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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 11:21:09 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t12586549439e20588ba3ovee3.htm/, Retrieved Fri, 29 Mar 2024 01:47:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57875, Retrieved Fri, 29 Mar 2024 01:47:08 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsy = aantal bouwvergunningen x= rente
Estimated Impact136
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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
- R  D      [Multiple Regression] [multiple regressi...] [2009-11-19 18:21:09] [03368d751914a6c247d86aff8eac7cbf] [Current]
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Dataseries X:
2360	2	2267	1746	2069	2299
2214	2	2360	2267	1746	2069
2825	2	2214	2360	2267	1746
2355	2	2825	2214	2360	2267
2333	2	2355	2825	2214	2360
3016	2	2333	2355	2825	2214
2155	2	3016	2333	2355	2825
2172	2	2155	3016	2333	2355
2150	2	2172	2155	3016	2333
2533	2	2150	2172	2155	3016
2058	2	2533	2150	2172	2155
2160	2	2058	2533	2150	2172
2260	2	2160	2058	2533	2150
2498	2	2260	2160	2058	2533
2695	2	2498	2260	2160	2058
2799	2	2695	2498	2260	2160
2947	2	2799	2695	2498	2260
2930	2	2947	2799	2695	2498
2318	2	2930	2947	2799	2695
2540	2	2318	2930	2947	2799
2570	2	2540	2318	2930	2947
2669	2	2570	2540	2318	2930
2450	2	2669	2570	2540	2318
2842	2	2450	2669	2570	2540
3440	2	2842	2450	2669	2570
2678	2	3440	2842	2450	2669
2981	2	2678	3440	2842	2450
2260	2,21	2981	2678	3440	2842
2844	2,25	2260	2981	2678	3440
2546	2,25	2844	2260	2981	2678
2456	2,45	2546	2844	2260	2981
2295	2,5	2456	2546	2844	2260
2379	2,5	2295	2456	2546	2844
2479	2,64	2379	2295	2456	2546
2057	2,75	2479	2379	2295	2456
2280	2,93	2057	2479	2379	2295
2351	3	2280	2057	2479	2379
2276	3,17	2351	2280	2057	2479
2548	3,25	2276	2351	2280	2057
2311	3,39	2548	2276	2351	2280
2201	3,5	2311	2548	2276	2351
2725	3,5	2201	2311	2548	2276
2408	3,65	2725	2201	2311	2548
2139	3,75	2408	2725	2201	2311
1898	3,75	2139	2408	2725	2201
2537	3,9	1898	2139	2408	2725
2069	4	2537	1898	2139	2408
2063	4	2069	2537	1898	2139
2524	4	2063	2069	2537	1898
2437	4	2524	2063	2069	2537
2189	4	2437	2524	2063	2069
2793	4	2189	2437	2524	2063
2074	4	2793	2189	2437	2524
2622	4	2074	2793	2189	2437
2278	4	2622	2074	2793	2189
2144	4	2278	2622	2074	2793
2427	4	2144	2278	2622	2074
2139	4	2427	2144	2278	2622
1828	4,18	2139	2427	2144	2278
2072	4,25	1828	2139	2427	2144
1800	4,25	2072	1828	2139	2427




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57875&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]3 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=57875&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57875&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 2543.65104195461 -332.852461340271X[t] -0.00897260626492228Y1[t] + 0.189452813460899Y2[t] + 0.0589636885733872Y3[t] -0.103950946153406Y4[t] + 254.931163373105M1[t] + 180.446464753134M2[t] + 295.839564571899M3[t] + 207.577948072379M4[t] + 175.647916374566M5[t] + 448.898639026695M6[t] + 55.4442951055209M7[t] -82.8238967667418M8[t] -2.92881928519086M9[t] + 260.561703653976M10[t] -149.418332236016M11[t] + 11.1347397110506t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  2543.65104195461 -332.852461340271X[t] -0.00897260626492228Y1[t] +  0.189452813460899Y2[t] +  0.0589636885733872Y3[t] -0.103950946153406Y4[t] +  254.931163373105M1[t] +  180.446464753134M2[t] +  295.839564571899M3[t] +  207.577948072379M4[t] +  175.647916374566M5[t] +  448.898639026695M6[t] +  55.4442951055209M7[t] -82.8238967667418M8[t] -2.92881928519086M9[t] +  260.561703653976M10[t] -149.418332236016M11[t] +  11.1347397110506t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57875&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  2543.65104195461 -332.852461340271X[t] -0.00897260626492228Y1[t] +  0.189452813460899Y2[t] +  0.0589636885733872Y3[t] -0.103950946153406Y4[t] +  254.931163373105M1[t] +  180.446464753134M2[t] +  295.839564571899M3[t] +  207.577948072379M4[t] +  175.647916374566M5[t] +  448.898639026695M6[t] +  