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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 19 Nov 2009 01:06:13 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258618008z9t0ngjvas9el2q.htm/, Retrieved Sat, 10 May 2025 01:28:44 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57642, Retrieved Sat, 10 May 2025 01:28:44 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

352

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 14:06:21] [b98453cac15ba1066b407e146608df68]
- R PD      [Multiple Regression] [] [2009-11-19 08:06:13] [2795ec65528c1a16d9df20713e7edc71] [Current]
-   PD        [Multiple Regression] [] [2009-11-19 08:07:59] [639dd97b6eeebe46a3c92d62cb04fb95] 
-   P           [Multiple Regression] [] [2009-11-19 08:09:26] [639dd97b6eeebe46a3c92d62cb04fb95] 
-    D            [Multiple Regression] [] [2009-11-19 08:16:57] [639dd97b6eeebe46a3c92d62cb04fb95] 
- R  D              [Multiple Regression] [model 4] [2010-12-28 21:11:46] [82643889efeee0b265cd2ff213e5137b] 
- R  D              [Multiple Regression] [Model 5] [2010-12-28 21:19:23] [82643889efeee0b265cd2ff213e5137b] 
-    D            [Multiple Regression] [] [2009-11-19 08:27:40] [639dd97b6eeebe46a3c92d62cb04fb95] 
-    D              [Multiple Regression] [] [2009-11-19 18:16:13] [639dd97b6eeebe46a3c92d62cb04fb95] 
- RMPD        [ARIMA Forecasting] [] [2009-12-14 08:41:55] [639dd97b6eeebe46a3c92d62cb04fb95] 
-   PD          [ARIMA Forecasting] [] [2009-12-19 10:06:44] [ea26ab7ea3bba830cfeb08d06278d52c] 
- R PD            [ARIMA Forecasting] [Arima forecast 6 ...] [2009-12-21 17:12:24] [9dbb467a28ad600d808a4e47d5e0774e] 
-   P               [ARIMA Forecasting] [Arima forecast 12...] [2009-12-21 17:19:37] [9dbb467a28ad600d808a4e47d5e0774e] 
-                     [ARIMA Forecasting] [paper] [2010-12-28 17:28:42] [654616a560d52fe6eb611aa3bbf6b3c7] 
-   P               [ARIMA Forecasting] [Arima forecast 24...] [2009-12-21 17:25:53] [9dbb467a28ad600d808a4e47d5e0774e] 
-                     [ARIMA Forecasting] [paper] [2010-12-28 17:32:12] [654616a560d52fe6eb611aa3bbf6b3c7] 
-                   [ARIMA Forecasting] [paper] [2010-12-28 17:26:19] [654616a560d52fe6eb611aa3bbf6b3c7] 
-   PD          [ARIMA Forecasting] [] [2009-12-19 10:31:03] [ea26ab7ea3bba830cfeb08d06278d52c] 
-   PD          [ARIMA Forecasting] [] [2009-12-19 10:32:11] [ea26ab7ea3bba830cfeb08d06278d52c] 
-   PD        [Multiple Regression] [Multiple regressi...] [2010-12-25 20:21:24] [1ec36cc0fd92fd0f07d0b885ce2c369b] 
-    D          [Multiple Regression] [] [2010-12-27 13:24:06] [1ec36cc0fd92fd0f07d0b885ce2c369b] 
- R  D        [Multiple Regression] [Model 1 ] [2010-12-28 19:46:23] [82643889efeee0b265cd2ff213e5137b] 

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100	0
108,1560276	0
114,0150276	0
102,1880309	0
110,3672031	0
96,8602511	0
94,1944583	0
99,51621961	0
94,06333487	0
97,5541476	0
78,15062422	0
81,2434643	0
92,36262465	0
96,06324371	0
114,0523777	0
110,6616666	0
104,9171949	0
90,00187193	0
95,7008067	0
86,02741157	0
84,85287668	0
100,04328	0
80,91713823	0
74,06539709	0
77,30281369	0
97,23043249	0
90,75515676	0
100,5614455	0
92,01293267	0
99,24012138	0
105,8672755	0
90,9920463	0
93,30624423	0
91,17419413	0
77,33295039	0
91,1277721	0
85,01249943	0
83,90390242	0
104,8626302	0
110,9039108	0
95,43714373	0
111,6238727	0
108,8925403	0
96,17511682	0
101,9740205	0
99,11953031	0
86,78158147	0
118,4195003	0
118,7441447	0
106,5296192	0
134,7772694	0
104,6778714	0
105,2954304	0
139,4139849	0
103,6060491	0
99,78182974	0
103,4610301	0
120,0594945	0
96,71377168	0
107,1308929	0
105,3608372	0
111,6942359	0
132,0519998	0
126,8037879	0
154,4824253	0
141,5570984	0
109,9506882	0
127,904198	0
133,0888617	0
120,0796299	0
117,5557142	0
143,0362309	0
159,982927	1
128,5991124	1
149,7373327	1
126,8169313	1
140,9639674	1
137,6691981	1
117,9402337	1
122,3095247	1
127,7804207	1
136,1677176	1
116,2405856	1
123,1576893	1
116,3400234	1
108,6119282	1
125,8982264	1
112,8003105	1
107,5182447	1
135,0955413	1
115,5096488	1
115,8640759	1
104,5883906	1
163,7213386	1
113,4482275	1
98,0428844	1
116,7868521	1
126,5330444	1
113,0336597	1
124,3392163	1
109,8298759	1
124,4434777	1
111,5039454	1
102,0350019	1
116,8726598	1
112,2073122	1
101,1513902	1
124,4255108	1

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	'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57642&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57642&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57642&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	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 103.885297340278 + 18.0026589708333X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  103.885297340278 +  18.0026589708333X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57642&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  103.885297340278 +  18.0026589708333X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57642&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57642&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] = + 103.885297340278 + 18.0026589708333X[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)	103.885297340278	1.912641	54.3151	0	0
X	18.0026589708333	3.312792	5.4343	0	0

\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) & 103.885297340278 & 1.912641 & 54.3151 & 0 & 0 \tabularnewline
X & 18.0026589708333 & 3.312792 & 5.4343 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57642&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]103.885297340278[/C][C]1.912641[/C][C]54.3151[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]18.0026589708333[/C][C]3.312792[/C][C]5.4343[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57642&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57642&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)	103.885297340278	1.912641	54.3151	0	0
X	18.0026589708333	3.312792	5.4343	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.4667909175743
R-squared	0.217893760729857
Adjusted R-squared	0.210515399982025
F-TEST (value)	29.5314593819359
F-TEST (DF numerator)	1
F-TEST (DF denominator)	106
p-value	3.53844414435756e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	16.2293012130874
Sum Squared Residuals	27919.3630937027

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.4667909175743 \tabularnewline
R-squared & 0.217893760729857 \tabularnewline
Adjusted R-squared & 0.210515399982025 \tabularnewline
F-TEST (value) & 29.5314593819359 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 106 \tabularnewline
p-value & 3.53844414435756e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 16.2293012130874 \tabularnewline
Sum Squared Residuals & 27919.3630937027 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57642&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.4667909175743[/C][/ROW]
[ROW][C]R-squared[/C][C]0.217893760729857[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.210515399982025[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]29.5314593819359[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]106[/C][/ROW]
[ROW][C]p-value[/C][C]3.53844414435756e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]16.2293012130874[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]27919.3630937027[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57642&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57642&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	0.4667909175743
R-squared	0.217893760729857
Adjusted R-squared	0.210515399982025
F-TEST (value)	29.5314593819359
F-TEST (DF numerator)	1
F-TEST (DF denominator)	106
p-value	3.53844414435756e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	16.2293012130874
Sum Squared Residuals	27919.3630937027

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	100	103.885297340278	-3.88529734027768
2	108.1560276	103.885297340278	4.27073025972225
3	114.0150276	103.885297340278	10.1297302597222
4	102.1880309	103.885297340278	-1.69726644027778
5	110.3672031	103.885297340278	6.48190575972222
6	96.8602511	103.885297340278	-7.02504624027778
7	94.1944583	103.885297340278	-9.69083904027779
8	99.51621961	103.885297340278	-4.36907773027779
9	94.06333487	103.885297340278	-9.82196247027777
