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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 computationFri, 20 Nov 2009 08:08:27 -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/t1258730093w6zgggqi6i2cv5h.htm/, Retrieved Thu, 28 Mar 2024 15:19:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58257, Retrieved Thu, 28 Mar 2024 15:19:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact126
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]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [Eco. Crisis] [2009-11-19 19:59:19] [36becc366f59efff5c3495030cea7527]
-   P         [Multiple Regression] [2de model] [2009-11-20 15:08:27] [e1f26cfd746b288ac2a466939c6f316e] [Current]
-   P           [Multiple Regression] [3de model] [2009-11-20 15:37:22] [36becc366f59efff5c3495030cea7527]
-    D            [Multiple Regression] [relatie lichten-o...] [2009-11-23 15:31:21] [74be16979710d4c4e7c6647856088456]
-    D          [Multiple Regression] [relatie ongevalle...] [2009-11-23 15:27:12] [74be16979710d4c4e7c6647856088456]
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Dataseries X:
105.7	0
105.7	0
111.1	0
82.4	0
60	0
107.3	0
99.3	0
113.5	0
108.9	0
100.2	0
103.9	0
138.7	0
120.2	0
100.2	0
143.2	0
70.9	0
85.2	0
133	0
136.6	0
117.9	0
106.3	0
122.3	0
125.5	0
148.4	0
126.3	0
99.6	0
140.4	0
80.3	0
92.6	0
138.5	0
110.9	0
119.6	0
105	0
109	0
129.4	0
148.6	0
101.4	0
134.8	0
143.7	0
81.6	0
90.3	0
141.5	0
140.7	0
140.2	0
100.2	0
125.7	0
119.6	0
134.7	0
109	0
116.3	0
146.9	0
97.4	0
89.4	0
132.1	0
139.8	0
129	0
112.5	0
121.9	0
121.7	0
123.1	0
131.6	0
119.3	0
132.5	0
98.3	0
85.1	0
131.7	0
129.3	0
90.7	1
78.6	1
68.9	1
79.1	1
83.5	1
74.1	1
59.7	1
93.3	1
61.3	1
56.6	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58257&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]6 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=58257&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58257&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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 136.045070422535 -39.2704225352113X[t] -20.6778672032194M1[t] -25.3492957746479M2[t] -0.277867203219396M3[t] -48.6921529175051M4[t] -50.5492957746479M5[t] -5.36173708920191M6[t] -9.94507042253522M7[t] -11.0166666666667M8[t] -27.5833333333334M9[t] -21.5M10[t] -16.3M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  136.045070422535 -39.2704225352113X[t] -20.6778672032194M1[t] -25.3492957746479M2[t] -0.277867203219396M3[t] -48.6921529175051M4[t] -50.5492957746479M5[t] -5.36173708920191M6[t] -9.94507042253522M7[t] -11.0166666666667M8[t] -27.5833333333334M9[t] -21.5M10[t] -16.3M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58257&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  136.045070422535 -39.2704225352113X[t] -20.6778672032194M1[t] -25.3492957746479M2[t] -0.277867203219396M3[t] -48.6921529175051M4[t] -50.5492957746479M5[t] -5.36173708920191M6[t] -9.94507042253522M7[t] -11.0166666666667M8[t] -27.5833333333334M9[t] -21.5M10[t] -16.3M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58257&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58257&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] = + 136.045070422535 -39.2704225352113X[t] -20.6778672032194M1[t] -25.3492957746479M2[t] -0.277867203219396M3[t] -48.6921529175051M4[t] -50.5492957746479M5[t] -5.36173708920191M6[t] -9.94507042253522M7[t] -11.0166666666667M8[t] -27.5833333333334M9[t] -21.5M10[t] -16.3M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)136.0450704225354.82698828.184300
X-39.27042253521134.027376-9.750900
M1-20.67786720321946.514871-3.17390.0023120.001156
M2-25.34929577464796.514871-3.8910.000240.00012
M3-0.2778672032193966.514871-0.04270.9661120.483056
M4-48.69215291750516.514871-7.47400
M5-50.54929577464796.514871-7.759100
M6-5.361737089201916.793312-0.78930.432870.216435
M7-9.945070422535226.793312-1.4640.1481020.074051
M8-11.01666666666676.760069-1.62970.1080840.054042
M9-27.58333333333346.760069-4.08030.0001276.4e-05
M10-21.56.760069-3.18040.0022680.001134
M11-16.36.760069-2.41120.018780.00939

\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) & 136.045070422535 & 4.826988 & 28.1843 & 0 & 0 \tabularnewline
X & -39.2704225352113 & 4.027376 & -9.7509 & 0 & 0 \tabularnewline
M1 & -20.6778672032194 & 6.514871 & -3.1739 & 0.002312 & 0.001156 \tabularnewline
M2 & -25.3492957746479 & 6.514871 & -3.891 & 0.00024 & 0.00012 \tabularnewline
M3 & -0.277867203219396 & 6.514871 & -0.0427 & 0.966112 & 0.483056 \tabularnewline
M4 & -48.6921529175051 & 6.514871 & -7.474 & 0 & 0 \tabularnewline
M5 & -50.5492957746479 & 6.514871 & -7.7591 & 0 & 0 \tabularnewline
M6 & -5.36173708920191 & 6.793312 & -0.7893 & 0.43287 & 0.216435 \tabularnewline
M7 & -9.94507042253522 & 6.793312 & -1.464 & 0.148102 & 0.074051 \tabularnewline
M8 & -11.0166666666667 & 6.760069 & -1.6297 & 0.108084 & 0.054042 \tabularnewline
M9 & -27.5833333333334 & 6.760069 & -4.0803 & 0.000127 & 6.4e-05 \tabularnewline
M10 & -21.5 & 6.760069 & -3.1804 & 0.002268 & 0.001134 \tabularnewline
