Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 19 Nov 2009 10:01:55 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258650302jheuf7ly064jxul.htm/, Retrieved Sat, 20 Apr 2024 13:58:29 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57832, Retrieved Sat, 20 Apr 2024 13:58:29 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

Uitleg in Word document

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

168

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 14:03:14] [b98453cac15ba1066b407e146608df68]
- R  D      [Multiple Regression] [Regressiemodel - ...] [2009-11-19 17:01:55] [8eb8270f5a1cfdf0409dcfcbf10be18b] [Current]
-    D        [Multiple Regression] [Multiple Regression] [2010-12-26 20:57:10] [fd57ceeb2f72ef497e1390930b11fced] 
- R  D        [Multiple Regression] [] [2010-12-29 14:23:28] [adca540665f1dd1a5a4406fd7f55bdf4] 

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

96.96	89.1
93.11	83.3
95.62	97.7
98.30	100.9
96.38	108.3
100.82	113.2
99.06	105
94.03	104
102.07	109.8
99.31	98.6
98.64	93.5
101.82	98.2
99.14	88
97.63	85.3
100.06	96.8
101.32	98.8
101.49	110.3
105.43	111.6
105.09	111.2
99.48	106.9
108.53	117.6
104.34	97
106.10	97.3
107.35	98.4
103.00	87.6
104.50	87.4
105.17	94.7
104.84	101.5
106.18	110.4
108.86	108.4
107.77	109.7
102.74	105.2
112.63	111.1
106.26	96.2
108.86	97.3
111.38	98.9
106.85	91.7
107.86	90.9
107.94	98.8
111.38	111.5
111.29	119
113.72	115.3
111.88	116.3
109.87	113.6
113.72	115.1
111.71	109.7
114.81	97.6
112.05	100.8
111.54	94
110.87	87.2
110.87	102.9
115.48	111.3
111.63	106.6
116.24	108.9
113.56	108.3
106.01	100.5
110.45	104
107.77	89.9
108.61	86.8
108.19	91.2

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57832&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]5 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=57832&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57832&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	5 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
BESTC[t] = + 53.3118683218126 + 0.562524427468587INDUSTR[t] -0.486068748183151M1[t] + 0.643760885364526M2[t] -4.60851661067863M3[t] -6.00042832052068M4[t] -10.3130778166284M5[t] -7.00809149601084M6[t] -7.7738077861042M7[t] -10.5359586105817M8[t] -6.56459247310959M9[t] -2.71876905342549M10[t] + 0.933573282405774M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
BESTC[t] =  +  53.3118683218126 +  0.562524427468587INDUSTR[t] -0.486068748183151M1[t] +  0.643760885364526M2[t] -4.60851661067863M3[t] -6.00042832052068M4[t] -10.3130778166284M5[t] -7.00809149601084M6[t] -7.7738077861042M7[t] -10.5359586105817M8[t] -6.56459247310959M9[t] -2.71876905342549M10[t] +  0.933573282405774M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57832&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]BESTC[t] =  +  53.3118683218126 +  0.562524427468587INDUSTR[t] -0.486068748183151M1[t] +  0.643760885364526M2[t] -4.60851661067863M3[t] -6.00042832052068M4[t] -10.3130778166284M5[t] -7.00809149601084M6[t] -7.7738077861042M7[t] -10.5359586105817M8[t] -6.56459247310959M9[t] -2.71876905342549M10[t] +  0.933573282405774M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57832&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57832&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
BESTC[t] = + 53.3118683218126 + 0.562524427468587INDUSTR[t] -0.486068748183151M1[t] + 0.643760885364526M2[t] -4.60851661067863M3[t] -6.00042832052068M4[t] -10.3130778166284M5[t] -7.00809149601084M6[t] -7.7738077861042M7[t] -10.5359586105817M8[t] -6.56459247310959M9[t] -2.71876905342549M10[t] + 0.933573282405774M11[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)	53.3118683218126	16.87502	3.1592	0.002765	0.001383
INDUSTR	0.562524427468587	0.171301	3.2838	0.001938	0.000969
M1	-0.486068748183151	3.639218	-0.1336	0.894318	0.447159
M2	0.643760885364526	3.869804	0.1664	0.868591	0.434296
M3	-4.60851661067863	3.412023	-1.3507	0.183269	0.091634
M4	-6.00042832052068	3.63209	-1.6521	0.105189	0.052594
M5	-10.3130778166284	4.112553	-2.5077	0.015664	0.007832
M6	-7.00809149601084	4.166935	-1.6818	0.099235	0.049617
M7	-7.7738077861042	4.035715	-1.9263	0.060133	0.030066
M8	-10.5359586105817	3.710585	-2.8394	0.006657	0.003328
M9	-6.56459247310959	4.170877	-1.5739	0.122216	0.061108
M10	-2.71876905342549	3.41265	-0.7967	0.429647	0.214823
M11	0.933573282405774	3.44854	0.2707	0.787795	0.393898

