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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 computationMon, 20 Dec 2010 18:51:12 +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/20/t1292870958hhfarkq8vrseto4.htm/, Retrieved Sat, 04 May 2024 03:48:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113062, Retrieved Sat, 04 May 2024 03:48:17 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact117

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Experiment 2] [2010-12-20 18:51:12] [21ba15a181629d0f70ea456ec39a7075] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
-999,0	-999,0	38,6	6654,000	5712,000	645,0	3	5	3
6,3	2,0	4,5	1,000	6,600	42,0	3	1	3
-999,0	-999,0	14,0	3,385	44,500	60,0	1	1	1
-999,0	-999,0	-999,0	0,920	5,700	25,0	5	2	3
2,1	1,8	69,0	2547,000	4603,000	624,0	3	5	4
9,1	0,7	27,0	10,550	179,500	180,0	4	4	4
15,8	3,9	19,0	0,023	0,300	35,0	1	1	1
5,2	1.0	30,4	160,000	169,000	392,0	4	5	4
10,9	36,0	28,0	3,300	25,600	63,0	1	2	1
8,3	1,4	50,0	52,160	440,000	230,0	1	1	1
11,0	1,5	7,0	0,425	6,400	112,0	5	4	4
3,2	0,7	30,0	465,000	423,000	281,0	5	5	5
7,6	2,7	-999,0	0,550	2,400	-999,0	2	1	2
-999,0	-999,0	40,0	187,100	419,000	365,0	5	5	5
6,3	2,1	3,5	0,075	1,200	42,0	1	1	1
8,6	0,0	50,0	3,000	25,000	28,0	2	2	2
6,6	4,1	6,0	0,785	3,500	42,0	2	2	2
9,5	1,2	10,4	0,200	5,000	120,0	2	2	2
4,8	1,3	34,0	1,410	17,500	-999,0	1	2	1
12,0	6,1	7,0	60,000	81,000	-999,0	1	1	1
-999,0	0,3	28,0	529,000	680,000	400,0	5	5	5
3,3	0,5	20,0	27,660	115,000	148,0	5	5	5
11,0	3,4	3,9	0,120	1,000	16,0	3	1	2
-999,0	-999,0	39,3	207,000	406,000	252,0	1	4	1
4,7	1,5	41,0	85,000	325,000	310,0	1	3	1
-999,0	-999,0	16,2	36,330	119,500	63,0	1	1	1
10,4	3,4	9,0	0,101	4,000	28,0	5	1	3
7,4	0,8	7,6	1,040	5,500	68,0	5	3	4
2,1	0,8	46,0	521,000	655,000	336,0	5	5	5
-999,0	-999,0	22,4	100,000	157,000	100,0	1	1	1
-999,0	-999,0	16,3	35,000	56,000	33,0	3	5	4
7,7	1,4	2,6	0,005	0,140	21,5	5	2	4
17,9	2,0	24,0	0,010	0,250	50,0	1	1	1
6,1	1,9	100,0	62,000	1320,000	267,0	1	1	1
8,2	2,4	-999,0	0,122	3,000	30,0	2	1	1
8,4	2,8	-999,0	1,350	8,100	45,0	3	1	3
11,9	1,3	3,2	0,023	0,400	19,0	4	1	3
10,8	2,0	2,0	0,048	0,330	30,0	4	1	3
13,8	5,6	5,0	1,700	6,300	12,0	2	1	1
14,3	3,1	6,5	3,500	10,800	120,0	2	1	1
-999,0	1,0	23,6	250,000	490,000	440,0	5	5	5
15,2	1,8	12,0	0,480	15,500	140,0	2	2	2
10,0	0,9	20,2	10,000	115,000	170,0	4	4	4
11,9	1,8	13,0	1,620	11,400	17,0	2	1	2
6,5	1,9	27,0	192,000	180,000	115,0	4	4	4
7,5	0,9	18,0	2,500	12,100	31,0	5	5	5
-999,0	-999,0	13,7	4,288	39,200	63,0	2	2	2
10,6	2,6	4,7	0,280	1,900	21,0	3	1	3
7,4	2,4	9,8	4,235	50,400	52,0	1	1	1
8,4	1,2	29,0	6,800	179,000	164,0	2	3	2
5,7	0,9	7,0	0,750	12,300	225,0	2	2	2
4,9	0,5	6,0	3,600	21,000	225,0	3	2	3
-999,0	-999,0	17,0	14,830	98,200	150,0	5	5	5
3,2	0,6	20,0	55,500	175,000	151,0	5	5	5
-999,0	-999,0	12,7	1,400	12,500	90,0	2	2	2
8,1	2,2	3,5	0,060	1,000	-999,0	3	1	1
11,0	2,3	4,5	0,900	2,600	38,0	2	1	2
-999,0	0,5	7,5	2,000	17,000	200,0	3	1	3
13,2	-999,0	2,3	0,104	2,500	46,0	3	2	2
9,7	0,6	13,0	4,050	58,000	210,0	4	3	4
12,8	6,6	3,0	3,500	3,900	14,0	2	1	1
4,9	2,6	24,0	4,190	12,300	60,0	3	1	2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113062&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113062&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113062&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 time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
PS[t] = -90.7471201795934 + 0.747139236735807SWS[t] + 0.0480756077894083L[t] -0.0585083359047774wb[t] + 0.0501497584438397wbr[t] + 0.00720177972023492tg[t] -56.5039953128544P[t] -57.1689370103844S[t] + 138.812334264370`D `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PS[t] =  -90.7471201795934 +  0.747139236735807SWS[t] +  0.0480756077894083L[t] -0.0585083359047774wb[t] +  0.0501497584438397wbr[t] +  0.00720177972023492tg[t] -56.5039953128544P[t] -57.1689370103844S[t] +  138.812334264370`D
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113062&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PS[t] =  -90.7471201795934 +  0.747139236735807SWS[t] +  0.0480756077894083L[t] -0.0585083359047774wb[t] +  0.0501497584438397wbr[t] +  0.00720177972023492tg[t] -56.5039953128544P[t] -57.1689370103844S[t] +  138.812334264370`D
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113062&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113062&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
PS[t] = -90.7471201795934 + 0.747139236735807SWS[t] + 0.0480756077894083L[t] -0.0585083359047774wb[t] + 0.0501497584438397wbr[t] + 0.00720177972023492tg[t] -56.5039953128544P[t] -57.1689370103844S[t] + 138.812334264370`D `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-90.747120179593472.068777-1.25920.2134850.106743