M11 & -16.3 & 6.760069 & -2.4112 & 0.01878 & 0.00939 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58257&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]136.045070422535[/C][C]4.826988[/C][C]28.1843[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-39.2704225352113[/C][C]4.027376[/C][C]-9.7509[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-20.6778672032194[/C][C]6.514871[/C][C]-3.1739[/C][C]0.002312[/C][C]0.001156[/C][/ROW]
[ROW][C]M2[/C][C]-25.3492957746479[/C][C]6.514871[/C][C]-3.891[/C][C]0.00024[/C][C]0.00012[/C][/ROW]
[ROW][C]M3[/C][C]-0.277867203219396[/C][C]6.514871[/C][C]-0.0427[/C][C]0.966112[/C][C]0.483056[/C][/ROW]
[ROW][C]M4[/C][C]-48.6921529175051[/C][C]6.514871[/C][C]-7.474[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M5[/C][C]-50.5492957746479[/C][C]6.514871[/C][C]-7.7591[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M6[/C][C]-5.36173708920191[/C][C]6.793312[/C][C]-0.7893[/C][C]0.43287[/C][C]0.216435[/C][/ROW]
[ROW][C]M7[/C][C]-9.94507042253522[/C][C]6.793312[/C][C]-1.464[/C][C]0.148102[/C][C]0.074051[/C][/ROW]
[ROW][C]M8[/C][C]-11.0166666666667[/C][C]6.760069[/C][C]-1.6297[/C][C]0.108084[/C][C]0.054042[/C][/ROW]
[ROW][C]M9[/C][C]-27.5833333333334[/C][C]6.760069[/C][C]-4.0803[/C][C]0.000127[/C][C]6.4e-05[/C][/ROW]
[ROW][C]M10[/C][C]-21.5[/C][C]6.760069[/C][C]-3.1804[/C][C]0.002268[/C][C]0.001134[/C][/ROW]
[ROW][C]M11[/C][C]-16.3[/C][C]6.760069[/C][C]-2.4112[/C][C]0.01878[/C][C]0.00939[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58257&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58257&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)136.0450704225354.82698828.184300
X-39.27042253521134.027376-9.750900
M1-20.67786720321946.514871-3.17390.0023120.001156
M2-25.34929577464796.514871-3.8910.000240.00012
M3-0.2778672032193966.514871-0.04270.9661120.483056
M4-48.69215291750516.514871-7.47400
M5-50.54929577464796.514871-7.759100
M6-5.361737089201916.793312-0.78930.432870.216435
M7-9.945070422535226.793312-1.4640.1481020.074051
M8-11.01666666666676.760069-1.62970.1080840.054042
M9-27.58333333333346.760069-4.08030.0001276.4e-05
M10-21.56.760069-3.18040.0022680.001134
M11-16.36.760069-2.41120.018780.00939







Multiple Linear Regression - Regression Statistics
Multiple R0.894877704308231
R-squared0.80080610566797
Adjusted R-squared0.763457250480714
F-TEST (value)21.4412490464024
F-TEST (DF numerator)12
F-TEST (DF denominator)64
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation11.7087829747050
Sum Squared Residuals8774.1183199195

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.894877704308231 \tabularnewline
R-squared & 0.80080610566797 \tabularnewline
Adjusted R-squared & 0.763457250480714 \tabularnewline
F-TEST (value) & 21.4412490464024 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 64 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 11.7087829747050 \tabularnewline
Sum Squared Residuals & 8774.1183199195 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58257&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.894877704308231[/C][/ROW]
[ROW][C]R-squared[/C][C]0.80080610566797[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.763457250480714[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]21.4412490464024[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]64[/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]11.7087829747050[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]8774.1183199195[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58257&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58257&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 R0.894877704308231
R-squared0.80080610566797
Adjusted R-squared0.763457250480714
F-TEST (value)21.4412490464024
F-TEST (DF numerator)12
F-TEST (DF denominator)64
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation11.7087829747050
Sum Squared Residuals8774.1183199195







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1105.7115.367203219316-9.66720321931598
2105.7110.695774647887-4.99577464788738
3111.1135.767203219316-24.6672032193159
482.487.3529175050302-4.95291750503018
56085.4957746478872-25.4957746478872
6107.3130.683333333333-23.3833333333333
799.3126.1-26.7999999999999
8113.5125.028403755869-11.5284037558685
9108.9108.4617370892020.438262910798133
10100.2114.545070422535-14.3450704225352
11103.9119.745070422535-15.8450704225352
12138.7136.0450704225352.65492957746474
13120.2115.3672032193164.83279678068412
14100.2110.695774647887-10.4957746478873
15143.2135.7672032193167.4327967806841
1670.987.3529175050302-16.4529175050302
1785.285.4957746478873-0.295774647887331
18133130.6833333333332.31666666666667
19136.6126.110.5000000000000
20117.9125.028403755869-7.12840375586854
21106.3108.461737089202-2.16173708920188
22122.3114.5450704225357.7549295774648
23125.5119.7450704225355.75492957746479
24148.4136.04507042253512.3549295774648
25126.3115.36720321931610.9327967806841
2699.6110.695774647887-11.0957746478873
27140.4135.7672032193164.63279678068412
2880.387.3529175050302-7.05291750503019
2992.685.49577464788737.10422535211266
30138.5130.6833333333337.81666666666667
