059981975130.412529990987565
910.5484650364474280.9030699271051440.451534963552572
920.5424733558139580.9150532883720840.457526644186042
930.5392200391750990.9215599216498020.460779960824901
940.5006673738552030.9986652522895940.499332626144797
950.6265912195953180.7468175608093640.373408780404682
960.6234278043510130.7531443912979740.376572195648987
970.709167015432370.581665969135260.29083298456763
980.6719178544097130.6561642911805740.328082145590287
990.6354471907686090.7291056184627820.364552809231391
1000.5912542761156360.8174914477687280.408745723884364
1010.5662188757643640.8675622484712720.433781124235636
1020.5184234219264370.9631531561471260.481576578073563
1030.5931967711609310.8136064576781370.406803228839069
1040.5890171832044710.8219656335910590.410982816795529
1050.5667971417950340.8664057164099320.433202858204966
1060.5434622021797860.9130755956404280.456537797820214
1070.4972090566477880.9944181132955760.502790943352212
1080.4685299121960690.9370598243921380.531470087803931
1090.4566917764160030.9133835528320070.543308223583997
1100.4222748890654630.8445497781309260.577725110934537
1110.4521608914121810.9043217828243620.547839108587819
1120.4516618950099750.9033237900199490.548338104990025
1130.4659861046309530.9319722092619070.534013895369047
1140.4246996528626350.849399305725270.575300347137365
1150.3739422494721340.7478844989442670.626057750527866
1160.325286386114260.650572772228520.67471361388574
1170.2910451136588350.5820902273176710.708954886341165
1180.2490454663628470.4980909327256950.750954533637153
1190.223389726317430.4467794526348610.77661027368257
1200.1986077246745210.3972154493490430.801392275325479
1210.1656671841117630.3313343682235250.834332815888237
1220.1589885749820240.3179771499640470.841011425017976
1230.1301755647298590.2603511294597180.869824435270141
1240.1047320568149230.2094641136298460.895267943185077
1250.2392699223652590.4785398447305180.760730077634741
1260.1966507759541350.3933015519082710.803349224045865
1270.1860166741656010.3720333483312010.813983325834399
1280.1675263050039880.3350526100079750.832473694996013
1290.1483005024107090.2966010048214170.851699497589291
1300.1309882327332870.2619764654665730.869011767266713
1310.14963893059580.2992778611916010.8503610694042
1320.1152508138145430.2305016276290870.884749186185457
1330.1990507789340150.398101557868030.800949221065985
1340.1886767300238810.3773534600477630.811323269976119
1350.184878394096190.369756788192380.81512160590381
1360.1652034846797220.3304069693594430.834796515320278
1370.136162313910880.272324627821760.86383768608912
1380.1397158227052980.2794316454105970.860284177294702
1390.1045611946891880.2091223893783760.895438805310812
1400.08533325895179590.1706665179035920.914666741048204
1410.06298483245032230.1259696649006450.937015167549678
1420.0459014180450030.09180283609000590.954098581954997
1430.9269649793080880.1460700413838240.0730350206919119
1440.9643173096013710.07136538079725760.0356826903986288
1450.933761421133120.1324771577337610.0662385788668804
1460.8849484103175370.2301031793649250.115051589682463
1470.8247115164368260.3505769671263480.175288483563174
1480.7910137044553130.4179725910893730.208986295544686
1490.6716865086262270.6566269827475450.328313491373772
1500.5389436264774430.9221127470451140.461056373522557

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.933847760479211 & 0.132304479041578 & 0.066152239520789 \tabularnewline
13 & 0.92191565251155 & 0.156168694976899 & 0.0780843474884495 \tabularnewline
14 & 0.866671029641433 & 0.266657940717135 & 0.133328970358567 \tabularnewline
15 & 0.795830122526878 & 0.408339754946245 & 0.204169877473122 \tabularnewline
16 & 0.768425250454816 & 0.463149499090369 & 0.231574749545184 \tabularnewline
17 & 0.689898543449899 & 0.620202913100202 & 0.310101456550101 \tabularnewline
18 & 0.599395357321046 & 0.801209285357908 & 0.400604642678954 \tabularnewline
19 & 0.621270232528421 & 0.757459534943158 & 0.378729767471579 \tabularnewline
20 & 0.664095834297203 & 0.671808331405594 & 0.335904165702797 \tabularnewline
21 & 0.598470989547408 & 0.803058020905183 & 0.401529010452592 \tabularnewline
22 & 0.531571313635339 & 0.936857372729321 & 0.468428686364661 \tabularnewline
23 & 0.584107473854247 & 0.831785052291506 & 0.415892526145753 \tabularnewline
24 & 0.561022815886842 & 0.877954368226315 & 0.438977184113158 \tabularnewline
25 & 0.495381749397361 & 0.990763498794721 & 0.504618250602639 \tabularnewline