10	97.5541476	103.885297340278	-6.33114974027779
11	78.15062422	103.885297340278	-25.7346731202778
12	81.2434643	103.885297340278	-22.6418330402778
13	92.36262465	103.885297340278	-11.5226726902778
14	96.06324371	103.885297340278	-7.82205363027778
15	114.0523777	103.885297340278	10.1670803597222
16	110.6616666	103.885297340278	6.77636925972222
17	104.9171949	103.885297340278	1.03189755972222
18	90.00187193	103.885297340278	-13.8834254102778
19	95.7008067	103.885297340278	-8.18449064027778
20	86.02741157	103.885297340278	-17.8578857702778
21	84.85287668	103.885297340278	-19.0324206602778
22	100.04328	103.885297340278	-3.84201734027778
23	80.91713823	103.885297340278	-22.9681591102778
24	74.06539709	103.885297340278	-29.8199002502778
25	77.30281369	103.885297340278	-26.5824836502778
26	97.23043249	103.885297340278	-6.65486485027778
27	90.75515676	103.885297340278	-13.1301405802778
28	100.5614455	103.885297340278	-3.32385184027777
29	92.01293267	103.885297340278	-11.8723646702778
30	99.24012138	103.885297340278	-4.64517596027777
31	105.8672755	103.885297340278	1.98197815972223
32	90.9920463	103.885297340278	-12.8932510402778
33	93.30624423	103.885297340278	-10.5790531102778
34	91.17419413	103.885297340278	-12.7111032102778
35	77.33295039	103.885297340278	-26.5523469502778
36	91.1277721	103.885297340278	-12.7575252402778
37	85.01249943	103.885297340278	-18.8727979102778
38	83.90390242	103.885297340278	-19.9813949202778
39	104.8626302	103.885297340278	0.97733285972222
40	110.9039108	103.885297340278	7.01861345972223
41	95.43714373	103.885297340278	-8.44815361027778
42	111.6238727	103.885297340278	7.73857535972223
43	108.8925403	103.885297340278	5.00724295972221
44	96.17511682	103.885297340278	-7.71018052027778
45	101.9740205	103.885297340278	-1.91127684027778
46	99.11953031	103.885297340278	-4.76576703027778
47	86.78158147	103.885297340278	-17.1037158702778
48	118.4195003	103.885297340278	14.5342029597222
49	118.7441447	103.885297340278	14.8588473597222
50	106.5296192	103.885297340278	2.64432185972222
51	134.7772694	103.885297340278	30.8919720597222
52	104.6778714	103.885297340278	0.792574059722222
53	105.2954304	103.885297340278	1.41013305972222
54	139.4139849	103.885297340278	35.5286875597222
55	103.6060491	103.885297340278	-0.279248240277772
56	99.78182974	103.885297340278	-4.10346760027777
57	103.4610301	103.885297340278	-0.424267240277777
58	120.0594945	103.885297340278	16.1741971597222
59	96.71377168	103.885297340278	-7.17152566027779
60	107.1308929	103.885297340278	3.24559555972223
61	105.3608372	103.885297340278	1.47553985972223
62	111.6942359	103.885297340278	7.80893855972222
63	132.0519998	103.885297340278	28.1667024597222
64	126.8037879	103.885297340278	22.9184905597222
65	154.4824253	103.885297340278	50.5971279597222
66	141.5570984	103.885297340278	37.6718010597222
67	109.9506882	103.885297340278	6.06539085972222
68	127.904198	103.885297340278	24.0189006597222
69	133.0888617	103.885297340278	29.2035643597222
70	120.0796299	103.885297340278	16.1943325597222
71	117.5557142	103.885297340278	13.6704168597222
72	143.0362309	103.885297340278	39.1509335597222
73	159.982927	121.887956311111	38.0949706888889
74	128.5991124	121.887956311111	6.71115608888889
75	149.7373327	121.887956311111	27.8493763888889
76	126.8169313	121.887956311111	4.92897498888888
77	140.9639674	121.887956311111	19.0760110888889
78	137.6691981	121.887956311111	15.7812417888889
79	117.9402337	121.887956311111	-3.94772261111112
80	122.3095247	121.887956311111	0.421568388888889
81	127.7804207	121.887956311111	5.89246438888889
82	136.1677176	121.887956311111	14.2797612888889
83	116.2405856	121.887956311111	-5.6473707111111
84	123.1576893	121.887956311111	1.26973298888889
85	116.3400234	121.887956311111	-5.5479329111111
86	108.6119282	121.887956311111	-13.2760281111111
87	125.8982264	121.887956311111	4.01027008888889
88	112.8003105	121.887956311111	-9.08764581111111
89	107.5182447	121.887956311111	-14.3697116111111
90	135.0955413	121.887956311111	13.2075849888889
91	115.5096488	121.887956311111	-6.37830751111111
92	115.8640759	121.887956311111	-6.0238804111111
93	104.5883906	121.887956311111	-17.2995657111111
94	163.7213386	121.887956311111	41.8333822888889
95	113.4482275	121.887956311111	-8.4397288111111
96	98.0428844	121.887956311111	-23.8450719111111
97	116.7868521	121.887956311111	-5.10110421111110
98	126.5330444	121.887956311111	4.64508808888889
99	113.0336597	121.887956311111	-8.85429661111111
100	124.3392163	121.887956311111	2.45125998888890
101	109.8298759	121.887956311111	-12.0580804111111
102	124.4434777	121.887956311111	2.55552138888889
103	111.5039454	121.887956311111	-10.3840109111111
104	102.0350019	121.887956311111	-19.8529544111111
105	116.8726598	121.887956311111	-5.01529651111111
106	112.2073122	121.887956311111	-9.6806441111111
107	101.1513902	121.887956311111	-20.7365661111111
108	124.4255108	121.887956311111	2.53755448888889

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 100 & 103.885297340278 & -3.88529734027768 \tabularnewline
2 & 108.1560276 & 103.885297340278 & 4.27073025972225 \tabularnewline
3 & 114.0150276 & 103.885297340278 & 10.1297302597222 \tabularnewline
4 & 102.1880309 & 103.885297340278 & -1.69726644027778 \tabularnewline
5 & 110.3672031 & 103.885297340278 & 6.48190575972222 \tabularnewline
6 & 96.8602511 & 103.885297340278 & -7.02504624027778 \tabularnewline
7 & 94.1944583 & 103.885297340278 & -9.69083904027779 \tabularnewline
8 & 99.51621961 & 103.885297340278 & -4.36907773027779 \tabularnewline
9 & 94.06333487 & 103.885297340278 & -9.82196247027777 \tabularnewline
10 & 97.5541476 & 103.885297340278 & -6.33114974027779 \tabularnewline
11 & 78.15062422 & 103.885297340278 & -25.7346731202778 \tabularnewline
12 & 81.2434643 & 103.885297340278 & -22.6418330402778 \tabularnewline
13 & 92.36262465 & 103.885297340278 & -11.5226726902778 \tabularnewline
14 & 96.06324371 & 103.885297340278 & -7.82205363027778 \tabularnewline
15 & 114.0523777 & 103.885297340278 & 10.1670803597222 \tabularnewline
16 & 110.6616666 & 103.885297340278 & 6.77636925972222 \tabularnewline
17 & 104.9171949 & 103.885297340278 & 1.03189755972222 \tabularnewline
18 & 90.00187193 & 103.885297340278 & -13.8834254102778 \tabularnewline
19 & 95.7008067 & 103.885297340278 & -8.18449064027778 \tabularnewline
20 & 86.02741157 & 103.885297340278 & -17.8578857702778 \tabularnewline
21 & 84.85287668 & 103.885297340278 & -19.0324206602778 \tabularnewline
22 & 100.04328 & 103.885297340278 & -3.84201734027778 \tabularnewline
23 & 80.91713823 & 103.885297340278 & -22.9681591102778 \tabularnewline
24 & 74.06539709 & 103.885297340278 & -29.8199002502778 \tabularnewline
25 & 77.30281369 & 103.885297340278 & -26.5824836502778 \tabularnewline
26 & 97.23043249 & 103.885297340278 & -6.65486485027778 \tabularnewline
27 & 90.75515676 & 103.885297340278 & -13.1301405802778 \tabularnewline
28 & 100.5614455 & 103.885297340278 & -3.32385184027777 \tabularnewline
29 & 92.01293267 & 103.885297340278 & -11.8723646702778 \tabularnewline
30 & 99.24012138 & 103.885297340278 & -4.64517596027777 \tabularnewline
31 & 105.8672755 & 103.885297340278 & 1.98197815972223 \tabularnewline
32 & 90.9920463 & 103.885297340278 & -12.8932510402778 \tabularnewline
33 & 93.30624423 & 103.885297340278 & -10.5790531102778 \tabularnewline
34 & 91.17419413 & 103.885297340278 & -12.7111032102778 \tabularnewline
35 & 77.33295039 & 103.885297340278 & -26.5523469502778 \tabularnewline
36 & 91.1277721 & 103.885297340278 & -12.7575252402778 \tabularnewline
37 & 85.01249943 & 103.885297340278 & -18.8727979102778 \tabularnewline
38 & 83.90390242 & 103.885297340278 & -19.9813949202778 \tabularnewline
39 & 104.8626302 & 103.885297340278 & 0.97733285972222 \tabularnewline
40 & 110.9039108 & 103.885297340278 & 7.01861345972223 \tabularnewline
41 & 95.43714373 & 103.885297340278 & -8.44815361027778 \tabularnewline
42 & 111.6238727 & 103.885297340278 & 7.73857535972223 \tabularnewline
43 & 108.8925403 & 103.885297340278 & 5.00724295972221 \tabularnewline
44 & 96.17511682 & 103.885297340278 & -7.71018052027778 \tabularnewline
45 & 101.9740205 & 103.885297340278 & -1.91127684027778 \tabularnewline
46 & 99.11953031 & 103.885297340278 & -4.76576703027778 \tabularnewline
