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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 computationFri, 18 Nov 2011 10:27:56 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/18/t1321630145htv89iecs2snbwg.htm/, Retrieved Thu, 28 Mar 2024 13:16:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145466, Retrieved Thu, 28 Mar 2024 13:16:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact169
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [tutorial multiple...] [2011-11-18 14:42:59] [379dab8110dbf77cfcc4b7951c3a599f]
-    D      [Multiple Regression] [met tijd] [2011-11-18 15:27:56] [e7912d585babb6fa20e6bf5178c462ce] [Current]
-   P         [Multiple Regression] [omzet doorheen de...] [2011-11-20 08:45:25] [74be16979710d4c4e7c6647856088456]
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Dataseries X:
1972	33907	71433	152	74272	99	765
1973	35981	53655	99	78867	128	1371
1974	36588	70556	92	80176	57	1880
1975	16967	74702	138	36541	95	232
1976	25333	61201	106	55107	205	230
1977	21027	686	95	45527	51	828
1978	21114	87586	145	46001	59	1833
1979	28777	6615	181	62854	194	906
1980	35612	89725	190	78112	27	1781
1981	24183	40420	150	52653	9	1264
1982	22262	49569	186	48467	24	1123
1983	20637	13963	174	44873	189	1461
1984	29948	62508	151	65605	37	820
1985	22093	90901	112	48016	81	107
1986	36997	89418	143	81110	72	1349
1987	31089	83237	120	68019	81	870
1988	19477	22183	169	42198	90	1471
1989	31301	24346	135	68531	216	731
1990	18497	74341	161	40071	216	1945
1991	30142	24188	98	65849	13	521
1992	21326	11781	142	46362	153	1920
1993	16779	23072	190	36313	185	1924
1994	38068	49119	169	83521	131	100
1995	29707	67776	130	64932	136	34
1996	35016	86910	160	76730	182	325
1997	26131	69358	176	56982	139	1677
1998	29251	16144	111	63793	42	1779
1999	22855	77863	165	49740	213	477
2000	31806	89070	117	69447	184	1007
2001	34124	34790	122	74708	44	1527




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145466&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145466&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145466&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 time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Jaar[t] = + 1941.73074167 + 0.0563566777893992Omzet_product[t] -1.11837153621192e-05Uitgaven_voor_promotie[t] + 0.0678938598601972Prijs_product[t] -0.0252914328395057Gem_budget[t] + 0.0297361210531898Index_cons_vertrouwen[t] + 0.000770357985107064uitgave_lok_prom[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Jaar[t] =  +  1941.73074167 +  0.0563566777893992Omzet_product[t] -1.11837153621192e-05Uitgaven_voor_promotie[t] +  0.0678938598601972Prijs_product[t] -0.0252914328395057Gem_budget[t] +  0.0297361210531898Index_cons_vertrouwen[t] +  0.000770357985107064uitgave_lok_prom[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145466&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Jaar[t] =  +  1941.73074167 +  0.0563566777893992Omzet_product[t] -1.11837153621192e-05Uitgaven_voor_promotie[t] +  0.0678938598601972Prijs_product[t] -0.0252914328395057Gem_budget[t] +  0.0297361210531898Index_cons_vertrouwen[t] +  0.000770357985107064uitgave_lok_prom[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145466&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145466&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
Jaar[t] = + 1941.73074167 + 0.0563566777893992Omzet_product[t] -1.11837153621192e-05Uitgaven_voor_promotie[t] + 0.0678938598601972Prijs_product[t] -0.0252914328395057Gem_budget[t] + 0.0297361210531898Index_cons_vertrouwen[t] + 0.000770357985107064uitgave_lok_prom[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1941.7307416740.35971248.110600
Omzet_product0.05635667778939920.0667340.84450.4070890.203544
Uitgaven_voor_promotie-1.11837153621192e-056.3e-05-0.17740.8607140.430357
Prijs_product0.06789385986019720.0747270.90860.3730050.186503
Gem_budget-0.02529143283950570.030048-0.84170.4086150.204308
Index_cons_vertrouwen0.02973612105318980.0267821.11030.2783470.139173
uitgave_lok_prom0.0007703579851070640.0029710.25930.7977350.398867

