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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Fri, 20 Nov 2009 05:28:48 -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/20/t1258720196sm5uhx6m9of3rzz.htm/, Retrieved Sat, 20 Apr 2024 06:38:08 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58081, Retrieved Sat, 20 Apr 2024 06:38:08 +0000

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Estimated Impact

129

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-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [ws77777] [2009-11-20 12:28:48] [9a1fef436e1d399a5ecd6808bfbd8489] [Current]

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101,8612953	1	118,1540031	105,5073942	95,84395716	100
109,8419174	1	101,8612953	118,1540031	105,5073942	95,84395716
105,6348802	1	109,8419174	101,8612953	118,1540031	105,5073942
112,927078	1	105,6348802	109,8419174	101,8612953	118,1540031
133,0698623	1	112,927078	105,6348802	109,8419174	101,8612953
125,6756757	1	133,0698623	112,927078	105,6348802	109,8419174
146,736359	1	125,6756757	133,0698623	112,927078	105,6348802
142,5803162	1	146,736359	125,6756757	133,0698623	112,927078
106,1448241	1	142,5803162	146,736359	125,6756757	133,0698623
126,5170831	1	106,1448241	142,5803162	146,736359	125,6756757
132,7893932	1	126,5170831	106,1448241	142,5803162	146,736359
121,2391637	1	132,7893932	126,5170831	106,1448241	142,5803162
114,5079041	1	121,2391637	132,7893932	126,5170831	106,1448241
146,1499235	1	114,5079041	121,2391637	132,7893932	126,5170831
146,1244263	1	146,1499235	114,5079041	121,2391637	132,7893932
128,5058644	1	146,1244263	146,1499235	114,5079041	121,2391637
155,5838858	1	128,5058644	146,1244263	146,1499235	114,5079041
125,0382458	1	155,5838858	128,5058644	146,1244263	146,1499235
136,8944416	1	125,0382458	155,5838858	128,5058644	146,1244263
142,2233554	1	136,8944416	125,0382458	155,5838858	128,5058644
117,7715451	1	142,2233554	136,8944416	125,0382458	155,5838858
120,627231	1	117,7715451	142,2233554	136,8944416	125,0382458
127,7664457	1	120,627231	117,7715451	142,2233554	136,8944416
135,1096379	1	127,7664457	120,627231	117,7715451	142,2233554
105,7113717	1	135,1096379	127,7664457	120,627231	117,7715451
117,9245283	1	105,7113717	135,1096379	127,7664457	120,627231
120,754717	1	117,9245283	105,7113717	135,1096379	127,7664457
107,572667	1	120,754717	117,9245283	105,7113717	135,1096379
130,4436512	1	107,572667	120,754717	117,9245283	105,7113717
107,2157063	1	130,4436512	107,572667	120,754717	117,9245283
105,0739419	1	107,2157063	130,4436512	107,572667	120,754717
130,1121877	1	105,0739419	107,2157063	130,4436512	107,572667
109,6379398	1	130,1121877	105,0739419	107,2157063	130,4436512
116,7261601	1	109,6379398	130,1121877	105,0739419	107,2157063
97,11881693	0	116,7261601	109,6379398	130,1121877	105,0739419
140,8975013	1	97,11881693	116,7261601	109,6379398	130,1121877
108,2865885	1	140,8975013	97,11881693	116,7261601	109,6379398
97,65425803	0	108,2865885	140,8975013	97,11881693	116,7261601
112,0346762	1	97,65425803	108,2865885	140,8975013	97,11881693
123,0494646	1	112,0346762	97,65425803	108,2865885	140,8975013
112,4171341	1	123,0494646	112,0346762	97,65425803	108,2865885
116,4966854	1	112,4171341	123,0494646	112,0346762	97,65425803
104,6914839	1	116,4966854	112,4171341	123,0494646	112,0346762
122,2335543	1	104,6914839	116,4966854	112,4171341	123,0494646
99,79602244	0	122,2335543	104,6914839	116,4966854	112,4171341
96,71086181	0	99,79602244	122,2335543	104,6914839	116,4966854
112,3151453	1	96,71086181	99,79602244	122,2335543	104,6914839
102,5497195	1	112,3151453	96,71086181	99,79602244	122,2335543
104,5385008	1	102,5497195	112,3151453	96,71086181	99,79602244
122,0805711	1	104,5385008	102,5497195	112,3151453	96,71086181
80,64762876	0	122,0805711	104,5385008	102,5497195	112,3151453
91,40744518	0	80,64762876	122,0805711	104,5385008	102,5497195
99,51555329	0	91,40744518	80,64762876	122,0805711	104,5385008
106,527282	1	99,51555329	91,40744518	80,64762876	122,0805711
98,49566548	0	106,527282	99,51555329	91,40744518	80,64762876
106,7567568	1	98,49566548	106,527282	99,51555329	91,40744518