47 & 86.78158147 & 103.885297340278 & -17.1037158702778 \tabularnewline
48 & 118.4195003 & 103.885297340278 & 14.5342029597222 \tabularnewline
49 & 118.7441447 & 103.885297340278 & 14.8588473597222 \tabularnewline
50 & 106.5296192 & 103.885297340278 & 2.64432185972222 \tabularnewline
51 & 134.7772694 & 103.885297340278 & 30.8919720597222 \tabularnewline
52 & 104.6778714 & 103.885297340278 & 0.792574059722222 \tabularnewline
53 & 105.2954304 & 103.885297340278 & 1.41013305972222 \tabularnewline
54 & 139.4139849 & 103.885297340278 & 35.5286875597222 \tabularnewline
55 & 103.6060491 & 103.885297340278 & -0.279248240277772 \tabularnewline
56 & 99.78182974 & 103.885297340278 & -4.10346760027777 \tabularnewline
57 & 103.4610301 & 103.885297340278 & -0.424267240277777 \tabularnewline
58 & 120.0594945 & 103.885297340278 & 16.1741971597222 \tabularnewline
59 & 96.71377168 & 103.885297340278 & -7.17152566027779 \tabularnewline
60 & 107.1308929 & 103.885297340278 & 3.24559555972223 \tabularnewline
61 & 105.3608372 & 103.885297340278 & 1.47553985972223 \tabularnewline
62 & 111.6942359 & 103.885297340278 & 7.80893855972222 \tabularnewline
63 & 132.0519998 & 103.885297340278 & 28.1667024597222 \tabularnewline
64 & 126.8037879 & 103.885297340278 & 22.9184905597222 \tabularnewline
65 & 154.4824253 & 103.885297340278 & 50.5971279597222 \tabularnewline
66 & 141.5570984 & 103.885297340278 & 37.6718010597222 \tabularnewline
67 & 109.9506882 & 103.885297340278 & 6.06539085972222 \tabularnewline
68 & 127.904198 & 103.885297340278 & 24.0189006597222 \tabularnewline
69 & 133.0888617 & 103.885297340278 & 29.2035643597222 \tabularnewline
70 & 120.0796299 & 103.885297340278 & 16.1943325597222 \tabularnewline
71 & 117.5557142 & 103.885297340278 & 13.6704168597222 \tabularnewline
72 & 143.0362309 & 103.885297340278 & 39.1509335597222 \tabularnewline
73 & 159.982927 & 121.887956311111 & 38.0949706888889 \tabularnewline
74 & 128.5991124 & 121.887956311111 & 6.71115608888889 \tabularnewline
75 & 149.7373327 & 121.887956311111 & 27.8493763888889 \tabularnewline
76 & 126.8169313 & 121.887956311111 & 4.92897498888888 \tabularnewline
77 & 140.9639674 & 121.887956311111 & 19.0760110888889 \tabularnewline
78 & 137.6691981 & 121.887956311111 & 15.7812417888889 \tabularnewline
79 & 117.9402337 & 121.887956311111 & -3.94772261111112 \tabularnewline
80 & 122.3095247 & 121.887956311111 & 0.421568388888889 \tabularnewline
81 & 127.7804207 & 121.887956311111 & 5.89246438888889 \tabularnewline
82 & 136.1677176 & 121.887956311111 & 14.2797612888889 \tabularnewline
83 & 116.2405856 & 121.887956311111 & -5.6473707111111 \tabularnewline
84 & 123.1576893 & 121.887956311111 & 1.26973298888889 \tabularnewline
85 & 116.3400234 & 121.887956311111 & -5.5479329111111 \tabularnewline
86 & 108.6119282 & 121.887956311111 & -13.2760281111111 \tabularnewline
87 & 125.8982264 & 121.887956311111 & 4.01027008888889 \tabularnewline
88 & 112.8003105 & 121.887956311111 & -9.08764581111111 \tabularnewline
89 & 107.5182447 & 121.887956311111 & -14.3697116111111 \tabularnewline
90 & 135.0955413 & 121.887956311111 & 13.2075849888889 \tabularnewline
91 & 115.5096488 & 121.887956311111 & -6.37830751111111 \tabularnewline
92 & 115.8640759 & 121.887956311111 & -6.0238804111111 \tabularnewline
93 & 104.5883906 & 121.887956311111 & -17.2995657111111 \tabularnewline
94 & 163.7213386 & 121.887956311111 & 41.8333822888889 \tabularnewline
95 & 113.4482275 & 121.887956311111 & -8.4397288111111 \tabularnewline
96 & 98.0428844 & 121.887956311111 & -23.8450719111111 \tabularnewline
97 & 116.7868521 & 121.887956311111 & -5.10110421111110 \tabularnewline
98 & 126.5330444 & 121.887956311111 & 4.64508808888889 \tabularnewline
99 & 113.0336597 & 121.887956311111 & -8.85429661111111 \tabularnewline
100 & 124.3392163 & 121.887956311111 & 2.45125998888890 \tabularnewline
101 & 109.8298759 & 121.887956311111 & -12.0580804111111 \tabularnewline
102 & 124.4434777 & 121.887956311111 & 2.55552138888889 \tabularnewline
103 & 111.5039454 & 121.887956311111 & -10.3840109111111 \tabularnewline
104 & 102.0350019 & 121.887956311111 & -19.8529544111111 \tabularnewline
105 & 116.8726598 & 121.887956311111 & -5.01529651111111 \tabularnewline
106 & 112.2073122 & 121.887956311111 & -9.6806441111111 \tabularnewline
107 & 101.1513902 & 121.887956311111 & -20.7365661111111 \tabularnewline
108 & 124.4255108 & 121.887956311111 & 2.53755448888889 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57642&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]100[/C][C]103.885297340278[/C][C]-3.88529734027768[/C][/ROW]
[ROW][C]2[/C][C]108.1560276[/C][C]103.885297340278[/C][C]4.27073025972225[/C][/ROW]
[ROW][C]3[/C][C]114.0150276[/C][C]103.885297340278[/C][C]10.1297302597222[/C][/ROW]
[ROW][C]4[/C][C]102.1880309[/C][C]103.885297340278[/C][C]-1.69726644027778[/C][/ROW]
[ROW][C]5[/C][C]110.3672031[/C][C]103.885297340278[/C][C]6.48190575972222[/C][/ROW]
[ROW][C]6[/C][C]96.8602511[/C][C]103.885297340278[/C][C]-7.02504624027778[/C][/ROW]
[ROW][C]7[/C][C]94.1944583[/C][C]103.885297340278[/C][C]-9.69083904027779[/C][/ROW]
[ROW][C]8[/C][C]99.51621961[/C][C]103.885297340278[/C][C]-4.36907773027779[/C][/ROW]
[ROW][C]9[/C][C]94.06333487[/C][C]103.885297340278[/C][C]-9.82196247027777[/C][/ROW]
[ROW][C]10[/C][C]97.5541476[/C][C]103.885297340278[/C][C]-6.33114974027779[/C][/ROW]
[ROW][C]11[/C][C]78.15062422[/C][C]103.885297340278[/C][C]-25.7346731202778[/C][/ROW]
[ROW][C]12[/C][C]81.2434643[/C][C]103.885297340278[/C][C]-22.6418330402778[/C][/ROW]
[ROW][C]13[/C][C]92.36262465[/C][C]103.885297340278[/C][C]-11.5226726902778[/C][/ROW]
[ROW][C]14[/C][C]96.06324371[/C][C]103.885297340278[/C][C]-7.82205363027778[/C][/ROW]
[ROW][C]15[/C][C]114.0523777[/C][C]103.885297340278[/C][C]10.1670803597222[/C][/ROW]
[ROW][C]16[/C][C]110.6616666[/C][C]103.885297340278[/C][C]6.77636925972222[/C][/ROW]
[ROW][C]17[/C][C]104.9171949[/C][C]103.885297340278[/C][C]1.03189755972222[/C][/ROW]
[ROW][C]18[/C][C]90.00187193[/C][C]103.885297340278[/C][C]-13.8834254102778[/C][/ROW]
[ROW][C]19[/C][C]95.7008067[/C][C]103.885297340278[/C][C]-8.18449064027778[/C][/ROW]
[ROW][C]20[/C][C]86.02741157[/C][C]103.885297340278[/C][C]-17.8578857702778[/C][/ROW]
[ROW][C]21[/C][C]84.85287668[/C][C]103.885297340278[/C][C]-19.0324206602778[/C][/ROW]
[ROW][C]22[/C][C]100.04328[/C][C]103.885297340278[/C][C]-3.84201734027778[/C][/ROW]
[ROW][C]23[/C][C]80.91713823[/C][C]103.885297340278[/C][C]-22.9681591102778[/C][/ROW]
[ROW][C]24[/C][C]74.06539709[/C][C]103.885297340278[/C][C]-29.8199002502778[/C][/ROW]
[ROW][C]25[/C][C]77.30281369[/C][C]103.885297340278[/C][C]-26.5824836502778[/C][/ROW]
[ROW][C]26[/C][C]97.23043249[/C][C]103.885297340278[/C][C]-6.65486485027778[/C][/ROW]
[ROW][C]27[/C][C]90.75515676[/C][C]103.885297340278[/C][C]-13.1301405802778[/C][/ROW]
[ROW][C]28[/C][C]100.5614455[/C][C]103.885297340278[/C][C]-3.32385184027777[/C][/ROW]
[ROW][C]29[/C][C]92.01293267[/C][C]103.885297340278[/C][C]-11.8723646702778[/C][/ROW]
[ROW][C]30[/C][C]99.24012138[/C][C]103.885297340278[/C][C]-4.64517596027777[/C][/ROW]
[ROW][C]31[/C][C]105.8672755[/C][C]103.885297340278[/C][C]1.98197815972223[/C][/ROW]
[ROW][C]32[/C][C]90.9920463[/C][C]103.885297340278[/C][C]-12.8932510402778[/C][/ROW]
[ROW][C]33[/C][C]93.30624423[/C][C]103.885297340278[/C][C]-10.5790531102778[/C][/ROW]
[ROW][C]34[/C][C]91.17419413[/C][C]103.885297340278[/C][C]-12.7111032102778[/C][/ROW]
[ROW][C]35[/C][C]77.33295039[/C][C]103.885297340278[/C][C]-26.5523469502778[/C][/ROW]
[ROW][C]36[/C][C]91.1277721[/C][C]103.885297340278[/C][C]-12.7575252402778[/C][/ROW]
[ROW][C]37[/C][C]85.01249943[/C][C]103.885297340278[/C][C]-18.8727979102778[/C][/ROW]
[ROW][C]38[/C][C]83.90390242[/C][C]103.885297340278[/C][C]-19.9813949202778[/C][/ROW]
[ROW][C]39[/C][C]104.8626302[/C][C]103.885297340278[/C][C]0.97733285972222[/C][/ROW]
[ROW][C]40[/C][C]110.9039108[/C][C]103.885297340278[/C][C]7.01861345972223[/C][/ROW]
[ROW][C]41[/C][C]95.43714373[/C][C]103.885297340278[/C][C]-8.44815361027778[/C][/ROW]
[ROW][C]42[/C][C]111.6238727[/C][C]103.885297340278[/C][C]7.73857535972223[/C][/ROW]
[ROW][C]43[/C][C]108.8925403[/C][C]103.885297340278[/C][C]5.00724295972221[/C][/ROW]
[ROW][C]44[/C][C]96.17511682[/C][C]103.885297340278[/C][C]-7.71018052027778[/C][/ROW]
