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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 12:06:11 -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/t1353431197clv3fhlirvn9dhw.htm/, Retrieved Mon, 29 Apr 2024 18:28:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=191191, Retrieved Mon, 29 Apr 2024 18:28:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [WS 7 + month] [2012-11-17 15:45:21] [f8ee2fa4f3a14474001c30fec05fcd2b]
- R PD    [Multiple Regression] [WS 7 + Month 1] [2012-11-20 17:03:30] [f8ee2fa4f3a14474001c30fec05fcd2b]
-   P         [Multiple Regression] [WS 7 Month + Trend] [2012-11-20 17:06:11] [4c93b3a0c48c946a3a36627369b78a37] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9	26	21	21	23	17	23	4	14	12	127
9	20	16	15	24	17	20	4	18	11	108
9	19	19	18	22	18	20	6	11	14	110
9	19	18	11	20	21	21	8	12	12	102
9	20	16	8	24	20	24	8	16	21	104
9	25	23	19	27	28	22	4	18	12	140
9	25	17	4	28	19	23	4	14	22	112
9	22	12	20	27	22	20	8	14	11	115
9	26	19	16	24	16	25	5	15	10	121
9	22	16	14	23	18	23	4	15	13	112
9	17	19	10	24	25	27	4	17	10	118
9	22	20	13	27	17	27	4	19	8	122
9	19	13	14	27	14	22	4	10	15	105
9	24	20	8	28	11	24	4	16	14	111
9	26	27	23	27	27	25	4	18	10	151
9	21	17	11	23	20	22	8	14	14	106
9	13	8	9	24	22	28	4	14	14	100
9	26	25	24	28	22	28	4	17	11	149
9	20	26	5	27	21	27	4	14	10	122
9	22	13	15	25	23	25	8	16	13	115
9	14	19	5	19	17	16	4	18	7	86
9	21	15	19	24	24	28	7	11	14	124
9	7	5	6	20	14	21	4	14	12	69
9	23	16	13	28	17	24	4	12	14	117
9	17	14	11	26	23	27	5	17	11	113
9	25	24	17	23	24	14	4	9	9	123
9	25	24	17	23	24	14	4	16	11	123
9	19	9	5	20	8	27	4	14	15	84
9	20	19	9	11	22	20	4	15	14	97
9	23	19	15	24	23	21	4	11	13	121
9	22	25	17	25	25	22	4	16	9	132
9	22	19	17	23	21	21	4	13	15	119
9	21	18	20	18	24	12	15	17	10	98
9	15	15	12	20	15	20	10	15	11	87
9	20	12	7	20	22	24	4	14	13	101
9	22	21	16	24	21	19	8	16	8	115
9	18	12	7	23	25	28	4	9	20	109
9	20	15	14	25	16	23	4	15	12	109
9	28	28	24	28	28	27	4	17	10	159
9	22	25	15	26	23	22	4	13	10	129
9	18	19	15	26	21	27	7	15	9	119
9	23	20	10	23	21	26	4	16	14	119
9	20	24	14	22	26	22	6	16	8	122
9	25	26	18	24	22	21	5	12	14	131
9	26	25	12	21	21	19	4	12	11	120
9	15	12	9	20	18	24	16	11	13	82
9	17	12	9	22	12	19	5	15	9	86
9	23	15	8	20	25	26	12	15	11	105
9	21	17	18	25	17	22	6	17	15	114
9	13	14	10	20	24	28	9	13	11	100
9	18	16	17	22	15	21	9	16	10	100
9	19	11	14	23	13	23	4	14	14	99
9	22	20	16	25	26	28	5	11	18	132
9	16	11	10	23	16	10	4	12	14	82
9	24	22	19	23	24	24	4	12	11	132
9	18	20	10	22	21	21	5	15	12	107
9	20	19	14	24	20	21	4	16	13	114
9	24	17	10	25	14	24	4	15	9	110
9	14	21	4	21	25	24	4	12	10	105
9	22	23	19	12	25	25	5	12	15	121
9	24	18	9	17	20	25	4	8	20	109
9	18	17	12	20	22	23	6	13	12	106
9	21	27	16	23	20	21	4	11	12	124
9	23	25	11	23	26	16	4	14	14	120
9	17	19	18	20	18	17	18	15	13	91
10	22	22	11	28	22	25	4	10	11	126
10	24	24	24	24	24	24	6	11	17	138
10	21	20	17	24	17	23	4	12	12	118
10	22	19	18	24	24	25	4	15	13	128
10	16	11	9	24	20	23	5	15	14	98
10	21	22	19	28	19	28	4	14	13	133
10	23	22	18	25	20	26	4	16	15	130
10	22	16	12	21	15	22	5	15	13	103
10	24	20	23	25	23	19	10	15	10	124
10	24	24	22	25	26	26	5	13	11	142
10	16	16	14	18	22	18	8	12	19	96
10	16	16	14	17	20	18	8	17	13	93
10	21	22	16	26	24	25	5	13	17	129
10	26	24	23	28	26	27	4	15	13	150
10	15	16	7	21	21	12	4	13	9	88
10	25	27	10	27	25	15	4	15	11	125
10	18	11	12	22	13	21	5	16	10	92
10	23	21	12	21	20	23	4	15	9	0
10	20	20	12	25	22	22	4	16	12	117
10	17	20	17	22	23	21	8	15	12	112
10	25	27	21	23	28	24	4	14	13	144
10	24	20	16	26	22	27	5	15	13	130
10	17	12	11	19	20	22	14	14	12	87
10	19	8	14	25	6	28	8	13	15	92
10	20	21	13	21	21	26	8	7	22	114
10	15	18	9	13	20	10	4	17	13	81
10	27	24	19	24	18	19	4	13	15	127
10	22	16	13	25	23	22	6	15	13	115
10	23	18	19	26	20	21	4	14	15	123
10	16	20	13	25	24	24	7	13	10	115
10	19	20	13	25	22	25	7	16	11	117
10	25	19	13	22	21	21	4	12	16	117
10	19	17	14	21	18	20	6	14	11	103
10	19	16	12	23	21	21	4	17	11	108
10	26	26	22	25	23	24	7	15	10	139
10	21	15	11	24	23	23	4	17	10	113
10	20	22	5	21	15	18	4	12	16	97
10	24	17	18	21	21	24	8	16	12	117
10	22	23	19	25	24	24	4	11	11	133
10	20	21	14	22	23	19	4	15	16	115
10	18	19	15	20	21	20	10	9	19	103
10	18	14	12	20	21	18	8	16	11	95
10	24	17	19	23	20	20	6	15	16	117
10	24	12	15	28	11	27	4	10	15	113
10	22	24	17	23	22	23	4	10	24	127
10	23	18	8	28	27	26	4	15	14	126
10	22	20	10	24	25	23	5	11	15	119
10	20	16	12	18	18	17	4	13	11	97
10	18	20	12	20	20	21	6	14	15	105
10	25	22	20	28	24	25	4	18	12	140
10	18	12	12	21	10	23	5	16	10	91
10	16	16	12	21	27	27	7	14	14	112
10	20	17	14	25	21	24	8	14	13	113
10	19	22	6	19	21	20	5	14	9	102
10	15	12	10	18	18	27	8	14	15	92
10	19	14	18	21	15	21	10	12	15	98
10	19	23	18	22	24	24	8	14	14	122
10	16	15	7	24	22	21	5	15	11	100
10	17	17	18	15	14	15	12	15	8	84
10	28	28	9	28	28	25	4	15	11	142
10	23	20	17	26	18	25	5	13	11	124
10	25	23	22	23	26	22	4	17	8	137
10	20	13	11	26	17	24	6	17	10	105
10	17	18	15	20	19	21	4	19	11	106
10	23	23	17	22	22	22	4	15	13	125
10	16	19	15	20	18	23	7	13	11	104
10	23	23	22	23	24	22	7	9	20	130
10	11	12	9	22	15	20	10	15	10	79
10	18	16	13	24	18	23	4	15	15	108
10	24	23	20	23	26	25	5	15	12	136
10	23	13	14	22	11	23	8	16	14	98
10	21	22	14	26	26	22	11	11	23	120
10	16	18	12	23	21	25	7	14	14	108
10	24	23	20	27	23	26	4	11	16	139
10	23	20	20	23	23	22	8	15	11	123
10	18	10	8	21	15	24	6	13	12	90
10	20	17	17	26	22	24	7	15	10	119
10	9	18	9	23	26	25	5	16	14	105
10	24	15	18	21	16	20	4	14	12	110
10	25	23	22	27	20	26	8	15	12	135
10	20	17	10	19	18	21	4	16	11	101
10	21	17	13	23	22	26	8	16	12	114
10	25	22	15	25	16	21	6	11	13	118
10	22	20	18	23	19	22	4	12	11	120
10	21	20	18	22	20	16	9	9	19	108
10	21	19	12	22	19	26	5	16	12	114
10	22	18	12	25	23	28	6	13	17	122
10	27	22	20	25	24	18	4	16	9	132
9	24	20	12	28	25	25	4	12	12	130
10	24	22	16	28	21	23	4	9	19	130
10	21	18	16	20	21	21	5	13	18	112
10	18	16	18	25	23	20	6	13	15	114
10	16	16	16	19	27	25	16	14	14	103
10	22	16	13	25	23	22	6	19	11	115
10	20	16	17	22	18	21	6	13	9	108
10	18	17	13	18	16	16	4	12	18	94
11	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 time21 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 21 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191191&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]21 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191191&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191191&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 time21 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Motivation[t] = + 31.2493029952481 -4.51155152959856Month[t] + 0.646256750774323I1[t] + 0.916477777271674I2[t] + 1.23147142872843I3[t] + 1.4067809878659E1[t] + 1.04413529591756E2[t] + 0.800316870309133E3[t] -0.944759235590694A[t] + 0.163273280683192Happiness[t] + 0.437721551104317Depression[t] + 0.0347231136916052t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Motivation[t] =  +  31.2493029952481 -4.51155152959856Month[t] +  0.646256750774323I1[t] +  0.916477777271674I2[t] +  1.23147142872843I3[t] +  1.4067809878659E1[t] +  1.04413529591756E2[t] +  0.800316870309133E3[t] -0.944759235590694A[t] +  0.163273280683192Happiness[t] +  0.437721551104317Depression[t] +  0.0347231136916052t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191191&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Motivation[t] =  +  31.2493029952481 -4.51155152959856Month[t] +  0.646256750774323I1[t] +  0.916477777271674I2[t] +  1.23147142872843I3[t] +  1.4067809878659E1[t] +  1.04413529591756E2[t] +  0.800316870309133E3[t] -0.944759235590694A[t] +  0.163273280683192Happiness[t] +  0.437721551104317Depression[t] +  0.0347231136916052t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191191&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191191&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
