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Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 18 Nov 2009 11:07:50 -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/18/t1258567734hudaqb0wp67ekp1.htm/, Retrieved Sat, 18 May 2024 22:21:42 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57572, Retrieved Sat, 18 May 2024 22:21:42 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

131

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]
- R PD      [Multiple Regression] [] [2009-11-18 18:07:50] [d5837f25ec8937f9733a894c487f865c] [Current]
-    D        [Multiple Regression] [] [2009-11-18 18:29:56] [c0117c881d5fcd069841276db0c34efe] 

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Dataseries X:

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Histogram

Boxplots

3030.29	101.2
2803.47	101.1
2767.63	100.7
2882.6	100.1
2863.36	99.9
2897.06	99.7
3012.61	99.5
3142.95	99.2
3032.93	99
3045.78	99
3110.52	99.3
3013.24	99.5
2987.1	99.7
2995.55	100
2833.18	100.4
2848.96	100.6
2794.83	100.7
2845.26	100.7
2915.02	100.6
2892.63	100.5
2604.42	100.6
2641.65	100.5
2659.81	100.4
2638.53	100.3
2720.25	100.4
2745.88	100.4
2735.7	100.4
2811.7	100.4
2799.43	100.4
2555.28	100.5
2304.98	100.6
2214.95	100.6
2065.81	100.5
1940.49	100.5
2042.00	100.7
1995.37	101.1
1946.81	101.5
1765.9	101.9
1635.25	102.1
1833.42	102.1
1910.43	102.1
1959.67	102.4
1969.6	102.8
2061.41	103.1
2093.48	103.1
2120.88	102.9
2174.56	102.4
2196.72	101.9
2350.44	101.3
2440.25	100.7
2408.64	100.6
2472.81	101
2407.6	101.5
2454.62	101.9
2448.05	102.1
2497.84	102.3
2645.64	102.5
2756.76	102.9
2849.27	103.6
2921.44	104.3

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57572&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]4 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=57572&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57572&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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
G.indx[t] = + 105.02459764215 -0.00156561284976838Bel20[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
G.indx[t] =  +  105.02459764215 -0.00156561284976838Bel20[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57572&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]G.indx[t] =  +  105.02459764215 -0.00156561284976838Bel20[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57572&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57572&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
G.indx[t] = + 105.02459764215 -0.00156561284976838Bel20[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)	105.02459764215	0.847463	123.9282	0	0
Bel20	-0.00156561284976838	0.000329	-4.7532	1.4e-05	7e-06

\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) & 105.02459764215 & 0.847463 & 123.9282 & 0 & 0 \tabularnewline
Bel20 & -0.00156561284976838 & 0.000329 & -4.7532 & 1.4e-05 & 7e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57572&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]105.02459764215[/C][C]0.847463[/C][C]123.9282[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Bel20[/C][C]-0.00156561284976838[/C][C]0.000329[/C][C]-4.7532[/C][C]1.4e-05[/C][C]7e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57572&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57572&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)	105.02459764215	0.847463	123.9282	0	0
Bel20	-0.00156561284976838	0.000329	-4.7532	1.4e-05	7e-06

Multiple Linear Regression - Regression Statistics
Multiple R	0.52946860542578
R-squared	0.28033700413152
Adjusted R-squared	0.267929021444133
F-TEST (value)	22.5932781495961
F-TEST (DF numerator)	1
F-TEST (DF denominator)	58
p-value	1.36235912630012e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.01658362472473
Sum Squared Residuals	59.9396514313913

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.52946860542578 \tabularnewline
R-squared & 0.28033700413152 \tabularnewline
Adjusted R-squared & 0.267929021444133 \tabularnewline
F-TEST (value) & 22.5932781495961 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 1.36235912630012e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.01658362472473 \tabularnewline
Sum Squared Residuals & 59.9396514313913 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57572&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.52946860542578[/C][/ROW]
[ROW][C]R-squared[/C][C]0.28033700413152[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.267929021444133[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]22.5932781495961[/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]1.36235912630012e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.01658362472473[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]59.9396514313913[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57572&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57572&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	0.52946860542578
R-squared	0.28033700413152
Adjusted R-squared	0.267929021444133
F-TEST (value)	22.5932781495961
F-TEST (DF numerator)	1
F-TEST (DF denominator)	58
p-value	1.36235912630012e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.01658362472473
Sum Squared Residuals	59.9396514313913

