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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, 23 Nov 2010 00:52:41 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t12904735038rk3zjr6cusqi51.htm/, Retrieved Fri, 29 Mar 2024 07:28:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98821, Retrieved Fri, 29 Mar 2024 07:28:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact155
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [Mini-Tutorial FMP...] [2010-11-23 00:52:41] [93ab421e12cd1017d2b38fdbcbdb62e0] [Current]
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Dataseries X:
1	25	25	11	11	7	7	8	8	25	25	23	23
1	17	17	6	6	17	17	8	8	30	30	25	25
1	18	18	8	8	12	12	9	9	22	22	19	19
1	16	16	10	10	12	12	7	7	22	22	29	29
1	20	20	10	10	11	11	4	4	25	25	25	25
1	16	16	11	11	11	11	11	11	23	23	21	21
1	18	18	16	16	12	12	7	7	17	17	22	22
1	17	17	11	11	13	13	7	7	21	21	25	25
1	30	30	12	12	16	16	10	10	19	19	18	18
1	23	23	8	8	11	11	10	10	15	15	22	22
1	18	18	12	12	10	10	8	8	16	16	15	15
1	21	21	9	9	9	9	9	9	22	22	20	20
1	31	31	14	14	17	17	11	11	23	23	20	20
1	27	27	15	15	11	11	9	9	23	23	21	21
1	21	21	9	9	14	14	13	13	19	19	21	21
1	16	16	8	8	15	15	9	9	23	23	24	24
1	20	20	9	9	15	15	6	6	25	25	24	24
1	17	17	9	9	13	13	6	6	22	22	23	23
1	25	25	16	16	18	18	16	16	26	26	24	24
1	26	26	11	11	18	18	5	5	29	29	18	18
1	25	25	8	8	12	12	7	7	32	32	25	25
1	17	17	9	9	17	17	9	9	25	25	21	21
1	32	32	12	12	18	18	12	12	28	28	22	22
1	22	22	9	9	14	14	9	9	25	25	23	23
1	17	17	9	9	16	16	5	5	25	25	23	23
1	20	20	14	14	14	14	10	10	18	18	24	24
1	29	29	10	10	12	12	8	8	25	25	23	23
1	23	23	14	14	17	17	7	7	25	25	21	21
1	20	20	10	10	12	12	8	8	20	20	28	28
1	11	11	6	6	6	6	4	4	15	15	16	16
1	26	26	13	13	12	12	8	8	24	24	29	29
1	22	22	10	10	12	12	8	8	26	26	27	27
1	14	14	15	15	13	13	8	8	14	14	16	16
1	19	19	12	12	14	14	7	7	24	24	28	28
1	20	20	11	11	11	11	8	8	25	25	25	25
1	28	28	8	8	12	12	7	7	20	20	22	22
1	19	19	9	9	9	9	7	7	21	21	23	23
1	30	30	9	9	15	15	9	9	27	27	26	26
1	29	29	15	15	18	18	11	11	23	23	23	23
1	26	26	9	9	15	15	6	6	25	25	25	25
1	23	23	10	10	12	12	8	8	20	20	21	21
1	21	21	12	12	14	14	9	9	22	22	24	24
1	28	28	11	11	13	13	6	6	25	25	22	22
1	23	23	14	14	13	13	10	10	25	25	27	27
1	18	18	6	6	11	11	8	8	17	17	26	26
1	20	20	8	8	16	16	10	10	25	25	24	24
1	21	21	10	10	11	11	5	5	26	26	24	24
1	28	28	12	12	16	16	14	14	27	27	22	22
1	10	10	5	5	8	8	6	6	19	19	24	24
1	22	22	10	10	15	15	6	6	22	22	20	20
1	31	31	10	10	21	21	12	12	32	32	26	26
1	29	29	13	13	18	18	12	12	21	21	21	21
1	22	22	10	10	13	13	8	8	18	18	19	19
1	23	23	10	10	15	15	10	10	23	23	21	21
1	20	20	9	9	19	19	10	10	20	20	16	16
1	18	18	8	8	15	15	10	10	21	21	22	22
1	25	25	14	14	11	11	5	5	17	17	15	15
1	21	21	8	8	10	10	7	7	18	18	17	17
1	24	24	9	9	13	13	10	10	19	19	15	15
1	25	25	14	14	15	15	11	11	22	22	21	21
1	13	13	8	8	12	12	7	7	14	14	19	19
1	28	28	8	8	16	16	12	12	18	18	24	24
1	25	25	7	7	18	18	11	11	35	35	17	17
1	9	9	6	6	8	8	11	11	29	29	23	23
1	16	16	8	8	13	13	5	5	21	21	24	24
1	19	19	6	6	17	17	8	8	25	25	14	14
1	29	29	11	11	7	7	4	4	26	26	22	22
1	14	14	11	11	12	12	7	7	17	17	16	16
1	22	22	14	14	14	14	11	11	25	25	19	19
1	15	15	8	8	6	6	6	6	20	20	25	25
1	15	15	8	8	10	10	4	4	22	22	24	24
1	20	20	11	11	11	11	8	8	24	24	26	26
1	18	18	10	10	14	14	9	9	21	21	26	26
1	33	33	14	14	11	11	8	8	26	26	25	25
1	22	22	11	11	13	13	11	11	24	24	18	18
1	16	16	9	9	12	12	8	8	16	16	21	21
1	16	16	8	8	9	9	4	4	18	18	23	23
1	18	18	13	13	12	12	6	6	19	19	20	20
1	18	18	12	12	13	13	9	9	21	21	13	13
1	22	22	13	13	12	12	13	13	22	22	15	15
1	30	30	14	14	9	9	9	9	23	23	14	14
1	30	30	12	12	15	15	10	10	29	29	22	22
1	24	24	14	14	24	24	20	20	21	21	10	10
1	21	21	13	13	17	17	11	11	23	23	22	22
1	29	29	16	16	11	11	6	6	27	27	24	24
1	31	31	9	9	17	17	9	9	25	25	19	19
1	20	20	9	9	11	11	7	7	21	21	20	20
1	16	16	9	9	12	12	9	9	10	10	13	13
1	22	22	8	8	14	14	10	10	20	20	20	20
1	20	20	7	7	11	11	9	9	26	26	22	22
1	28	28	16	16	16	16	8	8	24	24	24	24
1	38	38	11	11	21	21	7	7	29	29	29	29
1	22	22	9	9	14	14	6	6	19	19	12	12
1	20	20	11	11	20	20	13	13	24	24	20	20
1	17	17	9	9	13	13	6	6	19	19	21	21
1	22	22	13	13	15	15	10	10	22	22	22	22
1	31	31	16	16	19	19	16	16	17	17	20	20
2	24	48	14	28	11	22	12	24	24	48	26	52
2	18	36	12	24	10	20	8	16	19	38	23	46
2	23	46	13	26	14	28	12	24	19	38	24	48
2	15	30	11	22	11	22	8	16	23	46	22	44
2	12	24	4	8	15	30	4	8	27	54	28	56
2	15	30	8	16	11	22	8	16	14	28	12	24
2	20	40	8	16	17	34	7	14	22	44	24	48
2	34	68	16	32	18	36	11	22	21	42	20	40
2	31	62	14	28	10	20	8	16	18	36	23	46
2	19	38	11	22	11	22	8	16	20	40	28	56
2	21	42	9	18	13	26	9	18	19	38	24	48
2	22	44	9	18	16	32	9	18	24	48	23	46
2	24	48	10	20	9	18	6	12	25	50	29	58
2	32	64	16	32	9	18	6	12	29	58	26	52
2	33	66	11	22	9	18	6	12	28	56	22	44
2	13	26	16	32	12	24	5	10	17	34	22	44
2	25	50	12	24	12	24	7	14	29	58	23	46
2	29	58	14	28	18	36	10	20	26	52	30	60
2	18	36	10	20	15	30	8	16	14	28	17	34
2	20	40	10	20	10	20	8	16	26	52	23	46
2	15	30	12	24	11	22	8	16	20	40	25	50
2	33	66	14	28	9	18	6	12	32	64	24	48
2	26	52	16	32	5	10	4	8	23	46	24	48
2	18	36	9	18	12	24	8	16	21	42	24	48
2	28	56	8	16	24	48	20	40	30	60	20	40
2	17	34	8	16	14	28	6	12	24	48	22	44
2	12	24	7	14	7	14	4	8	22	44	28	56
2	17	34	9	18	12	24	9	18	24	48	25	50
2	21	42	10	20	13	26	6	12	24	48	24	48
2	18	36	13	26	8	16	9	18	24	48	24	48
2	10	20	10	20	11	22	5	10	19	38	23	46
2	29	58	11	22	9	18	5	10	31	62	30	60
2	31	62	8	16	11	22	8	16	22	44	24	48
2	19	38	9	18	13	26	8	16	27	54	21	42
2	9	18	13	26	10	20	6	12	19	38	25	50
2	13	26	14	28	13	26	6	12	21	42	25	50
2	19	38	12	24	10	20	8	16	23	46	29	58
2	21	42	12	24	13	26	8	16	19	38	22	44
2	23	46	14	28	8	16	5	10	19	38	27	54
2	21	42	11	22	16	32	7	14	20	40	24	48
2	15	30	14	28	9	18	8	16	23	46	29	58
2	19	38	10	20	12	24	7	14	17	34	21	42
2	26	52	14	28	14	28	8	16	17	34	24	48
2	16	32	11	22	9	18	5	10	17	34	23	46
2	19	38	9	18	11	22	10	20	21	42	27	54
2	31	62	16	32	14	28	9	18	21	42	25	50
2	19	38	9	18	12	24	7	14	18	36	21	42
2	15	30	7	14	12	24	6	12	19	38	21	42
2	23	46	14	28	11	22	10	20	20	40	29	58
2	17	34	14	28	12	24	6	12	15	30	21	42
2	21	42	8	16	9	18	11	22	24	48	20	40
2	17	34	11	22	9	18	6	12	20	40	19	38
2	25	50	14	28	15	30	9	18	22	44	24	48
2	20	40	11	22	8	16	4	8	13	26	13	26
2	19	38	20	40	8	16	7	14	19	38	25	50
2	20	40	11	22	17	34	8	16	21	42	23	46
2	17	34	9	18	11	22	5	10	23	46	26	52
2	21	42	10	20	12	24	8	16	16	32	23	46
2	26	52	13	26	20	40	10	20	26	52	22	44
2	17	34	8	16	12	24	9	18	21	42	24	48
2	21	42	15	30	7	14	5	10	21	42	24	48
2	28	56	14	28	11	22	8	16	24	48	24	48




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time14 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 14 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98821&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]14 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98821&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98821&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time14 seconds
R Server'George Udny Yule' @ 72.249.76.132







Multiple Linear Regression - Estimated Regression Equation
PS[t] = + 7.26199343419243 -4.64054039140916Gender[t] + 0.311331028298378CM[t] -0.209468291018821CM_G[t] -0.355090814190106D[t] + 0.247487525828207D_G[t] + 0.199005472933621PE[t] -0.100117147433695PE_G[t] + 0.000738451867712845PC[t] -0.0123779601043173PC_G[t] + 0.657302554450284PS_G[t] + 0.420848350295368O[t] -0.294086826997431O_G[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PS[t] =  +  7.26199343419243 -4.64054039140916Gender[t] +  0.311331028298378CM[t] -0.209468291018821CM_G[t] -0.355090814190106D[t] +  0.247487525828207D_G[t] +  0.199005472933621PE[t] -0.100117147433695PE_G[t] +  0.000738451867712845PC[t] -0.0123779601043173PC_G[t] +  0.657302554450284PS_G[t] +  0.420848350295368O[t] -0.294086826997431O_G[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98821&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PS[t] =  +  7.26199343419243 -4.64054039140916Gender[t] +  0.311331028298378CM[t] -0.209468291018821CM_G[t] -0.355090814190106D[t] +  0.247487525828207D_G[t] +  0.199005472933621PE[t] -0.100117147433695PE_G[t] +  0.000738451867712845PC[t] -0.0123779601043173PC_G[t] +  0.657302554450284PS_G[t] +  0.420848350295368O[t] -0.294086826997431O_G[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98821&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98821&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