55.4442951055209M7[t] -82.8238967667418M8[t] -2.92881928519086M9[t] +  260.561703653976M10[t] -149.418332236016M11[t] +  11.1347397110506t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57875&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57875&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 2543.65104195461 -332.852461340271X[t] -0.00897260626492228Y1[t] + 0.189452813460899Y2[t] + 0.0589636885733872Y3[t] -0.103950946153406Y4[t] + 254.931163373105M1[t] + 180.446464753134M2[t] + 295.839564571899M3[t] + 207.577948072379M4[t] + 175.647916374566M5[t] + 448.898639026695M6[t] + 55.4442951055209M7[t] -82.8238967667418M8[t] -2.92881928519086M9[t] + 260.561703653976M10[t] -149.418332236016M11[t] + 11.1347397110506t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2543.651041954611051.8993722.41820.0199090.009954
X-332.852461340271191.479044-1.73830.0893110.044655
Y1-0.008972606264922280.165852-0.05410.9571060.478553
Y20.1894528134608990.1671231.13360.2632380.131619
Y30.05896368857338720.1668550.35340.7255280.362764
Y4-0.1039509461534060.164072-0.63360.5297180.264859
M1254.931163373105171.5456171.48610.1445530.072276
M2180.446464753134188.3379340.95810.3433690.171684
M3295.839564571899172.4751631.71530.0934970.046748
M4207.577948072379190.8037041.08790.2826940.141347
M5175.647916374566187.7137060.93570.3546420.177321
M6448.898639026695182.0799822.46540.0177540.008877
M755.4442951055209208.180790.26630.791260.39563
M8-82.8238967667418179.470854-0.46150.6467740.323387
M9-2.92881928519086183.373895-0.0160.9873310.493665
M10260.561703653976185.9822121.4010.1683890.084195
M11-149.418332236016173.915755-0.85910.3950270.197513
t11.13473971105068.5739941.29870.2009810.10049

\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) & 2543.65104195461 & 1051.899372 & 2.4182 & 0.019909 & 0.009954 \tabularnewline
X & -332.852461340271 & 191.479044 & -1.7383 & 0.089311 & 0.044655 \tabularnewline
Y1 & -0.00897260626492228 & 0.165852 & -0.0541 & 0.957106 & 0.478553 \tabularnewline
Y2 & 0.189452813460899 & 0.167123 & 1.1336 & 0.263238 & 0.131619 \tabularnewline
Y3 & 0.0589636885733872 & 0.166855 & 0.3534 & 0.725528 & 0.362764 \tabularnewline
Y4 & -0.103950946153406 & 0.164072 & -0.6336 & 0.529718 & 0.264859 \tabularnewline
M1 & 254.931163373105 & 171.545617 & 1.4861 & 0.144553 & 0.072276 \tabularnewline
M2 & 180.446464753134 & 188.337934 & 0.9581 & 0.343369 & 0.171684 \tabularnewline
M3 & 295.839564571899 & 172.475163 & 1.7153 & 0.093497 & 0.046748 \tabularnewline
M4 & 207.577948072379 & 190.803704 & 1.0879 & 0.282694 & 0.141347 \tabularnewline
M5 & 175.647916374566 & 187.713706 & 0.9357 & 0.354642 & 0.177321 \tabularnewline
M6 & 448.898639026695 & 182.079982 & 2.4654 & 0.017754 & 0.008877 \tabularnewline
M7 & 55.4442951055209 & 208.18079 & 0.2663 & 0.79126 & 0.39563 \tabularnewline
M8 & -82.8238967667418 & 179.470854 & -0.4615 & 0.646774 & 0.323387 \tabularnewline
M9 & -2.92881928519086 & 183.373895 & -0.016 & 0.987331 & 0.493665 \tabularnewline
M10 & 260.561703653976 & 185.982212 & 1.401 & 0.168389 & 0.084195 \tabularnewline
M11 & -149.418332236016 & 173.915755 & -0.8591 & 0.395027 & 0.197513 \tabularnewline
t & 11.1347397110506 & 8.573994 & 1.2987 & 0.200981 & 0.10049 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57875&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]2543.65104195461[/C][C]1051.899372[/C][C]2.4182[/C][C]0.019909[/C][C]0.009954[/C][/ROW]
[ROW][C]X[/C][C]-332.852461340271[/C][C]191.479044[/C][C]-1.7383[/C][C]0.089311[/C][C]0.044655[/C][/ROW]
[ROW][C]Y1[/C][C]-0.00897260626492228[/C][C]0.165852[/C][C]-0.0541[/C][C]0.957106[/C][C]0.478553[/C][/ROW]
[ROW][C]Y2[/C][C]0.189452813460899[/C][C]0.167123[/C][C]1.1336[/C][C]0.263238[/C][C]0.131619[/C][/ROW]
[ROW][C]Y3[/C][C]0.0589636885733872[/C][C]0.166855[/C][C]0.3534[/C][C]0.725528[/C][C]0.362764[/C][/ROW]
[ROW][C]Y4[/C][C]-0.103950946153406[/C][C]0.164072[/C][C]-0.6336[/C][C]0.529718[/C][C]0.264859[/C][/ROW]
[ROW][C]M1[/C][C]254.931163373105[/C][C]171.545617[/C][C]1.4861[/C][C]0.144553[/C][C]0.072276[/C][/ROW]
