Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 09:10:52 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258733487ehmsf2to1dydc2j.htm/, Retrieved Thu, 28 Mar 2024 22:26:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58296, Retrieved Thu, 28 Mar 2024 22:26:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact212
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [WS 7 Multiple Reg...] [2009-11-20 13:27:42] [b103a1dc147def8132c7f643ad8c8f84]
-   PD        [Multiple Regression] [Workshop 7] [2009-11-20 16:10:52] [0bdf648420800d03e6dbfbd39fe2311c] [Current]
Feedback Forum

Post a new message
Dataseries X:
45	64	64	62	64	62
45	69	64	64	62	64
49	69	69	64	64	62
50	65	69	69	64	64
54	56	65	69	69	64
59	58	56	65	69	69
58	53	58	56	65	69
56	62	53	58	56	65
48	55	62	53	58	56
50	60	55	62	53	58
52	59	60	55	62	53
53	58	59	60	55	62
55	53	58	59	60	55
43	57	53	58	59	60
42	57	57	53	58	59
38	53	57	57	53	58
41	54	53	57	57	53
41	53	54	53	57	57
39	57	53	54	53	57
34	57	57	53	54	53
27	55	57	57	53	54
15	49	55	57	57	53
14	50	49	55	57	57
31	49	50	49	55	57
41	54	49	50	49	55
43	58	54	49	50	49
46	58	58	54	49	50
42	52	58	58	54	49
45	56	52	58	58	54
45	52	56	52	58	58
40	59	52	56	52	58
35	53	59	52	56	52
36	52	53	59	52	56
38	53	52	53	59	52
39	51	53	52	53	59
32	50	51	53	52	53
24	56	50	51	53	52
21	52	56	50	51	53
12	46	52	56	50	51
29	48	46	52	56	50
36	46	48	46	52	56
31	48	46	48	46	52
28	48	48	46	48	46
30	49	48	48	46	48
38	53	49	48	48	46
27	48	53	49	48	48
40	51	48	53	49	48
40	48	51	48	53	49
44	50	48	51	48	53
47	55	50	48	51	48
45	52	55	50	48	51
42	53	52	55	50	48
38	52	53	52	55	50
46	55	52	53	52	55
37	53	55	52	53	52
41	53	53	55	52	53
40	56	53	53	55	52
33	54	56	53	53	55
34	52	54	56	53	53
36	55	52	54	56	53
36	54	55	52	54	56
38	59	54	55	52	54
42	56	59	54	55	52
35	56	56	59	54	55
25	51	56	56	59	54
24	53	51	56	56	59
22	52	53	51	56	56
27	51	52	53	51	56
17	46	51	52	53	51
30	49	46	51	52	53
30	46	49	46	51	52
34	55	46	49	46	51
37	57	55	46	49	46
36	53	57	55	46	49
33	52	53	57	55	46
33	53	52	53	57	55
33	50	53	52	53	57
37	54	50	53	52	53
40	53	54	50	53	52
35	50	53	54	50	53
37	51	50	53	54	50
43	52	51	50	53	54
42	47	52	51	50	53
33	51	47	52	51	50
39	49	51	47	52	51
40	53	49	51	47	52
37	52	53	49	51	47
44	45	52	53	49	51
42	53	45	52	53	49
43	51	53	45	52	53
40	48	51	53	45	52
30	48	48	51	53	45
30	48	48	48	51	53
31	48	48	48	48	51
18	40	48	48	48	48
24	43	40	48	48	48
22	40	43	40	48	48
26	39	40	43	40	48
28	39	39	40	43	40
23	36	39	39	40	43
17	41	36	39	39	40
12	39	41	36	39	39
9	40	39	41	36	39
19	39	40	39	41	36
21	46	39	40	39	41
18	40	46	39	40	39
18	37	40	46	39	40
15	37	37	40	46	39
24	44	37	37	40	46
18	41	44	37	37	40
19	40	41	44	37	37
30	36	40	41	44	37
33	38	36	40	41	44
35	43	38	36	40	41
36	42	43	38	36	40
47	45	42	43	38	36
46	46	45	42	43	38




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58296&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58296&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58296&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 time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 14.9840080784092 + 0.128775996030082X[t] + 0.322177095577494Y1[t] + 0.306776902898658Y2[t] -0.0185667559362691Y3[t] + 0.0387471558376612Y4[t] + 1.23353128598870M1[t] + 2.06185157166443M2[t] -0.0664360866287527M3[t] -2.62061359157391M4[t] -1.34841144304865M5[t] + 0.113812162922448M6[t] + 0.0743039899866369M7[t] + 0.0868559660332346M8[t] + 0.37792301509465M9[t] -0.439014422798539M10[t] -2.34191110722947M11[t] -0.0278283031022919t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  14.9840080784092 +  0.128775996030082X[t] +  0.322177095577494Y1[t] +  0.306776902898658Y2[t] -0.0185667559362691Y3[t] +  0.0387471558376612Y4[t] +  1.23353128598870M1[t] +  2.06185157166443M2[t] -0.0664360866287527M3[t] -2.62061359157391M4[t] -1.34841144304865M5[t] +  0.113812162922448M6[t] +  0.0743039899866369M7[t] +  0.0868559660332346M8[t] +  0.37792301509465M9[t] -0.439014422798539M10[t] -2.34191110722947M11[t] -0.0278283031022919t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58296&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  14.9840080784092 +  0.128775996030082X[t] +  0.322177095577494Y1[t] +  0.306776902898658Y2[t] -0.0185667559362691Y3[t] +  0.0387471558376612Y4[t] +  1.23353128598870M1[t] +  2.06185157166443M2[t] -0.0664360866287527M3[t] -2.62061359157391M4[t] -1.34841144304865M5[t] +  0.113812162922448M6[t] +  0.0743039899866369M7[t] +  0.0868559660332346M8[t] +  0.37792301509465M9[t] -0.439014422798539M10[t] -2.34191110722947M11[t] -0.0278283031022919t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58296&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58296&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
Y[t] = + 14.9840080784092 + 0.128775996030082X[t] + 0.322177095577494Y1[t] + 0.306776902898658Y2[t] -0.0185667559362691Y3[t] + 0.0387471558376612Y4[t] + 1.23353128598870M1[t] + 2.06185157166443M2[t] -0.0664360866287527M3[t] -2.62061359157391M4[t] -1.34841144304865M5[t] + 0.113812162922448M6[t] + 0.0743039899866369M7[t] + 0.0868559660332346M8[t] + 0.37792301509465M9[t] -0.439014422798539M10[t] -2.34191110722947M11[t] -0.0278283031022919t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)14.98400807840924.9561963.02330.0031840.001592
X0.1287759960300820.0333523.86110.0002010.000101
Y10.3221770955774940.0957073.36630.0010860.000543
Y20.3067769028986580.1008273.04260.0030030.001502
Y3-0.01856675593626910.100796-0.18420.8542330.427116
Y40.03874715583766120.0935650.41410.6796810.339841
M11.233531285988701.40890.87550.3834060.191703
M22.061851571664431.4464691.42540.1571760.078588
M3-0.06643608662875271.471872-0.04510.9640890.482044
M4-2.620613591573911.420501-1.84490.0680490.034024
M5-1.348411443048651.383135-0.97490.3319890.165994
M60.1138121629224481.4017630.08120.9354530.467727
M70.07430398998663691.4202770.05230.9583820.479191
M80.08685596603323461.4112490.06150.9510490.475524
M90.377923015094651.4056040.26890.788590.394295
M10-0.4390144227985391.437652-0.30540.7607260.380363
M11-2.341911107229471.423159-1.64560.1030230.051512
t-0.02782830310229190.015057-1.84820.0675590.03378

\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) & 14.9840080784092 & 4.956196 & 3.0233 & 0.003184 & 0.001592 \tabularnewline
X & 0.128775996030082 & 0.033352 & 3.8611 & 0.000201 & 0.000101 \tabularnewline
Y1 & 0.322177095577494 & 0.095707 & 3.3663 & 0.001086 & 0.000543 \tabularnewline
Y2 & 0.306776902898658 & 0.100827 & 3.0426 & 0.003003 & 0.001502 \tabularnewline
Y3 & -0.0185667559362691 & 0.100796 & -0.1842 & 0.854233 & 0.427116 \tabularnewline
Y4 & 0.0387471558376612 & 0.093565 & 0.4141 & 0.679681 & 0.339841 \tabularnewline
M1 & 1.23353128598870 & 1.4089 & 0.8755 & 0.383406 & 0.191703 \tabularnewline
M2 & 2.06185157166443 & 1.446469 & 1.4254 & 0.157176 & 0.078588 \tabularnewline
M3 & -0.0664360866287527 & 1.471872 & -0.0451 & 0.964089 & 0.482044 \tabularnewline
M4 & -2.62061359157391 & 1.420501 & -1.8449 & 0.068049 & 0.034024 \tabularnewline
M5 & -1.34841144304865 & 1.383135 & -0.9749 & 0.331989 & 0.165994 \tabularnewline
M6 & 0.113812162922448 & 1.401763 & 0.0812 & 0.935453 & 0.467727 \tabularnewline
M7 & 0.0743039899866369 & 1.420277 & 0.0523 & 0.958382 & 0.479191 \tabularnewline
M8 & 0.0868559660332346 & 1.411249 & 0.0615 & 0.951049 & 0.475524 \tabularnewline
M9 & 0.37792301509465 & 1.405604 & 0.2689 & 0.78859 & 0.394295 \tabularnewline
M10 & -0.439014422798539 & 1.437652 & -0.3054 & 0.760726 & 0.380363 \tabularnewline
M11 & -2.34191110722947 & 1.423159 & -1.6456 & 0.103023 & 0.051512 \tabularnewline