\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) & 53.3118683218126 & 16.87502 & 3.1592 & 0.002765 & 0.001383 \tabularnewline
INDUSTR & 0.562524427468587 & 0.171301 & 3.2838 & 0.001938 & 0.000969 \tabularnewline
M1 & -0.486068748183151 & 3.639218 & -0.1336 & 0.894318 & 0.447159 \tabularnewline
M2 & 0.643760885364526 & 3.869804 & 0.1664 & 0.868591 & 0.434296 \tabularnewline
M3 & -4.60851661067863 & 3.412023 & -1.3507 & 0.183269 & 0.091634 \tabularnewline
M4 & -6.00042832052068 & 3.63209 & -1.6521 & 0.105189 & 0.052594 \tabularnewline
M5 & -10.3130778166284 & 4.112553 & -2.5077 & 0.015664 & 0.007832 \tabularnewline
M6 & -7.00809149601084 & 4.166935 & -1.6818 & 0.099235 & 0.049617 \tabularnewline
M7 & -7.7738077861042 & 4.035715 & -1.9263 & 0.060133 & 0.030066 \tabularnewline
M8 & -10.5359586105817 & 3.710585 & -2.8394 & 0.006657 & 0.003328 \tabularnewline
M9 & -6.56459247310959 & 4.170877 & -1.5739 & 0.122216 & 0.061108 \tabularnewline
M10 & -2.71876905342549 & 3.41265 & -0.7967 & 0.429647 & 0.214823 \tabularnewline
M11 & 0.933573282405774 & 3.44854 & 0.2707 & 0.787795 & 0.393898 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57832&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]53.3118683218126[/C][C]16.87502[/C][C]3.1592[/C][C]0.002765[/C][C]0.001383[/C][/ROW]
[ROW][C]INDUSTR[/C][C]0.562524427468587[/C][C]0.171301[/C][C]3.2838[/C][C]0.001938[/C][C]0.000969[/C][/ROW]
[ROW][C]M1[/C][C]-0.486068748183151[/C][C]3.639218[/C][C]-0.1336[/C][C]0.894318[/C][C]0.447159[/C][/ROW]
[ROW][C]M2[/C][C]0.643760885364526[/C][C]3.869804[/C][C]0.1664[/C][C]0.868591[/C][C]0.434296[/C][/ROW]
[ROW][C]M3[/C][C]-4.60851661067863[/C][C]3.412023[/C][C]-1.3507[/C][C]0.183269[/C][C]0.091634[/C][/ROW]
[ROW][C]M4[/C][C]-6.00042832052068[/C][C]3.63209[/C][C]-1.6521[/C][C]0.105189[/C][C]0.052594[/C][/ROW]
[ROW][C]M5[/C][C]-10.3130778166284[/C][C]4.112553[/C][C]-2.5077[/C][C]0.015664[/C][C]0.007832[/C][/ROW]
[ROW][C]M6[/C][C]-7.00809149601084[/C][C]4.166935[/C][C]-1.6818[/C][C]0.099235[/C][C]0.049617[/C][/ROW]
[ROW][C]M7[/C][C]-7.7738077861042[/C][C]4.035715[/C][C]-1.9263[/C][C]0.060133[/C][C]0.030066[/C][/ROW]
[ROW][C]M8[/C][C]-10.5359586105817[/C][C]3.710585[/C][C]-2.8394[/C][C]0.006657[/C][C]0.003328[/C][/ROW]
[ROW][C]M9[/C][C]-6.56459247310959[/C][C]4.170877[/C][C]-1.5739[/C][C]0.122216[/C][C]0.061108[/C][/ROW]
[ROW][C]M10[/C][C]-2.71876905342549[/C][C]3.41265[/C][C]-0.7967[/C][C]0.429647[/C][C]0.214823[/C][/ROW]
[ROW][C]M11[/C][C]0.933573282405774[/C][C]3.44854[/C][C]0.2707[/C][C]0.787795[/C][C]0.393898[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57832&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57832&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)	53.3118683218126	16.87502	3.1592	0.002765	0.001383
INDUSTR	0.562524427468587	0.171301	3.2838	0.001938	0.000969
M1	-0.486068748183151	3.639218	-0.1336	0.894318	0.447159
M2	0.643760885364526	3.869804	0.1664	0.868591	0.434296
M3	-4.60851661067863	3.412023	-1.3507	0.183269	0.091634
M4	-6.00042832052068	3.63209	-1.6521	0.105189	0.052594
M5	-10.3130778166284	4.112553	-2.5077	0.015664	0.007832
M6	-7.00809149601084	4.166935	-1.6818	0.099235	0.049617
M7	-7.7738077861042	4.035715	-1.9263	0.060133	0.030066
M8	-10.5359586105817	3.710585	-2.8394	0.006657	0.003328
M9	-6.56459247310959	4.170877	-1.5739	0.122216	0.061108
M10	-2.71876905342549	3.41265	-0.7967	0.429647	0.214823
M11	0.933573282405774	3.44854	0.2707	0.787795	0.393898

Multiple Linear Regression - Regression Statistics
Multiple R	0.56286069610102
R-squared	0.316812163215325
Adjusted R-squared	0.142381226163919
F-TEST (value)	1.81626131562865
F-TEST (DF numerator)	12
F-TEST (DF denominator)	47
p-value	0.0728147785907012
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.39173682080917
Sum Squared Residuals	1366.32881940886

