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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, 24 Nov 2011 10:46:51 -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/24/t1322149665xadev5yr8ezki36.htm/, Retrieved Fri, 29 Mar 2024 06:26:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147001, Retrieved Fri, 29 Mar 2024 06:26:45 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact85
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataseries X:
4	9,3	141	16	6	7
5	140002	135	20	20	0
7	23	308	8	15	0
8	160003	94	21	25	0
9	180004	160	7	4	0
10	14,2	108	17	6	0
11	901	79	20	2	0
12	5,9	40	18	1	1
1	7,2	35	26	4	2
2	6,8	48	18	4	2
3	8	144	20	0	2
4	14,3	284	0	3	0
5	14,6	164	22	14	0
6	17,5	130	19	17	0
7	17,2	178	18	14	0
8	17,5	150	13	10	0
9	14,1	103	16	7	0
10	10,4	110	11	4	0
11	6,8	51	22	1	1
12	4,1	70	19	6	0
1	6,5	41	23	2	1
2	6,1	125	11	2	0
3	6,3	68	24	8	7
4	9,3	135	14	10	0
5	16,4	231	11	13	0
6	16,1	184	17	10	0
7	18	181	20	14	0
8	17,6	138	19	13	0
9	14	157	12	6	0
10	10,5	122	19	6	2
11	6,9	39	26	9	3
12	2,8	61	13	2	5
1	0,7	88	12	4	5
2	3,6	32	20	3	7
3	6,7	149	15	4	2
4	12,5	196	15	10	0
5	14,4	195	17	15	0
6	16,5	224	11	14	0
7	18,7	212	20	18	0
8	19,4	257	9	10	0
9	15,8	156	10	5	0
10	11,3	89	17	5	0
11	9,7	48	25	7	0
12	2,9	46	19	2	7
1	0,1	48	18	0	4
2	2,5	28	24	4	10
3	6,7	117	13	7	2
4	10,3	223	6	8	0
5	11,2	171	14	6	0
6	17,4	258	9	3	0
7	20,5	252	13	12	0
8	17	136	23	15	0
9	14,2	142	18	8	0
10	10,6	118	16	6	0
11	6,1	23	21	1	6
12	-0,7	33	26	1	23
1	4	52	21	0	4
2	5,4	54	15	0	1
3	7,7	204	7	0	1
4	14,1	238	11	10	0
5	14,8	264	9	9	0
6	16,8	180	19	16	0
7	16	140	20	10	0
8	17,3	144	22	15	0
9	16,5	173	10	8	0
10	12,1	161	16	4	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'George Udny Yule' @ yule.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 & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147001&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147001&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147001&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 time6 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
maanden[t] = + 7.08579530057502 + 2.49554042294913e-06gemiddeldetemperatuur[t] -0.00779838678952947aantaldagenzonneschijn[t] + 0.00876250532166617aantaldagenregen[t] + 0.0576395321590154aantaldagenonweer[t] -0.0467467520537699aantaldagensneeuw[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
maanden[t] =  +  7.08579530057502 +  2.49554042294913e-06gemiddeldetemperatuur[t] -0.00779838678952947aantaldagenzonneschijn[t] +  0.00876250532166617aantaldagenregen[t] +  0.0576395321590154aantaldagenonweer[t] -0.0467467520537699aantaldagensneeuw[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147001&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]maanden[t] =  +  7.08579530057502 +  2.49554042294913e-06gemiddeldetemperatuur[t] -0.00779838678952947aantaldagenzonneschijn[t] +  0.00876250532166617aantaldagenregen[t] +  0.0576395321590154aantaldagenonweer[t] -0.0467467520537699aantaldagensneeuw[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147001&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147001&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
maanden[t] = + 7.08579530057502 + 2.49554042294913e-06gemiddeldetemperatuur[t] -0.00779838678952947aantaldagenzonneschijn[t] + 0.00876250532166617aantaldagenregen[t] + 0.0576395321590154aantaldagenonweer[t] -0.0467467520537699aantaldagensneeuw[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.085795300575023.3674132.10420.0395570.019779
gemiddeldetemperatuur2.49554042294913e-061.5e-050.16920.8662440.433122
aantaldagenzonneschijn-0.007798386789529470.012595-0.61920.5381540.269077
aantaldagenregen0.008762505321666170.1498680.05850.953570.476785
aantaldagenonweer0.05763953215901540.1288350.44740.6562030.328102
aantaldagensneeuw-0.04674675205376990.145511-0.32130.7491310.374565

\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) & 7.08579530057502 & 3.367413 & 2.1042 & 0.039557 & 0.019779 \tabularnewline
gemiddeldetemperatuur & 2.49554042294913e-06 & 1.5e-05 & 0.1692 & 0.866244 & 0.433122 \tabularnewline
aantaldagenzonneschijn & -0.00779838678952947 & 0.012595 & -0.6192 & 0.538154 & 0.269077 \tabularnewline
aantaldagenregen & 0.00876250532166617 & 0.149868 & 0.0585 & 0.95357 & 0.476785 \tabularnewline
aantaldagenonweer & 0.0576395321590154 & 0.128835 & 0.4474 & 0.656203 & 0.328102 \tabularnewline
aantaldagensneeuw & -0.0467467520537699 & 0.145511 & -0.3213 & 0.749131 & 0.374565 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147001&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]7.08579530057502[/C][C]3.367413[/C][C]2.1042[/C][C]0.039557[/C][C]0.019779[/C][/ROW]
