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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 04 Sep 2020 13:57:04 +0200

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2020/Sep/04/t1599220668oaywarpnhcqkklv.htm/, Retrieved Sat, 20 Apr 2024 15:02:48 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Sat, 20 Apr 2024 15:02:48 +0200

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Dataseries X:

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10 10 10 36
8 8 15 32
8 6 14 33
9 10 14 39
5 8 8 34
10 10 19 39
8 7 17 36
9 10 18 33
8 6 10 30
7 7 15 39
10 9 16 37
10 6 12 37
9 7 13 35
4 6 10 32
4 4 14 36
8 6 15 36
9 8 20 41
10 9 9 36
8 8 12 37
5 6 13 29
10 6 16 39
8 10 12 37
7 8 14 32
8 8 15 36
8 7 19 43
9 4 16 30
8 9 16 33
6 8 14 28
8 10 14 30
8 8 14 28
5 6 13 39
9 7 18 34
8 8 15 34
8 5 15 29
8 10 15 32
6 2 13 33
6 6 14 27
9 7 15 35
8 5 14 38
9 8 19 40
10 7 16 34
8 7 16 34
8 10 12 26
7 7 10 39
7 6 11 34
10 10 13 39
8 6 14 26
7 5 11 30
10 8 11 34
7 8 16 34
7 5 9 29
9 8 16 41
9 10 19 43
8 7 13 31
6 7 15 33
8 7 14 34
9 7 15 30
2 2 11 23
6 4 14 29
8 6 15 35
8 7 17 40
7 9 16 27
8 9 13 30
6 4 15 27
10 9 14 29
10 9 15 33
10 8 14 32
8 7 12 33
8 9 12 36
7 7 15 34
10 6 17 45
5 7 13 30
3 2 5 22
2 3 7 24
3 4 10 25
4 5 15 26
2 2 9 27
6 6 9 27
8 8 15 35
8 5 14 36
5 4 11 32
10 10 18 35
9 10 20 35
8 10 20 36
9 9 16 37
8 5 15 33
5 5 14 25
7 7 13 35
9 10 18 37
8 9 14 36
4 8 12 35
7 8 9 29
8 8 19 35
7 8 13 31
7 8 12 30
9 7 14 37
6 6 6 36
7 8 14 35
4 2 11 32
6 5 11 34
10 4 14 37
9 9 12 36
10 10 19 39
8 6 13 37
4 4 14 31
8 10 17 40
5 6 12 38
8 7 16 35
9 7 15 38
8 8 15 32
4 6 15 41
8 5 16 28
10 6 15 40
6 7 12 25
7 6 13 28
10 9 14 37
9 9 17 37
8 7 14 40
3 6 14 26
8 7 14 30
7 7 15 32
7 8 11 31
8 7 11 28
8 8 16 34
7 7 12 39
7 4 12 33
9 10 19 43
9 8 18 37
9 8 16 31
4 2 16 31
6 6 13 34
6 4 11 32
6 4 10 27
8 9 14 34
3 2 14 28
8 6 14 32
8 7 16 39
6 4 10 28
10 10 16 39
2 3 7 32
9 7 16 36
6 4 15 31
6 8 17 39
5 4 11 23
4 5 11 25
7 6 10 32
5 5 13 32
8 9 14 36
6 6 13 39
9 8 13 31
6 4 12 32
4 4 10 28
7 8 15 34
2 4 6 28
8 10 15 38
9 8 15 35
6 5 11 32
5 3 14 26
7 7 14 32
8 6 16 28
4 5 12 31
9 5 15 33
9 9 20 38
9 2 12 38
7 7 9 36
5 7 13 31
7 5 15 36
9 9 19 43
8 4 11 37
6 5 11 28
9 9 17 35
8 7 15 34
7 6 14 40
7 8 15 31
7 7 11 41
8 6 12 35
10 8 15 38
6 6 16 37
6 7 16 31

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time3 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

3 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	3 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
Intention_to_Use[t] = -1.06965 + 0.410284Relative_Advantage[t] + 0.141902Perceived_Ease_of_Use[t] + 0.107823System_Quality[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Intention_to_Use[t] =  -1.06965 +  0.410284Relative_Advantage[t] +  0.141902Perceived_Ease_of_Use[t] +  0.107823System_Quality[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Intention_to_Use[t] =  -1.06965 +  0.410284Relative_Advantage[t] +  0.141902Perceived_Ease_of_Use[t] +  0.107823System_Quality[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
Intention_to_Use[t] = -1.06965 + 0.410284Relative_Advantage[t] + 0.141902Perceived_Ease_of_Use[t] + 0.107823System_Quality[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)	-1.07	0.7781	-1.3750e+00	0.171	0.0855
Relative_Advantage	+0.4103	0.058	+7.0730e+00	3.486e-11	1.743e-11
Perceived_Ease_of_Use	+0.1419	0.04262	+3.3290e+00	0.001062	0.0005309
System_Quality	+0.1078	0.02607	+4.1360e+00	5.479e-05	2.739e-05

\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) & -1.07 &  0.7781 & -1.3750e+00 &  0.171 &  0.0855 \tabularnewline
Relative_Advantage & +0.4103 &  0.058 & +7.0730e+00 &  3.486e-11 &  1.743e-11 \tabularnewline
Perceived_Ease_of_Use & +0.1419 &  0.04262 & +3.3290e+00 &  0.001062 &  0.0005309 \tabularnewline
System_Quality & +0.1078 &  0.02607 & +4.1360e+00 &  5.479e-05 &  2.739e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]-1.07[/C][C] 0.7781[/C][C]-1.3750e+00[/C][C] 0.171[/C][C] 0.0855[/C][/ROW]
[ROW][C]Relative_Advantage[/C][C]+0.4103[/C][C] 0.058[/C][C]+7.0730e+00[/C][C] 3.486e-11[/C][C] 1.743e-11[/C][/ROW]
[ROW][C]Perceived_Ease_of_Use[/C][C]+0.1419[/C][C] 0.04262[/C][C]+3.3290e+00[/C][C] 0.001062[/C][C] 0.0005309[/C][/ROW]
[ROW][C]System_Quality[/C][C]+0.1078[/C][C] 0.02607[/C][C]+4.1360e+00[/C][C] 5.479e-05[/C][C] 2.739e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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)	-1.07	0.7781	-1.3750e+00	0.171	0.0855
Relative_Advantage	+0.4103	0.058	+7.0730e+00	3.486e-11	1.743e-11
Perceived_Ease_of_Use	+0.1419	0.04262	+3.3290e+00	0.001062	0.0005309
System_Quality	+0.1078	0.02607	+4.1360e+00	5.479e-05	2.739e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.7219
R-squared	0.5212
Adjusted R-squared	0.513
F-TEST (value)	63.49
F-TEST (DF numerator)	3
F-TEST (DF denominator)	175
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.37
Sum Squared Residuals	328.5

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.7219 \tabularnewline
R-squared &  0.5212 \tabularnewline
Adjusted R-squared &  0.513 \tabularnewline
F-TEST (value) &  63.49 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 175 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.37 \tabularnewline
Sum Squared Residuals &  328.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.7219[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.5212[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.513[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 63.49[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]175[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.37[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 328.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	0.7219
R-squared	0.5212
Adjusted R-squared	0.513
F-TEST (value)	63.49
F-TEST (DF numerator)	3
F-TEST (DF denominator)	175
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.37
Sum Squared Residuals	328.5