26 & 0.587049794851323 & 0.825900410297353 & 0.412950205148677 \tabularnewline
27 & 0.563479910971841 & 0.873040178056319 & 0.436520089028159 \tabularnewline
28 & 0.810894196965442 & 0.378211606069116 & 0.189105803034558 \tabularnewline
29 & 0.914701904541354 & 0.170596190917292 & 0.0852980954586458 \tabularnewline
30 & 0.896983867239886 & 0.206032265520228 & 0.103016132760114 \tabularnewline
31 & 0.895697367487303 & 0.208605265025394 & 0.104302632512697 \tabularnewline
32 & 0.864205480668222 & 0.271589038663556 & 0.135794519331778 \tabularnewline
33 & 0.86493222017174 & 0.27013555965652 & 0.13506777982826 \tabularnewline
34 & 0.904053647437143 & 0.191892705125714 & 0.0959463525628568 \tabularnewline
35 & 0.944532022355149 & 0.110935955289703 & 0.0554679776448514 \tabularnewline
36 & 0.927228135115723 & 0.145543729768554 & 0.0727718648842771 \tabularnewline
37 & 0.912975289513146 & 0.174049420973707 & 0.0870247104868537 \tabularnewline
38 & 0.889243699186567 & 0.221512601626865 & 0.110756300813433 \tabularnewline
39 & 0.86013793420916 & 0.279724131581679 & 0.13986206579084 \tabularnewline
40 & 0.848938284432152 & 0.302123431135697 & 0.151061715567848 \tabularnewline
41 & 0.87723323964317 & 0.24553352071366 & 0.12276676035683 \tabularnewline
42 & 0.875293598972991 & 0.249412802054018 & 0.124706401027009 \tabularnewline
43 & 0.861190257057793 & 0.277619485884414 & 0.138809742942207 \tabularnewline
44 & 0.830251517566276 & 0.339496964867448 & 0.169748482433724 \tabularnewline
45 & 0.859923106783972 & 0.280153786432056 & 0.140076893216028 \tabularnewline
46 & 0.82908987557518 & 0.34182024884964 & 0.17091012442482 \tabularnewline
47 & 0.795174553385703 & 0.409650893228593 & 0.204825446614297 \tabularnewline
48 & 0.946007344329041 & 0.107985311341917 & 0.0539926556709586 \tabularnewline
49 & 0.93127463868377 & 0.13745072263246 & 0.06872536131623 \tabularnewline
50 & 0.948716496785271 & 0.102567006429458 & 0.0512835032147289 \tabularnewline
51 & 0.942340309393723 & 0.115319381212554 & 0.0576596906062771 \tabularnewline
52 & 0.929884025809787 & 0.140231948380425 & 0.0701159741902127 \tabularnewline
53 & 0.911444792560746 & 0.177110414878508 & 0.0885552074392538 \tabularnewline
54 & 0.893907846710313 & 0.212184306579374 & 0.106092153289687 \tabularnewline
55 & 0.871238802531445 & 0.257522394937109 & 0.128761197468554 \tabularnewline
56 & 0.859408917037163 & 0.281182165925673 & 0.140591082962837 \tabularnewline
57 & 0.832966539652921 & 0.334066920694158 & 0.167033460347079 \tabularnewline
58 & 0.865708930292529 & 0.268582139414943 & 0.134291069707471 \tabularnewline
59 & 0.906221590456274 & 0.187556819087452 & 0.093778409543726 \tabularnewline
60 & 0.89539019444842 & 0.20921961110316 & 0.10460980555158 \tabularnewline
61 & 0.967080027827614 & 0.0658399443447723 & 0.0329199721723861 \tabularnewline
62 & 0.958482443354728 & 0.0830351132905435 & 0.0415175566452718 \tabularnewline
63 & 0.964511239522188 & 0.0709775209556248 & 0.0354887604778124 \tabularnewline
64 & 0.959205270277643 & 0.0815894594447146 & 0.0407947297223573 \tabularnewline
65 & 0.953284169782787 & 0.0934316604344257 & 0.0467158302172128 \tabularnewline
66 & 0.940942216482417 & 0.118115567035166 & 0.0590577835175829 \tabularnewline
67 & 0.925906783403442 & 0.148186433193117 & 0.0740932165965583 \tabularnewline
68 & 0.914243862352086 & 0.171512275295828 & 0.085756137647914 \tabularnewline
69 & 0.894893547860921 & 0.210212904278159 & 0.105106452139079 \tabularnewline
70 & 0.873677185981826 & 0.252645628036348 & 0.126322814018174 \tabularnewline
71 & 0.90263499850845 & 0.194730002983101 & 0.0973650014915504 \tabularnewline
72 & 0.88413467732843 & 0.23173064534314 & 0.11586532267157 \tabularnewline
73 & 0.892698329741236 & 0.214603340517529 & 0.107301670258764 \tabularnewline
74 & 0.88130152166515 & 0.237396956669701 & 0.11869847833485 \tabularnewline
75 & 0.85703851035193 & 0.28592297929614 & 0.14296148964807 \tabularnewline
76 & 0.836241831740153 & 0.327516336519693 & 0.163758168259847 \tabularnewline
77 & 0.812358552777093 & 0.375282894445814 & 0.187641447222907 \tabularnewline
78 & 0.795766272513746 & 0.408467454972508 & 0.204233727486254 \tabularnewline
79 & 0.767453375207655 & 0.465093249584689 & 0.232546624792345 \tabularnewline
80 & 0.753940652702964 & 0.492118694594073 & 0.246059347297036 \tabularnewline
81 & 0.741066760123547 & 0.517866479752907 & 0.258933239876453 \tabularnewline
