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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 24 Dec 2010 13:28:49 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/24/t1293197225mwanlz3njiqst8m.htm/, Retrieved Tue, 30 Apr 2024 06:20:25 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114921, Retrieved Tue, 30 Apr 2024 06:20:25 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

145

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [ws 8 auitoregressie] [2010-11-29 18:31:52] [bd591a1ebb67d263a02e7adae3fa1a4d]
-   PD        [Multiple Regression] [autoregressie] [2010-12-24 13:28:49] [09489ba95453d3f5c9e6f2eaeda915af] [Current]

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Dataseries X:

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98.1	102.8	104.7	95.9	94.6
113.9	98.1	102.8	104.7	95.9
80.9	113.9	98.1	102.8	104.7
95.7	80.9	113.9	98.1	102.8
113.2	95.7	80.9	113.9	98.1
105.9	113.2	95.7	80.9	113.9
108.8	105.9	113.2	95.7	80.9
102.3	108.8	105.9	113.2	95.7
99	102.3	108.8	105.9	113.2
100.7	99	102.3	108.8	105.9
115.5	100.7	99	102.3	108.8
100.7	115.5	100.7	99	102.3
109.9	100.7	115.5	100.7	99
114.6	109.9	100.7	115.5	100.7
85.4	114.6	109.9	100.7	115.5
100.5	85.4	114.6	109.9	100.7
114.8	100.5	85.4	114.6	109.9
116.5	114.8	100.5	85.4	114.6
112.9	116.5	114.8	100.5	85.4
102	112.9	116.5	114.8	100.5
106	102	112.9	116.5	114.8
105.3	106	102	112.9	116.5
118.8	105.3	106	102	112.9
106.1	118.8	105.3	106	102
109.3	106.1	118.8	105.3	106
117.2	109.3	106.1	118.8	105.3
92.5	117.2	109.3	106.1	118.8
104.2	92.5	117.2	109.3	106.1
112.5	104.2	92.5	117.2	109.3
122.4	112.5	104.2	92.5	117.2
113.3	122.4	112.5	104.2	92.5
100	113.3	122.4	112.5	104.2
110.7	100	113.3	122.4	112.5
112.8	110.7	100	113.3	122.4
109.8	112.8	110.7	100	113.3
117.3	109.8	112.8	110.7	100
109.1	117.3	109.8	112.8	110.7
115.9	109.1	117.3	109.8	112.8
96	115.9	109.1	117.3	109.8
99.8	96	115.9	109.1	117.3
116.8	99.8	96	115.9	109.1
115.7	116.8	99.8	96	115.9
99.4	115.7	116.8	99.8	96
94.3	99.4	115.7	116.8	99.8
91	94.3	99.4	115.7	116.8
93.2	91	94.3	99.4	115.7
103.1	93.2	91	94.3	99.4
94.1	103.1	93.2	91	94.3
91.8	94.1	103.1	93.2	91
102.7	91.8	94.1	103.1	93.2
82.6	102.7	91.8	94.1	103.1
89.1	82.6	102.7	91.8	94.1
104.5	89.1	82.6	102.7	91.8
105.1	104.5	89.1	82.6	102.7
95.1	105.1	104.5	89.1	82.6
88.7	95.1	105.1	104.5	89.1
86.3	88.7	95.1	105.1	104.5
91.8	86.3	88.7	95.1	105.1
111.5	91.8	86.3	88.7	95.1
99.7	111.5	91.8	86.3	88.7
97.5	99.7	111.5	91.8	86.3
111.7	97.5	99.7	111.5	91.8
86.2	111.7	97.5	99.7	111.5
95.4	86.2	111.7	97.5	99.7

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114921&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114921&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114921&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	5 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation
y[t] = + 60.5979205836162 + 0.27766008955589y1[t] -0.0968386935292209y2[t] + 0.223192273494244y3[t] + 0.00965031680418547y4[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y[t] =  +  60.5979205836162 +  0.27766008955589y1[t] -0.0968386935292209y2[t] +  0.223192273494244y3[t] +  0.00965031680418547y4[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114921&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y[t] =  +  60.5979205836162 +  0.27766008955589y1[t] -0.0968386935292209y2[t] +  0.223192273494244y3[t] +  0.00965031680418547y4[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114921&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114921&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] = + 60.5979205836162 + 0.27766008955589y1[t] -0.0968386935292209y2[t] + 0.223192273494244y3[t] + 0.00965031680418547y4[t] + 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)	60.5979205836162	20.885604	2.9014	0.005214	0.002607
y1	0.27766008955589	0.130195	2.1326	0.037125	0.018563
y2	-0.0968386935292209	0.136263	-0.7107	0.480087	0.240043
y3	0.223192273494244	0.138148	1.6156	0.111515	0.055758
y4	0.00965031680418547	0.134956	0.0715	0.943236	0.471618

\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) & 60.5979205836162 & 20.885604 & 2.9014 & 0.005214 & 0.002607 \tabularnewline
y1 & 0.27766008955589 & 0.130195 & 2.1326 & 0.037125 & 0.018563 \tabularnewline
y2 & -0.0968386935292209 & 0.136263 & -0.7107 & 0.480087 & 0.240043 \tabularnewline
y3 & 0.223192273494244 & 0.138148 & 1.6156 & 0.111515 & 0.055758 \tabularnewline
y4 & 0.00965031680418547 & 0.134956 & 0.0715 & 0.943236 & 0.471618 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114921&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]60.5979205836162[/C][C]20.885604[/C][C]2.9014[/C][C]0.005214[/C][C]0.002607[/C][/ROW]