[ROW][C]45[/C][C]101.9740205[/C][C]103.885297340278[/C][C]-1.91127684027778[/C][/ROW]
[ROW][C]46[/C][C]99.11953031[/C][C]103.885297340278[/C][C]-4.76576703027778[/C][/ROW]
[ROW][C]47[/C][C]86.78158147[/C][C]103.885297340278[/C][C]-17.1037158702778[/C][/ROW]
[ROW][C]48[/C][C]118.4195003[/C][C]103.885297340278[/C][C]14.5342029597222[/C][/ROW]
[ROW][C]49[/C][C]118.7441447[/C][C]103.885297340278[/C][C]14.8588473597222[/C][/ROW]
[ROW][C]50[/C][C]106.5296192[/C][C]103.885297340278[/C][C]2.64432185972222[/C][/ROW]
[ROW][C]51[/C][C]134.7772694[/C][C]103.885297340278[/C][C]30.8919720597222[/C][/ROW]
[ROW][C]52[/C][C]104.6778714[/C][C]103.885297340278[/C][C]0.792574059722222[/C][/ROW]
[ROW][C]53[/C][C]105.2954304[/C][C]103.885297340278[/C][C]1.41013305972222[/C][/ROW]
[ROW][C]54[/C][C]139.4139849[/C][C]103.885297340278[/C][C]35.5286875597222[/C][/ROW]
[ROW][C]55[/C][C]103.6060491[/C][C]103.885297340278[/C][C]-0.279248240277772[/C][/ROW]
[ROW][C]56[/C][C]99.78182974[/C][C]103.885297340278[/C][C]-4.10346760027777[/C][/ROW]
[ROW][C]57[/C][C]103.4610301[/C][C]103.885297340278[/C][C]-0.424267240277777[/C][/ROW]
[ROW][C]58[/C][C]120.0594945[/C][C]103.885297340278[/C][C]16.1741971597222[/C][/ROW]
[ROW][C]59[/C][C]96.71377168[/C][C]103.885297340278[/C][C]-7.17152566027779[/C][/ROW]
[ROW][C]60[/C][C]107.1308929[/C][C]103.885297340278[/C][C]3.24559555972223[/C][/ROW]
[ROW][C]61[/C][C]105.3608372[/C][C]103.885297340278[/C][C]1.47553985972223[/C][/ROW]
[ROW][C]62[/C][C]111.6942359[/C][C]103.885297340278[/C][C]7.80893855972222[/C][/ROW]
[ROW][C]63[/C][C]132.0519998[/C][C]103.885297340278[/C][C]28.1667024597222[/C][/ROW]
[ROW][C]64[/C][C]126.8037879[/C][C]103.885297340278[/C][C]22.9184905597222[/C][/ROW]
[ROW][C]65[/C][C]154.4824253[/C][C]103.885297340278[/C][C]50.5971279597222[/C][/ROW]
[ROW][C]66[/C][C]141.5570984[/C][C]103.885297340278[/C][C]37.6718010597222[/C][/ROW]
[ROW][C]67[/C][C]109.9506882[/C][C]103.885297340278[/C][C]6.06539085972222[/C][/ROW]
[ROW][C]68[/C][C]127.904198[/C][C]103.885297340278[/C][C]24.0189006597222[/C][/ROW]
[ROW][C]69[/C][C]133.0888617[/C][C]103.885297340278[/C][C]29.2035643597222[/C][/ROW]
[ROW][C]70[/C][C]120.0796299[/C][C]103.885297340278[/C][C]16.1943325597222[/C][/ROW]
[ROW][C]71[/C][C]117.5557142[/C][C]103.885297340278[/C][C]13.6704168597222[/C][/ROW]
[ROW][C]72[/C][C]143.0362309[/C][C]103.885297340278[/C][C]39.1509335597222[/C][/ROW]
[ROW][C]73[/C][C]159.982927[/C][C]121.887956311111[/C][C]38.0949706888889[/C][/ROW]
[ROW][C]74[/C][C]128.5991124[/C][C]121.887956311111[/C][C]6.71115608888889[/C][/ROW]
[ROW][C]75[/C][C]149.7373327[/C][C]121.887956311111[/C][C]27.8493763888889[/C][/ROW]
[ROW][C]76[/C][C]126.8169313[/C][C]121.887956311111[/C][C]4.92897498888888[/C][/ROW]
[ROW][C]77[/C][C]140.9639674[/C][C]121.887956311111[/C][C]19.0760110888889[/C][/ROW]
[ROW][C]78[/C][C]137.6691981[/C][C]121.887956311111[/C][C]15.7812417888889[/C][/ROW]
[ROW][C]79[/C][C]117.9402337[/C][C]121.887956311111[/C][C]-3.94772261111112[/C][/ROW]
[ROW][C]80[/C][C]122.3095247[/C][C]121.887956311111[/C][C]0.421568388888889[/C][/ROW]
[ROW][C]81[/C][C]127.7804207[/C][C]121.887956311111[/C][C]5.89246438888889[/C][/ROW]
[ROW][C]82[/C][C]136.1677176[/C][C]121.887956311111[/C][C]14.2797612888889[/C][/ROW]
[ROW][C]83[/C][C]116.2405856[/C][C]121.887956311111[/C][C]-5.6473707111111[/C][/ROW]
[ROW][C]84[/C][C]123.1576893[/C][C]121.887956311111[/C][C]1.26973298888889[/C][/ROW]
[ROW][C]85[/C][C]116.3400234[/C][C]121.887956311111[/C][C]-5.5479329111111[/C][/ROW]
[ROW][C]86[/C][C]108.6119282[/C][C]121.887956311111[/C][C]-13.2760281111111[/C][/ROW]
[ROW][C]87[/C][C]125.8982264[/C][C]121.887956311111[/C][C]4.01027008888889[/C][/ROW]
[ROW][C]88[/C][C]112.8003105[/C][C]121.887956311111[/C][C]-9.08764581111111[/C][/ROW]
[ROW][C]89[/C][C]107.5182447[/C][C]121.887956311111[/C][C]-14.3697116111111[/C][/ROW]
[ROW][C]90[/C][C]135.0955413[/C][C]121.887956311111[/C][C]13.2075849888889[/C][/ROW]
[ROW][C]91[/C][C]115.5096488[/C][C]121.887956311111[/C][C]-6.37830751111111[/C][/ROW]
[ROW][C]92[/C][C]115.8640759[/C][C]121.887956311111[/C][C]-6.0238804111111[/C][/ROW]
[ROW][C]93[/C][C]104.5883906[/C][C]121.887956311111[/C][C]-17.2995657111111[/C][/ROW]
[ROW][C]94[/C][C]163.7213386[/C][C]121.887956311111[/C][C]41.8333822888889[/C][/ROW]
[ROW][C]95[/C][C]113.4482275[/C][C]121.887956311111[/C][C]-8.4397288111111[/C][/ROW]
[ROW][C]96[/C][C]98.0428844[/C][C]121.887956311111[/C][C]-23.8450719111111[/C][/ROW]
[ROW][C]97[/C][C]116.7868521[/C][C]121.887956311111[/C][C]-5.10110421111110[/C][/ROW]
[ROW][C]98[/C][C]126.5330444[/C][C]121.887956311111[/C][C]4.64508808888889[/C][/ROW]
[ROW][C]99[/C][C]113.0336597[/C][C]121.887956311111[/C][C]-8.85429661111111[/C][/ROW]
[ROW][C]100[/C][C]124.3392163[/C][C]121.887956311111[/C][C]2.45125998888890[/C][/ROW]
[ROW][C]101[/C][C]109.8298759[/C][C]121.887956311111[/C][C]-12.0580804111111[/C][/ROW]
[ROW][C]102[/C][C]124.4434777[/C][C]121.887956311111[/C][C]2.55552138888889[/C][/ROW]
[ROW][C]103[/C][C]111.5039454[/C][C]121.887956311111[/C][C]-10.3840109111111[/C][/ROW]
[ROW][C]104[/C][C]102.0350019[/C][C]121.887956311111[/C][C]-19.8529544111111[/C][/ROW]
[ROW][C]105[/C][C]116.8726598[/C][C]121.887956311111[/C][C]-5.01529651111111[/C][/ROW]
[ROW][C]106[/C][C]112.2073122[/C][C]121.887956311111[/C][C]-9.6806441111111[/C][/ROW]
[ROW][C]107[/C][C]101.1513902[/C][C]121.887956311111[/C][C]-20.7365661111111[/C][/ROW]
[ROW][C]108[/C][C]124.4255108[/C][C]121.887956311111[/C][C]2.53755448888889[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57642&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57642&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	100	103.885297340278	-3.88529734027768
2	108.1560276	103.885297340278	4.27073025972225
3	114.0150276	103.885297340278	10.1297302597222
4	102.1880309	103.885297340278	-1.69726644027778
5	110.3672031	103.885297340278	6.48190575972222
6	96.8602511	103.885297340278	-7.02504624027778
7	94.1944583	103.885297340278	-9.69083904027779
8	99.51621961	103.885297340278	-4.36907773027779
9	94.06333487	103.885297340278	-9.82196247027777
10	97.5541476	103.885297340278	-6.33114974027779
11	78.15062422	103.885297340278	-25.7346731202778
12	81.2434643	103.885297340278	-22.6418330402778
13	92.36262465	103.885297340278	-11.5226726902778
14	96.06324371	103.885297340278	-7.82205363027778
15	114.0523777	103.885297340278	10.1670803597222
16	110.6616666	103.885297340278	6.77636925972222
17	104.9171949	103.885297340278	1.03189755972222
18	90.00187193	103.885297340278	-13.8834254102778
19	95.7008067	103.885297340278	-8.18449064027778
20	86.02741157	103.885297340278	-17.8578857702778
21	84.85287668	103.885297340278	-19.0324206602778
22	100.04328	103.885297340278	-3.84201734027778
23	80.91713823	103.885297340278	-22.9681591102778
24	74.06539709	103.885297340278	-29.8199002502778
25	77.30281369	103.885297340278	-26.5824836502778
26	97.23043249	103.885297340278	-6.65486485027778
27	90.75515676	103.885297340278	-13.1301405802778
28	100.5614455	103.885297340278	-3.32385184027777
29	92.01293267	103.885297340278	-11.8723646702778
30	99.24012138	103.885297340278	-4.64517596027777
31	105.8672755	103.885297340278	1.98197815972223
32	90.9920463	103.885297340278	-12.8932510402778
33	93.30624423	103.885297340278	-10.5790531102778
34	91.17419413	103.885297340278	-12.7111032102778
35	77.33295039	103.885297340278	-26.5523469502778
36	91.1277721	103.885297340278	-12.7575252402778
37	85.01249943	103.885297340278	-18.8727979102778
38	83.90390242	103.885297340278	-19.9813949202778
39	104.8626302	103.885297340278	0.97733285972222
40	110.9039108	103.885297340278	7.01861345972223
41	95.43714373	103.885297340278	-8.44815361027778
42	111.6238727	103.885297340278	7.73857535972223
43	108.8925403	103.885297340278	5.00724295972221
44	96.17511682	103.885297340278	-7.71018052027778
45	101.9740205	103.885297340278	-1.91127684027778
46	99.11953031	103.885297340278	-4.76576703027778
47	86.78158147	103.885297340278	-17.1037158702778
48	118.4195003	103.885297340278	14.5342029597222
49	118.7441447	103.885297340278	14.8588473597222
50	106.5296192	103.885297340278	2.64432185972222
51	134.7772694	103.885297340278	30.8919720597222
52	104.6778714	103.885297340278	0.792574059722222
53	105.2954304	103.885297340278	1.41013305972222