SWS0.7471392367358070.0774629.645300
L0.04807560778940830.1295880.3710.7121250.356063
wb-0.05850833590477740.099518-0.58790.5590840.279542
wbr0.05014975844383970.0993680.50470.6158690.307935
tg0.007201779720234920.1183310.06090.9516990.475849
P-56.503995312854459.041082-0.9570.3428980.171449
S-57.168937010384437.237168-1.53530.1306670.065334
`D `138.81233426437075.8489461.83010.0728590.036429

\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) & -90.7471201795934 & 72.068777 & -1.2592 & 0.213485 & 0.106743 \tabularnewline
SWS & 0.747139236735807 & 0.077462 & 9.6453 & 0 & 0 \tabularnewline
L & 0.0480756077894083 & 0.129588 & 0.371 & 0.712125 & 0.356063 \tabularnewline
wb & -0.0585083359047774 & 0.099518 & -0.5879 & 0.559084 & 0.279542 \tabularnewline
wbr & 0.0501497584438397 & 0.099368 & 0.5047 & 0.615869 & 0.307935 \tabularnewline
tg & 0.00720177972023492 & 0.118331 & 0.0609 & 0.951699 & 0.475849 \tabularnewline
P & -56.5039953128544 & 59.041082 & -0.957 & 0.342898 & 0.171449 \tabularnewline
S & -57.1689370103844 & 37.237168 & -1.5353 & 0.130667 & 0.065334 \tabularnewline
`D
` & 138.812334264370 & 75.848946 & 1.8301 & 0.072859 & 0.036429 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113062&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]-90.7471201795934[/C][C]72.068777[/C][C]-1.2592[/C][C]0.213485[/C][C]0.106743[/C][/ROW]
[ROW][C]SWS[/C][C]0.747139236735807[/C][C]0.077462[/C][C]9.6453[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]L[/C][C]0.0480756077894083[/C][C]0.129588[/C][C]0.371[/C][C]0.712125[/C][C]0.356063[/C][/ROW]
[ROW][C]wb[/C][C]-0.0585083359047774[/C][C]0.099518[/C][C]-0.5879[/C][C]0.559084[/C][C]0.279542[/C][/ROW]
[ROW][C]wbr[/C][C]0.0501497584438397[/C][C]0.099368[/C][C]0.5047[/C][C]0.615869[/C][C]0.307935[/C][/ROW]
[ROW][C]tg[/C][C]0.00720177972023492[/C][C]0.118331[/C][C]0.0609[/C][C]0.951699[/C][C]0.475849[/C][/ROW]
[ROW][C]P[/C][C]-56.5039953128544[/C][C]59.041082[/C][C]-0.957[/C][C]0.342898[/C][C]0.171449[/C][/ROW]
[ROW][C]S[/C][C]-57.1689370103844[/C][C]37.237168[/C][C]-1.5353[/C][C]0.130667[/C][C]0.065334[/C][/ROW]
[ROW][C]`D
`[/C][C]138.812334264370[/C][C]75.848946[/C][C]1.8301[/C][C]0.072859[/C][C]0.036429[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113062&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113062&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)-90.747120179593472.068777-1.25920.2134850.106743
SWS0.7471392367358070.0774629.645300
L0.04807560778940830.1295880.3710.7121250.356063
wb-0.05850833590477740.099518-0.58790.5590840.279542
wbr0.05014975844383970.0993680.50470.6158690.307935
tg0.007201779720234920.1183310.06090.9516990.475849
P-56.503995312854459.041082-0.9570.3428980.171449
S-57.168937010384437.237168-1.53530.1306670.065334
`D `138.81233426437075.8489461.83010.0728590.036429






Multiple Linear Regression - Regression Statistics
Multiple R0.833238085909915
R-squared0.694285707810819
Adjusted R-squared0.64814015427283
F-TEST (value)15.0455602886905
F-TEST (DF numerator)8
F-TEST (DF denominator)53
p-value3.10692582772276e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation236.662993065487
Sum Squared Residuals2968496.73119589

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.833238085909915 \tabularnewline
R-squared & 0.694285707810819 \tabularnewline
Adjusted R-squared & 0.64814015427283 \tabularnewline
F-TEST (value) & 15.0455602886905 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 53 \tabularnewline
p-value & 3.10692582772276e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 236.662993065487 \tabularnewline
Sum Squared Residuals & 2968496.73119589 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113062&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.833238085909915[/C][/ROW]
[ROW][C]R-squared[/C][C]0.694285707810819[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.64814015427283[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]15.0455602886905[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]53[/C][/ROW]
[ROW][C]p-value[/C][C]3.10692582772276e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]236.662993065487[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2968496.73119589[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113062&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113062&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 R0.833238085909915