82 & 0.708396296463813 & 0.583207407072375 & 0.291603703536187 \tabularnewline
83 & 0.703759019910282 & 0.592481960179435 & 0.296240980089718 \tabularnewline
84 & 0.675032765162155 & 0.64993446967569 & 0.324967234837845 \tabularnewline
85 & 0.719843805477241 & 0.560312389045518 & 0.280156194522759 \tabularnewline
86 & 0.6792397438141 & 0.6415205123718 & 0.3207602561859 \tabularnewline
87 & 0.656105910757756 & 0.687788178484487 & 0.343894089242244 \tabularnewline
88 & 0.665665655667808 & 0.668668688664384 & 0.334334344332192 \tabularnewline
89 & 0.632092493146437 & 0.735815013707125 & 0.367907506853563 \tabularnewline
90 & 0.587470009012435 & 0.82505998197513 & 0.412529990987565 \tabularnewline
91 & 0.548465036447428 & 0.903069927105144 & 0.451534963552572 \tabularnewline
92 & 0.542473355813958 & 0.915053288372084 & 0.457526644186042 \tabularnewline
93 & 0.539220039175099 & 0.921559921649802 & 0.460779960824901 \tabularnewline
94 & 0.500667373855203 & 0.998665252289594 & 0.499332626144797 \tabularnewline
95 & 0.626591219595318 & 0.746817560809364 & 0.373408780404682 \tabularnewline
96 & 0.623427804351013 & 0.753144391297974 & 0.376572195648987 \tabularnewline
97 & 0.70916701543237 & 0.58166596913526 & 0.29083298456763 \tabularnewline
98 & 0.671917854409713 & 0.656164291180574 & 0.328082145590287 \tabularnewline
99 & 0.635447190768609 & 0.729105618462782 & 0.364552809231391 \tabularnewline
100 & 0.591254276115636 & 0.817491447768728 & 0.408745723884364 \tabularnewline
101 & 0.566218875764364 & 0.867562248471272 & 0.433781124235636 \tabularnewline
102 & 0.518423421926437 & 0.963153156147126 & 0.481576578073563 \tabularnewline
103 & 0.593196771160931 & 0.813606457678137 & 0.406803228839069 \tabularnewline
104 & 0.589017183204471 & 0.821965633591059 & 0.410982816795529 \tabularnewline
105 & 0.566797141795034 & 0.866405716409932 & 0.433202858204966 \tabularnewline
106 & 0.543462202179786 & 0.913075595640428 & 0.456537797820214 \tabularnewline
107 & 0.497209056647788 & 0.994418113295576 & 0.502790943352212 \tabularnewline
108 & 0.468529912196069 & 0.937059824392138 & 0.531470087803931 \tabularnewline
109 & 0.456691776416003 & 0.913383552832007 & 0.543308223583997 \tabularnewline
110 & 0.422274889065463 & 0.844549778130926 & 0.577725110934537 \tabularnewline
111 & 0.452160891412181 & 0.904321782824362 & 0.547839108587819 \tabularnewline
112 & 0.451661895009975 & 0.903323790019949 & 0.548338104990025 \tabularnewline
113 & 0.465986104630953 & 0.931972209261907 & 0.534013895369047 \tabularnewline
114 & 0.424699652862635 & 0.84939930572527 & 0.575300347137365 \tabularnewline
115 & 0.373942249472134 & 0.747884498944267 & 0.626057750527866 \tabularnewline
116 & 0.32528638611426 & 0.65057277222852 & 0.67471361388574 \tabularnewline
117 & 0.291045113658835 & 0.582090227317671 & 0.708954886341165 \tabularnewline
118 & 0.249045466362847 & 0.498090932725695 & 0.750954533637153 \tabularnewline
119 & 0.22338972631743 & 0.446779452634861 & 0.77661027368257 \tabularnewline
120 & 0.198607724674521 & 0.397215449349043 & 0.801392275325479 \tabularnewline
121 & 0.165667184111763 & 0.331334368223525 & 0.834332815888237 \tabularnewline
122 & 0.158988574982024 & 0.317977149964047 & 0.841011425017976 \tabularnewline
123 & 0.130175564729859 & 0.260351129459718 & 0.869824435270141 \tabularnewline
124 & 0.104732056814923 & 0.209464113629846 & 0.895267943185077 \tabularnewline
125 & 0.239269922365259 & 0.478539844730518 & 0.760730077634741 \tabularnewline
126 & 0.196650775954135 & 0.393301551908271 & 0.803349224045865 \tabularnewline
127 & 0.186016674165601 & 0.372033348331201 & 0.813983325834399 \tabularnewline
128 & 0.167526305003988 & 0.335052610007975 & 0.832473694996013 \tabularnewline
129 & 0.148300502410709 & 0.296601004821417 & 0.851699497589291 \tabularnewline
130 & 0.130988232733287 & 0.261976465466573 & 0.869011767266713 \tabularnewline
131 & 0.1496389305958 & 0.299277861191601 & 0.8503610694042 \tabularnewline
132 & 0.115250813814543 & 0.230501627629087 & 0.884749186185457 \tabularnewline
133 & 0.199050778934015 & 0.39810155786803 & 0.800949221065985 \tabularnewline
134 & 0.188676730023881 & 0.377353460047763 & 0.811323269976119 \tabularnewline
135 & 0.18487839409619 & 0.36975678819238 & 0.81512160590381 \tabularnewline
136 & 0.165203484679722 & 0.330406969359443 & 0.834796515320278 \tabularnewline
137 & 0.13616231391088 & 0.27232462782176 & 0.86383768608912 \tabularnewline
138 & 0.139715822705298 & 0.279431645410597 & 0.860284177294702 \tabularnewline