31110.9126.1-15.2
32119.6125.028403755869-5.42840375586855
33105108.461737089202-3.46173708920187
34109114.545070422535-5.54507042253521
35129.4119.7450704225359.6549295774648
36148.6136.04507042253512.5549295774648
37101.4115.367203219316-13.9672032193159
38134.8110.69577464788724.1042253521127
39143.7135.7672032193167.9327967806841
4081.687.3529175050302-5.75291750503019
4190.385.49577464788734.80422535211267
42141.5130.68333333333310.8166666666667
43140.7126.114.6000000000000
44140.2125.02840375586915.1715962441314
45100.2108.461737089202-8.26173708920187
46125.7114.54507042253511.1549295774648
47119.6119.745070422535-0.145070422535225
48134.7136.045070422535-1.34507042253524
49109115.367203219316-6.36720321931588
50116.3110.6957746478875.60422535211269
51146.9135.76720321931611.1327967806841
5297.487.352917505030210.0470824949698
5389.485.49577464788733.90422535211267
54132.1130.6833333333331.41666666666666
55139.8126.113.7
56129125.0284037558693.97159624413146
57112.5108.4617370892024.03826291079813
58121.9114.5450704225357.3549295774648
59121.7119.7450704225351.95492957746479
60123.1136.045070422535-12.9450704225352
61131.6115.36720321931616.2327967806841
62119.3110.6957746478878.60422535211269
63132.5135.767203219316-3.26720321931589
6498.387.352917505030210.9470824949698
6585.185.4957746478873-0.39577464788734
66131.7130.6833333333331.01666666666666
67129.3126.13.20000000000001
6890.785.75798122065734.94201877934273
6978.669.19131455399069.40868544600938
7068.975.2746478873239-6.37464788732393
7179.180.474647887324-1.37464788732396
7283.596.774647887324-13.2746478873240
7374.176.0967806841046-1.99678068410461
7459.771.425352112676-11.7253521126760
7593.396.4967806841046-3.19678068410462
7661.348.082494969818913.2175050301811
7756.646.225352112676110.3746478873239

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 105.7 & 115.367203219316 & -9.66720321931598 \tabularnewline
2 & 105.7 & 110.695774647887 & -4.99577464788738 \tabularnewline
3 & 111.1 & 135.767203219316 & -24.6672032193159 \tabularnewline
4 & 82.4 & 87.3529175050302 & -4.95291750503018 \tabularnewline
5 & 60 & 85.4957746478872 & -25.4957746478872 \tabularnewline
6 & 107.3 & 130.683333333333 & -23.3833333333333 \tabularnewline
7 & 99.3 & 126.1 & -26.7999999999999 \tabularnewline
8 & 113.5 & 125.028403755869 & -11.5284037558685 \tabularnewline
9 & 108.9 & 108.461737089202 & 0.438262910798133 \tabularnewline
10 & 100.2 & 114.545070422535 & -14.3450704225352 \tabularnewline
11 & 103.9 & 119.745070422535 & -15.8450704225352 \tabularnewline
12 & 138.7 & 136.045070422535 & 2.65492957746474 \tabularnewline
13 & 120.2 & 115.367203219316 & 4.83279678068412 \tabularnewline
14 & 100.2 & 110.695774647887 & -10.4957746478873 \tabularnewline
15 & 143.2 & 135.767203219316 & 7.4327967806841 \tabularnewline
16 & 70.9 & 87.3529175050302 & -16.4529175050302 \tabularnewline
17 & 85.2 & 85.4957746478873 & -0.295774647887331 \tabularnewline
18 & 133 & 130.683333333333 & 2.31666666666667 \tabularnewline
19 & 136.6 & 126.1 & 10.5000000000000 \tabularnewline
20 & 117.9 & 125.028403755869 & -7.12840375586854 \tabularnewline
21 & 106.3 & 108.461737089202 & -2.16173708920188 \tabularnewline
22 & 122.3 & 114.545070422535 & 7.7549295774648 \tabularnewline
23 & 125.5 & 119.745070422535 & 5.75492957746479 \tabularnewline
24 & 148.4 & 136.045070422535 & 12.3549295774648 \tabularnewline
25 & 126.3 & 115.367203219316 & 10.9327967806841 \tabularnewline
26 & 99.6 & 110.695774647887 & -11.0957746478873 \tabularnewline
27 & 140.4 & 135.767203219316 & 4.63279678068412 \tabularnewline
28 & 80.3 & 87.3529175050302 & -7.05291750503019 \tabularnewline
29 & 92.6 & 85.4957746478873 & 7.10422535211266 \tabularnewline
30 & 138.5 & 130.683333333333 & 7.81666666666667 \tabularnewline
31 & 110.9 & 126.1 & -15.2 \tabularnewline
32 & 119.6 & 125.028403755869 & -5.42840375586855 \tabularnewline
33 & 105 & 108.461737089202 & -3.46173708920187 \tabularnewline
34 & 109 & 114.545070422535 & -5.54507042253521 \tabularnewline
35 & 129.4 & 119.745070422535 & 9.6549295774648 \tabularnewline
36 & 148.6 & 136.045070422535 & 12.5549295774648 \tabularnewline
37 & 101.4 & 115.367203219316 & -13.9672032193159 \tabularnewline
38 & 134.8 & 110.695774647887 & 24.1042253521127 \tabularnewline
39 & 143.7 & 135.767203219316 & 7.9327967806841 \tabularnewline
40 & 81.6 & 87.3529175050302 & -5.75291750503019 \tabularnewline
41 & 90.3 & 85.4957746478873 & 4.80422535211267 \tabularnewline
42 & 141.5 & 130.683333333333 & 10.8166666666667 \tabularnewline
43 & 140.7 & 126.1 & 14.6000000000000 \tabularnewline
44 & 140.2 & 125.028403755869 & 15.1715962441314 \tabularnewline
45 & 100.2 & 108.461737089202 & -8.26173708920187 \tabularnewline
46 & 125.7 & 114.545070422535 & 11.1549295774648 \tabularnewline
47 & 119.6 & 119.745070422535 & -0.145070422535225 \tabularnewline
48 & 134.7 & 136.045070422535 & -1.34507042253524 \tabularnewline
49 & 109 & 115.367203219316 & -6.36720321931588 \tabularnewline
50 & 116.3 & 110.695774647887 & 5.60422535211269 \tabularnewline
