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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 computationWed, 22 Dec 2010 08:41:16 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/22/t1293007179d1s0edwd9lkb4gd.htm/, Retrieved Fri, 07 Nov 2025 05:50:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114101, Retrieved Fri, 07 Nov 2025 05:50:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact442

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
-  M D  [Multiple Regression] [Workshop 8, Multi...] [2010-11-29 10:32:18] [d946de7cca328fbcf207448a112523ab]
-         [Multiple Regression] [Workshop 8, Multi...] [2010-11-29 20:25:47] [3635fb7041b1998c5a1332cf9de22bce]
-    D      [Multiple Regression] [Workshop 8, Multi...] [2010-11-29 20:46:24] [3635fb7041b1998c5a1332cf9de22bce]
- R PD        [Multiple Regression] [Paper, MR poging 2] [2010-12-19 20:57:32] [3635fb7041b1998c5a1332cf9de22bce]
-   P             [Multiple Regression] [Multiple Regression] [2010-12-22 08:41:16] [4d0f7ea43b071af5c75b527ee1ef14c2] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
97.06	21.454	631.923	130.678
97.73	23.899	654.294	120.877
98	24.939	671.833	137.114
97.76	23.580	586.840	134.406
97.48	24.562	600.969	120.262
97.77	24.696	625.568	130.846
97.96	23.785	558.110	120.343
98.22	23.812	630.577	98.881
98.51	21.917	628.654	115.678
98.19	19.713	603.184	120.796
98.37	19.282	656.255	94.261
98.31	18.788	600.730	89.151
98.6	21.453	670.326	119.880
98.96	24.482	678.423	131.468
99.11	27.474	641.502	155.089
99.64	27.264	625.311	149.581
100.02	27.349	628.177	122.788
99.98	30.632	589.767	143.900
100.32	29.429	582.471	112.115
100.44	30.084	636.248	109.600
100.51	26.290	599.885	117.446
101	24.379	621.694	118.456
100.88	23.335	637.406	101.901
100.55	21.346	595.994	89.940
100.82	21.106	696.308	129.143
101.5	24.514	674.201	126.102
102.15	28.353	648.861	143.048
102.39	30.805	649.605	142.258
102.54	31.348	672.392	131.011
102.85	34.556	598.396	146.471
103.47	33.855	613.177	114.073
103.56	34.787	638.104	114.642
103.69	32.529	615.632	118.226
103.49	29.998	634.465	111.338
103.47	29.257	638.686	108.701
103.45	28.155	604.243	80.512
103.48	30.466	706.669	146.865
103.93	35.704	677.185	137.179
103.89	39.327	644.328	166.536
104.4	39.351	664.825	137.070
104.79	42.234	605.707	127.090
104.77	43.630	600.136	139.966
105.13	43.722	612.166	122.243
105.26	43.121	599.659	109.097
104.96	37.985	634.210	116.591
104.75	37.135	618.234	111.964
105.01	34.646	613.576	109.754
105.15	33.026	627.200	77.609
105.2	35.087	668.973	138.445
105.77	38.846	651.479	127.901
105.78	42.013	619.661	156.615
106.26	43.908	644.260	133.264
106.13	42.868	579.936	143.521
106.12	44.423	601.752	152.139
106.57	44.167	595.376	131.523
106.44	43.636	588.902	113.925
106.54	44.382	634.341	86.495
107.1	42.142	594.305	127.877
108.1	43.452	606.200	107.017
108.4	36.912	610.926	78.716
108.84	42.413	633.685	138.278
109.62	45.344	639.696	144.238
110.42	44.873	659.451	143.679
110.67	47.510	593.248	159.932
111.66	49.554	606.677	136.781
112.28	47.369	599.434	148.173
112.87	45.998	569.578	125.673
112.18	48.140	629.873	105.573
112.36	48.441	613.438	122.405
112.16	44.928	604.172	128.045
111.49	40.454	658.328	94.467
111.25	38.661	612.633	85.573
111.36	37.246	707.372	121.501
111.74	36.843	739.770	125.074
111.1	36.424	777.535	144.979
111.33	37.594	685.030	142.120
111.25	38.144	730.234	124.213
111.04	38.737	714.154	144.407
110.97	34.560	630.872	125.170
111.31	36.080	719.492	109.267
111.02	33.508	677.023	122.354
111.07	35.462	679.272	122.589
111.36	33.374	718.317	104.982
111.54	32.110	645.672	90.542

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114101&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114101&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114101&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 time7 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
vacatures[t] = -93.4251087813653 + 1.51550935451971CPI[t] -0.070592650360014werklozen[t] + 0.110563161993499inschrijvingen[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
vacatures[t] =  -93.4251087813653 +  1.51550935451971CPI[t] -0.070592650360014werklozen[t] +  0.110563161993499inschrijvingen[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114101&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]vacatures[t] =  -93.4251087813653 +  1.51550935451971CPI[t] -0.070592650360014werklozen[t] +  0.110563161993499inschrijvingen[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114101&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114101&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
vacatures[t] = -93.4251087813653 + 1.51550935451971CPI[t] -0.070592650360014werklozen[t] + 0.110563161993499inschrijvingen[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-93.425108781365310.189264-9.16900
CPI1.515509354519710.08878117.070200
werklozen-0.0705926503600140.010499-6.723800
inschrijvingen0.1105631619934990.0215695.12612e-061e-06

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -93.4251087813653 & 10.189264 & -9.169 & 0 & 0 \tabularnewline
CPI & 1.51550935451971 & 0.088781 & 17.0702 & 0 & 0 \tabularnewline
werklozen & -0.070592650360014 & 0.010499 & -6.7238 & 0 & 0 \tabularnewline
inschrijvingen & 0.110563161993499 & 0.021569 & 5.1261 & 2e-06 & 1e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114101&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]-93.4251087813653[/C][C]10.189264[/C][C]-9.169[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]CPI[/C][C]1.51550935451971[/C][C]0.088781[/C][C]17.0702[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]werklozen[/C][C]-0.070592650360014[/C][C]0.010499[/C][C]-6.7238[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]inschrijvingen[/C][C]0.110563161993499[/C][C]0.021569[/C][C]5.1261[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114101&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114101&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)-93.425108781365310.189264-9.16900
CPI1.515509354519710.08878117.070200
werklozen-0.0705926503600140.010499-6.723800
inschrijvingen0.1105631619934990.0215695.12612e-061e-06






Multiple Linear Regression - Regression Statistics