PS[t] = + 7.26199343419243 -4.64054039140916Gender[t] + 0.311331028298378CM[t] -0.209468291018821CM_G[t] -0.355090814190106D[t] + 0.247487525828207D_G[t] + 0.199005472933621PE[t] -0.100117147433695PE_G[t] + 0.000738451867712845PC[t] -0.0123779601043173PC_G[t] + 0.657302554450284PS_G[t] + 0.420848350295368O[t] -0.294086826997431O_G[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.261993434192432.2887873.17290.0018410.00092
Gender-4.640540391409161.633075-2.84160.0051310.002566
CM0.3113310282983780.05855.321900
CM_G-0.2094682910188210.038783-5.40100
D-0.3550908141901060.11567-3.06990.0025540.001277
D_G0.2474875258282070.0763823.24010.001480.00074
PE0.1990054729336210.1058791.87960.0621610.03108
PE_G-0.1001171474336950.072109-1.38840.1671250.083563
PC0.0007384518677128450.131150.00560.9955150.497758
PC_G-0.01237796010431730.092816-0.13340.8940920.447046
PS_G0.6573025544502840.01921534.207300
O0.4208483502953680.0752525.592500
O_G-0.2940868269974310.054959-5.35100

\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) & 7.26199343419243 & 2.288787 & 3.1729 & 0.001841 & 0.00092 \tabularnewline
Gender & -4.64054039140916 & 1.633075 & -2.8416 & 0.005131 & 0.002566 \tabularnewline
CM & 0.311331028298378 & 0.0585 & 5.3219 & 0 & 0 \tabularnewline
CM_G & -0.209468291018821 & 0.038783 & -5.401 & 0 & 0 \tabularnewline
D & -0.355090814190106 & 0.11567 & -3.0699 & 0.002554 & 0.001277 \tabularnewline
D_G & 0.247487525828207 & 0.076382 & 3.2401 & 0.00148 & 0.00074 \tabularnewline
PE & 0.199005472933621 & 0.105879 & 1.8796 & 0.062161 & 0.03108 \tabularnewline
PE_G & -0.100117147433695 & 0.072109 & -1.3884 & 0.167125 & 0.083563 \tabularnewline
PC & 0.000738451867712845 & 0.13115 & 0.0056 & 0.995515 & 0.497758 \tabularnewline
PC_G & -0.0123779601043173 & 0.092816 & -0.1334 & 0.894092 & 0.447046 \tabularnewline
PS_G & 0.657302554450284 & 0.019215 & 34.2073 & 0 & 0 \tabularnewline
O & 0.420848350295368 & 0.075252 & 5.5925 & 0 & 0 \tabularnewline
O_G & -0.294086826997431 & 0.054959 & -5.351 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98821&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]7.26199343419243[/C][C]2.288787[/C][C]3.1729[/C][C]0.001841[/C][C]0.00092[/C][/ROW]
[ROW][C]Gender[/C][C]-4.64054039140916[/C][C]1.633075[/C][C]-2.8416[/C][C]0.005131[/C][C]0.002566[/C][/ROW]
[ROW][C]CM[/C][C]0.311331028298378[/C][C]0.0585[/C][C]5.3219[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]CM_G[/C][C]-0.209468291018821[/C][C]0.038783[/C][C]-5.401[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]D[/C][C]-0.355090814190106[/C][C]0.11567[/C][C]-3.0699[/C][C]0.002554[/C][C]0.001277[/C][/ROW]
[ROW][C]D_G[/C][C]0.247487525828207[/C][C]0.076382[/C][C]3.2401[/C][C]0.00148[/C][C]0.00074[/C][/ROW]
[ROW][C]PE[/C][C]0.199005472933621[/C][C]0.105879[/C][C]1.8796[/C][C]0.062161[/C][C]0.03108[/C][/ROW]
[ROW][C]PE_G[/C][C]-0.100117147433695[/C][C]0.072109[/C][C]-1.3884[/C][C]0.167125[/C][C]0.083563[/C][/ROW]
[ROW][C]PC[/C][C]0.000738451867712845[/C][C]0.13115[/C][C]0.0056[/C][C]0.995515[/C][C]0.497758[/C][/ROW]
[ROW][C]PC_G[/C][C]-0.0123779601043173[/C][C]0.092816[/C][C]-0.1334[/C][C]0.894092[/C][C]0.447046[/C][/ROW]
[ROW][C]PS_G[/C][C]0.657302554450284[/C][C]0.019215[/C][C]34.2073[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]O[/C][C]0.420848350295368[/C][C]0.075252[/C][C]5.5925[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]O_G[/C][C]-0.294086826997431[/C][C]0.054959[/C][C]-5.351[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98821&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98821&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.261993434192432.2887873.17290.0018410.00092
Gender-4.640540391409161.633075-2.84160.0051310.002566
CM0.3113310282983780.05855.321900
CM_G-0.2094682910188210.038783-5.40100
D-0.3550908141901060.11567-3.06990.0025540.001277
D_G0.2474875258282070.0763823.24010.001480.00074
PE0.1990054729336210.1058791.87960.0621610.03108
PE_G-0.1001171474336950.072109-1.38840.1671250.083563
PC0.0007384518677128450.131150.00560.9955150.497758
PC_G-0.01237796010431730.092816-0.13340.8940920.447046
PS_G0.6573025544502840.01921534.207300
O0.4208483502953680.0752525.592500
O_G-0.2940868269974310.054959-5.35100







Multiple Linear Regression - Regression Statistics
Multiple R0.965371417259928
R-squared0.931941973262442
Adjusted R-squared0.926348162845657
F-TEST (value)166.602352211638
F-TEST (DF numerator)12
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.14443500794398
Sum Squared Residuals191.220797161529

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.965371417259928 \tabularnewline
R-squared & 0.931941973262442 \tabularnewline
Adjusted R-squared & 0.926348162845657 \tabularnewline
F-TEST (value) & 166.602352211638 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 146 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.14443500794398 \tabularnewline
Sum Squared Residuals & 191.220797161529 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98821&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.965371417259928[/C][/ROW]
[ROW][C]R-squared[/C][C]0.931941973262442[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.926348162845657[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]166.602352211638[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]146[/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]1.14443500794398[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]191.220797161529[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98821&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98821&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.965371417259928
R-squared0.931941973262442
Adjusted R-squared0.926348162845657
F-TEST (value)166.602352211638
F-TEST (DF numerator)12
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.14443500794398
Sum Squared Residuals191.220797161529







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12523.93156641250761.06843358749244
23028.18360002992711.81639997007285
32221.54518547935680.454814520643217
42222.4171476775265-0.417147677526475
52524.22549039601370.774509603986297
62321.80730839878501.19269160121498
71717.8014099865772-0.801409986577212
82121.3459468043020-0.345946804302022
91920.6223697803782-1.62236978037821
101517.7231380207599-2.72313802075988
111616.4777737632525-0.477773763252486
122221.57326694963180.426733050368246
132323.4790080225943-0.479008022594289
142322.52066437188580.479335628114240
151920.1760044041320-1.17600440413203
162322.92923515223740.0707648477625605
172524.57860644660410.421393553395853
182221.97657239711680.0234276028831615
192625.17226956305270.827730436947342
202927.15152185630761.84847814369241
213229.61509834107742.38490165892263
222524.05559179116170.944408208838294
232827.42336197270830.57663802729174
242524.52176354765560.478236452344414
252524.25678454520410.743215454795929
261819.2940257651963-1.29402576519632
272524.94106227748730.0589377225126632
282524.15203078950280.847969210497239
292021.3715924862096-1.37159248620959
301515.5308180322614-0.530818032261378
312424.4159307859003-0.415930785900308
322625.39237176417250.607628235827527
331414.8563343399457-0.856334339945736
342423.89314954924380.106850450756172
352524.07132907470540.928670925294622
362021.6527713296188-1.65277132961882
372121.1158025069894-0.115802506989361
382727.1304434501863-0.130443450186342
392323.646852155067-0.646852155067015
402525.3165443935794-0.316544393579424
412020.7898500349627-0.789850034962703
422222.2519448052374-0.251944805237418
432524.72700207052110.272997929478921
442524.48212810258090.517871897419133
451719.2739611296514-2.27396112965142
462524.73854002751960.261459972480446
472624.8462546562091.15374534379101
482726.13755280166670.86244719833331
491919.354358622055-0.354358622054994
502222.1957748762588-0.195774876258763
513230.96962709964521.03037290035484
522122.2822910680578-1.28229106805777
531819.2187474676866-1.21874746768663
542323.0351436583401-0.0351436583401234