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=58081&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=58081&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58081&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
Y[t] = + 53.8696872319329 + 15.8488814290603X[t] + 0.133329923008086Y1[t] + 0.0532221071152637Y2[t] + 0.273745155025843Y3[t] + 0.0670592193928907Y4[t] -18.6035909016196M1[t] -3.10158370166338M2[t] -11.8809514270766M3[t] -7.94282530553286M4[t] + 3.99151966561568M5[t] -10.0326693378833M6[t] -3.40836044891388M7[t] + 0.564263857054042M8[t] -18.5636243442575M9[t] -9.04511375520523M10[t] -9.35771026063743M11[t] -0.174195328979543t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  53.8696872319329 +  15.8488814290603X[t] +  0.133329923008086Y1[t] +  0.0532221071152637Y2[t] +  0.273745155025843Y3[t] +  0.0670592193928907Y4[t] -18.6035909016196M1[t] -3.10158370166338M2[t] -11.8809514270766M3[t] -7.94282530553286M4[t] +  3.99151966561568M5[t] -10.0326693378833M6[t] -3.40836044891388M7[t] +  0.564263857054042M8[t] -18.5636243442575M9[t] -9.04511375520523M10[t] -9.35771026063743M11[t] -0.174195328979543t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58081&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  53.8696872319329 +  15.8488814290603X[t] +  0.133329923008086Y1[t] +  0.0532221071152637Y2[t] +  0.273745155025843Y3[t] +  0.0670592193928907Y4[t] -18.6035909016196M1[t] -3.10158370166338M2[t] -11.8809514270766M3[t] -7.94282530553286M4[t] +  3.99151966561568M5[t] -10.0326693378833M6[t] -3.40836044891388M7[t] +  0.564263857054042M8[t] -18.5636243442575M9[t] -9.04511375520523M10[t] -9.35771026063743M11[t] -0.174195328979543t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58081&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58081&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 53.8696872319329 + 15.8488814290603X[t] + 0.133329923008086Y1[t] + 0.0532221071152637Y2[t] + 0.273745155025843Y3[t] + 0.0670592193928907Y4[t] -18.6035909016196M1[t] -3.10158370166338M2[t] -11.8809514270766M3[t] -7.94282530553286M4[t] + 3.99151966561568M5[t] -10.0326693378833M6[t] -3.40836044891388M7[t] + 0.564263857054042M8[t] -18.5636243442575M9[t] -9.04511375520523M10[t] -9.35771026063743M11[t] -0.174195328979543t + 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)	53.8696872319329	22.794464	2.3633	0.023333	0.011666
X	15.8488814290603	5.162977	3.0697	0.003943	0.001971
Y1	0.133329923008086	0.142827	0.9335	0.356451	0.178226
Y2	0.0532221071152637	0.133863	0.3976	0.693159	0.34658
Y3	0.273745155025843	0.131522	2.0814	0.04419	0.022095
Y4	0.0670592193928907	0.141487	0.474	0.638241	0.31912
M1	-18.6035909016196	8.435979	-2.2053	0.033559	0.016779
M2	-3.10158370166338	8.170224	-0.3796	0.706341	0.35317
M3	-11.8809514270766	8.303704	-1.4308	0.16066	0.08033
M4	-7.94282530553286	7.447672	-1.0665	0.292934	0.146467
M5	3.99151966561568	8.380269	0.4763	0.636588	0.318294
M6	-10.0326693378833	7.867426	-1.2752	0.209974	0.104987
M7	-3.40836044891388	8.056909	-0.423	0.674654	0.337327
M8	0.564263857054042	8.458629	0.0667	0.947163	0.473582
M9	-18.5636243442575	8.116399	-2.2872	0.027844	0.013922
M10	-9.04511375520523	8.747493	-1.034	0.307661	0.153831
M11	-9.35771026063743	8.863449	-1.0558	0.297742	0.148871
t	-0.174195328979543	0.126072	-1.3817	0.175129	0.087564