[ROW][C]y1[/C][C]0.27766008955589[/C][C]0.130195[/C][C]2.1326[/C][C]0.037125[/C][C]0.018563[/C][/ROW]
[ROW][C]y2[/C][C]-0.0968386935292209[/C][C]0.136263[/C][C]-0.7107[/C][C]0.480087[/C][C]0.240043[/C][/ROW]
[ROW][C]y3[/C][C]0.223192273494244[/C][C]0.138148[/C][C]1.6156[/C][C]0.111515[/C][C]0.055758[/C][/ROW]
[ROW][C]y4[/C][C]0.00965031680418547[/C][C]0.134956[/C][C]0.0715[/C][C]0.943236[/C][C]0.471618[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114921&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114921&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)	60.5979205836162	20.885604	2.9014	0.005214	0.002607
y1	0.27766008955589	0.130195	2.1326	0.037125	0.018563
y2	-0.0968386935292209	0.136263	-0.7107	0.480087	0.240043
y3	0.223192273494244	0.138148	1.6156	0.111515	0.055758
y4	0.00965031680418547	0.134956	0.0715	0.943236	0.471618

Multiple Linear Regression - Regression Statistics
Multiple R	0.340153663156316
R-squared	0.115704514558661
Adjusted R-squared	0.0557522782575528
F-TEST (value)	1.92994493112049
F-TEST (DF numerator)	4
F-TEST (DF denominator)	59
p-value	0.117323585280604
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	9.8710825815571
Sum Squared Residuals	5748.85800858327

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.340153663156316 \tabularnewline
R-squared & 0.115704514558661 \tabularnewline
Adjusted R-squared & 0.0557522782575528 \tabularnewline
F-TEST (value) & 1.92994493112049 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0.117323585280604 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.8710825815571 \tabularnewline
Sum Squared Residuals & 5748.85800858327 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114921&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.340153663156316[/C][/ROW]
[ROW][C]R-squared[/C][C]0.115704514558661[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0557522782575528[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.92994493112049[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/C][/ROW]
[ROW][C]p-value[/C][C]0.117323585280604[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]9.8710825815571[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5748.85800858327[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114921&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114921&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	0.340153663156316
R-squared	0.115704514558661
Adjusted R-squared	0.0557522782575528
F-TEST (value)	1.92994493112049
F-TEST (DF numerator)	4
F-TEST (DF denominator)	59
p-value	0.117323585280604
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	9.8710825815571
Sum Squared Residuals	5748.85800858327

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	98.1	101.319425575226	-3.21942557522621
2	113.9	102.175054090614	11.7249459093861
3	80.9	106.678082833422	-25.778082833422
4	95.7	94.917909232965	0.78209076703493
5	113.2	105.704036877086	7.49596312291408
6	105.9	101.917005760278	3.9829942397224
7	108.8	101.180195162935	7.61980483706507
8	102.3	106.741021360262	-4.44102136026155
9	99	103.194975514479	-4.19497551447878
10	100.7	103.484959007347	-2.78495900734703
11	115.5	102.853784989258	12.646215010742
12	100.7	105.999266973927	-5.29926697392731
13	109.9	100.804265803754	9.09573419624593
14	114.6	108.111602478183	6.48839752181733
15	85.4	105.365267959614	-19.9652679596136
16	100.5	98.7129957124394	1.78700428756058
17	114.8	106.871139515808	7.92886048419194
18	116.5	102.907556627114	13.5924433728862
19	112.9	105.083199540972	7.81680045902818
20	102	107.256366734282	-5.25636673428184
21	106	105.095917450068	0.904082549932102
22	105.3	106.475012921748	-1.17501292174781
23	118.8	103.425759163359	15.3742408366405
24	106.1	108.029538098646	-1.9295380986458
25	109.3	103.078299274412	6.22170072558772
26	117.2	108.203003439222	8.9969965607784
27	92.5	107.382371730899	-14.8823717308992
28	104.2	100.350798091756	3.84920190824368
29	112.5	107.78543684411	4.71456315589008
30	122.4	103.520391220577	18.8796087794229
31	113.3	107.838451725707	5.46154827429279
32	100	106.318446421421	-6.31844642142051
33	110.7	105.796500478511	4.90349952148915
34	112.8	108.119906508261	4.68009349173868
35	109.8	104.610543555174	5.18945644482551
36	117.3	105.834010142988	11.4659898570118
37	109.1	108.778939059388	0.321060940612258
38	115.9	105.126524968366	10.7734750316337
39	96	109.45368196508	-13.4536819650803
40	99.8	101.511943800298	-1.71194380029795
41	116.8	105.932717003808	10.8672829961916
42	115.7	105.90904740258	9.79095259741955
43	99.4	104.613452848947	-5.21345284894706
44	94.3	104.025055805326	-9.72505580532626