552020.6269967570225-0.626996757022517
562121.5531929630635-0.553192963063506
571717.7667157521456-0.766715752145637
581818.7935427922718-0.793542792271826
591919.6570536753930-0.65705367539297
602222.1395139167648-0.139513916764754
611415.8007303738300-1.80073037382996
621820.9290450281308-2.92904502813081
633531.22728902645983.77271097354023
642925.53295907643543.46704092356458
652121.4634114252834-0.463411425283437
662523.70643597595761.29356402404244
672624.91611642572461.08388357427542
681717.1714063394809-0.171406339480863
692523.45342199618111.54657800381886
702020.1271498701154-0.127149870115450
712221.7338257741910.266174225809007
722423.54078804355300.459211956446970
732121.7577836622681-0.757783662268134
742625.73003734870420.269962651295798
752422.89327945801871.10672054198131
761617.5552039445666-1.55520394456657
771818.9808284448715-0.98082844487155
781919.1969414222042-0.196941422204213
792119.79578895717121.20421104282876
802220.86101586052741.13898413947262
812321.84874855800091.15125144199908
822927.60355309257291.39644690742714
832120.77121122412750.228788775872502
842322.82150698475650.178493015243502
852725.66119987048771.33880012951227
862525.2281470664796-0.228147066479624
872121.0351573253750-0.0351573253749579
881012.5856569232448-2.58565692324476
892020.9509299856356-0.95092998563565
902624.76712070447281.23287929552716
912424.0585920808837-0.058592080883744
922930.0416374199661-1.04163741996613
931919.2184899893864-0.218489989386392
942423.61201629208170.387983707918283
951919.7511416871701-0.751141687170112
962222.0799300248225-0.0799300248225236
971719.4595652289856-2.45956522898562
982424.2550967422243-0.255096742224325
991918.64721065162040.352789348379607
1001917.98075665559211.01924334440788
1012324.2546599927399-1.25465999273988
1022727.9439101907274-0.943910190727428
1031413.67681433723080.323185662769222
1042221.66436833208340.335631667916626
1052120.53436189006410.465638109935911
1061816.2135018190421.786498180958
1072018.87647062888411.12352937111586
1081917.70971204016191.29028795983805
1092424.3387708688237-0.338770868823711
1102524.65475144407420.345248555925757
1112930.3936087856587-1.39360878565874
1122828.9412781504853-0.941278150485337
1131717.3524852172355-0.352485217235538
1142931.0615826889246-2.06158268892461
1152625.71641115967660.283588840323385
1161412.79222434471311.20777565528695
1172627.3544668315132-1.35446683151322
1182019.9487529924060.0512470075940035
1193234.2847006910876-2.28470069108755
1202323.5392122865068-0.539212286506824
1212120.68698520945560.31301479054443
1223031.6688371462034-1.66883714620341
1232424.9787497526435-0.978749752643525
1242221.80036793409370.199632065906332
1252424.5470633178561-0.547063317856124
1262424.4946742271539-0.494674227153864
1272425.1712353054167-1.17123530541667
1281919.2991101896909-0.29911018969089
1293132.0009307308891-1.00093073088907
1302220.46406270421321.53593729578684
1312728.9677573982844-1.96775739828443
1321919.4689292020229-0.46892920202294
1332121.803914976532-0.803914976532019
1342322.79407371128640.205926288713565
1351918.48803282830080.511967171699217
1361917.7941601919491.20583980805099
1372019.34843409487570.651565905124333
1382323.5054932231099-0.50549322310988
1391715.98683683701971.01316316298025
1401715.26468388740321.73531611259682
1411716.1666085307880.83339146921199
1422120.03059762986970.969402370130253
1432120.07350225720130.926497742798667
1441817.1615577084540.838442291545996
1451918.65083402571990.349165974280063
1462018.65034088594471.34965911405533
1471514.15639214490330.843607855096707
1482424.7690349130497-0.769034913049695
1492020.6481020502075-0.648102050207509
1502221.92006869537060.0799313046294029
1511312.17626520749830.823734792501688
1521919.3505035024211-0.350503502421069
1532120.91272377094030.087276229059696
1542323.1624316005535-0.162431600553518
1551614.09835254490071.90164745509927
1562627.2354883691565-1.23548836915652
1572120.63068905738760.369310942612396
1582121.2816704877272-0.281670487727240
1592424.2553950080299-0.255395008029941

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 25 & 23.9315664125076 & 1.06843358749244 \tabularnewline
2 & 30 & 28.1836000299271 & 1.81639997007285 \tabularnewline
3 & 22 & 21.5451854793568 & 0.454814520643217 \tabularnewline
4 & 22 & 22.4171476775265 & -0.417147677526475 \tabularnewline
5 & 25 & 24.2254903960137 & 0.774509603986297 \tabularnewline
6 & 23 & 21.8073083987850 & 1.19269160121498 \tabularnewline
7 & 17 & 17.8014099865772 & -0.801409986577212 \tabularnewline
8 & 21 & 21.3459468043020 & -0.345946804302022 \tabularnewline
9 & 19 & 20.6223697803782 & -1.62236978037821 \tabularnewline
10 & 15 & 17.7231380207599 & -2.72313802075988 \tabularnewline
11 & 16 & 16.4777737632525 & -0.477773763252486 \tabularnewline
12 & 22 & 21.5732669496318 & 0.426733050368246 \tabularnewline
13 & 23 & 23.4790080225943 & -0.479008022594289 \tabularnewline
14 & 23 & 22.5206643718858 & 0.479335628114240 \tabularnewline
15 & 19 & 20.1760044041320 & -1.17600440413203 \tabularnewline
16 & 23 & 22.9292351522374 & 0.0707648477625605 \tabularnewline
17 & 25 & 24.5786064466041 & 0.421393553395853 \tabularnewline
18 & 22 & 21.9765723971168 & 0.0234276028831615 \tabularnewline
19 & 26 & 25.1722695630527 & 0.827730436947342 \tabularnewline
20 & 29 & 27.1515218563076 & 1.84847814369241 \tabularnewline
21 & 32 & 29.6150983410774 & 2.38490165892263 \tabularnewline
22 & 25 & 24.0555917911617 & 0.944408208838294 \tabularnewline
23 & 28 & 27.4233619727083 & 0.57663802729174 \tabularnewline
24 & 25 & 24.5217635476556 & 0.478236452344414 \tabularnewline
25 & 25 & 24.2567845452041 & 0.743215454795929 \tabularnewline
26 & 18 & 19.2940257651963 & -1.29402576519632 \tabularnewline
27 & 25 & 24.9410622774873 & 0.0589377225126632 \tabularnewline
28 & 25 & 24.1520307895028 & 0.847969210497239 \tabularnewline
29 & 20 & 21.3715924862096 & -1.37159248620959 \tabularnewline
30 & 15 & 15.5308180322614 & -0.530818032261378 \tabularnewline
31 & 24 & 24.4159307859003 & -0.415930785900308 \tabularnewline
32 & 26 & 25.3923717641725 & 0.607628235827527 \tabularnewline
33 & 14 & 14.8563343399457 & -0.856334339945736 \tabularnewline
34 & 24 & 23.8931495492438 & 0.106850450756172 \tabularnewline
35 & 25 & 24.0713290747054 & 0.928670925294622 \tabularnewline
36 & 20 & 21.6527713296188 & -1.65277132961882 \tabularnewline
37 & 21 & 21.1158025069894 & -0.115802506989361 \tabularnewline
38 & 27 & 27.1304434501863 & -0.130443450186342 \tabularnewline
39 & 23 & 23.646852155067 & -0.646852155067015 \tabularnewline
40 & 25 & 25.3165443935794 & -0.316544393579424 \tabularnewline
41 & 20 & 20.7898500349627 & -0.789850034962703 \tabularnewline
42 & 22 & 22.2519448052374 & -0.251944805237418 \tabularnewline
43 & 25 & 24.7270020705211 & 0.272997929478921 \tabularnewline
44 & 25 & 24.4821281025809 & 0.517871897419133 \tabularnewline
45 & 17 & 19.2739611296514 & -2.27396112965142 \tabularnewline
46 & 25 & 24.7385400275196 & 0.261459972480446 \tabularnewline
47 & 26 & 24.846254656209 & 1.15374534379101 \tabularnewline
48 & 27 & 26.1375528016667 & 0.86244719833331 \tabularnewline
49 & 19 & 19.354358622055 & -0.354358622054994 \tabularnewline
50 & 22 & 22.1957748762588 & -0.195774876258763 \tabularnewline
51 & 32 & 30.9696270996452 & 1.03037290035484 \tabularnewline
52 & 21 & 22.2822910680578 & -1.28229106805777 \tabularnewline
53 & 18 & 19.2187474676866 & -1.21874746768663 \tabularnewline
54 & 23 & 23.0351436583401 & -0.0351436583401234 \tabularnewline
55 & 20 & 20.6269967570225 & -0.626996757022517 \tabularnewline
56 & 21 & 21.5531929630635 & -0.553192963063506 \tabularnewline
57 & 17 & 17.7667157521456 & -0.766715752145637 \tabularnewline
58 & 18 & 18.7935427922718 & -0.793542792271826 \tabularnewline
59 & 19 & 19.6570536753930 & -0.65705367539297 \tabularnewline
60 & 22 & 22.1395139167648 & -0.139513916764754 \tabularnewline
61 & 14 & 15.8007303738300 & -1.80073037382996 \tabularnewline
62 & 18 & 20.9290450281308 & -2.92904502813081 \tabularnewline
63 & 35 & 31.2272890264598 & 3.77271097354023 \tabularnewline
64 & 29 & 25.5329590764354 & 3.46704092356458 \tabularnewline