\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) & 1941.73074167 & 40.359712 & 48.1106 & 0 & 0 \tabularnewline
Omzet_product & 0.0563566777893992 & 0.066734 & 0.8445 & 0.407089 & 0.203544 \tabularnewline
Uitgaven_voor_promotie & -1.11837153621192e-05 & 6.3e-05 & -0.1774 & 0.860714 & 0.430357 \tabularnewline
Prijs_product & 0.0678938598601972 & 0.074727 & 0.9086 & 0.373005 & 0.186503 \tabularnewline
Gem_budget & -0.0252914328395057 & 0.030048 & -0.8417 & 0.408615 & 0.204308 \tabularnewline
Index_cons_vertrouwen & 0.0297361210531898 & 0.026782 & 1.1103 & 0.278347 & 0.139173 \tabularnewline
uitgave_lok_prom & 0.000770357985107064 & 0.002971 & 0.2593 & 0.797735 & 0.398867 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145466&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]1941.73074167[/C][C]40.359712[/C][C]48.1106[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Omzet_product[/C][C]0.0563566777893992[/C][C]0.066734[/C][C]0.8445[/C][C]0.407089[/C][C]0.203544[/C][/ROW]
[ROW][C]Uitgaven_voor_promotie[/C][C]-1.11837153621192e-05[/C][C]6.3e-05[/C][C]-0.1774[/C][C]0.860714[/C][C]0.430357[/C][/ROW]
[ROW][C]Prijs_product[/C][C]0.0678938598601972[/C][C]0.074727[/C][C]0.9086[/C][C]0.373005[/C][C]0.186503[/C][/ROW]
[ROW][C]Gem_budget[/C][C]-0.0252914328395057[/C][C]0.030048[/C][C]-0.8417[/C][C]0.408615[/C][C]0.204308[/C][/ROW]
[ROW][C]Index_cons_vertrouwen[/C][C]0.0297361210531898[/C][C]0.026782[/C][C]1.1103[/C][C]0.278347[/C][C]0.139173[/C][/ROW]
[ROW][C]uitgave_lok_prom[/C][C]0.000770357985107064[/C][C]0.002971[/C][C]0.2593[/C][C]0.797735[/C][C]0.398867[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145466&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145466&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)1941.7307416740.35971248.110600
Omzet_product0.05635667778939920.0667340.84450.4070890.203544
Uitgaven_voor_promotie-1.11837153621192e-056.3e-05-0.17740.8607140.430357
Prijs_product0.06789385986019720.0747270.90860.3730050.186503
Gem_budget-0.02529143283950570.030048-0.84170.4086150.204308
Index_cons_vertrouwen0.02973612105318980.0267821.11030.2783470.139173
uitgave_lok_prom0.0007703579851070640.0029710.25930.7977350.398867







Multiple Linear Regression - Regression Statistics
Multiple R0.338359289691637
R-squared0.114487008920629
Adjusted R-squared-0.116516380056598
F-TEST (value)0.495607486225734
F-TEST (DF numerator)6
F-TEST (DF denominator)23
p-value0.804978518568233
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.3021513347391
Sum Squared Residuals1990.19044745093

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.338359289691637 \tabularnewline
R-squared & 0.114487008920629 \tabularnewline
Adjusted R-squared & -0.116516380056598 \tabularnewline
F-TEST (value) & 0.495607486225734 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 23 \tabularnewline
p-value & 0.804978518568233 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.3021513347391 \tabularnewline
Sum Squared Residuals & 1990.19044745093 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145466&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.338359289691637[/C][/ROW]
[ROW][C]R-squared[/C][C]0.114487008920629[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.116516380056598[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.495607486225734[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]23[/C][/ROW]
[ROW][C]p-value[/C][C]0.804978518568233[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]9.3021513347391[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1990.19044745093[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145466&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145466&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 R0.338359289691637
R-squared0.114487008920629
Adjusted R-squared-0.116516380056598
F-TEST (value)0.495607486225734
F-TEST (DF numerator)6
F-TEST (DF denominator)23
p-value0.804978518568233
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.3021513347391
Sum Squared Residuals1990.19044745093