45	91	104.106003937945	-13.106003937945
46	93.2	100.034953572969	-6.8349535729688
47	103.1	99.6697926999093	3.43020730009068
48	94.1	101.419831342516	-7.319831342516
49	91.8	98.4213644268072	-6.62136442680722
50	102.7	100.885128667154	1.81487133284612
51	82.6	102.221160313344	-19.6211603133435
52	89.1	94.9844556735272	-5.88445567352721
53	104.5	101.146304048015	3.35369595198453
54	105.1	100.411841675168	4.68815832483245
55	95.1	100.3439002585	-5.24390025849954
56	88.7	101.009084217862	-12.3090842178617
57	86.3	100.482976822877	-14.1829768228772
58	91.8	98.2102277016701	-6.41022770167014
59	111.5	98.4448373402927	13.0551626597074
60	99.7	102.7847048062	-3.08470480619999
61	97.5	98.8049902308031	-1.30499023080314
62	111.7	103.786799147685	7.91320085231538
63	86.2	105.499059958953	-19.2990599589529
64	95.4	96.438721487186	-1.03872148718605

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 98.1 & 101.319425575226 & -3.21942557522621 \tabularnewline
2 & 113.9 & 102.175054090614 & 11.7249459093861 \tabularnewline
3 & 80.9 & 106.678082833422 & -25.778082833422 \tabularnewline
4 & 95.7 & 94.917909232965 & 0.78209076703493 \tabularnewline
5 & 113.2 & 105.704036877086 & 7.49596312291408 \tabularnewline
6 & 105.9 & 101.917005760278 & 3.9829942397224 \tabularnewline
7 & 108.8 & 101.180195162935 & 7.61980483706507 \tabularnewline
8 & 102.3 & 106.741021360262 & -4.44102136026155 \tabularnewline
9 & 99 & 103.194975514479 & -4.19497551447878 \tabularnewline
10 & 100.7 & 103.484959007347 & -2.78495900734703 \tabularnewline
11 & 115.5 & 102.853784989258 & 12.646215010742 \tabularnewline
12 & 100.7 & 105.999266973927 & -5.29926697392731 \tabularnewline
13 & 109.9 & 100.804265803754 & 9.09573419624593 \tabularnewline
14 & 114.6 & 108.111602478183 & 6.48839752181733 \tabularnewline
15 & 85.4 & 105.365267959614 & -19.9652679596136 \tabularnewline
16 & 100.5 & 98.7129957124394 & 1.78700428756058 \tabularnewline
17 & 114.8 & 106.871139515808 & 7.92886048419194 \tabularnewline
18 & 116.5 & 102.907556627114 & 13.5924433728862 \tabularnewline
19 & 112.9 & 105.083199540972 & 7.81680045902818 \tabularnewline
20 & 102 & 107.256366734282 & -5.25636673428184 \tabularnewline
21 & 106 & 105.095917450068 & 0.904082549932102 \tabularnewline
22 & 105.3 & 106.475012921748 & -1.17501292174781 \tabularnewline
23 & 118.8 & 103.425759163359 & 15.3742408366405 \tabularnewline
24 & 106.1 & 108.029538098646 & -1.9295380986458 \tabularnewline
25 & 109.3 & 103.078299274412 & 6.22170072558772 \tabularnewline
26 & 117.2 & 108.203003439222 & 8.9969965607784 \tabularnewline
27 & 92.5 & 107.382371730899 & -14.8823717308992 \tabularnewline
28 & 104.2 & 100.350798091756 & 3.84920190824368 \tabularnewline
29 & 112.5 & 107.78543684411 & 4.71456315589008 \tabularnewline
30 & 122.4 & 103.520391220577 & 18.8796087794229 \tabularnewline
31 & 113.3 & 107.838451725707 & 5.46154827429279 \tabularnewline
32 & 100 & 106.318446421421 & -6.31844642142051 \tabularnewline
33 & 110.7 & 105.796500478511 & 4.90349952148915 \tabularnewline
34 & 112.8 & 108.119906508261 & 4.68009349173868 \tabularnewline
35 & 109.8 & 104.610543555174 & 5.18945644482551 \tabularnewline
36 & 117.3 & 105.834010142988 & 11.4659898570118 \tabularnewline
37 & 109.1 & 108.778939059388 & 0.321060940612258 \tabularnewline
38 & 115.9 & 105.126524968366 & 10.7734750316337 \tabularnewline
39 & 96 & 109.45368196508 & -13.4536819650803 \tabularnewline
40 & 99.8 & 101.511943800298 & -1.71194380029795 \tabularnewline
41 & 116.8 & 105.932717003808 & 10.8672829961916 \tabularnewline
42 & 115.7 & 105.90904740258 & 9.79095259741955 \tabularnewline
43 & 99.4 & 104.613452848947 & -5.21345284894706 \tabularnewline
44 & 94.3 & 104.025055805326 & -9.72505580532626 \tabularnewline
45 & 91 & 104.106003937945 & -13.106003937945 \tabularnewline
46 & 93.2 & 100.034953572969 & -6.8349535729688 \tabularnewline
47 & 103.1 & 99.6697926999093 & 3.43020730009068 \tabularnewline
48 & 94.1 & 101.419831342516 & -7.319831342516 \tabularnewline
49 & 91.8 & 98.4213644268072 & -6.62136442680722 \tabularnewline
50 & 102.7 & 100.885128667154 & 1.81487133284612 \tabularnewline
51 & 82.6 & 102.221160313344 & -19.6211603133435 \tabularnewline
52 & 89.1 & 94.9844556735272 & -5.88445567352721 \tabularnewline
53 & 104.5 & 101.146304048015 & 3.35369595198453 \tabularnewline
54 & 105.1 & 100.411841675168 & 4.68815832483245 \tabularnewline
55 & 95.1 & 100.3439002585 & -5.24390025849954 \tabularnewline
56 & 88.7 & 101.009084217862 & -12.3090842178617 \tabularnewline
57 & 86.3 & 100.482976822877 & -14.1829768228772 \tabularnewline
58 & 91.8 & 98.2102277016701 & -6.41022770167014 \tabularnewline