[ROW][C]gemiddeldetemperatuur[/C][C]2.49554042294913e-06[/C][C]1.5e-05[/C][C]0.1692[/C][C]0.866244[/C][C]0.433122[/C][/ROW]
[ROW][C]aantaldagenzonneschijn[/C][C]-0.00779838678952947[/C][C]0.012595[/C][C]-0.6192[/C][C]0.538154[/C][C]0.269077[/C][/ROW]
[ROW][C]aantaldagenregen[/C][C]0.00876250532166617[/C][C]0.149868[/C][C]0.0585[/C][C]0.95357[/C][C]0.476785[/C][/ROW]
[ROW][C]aantaldagenonweer[/C][C]0.0576395321590154[/C][C]0.128835[/C][C]0.4474[/C][C]0.656203[/C][C]0.328102[/C][/ROW]
[ROW][C]aantaldagensneeuw[/C][C]-0.0467467520537699[/C][C]0.145511[/C][C]-0.3213[/C][C]0.749131[/C][C]0.374565[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147001&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147001&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)7.085795300575023.3674132.10420.0395570.019779
gemiddeldetemperatuur2.49554042294913e-061.5e-050.16920.8662440.433122
aantaldagenzonneschijn-0.007798386789529470.012595-0.61920.5381540.269077
aantaldagenregen0.008762505321666170.1498680.05850.953570.476785
aantaldagenonweer0.05763953215901540.1288350.44740.6562030.328102
aantaldagensneeuw-0.04674675205376990.145511-0.32130.7491310.374565







Multiple Linear Regression - Regression Statistics
Multiple R0.162446509353474
R-squared0.0263888684011284
Adjusted R-squared-0.0547453925654442
F-TEST (value)0.325249384005613
F-TEST (DF numerator)5
F-TEST (DF denominator)60
p-value0.89584792733891
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.47519174659079
Sum Squared Residuals724.617460534366

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.162446509353474 \tabularnewline
R-squared & 0.0263888684011284 \tabularnewline
Adjusted R-squared & -0.0547453925654442 \tabularnewline
F-TEST (value) & 0.325249384005613 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 60 \tabularnewline
p-value & 0.89584792733891 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.47519174659079 \tabularnewline
Sum Squared Residuals & 724.617460534366 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147001&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.162446509353474[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0263888684011284[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0547453925654442[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.325249384005613[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]60[/C][/ROW]
[ROW][C]p-value[/C][C]0.89584792733891[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.47519174659079[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]724.617460534366[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147001&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147001&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 R0.162446509353474
R-squared0.0263888684011284
Adjusted R-squared-0.0547453925654442
F-TEST (value)0.325249384005613
F-TEST (DF numerator)5
F-TEST (DF denominator)60
p-value0.89584792733891
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.47519174659079
Sum Squared Residuals724.617460534366







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
146.14505598550167-2.14505598550167
257.7104344838959-2.7104344838959
375.618642591788241.38135740821176
488.37704181238276-0.37704181238276
596.579156338430572.42084366156943
6106.738404747402273.26159525259773
7116.762500396874634.23749960312537
8126.942492428577585.05750757142242
917.17775949372438-6.17775949372438
1027.006279424671-5.006279424671
1136.04460416953195-3.04460416953195
1245.04400773505375-1.04400773505375
1356.80662486928524-1.80662486928524
1467.21840833770852-1.21840833770852
1576.662403921350260.337596078649738
1686.606388844874821.39361115512518
1796.826273458633222.17372654136678
18106.554944394521573.44505560547843
19116.89176244116584.1082375588342
20127.052243251089454.94775674891055
2117.03614759787965-6.03614759787965
2226.32267879753678-4.32267879753678
2336.89970984140741-3.89970984140741
2446.73210668860796-2.73210668860796
2556.13011035566218-1.13011035566218
2666.3762902215609-0.376290221560892
2776.656535768057340.343464231942656
2886.925463364310261.07453663568974
2996.312470768998912.68752923100109
30106.553249605385083.44675039461492
31117.388016106645443.61198389335456
32126.605558567157755.39444143284225
3316.50151344220193-5.50151344220193
3426.85719734578959-4.85719734578959
3536.19235459340948-3.19235459340948
3646.26517558549768-2.26517558549768
3756.57870138525243-1.57870138525243
3866.24233884490195-0.242338844901947
3976.645345653096290.354654346903712