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.56286069610102 \tabularnewline
R-squared & 0.316812163215325 \tabularnewline
Adjusted R-squared & 0.142381226163919 \tabularnewline
F-TEST (value) & 1.81626131562865 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 0.0728147785907012 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5.39173682080917 \tabularnewline
Sum Squared Residuals & 1366.32881940886 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57832&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.56286069610102[/C][/ROW]
[ROW][C]R-squared[/C][C]0.316812163215325[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.142381226163919[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.81626131562865[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]0.0728147785907012[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]5.39173682080917[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1366.32881940886[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57832&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57832&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	0.56286069610102
R-squared	0.316812163215325
Adjusted R-squared	0.142381226163919
F-TEST (value)	1.81626131562865
F-TEST (DF numerator)	12
F-TEST (DF denominator)	47
p-value	0.0728147785907012
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.39173682080917
Sum Squared Residuals	1366.32881940886

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	96.96	102.946726061081	-5.9867260610811
2	93.11	100.813914015311	-7.70391401531056
3	95.62	103.661988274815	-8.04198827481507
4	98.3	104.070154732873	-5.77015473287252
5	96.38	103.920186000032	-7.5401860000323
6	100.82	109.981542015246	-9.16154201524597
7	99.06	104.603125419910	-5.5431254199102
8	94.03	101.278450167964	-7.24845016796409
9	102.07	108.512457984754	-6.44245798475403
10	99.31	106.05800781679	-6.74800781678995
11	98.64	106.841475572531	-8.20147557253142
12	101.82	108.551767099228	-6.73176709922801
13	99.14	102.327949190865	-3.18794919086526
14	97.63	101.938962870248	-4.30896287024775
15	100.06	103.155716290093	-3.09571629009335
16	101.32	102.888853435188	-1.56885343518848
17	101.49	105.045234854969	-3.55523485496948
18	105.43	109.081502931296	-3.65150293129622
19	105.09	108.090776870215	-3.00077687021545
20	99.48	102.909771007623	-3.42977100762299
21	108.53	112.900148519009	-4.37014851900901
22	104.34	105.157968732840	-0.817968732840203
23	106.1	108.979068396912	-2.87906839691205
24	107.35	108.664271984722	-1.31427198472173
25	103	102.102939419878	0.897060580122177
26	104.5	103.120264167932	1.37973583206821
27	105.17	101.974414992409	3.19558500759068
28	104.84	104.407669389354	0.432330610646340
29	106.18	105.101487297716	1.07851270228367
30	108.86	107.281424763397	1.57857523660325
31	107.77	107.246990229013	0.523009770987429
32	102.74	101.953479480926	0.786520519073607
33	112.63	109.243739740463	3.38626025953680
34	106.26	104.707949190865	1.55205080913467
35	108.86	108.979068396912	-0.119068396912045
36	111.38	108.945534198456	2.43446580154398
37	106.85	104.409289572499	2.44071042750096
38	107.86	105.089099664072	2.77090033592816
39	107.94	104.280765145031	3.65923485496947
40	111.38	110.032913664040	1.34708633596046
41	111.29	109.939197373946	1.35080262605382
42	113.72	111.16284331293	2.55715668707
43	111.88	110.959651450305	0.920348549694754
44	109.87	106.678684671663	3.19131532833748
45	113.72	111.493837450338	2.22616254966246
46	111.71	112.302028961691	-0.592028961691276
47	114.81	109.147825725153	5.66217427484738
48	112.05	110.014330610646	2.03566938935367
49	111.54	105.703095755677	5.83690424432322
50	110.87	103.007759282438	7.86224071756194
51	110.87	106.587115297652	4.28288470234826
52	115.48	109.920408778546	5.55959122145418
53	111.63	102.963894473336	8.6661055266643
54	116.24	107.562686977131	8.67731302286895
55	113.56	106.459456030557	7.10054396944346
56	106.01	99.309614671824	6.70038532817598
57	110.45	105.249816305436	5.20018369456378
58	107.77	101.164045297813	6.60595470218676
59	108.61	103.072561908492	5.53743809150813
60	108.19	104.614096106948	3.57590389305211