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.2	100.280336679626	0.919663320374281
2	101.1	100.635448986210	0.464551013790185
3	100.7	100.691560550746	0.00843944925449507
4	100.1	100.511562041408	-0.411562041407643
5	99.9	100.541684432637	-0.641684432637175
6	99.7	100.4889232796	-0.788923279599984
7	99.5	100.308016714809	-0.80801671480925
8	99.2	100.103954735970	-0.903954735970438
9	99	100.276203461702	-1.27620346170196
10	99	100.256085336582	-1.25608533658243
11	99.3	100.154727560688	-0.854727560688432
12	99.5	100.307030378714	-0.807030378713897
13	99.7	100.347955498607	-0.64795549860684
14	100	100.334726070026	-0.334726070026299
15	100.4	100.588934628443	-0.188934628443185
16	100.6	100.564229257674	0.0357707423261485
17	100.7	100.648975881232	0.0510241187681947
18	100.7	100.570022025218	0.129977974782014
19	100.6	100.460804872818	0.139195127181847
20	100.5	100.495858944524	0.00414105547553943
21	100.6	100.947084223956	-0.347084223956210
22	100.5	100.888796457559	-0.388796457559328
23	100.4	100.860364928208	-0.460364928207529
24	100.3	100.893681169651	-0.593681169650608
25	100.4	100.765739287568	-0.365739287567528
26	100.4	100.725612630228	-0.325612630227964
27	100.4	100.741550569039	-0.341550569038607
28	100.4	100.622563992456	-0.222563992456210
29	100.4	100.641774062123	-0.241774062122868
30	100.5	101.024018439394	-0.524018439393823
31	100.6	101.415891335691	-0.815891335690853
32	100.6	101.556843460555	-0.9568434605555
33	100.5	101.79033896097	-1.29033896096995
34	100.5	101.986541563303	-1.48654156330292
35	100.7	101.827616202923	-1.12761620292293
36	101.1	101.900620730108	-0.800620730107641
37	101.5	101.976646890092	-0.476646890092388
38	101.9	102.259881910744	-0.359881910743979
39	102.1	102.464429229566	-0.364429229566229
40	102.1	102.154171731128	-0.0541717311276294
41	102.1	102.033603885567	0.0663961144330334
42	102.4	101.956513108844	0.44348689115564
43	102.8	101.940966573246	0.859033426753831
44	103.1	101.797227657509	1.30277234249106
45	103.1	101.747018453417	1.35298154658313
46	102.9	101.704120661333	1.1958793386668
47	102.4	101.620078563558	0.779921436442366
48	101.9	101.585384582807	0.314615417193233
49	101.3	101.344718575540	-0.0447185755403801
50	100.7	101.204110885503	-0.504110885502676
51	100.6	101.253599907684	-0.653599907683864
52	101	101.153134531114	-0.153134531114221
53	101.5	101.255228145048	0.244771854952383
54	101.9	101.181613028852	0.718386971148498
55	102.1	101.191899105274	0.908100894725509
56	102.3	101.113947241485	1.18605275851548
57	102.5	100.882549662289	1.61745033771125
58	102.9	100.708578762422	2.19142123757752
59	103.6	100.563743917690	3.03625608230958
60	104.3	100.450753638323	3.84924636167736