4085.736916178635312.26308382136469
4196.245109104958852.75489089504115
42106.828927327177083.17107267282292
43117.334036299574483.66596370042552
44126.68161614637725.31838385362281
4516.68221107180657-5.68221107180657
4626.84083744513761-4.84083744513761
4736.59729655650814-3.59729655650814
4845.86047203977843-1.86047203977843
4956.22081137707564-1.22081137707564
5065.325636075651820.674363924348176
5175.926239943282121.07376005671788
5287.091387726169760.908612273830239
5396.597301166197962.40269883380204
54106.651649390239783.34835060976022
55116.867619258803814.13238074119619
56126.038736162927885.96126383707212
5716.6773147732211-5.6773147732211
5826.74938671762994-4.74938671762994
5935.50953439637016-2.50953439637016
6045.90259731191546-1.90259731191546
6155.62467645946364-0.62467645946364
6266.77084771919473-0.77084771919473
6376.745706506711150.254293493288855
6487.020238875193990.979761124806014
6596.285456872892192.71454312710781
66106.201043437282623.79895656271738

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4 & 6.14505598550167 & -2.14505598550167 \tabularnewline
2 & 5 & 7.7104344838959 & -2.7104344838959 \tabularnewline
3 & 7 & 5.61864259178824 & 1.38135740821176 \tabularnewline
4 & 8 & 8.37704181238276 & -0.37704181238276 \tabularnewline
5 & 9 & 6.57915633843057 & 2.42084366156943 \tabularnewline
6 & 10 & 6.73840474740227 & 3.26159525259773 \tabularnewline
7 & 11 & 6.76250039687463 & 4.23749960312537 \tabularnewline
8 & 12 & 6.94249242857758 & 5.05750757142242 \tabularnewline
9 & 1 & 7.17775949372438 & -6.17775949372438 \tabularnewline
10 & 2 & 7.006279424671 & -5.006279424671 \tabularnewline
11 & 3 & 6.04460416953195 & -3.04460416953195 \tabularnewline
12 & 4 & 5.04400773505375 & -1.04400773505375 \tabularnewline
13 & 5 & 6.80662486928524 & -1.80662486928524 \tabularnewline
14 & 6 & 7.21840833770852 & -1.21840833770852 \tabularnewline
15 & 7 & 6.66240392135026 & 0.337596078649738 \tabularnewline
16 & 8 & 6.60638884487482 & 1.39361115512518 \tabularnewline
17 & 9 & 6.82627345863322 & 2.17372654136678 \tabularnewline
18 & 10 & 6.55494439452157 & 3.44505560547843 \tabularnewline
19 & 11 & 6.8917624411658 & 4.1082375588342 \tabularnewline
20 & 12 & 7.05224325108945 & 4.94775674891055 \tabularnewline
21 & 1 & 7.03614759787965 & -6.03614759787965 \tabularnewline
22 & 2 & 6.32267879753678 & -4.32267879753678 \tabularnewline
23 & 3 & 6.89970984140741 & -3.89970984140741 \tabularnewline
24 & 4 & 6.73210668860796 & -2.73210668860796 \tabularnewline
25 & 5 & 6.13011035566218 & -1.13011035566218 \tabularnewline
26 & 6 & 6.3762902215609 & -0.376290221560892 \tabularnewline
27 & 7 & 6.65653576805734 & 0.343464231942656 \tabularnewline
28 & 8 & 6.92546336431026 & 1.07453663568974 \tabularnewline
29 & 9 & 6.31247076899891 & 2.68752923100109 \tabularnewline
30 & 10 & 6.55324960538508 & 3.44675039461492 \tabularnewline
31 & 11 & 7.38801610664544 & 3.61198389335456 \tabularnewline
32 & 12 & 6.60555856715775 & 5.39444143284225 \tabularnewline
33 & 1 & 6.50151344220193 & -5.50151344220193 \tabularnewline
34 & 2 & 6.85719734578959 & -4.85719734578959 \tabularnewline
35 & 3 & 6.19235459340948 & -3.19235459340948 \tabularnewline
36 & 4 & 6.26517558549768 & -2.26517558549768 \tabularnewline
37 & 5 & 6.57870138525243 & -1.57870138525243 \tabularnewline
38 & 6 & 6.24233884490195 & -0.242338844901947 \tabularnewline
39 & 7 & 6.64534565309629 & 0.354654346903712 \tabularnewline
40 & 8 & 5.73691617863531 & 2.26308382136469 \tabularnewline
41 & 9 & 6.24510910495885 & 2.75489089504115 \tabularnewline
42 & 10 & 6.82892732717708 & 3.17107267282292 \tabularnewline
43 & 11 & 7.33403629957448 & 3.66596370042552 \tabularnewline
44 & 12 & 6.6816161463772 & 5.31838385362281 \tabularnewline
45 & 1 & 6.68221107180657 & -5.68221107180657 \tabularnewline
46 & 2 & 6.84083744513761 & -4.84083744513761 \tabularnewline
47 & 3 & 6.59729655650814 & -3.59729655650814 \tabularnewline
48 & 4 & 5.86047203977843 & -1.86047203977843 \tabularnewline
49 & 5 & 6.22081137707564 & -1.22081137707564 \tabularnewline
50 & 6 & 5.32563607565182 & 0.674363924348176 \tabularnewline
51 & 7 & 5.92623994328212 & 1.07376005671788 \tabularnewline
52 & 8 & 7.09138772616976 & 0.908612273830239 \tabularnewline
53 & 9 & 6.59730116619796 & 2.40269883380204 \tabularnewline
54 & 10 & 6.65164939023978 & 3.34835060976022 \tabularnewline
55 & 11 & 6.86761925880381 & 4.13238074119619 \tabularnewline
56 & 12 & 6.03873616292788 & 5.96126383707212 \tabularnewline
57 & 1 & 6.6773147732211 & -5.6773147732211 \tabularnewline