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=4

As an alternative you can also use a QR Code:

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

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	10	8.334	1.666
2	8	7.791	0.2085
3	8	6.937	1.063
4	9	9.225	-0.2249
5	5	7.014	-2.014
6	10	9.934	0.06558
7	8	8.096	-0.09629
8	9	9.146	-0.1456
9	8	6.046	1.954
10	7	8.136	-1.136
11	10	8.883	1.117
12	10	7.084	2.916
13	9	7.421	1.579
14	4	6.261	-2.261
15	4	6.44	-2.44
16	8	7.402	0.5978
17	9	9.471	-0.4714
18	10	7.782	2.218
19	8	7.905	0.09511
20	5	6.364	-1.364
21	10	7.868	2.132
22	8	8.725	-0.7255
23	7	7.65	-0.6496
24	8	8.223	-0.2228
25	8	9.135	-1.135
26	9	6.077	2.923
27	8	8.451	-0.4515
28	6	7.218	-1.218
29	8	8.255	-0.2545
30	8	7.218	0.7817
31	5	7.442	-2.442
32	9	8.023	0.9774
33	8	8.007	-0.007129
34	8	6.237	1.763
35	8	8.612	-0.612
36	6	5.154	0.8462
37	6	6.29	-0.2899
38	9	7.705	1.295
39	8	7.066	0.9343
40	9	9.222	-0.2217
41	10	7.739	2.261
42	8	7.739	0.2613
43	8	7.539	0.4606
44	7	7.426	-0.4264
45	7	6.619	0.381
46	10	9.083	0.917
47	8	6.182	1.818
48	7	5.777	1.223
49	10	7.44	2.56
50	7	8.149	-1.149
51	7	5.386	1.614
52	9	8.904	0.09621
53	9	10.37	-1.366
54	8	6.99	1.01
55	6	7.489	-1.489
56	8	7.455	0.5451
57	9	7.166	1.834
58	2	3.792	-1.792
59	6	5.685	0.315
60	8	7.294	0.7056
61	8	8.528	-0.5276
62	7	7.805	-0.8046
63	8	7.702	0.2977
64	6	5.611	0.3888
65	10	7.736	2.264
66	10	8.31	1.69
67	10	7.65	2.35
68	8	7.063	0.9367
69	8	8.207	-0.2074
70	7	7.597	-0.5968
71	10	8.656	1.344
72	5	6.882	-1.882
73	3	2.833	0.1675
74	2	3.742	-1.742
75	3	4.686	-1.686
76	4	5.914	-1.914
77	2	3.939	-1.939
78	6	5.58	0.4196
79	8	8.115	-0.115
80	8	6.85	1.15
81	5	5.583	-0.5827
82	10	9.361	0.6388
83	9	9.645	-0.645
84	8	9.753	-1.753
85	9	8.883	0.1172
86	8	6.668	1.332
87	5	5.664	-0.664
88	7	7.421	-0.4209
89	9	9.577	-0.5769
90	8	8.491	-0.4912
91	4	7.689	-3.689
92	7	6.617	0.3834
93	8	8.683	-0.6826
94	7	7.4	-0.3999
95	7	7.15	-0.1501
96	9	7.778	1.222
97	6	6.125	-0.1251
98	7	7.973	-0.973
99	4	4.762	-0.7622
100	6	6.209	-0.2087
101	10	6.548	3.452
102	9	8.207	0.7926
103	10	9.934	0.06558
104	8	7.226	0.7738
105	4	5.901	-1.901
106	8	9.758	-1.758
107	5	7.192	-2.192
108	8	7.847	0.1534
109	9	8.028	0.9719
110	8	7.791	0.2085
111	4	7.941	-3.941
112	8	6.271	1.729
113	10	7.833	2.167
114	6	6.201	-0.2007
115	7	6.256	0.7442
116	10	8.599	1.401
117	9	9.025	-0.02468
118	8	8.102	-0.1019
119	3	6.182	-3.182
120	8	7.024	0.9763
121	7	7.381	-0.3812
122	7	7.116	-0.1161
123	8	6.382	1.618
124	8	8.149	-0.149
125	7	7.71	-0.7103
126	7	5.832	1.168
127	9	10.37	-1.366
128	9	8.756	0.2437
129	9	7.826	1.174
130	4	5.364	-1.364
131	6	6.903	-0.9028
132	6	5.583	0.4173
133	6	4.902	1.098
134	8	8.276	-0.2755
135	3	4.757	-1.757
136	8	6.829	1.171
137	8	8.278	-0.2779
138	6	5.01	0.9905
139	10	9.509	0.4913
140	2	4.605	-2.605
141	9	7.954	1.046
142	6	6.043	-0.04253
143	6	8.83	-2.83
144	5	4.612	0.3877
145	4	5.238	-1.238
146	7	6.261	0.7386
147	5	6.277	-1.277
148	8	8.491	-0.4912
149	6	7.442	-1.442
150	9	7.4	1.6
151	6	5.725	0.2754
152	4	5.01	-1.01
153	7	8.007	-1.007
154	2	4.442	-2.442
155	8	9.259	-1.259
156	9	8.115	0.885
157	6	5.993	0.006976
158	5	4.951	0.04877
159	7	7.239	-0.2393
160	8	6.682	1.318
161	4	6.027	-2.027
162	9	6.668	2.332
163	9	9.558	-0.5582
164	9	5.551	3.449
165	7	6.961	0.03892
166	5	6.99	-1.99
167	7	6.992	0.008076
168	9	9.955	-0.9554
169	8	6.122	1.878
170	6	5.562	0.4383
171	9	8.809	0.191
172	8	7.597	0.4032
173	7	7.692	-0.6916
174	7	7.684	-0.6837
175	7	7.784	-0.784
176	8	6.869	1.131
177	10	8.438	1.562
178	6	7.652	-1.652
179	6	7.415	-1.415