139 & 0.104561194689188 & 0.209122389378376 & 0.895438805310812 \tabularnewline
140 & 0.0853332589517959 & 0.170666517903592 & 0.914666741048204 \tabularnewline
141 & 0.0629848324503223 & 0.125969664900645 & 0.937015167549678 \tabularnewline
142 & 0.045901418045003 & 0.0918028360900059 & 0.954098581954997 \tabularnewline
143 & 0.926964979308088 & 0.146070041383824 & 0.0730350206919119 \tabularnewline
144 & 0.964317309601371 & 0.0713653807972576 & 0.0356826903986288 \tabularnewline
145 & 0.93376142113312 & 0.132477157733761 & 0.0662385788668804 \tabularnewline
146 & 0.884948410317537 & 0.230103179364925 & 0.115051589682463 \tabularnewline
147 & 0.824711516436826 & 0.350576967126348 & 0.175288483563174 \tabularnewline
148 & 0.791013704455313 & 0.417972591089373 & 0.208986295544686 \tabularnewline
149 & 0.671686508626227 & 0.656626982747545 & 0.328313491373772 \tabularnewline
150 & 0.538943626477443 & 0.922112747045114 & 0.461056373522557 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190233&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]12[/C][C]0.933847760479211[/C][C]0.132304479041578[/C][C]0.066152239520789[/C][/ROW]
[ROW][C]13[/C][C]0.92191565251155[/C][C]0.156168694976899[/C][C]0.0780843474884495[/C][/ROW]
[ROW][C]14[/C][C]0.866671029641433[/C][C]0.266657940717135[/C][C]0.133328970358567[/C][/ROW]
[ROW][C]15[/C][C]0.795830122526878[/C][C]0.408339754946245[/C][C]0.204169877473122[/C][/ROW]
[ROW][C]16[/C][C]0.768425250454816[/C][C]0.463149499090369[/C][C]0.231574749545184[/C][/ROW]
[ROW][C]17[/C][C]0.689898543449899[/C][C]0.620202913100202[/C][C]0.310101456550101[/C][/ROW]
[ROW][C]18[/C][C]0.599395357321046[/C][C]0.801209285357908[/C][C]0.400604642678954[/C][/ROW]
[ROW][C]19[/C][C]0.621270232528421[/C][C]0.757459534943158[/C][C]0.378729767471579[/C][/ROW]
[ROW][C]20[/C][C]0.664095834297203[/C][C]0.671808331405594[/C][C]0.335904165702797[/C][/ROW]
[ROW][C]21[/C][C]0.598470989547408[/C][C]0.803058020905183[/C][C]0.401529010452592[/C][/ROW]
[ROW][C]22[/C][C]0.531571313635339[/C][C]0.936857372729321[/C][C]0.468428686364661[/C][/ROW]
[ROW][C]23[/C][C]0.584107473854247[/C][C]0.831785052291506[/C][C]0.415892526145753[/C][/ROW]
[ROW][C]24[/C][C]0.561022815886842[/C][C]0.877954368226315[/C][C]0.438977184113158[/C][/ROW]
[ROW][C]25[/C][C]0.495381749397361[/C][C]0.990763498794721[/C][C]0.504618250602639[/C][/ROW]
[ROW][C]26[/C][C]0.587049794851323[/C][C]0.825900410297353[/C][C]0.412950205148677[/C][/ROW]
[ROW][C]27[/C][C]0.563479910971841[/C][C]0.873040178056319[/C][C]0.436520089028159[/C][/ROW]
[ROW][C]28[/C][C]0.810894196965442[/C][C]0.378211606069116[/C][C]0.189105803034558[/C][/ROW]
[ROW][C]29[/C][C]0.914701904541354[/C][C]0.170596190917292[/C][C]0.0852980954586458[/C][/ROW]
[ROW][C]30[/C][C]0.896983867239886[/C][C]0.206032265520228[/C][C]0.103016132760114[/C][/ROW]
[ROW][C]31[/C][C]0.895697367487303[/C][C]0.208605265025394[/C][C]0.104302632512697[/C][/ROW]
[ROW][C]32[/C][C]0.864205480668222[/C][C]0.271589038663556[/C][C]0.135794519331778[/C][/ROW]
[ROW][C]33[/C][C]0.86493222017174[/C][C]0.27013555965652[/C][C]0.13506777982826[/C][/ROW]
[ROW][C]34[/C][C]0.904053647437143[/C][C]0.191892705125714[/C][C]0.0959463525628568[/C][/ROW]
[ROW][C]35[/C][C]0.944532022355149[/C][C]0.110935955289703[/C][C]0.0554679776448514[/C][/ROW]
[ROW][C]36[/C][C]0.927228135115723[/C][C]0.145543729768554[/C][C]0.0727718648842771[/C][/ROW]
[ROW][C]37[/C][C]0.912975289513146[/C][C]0.174049420973707[/C][C]0.0870247104868537[/C][/ROW]
[ROW][C]38[/C][C]0.889243699186567[/C][C]0.221512601626865[/C][C]0.110756300813433[/C][/ROW]
[ROW][C]39[/C][C]0.86013793420916[/C][C]0.279724131581679[/C][C]0.13986206579084[/C][/ROW]
[ROW][C]40[/C][C]0.848938284432152[/C][C]0.302123431135697[/C][C]0.151061715567848[/C][/ROW]
[ROW][C]41[/C][C]0.87723323964317[/C][C]0.24553352071366[/C][C]0.12276676035683[/C][/ROW]
[ROW][C]42[/C][C]0.875293598972991[/C][C]0.249412802054018[/C][C]0.124706401027009[/C][/ROW]
[ROW][C]43[/C][C]0.861190257057793[/C][C]0.277619485884414[/C][C]0.138809742942207[/C][/ROW]
[ROW][C]44[/C][C]0.830251517566276[/C][C]0.339496964867448[/C][C]0.169748482433724[/C][/ROW]
[ROW][C]45[/C][C]0.859923106783972[/C][C]0.280153786432056[/C][C]0.140076893216028[/C][/ROW]
[ROW][C]46[/C][C]0.82908987557518[/C][C]0.34182024884964[/C][C]0.17091012442482[/C][/ROW]
[ROW][C]47[/C][C]0.795174553385703[/C][C]0.409650893228593[/C][C]0.204825446614297[/C][/ROW]