\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) & 53.8696872319329 & 22.794464 & 2.3633 & 0.023333 & 0.011666 \tabularnewline
X & 15.8488814290603 & 5.162977 & 3.0697 & 0.003943 & 0.001971 \tabularnewline
Y1 & 0.133329923008086 & 0.142827 & 0.9335 & 0.356451 & 0.178226 \tabularnewline
Y2 & 0.0532221071152637 & 0.133863 & 0.3976 & 0.693159 & 0.34658 \tabularnewline
Y3 & 0.273745155025843 & 0.131522 & 2.0814 & 0.04419 & 0.022095 \tabularnewline
Y4 & 0.0670592193928907 & 0.141487 & 0.474 & 0.638241 & 0.31912 \tabularnewline
M1 & -18.6035909016196 & 8.435979 & -2.2053 & 0.033559 & 0.016779 \tabularnewline
M2 & -3.10158370166338 & 8.170224 & -0.3796 & 0.706341 & 0.35317 \tabularnewline
M3 & -11.8809514270766 & 8.303704 & -1.4308 & 0.16066 & 0.08033 \tabularnewline
M4 & -7.94282530553286 & 7.447672 & -1.0665 & 0.292934 & 0.146467 \tabularnewline
M5 & 3.99151966561568 & 8.380269 & 0.4763 & 0.636588 & 0.318294 \tabularnewline
M6 & -10.0326693378833 & 7.867426 & -1.2752 & 0.209974 & 0.104987 \tabularnewline
M7 & -3.40836044891388 & 8.056909 & -0.423 & 0.674654 & 0.337327 \tabularnewline
M8 & 0.564263857054042 & 8.458629 & 0.0667 & 0.947163 & 0.473582 \tabularnewline
M9 & -18.5636243442575 & 8.116399 & -2.2872 & 0.027844 & 0.013922 \tabularnewline
M10 & -9.04511375520523 & 8.747493 & -1.034 & 0.307661 & 0.153831 \tabularnewline
M11 & -9.35771026063743 & 8.863449 & -1.0558 & 0.297742 & 0.148871 \tabularnewline
t & -0.174195328979543 & 0.126072 & -1.3817 & 0.175129 & 0.087564 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58081&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]53.8696872319329[/C][C]22.794464[/C][C]2.3633[/C][C]0.023333[/C][C]0.011666[/C][/ROW]
[ROW][C]X[/C][C]15.8488814290603[/C][C]5.162977[/C][C]3.0697[/C][C]0.003943[/C][C]0.001971[/C][/ROW]
[ROW][C]Y1[/C][C]0.133329923008086[/C][C]0.142827[/C][C]0.9335[/C][C]0.356451[/C][C]0.178226[/C][/ROW]
[ROW][C]Y2[/C][C]0.0532221071152637[/C][C]0.133863[/C][C]0.3976[/C][C]0.693159[/C][C]0.34658[/C][/ROW]
[ROW][C]Y3[/C][C]0.273745155025843[/C][C]0.131522[/C][C]2.0814[/C][C]0.04419[/C][C]0.022095[/C][/ROW]
[ROW][C]Y4[/C][C]0.0670592193928907[/C][C]0.141487[/C][C]0.474[/C][C]0.638241[/C][C]0.31912[/C][/ROW]
[ROW][C]M1[/C][C]-18.6035909016196[/C][C]8.435979[/C][C]-2.2053[/C][C]0.033559[/C][C]0.016779[/C][/ROW]
[ROW][C]M2[/C][C]-3.10158370166338[/C][C]8.170224[/C][C]-0.3796[/C][C]0.706341[/C][C]0.35317[/C][/ROW]
[ROW][C]M3[/C][C]-11.8809514270766[/C][C]8.303704[/C][C]-1.4308[/C][C]0.16066[/C][C]0.08033[/C][/ROW]
[ROW][C]M4[/C][C]-7.94282530553286[/C][C]7.447672[/C][C]-1.0665[/C][C]0.292934[/C][C]0.146467[/C][/ROW]
[ROW][C]M5[/C][C]3.99151966561568[/C][C]8.380269[/C][C]0.4763[/C][C]0.636588[/C][C]0.318294[/C][/ROW]
[ROW][C]M6[/C][C]-10.0326693378833[/C][C]7.867426[/C][C]-1.2752[/C][C]0.209974[/C][C]0.104987[/C][/ROW]
[ROW][C]M7[/C][C]-3.40836044891388[/C][C]8.056909[/C][C]-0.423[/C][C]0.674654[/C][C]0.337327[/C][/ROW]
[ROW][C]M8[/C][C]0.564263857054042[/C][C]8.458629[/C][C]0.0667[/C][C]0.947163[/C][C]0.473582[/C][/ROW]
[ROW][C]M9[/C][C]-18.5636243442575[/C][C]8.116399[/C][C]-2.2872[/C][C]0.027844[/C][C]0.013922[/C][/ROW]
[ROW][C]M10[/C][C]-9.04511375520523[/C][C]8.747493[/C][C]-1.034[/C][C]0.307661[/C][C]0.153831[/C][/ROW]
[ROW][C]M11[/C][C]-9.35771026063743[/C][C]8.863449[/C][C]-1.0558[/C][C]0.297742[/C][C]0.148871[/C][/ROW]
[ROW][C]t[/C][C]-0.174195328979543[/C][C]0.126072[/C][C]-1.3817[/C][C]0.175129[/C][C]0.087564[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58081&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58081&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)	53.8696872319329	22.794464	2.3633	0.023333	0.011666
X	15.8488814290603	5.162977	3.0697	0.003943	0.001971
Y1	0.133329923008086	0.142827	0.9335	0.356451	0.178226
Y2	0.0532221071152637	0.133863	0.3976	0.693159	0.34658
Y3	0.273745155025843	0.131522	2.0814	0.04419	0.022095
Y4	0.0670592193928907	0.141487	0.474	0.638241	0.31912
M1	-18.6035909016196	8.435979	-2.2053	0.033559	0.016779
M2	-3.10158370166338	8.170224	-0.3796	0.706341	0.35317
M3	-11.8809514270766	8.303704	-1.4308	0.16066	0.08033
M4	-7.94282530553286	7.447672	-1.0665	0.292934	0.146467
M5	3.99151966561568	8.380269	0.4763	0.636588	0.318294
M6	-10.0326693378833	7.867426	-1.2752	0.209974	0.104987
M7	-3.40836044891388	8.056909	-0.423	0.674654	0.337327
M8	0.564263857054042	8.458629	0.0667	0.947163	0.473582
M9	-18.5636243442575	8.116399	-2.2872	0.027844	0.013922
M10	-9.04511375520523	8.747493	-1.034	0.307661	0.153831
M11	-9.35771026063743	8.863449	-1.0558	0.297742	0.148871
t	-0.174195328979543	0.126072	-1.3817	0.175129	0.087564