58 & 2 & 6.74938671762994 & -4.74938671762994 \tabularnewline
59 & 3 & 5.50953439637016 & -2.50953439637016 \tabularnewline
60 & 4 & 5.90259731191546 & -1.90259731191546 \tabularnewline
61 & 5 & 5.62467645946364 & -0.62467645946364 \tabularnewline
62 & 6 & 6.77084771919473 & -0.77084771919473 \tabularnewline
63 & 7 & 6.74570650671115 & 0.254293493288855 \tabularnewline
64 & 8 & 7.02023887519399 & 0.979761124806014 \tabularnewline
65 & 9 & 6.28545687289219 & 2.71454312710781 \tabularnewline
66 & 10 & 6.20104343728262 & 3.79895656271738 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147001&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]4[/C][C]6.14505598550167[/C][C]-2.14505598550167[/C][/ROW]
[ROW][C]2[/C][C]5[/C][C]7.7104344838959[/C][C]-2.7104344838959[/C][/ROW]
[ROW][C]3[/C][C]7[/C][C]5.61864259178824[/C][C]1.38135740821176[/C][/ROW]
[ROW][C]4[/C][C]8[/C][C]8.37704181238276[/C][C]-0.37704181238276[/C][/ROW]
[ROW][C]5[/C][C]9[/C][C]6.57915633843057[/C][C]2.42084366156943[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]6.73840474740227[/C][C]3.26159525259773[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]6.76250039687463[/C][C]4.23749960312537[/C][/ROW]
[ROW][C]8[/C][C]12[/C][C]6.94249242857758[/C][C]5.05750757142242[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]7.17775949372438[/C][C]-6.17775949372438[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]7.006279424671[/C][C]-5.006279424671[/C][/ROW]
[ROW][C]11[/C][C]3[/C][C]6.04460416953195[/C][C]-3.04460416953195[/C][/ROW]
[ROW][C]12[/C][C]4[/C][C]5.04400773505375[/C][C]-1.04400773505375[/C][/ROW]
[ROW][C]13[/C][C]5[/C][C]6.80662486928524[/C][C]-1.80662486928524[/C][/ROW]
[ROW][C]14[/C][C]6[/C][C]7.21840833770852[/C][C]-1.21840833770852[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]6.66240392135026[/C][C]0.337596078649738[/C][/ROW]
[ROW][C]16[/C][C]8[/C][C]6.60638884487482[/C][C]1.39361115512518[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]6.82627345863322[/C][C]2.17372654136678[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]6.55494439452157[/C][C]3.44505560547843[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]6.8917624411658[/C][C]4.1082375588342[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]7.05224325108945[/C][C]4.94775674891055[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]7.03614759787965[/C][C]-6.03614759787965[/C][/ROW]
[ROW][C]22[/C][C]2[/C][C]6.32267879753678[/C][C]-4.32267879753678[/C][/ROW]
[ROW][C]23[/C][C]3[/C][C]6.89970984140741[/C][C]-3.89970984140741[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]6.73210668860796[/C][C]-2.73210668860796[/C][/ROW]
[ROW][C]25[/C][C]5[/C][C]6.13011035566218[/C][C]-1.13011035566218[/C][/ROW]
[ROW][C]26[/C][C]6[/C][C]6.3762902215609[/C][C]-0.376290221560892[/C][/ROW]
[ROW][C]27[/C][C]7[/C][C]6.65653576805734[/C][C]0.343464231942656[/C][/ROW]
[ROW][C]28[/C][C]8[/C][C]6.92546336431026[/C][C]1.07453663568974[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]6.31247076899891[/C][C]2.68752923100109[/C][/ROW]
[ROW][C]30[/C][C]10[/C][C]6.55324960538508[/C][C]3.44675039461492[/C][/ROW]
[ROW][C]31[/C][C]11[/C][C]7.38801610664544[/C][C]3.61198389335456[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]6.60555856715775[/C][C]5.39444143284225[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]6.50151344220193[/C][C]-5.50151344220193[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]6.85719734578959[/C][C]-4.85719734578959[/C][/ROW]
[ROW][C]35[/C][C]3[/C][C]6.19235459340948[/C][C]-3.19235459340948[/C][/ROW]
[ROW][C]36[/C][C]4[/C][C]6.26517558549768[/C][C]-2.26517558549768[/C][/ROW]
[ROW][C]37[/C][C]5[/C][C]6.57870138525243[/C][C]-1.57870138525243[/C][/ROW]
[ROW][C]38[/C][C]6[/C][C]6.24233884490195[/C][C]-0.242338844901947[/C][/ROW]
[ROW][C]39[/C][C]7[/C][C]6.64534565309629[/C][C]0.354654346903712[/C][/ROW]
[ROW][C]40[/C][C]8[/C][C]5.73691617863531[/C][C]2.26308382136469[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]6.24510910495885[/C][C]2.75489089504115[/C][/ROW]
[ROW][C]42[/C][C]10[/C][C]6.82892732717708[/C][C]3.17107267282292[/C][/ROW]
[ROW][C]43[/C][C]11[/C][C]7.33403629957448[/C][C]3.66596370042552[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]6.6816161463772[/C][C]5.31838385362281[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]6.68221107180657[/C][C]-5.68221107180657[/C][/ROW]
[ROW][C]46[/C][C]2[/C][C]6.84083744513761[/C][C]-4.84083744513761[/C][/ROW]
[ROW][C]47[/C][C]3[/C][C]6.59729655650814[/C][C]-3.59729655650814[/C][/ROW]
[ROW][C]48[/C][C]4[/C][C]5.86047203977843[/C][C]-1.86047203977843[/C][/ROW]