[ROW][C]48[/C][C]0.946007344329041[/C][C]0.107985311341917[/C][C]0.0539926556709586[/C][/ROW]
[ROW][C]49[/C][C]0.93127463868377[/C][C]0.13745072263246[/C][C]0.06872536131623[/C][/ROW]
[ROW][C]50[/C][C]0.948716496785271[/C][C]0.102567006429458[/C][C]0.0512835032147289[/C][/ROW]
[ROW][C]51[/C][C]0.942340309393723[/C][C]0.115319381212554[/C][C]0.0576596906062771[/C][/ROW]
[ROW][C]52[/C][C]0.929884025809787[/C][C]0.140231948380425[/C][C]0.0701159741902127[/C][/ROW]
[ROW][C]53[/C][C]0.911444792560746[/C][C]0.177110414878508[/C][C]0.0885552074392538[/C][/ROW]
[ROW][C]54[/C][C]0.893907846710313[/C][C]0.212184306579374[/C][C]0.106092153289687[/C][/ROW]
[ROW][C]55[/C][C]0.871238802531445[/C][C]0.257522394937109[/C][C]0.128761197468554[/C][/ROW]
[ROW][C]56[/C][C]0.859408917037163[/C][C]0.281182165925673[/C][C]0.140591082962837[/C][/ROW]
[ROW][C]57[/C][C]0.832966539652921[/C][C]0.334066920694158[/C][C]0.167033460347079[/C][/ROW]
[ROW][C]58[/C][C]0.865708930292529[/C][C]0.268582139414943[/C][C]0.134291069707471[/C][/ROW]
[ROW][C]59[/C][C]0.906221590456274[/C][C]0.187556819087452[/C][C]0.093778409543726[/C][/ROW]
[ROW][C]60[/C][C]0.89539019444842[/C][C]0.20921961110316[/C][C]0.10460980555158[/C][/ROW]
[ROW][C]61[/C][C]0.967080027827614[/C][C]0.0658399443447723[/C][C]0.0329199721723861[/C][/ROW]
[ROW][C]62[/C][C]0.958482443354728[/C][C]0.0830351132905435[/C][C]0.0415175566452718[/C][/ROW]
[ROW][C]63[/C][C]0.964511239522188[/C][C]0.0709775209556248[/C][C]0.0354887604778124[/C][/ROW]
[ROW][C]64[/C][C]0.959205270277643[/C][C]0.0815894594447146[/C][C]0.0407947297223573[/C][/ROW]
[ROW][C]65[/C][C]0.953284169782787[/C][C]0.0934316604344257[/C][C]0.0467158302172128[/C][/ROW]
[ROW][C]66[/C][C]0.940942216482417[/C][C]0.118115567035166[/C][C]0.0590577835175829[/C][/ROW]
[ROW][C]67[/C][C]0.925906783403442[/C][C]0.148186433193117[/C][C]0.0740932165965583[/C][/ROW]
[ROW][C]68[/C][C]0.914243862352086[/C][C]0.171512275295828[/C][C]0.085756137647914[/C][/ROW]
[ROW][C]69[/C][C]0.894893547860921[/C][C]0.210212904278159[/C][C]0.105106452139079[/C][/ROW]
[ROW][C]70[/C][C]0.873677185981826[/C][C]0.252645628036348[/C][C]0.126322814018174[/C][/ROW]
[ROW][C]71[/C][C]0.90263499850845[/C][C]0.194730002983101[/C][C]0.0973650014915504[/C][/ROW]
[ROW][C]72[/C][C]0.88413467732843[/C][C]0.23173064534314[/C][C]0.11586532267157[/C][/ROW]
[ROW][C]73[/C][C]0.892698329741236[/C][C]0.214603340517529[/C][C]0.107301670258764[/C][/ROW]
[ROW][C]74[/C][C]0.88130152166515[/C][C]0.237396956669701[/C][C]0.11869847833485[/C][/ROW]
[ROW][C]75[/C][C]0.85703851035193[/C][C]0.28592297929614[/C][C]0.14296148964807[/C][/ROW]
[ROW][C]76[/C][C]0.836241831740153[/C][C]0.327516336519693[/C][C]0.163758168259847[/C][/ROW]
[ROW][C]77[/C][C]0.812358552777093[/C][C]0.375282894445814[/C][C]0.187641447222907[/C][/ROW]
[ROW][C]78[/C][C]0.795766272513746[/C][C]0.408467454972508[/C][C]0.204233727486254[/C][/ROW]
[ROW][C]79[/C][C]0.767453375207655[/C][C]0.465093249584689[/C][C]0.232546624792345[/C][/ROW]
[ROW][C]80[/C][C]0.753940652702964[/C][C]0.492118694594073[/C][C]0.246059347297036[/C][/ROW]
[ROW][C]81[/C][C]0.741066760123547[/C][C]0.517866479752907[/C][C]0.258933239876453[/C][/ROW]
[ROW][C]82[/C][C]0.708396296463813[/C][C]0.583207407072375[/C][C]0.291603703536187[/C][/ROW]
[ROW][C]83[/C][C]0.703759019910282[/C][C]0.592481960179435[/C][C]0.296240980089718[/C][/ROW]
[ROW][C]84[/C][C]0.675032765162155[/C][C]0.64993446967569[/C][C]0.324967234837845[/C][/ROW]
[ROW][C]85[/C][C]0.719843805477241[/C][C]0.560312389045518[/C][C]0.280156194522759[/C][/ROW]
[ROW][C]86[/C][C]0.6792397438141[/C][C]0.6415205123718[/C][C]0.3207602561859[/C][/ROW]
[ROW][C]87[/C][C]0.656105910757756[/C][C]0.687788178484487[/C][C]0.343894089242244[/C][/ROW]
[ROW][C]88[/C][C]0.665665655667808[/C][C]0.668668688664384[/C][C]0.334334344332192[/C][/ROW]
[ROW][C]89[/C][C]0.632092493146437[/C][C]0.735815013707125[/C][C]0.367907506853563[/C][/ROW]
[ROW][C]90[/C][C]0.587470009012435[/C][C]0.82505998197513[/C][C]0.412529990987565[/C][/ROW]
[ROW][C]91[/C][C]0.548465036447428[/C][C]0.903069927105144[/C][C]0.451534963552572[/C][/ROW]
[ROW][C]92[/C][C]0.542473355813958[/C][C]0.915053288372084[/C][C]0.457526644186042[/C][/ROW]
[ROW][C]93[/C][C]0.539220039175099[/C][C]0.921559921649802[/C][C]0.460779960824901[/C][/ROW]
[ROW][C]94[/C][C]0.500667373855203[/C][C]0.998665252289594[/C][C]0.499332626144797[/C][/ROW]
[ROW][C]95[/C][C]0.626591219595318[/C][C]0.746817560809364[/C][C]0.373408780404682[/C][/ROW]