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.2 & 100.280336679626 & 0.919663320374281 \tabularnewline
2 & 101.1 & 100.635448986210 & 0.464551013790185 \tabularnewline
3 & 100.7 & 100.691560550746 & 0.00843944925449507 \tabularnewline
4 & 100.1 & 100.511562041408 & -0.411562041407643 \tabularnewline
5 & 99.9 & 100.541684432637 & -0.641684432637175 \tabularnewline
6 & 99.7 & 100.4889232796 & -0.788923279599984 \tabularnewline
7 & 99.5 & 100.308016714809 & -0.80801671480925 \tabularnewline
8 & 99.2 & 100.103954735970 & -0.903954735970438 \tabularnewline
9 & 99 & 100.276203461702 & -1.27620346170196 \tabularnewline
10 & 99 & 100.256085336582 & -1.25608533658243 \tabularnewline
11 & 99.3 & 100.154727560688 & -0.854727560688432 \tabularnewline
12 & 99.5 & 100.307030378714 & -0.807030378713897 \tabularnewline
13 & 99.7 & 100.347955498607 & -0.64795549860684 \tabularnewline
14 & 100 & 100.334726070026 & -0.334726070026299 \tabularnewline
15 & 100.4 & 100.588934628443 & -0.188934628443185 \tabularnewline
16 & 100.6 & 100.564229257674 & 0.0357707423261485 \tabularnewline
17 & 100.7 & 100.648975881232 & 0.0510241187681947 \tabularnewline
18 & 100.7 & 100.570022025218 & 0.129977974782014 \tabularnewline
19 & 100.6 & 100.460804872818 & 0.139195127181847 \tabularnewline
20 & 100.5 & 100.495858944524 & 0.00414105547553943 \tabularnewline
21 & 100.6 & 100.947084223956 & -0.347084223956210 \tabularnewline
22 & 100.5 & 100.888796457559 & -0.388796457559328 \tabularnewline
23 & 100.4 & 100.860364928208 & -0.460364928207529 \tabularnewline
24 & 100.3 & 100.893681169651 & -0.593681169650608 \tabularnewline
25 & 100.4 & 100.765739287568 & -0.365739287567528 \tabularnewline
26 & 100.4 & 100.725612630228 & -0.325612630227964 \tabularnewline
27 & 100.4 & 100.741550569039 & -0.341550569038607 \tabularnewline
28 & 100.4 & 100.622563992456 & -0.222563992456210 \tabularnewline
29 & 100.4 & 100.641774062123 & -0.241774062122868 \tabularnewline
30 & 100.5 & 101.024018439394 & -0.524018439393823 \tabularnewline
31 & 100.6 & 101.415891335691 & -0.815891335690853 \tabularnewline
32 & 100.6 & 101.556843460555 & -0.9568434605555 \tabularnewline
33 & 100.5 & 101.79033896097 & -1.29033896096995 \tabularnewline
34 & 100.5 & 101.986541563303 & -1.48654156330292 \tabularnewline
35 & 100.7 & 101.827616202923 & -1.12761620292293 \tabularnewline
36 & 101.1 & 101.900620730108 & -0.800620730107641 \tabularnewline
37 & 101.5 & 101.976646890092 & -0.476646890092388 \tabularnewline
38 & 101.9 & 102.259881910744 & -0.359881910743979 \tabularnewline
39 & 102.1 & 102.464429229566 & -0.364429229566229 \tabularnewline
40 & 102.1 & 102.154171731128 & -0.0541717311276294 \tabularnewline
41 & 102.1 & 102.033603885567 & 0.0663961144330334 \tabularnewline
42 & 102.4 & 101.956513108844 & 0.44348689115564 \tabularnewline
43 & 102.8 & 101.940966573246 & 0.859033426753831 \tabularnewline
44 & 103.1 & 101.797227657509 & 1.30277234249106 \tabularnewline
45 & 103.1 & 101.747018453417 & 1.35298154658313 \tabularnewline
46 & 102.9 & 101.704120661333 & 1.1958793386668 \tabularnewline
47 & 102.4 & 101.620078563558 & 0.779921436442366 \tabularnewline
48 & 101.9 & 101.585384582807 & 0.314615417193233 \tabularnewline
49 & 101.3 & 101.344718575540 & -0.0447185755403801 \tabularnewline
50 & 100.7 & 101.204110885503 & -0.504110885502676 \tabularnewline
51 & 100.6 & 101.253599907684 & -0.653599907683864 \tabularnewline
52 & 101 & 101.153134531114 & -0.153134531114221 \tabularnewline
53 & 101.5 & 101.255228145048 & 0.244771854952383 \tabularnewline
54 & 101.9 & 101.181613028852 & 0.718386971148498 \tabularnewline
55 & 102.1 & 101.191899105274 & 0.908100894725509 \tabularnewline
56 & 102.3 & 101.113947241485 & 1.18605275851548 \tabularnewline
57 & 102.5 & 100.882549662289 & 1.61745033771125 \tabularnewline
58 & 102.9 & 100.708578762422 & 2.19142123757752 \tabularnewline
59 & 103.6 & 100.563743917690 & 3.03625608230958 \tabularnewline
60 & 104.3 & 100.450753638323 & 3.84924636167736 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57572&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]101.2[/C][C]100.280336679626[/C][C]0.919663320374281[/C][/ROW]
[ROW][C]2[/C][C]101.1[/C][C]100.635448986210[/C][C]0.464551013790185[/C][/ROW]
[ROW][C]3[/C][C]100.7[/C][C]100.691560550746[/C][C]0.00843944925449507[/C][/ROW]
[ROW][C]4[/C][C]100.1[/C][C]100.511562041408[/C][C]-0.411562041407643[/C][/ROW]
[ROW][C]5[/C][C]99.9[/C][C]100.541684432637[/C][C]-0.641684432637175[/C][/ROW]
[ROW][C]6[/C][C]99.7[/C][C]100.4889232796[/C][C]-0.788923279599984[/C][/ROW]
[ROW][C]7[/C][C]99.5[/C][C]100.308016714809[/C][C]-0.80801671480925[/C][/ROW]
[ROW][C]8[/C][C]99.2[/C][C]100.103954735970[/C][C]-0.903954735970438[/C][/ROW]
[ROW][C]9[/C][C]99[/C][C]100.276203461702[/C][C]-1.27620346170196[/C][/ROW]
[ROW][C]10[/C][C]99[/C][C]100.256085336582[/C][C]-1.25608533658243[/C][/ROW]
[ROW][C]11[/C][C]99.3[/C][C]100.154727560688[/C][C]-0.854727560688432[/C][/ROW]
[ROW][C]12[/C][C]99.5[/C][C]100.307030378714[/C][C]-0.807030378713897[/C][/ROW]
[ROW][C]13[/C][C]99.7[/C][C]100.347955498607[/C][C]-0.64795549860684[/C][/ROW]
[ROW][C]14[/C][C]100[/C][C]100.334726070026[/C][C]-0.334726070026299[/C][/ROW]
[ROW][C]15[/C][C]100.4[/C][C]100.588934628443[/C][C]-0.188934628443185[/C][/ROW]
[ROW][C]16[/C][C]100.6[/C][C]100.564229257674[/C][C]0.0357707423261485[/C][/ROW]
[ROW][C]17[/C][C]100.7[/C][C]100.648975881232[/C][C]0.0510241187681947[/C][/ROW]
[ROW][C]18[/C][C]100.7[/C][C]100.570022025218[/C][C]0.129977974782014[/C][/ROW]
[ROW][C]19[/C][C]100.6[/C][C]100.460804872818[/C][C]0.139195127181847[/C][/ROW]
[ROW][C]20[/C][C]100.5[/C][C]100.495858944524[/C][C]0.00414105547553943[/C][/ROW]
[ROW][C]21[/C][C]100.6[/C][C]100.947084223956[/C][C]-0.347084223956210[/C][/ROW]
[ROW][C]22[/C][C]100.5[/C][C]100.888796457559[/C][C]-0.388796457559328[/C][/ROW]