Multiple R0.894854068958925
R-squared0.800763804732344
Adjusted R-squared0.793292447409807
F-TEST (value)107.177821935630
F-TEST (DF numerator)3
F-TEST (DF denominator)80
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.82827247699823
Sum Squared Residuals1172.45361265137

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.894854068958925 \tabularnewline
R-squared & 0.800763804732344 \tabularnewline
Adjusted R-squared & 0.793292447409807 \tabularnewline
F-TEST (value) & 107.177821935630 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 80 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.82827247699823 \tabularnewline
Sum Squared Residuals & 1172.45361265137 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114101&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.894854068958925[/C][/ROW]
[ROW][C]R-squared[/C][C]0.800763804732344[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.793292447409807[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]107.177821935630[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]80[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.82827247699823[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1172.45361265137[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114101&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114101&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 R0.894854068958925
R-squared0.800763804732344
Adjusted R-squared0.793292447409807
F-TEST (value)107.177821935630
F-TEST (DF numerator)3
F-TEST (DF denominator)80
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.82827247699823
Sum Squared Residuals1172.45361265137






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
121.45423.5092826578525-2.05528265785246
223.89921.86181619347892.03718380652106
324.93922.82809328582342.11090671417659
423.5828.1648471301090-4.58484713010895
524.56225.1792955906708-0.617295590670753
624.69625.0524852038147-0.356485203814674
723.78528.9412260987415-5.15622609874151
823.81221.84671435457301.96528564542697
921.91724.2790911660309-2.36209116603087
1019.71326.1579852403368-6.44498524033684
1119.28219.7505608733966-0.468560873396586
1218.78823.0143094655784-4.22630946557839
1321.45321.9383364888318-0.485336488831806
1424.48223.19353708767451.28846291232546
1527.47428.6388271842430-1.16482718424303
1627.26429.9760308478573-2.71203084785726
1727.34927.3872870673511-0.0382870673511174
1830.63232.3723398695052-1.74033986950523
1929.42929.8884069231052-0.459406923105207
2030.08425.99594073482354.08805926517655
2126.2929.5364655036820-3.24646550368203
2224.37928.8511787693086-4.47217876930857
2323.33525.7297927775073-2.39479277750728
2421.34626.8306115466204-5.48461154662042
2521.10624.4927755837575-3.38677558375746
2624.51426.7476910907175-2.23369109071747
2728.35331.3951932744199-3.04219327441988
2830.80531.6190496896619-0.814049689661886
2931.34828.99427748614532.35372251385468
3034.55636.3969656265055-1.84096562650552
3133.85532.7111261390711.14387386092902
3234.78731.1507694246283.63623057537202
3332.52933.3304020521905-0.801402052190478
3429.99830.9362697372452-0.938269737245161
3529.25730.3164329148083-1.05943291480830
3628.15529.6008804106331-1.44588041063312
3730.46629.75202037324860.713979626751412
3835.70431.44443849892814.25956150107192
3939.32736.94908358426942.37791641573058
4039.35133.01720166934486.33379833065518
4142.23436.67812626489575.55587373510431
4243.6338.46469900678925.16530099321079
4343.72236.20154187057457.52045812942546
4443.12135.82799703714837.29300296285173
4537.98533.76285790418284.22214209581721
4637.13534.06081337134133.07418662865868
4734.64634.53932178088780.106678219112237
4833.02630.23568597973472.79031402026535
4935.08734.08881518700830.998184812991698
5038.84635.02182536442323.82417463557685
5142.01340.45780804060461.55519195939537
5243.90836.86698352885797.0410164711421
5342.86842.34481530709520.523184692904821
5444.42341.74244428335592.68055571664408
5544.16740.59515208392723.57184791607277
5643.63638.90946216150884.7265378384912
5744.38232.820606123770411.5613938762296
5842.14241.07086348173001.07113651827005
5943.45239.44032570103294.0116742989671
6036.91236.43230959420940.479690405790627
6142.41342.07787863531130.335121364688697
6245.34443.49459995600391.84940004399612
6344.87343.25064482420321.62235517579680
6447.5150.0999504664975-2.58995046649748
6549.55448.09266826247581.46133173752415
6647.36950.8031221702656-3.43412217026561
6745.99851.3172157137271-5.31921571372708
6848.1443.79281084958214.34718915041791
6948.44147.0867918847371.35420811526296
7044.92848.0613777457123-3.13337774571233
7140.45439.51048105186950.943518948130518
7238.66141.3891412022154-2.72814120221541
7337.24638.8402834128577-1.59428341285767
7436.84337.5241584590142-0.681158459014184
7536.42436.08906077075630.334939229243744
7637.59442.6517009637095-5.05770096370947
7738.14437.35953550665620.784464493343774
7838.73740.4091208532928-1.67212085329285
7934.5644.0552287584902-9.4952287584902
8036.0836.5562952989398-0.476295298939846
8133.50840.5617369552775-7.05373695527747
8235.46240.5047318954123-5.04273189541225
8333.37436.2412539816967-2.86725398169669
8432.1140.0457166917273-7.93571669172734

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 21.454 & 23.5092826578525 & -2.05528265785246 \tabularnewline
2 & 23.899 & 21.8618161934789 & 2.03718380652106 \tabularnewline
3 & 24.939 & 22.8280932858234 & 2.11090671417659 \tabularnewline
4 & 23.58 & 28.1648471301090 & -4.58484713010895 \tabularnewline
5 & 24.562 & 25.1792955906708 & -0.617295590670753 \tabularnewline
6 & 24.696 & 25.0524852038147 & -0.356485203814674 \tabularnewline
7 & 23.785 & 28.9412260987415 & -5.15622609874151 \tabularnewline
8 & 23.812 & 21.8467143545730 & 1.96528564542697 \tabularnewline
9 & 21.917 & 24.2790911660309 & -2.36209116603087 \tabularnewline
10 & 19.713 & 26.1579852403368 & -6.44498524033684 \tabularnewline
11 & 19.282 & 19.7505608733966 & -0.468560873396586 \tabularnewline
12 & 18.788 & 23.0143094655784 & -4.22630946557839 \tabularnewline
13 & 21.453 & 21.9383364888318 & -0.485336488831806 \tabularnewline