51 & 146.9 & 135.767203219316 & 11.1327967806841 \tabularnewline
52 & 97.4 & 87.3529175050302 & 10.0470824949698 \tabularnewline
53 & 89.4 & 85.4957746478873 & 3.90422535211267 \tabularnewline
54 & 132.1 & 130.683333333333 & 1.41666666666666 \tabularnewline
55 & 139.8 & 126.1 & 13.7 \tabularnewline
56 & 129 & 125.028403755869 & 3.97159624413146 \tabularnewline
57 & 112.5 & 108.461737089202 & 4.03826291079813 \tabularnewline
58 & 121.9 & 114.545070422535 & 7.3549295774648 \tabularnewline
59 & 121.7 & 119.745070422535 & 1.95492957746479 \tabularnewline
60 & 123.1 & 136.045070422535 & -12.9450704225352 \tabularnewline
61 & 131.6 & 115.367203219316 & 16.2327967806841 \tabularnewline
62 & 119.3 & 110.695774647887 & 8.60422535211269 \tabularnewline
63 & 132.5 & 135.767203219316 & -3.26720321931589 \tabularnewline
64 & 98.3 & 87.3529175050302 & 10.9470824949698 \tabularnewline
65 & 85.1 & 85.4957746478873 & -0.39577464788734 \tabularnewline
66 & 131.7 & 130.683333333333 & 1.01666666666666 \tabularnewline
67 & 129.3 & 126.1 & 3.20000000000001 \tabularnewline
68 & 90.7 & 85.7579812206573 & 4.94201877934273 \tabularnewline
69 & 78.6 & 69.1913145539906 & 9.40868544600938 \tabularnewline
70 & 68.9 & 75.2746478873239 & -6.37464788732393 \tabularnewline
71 & 79.1 & 80.474647887324 & -1.37464788732396 \tabularnewline
72 & 83.5 & 96.774647887324 & -13.2746478873240 \tabularnewline
73 & 74.1 & 76.0967806841046 & -1.99678068410461 \tabularnewline
74 & 59.7 & 71.425352112676 & -11.7253521126760 \tabularnewline
75 & 93.3 & 96.4967806841046 & -3.19678068410462 \tabularnewline
76 & 61.3 & 48.0824949698189 & 13.2175050301811 \tabularnewline
77 & 56.6 & 46.2253521126761 & 10.3746478873239 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58257&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]105.7[/C][C]115.367203219316[/C][C]-9.66720321931598[/C][/ROW]
[ROW][C]2[/C][C]105.7[/C][C]110.695774647887[/C][C]-4.99577464788738[/C][/ROW]
[ROW][C]3[/C][C]111.1[/C][C]135.767203219316[/C][C]-24.6672032193159[/C][/ROW]
[ROW][C]4[/C][C]82.4[/C][C]87.3529175050302[/C][C]-4.95291750503018[/C][/ROW]
[ROW][C]5[/C][C]60[/C][C]85.4957746478872[/C][C]-25.4957746478872[/C][/ROW]
[ROW][C]6[/C][C]107.3[/C][C]130.683333333333[/C][C]-23.3833333333333[/C][/ROW]
[ROW][C]7[/C][C]99.3[/C][C]126.1[/C][C]-26.7999999999999[/C][/ROW]
[ROW][C]8[/C][C]113.5[/C][C]125.028403755869[/C][C]-11.5284037558685[/C][/ROW]
[ROW][C]9[/C][C]108.9[/C][C]108.461737089202[/C][C]0.438262910798133[/C][/ROW]
[ROW][C]10[/C][C]100.2[/C][C]114.545070422535[/C][C]-14.3450704225352[/C][/ROW]
[ROW][C]11[/C][C]103.9[/C][C]119.745070422535[/C][C]-15.8450704225352[/C][/ROW]
[ROW][C]12[/C][C]138.7[/C][C]136.045070422535[/C][C]2.65492957746474[/C][/ROW]
[ROW][C]13[/C][C]120.2[/C][C]115.367203219316[/C][C]4.83279678068412[/C][/ROW]
[ROW][C]14[/C][C]100.2[/C][C]110.695774647887[/C][C]-10.4957746478873[/C][/ROW]
[ROW][C]15[/C][C]143.2[/C][C]135.767203219316[/C][C]7.4327967806841[/C][/ROW]
[ROW][C]16[/C][C]70.9[/C][C]87.3529175050302[/C][C]-16.4529175050302[/C][/ROW]
[ROW][C]17[/C][C]85.2[/C][C]85.4957746478873[/C][C]-0.295774647887331[/C][/ROW]
[ROW][C]18[/C][C]133[/C][C]130.683333333333[/C][C]2.31666666666667[/C][/ROW]
[ROW][C]19[/C][C]136.6[/C][C]126.1[/C][C]10.5000000000000[/C][/ROW]
[ROW][C]20[/C][C]117.9[/C][C]125.028403755869[/C][C]-7.12840375586854[/C][/ROW]
[ROW][C]21[/C][C]106.3[/C][C]108.461737089202[/C][C]-2.16173708920188[/C][/ROW]
[ROW][C]22[/C][C]122.3[/C][C]114.545070422535[/C][C]7.7549295774648[/C][/ROW]
[ROW][C]23[/C][C]125.5[/C][C]119.745070422535[/C][C]5.75492957746479[/C][/ROW]
[ROW][C]24[/C][C]148.4[/C][C]136.045070422535[/C][C]12.3549295774648[/C][/ROW]
[ROW][C]25[/C][C]126.3[/C][C]115.367203219316[/C][C]10.9327967806841[/C][/ROW]
[ROW][C]26[/C][C]99.6[/C][C]110.695774647887[/C][C]-11.0957746478873[/C][/ROW]
[ROW][C]27[/C][C]140.4[/C][C]135.767203219316[/C][C]4.63279678068412[/C][/ROW]
[ROW][C]28[/C][C]80.3[/C][C]87.3529175050302[/C][C]-7.05291750503019[/C][/ROW]
[ROW][C]29[/C][C]92.6[/C][C]85.4957746478873[/C][C]7.10422535211266[/C][/ROW]
[ROW][C]30[/C][C]138.5[/C][C]130.683333333333[/C][C]7.81666666666667[/C][/ROW]
[ROW][C]31[/C][C]110.9[/C][C]126.1[/C][C]-15.2[/C][/ROW]
[ROW][C]32[/C][C]119.6[/C][C]125.028403755869[/C][C]-5.42840375586855[/C][/ROW]
[ROW][C]33[/C][C]105[/C][C]108.461737089202[/C][C]-3.46173708920187[/C][/ROW]
[ROW][C]34[/C][C]109[/C][C]114.545070422535[/C][C]-5.54507042253521[/C][/ROW]
[ROW][C]35[/C][C]129.4[/C][C]119.745070422535[/C][C]9.6549295774648[/C][/ROW]
[ROW][C]36[/C][C]148.6[/C][C]136.045070422535[/C][C]12.5549295774648[/C][/ROW]
[ROW][C]37[/C][C]101.4[/C][C]115.367203219316[/C][C]-13.9672032193159[/C][/ROW]
[ROW][C]38[/C][C]134.8[/C][C]110.695774647887[/C][C]24.1042253521127[/C][/ROW]
[ROW][C]39[/C][C]143.7[/C][C]135.767203219316[/C][C]7.9327967806841[/C][/ROW]
[ROW][C]40[/C][C]81.6[/C][C]87.3529175050302[/C][C]-5.75291750503019[/C][/ROW]
[ROW][C]41[/C][C]90.3[/C][C]85.4957746478873[/C][C]4.80422535211267[/C][/ROW]
[ROW][C]42[/C][C]141.5[/C][C]130.683333333333[/C][C]10.8166666666667[/C][/ROW]
[ROW][C]43[/C][C]140.7[/C][C]126.1[/C][C]14.6000000000000[/C][/ROW]