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 96.96 & 102.946726061081 & -5.9867260610811 \tabularnewline
2 & 93.11 & 100.813914015311 & -7.70391401531056 \tabularnewline
3 & 95.62 & 103.661988274815 & -8.04198827481507 \tabularnewline
4 & 98.3 & 104.070154732873 & -5.77015473287252 \tabularnewline
5 & 96.38 & 103.920186000032 & -7.5401860000323 \tabularnewline
6 & 100.82 & 109.981542015246 & -9.16154201524597 \tabularnewline
7 & 99.06 & 104.603125419910 & -5.5431254199102 \tabularnewline
8 & 94.03 & 101.278450167964 & -7.24845016796409 \tabularnewline
9 & 102.07 & 108.512457984754 & -6.44245798475403 \tabularnewline
10 & 99.31 & 106.05800781679 & -6.74800781678995 \tabularnewline
11 & 98.64 & 106.841475572531 & -8.20147557253142 \tabularnewline
12 & 101.82 & 108.551767099228 & -6.73176709922801 \tabularnewline
13 & 99.14 & 102.327949190865 & -3.18794919086526 \tabularnewline
14 & 97.63 & 101.938962870248 & -4.30896287024775 \tabularnewline
15 & 100.06 & 103.155716290093 & -3.09571629009335 \tabularnewline
16 & 101.32 & 102.888853435188 & -1.56885343518848 \tabularnewline
17 & 101.49 & 105.045234854969 & -3.55523485496948 \tabularnewline
18 & 105.43 & 109.081502931296 & -3.65150293129622 \tabularnewline
19 & 105.09 & 108.090776870215 & -3.00077687021545 \tabularnewline
20 & 99.48 & 102.909771007623 & -3.42977100762299 \tabularnewline
21 & 108.53 & 112.900148519009 & -4.37014851900901 \tabularnewline
22 & 104.34 & 105.157968732840 & -0.817968732840203 \tabularnewline
23 & 106.1 & 108.979068396912 & -2.87906839691205 \tabularnewline
24 & 107.35 & 108.664271984722 & -1.31427198472173 \tabularnewline
25 & 103 & 102.102939419878 & 0.897060580122177 \tabularnewline
26 & 104.5 & 103.120264167932 & 1.37973583206821 \tabularnewline
27 & 105.17 & 101.974414992409 & 3.19558500759068 \tabularnewline
28 & 104.84 & 104.407669389354 & 0.432330610646340 \tabularnewline
29 & 106.18 & 105.101487297716 & 1.07851270228367 \tabularnewline
30 & 108.86 & 107.281424763397 & 1.57857523660325 \tabularnewline
31 & 107.77 & 107.246990229013 & 0.523009770987429 \tabularnewline
32 & 102.74 & 101.953479480926 & 0.786520519073607 \tabularnewline
33 & 112.63 & 109.243739740463 & 3.38626025953680 \tabularnewline
34 & 106.26 & 104.707949190865 & 1.55205080913467 \tabularnewline
35 & 108.86 & 108.979068396912 & -0.119068396912045 \tabularnewline
36 & 111.38 & 108.945534198456 & 2.43446580154398 \tabularnewline
37 & 106.85 & 104.409289572499 & 2.44071042750096 \tabularnewline
38 & 107.86 & 105.089099664072 & 2.77090033592816 \tabularnewline
39 & 107.94 & 104.280765145031 & 3.65923485496947 \tabularnewline
40 & 111.38 & 110.032913664040 & 1.34708633596046 \tabularnewline
41 & 111.29 & 109.939197373946 & 1.35080262605382 \tabularnewline
42 & 113.72 & 111.16284331293 & 2.55715668707 \tabularnewline
43 & 111.88 & 110.959651450305 & 0.920348549694754 \tabularnewline
44 & 109.87 & 106.678684671663 & 3.19131532833748 \tabularnewline
45 & 113.72 & 111.493837450338 & 2.22616254966246 \tabularnewline
46 & 111.71 & 112.302028961691 & -0.592028961691276 \tabularnewline
47 & 114.81 & 109.147825725153 & 5.66217427484738 \tabularnewline
48 & 112.05 & 110.014330610646 & 2.03566938935367 \tabularnewline
49 & 111.54 & 105.703095755677 & 5.83690424432322 \tabularnewline
50 & 110.87 & 103.007759282438 & 7.86224071756194 \tabularnewline
51 & 110.87 & 106.587115297652 & 4.28288470234826 \tabularnewline
52 & 115.48 & 109.920408778546 & 5.55959122145418 \tabularnewline
53 & 111.63 & 102.963894473336 & 8.6661055266643 \tabularnewline
54 & 116.24 & 107.562686977131 & 8.67731302286895 \tabularnewline
55 & 113.56 & 106.459456030557 & 7.10054396944346 \tabularnewline
56 & 106.01 & 99.309614671824 & 6.70038532817598 \tabularnewline
57 & 110.45 & 105.249816305436 & 5.20018369456378 \tabularnewline
58 & 107.77 & 101.164045297813 & 6.60595470218676 \tabularnewline
