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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 computationMon, 05 Nov 2012 10:28:28 -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/2012/Nov/05/t1352129352ptoe313dwvztj95.htm/, Retrieved Sun, 05 Feb 2023 23:27:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186109, Retrieved Sun, 05 Feb 2023 23:27:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact55
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [MultipleRegressio...] [2011-11-24 14:29:00] [153847d967fd6c20972581ae976d765c]
- R       [Multiple Regression] [WS7] [2012-11-05 15:28:28] [59895c8d88061dfb2c96ecec1b803f61] [Current]
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Dataseries X:
-15	-7	55	23	39	24	-8	-2	19	4	-22	11	-8
-7	-1	54	20	19	23	-12	-3	18	6	-15	9	-1
-6	0	52	20	14	19	-10	0	20	5	-16	13	1
-6	-3	55	22	15	25	-11	-4	21	4	-22	12	-1
2	4	56	25	7	21	-13	-3	18	5	-21	5	2
-4	2	54	22	12	19	-10	-3	19	5	-11	13	2
-4	3	53	26	12	20	-10	-3	19	4	-10	11	1
-8	0	59	27	14	20	-11	-4	19	3	-6	8	-1
-10	-10	62	41	9	17	-11	-5	21	2	-8	8	-2
-16	-10	63	29	8	25	-11	-5	19	3	-15	8	-2
-14	-9	64	33	4	19	-10	-6	19	2	-16	8	-1
-30	-22	75	39	7	13	-13	-10	17	-1	-24	0	-8
-33	-16	77	27	3	15	-12	-11	16	0	-27	3	-4
-40	-18	79	27	5	15	-13	-13	16	-2	-33	0	-6
-38	-14	77	25	0	13	-15	-12	17	1	-29	-1	-3
-39	-12	82	19	-2	11	-16	-13	16	-2	-34	-1	-3
-46	-17	83	15	6	9	-18	-12	15	-2	-37	-4	-7
-50	-23	81	19	11	2	-17	-15	16	-2	-31	1	-9
-55	-28	78	23	9	-2	-18	-14	16	-6	-33	-1	-11
-66	-31	79	23	17	-4	-20	-16	16	-4	-25	0	-13
-63	-21	79	7	21	-2	-22	-16	18	-2	-27	-1	-11
-56	-19	73	1	21	1	-17	-12	19	0	-21	6	-9
-66	-22	72	7	41	-13	-19	-16	16	-5	-32	0	-17
-63	-22	67	4	57	-11	-18	-15	16	-4	-31	-3	-22
-69	-25	67	-8	65	-14	-26	-17	16	-5	-32	-3	-25
-69	-16	50	-14	68	-4	-19	-15	18	-1	-30	4	-20
-72	-22	45	-10	73	-9	-23	-14	16	-2	-34	1	-24
-69	-21	39	-11	71	-5	-21	-15	15	-4	-35	0	-24
-67	-10	39	-10	71	-4	-27	-14	15	-1	-37	-4	-22
-64	-7	37	-8	70	-8	-27	-16	16	1	-32	-2	-19
-61	-5	30	-8	69	-1	-21	-11	18	1	-28	3	-18
-58	-4	24	-7	65	-2	-22	-14	16	-2	-26	2	-17
-47	7	27	-8	57	-1	-24	-12	19	1	-24	5	-11
-44	6	19	-4	57	8	-21	-11	19	1	-27	6	-11
-42	3	19	3	57	8	-21	-13	18	3	-26	6	-12
-34	10	25	-5	55	6	-22	-12	17	3	-27	3	-10
-38	0	16	-4	65	7	-25	-12	19	1	-27	4	-15
-41	-2	20	5	65	2	-21	-10	22	1	-24	7	-15
-38	-1	25	3	64	3	-26	-12	19	0	-28	5	-15
-37	2	34	6	60	0	-27	-11	19	2	-23	6	-13
-22	8	39	10	43	5	-22	-10	16	2	-23	1	-8
-37	-6	40	16	47	-1	-22	-12	18	-1	-29	3	-13
-36	-4	38	11	40	3	-20	-12	20	1	-25	6	-9
-25	4	42	10	31	4	-21	-11	17	0	-24	0	-7
-15	7	46	21	27	8	-16	-12	17	1	-20	3	-4
-17	3	48	18	24	10	-17	-9	17	1	-22	4	-4
-19	3	51	20	23	14	-19	-6	20	3	-24	7	-2
-12	8	55	18	17	15	-20	-7	21	2	-27	6	0
-17	3	52	23	16	9	-20	-7	19	0	-25	6	-2
-21	-3	55	28	15	8	-20	-10	18	0	-26	6	-3
-10	4	58	31	8	10	-19	-8	20	3	-24	6	1
-19	-5	72	38	5	5	-20	-11	17	-2	-26	2	-2
-14	-1	70	27	6	4	-25	-12	15	0	-22	2	-1
-8	5	70	21	5	8	-25	-11	17	1	-20	2	1
-16	0	63	31	12	8	-22	-11	18	-1	-26	3	-3
-14	-6	66	31	8	10	-19	-9	20	-2	-22	-1	-4
-30	-13	65	29	17	8	-20	-9	19	-1	-29	-4	-9
-33	-15	55	24	22	10	-18	-12	20	-1	-30	4	-9
-37	-8	57	27	24	-8	-17	-10	22	1	-26	5	-7
-47	-20	60	36	36	-6	-17	-10	20	-2	-30	3	-14




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 8 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186109&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186109&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186109&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 time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Y_t[t] = + 0.0158598705609177 -0.00379664026461638X_1t[t] + 0.261788999309723X_2t[t] + 0.00183128940147963X_3t[t] -0.00295797398314777X_4t[t] -0.256111822739559X_5t[t] + 0.00494102191102556X_6t[t] -0.0142133503663819X_7t[t] + 0.0044653395713538X_8t[t] -0.00943433115097334X_9t[t] + 0.238885433628411X_10t[t] -0.00145084519409485X_11t[t] + 0.240612892049589X_12t[t] -0.00281276409301581t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y_t[t] =  +  0.0158598705609177 -0.00379664026461638X_1t[t] +  0.261788999309723X_2t[t] +  0.00183128940147963X_3t[t] -0.00295797398314777X_4t[t] -0.256111822739559X_5t[t] +  0.00494102191102556X_6t[t] -0.0142133503663819X_7t[t] +  0.0044653395713538X_8t[t] -0.00943433115097334X_9t[t] +  