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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 computationTue, 20 Nov 2012 11:52:47 -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/20/t1353430434gfzwuvqr2azwpef.htm/, Retrieved Mon, 29 Apr 2024 20:45:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=191176, Retrieved Mon, 29 Apr 2024 20:45:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact77

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [WS7 Multiple Regr...] [2012-11-17 13:02:00] [f8ee2fa4f3a14474001c30fec05fcd2b]
- R PD    [Multiple Regression] [WS7 Tabel 1] [2012-11-20 16:31:02] [f8ee2fa4f3a14474001c30fec05fcd2b]
-             [Multiple Regression] [WS 7 Tabel 3] [2012-11-20 16:52:47] [4c93b3a0c48c946a3a36627369b78a37] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
26	21	21	23	17	23	4	14	12	127
20	16	15	24	17	20	4	18	11	108
19	19	18	22	18	20	6	11	14	110
19	18	11	20	21	21	8	12	12	102
20	16	8	24	20	24	8	16	21	104
25	23	19	27	28	22	4	18	12	140
25	17	4	28	19	23	4	14	22	112
22	12	20	27	22	20	8	14	11	115
26	19	16	24	16	25	5	15	10	121
22	16	14	23	18	23	4	15	13	112
17	19	10	24	25	27	4	17	10	118
22	20	13	27	17	27	4	19	8	122
19	13	14	27	14	22	4	10	15	105
24	20	8	28	11	24	4	16	14	111
26	27	23	27	27	25	4	18	10	151
21	17	11	23	20	22	8	14	14	106
13	8	9	24	22	28	4	14	14	100
26	25	24	28	22	28	4	17	11	149
20	26	5	27	21	27	4	14	10	122
22	13	15	25	23	25	8	16	13	115
14	19	5	19	17	16	4	18	7	86
21	15	19	24	24	28	7	11	14	124
7	5	6	20	14	21	4	14	12	69
23	16	13	28	17	24	4	12	14	117
17	14	11	26	23	27	5	17	11	113
25	24	17	23	24	14	4	9	9	123
25	24	17	23	24	14	4	16	11	123
19	9	5	20	8	27	4	14	15	84
20	19	9	11	22	20	4	15	14	97
23	19	15	24	23	21	4	11	13	121
22	25	17	25	25	22	4	16	9	132
22	19	17	23	21	21	4	13	15	119
21	18	20	18	24	12	15	17	10	98
15	15	12	20	15	20	10	15	11	87
20	12	7	20	22	24	4	14	13	101
22	21	16	24	21	19	8	16	8	115
18	12	7	23	25	28	4	9	20	109
20	15	14	25	16	23	4	15	12	109
28	28	24	28	28	27	4	17	10	159
22	25	15	26	23	22	4	13	10	129
18	19	15	26	21	27	7	15	9	119
23	20	10	23	21	26	4	16	14	119
20	24	14	22	26	22	6	16	8	122
25	26	18	24	22	21	5	12	14	131
26	25	12	21	21	19	4	12	11	120
15	12	9	20	18	24	16	11	13	82
17	12	9	22	12	19	5	15	9	86
23	15	8	20	25	26	12	15	11	105
21	17	18	25	17	22	6	17	15	114
13	14	10	20	24	28	9	13	11	100
18	16	17	22	15	21	9	16	10	100
19	11	14	23	13	23	4	14	14	99
22	20	16	25	26	28	5	11	18	132
16	11	10	23	16	10	4	12	14	82
24	22	19	23	24	24	4	12	11	132
18	20	10	22	21	21	5	15	12	107
20	19	14	24	20	21	4	16	13	114
24	17	10	25	14	24	4	15	9	110
14	21	4	21	25	24	4	12	10	105
22	23	19	12	25	25	5	12	15	121
24	18	9	17	20	25	4	8	20	109
18	17	12	20	22	23	6	13	12	106
21	27	16	23	20	21	4	11	12	124
23	25	11	23	26	16	4	14	14	120
17	19	18	20	18	17	18	15	13	91
22	22	11	28	22	25	4	10	11	126
24	24	24	24	24	24	6	11	17	138
21	20	17	24	17	23	4	12	12	118
22	19	18	24	24	25	4	15	13	128
16	11	9	24	20	23	5	15	14	98
21	22	19	28	19	28	4	14	13	133
23	22	18	25	20	26	4	16	15	130
22	16	12	21	15	22	5	15	13	103
24	20	23	25	23	19	10	15	10	124
24	24	22	25	26	26	5	13	11	142
16	16	14	18	22	18	8	12	19	96
16	16	14	17	20	18	8	17	13	93
21	22	16	26	24	25	5	13	17	129
26	24	23	28	26	27	4	15	13	150
15	16	7	21	21	12	4	13	9	88
25	27	10	27	25	15	4	15	11	125
18	11	12	22	13	21	5	16	10	92
23	21	12	21	20	23	4	15	9	0
20	20	12	25	22	22	4	16	12	117
17	20	17	22	23	21	8	15	12	112
25	27	21	23	28	24	4	14	13	144
24	20	16	26	22	27	5	15	13	130
17	12	11	19	20	22	14	14	12	87
19	8	14	25	6	28	8	13	15	92
20	21	13	21	21	26	8	7	22	114
15	18	9	13	20	10	4	17	13	81
27	24	19	24	18	19	4	13	15	127
22	16	13	25	23	22	6	15	13	115
23	18	19	26	20	21	4	14	15	123
16	20	13	25	24	24	7	13	10	115
19	20	13	25	22	25	7	16	11	117
25	19	13	22	21	21	4	12	16	117
19	17	14	21	18	20	6	14	11	103
19	16	12	23	21	21	4	17	11	108
26	26	22	25	23	24	7	15	10	139
21	15	11	24	23	23	4	17	10	113
20	22	5	21	15	18	4	12	16	97
24	17	18	21	21	24	8	16	12	117
22	23	19	25	24	24	4	11	11	133
20	21	14	22	23	19	4	15	16	115
18	19	15	20	21	20	10	9	19	103
18	14	12	20	21	18	8	16	11	95
24	17	19	23	20	20	6	15	16	117
24	12	15	28	11	27	4	10	15	113
22	24	17	23	22	23	4	10	24	127
23	18	8	28	27	26	4	15	14	126
22	20	10	24	25	23	5	11	15	119
20	16	12	18	18	17	4	13	11	97
18	20	12	20	20	21	6	14	15	105
25	22	20	28	24	25	4	18	12	140
18	12	12	21	10	23	5	16	10	91
16	16	12	21	27	27	7	14	14	112
20	17	14	25	21	24	8	14	13	113
19	22	6	19	21	20	5	14	9	102
15	12	10	18	18	27	8	14	15	92
19	14	18	21	15	21	10	12	15	98
19	23	18	22	24	24	8	14	14	122
16	15	7	24	22	21	5	15	11	100
17	17	18	15	14	15	12	15	8	84
28	28	9	28	28	25	4	15	11	142
23	20	17	26	18	25	5	13	11	124
25	23	22	23	26	22	4	17	8	137
20	13	11	26	17	24	6	17	10	105
17	18	15	20	19	21	4	19	11	106
23	23	17	22	22	22	4	15	13	125
16	19	15	20	18	23	7	13	11	104
23	23	22	23	24	22	7	9	20	130
11	12	9	22	15	20	10	15	10	79
18	16	13	24	18	23	4	15	15	108
24	23	20	23	26	25	5	15	12	136
23	13	14	22	11	23	8	16	14	98
21	22	14	26	26	22	11	11	23	120
16	18	12	23	21	25	7	14	14	108
24	23	20	27	23	26	4	11	16	139
23	20	20	23	23	22	8	15	11	123
18	10	8	21	15	24	6	13	12	90
20	17	17	26	22	24	7	15	10	119
9	18	9	23	26	25	5	16	14	105
24	15	18	21	16	20	4	14	12	110
25	23	22	27	20	26	8	15	12	135
20	17	10	19	18	21	4	16	11	101
21	17	13	23	22	26	8	16	12	114
25	22	15	25	16	21	6	11	13	118
22	20	18	23	19	22	4	12	11	120
21	20	18	22	20	16	9	9	19	108
21	19	12	22	19	26	5	16	12	114
22	18	12	25	23	28	6	13	17	122
27	22	20	25	24	18	4	16	9	132
24	20	12	28	25	25	4	12	12	130
24	22	16	28	21	23	4	9	19	130
21	18	16	20	21	21	5	13	18	112
18	16	18	25	23	20	6	13	15	114
16	16	16	19	27	25	16	14	14	103
22	16	13	25	23	22	6	19	11	115
20	16	17	22	18	21	6	13	9	108
18	17	13	18	16	16	4	12	18	94
20	18	17	20	16	18	4	13	16	105

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191176&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191176&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191176&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 time11 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Motivatie [t] = -7.64892641196258 + 0.699182398878154I1[t] + 0.899995933217154I2[t] + 1.17049459923633I3[t] + 1.3824341738989E1[t] + 1.06977391077917E2[t] + 0.82021707934688E3[t] -0.899454015105466A[t] + 0.0818955064962485Happiness[t] + 0.357946924003673Depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Motivatie
[t] =  -7.64892641196258 +  0.699182398878154I1[t] +  0.899995933217154I2[t] +  1.17049459923633I3[t] +  1.3824341738989E1[t] +  1.06977391077917E2[t] +  0.82021707934688E3[t] -0.899454015105466A[t] +  0.0818955064962485Happiness[t] +  0.357946924003673Depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191176&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Motivatie
[t] =  -7.64892641196258 +  0.699182398878154I1[t] +  0.899995933217154I2[t] +  1.17049459923633I3[t] +  1.3824341738989E1[t] +  1.06977391077917E2[t] +  0.82021707934688E3[t] -0.899454015105466A[t] +  0.0818955064962485Happiness[t] +  0.357946924003673Depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191176&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191176&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
Motivatie [t] = -7.64892641196258 + 0.699182398878154I1[t] + 0.899995933217154I2[t] + 1.17049459923633I3[t] + 1.3824341738989E1[t] + 1.06977391077917E2[t] + 0.82021707934688E3[t] -0.899454015105466A[t] + 0.0818955064962485Happiness[t] + 0.357946924003673Depression[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-7.6489264119625810.685182-0.71580.4751860.237593
I10.6991823988781540.2986332.34130.0205160.010258
I20.8999959332171540.2587973.47760.000660.00033
I31.170494599236330.2001045.849400
E11.38243417389890.2883974.79354e-062e-06
E21.069773910779170.2207984.8453e-062e-06
E30.820217079346880.2276823.60250.0004260.000213
A-0.8994540151054660.316584-2.84110.0051120.002556
Happiness0.08189550649624850.3754410.21810.8276190.41381
Depression0.3579469240036730.2797831.27940.2027150.101357

\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) & -7.64892641196258 & 10.685182 & -0.7158 & 0.475186 & 0.237593 \tabularnewline
