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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 14 Nov 2009 06:01:08 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/14/t1258203886uim51hukqon4dy6.htm/, Retrieved Sat, 28 Jun 2025 06:02:00 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57221, Retrieved Sat, 28 Jun 2025 06:02:00 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

405

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] [WS 7 1] [2009-11-14 13:01:08] [2e4ef2c1b76db9b31c0a03b96e94ad77] [Current]

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Dataseries X:

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Histogram

Boxplots

103,63	100,30
103,64	98,50
103,66	95,10
103,77	93,10
103,88	92,20
103,91	89,00
103,91	86,40
103,92	84,50
104,05	82,70
104,23	80,80
104,30	81,80
104,31	81,80
104,31	82,90
104,34	83,80
104,55	86,20
104,65	86,10
104,73	86,20
104,75	88,80
104,75	89,60
104,76	87,80
104,94	88,30
105,29	88,60
105,38	91,00
105,43	91,50
105,43	95,40
105,42	98,70
105,52	99,90
105,69	98,60
105,72	100,30
105,74	100,20
105,74	100,40
105,74	101,40
105,95	103,00
106,17	109,10
106,34	111,40
106,37	114,10
106,37	121,80
106,36	127,60
106,44	129,90
106,29	128,00
106,23	123,50
106,23	124,00
106,23	127,40
106,23	127,60
106,34	128,40
106,44	131,40
106,44	135,10
106,48	134,00
106,50	144,50
106,57	147,30
106,40	150,90
106,37	148,70
106,25	141,40
106,21	138,90
106,21	139,80
106,24	145,60
106,19	147,90
106,08	148,50
106,13	151,10
106,09	157,50

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57221&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]3 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=57221&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57221&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 101.845973199319 + 0.0324017875946746X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  101.845973199319 +  0.0324017875946746X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57221&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  101.845973199319 +  0.0324017875946746X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57221&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57221&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] = + 101.845973199319 + 0.0324017875946746X[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	101.845973199319	0.360874	282.2201	0	0
X	0.0324017875946746	0.003184	10.1769	0	0

\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) & 101.845973199319 & 0.360874 & 282.2201 & 0 & 0 \tabularnewline
X & 0.0324017875946746 & 0.003184 & 10.1769 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57221&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]101.845973199319[/C][C]0.360874[/C][C]282.2201[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]0.0324017875946746[/C][C]0.003184[/C][C]10.1769[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57221&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57221&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
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	101.845973199319	0.360874	282.2201	0	0
X	0.0324017875946746	0.003184	10.1769	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.800637011015123
R-squared	0.64101962340723
Adjusted R-squared	0.634830306569423
F-TEST (value)	103.568720135908
F-TEST (DF numerator)	1
F-TEST (DF denominator)	58
p-value	1.62092561595273e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.58459632829669
Sum Squared Residuals	19.8216662893624

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.800637011015123 \tabularnewline
R-squared & 0.64101962340723 \tabularnewline
Adjusted R-squared & 0.634830306569423 \tabularnewline
F-TEST (value) & 103.568720135908 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 1.62092561595273e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.58459632829669 \tabularnewline
Sum Squared Residuals & 19.8216662893624 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57221&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.800637011015123[/C][/ROW]
[ROW][C]R-squared[/C][C]0.64101962340723[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.634830306569423[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]103.568720135908[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]1.62092561595273e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.58459632829669[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]19.8216662893624[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57221&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57221&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 R	0.800637011015123
R-squared	0.64101962340723
Adjusted R-squared	0.634830306569423
F-TEST (value)	103.568720135908
F-TEST (DF numerator)	1
F-TEST (DF denominator)	58
p-value	1.62092561595273e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.58459632829669
Sum Squared Residuals	19.8216662893624

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	103.63	105.095872495065	-1.46587249506480
2	103.64	105.037549277394	-1.39754927739437
3	103.66	104.927383199572	-1.26738319957248
4	103.77	104.862579624383	-1.09257962438313
5	103.88	104.833418015548	-0.953418015547927
6	103.91	104.729732295245	-0.819732295244966
7	103.91	104.645487647499	-0.735487647498813
8	103.92	104.583924251069	-0.663924251068925
9	104.05	104.525601033399	-0.475601033398516
10	104.23	104.464037636969	-0.234037636968627
11	104.3	104.496439424563	-0.196439424563308
12	104.31	104.496439424563	-0.186439424563303
13	104.31	104.532081390917	-0.222081390917445
14	104.34	104.561242999753	-0.221242999752651
15	104.55	104.639007289980	-0.0890072899798771
16	104.65	104.635767111220	0.0142328887795992
17	104.73	104.639007289980	0.0909927100201297
18	104.75	104.723251937726	0.026748062273972
19	104.75	104.749173367802	0.000826632198232341
20	104.76	104.690850150131	0.0691498498686516
21	104.94	104.707051043929	0.232948956071307
22	105.29	104.716771580207	0.573228419792913
23	105.38	104.794535870434	0.585464129565683
24	105.43	104.810736764232	0.619263235768357
25	105.43	104.937103735851	0.492896264149126
26	105.42	105.044029634913	0.375970365086694
27	105.52	105.082911780027	0.437088219973079
28	105.69	105.040789456154	0.649210543846158
29	105.72	105.095872495065	0.624127504935212
30	105.74	105.092632316305	0.647367683694675
31	105.74	105.099112673824	0.64088732617574
32	105.74	105.131514461419	0.608485538581066
33	105.95	105.183357321570	0.766642678429594
34	106.17	105.381008225898	0.788991774102078
35	106.34	105.455532337366	0.884467662634328
36	106.37	105.543017163871	0.826982836128708
37	106.37	105.792510928350	0.577489071649713
38	106.36	105.980441296399	0.379558703600595
39	106.44	106.054965407867	0.385034592132841
40	106.29	105.993402011437	0.296597988562731
41	106.23	105.847593967261	0.382406032738765
42	106.23	105.863794861059	0.366205138941428
43	106.23	105.973960938880	0.256039061119534
44	106.23	105.980441296399	0.249558703600599
45	106.34	106.006362726475	0.333637273524858
46	106.44	106.103568089259	0.336431910740829
47	106.44	106.223454703359	0.216545296640533
48	106.48	106.187812737005	0.292187262994681
49	106.5	106.528031506749	-0.0280315067494068
50	106.57	106.618756512014	-0.0487565120145032
51	106.4	106.735402947355	-0.335402947355319
52	106.37	106.664119014647	-0.294119014647036
53	106.25	106.427585965206	-0.177585965205916
54	106.21	106.346581496219	-0.136581496219235
55	106.21	106.375743105054	-0.165743105054443
56	106.24	106.563673473104	-0.323673473103554
57	106.19	106.638197584571	-0.448197584571303
58	106.08	106.657638657128	-0.577638657128107
59	106.13	106.741883304874	-0.611883304874264
60	106.09	106.949254745480	-0.859254745480174