R-squared0.694285707810819
Adjusted R-squared0.64814015427283
F-TEST (value)15.0455602886905
F-TEST (DF numerator)8
F-TEST (DF denominator)53
p-value3.10692582772276e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation236.662993065487
Sum Squared Residuals2968496.73119589






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-999-972.417066374995-26.5829336250051
22104.507231909130-102.507231909130
3-999-808.861036911555-190.138963088445
4-999-865.175527205112-133.824472794888
51.8100.344272334967-98.5442723349672
60.727.5884350960561-26.8884350960561
73.9-52.623720224022656.5237202240226
81-40.074753835053341.0747538350533
936-111.742252120272147.742252120272
101.4-36.332173933934637.7321739339346
111.5-37.035755129357138.5357551293571
120.734.8136750447066-34.1136750447066
132.7-32.755051374371235.4550513743712
14-999-696.824694923363-302.175305076637
152.1-60.374210086574462.4742100865744
160-30.359269686385130.3592696863851
174.1-34.816673829020838.9166738290208
181.2-31.767246535865232.9672465358652
191.3-123.955270169048125.255270169048
206.1-62.948465803957269.0484658039572
210.3-704.06445301863704.36445301863
220.543.5917062115877-43.0917062115877
233.4-31.238990891670134.6389908916701
24-999-971.552830497154-27.4471695028457
251.5-160.904923271592162.404923271592
26-999-806.899990478353-192.100009521647
273.4-5.399762904968088.79976290496808
280.817.0743302765808-16.2743302765808
290.843.5154066418439-42.7154066418439
30-999-808.180065665823-190.819934334177
31-999-735.464665757749-263.535334242251
321.473.6839016844503-72.2839016844503
332-50.708069971682152.7080699716821
341.98.25043138964159-6.35043138964159
352.4-163.653339341744166.053339341744
362.857.908703948864-55.108703948864
371.351.705311240132-50.405311240132
38250.9000137358092-48.9000137358092
395.6-111.257913381615116.857913381615
403.1-109.914079233409113.014079233409
411-697.19254289099698.19254289099
421.8-26.777406189996428.5774061899964
430.924.659448644068-23.759448644068
441.826.8159145288091-25.0159145288091
451.914.5864947280278-12.6864947280278
460.942.1025911505248-41.2025911505248
47-999-784.033079060675-214.966920939325
482.6107.384730511693-104.784730511693
492.4-56.953489361816459.3534893618164
501.2-80.20704914182981.407049141829
510.9-33.679732179428234.5797321794282
520.548.2523739160418-47.7523739160418
53-999-705.487628024838-293.512371975162
540.644.9187110621161-44.3187110621161
55-999-785.056733092375-213.943266907625
562.2-179.543555101573181.743555101573
572.325.4868920512075-23.1868920512075
580.5-644.8466856103645.3466856103
59-999-86.5489283910512-912.451071608949
600.679.0357590634919-78.4357590634919
616.6-112.312474699383118.912474699383
622.6-34.184778868218136.7847788682181

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -999 & -972.417066374995 & -26.5829336250051 \tabularnewline
2 & 2 & 104.507231909130 & -102.507231909130 \tabularnewline
3 & -999 & -808.861036911555 & -190.138963088445 \tabularnewline
4 & -999 & -865.175527205112 & -133.824472794888 \tabularnewline
5 & 1.8 & 100.344272334967 & -98.5442723349672 \tabularnewline
6 & 0.7 & 27.5884350960561 & -26.8884350960561 \tabularnewline
7 & 3.9 & -52.6237202240226 & 56.5237202240226 \tabularnewline
8 & 1 & -40.0747538350533 & 41.0747538350533 \tabularnewline
9 & 36 & -111.742252120272 & 147.742252120272 \tabularnewline
10 & 1.4 & -36.3321739339346 & 37.7321739339346 \tabularnewline
11 & 1.5 & -37.0357551293571 & 38.5357551293571 \tabularnewline
12 & 0.7 & 34.8136750447066 & -34.1136750447066 \tabularnewline
13 & 2.7 & -32.7550513743712 & 35.4550513743712 \tabularnewline
14 & -999 & -696.824694923363 & -302.175305076637 \tabularnewline
15 & 2.1 & -60.3742100865744 & 62.4742100865744 \tabularnewline
16 & 0 & -30.3592696863851 & 30.3592696863851 \tabularnewline
17 & 4.1 & -34.8166738290208 & 38.9166738290208 \tabularnewline
18 & 1.2 & -31.7672465358652 & 32.9672465358652 \tabularnewline
19 & 1.3 & -123.955270169048 & 125.255270169048 \tabularnewline
20 & 6.1 & -62.9484658039572 & 69.0484658039572 \tabularnewline
21 & 0.3 & -704.06445301863 & 704.36445301863 \tabularnewline
22 & 0.5 & 43.5917062115877 & -43.0917062115877 \tabularnewline
23 & 3.4 & -31.2389908916701 & 34.6389908916701 \tabularnewline
24 & -999 & -971.552830497154 & -27.4471695028457 \tabularnewline
25 & 1.5 & -160.904923271592 & 162.404923271592 \tabularnewline
26 & -999 & -806.899990478353 & -192.100009521647 \tabularnewline
27 & 3.4 & -5.39976290496808 & 8.79976290496808 \tabularnewline