I1 & 0.699182398878154 & 0.298633 & 2.3413 & 0.020516 & 0.010258 \tabularnewline
I2 & 0.899995933217154 & 0.258797 & 3.4776 & 0.00066 & 0.00033 \tabularnewline
I3 & 1.17049459923633 & 0.200104 & 5.8494 & 0 & 0 \tabularnewline
E1 & 1.3824341738989 & 0.288397 & 4.7935 & 4e-06 & 2e-06 \tabularnewline
E2 & 1.06977391077917 & 0.220798 & 4.845 & 3e-06 & 2e-06 \tabularnewline
E3 & 0.82021707934688 & 0.227682 & 3.6025 & 0.000426 & 0.000213 \tabularnewline
A & -0.899454015105466 & 0.316584 & -2.8411 & 0.005112 & 0.002556 \tabularnewline
Happiness & 0.0818955064962485 & 0.375441 & 0.2181 & 0.827619 & 0.41381 \tabularnewline
Depression & 0.357946924003673 & 0.279783 & 1.2794 & 0.202715 & 0.101357 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191176&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]-7.64892641196258[/C][C]10.685182[/C][C]-0.7158[/C][C]0.475186[/C][C]0.237593[/C][/ROW]
[ROW][C]I1[/C][C]0.699182398878154[/C][C]0.298633[/C][C]2.3413[/C][C]0.020516[/C][C]0.010258[/C][/ROW]
[ROW][C]I2[/C][C]0.899995933217154[/C][C]0.258797[/C][C]3.4776[/C][C]0.00066[/C][C]0.00033[/C][/ROW]
[ROW][C]I3[/C][C]1.17049459923633[/C][C]0.200104[/C][C]5.8494[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]E1[/C][C]1.3824341738989[/C][C]0.288397[/C][C]4.7935[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]E2[/C][C]1.06977391077917[/C][C]0.220798[/C][C]4.845[/C][C]3e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]E3[/C][C]0.82021707934688[/C][C]0.227682[/C][C]3.6025[/C][C]0.000426[/C][C]0.000213[/C][/ROW]
[ROW][C]A[/C][C]-0.899454015105466[/C][C]0.316584[/C][C]-2.8411[/C][C]0.005112[/C][C]0.002556[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0818955064962485[/C][C]0.375441[/C][C]0.2181[/C][C]0.827619[/C][C]0.41381[/C][/ROW]
[ROW][C]Depression[/C][C]0.357946924003673[/C][C]0.279783[/C][C]1.2794[/C][C]0.202715[/C][C]0.101357[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191176&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191176&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)-7.6489264119625810.685182-0.71580.4751860.237593
I10.6991823988781540.2986332.34130.0205160.010258
I20.8999959332171540.2587973.47760.000660.00033
I31.170494599236330.2001045.849400
E11.38243417389890.2883974.79354e-062e-06
E21.069773910779170.2207984.8453e-062e-06
E30.820217079346880.2276823.60250.0004260.000213
A-0.8994540151054660.316584-2.84110.0051120.002556
Happiness0.08189550649624850.3754410.21810.8276190.41381
Depression0.3579469240036730.2797831.27940.2027150.101357






Multiple Linear Regression - Regression Statistics
Multiple R0.873362373451491
R-squared0.762761835360821
Adjusted R-squared0.748714838770344
F-TEST (value)54.3007062362278
F-TEST (DF numerator)9
F-TEST (DF denominator)152
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.22544184762359
Sum Squared Residuals12936.5341471505

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.873362373451491 \tabularnewline
R-squared & 0.762761835360821 \tabularnewline
Adjusted R-squared & 0.748714838770344 \tabularnewline
F-TEST (value) & 54.3007062362278 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.22544184762359 \tabularnewline
Sum Squared Residuals & 12936.5341471505 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191176&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.873362373451491[/C][/ROW]
[ROW][C]R-squared[/C][C]0.762761835360821[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.748714838770344[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]54.3007062362278[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/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]9.22544184762359[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]12936.5341471505[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191176&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191176&T=3

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.873362373451491
R-squared0.762761835360821
Adjusted R-squared0.748714838770344
F-TEST (value)54.3007062362278
F-TEST (DF numerator)9
F-TEST (DF denominator)152
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.22544184762359
Sum Squared Residuals12936.5341471505






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1127124.7013365668612.29866343313892
2108107.8747129499280.125287050071958
3110110.393571907718-0.393571907718028
4102100.1318778720111.86812212798886
5104105.989302971621-1.98930297162116
6140140.265771496749-0.265771496749212
7112113.134950179186-1.13495017918639
8115117.096341000183-2.09634100018306
9121117.4685137694423.53148623055789
10112110.7207814518881.27921854811246
11118116.4845489679251.515451032075
12122119.428949093692.57105090630957
13105106.66008674426-1.66008674425977
14111109.3799753909821.62002460901835
15151149.9218995046951.0781004953046
16106105.4076772878150.592322712185428
17100101.414366031323-1.41436603132268
18149148.0626695130490.937330486951453
19122118.6521150599893.34788494001079
20115115.429539757934-0.429539757934193
218683.04983348105062.95016651894942
22124124.208242003984-0.208242003984454
236970.4624586554288-1.46245865542884
24117117.024343694107-0.0243436941065235
25113113.238877336213-0.238877336213234
26123120.6433089275292.35669107247089
27123121.932471321011.0675286789898
288480.85863635901633.14136364098372
299791.75711269989395.24288730010609
30121120.0537337927690.946266207230532
31132130.4154051029381.58459489706244
32119119.053243457907-0.0532434579065674
339898.5245993120478-0.524599312047803
348786.65562609733560.344373902664356
3510198.39908542087892.60091457912108
36115113.5669827313511.43301726864914
37109109.734364130089-0.734364130088818
38109108.689794323150.310205676850131
39159157.4015015717811.59849842821923
40129127.429562661321.5704373386796
41119118.3018770853530.698122914646648
42119116.4477845875672.55221541243293
43122119.3711770091232.62882299087703
44131129.734168425371.26583157462988
45120115.4784899475384.52151005246203
468281.92593228108980.0740677189102154
478684.35922506136491.64077493863508
48105101.3872404450783.61275955492239
49114115.559227571633-1.55922757163251
5010098.94164100179011.05835899820995
5110099.71413024811160.285869751888379
529999.0504364527789-0.0504364527788831
53132132.648597992922-0.64859799292226
548284.6536195470347-2.65361954703467
55132129.6488750216542.3511249783458
56107105.7711096529341.22889034706607
57114113.9858477970420.0141522029575846
58110106.2113658739293.78863412607098
59105102.1465947500882.85340524991171
60121116.3660549152624.63394508473763
61109105.4844019793073.51559802069283
62106104.2942057453321.70579425466773
63124122.0761282297471.92387177025337
64120119.1011666003240.898833399676295
659192.945874444839-1.94587444483901
66126124.3156025442981.68439745570215
67138138.950652065484-0.950652065483895
68118116.8420934032141.15790659678571
69128127.5442594457520.455740554248025
709899.1537093007067-1.15370930070665
71133133.275182319006-0.275182319005775
72130129.6647746089140.335225391085704
73103100.6859310788272.31406892117278
74124124.615886097302-0.615886097301705
75142140.6876425052381.31235749476183
769698.0957053996178-2.09570539961778
779392.83551939261970.164480607380255
78129130.337486663767-1.33748666376717
79150150.00316037938-0.00316037937970743
808887.4595292678030.540470732197007
81125123.7768291052331.22317089476721
829290.81994582470781.18005417529225
830111.521846096125-111.521846096125
84117116.5291066825870.470893317412633
85112112.706575224952-0.706575224951994
86144142.3478067892071.65219321079271
87130128.8679316480811.13206835191945
888786.46861339198040.531386608019574
899292.406220379291-0.406220379291012
90114114.525548547202-0.525548547202209
918179.59019859404551.40980140595453
92127125.9228025997231.0771974002766
93115115.044899644787-0.0448996447870464
94123124.352843239454-1.35284323945401
95115115.022911253751-0.0229112537506782