Motivation[t] = + 31.2493029952481 -4.51155152959856Month[t] + 0.646256750774323I1[t] + 0.916477777271674I2[t] + 1.23147142872843I3[t] + 1.4067809878659E1[t] + 1.04413529591756E2[t] + 0.800316870309133E3[t] -0.944759235590694A[t] + 0.163273280683192Happiness[t] + 0.437721551104317Depression[t] + 0.0347231136916052t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)31.249302995248125.0717791.24640.2145630.107281
Month-4.511551529598562.656561-1.69830.0915310.045765
I10.6462567507743230.2995912.15710.0325880.016294
I20.9164777772716740.2582073.54940.0005160.000258
I31.231471428728430.2036046.048400
E11.40678098786590.2883664.87853e-061e-06
E21.044135295917560.2205964.73335e-063e-06
E30.8003168703091330.2272513.52170.0005680.000284
A-0.9447592355906940.317306-2.97740.003390.001695
Happiness0.1632732806831920.3776230.43240.6660920.333046
Depression0.4377215511043170.283441.54430.1246190.062309
t0.03472311369160520.0283091.22660.2218980.110949

\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) & 31.2493029952481 & 25.071779 & 1.2464 & 0.214563 & 0.107281 \tabularnewline
Month & -4.51155152959856 & 2.656561 & -1.6983 & 0.091531 & 0.045765 \tabularnewline
I1 & 0.646256750774323 & 0.299591 & 2.1571 & 0.032588 & 0.016294 \tabularnewline
I2 & 0.916477777271674 & 0.258207 & 3.5494 & 0.000516 & 0.000258 \tabularnewline
I3 & 1.23147142872843 & 0.203604 & 6.0484 & 0 & 0 \tabularnewline
E1 & 1.4067809878659 & 0.288366 & 4.8785 & 3e-06 & 1e-06 \tabularnewline
E2 & 1.04413529591756 & 0.220596 & 4.7333 & 5e-06 & 3e-06 \tabularnewline
E3 & 0.800316870309133 & 0.227251 & 3.5217 & 0.000568 & 0.000284 \tabularnewline
A & -0.944759235590694 & 0.317306 & -2.9774 & 0.00339 & 0.001695 \tabularnewline
Happiness & 0.163273280683192 & 0.377623 & 0.4324 & 0.666092 & 0.333046 \tabularnewline
Depression & 0.437721551104317 & 0.28344 & 1.5443 & 0.124619 & 0.062309 \tabularnewline
t & 0.0347231136916052 & 0.028309 & 1.2266 & 0.221898 & 0.110949 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191191&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]31.2493029952481[/C][C]25.071779[/C][C]1.2464[/C][C]0.214563[/C][C]0.107281[/C][/ROW]
[ROW][C]Month[/C][C]-4.51155152959856[/C][C]2.656561[/C][C]-1.6983[/C][C]0.091531[/C][C]0.045765[/C][/ROW]
[ROW][C]I1[/C][C]0.646256750774323[/C][C]0.299591[/C][C]2.1571[/C][C]0.032588[/C][C]0.016294[/C][/ROW]
[ROW][C]I2[/C][C]0.916477777271674[/C][C]0.258207[/C][C]3.5494[/C][C]0.000516[/C][C]0.000258[/C][/ROW]
[ROW][C]I3[/C][C]1.23147142872843[/C][C]0.203604[/C][C]6.0484[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]E1[/C][C]1.4067809878659[/C][C]0.288366[/C][C]4.8785[/C][C]3e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]E2[/C][C]1.04413529591756[/C][C]0.220596[/C][C]4.7333[/C][C]5e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]E3[/C][C]0.800316870309133[/C][C]0.227251[/C][C]3.5217[/C][C]0.000568[/C][C]0.000284[/C][/ROW]
[ROW][C]A[/C][C]-0.944759235590694[/C][C]0.317306[/C][C]-2.9774[/C][C]0.00339[/C][C]0.001695[/C][/ROW]
[ROW][C]Happiness[/C][C]0.163273280683192[/C][C]0.377623[/C][C]0.4324[/C][C]0.666092[/C][C]0.333046[/C][/ROW]
[ROW][C]Depression[/C][C]0.437721551104317[/C][C]0.28344[/C][C]1.5443[/C][C]0.124619[/C][C]0.062309[/C][/ROW]
[ROW][C]t[/C][C]0.0347231136916052[/C][C]0.028309[/C][C]1.2266[/C][C]0.221898[/C][C]0.110949[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191191&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191191&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)31.249302995248125.0717791.24640.2145630.107281
Month-4.511551529598562.656561-1.69830.0915310.045765
I10.6462567507743230.2995912.15710.0325880.016294
I20.9164777772716740.2582073.54940.0005160.000258
I31.231471428728430.2036046.048400
E11.40678098786590.2883664.87853e-061e-06
E21.044135295917560.2205964.73335e-063e-06
E30.8003168703091330.2272513.52170.0005680.000284
A-0.9447592355906940.317306-2.97740.003390.001695
Happiness0.1632732806831920.3776230.43240.6660920.333046
Depression0.4377215511043170.283441.54430.1246190.062309
t0.03472311369160520.0283091.22660.2218980.110949






Multiple Linear Regression - Regression Statistics
Multiple R0.876009044196898
R-squared0.767391845514763
Adjusted R-squared0.750333914185846
F-TEST (value)44.9873921237945
F-TEST (DF numerator)11
F-TEST (DF denominator)150
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.19567322980735
Sum Squared Residuals12684.0609224093

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.876009044196898 \tabularnewline
R-squared & 0.767391845514763 \tabularnewline
Adjusted R-squared & 0.750333914185846 \tabularnewline
F-TEST (value) & 44.9873921237945 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 150 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.19567322980735 \tabularnewline
Sum Squared Residuals & 12684.0609224093 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191191&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.876009044196898[/C][/ROW]
[ROW][C]R-squared[/C][C]0.767391845514763[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.750333914185846[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]44.9873921237945[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]150[/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]9.19567322980735[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]12684.0609224093[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191191&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191191&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 R0.876009044196898
R-squared0.767391845514763
Adjusted R-squared0.750333914185846
F-TEST (value)44.9873921237945
F-TEST (DF numerator)11
F-TEST (DF denominator)150
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.19567322980735
Sum Squared Residuals12684.0609224093






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1127124.8626695577652.13733044223451
2108108.269836656649-0.269836656649063
3110110.613457175102-0.613457175101629
410299.62887500004572.37112499995426
5104106.359011372928-2.35901137292809
6140140.725429531516-0.725429531516037
7112113.323217133878-1.32321713387849
8115117.271024252993-2.27102425299277
9121117.4564921445633.54350785543679
10112111.032591818140.967408181859766
11118116.589956243441.41004375655967
12122119.7852192298752.21478077012452
13105107.157900171354-2.15790017135396
14111109.8673498811751.13265011882537
15151150.7573633423160.242636657683569
16106105.6001022901860.39989770981374
17100101.8295182887-1.82951828869995
18149149.121551947203-0.121551947202948
19122118.6184806404343.38151935956641
20115115.880969586228-0.880969586227998
218683.5006957316912.499304268309
22124124.667419634552-0.667419634552071
236971.2586013350266-2.25860133502664
24117117.671488954644-0.671488954644432
25113113.943416076903-0.943416076903167
26123120.8846024480942.11539755190567
27123122.9376816287770.0623183712230852
288481.47199169279722.52800830720281
299792.32383393436134.67616606563873
30121120.7279462073930.2720537926072
31132131.4390707404060.560929259594153
32119120.321016625333-1.32101662533254
339899.4552023885179-1.45520238851795
348787.6650306785593-0.665030678559354
3510198.91518685839452.08481314160552
36115114.5142724951180.485727504881908
37109110.49382037113-1.49382037113023
38109110.083417555477-1.08341755547722
39159158.9194575221820.0805424778177516
40129128.5550480112590.444951988741048
41119119.473738521424-0.473738521423743
42119117.6643649316861.33563506831351
43122120.4488948497341.55110515026636
44131131.22844232799-0.228442327990187