[ROW][C]23[/C][C]100.4[/C][C]100.860364928208[/C][C]-0.460364928207529[/C][/ROW]
[ROW][C]24[/C][C]100.3[/C][C]100.893681169651[/C][C]-0.593681169650608[/C][/ROW]
[ROW][C]25[/C][C]100.4[/C][C]100.765739287568[/C][C]-0.365739287567528[/C][/ROW]
[ROW][C]26[/C][C]100.4[/C][C]100.725612630228[/C][C]-0.325612630227964[/C][/ROW]
[ROW][C]27[/C][C]100.4[/C][C]100.741550569039[/C][C]-0.341550569038607[/C][/ROW]
[ROW][C]28[/C][C]100.4[/C][C]100.622563992456[/C][C]-0.222563992456210[/C][/ROW]
[ROW][C]29[/C][C]100.4[/C][C]100.641774062123[/C][C]-0.241774062122868[/C][/ROW]
[ROW][C]30[/C][C]100.5[/C][C]101.024018439394[/C][C]-0.524018439393823[/C][/ROW]
[ROW][C]31[/C][C]100.6[/C][C]101.415891335691[/C][C]-0.815891335690853[/C][/ROW]
[ROW][C]32[/C][C]100.6[/C][C]101.556843460555[/C][C]-0.9568434605555[/C][/ROW]
[ROW][C]33[/C][C]100.5[/C][C]101.79033896097[/C][C]-1.29033896096995[/C][/ROW]
[ROW][C]34[/C][C]100.5[/C][C]101.986541563303[/C][C]-1.48654156330292[/C][/ROW]
[ROW][C]35[/C][C]100.7[/C][C]101.827616202923[/C][C]-1.12761620292293[/C][/ROW]
[ROW][C]36[/C][C]101.1[/C][C]101.900620730108[/C][C]-0.800620730107641[/C][/ROW]
[ROW][C]37[/C][C]101.5[/C][C]101.976646890092[/C][C]-0.476646890092388[/C][/ROW]
[ROW][C]38[/C][C]101.9[/C][C]102.259881910744[/C][C]-0.359881910743979[/C][/ROW]
[ROW][C]39[/C][C]102.1[/C][C]102.464429229566[/C][C]-0.364429229566229[/C][/ROW]
[ROW][C]40[/C][C]102.1[/C][C]102.154171731128[/C][C]-0.0541717311276294[/C][/ROW]
[ROW][C]41[/C][C]102.1[/C][C]102.033603885567[/C][C]0.0663961144330334[/C][/ROW]
[ROW][C]42[/C][C]102.4[/C][C]101.956513108844[/C][C]0.44348689115564[/C][/ROW]
[ROW][C]43[/C][C]102.8[/C][C]101.940966573246[/C][C]0.859033426753831[/C][/ROW]
[ROW][C]44[/C][C]103.1[/C][C]101.797227657509[/C][C]1.30277234249106[/C][/ROW]
[ROW][C]45[/C][C]103.1[/C][C]101.747018453417[/C][C]1.35298154658313[/C][/ROW]
[ROW][C]46[/C][C]102.9[/C][C]101.704120661333[/C][C]1.1958793386668[/C][/ROW]
[ROW][C]47[/C][C]102.4[/C][C]101.620078563558[/C][C]0.779921436442366[/C][/ROW]
[ROW][C]48[/C][C]101.9[/C][C]101.585384582807[/C][C]0.314615417193233[/C][/ROW]
[ROW][C]49[/C][C]101.3[/C][C]101.344718575540[/C][C]-0.0447185755403801[/C][/ROW]
[ROW][C]50[/C][C]100.7[/C][C]101.204110885503[/C][C]-0.504110885502676[/C][/ROW]
[ROW][C]51[/C][C]100.6[/C][C]101.253599907684[/C][C]-0.653599907683864[/C][/ROW]
[ROW][C]52[/C][C]101[/C][C]101.153134531114[/C][C]-0.153134531114221[/C][/ROW]
[ROW][C]53[/C][C]101.5[/C][C]101.255228145048[/C][C]0.244771854952383[/C][/ROW]
[ROW][C]54[/C][C]101.9[/C][C]101.181613028852[/C][C]0.718386971148498[/C][/ROW]
[ROW][C]55[/C][C]102.1[/C][C]101.191899105274[/C][C]0.908100894725509[/C][/ROW]
[ROW][C]56[/C][C]102.3[/C][C]101.113947241485[/C][C]1.18605275851548[/C][/ROW]
[ROW][C]57[/C][C]102.5[/C][C]100.882549662289[/C][C]1.61745033771125[/C][/ROW]
[ROW][C]58[/C][C]102.9[/C][C]100.708578762422[/C][C]2.19142123757752[/C][/ROW]
[ROW][C]59[/C][C]103.6[/C][C]100.563743917690[/C][C]3.03625608230958[/C][/ROW]
[ROW][C]60[/C][C]104.3[/C][C]100.450753638323[/C][C]3.84924636167736[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57572&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57572&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	101.2	100.280336679626	0.919663320374281
2	101.1	100.635448986210	0.464551013790185
3	100.7	100.691560550746	0.00843944925449507
4	100.1	100.511562041408	-0.411562041407643
5	99.9	100.541684432637	-0.641684432637175
6	99.7	100.4889232796	-0.788923279599984
7	99.5	100.308016714809	-0.80801671480925
8	99.2	100.103954735970	-0.903954735970438
9	99	100.276203461702	-1.27620346170196
10	99	100.256085336582	-1.25608533658243
11	99.3	100.154727560688	-0.854727560688432
12	99.5	100.307030378714	-0.807030378713897
13	99.7	100.347955498607	-0.64795549860684
14	100	100.334726070026	-0.334726070026299
15	100.4	100.588934628443	-0.188934628443185
16	100.6	100.564229257674	0.0357707423261485
17	100.7	100.648975881232	0.0510241187681947
18	100.7	100.570022025218	0.129977974782014
19	100.6	100.460804872818	0.139195127181847
20	100.5	100.495858944524	0.00414105547553943
21	100.6	100.947084223956	-0.347084223956210
22	100.5	100.888796457559	-0.388796457559328
23	100.4	100.860364928208	-0.460364928207529
24	100.3	100.893681169651	-0.593681169650608
25	100.4	100.765739287568	-0.365739287567528
26	100.4	100.725612630228	-0.325612630227964
27	100.4	100.741550569039	-0.341550569038607
28	100.4	100.622563992456	-0.222563992456210
29	100.4	100.641774062123	-0.241774062122868
30	100.5	101.024018439394	-0.524018439393823
31	100.6	101.415891335691	-0.815891335690853
32	100.6	101.556843460555	-0.9568434605555
33	100.5	101.79033896097	-1.29033896096995
34	100.5	101.986541563303	-1.48654156330292
35	100.7	101.827616202923	-1.12761620292293
36	101.1	101.900620730108	-0.800620730107641
37	101.5	101.976646890092	-0.476646890092388
38	101.9	102.259881910744	-0.359881910743979
39	102.1	102.464429229566	-0.364429229566229
40	102.1	102.154171731128	-0.0541717311276294
41	102.1	102.033603885567	0.0663961144330334
42	102.4	101.956513108844	0.44348689115564
43	102.8	101.940966573246	0.859033426753831
44	103.1	101.797227657509	1.30277234249106
45	103.1	101.747018453417	1.35298154658313
46	102.9	101.704120661333	1.1958793386668
47	102.4	101.620078563558	0.779921436442366
48	101.9	101.585384582807	0.314615417193233
49	101.3	101.344718575540	-0.0447185755403801
50	100.7	101.204110885503	-0.504110885502676
51	100.6	101.253599907684	-0.653599907683864
52	101	101.153134531114	-0.153134531114221
53	101.5	101.255228145048	0.244771854952383
54	101.9	101.181613028852	0.718386971148498
55	102.1	101.191899105274	0.908100894725509
56	102.3	101.113947241485	1.18605275851548
57	102.5	100.882549662289	1.61745033771125
58	102.9	100.708578762422	2.19142123757752
59	103.6	100.563743917690	3.03625608230958
60	104.3	100.450753638323	3.84924636167736