96117116.4047611516660.595238848333896
97117115.3624295012041.63757049879575
98103101.7010132179171.29898678208275
99108107.2990297954090.700970204590686
100139137.0431793371751.95682066282458
101113111.4313732908591.56862670914053
1029794.94081963595982.05918036404023
103117115.0919235657641.90807643423641
104133131.8334789984171.1665210015834
105115115.581804154837-0.581804154837429
106103104.655486642275-1.6554866422746
1079594.51219020344140.487809796558615
108117117.82544450436-0.825444504360412
109113112.7006952429660.299304757034478
110127129.23926699001-2.23926699000958
111126125.5557223503330.444277649666606
112119117.9984961440811.00150385591918
1139794.26826924908292.73173075091707
114105104.3699478432390.630052156761093
115140140.100259931591-0.100259931591244
1169188.76862001038232.23137998961774
117112111.9063523989390.0936476010607919
118113113.337108179912-0.33710817991246
11910297.46504964175024.53495035824977
1209290.94942225890881.05057774109123
1219898.9640797848208-0.964079784820795
122122122.139845911927-0.139845911926995
12310099.83797672595620.162023274043825
1248481.92117137771422.07882862228582
125142138.3978043355533.6021956644467
126124122.5400291856281.45997081437155
127137134.5942875747932.40571242520671
128105104.2997329578060.700267042194244
129106105.0890813315340.910918668466262
130125123.3078635708661.69213642913437
131104102.6708200294931.32917997050656
132130131.998211946236-1.99821194623632
1337980.0543463282567-1.05434632825672
134108108.851885279045-0.851885279044838
135136134.38330988341.61669011660015
1369896.69115087183921.3088491281608
137120124.262560489209-4.26256048920895
138108108.412134989803-0.412134989802552
139139139.52760161114-0.52760161114
140123122.2578577451720.742142254827986
1419088.02648969365851.9735103063415
142119119.20830881189-0.208308811890207
143105107.317942997046-2.31794299704575
144110108.0962188754751.90378112452471
145135134.6564297457020.343570254298257
14610197.73626499460263.2637350053974
147114112.6169797902181.38302020978177
148118116.2471951584111.75280484158896
149120118.2907200456381.70927995436235
150108110.478193704572-2.47819370457178
151114111.3530831964962.6469168035038
152122121.8636960710880.136303928911497
153132130.3721708672191.62782913278084
154130128.815529744191.18447025581015
155130131.637912154296-1.63791215429599
156112112.310654761784-0.310654761783773
157114117.012311721777-3.01231172177714
158103104.087942153444-1.08794215344375
159115114.6565878227650.343412177235305
160108106.4165453800291.58345461997058
1619494.4043630444043-0.404363044404341
162105105.156006337304-0.156006337303583

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 127 & 124.701336566861 & 2.29866343313892 \tabularnewline
2 & 108 & 107.874712949928 & 0.125287050071958 \tabularnewline
3 & 110 & 110.393571907718 & -0.393571907718028 \tabularnewline
4 & 102 & 100.131877872011 & 1.86812212798886 \tabularnewline
5 & 104 & 105.989302971621 & -1.98930297162116 \tabularnewline
6 & 140 & 140.265771496749 & -0.265771496749212 \tabularnewline
7 & 112 & 113.134950179186 & -1.13495017918639 \tabularnewline
8 & 115 & 117.096341000183 & -2.09634100018306 \tabularnewline
9 & 121 & 117.468513769442 & 3.53148623055789 \tabularnewline
10 & 112 & 110.720781451888 & 1.27921854811246 \tabularnewline
11 & 118 & 116.484548967925 & 1.515451032075 \tabularnewline
12 & 122 & 119.42894909369 & 2.57105090630957 \tabularnewline
13 & 105 & 106.66008674426 & -1.66008674425977 \tabularnewline
14 & 111 & 109.379975390982 & 1.62002460901835 \tabularnewline
15 & 151 & 149.921899504695 & 1.0781004953046 \tabularnewline
16 & 106 & 105.407677287815 & 0.592322712185428 \tabularnewline
17 & 100 & 101.414366031323 & -1.41436603132268 \tabularnewline
18 & 149 & 148.062669513049 & 0.937330486951453 \tabularnewline
19 & 122 & 118.652115059989 & 3.34788494001079 \tabularnewline
20 & 115 & 115.429539757934 & -0.429539757934193 \tabularnewline
21 & 86 & 83.0498334810506 & 2.95016651894942 \tabularnewline
22 & 124 & 124.208242003984 & -0.208242003984454 \tabularnewline
23 & 69 & 70.4624586554288 & -1.46245865542884 \tabularnewline
24 & 117 & 117.024343694107 & -0.0243436941065235 \tabularnewline
25 & 113 & 113.238877336213 & -0.238877336213234 \tabularnewline
26 & 123 & 120.643308927529 & 2.35669107247089 \tabularnewline
27 & 123 & 121.93247132101 & 1.0675286789898 \tabularnewline
28 & 84 & 80.8586363590163 & 3.14136364098372 \tabularnewline
29 & 97 & 91.7571126998939 & 5.24288730010609 \tabularnewline
30 & 121 & 120.053733792769 & 0.946266207230532 \tabularnewline
31 & 132 & 130.415405102938 & 1.58459489706244 \tabularnewline
32 & 119 & 119.053243457907 & -0.0532434579065674 \tabularnewline
33 & 98 & 98.5245993120478 & -0.524599312047803 \tabularnewline
34 & 87 & 86.6556260973356 & 0.344373902664356 \tabularnewline
35 & 101 & 98.3990854208789 & 2.60091457912108 \tabularnewline
36 & 115 & 113.566982731351 & 1.43301726864914 \tabularnewline
37 & 109 & 109.734364130089 & -0.734364130088818 \tabularnewline
38 & 109 & 108.68979432315 & 0.310205676850131 \tabularnewline
39 & 159 & 157.401501571781 & 1.59849842821923 \tabularnewline
40 & 129 & 127.42956266132 & 1.5704373386796 \tabularnewline
41 & 119 & 118.301877085353 & 0.698122914646648 \tabularnewline
42 & 119 & 116.447784587567 & 2.55221541243293 \tabularnewline
43 & 122 & 119.371177009123 & 2.62882299087703 \tabularnewline
44 & 131 & 129.73416842537 & 1.26583157462988 \tabularnewline
45 & 120 & 115.478489947538 & 4.52151005246203 \tabularnewline
46 & 82 & 81.9259322810898 & 0.0740677189102154 \tabularnewline
47 & 86 & 84.3592250613649 & 1.64077493863508 \tabularnewline
48 & 105 & 101.387240445078 & 3.61275955492239 \tabularnewline
49 & 114 & 115.559227571633 & -1.55922757163251 \tabularnewline
50 & 100 & 98.9416410017901 & 1.05835899820995 \tabularnewline
51 & 100 & 99.7141302481116 & 0.285869751888379 \tabularnewline
52 & 99 & 99.0504364527789 & -0.0504364527788831 \tabularnewline
53 & 132 & 132.648597992922 & -0.64859799292226 \tabularnewline
54 & 82 & 84.6536195470347 & -2.65361954703467 \tabularnewline
55 & 132 & 129.648875021654 & 2.3511249783458 \tabularnewline
56 & 107 & 105.771109652934 & 1.22889034706607 \tabularnewline
57 & 114 & 113.985847797042 & 0.0141522029575846 \tabularnewline
58 & 110 & 106.211365873929 & 3.78863412607098 \tabularnewline
59 & 105 & 102.146594750088 & 2.85340524991171 \tabularnewline
60 & 121 & 116.366054915262 & 4.63394508473763 \tabularnewline
61 & 109 & 105.484401979307 & 3.51559802069283 \tabularnewline
62 & 106 & 104.294205745332 & 1.70579425466773 \tabularnewline
63 & 124 & 122.076128229747 & 1.92387177025337 \tabularnewline
64 & 120 & 119.101166600324 & 0.898833399676295 \tabularnewline
65 & 91 & 92.945874444839 & -1.94587444483901 \tabularnewline
66 & 126 & 124.315602544298 & 1.68439745570215 \tabularnewline
67 & 138 & 138.950652065484 & -0.950652065483895 \tabularnewline
68 & 118 & 116.842093403214 & 1.15790659678571 \tabularnewline
69 & 128 & 127.544259445752 & 0.455740554248025 \tabularnewline
70 & 98 & 99.1537093007067 & -1.15370930070665 \tabularnewline
71 & 133 & 133.275182319006 & -0.275182319005775 \tabularnewline
72 & 130 & 129.664774608914 & 0.335225391085704 \tabularnewline
73 & 103 & 100.685931078827 & 2.31406892117278 \tabularnewline
74 & 124 & 124.615886097302 & -0.615886097301705 \tabularnewline
75 & 142 & 140.687642505238 & 1.31235749476183 \tabularnewline
76 & 96 & 98.0957053996178 & -2.09570539961778 \tabularnewline
77 & 93 & 92.8355193926197 & 0.164480607380255 \tabularnewline
78 & 129 & 130.337486663767 & -1.33748666376717 \tabularnewline
79 & 150 & 150.00316037938 & -0.00316037937970743 \tabularnewline
80 & 88 & 87.459529267803 & 0.540470732197007 \tabularnewline
81 & 125 & 123.776829105233 & 1.22317089476721 \tabularnewline
82 & 92 & 90.8199458247078 & 1.18005417529225 \tabularnewline
83 & 0 & 111.521846096125 & -111.521846096125 \tabularnewline
