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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Wed, 17 Dec 2014 12:53:55 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/17/t1418820994wvpzz1xrlt2iu22.htm/, Retrieved Thu, 16 May 2024 10:30:12 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270146, Retrieved Thu, 16 May 2024 10:30:12 +0000

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Estimated Impact

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Testing Mean with unknown Variance - Critical Value] [] [2010-10-25 13:12:27] [b98453cac15ba1066b407e146608df68]
- RMP   [Testing Mean with unknown Variance - Critical Value] [] [2014-10-07 07:47:40] [32b17a345b130fdf5cc88718ed94a974]
- RMPD    [ARIMA Backward Selection] [] [2014-12-15 13:21:35] [1764622206627ac897c737076a0cb4c8]
- R P       [ARIMA Backward Selection] [] [2014-12-15 13:27:30] [32b17a345b130fdf5cc88718ed94a974]
- RMPD          [Multiple Regression] [] [2014-12-17 12:53:55] [baa7d013c3374cabca6c222951a47a9f] [Current]

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1	21	26	50	4	13	12	13	13	21	2	149	96	18	68	12,9
1	21	37	54	5	14	11	11	11	22	0	148	88	39	32	12,8
0	21	67	71	4	16	13	14	10	18	0	158	114	46	62	7,4
1	21	43	54	4	14	11	15	9	23	4	128	69	31	33	6,7
0	21	52	65	9	13	10	14	8	12	0	224	176	67	52	12,6
0	21	52	73	8	15	7	11	26	20	-1	159	114	35	62	14,8
1	23	43	52	11	13	10	13	10	22	0	105	121	52	77	13,3
1	22	84	84	4	20	15	16	10	21	1	159	110	77	76	11,1
0	25	67	42	4	17	12	14	8	19	0	167	158	37	41	8,2
1	21	49	66	6	15	12	14	13	22	3	165	116	32	48	11,4
0	23	70	65	4	16	10	15	11	15	-1	159	181	36	63	6,4
1	21	58	73	4	17	14	13	12	19	4	176	141	69	78	12,0
0	21	68	75	4	11	6	14	24	18	1	54	35	21	19	6,3
0	25	62	72	11	16	12	11	21	15	0	91	80	26	31	11,3
1	21	43	66	4	16	14	12	5	20	-2	163	152	54	66	11,9
1	21	56	70	4	15	11	14	14	21	-4	124	97	36	35	9,3
0	24	74	81	6	14	12	12	9	15	2	121	84	23	45	10,0
1	21	63	69	8	16	13	15	17	23	2	148	101	112	25	13,8
1	24	58	71	5	17	11	14	18	21	-4	221	107	35	44	10,8
0	21	63	68	9	15	7	12	23	25	2	149	112	47	54	11,7
1	22	53	70	4	14	11	12	9	9	2	244	171	37	74	10,9
0	20	57	68	7	14	7	12	14	30	0	148	137	109	80	16,1
1	21	64	67	4	15	12	14	10	23	-3	150	66	20	61	9,9
0	22	53	76	4	17	13	16	8	16	2	153	93	22	41	11,5
1	22	29	70	7	14	9	12	10	16	0	94	105	23	46	8,3
0	21	54	60	12	16	11	12	19	19	4	156	131	32	39	11,7
1	21	58	72	7	15	12	14	11	25	2	132	102	30	34	9,0
1	22	51	71	8	16	12	15	12	23	2	105	120	43	42	10,8
0	20	54	70	4	8	5	14	11	10	-4	151	77	16	39	10,4
0	21	56	64	9	17	13	13	10	14	3	131	108	49	20	12,7
1	21	47	76	4	10	6	16	14	26	2	157	168	43	53	11,8
1	24	50	68	4	16	6	15	11	24	-1	162	75	46	54	13,0
1	22	35	76	4	16	12	13	13	24	-3	163	107	19	49	10,8
1	22	30	65	7	16	11	16	15	18	0	59	62	23	34	12,3
0	21	68	67	4	8	6	16	15	23	1	187	121	59	46	11,3
0	20	56	75	4	14	11	15	14	23	-3	116	97	32	37	11,6
0	23	43	60	4	16	12	13	12	19	3	148	126	19	30	10,9
0	20	67	73	4	19	13	12	13	21	0	155	104	22	28	12,1
0	23	62	63	4	19	14	14	7	18	0	125	148	48	45	13,3
1	21	57	70	4	14	12	14	8	27	0	116	146	23	35	10,1
1	21	54	66	12	13	14	10	20	13	3	138	97	33	41	14,3
0	22	61	64	4	15	11	16	16	28	0	164	118	34	73	9,3
0	21	56	70	5	11	10	14	11	23	2	162	58	48	17	12,5
0	21	41	75	15	9	7	14	26	21	-1	99	63	18	40	7,6
1	21	53	60	10	12	7	15	15	19	3	186	50	33	37	9,2
1	22	46	66	5	13	10	16	20	17	2	188	94	67	28	14,5
1	21	51	59	9	17	12	15	15	25	2	177	127	80	56	12,3
0	21	37	78	4	7	5	13	17	14	-2	139	128	32	50	12,6
0	21	42	67	7	15	10	12	19	16	0	162	146	43	59	13,0
0	21	38	59	5	12	12	12	13	24	-2	108	69	38	27	12,6
0	20	66	66	4	15	11	14	8	20	0	159	186	29	61	13,2
0	21	53	71	4	16	12	15	9	24	6	110	85	32	51	7,7
0	19	49	66	4	14	11	11	12	22	0	96	54	35	35	10,5
1	19	49	72	4	16	12	14	9	22	-2	87	106	29	48	10,9
1	20	59	71	6	13	10	16	14	20	1	97	34	12	25	4,3
1	19	40	59	10	16	9	13	14	10	0	127	60	37	44	10,3
1	20	63	78	4	10	7	11	13	22	2	74	62	51	20	11,4
0	21	34	65	11	12	9	12	16	20	2	114	64	14	26	5,6
0	18	32	65	14	14	10	12	14	22	-3	95	98	20	23	8,8
0	21	67	71	4	16	12	14	11	20	1	121	35	11	21	9,0
0	20	61	72	4	18	14	12	11	17	-4	130	55	35	41	9,6
1	21	60	66	5	12	9	13	14	18	1	52	54	8	22	6,4
1	19	63	69	4	15	12	14	15	19	0	118	51	24	27	11,6