[ROW][C]49[/C][C]5[/C][C]6.22081137707564[/C][C]-1.22081137707564[/C][/ROW]
[ROW][C]50[/C][C]6[/C][C]5.32563607565182[/C][C]0.674363924348176[/C][/ROW]
[ROW][C]51[/C][C]7[/C][C]5.92623994328212[/C][C]1.07376005671788[/C][/ROW]
[ROW][C]52[/C][C]8[/C][C]7.09138772616976[/C][C]0.908612273830239[/C][/ROW]
[ROW][C]53[/C][C]9[/C][C]6.59730116619796[/C][C]2.40269883380204[/C][/ROW]
[ROW][C]54[/C][C]10[/C][C]6.65164939023978[/C][C]3.34835060976022[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]6.86761925880381[/C][C]4.13238074119619[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]6.03873616292788[/C][C]5.96126383707212[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]6.6773147732211[/C][C]-5.6773147732211[/C][/ROW]
[ROW][C]58[/C][C]2[/C][C]6.74938671762994[/C][C]-4.74938671762994[/C][/ROW]
[ROW][C]59[/C][C]3[/C][C]5.50953439637016[/C][C]-2.50953439637016[/C][/ROW]
[ROW][C]60[/C][C]4[/C][C]5.90259731191546[/C][C]-1.90259731191546[/C][/ROW]
[ROW][C]61[/C][C]5[/C][C]5.62467645946364[/C][C]-0.62467645946364[/C][/ROW]
[ROW][C]62[/C][C]6[/C][C]6.77084771919473[/C][C]-0.77084771919473[/C][/ROW]
[ROW][C]63[/C][C]7[/C][C]6.74570650671115[/C][C]0.254293493288855[/C][/ROW]
[ROW][C]64[/C][C]8[/C][C]7.02023887519399[/C][C]0.979761124806014[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]6.28545687289219[/C][C]2.71454312710781[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]6.20104343728262[/C][C]3.79895656271738[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147001&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147001&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
146.14505598550167-2.14505598550167
257.7104344838959-2.7104344838959
375.618642591788241.38135740821176
488.37704181238276-0.37704181238276
596.579156338430572.42084366156943
6106.738404747402273.26159525259773
7116.762500396874634.23749960312537
8126.942492428577585.05750757142242
917.17775949372438-6.17775949372438
1027.006279424671-5.006279424671
1136.04460416953195-3.04460416953195
1245.04400773505375-1.04400773505375
1356.80662486928524-1.80662486928524
1467.21840833770852-1.21840833770852
1576.662403921350260.337596078649738
1686.606388844874821.39361115512518
1796.826273458633222.17372654136678
18106.554944394521573.44505560547843
19116.89176244116584.1082375588342
20127.052243251089454.94775674891055
2117.03614759787965-6.03614759787965
2226.32267879753678-4.32267879753678
2336.89970984140741-3.89970984140741
2446.73210668860796-2.73210668860796
2556.13011035566218-1.13011035566218
2666.3762902215609-0.376290221560892
2776.656535768057340.343464231942656
2886.925463364310261.07453663568974
2996.312470768998912.68752923100109
30106.553249605385083.44675039461492
31117.388016106645443.61198389335456
32126.605558567157755.39444143284225
3316.50151344220193-5.50151344220193
3426.85719734578959-4.85719734578959
3536.19235459340948-3.19235459340948
3646.26517558549768-2.26517558549768
3756.57870138525243-1.57870138525243
3866.24233884490195-0.242338844901947
3976.645345653096290.354654346903712
4085.736916178635312.26308382136469
4196.245109104958852.75489089504115
42106.828927327177083.17107267282292
43117.334036299574483.66596370042552
44126.68161614637725.31838385362281
4516.68221107180657-5.68221107180657
4626.84083744513761-4.84083744513761
4736.59729655650814-3.59729655650814
4845.86047203977843-1.86047203977843
4956.22081137707564-1.22081137707564
5065.325636075651820.674363924348176
5175.926239943282121.07376005671788
5287.091387726169760.908612273830239
5396.597301166197962.40269883380204
54106.651649390239783.34835060976022
55116.867619258803814.13238074119619
56126.038736162927885.96126383707212
5716.6773147732211-5.6773147732211
5826.74938671762994-4.74938671762994
5935.50953439637016-2.50953439637016
6045.90259731191546-1.90259731191546
6155.62467645946364-0.62467645946364
6266.77084771919473-0.77084771919473
6376.745706506711150.254293493288855
6487.020238875193990.979761124806014
6596.285456872892192.71454312710781
66106.201043437282623.79895656271738







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.4414443201138750.882888640227750.558555679886125
100.9140735275827960.1718529448344070.0859264724172036
110.853912868267510.2921742634649790.14608713173249
120.9021216115115040.1957567769769920.0978783884884958
130.843670435671710.3126591286565790.156329564328289
140.784427751558910.4311444968821810.21557224844109
150.7086066193740160.5827867612519670.291393380625984
160.6228753650490230.7542492699019540.377124634950977
170.5336356650800780.9327286698398430.466364334919922