84 & 117 & 116.529106682587 & 0.470893317412633 \tabularnewline
85 & 112 & 112.706575224952 & -0.706575224951994 \tabularnewline
86 & 144 & 142.347806789207 & 1.65219321079271 \tabularnewline
87 & 130 & 128.867931648081 & 1.13206835191945 \tabularnewline
88 & 87 & 86.4686133919804 & 0.531386608019574 \tabularnewline
89 & 92 & 92.406220379291 & -0.406220379291012 \tabularnewline
90 & 114 & 114.525548547202 & -0.525548547202209 \tabularnewline
91 & 81 & 79.5901985940455 & 1.40980140595453 \tabularnewline
92 & 127 & 125.922802599723 & 1.0771974002766 \tabularnewline
93 & 115 & 115.044899644787 & -0.0448996447870464 \tabularnewline
94 & 123 & 124.352843239454 & -1.35284323945401 \tabularnewline
95 & 115 & 115.022911253751 & -0.0229112537506782 \tabularnewline
96 & 117 & 116.404761151666 & 0.595238848333896 \tabularnewline
97 & 117 & 115.362429501204 & 1.63757049879575 \tabularnewline
98 & 103 & 101.701013217917 & 1.29898678208275 \tabularnewline
99 & 108 & 107.299029795409 & 0.700970204590686 \tabularnewline
100 & 139 & 137.043179337175 & 1.95682066282458 \tabularnewline
101 & 113 & 111.431373290859 & 1.56862670914053 \tabularnewline
102 & 97 & 94.9408196359598 & 2.05918036404023 \tabularnewline
103 & 117 & 115.091923565764 & 1.90807643423641 \tabularnewline
104 & 133 & 131.833478998417 & 1.1665210015834 \tabularnewline
105 & 115 & 115.581804154837 & -0.581804154837429 \tabularnewline
106 & 103 & 104.655486642275 & -1.6554866422746 \tabularnewline
107 & 95 & 94.5121902034414 & 0.487809796558615 \tabularnewline
108 & 117 & 117.82544450436 & -0.825444504360412 \tabularnewline
109 & 113 & 112.700695242966 & 0.299304757034478 \tabularnewline
110 & 127 & 129.23926699001 & -2.23926699000958 \tabularnewline
111 & 126 & 125.555722350333 & 0.444277649666606 \tabularnewline
112 & 119 & 117.998496144081 & 1.00150385591918 \tabularnewline
113 & 97 & 94.2682692490829 & 2.73173075091707 \tabularnewline
114 & 105 & 104.369947843239 & 0.630052156761093 \tabularnewline
115 & 140 & 140.100259931591 & -0.100259931591244 \tabularnewline
116 & 91 & 88.7686200103823 & 2.23137998961774 \tabularnewline
117 & 112 & 111.906352398939 & 0.0936476010607919 \tabularnewline
118 & 113 & 113.337108179912 & -0.33710817991246 \tabularnewline
119 & 102 & 97.4650496417502 & 4.53495035824977 \tabularnewline
120 & 92 & 90.9494222589088 & 1.05057774109123 \tabularnewline
121 & 98 & 98.9640797848208 & -0.964079784820795 \tabularnewline
122 & 122 & 122.139845911927 & -0.139845911926995 \tabularnewline
123 & 100 & 99.8379767259562 & 0.162023274043825 \tabularnewline
124 & 84 & 81.9211713777142 & 2.07882862228582 \tabularnewline
125 & 142 & 138.397804335553 & 3.6021956644467 \tabularnewline
126 & 124 & 122.540029185628 & 1.45997081437155 \tabularnewline
127 & 137 & 134.594287574793 & 2.40571242520671 \tabularnewline
128 & 105 & 104.299732957806 & 0.700267042194244 \tabularnewline
129 & 106 & 105.089081331534 & 0.910918668466262 \tabularnewline
130 & 125 & 123.307863570866 & 1.69213642913437 \tabularnewline
131 & 104 & 102.670820029493 & 1.32917997050656 \tabularnewline
132 & 130 & 131.998211946236 & -1.99821194623632 \tabularnewline
133 & 79 & 80.0543463282567 & -1.05434632825672 \tabularnewline
134 & 108 & 108.851885279045 & -0.851885279044838 \tabularnewline
135 & 136 & 134.3833098834 & 1.61669011660015 \tabularnewline
136 & 98 & 96.6911508718392 & 1.3088491281608 \tabularnewline
137 & 120 & 124.262560489209 & -4.26256048920895 \tabularnewline
138 & 108 & 108.412134989803 & -0.412134989802552 \tabularnewline
139 & 139 & 139.52760161114 & -0.52760161114 \tabularnewline
140 & 123 & 122.257857745172 & 0.742142254827986 \tabularnewline
141 & 90 & 88.0264896936585 & 1.9735103063415 \tabularnewline
142 & 119 & 119.20830881189 & -0.208308811890207 \tabularnewline
143 & 105 & 107.317942997046 & -2.31794299704575 \tabularnewline
144 & 110 & 108.096218875475 & 1.90378112452471 \tabularnewline
145 & 135 & 134.656429745702 & 0.343570254298257 \tabularnewline
146 & 101 & 97.7362649946026 & 3.2637350053974 \tabularnewline
147 & 114 & 112.616979790218 & 1.38302020978177 \tabularnewline
148 & 118 & 116.247195158411 & 1.75280484158896 \tabularnewline
149 & 120 & 118.290720045638 & 1.70927995436235 \tabularnewline
150 & 108 & 110.478193704572 & -2.47819370457178 \tabularnewline
151 & 114 & 111.353083196496 & 2.6469168035038 \tabularnewline
152 & 122 & 121.863696071088 & 0.136303928911497 \tabularnewline
153 & 132 & 130.372170867219 & 1.62782913278084 \tabularnewline
154 & 130 & 128.81552974419 & 1.18447025581015 \tabularnewline
155 & 130 & 131.637912154296 & -1.63791215429599 \tabularnewline
156 & 112 & 112.310654761784 & -0.310654761783773 \tabularnewline
157 & 114 & 117.012311721777 & -3.01231172177714 \tabularnewline
158 & 103 & 104.087942153444 & -1.08794215344375 \tabularnewline
159 & 115 & 114.656587822765 & 0.343412177235305 \tabularnewline
160 & 108 & 106.416545380029 & 1.58345461997058 \tabularnewline
161 & 94 & 94.4043630444043 & -0.404363044404341 \tabularnewline
162 & 105 & 105.156006337304 & -0.156006337303583 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191176&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]127[/C][C]124.701336566861[/C][C]2.29866343313892[/C][/ROW]
[ROW][C]2[/C][C]108[/C][C]107.874712949928[/C][C]0.125287050071958[/C][/ROW]
[ROW][C]3[/C][C]110[/C][C]110.393571907718[/C][C]-0.393571907718028[/C][/ROW]
[ROW][C]4[/C][C]102[/C][C]100.131877872011[/C][C]1.86812212798886[/C][/ROW]
[ROW][C]5[/C][C]104[/C][C]105.989302971621[/C][C]-1.98930297162116[/C][/ROW]
[ROW][C]6[/C][C]140[/C][C]140.265771496749[/C][C]-0.265771496749212[/C][/ROW]
[ROW][C]7[/C][C]112[/C][C]113.134950179186[/C][C]-1.13495017918639[/C][/ROW]
[ROW][C]8[/C][C]115[/C][C]117.096341000183[/C][C]-2.09634100018306[/C][/ROW]
[ROW][C]9[/C][C]121[/C][C]117.468513769442[/C][C]3.53148623055789[/C][/ROW]
[ROW][C]10[/C][C]112[/C][C]110.720781451888[/C][C]1.27921854811246[/C][/ROW]
[ROW][C]11[/C][C]118[/C][C]116.484548967925[/C][C]1.515451032075[/C][/ROW]
[ROW][C]12[/C][C]122[/C][C]119.42894909369[/C][C]2.57105090630957[/C][/ROW]
[ROW][C]13[/C][C]105[/C][C]106.66008674426[/C][C]-1.66008674425977[/C][/ROW]
[ROW][C]14[/C][C]111[/C][C]109.379975390982[/C][C]1.62002460901835[/C][/ROW]
[ROW][C]15[/C][C]151[/C][C]149.921899504695[/C][C]1.0781004953046[/C][/ROW]
[ROW][C]16[/C][C]106[/C][C]105.407677287815[/C][C]0.592322712185428[/C][/ROW]
[ROW][C]17[/C][C]100[/C][C]101.414366031323[/C][C]-1.41436603132268[/C][/ROW]
[ROW][C]18[/C][C]149[/C][C]148.062669513049[/C][C]0.937330486951453[/C][/ROW]
[ROW][C]19[/C][C]122[/C][C]118.652115059989[/C][C]3.34788494001079[/C][/ROW]
[ROW][C]20[/C][C]115[/C][C]115.429539757934[/C][C]-0.429539757934193[/C][/ROW]
[ROW][C]21[/C][C]86[/C][C]83.0498334810506[/C][C]2.95016651894942[/C][/ROW]
[ROW][C]22[/C][C]124[/C][C]124.208242003984[/C][C]-0.208242003984454[/C][/ROW]
[ROW][C]23[/C][C]69[/C][C]70.4624586554288[/C][C]-1.46245865542884[/C][/ROW]
[ROW][C]24[/C][C]117[/C][C]117.024343694107[/C][C]-0.0243436941065235[/C][/ROW]
[ROW][C]25[/C][C]113[/C][C]113.238877336213[/C][C]-0.238877336213234[/C][/ROW]
[ROW][C]26[/C][C]123[/C][C]120.643308927529[/C][C]2.35669107247089[/C][/ROW]
[ROW][C]27[/C][C]123[/C][C]121.93247132101[/C][C]1.0675286789898[/C][/ROW]
[ROW][C]28[/C][C]84[/C][C]80.8586363590163[/C][C]3.14136364098372[/C][/ROW]
[ROW][C]29[/C][C]97[/C][C]91.7571126998939[/C][C]5.24288730010609[/C][/ROW]
[ROW][C]30[/C][C]121[/C][C]120.053733792769[/C][C]0.946266207230532[/C][/ROW]
[ROW][C]31[/C][C]132[/C][C]130.415405102938[/C][C]1.58459489706244[/C][/ROW]
[ROW][C]32[/C][C]119[/C][C]119.053243457907[/C][C]-0.0532434579065674[/C][/ROW]
[ROW][C]33[/C][C]98[/C][C]98.5245993120478[/C][C]-0.524599312047803[/C][/ROW]
[ROW][C]34[/C][C]87[/C][C]86.6556260973356[/C][C]0.344373902664356[/C][/ROW]
[ROW][C]35[/C][C]101[/C][C]98.3990854208789[/C][C]2.60091457912108[/C][/ROW]
[ROW][C]36[/C][C]115[/C][C]113.566982731351[/C][C]1.43301726864914[/C][/ROW]