14 & 24.482 & 23.1935370876745 & 1.28846291232546 \tabularnewline
15 & 27.474 & 28.6388271842430 & -1.16482718424303 \tabularnewline
16 & 27.264 & 29.9760308478573 & -2.71203084785726 \tabularnewline
17 & 27.349 & 27.3872870673511 & -0.0382870673511174 \tabularnewline
18 & 30.632 & 32.3723398695052 & -1.74033986950523 \tabularnewline
19 & 29.429 & 29.8884069231052 & -0.459406923105207 \tabularnewline
20 & 30.084 & 25.9959407348235 & 4.08805926517655 \tabularnewline
21 & 26.29 & 29.5364655036820 & -3.24646550368203 \tabularnewline
22 & 24.379 & 28.8511787693086 & -4.47217876930857 \tabularnewline
23 & 23.335 & 25.7297927775073 & -2.39479277750728 \tabularnewline
24 & 21.346 & 26.8306115466204 & -5.48461154662042 \tabularnewline
25 & 21.106 & 24.4927755837575 & -3.38677558375746 \tabularnewline
26 & 24.514 & 26.7476910907175 & -2.23369109071747 \tabularnewline
27 & 28.353 & 31.3951932744199 & -3.04219327441988 \tabularnewline
28 & 30.805 & 31.6190496896619 & -0.814049689661886 \tabularnewline
29 & 31.348 & 28.9942774861453 & 2.35372251385468 \tabularnewline
30 & 34.556 & 36.3969656265055 & -1.84096562650552 \tabularnewline
31 & 33.855 & 32.711126139071 & 1.14387386092902 \tabularnewline
32 & 34.787 & 31.150769424628 & 3.63623057537202 \tabularnewline
33 & 32.529 & 33.3304020521905 & -0.801402052190478 \tabularnewline
34 & 29.998 & 30.9362697372452 & -0.938269737245161 \tabularnewline
35 & 29.257 & 30.3164329148083 & -1.05943291480830 \tabularnewline
36 & 28.155 & 29.6008804106331 & -1.44588041063312 \tabularnewline
37 & 30.466 & 29.7520203732486 & 0.713979626751412 \tabularnewline
38 & 35.704 & 31.4444384989281 & 4.25956150107192 \tabularnewline
39 & 39.327 & 36.9490835842694 & 2.37791641573058 \tabularnewline
40 & 39.351 & 33.0172016693448 & 6.33379833065518 \tabularnewline
41 & 42.234 & 36.6781262648957 & 5.55587373510431 \tabularnewline
42 & 43.63 & 38.4646990067892 & 5.16530099321079 \tabularnewline
43 & 43.722 & 36.2015418705745 & 7.52045812942546 \tabularnewline
44 & 43.121 & 35.8279970371483 & 7.29300296285173 \tabularnewline
45 & 37.985 & 33.7628579041828 & 4.22214209581721 \tabularnewline
46 & 37.135 & 34.0608133713413 & 3.07418662865868 \tabularnewline
47 & 34.646 & 34.5393217808878 & 0.106678219112237 \tabularnewline
48 & 33.026 & 30.2356859797347 & 2.79031402026535 \tabularnewline
49 & 35.087 & 34.0888151870083 & 0.998184812991698 \tabularnewline
50 & 38.846 & 35.0218253644232 & 3.82417463557685 \tabularnewline
51 & 42.013 & 40.4578080406046 & 1.55519195939537 \tabularnewline
52 & 43.908 & 36.8669835288579 & 7.0410164711421 \tabularnewline
53 & 42.868 & 42.3448153070952 & 0.523184692904821 \tabularnewline
54 & 44.423 & 41.7424442833559 & 2.68055571664408 \tabularnewline
55 & 44.167 & 40.5951520839272 & 3.57184791607277 \tabularnewline
56 & 43.636 & 38.9094621615088 & 4.7265378384912 \tabularnewline
57 & 44.382 & 32.8206061237704 & 11.5613938762296 \tabularnewline
58 & 42.142 & 41.0708634817300 & 1.07113651827005 \tabularnewline
59 & 43.452 & 39.4403257010329 & 4.0116742989671 \tabularnewline
60 & 36.912 & 36.4323095942094 & 0.479690405790627 \tabularnewline
61 & 42.413 & 42.0778786353113 & 0.335121364688697 \tabularnewline
62 & 45.344 & 43.4945999560039 & 1.84940004399612 \tabularnewline
63 & 44.873 & 43.2506448242032 & 1.62235517579680 \tabularnewline
64 & 47.51 & 50.0999504664975 & -2.58995046649748 \tabularnewline
65 & 49.554 & 48.0926682624758 & 1.46133173752415 \tabularnewline
66 & 47.369 & 50.8031221702656 & -3.43412217026561 \tabularnewline
67 & 45.998 & 51.3172157137271 & -5.31921571372708 \tabularnewline
68 & 48.14 & 43.7928108495821 & 4.34718915041791 \tabularnewline
69 & 48.441 & 47.086791884737 & 1.35420811526296 \tabularnewline
70 & 44.928 & 48.0613777457123 & -3.13337774571233 \tabularnewline
71 & 40.454 & 39.5104810518695 & 0.943518948130518 \tabularnewline
72 & 38.661 & 41.3891412022154 & -2.72814120221541 \tabularnewline
73 & 37.246 & 38.8402834128577 & -1.59428341285767 \tabularnewline
74 & 36.843 & 37.5241584590142 & -0.681158459014184 \tabularnewline
75 & 36.424 & 36.0890607707563 & 0.334939229243744 \tabularnewline
76 & 37.594 & 42.6517009637095 & -5.05770096370947 \tabularnewline
77 & 38.144 & 37.3595355066562 & 0.784464493343774 \tabularnewline
78 & 38.737 & 40.4091208532928 & -1.67212085329285 \tabularnewline
79 & 34.56 & 44.0552287584902 & -9.4952287584902 \tabularnewline
80 & 36.08 & 36.5562952989398 & -0.476295298939846 \tabularnewline
81 & 33.508 & 40.5617369552775 & -7.05373695527747 \tabularnewline
82 & 35.462 & 40.5047318954123 & -5.04273189541225 \tabularnewline
83 & 33.374 & 36.2412539816967 & -2.86725398169669 \tabularnewline
84 & 32.11 & 40.0457166917273 & -7.93571669172734 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114101&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]21.454[/C][C]23.5092826578525[/C][C]-2.05528265785246[/C][/ROW]
[ROW][C]2[/C][C]23.899[/C][C]21.8618161934789[/C][C]2.03718380652106[/C][/ROW]
[ROW][C]3[/C][C]24.939[/C][C]22.8280932858234[/C][C]2.11090671417659[/C][/ROW]
[ROW][C]4[/C][C]23.58[/C][C]28.1648471301090[/C][C]-4.58484713010895[/C][/ROW]
[ROW][C]5[/C][C]24.562[/C][C]25.1792955906708[/C][C]-0.617295590670753[/C][/ROW]
[ROW][C]6[/C][C]24.696[/C][C]25.0524852038147[/C][C]-0.356485203814674[/C][/ROW]
[ROW][C]7[/C][C]23.785[/C][C]28.9412260987415[/C][C]-5.15622609874151[/C][/ROW]
[ROW][C]8[/C][C]23.812[/C][C]21.8467143545730[/C][C]1.96528564542697[/C][/ROW]
[ROW][C]9[/C][C]21.917[/C][C]24.2790911660309[/C][C]-2.36209116603087[/C][/ROW]
[ROW][C]10[/C][C]19.713[/C][C]26.1579852403368[/C][C]-6.44498524033684[/C][/ROW]
[ROW][C]11[/C][C]19.282[/C][C]19.7505608733966[/C][C]-0.468560873396586[/C][/ROW]
[ROW][C]12[/C][C]18.788[/C][C]23.0143094655784[/C][C]-4.22630946557839[/C][/ROW]
[ROW][C]13[/C][C]21.453[/C][C]21.9383364888318[/C][C]-0.485336488831806[/C][/ROW]
[ROW][C]14[/C][C]24.482[/C][C]23.1935370876745[/C][C]1.28846291232546[/C][/ROW]
[ROW][C]15[/C][C]27.474[/C][C]28.6388271842430[/C][C]-1.16482718424303[/C][/ROW]
[ROW][C]16[/C][C]27.264[/C][C]29.9760308478573[/C][C]-2.71203084785726[/C][/ROW]
[ROW][C]17[/C][C]27.349[/C][C]27.3872870673511[/C][C]-0.0382870673511174[/C][/ROW]