Multiple Linear Regression - Regression Statistics
Multiple R	0.829265907056123
R-squared	0.687681944605614
Adjusted R-squared	0.547960709297599
F-TEST (value)	4.92181408996007
F-TEST (DF numerator)	17
F-TEST (DF denominator)	38
p-value	2.20257367186116e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	10.7884149852318
Sum Squared Residuals	4422.81611995582

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.829265907056123 \tabularnewline
R-squared & 0.687681944605614 \tabularnewline
Adjusted R-squared & 0.547960709297599 \tabularnewline
F-TEST (value) & 4.92181408996007 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 38 \tabularnewline
p-value & 2.20257367186116e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 10.7884149852318 \tabularnewline
Sum Squared Residuals & 4422.81611995582 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58081&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.829265907056123[/C][/ROW]
[ROW][C]R-squared[/C][C]0.687681944605614[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.547960709297599[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.92181408996007[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]38[/C][/ROW]
[ROW][C]p-value[/C][C]2.20257367186116e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]10.7884149852318[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4422.81611995582[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58081&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58081&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	0.829265907056123
R-squared	0.687681944605614
Adjusted R-squared	0.547960709297599
F-TEST (value)	4.92181408996007
F-TEST (DF numerator)	17
F-TEST (DF denominator)	38
p-value	2.20257367186116e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	10.7884149852318
Sum Squared Residuals	4422.81611995582

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.8612953	105.252313252722	-3.39101795272242
2	109.8419174	121.447516902636	-11.6055995026361
3	105.6348802	116.800847797100	-11.1659675971001
4	112.927078	116.816622066248	-3.88954406624821
5	133.0698623	130.417212862257	2.65264943774267
6	125.6756757	118.676088779866	6.99958692013424
7	146.736359	126.926460620036	19.8098983799643
8	142.5803162	139.142373391638	3.43794280836247
9	106.1448241	119.733695571768	-13.5888714717678
10	126.5170831	129.268287753776	-2.75120465377585
11	132.7893932	129.833170381390	2.95622281860982
12	121.2391637	130.684486067153	-9.44532236715297
13	114.5079041	113.834005726355	0.673898373644709
14	146.1499235	130.732774007966	15.4171494920339
15	146.1244263	122.898583998578	23.2258423014216
16	128.5058644	125.725971120480	2.77989327952023
17	155.5838858	143.346138737976	12.2377470620241
18	125.0382458	133.935277311324	-8.89703151132393
19	136.8944416	132.929186616325	3.9652549836753
20	142.2233554	142.913688100608	-0.690332700608349
21	117.7715451	118.407229980460	-0.635684880459542
22	120.627231	125.972212659928	-5.34498165992803
23	127.7664457	126.818623909367	0.947821790632525
24	135.1096379	130.769783606576	4.3398542934241
25	105.7113717	112.493039544058	-6.7816678440582
26	117.9245283	126.437828509954	-8.51330020995356
27	120.754717	120.036920464059	0.71779653594113
28	107.572667	117.273005825247	-9.70033882524707
29	130.4436512	128.798090025921	1.64556117407938
30	107.2157063	118.541270971202	-11.3255646712015
31	105.0739419	119.692914321110	-14.6189724211104
32	130.1121877	127.346384976435	2.76580272356536
33	109.6379398	106.443832586220	3.19410721378048
34	116.7261601	112.246960669281	4.47919943071924
35	97.11881693	102.477150087142	-5.35833315714248
36	140.8975013	121.346869965567	19.5506313344331
37	108.2865885	107.929927116026	0.356661383973961
38	97.65425803	100.498756218487	-2.8444981884867
39	112.0346762	114.910194908369	-2.87551870836887
40	123.0494646	114.034275737134	9.01518886286626
41	112.4171341	122.930971115977	-10.5138370159773
42	116.4966854	111.124783244252	5.3719021557483
43	104.6914839	121.532532607381	-16.8410487073806
44	122.2335543	121.802191444423	0.431362855576731
45	99.79602244	88.7655733015532	11.0304491384468
46	96.71086181	93.0938749270153	3.61698688298465
47	112.3151453	110.860856752100	1.45428854790014
48	102.5497195	116.994882760704	-14.4451632607043
49	104.5385008	95.396374760838	9.14212603916197
50	122.0805711	114.534322690958	7.54624840904241
51	80.64762876	90.5497812918938	-9.90215253189376
52	91.40744518	89.6126444308912	1.7948007491088
53	99.51555329	105.537673947869	-6.02212065786882
54	106.527282	98.676174893357	7.85110710664293
55	98.49566548	90.8107977151485	7.68486776485149
56	106.7567568	112.701532486896	-5.94477568689619