0.238885433628411X_10t[t] -0.00145084519409485X_11t[t] +  0.240612892049589X_12t[t] -0.00281276409301581t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186109&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y_t[t] =  +  0.0158598705609177 -0.00379664026461638X_1t[t] +  0.261788999309723X_2t[t] +  0.00183128940147963X_3t[t] -0.00295797398314777X_4t[t] -0.256111822739559X_5t[t] +  0.00494102191102556X_6t[t] -0.0142133503663819X_7t[t] +  0.0044653395713538X_8t[t] -0.00943433115097334X_9t[t] +  0.238885433628411X_10t[t] -0.00145084519409485X_11t[t] +  0.240612892049589X_12t[t] -0.00281276409301581t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186109&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186109&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Y_t[t] = + 0.0158598705609177 -0.00379664026461638X_1t[t] + 0.261788999309723X_2t[t] + 0.00183128940147963X_3t[t] -0.00295797398314777X_4t[t] -0.256111822739559X_5t[t] + 0.00494102191102556X_6t[t] -0.0142133503663819X_7t[t] + 0.0044653395713538X_8t[t] -0.00943433115097334X_9t[t] + 0.238885433628411X_10t[t] -0.00145084519409485X_11t[t] + 0.240612892049589X_12t[t] -0.00281276409301581t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.01585987056091771.3015860.01220.9903310.495165
X_1t-0.003796640264616380.011363-0.33410.7398040.369902
X_2t0.2617889993097230.01640515.958100
X_3t0.001831289401479630.0088820.20620.8375550.418778
X_4t-0.002957973983147770.010088-0.29320.7706690.385335
X_5t-0.2561118227395590.007685-33.32600
X_6t0.004941021911025560.014220.34750.7298170.364909
X_7t-0.01421335036638190.021581-0.65860.5134240.256712
X_8t0.00446533957135380.0348420.12820.8985820.449291
X_9t-0.009434331150973340.042162-0.22380.8239330.411966
X_10t0.2388854336284110.0433855.50612e-061e-06
X_11t-0.001450845194094850.012426-0.11680.9075630.453781
X_12t0.2406128920495890.0231210.407100
t-0.002812764093015810.005994-0.46920.6411120.320556

\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) & 0.0158598705609177 & 1.301586 & 0.0122 & 0.990331 & 0.495165 \tabularnewline
X_1t & -0.00379664026461638 & 0.011363 & -0.3341 & 0.739804 & 0.369902 \tabularnewline
X_2t & 0.261788999309723 & 0.016405 & 15.9581 & 0 & 0 \tabularnewline
X_3t & 0.00183128940147963 & 0.008882 & 0.2062 & 0.837555 & 0.418778 \tabularnewline
X_4t & -0.00295797398314777 & 0.010088 & -0.2932 & 0.770669 & 0.385335 \tabularnewline
X_5t & -0.256111822739559 & 0.007685 & -33.326 & 0 & 0 \tabularnewline
X_6t & 0.00494102191102556 & 0.01422 & 0.3475 & 0.729817 & 0.364909 \tabularnewline
X_7t & -0.0142133503663819 & 0.021581 & -0.6586 & 0.513424 & 0.256712 \tabularnewline
X_8t & 0.0044653395713538 & 0.034842 & 0.1282 & 0.898582 & 0.449291 \tabularnewline
X_9t & -0.00943433115097334 & 0.042162 & -0.2238 & 0.823933 & 0.411966 \tabularnewline
X_10t & 0.238885433628411 & 0.043385 & 5.5061 & 2e-06 & 1e-06 \tabularnewline
X_11t & -0.00145084519409485 & 0.012426 & -0.1168 & 0.907563 & 0.453781 \tabularnewline
X_12t & 0.240612892049589 & 0.02312 & 10.4071 & 0 & 0 \tabularnewline
t & -0.00281276409301581 & 0.005994 & -0.4692 & 0.641112 & 0.320556 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186109&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]0.0158598705609177[/C][C]1.301586[/C][C]0.0122[/C][C]0.990331[/C][C]0.495165[/C][/ROW]
[ROW][C]X_1t[/C][C]-0.00379664026461638[/C][C]0.011363[/C][C]-0.3341[/C][C]0.739804[/C][C]0.369902[/C][/ROW]
[ROW][C]X_2t[/C][C]0.261788999309723[/C][C]0.016405[/C][C]15.9581[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X_3t[/C][C]0.00183128940147963[/C][C]0.008882[/C][C]0.2062[/C][C]0.837555[/C][C]0.418778[/C][/ROW]
[ROW][C]X_4t[/C][C]-0.00295797398314777[/C][C]0.010088[/C][C]-0.2932[/C][C]0.770669[/C][C]0.385335[/C][/ROW]
[ROW][C]X_5t[/C][C]-0.256111822739559[/C][C]0.007685[/C][C]-33.326[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X_6t[/C][C]0.00494102191102556[/C][C]0.01422[/C][C]0.3475[/C][C]0.729817[/C][C]0.364909[/C][/ROW]
[ROW][C]X_7t[/C][C]-0.0142133503663819[/C][C]0.021581[/C][C]-0.6586[/C][C]0.513424[/C][C]0.256712[/C][/ROW]
[ROW][C]X_8t[/C][C]0.0044653395713538[/C][C]0.034842[/C][C]0.1282[/C][C]0.898582[/C][C]0.449291[/C][/ROW]
[ROW][C]X_9t[/C][C]-0.00943433115097334[/C][C]0.042162[/C][C]-0.2238[/C][C]0.823933[/C][C]0.411966[/C][/ROW]