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.241371938313620	0.482743876627239	0.75862806168638
6	0.230782135270807	0.461564270541614	0.769217864729193
7	0.211859225093072	0.423718450186143	0.788140774906928
8	0.151111097477294	0.302222194954588	0.848888902522706
9	0.142734548599929	0.285469097199858	0.857265451400071
10	0.118501650320201	0.237003300640403	0.881498349679799
11	0.0752196796923327	0.150439359384665	0.924780320307667
12	0.0477231306173293	0.0954462612346585	0.95227686938267
13	0.0286674066341794	0.0573348132683588	0.97133259336582
14	0.0172999826618428	0.0345999653236855	0.982700017338157
15	0.00919060643075037	0.0183812128615007	0.99080939356925
16	0.00496068992844707	0.00992137985689413	0.995039310071553
17	0.0024307928184867	0.0048615856369734	0.997569207181513
18	0.00127655751368873	0.00255311502737746	0.998723442486311
19	0.00081127043081526	0.00162254086163052	0.999188729569185
20	0.000419628049802666	0.000839256099605332	0.999580371950197
21	0.00048549053006713	0.00097098106013426	0.999514509469933
22	0.000363009663422359	0.000726019326844719	0.999636990336578
23	0.000264264092686305	0.000528528185372609	0.999735735907314
24	0.000232238422872829	0.000464476845745657	0.999767761577127
25	0.000143209128701927	0.000286418257403854	0.999856790871298
26	9.13773895548201e-05	0.000182754779109640	0.999908622610445
27	6.44892410824026e-05	0.000128978482164805	0.999935510758918
28	5.3757833202351e-05	0.000107515666404702	0.999946242166798
29	5.82856594215919e-05	0.000116571318843184	0.999941714340578
30	8.40921699437727e-05	0.000168184339887545	0.999915907830056
31	0.00016874474640859	0.00033748949281718	0.999831255253591
32	0.000281920505697742	0.000563841011395483	0.999718079494302
33	0.00056039776539084	0.00112079553078168	0.99943960223461
34	0.000957142174098245	0.00191428434819649	0.999042857825902
35	0.00112153532362934	0.00224307064725869	0.99887846467637
36	0.000916082525558293	0.00183216505111659	0.999083917474442
37	0.000688791484274926	0.00137758296854985	0.999311208515725
38	0.000491906852837908	0.000983813705675817	0.999508093147162
39	0.000323713292406975	0.00064742658481395	0.999676286707593
40	0.000247764043012029	0.000495528086024057	0.999752235956988
41	0.000190679505071837	0.000381359010143675	0.999809320494928
42	0.000248257165568781	0.000496514331137562	0.999751742834431
43	0.000799200496923161	0.00159840099384632	0.999200799503077
44	0.00615913748616246	0.0123182749723249	0.993840862513838
45	0.0414775033243909	0.0829550066487818	0.95852249667561
46	0.206638776971325	0.413277553942651	0.793361223028675
47	0.500016355460788	0.999967289078424	0.499983644539212
48	0.840288248641335	0.31942350271733	0.159711751358665
49	0.796719205637204	0.406561588725591	0.203280794362796
50	0.831179505644961	0.337640988710079	0.168820494355039
51	0.899466823446679	0.201066353106642	0.100533176553321
52	0.984680284111943	0.0306394317761135	0.0153197158880568
53	0.968826411484034	0.0623471770319311	0.0311735885159656
54	0.926958061517207	0.146083876965586	0.073041938482793
55	0.86410377180552	0.271792456388961	0.135896228194481