t & -0.0278283031022919 & 0.015057 & -1.8482 & 0.067559 & 0.03378 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58296&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]14.9840080784092[/C][C]4.956196[/C][C]3.0233[/C][C]0.003184[/C][C]0.001592[/C][/ROW]
[ROW][C]X[/C][C]0.128775996030082[/C][C]0.033352[/C][C]3.8611[/C][C]0.000201[/C][C]0.000101[/C][/ROW]
[ROW][C]Y1[/C][C]0.322177095577494[/C][C]0.095707[/C][C]3.3663[/C][C]0.001086[/C][C]0.000543[/C][/ROW]
[ROW][C]Y2[/C][C]0.306776902898658[/C][C]0.100827[/C][C]3.0426[/C][C]0.003003[/C][C]0.001502[/C][/ROW]
[ROW][C]Y3[/C][C]-0.0185667559362691[/C][C]0.100796[/C][C]-0.1842[/C][C]0.854233[/C][C]0.427116[/C][/ROW]
[ROW][C]Y4[/C][C]0.0387471558376612[/C][C]0.093565[/C][C]0.4141[/C][C]0.679681[/C][C]0.339841[/C][/ROW]
[ROW][C]M1[/C][C]1.23353128598870[/C][C]1.4089[/C][C]0.8755[/C][C]0.383406[/C][C]0.191703[/C][/ROW]
[ROW][C]M2[/C][C]2.06185157166443[/C][C]1.446469[/C][C]1.4254[/C][C]0.157176[/C][C]0.078588[/C][/ROW]
[ROW][C]M3[/C][C]-0.0664360866287527[/C][C]1.471872[/C][C]-0.0451[/C][C]0.964089[/C][C]0.482044[/C][/ROW]
[ROW][C]M4[/C][C]-2.62061359157391[/C][C]1.420501[/C][C]-1.8449[/C][C]0.068049[/C][C]0.034024[/C][/ROW]
[ROW][C]M5[/C][C]-1.34841144304865[/C][C]1.383135[/C][C]-0.9749[/C][C]0.331989[/C][C]0.165994[/C][/ROW]
[ROW][C]M6[/C][C]0.113812162922448[/C][C]1.401763[/C][C]0.0812[/C][C]0.935453[/C][C]0.467727[/C][/ROW]
[ROW][C]M7[/C][C]0.0743039899866369[/C][C]1.420277[/C][C]0.0523[/C][C]0.958382[/C][C]0.479191[/C][/ROW]
[ROW][C]M8[/C][C]0.0868559660332346[/C][C]1.411249[/C][C]0.0615[/C][C]0.951049[/C][C]0.475524[/C][/ROW]
[ROW][C]M9[/C][C]0.37792301509465[/C][C]1.405604[/C][C]0.2689[/C][C]0.78859[/C][C]0.394295[/C][/ROW]
[ROW][C]M10[/C][C]-0.439014422798539[/C][C]1.437652[/C][C]-0.3054[/C][C]0.760726[/C][C]0.380363[/C][/ROW]
[ROW][C]M11[/C][C]-2.34191110722947[/C][C]1.423159[/C][C]-1.6456[/C][C]0.103023[/C][C]0.051512[/C][/ROW]
[ROW][C]t[/C][C]-0.0278283031022919[/C][C]0.015057[/C][C]-1.8482[/C][C]0.067559[/C][C]0.03378[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58296&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58296&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)14.98400807840924.9561963.02330.0031840.001592
X0.1287759960300820.0333523.86110.0002010.000101
Y10.3221770955774940.0957073.36630.0010860.000543
Y20.3067769028986580.1008273.04260.0030030.001502
Y3-0.01856675593626910.100796-0.18420.8542330.427116
Y40.03874715583766120.0935650.41410.6796810.339841
M11.233531285988701.40890.87550.3834060.191703
M22.061851571664431.4464691.42540.1571760.078588
M3-0.06643608662875271.471872-0.04510.9640890.482044
M4-2.620613591573911.420501-1.84490.0680490.034024
M5-1.348411443048651.383135-0.97490.3319890.165994
M60.1138121629224481.4017630.08120.9354530.467727
M70.07430398998663691.4202770.05230.9583820.479191
M80.08685596603323461.4112490.06150.9510490.475524
M90.377923015094651.4056040.26890.788590.394295
M10-0.4390144227985391.437652-0.30540.7607260.380363
M11-2.341911107229471.423159-1.64560.1030230.051512
t-0.02782830310229190.015057-1.84820.0675590.03378







Multiple Linear Regression - Regression Statistics
Multiple R0.909930197363436
R-squared0.827972964073862
Adjusted R-squared0.798432968005737
F-TEST (value)28.0288786147568
F-TEST (DF numerator)17
F-TEST (DF denominator)99
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.98603176154760
Sum Squared Residuals882.722182416138

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.909930197363436 \tabularnewline
R-squared & 0.827972964073862 \tabularnewline
Adjusted R-squared & 0.798432968005737 \tabularnewline
F-TEST (value) & 28.0288786147568 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 99 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.98603176154760 \tabularnewline
Sum Squared Residuals & 882.722182416138 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58296&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.909930197363436[/C][/ROW]
[ROW][C]R-squared[/C][C]0.827972964073862[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.798432968005737[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]28.0288786147568[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]99[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.98603176154760[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]882.722182416138[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58296&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58296&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 R0.909930197363436
R-squared0.827972964073862
Adjusted R-squared0.798432968005737
F-TEST (value)28.0288786147568
F-TEST (DF numerator)17
F-TEST (DF denominator)99
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.98603176154760
Sum Squared Residuals882.722182416138







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16462.83818426133971.16181573866032
26964.36685787325814.63314212674191
36964.22210355032264.77789644967742
46563.38025256447381.61974743552617
55663.7581882320258-7.7581882320258
65861.9034978224413-3.90349782244125
75359.6650144391852-6.66501443918521
86258.4069666280553.59303337194503
95558.6198488370661-3.6198488370661
106058.70871563653291.29128436346708
115956.1381532160422.861846783958
125860.2714111292079-2.27141112920789
135360.7416482351333-7.74164823513333
145758.2914684196842-1.29146841968424
155755.7412199301741.25878006982603
165353.9253043734445-0.925304373444513
175453.99929502171440.000704978285632499
185354.6837484319167-1.68374843191669
195754.41772679488472.58227320511534
205754.5669465878032.43305341219704
215555.2131748849201-0.213174884920069
224952.0657288208259-3.06572882082588
235047.61460008135102.38539991864905
244950.6365240080476-1.63652400804760
255453.14849298249830.851507017501724
265855.25959584115852.74040415884154
275856.36971467643041.63028532356958
285254.3681315603383-2.36813156033827
295654.18523957582971.81476042417025
305255.2226704669672-3.22267046696723
315954.5612537756814.43874622431901
325354.6234795671517-1.62347956715167
335255.4591257030622-3.45912570306223
345352.42411752625290.575882473747049
355151.0201993539099-0.0201993539098883
365051.8813567184805-1.88135671848047
375651.06380691997754.93619308002252
385253.1801372527372-1.18013725273718
394650.3580628064139-4.35806280641388
404846.6549310543631.34506894563704
414647.9111796045306-1.91117960453058
424848.6273064541583-0.627306454158276
434847.93482592848910.0651740715109061
444948.90528322283870.0947167771612615
455350.40627920906812.59372079093185
464849.8179571086257-1.81795710862572
475149.15897544725451.84102455274554
484850.8701851557134-2.87018515571341
495052.7926139477156-2.79261394771563
505553.45402135384121.54597864615878
515253.4367344193922-1.43673441939215
525350.88237887162972.11762112837025
535250.99815565180791.00184434819212
545553.69679477723561.30320522276438
555352.99542049731140.00457950268864583
565353.8285385836909-0.828538583690902
575653.25500010417622.74499989582384
585452.62870865708811.37129134291189
595251.02524187145060.974758128549368
605552.28326840287692.71673159712314
615453.9953238460840.00467615391604688
625955.61116063403333.38883936596674
635655.14106265226280.85893734773721
645652.35978632321483.64021367678517
655151.264488564122-0.264488564121994
665351.20865844007041.79134155992963
675249.87799818112082.12200181887918
685150.89081232411670.109187675883303
694649.006467820238-3.00646782023800
704948.01418871445910.985811285540944
714645.49593009926360.504069900736368
725548.33300293331836.66699706668169
735751.65486100879935.34513899120068
