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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 13 Dec 2011 16:07:06 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/13/t132381053930s67nzh93u4czv.htm/, Retrieved Thu, 02 May 2024 19:33:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154728, Retrieved Thu, 02 May 2024 19:33:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact105

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Paper] [2011-12-13 21:07:06] [858ef1d716a843f745df26a736207017] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
210907	79	30	94	112285	146283	0
179321	108	30	103	101193	96933	1
149061	43	26	93	116174	95757	1
237213	78	38	123	66198	143983	0
173326	86	44	148	71701	75851	-1
133131	44	30	90	57793	59238	-1
258873	104	40	124	80444	93163	1
324799	158	47	168	97668	151511	1
230964	102	30	115	133824	136368	0
236785	77	31	71	101481	112642	0
344297	80	30	108	67654	127766	1
174724	123	34	120	69112	85646	1
174415	73	31	114	82753	98579	1
223632	105	33	120	72654	131741	-1
294424	107	33	124	101494	171975	1
325107	84	36	126	79215	159676	1
106408	33	14	37	31081	58391	1
96560	42	17	38	22996	31580	0
265769	96	32	120	83122	136815	1
269651	106	30	93	70106	120642	-1
149112	56	35	95	60578	69107	-1
152871	59	28	90	79892	108016	1
362301	76	34	110	100708	79336	-1
183167	91	39	138	82875	93176	-1
277965	115	39	133	139077	161632	1
218946	76	29	96	80670	102996	1
244052	101	44	164	143558	160604	1
341570	94	21	78	117105	158051	-1
233328	92	28	102	120733	162647	1
206161	75	28	99	73107	60622	-1
311473	128	38	129	132068	179566	1
207176	56	32	114	87011	96144	-1
196553	41	29	99	95260	129847	1
143246	67	27	104	106671	71180	1
182192	77	40	138	70054	86767	-1
194979	66	40	151	74011	93487	-1
167488	69	28	72	83737	82981	1
143756	105	34	120	69094	73815	1
275541	116	33	115	93133	94552	1
152299	62	33	98	61370	67808	1
193339	100	35	71	84651	106175	-1
130585	67	29	107	95364	76669	-1
112611	46	20	73	26706	57283	-1
148446	135	37	129	126846	72413	1
182079	124	33	118	102860	96971	1
243060	58	29	104	111813	120336	1
162765	68	28	107	120293	93913	1
85574	37	21	36	24266	32036	1
225060	93	41	139	109825	102255	-1
133328	56	20	56	40909	63506	-1
100750	83	30	93	140867	68370	1
101523	59	22	87	61056	50517	-1
243511	133	42	110	101338	103950	-1
152474	106	32	83	65567	84396	0
132487	71	36	98	40735	55515	1
317394	116	31	82	91413	209056	-1
244749	98	33	115	76643	142775	-1
184510	64	40	140	110681	68847	-1
128423	32	38	120	92696	20112	1
97839	25	24	66	94785	61023	0
172494	46	43	139	86687	112494	1
229242	63	31	119	91721	78876	-1
351619	95	40	141	115168	170745	1
324598	113	37	133	135777	122037	-1
195838	111	31	98	102372	112283	-1
254488	120	39	117	103772	120691	1
199476	87	32	105	135400	122422	1
92499	25	18	55	21399	25899	-1
224330	131	39	132	130115	139296	-1
181633	47	30	73	64466	89455	1
271856	109	37	86	54990	147866	-1
95227	37	32	48	34777	14336	1
98146	15	17	48	27114	30059	-1
118612	54	12	43	30080	41907	1
65475	16	13	46	69008	35885	1
108446	22	17	65	46300	55764	0
121848	37	17	52	30594	35619	0
76302	29	20	68	30976	40557	1
98104	55	17	47	25568	44197	1
30989	5	17	41	4154	4103	-1
31774	0	17	47	4143	4694	0
150580	27	22	71	45588	62991	-1
54157	37	15	30	18625	24261	1
59382	29	12	24	26263	21425	1
84105	17	17	63	20055	27184	-1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154728&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154728&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154728&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
test2[t] = + 0.239526515302326 -3.72023219084456e-06time_in_rfc[t] + 0.00158418895052058blogged_computations[t] -0.00643291820388346compendiums_reviewed[t] -0.00280364860075766feedback_messages_p120[t] + 7.04277573099765e-06totsize[t] + 4.10606625565716e-06totseconds[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
test2[t] =  +  0.239526515302326 -3.72023219084456e-06time_in_rfc[t] +  0.00158418895052058blogged_computations[t] -0.00643291820388346compendiums_reviewed[t] -0.00280364860075766feedback_messages_p120[t] +  7.04277573099765e-06totsize[t] +  4.10606625565716e-06totseconds[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154728&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]test2[t] =  +  0.239526515302326 -3.72023219084456e-06time_in_rfc[t] +  0.00158418895052058blogged_computations[t] -0.00643291820388346compendiums_reviewed[t] -0.00280364860075766feedback_messages_p120[t] +  7.04277573099765e-06totsize[t] +  4.10606625565716e-06totseconds[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154728&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154728&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
test2[t] = + 0.239526515302326 -3.72023219084456e-06time_in_rfc[t] + 0.00158418895052058blogged_computations[t] -0.00643291820388346compendiums_reviewed[t] -0.00280364860075766feedback_messages_p120[t] + 7.04277573099765e-06totsize[t] + 4.10606625565716e-06totseconds[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.2395265153023260.3865340.61970.5372760.268638
time_in_rfc-3.72023219084456e-063e-06-1.3820.1709030.085452
blogged_computations0.001584188950520580.0050620.31290.7551550.377578
compendiums_reviewed-0.006432918203883460.028675-0.22430.8230790.411539
feedback_messages_p120-0.002803648600757660.007596-0.36910.7130430.356522
totsize7.04277573099765e-065e-061.50250.1369970.068498
totseconds4.10606625565716e-065e-060.8330.407410.203705

\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) & 0.239526515302326 & 0.386534 & 0.6197 & 0.537276 & 0.268638 \tabularnewline
time_in_rfc & -3.72023219084456e-06 & 3e-06 & -1.382 & 0.170903 & 0.085452 \tabularnewline
blogged_computations & 0.00158418895052058 & 0.005062 & 0.3129 & 0.755155 & 0.377578 \tabularnewline
compendiums_reviewed & -0.00643291820388346 & 0.028675 & -0.2243 & 0.823079 & 0.411539 \tabularnewline
feedback_messages_p120 & -0.00280364860075766 & 0.007596 & -0.3691 & 0.713043 & 0.356522 \tabularnewline
totsize & 7.04277573099765e-06 & 5e-06 & 1.5025 & 0.136997 & 0.068498 \tabularnewline