[ROW][C]X_10t[/C][C]0.238885433628411[/C][C]0.043385[/C][C]5.5061[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]X_11t[/C][C]-0.00145084519409485[/C][C]0.012426[/C][C]-0.1168[/C][C]0.907563[/C][C]0.453781[/C][/ROW]
[ROW][C]X_12t[/C][C]0.240612892049589[/C][C]0.02312[/C][C]10.4071[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]-0.00281276409301581[/C][C]0.005994[/C][C]-0.4692[/C][C]0.641112[/C][C]0.320556[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186109&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186109&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)0.01585987056091771.3015860.01220.9903310.495165
X_1t-0.003796640264616380.011363-0.33410.7398040.369902
X_2t0.2617889993097230.01640515.958100
X_3t0.001831289401479630.0088820.20620.8375550.418778
X_4t-0.002957973983147770.010088-0.29320.7706690.385335
X_5t-0.2561118227395590.007685-33.32600
X_6t0.004941021911025560.014220.34750.7298170.364909
X_7t-0.01421335036638190.021581-0.65860.5134240.256712
X_8t0.00446533957135380.0348420.12820.8985820.449291
X_9t-0.009434331150973340.042162-0.22380.8239330.411966
X_10t0.2388854336284110.0433855.50612e-061e-06
X_11t-0.001450845194094850.012426-0.11680.9075630.453781
X_12t0.2406128920495890.0231210.407100
t-0.002812764093015810.005994-0.46920.6411120.320556







Multiple Linear Regression - Regression Statistics
Multiple R0.999231404290997
R-squared0.998463399321359
Adjusted R-squared0.99802914260783
F-TEST (value)2299.24689294314
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.327670177865718
Sum Squared Residuals4.93891629127737

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999231404290997 \tabularnewline
R-squared & 0.998463399321359 \tabularnewline
Adjusted R-squared & 0.99802914260783 \tabularnewline
F-TEST (value) & 2299.24689294314 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.327670177865718 \tabularnewline
Sum Squared Residuals & 4.93891629127737 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186109&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999231404290997[/C][/ROW]
[ROW][C]R-squared[/C][C]0.998463399321359[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.99802914260783[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2299.24689294314[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]46[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.327670177865718[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4.93891629127737[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186109&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186109&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 R0.999231404290997
R-squared0.998463399321359
Adjusted R-squared0.99802914260783
F-TEST (value)2299.24689294314
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.327670177865718
Sum Squared Residuals4.93891629127737







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-8-8.039889356991060.0398893569910646
2-1-1.329791622070420.329791622070422
310.8736380555757990.126361944424201
4-1-0.625305071065087-0.374694928934913
521.810459277461660.189540722538337
621.879938844169180.120061155830816
711.40863085304116-0.408630853041161
8-1-0.825335710734478-0.174664289265522
9-2-2.467944652103920.467944652103924
10-2-1.8471005870932-0.152899412906797
11-1-0.867030357816808-0.132969642183192
12-8-7.59424444948332-0.405755550516685
13-4-3.98561340897042-0.0143865910295777
14-6-6.179610557524080.179610557524075
15-3-3.376232428655940.376232428655942
16-3-3.01264439427030.0126443942703029
17-7-7.018098882575460.0180988825754639
18-9-8.74978413442691-0.250215865573086
19-11-10.9826120383344-0.0173879616656237
20-13-13.05970107443910.0597010744391171
21-11-11.17363372560830.173633725608333
22-9-8.56714548396922-0.43285451603078
23-17-17.11162562559540.111625625595428
24-22-21.7081700540606-0.291829945939378
25-25-24.6344503989929-0.365549601007146
26-20-20.48597264158440.485972641584382
27-24-24.24911537259680.249115372596771
28-24-24.21796171074190.217961710741883
29-22-21.4998538501846-0.50014614981541
30-19-19.56854291372420.568542913724238
31-18-17.6658486368572-0.334151363142822
32-17-17.35318665314790.353186653147879
33-11-11.01114834866130.0111483486612728