59 & 108.61 & 103.072561908492 & 5.53743809150813 \tabularnewline
60 & 108.19 & 104.614096106948 & 3.57590389305211 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57832&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]96.96[/C][C]102.946726061081[/C][C]-5.9867260610811[/C][/ROW]
[ROW][C]2[/C][C]93.11[/C][C]100.813914015311[/C][C]-7.70391401531056[/C][/ROW]
[ROW][C]3[/C][C]95.62[/C][C]103.661988274815[/C][C]-8.04198827481507[/C][/ROW]
[ROW][C]4[/C][C]98.3[/C][C]104.070154732873[/C][C]-5.77015473287252[/C][/ROW]
[ROW][C]5[/C][C]96.38[/C][C]103.920186000032[/C][C]-7.5401860000323[/C][/ROW]
[ROW][C]6[/C][C]100.82[/C][C]109.981542015246[/C][C]-9.16154201524597[/C][/ROW]
[ROW][C]7[/C][C]99.06[/C][C]104.603125419910[/C][C]-5.5431254199102[/C][/ROW]
[ROW][C]8[/C][C]94.03[/C][C]101.278450167964[/C][C]-7.24845016796409[/C][/ROW]
[ROW][C]9[/C][C]102.07[/C][C]108.512457984754[/C][C]-6.44245798475403[/C][/ROW]
[ROW][C]10[/C][C]99.31[/C][C]106.05800781679[/C][C]-6.74800781678995[/C][/ROW]
[ROW][C]11[/C][C]98.64[/C][C]106.841475572531[/C][C]-8.20147557253142[/C][/ROW]
[ROW][C]12[/C][C]101.82[/C][C]108.551767099228[/C][C]-6.73176709922801[/C][/ROW]
[ROW][C]13[/C][C]99.14[/C][C]102.327949190865[/C][C]-3.18794919086526[/C][/ROW]
[ROW][C]14[/C][C]97.63[/C][C]101.938962870248[/C][C]-4.30896287024775[/C][/ROW]
[ROW][C]15[/C][C]100.06[/C][C]103.155716290093[/C][C]-3.09571629009335[/C][/ROW]
[ROW][C]16[/C][C]101.32[/C][C]102.888853435188[/C][C]-1.56885343518848[/C][/ROW]
[ROW][C]17[/C][C]101.49[/C][C]105.045234854969[/C][C]-3.55523485496948[/C][/ROW]
[ROW][C]18[/C][C]105.43[/C][C]109.081502931296[/C][C]-3.65150293129622[/C][/ROW]
[ROW][C]19[/C][C]105.09[/C][C]108.090776870215[/C][C]-3.00077687021545[/C][/ROW]
[ROW][C]20[/C][C]99.48[/C][C]102.909771007623[/C][C]-3.42977100762299[/C][/ROW]
[ROW][C]21[/C][C]108.53[/C][C]112.900148519009[/C][C]-4.37014851900901[/C][/ROW]
[ROW][C]22[/C][C]104.34[/C][C]105.157968732840[/C][C]-0.817968732840203[/C][/ROW]
[ROW][C]23[/C][C]106.1[/C][C]108.979068396912[/C][C]-2.87906839691205[/C][/ROW]
[ROW][C]24[/C][C]107.35[/C][C]108.664271984722[/C][C]-1.31427198472173[/C][/ROW]
[ROW][C]25[/C][C]103[/C][C]102.102939419878[/C][C]0.897060580122177[/C][/ROW]
[ROW][C]26[/C][C]104.5[/C][C]103.120264167932[/C][C]1.37973583206821[/C][/ROW]
[ROW][C]27[/C][C]105.17[/C][C]101.974414992409[/C][C]3.19558500759068[/C][/ROW]
[ROW][C]28[/C][C]104.84[/C][C]104.407669389354[/C][C]0.432330610646340[/C][/ROW]
[ROW][C]29[/C][C]106.18[/C][C]105.101487297716[/C][C]1.07851270228367[/C][/ROW]
[ROW][C]30[/C][C]108.86[/C][C]107.281424763397[/C][C]1.57857523660325[/C][/ROW]
[ROW][C]31[/C][C]107.77[/C][C]107.246990229013[/C][C]0.523009770987429[/C][/ROW]
[ROW][C]32[/C][C]102.74[/C][C]101.953479480926[/C][C]0.786520519073607[/C][/ROW]
[ROW][C]33[/C][C]112.63[/C][C]109.243739740463[/C][C]3.38626025953680[/C][/ROW]
[ROW][C]34[/C][C]106.26[/C][C]104.707949190865[/C][C]1.55205080913467[/C][/ROW]
[ROW][C]35[/C][C]108.86[/C][C]108.979068396912[/C][C]-0.119068396912045[/C][/ROW]
[ROW][C]36[/C][C]111.38[/C][C]108.945534198456[/C][C]2.43446580154398[/C][/ROW]
[ROW][C]37[/C][C]106.85[/C][C]104.409289572499[/C][C]2.44071042750096[/C][/ROW]
[ROW][C]38[/C][C]107.86[/C][C]105.089099664072[/C][C]2.77090033592816[/C][/ROW]
[ROW][C]39[/C][C]107.94[/C][C]104.280765145031[/C][C]3.65923485496947[/C][/ROW]
[ROW][C]40[/C][C]111.38[/C][C]110.032913664040[/C][C]1.34708633596046[/C][/ROW]
[ROW][C]41[/C][C]111.29[/C][C]109.939197373946[/C][C]1.35080262605382[/C][/ROW]
[ROW][C]42[/C][C]113.72[/C][C]111.16284331293[/C][C]2.55715668707[/C][/ROW]
[ROW][C]43[/C][C]111.88[/C][C]110.959651450305[/C][C]0.920348549694754[/C][/ROW]
[ROW][C]44[/C][C]109.87[/C][C]106.678684671663[/C][C]3.19131532833748[/C][/ROW]
[ROW][C]45[/C][C]113.72[/C][C]111.493837450338[/C][C]2.22616254966246[/C][/ROW]
[ROW][C]46[/C][C]111.71[/C][C]112.302028961691[/C][C]-0.592028961691276[/C][/ROW]