[ROW][C]96[/C][C]0.623427804351013[/C][C]0.753144391297974[/C][C]0.376572195648987[/C][/ROW]
[ROW][C]97[/C][C]0.70916701543237[/C][C]0.58166596913526[/C][C]0.29083298456763[/C][/ROW]
[ROW][C]98[/C][C]0.671917854409713[/C][C]0.656164291180574[/C][C]0.328082145590287[/C][/ROW]
[ROW][C]99[/C][C]0.635447190768609[/C][C]0.729105618462782[/C][C]0.364552809231391[/C][/ROW]
[ROW][C]100[/C][C]0.591254276115636[/C][C]0.817491447768728[/C][C]0.408745723884364[/C][/ROW]
[ROW][C]101[/C][C]0.566218875764364[/C][C]0.867562248471272[/C][C]0.433781124235636[/C][/ROW]
[ROW][C]102[/C][C]0.518423421926437[/C][C]0.963153156147126[/C][C]0.481576578073563[/C][/ROW]
[ROW][C]103[/C][C]0.593196771160931[/C][C]0.813606457678137[/C][C]0.406803228839069[/C][/ROW]
[ROW][C]104[/C][C]0.589017183204471[/C][C]0.821965633591059[/C][C]0.410982816795529[/C][/ROW]
[ROW][C]105[/C][C]0.566797141795034[/C][C]0.866405716409932[/C][C]0.433202858204966[/C][/ROW]
[ROW][C]106[/C][C]0.543462202179786[/C][C]0.913075595640428[/C][C]0.456537797820214[/C][/ROW]
[ROW][C]107[/C][C]0.497209056647788[/C][C]0.994418113295576[/C][C]0.502790943352212[/C][/ROW]
[ROW][C]108[/C][C]0.468529912196069[/C][C]0.937059824392138[/C][C]0.531470087803931[/C][/ROW]
[ROW][C]109[/C][C]0.456691776416003[/C][C]0.913383552832007[/C][C]0.543308223583997[/C][/ROW]
[ROW][C]110[/C][C]0.422274889065463[/C][C]0.844549778130926[/C][C]0.577725110934537[/C][/ROW]
[ROW][C]111[/C][C]0.452160891412181[/C][C]0.904321782824362[/C][C]0.547839108587819[/C][/ROW]
[ROW][C]112[/C][C]0.451661895009975[/C][C]0.903323790019949[/C][C]0.548338104990025[/C][/ROW]
[ROW][C]113[/C][C]0.465986104630953[/C][C]0.931972209261907[/C][C]0.534013895369047[/C][/ROW]
[ROW][C]114[/C][C]0.424699652862635[/C][C]0.84939930572527[/C][C]0.575300347137365[/C][/ROW]
[ROW][C]115[/C][C]0.373942249472134[/C][C]0.747884498944267[/C][C]0.626057750527866[/C][/ROW]
[ROW][C]116[/C][C]0.32528638611426[/C][C]0.65057277222852[/C][C]0.67471361388574[/C][/ROW]
[ROW][C]117[/C][C]0.291045113658835[/C][C]0.582090227317671[/C][C]0.708954886341165[/C][/ROW]
[ROW][C]118[/C][C]0.249045466362847[/C][C]0.498090932725695[/C][C]0.750954533637153[/C][/ROW]
[ROW][C]119[/C][C]0.22338972631743[/C][C]0.446779452634861[/C][C]0.77661027368257[/C][/ROW]
[ROW][C]120[/C][C]0.198607724674521[/C][C]0.397215449349043[/C][C]0.801392275325479[/C][/ROW]
[ROW][C]121[/C][C]0.165667184111763[/C][C]0.331334368223525[/C][C]0.834332815888237[/C][/ROW]
[ROW][C]122[/C][C]0.158988574982024[/C][C]0.317977149964047[/C][C]0.841011425017976[/C][/ROW]
[ROW][C]123[/C][C]0.130175564729859[/C][C]0.260351129459718[/C][C]0.869824435270141[/C][/ROW]
[ROW][C]124[/C][C]0.104732056814923[/C][C]0.209464113629846[/C][C]0.895267943185077[/C][/ROW]
[ROW][C]125[/C][C]0.239269922365259[/C][C]0.478539844730518[/C][C]0.760730077634741[/C][/ROW]
[ROW][C]126[/C][C]0.196650775954135[/C][C]0.393301551908271[/C][C]0.803349224045865[/C][/ROW]
[ROW][C]127[/C][C]0.186016674165601[/C][C]0.372033348331201[/C][C]0.813983325834399[/C][/ROW]
[ROW][C]128[/C][C]0.167526305003988[/C][C]0.335052610007975[/C][C]0.832473694996013[/C][/ROW]
[ROW][C]129[/C][C]0.148300502410709[/C][C]0.296601004821417[/C][C]0.851699497589291[/C][/ROW]
[ROW][C]130[/C][C]0.130988232733287[/C][C]0.261976465466573[/C][C]0.869011767266713[/C][/ROW]
[ROW][C]131[/C][C]0.1496389305958[/C][C]0.299277861191601[/C][C]0.8503610694042[/C][/ROW]
[ROW][C]132[/C][C]0.115250813814543[/C][C]0.230501627629087[/C][C]0.884749186185457[/C][/ROW]
[ROW][C]133[/C][C]0.199050778934015[/C][C]0.39810155786803[/C][C]0.800949221065985[/C][/ROW]
[ROW][C]134[/C][C]0.188676730023881[/C][C]0.377353460047763[/C][C]0.811323269976119[/C][/ROW]
[ROW][C]135[/C][C]0.18487839409619[/C][C]0.36975678819238[/C][C]0.81512160590381[/C][/ROW]
[ROW][C]136[/C][C]0.165203484679722[/C][C]0.330406969359443[/C][C]0.834796515320278[/C][/ROW]
[ROW][C]137[/C][C]0.13616231391088[/C][C]0.27232462782176[/C][C]0.86383768608912[/C][/ROW]
[ROW][C]138[/C][C]0.139715822705298[/C][C]0.279431645410597[/C][C]0.860284177294702[/C][/ROW]
[ROW][C]139[/C][C]0.104561194689188[/C][C]0.209122389378376[/C][C]0.895438805310812[/C][/ROW]
[ROW][C]140[/C][C]0.0853332589517959[/C][C]0.170666517903592[/C][C]0.914666741048204[/C][/ROW]
[ROW][C]141[/C][C]0.0629848324503223[/C][C]0.125969664900645[/C][C]0.937015167549678[/C][/ROW]
[ROW][C]142[/C][C]0.045901418045003[/C][C]0.0918028360900059[/C][C]0.954098581954997[/C][/ROW]
[ROW][C]143[/C][C]0.926964979308088[/C][C]0.146070041383824[/C][C]0.0730350206919119[/C][/ROW]