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 103.63 & 105.095872495065 & -1.46587249506480 \tabularnewline
2 & 103.64 & 105.037549277394 & -1.39754927739437 \tabularnewline
3 & 103.66 & 104.927383199572 & -1.26738319957248 \tabularnewline
4 & 103.77 & 104.862579624383 & -1.09257962438313 \tabularnewline
5 & 103.88 & 104.833418015548 & -0.953418015547927 \tabularnewline
6 & 103.91 & 104.729732295245 & -0.819732295244966 \tabularnewline
7 & 103.91 & 104.645487647499 & -0.735487647498813 \tabularnewline
8 & 103.92 & 104.583924251069 & -0.663924251068925 \tabularnewline
9 & 104.05 & 104.525601033399 & -0.475601033398516 \tabularnewline
10 & 104.23 & 104.464037636969 & -0.234037636968627 \tabularnewline
11 & 104.3 & 104.496439424563 & -0.196439424563308 \tabularnewline
12 & 104.31 & 104.496439424563 & -0.186439424563303 \tabularnewline
13 & 104.31 & 104.532081390917 & -0.222081390917445 \tabularnewline
14 & 104.34 & 104.561242999753 & -0.221242999752651 \tabularnewline
15 & 104.55 & 104.639007289980 & -0.0890072899798771 \tabularnewline
16 & 104.65 & 104.635767111220 & 0.0142328887795992 \tabularnewline
17 & 104.73 & 104.639007289980 & 0.0909927100201297 \tabularnewline
18 & 104.75 & 104.723251937726 & 0.026748062273972 \tabularnewline
19 & 104.75 & 104.749173367802 & 0.000826632198232341 \tabularnewline
20 & 104.76 & 104.690850150131 & 0.0691498498686516 \tabularnewline
21 & 104.94 & 104.707051043929 & 0.232948956071307 \tabularnewline
22 & 105.29 & 104.716771580207 & 0.573228419792913 \tabularnewline
23 & 105.38 & 104.794535870434 & 0.585464129565683 \tabularnewline
24 & 105.43 & 104.810736764232 & 0.619263235768357 \tabularnewline
25 & 105.43 & 104.937103735851 & 0.492896264149126 \tabularnewline
26 & 105.42 & 105.044029634913 & 0.375970365086694 \tabularnewline
27 & 105.52 & 105.082911780027 & 0.437088219973079 \tabularnewline
28 & 105.69 & 105.040789456154 & 0.649210543846158 \tabularnewline
29 & 105.72 & 105.095872495065 & 0.624127504935212 \tabularnewline
30 & 105.74 & 105.092632316305 & 0.647367683694675 \tabularnewline
31 & 105.74 & 105.099112673824 & 0.64088732617574 \tabularnewline
32 & 105.74 & 105.131514461419 & 0.608485538581066 \tabularnewline
33 & 105.95 & 105.183357321570 & 0.766642678429594 \tabularnewline
34 & 106.17 & 105.381008225898 & 0.788991774102078 \tabularnewline
35 & 106.34 & 105.455532337366 & 0.884467662634328 \tabularnewline
36 & 106.37 & 105.543017163871 & 0.826982836128708 \tabularnewline
37 & 106.37 & 105.792510928350 & 0.577489071649713 \tabularnewline
38 & 106.36 & 105.980441296399 & 0.379558703600595 \tabularnewline
39 & 106.44 & 106.054965407867 & 0.385034592132841 \tabularnewline
40 & 106.29 & 105.993402011437 & 0.296597988562731 \tabularnewline
41 & 106.23 & 105.847593967261 & 0.382406032738765 \tabularnewline
42 & 106.23 & 105.863794861059 & 0.366205138941428 \tabularnewline
43 & 106.23 & 105.973960938880 & 0.256039061119534 \tabularnewline
44 & 106.23 & 105.980441296399 & 0.249558703600599 \tabularnewline
45 & 106.34 & 106.006362726475 & 0.333637273524858 \tabularnewline
46 & 106.44 & 106.103568089259 & 0.336431910740829 \tabularnewline
47 & 106.44 & 106.223454703359 & 0.216545296640533 \tabularnewline
48 & 106.48 & 106.187812737005 & 0.292187262994681 \tabularnewline
49 & 106.5 & 106.528031506749 & -0.0280315067494068 \tabularnewline
50 & 106.57 & 106.618756512014 & -0.0487565120145032 \tabularnewline
51 & 106.4 & 106.735402947355 & -0.335402947355319 \tabularnewline
52 & 106.37 & 106.664119014647 & -0.294119014647036 \tabularnewline
53 & 106.25 & 106.427585965206 & -0.177585965205916 \tabularnewline
54 & 106.21 & 106.346581496219 & -0.136581496219235 \tabularnewline
55 & 106.21 & 106.375743105054 & -0.165743105054443 \tabularnewline
56 & 106.24 & 106.563673473104 & -0.323673473103554 \tabularnewline
57 & 106.19 & 106.638197584571 & -0.448197584571303 \tabularnewline
58 & 106.08 & 106.657638657128 & -0.577638657128107 \tabularnewline
59 & 106.13 & 106.741883304874 & -0.611883304874264 \tabularnewline