54	139.4139849	103.885297340278	35.5286875597222
55	103.6060491	103.885297340278	-0.279248240277772
56	99.78182974	103.885297340278	-4.10346760027777
57	103.4610301	103.885297340278	-0.424267240277777
58	120.0594945	103.885297340278	16.1741971597222
59	96.71377168	103.885297340278	-7.17152566027779
60	107.1308929	103.885297340278	3.24559555972223
61	105.3608372	103.885297340278	1.47553985972223
62	111.6942359	103.885297340278	7.80893855972222
63	132.0519998	103.885297340278	28.1667024597222
64	126.8037879	103.885297340278	22.9184905597222
65	154.4824253	103.885297340278	50.5971279597222
66	141.5570984	103.885297340278	37.6718010597222
67	109.9506882	103.885297340278	6.06539085972222
68	127.904198	103.885297340278	24.0189006597222
69	133.0888617	103.885297340278	29.2035643597222
70	120.0796299	103.885297340278	16.1943325597222
71	117.5557142	103.885297340278	13.6704168597222
72	143.0362309	103.885297340278	39.1509335597222
73	159.982927	121.887956311111	38.0949706888889
74	128.5991124	121.887956311111	6.71115608888889
75	149.7373327	121.887956311111	27.8493763888889
76	126.8169313	121.887956311111	4.92897498888888
77	140.9639674	121.887956311111	19.0760110888889
78	137.6691981	121.887956311111	15.7812417888889
79	117.9402337	121.887956311111	-3.94772261111112
80	122.3095247	121.887956311111	0.421568388888889
81	127.7804207	121.887956311111	5.89246438888889
82	136.1677176	121.887956311111	14.2797612888889
83	116.2405856	121.887956311111	-5.6473707111111
84	123.1576893	121.887956311111	1.26973298888889
85	116.3400234	121.887956311111	-5.5479329111111
86	108.6119282	121.887956311111	-13.2760281111111
87	125.8982264	121.887956311111	4.01027008888889
88	112.8003105	121.887956311111	-9.08764581111111
89	107.5182447	121.887956311111	-14.3697116111111
90	135.0955413	121.887956311111	13.2075849888889
91	115.5096488	121.887956311111	-6.37830751111111
92	115.8640759	121.887956311111	-6.0238804111111
93	104.5883906	121.887956311111	-17.2995657111111
94	163.7213386	121.887956311111	41.8333822888889
95	113.4482275	121.887956311111	-8.4397288111111
96	98.0428844	121.887956311111	-23.8450719111111
97	116.7868521	121.887956311111	-5.10110421111110
98	126.5330444	121.887956311111	4.64508808888889
99	113.0336597	121.887956311111	-8.85429661111111
100	124.3392163	121.887956311111	2.45125998888890
101	109.8298759	121.887956311111	-12.0580804111111
102	124.4434777	121.887956311111	2.55552138888889
103	111.5039454	121.887956311111	-10.3840109111111
104	102.0350019	121.887956311111	-19.8529544111111
105	116.8726598	121.887956311111	-5.01529651111111
106	112.2073122	121.887956311111	-9.6806441111111
107	101.1513902	121.887956311111	-20.7365661111111
108	124.4255108	121.887956311111	2.53755448888889

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.0787621459489388	0.157524291897878	0.921237854051061
6	0.0608673300550259	0.121734660110052	0.939132669944974
7	0.0524970373291991	0.104994074658398	0.94750296267080
8	0.0242007698072505	0.048401539614501	0.97579923019275
9	0.0170988855224703	0.0341977710449407	0.98290111447753
10	0.00806685693399365	0.0161337138679873	0.991933143066006
11	0.0529571894476661	0.105914378895332	0.947042810552334
12	0.080376905281759	0.160753810563518	0.919623094718241
13	0.0531355764199753	0.106271152839951	0.946864423580025
14	0.0313333000292319	0.0626666000584639	0.968666699970768
15	0.0388281831857341	0.0776563663714681	0.961171816814266
16	0.0334811211090817	0.0669622422181635	0.966518878890918
17	0.0214190042818892	0.0428380085637783	0.97858099571811
18	0.0167603065561136	0.0335206131122272	0.983239693443886
19	0.0101287447552609	0.0202574895105219	0.98987125524474
20	0.0101689434662982	0.0203378869325964	0.989831056533702
21	0.0107489191315675	0.0214978382631350	0.989251080868432
22	0.0063843752719433	0.0127687505438866	0.993615624728057
23	0.00950466856588625	0.0190093371317725	0.990495331434114
24	0.0253257833238490	0.0506515666476979	0.974674216676151
25	0.0403065992860307	0.0806131985720614	0.95969340071397
26	0.0281869950852667	0.0563739901705335	0.971813004914733
27	0.0212865245061066	0.0425730490122132	0.978713475493893
28	0.0149645781628233	0.0299291563256465	0.985035421837177
29	0.0108460157570762	0.0216920315141525	0.989153984242924
30	0.00734898567943645	0.0146979713588729	0.992651014320564
31	0.00585599677205853	0.0117119935441171	0.994144003227941
32	0.00439666802741335	0.0087933360548267	0.995603331972587
33	0.00307856369259617	0.00615712738519234	0.996921436307404
34	0.00231943102872675	0.0046388620574535	0.997680568971273
35	0.00523983629130157	0.0104796725826031	0.994760163708698
36	0.0042283107680864	0.0084566215361728	0.995771689231914
37	0.0048982100307368	0.0097964200614736	0.995101789969263
38	0.00641345089555615	0.0128269017911123	0.993586549104444
39	0.00552971063102585	0.0110594212620517	0.994470289368974
40	0.00623641653045005	0.0124728330609001	0.99376358346955
41	0.00505677097399696	0.0101135419479939	0.994943229026003
42	0.00574088931181939	0.0114817786236388	0.99425911068818
43	0.00546022080244678	0.0109204416048936	0.994539779197553
44	0.00454938218488873	0.00909876436977746	0.995450617815111
45	0.00366736859211772	0.00733473718423545	0.996332631407882
46	0.00301960571050053	0.00603921142100107	0.9969803942895
47	0.00462140526685546	0.00924281053371092	0.995378594733145
48	0.00771545813885953	0.0154309162777191	0.99228454186114
49	0.0116318344730961	0.0232636689461923	0.988368165526904
50	0.0103157418822994	0.0206314837645988	0.9896842581177
51	0.0472571861082816	0.0945143722165632	0.952742813891718
52	0.0416647248854043	0.0833294497708086	0.958335275114596
53	0.0370736014932048	0.0741472029864096	0.962926398506795
54	0.133790707391586	0.267581414783172	0.866209292608414
55	0.122781688376571	0.245563376753143	0.877218311623429
56	0.122062047883039	0.244124095766078	0.877937952116961
57	0.117982545739986	0.235965091479971	0.882017454260014
58	0.123970415073652	0.247940830147305	0.876029584926348
59	0.147593333261397	0.295186666522794	0.852406666738603
60	0.148935580984675	0.297871161969349	0.851064419015325
61	0.161496699523163	0.322993399046325	0.838503300476837
62	0.169790134247940	0.339580268495879	0.83020986575206
63	0.228722367136195	0.457444734272389	0.771277632863805
64	0.254764908355812	0.509529816711625	0.745235091644188
65	0.582584176854118	0.834831646291763	0.417415823145882
66	0.695816514858851	0.608366970282298	0.304183485141149
67	0.689278457524662	0.621443084950677	0.310721542475338
68	0.685551509441902	0.628896981116196	0.314448490558098
69	0.701387759397603	0.597224481204795	0.298612240602397
70	0.678362178601241	0.643275642797517	0.321637821398759
71	0.68617681831603	0.62764636336794	0.31382318168397
72	0.726914674971448	0.546170650057104	0.273085325028552
73	0.868582869815772	0.262834260368455	0.131417130184227
74	0.858357231250921	0.283285537498158	0.141642768749079
75	0.915328865301453	0.169342269397094	0.0846711346985468
76	0.900722355879453	0.198555288241095	0.0992776441205473
77	0.918872672913806	0.162254654172387	0.0811273270861937
78	0.927924440852928	0.144151118294143	0.0720755591470715
79	0.912493649175098	0.175012701649804	0.0875063508249022
80	0.890100064910757	0.219799870178487	0.109899935089243
81	0.870059934539163	0.259880130921674	0.129940065460837
82	0.882007780552894	0.235984438894212	0.117992219447106
83	0.853369913580667	0.293260172838665	0.146630086419333
84	0.819114939704816	0.361770120590368	0.180885060295184
85	0.776925065059802	0.446149869880396	0.223074934940198
86	0.752012419240148	0.495975161519704	0.247987580759852
87	0.709017808118574	0.581964383762852	0.290982191881426
88	0.65599546729325	0.6880090654135	0.34400453270675
89	0.623548262080461	0.752903475839079	0.376451737919539
90	0.636939273258257	0.726121453483485	0.363060726741743
91	0.564906251543936	0.870187496912128	0.435093748456064
92	0.487886389176181	0.975772778352363	0.512113610823819
93	0.462806317325371	0.925612634650742	0.537193682674629
94	0.982103362894111	0.0357932742117773	0.0178966371058886
95	0.967971722006086	0.0640565559878277	0.0320282779939139
96	0.9817479482404	0.036504103519199	0.0182520517595995
97	0.96489012471753	0.0702197505649413	0.0351098752824707
98	0.962759830354305	0.0744803392913907	0.0372401696456954