totseconds & 4.10606625565716e-06 & 5e-06 & 0.833 & 0.40741 & 0.203705 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154728&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]0.239526515302326[/C][C]0.386534[/C][C]0.6197[/C][C]0.537276[/C][C]0.268638[/C][/ROW]
[ROW][C]time_in_rfc[/C][C]-3.72023219084456e-06[/C][C]3e-06[/C][C]-1.382[/C][C]0.170903[/C][C]0.085452[/C][/ROW]
[ROW][C]blogged_computations[/C][C]0.00158418895052058[/C][C]0.005062[/C][C]0.3129[/C][C]0.755155[/C][C]0.377578[/C][/ROW]
[ROW][C]compendiums_reviewed[/C][C]-0.00643291820388346[/C][C]0.028675[/C][C]-0.2243[/C][C]0.823079[/C][C]0.411539[/C][/ROW]
[ROW][C]feedback_messages_p120[/C][C]-0.00280364860075766[/C][C]0.007596[/C][C]-0.3691[/C][C]0.713043[/C][C]0.356522[/C][/ROW]
[ROW][C]totsize[/C][C]7.04277573099765e-06[/C][C]5e-06[/C][C]1.5025[/C][C]0.136997[/C][C]0.068498[/C][/ROW]
[ROW][C]totseconds[/C][C]4.10606625565716e-06[/C][C]5e-06[/C][C]0.833[/C][C]0.40741[/C][C]0.203705[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154728&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154728&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.2395265153023260.3865340.61970.5372760.268638
time_in_rfc-3.72023219084456e-063e-06-1.3820.1709030.085452
blogged_computations0.001584188950520580.0050620.31290.7551550.377578
compendiums_reviewed-0.006432918203883460.028675-0.22430.8230790.411539
feedback_messages_p120-0.002803648600757660.007596-0.36910.7130430.356522
totsize7.04277573099765e-065e-061.50250.1369970.068498
totseconds4.10606625565716e-065e-060.8330.407410.203705






Multiple Linear Regression - Regression Statistics
Multiple R0.253937339120911
R-squared0.0644841721998085
Adjusted R-squared-0.00747858378482147
F-TEST (value)0.896077023697941
F-TEST (DF numerator)6
F-TEST (DF denominator)78
p-value0.502147111115584
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.939393918008795
Sum Squared Residuals68.8319527889694

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.253937339120911 \tabularnewline
R-squared & 0.0644841721998085 \tabularnewline
Adjusted R-squared & -0.00747858378482147 \tabularnewline
F-TEST (value) & 0.896077023697941 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 78 \tabularnewline
p-value & 0.502147111115584 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.939393918008795 \tabularnewline
Sum Squared Residuals & 68.8319527889694 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154728&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.253937339120911[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0644841721998085[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00747858378482147[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.896077023697941[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]78[/C][/ROW]
[ROW][C]p-value[/C][C]0.502147111115584[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.939393918008795[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]68.8319527889694[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154728&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154728&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.253937339120911
R-squared0.0644841721998085
Adjusted R-squared-0.00747858378482147
F-TEST (value)0.896077023697941
F-TEST (DF numerator)6
F-TEST (DF denominator)78
p-value0.502147111115584
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.939393918008795
Sum Squared Residuals68.8319527889694






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.514969680162642-0.514969680162642
210.3724327381760280.627567261823972
310.5364819306196810.463518069380319
40-0.05127244935577620.0512724493557762
5-1-0.150611299300121-0.849388700699879
6-10.0188949678153771-1.01889496781538
71-0.2146721549531631.21467215495316
81-0.1818914218234041.1818914218234
900.528895407902052-0.528895407902052
1000.259357809336206-0.259357809336206
111-0.4092971357403611.40929713574036
1210.06761932273299780.932380677267002
1310.1748543318010890.825145668198911
14-10.0778023595313957-1.0778023595314
1510.1747105875872210.825289412412779
161-0.2081862037888261.20818620378883
1710.160800257852930.83919974214707
1800.0225638176451048-0.0225638176451048
1910.007776109888264490.992223890111736
20-1-0.0603357718105731-0.939664228189427
21-1-0.00759373115555961-0.99240626884444
2210.3380102557314430.661989744268557
23-1-0.480016801672538-0.519983198327462
24-10.0307334913868025-1.0307334913868
2510.4070046512061980.592995348793802
2610.08073914298712640.919260857012874
2710.4192501844839020.580749815516098
28-10.237656823835183-1.23765682383518
2910.5692784957532310.430721504246769
30-1-0.102514869372588-0.897485130627412
3110.3448644570494530.655135542950547
32-10.03960154236416-1.03960154236416
3310.3131948263121220.686805173687878
3410.3910202746727610.608979725327239
35-1-0.110862052017923-0.889137947982077
36-1-0.156845142502255-0.843154857497745
3710.2742232900920210.725776709907979
3810.1056064322758640.894393567724136
391-0.09233834559017321.09233834559017
401-0.005245988526312531.00524598852631
41-10.286607844118053-1.28660784411805
42-10.359750884717916-1.35975088471792
43-1-0.0165724101550705-0.98342758984493
4410.4921302989240960.507869701075904
4510.3380622150402810.661937784959719
4610.2306172274852240.769382772514776
4710.4944252826815350.505574717318465
4810.04620565951804840.953794340481952
49-10.0894624785937661-1.08946247859377
50-10.0954400492816034-1.0954400492816
5110.8153003777787040.184699622221296
52-10.207292765989401-1.2072927659894
53-10.106248668353766-1.10624866835377
5400.209964888670282-0.209964888670282
551-0.1323853521081951.1323853521082
56-10.315392453039631-1.31539245303963
57-10.075571104163346-1.07557110416335
58-10.0668588385272815-1.06685883852728
591-0.03301319707000251.03301319707
6000.49383057598367-0.49383057598367
6110.07678375438331730.923216245616683
62-1-0.0767171817149657-0.923282818285034
631-0.05851835976324221.05851835976324
64-10.0573956686522326-1.05739566865223
65-10.394655104342499-1.3946551043425
6610.130372208959650.86962779104035
6710.5912821090200240.408717890979976
68-1-0.0779283512442807-0.922071648755719
69-10.479859524583674-1.47985952458367
7010.06194030565961560.938059694340384