[ROW][C]37[/C][C]109[/C][C]109.734364130089[/C][C]-0.734364130088818[/C][/ROW]
[ROW][C]38[/C][C]109[/C][C]108.68979432315[/C][C]0.310205676850131[/C][/ROW]
[ROW][C]39[/C][C]159[/C][C]157.401501571781[/C][C]1.59849842821923[/C][/ROW]
[ROW][C]40[/C][C]129[/C][C]127.42956266132[/C][C]1.5704373386796[/C][/ROW]
[ROW][C]41[/C][C]119[/C][C]118.301877085353[/C][C]0.698122914646648[/C][/ROW]
[ROW][C]42[/C][C]119[/C][C]116.447784587567[/C][C]2.55221541243293[/C][/ROW]
[ROW][C]43[/C][C]122[/C][C]119.371177009123[/C][C]2.62882299087703[/C][/ROW]
[ROW][C]44[/C][C]131[/C][C]129.73416842537[/C][C]1.26583157462988[/C][/ROW]
[ROW][C]45[/C][C]120[/C][C]115.478489947538[/C][C]4.52151005246203[/C][/ROW]
[ROW][C]46[/C][C]82[/C][C]81.9259322810898[/C][C]0.0740677189102154[/C][/ROW]
[ROW][C]47[/C][C]86[/C][C]84.3592250613649[/C][C]1.64077493863508[/C][/ROW]
[ROW][C]48[/C][C]105[/C][C]101.387240445078[/C][C]3.61275955492239[/C][/ROW]
[ROW][C]49[/C][C]114[/C][C]115.559227571633[/C][C]-1.55922757163251[/C][/ROW]
[ROW][C]50[/C][C]100[/C][C]98.9416410017901[/C][C]1.05835899820995[/C][/ROW]
[ROW][C]51[/C][C]100[/C][C]99.7141302481116[/C][C]0.285869751888379[/C][/ROW]
[ROW][C]52[/C][C]99[/C][C]99.0504364527789[/C][C]-0.0504364527788831[/C][/ROW]
[ROW][C]53[/C][C]132[/C][C]132.648597992922[/C][C]-0.64859799292226[/C][/ROW]
[ROW][C]54[/C][C]82[/C][C]84.6536195470347[/C][C]-2.65361954703467[/C][/ROW]
[ROW][C]55[/C][C]132[/C][C]129.648875021654[/C][C]2.3511249783458[/C][/ROW]
[ROW][C]56[/C][C]107[/C][C]105.771109652934[/C][C]1.22889034706607[/C][/ROW]
[ROW][C]57[/C][C]114[/C][C]113.985847797042[/C][C]0.0141522029575846[/C][/ROW]
[ROW][C]58[/C][C]110[/C][C]106.211365873929[/C][C]3.78863412607098[/C][/ROW]
[ROW][C]59[/C][C]105[/C][C]102.146594750088[/C][C]2.85340524991171[/C][/ROW]
[ROW][C]60[/C][C]121[/C][C]116.366054915262[/C][C]4.63394508473763[/C][/ROW]
[ROW][C]61[/C][C]109[/C][C]105.484401979307[/C][C]3.51559802069283[/C][/ROW]
[ROW][C]62[/C][C]106[/C][C]104.294205745332[/C][C]1.70579425466773[/C][/ROW]
[ROW][C]63[/C][C]124[/C][C]122.076128229747[/C][C]1.92387177025337[/C][/ROW]
[ROW][C]64[/C][C]120[/C][C]119.101166600324[/C][C]0.898833399676295[/C][/ROW]
[ROW][C]65[/C][C]91[/C][C]92.945874444839[/C][C]-1.94587444483901[/C][/ROW]
[ROW][C]66[/C][C]126[/C][C]124.315602544298[/C][C]1.68439745570215[/C][/ROW]
[ROW][C]67[/C][C]138[/C][C]138.950652065484[/C][C]-0.950652065483895[/C][/ROW]
[ROW][C]68[/C][C]118[/C][C]116.842093403214[/C][C]1.15790659678571[/C][/ROW]
[ROW][C]69[/C][C]128[/C][C]127.544259445752[/C][C]0.455740554248025[/C][/ROW]
[ROW][C]70[/C][C]98[/C][C]99.1537093007067[/C][C]-1.15370930070665[/C][/ROW]
[ROW][C]71[/C][C]133[/C][C]133.275182319006[/C][C]-0.275182319005775[/C][/ROW]
[ROW][C]72[/C][C]130[/C][C]129.664774608914[/C][C]0.335225391085704[/C][/ROW]
[ROW][C]73[/C][C]103[/C][C]100.685931078827[/C][C]2.31406892117278[/C][/ROW]
[ROW][C]74[/C][C]124[/C][C]124.615886097302[/C][C]-0.615886097301705[/C][/ROW]
[ROW][C]75[/C][C]142[/C][C]140.687642505238[/C][C]1.31235749476183[/C][/ROW]
[ROW][C]76[/C][C]96[/C][C]98.0957053996178[/C][C]-2.09570539961778[/C][/ROW]
[ROW][C]77[/C][C]93[/C][C]92.8355193926197[/C][C]0.164480607380255[/C][/ROW]
[ROW][C]78[/C][C]129[/C][C]130.337486663767[/C][C]-1.33748666376717[/C][/ROW]
[ROW][C]79[/C][C]150[/C][C]150.00316037938[/C][C]-0.00316037937970743[/C][/ROW]
[ROW][C]80[/C][C]88[/C][C]87.459529267803[/C][C]0.540470732197007[/C][/ROW]
[ROW][C]81[/C][C]125[/C][C]123.776829105233[/C][C]1.22317089476721[/C][/ROW]
[ROW][C]82[/C][C]92[/C][C]90.8199458247078[/C][C]1.18005417529225[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]111.521846096125[/C][C]-111.521846096125[/C][/ROW]
[ROW][C]84[/C][C]117[/C][C]116.529106682587[/C][C]0.470893317412633[/C][/ROW]
[ROW][C]85[/C][C]112[/C][C]112.706575224952[/C][C]-0.706575224951994[/C][/ROW]
[ROW][C]86[/C][C]144[/C][C]142.347806789207[/C][C]1.65219321079271[/C][/ROW]
[ROW][C]87[/C][C]130[/C][C]128.867931648081[/C][C]1.13206835191945[/C][/ROW]
[ROW][C]88[/C][C]87[/C][C]86.4686133919804[/C][C]0.531386608019574[/C][/ROW]
[ROW][C]89[/C][C]92[/C][C]92.406220379291[/C][C]-0.406220379291012[/C][/ROW]
[ROW][C]90[/C][C]114[/C][C]114.525548547202[/C][C]-0.525548547202209[/C][/ROW]
[ROW][C]91[/C][C]81[/C][C]79.5901985940455[/C][C]1.40980140595453[/C][/ROW]
[ROW][C]92[/C][C]127[/C][C]125.922802599723[/C][C]1.0771974002766[/C][/ROW]
[ROW][C]93[/C][C]115[/C][C]115.044899644787[/C][C]-0.0448996447870464[/C][/ROW]
[ROW][C]94[/C][C]123[/C][C]124.352843239454[/C][C]-1.35284323945401[/C][/ROW]
[ROW][C]95[/C][C]115[/C][C]115.022911253751[/C][C]-0.0229112537506782[/C][/ROW]
[ROW][C]96[/C][C]117[/C][C]116.404761151666[/C][C]0.595238848333896[/C][/ROW]
[ROW][C]97[/C][C]117[/C][C]115.362429501204[/C][C]1.63757049879575[/C][/ROW]
[ROW][C]98[/C][C]103[/C][C]101.701013217917[/C][C]1.29898678208275[/C][/ROW]
[ROW][C]99[/C][C]108[/C][C]107.299029795409[/C][C]0.700970204590686[/C][/ROW]
[ROW][C]100[/C][C]139[/C][C]137.043179337175[/C][C]1.95682066282458[/C][/ROW]
[ROW][C]101[/C][C]113[/C][C]111.431373290859[/C][C]1.56862670914053[/C][/ROW]
[ROW][C]102[/C][C]97[/C][C]94.9408196359598[/C][C]2.05918036404023[/C][/ROW]
[ROW][C]103[/C][C]117[/C][C]115.091923565764[/C][C]1.90807643423641[/C][/ROW]
[ROW][C]104[/C][C]133[/C][C]131.833478998417[/C][C]1.1665210015834[/C][/ROW]
[ROW][C]105[/C][C]115[/C][C]115.581804154837[/C][C]-0.581804154837429[/C][/ROW]
[ROW][C]106[/C][C]103[/C][C]104.655486642275[/C][C]-1.6554866422746[/C][/ROW]
[ROW][C]107[/C][C]95[/C][C]94.5121902034414[/C][C]0.487809796558615[/C][/ROW]
[ROW][C]108[/C][C]117[/C][C]117.82544450436[/C][C]-0.825444504360412[/C][/ROW]
[ROW][C]109[/C][C]113[/C][C]112.700695242966[/C][C]0.299304757034478[/C][/ROW]
[ROW][C]110[/C][C]127[/C][C]129.23926699001[/C][C]-2.23926699000958[/C][/ROW]
[ROW][C]111[/C][C]126[/C][C]125.555722350333[/C][C]0.444277649666606[/C][/ROW]
[ROW][C]112[/C][C]119[/C][C]117.998496144081[/C][C]1.00150385591918[/C][/ROW]
[ROW][C]113[/C][C]97[/C][C]94.2682692490829[/C][C]2.73173075091707[/C][/ROW]
[ROW][C]114[/C][C]105[/C][C]104.369947843239[/C][C]0.630052156761093[/C][/ROW]
[ROW][C]115[/C][C]140[/C][C]140.100259931591[/C][C]-0.100259931591244[/C][/ROW]
[ROW][C]116[/C][C]91[/C][C]88.7686200103823[/C][C]2.23137998961774[/C][/ROW]
[ROW][C]117[/C][C]112[/C][C]111.906352398939[/C][C]0.0936476010607919[/C][/ROW]
[ROW][C]118[/C][C]113[/C][C]113.337108179912[/C][C]-0.33710817991246[/C][/ROW]
[ROW][C]119[/C][C]102[/C][C]97.4650496417502[/C][C]4.53495035824977[/C][/ROW]
[ROW][C]120[/C][C]92[/C][C]90.9494222589088[/C][C]1.05057774109123[/C][/ROW]
[ROW][C]121[/C][C]98[/C][C]98.9640797848208[/C][C]-0.964079784820795[/C][/ROW]
[ROW][C]122[/C][C]122[/C][C]122.139845911927[/C][C]-0.139845911926995[/C][/ROW]
[ROW][C]123[/C][C]100[/C][C]99.8379767259562[/C][C]0.162023274043825[/C][/ROW]
[ROW][C]124[/C][C]84[/C][C]81.9211713777142[/C][C]2.07882862228582[/C][/ROW]
[ROW][C]125[/C][C]142[/C][C]138.397804335553[/C][C]3.6021956644467[/C][/ROW]
[ROW][C]126[/C][C]124[/C][C]122.540029185628[/C][C]1.45997081437155[/C][/ROW]
[ROW][C]127[/C][C]137[/C][C]134.594287574793[/C][C]2.40571242520671[/C][/ROW]
[ROW][C]128[/C][C]105[/C][C]104.299732957806[/C][C]0.700267042194244[/C][/ROW]
[ROW][C]129[/C][C]106[/C][C]105.089081331534[/C][C]0.910918668466262[/C][/ROW]
[ROW][C]130[/C][C]125[/C][C]123.307863570866[/C][C]1.69213642913437[/C][/ROW]
[ROW][C]131[/C][C]104[/C][C]102.670820029493[/C][C]1.32917997050656[/C][/ROW]
[ROW][C]132[/C][C]130[/C][C]131.998211946236[/C][C]-1.99821194623632[/C][/ROW]
[ROW][C]133[/C][C]79[/C][C]80.0543463282567[/C][C]-1.05434632825672[/C][/ROW]
[ROW][C]134[/C][C]108[/C][C]108.851885279045[/C][C]-0.851885279044838[/C][/ROW]
[ROW][C]135[/C][C]136[/C][C]134.3833098834[/C][C]1.61669011660015[/C][/ROW]
[ROW][C]136[/C][C]98[/C][C]96.6911508718392[/C][C]1.3088491281608[/C][/ROW]
[ROW][C]137[/C][C]120[/C][C]124.262560489209[/C][C]-4.26256048920895[/C][/ROW]
[ROW][C]138[/C][C]108[/C][C]108.412134989803[/C][C]-0.412134989802552[/C][/ROW]