[ROW][C]47[/C][C]114.81[/C][C]109.147825725153[/C][C]5.66217427484738[/C][/ROW]
[ROW][C]48[/C][C]112.05[/C][C]110.014330610646[/C][C]2.03566938935367[/C][/ROW]
[ROW][C]49[/C][C]111.54[/C][C]105.703095755677[/C][C]5.83690424432322[/C][/ROW]
[ROW][C]50[/C][C]110.87[/C][C]103.007759282438[/C][C]7.86224071756194[/C][/ROW]
[ROW][C]51[/C][C]110.87[/C][C]106.587115297652[/C][C]4.28288470234826[/C][/ROW]
[ROW][C]52[/C][C]115.48[/C][C]109.920408778546[/C][C]5.55959122145418[/C][/ROW]
[ROW][C]53[/C][C]111.63[/C][C]102.963894473336[/C][C]8.6661055266643[/C][/ROW]
[ROW][C]54[/C][C]116.24[/C][C]107.562686977131[/C][C]8.67731302286895[/C][/ROW]
[ROW][C]55[/C][C]113.56[/C][C]106.459456030557[/C][C]7.10054396944346[/C][/ROW]
[ROW][C]56[/C][C]106.01[/C][C]99.309614671824[/C][C]6.70038532817598[/C][/ROW]
[ROW][C]57[/C][C]110.45[/C][C]105.249816305436[/C][C]5.20018369456378[/C][/ROW]
[ROW][C]58[/C][C]107.77[/C][C]101.164045297813[/C][C]6.60595470218676[/C][/ROW]
[ROW][C]59[/C][C]108.61[/C][C]103.072561908492[/C][C]5.53743809150813[/C][/ROW]
[ROW][C]60[/C][C]108.19[/C][C]104.614096106948[/C][C]3.57590389305211[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57832&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57832&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	96.96	102.946726061081	-5.9867260610811
2	93.11	100.813914015311	-7.70391401531056
3	95.62	103.661988274815	-8.04198827481507
4	98.3	104.070154732873	-5.77015473287252
5	96.38	103.920186000032	-7.5401860000323
6	100.82	109.981542015246	-9.16154201524597
7	99.06	104.603125419910	-5.5431254199102
8	94.03	101.278450167964	-7.24845016796409
9	102.07	108.512457984754	-6.44245798475403
10	99.31	106.05800781679	-6.74800781678995
11	98.64	106.841475572531	-8.20147557253142
12	101.82	108.551767099228	-6.73176709922801
13	99.14	102.327949190865	-3.18794919086526
14	97.63	101.938962870248	-4.30896287024775
15	100.06	103.155716290093	-3.09571629009335
16	101.32	102.888853435188	-1.56885343518848
17	101.49	105.045234854969	-3.55523485496948
18	105.43	109.081502931296	-3.65150293129622
19	105.09	108.090776870215	-3.00077687021545
20	99.48	102.909771007623	-3.42977100762299
21	108.53	112.900148519009	-4.37014851900901
22	104.34	105.157968732840	-0.817968732840203
23	106.1	108.979068396912	-2.87906839691205
24	107.35	108.664271984722	-1.31427198472173
25	103	102.102939419878	0.897060580122177
26	104.5	103.120264167932	1.37973583206821
27	105.17	101.974414992409	3.19558500759068
28	104.84	104.407669389354	0.432330610646340
29	106.18	105.101487297716	1.07851270228367
30	108.86	107.281424763397	1.57857523660325
31	107.77	107.246990229013	0.523009770987429
32	102.74	101.953479480926	0.786520519073607
33	112.63	109.243739740463	3.38626025953680
34	106.26	104.707949190865	1.55205080913467
35	108.86	108.979068396912	-0.119068396912045
36	111.38	108.945534198456	2.43446580154398
37	106.85	104.409289572499	2.44071042750096
38	107.86	105.089099664072	2.77090033592816
39	107.94	104.280765145031	3.65923485496947
40	111.38	110.032913664040	1.34708633596046
41	111.29	109.939197373946	1.35080262605382
42	113.72	111.16284331293	2.55715668707
43	111.88	110.959651450305	0.920348549694754
44	109.87	106.678684671663	3.19131532833748
45	113.72	111.493837450338	2.22616254966246
46	111.71	112.302028961691	-0.592028961691276
47	114.81	109.147825725153	5.66217427484738
48	112.05	110.014330610646	2.03566938935367
49	111.54	105.703095755677	5.83690424432322
50	110.87	103.007759282438	7.86224071756194
51	110.87	106.587115297652	4.28288470234826
52	115.48	109.920408778546	5.55959122145418
53	111.63	102.963894473336	8.6661055266643
54	116.24	107.562686977131	8.67731302286895
55	113.56	106.459456030557	7.10054396944346
56	106.01	99.309614671824	6.70038532817598
57	110.45	105.249816305436	5.20018369456378
58	107.77	101.164045297813	6.60595470218676
59	108.61	103.072561908492	5.53743809150813
60	108.19	104.614096106948	3.57590389305211