45120116.3705984249583.62940157504195
468282.5253283597792-0.525328359779219
478685.69428933351340.305710666486624
48105101.7490582713723.25094172862828
49114117.864480995423-3.86448099542266
5010099.96663166130330.0333683386967008
51100101.552118595764-1.55211859576396
5299101.02357524534-2.02357524533962
53132134.413523765239-2.41352376523892
548286.6301055178537-4.6301055178537
55132131.243734944640.756265055360377
56107107.527363811603-0.527363811603255
57114116.179189111678-2.17918911167801
58110107.6688582971972.3311417028027
59105103.3243624525461.67563754745391
60121118.2173030573492.782696942651
61109106.9409388270682.05906117293222
62106106.009113283242-0.00911328324167099
63124124.167680677846-0.167680677846042
64120121.133094963119-1.1330949631187
659195.1375248803134-4.13752488031342
66126121.389257390714.61074260929024
67138137.1194920201530.880507979846945
68118114.6841538259473.3158461740531
69128125.5172495470652.48275045293532
709896.97515447060621.02484552939384
71133131.5654685863371.43453141366297
72130128.0863820281251.91361797187455
7310398.55460962427164.44539037572839
74124122.6362379412221.3637620587784
75142138.674995882883.32500411712049
769696.4337497025503-0.433749702550286
779391.16345833333091.83654166666908
78129128.7631336770650.236866322935042
79150148.5052820009961.49471799900444
808885.24548656327152.75451343672849
81125121.7386144062483.26138559375185
829289.06800391113232.93199608886772
830109.345352779391-109.345352779391
84117114.9163434704732.08365652952656
85112111.2508187144490.749181285551013
86144140.8787192813163.12128071868435
87130127.2694799038012.73052009619872
888784.25007658971122.74992341028881
899291.04895617442830.951043825571687
90114112.931358635011.0686413649898
918178.42830161356632.57169838643365
92127124.8432087744122.15679122558791
93115113.5159904100461.48400958995443
94123123.494500923936-0.49450092393635
95115113.4341058281351.56589417186466
96117115.0471868657781.95281313422219
97117113.9470193056533.05298069434722
98103100.4116343776192.58836562238079
99108106.1925599036071.80744009639313
100139135.9348096919713.06519030802865
101113110.0645341957542.9354658042464
1029793.71446964792813.28553035207193
103117113.9508424251413.04915757485889
104133130.7078689567012.29213104329864
105115115.035404137852-0.0354041378522152
106103103.739583482592-0.739583482592092
1079593.4275287102511.57247128974904
108117117.401220015821-0.401220015820747
109113111.8020044131371.19799558686324
110127129.194700004463-2.19470000446266
111126124.388253268961.61174673103982
112119116.7961420824322.20385791756761
1139793.30426881501223.69573118498784
114105103.8401305989641.15986940103565
115140139.9448814608820.0551185391177514
1169188.22651441891822.77348558108179
117112111.1210238238520.878976176147543
118113112.6990753537060.300924646294279
11910296.25959726707685.74040273292323
1209290.32548413626721.67451586373277
1219898.6999320588694-0.699932058869385
122122121.9662479115010.0337520884991126
12310099.1929199457950.807080054205019
1248481.51054929838082.48945070161924
125142137.4325635882554.56743641174508
126124122.2297314278641.77026857213554
127137134.4802350163882.51976498361221
128105103.9823945594631.01760544053655
129106105.4870426594690.512957340531429
130125123.4132727348911.58672726510915
131104102.5692909671791.4307090328214
132130132.385261152476-2.38526115247623
1337979.9380332079596-0.938033207959584
134108109.29243204455-1.29243204454953
135136134.5293513360771.47064866392296
1369896.89920586277911.10079413722089
137120124.667401840036-4.66740184003587
138108108.631281225528-0.631281225527681
139139140.005831042139-1.00583104213871
140123122.5019210653890.498078934610773
1419087.79760836621662.20239163378343
142119119.472628515234-0.472628515233945
143105108.023426764516-3.02342676451573
144110108.5720809521261.42791904787438
145135135.31413342072-0.314133420719566
14610198.0015347978022.99846520219805
147114112.8408630438741.15913695612575
148118116.5639843720291.43601562797111
149120118.8179053970251.18209460297456
150108111.329981234971-3.32998123497107
151114111.8763304556092.12366954439092
152122122.392379108571-0.392379108570879
153132131.0946010122710.905398987729093
154130133.544146301127-3.5441463011267
155130132.623215246069-2.62321524606894
156112113.468987690843-1.46898769084313
157114118.258862627077-4.25886262707715
158103104.553528363456-1.55352836345567
159115115.585365934216-0.585365934215644
160108107.157042161470.842957838530111
1619495.8386040915634-1.83860409156343
162105102.1986785642152.80132143578484

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 127 & 124.862669557765 & 2.13733044223451 \tabularnewline
2 & 108 & 108.269836656649 & -0.269836656649063 \tabularnewline
3 & 110 & 110.613457175102 & -0.613457175101629 \tabularnewline
4 & 102 & 99.6288750000457 & 2.37112499995426 \tabularnewline
5 & 104 & 106.359011372928 & -2.35901137292809 \tabularnewline
6 & 140 & 140.725429531516 & -0.725429531516037 \tabularnewline
7 & 112 & 113.323217133878 & -1.32321713387849 \tabularnewline
8 & 115 & 117.271024252993 & -2.27102425299277 \tabularnewline
9 & 121 & 117.456492144563 & 3.54350785543679 \tabularnewline
10 & 112 & 111.03259181814 & 0.967408181859766 \tabularnewline
11 & 118 & 116.58995624344 & 1.41004375655967 \tabularnewline
12 & 122 & 119.785219229875 & 2.21478077012452 \tabularnewline
13 & 105 & 107.157900171354 & -2.15790017135396 \tabularnewline
14 & 111 & 109.867349881175 & 1.13265011882537 \tabularnewline
15 & 151 & 150.757363342316 & 0.242636657683569 \tabularnewline
16 & 106 & 105.600102290186 & 0.39989770981374 \tabularnewline
17 & 100 & 101.8295182887 & -1.82951828869995 \tabularnewline
18 & 149 & 149.121551947203 & -0.121551947202948 \tabularnewline
19 & 122 & 118.618480640434 & 3.38151935956641 \tabularnewline
20 & 115 & 115.880969586228 & -0.880969586227998 \tabularnewline
21 & 86 & 83.500695731691 & 2.499304268309 \tabularnewline
22 & 124 & 124.667419634552 & -0.667419634552071 \tabularnewline
23 & 69 & 71.2586013350266 & -2.25860133502664 \tabularnewline
24 & 117 & 117.671488954644 & -0.671488954644432 \tabularnewline
25 & 113 & 113.943416076903 & -0.943416076903167 \tabularnewline
26 & 123 & 120.884602448094 & 2.11539755190567 \tabularnewline
27 & 123 & 122.937681628777 & 0.0623183712230852 \tabularnewline
28 & 84 & 81.4719916927972 & 2.52800830720281 \tabularnewline
29 & 97 & 92.3238339343613 & 4.67616606563873 \tabularnewline
30 & 121 & 120.727946207393 & 0.2720537926072 \tabularnewline
31 & 132 & 131.439070740406 & 0.560929259594153 \tabularnewline
32 & 119 & 120.321016625333 & -1.32101662533254 \tabularnewline
33 & 98 & 99.4552023885179 & -1.45520238851795 \tabularnewline
34 & 87 & 87.6650306785593 & -0.665030678559354 \tabularnewline
35 & 101 & 98.9151868583945 & 2.08481314160552 \tabularnewline
36 & 115 & 114.514272495118 & 0.485727504881908 \tabularnewline
37 & 109 & 110.49382037113 & -1.49382037113023 \tabularnewline
38 & 109 & 110.083417555477 & -1.08341755547722 \tabularnewline
39 & 159 & 158.919457522182 & 0.0805424778177516 \tabularnewline
40 & 129 & 128.555048011259 & 0.444951988741048 \tabularnewline
41 & 119 & 119.473738521424 & -0.473738521423743 \tabularnewline
42 & 119 & 117.664364931686 & 1.33563506831351 \tabularnewline
43 & 122 & 120.448894849734 & 1.55110515026636 \tabularnewline
44 & 131 & 131.22844232799 & -0.228442327990187 \tabularnewline
45 & 120 & 116.370598424958 & 3.62940157504195 \tabularnewline
46 & 82 & 82.5253283597792 & -0.525328359779219 \tabularnewline
47 & 86 & 85.6942893335134 & 0.305710666486624 \tabularnewline