59 & 111.5 & 98.4448373402927 & 13.0551626597074 \tabularnewline
60 & 99.7 & 102.7847048062 & -3.08470480619999 \tabularnewline
61 & 97.5 & 98.8049902308031 & -1.30499023080314 \tabularnewline
62 & 111.7 & 103.786799147685 & 7.91320085231538 \tabularnewline
63 & 86.2 & 105.499059958953 & -19.2990599589529 \tabularnewline
64 & 95.4 & 96.438721487186 & -1.03872148718605 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114921&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]98.1[/C][C]101.319425575226[/C][C]-3.21942557522621[/C][/ROW]
[ROW][C]2[/C][C]113.9[/C][C]102.175054090614[/C][C]11.7249459093861[/C][/ROW]
[ROW][C]3[/C][C]80.9[/C][C]106.678082833422[/C][C]-25.778082833422[/C][/ROW]
[ROW][C]4[/C][C]95.7[/C][C]94.917909232965[/C][C]0.78209076703493[/C][/ROW]
[ROW][C]5[/C][C]113.2[/C][C]105.704036877086[/C][C]7.49596312291408[/C][/ROW]
[ROW][C]6[/C][C]105.9[/C][C]101.917005760278[/C][C]3.9829942397224[/C][/ROW]
[ROW][C]7[/C][C]108.8[/C][C]101.180195162935[/C][C]7.61980483706507[/C][/ROW]
[ROW][C]8[/C][C]102.3[/C][C]106.741021360262[/C][C]-4.44102136026155[/C][/ROW]
[ROW][C]9[/C][C]99[/C][C]103.194975514479[/C][C]-4.19497551447878[/C][/ROW]
[ROW][C]10[/C][C]100.7[/C][C]103.484959007347[/C][C]-2.78495900734703[/C][/ROW]
[ROW][C]11[/C][C]115.5[/C][C]102.853784989258[/C][C]12.646215010742[/C][/ROW]
[ROW][C]12[/C][C]100.7[/C][C]105.999266973927[/C][C]-5.29926697392731[/C][/ROW]
[ROW][C]13[/C][C]109.9[/C][C]100.804265803754[/C][C]9.09573419624593[/C][/ROW]
[ROW][C]14[/C][C]114.6[/C][C]108.111602478183[/C][C]6.48839752181733[/C][/ROW]
[ROW][C]15[/C][C]85.4[/C][C]105.365267959614[/C][C]-19.9652679596136[/C][/ROW]
[ROW][C]16[/C][C]100.5[/C][C]98.7129957124394[/C][C]1.78700428756058[/C][/ROW]
[ROW][C]17[/C][C]114.8[/C][C]106.871139515808[/C][C]7.92886048419194[/C][/ROW]
[ROW][C]18[/C][C]116.5[/C][C]102.907556627114[/C][C]13.5924433728862[/C][/ROW]
[ROW][C]19[/C][C]112.9[/C][C]105.083199540972[/C][C]7.81680045902818[/C][/ROW]
[ROW][C]20[/C][C]102[/C][C]107.256366734282[/C][C]-5.25636673428184[/C][/ROW]
[ROW][C]21[/C][C]106[/C][C]105.095917450068[/C][C]0.904082549932102[/C][/ROW]
[ROW][C]22[/C][C]105.3[/C][C]106.475012921748[/C][C]-1.17501292174781[/C][/ROW]
[ROW][C]23[/C][C]118.8[/C][C]103.425759163359[/C][C]15.3742408366405[/C][/ROW]
[ROW][C]24[/C][C]106.1[/C][C]108.029538098646[/C][C]-1.9295380986458[/C][/ROW]
[ROW][C]25[/C][C]109.3[/C][C]103.078299274412[/C][C]6.22170072558772[/C][/ROW]
[ROW][C]26[/C][C]117.2[/C][C]108.203003439222[/C][C]8.9969965607784[/C][/ROW]
[ROW][C]27[/C][C]92.5[/C][C]107.382371730899[/C][C]-14.8823717308992[/C][/ROW]
[ROW][C]28[/C][C]104.2[/C][C]100.350798091756[/C][C]3.84920190824368[/C][/ROW]
[ROW][C]29[/C][C]112.5[/C][C]107.78543684411[/C][C]4.71456315589008[/C][/ROW]
[ROW][C]30[/C][C]122.4[/C][C]103.520391220577[/C][C]18.8796087794229[/C][/ROW]
[ROW][C]31[/C][C]113.3[/C][C]107.838451725707[/C][C]5.46154827429279[/C][/ROW]
[ROW][C]32[/C][C]100[/C][C]106.318446421421[/C][C]-6.31844642142051[/C][/ROW]
[ROW][C]33[/C][C]110.7[/C][C]105.796500478511[/C][C]4.90349952148915[/C][/ROW]
[ROW][C]34[/C][C]112.8[/C][C]108.119906508261[/C][C]4.68009349173868[/C][/ROW]
[ROW][C]35[/C][C]109.8[/C][C]104.610543555174[/C][C]5.18945644482551[/C][/ROW]
[ROW][C]36[/C][C]117.3[/C][C]105.834010142988[/C][C]11.4659898570118[/C][/ROW]
[ROW][C]37[/C][C]109.1[/C][C]108.778939059388[/C][C]0.321060940612258[/C][/ROW]
[ROW][C]38[/C][C]115.9[/C][C]105.126524968366[/C][C]10.7734750316337[/C][/ROW]
[ROW][C]39[/C][C]96[/C][C]109.45368196508[/C][C]-13.4536819650803[/C][/ROW]
[ROW][C]40[/C][C]99.8[/C][C]101.511943800298[/C][C]-1.71194380029795[/C][/ROW]
[ROW][C]41[/C][C]116.8[/C][C]105.932717003808[/C][C]10.8672829961916[/C][/ROW]
[ROW][C]42[/C][C]115.7[/C][C]105.90904740258[/C][C]9.79095259741955[/C][/ROW]
[ROW][C]43[/C][C]99.4[/C][C]104.613452848947[/C][C]-5.21345284894706[/C][/ROW]
[ROW][C]44[/C][C]94.3[/C][C]104.025055805326[/C][C]-9.72505580532626[/C][/ROW]
[ROW][C]45[/C][C]91[/C][C]104.106003937945[/C][C]-13.106003937945[/C][/ROW]
[ROW][C]46[/C][C]93.2[/C][C]100.034953572969[/C][C]-6.8349535729688[/C][/ROW]
[ROW][C]47[/C][C]103.1[/C][C]99.6697926999093[/C][C]3.43020730009068[/C][/ROW]
[ROW][C]48[/C][C]94.1[/C][C]101.419831342516[/C][C]-7.319831342516[/C][/ROW]
[ROW][C]49[/C][C]91.8[/C][C]98.4213644268072[/C][C]-6.62136442680722[/C][/ROW]
[ROW][C]50[/C][C]102.7[/C][C]100.885128667154[/C][C]1.81487133284612[/C][/ROW]
[ROW][C]51[/C][C]82.6[/C][C]102.221160313344[/C][C]-19.6211603133435[/C][/ROW]
[ROW][C]52[/C][C]89.1[/C][C]94.9844556735272[/C][C]-5.88445567352721[/C][/ROW]
[ROW][C]53[/C][C]104.5[/C][C]101.146304048015[/C][C]3.35369595198453[/C][/ROW]