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	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\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 & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270146&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270146&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270146&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	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Totaalscore[t] = + 11.3465 -0.0498912geslacht[t] + 0.0244063leeftijd[t] -0.0560965intrinsieke[t] + 0.0193198extrinsieke[t] -0.144469demotivatie[t] + 0.0187176statistiek[t] + 0.0951307software[t] -0.39529Stress[t] + 0.100445Depressie[t] -0.000893466NumeracyTest[t] -0.0552383TussentijdseTest[t] + 0.00943437LFM[t] + 0.00859947B[t] + 0.0642745PRH[t] -0.00701515CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totaalscore[t] =  +  11.3465 -0.0498912geslacht[t] +  0.0244063leeftijd[t] -0.0560965intrinsieke[t] +  0.0193198extrinsieke[t] -0.144469demotivatie[t] +  0.0187176statistiek[t] +  0.0951307software[t] -0.39529Stress[t] +  0.100445Depressie[t] -0.000893466NumeracyTest[t] -0.0552383TussentijdseTest[t] +  0.00943437LFM[t] +  0.00859947B[t] +  0.0642745PRH[t] -0.00701515CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270146&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totaalscore[t] =  +  11.3465 -0.0498912geslacht[t] +  0.0244063leeftijd[t] -0.0560965intrinsieke[t] +  0.0193198extrinsieke[t] -0.144469demotivatie[t] +  0.0187176statistiek[t] +  0.0951307software[t] -0.39529Stress[t] +  0.100445Depressie[t] -0.000893466NumeracyTest[t] -0.0552383TussentijdseTest[t] +  0.00943437LFM[t] +  0.00859947B[t] +  0.0642745PRH[t] -0.00701515CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270146&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270146&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
Totaalscore[t] = + 11.3465 -0.0498912geslacht[t] + 0.0244063leeftijd[t] -0.0560965intrinsieke[t] + 0.0193198extrinsieke[t] -0.144469demotivatie[t] + 0.0187176statistiek[t] + 0.0951307software[t] -0.39529Stress[t] + 0.100445Depressie[t] -0.000893466NumeracyTest[t] -0.0552383TussentijdseTest[t] + 0.00943437LFM[t] + 0.00859947B[t] + 0.0642745PRH[t] -0.00701515CH[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)	11.3465	5.84139	1.942	0.05809	0.029045
geslacht	-0.0498912	0.562479	-0.0887	0.929698	0.464849
leeftijd	0.0244063	0.206286	0.1183	0.906324	0.453162
intrinsieke	-0.0560965	0.0270157	-2.076	0.0433463	0.0216732
extrinsieke	0.0193198	0.0396009	0.4879	0.627915	0.313957
demotivatie	-0.144469	0.114401	-1.263	0.212877	0.106439
statistiek	0.0187176	0.154459	0.1212	0.904064	0.452032
software	0.0951307	0.170823	0.5569	0.58024	0.29012
Stress	-0.39529	0.184202	-2.146	0.0370709	0.0185355
Depressie	0.100445	0.0757178	1.327	0.19106	0.09553
NumeracyTest	-0.000893466	0.0654279	-0.01366	0.989162	0.494581
TussentijdseTest	-0.0552383	0.122708	-0.4502	0.654665	0.327333
LFM	0.00943437	0.00875541	1.078	0.286735	0.143368
B	0.00859947	0.00966887	0.8894	0.378319	0.18916
PRH	0.0642745	0.0152372	4.218	0.000111315	5.56573e-05
CH	-0.00701515	0.0203105	-0.3454	0.731338	0.365669