71-1-0.0838662543674543-0.916133745632546
721-0.09276238228859971.0927623822886
73-1-0.0313892345958129-0.968610765404187
7410.06997624229503520.930023757704965
7510.4420496187587130.557950381241287
7600.13438879855584-0.13438879855584
770-0.04859002754946320.0485900275494632
7810.06698711948648180.933012880513518
7910.08210165522014160.917898344779858
80-1-0.0460451371707106-0.953954862829289
810-0.07135914117361980.0713591411736198
82-1-0.0387649179354014-0.961235082064599
8310.1468506320493670.853149367950633
8410.1930074705965520.806992529403448
85-1-0.0795596998846149-0.920440300115385

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.514969680162642 & -0.514969680162642 \tabularnewline
2 & 1 & 0.372432738176028 & 0.627567261823972 \tabularnewline
3 & 1 & 0.536481930619681 & 0.463518069380319 \tabularnewline
4 & 0 & -0.0512724493557762 & 0.0512724493557762 \tabularnewline
5 & -1 & -0.150611299300121 & -0.849388700699879 \tabularnewline
6 & -1 & 0.0188949678153771 & -1.01889496781538 \tabularnewline
7 & 1 & -0.214672154953163 & 1.21467215495316 \tabularnewline
8 & 1 & -0.181891421823404 & 1.1818914218234 \tabularnewline
9 & 0 & 0.528895407902052 & -0.528895407902052 \tabularnewline
10 & 0 & 0.259357809336206 & -0.259357809336206 \tabularnewline
11 & 1 & -0.409297135740361 & 1.40929713574036 \tabularnewline
12 & 1 & 0.0676193227329978 & 0.932380677267002 \tabularnewline
13 & 1 & 0.174854331801089 & 0.825145668198911 \tabularnewline
14 & -1 & 0.0778023595313957 & -1.0778023595314 \tabularnewline
15 & 1 & 0.174710587587221 & 0.825289412412779 \tabularnewline
16 & 1 & -0.208186203788826 & 1.20818620378883 \tabularnewline
17 & 1 & 0.16080025785293 & 0.83919974214707 \tabularnewline
18 & 0 & 0.0225638176451048 & -0.0225638176451048 \tabularnewline
19 & 1 & 0.00777610988826449 & 0.992223890111736 \tabularnewline
20 & -1 & -0.0603357718105731 & -0.939664228189427 \tabularnewline
21 & -1 & -0.00759373115555961 & -0.99240626884444 \tabularnewline
22 & 1 & 0.338010255731443 & 0.661989744268557 \tabularnewline
23 & -1 & -0.480016801672538 & -0.519983198327462 \tabularnewline
24 & -1 & 0.0307334913868025 & -1.0307334913868 \tabularnewline
25 & 1 & 0.407004651206198 & 0.592995348793802 \tabularnewline
26 & 1 & 0.0807391429871264 & 0.919260857012874 \tabularnewline
27 & 1 & 0.419250184483902 & 0.580749815516098 \tabularnewline
28 & -1 & 0.237656823835183 & -1.23765682383518 \tabularnewline
29 & 1 & 0.569278495753231 & 0.430721504246769 \tabularnewline
30 & -1 & -0.102514869372588 & -0.897485130627412 \tabularnewline
31 & 1 & 0.344864457049453 & 0.655135542950547 \tabularnewline
32 & -1 & 0.03960154236416 & -1.03960154236416 \tabularnewline
33 & 1 & 0.313194826312122 & 0.686805173687878 \tabularnewline
34 & 1 & 0.391020274672761 & 0.608979725327239 \tabularnewline
35 & -1 & -0.110862052017923 & -0.889137947982077 \tabularnewline
36 & -1 & -0.156845142502255 & -0.843154857497745 \tabularnewline
37 & 1 & 0.274223290092021 & 0.725776709907979 \tabularnewline
38 & 1 & 0.105606432275864 & 0.894393567724136 \tabularnewline
39 & 1 & -0.0923383455901732 & 1.09233834559017 \tabularnewline
40 & 1 & -0.00524598852631253 & 1.00524598852631 \tabularnewline
41 & -1 & 0.286607844118053 & -1.28660784411805 \tabularnewline
42 & -1 & 0.359750884717916 & -1.35975088471792 \tabularnewline
43 & -1 & -0.0165724101550705 & -0.98342758984493 \tabularnewline
44 & 1 & 0.492130298924096 & 0.507869701075904 \tabularnewline
45 & 1 & 0.338062215040281 & 0.661937784959719 \tabularnewline
46 & 1 & 0.230617227485224 & 0.769382772514776 \tabularnewline
47 & 1 & 0.494425282681535 & 0.505574717318465 \tabularnewline
48 & 1 & 0.0462056595180484 & 0.953794340481952 \tabularnewline
49 & -1 & 0.0894624785937661 & -1.08946247859377 \tabularnewline
50 & -1 & 0.0954400492816034 & -1.0954400492816 \tabularnewline
51 & 1 & 0.815300377778704 & 0.184699622221296 \tabularnewline
52 & -1 & 0.207292765989401 & -1.2072927659894 \tabularnewline
53 & -1 & 0.106248668353766 & -1.10624866835377 \tabularnewline
54 & 0 & 0.209964888670282 & -0.209964888670282 \tabularnewline
55 & 1 & -0.132385352108195 & 1.1323853521082 \tabularnewline
56 & -1 & 0.315392453039631 & -1.31539245303963 \tabularnewline
57 & -1 & 0.075571104163346 & -1.07557110416335 \tabularnewline
58 & -1 & 0.0668588385272815 & -1.06685883852728 \tabularnewline
59 & 1 & -0.0330131970700025 & 1.03301319707 \tabularnewline
60 & 0 & 0.49383057598367 & -0.49383057598367 \tabularnewline
61 & 1 & 0.0767837543833173 & 0.923216245616683 \tabularnewline
62 & -1 & -0.0767171817149657 & -0.923282818285034 \tabularnewline
63 & 1 & -0.0585183597632422 & 1.05851835976324 \tabularnewline
64 & -1 & 0.0573956686522326 & -1.05739566865223 \tabularnewline
65 & -1 & 0.394655104342499 & -1.3946551043425 \tabularnewline
66 & 1 & 0.13037220895965 & 0.86962779104035 \tabularnewline
67 & 1 & 0.591282109020024 & 0.408717890979976 \tabularnewline
68 & -1 & -0.0779283512442807 & -0.922071648755719 \tabularnewline
69 & -1 & 0.479859524583674 & -1.47985952458367 \tabularnewline
70 & 1 & 0.0619403056596156 & 0.938059694340384 \tabularnewline
71 & -1 & -0.0838662543674543 & -0.916133745632546 \tabularnewline
72 & 1 & -0.0927623822885997 & 1.0927623822886 \tabularnewline
73 & -1 & -0.0313892345958129 & -0.968610765404187 \tabularnewline
74 & 1 & 0.0699762422950352 & 0.930023757704965 \tabularnewline
75 & 1 & 0.442049618758713 & 0.557950381241287 \tabularnewline
76 & 0 & 0.13438879855584 & -0.13438879855584 \tabularnewline
77 & 0 & -0.0485900275494632 & 0.0485900275494632 \tabularnewline
78 & 1 & 0.0669871194864818 & 0.933012880513518 \tabularnewline
79 & 1 & 0.0821016552201416 & 0.917898344779858 \tabularnewline
80 & -1 & -0.0460451371707106 & -0.953954862829289 \tabularnewline
81 & 0 & -0.0713591411736198 & 0.0713591411736198 \tabularnewline
82 & -1 & -0.0387649179354014 & -0.961235082064599 \tabularnewline
83 & 1 & 0.146850632049367 & 0.853149367950633 \tabularnewline
84 & 1 & 0.193007470596552 & 0.806992529403448 \tabularnewline