[ROW][C]M2[/C][C]180.446464753134[/C][C]188.337934[/C][C]0.9581[/C][C]0.343369[/C][C]0.171684[/C][/ROW]
[ROW][C]M3[/C][C]295.839564571899[/C][C]172.475163[/C][C]1.7153[/C][C]0.093497[/C][C]0.046748[/C][/ROW]
[ROW][C]M4[/C][C]207.577948072379[/C][C]190.803704[/C][C]1.0879[/C][C]0.282694[/C][C]0.141347[/C][/ROW]
[ROW][C]M5[/C][C]175.647916374566[/C][C]187.713706[/C][C]0.9357[/C][C]0.354642[/C][C]0.177321[/C][/ROW]
[ROW][C]M6[/C][C]448.898639026695[/C][C]182.079982[/C][C]2.4654[/C][C]0.017754[/C][C]0.008877[/C][/ROW]
[ROW][C]M7[/C][C]55.4442951055209[/C][C]208.18079[/C][C]0.2663[/C][C]0.79126[/C][C]0.39563[/C][/ROW]
[ROW][C]M8[/C][C]-82.8238967667418[/C][C]179.470854[/C][C]-0.4615[/C][C]0.646774[/C][C]0.323387[/C][/ROW]
[ROW][C]M9[/C][C]-2.92881928519086[/C][C]183.373895[/C][C]-0.016[/C][C]0.987331[/C][C]0.493665[/C][/ROW]
[ROW][C]M10[/C][C]260.561703653976[/C][C]185.982212[/C][C]1.401[/C][C]0.168389[/C][C]0.084195[/C][/ROW]
[ROW][C]M11[/C][C]-149.418332236016[/C][C]173.915755[/C][C]-0.8591[/C][C]0.395027[/C][C]0.197513[/C][/ROW]
[ROW][C]t[/C][C]11.1347397110506[/C][C]8.573994[/C][C]1.2987[/C][C]0.200981[/C][C]0.10049[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57875&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57875&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2543.651041954611051.8993722.41820.0199090.009954
X-332.852461340271191.479044-1.73830.0893110.044655
Y1-0.008972606264922280.165852-0.05410.9571060.478553
Y20.1894528134608990.1671231.13360.2632380.131619
Y30.05896368857338720.1668550.35340.7255280.362764
Y4-0.1039509461534060.164072-0.63360.5297180.264859
M1254.931163373105171.5456171.48610.1445530.072276
M2180.446464753134188.3379340.95810.3433690.171684
M3295.839564571899172.4751631.71530.0934970.046748
M4207.577948072379190.8037041.08790.2826940.141347
M5175.647916374566187.7137060.93570.3546420.177321
M6448.898639026695182.0799822.46540.0177540.008877
M755.4442951055209208.180790.26630.791260.39563
M8-82.8238967667418179.470854-0.46150.6467740.323387
M9-2.92881928519086183.373895-0.0160.9873310.493665
M10260.561703653976185.9822121.4010.1683890.084195
M11-149.418332236016173.915755-0.85910.3950270.197513
t11.13473971105068.5739941.29870.2009810.10049







Multiple Linear Regression - Regression Statistics
Multiple R0.741783509030645
R-squared0.550242774269817
Adjusted R-squared0.372431778050907
F-TEST (value)3.09453737941152
F-TEST (DF numerator)17
F-TEST (DF denominator)43
p-value0.00139595311085983
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation252.423589587147
Sum Squared Residuals2739859.74894261

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.741783509030645 \tabularnewline
R-squared & 0.550242774269817 \tabularnewline
Adjusted R-squared & 0.372431778050907 \tabularnewline
F-TEST (value) & 3.09453737941152 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 43 \tabularnewline
p-value & 0.00139595311085983 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 252.423589587147 \tabularnewline
Sum Squared Residuals & 2739859.74894261 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57875&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.741783509030645[/C][/ROW]
[ROW][C]R-squared[/C][C]0.550242774269817[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.372431778050907[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.09453737941152[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]43[/C][/ROW]
[ROW][C]p-value[/C][C]0.00139595311085983[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]252.423589587147[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2739859.74894261[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57875&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57875&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.741783509030645
R-squared0.550242774269817
Adjusted R-squared0.372431778050907
F-TEST (value)3.09453737941152
F-TEST (DF numerator)17
F-TEST (DF denominator)43
p-value0.00139595311085983
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation252.423589587147