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.422166437341422	0.844332874682844	0.577833562658578
17	0.464081372515983	0.928162745031967	0.535918627484017
18	0.520487944648759	0.959024110702481	0.479512055351241
19	0.497287614407888	0.994575228815777	0.502712385592112
20	0.50483742540742	0.99032514918516	0.49516257459258
21	0.456983759621668	0.913967519243337	0.543016240378332
22	0.564024419797397	0.871951160405205	0.435975580202603
23	0.623025739743727	0.753948520512545	0.376974260256273
24	0.665120866866675	0.66975826626665	0.334879133133325
25	0.75281645751648	0.494367084967039	0.247183542483519
26	0.8310576934753	0.3378846130494	0.1689423065247
27	0.923974538082534	0.152050923834932	0.076025461917466
28	0.943278779721542	0.113442440556917	0.0567212202784585
29	0.96307550906629	0.0738489818674179	0.0369244909337089
30	0.98704577675493	0.0259084464901413	0.0129542232450706
31	0.989769046034356	0.020461907931288	0.010230953965644
32	0.994630555697168	0.0107388886056639	0.00536944430283193
33	0.994288942542887	0.0114221149142253	0.00571105745711265
34	0.99300200657904	0.0139959868419201	0.00699799342096005
35	0.994060604180665	0.0118787916386701	0.00593939581933504
36	0.990009530158103	0.0199809396837944	0.00999046984189722
37	0.990414236008613	0.0191715279827733	0.00958576399138664
38	0.989596054003116	0.0208078919937676	0.0104039459968838
39	0.982581575771846	0.0348368484563071	0.0174184242281536
40	0.981285834854485	0.0374283302910302	0.0187141651455151
41	0.971934850741305	0.0561302985173909	0.0280651492586955
42	0.976133438930852	0.0477331221382952	0.0238665610691476
43	0.98321812228769	0.0335637554246196	0.0167818777123098
44	0.941674482385823	0.116651035228353	0.0583255176141767