[ROW][C]44[/C][C]140.2[/C][C]125.028403755869[/C][C]15.1715962441314[/C][/ROW]
[ROW][C]45[/C][C]100.2[/C][C]108.461737089202[/C][C]-8.26173708920187[/C][/ROW]
[ROW][C]46[/C][C]125.7[/C][C]114.545070422535[/C][C]11.1549295774648[/C][/ROW]
[ROW][C]47[/C][C]119.6[/C][C]119.745070422535[/C][C]-0.145070422535225[/C][/ROW]
[ROW][C]48[/C][C]134.7[/C][C]136.045070422535[/C][C]-1.34507042253524[/C][/ROW]
[ROW][C]49[/C][C]109[/C][C]115.367203219316[/C][C]-6.36720321931588[/C][/ROW]
[ROW][C]50[/C][C]116.3[/C][C]110.695774647887[/C][C]5.60422535211269[/C][/ROW]
[ROW][C]51[/C][C]146.9[/C][C]135.767203219316[/C][C]11.1327967806841[/C][/ROW]
[ROW][C]52[/C][C]97.4[/C][C]87.3529175050302[/C][C]10.0470824949698[/C][/ROW]
[ROW][C]53[/C][C]89.4[/C][C]85.4957746478873[/C][C]3.90422535211267[/C][/ROW]
[ROW][C]54[/C][C]132.1[/C][C]130.683333333333[/C][C]1.41666666666666[/C][/ROW]
[ROW][C]55[/C][C]139.8[/C][C]126.1[/C][C]13.7[/C][/ROW]
[ROW][C]56[/C][C]129[/C][C]125.028403755869[/C][C]3.97159624413146[/C][/ROW]
[ROW][C]57[/C][C]112.5[/C][C]108.461737089202[/C][C]4.03826291079813[/C][/ROW]
[ROW][C]58[/C][C]121.9[/C][C]114.545070422535[/C][C]7.3549295774648[/C][/ROW]
[ROW][C]59[/C][C]121.7[/C][C]119.745070422535[/C][C]1.95492957746479[/C][/ROW]
[ROW][C]60[/C][C]123.1[/C][C]136.045070422535[/C][C]-12.9450704225352[/C][/ROW]
[ROW][C]61[/C][C]131.6[/C][C]115.367203219316[/C][C]16.2327967806841[/C][/ROW]
[ROW][C]62[/C][C]119.3[/C][C]110.695774647887[/C][C]8.60422535211269[/C][/ROW]
[ROW][C]63[/C][C]132.5[/C][C]135.767203219316[/C][C]-3.26720321931589[/C][/ROW]
[ROW][C]64[/C][C]98.3[/C][C]87.3529175050302[/C][C]10.9470824949698[/C][/ROW]
[ROW][C]65[/C][C]85.1[/C][C]85.4957746478873[/C][C]-0.39577464788734[/C][/ROW]
[ROW][C]66[/C][C]131.7[/C][C]130.683333333333[/C][C]1.01666666666666[/C][/ROW]
[ROW][C]67[/C][C]129.3[/C][C]126.1[/C][C]3.20000000000001[/C][/ROW]
[ROW][C]68[/C][C]90.7[/C][C]85.7579812206573[/C][C]4.94201877934273[/C][/ROW]
[ROW][C]69[/C][C]78.6[/C][C]69.1913145539906[/C][C]9.40868544600938[/C][/ROW]
[ROW][C]70[/C][C]68.9[/C][C]75.2746478873239[/C][C]-6.37464788732393[/C][/ROW]
[ROW][C]71[/C][C]79.1[/C][C]80.474647887324[/C][C]-1.37464788732396[/C][/ROW]
[ROW][C]72[/C][C]83.5[/C][C]96.774647887324[/C][C]-13.2746478873240[/C][/ROW]
[ROW][C]73[/C][C]74.1[/C][C]76.0967806841046[/C][C]-1.99678068410461[/C][/ROW]
[ROW][C]74[/C][C]59.7[/C][C]71.425352112676[/C][C]-11.7253521126760[/C][/ROW]
[ROW][C]75[/C][C]93.3[/C][C]96.4967806841046[/C][C]-3.19678068410462[/C][/ROW]
[ROW][C]76[/C][C]61.3[/C][C]48.0824949698189[/C][C]13.2175050301811[/C][/ROW]
[ROW][C]77[/C][C]56.6[/C][C]46.2253521126761[/C][C]10.3746478873239[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58257&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58257&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
1105.7115.367203219316-9.66720321931598
2105.7110.695774647887-4.99577464788738
3111.1135.767203219316-24.6672032193159
482.487.3529175050302-4.95291750503018
56085.4957746478872-25.4957746478872
6107.3130.683333333333-23.3833333333333
799.3126.1-26.7999999999999
8113.5125.028403755869-11.5284037558685
9108.9108.4617370892020.438262910798133
10100.2114.545070422535-14.3450704225352
11103.9119.745070422535-15.8450704225352
12138.7136.0450704225352.65492957746474
13120.2115.3672032193164.83279678068412
14100.2110.695774647887-10.4957746478873
15143.2135.7672032193167.4327967806841
1670.987.3529175050302-16.4529175050302
1785.285.4957746478873-0.295774647887331
18133130.6833333333332.31666666666667
19136.6126.110.5000000000000
20117.9125.028403755869-7.12840375586854
21106.3108.461737089202-2.16173708920188
22122.3114.5450704225357.7549295774648
23125.5119.7450704225355.75492957746479
24148.4136.04507042253512.3549295774648
25126.3115.36720321931610.9327967806841
2699.6110.695774647887-11.0957746478873
27140.4135.7672032193164.63279678068412
2880.387.3529175050302-7.05291750503019
2992.685.49577464788737.10422535211266
30138.5130.6833333333337.81666666666667
31110.9126.1-15.2
32119.6125.028403755869-5.42840375586855
33105108.461737089202-3.46173708920187
34109114.545070422535-5.54507042253521
35129.4119.7450704225359.6549295774648
36148.6136.04507042253512.5549295774648
37101.4115.367203219316-13.9672032193159
38134.8110.69577464788724.1042253521127
39143.7135.7672032193167.9327967806841
4081.687.3529175050302-5.75291750503019
4190.385.49577464788734.80422535211267
42141.5130.68333333333310.8166666666667
43140.7126.114.6000000000000
44140.2125.02840375586915.1715962441314
45100.2108.461737089202-8.26173708920187
46125.7114.54507042253511.1549295774648
47119.6119.745070422535-0.145070422535225
48134.7136.045070422535-1.34507042253524
49109115.367203219316-6.36720321931588
50116.3110.6957746478875.60422535211269
51146.9135.76720321931611.1327967806841
5297.487.352917505030210.0470824949698
5389.485.49577464788733.90422535211267
54132.1130.6833333333331.41666666666666
55139.8126.113.7
56129125.0284037558693.97159624413146
57112.5108.4617370892024.03826291079813
58121.9114.5450704225357.3549295774648
59121.7119.7450704225351.95492957746479