99	0.929030183115254	0.141939633769492	0.0709698168847458
100	0.914925680949316	0.170148638101369	0.0850743190506845
101	0.847977570706551	0.304044858586898	0.152022429293449
102	0.831214362401126	0.337571275197748	0.168785637598874
103	0.688484830059195	0.623030339881609	0.311515169940805

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0787621459489388 & 0.157524291897878 & 0.921237854051061 \tabularnewline
6 & 0.0608673300550259 & 0.121734660110052 & 0.939132669944974 \tabularnewline
7 & 0.0524970373291991 & 0.104994074658398 & 0.94750296267080 \tabularnewline
8 & 0.0242007698072505 & 0.048401539614501 & 0.97579923019275 \tabularnewline
9 & 0.0170988855224703 & 0.0341977710449407 & 0.98290111447753 \tabularnewline
10 & 0.00806685693399365 & 0.0161337138679873 & 0.991933143066006 \tabularnewline
11 & 0.0529571894476661 & 0.105914378895332 & 0.947042810552334 \tabularnewline
12 & 0.080376905281759 & 0.160753810563518 & 0.919623094718241 \tabularnewline
13 & 0.0531355764199753 & 0.106271152839951 & 0.946864423580025 \tabularnewline
14 & 0.0313333000292319 & 0.0626666000584639 & 0.968666699970768 \tabularnewline
15 & 0.0388281831857341 & 0.0776563663714681 & 0.961171816814266 \tabularnewline
16 & 0.0334811211090817 & 0.0669622422181635 & 0.966518878890918 \tabularnewline
17 & 0.0214190042818892 & 0.0428380085637783 & 0.97858099571811 \tabularnewline
18 & 0.0167603065561136 & 0.0335206131122272 & 0.983239693443886 \tabularnewline
19 & 0.0101287447552609 & 0.0202574895105219 & 0.98987125524474 \tabularnewline
20 & 0.0101689434662982 & 0.0203378869325964 & 0.989831056533702 \tabularnewline
21 & 0.0107489191315675 & 0.0214978382631350 & 0.989251080868432 \tabularnewline
22 & 0.0063843752719433 & 0.0127687505438866 & 0.993615624728057 \tabularnewline
23 & 0.00950466856588625 & 0.0190093371317725 & 0.990495331434114 \tabularnewline
24 & 0.0253257833238490 & 0.0506515666476979 & 0.974674216676151 \tabularnewline
25 & 0.0403065992860307 & 0.0806131985720614 & 0.95969340071397 \tabularnewline
26 & 0.0281869950852667 & 0.0563739901705335 & 0.971813004914733 \tabularnewline
27 & 0.0212865245061066 & 0.0425730490122132 & 0.978713475493893 \tabularnewline
28 & 0.0149645781628233 & 0.0299291563256465 & 0.985035421837177 \tabularnewline
29 & 0.0108460157570762 & 0.0216920315141525 & 0.989153984242924 \tabularnewline
30 & 0.00734898567943645 & 0.0146979713588729 & 0.992651014320564 \tabularnewline
31 & 0.00585599677205853 & 0.0117119935441171 & 0.994144003227941 \tabularnewline
32 & 0.00439666802741335 & 0.0087933360548267 & 0.995603331972587 \tabularnewline
33 & 0.00307856369259617 & 0.00615712738519234 & 0.996921436307404 \tabularnewline
34 & 0.00231943102872675 & 0.0046388620574535 & 0.997680568971273 \tabularnewline
35 & 0.00523983629130157 & 0.0104796725826031 & 0.994760163708698 \tabularnewline
36 & 0.0042283107680864 & 0.0084566215361728 & 0.995771689231914 \tabularnewline
37 & 0.0048982100307368 & 0.0097964200614736 & 0.995101789969263 \tabularnewline
38 & 0.00641345089555615 & 0.0128269017911123 & 0.993586549104444 \tabularnewline
39 & 0.00552971063102585 & 0.0110594212620517 & 0.994470289368974 \tabularnewline
40 & 0.00623641653045005 & 0.0124728330609001 & 0.99376358346955 \tabularnewline
41 & 0.00505677097399696 & 0.0101135419479939 & 0.994943229026003 \tabularnewline
42 & 0.00574088931181939 & 0.0114817786236388 & 0.99425911068818 \tabularnewline
43 & 0.00546022080244678 & 0.0109204416048936 & 0.994539779197553 \tabularnewline
44 & 0.00454938218488873 & 0.00909876436977746 & 0.995450617815111 \tabularnewline
45 & 0.00366736859211772 & 0.00733473718423545 & 0.996332631407882 \tabularnewline
46 & 0.00301960571050053 & 0.00603921142100107 & 0.9969803942895 \tabularnewline
47 & 0.00462140526685546 & 0.00924281053371092 & 0.995378594733145 \tabularnewline
48 & 0.00771545813885953 & 0.0154309162777191 & 0.99228454186114 \tabularnewline
49 & 0.0116318344730961 & 0.0232636689461923 & 0.988368165526904 \tabularnewline
50 & 0.0103157418822994 & 0.0206314837645988 & 0.9896842581177 \tabularnewline
51 & 0.0472571861082816 & 0.0945143722165632 & 0.952742813891718 \tabularnewline
52 & 0.0416647248854043 & 0.0833294497708086 & 0.958335275114596 \tabularnewline
53 & 0.0370736014932048 & 0.0741472029864096 & 0.962926398506795 \tabularnewline
54 & 0.133790707391586 & 0.267581414783172 & 0.866209292608414 \tabularnewline
55 & 0.122781688376571 & 0.245563376753143 & 0.877218311623429 \tabularnewline
56 & 0.122062047883039 & 0.244124095766078 & 0.877937952116961 \tabularnewline
57 & 0.117982545739986 & 0.235965091479971 & 0.882017454260014 \tabularnewline
58 & 0.123970415073652 & 0.247940830147305 & 0.876029584926348 \tabularnewline
59 & 0.147593333261397 & 0.295186666522794 & 0.852406666738603 \tabularnewline
60 & 0.148935580984675 & 0.297871161969349 & 0.851064419015325 \tabularnewline
61 & 0.161496699523163 & 0.322993399046325 & 0.838503300476837 \tabularnewline
62 & 0.169790134247940 & 0.339580268495879 & 0.83020986575206 \tabularnewline
63 & 0.228722367136195 & 0.457444734272389 & 0.771277632863805 \tabularnewline
64 & 0.254764908355812 & 0.509529816711625 & 0.745235091644188 \tabularnewline
65 & 0.582584176854118 & 0.834831646291763 & 0.417415823145882 \tabularnewline
66 & 0.695816514858851 & 0.608366970282298 & 0.304183485141149 \tabularnewline
67 & 0.689278457524662 & 0.621443084950677 & 0.310721542475338 \tabularnewline
68 & 0.685551509441902 & 0.628896981116196 & 0.314448490558098 \tabularnewline
69 & 0.701387759397603 & 0.597224481204795 & 0.298612240602397 \tabularnewline
70 & 0.678362178601241 & 0.643275642797517 & 0.321637821398759 \tabularnewline
71 & 0.68617681831603 & 0.62764636336794 & 0.31382318168397 \tabularnewline
72 & 0.726914674971448 & 0.546170650057104 & 0.273085325028552 \tabularnewline
73 & 0.868582869815772 & 0.262834260368455 & 0.131417130184227 \tabularnewline
74 & 0.858357231250921 & 0.283285537498158 & 0.141642768749079 \tabularnewline
75 & 0.915328865301453 & 0.169342269397094 & 0.0846711346985468 \tabularnewline
76 & 0.900722355879453 & 0.198555288241095 & 0.0992776441205473 \tabularnewline
77 & 0.918872672913806 & 0.162254654172387 & 0.0811273270861937 \tabularnewline
78 & 0.927924440852928 & 0.144151118294143 & 0.0720755591470715 \tabularnewline
79 & 0.912493649175098 & 0.175012701649804 & 0.0875063508249022 \tabularnewline
80 & 0.890100064910757 & 0.219799870178487 & 0.109899935089243 \tabularnewline
81 & 0.870059934539163 & 0.259880130921674 & 0.129940065460837 \tabularnewline
82 & 0.882007780552894 & 0.235984438894212 & 0.117992219447106 \tabularnewline
83 & 0.853369913580667 & 0.293260172838665 & 0.146630086419333 \tabularnewline
84 & 0.819114939704816 & 0.361770120590368 & 0.180885060295184 \tabularnewline
85 & 0.776925065059802 & 0.446149869880396 & 0.223074934940198 \tabularnewline
86 & 0.752012419240148 & 0.495975161519704 & 0.247987580759852 \tabularnewline
87 & 0.709017808118574 & 0.581964383762852 & 0.290982191881426 \tabularnewline
88 & 0.65599546729325 & 0.6880090654135 & 0.34400453270675 \tabularnewline
89 & 0.623548262080461 & 0.752903475839079 & 0.376451737919539 \tabularnewline
90 & 0.636939273258257 & 0.726121453483485 & 0.363060726741743 \tabularnewline
91 & 0.564906251543936 & 0.870187496912128 & 0.435093748456064 \tabularnewline
92 & 0.487886389176181 & 0.975772778352363 & 0.512113610823819 \tabularnewline
93 & 0.462806317325371 & 0.925612634650742 & 0.537193682674629 \tabularnewline
94 & 0.982103362894111 & 0.0357932742117773 & 0.0178966371058886 \tabularnewline
95 & 0.967971722006086 & 0.0640565559878277 & 0.0320282779939139 \tabularnewline
96 & 0.9817479482404 & 0.036504103519199 & 0.0182520517595995 \tabularnewline
97 & 0.96489012471753 & 0.0702197505649413 & 0.0351098752824707 \tabularnewline
98 & 0.962759830354305 & 0.0744803392913907 & 0.0372401696456954 \tabularnewline
99 & 0.929030183115254 & 0.141939633769492 & 0.0709698168847458 \tabularnewline
100 & 0.914925680949316 & 0.170148638101369 & 0.0850743190506845 \tabularnewline
101 & 0.847977570706551 & 0.304044858586898 & 0.152022429293449 \tabularnewline
102 & 0.831214362401126 & 0.337571275197748 & 0.168785637598874 \tabularnewline