34-11-11.0623623306990.0623623306989769
35-12-11.4020175170614-0.597982482938564
36-10-9.75796208803816-0.242037911961839
37-15-15.1524816546610.152481654661005
38-15-15.07022378958640.0702237895864136
39-15-15.17038100634840.170381006348365
40-13-12.6445832816141-0.355416718385877
41-8-7.99904388567119-0.000956114328810113
42-13-12.934487800119-0.0655121998809817
43-9-9.447334268328910.447334268328908
44-7-6.71439947042818-0.285600529571817
45-4-4.071424179247030.0714241792470257
46-4-4.051921695738350.0519216957383477
47-2-2.555656388892640.555656388892641
480-0.1960796848204230.196079684820423
49-2-1.74447618632881-0.255523813671191
50-3-3.063472428473460.063472428473457
5111.21336278591619-0.213362785916191
52-2-2.487675338027540.487675338027538
53-1-1.137055454800720.137055454800717
5410.9232901101690840.0767098898309164
55-3-2.47380333946715-0.526196660532852
56-4-4.274838593824540.274838593824541
57-9-8.80938154340936-0.19061845659064
58-9-8.72348554830976-0.276514451690237
59-7-6.7965330941959-0.203466905804097
60-14-14.16064521596210.160645215962066

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -8 & -8.03988935699106 & 0.0398893569910646 \tabularnewline
2 & -1 & -1.32979162207042 & 0.329791622070422 \tabularnewline
3 & 1 & 0.873638055575799 & 0.126361944424201 \tabularnewline
4 & -1 & -0.625305071065087 & -0.374694928934913 \tabularnewline
5 & 2 & 1.81045927746166 & 0.189540722538337 \tabularnewline
6 & 2 & 1.87993884416918 & 0.120061155830816 \tabularnewline
7 & 1 & 1.40863085304116 & -0.408630853041161 \tabularnewline
8 & -1 & -0.825335710734478 & -0.174664289265522 \tabularnewline
9 & -2 & -2.46794465210392 & 0.467944652103924 \tabularnewline
10 & -2 & -1.8471005870932 & -0.152899412906797 \tabularnewline
11 & -1 & -0.867030357816808 & -0.132969642183192 \tabularnewline
12 & -8 & -7.59424444948332 & -0.405755550516685 \tabularnewline
13 & -4 & -3.98561340897042 & -0.0143865910295777 \tabularnewline
14 & -6 & -6.17961055752408 & 0.179610557524075 \tabularnewline
15 & -3 & -3.37623242865594 & 0.376232428655942 \tabularnewline
16 & -3 & -3.0126443942703 & 0.0126443942703029 \tabularnewline
17 & -7 & -7.01809888257546 & 0.0180988825754639 \tabularnewline
18 & -9 & -8.74978413442691 & -0.250215865573086 \tabularnewline
19 & -11 & -10.9826120383344 & -0.0173879616656237 \tabularnewline
20 & -13 & -13.0597010744391 & 0.0597010744391171 \tabularnewline
21 & -11 & -11.1736337256083 & 0.173633725608333 \tabularnewline
22 & -9 & -8.56714548396922 & -0.43285451603078 \tabularnewline
23 & -17 & -17.1116256255954 & 0.111625625595428 \tabularnewline
24 & -22 & -21.7081700540606 & -0.291829945939378 \tabularnewline
25 & -25 & -24.6344503989929 & -0.365549601007146 \tabularnewline
26 & -20 & -20.4859726415844 & 0.485972641584382 \tabularnewline
27 & -24 & -24.2491153725968 & 0.249115372596771 \tabularnewline
28 & -24 & -24.2179617107419 & 0.217961710741883 \tabularnewline
29 & -22 & -21.4998538501846 & -0.50014614981541 \tabularnewline
30 & -19 & -19.5685429137242 & 0.568542913724238 \tabularnewline
31 & -18 & -17.6658486368572 & -0.334151363142822 \tabularnewline
32 & -17 & -17.3531866531479 & 0.353186653147879 \tabularnewline
33 & -11 & -11.0111483486613 & 0.0111483486612728 \tabularnewline
34 & -11 & -11.062362330699 & 0.0623623306989769 \tabularnewline
35 & -12 & -11.4020175170614 & -0.597982482938564 \tabularnewline
36 & -10 & -9.75796208803816 & -0.242037911961839 \tabularnewline
37 & -15 & -15.152481654661 & 0.152481654661005 \tabularnewline
38 & -15 & -15.0702237895864 & 0.0702237895864136 \tabularnewline
39 & -15 & -15.1703810063484 & 0.170381006348365 \tabularnewline
40 & -13 & -12.6445832816141 & -0.355416718385877 \tabularnewline
41 & -8 & -7.99904388567119 & -0.000956114328810113 \tabularnewline
42 & -13 & -12.934487800119 & -0.0655121998809817 \tabularnewline
43 & -9 & -9.44733426832891 & 0.447334268328908 \tabularnewline
44 & -7 & -6.71439947042818 & -0.285600529571817 \tabularnewline
45 & -4 & -4.07142417924703 & 0.0714241792470257 \tabularnewline
46 & -4 & -4.05192169573835 & 0.0519216957383477 \tabularnewline
47 & -2 & -2.55565638889264 & 0.555656388892641 \tabularnewline
48 & 0 & -0.196079684820423 & 0.196079684820423 \tabularnewline