48 & 105 & 101.749058271372 & 3.25094172862828 \tabularnewline
49 & 114 & 117.864480995423 & -3.86448099542266 \tabularnewline
50 & 100 & 99.9666316613033 & 0.0333683386967008 \tabularnewline
51 & 100 & 101.552118595764 & -1.55211859576396 \tabularnewline
52 & 99 & 101.02357524534 & -2.02357524533962 \tabularnewline
53 & 132 & 134.413523765239 & -2.41352376523892 \tabularnewline
54 & 82 & 86.6301055178537 & -4.6301055178537 \tabularnewline
55 & 132 & 131.24373494464 & 0.756265055360377 \tabularnewline
56 & 107 & 107.527363811603 & -0.527363811603255 \tabularnewline
57 & 114 & 116.179189111678 & -2.17918911167801 \tabularnewline
58 & 110 & 107.668858297197 & 2.3311417028027 \tabularnewline
59 & 105 & 103.324362452546 & 1.67563754745391 \tabularnewline
60 & 121 & 118.217303057349 & 2.782696942651 \tabularnewline
61 & 109 & 106.940938827068 & 2.05906117293222 \tabularnewline
62 & 106 & 106.009113283242 & -0.00911328324167099 \tabularnewline
63 & 124 & 124.167680677846 & -0.167680677846042 \tabularnewline
64 & 120 & 121.133094963119 & -1.1330949631187 \tabularnewline
65 & 91 & 95.1375248803134 & -4.13752488031342 \tabularnewline
66 & 126 & 121.38925739071 & 4.61074260929024 \tabularnewline
67 & 138 & 137.119492020153 & 0.880507979846945 \tabularnewline
68 & 118 & 114.684153825947 & 3.3158461740531 \tabularnewline
69 & 128 & 125.517249547065 & 2.48275045293532 \tabularnewline
70 & 98 & 96.9751544706062 & 1.02484552939384 \tabularnewline
71 & 133 & 131.565468586337 & 1.43453141366297 \tabularnewline
72 & 130 & 128.086382028125 & 1.91361797187455 \tabularnewline
73 & 103 & 98.5546096242716 & 4.44539037572839 \tabularnewline
74 & 124 & 122.636237941222 & 1.3637620587784 \tabularnewline
75 & 142 & 138.67499588288 & 3.32500411712049 \tabularnewline
76 & 96 & 96.4337497025503 & -0.433749702550286 \tabularnewline
77 & 93 & 91.1634583333309 & 1.83654166666908 \tabularnewline
78 & 129 & 128.763133677065 & 0.236866322935042 \tabularnewline
79 & 150 & 148.505282000996 & 1.49471799900444 \tabularnewline
80 & 88 & 85.2454865632715 & 2.75451343672849 \tabularnewline
81 & 125 & 121.738614406248 & 3.26138559375185 \tabularnewline
82 & 92 & 89.0680039111323 & 2.93199608886772 \tabularnewline
83 & 0 & 109.345352779391 & -109.345352779391 \tabularnewline
84 & 117 & 114.916343470473 & 2.08365652952656 \tabularnewline
85 & 112 & 111.250818714449 & 0.749181285551013 \tabularnewline
86 & 144 & 140.878719281316 & 3.12128071868435 \tabularnewline
87 & 130 & 127.269479903801 & 2.73052009619872 \tabularnewline
88 & 87 & 84.2500765897112 & 2.74992341028881 \tabularnewline
89 & 92 & 91.0489561744283 & 0.951043825571687 \tabularnewline
90 & 114 & 112.93135863501 & 1.0686413649898 \tabularnewline
91 & 81 & 78.4283016135663 & 2.57169838643365 \tabularnewline
92 & 127 & 124.843208774412 & 2.15679122558791 \tabularnewline
93 & 115 & 113.515990410046 & 1.48400958995443 \tabularnewline
94 & 123 & 123.494500923936 & -0.49450092393635 \tabularnewline
95 & 115 & 113.434105828135 & 1.56589417186466 \tabularnewline
96 & 117 & 115.047186865778 & 1.95281313422219 \tabularnewline
97 & 117 & 113.947019305653 & 3.05298069434722 \tabularnewline
98 & 103 & 100.411634377619 & 2.58836562238079 \tabularnewline
99 & 108 & 106.192559903607 & 1.80744009639313 \tabularnewline
100 & 139 & 135.934809691971 & 3.06519030802865 \tabularnewline
101 & 113 & 110.064534195754 & 2.9354658042464 \tabularnewline
102 & 97 & 93.7144696479281 & 3.28553035207193 \tabularnewline
103 & 117 & 113.950842425141 & 3.04915757485889 \tabularnewline
104 & 133 & 130.707868956701 & 2.29213104329864 \tabularnewline
105 & 115 & 115.035404137852 & -0.0354041378522152 \tabularnewline
106 & 103 & 103.739583482592 & -0.739583482592092 \tabularnewline
107 & 95 & 93.427528710251 & 1.57247128974904 \tabularnewline
108 & 117 & 117.401220015821 & -0.401220015820747 \tabularnewline
109 & 113 & 111.802004413137 & 1.19799558686324 \tabularnewline
110 & 127 & 129.194700004463 & -2.19470000446266 \tabularnewline
111 & 126 & 124.38825326896 & 1.61174673103982 \tabularnewline
112 & 119 & 116.796142082432 & 2.20385791756761 \tabularnewline
113 & 97 & 93.3042688150122 & 3.69573118498784 \tabularnewline
114 & 105 & 103.840130598964 & 1.15986940103565 \tabularnewline
115 & 140 & 139.944881460882 & 0.0551185391177514 \tabularnewline
116 & 91 & 88.2265144189182 & 2.77348558108179 \tabularnewline
117 & 112 & 111.121023823852 & 0.878976176147543 \tabularnewline
118 & 113 & 112.699075353706 & 0.300924646294279 \tabularnewline
119 & 102 & 96.2595972670768 & 5.74040273292323 \tabularnewline
120 & 92 & 90.3254841362672 & 1.67451586373277 \tabularnewline
121 & 98 & 98.6999320588694 & -0.699932058869385 \tabularnewline
122 & 122 & 121.966247911501 & 0.0337520884991126 \tabularnewline
123 & 100 & 99.192919945795 & 0.807080054205019 \tabularnewline
124 & 84 & 81.5105492983808 & 2.48945070161924 \tabularnewline
125 & 142 & 137.432563588255 & 4.56743641174508 \tabularnewline
126 & 124 & 122.229731427864 & 1.77026857213554 \tabularnewline
127 & 137 & 134.480235016388 & 2.51976498361221 \tabularnewline
128 & 105 & 103.982394559463 & 1.01760544053655 \tabularnewline
129 & 106 & 105.487042659469 & 0.512957340531429 \tabularnewline
130 & 125 & 123.413272734891 & 1.58672726510915 \tabularnewline
131 & 104 & 102.569290967179 & 1.4307090328214 \tabularnewline
132 & 130 & 132.385261152476 & -2.38526115247623 \tabularnewline
133 & 79 & 79.9380332079596 & -0.938033207959584 \tabularnewline
134 & 108 & 109.29243204455 & -1.29243204454953 \tabularnewline
135 & 136 & 134.529351336077 & 1.47064866392296 \tabularnewline
136 & 98 & 96.8992058627791 & 1.10079413722089 \tabularnewline
137 & 120 & 124.667401840036 & -4.66740184003587 \tabularnewline
138 & 108 & 108.631281225528 & -0.631281225527681 \tabularnewline
139 & 139 & 140.005831042139 & -1.00583104213871 \tabularnewline
140 & 123 & 122.501921065389 & 0.498078934610773 \tabularnewline
141 & 90 & 87.7976083662166 & 2.20239163378343 \tabularnewline
142 & 119 & 119.472628515234 & -0.472628515233945 \tabularnewline
143 & 105 & 108.023426764516 & -3.02342676451573 \tabularnewline
144 & 110 & 108.572080952126 & 1.42791904787438 \tabularnewline
145 & 135 & 135.31413342072 & -0.314133420719566 \tabularnewline
146 & 101 & 98.001534797802 & 2.99846520219805 \tabularnewline
147 & 114 & 112.840863043874 & 1.15913695612575 \tabularnewline
148 & 118 & 116.563984372029 & 1.43601562797111 \tabularnewline
149 & 120 & 118.817905397025 & 1.18209460297456 \tabularnewline
150 & 108 & 111.329981234971 & -3.32998123497107 \tabularnewline
151 & 114 & 111.876330455609 & 2.12366954439092 \tabularnewline
152 & 122 & 122.392379108571 & -0.392379108570879 \tabularnewline
153 & 132 & 131.094601012271 & 0.905398987729093 \tabularnewline
154 & 130 & 133.544146301127 & -3.5441463011267 \tabularnewline
155 & 130 & 132.623215246069 & -2.62321524606894 \tabularnewline
156 & 112 & 113.468987690843 & -1.46898769084313 \tabularnewline
157 & 114 & 118.258862627077 & -4.25886262707715 \tabularnewline
158 & 103 & 104.553528363456 & -1.55352836345567 \tabularnewline
159 & 115 & 115.585365934216 & -0.585365934215644 \tabularnewline
160 & 108 & 107.15704216147 & 0.842957838530111 \tabularnewline
161 & 94 & 95.8386040915634 & -1.83860409156343 \tabularnewline
162 & 105 & 102.198678564215 & 2.80132143578484 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191191&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]124.862669557765[/C][C]2.13733044223451[/C][/ROW]
[ROW][C]2[/C][C]108[/C][C]108.269836656649[/C][C]-0.269836656649063[/C][/ROW]
[ROW][C]3[/C][C]110[/C][C]110.613457175102[/C][C]-0.613457175101629[/C][/ROW]