745355.9038650479074-2.90386504790738
755252.4029242509696-0.402924250969596
765348.58322462641644.41677537358357
775049.99475999993860.00524000006136917
785451.14808303567962.85191696432043
795351.77813830957181.22186169042822
805052.1183599420293-2.11835994202929
815151.1753339991594-0.175333999159375
825250.67862600051281.32137399948718
834749.2650321273968-2.26503212739681
845148.98121416881522.01878583118481
854950.7345773956002-1.73457739560016
865352.37817973016230.621820269837660
875250.24288755425591.75711244574411
884549.6593663696593-4.65936636965934
895347.93241031566065.06758968433937
905150.09911543817580.900884561824224
914851.5465321417979-3.54653214179793
924848.2436466235578-0.243646623557837
934847.93366541939480.0663345806051861
944847.19588163056290.8041183694371
954043.4748272271256-3.47482722712563
964343.9841492428134-0.98414924281335
974043.4446162971828-3.44461629718281
983944.8625457333302-5.86254573333022
993941.3557964452113-2.35579644521135
1003637.9950754894366-1.99507548943662
1014137.40258736036993.5974126396301
1023938.84491029644210.155089703557867
1034039.33647642346090.663523576539106
1043940.1085080992919-1.10850809929187
1054640.84476793569325.15523206430684
1064041.4660759051396-1.46607590513964
1073739.807040576206-2.80704057620599
1083738.7588882407270-1.75888824072695
1094440.58587510566943.41412489433064
1104142.6921681138876-1.69216811388761
1114041.7294937145674-1.72949371456736
1123639.1915487670235-3.19154876702346
1133839.5536956740005-1.55369567400047
1144340.56521483691312.43478516308691
1154242.8866135084973-0.886613508497264
1164545.3074584214651-0.307458421465081
1174646.0863360872219-0.0863360872219225

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 64 & 62.8381842613397 & 1.16181573866032 \tabularnewline
2 & 69 & 64.3668578732581 & 4.63314212674191 \tabularnewline
3 & 69 & 64.2221035503226 & 4.77789644967742 \tabularnewline
4 & 65 & 63.3802525644738 & 1.61974743552617 \tabularnewline
5 & 56 & 63.7581882320258 & -7.7581882320258 \tabularnewline
6 & 58 & 61.9034978224413 & -3.90349782244125 \tabularnewline
7 & 53 & 59.6650144391852 & -6.66501443918521 \tabularnewline
8 & 62 & 58.406966628055 & 3.59303337194503 \tabularnewline
9 & 55 & 58.6198488370661 & -3.6198488370661 \tabularnewline
10 & 60 & 58.7087156365329 & 1.29128436346708 \tabularnewline
11 & 59 & 56.138153216042 & 2.861846783958 \tabularnewline
12 & 58 & 60.2714111292079 & -2.27141112920789 \tabularnewline
13 & 53 & 60.7416482351333 & -7.74164823513333 \tabularnewline
14 & 57 & 58.2914684196842 & -1.29146841968424 \tabularnewline
15 & 57 & 55.741219930174 & 1.25878006982603 \tabularnewline
16 & 53 & 53.9253043734445 & -0.925304373444513 \tabularnewline
17 & 54 & 53.9992950217144 & 0.000704978285632499 \tabularnewline
18 & 53 & 54.6837484319167 & -1.68374843191669 \tabularnewline
19 & 57 & 54.4177267948847 & 2.58227320511534 \tabularnewline
20 & 57 & 54.566946587803 & 2.43305341219704 \tabularnewline
21 & 55 & 55.2131748849201 & -0.213174884920069 \tabularnewline
22 & 49 & 52.0657288208259 & -3.06572882082588 \tabularnewline
23 & 50 & 47.6146000813510 & 2.38539991864905 \tabularnewline
24 & 49 & 50.6365240080476 & -1.63652400804760 \tabularnewline
25 & 54 & 53.1484929824983 & 0.851507017501724 \tabularnewline
26 & 58 & 55.2595958411585 & 2.74040415884154 \tabularnewline
27 & 58 & 56.3697146764304 & 1.63028532356958 \tabularnewline
28 & 52 & 54.3681315603383 & -2.36813156033827 \tabularnewline
29 & 56 & 54.1852395758297 & 1.81476042417025 \tabularnewline
30 & 52 & 55.2226704669672 & -3.22267046696723 \tabularnewline
31 & 59 & 54.561253775681 & 4.43874622431901 \tabularnewline
32 & 53 & 54.6234795671517 & -1.62347956715167 \tabularnewline
33 & 52 & 55.4591257030622 & -3.45912570306223 \tabularnewline
34 & 53 & 52.4241175262529 & 0.575882473747049 \tabularnewline
35 & 51 & 51.0201993539099 & -0.0201993539098883 \tabularnewline
36 & 50 & 51.8813567184805 & -1.88135671848047 \tabularnewline
37 & 56 & 51.0638069199775 & 4.93619308002252 \tabularnewline
38 & 52 & 53.1801372527372 & -1.18013725273718 \tabularnewline
39 & 46 & 50.3580628064139 & -4.35806280641388 \tabularnewline
40 & 48 & 46.654931054363 & 1.34506894563704 \tabularnewline
41 & 46 & 47.9111796045306 & -1.91117960453058 \tabularnewline
42 & 48 & 48.6273064541583 & -0.627306454158276 \tabularnewline
43 & 48 & 47.9348259284891 & 0.0651740715109061 \tabularnewline
44 & 49 & 48.9052832228387 & 0.0947167771612615 \tabularnewline
45 & 53 & 50.4062792090681 & 2.59372079093185 \tabularnewline
46 & 48 & 49.8179571086257 & -1.81795710862572 \tabularnewline
47 & 51 & 49.1589754472545 & 1.84102455274554 \tabularnewline
48 & 48 & 50.8701851557134 & -2.87018515571341 \tabularnewline
49 & 50 & 52.7926139477156 & -2.79261394771563 \tabularnewline
50 & 55 & 53.4540213538412 & 1.54597864615878 \tabularnewline
51 & 52 & 53.4367344193922 & -1.43673441939215 \tabularnewline
52 & 53 & 50.8823788716297 & 2.11762112837025 \tabularnewline
53 & 52 & 50.9981556518079 & 1.00184434819212 \tabularnewline
54 & 55 & 53.6967947772356 & 1.30320522276438 \tabularnewline
55 & 53 & 52.9954204973114 & 0.00457950268864583 \tabularnewline
56 & 53 & 53.8285385836909 & -0.828538583690902 \tabularnewline
57 & 56 & 53.2550001041762 & 2.74499989582384 \tabularnewline
58 & 54 & 52.6287086570881 & 1.37129134291189 \tabularnewline
59 & 52 & 51.0252418714506 & 0.974758128549368 \tabularnewline
60 & 55 & 52.2832684028769 & 2.71673159712314 \tabularnewline
61 & 54 & 53.995323846084 & 0.00467615391604688 \tabularnewline
62 & 59 & 55.6111606340333 & 3.38883936596674 \tabularnewline
63 & 56 & 55.1410626522628 & 0.85893734773721 \tabularnewline
64 & 56 & 52.3597863232148 & 3.64021367678517 \tabularnewline
65 & 51 & 51.264488564122 & -0.264488564121994 \tabularnewline
66 & 53 & 51.2086584400704 & 1.79134155992963 \tabularnewline
67 & 52 & 49.8779981811208 & 2.12200181887918 \tabularnewline
68 & 51 & 50.8908123241167 & 0.109187675883303 \tabularnewline
69 & 46 & 49.006467820238 & -3.00646782023800 \tabularnewline
70 & 49 & 48.0141887144591 & 0.985811285540944 \tabularnewline
71 & 46 & 45.4959300992636 & 0.504069900736368 \tabularnewline
72 & 55 & 48.3330029333183 & 6.66699706668169 \tabularnewline
73 & 57 & 51.6548610087993 & 5.34513899120068 \tabularnewline
74 & 53 & 55.9038650479074 & -2.90386504790738 \tabularnewline
75 & 52 & 52.4029242509696 & -0.402924250969596 \tabularnewline
76 & 53 & 48.5832246264164 & 4.41677537358357 \tabularnewline
77 & 50 & 49.9947599999386 & 0.00524000006136917 \tabularnewline
78 & 54 & 51.1480830356796 & 2.85191696432043 \tabularnewline
79 & 53 & 51.7781383095718 & 1.22186169042822 \tabularnewline
80 & 50 & 52.1183599420293 & -2.11835994202929 \tabularnewline
81 & 51 & 51.1753339991594 & -0.175333999159375 \tabularnewline
82 & 52 & 50.6786260005128 & 1.32137399948718 \tabularnewline
83 & 47 & 49.2650321273968 & -2.26503212739681 \tabularnewline
84 & 51 & 48.9812141688152 & 2.01878583118481 \tabularnewline
85 & 49 & 50.7345773956002 & -1.73457739560016 \tabularnewline
86 & 53 & 52.3781797301623 & 0.621820269837660 \tabularnewline
87 & 52 & 50.2428875542559 & 1.75711244574411 \tabularnewline
88 & 45 & 49.6593663696593 & -4.65936636965934 \tabularnewline
89 & 53 & 47.9324103156606 & 5.06758968433937 \tabularnewline
90 & 51 & 50.0991154381758 & 0.900884561824224 \tabularnewline
91 & 48 & 51.5465321417979 & -3.54653214179793 \tabularnewline
92 & 48 & 48.2436466235578 & -0.243646623557837 \tabularnewline
93 & 48 & 47.9336654193948 & 0.0663345806051861 \tabularnewline