49 & -2 & -1.74447618632881 & -0.255523813671191 \tabularnewline
50 & -3 & -3.06347242847346 & 0.063472428473457 \tabularnewline
51 & 1 & 1.21336278591619 & -0.213362785916191 \tabularnewline
52 & -2 & -2.48767533802754 & 0.487675338027538 \tabularnewline
53 & -1 & -1.13705545480072 & 0.137055454800717 \tabularnewline
54 & 1 & 0.923290110169084 & 0.0767098898309164 \tabularnewline
55 & -3 & -2.47380333946715 & -0.526196660532852 \tabularnewline
56 & -4 & -4.27483859382454 & 0.274838593824541 \tabularnewline
57 & -9 & -8.80938154340936 & -0.19061845659064 \tabularnewline
58 & -9 & -8.72348554830976 & -0.276514451690237 \tabularnewline
59 & -7 & -6.7965330941959 & -0.203466905804097 \tabularnewline
60 & -14 & -14.1606452159621 & 0.160645215962066 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186109&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]-8[/C][C]-8.03988935699106[/C][C]0.0398893569910646[/C][/ROW]
[ROW][C]2[/C][C]-1[/C][C]-1.32979162207042[/C][C]0.329791622070422[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]0.873638055575799[/C][C]0.126361944424201[/C][/ROW]
[ROW][C]4[/C][C]-1[/C][C]-0.625305071065087[/C][C]-0.374694928934913[/C][/ROW]
[ROW][C]5[/C][C]2[/C][C]1.81045927746166[/C][C]0.189540722538337[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]1.87993884416918[/C][C]0.120061155830816[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]1.40863085304116[/C][C]-0.408630853041161[/C][/ROW]
[ROW][C]8[/C][C]-1[/C][C]-0.825335710734478[/C][C]-0.174664289265522[/C][/ROW]
[ROW][C]9[/C][C]-2[/C][C]-2.46794465210392[/C][C]0.467944652103924[/C][/ROW]
[ROW][C]10[/C][C]-2[/C][C]-1.8471005870932[/C][C]-0.152899412906797[/C][/ROW]
[ROW][C]11[/C][C]-1[/C][C]-0.867030357816808[/C][C]-0.132969642183192[/C][/ROW]
[ROW][C]12[/C][C]-8[/C][C]-7.59424444948332[/C][C]-0.405755550516685[/C][/ROW]
[ROW][C]13[/C][C]-4[/C][C]-3.98561340897042[/C][C]-0.0143865910295777[/C][/ROW]
[ROW][C]14[/C][C]-6[/C][C]-6.17961055752408[/C][C]0.179610557524075[/C][/ROW]
[ROW][C]15[/C][C]-3[/C][C]-3.37623242865594[/C][C]0.376232428655942[/C][/ROW]
[ROW][C]16[/C][C]-3[/C][C]-3.0126443942703[/C][C]0.0126443942703029[/C][/ROW]
[ROW][C]17[/C][C]-7[/C][C]-7.01809888257546[/C][C]0.0180988825754639[/C][/ROW]
[ROW][C]18[/C][C]-9[/C][C]-8.74978413442691[/C][C]-0.250215865573086[/C][/ROW]
[ROW][C]19[/C][C]-11[/C][C]-10.9826120383344[/C][C]-0.0173879616656237[/C][/ROW]
[ROW][C]20[/C][C]-13[/C][C]-13.0597010744391[/C][C]0.0597010744391171[/C][/ROW]
[ROW][C]21[/C][C]-11[/C][C]-11.1736337256083[/C][C]0.173633725608333[/C][/ROW]
[ROW][C]22[/C][C]-9[/C][C]-8.56714548396922[/C][C]-0.43285451603078[/C][/ROW]
[ROW][C]23[/C][C]-17[/C][C]-17.1116256255954[/C][C]0.111625625595428[/C][/ROW]
[ROW][C]24[/C][C]-22[/C][C]-21.7081700540606[/C][C]-0.291829945939378[/C][/ROW]
[ROW][C]25[/C][C]-25[/C][C]-24.6344503989929[/C][C]-0.365549601007146[/C][/ROW]
[ROW][C]26[/C][C]-20[/C][C]-20.4859726415844[/C][C]0.485972641584382[/C][/ROW]
[ROW][C]27[/C][C]-24[/C][C]-24.2491153725968[/C][C]0.249115372596771[/C][/ROW]
[ROW][C]28[/C][C]-24[/C][C]-24.2179617107419[/C][C]0.217961710741883[/C][/ROW]
[ROW][C]29[/C][C]-22[/C][C]-21.4998538501846[/C][C]-0.50014614981541[/C][/ROW]
[ROW][C]30[/C][C]-19[/C][C]-19.5685429137242[/C][C]0.568542913724238[/C][/ROW]
[ROW][C]31[/C][C]-18[/C][C]-17.6658486368572[/C][C]-0.334151363142822[/C][/ROW]
[ROW][C]32[/C][C]-17[/C][C]-17.3531866531479[/C][C]0.353186653147879[/C][/ROW]
[ROW][C]33[/C][C]-11[/C][C]-11.0111483486613[/C][C]0.0111483486612728[/C][/ROW]
[ROW][C]34[/C][C]-11[/C][C]-11.062362330699[/C][C]0.0623623306989769[/C][/ROW]
[ROW][C]35[/C][C]-12[/C][C]-11.4020175170614[/C][C]-0.597982482938564[/C][/ROW]
[ROW][C]36[/C][C]-10[/C][C]-9.75796208803816[/C][C]-0.242037911961839[/C][/ROW]
[ROW][C]37[/C][C]-15[/C][C]-15.152481654661[/C][C]0.152481654661005[/C][/ROW]
[ROW][C]38[/C][C]-15[/C][C]-15.0702237895864[/C][C]0.0702237895864136[/C][/ROW]
[ROW][C]39[/C][C]-15[/C][C]-15.1703810063484[/C][C]0.170381006348365[/C][/ROW]
[ROW][C]40[/C][C]-13[/C][C]-12.6445832816141[/C][C]-0.355416718385877[/C][/ROW]
[ROW][C]41[/C][C]-8[/C][C]-7.99904388567119[/C][C]-0.000956114328810113[/C][/ROW]
[ROW][C]42[/C][C]-13[/C][C]-12.934487800119[/C][C]-0.0655121998809817[/C][/ROW]
[ROW][C]43[/C][C]-9[/C][C]-9.44733426832891[/C][C]0.447334268328908[/C][/ROW]