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.422166437341422 & 0.844332874682844 & 0.577833562658578 \tabularnewline
17 & 0.464081372515983 & 0.928162745031967 & 0.535918627484017 \tabularnewline
18 & 0.520487944648759 & 0.959024110702481 & 0.479512055351241 \tabularnewline
19 & 0.497287614407888 & 0.994575228815777 & 0.502712385592112 \tabularnewline
20 & 0.50483742540742 & 0.99032514918516 & 0.49516257459258 \tabularnewline
21 & 0.456983759621668 & 0.913967519243337 & 0.543016240378332 \tabularnewline
22 & 0.564024419797397 & 0.871951160405205 & 0.435975580202603 \tabularnewline
23 & 0.623025739743727 & 0.753948520512545 & 0.376974260256273 \tabularnewline
24 & 0.665120866866675 & 0.66975826626665 & 0.334879133133325 \tabularnewline
25 & 0.75281645751648 & 0.494367084967039 & 0.247183542483519 \tabularnewline
26 & 0.8310576934753 & 0.3378846130494 & 0.1689423065247 \tabularnewline
27 & 0.923974538082534 & 0.152050923834932 & 0.076025461917466 \tabularnewline
28 & 0.943278779721542 & 0.113442440556917 & 0.0567212202784585 \tabularnewline
29 & 0.96307550906629 & 0.0738489818674179 & 0.0369244909337089 \tabularnewline
30 & 0.98704577675493 & 0.0259084464901413 & 0.0129542232450706 \tabularnewline
31 & 0.989769046034356 & 0.020461907931288 & 0.010230953965644 \tabularnewline
32 & 0.994630555697168 & 0.0107388886056639 & 0.00536944430283193 \tabularnewline
33 & 0.994288942542887 & 0.0114221149142253 & 0.00571105745711265 \tabularnewline
34 & 0.99300200657904 & 0.0139959868419201 & 0.00699799342096005 \tabularnewline
35 & 0.994060604180665 & 0.0118787916386701 & 0.00593939581933504 \tabularnewline
36 & 0.990009530158103 & 0.0199809396837944 & 0.00999046984189722 \tabularnewline
37 & 0.990414236008613 & 0.0191715279827733 & 0.00958576399138664 \tabularnewline
38 & 0.989596054003116 & 0.0208078919937676 & 0.0104039459968838 \tabularnewline
39 & 0.982581575771846 & 0.0348368484563071 & 0.0174184242281536 \tabularnewline
40 & 0.981285834854485 & 0.0374283302910302 & 0.0187141651455151 \tabularnewline
41 & 0.971934850741305 & 0.0561302985173909 & 0.0280651492586955 \tabularnewline
42 & 0.976133438930852 & 0.0477331221382952 & 0.0238665610691476 \tabularnewline
43 & 0.98321812228769 & 0.0335637554246196 & 0.0167818777123098 \tabularnewline
44 & 0.941674482385823 & 0.116651035228353 & 0.0583255176141767 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57832&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]16[/C][C]0.422166437341422[/C][C]0.844332874682844[/C][C]0.577833562658578[/C][/ROW]
[ROW][C]17[/C][C]0.464081372515983[/C][C]0.928162745031967[/C][C]0.535918627484017[/C][/ROW]
[ROW][C]18[/C][C]0.520487944648759[/C][C]0.959024110702481[/C][C]0.479512055351241[/C][/ROW]
[ROW][C]19[/C][C]0.497287614407888[/C][C]0.994575228815777[/C][C]0.502712385592112[/C][/ROW]
[ROW][C]20[/C][C]0.50483742540742[/C][C]0.99032514918516[/C][C]0.49516257459258[/C][/ROW]
[ROW][C]21[/C][C]0.456983759621668[/C][C]0.913967519243337[/C][C]0.543016240378332[/C][/ROW]
[ROW][C]22[/C][C]0.564024419797397[/C][C]0.871951160405205[/C][C]0.435975580202603[/C][/ROW]
[ROW][C]23[/C][C]0.623025739743727[/C][C]0.753948520512545[/C][C]0.376974260256273[/C][/ROW]
[ROW][C]24[/C][C]0.665120866866675[/C][C]0.66975826626665[/C][C]0.334879133133325[/C][/ROW]
[ROW][C]25[/C][C]0.75281645751648[/C][C]0.494367084967039[/C][C]0.247183542483519[/C][/ROW]
[ROW][C]26[/C][C]0.8310576934753[/C][C]0.3378846130494[/C][C]0.1689423065247[/C][/ROW]
[ROW][C]27[/C][C]0.923974538082534[/C][C]0.152050923834932[/C][C]0.076025461917466[/C][/ROW]
[ROW][C]28[/C][C]0.943278779721542[/C][C]0.113442440556917[/C][C]0.0567212202784585[/C][/ROW]
[ROW][C]29[/C][C]0.96307550906629[/C][C]0.0738489818674179[/C][C]0.0369244909337089[/C][/ROW]
[ROW][C]30[/C][C]0.98704577675493[/C][C]0.0259084464901413[/C][C]0.0129542232450706[/C][/ROW]
[ROW][C]31[/C][C]0.989769046034356[/C][C]0.020461907931288[/C][C]0.010230953965644[/C][/ROW]
[ROW][C]32[/C][C]0.994630555697168[/C][C]0.0107388886056639[/C][C]0.00536944430283193[/C][/ROW]
[ROW][C]33[/C][C]0.994288942542887[/C][C]0.0114221149142253[/C][C]0.00571105745711265[/C][/ROW]
[ROW][C]34[/C][C]0.99300200657904[/C][C]0.0139959868419201[/C][C]0.00699799342096005[/C][/ROW]
[ROW][C]35[/C][C]0.994060604180665[/C][C]0.0118787916386701[/C][C]0.00593939581933504[/C][/ROW]
[ROW][C]36[/C][C]0.990009530158103[/C][C]0.0199809396837944[/C][C]0.00999046984189722[/C][/ROW]
[ROW][C]37[/C][C]0.990414236008613[/C][C]0.0191715279827733[/C][C]0.00958576399138664[/C][/ROW]
[ROW][C]38[/C][C]0.989596054003116[/C][C]0.0208078919937676[/C][C]0.0104039459968838[/C][/ROW]
[ROW][C]39[/C][C]0.982581575771846[/C][C]0.0348368484563071[/C][C]0.0174184242281536[/C][/ROW]
[ROW][C]40[/C][C]0.981285834854485[/C][C]0.0374283302910302[/C][C]0.0187141651455151[/C][/ROW]
[ROW][C]41[/C][C]0.971934850741305[/C][C]0.0561302985173909[/C][C]0.0280651492586955[/C][/ROW]
[ROW][C]42[/C][C]0.976133438930852[/C][C]0.0477331221382952[/C][C]0.0238665610691476[/C][/ROW]
[ROW][C]43[/C][C]0.98321812228769[/C][C]0.0335637554246196[/C][C]0.0167818777123098[/C][/ROW]
[ROW][C]44[/C][C]0.941674482385823[/C][C]0.116651035228353[/C][C]0.0583255176141767[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57832&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57832&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
16	0.422166437341422	0.844332874682844	0.577833562658578
17	0.464081372515983	0.928162745031967	0.535918627484017
18	0.520487944648759	0.959024110702481	0.479512055351241
19	0.497287614407888	0.994575228815777	0.502712385592112
20	0.50483742540742	0.99032514918516	0.49516257459258
21	0.456983759621668	0.913967519243337	0.543016240378332
22	0.564024419797397	0.871951160405205	0.435975580202603
23	0.623025739743727	0.753948520512545	0.376974260256273
24	0.665120866866675	0.66975826626665	0.334879133133325
25	0.75281645751648	0.494367084967039	0.247183542483519
26	0.8310576934753	0.3378846130494	0.1689423065247
27	0.923974538082534	0.152050923834932	0.076025461917466
28	0.943278779721542	0.113442440556917	0.0567212202784585
29	0.96307550906629	0.0738489818674179	0.0369244909337089
30	0.98704577675493	0.0259084464901413	0.0129542232450706
31	0.989769046034356	0.020461907931288	0.010230953965644
32	0.994630555697168	0.0107388886056639	0.00536944430283193
33	0.994288942542887	0.0114221149142253	0.00571105745711265
34	0.99300200657904	0.0139959868419201	0.00699799342096005
35	0.994060604180665	0.0118787916386701	0.00593939581933504
36	0.990009530158103	0.0199809396837944	0.00999046984189722
37	0.990414236008613	0.0191715279827733	0.00958576399138664
38	0.989596054003116	0.0208078919937676	0.0104039459968838
39	0.982581575771846	0.0348368484563071	0.0174184242281536
40	0.981285834854485	0.0374283302910302	0.0187141651455151
41	0.971934850741305	0.0561302985173909	0.0280651492586955
42	0.976133438930852	0.0477331221382952	0.0238665610691476
43	0.98321812228769	0.0335637554246196	0.0167818777123098
44	0.941674482385823	0.116651035228353	0.0583255176141767

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	13	0.448275862068966	NOK
10% type I error level	15	0.517241379310345	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 & 0 & 0 & OK \tabularnewline
5% type I error level & 13 & 0.448275862068966 & NOK \tabularnewline
10% type I error level & 15 & 0.517241379310345 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57832&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]13[/C][C]0.448275862068966[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]15[/C][C]0.517241379310345[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57832&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57832&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	0	0	OK
5% type I error level	13	0.448275862068966	NOK
10% type I error level	15	0.517241379310345	NOK

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code