60 & 106.09 & 106.949254745480 & -0.859254745480174 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57221&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]103.63[/C][C]105.095872495065[/C][C]-1.46587249506480[/C][/ROW]
[ROW][C]2[/C][C]103.64[/C][C]105.037549277394[/C][C]-1.39754927739437[/C][/ROW]
[ROW][C]3[/C][C]103.66[/C][C]104.927383199572[/C][C]-1.26738319957248[/C][/ROW]
[ROW][C]4[/C][C]103.77[/C][C]104.862579624383[/C][C]-1.09257962438313[/C][/ROW]
[ROW][C]5[/C][C]103.88[/C][C]104.833418015548[/C][C]-0.953418015547927[/C][/ROW]
[ROW][C]6[/C][C]103.91[/C][C]104.729732295245[/C][C]-0.819732295244966[/C][/ROW]
[ROW][C]7[/C][C]103.91[/C][C]104.645487647499[/C][C]-0.735487647498813[/C][/ROW]
[ROW][C]8[/C][C]103.92[/C][C]104.583924251069[/C][C]-0.663924251068925[/C][/ROW]
[ROW][C]9[/C][C]104.05[/C][C]104.525601033399[/C][C]-0.475601033398516[/C][/ROW]
[ROW][C]10[/C][C]104.23[/C][C]104.464037636969[/C][C]-0.234037636968627[/C][/ROW]
[ROW][C]11[/C][C]104.3[/C][C]104.496439424563[/C][C]-0.196439424563308[/C][/ROW]
[ROW][C]12[/C][C]104.31[/C][C]104.496439424563[/C][C]-0.186439424563303[/C][/ROW]
[ROW][C]13[/C][C]104.31[/C][C]104.532081390917[/C][C]-0.222081390917445[/C][/ROW]
[ROW][C]14[/C][C]104.34[/C][C]104.561242999753[/C][C]-0.221242999752651[/C][/ROW]
[ROW][C]15[/C][C]104.55[/C][C]104.639007289980[/C][C]-0.0890072899798771[/C][/ROW]
[ROW][C]16[/C][C]104.65[/C][C]104.635767111220[/C][C]0.0142328887795992[/C][/ROW]
[ROW][C]17[/C][C]104.73[/C][C]104.639007289980[/C][C]0.0909927100201297[/C][/ROW]
[ROW][C]18[/C][C]104.75[/C][C]104.723251937726[/C][C]0.026748062273972[/C][/ROW]
[ROW][C]19[/C][C]104.75[/C][C]104.749173367802[/C][C]0.000826632198232341[/C][/ROW]
[ROW][C]20[/C][C]104.76[/C][C]104.690850150131[/C][C]0.0691498498686516[/C][/ROW]
[ROW][C]21[/C][C]104.94[/C][C]104.707051043929[/C][C]0.232948956071307[/C][/ROW]
[ROW][C]22[/C][C]105.29[/C][C]104.716771580207[/C][C]0.573228419792913[/C][/ROW]
[ROW][C]23[/C][C]105.38[/C][C]104.794535870434[/C][C]0.585464129565683[/C][/ROW]
[ROW][C]24[/C][C]105.43[/C][C]104.810736764232[/C][C]0.619263235768357[/C][/ROW]
[ROW][C]25[/C][C]105.43[/C][C]104.937103735851[/C][C]0.492896264149126[/C][/ROW]
[ROW][C]26[/C][C]105.42[/C][C]105.044029634913[/C][C]0.375970365086694[/C][/ROW]
[ROW][C]27[/C][C]105.52[/C][C]105.082911780027[/C][C]0.437088219973079[/C][/ROW]
[ROW][C]28[/C][C]105.69[/C][C]105.040789456154[/C][C]0.649210543846158[/C][/ROW]
[ROW][C]29[/C][C]105.72[/C][C]105.095872495065[/C][C]0.624127504935212[/C][/ROW]
[ROW][C]30[/C][C]105.74[/C][C]105.092632316305[/C][C]0.647367683694675[/C][/ROW]
[ROW][C]31[/C][C]105.74[/C][C]105.099112673824[/C][C]0.64088732617574[/C][/ROW]
[ROW][C]32[/C][C]105.74[/C][C]105.131514461419[/C][C]0.608485538581066[/C][/ROW]
[ROW][C]33[/C][C]105.95[/C][C]105.183357321570[/C][C]0.766642678429594[/C][/ROW]
[ROW][C]34[/C][C]106.17[/C][C]105.381008225898[/C][C]0.788991774102078[/C][/ROW]
[ROW][C]35[/C][C]106.34[/C][C]105.455532337366[/C][C]0.884467662634328[/C][/ROW]
[ROW][C]36[/C][C]106.37[/C][C]105.543017163871[/C][C]0.826982836128708[/C][/ROW]
[ROW][C]37[/C][C]106.37[/C][C]105.792510928350[/C][C]0.577489071649713[/C][/ROW]
[ROW][C]38[/C][C]106.36[/C][C]105.980441296399[/C][C]0.379558703600595[/C][/ROW]
[ROW][C]39[/C][C]106.44[/C][C]106.054965407867[/C][C]0.385034592132841[/C][/ROW]
[ROW][C]40[/C][C]106.29[/C][C]105.993402011437[/C][C]0.296597988562731[/C][/ROW]
[ROW][C]41[/C][C]106.23[/C][C]105.847593967261[/C][C]0.382406032738765[/C][/ROW]
[ROW][C]42[/C][C]106.23[/C][C]105.863794861059[/C][C]0.366205138941428[/C][/ROW]
[ROW][C]43[/C][C]106.23[/C][C]105.973960938880[/C][C]0.256039061119534[/C][/ROW]
[ROW][C]44[/C][C]106.23[/C][C]105.980441296399[/C][C]0.249558703600599[/C][/ROW]
[ROW][C]45[/C][C]106.34[/C][C]106.006362726475[/C][C]0.333637273524858[/C][/ROW]
[ROW][C]46[/C][C]106.44[/C][C]106.103568089259[/C][C]0.336431910740829[/C][/ROW]
[ROW][C]47[/C][C]106.44[/C][C]106.223454703359[/C][C]0.216545296640533[/C][/ROW]