[ROW][C]139[/C][C]139[/C][C]139.52760161114[/C][C]-0.52760161114[/C][/ROW]
[ROW][C]140[/C][C]123[/C][C]122.257857745172[/C][C]0.742142254827986[/C][/ROW]
[ROW][C]141[/C][C]90[/C][C]88.0264896936585[/C][C]1.9735103063415[/C][/ROW]
[ROW][C]142[/C][C]119[/C][C]119.20830881189[/C][C]-0.208308811890207[/C][/ROW]
[ROW][C]143[/C][C]105[/C][C]107.317942997046[/C][C]-2.31794299704575[/C][/ROW]
[ROW][C]144[/C][C]110[/C][C]108.096218875475[/C][C]1.90378112452471[/C][/ROW]
[ROW][C]145[/C][C]135[/C][C]134.656429745702[/C][C]0.343570254298257[/C][/ROW]
[ROW][C]146[/C][C]101[/C][C]97.7362649946026[/C][C]3.2637350053974[/C][/ROW]
[ROW][C]147[/C][C]114[/C][C]112.616979790218[/C][C]1.38302020978177[/C][/ROW]
[ROW][C]148[/C][C]118[/C][C]116.247195158411[/C][C]1.75280484158896[/C][/ROW]
[ROW][C]149[/C][C]120[/C][C]118.290720045638[/C][C]1.70927995436235[/C][/ROW]
[ROW][C]150[/C][C]108[/C][C]110.478193704572[/C][C]-2.47819370457178[/C][/ROW]
[ROW][C]151[/C][C]114[/C][C]111.353083196496[/C][C]2.6469168035038[/C][/ROW]
[ROW][C]152[/C][C]122[/C][C]121.863696071088[/C][C]0.136303928911497[/C][/ROW]
[ROW][C]153[/C][C]132[/C][C]130.372170867219[/C][C]1.62782913278084[/C][/ROW]
[ROW][C]154[/C][C]130[/C][C]128.81552974419[/C][C]1.18447025581015[/C][/ROW]
[ROW][C]155[/C][C]130[/C][C]131.637912154296[/C][C]-1.63791215429599[/C][/ROW]
[ROW][C]156[/C][C]112[/C][C]112.310654761784[/C][C]-0.310654761783773[/C][/ROW]
[ROW][C]157[/C][C]114[/C][C]117.012311721777[/C][C]-3.01231172177714[/C][/ROW]
[ROW][C]158[/C][C]103[/C][C]104.087942153444[/C][C]-1.08794215344375[/C][/ROW]
[ROW][C]159[/C][C]115[/C][C]114.656587822765[/C][C]0.343412177235305[/C][/ROW]
[ROW][C]160[/C][C]108[/C][C]106.416545380029[/C][C]1.58345461997058[/C][/ROW]
[ROW][C]161[/C][C]94[/C][C]94.4043630444043[/C][C]-0.404363044404341[/C][/ROW]
[ROW][C]162[/C][C]105[/C][C]105.156006337304[/C][C]-0.156006337303583[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191176&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191176&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
1127124.7013365668612.29866343313892
2108107.8747129499280.125287050071958
3110110.393571907718-0.393571907718028
4102100.1318778720111.86812212798886
5104105.989302971621-1.98930297162116
6140140.265771496749-0.265771496749212
7112113.134950179186-1.13495017918639
8115117.096341000183-2.09634100018306
9121117.4685137694423.53148623055789
10112110.7207814518881.27921854811246
11118116.4845489679251.515451032075
12122119.428949093692.57105090630957
13105106.66008674426-1.66008674425977
14111109.3799753909821.62002460901835
15151149.9218995046951.0781004953046
16106105.4076772878150.592322712185428
17100101.414366031323-1.41436603132268
18149148.0626695130490.937330486951453
19122118.6521150599893.34788494001079
20115115.429539757934-0.429539757934193
218683.04983348105062.95016651894942
22124124.208242003984-0.208242003984454
236970.4624586554288-1.46245865542884
24117117.024343694107-0.0243436941065235
25113113.238877336213-0.238877336213234
26123120.6433089275292.35669107247089
27123121.932471321011.0675286789898
288480.85863635901633.14136364098372
299791.75711269989395.24288730010609
30121120.0537337927690.946266207230532
31132130.4154051029381.58459489706244
32119119.053243457907-0.0532434579065674
339898.5245993120478-0.524599312047803
348786.65562609733560.344373902664356
3510198.39908542087892.60091457912108
36115113.5669827313511.43301726864914
37109109.734364130089-0.734364130088818
38109108.689794323150.310205676850131
39159157.4015015717811.59849842821923
40129127.429562661321.5704373386796
41119118.3018770853530.698122914646648
42119116.4477845875672.55221541243293
43122119.3711770091232.62882299087703
44131129.734168425371.26583157462988
45120115.4784899475384.52151005246203
468281.92593228108980.0740677189102154
478684.35922506136491.64077493863508
48105101.3872404450783.61275955492239
49114115.559227571633-1.55922757163251
5010098.94164100179011.05835899820995
5110099.71413024811160.285869751888379
529999.0504364527789-0.0504364527788831
53132132.648597992922-0.64859799292226
548284.6536195470347-2.65361954703467
55132129.6488750216542.3511249783458
56107105.7711096529341.22889034706607
57114113.9858477970420.0141522029575846
58110106.2113658739293.78863412607098
59105102.1465947500882.85340524991171
60121116.3660549152624.63394508473763
61109105.4844019793073.51559802069283
62106104.2942057453321.70579425466773
63124122.0761282297471.92387177025337
64120119.1011666003240.898833399676295
659192.945874444839-1.94587444483901
66126124.3156025442981.68439745570215
67138138.950652065484-0.950652065483895
68118116.8420934032141.15790659678571
69128127.5442594457520.455740554248025
709899.1537093007067-1.15370930070665
71133133.275182319006-0.275182319005775
72130129.6647746089140.335225391085704
73103100.6859310788272.31406892117278
74124124.615886097302-0.615886097301705
75142140.6876425052381.31235749476183
769698.0957053996178-2.09570539961778
779392.83551939261970.164480607380255
78129130.337486663767-1.33748666376717
79150150.00316037938-0.00316037937970743
808887.4595292678030.540470732197007
81125123.7768291052331.22317089476721
829290.81994582470781.18005417529225
830111.521846096125-111.521846096125
84117116.5291066825870.470893317412633
85112112.706575224952-0.706575224951994
86144142.3478067892071.65219321079271
87130128.8679316480811.13206835191945
888786.46861339198040.531386608019574
899292.406220379291-0.406220379291012
90114114.525548547202-0.525548547202209
918179.59019859404551.40980140595453
92127125.9228025997231.0771974002766
93115115.044899644787-0.0448996447870464
94123124.352843239454-1.35284323945401
95115115.022911253751-0.0229112537506782
96117116.4047611516660.595238848333896
97117115.3624295012041.63757049879575
98103101.7010132179171.29898678208275
99108107.2990297954090.700970204590686
100139137.0431793371751.95682066282458
101113111.4313732908591.56862670914053
1029794.94081963595982.05918036404023
103117115.0919235657641.90807643423641
104133131.8334789984171.1665210015834
105115115.581804154837-0.581804154837429
106103104.655486642275-1.6554866422746
1079594.51219020344140.487809796558615
108117117.82544450436-0.825444504360412
109113112.7006952429660.299304757034478
110127129.23926699001-2.23926699000958
111126125.5557223503330.444277649666606
112119117.9984961440811.00150385591918
1139794.26826924908292.73173075091707
114105104.3699478432390.630052156761093
115140140.100259931591-0.100259931591244
1169188.76862001038232.23137998961774
117112111.9063523989390.0936476010607919
118113113.337108179912-0.33710817991246
11910297.46504964175024.53495035824977
1209290.94942225890881.05057774109123
1219898.9640797848208-0.964079784820795
122122122.139845911927-0.139845911926995
12310099.83797672595620.162023274043825
1248481.92117137771422.07882862228582
125142138.3978043355533.6021956644467
126124122.5400291856281.45997081437155
127137134.5942875747932.40571242520671
128105104.2997329578060.700267042194244
129106105.0890813315340.910918668466262
130125123.3078635708661.69213642913437
131104102.6708200294931.32917997050656
132130131.998211946236-1.99821194623632
1337980.0543463282567-1.05434632825672
134108108.851885279045-0.851885279044838
135136134.38330988341.61669011660015
1369896.69115087183921.3088491281608
137120124.262560489209-4.26256048920895
138108108.412134989803-0.412134989802552
139139139.52760161114-0.52760161114
140123122.2578577451720.742142254827986
1419088.02648969365851.9735103063415
142119119.20830881189-0.208308811890207
143105107.317942997046-2.31794299704575
144110108.0962188754751.90378112452471
145135134.6564297457020.343570254298257
14610197.73626499460263.2637350053974
147114112.6169797902181.38302020978177
148118116.2471951584111.75280484158896
149120118.2907200456381.70927995436235
150108110.478193704572-2.47819370457178
151114111.3530831964962.6469168035038