180.450730266389710.9014605327794190.54926973361029
190.5017535977258390.9964928045483220.498246402274161
200.6708448696689810.6583102606620370.329155130331019
210.8534505888650670.2930988222698660.146549411134933
220.921859225623170.156281548753660.0781407743768298
230.9101426922478720.1797146155042550.0898573077521275
240.8997804718872910.2004390562254180.100219528112709
250.8630531971567980.2738936056864050.136946802843202
260.8191434278997230.3617131442005530.180856572100277
270.772870207226470.4542595855470590.22712979277353
280.7291006621988490.5417986756023020.270899337801151
290.675835937051040.6483281258979210.32416406294896
300.7137891726053270.5724216547893460.286210827394673
310.7979322174647430.4041355650705140.202067782535257
320.921565879927730.1568682401445410.0784341200722707
330.929052196077360.1418956078452790.0709478039226393
340.9493414908068370.1013170183863250.0506585091931626
350.9316015049631930.1367969900736150.0683984950368074
360.9062254820103690.1875490359792630.0937745179896313
370.8696582759241240.2606834481517510.130341724075876
380.822200889085110.3555982218297820.177799110914891
390.7703454081740490.4593091836519010.229654591825951
400.7176377591457830.5647244817084340.282362240854217
410.6574512856613170.6850974286773660.342548714338683
420.6050978182338230.7898043635323540.394902181766177
430.6685876746970810.6628246506058380.331412325302919
440.8988942309305750.202211538138850.101105769069425
450.8837700767209370.2324598465581260.116229923279063
460.9189091245079310.1621817509841390.0810908754920693
470.895155726920230.2096885461595420.104844273079771
480.8526284823031290.2947430353937420.147371517696871
490.7902751809325790.4194496381348420.209724819067421
500.7560721274475570.4878557451048860.243927872552443
510.7821447527563810.4357104944872380.217855247243619
520.6899554489671580.6200891020656840.310044551032842
530.5875967800774420.8248064398451160.412403219922558
540.828213008139690.343573983720620.17178699186031
550.8983410056611350.203317988677730.101658994338865
560.937170267725330.1256594645493390.0628297322746697
570.8665783052939180.2668433894121650.133421694706082

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.441444320113875 & 0.88288864022775 & 0.558555679886125 \tabularnewline
10 & 0.914073527582796 & 0.171852944834407 & 0.0859264724172036 \tabularnewline
11 & 0.85391286826751 & 0.292174263464979 & 0.14608713173249 \tabularnewline
12 & 0.902121611511504 & 0.195756776976992 & 0.0978783884884958 \tabularnewline
13 & 0.84367043567171 & 0.312659128656579 & 0.156329564328289 \tabularnewline
14 & 0.78442775155891 & 0.431144496882181 & 0.21557224844109 \tabularnewline
15 & 0.708606619374016 & 0.582786761251967 & 0.291393380625984 \tabularnewline
16 & 0.622875365049023 & 0.754249269901954 & 0.377124634950977 \tabularnewline
17 & 0.533635665080078 & 0.932728669839843 & 0.466364334919922 \tabularnewline
18 & 0.45073026638971 & 0.901460532779419 & 0.54926973361029 \tabularnewline
19 & 0.501753597725839 & 0.996492804548322 & 0.498246402274161 \tabularnewline
20 & 0.670844869668981 & 0.658310260662037 & 0.329155130331019 \tabularnewline
21 & 0.853450588865067 & 0.293098822269866 & 0.146549411134933 \tabularnewline
22 & 0.92185922562317 & 0.15628154875366 & 0.0781407743768298 \tabularnewline
23 & 0.910142692247872 & 0.179714615504255 & 0.0898573077521275 \tabularnewline
24 & 0.899780471887291 & 0.200439056225418 & 0.100219528112709 \tabularnewline
25 & 0.863053197156798 & 0.273893605686405 & 0.136946802843202 \tabularnewline
26 & 0.819143427899723 & 0.361713144200553 & 0.180856572100277 \tabularnewline
27 & 0.77287020722647 & 0.454259585547059 & 0.22712979277353 \tabularnewline
28 & 0.729100662198849 & 0.541798675602302 & 0.270899337801151 \tabularnewline
29 & 0.67583593705104 & 0.648328125897921 & 0.32416406294896 \tabularnewline
30 & 0.713789172605327 & 0.572421654789346 & 0.286210827394673 \tabularnewline
31 & 0.797932217464743 & 0.404135565070514 & 0.202067782535257 \tabularnewline
32 & 0.92156587992773 & 0.156868240144541 & 0.0784341200722707 \tabularnewline
33 & 0.92905219607736 & 0.141895607845279 & 0.0709478039226393 \tabularnewline
34 & 0.949341490806837 & 0.101317018386325 & 0.0506585091931626 \tabularnewline
35 & 0.931601504963193 & 0.136796990073615 & 0.0683984950368074 \tabularnewline
36 & 0.906225482010369 & 0.187549035979263 & 0.0937745179896313 \tabularnewline
37 & 0.869658275924124 & 0.260683448151751 & 0.130341724075876 \tabularnewline
38 & 0.82220088908511 & 0.355598221829782 & 0.177799110914891 \tabularnewline