65 & 21 & 21.4634114252834 & -0.463411425283437 \tabularnewline
66 & 25 & 23.7064359759576 & 1.29356402404244 \tabularnewline
67 & 26 & 24.9161164257246 & 1.08388357427542 \tabularnewline
68 & 17 & 17.1714063394809 & -0.171406339480863 \tabularnewline
69 & 25 & 23.4534219961811 & 1.54657800381886 \tabularnewline
70 & 20 & 20.1271498701154 & -0.127149870115450 \tabularnewline
71 & 22 & 21.733825774191 & 0.266174225809007 \tabularnewline
72 & 24 & 23.5407880435530 & 0.459211956446970 \tabularnewline
73 & 21 & 21.7577836622681 & -0.757783662268134 \tabularnewline
74 & 26 & 25.7300373487042 & 0.269962651295798 \tabularnewline
75 & 24 & 22.8932794580187 & 1.10672054198131 \tabularnewline
76 & 16 & 17.5552039445666 & -1.55520394456657 \tabularnewline
77 & 18 & 18.9808284448715 & -0.98082844487155 \tabularnewline
78 & 19 & 19.1969414222042 & -0.196941422204213 \tabularnewline
79 & 21 & 19.7957889571712 & 1.20421104282876 \tabularnewline
80 & 22 & 20.8610158605274 & 1.13898413947262 \tabularnewline
81 & 23 & 21.8487485580009 & 1.15125144199908 \tabularnewline
82 & 29 & 27.6035530925729 & 1.39644690742714 \tabularnewline
83 & 21 & 20.7712112241275 & 0.228788775872502 \tabularnewline
84 & 23 & 22.8215069847565 & 0.178493015243502 \tabularnewline
85 & 27 & 25.6611998704877 & 1.33880012951227 \tabularnewline
86 & 25 & 25.2281470664796 & -0.228147066479624 \tabularnewline
87 & 21 & 21.0351573253750 & -0.0351573253749579 \tabularnewline
88 & 10 & 12.5856569232448 & -2.58565692324476 \tabularnewline
89 & 20 & 20.9509299856356 & -0.95092998563565 \tabularnewline
90 & 26 & 24.7671207044728 & 1.23287929552716 \tabularnewline
91 & 24 & 24.0585920808837 & -0.058592080883744 \tabularnewline
92 & 29 & 30.0416374199661 & -1.04163741996613 \tabularnewline
93 & 19 & 19.2184899893864 & -0.218489989386392 \tabularnewline
94 & 24 & 23.6120162920817 & 0.387983707918283 \tabularnewline
95 & 19 & 19.7511416871701 & -0.751141687170112 \tabularnewline
96 & 22 & 22.0799300248225 & -0.0799300248225236 \tabularnewline
97 & 17 & 19.4595652289856 & -2.45956522898562 \tabularnewline
98 & 24 & 24.2550967422243 & -0.255096742224325 \tabularnewline
99 & 19 & 18.6472106516204 & 0.352789348379607 \tabularnewline
100 & 19 & 17.9807566555921 & 1.01924334440788 \tabularnewline
101 & 23 & 24.2546599927399 & -1.25465999273988 \tabularnewline
102 & 27 & 27.9439101907274 & -0.943910190727428 \tabularnewline
103 & 14 & 13.6768143372308 & 0.323185662769222 \tabularnewline
104 & 22 & 21.6643683320834 & 0.335631667916626 \tabularnewline
105 & 21 & 20.5343618900641 & 0.465638109935911 \tabularnewline
106 & 18 & 16.213501819042 & 1.786498180958 \tabularnewline
107 & 20 & 18.8764706288841 & 1.12352937111586 \tabularnewline
108 & 19 & 17.7097120401619 & 1.29028795983805 \tabularnewline
109 & 24 & 24.3387708688237 & -0.338770868823711 \tabularnewline
110 & 25 & 24.6547514440742 & 0.345248555925757 \tabularnewline
111 & 29 & 30.3936087856587 & -1.39360878565874 \tabularnewline
112 & 28 & 28.9412781504853 & -0.941278150485337 \tabularnewline
113 & 17 & 17.3524852172355 & -0.352485217235538 \tabularnewline
114 & 29 & 31.0615826889246 & -2.06158268892461 \tabularnewline
115 & 26 & 25.7164111596766 & 0.283588840323385 \tabularnewline
116 & 14 & 12.7922243447131 & 1.20777565528695 \tabularnewline
117 & 26 & 27.3544668315132 & -1.35446683151322 \tabularnewline
118 & 20 & 19.948752992406 & 0.0512470075940035 \tabularnewline
119 & 32 & 34.2847006910876 & -2.28470069108755 \tabularnewline
120 & 23 & 23.5392122865068 & -0.539212286506824 \tabularnewline
121 & 21 & 20.6869852094556 & 0.31301479054443 \tabularnewline
122 & 30 & 31.6688371462034 & -1.66883714620341 \tabularnewline
123 & 24 & 24.9787497526435 & -0.978749752643525 \tabularnewline
124 & 22 & 21.8003679340937 & 0.199632065906332 \tabularnewline
125 & 24 & 24.5470633178561 & -0.547063317856124 \tabularnewline
126 & 24 & 24.4946742271539 & -0.494674227153864 \tabularnewline
127 & 24 & 25.1712353054167 & -1.17123530541667 \tabularnewline
128 & 19 & 19.2991101896909 & -0.29911018969089 \tabularnewline
129 & 31 & 32.0009307308891 & -1.00093073088907 \tabularnewline
130 & 22 & 20.4640627042132 & 1.53593729578684 \tabularnewline
131 & 27 & 28.9677573982844 & -1.96775739828443 \tabularnewline
132 & 19 & 19.4689292020229 & -0.46892920202294 \tabularnewline
133 & 21 & 21.803914976532 & -0.803914976532019 \tabularnewline
134 & 23 & 22.7940737112864 & 0.205926288713565 \tabularnewline
135 & 19 & 18.4880328283008 & 0.511967171699217 \tabularnewline
136 & 19 & 17.794160191949 & 1.20583980805099 \tabularnewline
137 & 20 & 19.3484340948757 & 0.651565905124333 \tabularnewline
138 & 23 & 23.5054932231099 & -0.50549322310988 \tabularnewline
139 & 17 & 15.9868368370197 & 1.01316316298025 \tabularnewline
140 & 17 & 15.2646838874032 & 1.73531611259682 \tabularnewline
141 & 17 & 16.166608530788 & 0.83339146921199 \tabularnewline
142 & 21 & 20.0305976298697 & 0.969402370130253 \tabularnewline
143 & 21 & 20.0735022572013 & 0.926497742798667 \tabularnewline
144 & 18 & 17.161557708454 & 0.838442291545996 \tabularnewline
145 & 19 & 18.6508340257199 & 0.349165974280063 \tabularnewline
146 & 20 & 18.6503408859447 & 1.34965911405533 \tabularnewline
147 & 15 & 14.1563921449033 & 0.843607855096707 \tabularnewline
148 & 24 & 24.7690349130497 & -0.769034913049695 \tabularnewline
149 & 20 & 20.6481020502075 & -0.648102050207509 \tabularnewline
150 & 22 & 21.9200686953706 & 0.0799313046294029 \tabularnewline
151 & 13 & 12.1762652074983 & 0.823734792501688 \tabularnewline
152 & 19 & 19.3505035024211 & -0.350503502421069 \tabularnewline
153 & 21 & 20.9127237709403 & 0.087276229059696 \tabularnewline
154 & 23 & 23.1624316005535 & -0.162431600553518 \tabularnewline
155 & 16 & 14.0983525449007 & 1.90164745509927 \tabularnewline
156 & 26 & 27.2354883691565 & -1.23548836915652 \tabularnewline
157 & 21 & 20.6306890573876 & 0.369310942612396 \tabularnewline
158 & 21 & 21.2816704877272 & -0.281670487727240 \tabularnewline
159 & 24 & 24.2553950080299 & -0.255395008029941 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98821&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]25[/C][C]23.9315664125076[/C][C]1.06843358749244[/C][/ROW]
[ROW][C]2[/C][C]30[/C][C]28.1836000299271[/C][C]1.81639997007285[/C][/ROW]
[ROW][C]3[/C][C]22[/C][C]21.5451854793568[/C][C]0.454814520643217[/C][/ROW]
[ROW][C]4[/C][C]22[/C][C]22.4171476775265[/C][C]-0.417147677526475[/C][/ROW]
[ROW][C]5[/C][C]25[/C][C]24.2254903960137[/C][C]0.774509603986297[/C][/ROW]
[ROW][C]6[/C][C]23[/C][C]21.8073083987850[/C][C]1.19269160121498[/C][/ROW]
[ROW][C]7[/C][C]17[/C][C]17.8014099865772[/C][C]-0.801409986577212[/C][/ROW]
[ROW][C]8[/C][C]21[/C][C]21.3459468043020[/C][C]-0.345946804302022[/C][/ROW]
[ROW][C]9[/C][C]19[/C][C]20.6223697803782[/C][C]-1.62236978037821[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]17.7231380207599[/C][C]-2.72313802075988[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]16.4777737632525[/C][C]-0.477773763252486[/C][/ROW]
[ROW][C]12[/C][C]22[/C][C]21.5732669496318[/C][C]0.426733050368246[/C][/ROW]
[ROW][C]13[/C][C]23[/C][C]23.4790080225943[/C][C]-0.479008022594289[/C][/ROW]
[ROW][C]14[/C][C]23[/C][C]22.5206643718858[/C][C]0.479335628114240[/C][/ROW]
[ROW][C]15[/C][C]19[/C][C]20.1760044041320[/C][C]-1.17600440413203[/C][/ROW]
[ROW][C]16[/C][C]23[/C][C]22.9292351522374[/C][C]0.0707648477625605[/C][/ROW]
[ROW][C]17[/C][C]25[/C][C]24.5786064466041[/C][C]0.421393553395853[/C][/ROW]
[ROW][C]18[/C][C]22[/C][C]21.9765723971168[/C][C]0.0234276028831615[/C][/ROW]
[ROW][C]19[/C][C]26[/C][C]25.1722695630527[/C][C]0.827730436947342[/C][/ROW]
[ROW][C]20[/C][C]29[/C][C]27.1515218563076[/C][C]1.84847814369241[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]29.6150983410774[/C][C]2.38490165892263[/C][/ROW]
[ROW][C]22[/C][C]25[/C][C]24.0555917911617[/C][C]0.944408208838294[/C][/ROW]
[ROW][C]23[/C][C]28[/C][C]27.4233619727083[/C][C]0.57663802729174[/C][/ROW]
[ROW][C]24[/C][C]25[/C][C]24.5217635476556[/C][C]0.478236452344414[/C][/ROW]
[ROW][C]25[/C][C]25[/C][C]24.2567845452041[/C][C]0.743215454795929[/C][/ROW]