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  10 &  8.334 &  1.666 \tabularnewline
2 &  8 &  7.791 &  0.2085 \tabularnewline
3 &  8 &  6.937 &  1.063 \tabularnewline
4 &  9 &  9.225 & -0.2249 \tabularnewline
5 &  5 &  7.014 & -2.014 \tabularnewline
6 &  10 &  9.934 &  0.06558 \tabularnewline
7 &  8 &  8.096 & -0.09629 \tabularnewline
8 &  9 &  9.146 & -0.1456 \tabularnewline
9 &  8 &  6.046 &  1.954 \tabularnewline
10 &  7 &  8.136 & -1.136 \tabularnewline
11 &  10 &  8.883 &  1.117 \tabularnewline
12 &  10 &  7.084 &  2.916 \tabularnewline
13 &  9 &  7.421 &  1.579 \tabularnewline
14 &  4 &  6.261 & -2.261 \tabularnewline
15 &  4 &  6.44 & -2.44 \tabularnewline
16 &  8 &  7.402 &  0.5978 \tabularnewline
17 &  9 &  9.471 & -0.4714 \tabularnewline
18 &  10 &  7.782 &  2.218 \tabularnewline
19 &  8 &  7.905 &  0.09511 \tabularnewline
20 &  5 &  6.364 & -1.364 \tabularnewline
21 &  10 &  7.868 &  2.132 \tabularnewline
22 &  8 &  8.725 & -0.7255 \tabularnewline
23 &  7 &  7.65 & -0.6496 \tabularnewline
24 &  8 &  8.223 & -0.2228 \tabularnewline
25 &  8 &  9.135 & -1.135 \tabularnewline
26 &  9 &  6.077 &  2.923 \tabularnewline
27 &  8 &  8.451 & -0.4515 \tabularnewline
28 &  6 &  7.218 & -1.218 \tabularnewline
29 &  8 &  8.255 & -0.2545 \tabularnewline
30 &  8 &  7.218 &  0.7817 \tabularnewline
31 &  5 &  7.442 & -2.442 \tabularnewline
32 &  9 &  8.023 &  0.9774 \tabularnewline
33 &  8 &  8.007 & -0.007129 \tabularnewline
34 &  8 &  6.237 &  1.763 \tabularnewline
35 &  8 &  8.612 & -0.612 \tabularnewline
36 &  6 &  5.154 &  0.8462 \tabularnewline
37 &  6 &  6.29 & -0.2899 \tabularnewline
38 &  9 &  7.705 &  1.295 \tabularnewline
39 &  8 &  7.066 &  0.9343 \tabularnewline
40 &  9 &  9.222 & -0.2217 \tabularnewline
41 &  10 &  7.739 &  2.261 \tabularnewline
42 &  8 &  7.739 &  0.2613 \tabularnewline
43 &  8 &  7.539 &  0.4606 \tabularnewline
44 &  7 &  7.426 & -0.4264 \tabularnewline
45 &  7 &  6.619 &  0.381 \tabularnewline
46 &  10 &  9.083 &  0.917 \tabularnewline
47 &  8 &  6.182 &  1.818 \tabularnewline
48 &  7 &  5.777 &  1.223 \tabularnewline
49 &  10 &  7.44 &  2.56 \tabularnewline
50 &  7 &  8.149 & -1.149 \tabularnewline
51 &  7 &  5.386 &  1.614 \tabularnewline
52 &  9 &  8.904 &  0.09621 \tabularnewline
53 &  9 &  10.37 & -1.366 \tabularnewline
54 &  8 &  6.99 &  1.01 \tabularnewline
55 &  6 &  7.489 & -1.489 \tabularnewline
56 &  8 &  7.455 &  0.5451 \tabularnewline
57 &  9 &  7.166 &  1.834 \tabularnewline
58 &  2 &  3.792 & -1.792 \tabularnewline
59 &  6 &  5.685 &  0.315 \tabularnewline
60 &  8 &  7.294 &  0.7056 \tabularnewline
61 &  8 &  8.528 & -0.5276 \tabularnewline
62 &  7 &  7.805 & -0.8046 \tabularnewline
63 &  8 &  7.702 &  0.2977 \tabularnewline
64 &  6 &  5.611 &  0.3888 \tabularnewline
65 &  10 &  7.736 &  2.264 \tabularnewline
66 &  10 &  8.31 &  1.69 \tabularnewline
67 &  10 &  7.65 &  2.35 \tabularnewline
68 &  8 &  7.063 &  0.9367 \tabularnewline
69 &  8 &  8.207 & -0.2074 \tabularnewline
70 &  7 &  7.597 & -0.5968 \tabularnewline
71 &  10 &  8.656 &  1.344 \tabularnewline
72 &  5 &  6.882 & -1.882 \tabularnewline
73 &  3 &  2.833 &  0.1675 \tabularnewline
74 &  2 &  3.742 & -1.742 \tabularnewline
75 &  3 &  4.686 & -1.686 \tabularnewline
76 &  4 &  5.914 & -1.914 \tabularnewline
77 &  2 &  3.939 & -1.939 \tabularnewline
78 &  6 &  5.58 &  0.4196 \tabularnewline
79 &  8 &  8.115 & -0.115 \tabularnewline
80 &  8 &  6.85 &  1.15 \tabularnewline
81 &  5 &  5.583 & -0.5827 \tabularnewline
82 &  10 &  9.361 &  0.6388 \tabularnewline
83 &  9 &  9.645 & -0.645 \tabularnewline
84 &  8 &  9.753 & -1.753 \tabularnewline
85 &  9 &  8.883 &  0.1172 \tabularnewline
86 &  8 &  6.668 &  1.332 \tabularnewline
87 &  5 &  5.664 & -0.664 \tabularnewline
88 &  7 &  7.421 & -0.4209 \tabularnewline
89 &  9 &  9.577 & -0.5769 \tabularnewline
90 &  8 &  8.491 & -0.4912 \tabularnewline
91 &  4 &  7.689 & -3.689 \tabularnewline
92 &  7 &  6.617 &  0.3834 \tabularnewline
93 &  8 &  8.683 & -0.6826 \tabularnewline
94 &  7 &  7.4 & -0.3999 \tabularnewline
95 &  7 &  7.15 & -0.1501 \tabularnewline
96 &  9 &  7.778 &  1.222 \tabularnewline
97 &  6 &  6.125 & -0.1251 \tabularnewline
98 &  7 &  7.973 & -0.973 \tabularnewline
99 &  4 &  4.762 & -0.7622 \tabularnewline
100 &  6 &  6.209 & -0.2087 \tabularnewline
101 &  10 &  6.548 &  3.452 \tabularnewline
102 &  9 &  8.207 &  0.7926 \tabularnewline
103 &  10 &  9.934 &  0.06558 \tabularnewline
104 &  8 &  7.226 &  0.7738 \tabularnewline
105 &  4 &  5.901 & -1.901 \tabularnewline
106 &  8 &  9.758 & -1.758 \tabularnewline
107 &  5 &  7.192 & -2.192 \tabularnewline
108 &  8 &  7.847 &  0.1534 \tabularnewline
109 &  9 &  8.028 &  0.9719 \tabularnewline
110 &  8 &  7.791 &  0.2085 \tabularnewline
111 &  4 &  7.941 & -3.941 \tabularnewline
112 &  8 &  6.271 &  1.729 \tabularnewline
113 &  10 &  7.833 &  2.167 \tabularnewline
114 &  6 &  6.201 & -0.2007 \tabularnewline
115 &  7 &  6.256 &  0.7442 \tabularnewline
116 &  10 &  8.599 &  1.401 \tabularnewline
117 &  9 &  9.025 & -0.02468 \tabularnewline
118 &  8 &  8.102 & -0.1019 \tabularnewline
119 &  3 &  6.182 & -3.182 \tabularnewline
120 &  8 &  7.024 &  0.9763 \tabularnewline
121 &  7 &  7.381 & -0.3812 \tabularnewline
122 &  7 &  7.116 & -0.1161 \tabularnewline
123 &  8 &  6.382 &  1.618 \tabularnewline
124 &  8 &  8.149 & -0.149 \tabularnewline
125 &  7 &  7.71 & -0.7103 \tabularnewline
126 &  7 &  5.832 &  1.168 \tabularnewline
127 &  9 &  10.37 & -1.366 \tabularnewline
128 &  9 &  8.756 &  0.2437 \tabularnewline
129 &  9 &  7.826 &  1.174 \tabularnewline
130 &  4 &  5.364 & -1.364 \tabularnewline
131 &  6 &  6.903 & -0.9028 \tabularnewline
132 &  6 &  5.583 &  0.4173 \tabularnewline
133 &  6 &  4.902 &  1.098 \tabularnewline
134 &  8 &  8.276 & -0.2755 \tabularnewline
135 &  3 &  4.757 & -1.757 \tabularnewline
136 &  8 &  6.829 &  1.171 \tabularnewline
137 &  8 &  8.278 & -0.2779 \tabularnewline
138 &  6 &  5.01 &  0.9905 \tabularnewline
139 &  10 &  9.509 &  0.4913 \tabularnewline
140 &  2 &  4.605 & -2.605 \tabularnewline
141 &  9 &  7.954 &  1.046 \tabularnewline
142 &  6 &  6.043 & -0.04253 \tabularnewline
143 &  6 &  8.83 & -2.83 \tabularnewline
144 &  5 &  4.612 &  0.3877 \tabularnewline
145 &  4 &  5.238 & -1.238 \tabularnewline
146 &  7 &  6.261 &  0.7386 \tabularnewline
147 &  5 &  6.277 & -1.277 \tabularnewline
148 &  8 &  8.491 & -0.4912 \tabularnewline
149 &  6 &  7.442 & -1.442 \tabularnewline
150 &  9 &  7.4 &  1.6 \tabularnewline
151 &  6 &  5.725 &  0.2754 \tabularnewline
152 &  4 &  5.01 & -1.01 \tabularnewline
153 &  7 &  8.007 & -1.007 \tabularnewline
154 &  2 &  4.442 & -2.442 \tabularnewline
155 &  8 &  9.259 & -1.259 \tabularnewline
156 &  9 &  8.115 &  0.885 \tabularnewline
157 &  6 &  5.993 &  0.006976 \tabularnewline
158 &  5 &  4.951 &  0.04877 \tabularnewline
159 &  7 &  7.239 & -0.2393 \tabularnewline
160 &  8 &  6.682 &  1.318 \tabularnewline
161 &  4 &  6.027 & -2.027 \tabularnewline
162 &  9 &  6.668 &  2.332 \tabularnewline
163 &  9 &  9.558 & -0.5582 \tabularnewline
164 &  9 &  5.551 &  3.449 \tabularnewline
165 &  7 &  6.961 &  0.03892 \tabularnewline
166 &  5 &  6.99 & -1.99 \tabularnewline
167 &  7 &  6.992 &  0.008076 \tabularnewline
168 &  9 &  9.955 & -0.9554 \tabularnewline
169 &  8 &  6.122 &  1.878 \tabularnewline
170 &  6 &  5.562 &  0.4383 \tabularnewline
171 &  9 &  8.809 &  0.191 \tabularnewline
172 &  8 &  7.597 &  0.4032 \tabularnewline
173 &  7 &  7.692 & -0.6916 \tabularnewline
174 &  7 &  7.684 & -0.6837 \tabularnewline
175 &  7 &  7.784 & -0.784 \tabularnewline
176 &  8 &  6.869 &  1.131 \tabularnewline
177 &  10 &  8.438 &  1.562 \tabularnewline
178 &  6 &  7.652 & -1.652 \tabularnewline
179 &  6 &  7.415 & -1.415 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=5