94 & 48 & 47.1958816305629 & 0.8041183694371 \tabularnewline
95 & 40 & 43.4748272271256 & -3.47482722712563 \tabularnewline
96 & 43 & 43.9841492428134 & -0.98414924281335 \tabularnewline
97 & 40 & 43.4446162971828 & -3.44461629718281 \tabularnewline
98 & 39 & 44.8625457333302 & -5.86254573333022 \tabularnewline
99 & 39 & 41.3557964452113 & -2.35579644521135 \tabularnewline
100 & 36 & 37.9950754894366 & -1.99507548943662 \tabularnewline
101 & 41 & 37.4025873603699 & 3.5974126396301 \tabularnewline
102 & 39 & 38.8449102964421 & 0.155089703557867 \tabularnewline
103 & 40 & 39.3364764234609 & 0.663523576539106 \tabularnewline
104 & 39 & 40.1085080992919 & -1.10850809929187 \tabularnewline
105 & 46 & 40.8447679356932 & 5.15523206430684 \tabularnewline
106 & 40 & 41.4660759051396 & -1.46607590513964 \tabularnewline
107 & 37 & 39.807040576206 & -2.80704057620599 \tabularnewline
108 & 37 & 38.7588882407270 & -1.75888824072695 \tabularnewline
109 & 44 & 40.5858751056694 & 3.41412489433064 \tabularnewline
110 & 41 & 42.6921681138876 & -1.69216811388761 \tabularnewline
111 & 40 & 41.7294937145674 & -1.72949371456736 \tabularnewline
112 & 36 & 39.1915487670235 & -3.19154876702346 \tabularnewline
113 & 38 & 39.5536956740005 & -1.55369567400047 \tabularnewline
114 & 43 & 40.5652148369131 & 2.43478516308691 \tabularnewline
115 & 42 & 42.8866135084973 & -0.886613508497264 \tabularnewline
116 & 45 & 45.3074584214651 & -0.307458421465081 \tabularnewline
117 & 46 & 46.0863360872219 & -0.0863360872219225 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58296&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]64[/C][C]62.8381842613397[/C][C]1.16181573866032[/C][/ROW]
[ROW][C]2[/C][C]69[/C][C]64.3668578732581[/C][C]4.63314212674191[/C][/ROW]
[ROW][C]3[/C][C]69[/C][C]64.2221035503226[/C][C]4.77789644967742[/C][/ROW]
[ROW][C]4[/C][C]65[/C][C]63.3802525644738[/C][C]1.61974743552617[/C][/ROW]
[ROW][C]5[/C][C]56[/C][C]63.7581882320258[/C][C]-7.7581882320258[/C][/ROW]
[ROW][C]6[/C][C]58[/C][C]61.9034978224413[/C][C]-3.90349782244125[/C][/ROW]
[ROW][C]7[/C][C]53[/C][C]59.6650144391852[/C][C]-6.66501443918521[/C][/ROW]
[ROW][C]8[/C][C]62[/C][C]58.406966628055[/C][C]3.59303337194503[/C][/ROW]
[ROW][C]9[/C][C]55[/C][C]58.6198488370661[/C][C]-3.6198488370661[/C][/ROW]
[ROW][C]10[/C][C]60[/C][C]58.7087156365329[/C][C]1.29128436346708[/C][/ROW]
[ROW][C]11[/C][C]59[/C][C]56.138153216042[/C][C]2.861846783958[/C][/ROW]
[ROW][C]12[/C][C]58[/C][C]60.2714111292079[/C][C]-2.27141112920789[/C][/ROW]
[ROW][C]13[/C][C]53[/C][C]60.7416482351333[/C][C]-7.74164823513333[/C][/ROW]
[ROW][C]14[/C][C]57[/C][C]58.2914684196842[/C][C]-1.29146841968424[/C][/ROW]
[ROW][C]15[/C][C]57[/C][C]55.741219930174[/C][C]1.25878006982603[/C][/ROW]
[ROW][C]16[/C][C]53[/C][C]53.9253043734445[/C][C]-0.925304373444513[/C][/ROW]
[ROW][C]17[/C][C]54[/C][C]53.9992950217144[/C][C]0.000704978285632499[/C][/ROW]
[ROW][C]18[/C][C]53[/C][C]54.6837484319167[/C][C]-1.68374843191669[/C][/ROW]
[ROW][C]19[/C][C]57[/C][C]54.4177267948847[/C][C]2.58227320511534[/C][/ROW]
[ROW][C]20[/C][C]57[/C][C]54.566946587803[/C][C]2.43305341219704[/C][/ROW]
[ROW][C]21[/C][C]55[/C][C]55.2131748849201[/C][C]-0.213174884920069[/C][/ROW]
[ROW][C]22[/C][C]49[/C][C]52.0657288208259[/C][C]-3.06572882082588[/C][/ROW]
[ROW][C]23[/C][C]50[/C][C]47.6146000813510[/C][C]2.38539991864905[/C][/ROW]
[ROW][C]24[/C][C]49[/C][C]50.6365240080476[/C][C]-1.63652400804760[/C][/ROW]
[ROW][C]25[/C][C]54[/C][C]53.1484929824983[/C][C]0.851507017501724[/C][/ROW]
[ROW][C]26[/C][C]58[/C][C]55.2595958411585[/C][C]2.74040415884154[/C][/ROW]
[ROW][C]27[/C][C]58[/C][C]56.3697146764304[/C][C]1.63028532356958[/C][/ROW]
[ROW][C]28[/C][C]52[/C][C]54.3681315603383[/C][C]-2.36813156033827[/C][/ROW]
[ROW][C]29[/C][C]56[/C][C]54.1852395758297[/C][C]1.81476042417025[/C][/ROW]
[ROW][C]30[/C][C]52[/C][C]55.2226704669672[/C][C]-3.22267046696723[/C][/ROW]
[ROW][C]31[/C][C]59[/C][C]54.561253775681[/C][C]4.43874622431901[/C][/ROW]
[ROW][C]32[/C][C]53[/C][C]54.6234795671517[/C][C]-1.62347956715167[/C][/ROW]
[ROW][C]33[/C][C]52[/C][C]55.4591257030622[/C][C]-3.45912570306223[/C][/ROW]
[ROW][C]34[/C][C]53[/C][C]52.4241175262529[/C][C]0.575882473747049[/C][/ROW]
[ROW][C]35[/C][C]51[/C][C]51.0201993539099[/C][C]-0.0201993539098883[/C][/ROW]
[ROW][C]36[/C][C]50[/C][C]51.8813567184805[/C][C]-1.88135671848047[/C][/ROW]
[ROW][C]37[/C][C]56[/C][C]51.0638069199775[/C][C]4.93619308002252[/C][/ROW]
[ROW][C]38[/C][C]52[/C][C]53.1801372527372[/C][C]-1.18013725273718[/C][/ROW]
[ROW][C]39[/C][C]46[/C][C]50.3580628064139[/C][C]-4.35806280641388[/C][/ROW]
[ROW][C]40[/C][C]48[/C][C]46.654931054363[/C][C]1.34506894563704[/C][/ROW]
[ROW][C]41[/C][C]46[/C][C]47.9111796045306[/C][C]-1.91117960453058[/C][/ROW]
[ROW][C]42[/C][C]48[/C][C]48.6273064541583[/C][C]-0.627306454158276[/C][/ROW]
[ROW][C]43[/C][C]48[/C][C]47.9348259284891[/C][C]0.0651740715109061[/C][/ROW]
[ROW][C]44[/C][C]49[/C][C]48.9052832228387[/C][C]0.0947167771612615[/C][/ROW]
[ROW][C]45[/C][C]53[/C][C]50.4062792090681[/C][C]2.59372079093185[/C][/ROW]
[ROW][C]46[/C][C]48[/C][C]49.8179571086257[/C][C]-1.81795710862572[/C][/ROW]
[ROW][C]47[/C][C]51[/C][C]49.1589754472545[/C][C]1.84102455274554[/C][/ROW]
[ROW][C]48[/C][C]48[/C][C]50.8701851557134[/C][C]-2.87018515571341[/C][/ROW]
[ROW][C]49[/C][C]50[/C][C]52.7926139477156[/C][C]-2.79261394771563[/C][/ROW]
[ROW][C]50[/C][C]55[/C][C]53.4540213538412[/C][C]1.54597864615878[/C][/ROW]
[ROW][C]51[/C][C]52[/C][C]53.4367344193922[/C][C]-1.43673441939215[/C][/ROW]
[ROW][C]52[/C][C]53[/C][C]50.8823788716297[/C][C]2.11762112837025[/C][/ROW]
[ROW][C]53[/C][C]52[/C][C]50.9981556518079[/C][C]1.00184434819212[/C][/ROW]
[ROW][C]54[/C][C]55[/C][C]53.6967947772356[/C][C]1.30320522276438[/C][/ROW]
[ROW][C]55[/C][C]53[/C][C]52.9954204973114[/C][C]0.00457950268864583[/C][/ROW]
[ROW][C]56[/C][C]53[/C][C]53.8285385836909[/C][C]-0.828538583690902[/C][/ROW]
[ROW][C]57[/C][C]56[/C][C]53.2550001041762[/C][C]2.74499989582384[/C][/ROW]
[ROW][C]58[/C][C]54[/C][C]52.6287086570881[/C][C]1.37129134291189[/C][/ROW]
[ROW][C]59[/C][C]52[/C][C]51.0252418714506[/C][C]0.974758128549368[/C][/ROW]
[ROW][C]60[/C][C]55[/C][C]52.2832684028769[/C][C]2.71673159712314[/C][/ROW]
[ROW][C]61[/C][C]54[/C][C]53.995323846084[/C][C]0.00467615391604688[/C][/ROW]
[ROW][C]62[/C][C]59[/C][C]55.6111606340333[/C][C]3.38883936596674[/C][/ROW]
[ROW][C]63[/C][C]56[/C][C]55.1410626522628[/C][C]0.85893734773721[/C][/ROW]
[ROW][C]64[/C][C]56[/C][C]52.3597863232148[/C][C]3.64021367678517[/C][/ROW]
[ROW][C]65[/C][C]51[/C][C]51.264488564122[/C][C]-0.264488564121994[/C][/ROW]
[ROW][C]66[/C][C]53[/C][C]51.2086584400704[/C][C]1.79134155992963[/C][/ROW]
[ROW][C]67[/C][C]52[/C][C]49.8779981811208[/C][C]2.12200181887918[/C][/ROW]
[ROW][C]68[/C][C]51[/C][C]50.8908123241167[/C][C]0.109187675883303[/C][/ROW]
[ROW][C]69[/C][C]46[/C][C]49.006467820238[/C][C]-3.00646782023800[/C][/ROW]
[ROW][C]70[/C][C]49[/C][C]48.0141887144591[/C][C]0.985811285540944[/C][/ROW]
[ROW][C]71[/C][C]46[/C][C]45.4959300992636[/C][C]0.504069900736368[/C][/ROW]
[ROW][C]72[/C][C]55[/C][C]48.3330029333183[/C][C]6.66699706668169[/C][/ROW]
[ROW][C]73[/C][C]57[/C][C]51.6548610087993[/C][C]5.34513899120068[/C][/ROW]
[ROW][C]74[/C][C]53[/C][C]55.9038650479074[/C][C]-2.90386504790738[/C][/ROW]
[ROW][C]75[/C][C]52[/C][C]52.4029242509696[/C][C]-0.402924250969596[/C][/ROW]
[ROW][C]76[/C][C]53[/C][C]48.5832246264164[/C][C]4.41677537358357[/C][/ROW]
[ROW][C]77[/C][C]50[/C][C]49.9947599999386[/C][C]0.00524000006136917[/C][/ROW]
[ROW][C]78[/C][C]54[/C][C]51.1480830356796[/C][C]2.85191696432043[/C][/ROW]