[ROW][C]4[/C][C]102[/C][C]99.6288750000457[/C][C]2.37112499995426[/C][/ROW]
[ROW][C]5[/C][C]104[/C][C]106.359011372928[/C][C]-2.35901137292809[/C][/ROW]
[ROW][C]6[/C][C]140[/C][C]140.725429531516[/C][C]-0.725429531516037[/C][/ROW]
[ROW][C]7[/C][C]112[/C][C]113.323217133878[/C][C]-1.32321713387849[/C][/ROW]
[ROW][C]8[/C][C]115[/C][C]117.271024252993[/C][C]-2.27102425299277[/C][/ROW]
[ROW][C]9[/C][C]121[/C][C]117.456492144563[/C][C]3.54350785543679[/C][/ROW]
[ROW][C]10[/C][C]112[/C][C]111.03259181814[/C][C]0.967408181859766[/C][/ROW]
[ROW][C]11[/C][C]118[/C][C]116.58995624344[/C][C]1.41004375655967[/C][/ROW]
[ROW][C]12[/C][C]122[/C][C]119.785219229875[/C][C]2.21478077012452[/C][/ROW]
[ROW][C]13[/C][C]105[/C][C]107.157900171354[/C][C]-2.15790017135396[/C][/ROW]
[ROW][C]14[/C][C]111[/C][C]109.867349881175[/C][C]1.13265011882537[/C][/ROW]
[ROW][C]15[/C][C]151[/C][C]150.757363342316[/C][C]0.242636657683569[/C][/ROW]
[ROW][C]16[/C][C]106[/C][C]105.600102290186[/C][C]0.39989770981374[/C][/ROW]
[ROW][C]17[/C][C]100[/C][C]101.8295182887[/C][C]-1.82951828869995[/C][/ROW]
[ROW][C]18[/C][C]149[/C][C]149.121551947203[/C][C]-0.121551947202948[/C][/ROW]
[ROW][C]19[/C][C]122[/C][C]118.618480640434[/C][C]3.38151935956641[/C][/ROW]
[ROW][C]20[/C][C]115[/C][C]115.880969586228[/C][C]-0.880969586227998[/C][/ROW]
[ROW][C]21[/C][C]86[/C][C]83.500695731691[/C][C]2.499304268309[/C][/ROW]
[ROW][C]22[/C][C]124[/C][C]124.667419634552[/C][C]-0.667419634552071[/C][/ROW]
[ROW][C]23[/C][C]69[/C][C]71.2586013350266[/C][C]-2.25860133502664[/C][/ROW]
[ROW][C]24[/C][C]117[/C][C]117.671488954644[/C][C]-0.671488954644432[/C][/ROW]
[ROW][C]25[/C][C]113[/C][C]113.943416076903[/C][C]-0.943416076903167[/C][/ROW]
[ROW][C]26[/C][C]123[/C][C]120.884602448094[/C][C]2.11539755190567[/C][/ROW]
[ROW][C]27[/C][C]123[/C][C]122.937681628777[/C][C]0.0623183712230852[/C][/ROW]
[ROW][C]28[/C][C]84[/C][C]81.4719916927972[/C][C]2.52800830720281[/C][/ROW]
[ROW][C]29[/C][C]97[/C][C]92.3238339343613[/C][C]4.67616606563873[/C][/ROW]
[ROW][C]30[/C][C]121[/C][C]120.727946207393[/C][C]0.2720537926072[/C][/ROW]
[ROW][C]31[/C][C]132[/C][C]131.439070740406[/C][C]0.560929259594153[/C][/ROW]
[ROW][C]32[/C][C]119[/C][C]120.321016625333[/C][C]-1.32101662533254[/C][/ROW]
[ROW][C]33[/C][C]98[/C][C]99.4552023885179[/C][C]-1.45520238851795[/C][/ROW]
[ROW][C]34[/C][C]87[/C][C]87.6650306785593[/C][C]-0.665030678559354[/C][/ROW]
[ROW][C]35[/C][C]101[/C][C]98.9151868583945[/C][C]2.08481314160552[/C][/ROW]
[ROW][C]36[/C][C]115[/C][C]114.514272495118[/C][C]0.485727504881908[/C][/ROW]
[ROW][C]37[/C][C]109[/C][C]110.49382037113[/C][C]-1.49382037113023[/C][/ROW]
[ROW][C]38[/C][C]109[/C][C]110.083417555477[/C][C]-1.08341755547722[/C][/ROW]
[ROW][C]39[/C][C]159[/C][C]158.919457522182[/C][C]0.0805424778177516[/C][/ROW]
[ROW][C]40[/C][C]129[/C][C]128.555048011259[/C][C]0.444951988741048[/C][/ROW]
[ROW][C]41[/C][C]119[/C][C]119.473738521424[/C][C]-0.473738521423743[/C][/ROW]
[ROW][C]42[/C][C]119[/C][C]117.664364931686[/C][C]1.33563506831351[/C][/ROW]
[ROW][C]43[/C][C]122[/C][C]120.448894849734[/C][C]1.55110515026636[/C][/ROW]
[ROW][C]44[/C][C]131[/C][C]131.22844232799[/C][C]-0.228442327990187[/C][/ROW]
[ROW][C]45[/C][C]120[/C][C]116.370598424958[/C][C]3.62940157504195[/C][/ROW]
[ROW][C]46[/C][C]82[/C][C]82.5253283597792[/C][C]-0.525328359779219[/C][/ROW]
[ROW][C]47[/C][C]86[/C][C]85.6942893335134[/C][C]0.305710666486624[/C][/ROW]
[ROW][C]48[/C][C]105[/C][C]101.749058271372[/C][C]3.25094172862828[/C][/ROW]
[ROW][C]49[/C][C]114[/C][C]117.864480995423[/C][C]-3.86448099542266[/C][/ROW]
[ROW][C]50[/C][C]100[/C][C]99.9666316613033[/C][C]0.0333683386967008[/C][/ROW]
[ROW][C]51[/C][C]100[/C][C]101.552118595764[/C][C]-1.55211859576396[/C][/ROW]
[ROW][C]52[/C][C]99[/C][C]101.02357524534[/C][C]-2.02357524533962[/C][/ROW]
[ROW][C]53[/C][C]132[/C][C]134.413523765239[/C][C]-2.41352376523892[/C][/ROW]
[ROW][C]54[/C][C]82[/C][C]86.6301055178537[/C][C]-4.6301055178537[/C][/ROW]
[ROW][C]55[/C][C]132[/C][C]131.24373494464[/C][C]0.756265055360377[/C][/ROW]
[ROW][C]56[/C][C]107[/C][C]107.527363811603[/C][C]-0.527363811603255[/C][/ROW]
[ROW][C]57[/C][C]114[/C][C]116.179189111678[/C][C]-2.17918911167801[/C][/ROW]
[ROW][C]58[/C][C]110[/C][C]107.668858297197[/C][C]2.3311417028027[/C][/ROW]
[ROW][C]59[/C][C]105[/C][C]103.324362452546[/C][C]1.67563754745391[/C][/ROW]
[ROW][C]60[/C][C]121[/C][C]118.217303057349[/C][C]2.782696942651[/C][/ROW]
[ROW][C]61[/C][C]109[/C][C]106.940938827068[/C][C]2.05906117293222[/C][/ROW]
[ROW][C]62[/C][C]106[/C][C]106.009113283242[/C][C]-0.00911328324167099[/C][/ROW]
[ROW][C]63[/C][C]124[/C][C]124.167680677846[/C][C]-0.167680677846042[/C][/ROW]
[ROW][C]64[/C][C]120[/C][C]121.133094963119[/C][C]-1.1330949631187[/C][/ROW]
[ROW][C]65[/C][C]91[/C][C]95.1375248803134[/C][C]-4.13752488031342[/C][/ROW]
[ROW][C]66[/C][C]126[/C][C]121.38925739071[/C][C]4.61074260929024[/C][/ROW]
[ROW][C]67[/C][C]138[/C][C]137.119492020153[/C][C]0.880507979846945[/C][/ROW]
[ROW][C]68[/C][C]118[/C][C]114.684153825947[/C][C]3.3158461740531[/C][/ROW]
[ROW][C]69[/C][C]128[/C][C]125.517249547065[/C][C]2.48275045293532[/C][/ROW]
[ROW][C]70[/C][C]98[/C][C]96.9751544706062[/C][C]1.02484552939384[/C][/ROW]
[ROW][C]71[/C][C]133[/C][C]131.565468586337[/C][C]1.43453141366297[/C][/ROW]
[ROW][C]72[/C][C]130[/C][C]128.086382028125[/C][C]1.91361797187455[/C][/ROW]
[ROW][C]73[/C][C]103[/C][C]98.5546096242716[/C][C]4.44539037572839[/C][/ROW]
[ROW][C]74[/C][C]124[/C][C]122.636237941222[/C][C]1.3637620587784[/C][/ROW]
[ROW][C]75[/C][C]142[/C][C]138.67499588288[/C][C]3.32500411712049[/C][/ROW]
[ROW][C]76[/C][C]96[/C][C]96.4337497025503[/C][C]-0.433749702550286[/C][/ROW]
[ROW][C]77[/C][C]93[/C][C]91.1634583333309[/C][C]1.83654166666908[/C][/ROW]
[ROW][C]78[/C][C]129[/C][C]128.763133677065[/C][C]0.236866322935042[/C][/ROW]
[ROW][C]79[/C][C]150[/C][C]148.505282000996[/C][C]1.49471799900444[/C][/ROW]
[ROW][C]80[/C][C]88[/C][C]85.2454865632715[/C][C]2.75451343672849[/C][/ROW]
[ROW][C]81[/C][C]125[/C][C]121.738614406248[/C][C]3.26138559375185[/C][/ROW]
[ROW][C]82[/C][C]92[/C][C]89.0680039111323[/C][C]2.93199608886772[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]109.345352779391[/C][C]-109.345352779391[/C][/ROW]
[ROW][C]84[/C][C]117[/C][C]114.916343470473[/C][C]2.08365652952656[/C][/ROW]
[ROW][C]85[/C][C]112[/C][C]111.250818714449[/C][C]0.749181285551013[/C][/ROW]
[ROW][C]86[/C][C]144[/C][C]140.878719281316[/C][C]3.12128071868435[/C][/ROW]
[ROW][C]87[/C][C]130[/C][C]127.269479903801[/C][C]2.73052009619872[/C][/ROW]
[ROW][C]88[/C][C]87[/C][C]84.2500765897112[/C][C]2.74992341028881[/C][/ROW]
[ROW][C]89[/C][C]92[/C][C]91.0489561744283[/C][C]0.951043825571687[/C][/ROW]
[ROW][C]90[/C][C]114[/C][C]112.93135863501[/C][C]1.0686413649898[/C][/ROW]
[ROW][C]91[/C][C]81[/C][C]78.4283016135663[/C][C]2.57169838643365[/C][/ROW]
[ROW][C]92[/C][C]127[/C][C]124.843208774412[/C][C]2.15679122558791[/C][/ROW]
[ROW][C]93[/C][C]115[/C][C]113.515990410046[/C][C]1.48400958995443[/C][/ROW]
[ROW][C]94[/C][C]123[/C][C]123.494500923936[/C][C]-0.49450092393635[/C][/ROW]
[ROW][C]95[/C][C]115[/C][C]113.434105828135[/C][C]1.56589417186466[/C][/ROW]
[ROW][C]96[/C][C]117[/C][C]115.047186865778[/C][C]1.95281313422219[/C][/ROW]
[ROW][C]97[/C][C]117[/C][C]113.947019305653[/C][C]3.05298069434722[/C][/ROW]
[ROW][C]98[/C][C]103[/C][C]100.411634377619[/C][C]2.58836562238079[/C][/ROW]
[ROW][C]99[/C][C]108[/C][C]106.192559903607[/C][C]1.80744009639313[/C][/ROW]
[ROW][C]100[/C][C]139[/C][C]135.934809691971[/C][C]3.06519030802865[/C][/ROW]
[ROW][C]101[/C][C]113[/C][C]110.064534195754[/C][C]2.9354658042464[/C][/ROW]