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.8612953 & 105.252313252722 & -3.39101795272242 \tabularnewline
2 & 109.8419174 & 121.447516902636 & -11.6055995026361 \tabularnewline
3 & 105.6348802 & 116.800847797100 & -11.1659675971001 \tabularnewline
4 & 112.927078 & 116.816622066248 & -3.88954406624821 \tabularnewline
5 & 133.0698623 & 130.417212862257 & 2.65264943774267 \tabularnewline
6 & 125.6756757 & 118.676088779866 & 6.99958692013424 \tabularnewline
7 & 146.736359 & 126.926460620036 & 19.8098983799643 \tabularnewline
8 & 142.5803162 & 139.142373391638 & 3.43794280836247 \tabularnewline
9 & 106.1448241 & 119.733695571768 & -13.5888714717678 \tabularnewline
10 & 126.5170831 & 129.268287753776 & -2.75120465377585 \tabularnewline
11 & 132.7893932 & 129.833170381390 & 2.95622281860982 \tabularnewline
12 & 121.2391637 & 130.684486067153 & -9.44532236715297 \tabularnewline
13 & 114.5079041 & 113.834005726355 & 0.673898373644709 \tabularnewline
14 & 146.1499235 & 130.732774007966 & 15.4171494920339 \tabularnewline
15 & 146.1244263 & 122.898583998578 & 23.2258423014216 \tabularnewline
16 & 128.5058644 & 125.725971120480 & 2.77989327952023 \tabularnewline
17 & 155.5838858 & 143.346138737976 & 12.2377470620241 \tabularnewline
18 & 125.0382458 & 133.935277311324 & -8.89703151132393 \tabularnewline
19 & 136.8944416 & 132.929186616325 & 3.9652549836753 \tabularnewline
20 & 142.2233554 & 142.913688100608 & -0.690332700608349 \tabularnewline
21 & 117.7715451 & 118.407229980460 & -0.635684880459542 \tabularnewline
22 & 120.627231 & 125.972212659928 & -5.34498165992803 \tabularnewline
23 & 127.7664457 & 126.818623909367 & 0.947821790632525 \tabularnewline
24 & 135.1096379 & 130.769783606576 & 4.3398542934241 \tabularnewline
25 & 105.7113717 & 112.493039544058 & -6.7816678440582 \tabularnewline
26 & 117.9245283 & 126.437828509954 & -8.51330020995356 \tabularnewline
27 & 120.754717 & 120.036920464059 & 0.71779653594113 \tabularnewline
28 & 107.572667 & 117.273005825247 & -9.70033882524707 \tabularnewline
29 & 130.4436512 & 128.798090025921 & 1.64556117407938 \tabularnewline
30 & 107.2157063 & 118.541270971202 & -11.3255646712015 \tabularnewline
31 & 105.0739419 & 119.692914321110 & -14.6189724211104 \tabularnewline
32 & 130.1121877 & 127.346384976435 & 2.76580272356536 \tabularnewline
33 & 109.6379398 & 106.443832586220 & 3.19410721378048 \tabularnewline
34 & 116.7261601 & 112.246960669281 & 4.47919943071924 \tabularnewline
35 & 97.11881693 & 102.477150087142 & -5.35833315714248 \tabularnewline
36 & 140.8975013 & 121.346869965567 & 19.5506313344331 \tabularnewline
37 & 108.2865885 & 107.929927116026 & 0.356661383973961 \tabularnewline
38 & 97.65425803 & 100.498756218487 & -2.8444981884867 \tabularnewline
39 & 112.0346762 & 114.910194908369 & -2.87551870836887 \tabularnewline
40 & 123.0494646 & 114.034275737134 & 9.01518886286626 \tabularnewline
41 & 112.4171341 & 122.930971115977 & -10.5138370159773 \tabularnewline
42 & 116.4966854 & 111.124783244252 & 5.3719021557483 \tabularnewline
43 & 104.6914839 & 121.532532607381 & -16.8410487073806 \tabularnewline
44 & 122.2335543 & 121.802191444423 & 0.431362855576731 \tabularnewline
45 & 99.79602244 & 88.7655733015532 & 11.0304491384468 \tabularnewline
46 & 96.71086181 & 93.0938749270153 & 3.61698688298465 \tabularnewline
47 & 112.3151453 & 110.860856752100 & 1.45428854790014 \tabularnewline
48 & 102.5497195 & 116.994882760704 & -14.4451632607043 \tabularnewline
49 & 104.5385008 & 95.396374760838 & 9.14212603916197 \tabularnewline
50 & 122.0805711 & 114.534322690958 & 7.54624840904241 \tabularnewline
51 & 80.64762876 & 90.5497812918938 & -9.90215253189376 \tabularnewline
52 & 91.40744518 & 89.6126444308912 & 1.7948007491088 \tabularnewline
53 & 99.51555329 & 105.537673947869 & -6.02212065786882 \tabularnewline
54 & 106.527282 & 98.676174893357 & 7.85110710664293 \tabularnewline
55 & 98.49566548 & 90.8107977151485 & 7.68486776485149 \tabularnewline