[ROW][C]144[/C][C]0.964317309601371[/C][C]0.0713653807972576[/C][C]0.0356826903986288[/C][/ROW]
[ROW][C]145[/C][C]0.93376142113312[/C][C]0.132477157733761[/C][C]0.0662385788668804[/C][/ROW]
[ROW][C]146[/C][C]0.884948410317537[/C][C]0.230103179364925[/C][C]0.115051589682463[/C][/ROW]
[ROW][C]147[/C][C]0.824711516436826[/C][C]0.350576967126348[/C][C]0.175288483563174[/C][/ROW]
[ROW][C]148[/C][C]0.791013704455313[/C][C]0.417972591089373[/C][C]0.208986295544686[/C][/ROW]
[ROW][C]149[/C][C]0.671686508626227[/C][C]0.656626982747545[/C][C]0.328313491373772[/C][/ROW]
[ROW][C]150[/C][C]0.538943626477443[/C][C]0.922112747045114[/C][C]0.461056373522557[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190233&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190233&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.9338477604792110.1323044790415780.066152239520789
130.921915652511550.1561686949768990.0780843474884495
140.8666710296414330.2666579407171350.133328970358567
150.7958301225268780.4083397549462450.204169877473122
160.7684252504548160.4631494990903690.231574749545184
170.6898985434498990.6202029131002020.310101456550101
180.5993953573210460.8012092853579080.400604642678954
190.6212702325284210.7574595349431580.378729767471579
200.6640958342972030.6718083314055940.335904165702797
210.5984709895474080.8030580209051830.401529010452592
220.5315713136353390.9368573727293210.468428686364661
230.5841074738542470.8317850522915060.415892526145753
240.5610228158868420.8779543682263150.438977184113158
250.4953817493973610.9907634987947210.504618250602639
260.5870497948513230.8259004102973530.412950205148677
270.5634799109718410.8730401780563190.436520089028159
280.8108941969654420.3782116060691160.189105803034558
290.9147019045413540.1705961909172920.0852980954586458
300.8969838672398860.2060322655202280.103016132760114
310.8956973674873030.2086052650253940.104302632512697
320.8642054806682220.2715890386635560.135794519331778
330.864932220171740.270135559656520.13506777982826
340.9040536474371430.1918927051257140.0959463525628568
350.9445320223551490.1109359552897030.0554679776448514
360.9272281351157230.1455437297685540.0727718648842771
370.9129752895131460.1740494209737070.0870247104868537
380.8892436991865670.2215126016268650.110756300813433
390.860137934209160.2797241315816790.13986206579084
400.8489382844321520.3021234311356970.151061715567848
410.877233239643170.245533520713660.12276676035683
420.8752935989729910.2494128020540180.124706401027009
430.8611902570577930.2776194858844140.138809742942207
440.8302515175662760.3394969648674480.169748482433724
450.8599231067839720.2801537864320560.140076893216028
460.829089875575180.341820248849640.17091012442482
470.7951745533857030.4096508932285930.204825446614297
480.9460073443290410.1079853113419170.0539926556709586
490.931274638683770.137450722632460.06872536131623
500.9487164967852710.1025670064294580.0512835032147289
510.9423403093937230.1153193812125540.0576596906062771
520.9298840258097870.1402319483804250.0701159741902127
530.9114447925607460.1771104148785080.0885552074392538
540.8939078467103130.2121843065793740.106092153289687
550.8712388025314450.2575223949371090.128761197468554
560.8594089170371630.2811821659256730.140591082962837
570.8329665396529210.3340669206941580.167033460347079
580.8657089302925290.2685821394149430.134291069707471
590.9062215904562740.1875568190874520.093778409543726
600.895390194448420.209219611103160.10460980555158
610.9670800278276140.06583994434477230.0329199721723861
620.9584824433547280.08303511329054350.0415175566452718
630.9645112395221880.07097752095562480.0354887604778124
640.9592052702776430.08158945944471460.0407947297223573
650.9532841697827870.09343166043442570.0467158302172128
660.9409422164824170.1181155670351660.0590577835175829
670.9259067834034420.1481864331931170.0740932165965583
680.9142438623520860.1715122752958280.085756137647914
690.8948935478609210.2102129042781590.105106452139079
700.8736771859818260.2526456280363480.126322814018174
710.902634998508450.1947300029831010.0973650014915504
720.884134677328430.231730645343140.11586532267157
730.8926983297412360.2146033405175290.107301670258764
740.881301521665150.2373969566697010.11869847833485
750.857038510351930.285922979296140.14296148964807
760.8362418317401530.3275163365196930.163758168259847
770.8123585527770930.3752828944458140.187641447222907
780.7957662725137460.4084674549725080.204233727486254
790.7674533752076550.4650932495846890.232546624792345