[ROW][C]54[/C][C]105.1[/C][C]100.411841675168[/C][C]4.68815832483245[/C][/ROW]
[ROW][C]55[/C][C]95.1[/C][C]100.3439002585[/C][C]-5.24390025849954[/C][/ROW]
[ROW][C]56[/C][C]88.7[/C][C]101.009084217862[/C][C]-12.3090842178617[/C][/ROW]
[ROW][C]57[/C][C]86.3[/C][C]100.482976822877[/C][C]-14.1829768228772[/C][/ROW]
[ROW][C]58[/C][C]91.8[/C][C]98.2102277016701[/C][C]-6.41022770167014[/C][/ROW]
[ROW][C]59[/C][C]111.5[/C][C]98.4448373402927[/C][C]13.0551626597074[/C][/ROW]
[ROW][C]60[/C][C]99.7[/C][C]102.7847048062[/C][C]-3.08470480619999[/C][/ROW]
[ROW][C]61[/C][C]97.5[/C][C]98.8049902308031[/C][C]-1.30499023080314[/C][/ROW]
[ROW][C]62[/C][C]111.7[/C][C]103.786799147685[/C][C]7.91320085231538[/C][/ROW]
[ROW][C]63[/C][C]86.2[/C][C]105.499059958953[/C][C]-19.2990599589529[/C][/ROW]
[ROW][C]64[/C][C]95.4[/C][C]96.438721487186[/C][C]-1.03872148718605[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114921&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114921&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	98.1	101.319425575226	-3.21942557522621
2	113.9	102.175054090614	11.7249459093861
3	80.9	106.678082833422	-25.778082833422
4	95.7	94.917909232965	0.78209076703493
5	113.2	105.704036877086	7.49596312291408
6	105.9	101.917005760278	3.9829942397224
7	108.8	101.180195162935	7.61980483706507
8	102.3	106.741021360262	-4.44102136026155
9	99	103.194975514479	-4.19497551447878
10	100.7	103.484959007347	-2.78495900734703
11	115.5	102.853784989258	12.646215010742
12	100.7	105.999266973927	-5.29926697392731
13	109.9	100.804265803754	9.09573419624593
14	114.6	108.111602478183	6.48839752181733
15	85.4	105.365267959614	-19.9652679596136
16	100.5	98.7129957124394	1.78700428756058
17	114.8	106.871139515808	7.92886048419194
18	116.5	102.907556627114	13.5924433728862
19	112.9	105.083199540972	7.81680045902818
20	102	107.256366734282	-5.25636673428184
21	106	105.095917450068	0.904082549932102
22	105.3	106.475012921748	-1.17501292174781
23	118.8	103.425759163359	15.3742408366405
24	106.1	108.029538098646	-1.9295380986458
25	109.3	103.078299274412	6.22170072558772
26	117.2	108.203003439222	8.9969965607784
27	92.5	107.382371730899	-14.8823717308992
28	104.2	100.350798091756	3.84920190824368
29	112.5	107.78543684411	4.71456315589008
30	122.4	103.520391220577	18.8796087794229
31	113.3	107.838451725707	5.46154827429279
32	100	106.318446421421	-6.31844642142051
33	110.7	105.796500478511	4.90349952148915
34	112.8	108.119906508261	4.68009349173868
35	109.8	104.610543555174	5.18945644482551
36	117.3	105.834010142988	11.4659898570118
37	109.1	108.778939059388	0.321060940612258
38	115.9	105.126524968366	10.7734750316337
39	96	109.45368196508	-13.4536819650803
40	99.8	101.511943800298	-1.71194380029795
41	116.8	105.932717003808	10.8672829961916
42	115.7	105.90904740258	9.79095259741955
43	99.4	104.613452848947	-5.21345284894706
44	94.3	104.025055805326	-9.72505580532626
45	91	104.106003937945	-13.106003937945
46	93.2	100.034953572969	-6.8349535729688
47	103.1	99.6697926999093	3.43020730009068
48	94.1	101.419831342516	-7.319831342516
49	91.8	98.4213644268072	-6.62136442680722
50	102.7	100.885128667154	1.81487133284612
51	82.6	102.221160313344	-19.6211603133435
52	89.1	94.9844556735272	-5.88445567352721
53	104.5	101.146304048015	3.35369595198453
54	105.1	100.411841675168	4.68815832483245
55	95.1	100.3439002585	-5.24390025849954
56	88.7	101.009084217862	-12.3090842178617
57	86.3	100.482976822877	-14.1829768228772
58	91.8	98.2102277016701	-6.41022770167014
59	111.5	98.4448373402927	13.0551626597074
60	99.7	102.7847048062	-3.08470480619999
61	97.5	98.8049902308031	-1.30499023080314
62	111.7	103.786799147685	7.91320085231538
63	86.2	105.499059958953	-19.2990599589529
64	95.4	96.438721487186	-1.03872148718605

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.82991480519594	0.34017038960812	0.17008519480406
9	0.798040442983934	0.403919114032131	0.201959557016066
10	0.686996723193065	0.626006553613869	0.313003276806935
11	0.757800565083879	0.484398869832242	0.242199434916121
12	0.661294332590711	0.677411334818578	0.338705667409289
13	0.654017250509373	0.691965498981253	0.345982749490627
14	0.663309500435914	0.673380999128172	0.336690499564086
15	0.705127069948046	0.589745860103908	0.294872930051954
16	0.628053123846523	0.743893752306954	0.371946876153477
17	0.575231269855698	0.849537460288605	0.424768730144302
18	0.696538705207561	0.606922589584878	0.303461294792439
19	0.683485245246839	0.633029509506323	0.316514754753162
20	0.618117550839415	0.76376489832117	0.381882449160585
21	0.579353473945914	0.841293052108173	0.420646526054086