39 & 0.770345408174049 & 0.459309183651901 & 0.229654591825951 \tabularnewline
40 & 0.717637759145783 & 0.564724481708434 & 0.282362240854217 \tabularnewline
41 & 0.657451285661317 & 0.685097428677366 & 0.342548714338683 \tabularnewline
42 & 0.605097818233823 & 0.789804363532354 & 0.394902181766177 \tabularnewline
43 & 0.668587674697081 & 0.662824650605838 & 0.331412325302919 \tabularnewline
44 & 0.898894230930575 & 0.20221153813885 & 0.101105769069425 \tabularnewline
45 & 0.883770076720937 & 0.232459846558126 & 0.116229923279063 \tabularnewline
46 & 0.918909124507931 & 0.162181750984139 & 0.0810908754920693 \tabularnewline
47 & 0.89515572692023 & 0.209688546159542 & 0.104844273079771 \tabularnewline
48 & 0.852628482303129 & 0.294743035393742 & 0.147371517696871 \tabularnewline
49 & 0.790275180932579 & 0.419449638134842 & 0.209724819067421 \tabularnewline
50 & 0.756072127447557 & 0.487855745104886 & 0.243927872552443 \tabularnewline
51 & 0.782144752756381 & 0.435710494487238 & 0.217855247243619 \tabularnewline
52 & 0.689955448967158 & 0.620089102065684 & 0.310044551032842 \tabularnewline
53 & 0.587596780077442 & 0.824806439845116 & 0.412403219922558 \tabularnewline
54 & 0.82821300813969 & 0.34357398372062 & 0.17178699186031 \tabularnewline
55 & 0.898341005661135 & 0.20331798867773 & 0.101658994338865 \tabularnewline
56 & 0.93717026772533 & 0.125659464549339 & 0.0628297322746697 \tabularnewline
57 & 0.866578305293918 & 0.266843389412165 & 0.133421694706082 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147001&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]9[/C][C]0.441444320113875[/C][C]0.88288864022775[/C][C]0.558555679886125[/C][/ROW]
[ROW][C]10[/C][C]0.914073527582796[/C][C]0.171852944834407[/C][C]0.0859264724172036[/C][/ROW]
[ROW][C]11[/C][C]0.85391286826751[/C][C]0.292174263464979[/C][C]0.14608713173249[/C][/ROW]
[ROW][C]12[/C][C]0.902121611511504[/C][C]0.195756776976992[/C][C]0.0978783884884958[/C][/ROW]
[ROW][C]13[/C][C]0.84367043567171[/C][C]0.312659128656579[/C][C]0.156329564328289[/C][/ROW]
[ROW][C]14[/C][C]0.78442775155891[/C][C]0.431144496882181[/C][C]0.21557224844109[/C][/ROW]
[ROW][C]15[/C][C]0.708606619374016[/C][C]0.582786761251967[/C][C]0.291393380625984[/C][/ROW]
[ROW][C]16[/C][C]0.622875365049023[/C][C]0.754249269901954[/C][C]0.377124634950977[/C][/ROW]
[ROW][C]17[/C][C]0.533635665080078[/C][C]0.932728669839843[/C][C]0.466364334919922[/C][/ROW]
[ROW][C]18[/C][C]0.45073026638971[/C][C]0.901460532779419[/C][C]0.54926973361029[/C][/ROW]
[ROW][C]19[/C][C]0.501753597725839[/C][C]0.996492804548322[/C][C]0.498246402274161[/C][/ROW]
[ROW][C]20[/C][C]0.670844869668981[/C][C]0.658310260662037[/C][C]0.329155130331019[/C][/ROW]
[ROW][C]21[/C][C]0.853450588865067[/C][C]0.293098822269866[/C][C]0.146549411134933[/C][/ROW]
[ROW][C]22[/C][C]0.92185922562317[/C][C]0.15628154875366[/C][C]0.0781407743768298[/C][/ROW]
[ROW][C]23[/C][C]0.910142692247872[/C][C]0.179714615504255[/C][C]0.0898573077521275[/C][/ROW]
[ROW][C]24[/C][C]0.899780471887291[/C][C]0.200439056225418[/C][C]0.100219528112709[/C][/ROW]
[ROW][C]25[/C][C]0.863053197156798[/C][C]0.273893605686405[/C][C]0.136946802843202[/C][/ROW]
[ROW][C]26[/C][C]0.819143427899723[/C][C]0.361713144200553[/C][C]0.180856572100277[/C][/ROW]
[ROW][C]27[/C][C]0.77287020722647[/C][C]0.454259585547059[/C][C]0.22712979277353[/C][/ROW]
[ROW][C]28[/C][C]0.729100662198849[/C][C]0.541798675602302[/C][C]0.270899337801151[/C][/ROW]
[ROW][C]29[/C][C]0.67583593705104[/C][C]0.648328125897921[/C][C]0.32416406294896[/C][/ROW]
[ROW][C]30[/C][C]0.713789172605327[/C][C]0.572421654789346[/C][C]0.286210827394673[/C][/ROW]
[ROW][C]31[/C][C]0.797932217464743[/C][C]0.404135565070514[/C][C]0.202067782535257[/C][/ROW]
[ROW][C]32[/C][C]0.92156587992773[/C][C]0.156868240144541[/C][C]0.0784341200722707[/C][/ROW]
[ROW][C]33[/C][C]0.92905219607736[/C][C]0.141895607845279[/C][C]0.0709478039226393[/C][/ROW]
[ROW][C]34[/C][C]0.949341490806837[/C][C]0.101317018386325[/C][C]0.0506585091931626[/C][/ROW]
[ROW][C]35[/C][C]0.931601504963193[/C][C]0.136796990073615[/C][C]0.0683984950368074[/C][/ROW]
[ROW][C]36[/C][C]0.906225482010369[/C][C]0.187549035979263[/C][C]0.0937745179896313[/C][/ROW]
[ROW][C]37[/C][C]0.869658275924124[/C][C]0.260683448151751[/C][C]0.130341724075876[/C][/ROW]
[ROW][C]38[/C][C]0.82220088908511[/C][C]0.355598221829782[/C][C]0.177799110914891[/C][/ROW]
[ROW][C]39[/C][C]0.770345408174049[/C][C]0.459309183651901[/C][C]0.229654591825951[/C][/ROW]
[ROW][C]40[/C][C]0.717637759145783[/C][C]0.564724481708434[/C][C]0.282362240854217[/C][/ROW]