[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] 10[/C][C] 8.334[/C][C] 1.666[/C][/ROW]
[ROW][C]2[/C][C] 8[/C][C] 7.791[/C][C] 0.2085[/C][/ROW]
[ROW][C]3[/C][C] 8[/C][C] 6.937[/C][C] 1.063[/C][/ROW]
[ROW][C]4[/C][C] 9[/C][C] 9.225[/C][C]-0.2249[/C][/ROW]
[ROW][C]5[/C][C] 5[/C][C] 7.014[/C][C]-2.014[/C][/ROW]
[ROW][C]6[/C][C] 10[/C][C] 9.934[/C][C] 0.06558[/C][/ROW]
[ROW][C]7[/C][C] 8[/C][C] 8.096[/C][C]-0.09629[/C][/ROW]
[ROW][C]8[/C][C] 9[/C][C] 9.146[/C][C]-0.1456[/C][/ROW]
[ROW][C]9[/C][C] 8[/C][C] 6.046[/C][C] 1.954[/C][/ROW]
[ROW][C]10[/C][C] 7[/C][C] 8.136[/C][C]-1.136[/C][/ROW]
[ROW][C]11[/C][C] 10[/C][C] 8.883[/C][C] 1.117[/C][/ROW]
[ROW][C]12[/C][C] 10[/C][C] 7.084[/C][C] 2.916[/C][/ROW]
[ROW][C]13[/C][C] 9[/C][C] 7.421[/C][C] 1.579[/C][/ROW]
[ROW][C]14[/C][C] 4[/C][C] 6.261[/C][C]-2.261[/C][/ROW]
[ROW][C]15[/C][C] 4[/C][C] 6.44[/C][C]-2.44[/C][/ROW]
[ROW][C]16[/C][C] 8[/C][C] 7.402[/C][C] 0.5978[/C][/ROW]
[ROW][C]17[/C][C] 9[/C][C] 9.471[/C][C]-0.4714[/C][/ROW]
[ROW][C]18[/C][C] 10[/C][C] 7.782[/C][C] 2.218[/C][/ROW]
[ROW][C]19[/C][C] 8[/C][C] 7.905[/C][C] 0.09511[/C][/ROW]
[ROW][C]20[/C][C] 5[/C][C] 6.364[/C][C]-1.364[/C][/ROW]
[ROW][C]21[/C][C] 10[/C][C] 7.868[/C][C] 2.132[/C][/ROW]
[ROW][C]22[/C][C] 8[/C][C] 8.725[/C][C]-0.7255[/C][/ROW]
[ROW][C]23[/C][C] 7[/C][C] 7.65[/C][C]-0.6496[/C][/ROW]
[ROW][C]24[/C][C] 8[/C][C] 8.223[/C][C]-0.2228[/C][/ROW]
[ROW][C]25[/C][C] 8[/C][C] 9.135[/C][C]-1.135[/C][/ROW]
[ROW][C]26[/C][C] 9[/C][C] 6.077[/C][C] 2.923[/C][/ROW]
[ROW][C]27[/C][C] 8[/C][C] 8.451[/C][C]-0.4515[/C][/ROW]
[ROW][C]28[/C][C] 6[/C][C] 7.218[/C][C]-1.218[/C][/ROW]
[ROW][C]29[/C][C] 8[/C][C] 8.255[/C][C]-0.2545[/C][/ROW]
[ROW][C]30[/C][C] 8[/C][C] 7.218[/C][C] 0.7817[/C][/ROW]
[ROW][C]31[/C][C] 5[/C][C] 7.442[/C][C]-2.442[/C][/ROW]
[ROW][C]32[/C][C] 9[/C][C] 8.023[/C][C] 0.9774[/C][/ROW]
[ROW][C]33[/C][C] 8[/C][C] 8.007[/C][C]-0.007129[/C][/ROW]
[ROW][C]34[/C][C] 8[/C][C] 6.237[/C][C] 1.763[/C][/ROW]
[ROW][C]35[/C][C] 8[/C][C] 8.612[/C][C]-0.612[/C][/ROW]
[ROW][C]36[/C][C] 6[/C][C] 5.154[/C][C] 0.8462[/C][/ROW]
[ROW][C]37[/C][C] 6[/C][C] 6.29[/C][C]-0.2899[/C][/ROW]
[ROW][C]38[/C][C] 9[/C][C] 7.705[/C][C] 1.295[/C][/ROW]
[ROW][C]39[/C][C] 8[/C][C] 7.066[/C][C] 0.9343[/C][/ROW]
[ROW][C]40[/C][C] 9[/C][C] 9.222[/C][C]-0.2217[/C][/ROW]
[ROW][C]41[/C][C] 10[/C][C] 7.739[/C][C] 2.261[/C][/ROW]
[ROW][C]42[/C][C] 8[/C][C] 7.739[/C][C] 0.2613[/C][/ROW]
[ROW][C]43[/C][C] 8[/C][C] 7.539[/C][C] 0.4606[/C][/ROW]
[ROW][C]44[/C][C] 7[/C][C] 7.426[/C][C]-0.4264[/C][/ROW]
[ROW][C]45[/C][C] 7[/C][C] 6.619[/C][C] 0.381[/C][/ROW]
[ROW][C]46[/C][C] 10[/C][C] 9.083[/C][C] 0.917[/C][/ROW]
[ROW][C]47[/C][C] 8[/C][C] 6.182[/C][C] 1.818[/C][/ROW]
[ROW][C]48[/C][C] 7[/C][C] 5.777[/C][C] 1.223[/C][/ROW]
[ROW][C]49[/C][C] 10[/C][C] 7.44[/C][C] 2.56[/C][/ROW]
[ROW][C]50[/C][C] 7[/C][C] 8.149[/C][C]-1.149[/C][/ROW]
[ROW][C]51[/C][C] 7[/C][C] 5.386[/C][C] 1.614[/C][/ROW]
[ROW][C]52[/C][C] 9[/C][C] 8.904[/C][C] 0.09621[/C][/ROW]
[ROW][C]53[/C][C] 9[/C][C] 10.37[/C][C]-1.366[/C][/ROW]
[ROW][C]54[/C][C] 8[/C][C] 6.99[/C][C] 1.01[/C][/ROW]
[ROW][C]55[/C][C] 6[/C][C] 7.489[/C][C]-1.489[/C][/ROW]
[ROW][C]56[/C][C] 8[/C][C] 7.455[/C][C] 0.5451[/C][/ROW]
[ROW][C]57[/C][C] 9[/C][C] 7.166[/C][C] 1.834[/C][/ROW]
[ROW][C]58[/C][C] 2[/C][C] 3.792[/C][C]-1.792[/C][/ROW]
[ROW][C]59[/C][C] 6[/C][C] 5.685[/C][C] 0.315[/C][/ROW]
[ROW][C]60[/C][C] 8[/C][C] 7.294[/C][C] 0.7056[/C][/ROW]