[ROW][C]48[/C][C]106.48[/C][C]106.187812737005[/C][C]0.292187262994681[/C][/ROW]
[ROW][C]49[/C][C]106.5[/C][C]106.528031506749[/C][C]-0.0280315067494068[/C][/ROW]
[ROW][C]50[/C][C]106.57[/C][C]106.618756512014[/C][C]-0.0487565120145032[/C][/ROW]
[ROW][C]51[/C][C]106.4[/C][C]106.735402947355[/C][C]-0.335402947355319[/C][/ROW]
[ROW][C]52[/C][C]106.37[/C][C]106.664119014647[/C][C]-0.294119014647036[/C][/ROW]
[ROW][C]53[/C][C]106.25[/C][C]106.427585965206[/C][C]-0.177585965205916[/C][/ROW]
[ROW][C]54[/C][C]106.21[/C][C]106.346581496219[/C][C]-0.136581496219235[/C][/ROW]
[ROW][C]55[/C][C]106.21[/C][C]106.375743105054[/C][C]-0.165743105054443[/C][/ROW]
[ROW][C]56[/C][C]106.24[/C][C]106.563673473104[/C][C]-0.323673473103554[/C][/ROW]
[ROW][C]57[/C][C]106.19[/C][C]106.638197584571[/C][C]-0.448197584571303[/C][/ROW]
[ROW][C]58[/C][C]106.08[/C][C]106.657638657128[/C][C]-0.577638657128107[/C][/ROW]
[ROW][C]59[/C][C]106.13[/C][C]106.741883304874[/C][C]-0.611883304874264[/C][/ROW]
[ROW][C]60[/C][C]106.09[/C][C]106.949254745480[/C][C]-0.859254745480174[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57221&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57221&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 Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	103.63	105.095872495065	-1.46587249506480
2	103.64	105.037549277394	-1.39754927739437
3	103.66	104.927383199572	-1.26738319957248
4	103.77	104.862579624383	-1.09257962438313
5	103.88	104.833418015548	-0.953418015547927
6	103.91	104.729732295245	-0.819732295244966
7	103.91	104.645487647499	-0.735487647498813
8	103.92	104.583924251069	-0.663924251068925
9	104.05	104.525601033399	-0.475601033398516
10	104.23	104.464037636969	-0.234037636968627
11	104.3	104.496439424563	-0.196439424563308
12	104.31	104.496439424563	-0.186439424563303
13	104.31	104.532081390917	-0.222081390917445
14	104.34	104.561242999753	-0.221242999752651
15	104.55	104.639007289980	-0.0890072899798771
16	104.65	104.635767111220	0.0142328887795992
17	104.73	104.639007289980	0.0909927100201297
18	104.75	104.723251937726	0.026748062273972
19	104.75	104.749173367802	0.000826632198232341
20	104.76	104.690850150131	0.0691498498686516
21	104.94	104.707051043929	0.232948956071307
22	105.29	104.716771580207	0.573228419792913
23	105.38	104.794535870434	0.585464129565683
24	105.43	104.810736764232	0.619263235768357
25	105.43	104.937103735851	0.492896264149126
26	105.42	105.044029634913	0.375970365086694
27	105.52	105.082911780027	0.437088219973079
28	105.69	105.040789456154	0.649210543846158
29	105.72	105.095872495065	0.624127504935212
30	105.74	105.092632316305	0.647367683694675
31	105.74	105.099112673824	0.64088732617574
32	105.74	105.131514461419	0.608485538581066
33	105.95	105.183357321570	0.766642678429594
34	106.17	105.381008225898	0.788991774102078
35	106.34	105.455532337366	0.884467662634328
36	106.37	105.543017163871	0.826982836128708
37	106.37	105.792510928350	0.577489071649713
38	106.36	105.980441296399	0.379558703600595
39	106.44	106.054965407867	0.385034592132841
40	106.29	105.993402011437	0.296597988562731
41	106.23	105.847593967261	0.382406032738765
42	106.23	105.863794861059	0.366205138941428
43	106.23	105.973960938880	0.256039061119534
44	106.23	105.980441296399	0.249558703600599
45	106.34	106.006362726475	0.333637273524858
46	106.44	106.103568089259	0.336431910740829
47	106.44	106.223454703359	0.216545296640533
48	106.48	106.187812737005	0.292187262994681
49	106.5	106.528031506749	-0.0280315067494068
50	106.57	106.618756512014	-0.0487565120145032
51	106.4	106.735402947355	-0.335402947355319
52	106.37	106.664119014647	-0.294119014647036
53	106.25	106.427585965206	-0.177585965205916
54	106.21	106.346581496219	-0.136581496219235
55	106.21	106.375743105054	-0.165743105054443
56	106.24	106.563673473104	-0.323673473103554
57	106.19	106.638197584571	-0.448197584571303
58	106.08	106.657638657128	-0.577638657128107
59	106.13	106.741883304874	-0.611883304874264
60	106.09	106.949254745480	-0.859254745480174