[ROW][C]26[/C][C]18[/C][C]19.2940257651963[/C][C]-1.29402576519632[/C][/ROW]
[ROW][C]27[/C][C]25[/C][C]24.9410622774873[/C][C]0.0589377225126632[/C][/ROW]
[ROW][C]28[/C][C]25[/C][C]24.1520307895028[/C][C]0.847969210497239[/C][/ROW]
[ROW][C]29[/C][C]20[/C][C]21.3715924862096[/C][C]-1.37159248620959[/C][/ROW]
[ROW][C]30[/C][C]15[/C][C]15.5308180322614[/C][C]-0.530818032261378[/C][/ROW]
[ROW][C]31[/C][C]24[/C][C]24.4159307859003[/C][C]-0.415930785900308[/C][/ROW]
[ROW][C]32[/C][C]26[/C][C]25.3923717641725[/C][C]0.607628235827527[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.8563343399457[/C][C]-0.856334339945736[/C][/ROW]
[ROW][C]34[/C][C]24[/C][C]23.8931495492438[/C][C]0.106850450756172[/C][/ROW]
[ROW][C]35[/C][C]25[/C][C]24.0713290747054[/C][C]0.928670925294622[/C][/ROW]
[ROW][C]36[/C][C]20[/C][C]21.6527713296188[/C][C]-1.65277132961882[/C][/ROW]
[ROW][C]37[/C][C]21[/C][C]21.1158025069894[/C][C]-0.115802506989361[/C][/ROW]
[ROW][C]38[/C][C]27[/C][C]27.1304434501863[/C][C]-0.130443450186342[/C][/ROW]
[ROW][C]39[/C][C]23[/C][C]23.646852155067[/C][C]-0.646852155067015[/C][/ROW]
[ROW][C]40[/C][C]25[/C][C]25.3165443935794[/C][C]-0.316544393579424[/C][/ROW]
[ROW][C]41[/C][C]20[/C][C]20.7898500349627[/C][C]-0.789850034962703[/C][/ROW]
[ROW][C]42[/C][C]22[/C][C]22.2519448052374[/C][C]-0.251944805237418[/C][/ROW]
[ROW][C]43[/C][C]25[/C][C]24.7270020705211[/C][C]0.272997929478921[/C][/ROW]
[ROW][C]44[/C][C]25[/C][C]24.4821281025809[/C][C]0.517871897419133[/C][/ROW]
[ROW][C]45[/C][C]17[/C][C]19.2739611296514[/C][C]-2.27396112965142[/C][/ROW]
[ROW][C]46[/C][C]25[/C][C]24.7385400275196[/C][C]0.261459972480446[/C][/ROW]
[ROW][C]47[/C][C]26[/C][C]24.846254656209[/C][C]1.15374534379101[/C][/ROW]
[ROW][C]48[/C][C]27[/C][C]26.1375528016667[/C][C]0.86244719833331[/C][/ROW]
[ROW][C]49[/C][C]19[/C][C]19.354358622055[/C][C]-0.354358622054994[/C][/ROW]
[ROW][C]50[/C][C]22[/C][C]22.1957748762588[/C][C]-0.195774876258763[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]30.9696270996452[/C][C]1.03037290035484[/C][/ROW]
[ROW][C]52[/C][C]21[/C][C]22.2822910680578[/C][C]-1.28229106805777[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]19.2187474676866[/C][C]-1.21874746768663[/C][/ROW]
[ROW][C]54[/C][C]23[/C][C]23.0351436583401[/C][C]-0.0351436583401234[/C][/ROW]
[ROW][C]55[/C][C]20[/C][C]20.6269967570225[/C][C]-0.626996757022517[/C][/ROW]
[ROW][C]56[/C][C]21[/C][C]21.5531929630635[/C][C]-0.553192963063506[/C][/ROW]
[ROW][C]57[/C][C]17[/C][C]17.7667157521456[/C][C]-0.766715752145637[/C][/ROW]
[ROW][C]58[/C][C]18[/C][C]18.7935427922718[/C][C]-0.793542792271826[/C][/ROW]
[ROW][C]59[/C][C]19[/C][C]19.6570536753930[/C][C]-0.65705367539297[/C][/ROW]
[ROW][C]60[/C][C]22[/C][C]22.1395139167648[/C][C]-0.139513916764754[/C][/ROW]
[ROW][C]61[/C][C]14[/C][C]15.8007303738300[/C][C]-1.80073037382996[/C][/ROW]
[ROW][C]62[/C][C]18[/C][C]20.9290450281308[/C][C]-2.92904502813081[/C][/ROW]
[ROW][C]63[/C][C]35[/C][C]31.2272890264598[/C][C]3.77271097354023[/C][/ROW]
[ROW][C]64[/C][C]29[/C][C]25.5329590764354[/C][C]3.46704092356458[/C][/ROW]
[ROW][C]65[/C][C]21[/C][C]21.4634114252834[/C][C]-0.463411425283437[/C][/ROW]
[ROW][C]66[/C][C]25[/C][C]23.7064359759576[/C][C]1.29356402404244[/C][/ROW]
[ROW][C]67[/C][C]26[/C][C]24.9161164257246[/C][C]1.08388357427542[/C][/ROW]
[ROW][C]68[/C][C]17[/C][C]17.1714063394809[/C][C]-0.171406339480863[/C][/ROW]
[ROW][C]69[/C][C]25[/C][C]23.4534219961811[/C][C]1.54657800381886[/C][/ROW]
[ROW][C]70[/C][C]20[/C][C]20.1271498701154[/C][C]-0.127149870115450[/C][/ROW]
[ROW][C]71[/C][C]22[/C][C]21.733825774191[/C][C]0.266174225809007[/C][/ROW]
[ROW][C]72[/C][C]24[/C][C]23.5407880435530[/C][C]0.459211956446970[/C][/ROW]
[ROW][C]73[/C][C]21[/C][C]21.7577836622681[/C][C]-0.757783662268134[/C][/ROW]
[ROW][C]74[/C][C]26[/C][C]25.7300373487042[/C][C]0.269962651295798[/C][/ROW]
[ROW][C]75[/C][C]24[/C][C]22.8932794580187[/C][C]1.10672054198131[/C][/ROW]
[ROW][C]76[/C][C]16[/C][C]17.5552039445666[/C][C]-1.55520394456657[/C][/ROW]
[ROW][C]77[/C][C]18[/C][C]18.9808284448715[/C][C]-0.98082844487155[/C][/ROW]
[ROW][C]78[/C][C]19[/C][C]19.1969414222042[/C][C]-0.196941422204213[/C][/ROW]
[ROW][C]79[/C][C]21[/C][C]19.7957889571712[/C][C]1.20421104282876[/C][/ROW]
[ROW][C]80[/C][C]22[/C][C]20.8610158605274[/C][C]1.13898413947262[/C][/ROW]
[ROW][C]81[/C][C]23[/C][C]21.8487485580009[/C][C]1.15125144199908[/C][/ROW]
[ROW][C]82[/C][C]29[/C][C]27.6035530925729[/C][C]1.39644690742714[/C][/ROW]
[ROW][C]83[/C][C]21[/C][C]20.7712112241275[/C][C]0.228788775872502[/C][/ROW]
[ROW][C]84[/C][C]23[/C][C]22.8215069847565[/C][C]0.178493015243502[/C][/ROW]
[ROW][C]85[/C][C]27[/C][C]25.6611998704877[/C][C]1.33880012951227[/C][/ROW]
[ROW][C]86[/C][C]25[/C][C]25.2281470664796[/C][C]-0.228147066479624[/C][/ROW]
[ROW][C]87[/C][C]21[/C][C]21.0351573253750[/C][C]-0.0351573253749579[/C][/ROW]
[ROW][C]88[/C][C]10[/C][C]12.5856569232448[/C][C]-2.58565692324476[/C][/ROW]
[ROW][C]89[/C][C]20[/C][C]20.9509299856356[/C][C]-0.95092998563565[/C][/ROW]
[ROW][C]90[/C][C]26[/C][C]24.7671207044728[/C][C]1.23287929552716[/C][/ROW]
[ROW][C]91[/C][C]24[/C][C]24.0585920808837[/C][C]-0.058592080883744[/C][/ROW]
[ROW][C]92[/C][C]29[/C][C]30.0416374199661[/C][C]-1.04163741996613[/C][/ROW]
[ROW][C]93[/C][C]19[/C][C]19.2184899893864[/C][C]-0.218489989386392[/C][/ROW]
[ROW][C]94[/C][C]24[/C][C]23.6120162920817[/C][C]0.387983707918283[/C][/ROW]
[ROW][C]95[/C][C]19[/C][C]19.7511416871701[/C][C]-0.751141687170112[/C][/ROW]
[ROW][C]96[/C][C]22[/C][C]22.0799300248225[/C][C]-0.0799300248225236[/C][/ROW]
[ROW][C]97[/C][C]17[/C][C]19.4595652289856[/C][C]-2.45956522898562[/C][/ROW]
[ROW][C]98[/C][C]24[/C][C]24.2550967422243[/C][C]-0.255096742224325[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]18.6472106516204[/C][C]0.352789348379607[/C][/ROW]
[ROW][C]100[/C][C]19[/C][C]17.9807566555921[/C][C]1.01924334440788[/C][/ROW]
[ROW][C]101[/C][C]23[/C][C]24.2546599927399[/C][C]-1.25465999273988[/C][/ROW]
[ROW][C]102[/C][C]27[/C][C]27.9439101907274[/C][C]-0.943910190727428[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]13.6768143372308[/C][C]0.323185662769222[/C][/ROW]
[ROW][C]104[/C][C]22[/C][C]21.6643683320834[/C][C]0.335631667916626[/C][/ROW]
[ROW][C]105[/C][C]21[/C][C]20.5343618900641[/C][C]0.465638109935911[/C][/ROW]
[ROW][C]106[/C][C]18[/C][C]16.213501819042[/C][C]1.786498180958[/C][/ROW]
[ROW][C]107[/C][C]20[/C][C]18.8764706288841[/C][C]1.12352937111586[/C][/ROW]
[ROW][C]108[/C][C]19[/C][C]17.7097120401619[/C][C]1.29028795983805[/C][/ROW]
[ROW][C]109[/C][C]24[/C][C]24.3387708688237[/C][C]-0.338770868823711[/C][/ROW]
[ROW][C]110[/C][C]25[/C][C]24.6547514440742[/C][C]0.345248555925757[/C][/ROW]
[ROW][C]111[/C][C]29[/C][C]30.3936087856587[/C][C]-1.39360878565874[/C][/ROW]
[ROW][C]112[/C][C]28[/C][C]28.9412781504853[/C][C]-0.941278150485337[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]17.3524852172355[/C][C]-0.352485217235538[/C][/ROW]
[ROW][C]114[/C][C]29[/C][C]31.0615826889246[/C][C]-2.06158268892461[/C][/ROW]
[ROW][C]115[/C][C]26[/C][C]25.7164111596766[/C][C]0.283588840323385[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]12.7922243447131[/C][C]1.20777565528695[/C][/ROW]
[ROW][C]117[/C][C]26[/C][C]27.3544668315132[/C][C]-1.35446683151322[/C][/ROW]
[ROW][C]118[/C][C]20[/C][C]19.948752992406[/C][C]0.0512470075940035[/C][/ROW]
[ROW][C]119[/C][C]32[/C][C]34.2847006910876[/C][C]-2.28470069108755[/C][/ROW]
[ROW][C]120[/C][C]23[/C][C]23.5392122865068[/C][C]-0.539212286506824[/C][/ROW]
[ROW][C]121[/C][C]21[/C][C]20.6869852094556[/C][C]0.31301479054443[/C][/ROW]
[ROW][C]122[/C][C]30[/C][C]31.6688371462034[/C][C]-1.66883714620341[/C][/ROW]
[ROW][C]123[/C][C]24[/C][C]24.9787497526435[/C][C]-0.978749752643525[/C][/ROW]
[ROW][C]124[/C][C]22[/C][C]21.8003679340937[/C][C]0.199632065906332[/C][/ROW]
[ROW][C]125[/C][C]24[/C][C]24.5470633178561[/C][C]-0.547063317856124[/C][/ROW]
[ROW][C]126[/C][C]24[/C][C]24.4946742271539[/C][C]-0.494674227153864[/C][/ROW]
[ROW][C]127[/C][C]24[/C][C]25.1712353054167[/C][C]-1.17123530541667[/C][/ROW]
[ROW][C]128[/C][C]19[/C][C]19.2991101896909[/C][C]-0.29911018969089[/C][/ROW]