\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) & 11.3465 & 5.84139 & 1.942 & 0.05809 & 0.029045 \tabularnewline
geslacht & -0.0498912 & 0.562479 & -0.0887 & 0.929698 & 0.464849 \tabularnewline
leeftijd & 0.0244063 & 0.206286 & 0.1183 & 0.906324 & 0.453162 \tabularnewline
intrinsieke & -0.0560965 & 0.0270157 & -2.076 & 0.0433463 & 0.0216732 \tabularnewline
extrinsieke & 0.0193198 & 0.0396009 & 0.4879 & 0.627915 & 0.313957 \tabularnewline
demotivatie & -0.144469 & 0.114401 & -1.263 & 0.212877 & 0.106439 \tabularnewline
statistiek & 0.0187176 & 0.154459 & 0.1212 & 0.904064 & 0.452032 \tabularnewline
software & 0.0951307 & 0.170823 & 0.5569 & 0.58024 & 0.29012 \tabularnewline
Stress & -0.39529 & 0.184202 & -2.146 & 0.0370709 & 0.0185355 \tabularnewline
Depressie & 0.100445 & 0.0757178 & 1.327 & 0.19106 & 0.09553 \tabularnewline
NumeracyTest & -0.000893466 & 0.0654279 & -0.01366 & 0.989162 & 0.494581 \tabularnewline
TussentijdseTest & -0.0552383 & 0.122708 & -0.4502 & 0.654665 & 0.327333 \tabularnewline
LFM & 0.00943437 & 0.00875541 & 1.078 & 0.286735 & 0.143368 \tabularnewline
B & 0.00859947 & 0.00966887 & 0.8894 & 0.378319 & 0.18916 \tabularnewline
PRH & 0.0642745 & 0.0152372 & 4.218 & 0.000111315 & 5.56573e-05 \tabularnewline
CH & -0.00701515 & 0.0203105 & -0.3454 & 0.731338 & 0.365669 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270146&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]11.3465[/C][C]5.84139[/C][C]1.942[/C][C]0.05809[/C][C]0.029045[/C][/ROW]
[ROW][C]geslacht[/C][C]-0.0498912[/C][C]0.562479[/C][C]-0.0887[/C][C]0.929698[/C][C]0.464849[/C][/ROW]
[ROW][C]leeftijd[/C][C]0.0244063[/C][C]0.206286[/C][C]0.1183[/C][C]0.906324[/C][C]0.453162[/C][/ROW]
[ROW][C]intrinsieke[/C][C]-0.0560965[/C][C]0.0270157[/C][C]-2.076[/C][C]0.0433463[/C][C]0.0216732[/C][/ROW]
[ROW][C]extrinsieke[/C][C]0.0193198[/C][C]0.0396009[/C][C]0.4879[/C][C]0.627915[/C][C]0.313957[/C][/ROW]
[ROW][C]demotivatie[/C][C]-0.144469[/C][C]0.114401[/C][C]-1.263[/C][C]0.212877[/C][C]0.106439[/C][/ROW]
[ROW][C]statistiek[/C][C]0.0187176[/C][C]0.154459[/C][C]0.1212[/C][C]0.904064[/C][C]0.452032[/C][/ROW]
[ROW][C]software[/C][C]0.0951307[/C][C]0.170823[/C][C]0.5569[/C][C]0.58024[/C][C]0.29012[/C][/ROW]
[ROW][C]Stress[/C][C]-0.39529[/C][C]0.184202[/C][C]-2.146[/C][C]0.0370709[/C][C]0.0185355[/C][/ROW]
[ROW][C]Depressie[/C][C]0.100445[/C][C]0.0757178[/C][C]1.327[/C][C]0.19106[/C][C]0.09553[/C][/ROW]
[ROW][C]NumeracyTest[/C][C]-0.000893466[/C][C]0.0654279[/C][C]-0.01366[/C][C]0.989162[/C][C]0.494581[/C][/ROW]
[ROW][C]TussentijdseTest[/C][C]-0.0552383[/C][C]0.122708[/C][C]-0.4502[/C][C]0.654665[/C][C]0.327333[/C][/ROW]
[ROW][C]LFM[/C][C]0.00943437[/C][C]0.00875541[/C][C]1.078[/C][C]0.286735[/C][C]0.143368[/C][/ROW]
[ROW][C]B[/C][C]0.00859947[/C][C]0.00966887[/C][C]0.8894[/C][C]0.378319[/C][C]0.18916[/C][/ROW]
[ROW][C]PRH[/C][C]0.0642745[/C][C]0.0152372[/C][C]4.218[/C][C]0.000111315[/C][C]5.56573e-05[/C][/ROW]
[ROW][C]CH[/C][C]-0.00701515[/C][C]0.0203105[/C][C]-0.3454[/C][C]0.731338[/C][C]0.365669[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270146&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270146&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)	11.3465	5.84139	1.942	0.05809	0.029045
geslacht	-0.0498912	0.562479	-0.0887	0.929698	0.464849
leeftijd	0.0244063	0.206286	0.1183	0.906324	0.453162
intrinsieke	-0.0560965	0.0270157	-2.076	0.0433463	0.0216732
extrinsieke	0.0193198	0.0396009	0.4879	0.627915	0.313957
demotivatie	-0.144469	0.114401	-1.263	0.212877	0.106439
statistiek	0.0187176	0.154459	0.1212	0.904064	0.452032
software	0.0951307	0.170823	0.5569	0.58024	0.29012
Stress	-0.39529	0.184202	-2.146	0.0370709	0.0185355
Depressie	0.100445	0.0757178	1.327	0.19106	0.09553
NumeracyTest	-0.000893466	0.0654279	-0.01366	0.989162	0.494581
TussentijdseTest	-0.0552383	0.122708	-0.4502	0.654665	0.327333
LFM	0.00943437	0.00875541	1.078	0.286735	0.143368
B	0.00859947	0.00966887	0.8894	0.378319	0.18916
PRH	0.0642745	0.0152372	4.218	0.000111315	5.56573e-05
CH	-0.00701515	0.0203105	-0.3454	0.731338	0.365669