22	0.502439888524176	0.995120222951647	0.497560111475824
23	0.627007383767645	0.74598523246471	0.372992616232355
24	0.550170468170604	0.899659063658793	0.449829531829396
25	0.515911293503391	0.968177412993219	0.484088706496609
26	0.522364513199622	0.955270973600755	0.477635486800378
27	0.573312674948786	0.853374650102428	0.426687325051214
28	0.51019538366659	0.97960923266682	0.48980461633341
29	0.446712730612908	0.893425461225815	0.553287269387092
30	0.645412424408368	0.709175151183263	0.354587575591632
31	0.603070058499436	0.793859883001128	0.396929941500564
32	0.539523504511814	0.920952990976373	0.460476495488186
33	0.497506506919947	0.995013013839895	0.502493493080053
34	0.448662031742891	0.897324063485781	0.55133796825711
35	0.404831524236407	0.809663048472814	0.595168475763593
36	0.463240869927506	0.926481739855012	0.536759130072494
37	0.396317119224367	0.792634238448734	0.603682880775633
38	0.517324454596569	0.965351090806861	0.482675545403431
39	0.508010768607009	0.983978462785983	0.491989231392991
40	0.469196984544606	0.938393969089213	0.530803015455394
41	0.570977416373508	0.858045167252983	0.429022583626492
42	0.813316820488018	0.373366359023964	0.186683179511982
43	0.808981517504465	0.38203696499107	0.191018482495535
44	0.783360295935548	0.433279408128904	0.216639704064452
45	0.771071096988574	0.457857806022851	0.228928903011426
46	0.747540591491066	0.504918817017867	0.252459408508934
47	0.69900746352573	0.60198507294854	0.30099253647427
48	0.657065960055783	0.685868079888434	0.342934039944217
49	0.599767457016662	0.800465085966677	0.400232542983338
50	0.504407932055155	0.99118413588969	0.495592067944845
51	0.634666307338715	0.73066738532257	0.365333692661285
52	0.58168282439732	0.83663435120536	0.41831717560268
53	0.465330490546769	0.930660981093538	0.534669509453231
54	0.427123544514592	0.854247089029185	0.572876455485408
55	0.338076957562247	0.676153915124493	0.661923042437753
56	0.469255693420774	0.938511386841548	0.530744306579226

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.82991480519594 & 0.34017038960812 & 0.17008519480406 \tabularnewline
9 & 0.798040442983934 & 0.403919114032131 & 0.201959557016066 \tabularnewline
10 & 0.686996723193065 & 0.626006553613869 & 0.313003276806935 \tabularnewline
11 & 0.757800565083879 & 0.484398869832242 & 0.242199434916121 \tabularnewline
12 & 0.661294332590711 & 0.677411334818578 & 0.338705667409289 \tabularnewline
13 & 0.654017250509373 & 0.691965498981253 & 0.345982749490627 \tabularnewline
14 & 0.663309500435914 & 0.673380999128172 & 0.336690499564086 \tabularnewline
15 & 0.705127069948046 & 0.589745860103908 & 0.294872930051954 \tabularnewline
16 & 0.628053123846523 & 0.743893752306954 & 0.371946876153477 \tabularnewline
17 & 0.575231269855698 & 0.849537460288605 & 0.424768730144302 \tabularnewline
18 & 0.696538705207561 & 0.606922589584878 & 0.303461294792439 \tabularnewline
19 & 0.683485245246839 & 0.633029509506323 & 0.316514754753162 \tabularnewline
20 & 0.618117550839415 & 0.76376489832117 & 0.381882449160585 \tabularnewline
21 & 0.579353473945914 & 0.841293052108173 & 0.420646526054086 \tabularnewline
22 & 0.502439888524176 & 0.995120222951647 & 0.497560111475824 \tabularnewline
23 & 0.627007383767645 & 0.74598523246471 & 0.372992616232355 \tabularnewline
24 & 0.550170468170604 & 0.899659063658793 & 0.449829531829396 \tabularnewline
25 & 0.515911293503391 & 0.968177412993219 & 0.484088706496609 \tabularnewline
26 & 0.522364513199622 & 0.955270973600755 & 0.477635486800378 \tabularnewline
27 & 0.573312674948786 & 0.853374650102428 & 0.426687325051214 \tabularnewline
28 & 0.51019538366659 & 0.97960923266682 & 0.48980461633341 \tabularnewline
29 & 0.446712730612908 & 0.893425461225815 & 0.553287269387092 \tabularnewline
30 & 0.645412424408368 & 0.709175151183263 & 0.354587575591632 \tabularnewline
31 & 0.603070058499436 & 0.793859883001128 & 0.396929941500564 \tabularnewline
32 & 0.539523504511814 & 0.920952990976373 & 0.460476495488186 \tabularnewline
33 & 0.497506506919947 & 0.995013013839895 & 0.502493493080053 \tabularnewline
34 & 0.448662031742891 & 0.897324063485781 & 0.55133796825711 \tabularnewline
35 & 0.404831524236407 & 0.809663048472814 & 0.595168475763593 \tabularnewline
36 & 0.463240869927506 & 0.926481739855012 & 0.536759130072494 \tabularnewline
37 & 0.396317119224367 & 0.792634238448734 & 0.603682880775633 \tabularnewline
38 & 0.517324454596569 & 0.965351090806861 & 0.482675545403431 \tabularnewline
39 & 0.508010768607009 & 0.983978462785983 & 0.491989231392991 \tabularnewline
40 & 0.469196984544606 & 0.938393969089213 & 0.530803015455394 \tabularnewline