[ROW][C]41[/C][C]0.657451285661317[/C][C]0.685097428677366[/C][C]0.342548714338683[/C][/ROW]
[ROW][C]42[/C][C]0.605097818233823[/C][C]0.789804363532354[/C][C]0.394902181766177[/C][/ROW]
[ROW][C]43[/C][C]0.668587674697081[/C][C]0.662824650605838[/C][C]0.331412325302919[/C][/ROW]
[ROW][C]44[/C][C]0.898894230930575[/C][C]0.20221153813885[/C][C]0.101105769069425[/C][/ROW]
[ROW][C]45[/C][C]0.883770076720937[/C][C]0.232459846558126[/C][C]0.116229923279063[/C][/ROW]
[ROW][C]46[/C][C]0.918909124507931[/C][C]0.162181750984139[/C][C]0.0810908754920693[/C][/ROW]
[ROW][C]47[/C][C]0.89515572692023[/C][C]0.209688546159542[/C][C]0.104844273079771[/C][/ROW]
[ROW][C]48[/C][C]0.852628482303129[/C][C]0.294743035393742[/C][C]0.147371517696871[/C][/ROW]
[ROW][C]49[/C][C]0.790275180932579[/C][C]0.419449638134842[/C][C]0.209724819067421[/C][/ROW]
[ROW][C]50[/C][C]0.756072127447557[/C][C]0.487855745104886[/C][C]0.243927872552443[/C][/ROW]
[ROW][C]51[/C][C]0.782144752756381[/C][C]0.435710494487238[/C][C]0.217855247243619[/C][/ROW]
[ROW][C]52[/C][C]0.689955448967158[/C][C]0.620089102065684[/C][C]0.310044551032842[/C][/ROW]
[ROW][C]53[/C][C]0.587596780077442[/C][C]0.824806439845116[/C][C]0.412403219922558[/C][/ROW]
[ROW][C]54[/C][C]0.82821300813969[/C][C]0.34357398372062[/C][C]0.17178699186031[/C][/ROW]
[ROW][C]55[/C][C]0.898341005661135[/C][C]0.20331798867773[/C][C]0.101658994338865[/C][/ROW]
[ROW][C]56[/C][C]0.93717026772533[/C][C]0.125659464549339[/C][C]0.0628297322746697[/C][/ROW]
[ROW][C]57[/C][C]0.866578305293918[/C][C]0.266843389412165[/C][C]0.133421694706082[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147001&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147001&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
90.4414443201138750.882888640227750.558555679886125
100.9140735275827960.1718529448344070.0859264724172036
110.853912868267510.2921742634649790.14608713173249
120.9021216115115040.1957567769769920.0978783884884958
130.843670435671710.3126591286565790.156329564328289
140.784427751558910.4311444968821810.21557224844109
150.7086066193740160.5827867612519670.291393380625984
160.6228753650490230.7542492699019540.377124634950977
170.5336356650800780.9327286698398430.466364334919922
180.450730266389710.9014605327794190.54926973361029
190.5017535977258390.9964928045483220.498246402274161
200.6708448696689810.6583102606620370.329155130331019
210.8534505888650670.2930988222698660.146549411134933
220.921859225623170.156281548753660.0781407743768298
230.9101426922478720.1797146155042550.0898573077521275
240.8997804718872910.2004390562254180.100219528112709
250.8630531971567980.2738936056864050.136946802843202
260.8191434278997230.3617131442005530.180856572100277
270.772870207226470.4542595855470590.22712979277353
280.7291006621988490.5417986756023020.270899337801151
290.675835937051040.6483281258979210.32416406294896
300.7137891726053270.5724216547893460.286210827394673
310.7979322174647430.4041355650705140.202067782535257
320.921565879927730.1568682401445410.0784341200722707
330.929052196077360.1418956078452790.0709478039226393
340.9493414908068370.1013170183863250.0506585091931626
350.9316015049631930.1367969900736150.0683984950368074
360.9062254820103690.1875490359792630.0937745179896313
370.8696582759241240.2606834481517510.130341724075876
380.822200889085110.3555982218297820.177799110914891
390.7703454081740490.4593091836519010.229654591825951
400.7176377591457830.5647244817084340.282362240854217
410.6574512856613170.6850974286773660.342548714338683
420.6050978182338230.7898043635323540.394902181766177
430.6685876746970810.6628246506058380.331412325302919
440.8988942309305750.202211538138850.101105769069425
450.8837700767209370.2324598465581260.116229923279063
460.9189091245079310.1621817509841390.0810908754920693
470.895155726920230.2096885461595420.104844273079771
480.8526284823031290.2947430353937420.147371517696871
490.7902751809325790.4194496381348420.209724819067421
500.7560721274475570.4878557451048860.243927872552443
510.7821447527563810.4357104944872380.217855247243619
520.6899554489671580.6200891020656840.310044551032842
530.5875967800774420.8248064398451160.412403219922558
540.828213008139690.343573983720620.17178699186031
550.8983410056611350.203317988677730.101658994338865
560.937170267725330.1256594645493390.0628297322746697
570.8665783052939180.2668433894121650.133421694706082







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=147001&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=147001&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147001&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')
}