Sum Squared Residuals2739859.74894261







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
123602337.4683827100322.5316172899689
222142376.85233343769-162.852333437686
328252586.60552248833238.394477511670
423552427.6614525981-72.661452598102
523332508.56281805648-175.562818056482
630162755.30650728760260.693492712397
721552271.46368937317-116.463689373175
821722329.01166634334-157.011666343342
921502299.32919695060-149.329196950604
1025332455.6063226830277.3936773169778
1120582139.66070375231-81.6607037523058
1221602373.97182399751-213.971823997512
1322602574.0024483877-314.002448387701
1424982461.2584513761936.7415486238139
1526952659.9870876183935.0129123816067
1627992621.47574934912177.52425065088
1729472640.70777382773306.292226172271
1829302930.34390452808-0.343904528079557
1923182561.86973823608-243.869738236084
2025402434.92255078908105.077449210923
2125702391.62820481634178.371795183657
2226692673.72420254463-4.72420254462778
2324502356.3821206584693.6178793415375
2428422516.34782252132325.652177478679
2534402740.12517458585699.874825414146
2626782722.47090854042-44.4709085404226
2729813015.00767962209-34.0076796220907
2822602715.41155727144-455.411557271438
2928442638.08262193407205.917378065931
3025462877.70922233986-331.709222339856
3124562468.12344944390-12.1234494438971
3222952378.08139667163-83.0813966716299
3323792375.226518512943.77348148705526
3424792597.66748466418-118.667484664176
3520572177.08762473552-120.087624735520
3622802322.14802700209-42.1480270020869
3723512480.1287686953-129.128768695299
3822762366.52704252219-90.5270425221932
3925482537.5669821989810.4330178010174
4023112378.19661180575-67.1966118057504
4122012362.64242819781-161.642428197814
4227252626.9490047133798.0509952866323
4324082126.91102459307281.088975406933
4421392086.7602852326052.2397147673964
4518982162.47876853269-264.478768532693
4625372265.21396820859271.786031791413
4720691798.78302015264270.216979847355
4820632098.34817520229-35.348175202289
4925242338.5339722457185.466027754302
5024372175.89126412351261.108735876489
5121892438.83272807220-249.832728072203
5227932375.25462897559417.745371024411
5320742249.00435798391-175.004357983906
5426222648.69136113109-26.6913611310936
5522782186.6320983537891.3679016462235
5621442061.2241009633582.7758990366518
5724272195.33731118741231.662688812585
5821392364.78802189959-225.788021899587
5918281990.08653070107-162.086530701067
6020722106.18415127679-34.1841512767911
6118002264.74125337542-464.741253375417

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2360 & 2337.46838271003 & 22.5316172899689 \tabularnewline
2 & 2214 & 2376.85233343769 & -162.852333437686 \tabularnewline
3 & 2825 & 2586.60552248833 & 238.394477511670 \tabularnewline
4 & 2355 & 2427.6614525981 & -72.661452598102 \tabularnewline
5 & 2333 & 2508.56281805648 & -175.562818056482 \tabularnewline
6 & 3016 & 2755.30650728760 & 260.693492712397 \tabularnewline
7 & 2155 & 2271.46368937317 & -116.463689373175 \tabularnewline
8 & 2172 & 2329.01166634334 & -157.011666343342 \tabularnewline
9 & 2150 & 2299.32919695060 & -149.329196950604 \tabularnewline
10 & 2533 & 2455.60632268302 & 77.3936773169778 \tabularnewline
11 & 2058 & 2139.66070375231 & -81.6607037523058 \tabularnewline
12 & 2160 & 2373.97182399751 & -213.971823997512 \tabularnewline
13 & 2260 & 2574.0024483877 & -314.002448387701 \tabularnewline
14 & 2498 & 2461.25845137619 & 36.7415486238139 \tabularnewline
15 & 2695 & 2659.98708761839 & 35.0129123816067 \tabularnewline
16 & 2799 & 2621.47574934912 & 177.52425065088 \tabularnewline
17 & 2947 & 2640.70777382773 & 306.292226172271 \tabularnewline
18 & 2930 & 2930.34390452808 & -0.343904528079557 \tabularnewline
19 & 2318 & 2561.86973823608 & -243.869738236084 \tabularnewline
20 & 2540 & 2434.92255078908 & 105.077449210923 \tabularnewline
21 & 2570 & 2391.62820481634 & 178.371795183657 \tabularnewline
22 & 2669 & 2673.72420254463 & -4.72420254462778 \tabularnewline
23 & 2450 & 2356.38212065846 & 93.6178793415375 \tabularnewline