152122121.8636960710880.136303928911497
153132130.3721708672191.62782913278084
154130128.815529744191.18447025581015
155130131.637912154296-1.63791215429599
156112112.310654761784-0.310654761783773
157114117.012311721777-3.01231172177714
158103104.087942153444-1.08794215344375
159115114.6565878227650.343412177235305
160108106.4165453800291.58345461997058
1619494.4043630444043-0.404363044404341
162105105.156006337304-0.156006337303583






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
136.19613467228262e-461.23922693445652e-451
145.14596865358409e-611.02919373071682e-601
155.20191374048204e-761.04038274809641e-751
161.16889374181973e-902.33778748363946e-901
172.39199113514881e-1104.78398227029762e-1101
181.71971953832417e-1263.43943907664835e-1261
193.76703087815383e-1347.53406175630765e-1341
204.58814625041776e-1499.17629250083553e-1491
214.60859904689754e-1689.21719809379508e-1681
226.53073672003436e-1871.30614734400687e-1861
237.1568672563032e-1931.43137345126064e-1921
243.67273096000514e-2097.34546192001029e-2091
253.57904782763809e-2327.15809565527617e-2321
263.19236862602127e-2456.38473725204255e-2451
271.9578088446052e-2593.9156176892104e-2591
285.06158799842713e-2771.01231759968543e-2761
297.06366500202567e-2811.41273300040513e-2801
303.00168547614631e-3106.00337095229261e-3101
319.88131291682493e-3241.97626258336499e-3231
32001
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
55001
56001
57001
58001
59001
60001
61001
62001
63001
64001
65001
66001
67001
68001
69001
70001
71001
72001
73001
74001
75001
76001
77001
78001
79001
80001
81001
82001
83100
84100
85100
86100
87100
88100
89100
90100
91100
92100
93100
94100
95100
96100
97100
98100
99100
100100
101100
102100
103100
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
124100
125100
126100
127100
128100
129100
130100
13111.00734846904313e-3175.03674234521567e-318
13215.90568789478025e-3012.95284394739012e-301
13311.02137262022903e-2795.10686310114517e-280
13419.79407137186415e-2834.89703568593208e-283
13511.16400567482541e-2665.82002837412706e-267
13611.90097525497101e-2409.50487627485504e-241
13712.49313006360352e-2231.24656503180176e-223
13814.48256135513858e-2152.24128067756929e-215
13915.91766434775965e-1982.95883217387982e-198
14014.44440947341153e-1792.22220473670576e-179
14119.88409710341578e-1644.94204855170789e-164
14211.16937011408297e-1545.84685057041484e-155
14314.1384984240254e-1422.0692492120127e-142
14419.362451329071e-1254.6812256645355e-125
14513.03131485373578e-1121.51565742686789e-112
14617.02067029702497e-913.51033514851249e-91
14711.93533519069677e-759.67667595348385e-76
14813.89642009782747e-651.94821004891374e-65
14911.88929018934962e-469.44645094674809e-47

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 6.19613467228262e-46 & 1.23922693445652e-45 & 1 \tabularnewline
14 & 5.14596865358409e-61 & 1.02919373071682e-60 & 1 \tabularnewline
15 & 5.20191374048204e-76 & 1.04038274809641e-75 & 1 \tabularnewline
16 & 1.16889374181973e-90 & 2.33778748363946e-90 & 1 \tabularnewline
17 & 2.39199113514881e-110 & 4.78398227029762e-110 & 1 \tabularnewline
18 & 1.71971953832417e-126 & 3.43943907664835e-126 & 1 \tabularnewline
19 & 3.76703087815383e-134 & 7.53406175630765e-134 & 1 \tabularnewline
20 & 4.58814625041776e-149 & 9.17629250083553e-149 & 1 \tabularnewline
21 & 4.60859904689754e-168 & 9.21719809379508e-168 & 1 \tabularnewline
22 & 6.53073672003436e-187 & 1.30614734400687e-186 & 1 \tabularnewline
23 & 7.1568672563032e-193 & 1.43137345126064e-192 & 1 \tabularnewline
24 & 3.67273096000514e-209 & 7.34546192001029e-209 & 1 \tabularnewline
25 & 3.57904782763809e-232 & 7.15809565527617e-232 & 1 \tabularnewline
26 & 3.19236862602127e-245 & 6.38473725204255e-245 & 1 \tabularnewline
27 & 1.9578088446052e-259 & 3.9156176892104e-259 & 1 \tabularnewline
28 & 5.06158799842713e-277 & 1.01231759968543e-276 & 1 \tabularnewline
29 & 7.06366500202567e-281 & 1.41273300040513e-280 & 1 \tabularnewline
30 & 3.00168547614631e-310 & 6.00337095229261e-310 & 1 \tabularnewline
31 & 9.88131291682493e-324 & 1.97626258336499e-323 & 1 \tabularnewline
32 & 0 & 0 & 1 \tabularnewline
33 & 0 & 0 & 1 \tabularnewline
34 & 0 & 0 & 1 \tabularnewline
35 & 0 & 0 & 1 \tabularnewline
36 & 0 & 0 & 1 \tabularnewline
37 & 0 & 0 & 1 \tabularnewline
38 & 0 & 0 & 1 \tabularnewline
39 & 0 & 0 & 1 \tabularnewline
40 & 0 & 0 & 1 \tabularnewline
41 & 0 & 0 & 1 \tabularnewline
42 & 0 & 0 & 1 \tabularnewline
43 & 0 & 0 & 1 \tabularnewline
44 & 0 & 0 & 1 \tabularnewline
45 & 0 & 0 & 1 \tabularnewline
46 & 0 & 0 & 1 \tabularnewline
47 & 0 & 0 & 1 \tabularnewline
48 & 0 & 0 & 1 \tabularnewline
49 & 0 & 0 & 1 \tabularnewline
50 & 0 & 0 & 1 \tabularnewline
51 & 0 & 0 & 1 \tabularnewline
52 & 0 & 0 & 1 \tabularnewline
53 & 0 & 0 & 1 \tabularnewline
54 & 0 & 0 & 1 \tabularnewline
55 & 0 & 0 & 1 \tabularnewline
56 & 0 & 0 & 1 \tabularnewline
57 & 0 & 0 & 1 \tabularnewline
58 & 0 & 0 & 1 \tabularnewline
59 & 0 & 0 & 1 \tabularnewline
60 & 0 & 0 & 1 \tabularnewline
61 & 0 & 0 & 1 \tabularnewline
62 & 0 & 0 & 1 \tabularnewline
63 & 0 & 0 & 1 \tabularnewline
64 & 0 & 0 & 1 \tabularnewline
65 & 0 & 0 & 1 \tabularnewline
66 & 0 & 0 & 1 \tabularnewline
67 & 0 & 0 & 1 \tabularnewline
68 & 0 & 0 & 1 \tabularnewline
69 & 0 & 0 & 1 \tabularnewline
70 & 0 & 0 & 1 \tabularnewline
71 & 0 & 0 & 1 \tabularnewline
72 & 0 & 0 & 1 \tabularnewline
73 & 0 & 0 & 1 \tabularnewline
74 & 0 & 0 & 1 \tabularnewline
75 & 0 & 0 & 1 \tabularnewline
76 & 0 & 0 & 1 \tabularnewline
77 & 0 & 0 & 1 \tabularnewline
78 & 0 & 0 & 1 \tabularnewline
79 & 0 & 0 & 1 \tabularnewline
80 & 0 & 0 & 1 \tabularnewline
81 & 0 & 0 & 1 \tabularnewline
82 & 0 & 0 & 1 \tabularnewline
83 & 1 & 0 & 0 \tabularnewline
84 & 1 & 0 & 0 \tabularnewline
85 & 1 & 0 & 0 \tabularnewline
86 & 1 & 0 & 0 \tabularnewline
87 & 1 & 0 & 0 \tabularnewline
88 & 1 & 0 & 0 \tabularnewline
89 & 1 & 0 & 0 \tabularnewline
90 & 1 & 0 & 0 \tabularnewline
91 & 1 & 0 & 0 \tabularnewline
92 & 1 & 0 & 0 \tabularnewline
93 & 1 & 0 & 0 \tabularnewline
94 & 1 & 0 & 0 \tabularnewline
95 & 1 & 0 & 0 \tabularnewline
96 & 1 & 0 & 0 \tabularnewline
97 & 1 & 0 & 0 \tabularnewline
98 & 1 & 0 & 0 \tabularnewline
99 & 1 & 0 & 0 \tabularnewline
100 & 1 & 0 & 0 \tabularnewline
101 & 1 & 0 & 0 \tabularnewline
102 & 1 & 0 & 0 \tabularnewline
103 & 1 & 0 & 0 \tabularnewline
104 & 1 & 0 & 0 \tabularnewline
105 & 1 & 0 & 0 \tabularnewline
106 & 1 & 0 & 0 \tabularnewline
107 & 1 & 0 & 0 \tabularnewline
108 & 1 & 0 & 0 \tabularnewline
109 & 1 & 0 & 0 \tabularnewline
110 & 1 & 0 & 0 \tabularnewline
111 & 1 & 0 & 0 \tabularnewline
112 & 1 & 0 & 0 \tabularnewline
113 & 1 & 0 & 0 \tabularnewline
114 & 1 & 0 & 0 \tabularnewline
115 & 1 & 0 & 0 \tabularnewline
116 & 1 & 0 & 0 \tabularnewline
117 & 1 & 0 & 0 \tabularnewline
118 & 1 & 0 & 0 \tabularnewline
119 & 1 & 0 & 0 \tabularnewline
120 & 1 & 0 & 0 \tabularnewline
121 & 1 & 0 & 0 \tabularnewline
122 & 1 & 0 & 0 \tabularnewline
123 & 1 & 0 & 0 \tabularnewline
124 & 1 & 0 & 0 \tabularnewline
125 & 1 & 0 & 0 \tabularnewline
126 & 1 & 0 & 0 \tabularnewline
127 & 1 & 0 & 0 \tabularnewline
128 & 1 & 0 & 0 \tabularnewline
129 & 1 & 0 & 0 \tabularnewline
130 & 1 & 0 & 0 \tabularnewline
131 & 1 & 1.00734846904313e-317 & 5.03674234521567e-318 \tabularnewline
132 & 1 & 5.90568789478025e-301 & 2.95284394739012e-301 \tabularnewline
133 & 1 & 1.02137262022903e-279 & 5.10686310114517e-280 \tabularnewline
134 & 1 & 9.79407137186415e-283 & 4.89703568593208e-283 \tabularnewline
135 & 1 & 1.16400567482541e-266 & 5.82002837412706e-267 \tabularnewline
136 & 1 & 1.90097525497101e-240 & 9.50487627485504e-241 \tabularnewline
137 & 1 & 2.49313006360352e-223 & 1.24656503180176e-223 \tabularnewline
138 & 1 & 4.48256135513858e-215 & 2.24128067756929e-215 \tabularnewline
139 & 1 & 5.91766434775965e-198 & 2.95883217387982e-198 \tabularnewline
140 & 1 & 4.44440947341153e-179 & 2.22220473670576e-179 \tabularnewline