[ROW][C]18[/C][C]30.632[/C][C]32.3723398695052[/C][C]-1.74033986950523[/C][/ROW]
[ROW][C]19[/C][C]29.429[/C][C]29.8884069231052[/C][C]-0.459406923105207[/C][/ROW]
[ROW][C]20[/C][C]30.084[/C][C]25.9959407348235[/C][C]4.08805926517655[/C][/ROW]
[ROW][C]21[/C][C]26.29[/C][C]29.5364655036820[/C][C]-3.24646550368203[/C][/ROW]
[ROW][C]22[/C][C]24.379[/C][C]28.8511787693086[/C][C]-4.47217876930857[/C][/ROW]
[ROW][C]23[/C][C]23.335[/C][C]25.7297927775073[/C][C]-2.39479277750728[/C][/ROW]
[ROW][C]24[/C][C]21.346[/C][C]26.8306115466204[/C][C]-5.48461154662042[/C][/ROW]
[ROW][C]25[/C][C]21.106[/C][C]24.4927755837575[/C][C]-3.38677558375746[/C][/ROW]
[ROW][C]26[/C][C]24.514[/C][C]26.7476910907175[/C][C]-2.23369109071747[/C][/ROW]
[ROW][C]27[/C][C]28.353[/C][C]31.3951932744199[/C][C]-3.04219327441988[/C][/ROW]
[ROW][C]28[/C][C]30.805[/C][C]31.6190496896619[/C][C]-0.814049689661886[/C][/ROW]
[ROW][C]29[/C][C]31.348[/C][C]28.9942774861453[/C][C]2.35372251385468[/C][/ROW]
[ROW][C]30[/C][C]34.556[/C][C]36.3969656265055[/C][C]-1.84096562650552[/C][/ROW]
[ROW][C]31[/C][C]33.855[/C][C]32.711126139071[/C][C]1.14387386092902[/C][/ROW]
[ROW][C]32[/C][C]34.787[/C][C]31.150769424628[/C][C]3.63623057537202[/C][/ROW]
[ROW][C]33[/C][C]32.529[/C][C]33.3304020521905[/C][C]-0.801402052190478[/C][/ROW]
[ROW][C]34[/C][C]29.998[/C][C]30.9362697372452[/C][C]-0.938269737245161[/C][/ROW]
[ROW][C]35[/C][C]29.257[/C][C]30.3164329148083[/C][C]-1.05943291480830[/C][/ROW]
[ROW][C]36[/C][C]28.155[/C][C]29.6008804106331[/C][C]-1.44588041063312[/C][/ROW]
[ROW][C]37[/C][C]30.466[/C][C]29.7520203732486[/C][C]0.713979626751412[/C][/ROW]
[ROW][C]38[/C][C]35.704[/C][C]31.4444384989281[/C][C]4.25956150107192[/C][/ROW]
[ROW][C]39[/C][C]39.327[/C][C]36.9490835842694[/C][C]2.37791641573058[/C][/ROW]
[ROW][C]40[/C][C]39.351[/C][C]33.0172016693448[/C][C]6.33379833065518[/C][/ROW]
[ROW][C]41[/C][C]42.234[/C][C]36.6781262648957[/C][C]5.55587373510431[/C][/ROW]
[ROW][C]42[/C][C]43.63[/C][C]38.4646990067892[/C][C]5.16530099321079[/C][/ROW]
[ROW][C]43[/C][C]43.722[/C][C]36.2015418705745[/C][C]7.52045812942546[/C][/ROW]
[ROW][C]44[/C][C]43.121[/C][C]35.8279970371483[/C][C]7.29300296285173[/C][/ROW]
[ROW][C]45[/C][C]37.985[/C][C]33.7628579041828[/C][C]4.22214209581721[/C][/ROW]
[ROW][C]46[/C][C]37.135[/C][C]34.0608133713413[/C][C]3.07418662865868[/C][/ROW]
[ROW][C]47[/C][C]34.646[/C][C]34.5393217808878[/C][C]0.106678219112237[/C][/ROW]
[ROW][C]48[/C][C]33.026[/C][C]30.2356859797347[/C][C]2.79031402026535[/C][/ROW]
[ROW][C]49[/C][C]35.087[/C][C]34.0888151870083[/C][C]0.998184812991698[/C][/ROW]
[ROW][C]50[/C][C]38.846[/C][C]35.0218253644232[/C][C]3.82417463557685[/C][/ROW]
[ROW][C]51[/C][C]42.013[/C][C]40.4578080406046[/C][C]1.55519195939537[/C][/ROW]
[ROW][C]52[/C][C]43.908[/C][C]36.8669835288579[/C][C]7.0410164711421[/C][/ROW]
[ROW][C]53[/C][C]42.868[/C][C]42.3448153070952[/C][C]0.523184692904821[/C][/ROW]
[ROW][C]54[/C][C]44.423[/C][C]41.7424442833559[/C][C]2.68055571664408[/C][/ROW]
[ROW][C]55[/C][C]44.167[/C][C]40.5951520839272[/C][C]3.57184791607277[/C][/ROW]
[ROW][C]56[/C][C]43.636[/C][C]38.9094621615088[/C][C]4.7265378384912[/C][/ROW]
[ROW][C]57[/C][C]44.382[/C][C]32.8206061237704[/C][C]11.5613938762296[/C][/ROW]
[ROW][C]58[/C][C]42.142[/C][C]41.0708634817300[/C][C]1.07113651827005[/C][/ROW]
[ROW][C]59[/C][C]43.452[/C][C]39.4403257010329[/C][C]4.0116742989671[/C][/ROW]
[ROW][C]60[/C][C]36.912[/C][C]36.4323095942094[/C][C]0.479690405790627[/C][/ROW]
[ROW][C]61[/C][C]42.413[/C][C]42.0778786353113[/C][C]0.335121364688697[/C][/ROW]
[ROW][C]62[/C][C]45.344[/C][C]43.4945999560039[/C][C]1.84940004399612[/C][/ROW]
[ROW][C]63[/C][C]44.873[/C][C]43.2506448242032[/C][C]1.62235517579680[/C][/ROW]
[ROW][C]64[/C][C]47.51[/C][C]50.0999504664975[/C][C]-2.58995046649748[/C][/ROW]
[ROW][C]65[/C][C]49.554[/C][C]48.0926682624758[/C][C]1.46133173752415[/C][/ROW]
[ROW][C]66[/C][C]47.369[/C][C]50.8031221702656[/C][C]-3.43412217026561[/C][/ROW]
[ROW][C]67[/C][C]45.998[/C][C]51.3172157137271[/C][C]-5.31921571372708[/C][/ROW]
[ROW][C]68[/C][C]48.14[/C][C]43.7928108495821[/C][C]4.34718915041791[/C][/ROW]
[ROW][C]69[/C][C]48.441[/C][C]47.086791884737[/C][C]1.35420811526296[/C][/ROW]
[ROW][C]70[/C][C]44.928[/C][C]48.0613777457123[/C][C]-3.13337774571233[/C][/ROW]
[ROW][C]71[/C][C]40.454[/C][C]39.5104810518695[/C][C]0.943518948130518[/C][/ROW]
[ROW][C]72[/C][C]38.661[/C][C]41.3891412022154[/C][C]-2.72814120221541[/C][/ROW]
[ROW][C]73[/C][C]37.246[/C][C]38.8402834128577[/C][C]-1.59428341285767[/C][/ROW]
[ROW][C]74[/C][C]36.843[/C][C]37.5241584590142[/C][C]-0.681158459014184[/C][/ROW]
[ROW][C]75[/C][C]36.424[/C][C]36.0890607707563[/C][C]0.334939229243744[/C][/ROW]
[ROW][C]76[/C][C]37.594[/C][C]42.6517009637095[/C][C]-5.05770096370947[/C][/ROW]
[ROW][C]77[/C][C]38.144[/C][C]37.3595355066562[/C][C]0.784464493343774[/C][/ROW]
[ROW][C]78[/C][C]38.737[/C][C]40.4091208532928[/C][C]-1.67212085329285[/C][/ROW]
[ROW][C]79[/C][C]34.56[/C][C]44.0552287584902[/C][C]-9.4952287584902[/C][/ROW]
[ROW][C]80[/C][C]36.08[/C][C]36.5562952989398[/C][C]-0.476295298939846[/C][/ROW]
[ROW][C]81[/C][C]33.508[/C][C]40.5617369552775[/C][C]-7.05373695527747[/C][/ROW]
[ROW][C]82[/C][C]35.462[/C][C]40.5047318954123[/C][C]-5.04273189541225[/C][/ROW]
[ROW][C]83[/C][C]33.374[/C][C]36.2412539816967[/C][C]-2.86725398169669[/C][/ROW]
[ROW][C]84[/C][C]32.11[/C][C]40.0457166917273[/C][C]-7.93571669172734[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114101&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114101&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
121.45423.5092826578525-2.05528265785246
223.89921.86181619347892.03718380652106
324.93922.82809328582342.11090671417659
423.5828.1648471301090-4.58484713010895
524.56225.1792955906708-0.617295590670753
624.69625.0524852038147-0.356485203814674
723.78528.9412260987415-5.15622609874151
823.81221.84671435457301.96528564542697
921.91724.2790911660309-2.36209116603087
1019.71326.1579852403368-6.44498524033684
1119.28219.7505608733966-0.468560873396586
1218.78823.0143094655784-4.22630946557839
1321.45321.9383364888318-0.485336488831806
1424.48223.19353708767451.28846291232546
1527.47428.6388271842430-1.16482718424303
