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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/t1352129357t5hf5lx9hb73a9a.htm/, Retrieved Sun, 05 Feb 2023 23:30:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186110, Retrieved Sun, 05 Feb 2023 23:30:52 +0000
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User-defined keywords
Estimated Impact43
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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- 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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 8 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=186110&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=186110&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186110&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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 8 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=186110&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=186110&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186110&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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 0.0158598705609177 1.301586 0.0122 0.990331 0.495165 X_1t -0.00379664026461638 0.011363 -0.3341 0.739804 0.369902 X_2t 0.261788999309723 0.016405 15.9581 0 0 X_3t 0.00183128940147963 0.008882 0.2062 0.837555 0.418778 X_4t -0.00295797398314777 0.010088 -0.2932 0.770669 0.385335 X_5t -0.256111822739559 0.007685 -33.326 0 0 X_6t 0.00494102191102556 0.01422 0.3475 0.729817 0.364909 X_7t -0.0142133503663819 0.021581 -0.6586 0.513424 0.256712 X_8t 0.0044653395713538 0.034842 0.1282 0.898582 0.449291 X_9t -0.00943433115097334 0.042162 -0.2238 0.823933 0.411966 X_10t 0.238885433628411 0.043385 5.5061 2e-06 1e-06 X_11t -0.00145084519409485 0.012426 -0.1168 0.907563 0.453781 X_12t 0.240612892049589 0.02312 10.4071 0 0 t -0.00281276409301581 0.005994 -0.4692 0.641112 0.320556

\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=186110&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=186110&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186110&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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 0.0158598705609177 1.301586 0.0122 0.990331 0.495165 X_1t -0.00379664026461638 0.011363 -0.3341 0.739804 0.369902 X_2t 0.261788999309723 0.016405 15.9581 0 0 X_3t 0.00183128940147963 0.008882 0.2062 0.837555 0.418778 X_4t -0.00295797398314777 0.010088 -0.2932 0.770669 0.385335 X_5t -0.256111822739559 0.007685 -33.326 0 0 X_6t 0.00494102191102556 0.01422 0.3475 0.729817 0.364909 X_7t -0.0142133503663819 0.021581 -0.6586 0.513424 0.256712 X_8t 0.0044653395713538 0.034842 0.1282 0.898582 0.449291 X_9t -0.00943433115097334 0.042162 -0.2238 0.823933 0.411966 X_10t 0.238885433628411 0.043385 5.5061 2e-06 1e-06 X_11t -0.00145084519409485 0.012426 -0.1168 0.907563 0.453781 X_12t 0.240612892049589 0.02312 10.4071 0 0 t -0.00281276409301581 0.005994 -0.4692 0.641112 0.320556

 Multiple Linear Regression - Regression Statistics Multiple R 0.999231404290997 R-squared 0.998463399321359 Adjusted R-squared 0.99802914260783 F-TEST (value) 2299.24689294314 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.327670177865718 Sum Squared Residuals 4.93891629127737

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999231404290997 \tabularnewline
R-squared & 0.998463399321359 \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=186110&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]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=186110&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186110&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 R 0.999231404290997 R-squared 0.998463399321359 Adjusted R-squared 0.99802914260783 F-TEST (value) 2299.24689294314 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.327670177865718 Sum Squared Residuals 4.93891629127737