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.241371938313620 & 0.482743876627239 & 0.75862806168638 \tabularnewline
6 & 0.230782135270807 & 0.461564270541614 & 0.769217864729193 \tabularnewline
7 & 0.211859225093072 & 0.423718450186143 & 0.788140774906928 \tabularnewline
8 & 0.151111097477294 & 0.302222194954588 & 0.848888902522706 \tabularnewline
9 & 0.142734548599929 & 0.285469097199858 & 0.857265451400071 \tabularnewline
10 & 0.118501650320201 & 0.237003300640403 & 0.881498349679799 \tabularnewline
11 & 0.0752196796923327 & 0.150439359384665 & 0.924780320307667 \tabularnewline
12 & 0.0477231306173293 & 0.0954462612346585 & 0.95227686938267 \tabularnewline
13 & 0.0286674066341794 & 0.0573348132683588 & 0.97133259336582 \tabularnewline
14 & 0.0172999826618428 & 0.0345999653236855 & 0.982700017338157 \tabularnewline
15 & 0.00919060643075037 & 0.0183812128615007 & 0.99080939356925 \tabularnewline
16 & 0.00496068992844707 & 0.00992137985689413 & 0.995039310071553 \tabularnewline
17 & 0.0024307928184867 & 0.0048615856369734 & 0.997569207181513 \tabularnewline
18 & 0.00127655751368873 & 0.00255311502737746 & 0.998723442486311 \tabularnewline
19 & 0.00081127043081526 & 0.00162254086163052 & 0.999188729569185 \tabularnewline
20 & 0.000419628049802666 & 0.000839256099605332 & 0.999580371950197 \tabularnewline
21 & 0.00048549053006713 & 0.00097098106013426 & 0.999514509469933 \tabularnewline
22 & 0.000363009663422359 & 0.000726019326844719 & 0.999636990336578 \tabularnewline
23 & 0.000264264092686305 & 0.000528528185372609 & 0.999735735907314 \tabularnewline
24 & 0.000232238422872829 & 0.000464476845745657 & 0.999767761577127 \tabularnewline
25 & 0.000143209128701927 & 0.000286418257403854 & 0.999856790871298 \tabularnewline
26 & 9.13773895548201e-05 & 0.000182754779109640 & 0.999908622610445 \tabularnewline
27 & 6.44892410824026e-05 & 0.000128978482164805 & 0.999935510758918 \tabularnewline
28 & 5.3757833202351e-05 & 0.000107515666404702 & 0.999946242166798 \tabularnewline
29 & 5.82856594215919e-05 & 0.000116571318843184 & 0.999941714340578 \tabularnewline
30 & 8.40921699437727e-05 & 0.000168184339887545 & 0.999915907830056 \tabularnewline
31 & 0.00016874474640859 & 0.00033748949281718 & 0.999831255253591 \tabularnewline
32 & 0.000281920505697742 & 0.000563841011395483 & 0.999718079494302 \tabularnewline
33 & 0.00056039776539084 & 0.00112079553078168 & 0.99943960223461 \tabularnewline
34 & 0.000957142174098245 & 0.00191428434819649 & 0.999042857825902 \tabularnewline
35 & 0.00112153532362934 & 0.00224307064725869 & 0.99887846467637 \tabularnewline
36 & 0.000916082525558293 & 0.00183216505111659 & 0.999083917474442 \tabularnewline
37 & 0.000688791484274926 & 0.00137758296854985 & 0.999311208515725 \tabularnewline
38 & 0.000491906852837908 & 0.000983813705675817 & 0.999508093147162 \tabularnewline
39 & 0.000323713292406975 & 0.00064742658481395 & 0.999676286707593 \tabularnewline
40 & 0.000247764043012029 & 0.000495528086024057 & 0.999752235956988 \tabularnewline
41 & 0.000190679505071837 & 0.000381359010143675 & 0.999809320494928 \tabularnewline
42 & 0.000248257165568781 & 0.000496514331137562 & 0.999751742834431 \tabularnewline
43 & 0.000799200496923161 & 0.00159840099384632 & 0.999200799503077 \tabularnewline
44 & 0.00615913748616246 & 0.0123182749723249 & 0.993840862513838 \tabularnewline
45 & 0.0414775033243909 & 0.0829550066487818 & 0.95852249667561 \tabularnewline
46 & 0.206638776971325 & 0.413277553942651 & 0.793361223028675 \tabularnewline
47 & 0.500016355460788 & 0.999967289078424 & 0.499983644539212 \tabularnewline
48 & 0.840288248641335 & 0.31942350271733 & 0.159711751358665 \tabularnewline
49 & 0.796719205637204 & 0.406561588725591 & 0.203280794362796 \tabularnewline
50 & 0.831179505644961 & 0.337640988710079 & 0.168820494355039 \tabularnewline
51 & 0.899466823446679 & 0.201066353106642 & 0.100533176553321 \tabularnewline
52 & 0.984680284111943 & 0.0306394317761135 & 0.0153197158880568 \tabularnewline
53 & 0.968826411484034 & 0.0623471770319311 & 0.0311735885159656 \tabularnewline
54 & 0.926958061517207 & 0.146083876965586 & 0.073041938482793 \tabularnewline
55 & 0.86410377180552 & 0.271792456388961 & 0.135896228194481 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57572&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]0.241371938313620[/C][C]0.482743876627239[/C][C]0.75862806168638[/C][/ROW]
[ROW][C]6[/C][C]0.230782135270807[/C][C]0.461564270541614[/C][C]0.769217864729193[/C][/ROW]