1627.26429.9760308478573-2.71203084785726
1727.34927.3872870673511-0.0382870673511174
1830.63232.3723398695052-1.74033986950523
1929.42929.8884069231052-0.459406923105207
2030.08425.99594073482354.08805926517655
2126.2929.5364655036820-3.24646550368203
2224.37928.8511787693086-4.47217876930857
2323.33525.7297927775073-2.39479277750728
2421.34626.8306115466204-5.48461154662042
2521.10624.4927755837575-3.38677558375746
2624.51426.7476910907175-2.23369109071747
2728.35331.3951932744199-3.04219327441988
2830.80531.6190496896619-0.814049689661886
2931.34828.99427748614532.35372251385468
3034.55636.3969656265055-1.84096562650552
3133.85532.7111261390711.14387386092902
3234.78731.1507694246283.63623057537202
3332.52933.3304020521905-0.801402052190478
3429.99830.9362697372452-0.938269737245161
3529.25730.3164329148083-1.05943291480830
3628.15529.6008804106331-1.44588041063312
3730.46629.75202037324860.713979626751412
3835.70431.44443849892814.25956150107192
3939.32736.94908358426942.37791641573058
4039.35133.01720166934486.33379833065518
4142.23436.67812626489575.55587373510431
4243.6338.46469900678925.16530099321079
4343.72236.20154187057457.52045812942546
4443.12135.82799703714837.29300296285173
4537.98533.76285790418284.22214209581721
4637.13534.06081337134133.07418662865868
4734.64634.53932178088780.106678219112237
4833.02630.23568597973472.79031402026535
4935.08734.08881518700830.998184812991698
5038.84635.02182536442323.82417463557685
5142.01340.45780804060461.55519195939537
5243.90836.86698352885797.0410164711421
5342.86842.34481530709520.523184692904821
5444.42341.74244428335592.68055571664408
5544.16740.59515208392723.57184791607277
5643.63638.90946216150884.7265378384912
5744.38232.820606123770411.5613938762296
5842.14241.07086348173001.07113651827005
5943.45239.44032570103294.0116742989671
6036.91236.43230959420940.479690405790627
6142.41342.07787863531130.335121364688697
6245.34443.49459995600391.84940004399612
6344.87343.25064482420321.62235517579680
6447.5150.0999504664975-2.58995046649748
6549.55448.09266826247581.46133173752415
6647.36950.8031221702656-3.43412217026561
6745.99851.3172157137271-5.31921571372708
6848.1443.79281084958214.34718915041791
6948.44147.0867918847371.35420811526296
7044.92848.0613777457123-3.13337774571233
7140.45439.51048105186950.943518948130518
7238.66141.3891412022154-2.72814120221541
7337.24638.8402834128577-1.59428341285767
7436.84337.5241584590142-0.681158459014184
7536.42436.08906077075630.334939229243744
7637.59442.6517009637095-5.05770096370947
7738.14437.35953550665620.784464493343774
7838.73740.4091208532928-1.67212085329285
7934.5644.0552287584902-9.4952287584902
8036.0836.5562952989398-0.476295298939846
8133.50840.5617369552775-7.05373695527747
8235.46240.5047318954123-5.04273189541225
8333.37436.2412539816967-2.86725398169669
8432.1140.0457166917273-7.93571669172734






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.02178615433595730.04357230867191460.978213845664043
80.008598579249076210.01719715849815240.991401420750924
90.01826071957586420.03652143915172830.981739280424136
100.05062877166787220.1012575433357440.949371228332128
110.04500479958396290.09000959916792580.954995200416037
120.02947785659480740.05895571318961480.970522143405193
130.01527224888252670.03054449776505340.984727751117473
140.007988277472756920.01597655494551380.992011722527243
150.004170422093506760.008340844187013520.995829577906493
160.002030184242707770.004060368485415540.997969815757292
170.001917959551172570.003835919102345140.998082040448827
180.001653014826244550.00330602965248910.998346985173755
190.001673620445543910.003347240891087820.998326379554456
200.002156300161128100.004312600322256190.997843699838872
210.001909912077177930.003819824154355860.998090087922822
220.004855642583548740.009711285167097490.995144357416451
230.00442501326091750.0088500265218350.995574986739083
240.00705935634245880.01411871268491760.992940643657541
250.02053423578270680.04106847156541360.979465764217293
260.01844359179315240.03688718358630490.981556408206848
270.01687651280022580.03375302560045150.983123487199774
280.01397706452051850.02795412904103700.986022935479481
290.01444970640117470.02889941280234940.985550293598825
300.01454337970788650.02908675941577300.985456620292114
310.01713842862811420.03427685725622840.982861571371886
320.02269003483594980.04538006967189950.97730996516405
330.01908936153893930.03817872307787860.98091063846106
340.01765109325822520.03530218651645030.982348906741775
350.01846096909126150.03692193818252290.981539030908739
360.02684929414274180.05369858828548350.973150705857258
370.02512101870552610.05024203741105230.974878981294474
380.02601981922625690.05203963845251390.973980180773743
390.02373411642559070.04746823285118130.97626588357441
400.0369206940675680.0738413881351360.963079305932432
410.06155084231045740.1231016846209150.938449157689543
420.07207580507595740.1441516101519150.927924194924043
430.1200051093621130.2400102187242260.879994890637887
440.1617149323664450.3234298647328890.838285067633555
450.1284122452265150.2568244904530290.871587754773485
460.1003200188660020.2006400377320040.899679981133998
470.1073773401887990.2147546803775990.892622659811201
480.0923990791828060.1847981583656120.907600920817194
490.09617355012586270.1923471002517250.903826449874137
500.07212885726451830.1442577145290370.927871142735482
510.05978355594596490.1195671118919300.940216444054035
520.05852288791755410.1170457758351080.941477112082446
530.05350287940454840.1070057588090970.946497120595452
540.03945609862173070.07891219724346140.96054390137827
550.02755120951126040.05510241902252070.97244879048874
560.0197226403186590.0394452806373180.98027735968134
570.0948689580445540.1897379160891080.905131041955446
580.07632317017608330.1526463403521670.923676829823917
590.07190920994308850.1438184198861770.928090790056912
600.07378070640603440.1475614128120690.926219293593966
610.07525618741356110.1505123748271220.924743812586439
620.1064407190230660.2128814380461330.893559280976933
630.1835102526799750.367020505359950.816489747320025
640.2879788890750050.5759577781500110.712021110924995