28 & 0.8 & 17.0743302765808 & -16.2743302765808 \tabularnewline
29 & 0.8 & 43.5154066418439 & -42.7154066418439 \tabularnewline
30 & -999 & -808.180065665823 & -190.819934334177 \tabularnewline
31 & -999 & -735.464665757749 & -263.535334242251 \tabularnewline
32 & 1.4 & 73.6839016844503 & -72.2839016844503 \tabularnewline
33 & 2 & -50.7080699716821 & 52.7080699716821 \tabularnewline
34 & 1.9 & 8.25043138964159 & -6.35043138964159 \tabularnewline
35 & 2.4 & -163.653339341744 & 166.053339341744 \tabularnewline
36 & 2.8 & 57.908703948864 & -55.108703948864 \tabularnewline
37 & 1.3 & 51.705311240132 & -50.405311240132 \tabularnewline
38 & 2 & 50.9000137358092 & -48.9000137358092 \tabularnewline
39 & 5.6 & -111.257913381615 & 116.857913381615 \tabularnewline
40 & 3.1 & -109.914079233409 & 113.014079233409 \tabularnewline
41 & 1 & -697.19254289099 & 698.19254289099 \tabularnewline
42 & 1.8 & -26.7774061899964 & 28.5774061899964 \tabularnewline
43 & 0.9 & 24.659448644068 & -23.759448644068 \tabularnewline
44 & 1.8 & 26.8159145288091 & -25.0159145288091 \tabularnewline
45 & 1.9 & 14.5864947280278 & -12.6864947280278 \tabularnewline
46 & 0.9 & 42.1025911505248 & -41.2025911505248 \tabularnewline
47 & -999 & -784.033079060675 & -214.966920939325 \tabularnewline
48 & 2.6 & 107.384730511693 & -104.784730511693 \tabularnewline
49 & 2.4 & -56.9534893618164 & 59.3534893618164 \tabularnewline
50 & 1.2 & -80.207049141829 & 81.407049141829 \tabularnewline
51 & 0.9 & -33.6797321794282 & 34.5797321794282 \tabularnewline
52 & 0.5 & 48.2523739160418 & -47.7523739160418 \tabularnewline
53 & -999 & -705.487628024838 & -293.512371975162 \tabularnewline
54 & 0.6 & 44.9187110621161 & -44.3187110621161 \tabularnewline
55 & -999 & -785.056733092375 & -213.943266907625 \tabularnewline
56 & 2.2 & -179.543555101573 & 181.743555101573 \tabularnewline
57 & 2.3 & 25.4868920512075 & -23.1868920512075 \tabularnewline
58 & 0.5 & -644.8466856103 & 645.3466856103 \tabularnewline
59 & -999 & -86.5489283910512 & -912.451071608949 \tabularnewline
60 & 0.6 & 79.0357590634919 & -78.4357590634919 \tabularnewline
61 & 6.6 & -112.312474699383 & 118.912474699383 \tabularnewline
62 & 2.6 & -34.1847788682181 & 36.7847788682181 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113062&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]-999[/C][C]-972.417066374995[/C][C]-26.5829336250051[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]104.507231909130[/C][C]-102.507231909130[/C][/ROW]
[ROW][C]3[/C][C]-999[/C][C]-808.861036911555[/C][C]-190.138963088445[/C][/ROW]
[ROW][C]4[/C][C]-999[/C][C]-865.175527205112[/C][C]-133.824472794888[/C][/ROW]
[ROW][C]5[/C][C]1.8[/C][C]100.344272334967[/C][C]-98.5442723349672[/C][/ROW]
[ROW][C]6[/C][C]0.7[/C][C]27.5884350960561[/C][C]-26.8884350960561[/C][/ROW]
[ROW][C]7[/C][C]3.9[/C][C]-52.6237202240226[/C][C]56.5237202240226[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]-40.0747538350533[/C][C]41.0747538350533[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]-111.742252120272[/C][C]147.742252120272[/C][/ROW]
[ROW][C]10[/C][C]1.4[/C][C]-36.3321739339346[/C][C]37.7321739339346[/C][/ROW]
[ROW][C]11[/C][C]1.5[/C][C]-37.0357551293571[/C][C]38.5357551293571[/C][/ROW]
[ROW][C]12[/C][C]0.7[/C][C]34.8136750447066[/C][C]-34.1136750447066[/C][/ROW]
[ROW][C]13[/C][C]2.7[/C][C]-32.7550513743712[/C][C]35.4550513743712[/C][/ROW]
[ROW][C]14[/C][C]-999[/C][C]-696.824694923363[/C][C]-302.175305076637[/C][/ROW]
[ROW][C]15[/C][C]2.1[/C][C]-60.3742100865744[/C][C]62.4742100865744[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]-30.3592696863851[/C][C]30.3592696863851[/C][/ROW]
[ROW][C]17[/C][C]4.1[/C][C]-34.8166738290208[/C][C]38.9166738290208[/C][/ROW]
[ROW][C]18[/C][C]1.2[/C][C]-31.7672465358652[/C][C]32.9672465358652[/C][/ROW]
[ROW][C]19[/C][C]1.3[/C][C]-123.955270169048[/C][C]125.255270169048[/C][/ROW]
[ROW][C]20[/C][C]6.1[/C][C]-62.9484658039572[/C][C]69.0484658039572[/C][/ROW]
[ROW][C]21[/C][C]0.3[/C][C]-704.06445301863[/C][C]704.36445301863[/C][/ROW]
[ROW][C]22[/C][C]0.5[/C][C]43.5917062115877[/C][C]-43.0917062115877[/C][/ROW]
[ROW][C]23[/C][C]3.4[/C][C]-31.2389908916701[/C][C]34.6389908916701[/C][/ROW]
[ROW][C]24[/C][C]-999[/C][C]-971.552830497154[/C][C]-27.4471695028457[/C][/ROW]
[ROW][C]25[/C][C]1.5[/C][C]-160.904923271592[/C][C]162.404923271592[/C][/ROW]
[ROW][C]26[/C][C]-999[/C][C]-806.899990478353[/C][C]-192.100009521647[/C][/ROW]
[ROW][C]27[/C][C]3.4[/C][C]-5.39976290496808[/C][C]8.79976290496808[/C][/ROW]
[ROW][C]28[/C][C]0.8[/C][C]17.0743302765808[/C][C]-16.2743302765808[/C][/ROW]