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
119721987.22549582155-15.2254958215536
219731985.82474562787-12.824745627871
319741984.54333808641-10.5433380864098
419751985.29780764268-10.2978076426802
519761988.46485235599-12.4648523559901
619771983.89618590688-6.89618590688065
719781980.24600458013-2.24600458013256
819791992.52069889765-13.5206988976493
919801987.2116064991-7.21160649909742
1019811983.90785813929-2.90785813929229
1119821984.19589845504-2.19589845504395
1219831988.26402869606-5.26402869605737
1319841981.540907856942.4590921430552
1419851981.503940353413.49605964658612
1519861987.25964236959-1.25964236959146
1619871983.800728674273.19927132572914
1719881987.177293462040.822706537960378
1819891988.383455413340.616544586659173
1919901988.728056710961.27194328903943
2019911982.189175192518.81082480748874
2119921986.569729503575.43027049642737
2219931988.556791448154.44320855185485
2319941989.648186626474.3518133735275
2419951985.833770549079.16622945093324
2519961990.057910620685.94208937932068
2619971989.82951329687.17048670319807
2719981986.7786010405411.2213989594572
2819991988.7996869006210.2003130993793
2920001990.991743875619.00825612439363
3020011985.7523453967615.2476546032397

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1972 & 1987.22549582155 & -15.2254958215536 \tabularnewline
2 & 1973 & 1985.82474562787 & -12.824745627871 \tabularnewline
3 & 1974 & 1984.54333808641 & -10.5433380864098 \tabularnewline
4 & 1975 & 1985.29780764268 & -10.2978076426802 \tabularnewline
5 & 1976 & 1988.46485235599 & -12.4648523559901 \tabularnewline
6 & 1977 & 1983.89618590688 & -6.89618590688065 \tabularnewline
7 & 1978 & 1980.24600458013 & -2.24600458013256 \tabularnewline
8 & 1979 & 1992.52069889765 & -13.5206988976493 \tabularnewline
9 & 1980 & 1987.2116064991 & -7.21160649909742 \tabularnewline
10 & 1981 & 1983.90785813929 & -2.90785813929229 \tabularnewline
11 & 1982 & 1984.19589845504 & -2.19589845504395 \tabularnewline
12 & 1983 & 1988.26402869606 & -5.26402869605737 \tabularnewline
13 & 1984 & 1981.54090785694 & 2.4590921430552 \tabularnewline
14 & 1985 & 1981.50394035341 & 3.49605964658612 \tabularnewline
15 & 1986 & 1987.25964236959 & -1.25964236959146 \tabularnewline
16 & 1987 & 1983.80072867427 & 3.19927132572914 \tabularnewline
17 & 1988 & 1987.17729346204 & 0.822706537960378 \tabularnewline
18 & 1989 & 1988.38345541334 & 0.616544586659173 \tabularnewline
19 & 1990 & 1988.72805671096 & 1.27194328903943 \tabularnewline
20 & 1991 & 1982.18917519251 & 8.81082480748874 \tabularnewline
21 & 1992 & 1986.56972950357 & 5.43027049642737 \tabularnewline
22 & 1993 & 1988.55679144815 & 4.44320855185485 \tabularnewline
23 & 1994 & 1989.64818662647 & 4.3518133735275 \tabularnewline
24 & 1995 & 1985.83377054907 & 9.16622945093324 \tabularnewline
25 & 1996 & 1990.05791062068 & 5.94208937932068 \tabularnewline
26 & 1997 & 1989.8295132968 & 7.17048670319807 \tabularnewline
27 & 1998 & 1986.77860104054 & 11.2213989594572 \tabularnewline
28 & 1999 & 1988.79968690062 & 10.2003130993793 \tabularnewline
29 & 2000 & 1990.99174387561 & 9.00825612439363 \tabularnewline
30 & 2001 & 1985.75234539676 & 15.2476546032397 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145466&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]1972[/C][C]1987.22549582155[/C][C]-15.2254958215536[/C][/ROW]
[ROW][C]2[/C][C]1973[/C][C]1985.82474562787[/C][C]-12.824745627871[/C][/ROW]
[ROW][C]3[/C][C]1974[/C][C]1984.54333808641[/C][C]-10.5433380864098[/C][/ROW]