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.00353190085278627	0.00706380170557254	0.996468099147214
6	0.000475962731378316	0.000951925462756633	0.999524037268622
7	9.76489486426595e-05	0.000195297897285319	0.999902351051357
8	2.54017830116207e-05	5.08035660232414e-05	0.999974598216988
9	5.45945213473123e-06	1.09189042694625e-05	0.999994540547865
10	1.30048312140784e-05	2.60096624281569e-05	0.999986995168786
11	4.60562124556479e-05	9.21124249112957e-05	0.999953943787544
12	5.60890404279288e-05	0.000112178080855858	0.999943910959572
13	7.94211420511616e-05	0.000158842284102323	0.99992057885795
14	0.000193200648112691	0.000386401296225381	0.999806799351887
15	0.00879090788830782	0.0175818157766156	0.991209092111692
16	0.0722700636989494	0.144540127397899	0.92772993630105
17	0.249870783920006	0.499741567840012	0.750129216079994
18	0.583718929259909	0.832562141480181	0.416281070740091
19	0.851361926118107	0.297276147763785	0.148638073881893
20	0.961234187388	0.0775316252239987	0.0387658126119994
21	0.995343661158418	0.00931267768316422	0.00465633884158211
22	0.999690000979959	0.000619998040082617	0.000309999020041309
23	0.9999811208615	3.77582770001261e-05	1.88791385000630e-05
24	0.999997977969165	4.0440616693207e-06	2.02203083466035e-06
25	0.999999776791037	4.46417926142944e-07	2.23208963071472e-07
26	0.99999997630481	4.73903783442113e-08	2.36951891721056e-08
27	0.999999995941818	8.11636350331531e-09	4.05818175165766e-09
28	0.999999998185024	3.62995130718905e-09	1.81497565359452e-09
29	0.99999999898168	2.03664028809581e-09	1.01832014404791e-09
30	0.999999999451629	1.09674264922134e-09	5.48371324610669e-10
31	0.999999999832665	3.34670152761284e-10	1.67335076380642e-10
32	0.999999999990368	1.92645708574254e-11	9.6322854287127e-12
33	0.999999999998263	3.47488330103234e-12	1.73744165051617e-12
34	0.999999999996886	6.22712989073223e-12	3.11356494536612e-12
35	0.999999999985738	2.85249216238025e-11	1.42624608119012e-11
36	0.999999999929737	1.4052575431908e-10	7.026287715954e-11
37	0.999999999688492	6.23016730731018e-10	3.11508365365509e-10
38	0.999999998842634	2.31473134142511e-09	1.15736567071256e-09
39	0.999999996815978	6.36804440198637e-09	3.18402220099319e-09
40	0.999999987084253	2.58314938390459e-08	1.29157469195230e-08
41	0.99999995549434	8.90113211704709e-08	4.45056605852354e-08
42	0.999999865481972	2.69036056203165e-07	1.34518028101583e-07
43	0.999999647030413	7.05939174139923e-07	3.52969587069961e-07
44	0.999999286882236	1.42623552745598e-06	7.13117763727992e-07
45	0.999997673319522	4.65336095611723e-06	2.32668047805862e-06
46	0.9999906847504	1.86304991985127e-05	9.31524959925635e-06
47	0.999966955854387	6.6088291226802e-05	3.3044145613401e-05
48	0.999902523125526	0.000194953748947289	9.74768744736445e-05
49	0.999875226148606	0.000249547702788523	0.000124773851394262
50	0.99997630334149	4.73933170194155e-05	2.36966585097078e-05
51	0.999986881937834	2.62361243313835e-05	1.31180621656918e-05
52	0.999998811299283	2.37740143354248e-06	1.18870071677124e-06
53	0.999988964854605	2.20702907890516e-05	1.10351453945258e-05
54	0.999866851765607	0.000266296468786153	0.000133148234393076
55	0.998576033479415	0.00284793304117074	0.00142396652058537