Multiple Linear Regression - Regression Statistics
Multiple R	0.695623
R-squared	0.483891
Adjusted R-squared	0.319175
F-TEST (value)	2.93773
F-TEST (DF numerator)	15
F-TEST (DF denominator)	47
p-value	0.00242957
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.98018
Sum Squared Residuals	184.292

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.695623 \tabularnewline
R-squared & 0.483891 \tabularnewline
Adjusted R-squared & 0.319175 \tabularnewline
F-TEST (value) & 2.93773 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 0.00242957 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.98018 \tabularnewline
Sum Squared Residuals & 184.292 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270146&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.695623[/C][/ROW]
[ROW][C]R-squared[/C][C]0.483891[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.319175[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.93773[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]0.00242957[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.98018[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]184.292[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270146&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270146&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	0.695623
R-squared	0.483891
Adjusted R-squared	0.319175
F-TEST (value)	2.93773
F-TEST (DF numerator)	15
F-TEST (DF denominator)	47
p-value	0.00242957
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.98018
Sum Squared Residuals	184.292

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	12.9	11.0726	1.82739
2	12.8	12.5353	0.264705
3	7.4	10.8775	-3.47755
4	6.7	9.466	-2.766
5	12.6	12.9194	-0.319407
6	14.8	12.7391	2.06088
7	13.3	10.7352	2.56481
8	11.1	11.4355	-0.335547
9	8.2	10.1688	-1.96884
10	11.4	10.7517	0.648335
11	6.4	10.0557	-3.65567
12	12	13.6274	-1.62742
13	6.3	8.52448	-2.22448
14	11.3	10.4695	0.830498
15	11.9	13.4296	-1.52964
16	9.3	10.9162	-1.61618
17	10	8.94601	1.05399
18	13.8	14.9239	-1.12395
19	10.8	12.065	-1.26499
20	11.7	11.7301	-0.0300505
21	10.9	12.617	-1.71697
22	16.1	15.5226	0.577378
23	9.9	8.8137	1.0863
24	11.5	9.07943	2.42057
25	8.3	10.8581	-2.55815
26	11.7	10.9098	0.790163
27	9	9.60785	-0.60785
28	10.8	10.2663	0.533689
29	10.4	8.82973	1.57027
30	12.7	11.07	1.62997
31	11.8	11.0868	0.713207
32	13	10.6442	2.35585
33	10.8	11.8484	-1.04836
34	12.3	9.23663	3.06337
35	11.3	10.8621	0.437938
36	11.6	10.2206	1.37937
37	10.9	10.8925	0.00747416
38	12.1	10.6188	1.4812
39	13.3	11.1311	2.16891
40	10.1	9.61784	0.482158
41	14.3	11.7448	2.55523
42	9.3	9.94002	-0.640018
43	12.5	10.9377	1.56226
44	7.6	9.14149	-1.54149
45	9.2	8.88195	0.318045
46	14.5	13.2511	1.24891
47	12.3	13.2033	-0.903278
48	12.6	12.1041	0.495885
49	13	13.3027	-0.302681
50	12.6	12.0277	0.572289
51	13.2	9.94393	3.25607
52	7.7	9.21033	-1.51033
53	10.5	11.2789	-0.778855
54	10.9	9.98614	0.913861
55	4.3	6.98649	-2.68649
56	10.3	10.4095	-0.109458
57	11.4	11.2293	0.17071
58	5.6	9.7778	-4.1778
59	8.8	10.1093	-1.30927
60	9	7.83542	1.16458
61	9.6	11.1232	-1.52325
62	6.4	7.58787	-1.18787
63	11.6	9.26443	2.33557