[ROW][C]29[/C][C]0.8[/C][C]43.5154066418439[/C][C]-42.7154066418439[/C][/ROW]
[ROW][C]30[/C][C]-999[/C][C]-808.180065665823[/C][C]-190.819934334177[/C][/ROW]
[ROW][C]31[/C][C]-999[/C][C]-735.464665757749[/C][C]-263.535334242251[/C][/ROW]
[ROW][C]32[/C][C]1.4[/C][C]73.6839016844503[/C][C]-72.2839016844503[/C][/ROW]
[ROW][C]33[/C][C]2[/C][C]-50.7080699716821[/C][C]52.7080699716821[/C][/ROW]
[ROW][C]34[/C][C]1.9[/C][C]8.25043138964159[/C][C]-6.35043138964159[/C][/ROW]
[ROW][C]35[/C][C]2.4[/C][C]-163.653339341744[/C][C]166.053339341744[/C][/ROW]
[ROW][C]36[/C][C]2.8[/C][C]57.908703948864[/C][C]-55.108703948864[/C][/ROW]
[ROW][C]37[/C][C]1.3[/C][C]51.705311240132[/C][C]-50.405311240132[/C][/ROW]
[ROW][C]38[/C][C]2[/C][C]50.9000137358092[/C][C]-48.9000137358092[/C][/ROW]
[ROW][C]39[/C][C]5.6[/C][C]-111.257913381615[/C][C]116.857913381615[/C][/ROW]
[ROW][C]40[/C][C]3.1[/C][C]-109.914079233409[/C][C]113.014079233409[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]-697.19254289099[/C][C]698.19254289099[/C][/ROW]
[ROW][C]42[/C][C]1.8[/C][C]-26.7774061899964[/C][C]28.5774061899964[/C][/ROW]
[ROW][C]43[/C][C]0.9[/C][C]24.659448644068[/C][C]-23.759448644068[/C][/ROW]
[ROW][C]44[/C][C]1.8[/C][C]26.8159145288091[/C][C]-25.0159145288091[/C][/ROW]
[ROW][C]45[/C][C]1.9[/C][C]14.5864947280278[/C][C]-12.6864947280278[/C][/ROW]
[ROW][C]46[/C][C]0.9[/C][C]42.1025911505248[/C][C]-41.2025911505248[/C][/ROW]
[ROW][C]47[/C][C]-999[/C][C]-784.033079060675[/C][C]-214.966920939325[/C][/ROW]
[ROW][C]48[/C][C]2.6[/C][C]107.384730511693[/C][C]-104.784730511693[/C][/ROW]
[ROW][C]49[/C][C]2.4[/C][C]-56.9534893618164[/C][C]59.3534893618164[/C][/ROW]
[ROW][C]50[/C][C]1.2[/C][C]-80.207049141829[/C][C]81.407049141829[/C][/ROW]
[ROW][C]51[/C][C]0.9[/C][C]-33.6797321794282[/C][C]34.5797321794282[/C][/ROW]
[ROW][C]52[/C][C]0.5[/C][C]48.2523739160418[/C][C]-47.7523739160418[/C][/ROW]
[ROW][C]53[/C][C]-999[/C][C]-705.487628024838[/C][C]-293.512371975162[/C][/ROW]
[ROW][C]54[/C][C]0.6[/C][C]44.9187110621161[/C][C]-44.3187110621161[/C][/ROW]
[ROW][C]55[/C][C]-999[/C][C]-785.056733092375[/C][C]-213.943266907625[/C][/ROW]
[ROW][C]56[/C][C]2.2[/C][C]-179.543555101573[/C][C]181.743555101573[/C][/ROW]
[ROW][C]57[/C][C]2.3[/C][C]25.4868920512075[/C][C]-23.1868920512075[/C][/ROW]
[ROW][C]58[/C][C]0.5[/C][C]-644.8466856103[/C][C]645.3466856103[/C][/ROW]
[ROW][C]59[/C][C]-999[/C][C]-86.5489283910512[/C][C]-912.451071608949[/C][/ROW]
[ROW][C]60[/C][C]0.6[/C][C]79.0357590634919[/C][C]-78.4357590634919[/C][/ROW]
[ROW][C]61[/C][C]6.6[/C][C]-112.312474699383[/C][C]118.912474699383[/C][/ROW]
[ROW][C]62[/C][C]2.6[/C][C]-34.1847788682181[/C][C]36.7847788682181[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113062&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113062&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
1-999-972.417066374995-26.5829336250051
22104.507231909130-102.507231909130
3-999-808.861036911555-190.138963088445
4-999-865.175527205112-133.824472794888
51.8100.344272334967-98.5442723349672
60.727.5884350960561-26.8884350960561
73.9-52.623720224022656.5237202240226
81-40.074753835053341.0747538350533
936-111.742252120272147.742252120272
101.4-36.332173933934637.7321739339346
111.5-37.035755129357138.5357551293571
120.734.8136750447066-34.1136750447066
132.7-32.755051374371235.4550513743712
14-999-696.824694923363-302.175305076637
152.1-60.374210086574462.4742100865744
160-30.359269686385130.3592696863851
174.1-34.816673829020838.9166738290208
181.2-31.767246535865232.9672465358652
191.3-123.955270169048125.255270169048
206.1-62.948465803957269.0484658039572
210.3-704.06445301863704.36445301863
220.543.5917062115877-43.0917062115877
233.4-31.238990891670134.6389908916701
24-999-971.552830497154-27.4471695028457
251.5-160.904923271592162.404923271592
26-999-806.899990478353-192.100009521647
273.4-5.399762904968088.79976290496808
280.817.0743302765808-16.2743302765808
290.843.5154066418439-42.7154066418439
30-999-808.180065665823-190.819934334177
31-999-735.464665757749-263.535334242251
321.473.6839016844503-72.2839016844503
332-50.708069971682152.7080699716821
341.98.25043138964159-6.35043138964159
352.4-163.653339341744166.053339341744
362.857.908703948864-55.108703948864
371.351.705311240132-50.405311240132
38250.9000137358092-48.9000137358092
395.6-111.257913381615116.857913381615
403.1-109.914079233409113.014079233409
411-697.19254289099698.19254289099
421.8-26.777406189996428.5774061899964
430.924.659448644068-23.759448644068
441.826.8159145288091-25.0159145288091