24 & 2842 & 2516.34782252132 & 325.652177478679 \tabularnewline
25 & 3440 & 2740.12517458585 & 699.874825414146 \tabularnewline
26 & 2678 & 2722.47090854042 & -44.4709085404226 \tabularnewline
27 & 2981 & 3015.00767962209 & -34.0076796220907 \tabularnewline
28 & 2260 & 2715.41155727144 & -455.411557271438 \tabularnewline
29 & 2844 & 2638.08262193407 & 205.917378065931 \tabularnewline
30 & 2546 & 2877.70922233986 & -331.709222339856 \tabularnewline
31 & 2456 & 2468.12344944390 & -12.1234494438971 \tabularnewline
32 & 2295 & 2378.08139667163 & -83.0813966716299 \tabularnewline
33 & 2379 & 2375.22651851294 & 3.77348148705526 \tabularnewline
34 & 2479 & 2597.66748466418 & -118.667484664176 \tabularnewline
35 & 2057 & 2177.08762473552 & -120.087624735520 \tabularnewline
36 & 2280 & 2322.14802700209 & -42.1480270020869 \tabularnewline
37 & 2351 & 2480.1287686953 & -129.128768695299 \tabularnewline
38 & 2276 & 2366.52704252219 & -90.5270425221932 \tabularnewline
39 & 2548 & 2537.56698219898 & 10.4330178010174 \tabularnewline
40 & 2311 & 2378.19661180575 & -67.1966118057504 \tabularnewline
41 & 2201 & 2362.64242819781 & -161.642428197814 \tabularnewline
42 & 2725 & 2626.94900471337 & 98.0509952866323 \tabularnewline
43 & 2408 & 2126.91102459307 & 281.088975406933 \tabularnewline
44 & 2139 & 2086.76028523260 & 52.2397147673964 \tabularnewline
45 & 1898 & 2162.47876853269 & -264.478768532693 \tabularnewline
46 & 2537 & 2265.21396820859 & 271.786031791413 \tabularnewline
47 & 2069 & 1798.78302015264 & 270.216979847355 \tabularnewline
48 & 2063 & 2098.34817520229 & -35.348175202289 \tabularnewline
49 & 2524 & 2338.5339722457 & 185.466027754302 \tabularnewline
50 & 2437 & 2175.89126412351 & 261.108735876489 \tabularnewline
51 & 2189 & 2438.83272807220 & -249.832728072203 \tabularnewline
52 & 2793 & 2375.25462897559 & 417.745371024411 \tabularnewline
53 & 2074 & 2249.00435798391 & -175.004357983906 \tabularnewline
54 & 2622 & 2648.69136113109 & -26.6913611310936 \tabularnewline
55 & 2278 & 2186.63209835378 & 91.3679016462235 \tabularnewline
56 & 2144 & 2061.22410096335 & 82.7758990366518 \tabularnewline
57 & 2427 & 2195.33731118741 & 231.662688812585 \tabularnewline
58 & 2139 & 2364.78802189959 & -225.788021899587 \tabularnewline
59 & 1828 & 1990.08653070107 & -162.086530701067 \tabularnewline
60 & 2072 & 2106.18415127679 & -34.1841512767911 \tabularnewline
61 & 1800 & 2264.74125337542 & -464.741253375417 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57875&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]2360[/C][C]2337.46838271003[/C][C]22.5316172899689[/C][/ROW]
[ROW][C]2[/C][C]2214[/C][C]2376.85233343769[/C][C]-162.852333437686[/C][/ROW]
[ROW][C]3[/C][C]2825[/C][C]2586.60552248833[/C][C]238.394477511670[/C][/ROW]
[ROW][C]4[/C][C]2355[/C][C]2427.6614525981[/C][C]-72.661452598102[/C][/ROW]
[ROW][C]5[/C][C]2333[/C][C]2508.56281805648[/C][C]-175.562818056482[/C][/ROW]
[ROW][C]6[/C][C]3016[/C][C]2755.30650728760[/C][C]260.693492712397[/C][/ROW]
[ROW][C]7[/C][C]2155[/C][C]2271.46368937317[/C][C]-116.463689373175[/C][/ROW]
[ROW][C]8[/C][C]2172[/C][C]2329.01166634334[/C][C]-157.011666343342[/C][/ROW]
[ROW][C]9[/C][C]2150[/C][C]2299.32919695060[/C][C]-149.329196950604[/C][/ROW]
[ROW][C]10[/C][C]2533[/C][C]2455.60632268302[/C][C]77.3936773169778[/C][/ROW]
[ROW][C]11[/C][C]2058[/C][C]2139.66070375231[/C][C]-81.6607037523058[/C][/ROW]
[ROW][C]12[/C][C]2160[/C][C]2373.97182399751[/C][C]-213.971823997512[/C][/ROW]
[ROW][C]13[/C][C]2260[/C][C]2574.0024483877[/C][C]-314.002448387701[/C][/ROW]
[ROW][C]14[/C][C]2498[/C][C]2461.25845137619[/C][C]36.7415486238139[/C][/ROW]
[ROW][C]15[/C][C]2695[/C][C]2659.98708761839[/C][C]35.0129123816067[/C][/ROW]
[ROW][C]16[/C][C]2799[/C][C]2621.47574934912[/C][C]177.52425065088[/C][/ROW]
[ROW][C]17[/C][C]2947[/C][C]2640.70777382773[/C][C]306.292226172271[/C][/ROW]
[ROW][C]18[/C][C]2930[/C][C]2930.34390452808[/C][C]-0.343904528079557[/C][/ROW]
[ROW][C]19[/C][C]2318[/C][C]2561.86973823608[/C][C]-243.869738236084[/C][/ROW]
[ROW][C]20[/C][C]2540[/C][C]2434.92255078908[/C][C]105.077449210923[/C][/ROW]