85 & -1 & -0.0795596998846149 & -0.920440300115385 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154728&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]0[/C][C]0.514969680162642[/C][C]-0.514969680162642[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]0.372432738176028[/C][C]0.627567261823972[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]0.536481930619681[/C][C]0.463518069380319[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]-0.0512724493557762[/C][C]0.0512724493557762[/C][/ROW]
[ROW][C]5[/C][C]-1[/C][C]-0.150611299300121[/C][C]-0.849388700699879[/C][/ROW]
[ROW][C]6[/C][C]-1[/C][C]0.0188949678153771[/C][C]-1.01889496781538[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]-0.214672154953163[/C][C]1.21467215495316[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]-0.181891421823404[/C][C]1.1818914218234[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0.528895407902052[/C][C]-0.528895407902052[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.259357809336206[/C][C]-0.259357809336206[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]-0.409297135740361[/C][C]1.40929713574036[/C][/ROW]
[ROW][C]12[/C][C]1[/C][C]0.0676193227329978[/C][C]0.932380677267002[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]0.174854331801089[/C][C]0.825145668198911[/C][/ROW]
[ROW][C]14[/C][C]-1[/C][C]0.0778023595313957[/C][C]-1.0778023595314[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]0.174710587587221[/C][C]0.825289412412779[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]-0.208186203788826[/C][C]1.20818620378883[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.16080025785293[/C][C]0.83919974214707[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.0225638176451048[/C][C]-0.0225638176451048[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]0.00777610988826449[/C][C]0.992223890111736[/C][/ROW]
[ROW][C]20[/C][C]-1[/C][C]-0.0603357718105731[/C][C]-0.939664228189427[/C][/ROW]
[ROW][C]21[/C][C]-1[/C][C]-0.00759373115555961[/C][C]-0.99240626884444[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]0.338010255731443[/C][C]0.661989744268557[/C][/ROW]
[ROW][C]23[/C][C]-1[/C][C]-0.480016801672538[/C][C]-0.519983198327462[/C][/ROW]
[ROW][C]24[/C][C]-1[/C][C]0.0307334913868025[/C][C]-1.0307334913868[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]0.407004651206198[/C][C]0.592995348793802[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]0.0807391429871264[/C][C]0.919260857012874[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]0.419250184483902[/C][C]0.580749815516098[/C][/ROW]
[ROW][C]28[/C][C]-1[/C][C]0.237656823835183[/C][C]-1.23765682383518[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]0.569278495753231[/C][C]0.430721504246769[/C][/ROW]
[ROW][C]30[/C][C]-1[/C][C]-0.102514869372588[/C][C]-0.897485130627412[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]0.344864457049453[/C][C]0.655135542950547[/C][/ROW]
[ROW][C]32[/C][C]-1[/C][C]0.03960154236416[/C][C]-1.03960154236416[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]0.313194826312122[/C][C]0.686805173687878[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]0.391020274672761[/C][C]0.608979725327239[/C][/ROW]
[ROW][C]35[/C][C]-1[/C][C]-0.110862052017923[/C][C]-0.889137947982077[/C][/ROW]
[ROW][C]36[/C][C]-1[/C][C]-0.156845142502255[/C][C]-0.843154857497745[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]0.274223290092021[/C][C]0.725776709907979[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]0.105606432275864[/C][C]0.894393567724136[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]-0.0923383455901732[/C][C]1.09233834559017[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]-0.00524598852631253[/C][C]1.00524598852631[/C][/ROW]
[ROW][C]41[/C][C]-1[/C][C]0.286607844118053[/C][C]-1.28660784411805[/C][/ROW]
[ROW][C]42[/C][C]-1[/C][C]0.359750884717916[/C][C]-1.35975088471792[/C][/ROW]
[ROW][C]43[/C][C]-1[/C][C]-0.0165724101550705[/C][C]-0.98342758984493[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]0.492130298924096[/C][C]0.507869701075904[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]0.338062215040281[/C][C]0.661937784959719[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]0.230617227485224[/C][C]0.769382772514776[/C][/ROW]
[ROW][C]47[/C][C]1[/C][C]0.494425282681535[/C][C]0.505574717318465[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]0.0462056595180484[/C][C]0.953794340481952[/C][/ROW]
[ROW][C]49[/C][C]-1[/C][C]0.0894624785937661[/C][C]-1.08946247859377[/C][/ROW]
[ROW][C]50[/C][C]-1[/C][C]0.0954400492816034[/C][C]-1.0954400492816[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]0.815300377778704[/C][C]0.184699622221296[/C][/ROW]
[ROW][C]52[/C][C]-1[/C][C]0.207292765989401[/C][C]-1.2072927659894[/C][/ROW]
[ROW][C]53[/C][C]-1[/C][C]0.106248668353766[/C][C]-1.10624866835377[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.209964888670282[/C][C]-0.209964888670282[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]-0.132385352108195[/C][C]1.1323853521082[/C][/ROW]
[ROW][C]56[/C][C]-1[/C][C]0.315392453039631[/C][C]-1.31539245303963[/C][/ROW]
[ROW][C]57[/C][C]-1[/C][C]0.075571104163346[/C][C]-1.07557110416335[/C][/ROW]
[ROW][C]58[/C][C]-1[/C][C]0.0668588385272815[/C][C]-1.06685883852728[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]-0.0330131970700025[/C][C]1.03301319707[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]0.49383057598367[/C][C]-0.49383057598367[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]0.0767837543833173[/C][C]0.923216245616683[/C][/ROW]
[ROW][C]62[/C][C]-1[/C][C]-0.0767171817149657[/C][C]-0.923282818285034[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]-0.0585183597632422[/C][C]1.05851835976324[/C][/ROW]
[ROW][C]64[/C][C]-1[/C][C]0.0573956686522326[/C][C]-1.05739566865223[/C][/ROW]
[ROW][C]65[/C][C]-1[/C][C]0.394655104342499[/C][C]-1.3946551043425[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]0.13037220895965[/C][C]0.86962779104035[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.591282109020024[/C][C]0.408717890979976[/C][/ROW]
[ROW][C]68[/C][C]-1[/C][C]-0.0779283512442807[/C][C]-0.922071648755719[/C][/ROW]