56 & 106.7567568 & 112.701532486896 & -5.94477568689619 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58081&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]101.8612953[/C][C]105.252313252722[/C][C]-3.39101795272242[/C][/ROW]
[ROW][C]2[/C][C]109.8419174[/C][C]121.447516902636[/C][C]-11.6055995026361[/C][/ROW]
[ROW][C]3[/C][C]105.6348802[/C][C]116.800847797100[/C][C]-11.1659675971001[/C][/ROW]
[ROW][C]4[/C][C]112.927078[/C][C]116.816622066248[/C][C]-3.88954406624821[/C][/ROW]
[ROW][C]5[/C][C]133.0698623[/C][C]130.417212862257[/C][C]2.65264943774267[/C][/ROW]
[ROW][C]6[/C][C]125.6756757[/C][C]118.676088779866[/C][C]6.99958692013424[/C][/ROW]
[ROW][C]7[/C][C]146.736359[/C][C]126.926460620036[/C][C]19.8098983799643[/C][/ROW]
[ROW][C]8[/C][C]142.5803162[/C][C]139.142373391638[/C][C]3.43794280836247[/C][/ROW]
[ROW][C]9[/C][C]106.1448241[/C][C]119.733695571768[/C][C]-13.5888714717678[/C][/ROW]
[ROW][C]10[/C][C]126.5170831[/C][C]129.268287753776[/C][C]-2.75120465377585[/C][/ROW]
[ROW][C]11[/C][C]132.7893932[/C][C]129.833170381390[/C][C]2.95622281860982[/C][/ROW]
[ROW][C]12[/C][C]121.2391637[/C][C]130.684486067153[/C][C]-9.44532236715297[/C][/ROW]
[ROW][C]13[/C][C]114.5079041[/C][C]113.834005726355[/C][C]0.673898373644709[/C][/ROW]
[ROW][C]14[/C][C]146.1499235[/C][C]130.732774007966[/C][C]15.4171494920339[/C][/ROW]
[ROW][C]15[/C][C]146.1244263[/C][C]122.898583998578[/C][C]23.2258423014216[/C][/ROW]
[ROW][C]16[/C][C]128.5058644[/C][C]125.725971120480[/C][C]2.77989327952023[/C][/ROW]
[ROW][C]17[/C][C]155.5838858[/C][C]143.346138737976[/C][C]12.2377470620241[/C][/ROW]
[ROW][C]18[/C][C]125.0382458[/C][C]133.935277311324[/C][C]-8.89703151132393[/C][/ROW]
[ROW][C]19[/C][C]136.8944416[/C][C]132.929186616325[/C][C]3.9652549836753[/C][/ROW]
[ROW][C]20[/C][C]142.2233554[/C][C]142.913688100608[/C][C]-0.690332700608349[/C][/ROW]
[ROW][C]21[/C][C]117.7715451[/C][C]118.407229980460[/C][C]-0.635684880459542[/C][/ROW]
[ROW][C]22[/C][C]120.627231[/C][C]125.972212659928[/C][C]-5.34498165992803[/C][/ROW]
[ROW][C]23[/C][C]127.7664457[/C][C]126.818623909367[/C][C]0.947821790632525[/C][/ROW]
[ROW][C]24[/C][C]135.1096379[/C][C]130.769783606576[/C][C]4.3398542934241[/C][/ROW]
[ROW][C]25[/C][C]105.7113717[/C][C]112.493039544058[/C][C]-6.7816678440582[/C][/ROW]
[ROW][C]26[/C][C]117.9245283[/C][C]126.437828509954[/C][C]-8.51330020995356[/C][/ROW]
[ROW][C]27[/C][C]120.754717[/C][C]120.036920464059[/C][C]0.71779653594113[/C][/ROW]
[ROW][C]28[/C][C]107.572667[/C][C]117.273005825247[/C][C]-9.70033882524707[/C][/ROW]
[ROW][C]29[/C][C]130.4436512[/C][C]128.798090025921[/C][C]1.64556117407938[/C][/ROW]
[ROW][C]30[/C][C]107.2157063[/C][C]118.541270971202[/C][C]-11.3255646712015[/C][/ROW]
[ROW][C]31[/C][C]105.0739419[/C][C]119.692914321110[/C][C]-14.6189724211104[/C][/ROW]
[ROW][C]32[/C][C]130.1121877[/C][C]127.346384976435[/C][C]2.76580272356536[/C][/ROW]
[ROW][C]33[/C][C]109.6379398[/C][C]106.443832586220[/C][C]3.19410721378048[/C][/ROW]
[ROW][C]34[/C][C]116.7261601[/C][C]112.246960669281[/C][C]4.47919943071924[/C][/ROW]
[ROW][C]35[/C][C]97.11881693[/C][C]102.477150087142[/C][C]-5.35833315714248[/C][/ROW]
[ROW][C]36[/C][C]140.8975013[/C][C]121.346869965567[/C][C]19.5506313344331[/C][/ROW]
[ROW][C]37[/C][C]108.2865885[/C][C]107.929927116026[/C][C]0.356661383973961[/C][/ROW]
[ROW][C]38[/C][C]97.65425803[/C][C]100.498756218487[/C][C]-2.8444981884867[/C][/ROW]
[ROW][C]39[/C][C]112.0346762[/C][C]114.910194908369[/C][C]-2.87551870836887[/C][/ROW]
[ROW][C]40[/C][C]123.0494646[/C][C]114.034275737134[/C][C]9.01518886286626[/C][/ROW]
[ROW][C]41[/C][C]112.4171341[/C][C]122.930971115977[/C][C]-10.5138370159773[/C][/ROW]
[ROW][C]42[/C][C]116.4966854[/C][C]111.124783244252[/C][C]5.3719021557483[/C][/ROW]
[ROW][C]43[/C][C]104.6914839[/C][C]121.532532607381[/C][C]-16.8410487073806[/C][/ROW]
[ROW][C]44[/C][C]122.2335543[/C][C]121.802191444423[/C][C]0.431362855576731[/C][/ROW]