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 11.0726 & 1.82739 \tabularnewline
2 & 12.8 & 12.5353 & 0.264705 \tabularnewline
3 & 7.4 & 10.8775 & -3.47755 \tabularnewline
4 & 6.7 & 9.466 & -2.766 \tabularnewline
5 & 12.6 & 12.9194 & -0.319407 \tabularnewline
6 & 14.8 & 12.7391 & 2.06088 \tabularnewline
7 & 13.3 & 10.7352 & 2.56481 \tabularnewline
8 & 11.1 & 11.4355 & -0.335547 \tabularnewline
9 & 8.2 & 10.1688 & -1.96884 \tabularnewline
10 & 11.4 & 10.7517 & 0.648335 \tabularnewline
11 & 6.4 & 10.0557 & -3.65567 \tabularnewline
12 & 12 & 13.6274 & -1.62742 \tabularnewline
13 & 6.3 & 8.52448 & -2.22448 \tabularnewline
14 & 11.3 & 10.4695 & 0.830498 \tabularnewline
15 & 11.9 & 13.4296 & -1.52964 \tabularnewline
16 & 9.3 & 10.9162 & -1.61618 \tabularnewline
17 & 10 & 8.94601 & 1.05399 \tabularnewline
18 & 13.8 & 14.9239 & -1.12395 \tabularnewline
19 & 10.8 & 12.065 & -1.26499 \tabularnewline
20 & 11.7 & 11.7301 & -0.0300505 \tabularnewline
21 & 10.9 & 12.617 & -1.71697 \tabularnewline
22 & 16.1 & 15.5226 & 0.577378 \tabularnewline
23 & 9.9 & 8.8137 & 1.0863 \tabularnewline
24 & 11.5 & 9.07943 & 2.42057 \tabularnewline
25 & 8.3 & 10.8581 & -2.55815 \tabularnewline
26 & 11.7 & 10.9098 & 0.790163 \tabularnewline
27 & 9 & 9.60785 & -0.60785 \tabularnewline
28 & 10.8 & 10.2663 & 0.533689 \tabularnewline
29 & 10.4 & 8.82973 & 1.57027 \tabularnewline
30 & 12.7 & 11.07 & 1.62997 \tabularnewline
31 & 11.8 & 11.0868 & 0.713207 \tabularnewline
32 & 13 & 10.6442 & 2.35585 \tabularnewline
33 & 10.8 & 11.8484 & -1.04836 \tabularnewline
34 & 12.3 & 9.23663 & 3.06337 \tabularnewline
35 & 11.3 & 10.8621 & 0.437938 \tabularnewline
36 & 11.6 & 10.2206 & 1.37937 \tabularnewline
37 & 10.9 & 10.8925 & 0.00747416 \tabularnewline
38 & 12.1 & 10.6188 & 1.4812 \tabularnewline
39 & 13.3 & 11.1311 & 2.16891 \tabularnewline
40 & 10.1 & 9.61784 & 0.482158 \tabularnewline
41 & 14.3 & 11.7448 & 2.55523 \tabularnewline
42 & 9.3 & 9.94002 & -0.640018 \tabularnewline
43 & 12.5 & 10.9377 & 1.56226 \tabularnewline
44 & 7.6 & 9.14149 & -1.54149 \tabularnewline
45 & 9.2 & 8.88195 & 0.318045 \tabularnewline
46 & 14.5 & 13.2511 & 1.24891 \tabularnewline
47 & 12.3 & 13.2033 & -0.903278 \tabularnewline
48 & 12.6 & 12.1041 & 0.495885 \tabularnewline
49 & 13 & 13.3027 & -0.302681 \tabularnewline
50 & 12.6 & 12.0277 & 0.572289 \tabularnewline
51 & 13.2 & 9.94393 & 3.25607 \tabularnewline
52 & 7.7 & 9.21033 & -1.51033 \tabularnewline
53 & 10.5 & 11.2789 & -0.778855 \tabularnewline
54 & 10.9 & 9.98614 & 0.913861 \tabularnewline
55 & 4.3 & 6.98649 & -2.68649 \tabularnewline
56 & 10.3 & 10.4095 & -0.109458 \tabularnewline
57 & 11.4 & 11.2293 & 0.17071 \tabularnewline
58 & 5.6 & 9.7778 & -4.1778 \tabularnewline
59 & 8.8 & 10.1093 & -1.30927 \tabularnewline
60 & 9 & 7.83542 & 1.16458 \tabularnewline
61 & 9.6 & 11.1232 & -1.52325 \tabularnewline