650.5179962791509780.9640074416980430.482003720849022
660.5174914019890340.9650171960219330.482508598010966
670.7196121422289040.5607757155421930.280387857771096
680.7716601423468530.4566797153062940.228339857653147
690.7655787412531290.4688425174937420.234421258746871
700.763159954717050.4736800905659010.236840045282951
710.877393493743570.2452130125128580.122606506256429
720.991797369235110.01640526152978040.00820263076489021
730.9877830971078420.0244338057843170.0122169028921585
740.973794880944880.05241023811023950.0262051190551198
750.9872275239112350.02554495217753060.0127724760887653
760.9808302245210370.03833955095792550.0191697754789627
770.9492285457522240.1015429084955510.0507714542477757

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.0217861543359573 & 0.0435723086719146 & 0.978213845664043 \tabularnewline
8 & 0.00859857924907621 & 0.0171971584981524 & 0.991401420750924 \tabularnewline
9 & 0.0182607195758642 & 0.0365214391517283 & 0.981739280424136 \tabularnewline
10 & 0.0506287716678722 & 0.101257543335744 & 0.949371228332128 \tabularnewline
11 & 0.0450047995839629 & 0.0900095991679258 & 0.954995200416037 \tabularnewline
12 & 0.0294778565948074 & 0.0589557131896148 & 0.970522143405193 \tabularnewline
13 & 0.0152722488825267 & 0.0305444977650534 & 0.984727751117473 \tabularnewline
14 & 0.00798827747275692 & 0.0159765549455138 & 0.992011722527243 \tabularnewline
15 & 0.00417042209350676 & 0.00834084418701352 & 0.995829577906493 \tabularnewline
16 & 0.00203018424270777 & 0.00406036848541554 & 0.997969815757292 \tabularnewline
17 & 0.00191795955117257 & 0.00383591910234514 & 0.998082040448827 \tabularnewline
18 & 0.00165301482624455 & 0.0033060296524891 & 0.998346985173755 \tabularnewline
19 & 0.00167362044554391 & 0.00334724089108782 & 0.998326379554456 \tabularnewline
20 & 0.00215630016112810 & 0.00431260032225619 & 0.997843699838872 \tabularnewline
21 & 0.00190991207717793 & 0.00381982415435586 & 0.998090087922822 \tabularnewline
22 & 0.00485564258354874 & 0.00971128516709749 & 0.995144357416451 \tabularnewline
23 & 0.0044250132609175 & 0.008850026521835 & 0.995574986739083 \tabularnewline
24 & 0.0070593563424588 & 0.0141187126849176 & 0.992940643657541 \tabularnewline
25 & 0.0205342357827068 & 0.0410684715654136 & 0.979465764217293 \tabularnewline
26 & 0.0184435917931524 & 0.0368871835863049 & 0.981556408206848 \tabularnewline
27 & 0.0168765128002258 & 0.0337530256004515 & 0.983123487199774 \tabularnewline
28 & 0.0139770645205185 & 0.0279541290410370 & 0.986022935479481 \tabularnewline
29 & 0.0144497064011747 & 0.0288994128023494 & 0.985550293598825 \tabularnewline
30 & 0.0145433797078865 & 0.0290867594157730 & 0.985456620292114 \tabularnewline
31 & 0.0171384286281142 & 0.0342768572562284 & 0.982861571371886 \tabularnewline
32 & 0.0226900348359498 & 0.0453800696718995 & 0.97730996516405 \tabularnewline
33 & 0.0190893615389393 & 0.0381787230778786 & 0.98091063846106 \tabularnewline
34 & 0.0176510932582252 & 0.0353021865164503 & 0.982348906741775 \tabularnewline
35 & 0.0184609690912615 & 0.0369219381825229 & 0.981539030908739 \tabularnewline
36 & 0.0268492941427418 & 0.0536985882854835 & 0.973150705857258 \tabularnewline
37 & 0.0251210187055261 & 0.0502420374110523 & 0.974878981294474 \tabularnewline
38 & 0.0260198192262569 & 0.0520396384525139 & 0.973980180773743 \tabularnewline
39 & 0.0237341164255907 & 0.0474682328511813 & 0.97626588357441 \tabularnewline
40 & 0.036920694067568 & 0.073841388135136 & 0.963079305932432 \tabularnewline
41 & 0.0615508423104574 & 0.123101684620915 & 0.938449157689543 \tabularnewline
42 & 0.0720758050759574 & 0.144151610151915 & 0.927924194924043 \tabularnewline
43 & 0.120005109362113 & 0.240010218724226 & 0.879994890637887 \tabularnewline
44 & 0.161714932366445 & 0.323429864732889 & 0.838285067633555 \tabularnewline
45 & 0.128412245226515 & 0.256824490453029 & 0.871587754773485 \tabularnewline
46 & 0.100320018866002 & 0.200640037732004 & 0.899679981133998 \tabularnewline
47 & 0.107377340188799 & 0.214754680377599 & 0.892622659811201 \tabularnewline
48 & 0.092399079182806 & 0.184798158365612 & 0.907600920817194 \tabularnewline
49 & 0.0961735501258627 & 0.192347100251725 & 0.903826449874137 \tabularnewline
50 & 0.0721288572645183 & 0.144257714529037 & 0.927871142735482 \tabularnewline
51 & 0.0597835559459649 & 0.119567111891930 & 0.940216444054035 \tabularnewline
52 & 0.0585228879175541 & 0.117045775835108 & 0.941477112082446 \tabularnewline
53 & 0.0535028794045484 & 0.107005758809097 & 0.946497120595452 \tabularnewline
54 & 0.0394560986217307 & 0.0789121972434614 & 0.96054390137827 \tabularnewline
55 & 0.0275512095112604 & 0.0551024190225207 & 0.97244879048874 \tabularnewline
56 & 0.019722640318659 & 0.039445280637318 & 0.98027735968134 \tabularnewline
57 & 0.094868958044554 & 0.189737916089108 & 0.905131041955446 \tabularnewline
58 & 0.0763231701760833 & 0.152646340352167 & 0.923676829823917 \tabularnewline
59 & 0.0719092099430885 & 0.143818419886177 & 0.928090790056912 \tabularnewline
60 & 0.0737807064060344 & 0.147561412812069 & 0.926219293593966 \tabularnewline
61 & 0.0752561874135611 & 0.150512374827122 & 0.924743812586439 \tabularnewline
62 & 0.106440719023066 & 0.212881438046133 & 0.893559280976933 \tabularnewline
63 & 0.183510252679975 & 0.36702050535995 & 0.816489747320025 \tabularnewline
64 & 0.287978889075005 & 0.575957778150011 & 0.712021110924995 \tabularnewline
65 & 0.517996279150978 & 0.964007441698043 & 0.482003720849022 \tabularnewline
66 & 0.517491401989034 & 0.965017196021933 & 0.482508598010966 \tabularnewline
67 & 0.719612142228904 & 0.560775715542193 & 0.280387857771096 \tabularnewline
68 & 0.771660142346853 & 0.456679715306294 & 0.228339857653147 \tabularnewline
69 & 0.765578741253129 & 0.468842517493742 & 0.234421258746871 \tabularnewline
70 & 0.76315995471705 & 0.473680090565901 & 0.236840045282951 \tabularnewline
71 & 0.87739349374357 & 0.245213012512858 & 0.122606506256429 \tabularnewline
72 & 0.99179736923511 & 0.0164052615297804 & 0.00820263076489021 \tabularnewline
73 & 0.987783097107842 & 0.024433805784317 & 0.0122169028921585 \tabularnewline