[ROW][C]4[/C][C]1975[/C][C]1985.29780764268[/C][C]-10.2978076426802[/C][/ROW]
[ROW][C]5[/C][C]1976[/C][C]1988.46485235599[/C][C]-12.4648523559901[/C][/ROW]
[ROW][C]6[/C][C]1977[/C][C]1983.89618590688[/C][C]-6.89618590688065[/C][/ROW]
[ROW][C]7[/C][C]1978[/C][C]1980.24600458013[/C][C]-2.24600458013256[/C][/ROW]
[ROW][C]8[/C][C]1979[/C][C]1992.52069889765[/C][C]-13.5206988976493[/C][/ROW]
[ROW][C]9[/C][C]1980[/C][C]1987.2116064991[/C][C]-7.21160649909742[/C][/ROW]
[ROW][C]10[/C][C]1981[/C][C]1983.90785813929[/C][C]-2.90785813929229[/C][/ROW]
[ROW][C]11[/C][C]1982[/C][C]1984.19589845504[/C][C]-2.19589845504395[/C][/ROW]
[ROW][C]12[/C][C]1983[/C][C]1988.26402869606[/C][C]-5.26402869605737[/C][/ROW]
[ROW][C]13[/C][C]1984[/C][C]1981.54090785694[/C][C]2.4590921430552[/C][/ROW]
[ROW][C]14[/C][C]1985[/C][C]1981.50394035341[/C][C]3.49605964658612[/C][/ROW]
[ROW][C]15[/C][C]1986[/C][C]1987.25964236959[/C][C]-1.25964236959146[/C][/ROW]
[ROW][C]16[/C][C]1987[/C][C]1983.80072867427[/C][C]3.19927132572914[/C][/ROW]
[ROW][C]17[/C][C]1988[/C][C]1987.17729346204[/C][C]0.822706537960378[/C][/ROW]
[ROW][C]18[/C][C]1989[/C][C]1988.38345541334[/C][C]0.616544586659173[/C][/ROW]
[ROW][C]19[/C][C]1990[/C][C]1988.72805671096[/C][C]1.27194328903943[/C][/ROW]
[ROW][C]20[/C][C]1991[/C][C]1982.18917519251[/C][C]8.81082480748874[/C][/ROW]
[ROW][C]21[/C][C]1992[/C][C]1986.56972950357[/C][C]5.43027049642737[/C][/ROW]
[ROW][C]22[/C][C]1993[/C][C]1988.55679144815[/C][C]4.44320855185485[/C][/ROW]
[ROW][C]23[/C][C]1994[/C][C]1989.64818662647[/C][C]4.3518133735275[/C][/ROW]
[ROW][C]24[/C][C]1995[/C][C]1985.83377054907[/C][C]9.16622945093324[/C][/ROW]
[ROW][C]25[/C][C]1996[/C][C]1990.05791062068[/C][C]5.94208937932068[/C][/ROW]
[ROW][C]26[/C][C]1997[/C][C]1989.8295132968[/C][C]7.17048670319807[/C][/ROW]
[ROW][C]27[/C][C]1998[/C][C]1986.77860104054[/C][C]11.2213989594572[/C][/ROW]
[ROW][C]28[/C][C]1999[/C][C]1988.79968690062[/C][C]10.2003130993793[/C][/ROW]
[ROW][C]29[/C][C]2000[/C][C]1990.99174387561[/C][C]9.00825612439363[/C][/ROW]
[ROW][C]30[/C][C]2001[/C][C]1985.75234539676[/C][C]15.2476546032397[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145466&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145466&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
119721987.22549582155-15.2254958215536
219731985.82474562787-12.824745627871
319741984.54333808641-10.5433380864098
419751985.29780764268-10.2978076426802
519761988.46485235599-12.4648523559901
619771983.89618590688-6.89618590688065
719781980.24600458013-2.24600458013256
819791992.52069889765-13.5206988976493
919801987.2116064991-7.21160649909742
1019811983.90785813929-2.90785813929229
1119821984.19589845504-2.19589845504395
1219831988.26402869606-5.26402869605737
1319841981.540907856942.4590921430552
1419851981.503940353413.49605964658612
1519861987.25964236959-1.25964236959146
1619871983.800728674273.19927132572914
1719881987.177293462040.822706537960378
1819891988.383455413340.616544586659173
1919901988.728056710961.27194328903943
2019911982.189175192518.81082480748874
2119921986.569729503575.43027049642737
2219931988.556791448154.44320855185485
2319941989.648186626474.3518133735275
2419951985.833770549079.16622945093324
2519961990.057910620685.94208937932068
2619971989.82951329687.17048670319807
2719981986.7786010405411.2213989594572
2819991988.7996869006210.2003130993793
2920001990.991743875619.00825612439363
3020011985.7523453967615.2476546032397