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.00353190085278627 & 0.00706380170557254 & 0.996468099147214 \tabularnewline
6 & 0.000475962731378316 & 0.000951925462756633 & 0.999524037268622 \tabularnewline
7 & 9.76489486426595e-05 & 0.000195297897285319 & 0.999902351051357 \tabularnewline
8 & 2.54017830116207e-05 & 5.08035660232414e-05 & 0.999974598216988 \tabularnewline
9 & 5.45945213473123e-06 & 1.09189042694625e-05 & 0.999994540547865 \tabularnewline
10 & 1.30048312140784e-05 & 2.60096624281569e-05 & 0.999986995168786 \tabularnewline
11 & 4.60562124556479e-05 & 9.21124249112957e-05 & 0.999953943787544 \tabularnewline
12 & 5.60890404279288e-05 & 0.000112178080855858 & 0.999943910959572 \tabularnewline
13 & 7.94211420511616e-05 & 0.000158842284102323 & 0.99992057885795 \tabularnewline
14 & 0.000193200648112691 & 0.000386401296225381 & 0.999806799351887 \tabularnewline
15 & 0.00879090788830782 & 0.0175818157766156 & 0.991209092111692 \tabularnewline
16 & 0.0722700636989494 & 0.144540127397899 & 0.92772993630105 \tabularnewline
17 & 0.249870783920006 & 0.499741567840012 & 0.750129216079994 \tabularnewline
18 & 0.583718929259909 & 0.832562141480181 & 0.416281070740091 \tabularnewline
19 & 0.851361926118107 & 0.297276147763785 & 0.148638073881893 \tabularnewline
20 & 0.961234187388 & 0.0775316252239987 & 0.0387658126119994 \tabularnewline
21 & 0.995343661158418 & 0.00931267768316422 & 0.00465633884158211 \tabularnewline
22 & 0.999690000979959 & 0.000619998040082617 & 0.000309999020041309 \tabularnewline
23 & 0.9999811208615 & 3.77582770001261e-05 & 1.88791385000630e-05 \tabularnewline
24 & 0.999997977969165 & 4.0440616693207e-06 & 2.02203083466035e-06 \tabularnewline
25 & 0.999999776791037 & 4.46417926142944e-07 & 2.23208963071472e-07 \tabularnewline
26 & 0.99999997630481 & 4.73903783442113e-08 & 2.36951891721056e-08 \tabularnewline
27 & 0.999999995941818 & 8.11636350331531e-09 & 4.05818175165766e-09 \tabularnewline
28 & 0.999999998185024 & 3.62995130718905e-09 & 1.81497565359452e-09 \tabularnewline
29 & 0.99999999898168 & 2.03664028809581e-09 & 1.01832014404791e-09 \tabularnewline
30 & 0.999999999451629 & 1.09674264922134e-09 & 5.48371324610669e-10 \tabularnewline
31 & 0.999999999832665 & 3.34670152761284e-10 & 1.67335076380642e-10 \tabularnewline
32 & 0.999999999990368 & 1.92645708574254e-11 & 9.6322854287127e-12 \tabularnewline
33 & 0.999999999998263 & 3.47488330103234e-12 & 1.73744165051617e-12 \tabularnewline
34 & 0.999999999996886 & 6.22712989073223e-12 & 3.11356494536612e-12 \tabularnewline
35 & 0.999999999985738 & 2.85249216238025e-11 & 1.42624608119012e-11 \tabularnewline
36 & 0.999999999929737 & 1.4052575431908e-10 & 7.026287715954e-11 \tabularnewline
37 & 0.999999999688492 & 6.23016730731018e-10 & 3.11508365365509e-10 \tabularnewline
38 & 0.999999998842634 & 2.31473134142511e-09 & 1.15736567071256e-09 \tabularnewline
39 & 0.999999996815978 & 6.36804440198637e-09 & 3.18402220099319e-09 \tabularnewline
40 & 0.999999987084253 & 2.58314938390459e-08 & 1.29157469195230e-08 \tabularnewline
41 & 0.99999995549434 & 8.90113211704709e-08 & 4.45056605852354e-08 \tabularnewline
42 & 0.999999865481972 & 2.69036056203165e-07 & 1.34518028101583e-07 \tabularnewline
43 & 0.999999647030413 & 7.05939174139923e-07 & 3.52969587069961e-07 \tabularnewline
44 & 0.999999286882236 & 1.42623552745598e-06 & 7.13117763727992e-07 \tabularnewline
45 & 0.999997673319522 & 4.65336095611723e-06 & 2.32668047805862e-06 \tabularnewline
46 & 0.9999906847504 & 1.86304991985127e-05 & 9.31524959925635e-06 \tabularnewline
47 & 0.999966955854387 & 6.6088291226802e-05 & 3.3044145613401e-05 \tabularnewline
48 & 0.999902523125526 & 0.000194953748947289 & 9.74768744736445e-05 \tabularnewline
49 & 0.999875226148606 & 0.000249547702788523 & 0.000124773851394262 \tabularnewline
50 & 0.99997630334149 & 4.73933170194155e-05 & 2.36966585097078e-05 \tabularnewline
51 & 0.999986881937834 & 2.62361243313835e-05 & 1.31180621656918e-05 \tabularnewline
52 & 0.999998811299283 & 2.37740143354248e-06 & 1.18870071677124e-06 \tabularnewline
53 & 0.999988964854605 & 2.20702907890516e-05 & 1.10351453945258e-05 \tabularnewline
54 & 0.999866851765607 & 0.000266296468786153 & 0.000133148234393076 \tabularnewline
55 & 0.998576033479415 & 0.00284793304117074 & 0.00142396652058537 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57221&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]5[/C][C]0.00353190085278627[/C][C]0.00706380170557254[/C][C]0.996468099147214[/C][/ROW]
[ROW][C]6[/C][C]0.000475962731378316[/C][C]0.000951925462756633[/C][C]0.999524037268622[/C][/ROW]