[ROW][C]44[/C][C]-7[/C][C]-6.71439947042818[/C][C]-0.285600529571817[/C][/ROW]
[ROW][C]45[/C][C]-4[/C][C]-4.07142417924703[/C][C]0.0714241792470257[/C][/ROW]
[ROW][C]46[/C][C]-4[/C][C]-4.05192169573835[/C][C]0.0519216957383477[/C][/ROW]
[ROW][C]47[/C][C]-2[/C][C]-2.55565638889264[/C][C]0.555656388892641[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]-0.196079684820423[/C][C]0.196079684820423[/C][/ROW]
[ROW][C]49[/C][C]-2[/C][C]-1.74447618632881[/C][C]-0.255523813671191[/C][/ROW]
[ROW][C]50[/C][C]-3[/C][C]-3.06347242847346[/C][C]0.063472428473457[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]1.21336278591619[/C][C]-0.213362785916191[/C][/ROW]
[ROW][C]52[/C][C]-2[/C][C]-2.48767533802754[/C][C]0.487675338027538[/C][/ROW]
[ROW][C]53[/C][C]-1[/C][C]-1.13705545480072[/C][C]0.137055454800717[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]0.923290110169084[/C][C]0.0767098898309164[/C][/ROW]
[ROW][C]55[/C][C]-3[/C][C]-2.47380333946715[/C][C]-0.526196660532852[/C][/ROW]
[ROW][C]56[/C][C]-4[/C][C]-4.27483859382454[/C][C]0.274838593824541[/C][/ROW]
[ROW][C]57[/C][C]-9[/C][C]-8.80938154340936[/C][C]-0.19061845659064[/C][/ROW]
[ROW][C]58[/C][C]-9[/C][C]-8.72348554830976[/C][C]-0.276514451690237[/C][/ROW]
[ROW][C]59[/C][C]-7[/C][C]-6.7965330941959[/C][C]-0.203466905804097[/C][/ROW]
[ROW][C]60[/C][C]-14[/C][C]-14.1606452159621[/C][C]0.160645215962066[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186109&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186109&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
1-8-8.039889356991060.0398893569910646
2-1-1.329791622070420.329791622070422
310.8736380555757990.126361944424201
4-1-0.625305071065087-0.374694928934913
521.810459277461660.189540722538337
621.879938844169180.120061155830816
711.40863085304116-0.408630853041161
8-1-0.825335710734478-0.174664289265522
9-2-2.467944652103920.467944652103924
10-2-1.8471005870932-0.152899412906797
11-1-0.867030357816808-0.132969642183192
12-8-7.59424444948332-0.405755550516685
13-4-3.98561340897042-0.0143865910295777
14-6-6.179610557524080.179610557524075
15-3-3.376232428655940.376232428655942
16-3-3.01264439427030.0126443942703029
17-7-7.018098882575460.0180988825754639
18-9-8.74978413442691-0.250215865573086
19-11-10.9826120383344-0.0173879616656237
20-13-13.05970107443910.0597010744391171
21-11-11.17363372560830.173633725608333
22-9-8.56714548396922-0.43285451603078
23-17-17.11162562559540.111625625595428
24-22-21.7081700540606-0.291829945939378
25-25-24.6344503989929-0.365549601007146
26-20-20.48597264158440.485972641584382
27-24-24.24911537259680.249115372596771
28-24-24.21796171074190.217961710741883
29-22-21.4998538501846-0.50014614981541
30-19-19.56854291372420.568542913724238
31-18-17.6658486368572-0.334151363142822
32-17-17.35318665314790.353186653147879
33-11-11.01114834866130.0111483486612728
34-11-11.0623623306990.0623623306989769
35-12-11.4020175170614-0.597982482938564
36-10-9.75796208803816-0.242037911961839
37-15-15.1524816546610.152481654661005
38-15-15.07022378958640.0702237895864136
39-15-15.17038100634840.170381006348365
40-13-12.6445832816141-0.355416718385877
41-8-7.99904388567119-0.000956114328810113
42-13-12.934487800119-0.0655121998809817
43-9-9.447334268328910.447334268328908
44-7-6.71439947042818-0.285600529571817
45-4-4.071424179247030.0714241792470257
46-4-4.051921695738350.0519216957383477
47-2-2.555656388892640.555656388892641
480-0.1960796848204230.196079684820423
49-2-1.74447618632881-0.255523813671191
50-3-3.063472428473460.063472428473457
5111.21336278591619-0.213362785916191
52-2-2.487675338027540.487675338027538
53-1-1.137055454800720.137055454800717
5410.9232901101690840.0767098898309164
55-3-2.47380333946715-0.526196660532852
56-4-4.274838593824540.274838593824541
57-9-8.80938154340936-0.19061845659064
58-9-8.72348554830976-0.276514451690237
59-7-6.7965330941959-0.203466905804097
60-14-14.16064521596210.160645215962066







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.6819013670761150.636197265847770.318098632923885
180.5221994054225760.9556011891548470.477800594577424
190.5587070064301380.8825859871397240.441292993569862
200.4596779825158020.9193559650316050.540322017484198