[ROW][C]69[/C][C]-1[/C][C]0.479859524583674[/C][C]-1.47985952458367[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]0.0619403056596156[/C][C]0.938059694340384[/C][/ROW]
[ROW][C]71[/C][C]-1[/C][C]-0.0838662543674543[/C][C]-0.916133745632546[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]-0.0927623822885997[/C][C]1.0927623822886[/C][/ROW]
[ROW][C]73[/C][C]-1[/C][C]-0.0313892345958129[/C][C]-0.968610765404187[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]0.0699762422950352[/C][C]0.930023757704965[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]0.442049618758713[/C][C]0.557950381241287[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.13438879855584[/C][C]-0.13438879855584[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]-0.0485900275494632[/C][C]0.0485900275494632[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]0.0669871194864818[/C][C]0.933012880513518[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.0821016552201416[/C][C]0.917898344779858[/C][/ROW]
[ROW][C]80[/C][C]-1[/C][C]-0.0460451371707106[/C][C]-0.953954862829289[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]-0.0713591411736198[/C][C]0.0713591411736198[/C][/ROW]
[ROW][C]82[/C][C]-1[/C][C]-0.0387649179354014[/C][C]-0.961235082064599[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]0.146850632049367[/C][C]0.853149367950633[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.193007470596552[/C][C]0.806992529403448[/C][/ROW]
[ROW][C]85[/C][C]-1[/C][C]-0.0795596998846149[/C][C]-0.920440300115385[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154728&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154728&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.514969680162642-0.514969680162642
210.3724327381760280.627567261823972
310.5364819306196810.463518069380319
40-0.05127244935577620.0512724493557762
5-1-0.150611299300121-0.849388700699879
6-10.0188949678153771-1.01889496781538
71-0.2146721549531631.21467215495316
81-0.1818914218234041.1818914218234
900.528895407902052-0.528895407902052
1000.259357809336206-0.259357809336206
111-0.4092971357403611.40929713574036
1210.06761932273299780.932380677267002
1310.1748543318010890.825145668198911
14-10.0778023595313957-1.0778023595314
1510.1747105875872210.825289412412779
161-0.2081862037888261.20818620378883
1710.160800257852930.83919974214707
1800.0225638176451048-0.0225638176451048
1910.007776109888264490.992223890111736
20-1-0.0603357718105731-0.939664228189427
21-1-0.00759373115555961-0.99240626884444
2210.3380102557314430.661989744268557
23-1-0.480016801672538-0.519983198327462
24-10.0307334913868025-1.0307334913868
2510.4070046512061980.592995348793802
2610.08073914298712640.919260857012874
2710.4192501844839020.580749815516098
28-10.237656823835183-1.23765682383518
2910.5692784957532310.430721504246769
30-1-0.102514869372588-0.897485130627412
3110.3448644570494530.655135542950547
32-10.03960154236416-1.03960154236416
3310.3131948263121220.686805173687878
3410.3910202746727610.608979725327239
35-1-0.110862052017923-0.889137947982077
36-1-0.156845142502255-0.843154857497745
3710.2742232900920210.725776709907979
3810.1056064322758640.894393567724136
391-0.09233834559017321.09233834559017
401-0.005245988526312531.00524598852631
41-10.286607844118053-1.28660784411805
42-10.359750884717916-1.35975088471792
43-1-0.0165724101550705-0.98342758984493
4410.4921302989240960.507869701075904
4510.3380622150402810.661937784959719
4610.2306172274852240.769382772514776
4710.4944252826815350.505574717318465
4810.04620565951804840.953794340481952
49-10.0894624785937661-1.08946247859377
50-10.0954400492816034-1.0954400492816
5110.8153003777787040.184699622221296
52-10.207292765989401-1.2072927659894
53-10.106248668353766-1.10624866835377
5400.209964888670282-0.209964888670282
551-0.1323853521081951.1323853521082
56-10.315392453039631-1.31539245303963
57-10.075571104163346-1.07557110416335
58-10.0668588385272815-1.06685883852728
591-0.03301319707000251.03301319707
6000.49383057598367-0.49383057598367
6110.07678375438331730.923216245616683
62-1-0.0767171817149657-0.923282818285034
631-0.05851835976324221.05851835976324
64-10.0573956686522326-1.05739566865223
65-10.394655104342499-1.3946551043425
6610.130372208959650.86962779104035
6710.5912821090200240.408717890979976
68-1-0.0779283512442807-0.922071648755719
69-10.479859524583674-1.47985952458367
7010.06194030565961560.938059694340384
71-1-0.0838662543674543-0.916133745632546
721-0.09276238228859971.0927623822886
73-1-0.0313892345958129-0.968610765404187
7410.06997624229503520.930023757704965
7510.4420496187587130.557950381241287
7600.13438879855584-0.13438879855584
770-0.04859002754946320.0485900275494632
7810.06698711948648180.933012880513518
7910.08210165522014160.917898344779858
80-1-0.0460451371707106-0.953954862829289
810-0.07135914117361980.0713591411736198
82-1-0.0387649179354014-0.961235082064599
8310.1468506320493670.853149367950633
8410.1930074705965520.806992529403448
85-1-0.0795596998846149-0.920440300115385






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.6049969249412210.7900061501175570.395003075058779
110.5013650607239520.9972698785520960.498634939276048
120.3883530181294790.7767060362589580.611646981870521
130.3430820246026620.6861640492053240.656917975397338
140.4928522603259540.9857045206519080.507147739674046
150.4129449062466990.8258898124933980.587055093753301
160.3698653296194980.7397306592389950.630134670380502
170.331801560771170.6636031215423390.66819843922883
180.2626660829042710.5253321658085430.737333917095728
190.2158110965739710.4316221931479420.784188903426029
200.3859044239309610.7718088478619220.614095576069039
210.3330482755869370.6660965511738740.666951724413063
220.3425272081519880.6850544163039750.657472791848012
230.3872463680061630.7744927360123260.612753631993837
240.3981476785535010.7962953571070010.601852321446499
250.3536831961586720.7073663923173430.646316803841328
260.3379410730629550.675882146125910.662058926937045
270.2971394445160890.5942788890321790.702860555483911
280.4706940595849550.941388119169910.529305940415045
290.4115267096742470.8230534193484940.588473290325753
300.4023532279074020.8047064558148040.597646772092598