[ROW][C]45[/C][C]99.79602244[/C][C]88.7655733015532[/C][C]11.0304491384468[/C][/ROW]
[ROW][C]46[/C][C]96.71086181[/C][C]93.0938749270153[/C][C]3.61698688298465[/C][/ROW]
[ROW][C]47[/C][C]112.3151453[/C][C]110.860856752100[/C][C]1.45428854790014[/C][/ROW]
[ROW][C]48[/C][C]102.5497195[/C][C]116.994882760704[/C][C]-14.4451632607043[/C][/ROW]
[ROW][C]49[/C][C]104.5385008[/C][C]95.396374760838[/C][C]9.14212603916197[/C][/ROW]
[ROW][C]50[/C][C]122.0805711[/C][C]114.534322690958[/C][C]7.54624840904241[/C][/ROW]
[ROW][C]51[/C][C]80.64762876[/C][C]90.5497812918938[/C][C]-9.90215253189376[/C][/ROW]
[ROW][C]52[/C][C]91.40744518[/C][C]89.6126444308912[/C][C]1.7948007491088[/C][/ROW]
[ROW][C]53[/C][C]99.51555329[/C][C]105.537673947869[/C][C]-6.02212065786882[/C][/ROW]
[ROW][C]54[/C][C]106.527282[/C][C]98.676174893357[/C][C]7.85110710664293[/C][/ROW]
[ROW][C]55[/C][C]98.49566548[/C][C]90.8107977151485[/C][C]7.68486776485149[/C][/ROW]
[ROW][C]56[/C][C]106.7567568[/C][C]112.701532486896[/C][C]-5.94477568689619[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58081&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58081&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	101.8612953	105.252313252722	-3.39101795272242
2	109.8419174	121.447516902636	-11.6055995026361
3	105.6348802	116.800847797100	-11.1659675971001
4	112.927078	116.816622066248	-3.88954406624821
5	133.0698623	130.417212862257	2.65264943774267
6	125.6756757	118.676088779866	6.99958692013424
7	146.736359	126.926460620036	19.8098983799643
8	142.5803162	139.142373391638	3.43794280836247
9	106.1448241	119.733695571768	-13.5888714717678
10	126.5170831	129.268287753776	-2.75120465377585
11	132.7893932	129.833170381390	2.95622281860982
12	121.2391637	130.684486067153	-9.44532236715297
13	114.5079041	113.834005726355	0.673898373644709
14	146.1499235	130.732774007966	15.4171494920339
15	146.1244263	122.898583998578	23.2258423014216
16	128.5058644	125.725971120480	2.77989327952023
17	155.5838858	143.346138737976	12.2377470620241
18	125.0382458	133.935277311324	-8.89703151132393
19	136.8944416	132.929186616325	3.9652549836753
20	142.2233554	142.913688100608	-0.690332700608349
21	117.7715451	118.407229980460	-0.635684880459542
22	120.627231	125.972212659928	-5.34498165992803
23	127.7664457	126.818623909367	0.947821790632525
24	135.1096379	130.769783606576	4.3398542934241
25	105.7113717	112.493039544058	-6.7816678440582
26	117.9245283	126.437828509954	-8.51330020995356
27	120.754717	120.036920464059	0.71779653594113
28	107.572667	117.273005825247	-9.70033882524707
29	130.4436512	128.798090025921	1.64556117407938
30	107.2157063	118.541270971202	-11.3255646712015
31	105.0739419	119.692914321110	-14.6189724211104
32	130.1121877	127.346384976435	2.76580272356536
33	109.6379398	106.443832586220	3.19410721378048
34	116.7261601	112.246960669281	4.47919943071924
35	97.11881693	102.477150087142	-5.35833315714248
36	140.8975013	121.346869965567	19.5506313344331
37	108.2865885	107.929927116026	0.356661383973961
38	97.65425803	100.498756218487	-2.8444981884867
39	112.0346762	114.910194908369	-2.87551870836887
40	123.0494646	114.034275737134	9.01518886286626
41	112.4171341	122.930971115977	-10.5138370159773
42	116.4966854	111.124783244252	5.3719021557483
43	104.6914839	121.532532607381	-16.8410487073806
44	122.2335543	121.802191444423	0.431362855576731
45	99.79602244	88.7655733015532	11.0304491384468
46	96.71086181	93.0938749270153	3.61698688298465
47	112.3151453	110.860856752100	1.45428854790014
48	102.5497195	116.994882760704	-14.4451632607043
49	104.5385008	95.396374760838	9.14212603916197
50	122.0805711	114.534322690958	7.54624840904241
51	80.64762876	90.5497812918938	-9.90215253189376
52	91.40744518	89.6126444308912	1.7948007491088
53	99.51555329	105.537673947869	-6.02212065786882
54	106.527282	98.676174893357	7.85110710664293
55	98.49566548	90.8107977151485	7.68486776485149
56	106.7567568	112.701532486896	-5.94477568689619