62 & 6.4 & 7.58787 & -1.18787 \tabularnewline
63 & 11.6 & 9.26443 & 2.33557 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270146&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]12.9[/C][C]11.0726[/C][C]1.82739[/C][/ROW]
[ROW][C]2[/C][C]12.8[/C][C]12.5353[/C][C]0.264705[/C][/ROW]
[ROW][C]3[/C][C]7.4[/C][C]10.8775[/C][C]-3.47755[/C][/ROW]
[ROW][C]4[/C][C]6.7[/C][C]9.466[/C][C]-2.766[/C][/ROW]
[ROW][C]5[/C][C]12.6[/C][C]12.9194[/C][C]-0.319407[/C][/ROW]
[ROW][C]6[/C][C]14.8[/C][C]12.7391[/C][C]2.06088[/C][/ROW]
[ROW][C]7[/C][C]13.3[/C][C]10.7352[/C][C]2.56481[/C][/ROW]
[ROW][C]8[/C][C]11.1[/C][C]11.4355[/C][C]-0.335547[/C][/ROW]
[ROW][C]9[/C][C]8.2[/C][C]10.1688[/C][C]-1.96884[/C][/ROW]
[ROW][C]10[/C][C]11.4[/C][C]10.7517[/C][C]0.648335[/C][/ROW]
[ROW][C]11[/C][C]6.4[/C][C]10.0557[/C][C]-3.65567[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]13.6274[/C][C]-1.62742[/C][/ROW]
[ROW][C]13[/C][C]6.3[/C][C]8.52448[/C][C]-2.22448[/C][/ROW]
[ROW][C]14[/C][C]11.3[/C][C]10.4695[/C][C]0.830498[/C][/ROW]
[ROW][C]15[/C][C]11.9[/C][C]13.4296[/C][C]-1.52964[/C][/ROW]
[ROW][C]16[/C][C]9.3[/C][C]10.9162[/C][C]-1.61618[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]8.94601[/C][C]1.05399[/C][/ROW]
[ROW][C]18[/C][C]13.8[/C][C]14.9239[/C][C]-1.12395[/C][/ROW]
[ROW][C]19[/C][C]10.8[/C][C]12.065[/C][C]-1.26499[/C][/ROW]
[ROW][C]20[/C][C]11.7[/C][C]11.7301[/C][C]-0.0300505[/C][/ROW]
[ROW][C]21[/C][C]10.9[/C][C]12.617[/C][C]-1.71697[/C][/ROW]
[ROW][C]22[/C][C]16.1[/C][C]15.5226[/C][C]0.577378[/C][/ROW]
[ROW][C]23[/C][C]9.9[/C][C]8.8137[/C][C]1.0863[/C][/ROW]
[ROW][C]24[/C][C]11.5[/C][C]9.07943[/C][C]2.42057[/C][/ROW]
[ROW][C]25[/C][C]8.3[/C][C]10.8581[/C][C]-2.55815[/C][/ROW]
[ROW][C]26[/C][C]11.7[/C][C]10.9098[/C][C]0.790163[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]9.60785[/C][C]-0.60785[/C][/ROW]
[ROW][C]28[/C][C]10.8[/C][C]10.2663[/C][C]0.533689[/C][/ROW]
[ROW][C]29[/C][C]10.4[/C][C]8.82973[/C][C]1.57027[/C][/ROW]
[ROW][C]30[/C][C]12.7[/C][C]11.07[/C][C]1.62997[/C][/ROW]
[ROW][C]31[/C][C]11.8[/C][C]11.0868[/C][C]0.713207[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]10.6442[/C][C]2.35585[/C][/ROW]
[ROW][C]33[/C][C]10.8[/C][C]11.8484[/C][C]-1.04836[/C][/ROW]
[ROW][C]34[/C][C]12.3[/C][C]9.23663[/C][C]3.06337[/C][/ROW]
[ROW][C]35[/C][C]11.3[/C][C]10.8621[/C][C]0.437938[/C][/ROW]
[ROW][C]36[/C][C]11.6[/C][C]10.2206[/C][C]1.37937[/C][/ROW]
[ROW][C]37[/C][C]10.9[/C][C]10.8925[/C][C]0.00747416[/C][/ROW]
[ROW][C]38[/C][C]12.1[/C][C]10.6188[/C][C]1.4812[/C][/ROW]
[ROW][C]39[/C][C]13.3[/C][C]11.1311[/C][C]2.16891[/C][/ROW]
[ROW][C]40[/C][C]10.1[/C][C]9.61784[/C][C]0.482158[/C][/ROW]
[ROW][C]41[/C][C]14.3[/C][C]11.7448[/C][C]2.55523[/C][/ROW]
[ROW][C]42[/C][C]9.3[/C][C]9.94002[/C][C]-0.640018[/C][/ROW]
[ROW][C]43[/C][C]12.5[/C][C]10.9377[/C][C]1.56226[/C][/ROW]