[ROW][C]102[/C][C]97[/C][C]93.7144696479281[/C][C]3.28553035207193[/C][/ROW]
[ROW][C]103[/C][C]117[/C][C]113.950842425141[/C][C]3.04915757485889[/C][/ROW]
[ROW][C]104[/C][C]133[/C][C]130.707868956701[/C][C]2.29213104329864[/C][/ROW]
[ROW][C]105[/C][C]115[/C][C]115.035404137852[/C][C]-0.0354041378522152[/C][/ROW]
[ROW][C]106[/C][C]103[/C][C]103.739583482592[/C][C]-0.739583482592092[/C][/ROW]
[ROW][C]107[/C][C]95[/C][C]93.427528710251[/C][C]1.57247128974904[/C][/ROW]
[ROW][C]108[/C][C]117[/C][C]117.401220015821[/C][C]-0.401220015820747[/C][/ROW]
[ROW][C]109[/C][C]113[/C][C]111.802004413137[/C][C]1.19799558686324[/C][/ROW]
[ROW][C]110[/C][C]127[/C][C]129.194700004463[/C][C]-2.19470000446266[/C][/ROW]
[ROW][C]111[/C][C]126[/C][C]124.38825326896[/C][C]1.61174673103982[/C][/ROW]
[ROW][C]112[/C][C]119[/C][C]116.796142082432[/C][C]2.20385791756761[/C][/ROW]
[ROW][C]113[/C][C]97[/C][C]93.3042688150122[/C][C]3.69573118498784[/C][/ROW]
[ROW][C]114[/C][C]105[/C][C]103.840130598964[/C][C]1.15986940103565[/C][/ROW]
[ROW][C]115[/C][C]140[/C][C]139.944881460882[/C][C]0.0551185391177514[/C][/ROW]
[ROW][C]116[/C][C]91[/C][C]88.2265144189182[/C][C]2.77348558108179[/C][/ROW]
[ROW][C]117[/C][C]112[/C][C]111.121023823852[/C][C]0.878976176147543[/C][/ROW]
[ROW][C]118[/C][C]113[/C][C]112.699075353706[/C][C]0.300924646294279[/C][/ROW]
[ROW][C]119[/C][C]102[/C][C]96.2595972670768[/C][C]5.74040273292323[/C][/ROW]
[ROW][C]120[/C][C]92[/C][C]90.3254841362672[/C][C]1.67451586373277[/C][/ROW]
[ROW][C]121[/C][C]98[/C][C]98.6999320588694[/C][C]-0.699932058869385[/C][/ROW]
[ROW][C]122[/C][C]122[/C][C]121.966247911501[/C][C]0.0337520884991126[/C][/ROW]
[ROW][C]123[/C][C]100[/C][C]99.192919945795[/C][C]0.807080054205019[/C][/ROW]
[ROW][C]124[/C][C]84[/C][C]81.5105492983808[/C][C]2.48945070161924[/C][/ROW]
[ROW][C]125[/C][C]142[/C][C]137.432563588255[/C][C]4.56743641174508[/C][/ROW]
[ROW][C]126[/C][C]124[/C][C]122.229731427864[/C][C]1.77026857213554[/C][/ROW]
[ROW][C]127[/C][C]137[/C][C]134.480235016388[/C][C]2.51976498361221[/C][/ROW]
[ROW][C]128[/C][C]105[/C][C]103.982394559463[/C][C]1.01760544053655[/C][/ROW]
[ROW][C]129[/C][C]106[/C][C]105.487042659469[/C][C]0.512957340531429[/C][/ROW]
[ROW][C]130[/C][C]125[/C][C]123.413272734891[/C][C]1.58672726510915[/C][/ROW]
[ROW][C]131[/C][C]104[/C][C]102.569290967179[/C][C]1.4307090328214[/C][/ROW]
[ROW][C]132[/C][C]130[/C][C]132.385261152476[/C][C]-2.38526115247623[/C][/ROW]
[ROW][C]133[/C][C]79[/C][C]79.9380332079596[/C][C]-0.938033207959584[/C][/ROW]
[ROW][C]134[/C][C]108[/C][C]109.29243204455[/C][C]-1.29243204454953[/C][/ROW]
[ROW][C]135[/C][C]136[/C][C]134.529351336077[/C][C]1.47064866392296[/C][/ROW]
[ROW][C]136[/C][C]98[/C][C]96.8992058627791[/C][C]1.10079413722089[/C][/ROW]
[ROW][C]137[/C][C]120[/C][C]124.667401840036[/C][C]-4.66740184003587[/C][/ROW]
[ROW][C]138[/C][C]108[/C][C]108.631281225528[/C][C]-0.631281225527681[/C][/ROW]
[ROW][C]139[/C][C]139[/C][C]140.005831042139[/C][C]-1.00583104213871[/C][/ROW]
[ROW][C]140[/C][C]123[/C][C]122.501921065389[/C][C]0.498078934610773[/C][/ROW]
[ROW][C]141[/C][C]90[/C][C]87.7976083662166[/C][C]2.20239163378343[/C][/ROW]
[ROW][C]142[/C][C]119[/C][C]119.472628515234[/C][C]-0.472628515233945[/C][/ROW]
[ROW][C]143[/C][C]105[/C][C]108.023426764516[/C][C]-3.02342676451573[/C][/ROW]
[ROW][C]144[/C][C]110[/C][C]108.572080952126[/C][C]1.42791904787438[/C][/ROW]
[ROW][C]145[/C][C]135[/C][C]135.31413342072[/C][C]-0.314133420719566[/C][/ROW]
[ROW][C]146[/C][C]101[/C][C]98.001534797802[/C][C]2.99846520219805[/C][/ROW]
[ROW][C]147[/C][C]114[/C][C]112.840863043874[/C][C]1.15913695612575[/C][/ROW]
[ROW][C]148[/C][C]118[/C][C]116.563984372029[/C][C]1.43601562797111[/C][/ROW]
[ROW][C]149[/C][C]120[/C][C]118.817905397025[/C][C]1.18209460297456[/C][/ROW]
[ROW][C]150[/C][C]108[/C][C]111.329981234971[/C][C]-3.32998123497107[/C][/ROW]
[ROW][C]151[/C][C]114[/C][C]111.876330455609[/C][C]2.12366954439092[/C][/ROW]
[ROW][C]152[/C][C]122[/C][C]122.392379108571[/C][C]-0.392379108570879[/C][/ROW]
[ROW][C]153[/C][C]132[/C][C]131.094601012271[/C][C]0.905398987729093[/C][/ROW]
[ROW][C]154[/C][C]130[/C][C]133.544146301127[/C][C]-3.5441463011267[/C][/ROW]
[ROW][C]155[/C][C]130[/C][C]132.623215246069[/C][C]-2.62321524606894[/C][/ROW]
[ROW][C]156[/C][C]112[/C][C]113.468987690843[/C][C]-1.46898769084313[/C][/ROW]
[ROW][C]157[/C][C]114[/C][C]118.258862627077[/C][C]-4.25886262707715[/C][/ROW]
[ROW][C]158[/C][C]103[/C][C]104.553528363456[/C][C]-1.55352836345567[/C][/ROW]
[ROW][C]159[/C][C]115[/C][C]115.585365934216[/C][C]-0.585365934215644[/C][/ROW]
[ROW][C]160[/C][C]108[/C][C]107.15704216147[/C][C]0.842957838530111[/C][/ROW]
[ROW][C]161[/C][C]94[/C][C]95.8386040915634[/C][C]-1.83860409156343[/C][/ROW]
[ROW][C]162[/C][C]105[/C][C]102.198678564215[/C][C]2.80132143578484[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191191&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191191&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
1127124.8626695577652.13733044223451
2108108.269836656649-0.269836656649063
3110110.613457175102-0.613457175101629
410299.62887500004572.37112499995426
5104106.359011372928-2.35901137292809
6140140.725429531516-0.725429531516037
7112113.323217133878-1.32321713387849
8115117.271024252993-2.27102425299277
9121117.4564921445633.54350785543679
10112111.032591818140.967408181859766
11118116.589956243441.41004375655967
12122119.7852192298752.21478077012452
13105107.157900171354-2.15790017135396
14111109.8673498811751.13265011882537
15151150.7573633423160.242636657683569
16106105.6001022901860.39989770981374
17100101.8295182887-1.82951828869995
18149149.121551947203-0.121551947202948
19122118.6184806404343.38151935956641
20115115.880969586228-0.880969586227998
218683.5006957316912.499304268309
22124124.667419634552-0.667419634552071
236971.2586013350266-2.25860133502664
24117117.671488954644-0.671488954644432
25113113.943416076903-0.943416076903167
26123120.8846024480942.11539755190567
27123122.9376816287770.0623183712230852
288481.47199169279722.52800830720281
299792.32383393436134.67616606563873
30121120.7279462073930.2720537926072
31132131.4390707404060.560929259594153
32119120.321016625333-1.32101662533254
339899.4552023885179-1.45520238851795
348787.6650306785593-0.665030678559354
3510198.91518685839452.08481314160552
36115114.5142724951180.485727504881908
37109110.49382037113-1.49382037113023
38109110.083417555477-1.08341755547722
39159158.9194575221820.0805424778177516
40129128.5550480112590.444951988741048
41119119.473738521424-0.473738521423743
42119117.6643649316861.33563506831351
43122120.4488948497341.55110515026636
44131131.22844232799-0.228442327990187
45120116.3705984249583.62940157504195
468282.5253283597792-0.525328359779219
478685.69428933351340.305710666486624
48105101.7490582713723.25094172862828
49114117.864480995423-3.86448099542266
5010099.96663166130330.0333683386967008
51100101.552118595764-1.55211859576396
5299101.02357524534-2.02357524533962
53132134.413523765239-2.41352376523892
548286.6301055178537-4.6301055178537
55132131.243734944640.756265055360377
56107107.527363811603-0.527363811603255
57114116.179189111678-2.17918911167801
58110107.6688582971972.3311417028027
59105103.3243624525461.67563754745391
60121118.2173030573492.782696942651
61109106.9409388270682.05906117293222
62106106.009113283242-0.00911328324167099
63124124.167680677846-0.167680677846042
64120121.133094963119-1.1330949631187
659195.1375248803134-4.13752488031342
66126121.389257390714.61074260929024
67138137.1194920201530.880507979846945
68118114.6841538259473.3158461740531
69128125.5172495470652.48275045293532
709896.97515447060621.02484552939384