[ROW][C]79[/C][C]53[/C][C]51.7781383095718[/C][C]1.22186169042822[/C][/ROW]
[ROW][C]80[/C][C]50[/C][C]52.1183599420293[/C][C]-2.11835994202929[/C][/ROW]
[ROW][C]81[/C][C]51[/C][C]51.1753339991594[/C][C]-0.175333999159375[/C][/ROW]
[ROW][C]82[/C][C]52[/C][C]50.6786260005128[/C][C]1.32137399948718[/C][/ROW]
[ROW][C]83[/C][C]47[/C][C]49.2650321273968[/C][C]-2.26503212739681[/C][/ROW]
[ROW][C]84[/C][C]51[/C][C]48.9812141688152[/C][C]2.01878583118481[/C][/ROW]
[ROW][C]85[/C][C]49[/C][C]50.7345773956002[/C][C]-1.73457739560016[/C][/ROW]
[ROW][C]86[/C][C]53[/C][C]52.3781797301623[/C][C]0.621820269837660[/C][/ROW]
[ROW][C]87[/C][C]52[/C][C]50.2428875542559[/C][C]1.75711244574411[/C][/ROW]
[ROW][C]88[/C][C]45[/C][C]49.6593663696593[/C][C]-4.65936636965934[/C][/ROW]
[ROW][C]89[/C][C]53[/C][C]47.9324103156606[/C][C]5.06758968433937[/C][/ROW]
[ROW][C]90[/C][C]51[/C][C]50.0991154381758[/C][C]0.900884561824224[/C][/ROW]
[ROW][C]91[/C][C]48[/C][C]51.5465321417979[/C][C]-3.54653214179793[/C][/ROW]
[ROW][C]92[/C][C]48[/C][C]48.2436466235578[/C][C]-0.243646623557837[/C][/ROW]
[ROW][C]93[/C][C]48[/C][C]47.9336654193948[/C][C]0.0663345806051861[/C][/ROW]
[ROW][C]94[/C][C]48[/C][C]47.1958816305629[/C][C]0.8041183694371[/C][/ROW]
[ROW][C]95[/C][C]40[/C][C]43.4748272271256[/C][C]-3.47482722712563[/C][/ROW]
[ROW][C]96[/C][C]43[/C][C]43.9841492428134[/C][C]-0.98414924281335[/C][/ROW]
[ROW][C]97[/C][C]40[/C][C]43.4446162971828[/C][C]-3.44461629718281[/C][/ROW]
[ROW][C]98[/C][C]39[/C][C]44.8625457333302[/C][C]-5.86254573333022[/C][/ROW]
[ROW][C]99[/C][C]39[/C][C]41.3557964452113[/C][C]-2.35579644521135[/C][/ROW]
[ROW][C]100[/C][C]36[/C][C]37.9950754894366[/C][C]-1.99507548943662[/C][/ROW]
[ROW][C]101[/C][C]41[/C][C]37.4025873603699[/C][C]3.5974126396301[/C][/ROW]
[ROW][C]102[/C][C]39[/C][C]38.8449102964421[/C][C]0.155089703557867[/C][/ROW]
[ROW][C]103[/C][C]40[/C][C]39.3364764234609[/C][C]0.663523576539106[/C][/ROW]
[ROW][C]104[/C][C]39[/C][C]40.1085080992919[/C][C]-1.10850809929187[/C][/ROW]
[ROW][C]105[/C][C]46[/C][C]40.8447679356932[/C][C]5.15523206430684[/C][/ROW]
[ROW][C]106[/C][C]40[/C][C]41.4660759051396[/C][C]-1.46607590513964[/C][/ROW]
[ROW][C]107[/C][C]37[/C][C]39.807040576206[/C][C]-2.80704057620599[/C][/ROW]
[ROW][C]108[/C][C]37[/C][C]38.7588882407270[/C][C]-1.75888824072695[/C][/ROW]
[ROW][C]109[/C][C]44[/C][C]40.5858751056694[/C][C]3.41412489433064[/C][/ROW]
[ROW][C]110[/C][C]41[/C][C]42.6921681138876[/C][C]-1.69216811388761[/C][/ROW]
[ROW][C]111[/C][C]40[/C][C]41.7294937145674[/C][C]-1.72949371456736[/C][/ROW]
[ROW][C]112[/C][C]36[/C][C]39.1915487670235[/C][C]-3.19154876702346[/C][/ROW]
[ROW][C]113[/C][C]38[/C][C]39.5536956740005[/C][C]-1.55369567400047[/C][/ROW]
[ROW][C]114[/C][C]43[/C][C]40.5652148369131[/C][C]2.43478516308691[/C][/ROW]
[ROW][C]115[/C][C]42[/C][C]42.8866135084973[/C][C]-0.886613508497264[/C][/ROW]
[ROW][C]116[/C][C]45[/C][C]45.3074584214651[/C][C]-0.307458421465081[/C][/ROW]
[ROW][C]117[/C][C]46[/C][C]46.0863360872219[/C][C]-0.0863360872219225[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58296&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58296&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
16462.83818426133971.16181573866032
26964.36685787325814.63314212674191
36964.22210355032264.77789644967742
46563.38025256447381.61974743552617
55663.7581882320258-7.7581882320258
65861.9034978224413-3.90349782244125
75359.6650144391852-6.66501443918521
86258.4069666280553.59303337194503
95558.6198488370661-3.6198488370661
106058.70871563653291.29128436346708
115956.1381532160422.861846783958
125860.2714111292079-2.27141112920789
135360.7416482351333-7.74164823513333
145758.2914684196842-1.29146841968424
155755.7412199301741.25878006982603
165353.9253043734445-0.925304373444513
175453.99929502171440.000704978285632499
185354.6837484319167-1.68374843191669
195754.41772679488472.58227320511534
205754.5669465878032.43305341219704
215555.2131748849201-0.213174884920069
224952.0657288208259-3.06572882082588
235047.61460008135102.38539991864905
244950.6365240080476-1.63652400804760
255453.14849298249830.851507017501724
265855.25959584115852.74040415884154
275856.36971467643041.63028532356958
285254.3681315603383-2.36813156033827
295654.18523957582971.81476042417025
305255.2226704669672-3.22267046696723
315954.5612537756814.43874622431901
325354.6234795671517-1.62347956715167
335255.4591257030622-3.45912570306223
345352.42411752625290.575882473747049
355151.0201993539099-0.0201993539098883
365051.8813567184805-1.88135671848047
375651.06380691997754.93619308002252
385253.1801372527372-1.18013725273718
394650.3580628064139-4.35806280641388
404846.6549310543631.34506894563704
414647.9111796045306-1.91117960453058
424848.6273064541583-0.627306454158276
434847.93482592848910.0651740715109061
444948.90528322283870.0947167771612615
455350.40627920906812.59372079093185
464849.8179571086257-1.81795710862572
475149.15897544725451.84102455274554
484850.8701851557134-2.87018515571341
495052.7926139477156-2.79261394771563
505553.45402135384121.54597864615878
515253.4367344193922-1.43673441939215
525350.88237887162972.11762112837025
535250.99815565180791.00184434819212
545553.69679477723561.30320522276438
555352.99542049731140.00457950268864583
565353.8285385836909-0.828538583690902
575653.25500010417622.74499989582384
585452.62870865708811.37129134291189
595251.02524187145060.974758128549368
605552.28326840287692.71673159712314
615453.9953238460840.00467615391604688
625955.61116063403333.38883936596674
635655.14106265226280.85893734773721
645652.35978632321483.64021367678517
655151.264488564122-0.264488564121994
665351.20865844007041.79134155992963
675249.87799818112082.12200181887918
685150.89081232411670.109187675883303
694649.006467820238-3.00646782023800
704948.01418871445910.985811285540944
714645.49593009926360.504069900736368
725548.33300293331836.66699706668169
735751.65486100879935.34513899120068
745355.9038650479074-2.90386504790738
755252.4029242509696-0.402924250969596
765348.58322462641644.41677537358357
775049.99475999993860.00524000006136917
785451.14808303567962.85191696432043
795351.77813830957181.22186169042822
805052.1183599420293-2.11835994202929
815151.1753339991594-0.175333999159375
825250.67862600051281.32137399948718
834749.2650321273968-2.26503212739681
845148.98121416881522.01878583118481
854950.7345773956002-1.73457739560016
865352.37817973016230.621820269837660
875250.24288755425591.75711244574411
884549.6593663696593-4.65936636965934
895347.93241031566065.06758968433937
905150.09911543817580.900884561824224
914851.5465321417979-3.54653214179793
924848.2436466235578-0.243646623557837
934847.93366541939480.0663345806051861
944847.19588163056290.8041183694371
954043.4748272271256-3.47482722712563
964343.9841492428134-0.98414924281335
974043.4446162971828-3.44461629718281
983944.8625457333302-5.86254573333022
993941.3557964452113-2.35579644521135
1003637.9950754894366-1.99507548943662
1014137.40258736036993.5974126396301
1023938.84491029644210.155089703557867
1034039.33647642346090.663523576539106
1043940.1085080992919-1.10850809929187
1054640.84476793569325.15523206430684
1064041.4660759051396-1.46607590513964
1073739.807040576206-2.80704057620599
1083738.7588882407270-1.75888824072695
1094440.58587510566943.41412489433064
1104142.6921681138876-1.69216811388761
1114041.7294937145674-1.72949371456736
1123639.1915487670235-3.19154876702346
1133839.5536956740005-1.55369567400047
1144340.56521483691312.43478516308691
1154242.8866135084973-0.886613508497264
1164545.3074584214651-0.307458421465081