[ROW][C]44[/C][C]7.6[/C][C]9.14149[/C][C]-1.54149[/C][/ROW]
[ROW][C]45[/C][C]9.2[/C][C]8.88195[/C][C]0.318045[/C][/ROW]
[ROW][C]46[/C][C]14.5[/C][C]13.2511[/C][C]1.24891[/C][/ROW]
[ROW][C]47[/C][C]12.3[/C][C]13.2033[/C][C]-0.903278[/C][/ROW]
[ROW][C]48[/C][C]12.6[/C][C]12.1041[/C][C]0.495885[/C][/ROW]
[ROW][C]49[/C][C]13[/C][C]13.3027[/C][C]-0.302681[/C][/ROW]
[ROW][C]50[/C][C]12.6[/C][C]12.0277[/C][C]0.572289[/C][/ROW]
[ROW][C]51[/C][C]13.2[/C][C]9.94393[/C][C]3.25607[/C][/ROW]
[ROW][C]52[/C][C]7.7[/C][C]9.21033[/C][C]-1.51033[/C][/ROW]
[ROW][C]53[/C][C]10.5[/C][C]11.2789[/C][C]-0.778855[/C][/ROW]
[ROW][C]54[/C][C]10.9[/C][C]9.98614[/C][C]0.913861[/C][/ROW]
[ROW][C]55[/C][C]4.3[/C][C]6.98649[/C][C]-2.68649[/C][/ROW]
[ROW][C]56[/C][C]10.3[/C][C]10.4095[/C][C]-0.109458[/C][/ROW]
[ROW][C]57[/C][C]11.4[/C][C]11.2293[/C][C]0.17071[/C][/ROW]
[ROW][C]58[/C][C]5.6[/C][C]9.7778[/C][C]-4.1778[/C][/ROW]
[ROW][C]59[/C][C]8.8[/C][C]10.1093[/C][C]-1.30927[/C][/ROW]
[ROW][C]60[/C][C]9[/C][C]7.83542[/C][C]1.16458[/C][/ROW]
[ROW][C]61[/C][C]9.6[/C][C]11.1232[/C][C]-1.52325[/C][/ROW]
[ROW][C]62[/C][C]6.4[/C][C]7.58787[/C][C]-1.18787[/C][/ROW]
[ROW][C]63[/C][C]11.6[/C][C]9.26443[/C][C]2.33557[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270146&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270146&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	12.9	11.0726	1.82739
2	12.8	12.5353	0.264705
3	7.4	10.8775	-3.47755
4	6.7	9.466	-2.766
5	12.6	12.9194	-0.319407
6	14.8	12.7391	2.06088
7	13.3	10.7352	2.56481
8	11.1	11.4355	-0.335547
9	8.2	10.1688	-1.96884
10	11.4	10.7517	0.648335
11	6.4	10.0557	-3.65567
12	12	13.6274	-1.62742
13	6.3	8.52448	-2.22448
14	11.3	10.4695	0.830498
15	11.9	13.4296	-1.52964
16	9.3	10.9162	-1.61618
17	10	8.94601	1.05399
18	13.8	14.9239	-1.12395
19	10.8	12.065	-1.26499
20	11.7	11.7301	-0.0300505
21	10.9	12.617	-1.71697
22	16.1	15.5226	0.577378
23	9.9	8.8137	1.0863
24	11.5	9.07943	2.42057
25	8.3	10.8581	-2.55815
26	11.7	10.9098	0.790163
27	9	9.60785	-0.60785
28	10.8	10.2663	0.533689
29	10.4	8.82973	1.57027
30	12.7	11.07	1.62997
31	11.8	11.0868	0.713207
32	13	10.6442	2.35585
33	10.8	11.8484	-1.04836
34	12.3	9.23663	3.06337
35	11.3	10.8621	0.437938
36	11.6	10.2206	1.37937
37	10.9	10.8925	0.00747416
38	12.1	10.6188	1.4812
39	13.3	11.1311	2.16891
40	10.1	9.61784	0.482158
41	14.3	11.7448	2.55523
42	9.3	9.94002	-0.640018
43	12.5	10.9377	1.56226
44	7.6	9.14149	-1.54149
45	9.2	8.88195	0.318045
46	14.5	13.2511	1.24891
47	12.3	13.2033	-0.903278
48	12.6	12.1041	0.495885
49	13	13.3027	-0.302681
50	12.6	12.0277	0.572289
51	13.2	9.94393	3.25607
52	7.7	9.21033	-1.51033
53	10.5	11.2789	-0.778855
54	10.9	9.98614	0.913861
55	4.3	6.98649	-2.68649
56	10.3	10.4095	-0.109458
57	11.4	11.2293	0.17071
58	5.6	9.7778	-4.1778
59	8.8	10.1093	-1.30927
60	9	7.83542	1.16458
61	9.6	11.1232	-1.52325
62	6.4	7.58787	-1.18787
63	11.6	9.26443	2.33557