141 & 1 & 9.88409710341578e-164 & 4.94204855170789e-164 \tabularnewline
142 & 1 & 1.16937011408297e-154 & 5.84685057041484e-155 \tabularnewline
143 & 1 & 4.1384984240254e-142 & 2.0692492120127e-142 \tabularnewline
144 & 1 & 9.362451329071e-125 & 4.6812256645355e-125 \tabularnewline
145 & 1 & 3.03131485373578e-112 & 1.51565742686789e-112 \tabularnewline
146 & 1 & 7.02067029702497e-91 & 3.51033514851249e-91 \tabularnewline
147 & 1 & 1.93533519069677e-75 & 9.67667595348385e-76 \tabularnewline
148 & 1 & 3.89642009782747e-65 & 1.94821004891374e-65 \tabularnewline
149 & 1 & 1.88929018934962e-46 & 9.44645094674809e-47 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191176&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]13[/C][C]6.19613467228262e-46[/C][C]1.23922693445652e-45[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]5.14596865358409e-61[/C][C]1.02919373071682e-60[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]5.20191374048204e-76[/C][C]1.04038274809641e-75[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]1.16889374181973e-90[/C][C]2.33778748363946e-90[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]2.39199113514881e-110[/C][C]4.78398227029762e-110[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]1.71971953832417e-126[/C][C]3.43943907664835e-126[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]3.76703087815383e-134[/C][C]7.53406175630765e-134[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]4.58814625041776e-149[/C][C]9.17629250083553e-149[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]4.60859904689754e-168[/C][C]9.21719809379508e-168[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]6.53073672003436e-187[/C][C]1.30614734400687e-186[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]7.1568672563032e-193[/C][C]1.43137345126064e-192[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]3.67273096000514e-209[/C][C]7.34546192001029e-209[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]3.57904782763809e-232[/C][C]7.15809565527617e-232[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]3.19236862602127e-245[/C][C]6.38473725204255e-245[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]1.9578088446052e-259[/C][C]3.9156176892104e-259[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]5.06158799842713e-277[/C][C]1.01231759968543e-276[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]7.06366500202567e-281[/C][C]1.41273300040513e-280[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]3.00168547614631e-310[/C][C]6.00337095229261e-310[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]9.88131291682493e-324[/C][C]1.97626258336499e-323[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]79[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]119[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]1.00734846904313e-317[/C][C]5.03674234521567e-318[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]5.90568789478025e-301[/C][C]2.95284394739012e-301[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]1.02137262022903e-279[/C][C]5.10686310114517e-280[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]9.79407137186415e-283[/C][C]4.89703568593208e-283[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]1.16400567482541e-266[/C][C]5.82002837412706e-267[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]1.90097525497101e-240[/C][C]9.50487627485504e-241[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]2.49313006360352e-223[/C][C]1.24656503180176e-223[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]4.48256135513858e-215[/C][C]2.24128067756929e-215[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]5.91766434775965e-198[/C][C]2.95883217387982e-198[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]4.44440947341153e-179[/C][C]2.22220473670576e-179[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]9.88409710341578e-164[/C][C]4.94204855170789e-164[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]1.16937011408297e-154[/C][C]5.84685057041484e-155[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]4.1384984240254e-142[/C][C]2.0692492120127e-142[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]9.362451329071e-125[/C][C]4.6812256645355e-125[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]3.03131485373578e-112[/C][C]1.51565742686789e-112[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]7.02067029702497e-91[/C][C]3.51033514851249e-91[/C][/ROW]
[ROW][C]147[/C][C]1[/C][C]1.93533519069677e-75[/C][C]9.67667595348385e-76[/C][/ROW]
[ROW][C]148[/C][C]1[/C][C]3.89642009782747e-65[/C][C]1.94821004891374e-65[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]1.88929018934962e-46[/C][C]9.44645094674809e-47[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191176&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191176&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
136.19613467228262e-461.23922693445652e-451
145.14596865358409e-611.02919373071682e-601
155.20191374048204e-761.04038274809641e-751
161.16889374181973e-902.33778748363946e-901
172.39199113514881e-1104.78398227029762e-1101
181.71971953832417e-1263.43943907664835e-1261
193.76703087815383e-1347.53406175630765e-1341
204.58814625041776e-1499.17629250083553e-1491
214.60859904689754e-1689.21719809379508e-1681
226.53073672003436e-1871.30614734400687e-1861
237.1568672563032e-1931.43137345126064e-1921
243.67273096000514e-2097.34546192001029e-2091
253.57904782763809e-2327.15809565527617e-2321
263.19236862602127e-2456.38473725204255e-2451
271.9578088446052e-2593.9156176892104e-2591
285.06158799842713e-2771.01231759968543e-2761
297.06366500202567e-2811.41273300040513e-2801
303.00168547614631e-3106.00337095229261e-3101
319.88131291682493e-3241.97626258336499e-3231
32001
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
55001
56001
57001
58001
59001
60001
61001
62001
63001
64001
65001
66001
67001
68001
69001
70001
71001
72001
73001
74001
75001
76001
77001
78001
79001
80001
81001
82001
83100
84100
85100
86100
87100
88100
89100
90100
91100
92100
93100
94100
95100
96100
97100
98100
99100
100100
101100
102100
103100
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
124100
125100
126100
127100
128100
129100
130100
13111.00734846904313e-3175.03674234521567e-318
13215.90568789478025e-3012.95284394739012e-301
13311.02137262022903e-2795.10686310114517e-280
13419.79407137186415e-2834.89703568593208e-283
13511.16400567482541e-2665.82002837412706e-267
13611.90097525497101e-2409.50487627485504e-241
13712.49313006360352e-2231.24656503180176e-223
13814.48256135513858e-2152.24128067756929e-215
13915.91766434775965e-1982.95883217387982e-198
14014.44440947341153e-1792.22220473670576e-179
14119.88409710341578e-1644.94204855170789e-164
14211.16937011408297e-1545.84685057041484e-155
14314.1384984240254e-1422.0692492120127e-142
14419.362451329071e-1254.6812256645355e-125
14513.03131485373578e-1121.51565742686789e-112
14617.02067029702497e-913.51033514851249e-91
14711.93533519069677e-759.67667595348385e-76
14813.89642009782747e-651.94821004891374e-65
14911.88929018934962e-469.44645094674809e-47






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1371NOK
5% type I error level1371NOK
10% type I error level1371NOK

\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 & 137 & 1 & NOK \tabularnewline
5% type I error level & 137 & 1 & NOK \tabularnewline
10% type I error level & 137 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191176&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]137[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]137[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]137[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191176&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191176&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 level1371NOK
5% type I error level1371NOK
10% type I error level1371NOK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

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