60123.1136.045070422535-12.9450704225352
61131.6115.36720321931616.2327967806841
62119.3110.6957746478878.60422535211269
63132.5135.767203219316-3.26720321931589
6498.387.352917505030210.9470824949698
6585.185.4957746478873-0.39577464788734
66131.7130.6833333333331.01666666666666
67129.3126.13.20000000000001
6890.785.75798122065734.94201877934273
6978.669.19131455399069.40868544600938
7068.975.2746478873239-6.37464788732393
7179.180.474647887324-1.37464788732396
7283.596.774647887324-13.2746478873240
7374.176.0967806841046-1.99678068410461
7459.771.425352112676-11.7253521126760
7593.396.4967806841046-3.19678068410462
7661.348.082494969818913.2175050301811
7756.646.225352112676110.3746478873239







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.9380278444874520.1239443110250960.0619721555125482
170.9639831248410720.07203375031785520.0360168751589276
180.9771664473381370.0456671053237270.0228335526618635
190.9949304427409340.01013911451813100.00506955725906551
200.991307575389010.01738484922198020.00869242461099011
210.9833821804004850.03323563919902990.0166178195995150
220.983959523427130.03208095314574080.0160404765728704
230.9833044702712920.03339105945741620.0166955297287081
240.9797242234869620.04055155302607650.0202757765130383
250.9770394711630370.04592105767392530.0229605288369627
260.9750379854257930.04992402914841490.0249620145742075
270.9675023918385950.064995216322810.032497608161405
280.9613852411633560.07722951767328830.0386147588366441
290.9622638177416250.07547236451675010.0377361822583751
300.9597484535403960.08050309291920810.0402515464596041
310.9755541555047460.04889168899050810.0244458444952540
320.9725628394751850.05487432104963010.0274371605248151
330.959928120744450.0801437585110990.0400718792555495
340.9492976982501040.1014046034997910.0507023017498956
350.9439013305662870.1121973388674260.0560986694337128
360.958634994582150.08273001083570210.0413650054178511
370.974502184778130.05099563044373940.0254978152218697
380.996785454703790.006429090592421650.00321454529621083
390.995430296900550.009139406198900280.00456970309945014
400.9975629272548750.004874145490250820.00243707274512541
410.9959809896473540.008038020705291330.00401901035264567
420.9957023539394250.008595292121149980.00429764606057499
430.996068027834940.00786394433011920.0039319721650596
440.996413016054940.007173967890121880.00358698394506094
450.9978343479037260.004331304192548210.00216565209627410
460.997365621475410.005268757049178660.00263437852458933
470.9948659972921350.01026800541573070.00513400270786537
480.993840421222050.01231915755589940.00615957877794969
490.9960241184269530.007951763146093570.00397588157304679
500.993032509331560.01393498133688170.00696749066844085
510.9943525331100220.01129493377995650.00564746688997827
520.9909731127122280.0180537745755440.009026887287772
530.98281493925320.03437012149360170.0171850607468009
540.9665740447310.06685191053800150.0334259552690007
550.9607289018429320.0785421963141360.039271098157068
560.9321342428513560.1357315142972890.0678657571486444
570.9089675700202080.1820648599595850.0910324299797923
580.8701489562532780.2597020874934440.129851043746722
590.7752570498275810.4494859003448380.224742950172419
600.6675950075143850.664809984971230.332404992485615
610.6501897735130470.6996204529739060.349810226486953

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.938027844487452 & 0.123944311025096 & 0.0619721555125482 \tabularnewline
17 & 0.963983124841072 & 0.0720337503178552 & 0.0360168751589276 \tabularnewline
18 & 0.977166447338137 & 0.045667105323727 & 0.0228335526618635 \tabularnewline
19 & 0.994930442740934 & 0.0101391145181310 & 0.00506955725906551 \tabularnewline
20 & 0.99130757538901 & 0.0173848492219802 & 0.00869242461099011 \tabularnewline
21 & 0.983382180400485 & 0.0332356391990299 & 0.0166178195995150 \tabularnewline
22 & 0.98395952342713 & 0.0320809531457408 & 0.0160404765728704 \tabularnewline
23 & 0.983304470271292 & 0.0333910594574162 & 0.0166955297287081 \tabularnewline
24 & 0.979724223486962 & 0.0405515530260765 & 0.0202757765130383 \tabularnewline
25 & 0.977039471163037 & 0.0459210576739253 & 0.0229605288369627 \tabularnewline
26 & 0.975037985425793 & 0.0499240291484149 & 0.0249620145742075 \tabularnewline
27 & 0.967502391838595 & 0.06499521632281 & 0.032497608161405 \tabularnewline
28 & 0.961385241163356 & 0.0772295176732883 & 0.0386147588366441 \tabularnewline
29 & 0.962263817741625 & 0.0754723645167501 & 0.0377361822583751 \tabularnewline
30 & 0.959748453540396 & 0.0805030929192081 & 0.0402515464596041 \tabularnewline
31 & 0.975554155504746 & 0.0488916889905081 & 0.0244458444952540 \tabularnewline
32 & 0.972562839475185 & 0.0548743210496301 & 0.0274371605248151 \tabularnewline
33 & 0.95992812074445 & 0.080143758511099 & 0.0400718792555495 \tabularnewline
34 & 0.949297698250104 & 0.101404603499791 & 0.0507023017498956 \tabularnewline
35 & 0.943901330566287 & 0.112197338867426 & 0.0560986694337128 \tabularnewline
36 & 0.95863499458215 & 0.0827300108357021 & 0.0413650054178511 \tabularnewline