210.3551301231696350.710260246339270.644869876830365
220.2903021605904210.5806043211808420.709697839409579
230.2478363836337510.4956727672675020.752163616366249
240.1861895427059780.3723790854119570.813810457294022
250.2484853994062570.4969707988125150.751514600593743
260.2476979053401020.4953958106802040.752302094659898
270.1907302814382670.3814605628765350.809269718561733
280.1426460758082730.2852921516165450.857353924191727
290.4345074701649710.8690149403299420.565492529835029
300.4830235259650210.9660470519300430.516976474034979
310.7569868170005120.4860263659989760.243013182999488
320.6771865642449850.645626871510030.322813435755015
330.6261811546747750.747637690650450.373818845325225
340.5909602180725990.8180795638548030.409039781927401
350.5755090332604550.848981933479090.424490966739545
360.5075877738982790.9848244522034410.492412226101721
370.6664611059413550.6670777881172890.333538894058645
380.5847638940672150.830472211865570.415236105932785
390.8845024884572350.230995023085530.115497511542765
400.9729633655166590.05407326896668160.0270366344833408
410.9792097607550660.04158047848986750.0207902392449338
420.9594829366976560.08103412660468730.0405170633023436
430.9907112487360350.01857750252793030.00928875126396515

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.681901367076115 & 0.63619726584777 & 0.318098632923885 \tabularnewline
18 & 0.522199405422576 & 0.955601189154847 & 0.477800594577424 \tabularnewline
19 & 0.558707006430138 & 0.882585987139724 & 0.441292993569862 \tabularnewline
20 & 0.459677982515802 & 0.919355965031605 & 0.540322017484198 \tabularnewline
21 & 0.355130123169635 & 0.71026024633927 & 0.644869876830365 \tabularnewline
22 & 0.290302160590421 & 0.580604321180842 & 0.709697839409579 \tabularnewline
23 & 0.247836383633751 & 0.495672767267502 & 0.752163616366249 \tabularnewline
24 & 0.186189542705978 & 0.372379085411957 & 0.813810457294022 \tabularnewline
25 & 0.248485399406257 & 0.496970798812515 & 0.751514600593743 \tabularnewline
26 & 0.247697905340102 & 0.495395810680204 & 0.752302094659898 \tabularnewline
27 & 0.190730281438267 & 0.381460562876535 & 0.809269718561733 \tabularnewline
28 & 0.142646075808273 & 0.285292151616545 & 0.857353924191727 \tabularnewline
29 & 0.434507470164971 & 0.869014940329942 & 0.565492529835029 \tabularnewline
30 & 0.483023525965021 & 0.966047051930043 & 0.516976474034979 \tabularnewline
31 & 0.756986817000512 & 0.486026365998976 & 0.243013182999488 \tabularnewline
32 & 0.677186564244985 & 0.64562687151003 & 0.322813435755015 \tabularnewline
33 & 0.626181154674775 & 0.74763769065045 & 0.373818845325225 \tabularnewline
34 & 0.590960218072599 & 0.818079563854803 & 0.409039781927401 \tabularnewline
35 & 0.575509033260455 & 0.84898193347909 & 0.424490966739545 \tabularnewline
36 & 0.507587773898279 & 0.984824452203441 & 0.492412226101721 \tabularnewline
37 & 0.666461105941355 & 0.667077788117289 & 0.333538894058645 \tabularnewline
38 & 0.584763894067215 & 0.83047221186557 & 0.415236105932785 \tabularnewline
39 & 0.884502488457235 & 0.23099502308553 & 0.115497511542765 \tabularnewline
40 & 0.972963365516659 & 0.0540732689666816 & 0.0270366344833408 \tabularnewline
41 & 0.979209760755066 & 0.0415804784898675 & 0.0207902392449338 \tabularnewline
42 & 0.959482936697656 & 0.0810341266046873 & 0.0405170633023436 \tabularnewline
43 & 0.990711248736035 & 0.0185775025279303 & 0.00928875126396515 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186109&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]17[/C][C]0.681901367076115[/C][C]0.63619726584777[/C][C]0.318098632923885[/C][/ROW]
[ROW][C]18[/C][C]0.522199405422576[/C][C]0.955601189154847[/C][C]0.477800594577424[/C][/ROW]
[ROW][C]19[/C][C]0.558707006430138[/C][C]0.882585987139724[/C][C]0.441292993569862[/C][/ROW]
[ROW][C]20[/C][C]0.459677982515802[/C][C]0.919355965031605[/C][C]0.540322017484198[/C][/ROW]
[ROW][C]21[/C][C]0.355130123169635[/C][C]0.71026024633927[/C][C]0.644869876830365[/C][/ROW]
[ROW][C]22[/C][C]0.290302160590421[/C][C]0.580604321180842[/C][C]0.709697839409579[/C][/ROW]
[ROW][C]23[/C][C]0.247836383633751[/C][C]0.495672767267502[/C][C]0.752163616366249[/C][/ROW]
[ROW][C]24[/C][C]0.186189542705978[/C][C]0.372379085411957[/C][C]0.813810457294022[/C][/ROW]