[ROW][C]7[/C][C]9.76489486426595e-05[/C][C]0.000195297897285319[/C][C]0.999902351051357[/C][/ROW]
[ROW][C]8[/C][C]2.54017830116207e-05[/C][C]5.08035660232414e-05[/C][C]0.999974598216988[/C][/ROW]
[ROW][C]9[/C][C]5.45945213473123e-06[/C][C]1.09189042694625e-05[/C][C]0.999994540547865[/C][/ROW]
[ROW][C]10[/C][C]1.30048312140784e-05[/C][C]2.60096624281569e-05[/C][C]0.999986995168786[/C][/ROW]
[ROW][C]11[/C][C]4.60562124556479e-05[/C][C]9.21124249112957e-05[/C][C]0.999953943787544[/C][/ROW]
[ROW][C]12[/C][C]5.60890404279288e-05[/C][C]0.000112178080855858[/C][C]0.999943910959572[/C][/ROW]
[ROW][C]13[/C][C]7.94211420511616e-05[/C][C]0.000158842284102323[/C][C]0.99992057885795[/C][/ROW]
[ROW][C]14[/C][C]0.000193200648112691[/C][C]0.000386401296225381[/C][C]0.999806799351887[/C][/ROW]
[ROW][C]15[/C][C]0.00879090788830782[/C][C]0.0175818157766156[/C][C]0.991209092111692[/C][/ROW]
[ROW][C]16[/C][C]0.0722700636989494[/C][C]0.144540127397899[/C][C]0.92772993630105[/C][/ROW]
[ROW][C]17[/C][C]0.249870783920006[/C][C]0.499741567840012[/C][C]0.750129216079994[/C][/ROW]
[ROW][C]18[/C][C]0.583718929259909[/C][C]0.832562141480181[/C][C]0.416281070740091[/C][/ROW]
[ROW][C]19[/C][C]0.851361926118107[/C][C]0.297276147763785[/C][C]0.148638073881893[/C][/ROW]
[ROW][C]20[/C][C]0.961234187388[/C][C]0.0775316252239987[/C][C]0.0387658126119994[/C][/ROW]
[ROW][C]21[/C][C]0.995343661158418[/C][C]0.00931267768316422[/C][C]0.00465633884158211[/C][/ROW]
[ROW][C]22[/C][C]0.999690000979959[/C][C]0.000619998040082617[/C][C]0.000309999020041309[/C][/ROW]
[ROW][C]23[/C][C]0.9999811208615[/C][C]3.77582770001261e-05[/C][C]1.88791385000630e-05[/C][/ROW]
[ROW][C]24[/C][C]0.999997977969165[/C][C]4.0440616693207e-06[/C][C]2.02203083466035e-06[/C][/ROW]
[ROW][C]25[/C][C]0.999999776791037[/C][C]4.46417926142944e-07[/C][C]2.23208963071472e-07[/C][/ROW]
[ROW][C]26[/C][C]0.99999997630481[/C][C]4.73903783442113e-08[/C][C]2.36951891721056e-08[/C][/ROW]
[ROW][C]27[/C][C]0.999999995941818[/C][C]8.11636350331531e-09[/C][C]4.05818175165766e-09[/C][/ROW]
[ROW][C]28[/C][C]0.999999998185024[/C][C]3.62995130718905e-09[/C][C]1.81497565359452e-09[/C][/ROW]
[ROW][C]29[/C][C]0.99999999898168[/C][C]2.03664028809581e-09[/C][C]1.01832014404791e-09[/C][/ROW]
[ROW][C]30[/C][C]0.999999999451629[/C][C]1.09674264922134e-09[/C][C]5.48371324610669e-10[/C][/ROW]
[ROW][C]31[/C][C]0.999999999832665[/C][C]3.34670152761284e-10[/C][C]1.67335076380642e-10[/C][/ROW]
[ROW][C]32[/C][C]0.999999999990368[/C][C]1.92645708574254e-11[/C][C]9.6322854287127e-12[/C][/ROW]
[ROW][C]33[/C][C]0.999999999998263[/C][C]3.47488330103234e-12[/C][C]1.73744165051617e-12[/C][/ROW]
[ROW][C]34[/C][C]0.999999999996886[/C][C]6.22712989073223e-12[/C][C]3.11356494536612e-12[/C][/ROW]
[ROW][C]35[/C][C]0.999999999985738[/C][C]2.85249216238025e-11[/C][C]1.42624608119012e-11[/C][/ROW]
[ROW][C]36[/C][C]0.999999999929737[/C][C]1.4052575431908e-10[/C][C]7.026287715954e-11[/C][/ROW]
[ROW][C]37[/C][C]0.999999999688492[/C][C]6.23016730731018e-10[/C][C]3.11508365365509e-10[/C][/ROW]
[ROW][C]38[/C][C]0.999999998842634[/C][C]2.31473134142511e-09[/C][C]1.15736567071256e-09[/C][/ROW]
[ROW][C]39[/C][C]0.999999996815978[/C][C]6.36804440198637e-09[/C][C]3.18402220099319e-09[/C][/ROW]
[ROW][C]40[/C][C]0.999999987084253[/C][C]2.58314938390459e-08[/C][C]1.29157469195230e-08[/C][/ROW]
[ROW][C]41[/C][C]0.99999995549434[/C][C]8.90113211704709e-08[/C][C]4.45056605852354e-08[/C][/ROW]
[ROW][C]42[/C][C]0.999999865481972[/C][C]2.69036056203165e-07[/C][C]1.34518028101583e-07[/C][/ROW]
[ROW][C]43[/C][C]0.999999647030413[/C][C]7.05939174139923e-07[/C][C]3.52969587069961e-07[/C][/ROW]
[ROW][C]44[/C][C]0.999999286882236[/C][C]1.42623552745598e-06[/C][C]7.13117763727992e-07[/C][/ROW]
[ROW][C]45[/C][C]0.999997673319522[/C][C]4.65336095611723e-06[/C][C]2.32668047805862e-06[/C][/ROW]
[ROW][C]46[/C][C]0.9999906847504[/C][C]1.86304991985127e-05[/C][C]9.31524959925635e-06[/C][/ROW]
[ROW][C]47[/C][C]0.999966955854387[/C][C]6.6088291226802e-05[/C][C]3.3044145613401e-05[/C][/ROW]
[ROW][C]48[/C][C]0.999902523125526[/C][C]0.000194953748947289[/C][C]9.74768744736445e-05[/C][/ROW]
[ROW][C]49[/C][C]0.999875226148606[/C][C]0.000249547702788523[/C][C]0.000124773851394262[/C][/ROW]
[ROW][C]50[/C][C]0.99997630334149[/C][C]4.73933170194155e-05[/C][C]2.36966585097078e-05[/C][/ROW]
[ROW][C]51[/C][C]0.999986881937834[/C][C]2.62361243313835e-05[/C][C]1.31180621656918e-05[/C][/ROW]
[ROW][C]52[/C][C]0.999998811299283[/C][C]2.37740143354248e-06[/C][C]1.18870071677124e-06[/C][/ROW]
[ROW][C]53[/C][C]0.999988964854605[/C][C]2.20702907890516e-05[/C][C]1.10351453945258e-05[/C][/ROW]