71133131.5654685863371.43453141366297
72130128.0863820281251.91361797187455
7310398.55460962427164.44539037572839
74124122.6362379412221.3637620587784
75142138.674995882883.32500411712049
769696.4337497025503-0.433749702550286
779391.16345833333091.83654166666908
78129128.7631336770650.236866322935042
79150148.5052820009961.49471799900444
808885.24548656327152.75451343672849
81125121.7386144062483.26138559375185
829289.06800391113232.93199608886772
830109.345352779391-109.345352779391
84117114.9163434704732.08365652952656
85112111.2508187144490.749181285551013
86144140.8787192813163.12128071868435
87130127.2694799038012.73052009619872
888784.25007658971122.74992341028881
899291.04895617442830.951043825571687
90114112.931358635011.0686413649898
918178.42830161356632.57169838643365
92127124.8432087744122.15679122558791
93115113.5159904100461.48400958995443
94123123.494500923936-0.49450092393635
95115113.4341058281351.56589417186466
96117115.0471868657781.95281313422219
97117113.9470193056533.05298069434722
98103100.4116343776192.58836562238079
99108106.1925599036071.80744009639313
100139135.9348096919713.06519030802865
101113110.0645341957542.9354658042464
1029793.71446964792813.28553035207193
103117113.9508424251413.04915757485889
104133130.7078689567012.29213104329864
105115115.035404137852-0.0354041378522152
106103103.739583482592-0.739583482592092
1079593.4275287102511.57247128974904
108117117.401220015821-0.401220015820747
109113111.8020044131371.19799558686324
110127129.194700004463-2.19470000446266
111126124.388253268961.61174673103982
112119116.7961420824322.20385791756761
1139793.30426881501223.69573118498784
114105103.8401305989641.15986940103565
115140139.9448814608820.0551185391177514
1169188.22651441891822.77348558108179
117112111.1210238238520.878976176147543
118113112.6990753537060.300924646294279
11910296.25959726707685.74040273292323
1209290.32548413626721.67451586373277
1219898.6999320588694-0.699932058869385
122122121.9662479115010.0337520884991126
12310099.1929199457950.807080054205019
1248481.51054929838082.48945070161924
125142137.4325635882554.56743641174508
126124122.2297314278641.77026857213554
127137134.4802350163882.51976498361221
128105103.9823945594631.01760544053655
129106105.4870426594690.512957340531429
130125123.4132727348911.58672726510915
131104102.5692909671791.4307090328214
132130132.385261152476-2.38526115247623
1337979.9380332079596-0.938033207959584
134108109.29243204455-1.29243204454953
135136134.5293513360771.47064866392296
1369896.89920586277911.10079413722089
137120124.667401840036-4.66740184003587
138108108.631281225528-0.631281225527681
139139140.005831042139-1.00583104213871
140123122.5019210653890.498078934610773
1419087.79760836621662.20239163378343
142119119.472628515234-0.472628515233945
143105108.023426764516-3.02342676451573
144110108.5720809521261.42791904787438
145135135.31413342072-0.314133420719566
14610198.0015347978022.99846520219805
147114112.8408630438741.15913695612575
148118116.5639843720291.43601562797111
149120118.8179053970251.18209460297456
150108111.329981234971-3.32998123497107
151114111.8763304556092.12366954439092
152122122.392379108571-0.392379108570879
153132131.0946010122710.905398987729093
154130133.544146301127-3.5441463011267
155130132.623215246069-2.62321524606894
156112113.468987690843-1.46898769084313
157114118.258862627077-4.25886262707715
158103104.553528363456-1.55352836345567
159115115.585365934216-0.585365934215644
160108107.157042161470.842957838530111
1619495.8386040915634-1.83860409156343
162105102.1986785642152.80132143578484






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
154.3961443679384e-468.7922887358768e-461
161.12221471856813e-602.24442943713627e-601
175.99671501062401e-791.1993430021248e-781
185.32631583480094e-951.06526316696019e-941
191.4720224798454e-1042.9440449596908e-1041
202.44561317863365e-1194.89122635726729e-1191
212.14155152992171e-1374.28310305984341e-1371
229.94150224642491e-1561.98830044928498e-1551
234.17429663289577e-1638.34859326579154e-1631
243.70193208154878e-1797.40386416309755e-1791
252.80820170756337e-2015.61640341512673e-2011
261.71765420184186e-2143.43530840368372e-2141
277.79588057965603e-2291.55917611593121e-2281
285.15258856484324e-2461.03051771296865e-2451
291.52797521124352e-2513.05595042248705e-2511
305.50363328832411e-2791.10072665766482e-2781
316.47102081610366e-2931.29420416322073e-2921
322.39805282214752e-3084.79610564429504e-3081
331.18995710800864e-3192.37991421601728e-3191
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
12914.94065645841247e-3232.47032822920623e-323
13011.70732243613907e-2988.53661218069533e-299
13115.26242879649718e-2882.63121439824859e-288
13211.28955340515892e-2706.44776702579459e-271
13319.93391705898235e-2514.96695852949118e-251
13417.79117096707144e-2513.89558548353572e-251
13514.77822810408425e-2332.38911405204213e-233
13615.96737173054915e-2122.98368586527458e-212
13712.74094761413078e-1941.37047380706539e-194
13814.41484685992468e-1832.20742342996234e-183
13915.14291306299364e-1682.57145653149682e-168
14012.81237820049952e-1491.40618910024976e-149
14113.03123157598842e-1341.51561578799421e-134
14211.19426104293487e-1245.97130521467434e-125
14316.18615291565865e-1093.09307645782933e-109
14413.91663943767624e-941.95831971883812e-94
14511.63048519310666e-788.15242596553329e-79
14611.98365506502173e-619.91827532510865e-62
14714.32101645611691e-452.16050822805846e-45

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 4.3961443679384e-46 & 8.7922887358768e-46 & 1 \tabularnewline
16 & 1.12221471856813e-60 & 2.24442943713627e-60 & 1 \tabularnewline
17 & 5.99671501062401e-79 & 1.1993430021248e-78 & 1 \tabularnewline
18 & 5.32631583480094e-95 & 1.06526316696019e-94 & 1 \tabularnewline
19 & 1.4720224798454e-104 & 2.9440449596908e-104 & 1 \tabularnewline
20 & 2.44561317863365e-119 & 4.89122635726729e-119 & 1 \tabularnewline
21 & 2.14155152992171e-137 & 4.28310305984341e-137 & 1 \tabularnewline
22 & 9.94150224642491e-156 & 1.98830044928498e-155 & 1 \tabularnewline
23 & 4.17429663289577e-163 & 8.34859326579154e-163 & 1 \tabularnewline
24 & 3.70193208154878e-179 & 7.40386416309755e-179 & 1 \tabularnewline
25 & 2.80820170756337e-201 & 5.61640341512673e-201 & 1 \tabularnewline
26 & 1.71765420184186e-214 & 3.43530840368372e-214 & 1 \tabularnewline
27 & 7.79588057965603e-229 & 1.55917611593121e-228 & 1 \tabularnewline
28 & 5.15258856484324e-246 & 1.03051771296865e-245 & 1 \tabularnewline
29 & 1.52797521124352e-251 & 3.05595042248705e-251 & 1 \tabularnewline
30 & 5.50363328832411e-279 & 1.10072665766482e-278 & 1 \tabularnewline
31 & 6.47102081610366e-293 & 1.29420416322073e-292 & 1 \tabularnewline
32 & 2.39805282214752e-308 & 4.79610564429504e-308 & 1 \tabularnewline
33 & 1.18995710800864e-319 & 2.37991421601728e-319 & 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 & 4.94065645841247e-323 & 2.47032822920623e-323 \tabularnewline
130 & 1 & 1.70732243613907e-298 & 8.53661218069533e-299 \tabularnewline
131 & 1 & 5.26242879649718e-288 & 2.63121439824859e-288 \tabularnewline
132 & 1 & 1.28955340515892e-270 & 6.44776702579459e-271 \tabularnewline
133 & 1 & 9.93391705898235e-251 & 4.96695852949118e-251 \tabularnewline
134 & 1 & 7.79117096707144e-251 & 3.89558548353572e-251 \tabularnewline
135 & 1 & 4.77822810408425e-233 & 2.38911405204213e-233 \tabularnewline
136 & 1 & 5.96737173054915e-212 & 2.98368586527458e-212 \tabularnewline
137 & 1 & 2.74094761413078e-194 & 1.37047380706539e-194 \tabularnewline
138 & 1 & 4.41484685992468e-183 & 2.20742342996234e-183 \tabularnewline