[ROW][C]129[/C][C]31[/C][C]32.0009307308891[/C][C]-1.00093073088907[/C][/ROW]
[ROW][C]130[/C][C]22[/C][C]20.4640627042132[/C][C]1.53593729578684[/C][/ROW]
[ROW][C]131[/C][C]27[/C][C]28.9677573982844[/C][C]-1.96775739828443[/C][/ROW]
[ROW][C]132[/C][C]19[/C][C]19.4689292020229[/C][C]-0.46892920202294[/C][/ROW]
[ROW][C]133[/C][C]21[/C][C]21.803914976532[/C][C]-0.803914976532019[/C][/ROW]
[ROW][C]134[/C][C]23[/C][C]22.7940737112864[/C][C]0.205926288713565[/C][/ROW]
[ROW][C]135[/C][C]19[/C][C]18.4880328283008[/C][C]0.511967171699217[/C][/ROW]
[ROW][C]136[/C][C]19[/C][C]17.794160191949[/C][C]1.20583980805099[/C][/ROW]
[ROW][C]137[/C][C]20[/C][C]19.3484340948757[/C][C]0.651565905124333[/C][/ROW]
[ROW][C]138[/C][C]23[/C][C]23.5054932231099[/C][C]-0.50549322310988[/C][/ROW]
[ROW][C]139[/C][C]17[/C][C]15.9868368370197[/C][C]1.01316316298025[/C][/ROW]
[ROW][C]140[/C][C]17[/C][C]15.2646838874032[/C][C]1.73531611259682[/C][/ROW]
[ROW][C]141[/C][C]17[/C][C]16.166608530788[/C][C]0.83339146921199[/C][/ROW]
[ROW][C]142[/C][C]21[/C][C]20.0305976298697[/C][C]0.969402370130253[/C][/ROW]
[ROW][C]143[/C][C]21[/C][C]20.0735022572013[/C][C]0.926497742798667[/C][/ROW]
[ROW][C]144[/C][C]18[/C][C]17.161557708454[/C][C]0.838442291545996[/C][/ROW]
[ROW][C]145[/C][C]19[/C][C]18.6508340257199[/C][C]0.349165974280063[/C][/ROW]
[ROW][C]146[/C][C]20[/C][C]18.6503408859447[/C][C]1.34965911405533[/C][/ROW]
[ROW][C]147[/C][C]15[/C][C]14.1563921449033[/C][C]0.843607855096707[/C][/ROW]
[ROW][C]148[/C][C]24[/C][C]24.7690349130497[/C][C]-0.769034913049695[/C][/ROW]
[ROW][C]149[/C][C]20[/C][C]20.6481020502075[/C][C]-0.648102050207509[/C][/ROW]
[ROW][C]150[/C][C]22[/C][C]21.9200686953706[/C][C]0.0799313046294029[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]12.1762652074983[/C][C]0.823734792501688[/C][/ROW]
[ROW][C]152[/C][C]19[/C][C]19.3505035024211[/C][C]-0.350503502421069[/C][/ROW]
[ROW][C]153[/C][C]21[/C][C]20.9127237709403[/C][C]0.087276229059696[/C][/ROW]
[ROW][C]154[/C][C]23[/C][C]23.1624316005535[/C][C]-0.162431600553518[/C][/ROW]
[ROW][C]155[/C][C]16[/C][C]14.0983525449007[/C][C]1.90164745509927[/C][/ROW]
[ROW][C]156[/C][C]26[/C][C]27.2354883691565[/C][C]-1.23548836915652[/C][/ROW]
[ROW][C]157[/C][C]21[/C][C]20.6306890573876[/C][C]0.369310942612396[/C][/ROW]
[ROW][C]158[/C][C]21[/C][C]21.2816704877272[/C][C]-0.281670487727240[/C][/ROW]
[ROW][C]159[/C][C]24[/C][C]24.2553950080299[/C][C]-0.255395008029941[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98821&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98821&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12523.93156641250761.06843358749244
23028.18360002992711.81639997007285
32221.54518547935680.454814520643217
42222.4171476775265-0.417147677526475
52524.22549039601370.774509603986297
62321.80730839878501.19269160121498
71717.8014099865772-0.801409986577212
82121.3459468043020-0.345946804302022
91920.6223697803782-1.62236978037821
101517.7231380207599-2.72313802075988
111616.4777737632525-0.477773763252486
122221.57326694963180.426733050368246
132323.4790080225943-0.479008022594289
142322.52066437188580.479335628114240
151920.1760044041320-1.17600440413203
162322.92923515223740.0707648477625605
172524.57860644660410.421393553395853
182221.97657239711680.0234276028831615
192625.17226956305270.827730436947342
202927.15152185630761.84847814369241
213229.61509834107742.38490165892263
222524.05559179116170.944408208838294
232827.42336197270830.57663802729174
242524.52176354765560.478236452344414
252524.25678454520410.743215454795929
261819.2940257651963-1.29402576519632
272524.94106227748730.0589377225126632
282524.15203078950280.847969210497239
292021.3715924862096-1.37159248620959
301515.5308180322614-0.530818032261378
312424.4159307859003-0.415930785900308
322625.39237176417250.607628235827527
331414.8563343399457-0.856334339945736
342423.89314954924380.106850450756172
352524.07132907470540.928670925294622
362021.6527713296188-1.65277132961882
372121.1158025069894-0.115802506989361
382727.1304434501863-0.130443450186342
392323.646852155067-0.646852155067015
402525.3165443935794-0.316544393579424
412020.7898500349627-0.789850034962703
422222.2519448052374-0.251944805237418
432524.72700207052110.272997929478921
442524.48212810258090.517871897419133
451719.2739611296514-2.27396112965142
462524.73854002751960.261459972480446
472624.8462546562091.15374534379101
482726.13755280166670.86244719833331
491919.354358622055-0.354358622054994
502222.1957748762588-0.195774876258763
513230.96962709964521.03037290035484
522122.2822910680578-1.28229106805777
531819.2187474676866-1.21874746768663
542323.0351436583401-0.0351436583401234
552020.6269967570225-0.626996757022517
562121.5531929630635-0.553192963063506
571717.7667157521456-0.766715752145637
581818.7935427922718-0.793542792271826
591919.6570536753930-0.65705367539297
602222.1395139167648-0.139513916764754
611415.8007303738300-1.80073037382996
621820.9290450281308-2.92904502813081
633531.22728902645983.77271097354023
642925.53295907643543.46704092356458
652121.4634114252834-0.463411425283437
662523.70643597595761.29356402404244
672624.91611642572461.08388357427542
681717.1714063394809-0.171406339480863
692523.45342199618111.54657800381886
702020.1271498701154-0.127149870115450
712221.7338257741910.266174225809007
722423.54078804355300.459211956446970
732121.7577836622681-0.757783662268134
742625.73003734870420.269962651295798
752422.89327945801871.10672054198131
761617.5552039445666-1.55520394456657
771818.9808284448715-0.98082844487155
781919.1969414222042-0.196941422204213
792119.79578895717121.20421104282876
802220.86101586052741.13898413947262
812321.84874855800091.15125144199908
822927.60355309257291.39644690742714
832120.77121122412750.228788775872502
842322.82150698475650.178493015243502
852725.66119987048771.33880012951227
862525.2281470664796-0.228147066479624
872121.0351573253750-0.0351573253749579
881012.5856569232448-2.58565692324476
892020.9509299856356-0.95092998563565
902624.76712070447281.23287929552716
912424.0585920808837-0.058592080883744
922930.0416374199661-1.04163741996613
931919.2184899893864-0.218489989386392
942423.61201629208170.387983707918283
951919.7511416871701-0.751141687170112
962222.0799300248225-0.0799300248225236
971719.4595652289856-2.45956522898562
982424.2550967422243-0.255096742224325
991918.64721065162040.352789348379607
1001917.98075665559211.01924334440788
1012324.2546599927399-1.25465999273988
1022727.9439101907274-0.943910190727428
1031413.67681433723080.323185662769222
1042221.66436833208340.335631667916626
1052120.53436189006410.465638109935911
1061816.2135018190421.786498180958
1072018.87647062888411.12352937111586
1081917.70971204016191.29028795983805
1092424.3387708688237-0.338770868823711
1102524.65475144407420.345248555925757
1112930.3936087856587-1.39360878565874
1122828.9412781504853-0.941278150485337
1131717.3524852172355-0.352485217235538
1142931.0615826889246-2.06158268892461
1152625.71641115967660.283588840323385
1161412.79222434471311.20777565528695
1172627.3544668315132-1.35446683151322
1182019.9487529924060.0512470075940035
1193234.2847006910876-2.28470069108755
1202323.5392122865068-0.539212286506824
1212120.68698520945560.31301479054443
1223031.6688371462034-1.66883714620341
1232424.9787497526435-0.978749752643525
1242221.80036793409370.199632065906332
1252424.5470633178561-0.547063317856124
1262424.4946742271539-0.494674227153864
1272425.1712353054167-1.17123530541667
1281919.2991101896909-0.29911018969089
1293132.0009307308891-1.00093073088907
1302220.46406270421321.53593729578684
1312728.9677573982844-1.96775739828443
1321919.4689292020229-0.46892920202294
1332121.803914976532-0.803914976532019
1342322.79407371128640.205926288713565
1351918.48803282830080.511967171699217
1361917.7941601919491.20583980805099
1372019.34843409487570.651565905124333
1382323.5054932231099-0.50549322310988
1391715.98683683701971.01316316298025
1401715.26468388740321.73531611259682
1411716.1666085307880.83339146921199
1422120.03059762986970.969402370130253
1432120.07350225720130.926497742798667