451.914.5864947280278-12.6864947280278
460.942.1025911505248-41.2025911505248
47-999-784.033079060675-214.966920939325
482.6107.384730511693-104.784730511693
492.4-56.953489361816459.3534893618164
501.2-80.20704914182981.407049141829
510.9-33.679732179428234.5797321794282
520.548.2523739160418-47.7523739160418
53-999-705.487628024838-293.512371975162
540.644.9187110621161-44.3187110621161
55-999-785.056733092375-213.943266907625
562.2-179.543555101573181.743555101573
572.325.4868920512075-23.1868920512075
580.5-644.8466856103645.3466856103
59-999-86.5489283910512-912.451071608949
600.679.0357590634919-78.4357590634919
616.6-112.312474699383118.912474699383
622.6-34.184778868218136.7847788682181






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.0002469784581179110.0004939569162358230.999753021541882
131.99337427561908e-053.98674855123816e-050.999980066257244
141.08838234503863e-062.17676469007725e-060.999998911617655
155.02492959669024e-081.00498591933805e-070.999999949750704
162.35559126978631e-094.71118253957262e-090.999999997644409
179.1580238955034e-111.83160477910068e-100.99999999990842
183.95044996609602e-127.90089993219204e-120.99999999999605
191.47601259810091e-132.95202519620182e-130.999999999999852
205.06193861256486e-151.01238772251297e-140.999999999999995
210.3630901680964870.7261803361929740.636909831903513
220.2900966014000350.580193202800070.709903398599965
230.2215533512457510.4431067024915020.778446648754249
240.1676602703573160.3353205407146330.832339729642684
250.1340729698549290.2681459397098570.865927030145071
260.1126998529413440.2253997058826870.887300147058656
270.07632346938539960.1526469387707990.9236765306146
280.0494579933716180.0989159867432360.950542006628382
290.05150217948043650.1030043589608730.948497820519563
300.05909410178518650.1181882035703730.940905898214814
310.05741793587119550.1148358717423910.942582064128805
320.03706055774199270.07412111548398550.962939442258007
330.02301069077603340.04602138155206670.976989309223967
340.03432208371728960.06864416743457920.96567791628271
350.03102231905245160.06204463810490320.968977680947548
360.01984750347140680.03969500694281360.980152496528593
370.01207691115028440.02415382230056880.987923088849716
380.007397857525222440.01479571505044490.992602142474778
390.005218207013909760.01043641402781950.99478179298609
400.004675677577103910.009351355154207820.995324322422896
410.05710476544703870.1142095308940770.942895234552961
420.03920765701280150.0784153140256030.960792342987199
430.02357351975613880.04714703951227760.976426480243861
440.01590173972295460.03180347944590930.984098260277045
450.008767055624165110.01753411124833020.991232944375835
460.01831791437432120.03663582874864240.981682085625679
470.01799897883083150.0359979576616630.982001021169169
480.02623105059576060.05246210119152120.97376894940424
490.01553612640216240.03107225280432470.984463873597838
500.05841096729032420.1168219345806480.941589032709676

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.000246978458117911 & 0.000493956916235823 & 0.999753021541882 \tabularnewline
13 & 1.99337427561908e-05 & 3.98674855123816e-05 & 0.999980066257244 \tabularnewline
14 & 1.08838234503863e-06 & 2.17676469007725e-06 & 0.999998911617655 \tabularnewline
15 & 5.02492959669024e-08 & 1.00498591933805e-07 & 0.999999949750704 \tabularnewline
16 & 2.35559126978631e-09 & 4.71118253957262e-09 & 0.999999997644409 \tabularnewline
17 & 9.1580238955034e-11 & 1.83160477910068e-10 & 0.99999999990842 \tabularnewline
18 & 3.95044996609602e-12 & 7.90089993219204e-12 & 0.99999999999605 \tabularnewline
19 & 1.47601259810091e-13 & 2.95202519620182e-13 & 0.999999999999852 \tabularnewline
20 & 5.06193861256486e-15 & 1.01238772251297e-14 & 0.999999999999995 \tabularnewline
21 & 0.363090168096487 & 0.726180336192974 & 0.636909831903513 \tabularnewline
22 & 0.290096601400035 & 0.58019320280007 & 0.709903398599965 \tabularnewline
23 & 0.221553351245751 & 0.443106702491502 & 0.778446648754249 \tabularnewline
24 & 0.167660270357316 & 0.335320540714633 & 0.832339729642684 \tabularnewline
25 & 0.134072969854929 & 0.268145939709857 & 0.865927030145071 \tabularnewline
26 & 0.112699852941344 & 0.225399705882687 & 0.887300147058656 \tabularnewline
27 & 0.0763234693853996 & 0.152646938770799 & 0.9236765306146 \tabularnewline
28 & 0.049457993371618 & 0.098915986743236 & 0.950542006628382 \tabularnewline
29 & 0.0515021794804365 & 0.103004358960873 & 0.948497820519563 \tabularnewline
30 & 0.0590941017851865 & 0.118188203570373 & 0.940905898214814 \tabularnewline