139 & 1 & 5.14291306299364e-168 & 2.57145653149682e-168 \tabularnewline
140 & 1 & 2.81237820049952e-149 & 1.40618910024976e-149 \tabularnewline
141 & 1 & 3.03123157598842e-134 & 1.51561578799421e-134 \tabularnewline
142 & 1 & 1.19426104293487e-124 & 5.97130521467434e-125 \tabularnewline
143 & 1 & 6.18615291565865e-109 & 3.09307645782933e-109 \tabularnewline
144 & 1 & 3.91663943767624e-94 & 1.95831971883812e-94 \tabularnewline
145 & 1 & 1.63048519310666e-78 & 8.15242596553329e-79 \tabularnewline
146 & 1 & 1.98365506502173e-61 & 9.91827532510865e-62 \tabularnewline
147 & 1 & 4.32101645611691e-45 & 2.16050822805846e-45 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191191&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]15[/C][C]4.3961443679384e-46[/C][C]8.7922887358768e-46[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]1.12221471856813e-60[/C][C]2.24442943713627e-60[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]5.99671501062401e-79[/C][C]1.1993430021248e-78[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]5.32631583480094e-95[/C][C]1.06526316696019e-94[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]1.4720224798454e-104[/C][C]2.9440449596908e-104[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]2.44561317863365e-119[/C][C]4.89122635726729e-119[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]2.14155152992171e-137[/C][C]4.28310305984341e-137[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]9.94150224642491e-156[/C][C]1.98830044928498e-155[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]4.17429663289577e-163[/C][C]8.34859326579154e-163[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]3.70193208154878e-179[/C][C]7.40386416309755e-179[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]2.80820170756337e-201[/C][C]5.61640341512673e-201[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]1.71765420184186e-214[/C][C]3.43530840368372e-214[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]7.79588057965603e-229[/C][C]1.55917611593121e-228[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]5.15258856484324e-246[/C][C]1.03051771296865e-245[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]1.52797521124352e-251[/C][C]3.05595042248705e-251[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]5.50363328832411e-279[/C][C]1.10072665766482e-278[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]6.47102081610366e-293[/C][C]1.29420416322073e-292[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]2.39805282214752e-308[/C][C]4.79610564429504e-308[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]1.18995710800864e-319[/C][C]2.37991421601728e-319[/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]4.94065645841247e-323[/C][C]2.47032822920623e-323[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]1.70732243613907e-298[/C][C]8.53661218069533e-299[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]5.26242879649718e-288[/C][C]2.63121439824859e-288[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]1.28955340515892e-270[/C][C]6.44776702579459e-271[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]9.93391705898235e-251[/C][C]4.96695852949118e-251[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]7.79117096707144e-251[/C][C]3.89558548353572e-251[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]4.77822810408425e-233[/C][C]2.38911405204213e-233[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]5.96737173054915e-212[/C][C]2.98368586527458e-212[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]2.74094761413078e-194[/C][C]1.37047380706539e-194[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]4.41484685992468e-183[/C][C]2.20742342996234e-183[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]5.14291306299364e-168[/C][C]2.57145653149682e-168[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]2.81237820049952e-149[/C][C]1.40618910024976e-149[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]3.03123157598842e-134[/C][C]1.51561578799421e-134[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]1.19426104293487e-124[/C][C]5.97130521467434e-125[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]6.18615291565865e-109[/C][C]3.09307645782933e-109[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]3.91663943767624e-94[/C][C]1.95831971883812e-94[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]1.63048519310666e-78[/C][C]8.15242596553329e-79[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]1.98365506502173e-61[/C][C]9.91827532510865e-62[/C][/ROW]
[ROW][C]147[/C][C]1[/C][C]4.32101645611691e-45[/C][C]2.16050822805846e-45[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191191&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191191&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
154.3961443679384e-468.7922887358768e-461
161.12221471856813e-602.24442943713627e-601
175.99671501062401e-791.1993430021248e-781
185.32631583480094e-951.06526316696019e-941
191.4720224798454e-1042.9440449596908e-1041
202.44561317863365e-1194.89122635726729e-1191
212.14155152992171e-1374.28310305984341e-1371
229.94150224642491e-1561.98830044928498e-1551
234.17429663289577e-1638.34859326579154e-1631
243.70193208154878e-1797.40386416309755e-1791
252.80820170756337e-2015.61640341512673e-2011
261.71765420184186e-2143.43530840368372e-2141
277.79588057965603e-2291.55917611593121e-2281
285.15258856484324e-2461.03051771296865e-2451
291.52797521124352e-2513.05595042248705e-2511
305.50363328832411e-2791.10072665766482e-2781
316.47102081610366e-2931.29420416322073e-2921
322.39805282214752e-3084.79610564429504e-3081
331.18995710800864e-3192.37991421601728e-3191
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
12914.94065645841247e-3232.47032822920623e-323
13011.70732243613907e-2988.53661218069533e-299
13115.26242879649718e-2882.63121439824859e-288
13211.28955340515892e-2706.44776702579459e-271
13319.93391705898235e-2514.96695852949118e-251
13417.79117096707144e-2513.89558548353572e-251
13514.77822810408425e-2332.38911405204213e-233
13615.96737173054915e-2122.98368586527458e-212
13712.74094761413078e-1941.37047380706539e-194
13814.41484685992468e-1832.20742342996234e-183
13915.14291306299364e-1682.57145653149682e-168
14012.81237820049952e-1491.40618910024976e-149
14113.03123157598842e-1341.51561578799421e-134
14211.19426104293487e-1245.97130521467434e-125
14316.18615291565865e-1093.09307645782933e-109
14413.91663943767624e-941.95831971883812e-94
14511.63048519310666e-788.15242596553329e-79
14611.98365506502173e-619.91827532510865e-62
14714.32101645611691e-452.16050822805846e-45






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1331NOK
5% type I error level1331NOK
10% type I error level1331NOK

\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 & 133 & 1 & NOK \tabularnewline
5% type I error level & 133 & 1 & NOK \tabularnewline
10% type I error level & 133 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191191&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]133[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]133[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]133[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191191&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191191&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 level1331NOK
5% type I error level1331NOK
10% type I error level1331NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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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 = 11 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 11 ; 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')
}