1441817.1615577084540.838442291545996
1451918.65083402571990.349165974280063
1462018.65034088594471.34965911405533
1471514.15639214490330.843607855096707
1482424.7690349130497-0.769034913049695
1492020.6481020502075-0.648102050207509
1502221.92006869537060.0799313046294029
1511312.17626520749830.823734792501688
1521919.3505035024211-0.350503502421069
1532120.91272377094030.087276229059696
1542323.1624316005535-0.162431600553518
1551614.09835254490071.90164745509927
1562627.2354883691565-1.23548836915652
1572120.63068905738760.369310942612396
1582121.2816704877272-0.281670487727240
1592424.2553950080299-0.255395008029941







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
161.29756876392999e-462.59513752785998e-461
171.36622729011009e-592.73245458022018e-591
181.6790581738272e-763.3581163476544e-761
194.48554595174962e-898.97109190349924e-891
205.20915658563737e-1021.04183131712747e-1011
218.82611643193157e-1191.76522328638631e-1181
226.91656942940653e-1371.38331388588131e-1361
236.11175439829846e-1461.22235087965969e-1451
242.01969521373594e-1654.03939042747188e-1651
252.66351445284917e-1845.32702890569834e-1841
261.20729510146235e-2012.41459020292469e-2011
279.92288897420879e-2061.98457779484176e-2051
282.55451406966593e-2465.10902813933187e-2461
291.36289751799107e-2392.72579503598213e-2391
307.70032334006682e-2501.54006466801336e-2491
311.09240602643824e-2792.18481205287648e-2791
321.64647057830898e-2803.29294115661795e-2801
336.04542558596022e-3041.20908511719204e-3031
343.21690106884044e-3156.43380213768087e-3151
356.22522713759971e-3221.24504542751994e-3211
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
83001
84001
85001
86001
87001
88001
89001
90001
911.41525658376083e-352.83051316752167e-351
928.82148611599365e-301.76429722319873e-291
931.40455807523342e-872.80911615046683e-871
940.9728018212694220.05439635746115680.0271981787305784
950.0001321173897049020.0002642347794098040.999867882610295
961.44939012853835e-132.8987802570767e-130.999999999999855
976.55802392624468e-241.31160478524894e-231
9811.90283839691508e-749.51419198457538e-75
9917.41866399569162e-213.70933199784581e-21
10012.59040308760938e-611.29520154380469e-61
10113.01424673228369e-701.50712336614184e-70
10211.54218903122032e-697.7109451561016e-70
10319.32761823045113e-374.66380911522557e-37
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
12411.77863632502849e-3228.89318162514244e-323
125100
12611.73330036219458e-2978.66650181097291e-298
12712.15530627154343e-2831.07765313577172e-283
12811.21028861735151e-2756.05144308675757e-276
12911.36770249851865e-2556.83851249259327e-256
13013.48834161612654e-2331.74417080806327e-233
13111.94699091611288e-2309.7349545805644e-231
13214.83663542918988e-2062.41831771459494e-206
13313.72666988361690e-1951.86333494180845e-195
13418.37515702102717e-1824.18757851051359e-182
13511.93405502257998e-1629.67027511289989e-163
13613.9301989953312e-1541.9650994976656e-154
13712.25751757306952e-1361.12875878653476e-136
13812.97875046288305e-1201.48937523144152e-120
13913.89346238772773e-1131.94673119386387e-113
14015.29816569519424e-892.64908284759712e-89
14114.0419857455965e-742.02099287279825e-74
142100
14311.82431973496946e-459.12159867484728e-46

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 1.29756876392999e-46 & 2.59513752785998e-46 & 1 \tabularnewline
17 & 1.36622729011009e-59 & 2.73245458022018e-59 & 1 \tabularnewline
18 & 1.6790581738272e-76 & 3.3581163476544e-76 & 1 \tabularnewline
19 & 4.48554595174962e-89 & 8.97109190349924e-89 & 1 \tabularnewline
20 & 5.20915658563737e-102 & 1.04183131712747e-101 & 1 \tabularnewline
21 & 8.82611643193157e-119 & 1.76522328638631e-118 & 1 \tabularnewline
22 & 6.91656942940653e-137 & 1.38331388588131e-136 & 1 \tabularnewline
23 & 6.11175439829846e-146 & 1.22235087965969e-145 & 1 \tabularnewline
24 & 2.01969521373594e-165 & 4.03939042747188e-165 & 1 \tabularnewline
25 & 2.66351445284917e-184 & 5.32702890569834e-184 & 1 \tabularnewline
26 & 1.20729510146235e-201 & 2.41459020292469e-201 & 1 \tabularnewline
27 & 9.92288897420879e-206 & 1.98457779484176e-205 & 1 \tabularnewline
28 & 2.55451406966593e-246 & 5.10902813933187e-246 & 1 \tabularnewline
29 & 1.36289751799107e-239 & 2.72579503598213e-239 & 1 \tabularnewline
30 & 7.70032334006682e-250 & 1.54006466801336e-249 & 1 \tabularnewline
31 & 1.09240602643824e-279 & 2.18481205287648e-279 & 1 \tabularnewline
32 & 1.64647057830898e-280 & 3.29294115661795e-280 & 1 \tabularnewline
33 & 6.04542558596022e-304 & 1.20908511719204e-303 & 1 \tabularnewline
34 & 3.21690106884044e-315 & 6.43380213768087e-315 & 1 \tabularnewline
35 & 6.22522713759971e-322 & 1.24504542751994e-321 & 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 & 0 & 0 & 1 \tabularnewline
84 & 0 & 0 & 1 \tabularnewline
85 & 0 & 0 & 1 \tabularnewline
86 & 0 & 0 & 1 \tabularnewline
87 & 0 & 0 & 1 \tabularnewline
88 & 0 & 0 & 1 \tabularnewline
89 & 0 & 0 & 1 \tabularnewline
90 & 0 & 0 & 1 \tabularnewline
91 & 1.41525658376083e-35 & 2.83051316752167e-35 & 1 \tabularnewline
92 & 8.82148611599365e-30 & 1.76429722319873e-29 & 1 \tabularnewline
93 & 1.40455807523342e-87 & 2.80911615046683e-87 & 1 \tabularnewline
94 & 0.972801821269422 & 0.0543963574611568 & 0.0271981787305784 \tabularnewline
95 & 0.000132117389704902 & 0.000264234779409804 & 0.999867882610295 \tabularnewline
96 & 1.44939012853835e-13 & 2.8987802570767e-13 & 0.999999999999855 \tabularnewline
97 & 6.55802392624468e-24 & 1.31160478524894e-23 & 1 \tabularnewline
98 & 1 & 1.90283839691508e-74 & 9.51419198457538e-75 \tabularnewline
99 & 1 & 7.41866399569162e-21 & 3.70933199784581e-21 \tabularnewline
100 & 1 & 2.59040308760938e-61 & 1.29520154380469e-61 \tabularnewline
101 & 1 & 3.01424673228369e-70 & 1.50712336614184e-70 \tabularnewline
102 & 1 & 1.54218903122032e-69 & 7.7109451561016e-70 \tabularnewline
103 & 1 & 9.32761823045113e-37 & 4.66380911522557e-37 \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 & 1.77863632502849e-322 & 8.89318162514244e-323 \tabularnewline
125 & 1 & 0 & 0 \tabularnewline
126 & 1 & 1.73330036219458e-297 & 8.66650181097291e-298 \tabularnewline
127 & 1 & 2.15530627154343e-283 & 1.07765313577172e-283 \tabularnewline
128 & 1 & 1.21028861735151e-275 & 6.05144308675757e-276 \tabularnewline
129 & 1 & 1.36770249851865e-255 & 6.83851249259327e-256 \tabularnewline
130 & 1 & 3.48834161612654e-233 & 1.74417080806327e-233 \tabularnewline
131 & 1 & 1.94699091611288e-230 & 9.7349545805644e-231 \tabularnewline
132 & 1 & 4.83663542918988e-206 & 2.41831771459494e-206 \tabularnewline
133 & 1 & 3.72666988361690e-195 & 1.86333494180845e-195 \tabularnewline
134 & 1 & 8.37515702102717e-182 & 4.18757851051359e-182 \tabularnewline
135 & 1 & 1.93405502257998e-162 & 9.67027511289989e-163 \tabularnewline
136 & 1 & 3.9301989953312e-154 & 1.9650994976656e-154 \tabularnewline
137 & 1 & 2.25751757306952e-136 & 1.12875878653476e-136 \tabularnewline
138 & 1 & 2.97875046288305e-120 & 1.48937523144152e-120 \tabularnewline
139 & 1 & 3.89346238772773e-113 & 1.94673119386387e-113 \tabularnewline
140 & 1 & 5.29816569519424e-89 & 2.64908284759712e-89 \tabularnewline
141 & 1 & 4.0419857455965e-74 & 2.02099287279825e-74 \tabularnewline
142 & 1 & 0 & 0 \tabularnewline