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.1546502624035540.3093005248071080.845349737596446
110.09113552538612910.1822710507722580.908864474613871
120.05863647767559320.1172729553511860.941363522324407
130.1482192883356780.2964385766713570.851780711664322
140.26619819331910.5323963866381990.7338018066809
150.6184113062487960.7631773875024080.381588693751204
160.7630992970381760.4738014059236470.236900702961824
170.8529110733109990.2941778533780020.147088926689001
180.8051768930370410.3896462139259190.194823106962959
190.8707755960460670.2584488079078660.129224403953933
200.9022016197568730.1955967604862540.0977983802431272

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.154650262403554 & 0.309300524807108 & 0.845349737596446 \tabularnewline
11 & 0.0911355253861291 & 0.182271050772258 & 0.908864474613871 \tabularnewline
12 & 0.0586364776755932 & 0.117272955351186 & 0.941363522324407 \tabularnewline
13 & 0.148219288335678 & 0.296438576671357 & 0.851780711664322 \tabularnewline
14 & 0.2661981933191 & 0.532396386638199 & 0.7338018066809 \tabularnewline
15 & 0.618411306248796 & 0.763177387502408 & 0.381588693751204 \tabularnewline
16 & 0.763099297038176 & 0.473801405923647 & 0.236900702961824 \tabularnewline
17 & 0.852911073310999 & 0.294177853378002 & 0.147088926689001 \tabularnewline
18 & 0.805176893037041 & 0.389646213925919 & 0.194823106962959 \tabularnewline
19 & 0.870775596046067 & 0.258448807907866 & 0.129224403953933 \tabularnewline
20 & 0.902201619756873 & 0.195596760486254 & 0.0977983802431272 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145466&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.154650262403554[/C][C]0.309300524807108[/C][C]0.845349737596446[/C][/ROW]
[ROW][C]11[/C][C]0.0911355253861291[/C][C]0.182271050772258[/C][C]0.908864474613871[/C][/ROW]
[ROW][C]12[/C][C]0.0586364776755932[/C][C]0.117272955351186[/C][C]0.941363522324407[/C][/ROW]
[ROW][C]13[/C][C]0.148219288335678[/C][C]0.296438576671357[/C][C]0.851780711664322[/C][/ROW]
[ROW][C]14[/C][C]0.2661981933191[/C][C]0.532396386638199[/C][C]0.7338018066809[/C][/ROW]
[ROW][C]15[/C][C]0.618411306248796[/C][C]0.763177387502408[/C][C]0.381588693751204[/C][/ROW]
[ROW][C]16[/C][C]0.763099297038176[/C][C]0.473801405923647[/C][C]0.236900702961824[/C][/ROW]
[ROW][C]17[/C][C]0.852911073310999[/C][C]0.294177853378002[/C][C]0.147088926689001[/C][/ROW]
[ROW][C]18[/C][C]0.805176893037041[/C][C]0.389646213925919[/C][C]0.194823106962959[/C][/ROW]
[ROW][C]19[/C][C]0.870775596046067[/C][C]0.258448807907866[/C][C]0.129224403953933[/C][/ROW]
[ROW][C]20[/C][C]0.902201619756873[/C][C]0.195596760486254[/C][C]0.0977983802431272[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145466&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145466&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
100.1546502624035540.3093005248071080.845349737596446
110.09113552538612910.1822710507722580.908864474613871
120.05863647767559320.1172729553511860.941363522324407
130.1482192883356780.2964385766713570.851780711664322
140.26619819331910.5323963866381990.7338018066809
150.6184113062487960.7631773875024080.381588693751204
160.7630992970381760.4738014059236470.236900702961824
170.8529110733109990.2941778533780020.147088926689001
180.8051768930370410.3896462139259190.194823106962959
190.8707755960460670.2584488079078660.129224403953933
200.9022016197568730.1955967604862540.0977983802431272







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=145466&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=145466&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145466&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 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}