800.7539406527029640.4921186945940730.246059347297036
810.7410667601235470.5178664797529070.258933239876453
820.7083962964638130.5832074070723750.291603703536187
830.7037590199102820.5924819601794350.296240980089718
840.6750327651621550.649934469675690.324967234837845
850.7198438054772410.5603123890455180.280156194522759
860.67923974381410.64152051237180.3207602561859
870.6561059107577560.6877881784844870.343894089242244
880.6656656556678080.6686686886643840.334334344332192
890.6320924931464370.7358150137071250.367907506853563
900.5874700090124350.825059981975130.412529990987565
910.5484650364474280.9030699271051440.451534963552572
920.5424733558139580.9150532883720840.457526644186042
930.5392200391750990.9215599216498020.460779960824901
940.5006673738552030.9986652522895940.499332626144797
950.6265912195953180.7468175608093640.373408780404682
960.6234278043510130.7531443912979740.376572195648987
970.709167015432370.581665969135260.29083298456763
980.6719178544097130.6561642911805740.328082145590287
990.6354471907686090.7291056184627820.364552809231391
1000.5912542761156360.8174914477687280.408745723884364
1010.5662188757643640.8675622484712720.433781124235636
1020.5184234219264370.9631531561471260.481576578073563
1030.5931967711609310.8136064576781370.406803228839069
1040.5890171832044710.8219656335910590.410982816795529
1050.5667971417950340.8664057164099320.433202858204966
1060.5434622021797860.9130755956404280.456537797820214
1070.4972090566477880.9944181132955760.502790943352212
1080.4685299121960690.9370598243921380.531470087803931
1090.4566917764160030.9133835528320070.543308223583997
1100.4222748890654630.8445497781309260.577725110934537
1110.4521608914121810.9043217828243620.547839108587819
1120.4516618950099750.9033237900199490.548338104990025
1130.4659861046309530.9319722092619070.534013895369047
1140.4246996528626350.849399305725270.575300347137365
1150.3739422494721340.7478844989442670.626057750527866
1160.325286386114260.650572772228520.67471361388574
1170.2910451136588350.5820902273176710.708954886341165
1180.2490454663628470.4980909327256950.750954533637153
1190.223389726317430.4467794526348610.77661027368257
1200.1986077246745210.3972154493490430.801392275325479
1210.1656671841117630.3313343682235250.834332815888237
1220.1589885749820240.3179771499640470.841011425017976
1230.1301755647298590.2603511294597180.869824435270141
1240.1047320568149230.2094641136298460.895267943185077
1250.2392699223652590.4785398447305180.760730077634741
1260.1966507759541350.3933015519082710.803349224045865
1270.1860166741656010.3720333483312010.813983325834399
1280.1675263050039880.3350526100079750.832473694996013
1290.1483005024107090.2966010048214170.851699497589291
1300.1309882327332870.2619764654665730.869011767266713
1310.14963893059580.2992778611916010.8503610694042
1320.1152508138145430.2305016276290870.884749186185457
1330.1990507789340150.398101557868030.800949221065985
1340.1886767300238810.3773534600477630.811323269976119
1350.184878394096190.369756788192380.81512160590381
1360.1652034846797220.3304069693594430.834796515320278
1370.136162313910880.272324627821760.86383768608912
1380.1397158227052980.2794316454105970.860284177294702
1390.1045611946891880.2091223893783760.895438805310812
1400.08533325895179590.1706665179035920.914666741048204
1410.06298483245032230.1259696649006450.937015167549678
1420.0459014180450030.09180283609000590.954098581954997
1430.9269649793080880.1460700413838240.0730350206919119
1440.9643173096013710.07136538079725760.0356826903986288
1450.933761421133120.1324771577337610.0662385788668804
1460.8849484103175370.2301031793649250.115051589682463
1470.8247115164368260.3505769671263480.175288483563174
1480.7910137044553130.4179725910893730.208986295544686
1490.6716865086262270.6566269827475450.328313491373772
1500.5389436264774430.9221127470451140.461056373522557






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 level70.0503597122302158OK

\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 & 7 & 0.0503597122302158 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190233&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]7[/C][C]0.0503597122302158[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190233&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190233&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 level70.0503597122302158OK


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')
}