[ROW][C]61[/C][C] 8[/C][C] 8.528[/C][C]-0.5276[/C][/ROW]
[ROW][C]62[/C][C] 7[/C][C] 7.805[/C][C]-0.8046[/C][/ROW]
[ROW][C]63[/C][C] 8[/C][C] 7.702[/C][C] 0.2977[/C][/ROW]
[ROW][C]64[/C][C] 6[/C][C] 5.611[/C][C] 0.3888[/C][/ROW]
[ROW][C]65[/C][C] 10[/C][C] 7.736[/C][C] 2.264[/C][/ROW]
[ROW][C]66[/C][C] 10[/C][C] 8.31[/C][C] 1.69[/C][/ROW]
[ROW][C]67[/C][C] 10[/C][C] 7.65[/C][C] 2.35[/C][/ROW]
[ROW][C]68[/C][C] 8[/C][C] 7.063[/C][C] 0.9367[/C][/ROW]
[ROW][C]69[/C][C] 8[/C][C] 8.207[/C][C]-0.2074[/C][/ROW]
[ROW][C]70[/C][C] 7[/C][C] 7.597[/C][C]-0.5968[/C][/ROW]
[ROW][C]71[/C][C] 10[/C][C] 8.656[/C][C] 1.344[/C][/ROW]
[ROW][C]72[/C][C] 5[/C][C] 6.882[/C][C]-1.882[/C][/ROW]
[ROW][C]73[/C][C] 3[/C][C] 2.833[/C][C] 0.1675[/C][/ROW]
[ROW][C]74[/C][C] 2[/C][C] 3.742[/C][C]-1.742[/C][/ROW]
[ROW][C]75[/C][C] 3[/C][C] 4.686[/C][C]-1.686[/C][/ROW]
[ROW][C]76[/C][C] 4[/C][C] 5.914[/C][C]-1.914[/C][/ROW]
[ROW][C]77[/C][C] 2[/C][C] 3.939[/C][C]-1.939[/C][/ROW]
[ROW][C]78[/C][C] 6[/C][C] 5.58[/C][C] 0.4196[/C][/ROW]
[ROW][C]79[/C][C] 8[/C][C] 8.115[/C][C]-0.115[/C][/ROW]
[ROW][C]80[/C][C] 8[/C][C] 6.85[/C][C] 1.15[/C][/ROW]
[ROW][C]81[/C][C] 5[/C][C] 5.583[/C][C]-0.5827[/C][/ROW]
[ROW][C]82[/C][C] 10[/C][C] 9.361[/C][C] 0.6388[/C][/ROW]
[ROW][C]83[/C][C] 9[/C][C] 9.645[/C][C]-0.645[/C][/ROW]
[ROW][C]84[/C][C] 8[/C][C] 9.753[/C][C]-1.753[/C][/ROW]
[ROW][C]85[/C][C] 9[/C][C] 8.883[/C][C] 0.1172[/C][/ROW]
[ROW][C]86[/C][C] 8[/C][C] 6.668[/C][C] 1.332[/C][/ROW]
[ROW][C]87[/C][C] 5[/C][C] 5.664[/C][C]-0.664[/C][/ROW]
[ROW][C]88[/C][C] 7[/C][C] 7.421[/C][C]-0.4209[/C][/ROW]
[ROW][C]89[/C][C] 9[/C][C] 9.577[/C][C]-0.5769[/C][/ROW]
[ROW][C]90[/C][C] 8[/C][C] 8.491[/C][C]-0.4912[/C][/ROW]
[ROW][C]91[/C][C] 4[/C][C] 7.689[/C][C]-3.689[/C][/ROW]
[ROW][C]92[/C][C] 7[/C][C] 6.617[/C][C] 0.3834[/C][/ROW]
[ROW][C]93[/C][C] 8[/C][C] 8.683[/C][C]-0.6826[/C][/ROW]
[ROW][C]94[/C][C] 7[/C][C] 7.4[/C][C]-0.3999[/C][/ROW]
[ROW][C]95[/C][C] 7[/C][C] 7.15[/C][C]-0.1501[/C][/ROW]
[ROW][C]96[/C][C] 9[/C][C] 7.778[/C][C] 1.222[/C][/ROW]
[ROW][C]97[/C][C] 6[/C][C] 6.125[/C][C]-0.1251[/C][/ROW]
[ROW][C]98[/C][C] 7[/C][C] 7.973[/C][C]-0.973[/C][/ROW]
[ROW][C]99[/C][C] 4[/C][C] 4.762[/C][C]-0.7622[/C][/ROW]
[ROW][C]100[/C][C] 6[/C][C] 6.209[/C][C]-0.2087[/C][/ROW]
[ROW][C]101[/C][C] 10[/C][C] 6.548[/C][C] 3.452[/C][/ROW]
[ROW][C]102[/C][C] 9[/C][C] 8.207[/C][C] 0.7926[/C][/ROW]
[ROW][C]103[/C][C] 10[/C][C] 9.934[/C][C] 0.06558[/C][/ROW]
[ROW][C]104[/C][C] 8[/C][C] 7.226[/C][C] 0.7738[/C][/ROW]
[ROW][C]105[/C][C] 4[/C][C] 5.901[/C][C]-1.901[/C][/ROW]
[ROW][C]106[/C][C] 8[/C][C] 9.758[/C][C]-1.758[/C][/ROW]
[ROW][C]107[/C][C] 5[/C][C] 7.192[/C][C]-2.192[/C][/ROW]
[ROW][C]108[/C][C] 8[/C][C] 7.847[/C][C] 0.1534[/C][/ROW]
[ROW][C]109[/C][C] 9[/C][C] 8.028[/C][C] 0.9719[/C][/ROW]
[ROW][C]110[/C][C] 8[/C][C] 7.791[/C][C] 0.2085[/C][/ROW]
[ROW][C]111[/C][C] 4[/C][C] 7.941[/C][C]-3.941[/C][/ROW]
[ROW][C]112[/C][C] 8[/C][C] 6.271[/C][C] 1.729[/C][/ROW]
[ROW][C]113[/C][C] 10[/C][C] 7.833[/C][C] 2.167[/C][/ROW]
[ROW][C]114[/C][C] 6[/C][C] 6.201[/C][C]-0.2007[/C][/ROW]
[ROW][C]115[/C][C] 7[/C][C] 6.256[/C][C] 0.7442[/C][/ROW]
[ROW][C]116[/C][C] 10[/C][C] 8.599[/C][C] 1.401[/C][/ROW]
[ROW][C]117[/C][C] 9[/C][C] 9.025[/C][C]-0.02468[/C][/ROW]
[ROW][C]118[/C][C] 8[/C][C] 8.102[/C][C]-0.1019[/C][/ROW]
[ROW][C]119[/C][C] 3[/C][C] 6.182[/C][C]-3.182[/C][/ROW]
[ROW][C]120[/C][C] 8[/C][C] 7.024[/C][C] 0.9763[/C][/ROW]