31 & 0.0574179358711955 & 0.114835871742391 & 0.942582064128805 \tabularnewline
32 & 0.0370605577419927 & 0.0741211154839855 & 0.962939442258007 \tabularnewline
33 & 0.0230106907760334 & 0.0460213815520667 & 0.976989309223967 \tabularnewline
34 & 0.0343220837172896 & 0.0686441674345792 & 0.96567791628271 \tabularnewline
35 & 0.0310223190524516 & 0.0620446381049032 & 0.968977680947548 \tabularnewline
36 & 0.0198475034714068 & 0.0396950069428136 & 0.980152496528593 \tabularnewline
37 & 0.0120769111502844 & 0.0241538223005688 & 0.987923088849716 \tabularnewline
38 & 0.00739785752522244 & 0.0147957150504449 & 0.992602142474778 \tabularnewline
39 & 0.00521820701390976 & 0.0104364140278195 & 0.99478179298609 \tabularnewline
40 & 0.00467567757710391 & 0.00935135515420782 & 0.995324322422896 \tabularnewline
41 & 0.0571047654470387 & 0.114209530894077 & 0.942895234552961 \tabularnewline
42 & 0.0392076570128015 & 0.078415314025603 & 0.960792342987199 \tabularnewline
43 & 0.0235735197561388 & 0.0471470395122776 & 0.976426480243861 \tabularnewline
44 & 0.0159017397229546 & 0.0318034794459093 & 0.984098260277045 \tabularnewline
45 & 0.00876705562416511 & 0.0175341112483302 & 0.991232944375835 \tabularnewline
46 & 0.0183179143743212 & 0.0366358287486424 & 0.981682085625679 \tabularnewline
47 & 0.0179989788308315 & 0.035997957661663 & 0.982001021169169 \tabularnewline
48 & 0.0262310505957606 & 0.0524621011915212 & 0.97376894940424 \tabularnewline
49 & 0.0155361264021624 & 0.0310722528043247 & 0.984463873597838 \tabularnewline
50 & 0.0584109672903242 & 0.116821934580648 & 0.941589032709676 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113062&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]12[/C][C]0.000246978458117911[/C][C]0.000493956916235823[/C][C]0.999753021541882[/C][/ROW]
[ROW][C]13[/C][C]1.99337427561908e-05[/C][C]3.98674855123816e-05[/C][C]0.999980066257244[/C][/ROW]
[ROW][C]14[/C][C]1.08838234503863e-06[/C][C]2.17676469007725e-06[/C][C]0.999998911617655[/C][/ROW]
[ROW][C]15[/C][C]5.02492959669024e-08[/C][C]1.00498591933805e-07[/C][C]0.999999949750704[/C][/ROW]
[ROW][C]16[/C][C]2.35559126978631e-09[/C][C]4.71118253957262e-09[/C][C]0.999999997644409[/C][/ROW]
[ROW][C]17[/C][C]9.1580238955034e-11[/C][C]1.83160477910068e-10[/C][C]0.99999999990842[/C][/ROW]
[ROW][C]18[/C][C]3.95044996609602e-12[/C][C]7.90089993219204e-12[/C][C]0.99999999999605[/C][/ROW]
[ROW][C]19[/C][C]1.47601259810091e-13[/C][C]2.95202519620182e-13[/C][C]0.999999999999852[/C][/ROW]
[ROW][C]20[/C][C]5.06193861256486e-15[/C][C]1.01238772251297e-14[/C][C]0.999999999999995[/C][/ROW]
[ROW][C]21[/C][C]0.363090168096487[/C][C]0.726180336192974[/C][C]0.636909831903513[/C][/ROW]
[ROW][C]22[/C][C]0.290096601400035[/C][C]0.58019320280007[/C][C]0.709903398599965[/C][/ROW]
[ROW][C]23[/C][C]0.221553351245751[/C][C]0.443106702491502[/C][C]0.778446648754249[/C][/ROW]
[ROW][C]24[/C][C]0.167660270357316[/C][C]0.335320540714633[/C][C]0.832339729642684[/C][/ROW]
[ROW][C]25[/C][C]0.134072969854929[/C][C]0.268145939709857[/C][C]0.865927030145071[/C][/ROW]
[ROW][C]26[/C][C]0.112699852941344[/C][C]0.225399705882687[/C][C]0.887300147058656[/C][/ROW]
[ROW][C]27[/C][C]0.0763234693853996[/C][C]0.152646938770799[/C][C]0.9236765306146[/C][/ROW]
[ROW][C]28[/C][C]0.049457993371618[/C][C]0.098915986743236[/C][C]0.950542006628382[/C][/ROW]
[ROW][C]29[/C][C]0.0515021794804365[/C][C]0.103004358960873[/C][C]0.948497820519563[/C][/ROW]
[ROW][C]30[/C][C]0.0590941017851865[/C][C]0.118188203570373[/C][C]0.940905898214814[/C][/ROW]
[ROW][C]31[/C][C]0.0574179358711955[/C][C]0.114835871742391[/C][C]0.942582064128805[/C][/ROW]
[ROW][C]32[/C][C]0.0370605577419927[/C][C]0.0741211154839855[/C][C]0.962939442258007[/C][/ROW]
[ROW][C]33[/C][C]0.0230106907760334[/C][C]0.0460213815520667[/C][C]0.976989309223967[/C][/ROW]
[ROW][C]34[/C][C]0.0343220837172896[/C][C]0.0686441674345792[/C][C]0.96567791628271[/C][/ROW]
[ROW][C]35[/C][C]0.0310223190524516[/C][C]0.0620446381049032[/C][C]0.968977680947548[/C][/ROW]
[ROW][C]36[/C][C]0.0198475034714068[/C][C]0.0396950069428136[/C][C]0.980152496528593[/C][/ROW]
[ROW][C]37[/C][C]0.0120769111502844[/C][C]0.0241538223005688[/C][C]0.987923088849716[/C][/ROW]
[ROW][C]38[/C][C]0.00739785752522244[/C][C]0.0147957150504449[/C][C]0.992602142474778[/C][/ROW]
[ROW][C]39[/C][C]0.00521820701390976[/C][C]0.0104364140278195[/C][C]0.99478179298609[/C][/ROW]
[ROW][C]40[/C][C]0.00467567757710391[/C][C]0.00935135515420782[/C][C]0.995324322422896[/C][/ROW]