103 & 0.688484830059195 & 0.623030339881609 & 0.311515169940805 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57642&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]5[/C][C]0.0787621459489388[/C][C]0.157524291897878[/C][C]0.921237854051061[/C][/ROW]
[ROW][C]6[/C][C]0.0608673300550259[/C][C]0.121734660110052[/C][C]0.939132669944974[/C][/ROW]
[ROW][C]7[/C][C]0.0524970373291991[/C][C]0.104994074658398[/C][C]0.94750296267080[/C][/ROW]
[ROW][C]8[/C][C]0.0242007698072505[/C][C]0.048401539614501[/C][C]0.97579923019275[/C][/ROW]
[ROW][C]9[/C][C]0.0170988855224703[/C][C]0.0341977710449407[/C][C]0.98290111447753[/C][/ROW]
[ROW][C]10[/C][C]0.00806685693399365[/C][C]0.0161337138679873[/C][C]0.991933143066006[/C][/ROW]
[ROW][C]11[/C][C]0.0529571894476661[/C][C]0.105914378895332[/C][C]0.947042810552334[/C][/ROW]
[ROW][C]12[/C][C]0.080376905281759[/C][C]0.160753810563518[/C][C]0.919623094718241[/C][/ROW]
[ROW][C]13[/C][C]0.0531355764199753[/C][C]0.106271152839951[/C][C]0.946864423580025[/C][/ROW]
[ROW][C]14[/C][C]0.0313333000292319[/C][C]0.0626666000584639[/C][C]0.968666699970768[/C][/ROW]
[ROW][C]15[/C][C]0.0388281831857341[/C][C]0.0776563663714681[/C][C]0.961171816814266[/C][/ROW]
[ROW][C]16[/C][C]0.0334811211090817[/C][C]0.0669622422181635[/C][C]0.966518878890918[/C][/ROW]
[ROW][C]17[/C][C]0.0214190042818892[/C][C]0.0428380085637783[/C][C]0.97858099571811[/C][/ROW]
[ROW][C]18[/C][C]0.0167603065561136[/C][C]0.0335206131122272[/C][C]0.983239693443886[/C][/ROW]
[ROW][C]19[/C][C]0.0101287447552609[/C][C]0.0202574895105219[/C][C]0.98987125524474[/C][/ROW]
[ROW][C]20[/C][C]0.0101689434662982[/C][C]0.0203378869325964[/C][C]0.989831056533702[/C][/ROW]
[ROW][C]21[/C][C]0.0107489191315675[/C][C]0.0214978382631350[/C][C]0.989251080868432[/C][/ROW]
[ROW][C]22[/C][C]0.0063843752719433[/C][C]0.0127687505438866[/C][C]0.993615624728057[/C][/ROW]
[ROW][C]23[/C][C]0.00950466856588625[/C][C]0.0190093371317725[/C][C]0.990495331434114[/C][/ROW]
[ROW][C]24[/C][C]0.0253257833238490[/C][C]0.0506515666476979[/C][C]0.974674216676151[/C][/ROW]
[ROW][C]25[/C][C]0.0403065992860307[/C][C]0.0806131985720614[/C][C]0.95969340071397[/C][/ROW]
[ROW][C]26[/C][C]0.0281869950852667[/C][C]0.0563739901705335[/C][C]0.971813004914733[/C][/ROW]
[ROW][C]27[/C][C]0.0212865245061066[/C][C]0.0425730490122132[/C][C]0.978713475493893[/C][/ROW]
[ROW][C]28[/C][C]0.0149645781628233[/C][C]0.0299291563256465[/C][C]0.985035421837177[/C][/ROW]
[ROW][C]29[/C][C]0.0108460157570762[/C][C]0.0216920315141525[/C][C]0.989153984242924[/C][/ROW]
[ROW][C]30[/C][C]0.00734898567943645[/C][C]0.0146979713588729[/C][C]0.992651014320564[/C][/ROW]
[ROW][C]31[/C][C]0.00585599677205853[/C][C]0.0117119935441171[/C][C]0.994144003227941[/C][/ROW]
[ROW][C]32[/C][C]0.00439666802741335[/C][C]0.0087933360548267[/C][C]0.995603331972587[/C][/ROW]
[ROW][C]33[/C][C]0.00307856369259617[/C][C]0.00615712738519234[/C][C]0.996921436307404[/C][/ROW]
[ROW][C]34[/C][C]0.00231943102872675[/C][C]0.0046388620574535[/C][C]0.997680568971273[/C][/ROW]
[ROW][C]35[/C][C]0.00523983629130157[/C][C]0.0104796725826031[/C][C]0.994760163708698[/C][/ROW]
[ROW][C]36[/C][C]0.0042283107680864[/C][C]0.0084566215361728[/C][C]0.995771689231914[/C][/ROW]
[ROW][C]37[/C][C]0.0048982100307368[/C][C]0.0097964200614736[/C][C]0.995101789969263[/C][/ROW]
[ROW][C]38[/C][C]0.00641345089555615[/C][C]0.0128269017911123[/C][C]0.993586549104444[/C][/ROW]
[ROW][C]39[/C][C]0.00552971063102585[/C][C]0.0110594212620517[/C][C]0.994470289368974[/C][/ROW]
[ROW][C]40[/C][C]0.00623641653045005[/C][C]0.0124728330609001[/C][C]0.99376358346955[/C][/ROW]
[ROW][C]41[/C][C]0.00505677097399696[/C][C]0.0101135419479939[/C][C]0.994943229026003[/C][/ROW]
[ROW][C]42[/C][C]0.00574088931181939[/C][C]0.0114817786236388[/C][C]0.99425911068818[/C][/ROW]
[ROW][C]43[/C][C]0.00546022080244678[/C][C]0.0109204416048936[/C][C]0.994539779197553[/C][/ROW]
[ROW][C]44[/C][C]0.00454938218488873[/C][C]0.00909876436977746[/C][C]0.995450617815111[/C][/ROW]
[ROW][C]45[/C][C]0.00366736859211772[/C][C]0.00733473718423545[/C][C]0.996332631407882[/C][/ROW]
[ROW][C]46[/C][C]0.00301960571050053[/C][C]0.00603921142100107[/C][C]0.9969803942895[/C][/ROW]
[ROW][C]47[/C][C]0.00462140526685546[/C][C]0.00924281053371092[/C][C]0.995378594733145[/C][/ROW]
[ROW][C]48[/C][C]0.00771545813885953[/C][C]0.0154309162777191[/C][C]0.99228454186114[/C][/ROW]
[ROW][C]49[/C][C]0.0116318344730961[/C][C]0.0232636689461923[/C][C]0.988368165526904[/C][/ROW]
[ROW][C]50[/C][C]0.0103157418822994[/C][C]0.0206314837645988[/C][C]0.9896842581177[/C][/ROW]
[ROW][C]51[/C][C]0.0472571861082816[/C][C]0.0945143722165632[/C][C]0.952742813891718[/C][/ROW]
[ROW][C]52[/C][C]0.0416647248854043[/C][C]0.0833294497708086[/C][C]0.958335275114596[/C][/ROW]
[ROW][C]53[/C][C]0.0370736014932048[/C][C]0.0741472029864096[/C][C]0.962926398506795[/C][/ROW]
[ROW][C]54[/C][C]0.133790707391586[/C][C]0.267581414783172[/C][C]0.866209292608414[/C][/ROW]
[ROW][C]55[/C][C]0.122781688376571[/C][C]0.245563376753143[/C][C]0.877218311623429[/C][/ROW]
[ROW][C]56[/C][C]0.122062047883039[/C][C]0.244124095766078[/C][C]0.877937952116961[/C][/ROW]
[ROW][C]57[/C][C]0.117982545739986[/C][C]0.235965091479971[/C][C]0.882017454260014[/C][/ROW]
[ROW][C]58[/C][C]0.123970415073652[/C][C]0.247940830147305[/C][C]0.876029584926348[/C][/ROW]
[ROW][C]59[/C][C]0.147593333261397[/C][C]0.295186666522794[/C][C]0.852406666738603[/C][/ROW]
[ROW][C]60[/C][C]0.148935580984675[/C][C]0.297871161969349[/C][C]0.851064419015325[/C][/ROW]
[ROW][C]61[/C][C]0.161496699523163[/C][C]0.322993399046325[/C][C]0.838503300476837[/C][/ROW]
[ROW][C]62[/C][C]0.169790134247940[/C][C]0.339580268495879[/C][C]0.83020986575206[/C][/ROW]
[ROW][C]63[/C][C]0.228722367136195[/C][C]0.457444734272389[/C][C]0.771277632863805[/C][/ROW]
[ROW][C]64[/C][C]0.254764908355812[/C][C]0.509529816711625[/C][C]0.745235091644188[/C][/ROW]
[ROW][C]65[/C][C]0.582584176854118[/C][C]0.834831646291763[/C][C]0.417415823145882[/C][/ROW]
[ROW][C]66[/C][C]0.695816514858851[/C][C]0.608366970282298[/C][C]0.304183485141149[/C][/ROW]
[ROW][C]67[/C][C]0.689278457524662[/C][C]0.621443084950677[/C][C]0.310721542475338[/C][/ROW]
[ROW][C]68[/C][C]0.685551509441902[/C][C]0.628896981116196[/C][C]0.314448490558098[/C][/ROW]
[ROW][C]69[/C][C]0.701387759397603[/C][C]0.597224481204795[/C][C]0.298612240602397[/C][/ROW]
[ROW][C]70[/C][C]0.678362178601241[/C][C]0.643275642797517[/C][C]0.321637821398759[/C][/ROW]
[ROW][C]71[/C][C]0.68617681831603[/C][C]0.62764636336794[/C][C]0.31382318168397[/C][/ROW]
[ROW][C]72[/C][C]0.726914674971448[/C][C]0.546170650057104[/C][C]0.273085325028552[/C][/ROW]
[ROW][C]73[/C][C]0.868582869815772[/C][C]0.262834260368455[/C][C]0.131417130184227[/C][/ROW]
[ROW][C]74[/C][C]0.858357231250921[/C][C]0.283285537498158[/C][C]0.141642768749079[/C][/ROW]
[ROW][C]75[/C][C]0.915328865301453[/C][C]0.169342269397094[/C][C]0.0846711346985468[/C][/ROW]
[ROW][C]76[/C][C]0.900722355879453[/C][C]0.198555288241095[/C][C]0.0992776441205473[/C][/ROW]
[ROW][C]77[/C][C]0.918872672913806[/C][C]0.162254654172387[/C][C]0.0811273270861937[/C][/ROW]
[ROW][C]78[/C][C]0.927924440852928[/C][C]0.144151118294143[/C][C]0.0720755591470715[/C][/ROW]
[ROW][C]79[/C][C]0.912493649175098[/C][C]0.175012701649804[/C][C]0.0875063508249022[/C][/ROW]
[ROW][C]80[/C][C]0.890100064910757[/C][C]0.219799870178487[/C][C]0.109899935089243[/C][/ROW]
[ROW][C]81[/C][C]0.870059934539163[/C][C]0.259880130921674[/C][C]0.129940065460837[/C][/ROW]
[ROW][C]82[/C][C]0.882007780552894[/C][C]0.235984438894212[/C][C]0.117992219447106[/C][/ROW]
[ROW][C]83[/C][C]0.853369913580667[/C][C]0.293260172838665[/C][C]0.146630086419333[/C][/ROW]
[ROW][C]84[/C][C]0.819114939704816[/C][C]0.361770120590368[/C][C]0.180885060295184[/C][/ROW]
[ROW][C]85[/C][C]0.776925065059802[/C][C]0.446149869880396[/C][C]0.223074934940198[/C][/ROW]
[ROW][C]86[/C][C]0.752012419240148[/C][C]0.495975161519704[/C][C]0.247987580759852[/C][/ROW]
[ROW][C]87[/C][C]0.709017808118574[/C][C]0.581964383762852[/C][C]0.290982191881426[/C][/ROW]
[ROW][C]88[/C][C]0.65599546729325[/C][C]0.6880090654135[/C][C]0.34400453270675[/C][/ROW]
[ROW][C]89[/C][C]0.623548262080461[/C][C]0.752903475839079[/C][C]0.376451737919539[/C][/ROW]
[ROW][C]90[/C][C]0.636939273258257[/C][C]0.726121453483485[/C][C]0.363060726741743[/C][/ROW]