143 & 1 & 1.82431973496946e-45 & 9.12159867484728e-46 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98821&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]16[/C][C]1.29756876392999e-46[/C][C]2.59513752785998e-46[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]1.36622729011009e-59[/C][C]2.73245458022018e-59[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]1.6790581738272e-76[/C][C]3.3581163476544e-76[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]4.48554595174962e-89[/C][C]8.97109190349924e-89[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]5.20915658563737e-102[/C][C]1.04183131712747e-101[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]8.82611643193157e-119[/C][C]1.76522328638631e-118[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]6.91656942940653e-137[/C][C]1.38331388588131e-136[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]6.11175439829846e-146[/C][C]1.22235087965969e-145[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]2.01969521373594e-165[/C][C]4.03939042747188e-165[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]2.66351445284917e-184[/C][C]5.32702890569834e-184[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]1.20729510146235e-201[/C][C]2.41459020292469e-201[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]9.92288897420879e-206[/C][C]1.98457779484176e-205[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]2.55451406966593e-246[/C][C]5.10902813933187e-246[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]1.36289751799107e-239[/C][C]2.72579503598213e-239[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]7.70032334006682e-250[/C][C]1.54006466801336e-249[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]1.09240602643824e-279[/C][C]2.18481205287648e-279[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]1.64647057830898e-280[/C][C]3.29294115661795e-280[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]6.04542558596022e-304[/C][C]1.20908511719204e-303[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]3.21690106884044e-315[/C][C]6.43380213768087e-315[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]6.22522713759971e-322[/C][C]1.24504542751994e-321[/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]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]91[/C][C]1.41525658376083e-35[/C][C]2.83051316752167e-35[/C][C]1[/C][/ROW]
[ROW][C]92[/C][C]8.82148611599365e-30[/C][C]1.76429722319873e-29[/C][C]1[/C][/ROW]
[ROW][C]93[/C][C]1.40455807523342e-87[/C][C]2.80911615046683e-87[/C][C]1[/C][/ROW]
[ROW][C]94[/C][C]0.972801821269422[/C][C]0.0543963574611568[/C][C]0.0271981787305784[/C][/ROW]
[ROW][C]95[/C][C]0.000132117389704902[/C][C]0.000264234779409804[/C][C]0.999867882610295[/C][/ROW]
[ROW][C]96[/C][C]1.44939012853835e-13[/C][C]2.8987802570767e-13[/C][C]0.999999999999855[/C][/ROW]
[ROW][C]97[/C][C]6.55802392624468e-24[/C][C]1.31160478524894e-23[/C][C]1[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]1.90283839691508e-74[/C][C]9.51419198457538e-75[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]7.41866399569162e-21[/C][C]3.70933199784581e-21[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]2.59040308760938e-61[/C][C]1.29520154380469e-61[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]3.01424673228369e-70[/C][C]1.50712336614184e-70[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]1.54218903122032e-69[/C][C]7.7109451561016e-70[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]9.32761823045113e-37[/C][C]4.66380911522557e-37[/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]1.77863632502849e-322[/C][C]8.89318162514244e-323[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]1.73330036219458e-297[/C][C]8.66650181097291e-298[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]2.15530627154343e-283[/C][C]1.07765313577172e-283[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]1.21028861735151e-275[/C][C]6.05144308675757e-276[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]1.36770249851865e-255[/C][C]6.83851249259327e-256[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]3.48834161612654e-233[/C][C]1.74417080806327e-233[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]1.94699091611288e-230[/C][C]9.7349545805644e-231[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]4.83663542918988e-206[/C][C]2.41831771459494e-206[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]3.72666988361690e-195[/C][C]1.86333494180845e-195[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]8.37515702102717e-182[/C][C]4.18757851051359e-182[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]1.93405502257998e-162[/C][C]9.67027511289989e-163[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]3.9301989953312e-154[/C][C]1.9650994976656e-154[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]2.25751757306952e-136[/C][C]1.12875878653476e-136[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]2.97875046288305e-120[/C][C]1.48937523144152e-120[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]3.89346238772773e-113[/C][C]1.94673119386387e-113[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]5.29816569519424e-89[/C][C]2.64908284759712e-89[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]4.0419857455965e-74[/C][C]2.02099287279825e-74[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]1.82431973496946e-45[/C][C]9.12159867484728e-46[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98821&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98821&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
161.29756876392999e-462.59513752785998e-461
171.36622729011009e-592.73245458022018e-591
181.6790581738272e-763.3581163476544e-761
194.48554595174962e-898.97109190349924e-891
205.20915658563737e-1021.04183131712747e-1011
218.82611643193157e-1191.76522328638631e-1181
226.91656942940653e-1371.38331388588131e-1361
236.11175439829846e-1461.22235087965969e-1451
242.01969521373594e-1654.03939042747188e-1651
252.66351445284917e-1845.32702890569834e-1841
261.20729510146235e-2012.41459020292469e-2011
279.92288897420879e-2061.98457779484176e-2051
282.55451406966593e-2465.10902813933187e-2461
291.36289751799107e-2392.72579503598213e-2391
307.70032334006682e-2501.54006466801336e-2491
311.09240602643824e-2792.18481205287648e-2791
321.64647057830898e-2803.29294115661795e-2801
336.04542558596022e-3041.20908511719204e-3031
343.21690106884044e-3156.43380213768087e-3151
356.22522713759971e-3221.24504542751994e-3211
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
83001
84001
85001
86001
87001
88001
89001
90001
911.41525658376083e-352.83051316752167e-351
928.82148611599365e-301.76429722319873e-291
931.40455807523342e-872.80911615046683e-871
940.9728018212694220.05439635746115680.0271981787305784
950.0001321173897049020.0002642347794098040.999867882610295
961.44939012853835e-132.8987802570767e-130.999999999999855
976.55802392624468e-241.31160478524894e-231
9811.90283839691508e-749.51419198457538e-75
9917.41866399569162e-213.70933199784581e-21
10012.59040308760938e-611.29520154380469e-61
10113.01424673228369e-701.50712336614184e-70
10211.54218903122032e-697.7109451561016e-70
10319.32761823045113e-374.66380911522557e-37
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
12411.77863632502849e-3228.89318162514244e-323
125100
12611.73330036219458e-2978.66650181097291e-298
12712.15530627154343e-2831.07765313577172e-283
12811.21028861735151e-2756.05144308675757e-276
12911.36770249851865e-2556.83851249259327e-256
13013.48834161612654e-2331.74417080806327e-233
13111.94699091611288e-2309.7349545805644e-231
13214.83663542918988e-2062.41831771459494e-206
13313.72666988361690e-1951.86333494180845e-195
13418.37515702102717e-1824.18757851051359e-182
13511.93405502257998e-1629.67027511289989e-163
13613.9301989953312e-1541.9650994976656e-154
13712.25751757306952e-1361.12875878653476e-136
13812.97875046288305e-1201.48937523144152e-120
13913.89346238772773e-1131.94673119386387e-113
14015.29816569519424e-892.64908284759712e-89
14114.0419857455965e-742.02099287279825e-74
142100
14311.82431973496946e-459.12159867484728e-46







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1270.9921875NOK
5% type I error level1270.9921875NOK
10% type I error level1281NOK

\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 & 127 & 0.9921875 & NOK \tabularnewline
5% type I error level & 127 & 0.9921875 & NOK \tabularnewline
10% type I error level & 128 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98821&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]127[/C][C]0.9921875[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]127[/C][C]0.9921875[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]128[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98821&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98821&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1270.9921875NOK
5% type I error level1270.9921875NOK
10% type I error level1281NOK



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')
}