37 & 0.97450218477813 & 0.0509956304437394 & 0.0254978152218697 \tabularnewline
38 & 0.99678545470379 & 0.00642909059242165 & 0.00321454529621083 \tabularnewline
39 & 0.99543029690055 & 0.00913940619890028 & 0.00456970309945014 \tabularnewline
40 & 0.997562927254875 & 0.00487414549025082 & 0.00243707274512541 \tabularnewline
41 & 0.995980989647354 & 0.00803802070529133 & 0.00401901035264567 \tabularnewline
42 & 0.995702353939425 & 0.00859529212114998 & 0.00429764606057499 \tabularnewline
43 & 0.99606802783494 & 0.0078639443301192 & 0.0039319721650596 \tabularnewline
44 & 0.99641301605494 & 0.00717396789012188 & 0.00358698394506094 \tabularnewline
45 & 0.997834347903726 & 0.00433130419254821 & 0.00216565209627410 \tabularnewline
46 & 0.99736562147541 & 0.00526875704917866 & 0.00263437852458933 \tabularnewline
47 & 0.994865997292135 & 0.0102680054157307 & 0.00513400270786537 \tabularnewline
48 & 0.99384042122205 & 0.0123191575558994 & 0.00615957877794969 \tabularnewline
49 & 0.996024118426953 & 0.00795176314609357 & 0.00397588157304679 \tabularnewline
50 & 0.99303250933156 & 0.0139349813368817 & 0.00696749066844085 \tabularnewline
51 & 0.994352533110022 & 0.0112949337799565 & 0.00564746688997827 \tabularnewline
52 & 0.990973112712228 & 0.018053774575544 & 0.009026887287772 \tabularnewline
53 & 0.9828149392532 & 0.0343701214936017 & 0.0171850607468009 \tabularnewline
54 & 0.966574044731 & 0.0668519105380015 & 0.0334259552690007 \tabularnewline
55 & 0.960728901842932 & 0.078542196314136 & 0.039271098157068 \tabularnewline
56 & 0.932134242851356 & 0.135731514297289 & 0.0678657571486444 \tabularnewline
57 & 0.908967570020208 & 0.182064859959585 & 0.0910324299797923 \tabularnewline
58 & 0.870148956253278 & 0.259702087493444 & 0.129851043746722 \tabularnewline
59 & 0.775257049827581 & 0.449485900344838 & 0.224742950172419 \tabularnewline
60 & 0.667595007514385 & 0.66480998497123 & 0.332404992485615 \tabularnewline
61 & 0.650189773513047 & 0.699620452973906 & 0.349810226486953 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58257&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]0.938027844487452[/C][C]0.123944311025096[/C][C]0.0619721555125482[/C][/ROW]
[ROW][C]17[/C][C]0.963983124841072[/C][C]0.0720337503178552[/C][C]0.0360168751589276[/C][/ROW]
[ROW][C]18[/C][C]0.977166447338137[/C][C]0.045667105323727[/C][C]0.0228335526618635[/C][/ROW]
[ROW][C]19[/C][C]0.994930442740934[/C][C]0.0101391145181310[/C][C]0.00506955725906551[/C][/ROW]
[ROW][C]20[/C][C]0.99130757538901[/C][C]0.0173848492219802[/C][C]0.00869242461099011[/C][/ROW]
[ROW][C]21[/C][C]0.983382180400485[/C][C]0.0332356391990299[/C][C]0.0166178195995150[/C][/ROW]
[ROW][C]22[/C][C]0.98395952342713[/C][C]0.0320809531457408[/C][C]0.0160404765728704[/C][/ROW]
[ROW][C]23[/C][C]0.983304470271292[/C][C]0.0333910594574162[/C][C]0.0166955297287081[/C][/ROW]
[ROW][C]24[/C][C]0.979724223486962[/C][C]0.0405515530260765[/C][C]0.0202757765130383[/C][/ROW]
[ROW][C]25[/C][C]0.977039471163037[/C][C]0.0459210576739253[/C][C]0.0229605288369627[/C][/ROW]
[ROW][C]26[/C][C]0.975037985425793[/C][C]0.0499240291484149[/C][C]0.0249620145742075[/C][/ROW]
[ROW][C]27[/C][C]0.967502391838595[/C][C]0.06499521632281[/C][C]0.032497608161405[/C][/ROW]
[ROW][C]28[/C][C]0.961385241163356[/C][C]0.0772295176732883[/C][C]0.0386147588366441[/C][/ROW]
[ROW][C]29[/C][C]0.962263817741625[/C][C]0.0754723645167501[/C][C]0.0377361822583751[/C][/ROW]
[ROW][C]30[/C][C]0.959748453540396[/C][C]0.0805030929192081[/C][C]0.0402515464596041[/C][/ROW]
[ROW][C]31[/C][C]0.975554155504746[/C][C]0.0488916889905081[/C][C]0.0244458444952540[/C][/ROW]
[ROW][C]32[/C][C]0.972562839475185[/C][C]0.0548743210496301[/C][C]0.0274371605248151[/C][/ROW]
[ROW][C]33[/C][C]0.95992812074445[/C][C]0.080143758511099[/C][C]0.0400718792555495[/C][/ROW]
[ROW][C]34[/C][C]0.949297698250104[/C][C]0.101404603499791[/C][C]0.0507023017498956[/C][/ROW]
[ROW][C]35[/C][C]0.943901330566287[/C][C]0.112197338867426[/C][C]0.0560986694337128[/C][/ROW]
[ROW][C]36[/C][C]0.95863499458215[/C][C]0.0827300108357021[/C][C]0.0413650054178511[/C][/ROW]
[ROW][C]37[/C][C]0.97450218477813[/C][C]0.0509956304437394[/C][C]0.0254978152218697[/C][/ROW]
[ROW][C]38[/C][C]0.99678545470379[/C][C]0.00642909059242165[/C][C]0.00321454529621083[/C][/ROW]
[ROW][C]39[/C][C]0.99543029690055[/C][C]0.00913940619890028[/C][C]0.00456970309945014[/C][/ROW]
[ROW][C]40[/C][C]0.997562927254875[/C][C]0.00487414549025082[/C][C]0.00243707274512541[/C][/ROW]
[ROW][C]41[/C][C]0.995980989647354[/C][C]0.00803802070529133[/C][C]0.00401901035264567[/C][/ROW]
[ROW][C]42[/C][C]0.995702353939425[/C][C]0.00859529212114998[/C][C]0.00429764606057499[/C][/ROW]
[ROW][C]43[/C][C]0.99606802783494[/C][C]0.0078639443301192[/C][C]0.0039319721650596[/C][/ROW]
[ROW][C]44[/C][C]0.99641301605494[/C][C]0.00717396789012188[/C][C]0.00358698394506094[/C][/ROW]
[ROW][C]45[/C][C]0.997834347903726[/C][C]0.00433130419254821[/C][C]0.00216565209627410[/C][/ROW]
[ROW][C]46[/C][C]0.99736562147541[/C][C]0.00526875704917866[/C][C]0.00263437852458933[/C][/ROW]
[ROW][C]47[/C][C]0.994865997292135[/C][C]0.0102680054157307[/C][C]0.00513400270786537[/C][/ROW]