310.3713203851635510.7426407703271020.628679614836449
320.3753655196786990.7507310393573990.624634480321301
330.3565617264433060.7131234528866120.643438273556694
340.3354685380561090.6709370761122170.664531461943891
350.322041579492680.644083158985360.67795842050732
360.2993880429069680.5987760858139350.700611957093032
370.28751675439550.5750335087909990.7124832456045
380.2849475760295680.5698951520591370.715052423970432
390.3116526633219080.6233053266438170.688347336678092
400.3389011678993480.6778023357986970.661098832100652
410.4038025384261130.8076050768522270.596197461573887
420.4639178693322260.9278357386644520.536082130667774
430.4597197381712550.919439476342510.540280261828745
440.4213058116189760.8426116232379520.578694188381024
450.4388802692060420.8777605384120840.561119730793958
460.4332119472774350.866423894554870.566788052722565
470.4092777622923250.818555524584650.590722237707675
480.4168055461566620.8336110923133240.583194453843338
490.4061384568730490.8122769137460970.593861543126951
500.4202868308139080.8405736616278160.579713169186092
510.359489987040270.7189799740805390.64051001295973
520.3619376523641850.7238753047283690.638062347635815
530.3839489540023330.7678979080046670.616051045997667
540.3214948154674380.6429896309348770.678505184532561
550.3525477407929230.7050954815858460.647452259207077
560.3957623323637780.7915246647275570.604237667636222
570.3750672380852970.7501344761705940.624932761914703
580.3670455034683480.7340910069366960.632954496531652
590.3830683325797930.7661366651595870.616931667420207
600.3753928166355330.7507856332710660.624607183364467
610.4232731641237970.8465463282475930.576726835876203
620.3694194531843990.7388389063687990.630580546815601
630.5872019666005910.8255960667988190.412798033399409
640.5400730551984120.9198538896031760.459926944801588
650.6658892901294060.6682214197411890.334110709870594
660.7432713760143060.5134572479713890.256728623985694
670.6772418769456680.6455162461086640.322758123054332
680.6339532322270350.732093535545930.366046767772965
690.8953514400802730.2092971198394540.104648559919727
700.9812827653040790.03743446939184270.0187172346959214
710.9770706114392620.04585877712147620.0229293885607381
720.9578365704718390.08432685905632260.0421634295281613
730.9120286424782910.1759427150434180.0879713575217088
740.8353111143222930.3293777713554140.164688885677707
750.7797198757004790.4405602485990430.220280124299521

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.604996924941221 & 0.790006150117557 & 0.395003075058779 \tabularnewline
11 & 0.501365060723952 & 0.997269878552096 & 0.498634939276048 \tabularnewline
12 & 0.388353018129479 & 0.776706036258958 & 0.611646981870521 \tabularnewline
13 & 0.343082024602662 & 0.686164049205324 & 0.656917975397338 \tabularnewline
14 & 0.492852260325954 & 0.985704520651908 & 0.507147739674046 \tabularnewline
15 & 0.412944906246699 & 0.825889812493398 & 0.587055093753301 \tabularnewline
16 & 0.369865329619498 & 0.739730659238995 & 0.630134670380502 \tabularnewline
17 & 0.33180156077117 & 0.663603121542339 & 0.66819843922883 \tabularnewline
18 & 0.262666082904271 & 0.525332165808543 & 0.737333917095728 \tabularnewline
19 & 0.215811096573971 & 0.431622193147942 & 0.784188903426029 \tabularnewline
20 & 0.385904423930961 & 0.771808847861922 & 0.614095576069039 \tabularnewline
21 & 0.333048275586937 & 0.666096551173874 & 0.666951724413063 \tabularnewline
22 & 0.342527208151988 & 0.685054416303975 & 0.657472791848012 \tabularnewline
23 & 0.387246368006163 & 0.774492736012326 & 0.612753631993837 \tabularnewline
24 & 0.398147678553501 & 0.796295357107001 & 0.601852321446499 \tabularnewline
25 & 0.353683196158672 & 0.707366392317343 & 0.646316803841328 \tabularnewline
26 & 0.337941073062955 & 0.67588214612591 & 0.662058926937045 \tabularnewline
27 & 0.297139444516089 & 0.594278889032179 & 0.702860555483911 \tabularnewline
28 & 0.470694059584955 & 0.94138811916991 & 0.529305940415045 \tabularnewline
29 & 0.411526709674247 & 0.823053419348494 & 0.588473290325753 \tabularnewline
30 & 0.402353227907402 & 0.804706455814804 & 0.597646772092598 \tabularnewline
31 & 0.371320385163551 & 0.742640770327102 & 0.628679614836449 \tabularnewline
32 & 0.375365519678699 & 0.750731039357399 & 0.624634480321301 \tabularnewline
33 & 0.356561726443306 & 0.713123452886612 & 0.643438273556694 \tabularnewline
34 & 0.335468538056109 & 0.670937076112217 & 0.664531461943891 \tabularnewline
35 & 0.32204157949268 & 0.64408315898536 & 0.67795842050732 \tabularnewline
36 & 0.299388042906968 & 0.598776085813935 & 0.700611957093032 \tabularnewline
37 & 0.2875167543955 & 0.575033508790999 & 0.7124832456045 \tabularnewline
38 & 0.284947576029568 & 0.569895152059137 & 0.715052423970432 \tabularnewline
39 & 0.311652663321908 & 0.623305326643817 & 0.688347336678092 \tabularnewline
40 & 0.338901167899348 & 0.677802335798697 & 0.661098832100652 \tabularnewline
41 & 0.403802538426113 & 0.807605076852227 & 0.596197461573887 \tabularnewline
42 & 0.463917869332226 & 0.927835738664452 & 0.536082130667774 \tabularnewline
43 & 0.459719738171255 & 0.91943947634251 & 0.540280261828745 \tabularnewline
44 & 0.421305811618976 & 0.842611623237952 & 0.578694188381024 \tabularnewline
45 & 0.438880269206042 & 0.877760538412084 & 0.561119730793958 \tabularnewline
46 & 0.433211947277435 & 0.86642389455487 & 0.566788052722565 \tabularnewline
47 & 0.409277762292325 & 0.81855552458465 & 0.590722237707675 \tabularnewline
48 & 0.416805546156662 & 0.833611092313324 & 0.583194453843338 \tabularnewline
49 & 0.406138456873049 & 0.812276913746097 & 0.593861543126951 \tabularnewline
50 & 0.420286830813908 & 0.840573661627816 & 0.579713169186092 \tabularnewline
51 & 0.35948998704027 & 0.718979974080539 & 0.64051001295973 \tabularnewline
52 & 0.361937652364185 & 0.723875304728369 & 0.638062347635815 \tabularnewline
53 & 0.383948954002333 & 0.767897908004667 & 0.616051045997667 \tabularnewline
54 & 0.321494815467438 & 0.642989630934877 & 0.678505184532561 \tabularnewline
55 & 0.352547740792923 & 0.705095481585846 & 0.647452259207077 \tabularnewline