[ROW][C]7[/C][C]0.211859225093072[/C][C]0.423718450186143[/C][C]0.788140774906928[/C][/ROW]
[ROW][C]8[/C][C]0.151111097477294[/C][C]0.302222194954588[/C][C]0.848888902522706[/C][/ROW]
[ROW][C]9[/C][C]0.142734548599929[/C][C]0.285469097199858[/C][C]0.857265451400071[/C][/ROW]
[ROW][C]10[/C][C]0.118501650320201[/C][C]0.237003300640403[/C][C]0.881498349679799[/C][/ROW]
[ROW][C]11[/C][C]0.0752196796923327[/C][C]0.150439359384665[/C][C]0.924780320307667[/C][/ROW]
[ROW][C]12[/C][C]0.0477231306173293[/C][C]0.0954462612346585[/C][C]0.95227686938267[/C][/ROW]
[ROW][C]13[/C][C]0.0286674066341794[/C][C]0.0573348132683588[/C][C]0.97133259336582[/C][/ROW]
[ROW][C]14[/C][C]0.0172999826618428[/C][C]0.0345999653236855[/C][C]0.982700017338157[/C][/ROW]
[ROW][C]15[/C][C]0.00919060643075037[/C][C]0.0183812128615007[/C][C]0.99080939356925[/C][/ROW]
[ROW][C]16[/C][C]0.00496068992844707[/C][C]0.00992137985689413[/C][C]0.995039310071553[/C][/ROW]
[ROW][C]17[/C][C]0.0024307928184867[/C][C]0.0048615856369734[/C][C]0.997569207181513[/C][/ROW]
[ROW][C]18[/C][C]0.00127655751368873[/C][C]0.00255311502737746[/C][C]0.998723442486311[/C][/ROW]
[ROW][C]19[/C][C]0.00081127043081526[/C][C]0.00162254086163052[/C][C]0.999188729569185[/C][/ROW]
[ROW][C]20[/C][C]0.000419628049802666[/C][C]0.000839256099605332[/C][C]0.999580371950197[/C][/ROW]
[ROW][C]21[/C][C]0.00048549053006713[/C][C]0.00097098106013426[/C][C]0.999514509469933[/C][/ROW]
[ROW][C]22[/C][C]0.000363009663422359[/C][C]0.000726019326844719[/C][C]0.999636990336578[/C][/ROW]
[ROW][C]23[/C][C]0.000264264092686305[/C][C]0.000528528185372609[/C][C]0.999735735907314[/C][/ROW]
[ROW][C]24[/C][C]0.000232238422872829[/C][C]0.000464476845745657[/C][C]0.999767761577127[/C][/ROW]
[ROW][C]25[/C][C]0.000143209128701927[/C][C]0.000286418257403854[/C][C]0.999856790871298[/C][/ROW]
[ROW][C]26[/C][C]9.13773895548201e-05[/C][C]0.000182754779109640[/C][C]0.999908622610445[/C][/ROW]
[ROW][C]27[/C][C]6.44892410824026e-05[/C][C]0.000128978482164805[/C][C]0.999935510758918[/C][/ROW]
[ROW][C]28[/C][C]5.3757833202351e-05[/C][C]0.000107515666404702[/C][C]0.999946242166798[/C][/ROW]
[ROW][C]29[/C][C]5.82856594215919e-05[/C][C]0.000116571318843184[/C][C]0.999941714340578[/C][/ROW]
[ROW][C]30[/C][C]8.40921699437727e-05[/C][C]0.000168184339887545[/C][C]0.999915907830056[/C][/ROW]
[ROW][C]31[/C][C]0.00016874474640859[/C][C]0.00033748949281718[/C][C]0.999831255253591[/C][/ROW]
[ROW][C]32[/C][C]0.000281920505697742[/C][C]0.000563841011395483[/C][C]0.999718079494302[/C][/ROW]
[ROW][C]33[/C][C]0.00056039776539084[/C][C]0.00112079553078168[/C][C]0.99943960223461[/C][/ROW]
[ROW][C]34[/C][C]0.000957142174098245[/C][C]0.00191428434819649[/C][C]0.999042857825902[/C][/ROW]
[ROW][C]35[/C][C]0.00112153532362934[/C][C]0.00224307064725869[/C][C]0.99887846467637[/C][/ROW]
[ROW][C]36[/C][C]0.000916082525558293[/C][C]0.00183216505111659[/C][C]0.999083917474442[/C][/ROW]
[ROW][C]37[/C][C]0.000688791484274926[/C][C]0.00137758296854985[/C][C]0.999311208515725[/C][/ROW]
[ROW][C]38[/C][C]0.000491906852837908[/C][C]0.000983813705675817[/C][C]0.999508093147162[/C][/ROW]
[ROW][C]39[/C][C]0.000323713292406975[/C][C]0.00064742658481395[/C][C]0.999676286707593[/C][/ROW]
[ROW][C]40[/C][C]0.000247764043012029[/C][C]0.000495528086024057[/C][C]0.999752235956988[/C][/ROW]
[ROW][C]41[/C][C]0.000190679505071837[/C][C]0.000381359010143675[/C][C]0.999809320494928[/C][/ROW]
[ROW][C]42[/C][C]0.000248257165568781[/C][C]0.000496514331137562[/C][C]0.999751742834431[/C][/ROW]
[ROW][C]43[/C][C]0.000799200496923161[/C][C]0.00159840099384632[/C][C]0.999200799503077[/C][/ROW]
[ROW][C]44[/C][C]0.00615913748616246[/C][C]0.0123182749723249[/C][C]0.993840862513838[/C][/ROW]
[ROW][C]45[/C][C]0.0414775033243909[/C][C]0.0829550066487818[/C][C]0.95852249667561[/C][/ROW]
[ROW][C]46[/C][C]0.206638776971325[/C][C]0.413277553942651[/C][C]0.793361223028675[/C][/ROW]
[ROW][C]47[/C][C]0.500016355460788[/C][C]0.999967289078424[/C][C]0.499983644539212[/C][/ROW]
[ROW][C]48[/C][C]0.840288248641335[/C][C]0.31942350271733[/C][C]0.159711751358665[/C][/ROW]
[ROW][C]49[/C][C]0.796719205637204[/C][C]0.406561588725591[/C][C]0.203280794362796[/C][/ROW]
[ROW][C]50[/C][C]0.831179505644961[/C][C]0.337640988710079[/C][C]0.168820494355039[/C][/ROW]
[ROW][C]51[/C][C]0.899466823446679[/C][C]0.201066353106642[/C][C]0.100533176553321[/C][/ROW]
[ROW][C]52[/C][C]0.984680284111943[/C][C]0.0306394317761135[/C][C]0.0153197158880568[/C][/ROW]
[ROW][C]53[/C][C]0.968826411484034[/C][C]0.0623471770319311[/C][C]0.0311735885159656[/C][/ROW]