41 & 0.570977416373508 & 0.858045167252983 & 0.429022583626492 \tabularnewline
42 & 0.813316820488018 & 0.373366359023964 & 0.186683179511982 \tabularnewline
43 & 0.808981517504465 & 0.38203696499107 & 0.191018482495535 \tabularnewline
44 & 0.783360295935548 & 0.433279408128904 & 0.216639704064452 \tabularnewline
45 & 0.771071096988574 & 0.457857806022851 & 0.228928903011426 \tabularnewline
46 & 0.747540591491066 & 0.504918817017867 & 0.252459408508934 \tabularnewline
47 & 0.69900746352573 & 0.60198507294854 & 0.30099253647427 \tabularnewline
48 & 0.657065960055783 & 0.685868079888434 & 0.342934039944217 \tabularnewline
49 & 0.599767457016662 & 0.800465085966677 & 0.400232542983338 \tabularnewline
50 & 0.504407932055155 & 0.99118413588969 & 0.495592067944845 \tabularnewline
51 & 0.634666307338715 & 0.73066738532257 & 0.365333692661285 \tabularnewline
52 & 0.58168282439732 & 0.83663435120536 & 0.41831717560268 \tabularnewline
53 & 0.465330490546769 & 0.930660981093538 & 0.534669509453231 \tabularnewline
54 & 0.427123544514592 & 0.854247089029185 & 0.572876455485408 \tabularnewline
55 & 0.338076957562247 & 0.676153915124493 & 0.661923042437753 \tabularnewline
56 & 0.469255693420774 & 0.938511386841548 & 0.530744306579226 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114921&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]8[/C][C]0.82991480519594[/C][C]0.34017038960812[/C][C]0.17008519480406[/C][/ROW]
[ROW][C]9[/C][C]0.798040442983934[/C][C]0.403919114032131[/C][C]0.201959557016066[/C][/ROW]
[ROW][C]10[/C][C]0.686996723193065[/C][C]0.626006553613869[/C][C]0.313003276806935[/C][/ROW]
[ROW][C]11[/C][C]0.757800565083879[/C][C]0.484398869832242[/C][C]0.242199434916121[/C][/ROW]
[ROW][C]12[/C][C]0.661294332590711[/C][C]0.677411334818578[/C][C]0.338705667409289[/C][/ROW]
[ROW][C]13[/C][C]0.654017250509373[/C][C]0.691965498981253[/C][C]0.345982749490627[/C][/ROW]
[ROW][C]14[/C][C]0.663309500435914[/C][C]0.673380999128172[/C][C]0.336690499564086[/C][/ROW]
[ROW][C]15[/C][C]0.705127069948046[/C][C]0.589745860103908[/C][C]0.294872930051954[/C][/ROW]
[ROW][C]16[/C][C]0.628053123846523[/C][C]0.743893752306954[/C][C]0.371946876153477[/C][/ROW]
[ROW][C]17[/C][C]0.575231269855698[/C][C]0.849537460288605[/C][C]0.424768730144302[/C][/ROW]
[ROW][C]18[/C][C]0.696538705207561[/C][C]0.606922589584878[/C][C]0.303461294792439[/C][/ROW]
[ROW][C]19[/C][C]0.683485245246839[/C][C]0.633029509506323[/C][C]0.316514754753162[/C][/ROW]
[ROW][C]20[/C][C]0.618117550839415[/C][C]0.76376489832117[/C][C]0.381882449160585[/C][/ROW]
[ROW][C]21[/C][C]0.579353473945914[/C][C]0.841293052108173[/C][C]0.420646526054086[/C][/ROW]
[ROW][C]22[/C][C]0.502439888524176[/C][C]0.995120222951647[/C][C]0.497560111475824[/C][/ROW]
[ROW][C]23[/C][C]0.627007383767645[/C][C]0.74598523246471[/C][C]0.372992616232355[/C][/ROW]
[ROW][C]24[/C][C]0.550170468170604[/C][C]0.899659063658793[/C][C]0.449829531829396[/C][/ROW]
[ROW][C]25[/C][C]0.515911293503391[/C][C]0.968177412993219[/C][C]0.484088706496609[/C][/ROW]
[ROW][C]26[/C][C]0.522364513199622[/C][C]0.955270973600755[/C][C]0.477635486800378[/C][/ROW]
[ROW][C]27[/C][C]0.573312674948786[/C][C]0.853374650102428[/C][C]0.426687325051214[/C][/ROW]
[ROW][C]28[/C][C]0.51019538366659[/C][C]0.97960923266682[/C][C]0.48980461633341[/C][/ROW]
[ROW][C]29[/C][C]0.446712730612908[/C][C]0.893425461225815[/C][C]0.553287269387092[/C][/ROW]
[ROW][C]30[/C][C]0.645412424408368[/C][C]0.709175151183263[/C][C]0.354587575591632[/C][/ROW]
[ROW][C]31[/C][C]0.603070058499436[/C][C]0.793859883001128[/C][C]0.396929941500564[/C][/ROW]
[ROW][C]32[/C][C]0.539523504511814[/C][C]0.920952990976373[/C][C]0.460476495488186[/C][/ROW]
[ROW][C]33[/C][C]0.497506506919947[/C][C]0.995013013839895[/C][C]0.502493493080053[/C][/ROW]
[ROW][C]34[/C][C]0.448662031742891[/C][C]0.897324063485781[/C][C]0.55133796825711[/C][/ROW]
[ROW][C]35[/C][C]0.404831524236407[/C][C]0.809663048472814[/C][C]0.595168475763593[/C][/ROW]
[ROW][C]36[/C][C]0.463240869927506[/C][C]0.926481739855012[/C][C]0.536759130072494[/C][/ROW]
[ROW][C]37[/C][C]0.396317119224367[/C][C]0.792634238448734[/C][C]0.603682880775633[/C][/ROW]
[ROW][C]38[/C][C]0.517324454596569[/C][C]0.965351090806861[/C][C]0.482675545403431[/C][/ROW]
[ROW][C]39[/C][C]0.508010768607009[/C][C]0.983978462785983[/C][C]0.491989231392991[/C][/ROW]
[ROW][C]40[/C][C]0.469196984544606[/C][C]0.938393969089213[/C][C]0.530803015455394[/C][/ROW]
[ROW][C]41[/C][C]0.570977416373508[/C][C]0.858045167252983[/C][C]0.429022583626492[/C][/ROW]
[ROW][C]42[/C][C]0.813316820488018[/C][C]0.373366359023964[/C][C]0.186683179511982[/C][/ROW]