[ROW][C]91[/C][C]0.564906251543936[/C][C]0.870187496912128[/C][C]0.435093748456064[/C][/ROW]
[ROW][C]92[/C][C]0.487886389176181[/C][C]0.975772778352363[/C][C]0.512113610823819[/C][/ROW]
[ROW][C]93[/C][C]0.462806317325371[/C][C]0.925612634650742[/C][C]0.537193682674629[/C][/ROW]
[ROW][C]94[/C][C]0.982103362894111[/C][C]0.0357932742117773[/C][C]0.0178966371058886[/C][/ROW]
[ROW][C]95[/C][C]0.967971722006086[/C][C]0.0640565559878277[/C][C]0.0320282779939139[/C][/ROW]
[ROW][C]96[/C][C]0.9817479482404[/C][C]0.036504103519199[/C][C]0.0182520517595995[/C][/ROW]
[ROW][C]97[/C][C]0.96489012471753[/C][C]0.0702197505649413[/C][C]0.0351098752824707[/C][/ROW]
[ROW][C]98[/C][C]0.962759830354305[/C][C]0.0744803392913907[/C][C]0.0372401696456954[/C][/ROW]
[ROW][C]99[/C][C]0.929030183115254[/C][C]0.141939633769492[/C][C]0.0709698168847458[/C][/ROW]
[ROW][C]100[/C][C]0.914925680949316[/C][C]0.170148638101369[/C][C]0.0850743190506845[/C][/ROW]
[ROW][C]101[/C][C]0.847977570706551[/C][C]0.304044858586898[/C][C]0.152022429293449[/C][/ROW]
[ROW][C]102[/C][C]0.831214362401126[/C][C]0.337571275197748[/C][C]0.168785637598874[/C][/ROW]
[ROW][C]103[/C][C]0.688484830059195[/C][C]0.623030339881609[/C][C]0.311515169940805[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57642&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57642&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
5	0.0787621459489388	0.157524291897878	0.921237854051061
6	0.0608673300550259	0.121734660110052	0.939132669944974
7	0.0524970373291991	0.104994074658398	0.94750296267080
8	0.0242007698072505	0.048401539614501	0.97579923019275
9	0.0170988855224703	0.0341977710449407	0.98290111447753
10	0.00806685693399365	0.0161337138679873	0.991933143066006
11	0.0529571894476661	0.105914378895332	0.947042810552334
12	0.080376905281759	0.160753810563518	0.919623094718241
13	0.0531355764199753	0.106271152839951	0.946864423580025
14	0.0313333000292319	0.0626666000584639	0.968666699970768
15	0.0388281831857341	0.0776563663714681	0.961171816814266
16	0.0334811211090817	0.0669622422181635	0.966518878890918
17	0.0214190042818892	0.0428380085637783	0.97858099571811
18	0.0167603065561136	0.0335206131122272	0.983239693443886
19	0.0101287447552609	0.0202574895105219	0.98987125524474
20	0.0101689434662982	0.0203378869325964	0.989831056533702
21	0.0107489191315675	0.0214978382631350	0.989251080868432
22	0.0063843752719433	0.0127687505438866	0.993615624728057
23	0.00950466856588625	0.0190093371317725	0.990495331434114
24	0.0253257833238490	0.0506515666476979	0.974674216676151
25	0.0403065992860307	0.0806131985720614	0.95969340071397
26	0.0281869950852667	0.0563739901705335	0.971813004914733
27	0.0212865245061066	0.0425730490122132	0.978713475493893
28	0.0149645781628233	0.0299291563256465	0.985035421837177
29	0.0108460157570762	0.0216920315141525	0.989153984242924
30	0.00734898567943645	0.0146979713588729	0.992651014320564
31	0.00585599677205853	0.0117119935441171	0.994144003227941
32	0.00439666802741335	0.0087933360548267	0.995603331972587
33	0.00307856369259617	0.00615712738519234	0.996921436307404
34	0.00231943102872675	0.0046388620574535	0.997680568971273
35	0.00523983629130157	0.0104796725826031	0.994760163708698
36	0.0042283107680864	0.0084566215361728	0.995771689231914
37	0.0048982100307368	0.0097964200614736	0.995101789969263
38	0.00641345089555615	0.0128269017911123	0.993586549104444
39	0.00552971063102585	0.0110594212620517	0.994470289368974
40	0.00623641653045005	0.0124728330609001	0.99376358346955
41	0.00505677097399696	0.0101135419479939	0.994943229026003
42	0.00574088931181939	0.0114817786236388	0.99425911068818
43	0.00546022080244678	0.0109204416048936	0.994539779197553
44	0.00454938218488873	0.00909876436977746	0.995450617815111
45	0.00366736859211772	0.00733473718423545	0.996332631407882
46	0.00301960571050053	0.00603921142100107	0.9969803942895
47	0.00462140526685546	0.00924281053371092	0.995378594733145
48	0.00771545813885953	0.0154309162777191	0.99228454186114
49	0.0116318344730961	0.0232636689461923	0.988368165526904
50	0.0103157418822994	0.0206314837645988	0.9896842581177
51	0.0472571861082816	0.0945143722165632	0.952742813891718
52	0.0416647248854043	0.0833294497708086	0.958335275114596
53	0.0370736014932048	0.0741472029864096	0.962926398506795
54	0.133790707391586	0.267581414783172	0.866209292608414
55	0.122781688376571	0.245563376753143	0.877218311623429
56	0.122062047883039	0.244124095766078	0.877937952116961
57	0.117982545739986	0.235965091479971	0.882017454260014
58	0.123970415073652	0.247940830147305	0.876029584926348
59	0.147593333261397	0.295186666522794	0.852406666738603
60	0.148935580984675	0.297871161969349	0.851064419015325
61	0.161496699523163	0.322993399046325	0.838503300476837
62	0.169790134247940	0.339580268495879	0.83020986575206
63	0.228722367136195	0.457444734272389	0.771277632863805
64	0.254764908355812	0.509529816711625	0.745235091644188
65	0.582584176854118	0.834831646291763	0.417415823145882
66	0.695816514858851	0.608366970282298	0.304183485141149
67	0.689278457524662	0.621443084950677	0.310721542475338
68	0.685551509441902	0.628896981116196	0.314448490558098
69	0.701387759397603	0.597224481204795	0.298612240602397
70	0.678362178601241	0.643275642797517	0.321637821398759
71	0.68617681831603	0.62764636336794	0.31382318168397
72	0.726914674971448	0.546170650057104	0.273085325028552
73	0.868582869815772	0.262834260368455	0.131417130184227
74	0.858357231250921	0.283285537498158	0.141642768749079
75	0.915328865301453	0.169342269397094	0.0846711346985468
76	0.900722355879453	0.198555288241095	0.0992776441205473
77	0.918872672913806	0.162254654172387	0.0811273270861937
78	0.927924440852928	0.144151118294143	0.0720755591470715
79	0.912493649175098	0.175012701649804	0.0875063508249022
80	0.890100064910757	0.219799870178487	0.109899935089243
81	0.870059934539163	0.259880130921674	0.129940065460837
82	0.882007780552894	0.235984438894212	0.117992219447106
83	0.853369913580667	0.293260172838665	0.146630086419333
84	0.819114939704816	0.361770120590368	0.180885060295184
85	0.776925065059802	0.446149869880396	0.223074934940198
86	0.752012419240148	0.495975161519704	0.247987580759852
87	0.709017808118574	0.581964383762852	0.290982191881426
88	0.65599546729325	0.6880090654135	0.34400453270675
89	0.623548262080461	0.752903475839079	0.376451737919539
90	0.636939273258257	0.726121453483485	0.363060726741743
91	0.564906251543936	0.870187496912128	0.435093748456064
92	0.487886389176181	0.975772778352363	0.512113610823819
93	0.462806317325371	0.925612634650742	0.537193682674629
94	0.982103362894111	0.0357932742117773	0.0178966371058886
95	0.967971722006086	0.0640565559878277	0.0320282779939139
96	0.9817479482404	0.036504103519199	0.0182520517595995
97	0.96489012471753	0.0702197505649413	0.0351098752824707
98	0.962759830354305	0.0744803392913907	0.0372401696456954
99	0.929030183115254	0.141939633769492	0.0709698168847458
100	0.914925680949316	0.170148638101369	0.0850743190506845
101	0.847977570706551	0.304044858586898	0.152022429293449
102	0.831214362401126	0.337571275197748	0.168785637598874
103	0.688484830059195	0.623030339881609	0.311515169940805

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	9	0.090909090909091	NOK
5% type I error level	36	0.363636363636364	NOK
10% type I error level	48	0.484848484848485	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 & 9 & 0.090909090909091 & NOK \tabularnewline
5% type I error level & 36 & 0.363636363636364 & NOK \tabularnewline
10% type I error level & 48 & 0.484848484848485 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57642&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]9[/C][C]0.090909090909091[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]36[/C][C]0.363636363636364[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]48[/C][C]0.484848484848485[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57642&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57642&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	9	0.090909090909091	NOK
5% type I error level	36	0.363636363636364	NOK
10% type I error level	48	0.484848484848485	NOK

Figure 1

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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