56 & 0.395762332363778 & 0.791524664727557 & 0.604237667636222 \tabularnewline
57 & 0.375067238085297 & 0.750134476170594 & 0.624932761914703 \tabularnewline
58 & 0.367045503468348 & 0.734091006936696 & 0.632954496531652 \tabularnewline
59 & 0.383068332579793 & 0.766136665159587 & 0.616931667420207 \tabularnewline
60 & 0.375392816635533 & 0.750785633271066 & 0.624607183364467 \tabularnewline
61 & 0.423273164123797 & 0.846546328247593 & 0.576726835876203 \tabularnewline
62 & 0.369419453184399 & 0.738838906368799 & 0.630580546815601 \tabularnewline
63 & 0.587201966600591 & 0.825596066798819 & 0.412798033399409 \tabularnewline
64 & 0.540073055198412 & 0.919853889603176 & 0.459926944801588 \tabularnewline
65 & 0.665889290129406 & 0.668221419741189 & 0.334110709870594 \tabularnewline
66 & 0.743271376014306 & 0.513457247971389 & 0.256728623985694 \tabularnewline
67 & 0.677241876945668 & 0.645516246108664 & 0.322758123054332 \tabularnewline
68 & 0.633953232227035 & 0.73209353554593 & 0.366046767772965 \tabularnewline
69 & 0.895351440080273 & 0.209297119839454 & 0.104648559919727 \tabularnewline
70 & 0.981282765304079 & 0.0374344693918427 & 0.0187172346959214 \tabularnewline
71 & 0.977070611439262 & 0.0458587771214762 & 0.0229293885607381 \tabularnewline
72 & 0.957836570471839 & 0.0843268590563226 & 0.0421634295281613 \tabularnewline
73 & 0.912028642478291 & 0.175942715043418 & 0.0879713575217088 \tabularnewline
74 & 0.835311114322293 & 0.329377771355414 & 0.164688885677707 \tabularnewline
75 & 0.779719875700479 & 0.440560248599043 & 0.220280124299521 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154728&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]10[/C][C]0.604996924941221[/C][C]0.790006150117557[/C][C]0.395003075058779[/C][/ROW]
[ROW][C]11[/C][C]0.501365060723952[/C][C]0.997269878552096[/C][C]0.498634939276048[/C][/ROW]
[ROW][C]12[/C][C]0.388353018129479[/C][C]0.776706036258958[/C][C]0.611646981870521[/C][/ROW]
[ROW][C]13[/C][C]0.343082024602662[/C][C]0.686164049205324[/C][C]0.656917975397338[/C][/ROW]
[ROW][C]14[/C][C]0.492852260325954[/C][C]0.985704520651908[/C][C]0.507147739674046[/C][/ROW]
[ROW][C]15[/C][C]0.412944906246699[/C][C]0.825889812493398[/C][C]0.587055093753301[/C][/ROW]
[ROW][C]16[/C][C]0.369865329619498[/C][C]0.739730659238995[/C][C]0.630134670380502[/C][/ROW]
[ROW][C]17[/C][C]0.33180156077117[/C][C]0.663603121542339[/C][C]0.66819843922883[/C][/ROW]
[ROW][C]18[/C][C]0.262666082904271[/C][C]0.525332165808543[/C][C]0.737333917095728[/C][/ROW]
[ROW][C]19[/C][C]0.215811096573971[/C][C]0.431622193147942[/C][C]0.784188903426029[/C][/ROW]
[ROW][C]20[/C][C]0.385904423930961[/C][C]0.771808847861922[/C][C]0.614095576069039[/C][/ROW]
[ROW][C]21[/C][C]0.333048275586937[/C][C]0.666096551173874[/C][C]0.666951724413063[/C][/ROW]
[ROW][C]22[/C][C]0.342527208151988[/C][C]0.685054416303975[/C][C]0.657472791848012[/C][/ROW]
[ROW][C]23[/C][C]0.387246368006163[/C][C]0.774492736012326[/C][C]0.612753631993837[/C][/ROW]
[ROW][C]24[/C][C]0.398147678553501[/C][C]0.796295357107001[/C][C]0.601852321446499[/C][/ROW]
[ROW][C]25[/C][C]0.353683196158672[/C][C]0.707366392317343[/C][C]0.646316803841328[/C][/ROW]
[ROW][C]26[/C][C]0.337941073062955[/C][C]0.67588214612591[/C][C]0.662058926937045[/C][/ROW]
[ROW][C]27[/C][C]0.297139444516089[/C][C]0.594278889032179[/C][C]0.702860555483911[/C][/ROW]
[ROW][C]28[/C][C]0.470694059584955[/C][C]0.94138811916991[/C][C]0.529305940415045[/C][/ROW]
[ROW][C]29[/C][C]0.411526709674247[/C][C]0.823053419348494[/C][C]0.588473290325753[/C][/ROW]
[ROW][C]30[/C][C]0.402353227907402[/C][C]0.804706455814804[/C][C]0.597646772092598[/C][/ROW]
[ROW][C]31[/C][C]0.371320385163551[/C][C]0.742640770327102[/C][C]0.628679614836449[/C][/ROW]
[ROW][C]32[/C][C]0.375365519678699[/C][C]0.750731039357399[/C][C]0.624634480321301[/C][/ROW]
[ROW][C]33[/C][C]0.356561726443306[/C][C]0.713123452886612[/C][C]0.643438273556694[/C][/ROW]
[ROW][C]34[/C][C]0.335468538056109[/C][C]0.670937076112217[/C][C]0.664531461943891[/C][/ROW]
[ROW][C]35[/C][C]0.32204157949268[/C][C]0.64408315898536[/C][C]0.67795842050732[/C][/ROW]
[ROW][C]36[/C][C]0.299388042906968[/C][C]0.598776085813935[/C][C]0.700611957093032[/C][/ROW]
[ROW][C]37[/C][C]0.2875167543955[/C][C]0.575033508790999[/C][C]0.7124832456045[/C][/ROW]
[ROW][C]38[/C][C]0.284947576029568[/C][C]0.569895152059137[/C][C]0.715052423970432[/C][/ROW]
[ROW][C]39[/C][C]0.311652663321908[/C][C]0.623305326643817[/C][C]0.688347336678092[/C][/ROW]
[ROW][C]40[/C][C]0.338901167899348[/C][C]0.677802335798697[/C][C]0.661098832100652[/C][/ROW]
[ROW][C]41[/C][C]0.403802538426113[/C][C]0.807605076852227[/C][C]0.596197461573887[/C][/ROW]
[ROW][C]42[/C][C]0.463917869332226[/C][C]0.927835738664452[/C][C]0.536082130667774[/C][/ROW]
[ROW][C]43[/C][C]0.459719738171255[/C][C]0.91943947634251[/C][C]0.540280261828745[/C][/ROW]
[ROW][C]44[/C][C]0.421305811618976[/C][C]0.842611623237952[/C][C]0.578694188381024[/C][/ROW]
[ROW][C]45[/C][C]0.438880269206042[/C][C]0.877760538412084[/C][C]0.561119730793958[/C][/ROW]
[ROW][C]46[/C][C]0.433211947277435[/C][C]0.86642389455487[/C][C]0.566788052722565[/C][/ROW]
[ROW][C]47[/C][C]0.409277762292325[/C][C]0.81855552458465[/C][C]0.590722237707675[/C][/ROW]
[ROW][C]48[/C][C]0.416805546156662[/C][C]0.833611092313324[/C][C]0.583194453843338[/C][/ROW]
[ROW][C]49[/C][C]0.406138456873049[/C][C]0.812276913746097[/C][C]0.593861543126951[/C][/ROW]
[ROW][C]50[/C][C]0.420286830813908[/C][C]0.840573661627816[/C][C]0.579713169186092[/C][/ROW]
[ROW][C]51[/C][C]0.35948998704027[/C][C]0.718979974080539[/C][C]0.64051001295973[/C][/ROW]
[ROW][C]52[/C][C]0.361937652364185[/C][C]0.723875304728369[/C][C]0.638062347635815[/C][/ROW]
[ROW][C]53[/C][C]0.383948954002333[/C][C]0.767897908004667[/C][C]0.616051045997667[/C][/ROW]
[ROW][C]54[/C][C]0.321494815467438[/C][C]0.642989630934877[/C][C]0.678505184532561[/C][/ROW]
[ROW][C]55[/C][C]0.352547740792923[/C][C]0.705095481585846[/C][C]0.647452259207077[/C][/ROW]
[ROW][C]56[/C][C]0.395762332363778[/C][C]0.791524664727557[/C][C]0.604237667636222[/C][/ROW]
[ROW][C]57[/C][C]0.375067238085297[/C][C]0.750134476170594[/C][C]0.624932761914703[/C][/ROW]