[ROW][C]54[/C][C]0.999866851765607[/C][C]0.000266296468786153[/C][C]0.000133148234393076[/C][/ROW]
[ROW][C]55[/C][C]0.998576033479415[/C][C]0.00284793304117074[/C][C]0.00142396652058537[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57221&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57221&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-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.00353190085278627	0.00706380170557254	0.996468099147214
6	0.000475962731378316	0.000951925462756633	0.999524037268622
7	9.76489486426595e-05	0.000195297897285319	0.999902351051357
8	2.54017830116207e-05	5.08035660232414e-05	0.999974598216988
9	5.45945213473123e-06	1.09189042694625e-05	0.999994540547865
10	1.30048312140784e-05	2.60096624281569e-05	0.999986995168786
11	4.60562124556479e-05	9.21124249112957e-05	0.999953943787544
12	5.60890404279288e-05	0.000112178080855858	0.999943910959572
13	7.94211420511616e-05	0.000158842284102323	0.99992057885795
14	0.000193200648112691	0.000386401296225381	0.999806799351887
15	0.00879090788830782	0.0175818157766156	0.991209092111692
16	0.0722700636989494	0.144540127397899	0.92772993630105
17	0.249870783920006	0.499741567840012	0.750129216079994
18	0.583718929259909	0.832562141480181	0.416281070740091
19	0.851361926118107	0.297276147763785	0.148638073881893
20	0.961234187388	0.0775316252239987	0.0387658126119994
21	0.995343661158418	0.00931267768316422	0.00465633884158211
22	0.999690000979959	0.000619998040082617	0.000309999020041309
23	0.9999811208615	3.77582770001261e-05	1.88791385000630e-05
24	0.999997977969165	4.0440616693207e-06	2.02203083466035e-06
25	0.999999776791037	4.46417926142944e-07	2.23208963071472e-07
26	0.99999997630481	4.73903783442113e-08	2.36951891721056e-08
27	0.999999995941818	8.11636350331531e-09	4.05818175165766e-09
28	0.999999998185024	3.62995130718905e-09	1.81497565359452e-09
29	0.99999999898168	2.03664028809581e-09	1.01832014404791e-09
30	0.999999999451629	1.09674264922134e-09	5.48371324610669e-10
31	0.999999999832665	3.34670152761284e-10	1.67335076380642e-10
32	0.999999999990368	1.92645708574254e-11	9.6322854287127e-12
33	0.999999999998263	3.47488330103234e-12	1.73744165051617e-12
34	0.999999999996886	6.22712989073223e-12	3.11356494536612e-12
35	0.999999999985738	2.85249216238025e-11	1.42624608119012e-11
36	0.999999999929737	1.4052575431908e-10	7.026287715954e-11
37	0.999999999688492	6.23016730731018e-10	3.11508365365509e-10
38	0.999999998842634	2.31473134142511e-09	1.15736567071256e-09
39	0.999999996815978	6.36804440198637e-09	3.18402220099319e-09
40	0.999999987084253	2.58314938390459e-08	1.29157469195230e-08
41	0.99999995549434	8.90113211704709e-08	4.45056605852354e-08
42	0.999999865481972	2.69036056203165e-07	1.34518028101583e-07
43	0.999999647030413	7.05939174139923e-07	3.52969587069961e-07
44	0.999999286882236	1.42623552745598e-06	7.13117763727992e-07
45	0.999997673319522	4.65336095611723e-06	2.32668047805862e-06
46	0.9999906847504	1.86304991985127e-05	9.31524959925635e-06
47	0.999966955854387	6.6088291226802e-05	3.3044145613401e-05
48	0.999902523125526	0.000194953748947289	9.74768744736445e-05
49	0.999875226148606	0.000249547702788523	0.000124773851394262
50	0.99997630334149	4.73933170194155e-05	2.36966585097078e-05
51	0.999986881937834	2.62361243313835e-05	1.31180621656918e-05
52	0.999998811299283	2.37740143354248e-06	1.18870071677124e-06
53	0.999988964854605	2.20702907890516e-05	1.10351453945258e-05
54	0.999866851765607	0.000266296468786153	0.000133148234393076
55	0.998576033479415	0.00284793304117074	0.00142396652058537

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	45	0.88235294117647	NOK
5% type I error level	46	0.901960784313726	NOK
10% type I error level	47	0.92156862745098	NOK

\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 & 45 & 0.88235294117647 & NOK \tabularnewline
5% type I error level & 46 & 0.901960784313726 & NOK \tabularnewline
10% type I error level & 47 & 0.92156862745098 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57221&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]45[/C][C]0.88235294117647[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]46[/C][C]0.901960784313726[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]47[/C][C]0.92156862745098[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57221&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57221&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 tests	OK/NOK
1% type I error level	45	0.88235294117647	NOK
5% type I error level	46	0.901960784313726	NOK
10% type I error level	47	0.92156862745098	NOK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code