1174646.0863360872219-0.0863360872219225







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.2859946584547940.5719893169095890.714005341545206
220.1970325868538220.3940651737076440.802967413146178
230.1872653928399090.3745307856798180.812734607160091
240.1970756574486540.3941513148973080.802924342551346
250.765610127815880.4687797443682410.234389872184121
260.7269259582841020.5461480834317950.273074041715898
270.6657401668786430.6685196662427140.334259833121357
280.6095380966466680.7809238067066650.390461903353332
290.910431816551990.1791363668960190.0895681834480097
300.8955142892753960.2089714214492080.104485710724604
310.915202850310090.1695942993798200.0847971496899102
320.889406574692060.2211868506158810.110593425307940
330.8758088509026460.2483822981947090.124191149097354
340.9461397973430690.1077204053138630.0538602026569314
350.9364403840410470.1271192319179050.0635596159589526
360.9179778240185650.1640443519628710.0820221759814355
370.9603785646336570.07924287073268580.0396214353663429
380.958120927395110.08375814520977950.0418790726048898
390.9874076460036220.02518470799275570.0125923539963778
400.9848226371362380.03035472572752370.0151773628637619
410.9810847127609850.03783057447803010.0189152872390150
420.9751517761814130.04969644763717450.0248482238185873
430.964624871676660.0707502566466780.035375128323339
440.952564237494550.09487152501089840.0474357625054492
450.9552641032344240.08947179353115120.0447358967655756
460.9463945006146950.1072109987706110.0536054993853054
470.929590294303880.1408194113922410.0704097056961207
480.94470210467150.1105957906569980.0552978953284992
490.955895621184740.0882087576305180.044104378815259
500.940270430204740.1194591395905190.0597295697952597
510.9345669931942170.1308660136115660.0654330068057828
520.9211238887248920.1577522225502160.0788761112751082
530.9286200063412830.1427599873174340.0713799936587169
540.9393377150691790.1213245698616420.060662284930821
550.9314449145011390.1371101709977230.0685550854988615
560.9283115459238960.1433769081522090.0716884540761044
570.9349657376756330.1300685246487350.0650342623243674
580.9228215862308550.1543568275382900.0771784137691452
590.8978821390169480.2042357219661040.102117860983052
600.9014153343417750.1971693313164500.0985846656582248
610.8908181036202440.2183637927595120.109181896379756
620.8794450833764360.2411098332471270.120554916623564
630.8460415183977920.3079169632044150.153958481602208
640.8535319683063430.2929360633873140.146468031693657
650.8263691209464010.3472617581071970.173630879053599
660.7909939832763620.4180120334472750.209006016723638
670.7583869349916630.4832261300166740.241613065008337
680.7115614180855570.5768771638288870.288438581914443
690.7967176495256410.4065647009487170.203282350474359
700.7696726437857840.4606547124284330.230327356214216
710.7236662397862450.552667520427510.276333760213755
720.777034796830990.445930406338020.22296520316901
730.8069249605217830.3861500789564350.193075039478217
740.8174855632284810.3650288735430380.182514436771519
750.7771101562375690.4457796875248620.222889843762431
760.9165221838035960.1669556323928090.0834778161964043
770.8904000479955820.2191999040088350.109599952004418
780.8590433167206320.2819133665587370.140956683279368
790.8281723377971890.3436553244056230.171827662202811
800.8034634228481690.3930731543036630.196536577151831
810.7987136134164530.4025727731670940.201286386583547
820.7392214031257650.521557193748470.260778596874235
830.6980499128183540.6039001743632920.301950087181646
840.6469497218486280.7061005563027450.353050278151372
850.6374092618665140.7251814762669720.362590738133486
860.6146482911342610.7707034177314780.385351708865739
870.6605084140740520.6789831718518960.339491585925948
880.6407451150384160.7185097699231670.359254884961583
890.7595617891007740.4808764217984530.240438210899226
900.7294226419813140.5411547160373720.270577358018686
910.6614908722680950.677018255463810.338509127731905
920.6597579345187470.6804841309625050.340242065481253
930.543539050746170.9129218985076590.456460949253829
940.5899910583569260.8200178832861470.410008941643074
950.6949684030503990.6100631938992020.305031596949601
960.9026596831074640.1946806337850730.0973403168925365

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.285994658454794 & 0.571989316909589 & 0.714005341545206 \tabularnewline
22 & 0.197032586853822 & 0.394065173707644 & 0.802967413146178 \tabularnewline
23 & 0.187265392839909 & 0.374530785679818 & 0.812734607160091 \tabularnewline
24 & 0.197075657448654 & 0.394151314897308 & 0.802924342551346 \tabularnewline
25 & 0.76561012781588 & 0.468779744368241 & 0.234389872184121 \tabularnewline
26 & 0.726925958284102 & 0.546148083431795 & 0.273074041715898 \tabularnewline
27 & 0.665740166878643 & 0.668519666242714 & 0.334259833121357 \tabularnewline
28 & 0.609538096646668 & 0.780923806706665 & 0.390461903353332 \tabularnewline
29 & 0.91043181655199 & 0.179136366896019 & 0.0895681834480097 \tabularnewline
30 & 0.895514289275396 & 0.208971421449208 & 0.104485710724604 \tabularnewline
31 & 0.91520285031009 & 0.169594299379820 & 0.0847971496899102 \tabularnewline
32 & 0.88940657469206 & 0.221186850615881 & 0.110593425307940 \tabularnewline
33 & 0.875808850902646 & 0.248382298194709 & 0.124191149097354 \tabularnewline
34 & 0.946139797343069 & 0.107720405313863 & 0.0538602026569314 \tabularnewline
35 & 0.936440384041047 & 0.127119231917905 & 0.0635596159589526 \tabularnewline
36 & 0.917977824018565 & 0.164044351962871 & 0.0820221759814355 \tabularnewline
37 & 0.960378564633657 & 0.0792428707326858 & 0.0396214353663429 \tabularnewline
38 & 0.95812092739511 & 0.0837581452097795 & 0.0418790726048898 \tabularnewline
39 & 0.987407646003622 & 0.0251847079927557 & 0.0125923539963778 \tabularnewline
40 & 0.984822637136238 & 0.0303547257275237 & 0.0151773628637619 \tabularnewline
41 & 0.981084712760985 & 0.0378305744780301 & 0.0189152872390150 \tabularnewline
42 & 0.975151776181413 & 0.0496964476371745 & 0.0248482238185873 \tabularnewline
43 & 0.96462487167666 & 0.070750256646678 & 0.035375128323339 \tabularnewline
44 & 0.95256423749455 & 0.0948715250108984 & 0.0474357625054492 \tabularnewline
45 & 0.955264103234424 & 0.0894717935311512 & 0.0447358967655756 \tabularnewline
46 & 0.946394500614695 & 0.107210998770611 & 0.0536054993853054 \tabularnewline
47 & 0.92959029430388 & 0.140819411392241 & 0.0704097056961207 \tabularnewline
48 & 0.9447021046715 & 0.110595790656998 & 0.0552978953284992 \tabularnewline
49 & 0.95589562118474 & 0.088208757630518 & 0.044104378815259 \tabularnewline
50 & 0.94027043020474 & 0.119459139590519 & 0.0597295697952597 \tabularnewline
51 & 0.934566993194217 & 0.130866013611566 & 0.0654330068057828 \tabularnewline
52 & 0.921123888724892 & 0.157752222550216 & 0.0788761112751082 \tabularnewline
53 & 0.928620006341283 & 0.142759987317434 & 0.0713799936587169 \tabularnewline
54 & 0.939337715069179 & 0.121324569861642 & 0.060662284930821 \tabularnewline
55 & 0.931444914501139 & 0.137110170997723 & 0.0685550854988615 \tabularnewline
56 & 0.928311545923896 & 0.143376908152209 & 0.0716884540761044 \tabularnewline
57 & 0.934965737675633 & 0.130068524648735 & 0.0650342623243674 \tabularnewline
58 & 0.922821586230855 & 0.154356827538290 & 0.0771784137691452 \tabularnewline
59 & 0.897882139016948 & 0.204235721966104 & 0.102117860983052 \tabularnewline
60 & 0.901415334341775 & 0.197169331316450 & 0.0985846656582248 \tabularnewline
61 & 0.890818103620244 & 0.218363792759512 & 0.109181896379756 \tabularnewline