[ROW][C]58[/C][C]0.367045503468348[/C][C]0.734091006936696[/C][C]0.632954496531652[/C][/ROW]
[ROW][C]59[/C][C]0.383068332579793[/C][C]0.766136665159587[/C][C]0.616931667420207[/C][/ROW]
[ROW][C]60[/C][C]0.375392816635533[/C][C]0.750785633271066[/C][C]0.624607183364467[/C][/ROW]
[ROW][C]61[/C][C]0.423273164123797[/C][C]0.846546328247593[/C][C]0.576726835876203[/C][/ROW]
[ROW][C]62[/C][C]0.369419453184399[/C][C]0.738838906368799[/C][C]0.630580546815601[/C][/ROW]
[ROW][C]63[/C][C]0.587201966600591[/C][C]0.825596066798819[/C][C]0.412798033399409[/C][/ROW]
[ROW][C]64[/C][C]0.540073055198412[/C][C]0.919853889603176[/C][C]0.459926944801588[/C][/ROW]
[ROW][C]65[/C][C]0.665889290129406[/C][C]0.668221419741189[/C][C]0.334110709870594[/C][/ROW]
[ROW][C]66[/C][C]0.743271376014306[/C][C]0.513457247971389[/C][C]0.256728623985694[/C][/ROW]
[ROW][C]67[/C][C]0.677241876945668[/C][C]0.645516246108664[/C][C]0.322758123054332[/C][/ROW]
[ROW][C]68[/C][C]0.633953232227035[/C][C]0.73209353554593[/C][C]0.366046767772965[/C][/ROW]
[ROW][C]69[/C][C]0.895351440080273[/C][C]0.209297119839454[/C][C]0.104648559919727[/C][/ROW]
[ROW][C]70[/C][C]0.981282765304079[/C][C]0.0374344693918427[/C][C]0.0187172346959214[/C][/ROW]
[ROW][C]71[/C][C]0.977070611439262[/C][C]0.0458587771214762[/C][C]0.0229293885607381[/C][/ROW]
[ROW][C]72[/C][C]0.957836570471839[/C][C]0.0843268590563226[/C][C]0.0421634295281613[/C][/ROW]
[ROW][C]73[/C][C]0.912028642478291[/C][C]0.175942715043418[/C][C]0.0879713575217088[/C][/ROW]
[ROW][C]74[/C][C]0.835311114322293[/C][C]0.329377771355414[/C][C]0.164688885677707[/C][/ROW]
[ROW][C]75[/C][C]0.779719875700479[/C][C]0.440560248599043[/C][C]0.220280124299521[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154728&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154728&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.6049969249412210.7900061501175570.395003075058779
110.5013650607239520.9972698785520960.498634939276048
120.3883530181294790.7767060362589580.611646981870521
130.3430820246026620.6861640492053240.656917975397338
140.4928522603259540.9857045206519080.507147739674046
150.4129449062466990.8258898124933980.587055093753301
160.3698653296194980.7397306592389950.630134670380502
170.331801560771170.6636031215423390.66819843922883
180.2626660829042710.5253321658085430.737333917095728
190.2158110965739710.4316221931479420.784188903426029
200.3859044239309610.7718088478619220.614095576069039
210.3330482755869370.6660965511738740.666951724413063
220.3425272081519880.6850544163039750.657472791848012
230.3872463680061630.7744927360123260.612753631993837
240.3981476785535010.7962953571070010.601852321446499
250.3536831961586720.7073663923173430.646316803841328
260.3379410730629550.675882146125910.662058926937045
270.2971394445160890.5942788890321790.702860555483911
280.4706940595849550.941388119169910.529305940415045
290.4115267096742470.8230534193484940.588473290325753
300.4023532279074020.8047064558148040.597646772092598
310.3713203851635510.7426407703271020.628679614836449
320.3753655196786990.7507310393573990.624634480321301
330.3565617264433060.7131234528866120.643438273556694
340.3354685380561090.6709370761122170.664531461943891
350.322041579492680.644083158985360.67795842050732
360.2993880429069680.5987760858139350.700611957093032
370.28751675439550.5750335087909990.7124832456045
380.2849475760295680.5698951520591370.715052423970432
390.3116526633219080.6233053266438170.688347336678092
400.3389011678993480.6778023357986970.661098832100652
410.4038025384261130.8076050768522270.596197461573887
420.4639178693322260.9278357386644520.536082130667774
430.4597197381712550.919439476342510.540280261828745
440.4213058116189760.8426116232379520.578694188381024
450.4388802692060420.8777605384120840.561119730793958
460.4332119472774350.866423894554870.566788052722565
470.4092777622923250.818555524584650.590722237707675
480.4168055461566620.8336110923133240.583194453843338
490.4061384568730490.8122769137460970.593861543126951
500.4202868308139080.8405736616278160.579713169186092
510.359489987040270.7189799740805390.64051001295973
520.3619376523641850.7238753047283690.638062347635815
530.3839489540023330.7678979080046670.616051045997667
540.3214948154674380.6429896309348770.678505184532561
550.3525477407929230.7050954815858460.647452259207077
560.3957623323637780.7915246647275570.604237667636222
570.3750672380852970.7501344761705940.624932761914703
580.3670455034683480.7340910069366960.632954496531652
590.3830683325797930.7661366651595870.616931667420207
600.3753928166355330.7507856332710660.624607183364467
610.4232731641237970.8465463282475930.576726835876203
620.3694194531843990.7388389063687990.630580546815601
630.5872019666005910.8255960667988190.412798033399409
640.5400730551984120.9198538896031760.459926944801588
650.6658892901294060.6682214197411890.334110709870594
660.7432713760143060.5134572479713890.256728623985694
670.6772418769456680.6455162461086640.322758123054332
680.6339532322270350.732093535545930.366046767772965
690.8953514400802730.2092971198394540.104648559919727
700.9812827653040790.03743446939184270.0187172346959214
710.9770706114392620.04585877712147620.0229293885607381
720.9578365704718390.08432685905632260.0421634295281613
730.9120286424782910.1759427150434180.0879713575217088
740.8353111143222930.3293777713554140.164688885677707
750.7797198757004790.4405602485990430.220280124299521






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0303030303030303OK
10% type I error level30.0454545454545455OK

\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 & 2 & 0.0303030303030303 & OK \tabularnewline
10% type I error level & 3 & 0.0454545454545455 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154728&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]2[/C][C]0.0303030303030303[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0454545454545455[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154728&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154728&T=6

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0303030303030303OK
10% type I error level30.0454545454545455OK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}