[ROW][C]41[/C][C]0.0571047654470387[/C][C]0.114209530894077[/C][C]0.942895234552961[/C][/ROW]
[ROW][C]42[/C][C]0.0392076570128015[/C][C]0.078415314025603[/C][C]0.960792342987199[/C][/ROW]
[ROW][C]43[/C][C]0.0235735197561388[/C][C]0.0471470395122776[/C][C]0.976426480243861[/C][/ROW]
[ROW][C]44[/C][C]0.0159017397229546[/C][C]0.0318034794459093[/C][C]0.984098260277045[/C][/ROW]
[ROW][C]45[/C][C]0.00876705562416511[/C][C]0.0175341112483302[/C][C]0.991232944375835[/C][/ROW]
[ROW][C]46[/C][C]0.0183179143743212[/C][C]0.0366358287486424[/C][C]0.981682085625679[/C][/ROW]
[ROW][C]47[/C][C]0.0179989788308315[/C][C]0.035997957661663[/C][C]0.982001021169169[/C][/ROW]
[ROW][C]48[/C][C]0.0262310505957606[/C][C]0.0524621011915212[/C][C]0.97376894940424[/C][/ROW]
[ROW][C]49[/C][C]0.0155361264021624[/C][C]0.0310722528043247[/C][C]0.984463873597838[/C][/ROW]
[ROW][C]50[/C][C]0.0584109672903242[/C][C]0.116821934580648[/C][C]0.941589032709676[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113062&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113062&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.0002469784581179110.0004939569162358230.999753021541882
131.99337427561908e-053.98674855123816e-050.999980066257244
141.08838234503863e-062.17676469007725e-060.999998911617655
155.02492959669024e-081.00498591933805e-070.999999949750704
162.35559126978631e-094.71118253957262e-090.999999997644409
179.1580238955034e-111.83160477910068e-100.99999999990842
183.95044996609602e-127.90089993219204e-120.99999999999605
191.47601259810091e-132.95202519620182e-130.999999999999852
205.06193861256486e-151.01238772251297e-140.999999999999995
210.3630901680964870.7261803361929740.636909831903513
220.2900966014000350.580193202800070.709903398599965
230.2215533512457510.4431067024915020.778446648754249
240.1676602703573160.3353205407146330.832339729642684
250.1340729698549290.2681459397098570.865927030145071
260.1126998529413440.2253997058826870.887300147058656
270.07632346938539960.1526469387707990.9236765306146
280.0494579933716180.0989159867432360.950542006628382
290.05150217948043650.1030043589608730.948497820519563
300.05909410178518650.1181882035703730.940905898214814
310.05741793587119550.1148358717423910.942582064128805
320.03706055774199270.07412111548398550.962939442258007
330.02301069077603340.04602138155206670.976989309223967
340.03432208371728960.06864416743457920.96567791628271
350.03102231905245160.06204463810490320.968977680947548
360.01984750347140680.03969500694281360.980152496528593
370.01207691115028440.02415382230056880.987923088849716
380.007397857525222440.01479571505044490.992602142474778
390.005218207013909760.01043641402781950.99478179298609
400.004675677577103910.009351355154207820.995324322422896
410.05710476544703870.1142095308940770.942895234552961
420.03920765701280150.0784153140256030.960792342987199
430.02357351975613880.04714703951227760.976426480243861
440.01590173972295460.03180347944590930.984098260277045
450.008767055624165110.01753411124833020.991232944375835
460.01831791437432120.03663582874864240.981682085625679
470.01799897883083150.0359979576616630.982001021169169
480.02623105059576060.05246210119152120.97376894940424
490.01553612640216240.03107225280432470.984463873597838
500.05841096729032420.1168219345806480.941589032709676






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.256410256410256NOK
5% type I error level210.538461538461538NOK
10% type I error level270.692307692307692NOK

\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 & 10 & 0.256410256410256 & NOK \tabularnewline
5% type I error level & 21 & 0.538461538461538 & NOK \tabularnewline
10% type I error level & 27 & 0.692307692307692 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113062&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]10[/C][C]0.256410256410256[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]21[/C][C]0.538461538461538[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]27[/C][C]0.692307692307692[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113062&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113062&T=6

As an alternative you can also use a QR Code:  
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 level100.256410256410256NOK
5% type I error level210.538461538461538NOK
10% type I error level270.692307692307692NOK


Figures (Output of Computation)

Figure 1
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Figure 6
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = First Differences ;
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')
}