[ROW][C]121[/C][C] 7[/C][C] 7.381[/C][C]-0.3812[/C][/ROW]
[ROW][C]122[/C][C] 7[/C][C] 7.116[/C][C]-0.1161[/C][/ROW]
[ROW][C]123[/C][C] 8[/C][C] 6.382[/C][C] 1.618[/C][/ROW]
[ROW][C]124[/C][C] 8[/C][C] 8.149[/C][C]-0.149[/C][/ROW]
[ROW][C]125[/C][C] 7[/C][C] 7.71[/C][C]-0.7103[/C][/ROW]
[ROW][C]126[/C][C] 7[/C][C] 5.832[/C][C] 1.168[/C][/ROW]
[ROW][C]127[/C][C] 9[/C][C] 10.37[/C][C]-1.366[/C][/ROW]
[ROW][C]128[/C][C] 9[/C][C] 8.756[/C][C] 0.2437[/C][/ROW]
[ROW][C]129[/C][C] 9[/C][C] 7.826[/C][C] 1.174[/C][/ROW]
[ROW][C]130[/C][C] 4[/C][C] 5.364[/C][C]-1.364[/C][/ROW]
[ROW][C]131[/C][C] 6[/C][C] 6.903[/C][C]-0.9028[/C][/ROW]
[ROW][C]132[/C][C] 6[/C][C] 5.583[/C][C] 0.4173[/C][/ROW]
[ROW][C]133[/C][C] 6[/C][C] 4.902[/C][C] 1.098[/C][/ROW]
[ROW][C]134[/C][C] 8[/C][C] 8.276[/C][C]-0.2755[/C][/ROW]
[ROW][C]135[/C][C] 3[/C][C] 4.757[/C][C]-1.757[/C][/ROW]
[ROW][C]136[/C][C] 8[/C][C] 6.829[/C][C] 1.171[/C][/ROW]
[ROW][C]137[/C][C] 8[/C][C] 8.278[/C][C]-0.2779[/C][/ROW]
[ROW][C]138[/C][C] 6[/C][C] 5.01[/C][C] 0.9905[/C][/ROW]
[ROW][C]139[/C][C] 10[/C][C] 9.509[/C][C] 0.4913[/C][/ROW]
[ROW][C]140[/C][C] 2[/C][C] 4.605[/C][C]-2.605[/C][/ROW]
[ROW][C]141[/C][C] 9[/C][C] 7.954[/C][C] 1.046[/C][/ROW]
[ROW][C]142[/C][C] 6[/C][C] 6.043[/C][C]-0.04253[/C][/ROW]
[ROW][C]143[/C][C] 6[/C][C] 8.83[/C][C]-2.83[/C][/ROW]
[ROW][C]144[/C][C] 5[/C][C] 4.612[/C][C] 0.3877[/C][/ROW]
[ROW][C]145[/C][C] 4[/C][C] 5.238[/C][C]-1.238[/C][/ROW]
[ROW][C]146[/C][C] 7[/C][C] 6.261[/C][C] 0.7386[/C][/ROW]
[ROW][C]147[/C][C] 5[/C][C] 6.277[/C][C]-1.277[/C][/ROW]
[ROW][C]148[/C][C] 8[/C][C] 8.491[/C][C]-0.4912[/C][/ROW]
[ROW][C]149[/C][C] 6[/C][C] 7.442[/C][C]-1.442[/C][/ROW]
[ROW][C]150[/C][C] 9[/C][C] 7.4[/C][C] 1.6[/C][/ROW]
[ROW][C]151[/C][C] 6[/C][C] 5.725[/C][C] 0.2754[/C][/ROW]
[ROW][C]152[/C][C] 4[/C][C] 5.01[/C][C]-1.01[/C][/ROW]
[ROW][C]153[/C][C] 7[/C][C] 8.007[/C][C]-1.007[/C][/ROW]
[ROW][C]154[/C][C] 2[/C][C] 4.442[/C][C]-2.442[/C][/ROW]
[ROW][C]155[/C][C] 8[/C][C] 9.259[/C][C]-1.259[/C][/ROW]
[ROW][C]156[/C][C] 9[/C][C] 8.115[/C][C] 0.885[/C][/ROW]
[ROW][C]157[/C][C] 6[/C][C] 5.993[/C][C] 0.006976[/C][/ROW]
[ROW][C]158[/C][C] 5[/C][C] 4.951[/C][C] 0.04877[/C][/ROW]
[ROW][C]159[/C][C] 7[/C][C] 7.239[/C][C]-0.2393[/C][/ROW]
[ROW][C]160[/C][C] 8[/C][C] 6.682[/C][C] 1.318[/C][/ROW]
[ROW][C]161[/C][C] 4[/C][C] 6.027[/C][C]-2.027[/C][/ROW]
[ROW][C]162[/C][C] 9[/C][C] 6.668[/C][C] 2.332[/C][/ROW]
[ROW][C]163[/C][C] 9[/C][C] 9.558[/C][C]-0.5582[/C][/ROW]
[ROW][C]164[/C][C] 9[/C][C] 5.551[/C][C] 3.449[/C][/ROW]
[ROW][C]165[/C][C] 7[/C][C] 6.961[/C][C] 0.03892[/C][/ROW]
[ROW][C]166[/C][C] 5[/C][C] 6.99[/C][C]-1.99[/C][/ROW]
[ROW][C]167[/C][C] 7[/C][C] 6.992[/C][C] 0.008076[/C][/ROW]
[ROW][C]168[/C][C] 9[/C][C] 9.955[/C][C]-0.9554[/C][/ROW]
[ROW][C]169[/C][C] 8[/C][C] 6.122[/C][C] 1.878[/C][/ROW]
[ROW][C]170[/C][C] 6[/C][C] 5.562[/C][C] 0.4383[/C][/ROW]
[ROW][C]171[/C][C] 9[/C][C] 8.809[/C][C] 0.191[/C][/ROW]
[ROW][C]172[/C][C] 8[/C][C] 7.597[/C][C] 0.4032[/C][/ROW]
[ROW][C]173[/C][C] 7[/C][C] 7.692[/C][C]-0.6916[/C][/ROW]
[ROW][C]174[/C][C] 7[/C][C] 7.684[/C][C]-0.6837[/C][/ROW]
[ROW][C]175[/C][C] 7[/C][C] 7.784[/C][C]-0.784[/C][/ROW]
[ROW][C]176[/C][C] 8[/C][C] 6.869[/C][C] 1.131[/C][/ROW]
[ROW][C]177[/C][C] 10[/C][C] 8.438[/C][C] 1.562[/C][/ROW]
[ROW][C]178[/C][C] 6[/C][C] 7.652[/C][C]-1.652[/C][/ROW]
[ROW][C]179[/C][C] 6[/C][C] 7.415[/C][C]-1.415[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=5