62 & 0.879445083376436 & 0.241109833247127 & 0.120554916623564 \tabularnewline
63 & 0.846041518397792 & 0.307916963204415 & 0.153958481602208 \tabularnewline
64 & 0.853531968306343 & 0.292936063387314 & 0.146468031693657 \tabularnewline
65 & 0.826369120946401 & 0.347261758107197 & 0.173630879053599 \tabularnewline
66 & 0.790993983276362 & 0.418012033447275 & 0.209006016723638 \tabularnewline
67 & 0.758386934991663 & 0.483226130016674 & 0.241613065008337 \tabularnewline
68 & 0.711561418085557 & 0.576877163828887 & 0.288438581914443 \tabularnewline
69 & 0.796717649525641 & 0.406564700948717 & 0.203282350474359 \tabularnewline
70 & 0.769672643785784 & 0.460654712428433 & 0.230327356214216 \tabularnewline
71 & 0.723666239786245 & 0.55266752042751 & 0.276333760213755 \tabularnewline
72 & 0.77703479683099 & 0.44593040633802 & 0.22296520316901 \tabularnewline
73 & 0.806924960521783 & 0.386150078956435 & 0.193075039478217 \tabularnewline
74 & 0.817485563228481 & 0.365028873543038 & 0.182514436771519 \tabularnewline
75 & 0.777110156237569 & 0.445779687524862 & 0.222889843762431 \tabularnewline
76 & 0.916522183803596 & 0.166955632392809 & 0.0834778161964043 \tabularnewline
77 & 0.890400047995582 & 0.219199904008835 & 0.109599952004418 \tabularnewline
78 & 0.859043316720632 & 0.281913366558737 & 0.140956683279368 \tabularnewline
79 & 0.828172337797189 & 0.343655324405623 & 0.171827662202811 \tabularnewline
80 & 0.803463422848169 & 0.393073154303663 & 0.196536577151831 \tabularnewline
81 & 0.798713613416453 & 0.402572773167094 & 0.201286386583547 \tabularnewline
82 & 0.739221403125765 & 0.52155719374847 & 0.260778596874235 \tabularnewline
83 & 0.698049912818354 & 0.603900174363292 & 0.301950087181646 \tabularnewline
84 & 0.646949721848628 & 0.706100556302745 & 0.353050278151372 \tabularnewline
85 & 0.637409261866514 & 0.725181476266972 & 0.362590738133486 \tabularnewline
86 & 0.614648291134261 & 0.770703417731478 & 0.385351708865739 \tabularnewline
87 & 0.660508414074052 & 0.678983171851896 & 0.339491585925948 \tabularnewline
88 & 0.640745115038416 & 0.718509769923167 & 0.359254884961583 \tabularnewline
89 & 0.759561789100774 & 0.480876421798453 & 0.240438210899226 \tabularnewline
90 & 0.729422641981314 & 0.541154716037372 & 0.270577358018686 \tabularnewline
91 & 0.661490872268095 & 0.67701825546381 & 0.338509127731905 \tabularnewline
92 & 0.659757934518747 & 0.680484130962505 & 0.340242065481253 \tabularnewline
93 & 0.54353905074617 & 0.912921898507659 & 0.456460949253829 \tabularnewline
94 & 0.589991058356926 & 0.820017883286147 & 0.410008941643074 \tabularnewline
95 & 0.694968403050399 & 0.610063193899202 & 0.305031596949601 \tabularnewline
96 & 0.902659683107464 & 0.194680633785073 & 0.0973403168925365 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58296&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]21[/C][C]0.285994658454794[/C][C]0.571989316909589[/C][C]0.714005341545206[/C][/ROW]
[ROW][C]22[/C][C]0.197032586853822[/C][C]0.394065173707644[/C][C]0.802967413146178[/C][/ROW]
[ROW][C]23[/C][C]0.187265392839909[/C][C]0.374530785679818[/C][C]0.812734607160091[/C][/ROW]
[ROW][C]24[/C][C]0.197075657448654[/C][C]0.394151314897308[/C][C]0.802924342551346[/C][/ROW]
[ROW][C]25[/C][C]0.76561012781588[/C][C]0.468779744368241[/C][C]0.234389872184121[/C][/ROW]
[ROW][C]26[/C][C]0.726925958284102[/C][C]0.546148083431795[/C][C]0.273074041715898[/C][/ROW]
[ROW][C]27[/C][C]0.665740166878643[/C][C]0.668519666242714[/C][C]0.334259833121357[/C][/ROW]
[ROW][C]28[/C][C]0.609538096646668[/C][C]0.780923806706665[/C][C]0.390461903353332[/C][/ROW]
[ROW][C]29[/C][C]0.91043181655199[/C][C]0.179136366896019[/C][C]0.0895681834480097[/C][/ROW]
[ROW][C]30[/C][C]0.895514289275396[/C][C]0.208971421449208[/C][C]0.104485710724604[/C][/ROW]
[ROW][C]31[/C][C]0.91520285031009[/C][C]0.169594299379820[/C][C]0.0847971496899102[/C][/ROW]
[ROW][C]32[/C][C]0.88940657469206[/C][C]0.221186850615881[/C][C]0.110593425307940[/C][/ROW]
[ROW][C]33[/C][C]0.875808850902646[/C][C]0.248382298194709[/C][C]0.124191149097354[/C][/ROW]
[ROW][C]34[/C][C]0.946139797343069[/C][C]0.107720405313863[/C][C]0.0538602026569314[/C][/ROW]
[ROW][C]35[/C][C]0.936440384041047[/C][C]0.127119231917905[/C][C]0.0635596159589526[/C][/ROW]
[ROW][C]36[/C][C]0.917977824018565[/C][C]0.164044351962871[/C][C]0.0820221759814355[/C][/ROW]
[ROW][C]37[/C][C]0.960378564633657[/C][C]0.0792428707326858[/C][C]0.0396214353663429[/C][/ROW]
[ROW][C]38[/C][C]0.95812092739511[/C][C]0.0837581452097795[/C][C]0.0418790726048898[/C][/ROW]
[ROW][C]39[/C][C]0.987407646003622[/C][C]0.0251847079927557[/C][C]0.0125923539963778[/C][/ROW]
[ROW][C]40[/C][C]0.984822637136238[/C][C]0.0303547257275237[/C][C]0.0151773628637619[/C][/ROW]
[ROW][C]41[/C][C]0.981084712760985[/C][C]0.0378305744780301[/C][C]0.0189152872390150[/C][/ROW]
[ROW][C]42[/C][C]0.975151776181413[/C][C]0.0496964476371745[/C][C]0.0248482238185873[/C][/ROW]
[ROW][C]43[/C][C]0.96462487167666[/C][C]0.070750256646678[/C][C]0.035375128323339[/C][/ROW]
[ROW][C]44[/C][C]0.95256423749455[/C][C]0.0948715250108984[/C][C]0.0474357625054492[/C][/ROW]
[ROW][C]45[/C][C]0.955264103234424[/C][C]0.0894717935311512[/C][C]0.0447358967655756[/C][/ROW]
[ROW][C]46[/C][C]0.946394500614695[/C][C]0.107210998770611[/C][C]0.0536054993853054[/C][/ROW]
[ROW][C]47[/C][C]0.92959029430388[/C][C]0.140819411392241[/C][C]0.0704097056961207[/C][/ROW]
[ROW][C]48[/C][C]0.9447021046715[/C][C]0.110595790656998[/C][C]0.0552978953284992[/C][/ROW]
[ROW][C]49[/C][C]0.95589562118474[/C][C]0.088208757630518[/C][C]0.044104378815259[/C][/ROW]
[ROW][C]50[/C][C]0.94027043020474[/C][C]0.119459139590519[/C][C]0.0597295697952597[/C][/ROW]
[ROW][C]51[/C][C]0.934566993194217[/C][C]0.130866013611566[/C][C]0.0654330068057828[/C][/ROW]
[ROW][C]52[/C][C]0.921123888724892[/C][C]0.157752222550216[/C][C]0.0788761112751082[/C][/ROW]
[ROW][C]53[/C][C]0.928620006341283[/C][C]0.142759987317434[/C][C]0.0713799936587169[/C][/ROW]
[ROW][C]54[/C][C]0.939337715069179[/C][C]0.121324569861642[/C][C]0.060662284930821[/C][/ROW]
[ROW][C]55[/C][C]0.931444914501139[/C][C]0.137110170997723[/C][C]0.0685550854988615[/C][/ROW]
[ROW][C]56[/C][C]0.928311545923896[/C][C]0.143376908152209[/C][C]0.0716884540761044[/C][/ROW]
[ROW][C]57[/C][C]0.934965737675633[/C][C]0.130068524648735[/C][C]0.0650342623243674[/C][/ROW]
[ROW][C]58[/C][C]0.922821586230855[/C][C]0.154356827538290[/C][C]0.0771784137691452[/C][/ROW]
[ROW][C]59[/C][C]0.897882139016948[/C][C]0.204235721966104[/C][C]0.102117860983052[/C][/ROW]
[ROW][C]60[/C][C]0.901415334341775[/C][C]0.197169331316450[/C][C]0.0985846656582248[/C][/ROW]
[ROW][C]61[/C][C]0.890818103620244[/C][C]0.218363792759512[/C][C]0.109181896379756[/C][/ROW]
[ROW][C]62[/C][C]0.879445083376436[/C][C]0.241109833247127[/C][C]0.120554916623564[/C][/ROW]
[ROW][C]63[/C][C]0.846041518397792[/C][C]0.307916963204415[/C][C]0.153958481602208[/C][/ROW]
[ROW][C]64[/C][C]0.853531968306343[/C][C]0.292936063387314[/C][C]0.146468031693657[/C][/ROW]
[ROW][C]65[/C][C]0.826369120946401[/C][C]0.347261758107197[/C][C]0.173630879053599[/C][/ROW]
[ROW][C]66[/C][C]0.790993983276362[/C][C]0.418012033447275[/C][C]0.209006016723638[/C][/ROW]
[ROW][C]67[/C][C]0.758386934991663[/C][C]0.483226130016674[/C][C]0.241613065008337[/C][/ROW]
[ROW][C]68[/C][C]0.711561418085557[/C][C]0.576877163828887[/C][C]0.288438581914443[/C][/ROW]
[ROW][C]69[/C][C]0.796717649525641[/C][C]0.406564700948717[/C][C]0.203282350474359[/C][/ROW]
[ROW][C]70[/C][C]0.769672643785784[/C][C]0.460654712428433[/C][C]0.230327356214216[/C][/ROW]
[ROW][C]71[/C][C]0.723666239786245[/C][C]0.55266752042751[/C][C]0.276333760213755[/C][/ROW]