[ROW][C]54[/C][C]0.926958061517207[/C][C]0.146083876965586[/C][C]0.073041938482793[/C][/ROW]
[ROW][C]55[/C][C]0.86410377180552[/C][C]0.271792456388961[/C][C]0.135896228194481[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57572&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57572&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	0.241371938313620	0.482743876627239	0.75862806168638
6	0.230782135270807	0.461564270541614	0.769217864729193
7	0.211859225093072	0.423718450186143	0.788140774906928
8	0.151111097477294	0.302222194954588	0.848888902522706
9	0.142734548599929	0.285469097199858	0.857265451400071
10	0.118501650320201	0.237003300640403	0.881498349679799
11	0.0752196796923327	0.150439359384665	0.924780320307667
12	0.0477231306173293	0.0954462612346585	0.95227686938267
13	0.0286674066341794	0.0573348132683588	0.97133259336582
14	0.0172999826618428	0.0345999653236855	0.982700017338157
15	0.00919060643075037	0.0183812128615007	0.99080939356925
16	0.00496068992844707	0.00992137985689413	0.995039310071553
17	0.0024307928184867	0.0048615856369734	0.997569207181513
18	0.00127655751368873	0.00255311502737746	0.998723442486311
19	0.00081127043081526	0.00162254086163052	0.999188729569185
20	0.000419628049802666	0.000839256099605332	0.999580371950197
21	0.00048549053006713	0.00097098106013426	0.999514509469933
22	0.000363009663422359	0.000726019326844719	0.999636990336578
23	0.000264264092686305	0.000528528185372609	0.999735735907314
24	0.000232238422872829	0.000464476845745657	0.999767761577127
25	0.000143209128701927	0.000286418257403854	0.999856790871298
26	9.13773895548201e-05	0.000182754779109640	0.999908622610445
27	6.44892410824026e-05	0.000128978482164805	0.999935510758918
28	5.3757833202351e-05	0.000107515666404702	0.999946242166798
29	5.82856594215919e-05	0.000116571318843184	0.999941714340578
30	8.40921699437727e-05	0.000168184339887545	0.999915907830056
31	0.00016874474640859	0.00033748949281718	0.999831255253591
32	0.000281920505697742	0.000563841011395483	0.999718079494302
33	0.00056039776539084	0.00112079553078168	0.99943960223461
34	0.000957142174098245	0.00191428434819649	0.999042857825902
35	0.00112153532362934	0.00224307064725869	0.99887846467637
36	0.000916082525558293	0.00183216505111659	0.999083917474442
37	0.000688791484274926	0.00137758296854985	0.999311208515725
38	0.000491906852837908	0.000983813705675817	0.999508093147162
39	0.000323713292406975	0.00064742658481395	0.999676286707593
40	0.000247764043012029	0.000495528086024057	0.999752235956988
41	0.000190679505071837	0.000381359010143675	0.999809320494928
42	0.000248257165568781	0.000496514331137562	0.999751742834431
43	0.000799200496923161	0.00159840099384632	0.999200799503077
44	0.00615913748616246	0.0123182749723249	0.993840862513838
45	0.0414775033243909	0.0829550066487818	0.95852249667561
46	0.206638776971325	0.413277553942651	0.793361223028675
47	0.500016355460788	0.999967289078424	0.499983644539212
48	0.840288248641335	0.31942350271733	0.159711751358665
49	0.796719205637204	0.406561588725591	0.203280794362796
50	0.831179505644961	0.337640988710079	0.168820494355039
51	0.899466823446679	0.201066353106642	0.100533176553321
52	0.984680284111943	0.0306394317761135	0.0153197158880568
53	0.968826411484034	0.0623471770319311	0.0311735885159656
54	0.926958061517207	0.146083876965586	0.073041938482793
55	0.86410377180552	0.271792456388961	0.135896228194481

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	28	0.549019607843137	NOK
5% type I error level	32	0.627450980392157	NOK
10% type I error level	36	0.705882352941177	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 & 28 & 0.549019607843137 & NOK \tabularnewline
5% type I error level & 32 & 0.627450980392157 & NOK \tabularnewline
10% type I error level & 36 & 0.705882352941177 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57572&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]28[/C][C]0.549019607843137[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]32[/C][C]0.627450980392157[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]36[/C][C]0.705882352941177[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57572&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57572&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	28	0.549019607843137	NOK
5% type I error level	32	0.627450980392157	NOK
10% type I error level	36	0.705882352941177	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 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; 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