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	10	8.334	1.666
2	8	7.791	0.2085
3	8	6.937	1.063
4	9	9.225	-0.2249
5	5	7.014	-2.014
6	10	9.934	0.06558
7	8	8.096	-0.09629
8	9	9.146	-0.1456
9	8	6.046	1.954
10	7	8.136	-1.136
11	10	8.883	1.117
12	10	7.084	2.916
13	9	7.421	1.579
14	4	6.261	-2.261
15	4	6.44	-2.44
16	8	7.402	0.5978
17	9	9.471	-0.4714
18	10	7.782	2.218
19	8	7.905	0.09511
20	5	6.364	-1.364
21	10	7.868	2.132
22	8	8.725	-0.7255
23	7	7.65	-0.6496
24	8	8.223	-0.2228
25	8	9.135	-1.135
26	9	6.077	2.923
27	8	8.451	-0.4515
28	6	7.218	-1.218
29	8	8.255	-0.2545
30	8	7.218	0.7817
31	5	7.442	-2.442
32	9	8.023	0.9774
33	8	8.007	-0.007129
34	8	6.237	1.763
35	8	8.612	-0.612
36	6	5.154	0.8462
37	6	6.29	-0.2899
38	9	7.705	1.295
39	8	7.066	0.9343
40	9	9.222	-0.2217
41	10	7.739	2.261
42	8	7.739	0.2613
43	8	7.539	0.4606
44	7	7.426	-0.4264
45	7	6.619	0.381
46	10	9.083	0.917
47	8	6.182	1.818
48	7	5.777	1.223
49	10	7.44	2.56
50	7	8.149	-1.149
51	7	5.386	1.614
52	9	8.904	0.09621
53	9	10.37	-1.366
54	8	6.99	1.01
55	6	7.489	-1.489
56	8	7.455	0.5451
57	9	7.166	1.834
58	2	3.792	-1.792
59	6	5.685	0.315
60	8	7.294	0.7056
61	8	8.528	-0.5276
62	7	7.805	-0.8046
63	8	7.702	0.2977
64	6	5.611	0.3888
65	10	7.736	2.264
66	10	8.31	1.69
67	10	7.65	2.35
68	8	7.063	0.9367
69	8	8.207	-0.2074
70	7	7.597	-0.5968
71	10	8.656	1.344
72	5	6.882	-1.882
73	3	2.833	0.1675
74	2	3.742	-1.742
75	3	4.686	-1.686
76	4	5.914	-1.914
77	2	3.939	-1.939
78	6	5.58	0.4196
79	8	8.115	-0.115
80	8	6.85	1.15
81	5	5.583	-0.5827
82	10	9.361	0.6388
83	9	9.645	-0.645
84	8	9.753	-1.753
85	9	8.883	0.1172
86	8	6.668	1.332
87	5	5.664	-0.664
88	7	7.421	-0.4209
89	9	9.577	-0.5769
90	8	8.491	-0.4912
91	4	7.689	-3.689
92	7	6.617	0.3834
93	8	8.683	-0.6826
94	7	7.4	-0.3999
95	7	7.15	-0.1501
96	9	7.778	1.222
97	6	6.125	-0.1251
98	7	7.973	-0.973
99	4	4.762	-0.7622
100	6	6.209	-0.2087
101	10	6.548	3.452
102	9	8.207	0.7926
103	10	9.934	0.06558
104	8	7.226	0.7738
105	4	5.901	-1.901
106	8	9.758	-1.758
107	5	7.192	-2.192
108	8	7.847	0.1534
109	9	8.028	0.9719
110	8	7.791	0.2085
111	4	7.941	-3.941
112	8	6.271	1.729
113	10	7.833	2.167
114	6	6.201	-0.2007
115	7	6.256	0.7442
116	10	8.599	1.401
117	9	9.025	-0.02468
118	8	8.102	-0.1019
119	3	6.182	-3.182
120	8	7.024	0.9763
121	7	7.381	-0.3812
122	7	7.116	-0.1161
123	8	6.382	1.618
124	8	8.149	-0.149
125	7	7.71	-0.7103
126	7	5.832	1.168
127	9	10.37	-1.366
128	9	8.756	0.2437
129	9	7.826	1.174
130	4	5.364	-1.364
131	6	6.903	-0.9028
132	6	5.583	0.4173
133	6	4.902	1.098
134	8	8.276	-0.2755
135	3	4.757	-1.757
136	8	6.829	1.171
137	8	8.278	-0.2779
138	6	5.01	0.9905
139	10	9.509	0.4913
140	2	4.605	-2.605
141	9	7.954	1.046
142	6	6.043	-0.04253
143	6	8.83	-2.83
144	5	4.612	0.3877
145	4	5.238	-1.238
146	7	6.261	0.7386
147	5	6.277	-1.277
148	8	8.491	-0.4912
149	6	7.442	-1.442
150	9	7.4	1.6
151	6	5.725	0.2754
152	4	5.01	-1.01
153	7	8.007	-1.007
154	2	4.442	-2.442
155	8	9.259	-1.259
156	9	8.115	0.885
157	6	5.993	0.006976
158	5	4.951	0.04877
159	7	7.239	-0.2393
160	8	6.682	1.318
161	4	6.027	-2.027
162	9	6.668	2.332
163	9	9.558	-0.5582
164	9	5.551	3.449
165	7	6.961	0.03892
166	5	6.99	-1.99
167	7	6.992	0.008076
168	9	9.955	-0.9554
169	8	6.122	1.878
170	6	5.562	0.4383
171	9	8.809	0.191
172	8	7.597	0.4032
173	7	7.692	-0.6916
174	7	7.684	-0.6837
175	7	7.784	-0.784
176	8	6.869	1.131
177	10	8.438	1.562
178	6	7.652	-1.652
179	6	7.415	-1.415