[ROW][C]21[/C][C]2570[/C][C]2391.62820481634[/C][C]178.371795183657[/C][/ROW]
[ROW][C]22[/C][C]2669[/C][C]2673.72420254463[/C][C]-4.72420254462778[/C][/ROW]
[ROW][C]23[/C][C]2450[/C][C]2356.38212065846[/C][C]93.6178793415375[/C][/ROW]
[ROW][C]24[/C][C]2842[/C][C]2516.34782252132[/C][C]325.652177478679[/C][/ROW]
[ROW][C]25[/C][C]3440[/C][C]2740.12517458585[/C][C]699.874825414146[/C][/ROW]
[ROW][C]26[/C][C]2678[/C][C]2722.47090854042[/C][C]-44.4709085404226[/C][/ROW]
[ROW][C]27[/C][C]2981[/C][C]3015.00767962209[/C][C]-34.0076796220907[/C][/ROW]
[ROW][C]28[/C][C]2260[/C][C]2715.41155727144[/C][C]-455.411557271438[/C][/ROW]
[ROW][C]29[/C][C]2844[/C][C]2638.08262193407[/C][C]205.917378065931[/C][/ROW]
[ROW][C]30[/C][C]2546[/C][C]2877.70922233986[/C][C]-331.709222339856[/C][/ROW]
[ROW][C]31[/C][C]2456[/C][C]2468.12344944390[/C][C]-12.1234494438971[/C][/ROW]
[ROW][C]32[/C][C]2295[/C][C]2378.08139667163[/C][C]-83.0813966716299[/C][/ROW]
[ROW][C]33[/C][C]2379[/C][C]2375.22651851294[/C][C]3.77348148705526[/C][/ROW]
[ROW][C]34[/C][C]2479[/C][C]2597.66748466418[/C][C]-118.667484664176[/C][/ROW]
[ROW][C]35[/C][C]2057[/C][C]2177.08762473552[/C][C]-120.087624735520[/C][/ROW]
[ROW][C]36[/C][C]2280[/C][C]2322.14802700209[/C][C]-42.1480270020869[/C][/ROW]
[ROW][C]37[/C][C]2351[/C][C]2480.1287686953[/C][C]-129.128768695299[/C][/ROW]
[ROW][C]38[/C][C]2276[/C][C]2366.52704252219[/C][C]-90.5270425221932[/C][/ROW]
[ROW][C]39[/C][C]2548[/C][C]2537.56698219898[/C][C]10.4330178010174[/C][/ROW]
[ROW][C]40[/C][C]2311[/C][C]2378.19661180575[/C][C]-67.1966118057504[/C][/ROW]
[ROW][C]41[/C][C]2201[/C][C]2362.64242819781[/C][C]-161.642428197814[/C][/ROW]
[ROW][C]42[/C][C]2725[/C][C]2626.94900471337[/C][C]98.0509952866323[/C][/ROW]
[ROW][C]43[/C][C]2408[/C][C]2126.91102459307[/C][C]281.088975406933[/C][/ROW]
[ROW][C]44[/C][C]2139[/C][C]2086.76028523260[/C][C]52.2397147673964[/C][/ROW]
[ROW][C]45[/C][C]1898[/C][C]2162.47876853269[/C][C]-264.478768532693[/C][/ROW]
[ROW][C]46[/C][C]2537[/C][C]2265.21396820859[/C][C]271.786031791413[/C][/ROW]
[ROW][C]47[/C][C]2069[/C][C]1798.78302015264[/C][C]270.216979847355[/C][/ROW]
[ROW][C]48[/C][C]2063[/C][C]2098.34817520229[/C][C]-35.348175202289[/C][/ROW]
[ROW][C]49[/C][C]2524[/C][C]2338.5339722457[/C][C]185.466027754302[/C][/ROW]
[ROW][C]50[/C][C]2437[/C][C]2175.89126412351[/C][C]261.108735876489[/C][/ROW]
[ROW][C]51[/C][C]2189[/C][C]2438.83272807220[/C][C]-249.832728072203[/C][/ROW]
[ROW][C]52[/C][C]2793[/C][C]2375.25462897559[/C][C]417.745371024411[/C][/ROW]
[ROW][C]53[/C][C]2074[/C][C]2249.00435798391[/C][C]-175.004357983906[/C][/ROW]
[ROW][C]54[/C][C]2622[/C][C]2648.69136113109[/C][C]-26.6913611310936[/C][/ROW]
[ROW][C]55[/C][C]2278[/C][C]2186.63209835378[/C][C]91.3679016462235[/C][/ROW]
[ROW][C]56[/C][C]2144[/C][C]2061.22410096335[/C][C]82.7758990366518[/C][/ROW]
[ROW][C]57[/C][C]2427[/C][C]2195.33731118741[/C][C]231.662688812585[/C][/ROW]
[ROW][C]58[/C][C]2139[/C][C]2364.78802189959[/C][C]-225.788021899587[/C][/ROW]
[ROW][C]59[/C][C]1828[/C][C]1990.08653070107[/C][C]-162.086530701067[/C][/ROW]
[ROW][C]60[/C][C]2072[/C][C]2106.18415127679[/C][C]-34.1841512767911[/C][/ROW]
[ROW][C]61[/C][C]1800[/C][C]2264.74125337542[/C][C]-464.741253375417[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57875&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57875&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
123602337.4683827100322.5316172899689
222142376.85233343769-162.852333437686
328252586.60552248833238.394477511670
423552427.6614525981-72.661452598102
523332508.56281805648-175.562818056482
630162755.30650728760260.693492712397
721552271.46368937317-116.463689373175
821722329.01166634334-157.011666343342
921502299.32919695060-149.329196950604
1025332455.6063226830277.3936773169778
1120582139.66070375231-81.6607037523058
1221602373.97182399751-213.971823997512
1322602574.0024483877-314.002448387701
1424982461.2584513761936.7415486238139
1526952659.9870876183935.0129123816067
1627992621.47574934912177.52425065088
1729472640.70777382773306.292226172271