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.789964	0.420072	0.210036
20	0.710541	0.578919	0.289459
21	0.714482	0.571036	0.285518
22	0.599634	0.800732	0.400366
23	0.479131	0.958262	0.520869
24	0.633031	0.733938	0.366969
25	0.712158	0.575684	0.287842
26	0.625158	0.749683	0.374842
27	0.5278	0.944401	0.4722
28	0.445104	0.890208	0.554896
29	0.428647	0.857294	0.571353
30	0.40707	0.81414	0.59293
31	0.393053	0.786105	0.606947
32	0.464536	0.929072	0.535464
33	0.395767	0.791533	0.604233
34	0.693888	0.612225	0.306112
35	0.725895	0.548209	0.274105
36	0.66719	0.665619	0.33281
37	0.57332	0.853359	0.42668
38	0.578087	0.843826	0.421913
39	0.631452	0.737096	0.368548
40	0.531275	0.937449	0.468725
41	0.465718	0.931437	0.534282
42	0.365041	0.730082	0.634959
43	0.246664	0.493329	0.753336
44	0.578682	0.842636	0.421318

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.789964 & 0.420072 & 0.210036 \tabularnewline
20 & 0.710541 & 0.578919 & 0.289459 \tabularnewline
21 & 0.714482 & 0.571036 & 0.285518 \tabularnewline
22 & 0.599634 & 0.800732 & 0.400366 \tabularnewline
23 & 0.479131 & 0.958262 & 0.520869 \tabularnewline
24 & 0.633031 & 0.733938 & 0.366969 \tabularnewline
25 & 0.712158 & 0.575684 & 0.287842 \tabularnewline
26 & 0.625158 & 0.749683 & 0.374842 \tabularnewline
27 & 0.5278 & 0.944401 & 0.4722 \tabularnewline
28 & 0.445104 & 0.890208 & 0.554896 \tabularnewline
29 & 0.428647 & 0.857294 & 0.571353 \tabularnewline
30 & 0.40707 & 0.81414 & 0.59293 \tabularnewline
31 & 0.393053 & 0.786105 & 0.606947 \tabularnewline
32 & 0.464536 & 0.929072 & 0.535464 \tabularnewline
33 & 0.395767 & 0.791533 & 0.604233 \tabularnewline
34 & 0.693888 & 0.612225 & 0.306112 \tabularnewline
35 & 0.725895 & 0.548209 & 0.274105 \tabularnewline
36 & 0.66719 & 0.665619 & 0.33281 \tabularnewline
37 & 0.57332 & 0.853359 & 0.42668 \tabularnewline
38 & 0.578087 & 0.843826 & 0.421913 \tabularnewline
39 & 0.631452 & 0.737096 & 0.368548 \tabularnewline
40 & 0.531275 & 0.937449 & 0.468725 \tabularnewline
41 & 0.465718 & 0.931437 & 0.534282 \tabularnewline
42 & 0.365041 & 0.730082 & 0.634959 \tabularnewline
43 & 0.246664 & 0.493329 & 0.753336 \tabularnewline
44 & 0.578682 & 0.842636 & 0.421318 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270146&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]19[/C][C]0.789964[/C][C]0.420072[/C][C]0.210036[/C][/ROW]
[ROW][C]20[/C][C]0.710541[/C][C]0.578919[/C][C]0.289459[/C][/ROW]
[ROW][C]21[/C][C]0.714482[/C][C]0.571036[/C][C]0.285518[/C][/ROW]
[ROW][C]22[/C][C]0.599634[/C][C]0.800732[/C][C]0.400366[/C][/ROW]
[ROW][C]23[/C][C]0.479131[/C][C]0.958262[/C][C]0.520869[/C][/ROW]
[ROW][C]24[/C][C]0.633031[/C][C]0.733938[/C][C]0.366969[/C][/ROW]
[ROW][C]25[/C][C]0.712158[/C][C]0.575684[/C][C]0.287842[/C][/ROW]
[ROW][C]26[/C][C]0.625158[/C][C]0.749683[/C][C]0.374842[/C][/ROW]
[ROW][C]27[/C][C]0.5278[/C][C]0.944401[/C][C]0.4722[/C][/ROW]
[ROW][C]28[/C][C]0.445104[/C][C]0.890208[/C][C]0.554896[/C][/ROW]
[ROW][C]29[/C][C]0.428647[/C][C]0.857294[/C][C]0.571353[/C][/ROW]
[ROW][C]30[/C][C]0.40707[/C][C]0.81414[/C][C]0.59293[/C][/ROW]
[ROW][C]31[/C][C]0.393053[/C][C]0.786105[/C][C]0.606947[/C][/ROW]
[ROW][C]32[/C][C]0.464536[/C][C]0.929072[/C][C]0.535464[/C][/ROW]
[ROW][C]33[/C][C]0.395767[/C][C]0.791533[/C][C]0.604233[/C][/ROW]
[ROW][C]34[/C][C]0.693888[/C][C]0.612225[/C][C]0.306112[/C][/ROW]
[ROW][C]35[/C][C]0.725895[/C][C]0.548209[/C][C]0.274105[/C][/ROW]
[ROW][C]36[/C][C]0.66719[/C][C]0.665619[/C][C]0.33281[/C][/ROW]
[ROW][C]37[/C][C]0.57332[/C][C]0.853359[/C][C]0.42668[/C][/ROW]
[ROW][C]38[/C][C]0.578087[/C][C]0.843826[/C][C]0.421913[/C][/ROW]
[ROW][C]39[/C][C]0.631452[/C][C]0.737096[/C][C]0.368548[/C][/ROW]
[ROW][C]40[/C][C]0.531275[/C][C]0.937449[/C][C]0.468725[/C][/ROW]
[ROW][C]41[/C][C]0.465718[/C][C]0.931437[/C][C]0.534282[/C][/ROW]
[ROW][C]42[/C][C]0.365041[/C][C]0.730082[/C][C]0.634959[/C][/ROW]
[ROW][C]43[/C][C]0.246664[/C][C]0.493329[/C][C]0.753336[/C][/ROW]
[ROW][C]44[/C][C]0.578682[/C][C]0.842636[/C][C]0.421318[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270146&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270146&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
19	0.789964	0.420072	0.210036
20	0.710541	0.578919	0.289459
21	0.714482	0.571036	0.285518
22	0.599634	0.800732	0.400366
23	0.479131	0.958262	0.520869
24	0.633031	0.733938	0.366969
25	0.712158	0.575684	0.287842
26	0.625158	0.749683	0.374842
27	0.5278	0.944401	0.4722
28	0.445104	0.890208	0.554896
29	0.428647	0.857294	0.571353
30	0.40707	0.81414	0.59293
31	0.393053	0.786105	0.606947
32	0.464536	0.929072	0.535464
33	0.395767	0.791533	0.604233
34	0.693888	0.612225	0.306112
35	0.725895	0.548209	0.274105
36	0.66719	0.665619	0.33281
37	0.57332	0.853359	0.42668
38	0.578087	0.843826	0.421913
39	0.631452	0.737096	0.368548
40	0.531275	0.937449	0.468725
41	0.465718	0.931437	0.534282
42	0.365041	0.730082	0.634959
43	0.246664	0.493329	0.753336
44	0.578682	0.842636	0.421318

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=270146&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=270146&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270146&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

PNG link

Postscript link

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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

PNG link

Postscript link

PDF link

Parameters (Session):

Parameters (R input):

par1 = 16 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '16'
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), 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,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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