74 & 0.97379488094488 & 0.0524102381102395 & 0.0262051190551198 \tabularnewline
75 & 0.987227523911235 & 0.0255449521775306 & 0.0127724760887653 \tabularnewline
76 & 0.980830224521037 & 0.0383395509579255 & 0.0191697754789627 \tabularnewline
77 & 0.949228545752224 & 0.101542908495551 & 0.0507714542477757 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114101&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]7[/C][C]0.0217861543359573[/C][C]0.0435723086719146[/C][C]0.978213845664043[/C][/ROW]
[ROW][C]8[/C][C]0.00859857924907621[/C][C]0.0171971584981524[/C][C]0.991401420750924[/C][/ROW]
[ROW][C]9[/C][C]0.0182607195758642[/C][C]0.0365214391517283[/C][C]0.981739280424136[/C][/ROW]
[ROW][C]10[/C][C]0.0506287716678722[/C][C]0.101257543335744[/C][C]0.949371228332128[/C][/ROW]
[ROW][C]11[/C][C]0.0450047995839629[/C][C]0.0900095991679258[/C][C]0.954995200416037[/C][/ROW]
[ROW][C]12[/C][C]0.0294778565948074[/C][C]0.0589557131896148[/C][C]0.970522143405193[/C][/ROW]
[ROW][C]13[/C][C]0.0152722488825267[/C][C]0.0305444977650534[/C][C]0.984727751117473[/C][/ROW]
[ROW][C]14[/C][C]0.00798827747275692[/C][C]0.0159765549455138[/C][C]0.992011722527243[/C][/ROW]
[ROW][C]15[/C][C]0.00417042209350676[/C][C]0.00834084418701352[/C][C]0.995829577906493[/C][/ROW]
[ROW][C]16[/C][C]0.00203018424270777[/C][C]0.00406036848541554[/C][C]0.997969815757292[/C][/ROW]
[ROW][C]17[/C][C]0.00191795955117257[/C][C]0.00383591910234514[/C][C]0.998082040448827[/C][/ROW]
[ROW][C]18[/C][C]0.00165301482624455[/C][C]0.0033060296524891[/C][C]0.998346985173755[/C][/ROW]
[ROW][C]19[/C][C]0.00167362044554391[/C][C]0.00334724089108782[/C][C]0.998326379554456[/C][/ROW]
[ROW][C]20[/C][C]0.00215630016112810[/C][C]0.00431260032225619[/C][C]0.997843699838872[/C][/ROW]
[ROW][C]21[/C][C]0.00190991207717793[/C][C]0.00381982415435586[/C][C]0.998090087922822[/C][/ROW]
[ROW][C]22[/C][C]0.00485564258354874[/C][C]0.00971128516709749[/C][C]0.995144357416451[/C][/ROW]
[ROW][C]23[/C][C]0.0044250132609175[/C][C]0.008850026521835[/C][C]0.995574986739083[/C][/ROW]
[ROW][C]24[/C][C]0.0070593563424588[/C][C]0.0141187126849176[/C][C]0.992940643657541[/C][/ROW]
[ROW][C]25[/C][C]0.0205342357827068[/C][C]0.0410684715654136[/C][C]0.979465764217293[/C][/ROW]
[ROW][C]26[/C][C]0.0184435917931524[/C][C]0.0368871835863049[/C][C]0.981556408206848[/C][/ROW]
[ROW][C]27[/C][C]0.0168765128002258[/C][C]0.0337530256004515[/C][C]0.983123487199774[/C][/ROW]
[ROW][C]28[/C][C]0.0139770645205185[/C][C]0.0279541290410370[/C][C]0.986022935479481[/C][/ROW]
[ROW][C]29[/C][C]0.0144497064011747[/C][C]0.0288994128023494[/C][C]0.985550293598825[/C][/ROW]
[ROW][C]30[/C][C]0.0145433797078865[/C][C]0.0290867594157730[/C][C]0.985456620292114[/C][/ROW]
[ROW][C]31[/C][C]0.0171384286281142[/C][C]0.0342768572562284[/C][C]0.982861571371886[/C][/ROW]
[ROW][C]32[/C][C]0.0226900348359498[/C][C]0.0453800696718995[/C][C]0.97730996516405[/C][/ROW]
[ROW][C]33[/C][C]0.0190893615389393[/C][C]0.0381787230778786[/C][C]0.98091063846106[/C][/ROW]
[ROW][C]34[/C][C]0.0176510932582252[/C][C]0.0353021865164503[/C][C]0.982348906741775[/C][/ROW]
[ROW][C]35[/C][C]0.0184609690912615[/C][C]0.0369219381825229[/C][C]0.981539030908739[/C][/ROW]
[ROW][C]36[/C][C]0.0268492941427418[/C][C]0.0536985882854835[/C][C]0.973150705857258[/C][/ROW]
[ROW][C]37[/C][C]0.0251210187055261[/C][C]0.0502420374110523[/C][C]0.974878981294474[/C][/ROW]
[ROW][C]38[/C][C]0.0260198192262569[/C][C]0.0520396384525139[/C][C]0.973980180773743[/C][/ROW]
[ROW][C]39[/C][C]0.0237341164255907[/C][C]0.0474682328511813[/C][C]0.97626588357441[/C][/ROW]
[ROW][C]40[/C][C]0.036920694067568[/C][C]0.073841388135136[/C][C]0.963079305932432[/C][/ROW]
[ROW][C]41[/C][C]0.0615508423104574[/C][C]0.123101684620915[/C][C]0.938449157689543[/C][/ROW]
[ROW][C]42[/C][C]0.0720758050759574[/C][C]0.144151610151915[/C][C]0.927924194924043[/C][/ROW]
[ROW][C]43[/C][C]0.120005109362113[/C][C]0.240010218724226[/C][C]0.879994890637887[/C][/ROW]
[ROW][C]44[/C][C]0.161714932366445[/C][C]0.323429864732889[/C][C]0.838285067633555[/C][/ROW]
[ROW][C]45[/C][C]0.128412245226515[/C][C]0.256824490453029[/C][C]0.871587754773485[/C][/ROW]
[ROW][C]46[/C][C]0.100320018866002[/C][C]0.200640037732004[/C][C]0.899679981133998[/C][/ROW]
[ROW][C]47[/C][C]0.107377340188799[/C][C]0.214754680377599[/C][C]0.892622659811201[/C][/ROW]
[ROW][C]48[/C][C]0.092399079182806[/C][C]0.184798158365612[/C][C]0.907600920817194[/C][/ROW]
[ROW][C]49[/C][C]0.0961735501258627[/C][C]0.192347100251725[/C][C]0.903826449874137[/C][/ROW]
[ROW][C]50[/C][C]0.0721288572645183[/C][C]0.144257714529037[/C][C]0.927871142735482[/C][/ROW]
[ROW][C]51[/C][C]0.0597835559459649[/C][C]0.119567111891930[/C][C]0.940216444054035[/C][/ROW]
[ROW][C]52[/C][C]0.0585228879175541[/C][C]0.117045775835108[/C][C]0.941477112082446[/C][/ROW]
[ROW][C]53[/C][C]0.0535028794045484[/C][C]0.107005758809097[/C][C]0.946497120595452[/C][/ROW]
[ROW][C]54[/C][C]0.0394560986217307[/C][C]0.0789121972434614[/C][C]0.96054390137827[/C][/ROW]
[ROW][C]55[/C][C]0.0275512095112604[/C][C]0.0551024190225207[/C][C]0.97244879048874[/C][/ROW]
[ROW][C]56[/C][C]0.019722640318659[/C][C]0.039445280637318[/C][C]0.98027735968134[/C][/ROW]
[ROW][C]57[/C][C]0.094868958044554[/C][C]0.189737916089108[/C][C]0.905131041955446[/C][/ROW]
[ROW][C]58[/C][C]0.0763231701760833[/C][C]0.152646340352167[/C][C]0.923676829823917[/C][/ROW]
[ROW][C]59[/C][C]0.0719092099430885[/C][C]0.143818419886177[/C][C]0.928090790056912[/C][/ROW]
[ROW][C]60[/C][C]0.0737807064060344[/C][C]0.147561412812069[/C][C]0.926219293593966[/C][/ROW]
[ROW][C]61[/C][C]0.0752561874135611[/C][C]0.150512374827122[/C][C]0.924743812586439[/C][/ROW]
[ROW][C]62[/C][C]0.106440719023066[/C][C]0.212881438046133[/C][C]0.893559280976933[/C][/ROW]
[ROW][C]63[/C][C]0.183510252679975[/C][C]0.36702050535995[/C][C]0.816489747320025[/C][/ROW]
[ROW][C]64[/C][C]0.287978889075005[/C][C]0.575957778150011[/C][C]0.712021110924995[/C][/ROW]
[ROW][C]65[/C][C]0.517996279150978[/C][C]0.964007441698043[/C][C]0.482003720849022[/C][/ROW]
[ROW][C]66[/C][C]0.517491401989034[/C][C]0.965017196021933[/C][C]0.482508598010966[/C][/ROW]
[ROW][C]67[/C][C]0.719612142228904[/C][C]0.560775715542193[/C][C]0.280387857771096[/C][/ROW]