1829302930.34390452808-0.343904528079557
1923182561.86973823608-243.869738236084
2025402434.92255078908105.077449210923
2125702391.62820481634178.371795183657
2226692673.72420254463-4.72420254462778
2324502356.3821206584693.6178793415375
2428422516.34782252132325.652177478679
2534402740.12517458585699.874825414146
2626782722.47090854042-44.4709085404226
2729813015.00767962209-34.0076796220907
2822602715.41155727144-455.411557271438
2928442638.08262193407205.917378065931
3025462877.70922233986-331.709222339856
3124562468.12344944390-12.1234494438971
3222952378.08139667163-83.0813966716299
3323792375.226518512943.77348148705526
3424792597.66748466418-118.667484664176
3520572177.08762473552-120.087624735520
3622802322.14802700209-42.1480270020869
3723512480.1287686953-129.128768695299
3822762366.52704252219-90.5270425221932
3925482537.5669821989810.4330178010174
4023112378.19661180575-67.1966118057504
4122012362.64242819781-161.642428197814
4227252626.9490047133798.0509952866323
4324082126.91102459307281.088975406933
4421392086.7602852326052.2397147673964
4518982162.47876853269-264.478768532693
4625372265.21396820859271.786031791413
4720691798.78302015264270.216979847355
4820632098.34817520229-35.348175202289
4925242338.5339722457185.466027754302
5024372175.89126412351261.108735876489
5121892438.83272807220-249.832728072203
5227932375.25462897559417.745371024411
5320742249.00435798391-175.004357983906
5426222648.69136113109-26.6913611310936
5522782186.6320983537891.3679016462235
5621442061.2241009633582.7758990366518
5724272195.33731118741231.662688812585
5821392364.78802189959-225.788021899587
5918281990.08653070107-162.086530701067
6020722106.18415127679-34.1841512767911
6118002264.74125337542-464.741253375417







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.6087574157315140.7824851685369710.391242584268486
220.4323827267134730.8647654534269470.567617273286527
230.3247431887245230.6494863774490460.675256811275477
240.3129030346285540.6258060692571070.687096965371446
250.7602062698386330.4795874603227350.239793730161367
260.8068330218588970.3863339562822070.193166978141103
270.8111059190856280.3777881618287450.188894080914372
280.796971113296990.4060577734060220.203028886703011
290.826869224787830.3462615504243410.173130775212170
300.7762322695936930.4475354608126130.223767730406307
310.8149782861702950.3700434276594110.185021713829705
320.7583415931750530.4833168136498930.241658406824947
330.7022344427900760.5955311144198470.297765557209924
340.5998460677333040.8003078645333930.400153932266696
350.4814745717035890.9629491434071790.518525428296411
360.3867794021402680.7735588042805360.613220597859732
370.4109128752734930.8218257505469860.589087124726507
380.2996265173855970.5992530347711940.700373482614403
390.2929590890749450.5859181781498910.707040910925055
400.1770050578806310.3540101157612610.82299494211937

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.608757415731514 & 0.782485168536971 & 0.391242584268486 \tabularnewline
22 & 0.432382726713473 & 0.864765453426947 & 0.567617273286527 \tabularnewline
23 & 0.324743188724523 & 0.649486377449046 & 0.675256811275477 \tabularnewline
24 & 0.312903034628554 & 0.625806069257107 & 0.687096965371446 \tabularnewline
25 & 0.760206269838633 & 0.479587460322735 & 0.239793730161367 \tabularnewline
26 & 0.806833021858897 & 0.386333956282207 & 0.193166978141103 \tabularnewline
27 & 0.811105919085628 & 0.377788161828745 & 0.188894080914372 \tabularnewline
28 & 0.79697111329699 & 0.406057773406022 & 0.203028886703011 \tabularnewline
29 & 0.82686922478783 & 0.346261550424341 & 0.173130775212170 \tabularnewline
30 & 0.776232269593693 & 0.447535460812613 & 0.223767730406307 \tabularnewline
31 & 0.814978286170295 & 0.370043427659411 & 0.185021713829705 \tabularnewline
32 & 0.758341593175053 & 0.483316813649893 & 0.241658406824947 \tabularnewline
33 & 0.702234442790076 & 0.595531114419847 & 0.297765557209924 \tabularnewline
34 & 0.599846067733304 & 0.800307864533393 & 0.400153932266696 \tabularnewline