[ROW][C]48[/C][C]0.99384042122205[/C][C]0.0123191575558994[/C][C]0.00615957877794969[/C][/ROW]
[ROW][C]49[/C][C]0.996024118426953[/C][C]0.00795176314609357[/C][C]0.00397588157304679[/C][/ROW]
[ROW][C]50[/C][C]0.99303250933156[/C][C]0.0139349813368817[/C][C]0.00696749066844085[/C][/ROW]
[ROW][C]51[/C][C]0.994352533110022[/C][C]0.0112949337799565[/C][C]0.00564746688997827[/C][/ROW]
[ROW][C]52[/C][C]0.990973112712228[/C][C]0.018053774575544[/C][C]0.009026887287772[/C][/ROW]
[ROW][C]53[/C][C]0.9828149392532[/C][C]0.0343701214936017[/C][C]0.0171850607468009[/C][/ROW]
[ROW][C]54[/C][C]0.966574044731[/C][C]0.0668519105380015[/C][C]0.0334259552690007[/C][/ROW]
[ROW][C]55[/C][C]0.960728901842932[/C][C]0.078542196314136[/C][C]0.039271098157068[/C][/ROW]
[ROW][C]56[/C][C]0.932134242851356[/C][C]0.135731514297289[/C][C]0.0678657571486444[/C][/ROW]
[ROW][C]57[/C][C]0.908967570020208[/C][C]0.182064859959585[/C][C]0.0910324299797923[/C][/ROW]
[ROW][C]58[/C][C]0.870148956253278[/C][C]0.259702087493444[/C][C]0.129851043746722[/C][/ROW]
[ROW][C]59[/C][C]0.775257049827581[/C][C]0.449485900344838[/C][C]0.224742950172419[/C][/ROW]
[ROW][C]60[/C][C]0.667595007514385[/C][C]0.66480998497123[/C][C]0.332404992485615[/C][/ROW]
[ROW][C]61[/C][C]0.650189773513047[/C][C]0.699620452973906[/C][C]0.349810226486953[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58257&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58257&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
160.9380278444874520.1239443110250960.0619721555125482
170.9639831248410720.07203375031785520.0360168751589276
180.9771664473381370.0456671053237270.0228335526618635
190.9949304427409340.01013911451813100.00506955725906551
200.991307575389010.01738484922198020.00869242461099011
210.9833821804004850.03323563919902990.0166178195995150
220.983959523427130.03208095314574080.0160404765728704
230.9833044702712920.03339105945741620.0166955297287081
240.9797242234869620.04055155302607650.0202757765130383
250.9770394711630370.04592105767392530.0229605288369627
260.9750379854257930.04992402914841490.0249620145742075
270.9675023918385950.064995216322810.032497608161405
280.9613852411633560.07722951767328830.0386147588366441
290.9622638177416250.07547236451675010.0377361822583751
300.9597484535403960.08050309291920810.0402515464596041
310.9755541555047460.04889168899050810.0244458444952540
320.9725628394751850.05487432104963010.0274371605248151
330.959928120744450.0801437585110990.0400718792555495
340.9492976982501040.1014046034997910.0507023017498956
350.9439013305662870.1121973388674260.0560986694337128
360.958634994582150.08273001083570210.0413650054178511
370.974502184778130.05099563044373940.0254978152218697
380.996785454703790.006429090592421650.00321454529621083
390.995430296900550.009139406198900280.00456970309945014
400.9975629272548750.004874145490250820.00243707274512541
410.9959809896473540.008038020705291330.00401901035264567
420.9957023539394250.008595292121149980.00429764606057499
430.996068027834940.00786394433011920.0039319721650596
440.996413016054940.007173967890121880.00358698394506094
450.9978343479037260.004331304192548210.00216565209627410
460.997365621475410.005268757049178660.00263437852458933
470.9948659972921350.01026800541573070.00513400270786537
480.993840421222050.01231915755589940.00615957877794969
490.9960241184269530.007951763146093570.00397588157304679
500.993032509331560.01393498133688170.00696749066844085
510.9943525331100220.01129493377995650.00564746688997827
520.9909731127122280.0180537745755440.009026887287772
530.98281493925320.03437012149360170.0171850607468009
540.9665740447310.06685191053800150.0334259552690007
550.9607289018429320.0785421963141360.039271098157068
560.9321342428513560.1357315142972890.0678657571486444
570.9089675700202080.1820648599595850.0910324299797923
580.8701489562532780.2597020874934440.129851043746722
590.7752570498275810.4494859003448380.224742950172419
600.6675950075143850.664809984971230.332404992485615
610.6501897735130470.6996204529739060.349810226486953







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.217391304347826NOK
5% type I error level260.565217391304348NOK
10% type I error level370.804347826086957NOK

\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 & 10 & 0.217391304347826 & NOK \tabularnewline
5% type I error level & 26 & 0.565217391304348 & NOK \tabularnewline
10% type I error level & 37 & 0.804347826086957 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58257&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]10[/C][C]0.217391304347826[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]26[/C][C]0.565217391304348[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]37[/C][C]0.804347826086957[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58257&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58257&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 level100.217391304347826NOK
5% type I error level260.565217391304348NOK
10% type I error level370.804347826086957NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}