[ROW][C]25[/C][C]0.248485399406257[/C][C]0.496970798812515[/C][C]0.751514600593743[/C][/ROW]
[ROW][C]26[/C][C]0.247697905340102[/C][C]0.495395810680204[/C][C]0.752302094659898[/C][/ROW]
[ROW][C]27[/C][C]0.190730281438267[/C][C]0.381460562876535[/C][C]0.809269718561733[/C][/ROW]
[ROW][C]28[/C][C]0.142646075808273[/C][C]0.285292151616545[/C][C]0.857353924191727[/C][/ROW]
[ROW][C]29[/C][C]0.434507470164971[/C][C]0.869014940329942[/C][C]0.565492529835029[/C][/ROW]
[ROW][C]30[/C][C]0.483023525965021[/C][C]0.966047051930043[/C][C]0.516976474034979[/C][/ROW]
[ROW][C]31[/C][C]0.756986817000512[/C][C]0.486026365998976[/C][C]0.243013182999488[/C][/ROW]
[ROW][C]32[/C][C]0.677186564244985[/C][C]0.64562687151003[/C][C]0.322813435755015[/C][/ROW]
[ROW][C]33[/C][C]0.626181154674775[/C][C]0.74763769065045[/C][C]0.373818845325225[/C][/ROW]
[ROW][C]34[/C][C]0.590960218072599[/C][C]0.818079563854803[/C][C]0.409039781927401[/C][/ROW]
[ROW][C]35[/C][C]0.575509033260455[/C][C]0.84898193347909[/C][C]0.424490966739545[/C][/ROW]
[ROW][C]36[/C][C]0.507587773898279[/C][C]0.984824452203441[/C][C]0.492412226101721[/C][/ROW]
[ROW][C]37[/C][C]0.666461105941355[/C][C]0.667077788117289[/C][C]0.333538894058645[/C][/ROW]
[ROW][C]38[/C][C]0.584763894067215[/C][C]0.83047221186557[/C][C]0.415236105932785[/C][/ROW]
[ROW][C]39[/C][C]0.884502488457235[/C][C]0.23099502308553[/C][C]0.115497511542765[/C][/ROW]
[ROW][C]40[/C][C]0.972963365516659[/C][C]0.0540732689666816[/C][C]0.0270366344833408[/C][/ROW]
[ROW][C]41[/C][C]0.979209760755066[/C][C]0.0415804784898675[/C][C]0.0207902392449338[/C][/ROW]
[ROW][C]42[/C][C]0.959482936697656[/C][C]0.0810341266046873[/C][C]0.0405170633023436[/C][/ROW]
[ROW][C]43[/C][C]0.990711248736035[/C][C]0.0185775025279303[/C][C]0.00928875126396515[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186109&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186109&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
170.6819013670761150.636197265847770.318098632923885
180.5221994054225760.9556011891548470.477800594577424
190.5587070064301380.8825859871397240.441292993569862
200.4596779825158020.9193559650316050.540322017484198
210.3551301231696350.710260246339270.644869876830365
220.2903021605904210.5806043211808420.709697839409579
230.2478363836337510.4956727672675020.752163616366249
240.1861895427059780.3723790854119570.813810457294022
250.2484853994062570.4969707988125150.751514600593743
260.2476979053401020.4953958106802040.752302094659898
270.1907302814382670.3814605628765350.809269718561733
280.1426460758082730.2852921516165450.857353924191727
290.4345074701649710.8690149403299420.565492529835029
300.4830235259650210.9660470519300430.516976474034979
310.7569868170005120.4860263659989760.243013182999488
320.6771865642449850.645626871510030.322813435755015
330.6261811546747750.747637690650450.373818845325225
340.5909602180725990.8180795638548030.409039781927401
350.5755090332604550.848981933479090.424490966739545
360.5075877738982790.9848244522034410.492412226101721
370.6664611059413550.6670777881172890.333538894058645
380.5847638940672150.830472211865570.415236105932785
390.8845024884572350.230995023085530.115497511542765
400.9729633655166590.05407326896668160.0270366344833408
410.9792097607550660.04158047848986750.0207902392449338
420.9594829366976560.08103412660468730.0405170633023436
430.9907112487360350.01857750252793030.00928875126396515







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0740740740740741NOK
10% type I error level40.148148148148148NOK

\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 & 2 & 0.0740740740740741 & NOK \tabularnewline
10% type I error level & 4 & 0.148148148148148 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186109&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]2[/C][C]0.0740740740740741[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.148148148148148[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186109&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186109&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 level20.0740740740740741NOK
10% type I error level40.148148148148148NOK



Parameters (Session):
par1 = 13 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 13 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}