[ROW][C]68[/C][C]0.771660142346853[/C][C]0.456679715306294[/C][C]0.228339857653147[/C][/ROW]
[ROW][C]69[/C][C]0.765578741253129[/C][C]0.468842517493742[/C][C]0.234421258746871[/C][/ROW]
[ROW][C]70[/C][C]0.76315995471705[/C][C]0.473680090565901[/C][C]0.236840045282951[/C][/ROW]
[ROW][C]71[/C][C]0.87739349374357[/C][C]0.245213012512858[/C][C]0.122606506256429[/C][/ROW]
[ROW][C]72[/C][C]0.99179736923511[/C][C]0.0164052615297804[/C][C]0.00820263076489021[/C][/ROW]
[ROW][C]73[/C][C]0.987783097107842[/C][C]0.024433805784317[/C][C]0.0122169028921585[/C][/ROW]
[ROW][C]74[/C][C]0.97379488094488[/C][C]0.0524102381102395[/C][C]0.0262051190551198[/C][/ROW]
[ROW][C]75[/C][C]0.987227523911235[/C][C]0.0255449521775306[/C][C]0.0127724760887653[/C][/ROW]
[ROW][C]76[/C][C]0.980830224521037[/C][C]0.0383395509579255[/C][C]0.0191697754789627[/C][/ROW]
[ROW][C]77[/C][C]0.949228545752224[/C][C]0.101542908495551[/C][C]0.0507714542477757[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114101&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114101&T=5

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.02178615433595730.04357230867191460.978213845664043
80.008598579249076210.01719715849815240.991401420750924
90.01826071957586420.03652143915172830.981739280424136
100.05062877166787220.1012575433357440.949371228332128
110.04500479958396290.09000959916792580.954995200416037
120.02947785659480740.05895571318961480.970522143405193
130.01527224888252670.03054449776505340.984727751117473
140.007988277472756920.01597655494551380.992011722527243
150.004170422093506760.008340844187013520.995829577906493
160.002030184242707770.004060368485415540.997969815757292
170.001917959551172570.003835919102345140.998082040448827
180.001653014826244550.00330602965248910.998346985173755
190.001673620445543910.003347240891087820.998326379554456
200.002156300161128100.004312600322256190.997843699838872
210.001909912077177930.003819824154355860.998090087922822
220.004855642583548740.009711285167097490.995144357416451
230.00442501326091750.0088500265218350.995574986739083
240.00705935634245880.01411871268491760.992940643657541
250.02053423578270680.04106847156541360.979465764217293
260.01844359179315240.03688718358630490.981556408206848
270.01687651280022580.03375302560045150.983123487199774
280.01397706452051850.02795412904103700.986022935479481
290.01444970640117470.02889941280234940.985550293598825
300.01454337970788650.02908675941577300.985456620292114
310.01713842862811420.03427685725622840.982861571371886
320.02269003483594980.04538006967189950.97730996516405
330.01908936153893930.03817872307787860.98091063846106
340.01765109325822520.03530218651645030.982348906741775
350.01846096909126150.03692193818252290.981539030908739
360.02684929414274180.05369858828548350.973150705857258
370.02512101870552610.05024203741105230.974878981294474
380.02601981922625690.05203963845251390.973980180773743
390.02373411642559070.04746823285118130.97626588357441
400.0369206940675680.0738413881351360.963079305932432
410.06155084231045740.1231016846209150.938449157689543
420.07207580507595740.1441516101519150.927924194924043
430.1200051093621130.2400102187242260.879994890637887
440.1617149323664450.3234298647328890.838285067633555
450.1284122452265150.2568244904530290.871587754773485
460.1003200188660020.2006400377320040.899679981133998
470.1073773401887990.2147546803775990.892622659811201
480.0923990791828060.1847981583656120.907600920817194
490.09617355012586270.1923471002517250.903826449874137
500.07212885726451830.1442577145290370.927871142735482
510.05978355594596490.1195671118919300.940216444054035
520.05852288791755410.1170457758351080.941477112082446
530.05350287940454840.1070057588090970.946497120595452
540.03945609862173070.07891219724346140.96054390137827
550.02755120951126040.05510241902252070.97244879048874
560.0197226403186590.0394452806373180.98027735968134
570.0948689580445540.1897379160891080.905131041955446
580.07632317017608330.1526463403521670.923676829823917
590.07190920994308850.1438184198861770.928090790056912
600.07378070640603440.1475614128120690.926219293593966
610.07525618741356110.1505123748271220.924743812586439
620.1064407190230660.2128814380461330.893559280976933
630.1835102526799750.367020505359950.816489747320025
640.2879788890750050.5759577781500110.712021110924995
650.5179962791509780.9640074416980430.482003720849022
660.5174914019890340.9650171960219330.482508598010966
670.7196121422289040.5607757155421930.280387857771096
680.7716601423468530.4566797153062940.228339857653147
690.7655787412531290.4688425174937420.234421258746871
700.763159954717050.4736800905659010.236840045282951
710.877393493743570.2452130125128580.122606506256429
720.991797369235110.01640526152978040.00820263076489021
730.9877830971078420.0244338057843170.0122169028921585
740.973794880944880.05241023811023950.0262051190551198
750.9872275239112350.02554495217753060.0127724760887653
760.9808302245210370.03833955095792550.0191697754789627
770.9492285457522240.1015429084955510.0507714542477757






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level90.126760563380282NOK
5% type I error level320.450704225352113NOK
10% type I error level410.577464788732394NOK

\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 & 9 & 0.126760563380282 & NOK \tabularnewline
5% type I error level & 32 & 0.450704225352113 & NOK \tabularnewline
10% type I error level & 41 & 0.577464788732394 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114101&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]9[/C][C]0.126760563380282[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]32[/C][C]0.450704225352113[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]41[/C][C]0.577464788732394[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114101&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114101&T=6

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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 level90.126760563380282NOK
5% type I error level320.450704225352113NOK
10% type I error level410.577464788732394NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

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