[ROW][C]72[/C][C]0.77703479683099[/C][C]0.44593040633802[/C][C]0.22296520316901[/C][/ROW]
[ROW][C]73[/C][C]0.806924960521783[/C][C]0.386150078956435[/C][C]0.193075039478217[/C][/ROW]
[ROW][C]74[/C][C]0.817485563228481[/C][C]0.365028873543038[/C][C]0.182514436771519[/C][/ROW]
[ROW][C]75[/C][C]0.777110156237569[/C][C]0.445779687524862[/C][C]0.222889843762431[/C][/ROW]
[ROW][C]76[/C][C]0.916522183803596[/C][C]0.166955632392809[/C][C]0.0834778161964043[/C][/ROW]
[ROW][C]77[/C][C]0.890400047995582[/C][C]0.219199904008835[/C][C]0.109599952004418[/C][/ROW]
[ROW][C]78[/C][C]0.859043316720632[/C][C]0.281913366558737[/C][C]0.140956683279368[/C][/ROW]
[ROW][C]79[/C][C]0.828172337797189[/C][C]0.343655324405623[/C][C]0.171827662202811[/C][/ROW]
[ROW][C]80[/C][C]0.803463422848169[/C][C]0.393073154303663[/C][C]0.196536577151831[/C][/ROW]
[ROW][C]81[/C][C]0.798713613416453[/C][C]0.402572773167094[/C][C]0.201286386583547[/C][/ROW]
[ROW][C]82[/C][C]0.739221403125765[/C][C]0.52155719374847[/C][C]0.260778596874235[/C][/ROW]
[ROW][C]83[/C][C]0.698049912818354[/C][C]0.603900174363292[/C][C]0.301950087181646[/C][/ROW]
[ROW][C]84[/C][C]0.646949721848628[/C][C]0.706100556302745[/C][C]0.353050278151372[/C][/ROW]
[ROW][C]85[/C][C]0.637409261866514[/C][C]0.725181476266972[/C][C]0.362590738133486[/C][/ROW]
[ROW][C]86[/C][C]0.614648291134261[/C][C]0.770703417731478[/C][C]0.385351708865739[/C][/ROW]
[ROW][C]87[/C][C]0.660508414074052[/C][C]0.678983171851896[/C][C]0.339491585925948[/C][/ROW]
[ROW][C]88[/C][C]0.640745115038416[/C][C]0.718509769923167[/C][C]0.359254884961583[/C][/ROW]
[ROW][C]89[/C][C]0.759561789100774[/C][C]0.480876421798453[/C][C]0.240438210899226[/C][/ROW]
[ROW][C]90[/C][C]0.729422641981314[/C][C]0.541154716037372[/C][C]0.270577358018686[/C][/ROW]
[ROW][C]91[/C][C]0.661490872268095[/C][C]0.67701825546381[/C][C]0.338509127731905[/C][/ROW]
[ROW][C]92[/C][C]0.659757934518747[/C][C]0.680484130962505[/C][C]0.340242065481253[/C][/ROW]
[ROW][C]93[/C][C]0.54353905074617[/C][C]0.912921898507659[/C][C]0.456460949253829[/C][/ROW]
[ROW][C]94[/C][C]0.589991058356926[/C][C]0.820017883286147[/C][C]0.410008941643074[/C][/ROW]
[ROW][C]95[/C][C]0.694968403050399[/C][C]0.610063193899202[/C][C]0.305031596949601[/C][/ROW]
[ROW][C]96[/C][C]0.902659683107464[/C][C]0.194680633785073[/C][C]0.0973403168925365[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58296&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58296&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
210.2859946584547940.5719893169095890.714005341545206
220.1970325868538220.3940651737076440.802967413146178
230.1872653928399090.3745307856798180.812734607160091
240.1970756574486540.3941513148973080.802924342551346
250.765610127815880.4687797443682410.234389872184121
260.7269259582841020.5461480834317950.273074041715898
270.6657401668786430.6685196662427140.334259833121357
280.6095380966466680.7809238067066650.390461903353332
290.910431816551990.1791363668960190.0895681834480097
300.8955142892753960.2089714214492080.104485710724604
310.915202850310090.1695942993798200.0847971496899102
320.889406574692060.2211868506158810.110593425307940
330.8758088509026460.2483822981947090.124191149097354
340.9461397973430690.1077204053138630.0538602026569314
350.9364403840410470.1271192319179050.0635596159589526
360.9179778240185650.1640443519628710.0820221759814355
370.9603785646336570.07924287073268580.0396214353663429
380.958120927395110.08375814520977950.0418790726048898
390.9874076460036220.02518470799275570.0125923539963778
400.9848226371362380.03035472572752370.0151773628637619
410.9810847127609850.03783057447803010.0189152872390150
420.9751517761814130.04969644763717450.0248482238185873
430.964624871676660.0707502566466780.035375128323339
440.952564237494550.09487152501089840.0474357625054492
450.9552641032344240.08947179353115120.0447358967655756
460.9463945006146950.1072109987706110.0536054993853054
470.929590294303880.1408194113922410.0704097056961207
480.94470210467150.1105957906569980.0552978953284992
490.955895621184740.0882087576305180.044104378815259
500.940270430204740.1194591395905190.0597295697952597
510.9345669931942170.1308660136115660.0654330068057828
520.9211238887248920.1577522225502160.0788761112751082
530.9286200063412830.1427599873174340.0713799936587169
540.9393377150691790.1213245698616420.060662284930821
550.9314449145011390.1371101709977230.0685550854988615
560.9283115459238960.1433769081522090.0716884540761044
570.9349657376756330.1300685246487350.0650342623243674
580.9228215862308550.1543568275382900.0771784137691452
590.8978821390169480.2042357219661040.102117860983052
600.9014153343417750.1971693313164500.0985846656582248
610.8908181036202440.2183637927595120.109181896379756
620.8794450833764360.2411098332471270.120554916623564
630.8460415183977920.3079169632044150.153958481602208
640.8535319683063430.2929360633873140.146468031693657
650.8263691209464010.3472617581071970.173630879053599
660.7909939832763620.4180120334472750.209006016723638
670.7583869349916630.4832261300166740.241613065008337
680.7115614180855570.5768771638288870.288438581914443
690.7967176495256410.4065647009487170.203282350474359
700.7696726437857840.4606547124284330.230327356214216
710.7236662397862450.552667520427510.276333760213755
720.777034796830990.445930406338020.22296520316901
730.8069249605217830.3861500789564350.193075039478217
740.8174855632284810.3650288735430380.182514436771519
750.7771101562375690.4457796875248620.222889843762431
760.9165221838035960.1669556323928090.0834778161964043
770.8904000479955820.2191999040088350.109599952004418
780.8590433167206320.2819133665587370.140956683279368
790.8281723377971890.3436553244056230.171827662202811
800.8034634228481690.3930731543036630.196536577151831
810.7987136134164530.4025727731670940.201286386583547
820.7392214031257650.521557193748470.260778596874235
830.6980499128183540.6039001743632920.301950087181646
840.6469497218486280.7061005563027450.353050278151372
850.6374092618665140.7251814762669720.362590738133486
860.6146482911342610.7707034177314780.385351708865739
870.6605084140740520.6789831718518960.339491585925948
880.6407451150384160.7185097699231670.359254884961583
890.7595617891007740.4808764217984530.240438210899226
900.7294226419813140.5411547160373720.270577358018686
910.6614908722680950.677018255463810.338509127731905
920.6597579345187470.6804841309625050.340242065481253
930.543539050746170.9129218985076590.456460949253829
940.5899910583569260.8200178832861470.410008941643074
950.6949684030503990.6100631938992020.305031596949601
960.9026596831074640.1946806337850730.0973403168925365







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level40.0526315789473684NOK
10% type I error level100.131578947368421NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 4 & 0.0526315789473684 & NOK \tabularnewline
10% type I error level & 10 & 0.131578947368421 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58296&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]4[/C][C]0.0526315789473684[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]10[/C][C]0.131578947368421[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58296&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58296&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 level00OK
5% type I error level40.0526315789473684NOK
10% type I error level100.131578947368421NOK



Parameters (Session):
par1 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Include Monthly 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')
}