[ROW][C]43[/C][C]0.808981517504465[/C][C]0.38203696499107[/C][C]0.191018482495535[/C][/ROW]
[ROW][C]44[/C][C]0.783360295935548[/C][C]0.433279408128904[/C][C]0.216639704064452[/C][/ROW]
[ROW][C]45[/C][C]0.771071096988574[/C][C]0.457857806022851[/C][C]0.228928903011426[/C][/ROW]
[ROW][C]46[/C][C]0.747540591491066[/C][C]0.504918817017867[/C][C]0.252459408508934[/C][/ROW]
[ROW][C]47[/C][C]0.69900746352573[/C][C]0.60198507294854[/C][C]0.30099253647427[/C][/ROW]
[ROW][C]48[/C][C]0.657065960055783[/C][C]0.685868079888434[/C][C]0.342934039944217[/C][/ROW]
[ROW][C]49[/C][C]0.599767457016662[/C][C]0.800465085966677[/C][C]0.400232542983338[/C][/ROW]
[ROW][C]50[/C][C]0.504407932055155[/C][C]0.99118413588969[/C][C]0.495592067944845[/C][/ROW]
[ROW][C]51[/C][C]0.634666307338715[/C][C]0.73066738532257[/C][C]0.365333692661285[/C][/ROW]
[ROW][C]52[/C][C]0.58168282439732[/C][C]0.83663435120536[/C][C]0.41831717560268[/C][/ROW]
[ROW][C]53[/C][C]0.465330490546769[/C][C]0.930660981093538[/C][C]0.534669509453231[/C][/ROW]
[ROW][C]54[/C][C]0.427123544514592[/C][C]0.854247089029185[/C][C]0.572876455485408[/C][/ROW]
[ROW][C]55[/C][C]0.338076957562247[/C][C]0.676153915124493[/C][C]0.661923042437753[/C][/ROW]
[ROW][C]56[/C][C]0.469255693420774[/C][C]0.938511386841548[/C][C]0.530744306579226[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114921&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114921&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
8	0.82991480519594	0.34017038960812	0.17008519480406
9	0.798040442983934	0.403919114032131	0.201959557016066
10	0.686996723193065	0.626006553613869	0.313003276806935
11	0.757800565083879	0.484398869832242	0.242199434916121
12	0.661294332590711	0.677411334818578	0.338705667409289
13	0.654017250509373	0.691965498981253	0.345982749490627
14	0.663309500435914	0.673380999128172	0.336690499564086
15	0.705127069948046	0.589745860103908	0.294872930051954
16	0.628053123846523	0.743893752306954	0.371946876153477
17	0.575231269855698	0.849537460288605	0.424768730144302
18	0.696538705207561	0.606922589584878	0.303461294792439
19	0.683485245246839	0.633029509506323	0.316514754753162
20	0.618117550839415	0.76376489832117	0.381882449160585
21	0.579353473945914	0.841293052108173	0.420646526054086
22	0.502439888524176	0.995120222951647	0.497560111475824
23	0.627007383767645	0.74598523246471	0.372992616232355
24	0.550170468170604	0.899659063658793	0.449829531829396
25	0.515911293503391	0.968177412993219	0.484088706496609
26	0.522364513199622	0.955270973600755	0.477635486800378
27	0.573312674948786	0.853374650102428	0.426687325051214
28	0.51019538366659	0.97960923266682	0.48980461633341
29	0.446712730612908	0.893425461225815	0.553287269387092
30	0.645412424408368	0.709175151183263	0.354587575591632
31	0.603070058499436	0.793859883001128	0.396929941500564
32	0.539523504511814	0.920952990976373	0.460476495488186
33	0.497506506919947	0.995013013839895	0.502493493080053
34	0.448662031742891	0.897324063485781	0.55133796825711
35	0.404831524236407	0.809663048472814	0.595168475763593
36	0.463240869927506	0.926481739855012	0.536759130072494
37	0.396317119224367	0.792634238448734	0.603682880775633
38	0.517324454596569	0.965351090806861	0.482675545403431
39	0.508010768607009	0.983978462785983	0.491989231392991
40	0.469196984544606	0.938393969089213	0.530803015455394
41	0.570977416373508	0.858045167252983	0.429022583626492
42	0.813316820488018	0.373366359023964	0.186683179511982
43	0.808981517504465	0.38203696499107	0.191018482495535
44	0.783360295935548	0.433279408128904	0.216639704064452
45	0.771071096988574	0.457857806022851	0.228928903011426
46	0.747540591491066	0.504918817017867	0.252459408508934
47	0.69900746352573	0.60198507294854	0.30099253647427
48	0.657065960055783	0.685868079888434	0.342934039944217
49	0.599767457016662	0.800465085966677	0.400232542983338
50	0.504407932055155	0.99118413588969	0.495592067944845
51	0.634666307338715	0.73066738532257	0.365333692661285
52	0.58168282439732	0.83663435120536	0.41831717560268
53	0.465330490546769	0.930660981093538	0.534669509453231
54	0.427123544514592	0.854247089029185	0.572876455485408
55	0.338076957562247	0.676153915124493	0.661923042437753
56	0.469255693420774	0.938511386841548	0.530744306579226

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	0	0	OK
10% type I error level	0	0	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114921&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114921&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114921&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	0	0	OK
10% type I error level	0	0	OK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code