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

\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=186110&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=186110&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186110&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 Index Actuals InterpolationForecast ResidualsPrediction Error 1 -8 -8.03988935699106 0.0398893569910646 2 -1 -1.32979162207042 0.329791622070422 3 1 0.873638055575799 0.126361944424201 4 -1 -0.625305071065087 -0.374694928934913 5 2 1.81045927746166 0.189540722538337 6 2 1.87993884416918 0.120061155830816 7 1 1.40863085304116 -0.408630853041161 8 -1 -0.825335710734478 -0.174664289265522 9 -2 -2.46794465210392 0.467944652103924 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 -6.17961055752408 0.179610557524075 15 -3 -3.37623242865594 0.376232428655942 16 -3 -3.0126443942703 0.0126443942703029 17 -7 -7.01809888257546 0.0180988825754639 18 -9 -8.74978413442691 -0.250215865573086 19 -11 -10.9826120383344 -0.0173879616656237 20 -13 -13.0597010744391 0.0597010744391171 21 -11 -11.1736337256083 0.173633725608333 22 -9 -8.56714548396922 -0.43285451603078 23 -17 -17.1116256255954 0.111625625595428 24 -22 -21.7081700540606 -0.291829945939378 25 -25 -24.6344503989929 -0.365549601007146 26 -20 -20.4859726415844 0.485972641584382 27 -24 -24.2491153725968 0.249115372596771 28 -24 -24.2179617107419 0.217961710741883 29 -22 -21.4998538501846 -0.50014614981541 30 -19 -19.5685429137242 0.568542913724238 31 -18 -17.6658486368572 -0.334151363142822 32 -17 -17.3531866531479 0.353186653147879 33 -11 -11.0111483486613 0.0111483486612728 34 -11 -11.062362330699 0.0623623306989769 35 -12 -11.4020175170614 -0.597982482938564 36 -10 -9.75796208803816 -0.242037911961839 37 -15 -15.152481654661 0.152481654661005 38 -15 -15.0702237895864 0.0702237895864136 39 -15 -15.1703810063484 0.170381006348365 40 -13 -12.6445832816141 -0.355416718385877 41 -8 -7.99904388567119 -0.000956114328810113 42 -13 -12.934487800119 -0.0655121998809817 43 -9 -9.44733426832891 0.447334268328908 44 -7 -6.71439947042818 -0.285600529571817 45 -4 -4.07142417924703 0.0714241792470257 46 -4 -4.05192169573835 0.0519216957383477 47 -2 -2.55565638889264 0.555656388892641 48 0 -0.196079684820423 0.196079684820423 49 -2 -1.74447618632881 -0.255523813671191 50 -3 -3.06347242847346 0.063472428473457 51 1 1.21336278591619 -0.213362785916191 52 -2 -2.48767533802754 0.487675338027538 53 -1 -1.13705545480072 0.137055454800717 54 1 0.923290110169084 0.0767098898309164 55 -3 -2.47380333946715 -0.526196660532852 56 -4 -4.27483859382454 0.274838593824541 57 -9 -8.80938154340936 -0.19061845659064 58 -9 -8.72348554830976 -0.276514451690237 59 -7 -6.7965330941959 -0.203466905804097 60 -14 -14.1606452159621 0.160645215962066

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

\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=186110&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=186110&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186110&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-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.681901367076115 0.63619726584777 0.318098632923885 18 0.522199405422576 0.955601189154847 0.477800594577424 19 0.558707006430138 0.882585987139724 0.441292993569862 20 0.459677982515802 0.919355965031605 0.540322017484198 21 0.355130123169635 0.71026024633927 0.644869876830365 22 0.290302160590421 0.580604321180842 0.709697839409579 23 0.247836383633751 0.495672767267502 0.752163616366249 24 0.186189542705978 0.372379085411957 0.813810457294022 25 0.248485399406257 0.496970798812515 0.751514600593743 26 0.247697905340102 0.495395810680204 0.752302094659898 27 0.190730281438267 0.381460562876535 0.809269718561733 28 0.142646075808273 0.285292151616545 0.857353924191727 29 0.434507470164971 0.869014940329942 0.565492529835029 30 0.483023525965021 0.966047051930043 0.516976474034979 31 0.756986817000512 0.486026365998976 0.243013182999488 32 0.677186564244985 0.64562687151003 0.322813435755015 33 0.626181154674775 0.74763769065045 0.373818845325225 34 0.590960218072599 0.818079563854803 0.409039781927401 35 0.575509033260455 0.84898193347909 0.424490966739545 36 0.507587773898279 0.984824452203441 0.492412226101721 37 0.666461105941355 0.667077788117289 0.333538894058645 38 0.584763894067215 0.83047221186557 0.415236105932785 39 0.884502488457235 0.23099502308553 0.115497511542765 40 0.972963365516659 0.0540732689666816 0.0270366344833408 41 0.979209760755066 0.0415804784898675 0.0207902392449338 42 0.959482936697656 0.0810341266046873 0.0405170633023436 43 0.990711248736035 0.0185775025279303 0.00928875126396515

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 2 0.0740740740740741 NOK 10% type I error level 4 0.148148148148148 NOK

\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=186110&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=186110&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186110&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 tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 2 0.0740740740740741 NOK 10% type I error level 4 0.148148148148148 NOK

library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- 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 <- x1if (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'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$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')}