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
21	0.928239601615206	0.143520796769588	0.0717603983847939
22	0.928767890427032	0.142464219145937	0.0712321095729685
23	0.878340022418805	0.243319955162390	0.121659977581195
24	0.82777064227862	0.344458715442759	0.172229357721379
25	0.811051026809998	0.377897946380003	0.188948973190002
26	0.752190062655804	0.495619874688392	0.247809937344196
27	0.658220725469022	0.683558549061955	0.341779274530978
28	0.594333300686514	0.811333398626973	0.405666699313486
29	0.48837974109318	0.97675948218636	0.51162025890682
30	0.446401505846252	0.892803011692504	0.553598494153748
31	0.47124142380337	0.94248284760674	0.52875857619663
32	0.45479924784171	0.90959849568342	0.54520075215829
33	0.439264208038213	0.878528416076427	0.560735791961787
34	0.348294809685982	0.696589619371965	0.651705190314018
35	0.232739428589462	0.465478857178924	0.767260571410538

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.928239601615206 & 0.143520796769588 & 0.0717603983847939 \tabularnewline
22 & 0.928767890427032 & 0.142464219145937 & 0.0712321095729685 \tabularnewline
23 & 0.878340022418805 & 0.243319955162390 & 0.121659977581195 \tabularnewline
24 & 0.82777064227862 & 0.344458715442759 & 0.172229357721379 \tabularnewline
25 & 0.811051026809998 & 0.377897946380003 & 0.188948973190002 \tabularnewline
26 & 0.752190062655804 & 0.495619874688392 & 0.247809937344196 \tabularnewline
27 & 0.658220725469022 & 0.683558549061955 & 0.341779274530978 \tabularnewline
28 & 0.594333300686514 & 0.811333398626973 & 0.405666699313486 \tabularnewline
29 & 0.48837974109318 & 0.97675948218636 & 0.51162025890682 \tabularnewline
30 & 0.446401505846252 & 0.892803011692504 & 0.553598494153748 \tabularnewline
31 & 0.47124142380337 & 0.94248284760674 & 0.52875857619663 \tabularnewline
32 & 0.45479924784171 & 0.90959849568342 & 0.54520075215829 \tabularnewline
33 & 0.439264208038213 & 0.878528416076427 & 0.560735791961787 \tabularnewline
34 & 0.348294809685982 & 0.696589619371965 & 0.651705190314018 \tabularnewline
35 & 0.232739428589462 & 0.465478857178924 & 0.767260571410538 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58081&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]21[/C][C]0.928239601615206[/C][C]0.143520796769588[/C][C]0.0717603983847939[/C][/ROW]
[ROW][C]22[/C][C]0.928767890427032[/C][C]0.142464219145937[/C][C]0.0712321095729685[/C][/ROW]
[ROW][C]23[/C][C]0.878340022418805[/C][C]0.243319955162390[/C][C]0.121659977581195[/C][/ROW]
[ROW][C]24[/C][C]0.82777064227862[/C][C]0.344458715442759[/C][C]0.172229357721379[/C][/ROW]
[ROW][C]25[/C][C]0.811051026809998[/C][C]0.377897946380003[/C][C]0.188948973190002[/C][/ROW]
[ROW][C]26[/C][C]0.752190062655804[/C][C]0.495619874688392[/C][C]0.247809937344196[/C][/ROW]
[ROW][C]27[/C][C]0.658220725469022[/C][C]0.683558549061955[/C][C]0.341779274530978[/C][/ROW]
[ROW][C]28[/C][C]0.594333300686514[/C][C]0.811333398626973[/C][C]0.405666699313486[/C][/ROW]
[ROW][C]29[/C][C]0.48837974109318[/C][C]0.97675948218636[/C][C]0.51162025890682[/C][/ROW]
[ROW][C]30[/C][C]0.446401505846252[/C][C]0.892803011692504[/C][C]0.553598494153748[/C][/ROW]
[ROW][C]31[/C][C]0.47124142380337[/C][C]0.94248284760674[/C][C]0.52875857619663[/C][/ROW]
[ROW][C]32[/C][C]0.45479924784171[/C][C]0.90959849568342[/C][C]0.54520075215829[/C][/ROW]
[ROW][C]33[/C][C]0.439264208038213[/C][C]0.878528416076427[/C][C]0.560735791961787[/C][/ROW]
[ROW][C]34[/C][C]0.348294809685982[/C][C]0.696589619371965[/C][C]0.651705190314018[/C][/ROW]
[ROW][C]35[/C][C]0.232739428589462[/C][C]0.465478857178924[/C][C]0.767260571410538[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58081&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58081&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
21	0.928239601615206	0.143520796769588	0.0717603983847939
22	0.928767890427032	0.142464219145937	0.0712321095729685
23	0.878340022418805	0.243319955162390	0.121659977581195
24	0.82777064227862	0.344458715442759	0.172229357721379
25	0.811051026809998	0.377897946380003	0.188948973190002
26	0.752190062655804	0.495619874688392	0.247809937344196
27	0.658220725469022	0.683558549061955	0.341779274530978
28	0.594333300686514	0.811333398626973	0.405666699313486
29	0.48837974109318	0.97675948218636	0.51162025890682
30	0.446401505846252	0.892803011692504	0.553598494153748
31	0.47124142380337	0.94248284760674	0.52875857619663
32	0.45479924784171	0.90959849568342	0.54520075215829
33	0.439264208038213	0.878528416076427	0.560735791961787
34	0.348294809685982	0.696589619371965	0.651705190314018
35	0.232739428589462	0.465478857178924	0.767260571410538

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	0	0	OK
10% type I error level	0	0	OK

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58081&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58081&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58081&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	0	0	OK
10% type I error level	0	0	OK

Figure 1

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Figure 2

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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 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code