35 & 0.481474571703589 & 0.962949143407179 & 0.518525428296411 \tabularnewline
36 & 0.386779402140268 & 0.773558804280536 & 0.613220597859732 \tabularnewline
37 & 0.410912875273493 & 0.821825750546986 & 0.589087124726507 \tabularnewline
38 & 0.299626517385597 & 0.599253034771194 & 0.700373482614403 \tabularnewline
39 & 0.292959089074945 & 0.585918178149891 & 0.707040910925055 \tabularnewline
40 & 0.177005057880631 & 0.354010115761261 & 0.82299494211937 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57875&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]21[/C][C]0.608757415731514[/C][C]0.782485168536971[/C][C]0.391242584268486[/C][/ROW]
[ROW][C]22[/C][C]0.432382726713473[/C][C]0.864765453426947[/C][C]0.567617273286527[/C][/ROW]
[ROW][C]23[/C][C]0.324743188724523[/C][C]0.649486377449046[/C][C]0.675256811275477[/C][/ROW]
[ROW][C]24[/C][C]0.312903034628554[/C][C]0.625806069257107[/C][C]0.687096965371446[/C][/ROW]
[ROW][C]25[/C][C]0.760206269838633[/C][C]0.479587460322735[/C][C]0.239793730161367[/C][/ROW]
[ROW][C]26[/C][C]0.806833021858897[/C][C]0.386333956282207[/C][C]0.193166978141103[/C][/ROW]
[ROW][C]27[/C][C]0.811105919085628[/C][C]0.377788161828745[/C][C]0.188894080914372[/C][/ROW]
[ROW][C]28[/C][C]0.79697111329699[/C][C]0.406057773406022[/C][C]0.203028886703011[/C][/ROW]
[ROW][C]29[/C][C]0.82686922478783[/C][C]0.346261550424341[/C][C]0.173130775212170[/C][/ROW]
[ROW][C]30[/C][C]0.776232269593693[/C][C]0.447535460812613[/C][C]0.223767730406307[/C][/ROW]
[ROW][C]31[/C][C]0.814978286170295[/C][C]0.370043427659411[/C][C]0.185021713829705[/C][/ROW]
[ROW][C]32[/C][C]0.758341593175053[/C][C]0.483316813649893[/C][C]0.241658406824947[/C][/ROW]
[ROW][C]33[/C][C]0.702234442790076[/C][C]0.595531114419847[/C][C]0.297765557209924[/C][/ROW]
[ROW][C]34[/C][C]0.599846067733304[/C][C]0.800307864533393[/C][C]0.400153932266696[/C][/ROW]
[ROW][C]35[/C][C]0.481474571703589[/C][C]0.962949143407179[/C][C]0.518525428296411[/C][/ROW]
[ROW][C]36[/C][C]0.386779402140268[/C][C]0.773558804280536[/C][C]0.613220597859732[/C][/ROW]
[ROW][C]37[/C][C]0.410912875273493[/C][C]0.821825750546986[/C][C]0.589087124726507[/C][/ROW]
[ROW][C]38[/C][C]0.299626517385597[/C][C]0.599253034771194[/C][C]0.700373482614403[/C][/ROW]
[ROW][C]39[/C][C]0.292959089074945[/C][C]0.585918178149891[/C][C]0.707040910925055[/C][/ROW]
[ROW][C]40[/C][C]0.177005057880631[/C][C]0.354010115761261[/C][C]0.82299494211937[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57875&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57875&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.6087574157315140.7824851685369710.391242584268486
220.4323827267134730.8647654534269470.567617273286527
230.3247431887245230.6494863774490460.675256811275477
240.3129030346285540.6258060692571070.687096965371446
250.7602062698386330.4795874603227350.239793730161367
260.8068330218588970.3863339562822070.193166978141103
270.8111059190856280.3777881618287450.188894080914372
280.796971113296990.4060577734060220.203028886703011
290.826869224787830.3462615504243410.173130775212170
300.7762322695936930.4475354608126130.223767730406307
310.8149782861702950.3700434276594110.185021713829705
320.7583415931750530.4833168136498930.241658406824947
330.7022344427900760.5955311144198470.297765557209924
340.5998460677333040.8003078645333930.400153932266696
350.4814745717035890.9629491434071790.518525428296411
360.3867794021402680.7735588042805360.613220597859732
370.4109128752734930.8218257505469860.589087124726507
380.2996265173855970.5992530347711940.700373482614403
390.2929590890749450.5859181781498910.707040910925055
400.1770050578806310.3540101157612610.82299494211937







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57875&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57875&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57875&T=6

As an alternative you can also use a QR Code:  

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

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



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