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.7074	0.5853	0.2926
8	0.6007	0.7986	0.3993
9	0.6211	0.7577	0.3789
10	0.5359	0.9282	0.4641
11	0.4973	0.9947	0.5027
12	0.7253	0.5494	0.2747
13	0.6712	0.6576	0.3288
14	0.871	0.258	0.129
15	0.9314	0.1372	0.0686
16	0.907	0.1861	0.09303
17	0.8721	0.2557	0.1279
18	0.8818	0.2363	0.1182
19	0.8423	0.3154	0.1577
20	0.8319	0.3361	0.1681
21	0.8764	0.2473	0.1236
22	0.869	0.262	0.131
23	0.8362	0.3277	0.1638
24	0.7945	0.411	0.2055
25	0.7853	0.4294	0.2147
26	0.899	0.202	0.101
27	0.8739	0.2523	0.1261
28	0.8669	0.2661	0.1331
29	0.8321	0.3358	0.1679
30	0.8042	0.3917	0.1958
31	0.8784	0.2433	0.1216
32	0.8592	0.2815	0.1408
33	0.8252	0.3496	0.1748
34	0.8252	0.3496	0.1748
35	0.7962	0.4077	0.2039
36	0.7598	0.4805	0.2402
37	0.7265	0.547	0.2735
38	0.7125	0.575	0.2875
39	0.6785	0.6429	0.3215
40	0.6307	0.7386	0.3693
41	0.6889	0.6222	0.3111
42	0.6422	0.7156	0.3578
43	0.5956	0.8089	0.4044
44	0.5501	0.8999	0.4499
45	0.5004	0.9993	0.4996
46	0.4786	0.9572	0.5214
47	0.4807	0.9613	0.5193
48	0.4501	0.9002	0.5499
49	0.5524	0.8953	0.4476
50	0.5481	0.9038	0.4519
51	0.5339	0.9322	0.4661
52	0.4861	0.9723	0.5139
53	0.467	0.934	0.533
54	0.4337	0.8674	0.5663
55	0.4628	0.9256	0.5372
56	0.4199	0.8399	0.5801
57	0.4348	0.8697	0.5652
58	0.5576	0.8848	0.4424
59	0.5135	0.9729	0.4865
60	0.4756	0.9512	0.5244
61	0.4367	0.8733	0.5633
62	0.4139	0.8278	0.5861
63	0.3717	0.7435	0.6283
64	0.3316	0.6632	0.6684
65	0.3967	0.7935	0.6033
66	0.4165	0.833	0.5835
67	0.4941	0.9883	0.5059
68	0.4664	0.9328	0.5336
69	0.4265	0.853	0.5735
70	0.3961	0.7922	0.6039
71	0.3955	0.7911	0.6045
72	0.4531	0.9061	0.5469
73	0.4181	0.8363	0.5819
74	0.4653	0.9306	0.5347
75	0.4957	0.9913	0.5043
76	0.5414	0.9172	0.4586
77	0.5848	0.8303	0.4152
78	0.5466	0.9068	0.4534
79	0.5049	0.9902	0.4951
80	0.4896	0.9792	0.5104
81	0.4544	0.9088	0.5456
82	0.4223	0.8446	0.5777
83	0.3927	0.7853	0.6073
84	0.4204	0.8409	0.5796
85	0.38	0.7601	0.62
86	0.376	0.7519	0.624
87	0.3441	0.6883	0.6559
88	0.3099	0.6198	0.6901
89	0.2796	0.5592	0.7204
90	0.2497	0.4995	0.7503
91	0.4952	0.9904	0.5048
92	0.4565	0.913	0.5435
93	0.4237	0.8473	0.5763
94	0.3847	0.7695	0.6153
95	0.3445	0.689	0.6555
96	0.3362	0.6724	0.6638
97	0.2988	0.5976	0.7012
98	0.279	0.5579	0.721
99	0.2552	0.5104	0.7448
100	0.2222	0.4444	0.7778
101	0.4199	0.8398	0.5801
102	0.3943	0.7886	0.6057
103	0.3539	0.7077	0.6461
104	0.3287	0.6574	0.6713
105	0.3658	0.7317	0.6342
106	0.3856	0.7713	0.6144
107	0.4448	0.8896	0.5552
108	0.4022	0.8044	0.5978
109	0.3832	0.7663	0.6168
110	0.3429	0.6858	0.6571
111	0.6475	0.7049	0.3525
112	0.6731	0.6538	0.3269
113	0.7315	0.537	0.2685
114	0.6932	0.6136	0.3068
115	0.6674	0.6652	0.3326
116	0.6773	0.6455	0.3227
117	0.6359	0.7282	0.3641
118	0.592	0.8161	0.408
119	0.7653	0.4694	0.2347
120	0.7497	0.5006	0.2503
121	0.7129	0.5741	0.2871
122	0.6718	0.6565	0.3282
123	0.6999	0.6003	0.3001
124	0.6575	0.6851	0.3425
125	0.6204	0.7593	0.3796
126	0.6092	0.7815	0.3908
127	0.6008	0.7983	0.3992
128	0.5554	0.8892	0.4446
129	0.5537	0.8925	0.4463
130	0.5711	0.8577	0.4289
131	0.5388	0.9224	0.4612
132	0.4939	0.9878	0.5061
133	0.4862	0.9724	0.5138
134	0.4379	0.8758	0.5621
135	0.5088	0.9824	0.4912
136	0.4988	0.9977	0.5012
137	0.4498	0.8996	0.5502
138	0.4351	0.8703	0.5649
139	0.4111	0.8222	0.5889
140	0.5442	0.9115	0.4558
141	0.5281	0.9438	0.4719
142	0.4785	0.9571	0.5215
143	0.6441	0.7117	0.3559
144	0.6027	0.7947	0.3973
145	0.5696	0.8609	0.4304
146	0.5542	0.8916	0.4458
147	0.5552	0.8896	0.4448
148	0.5001	0.9999	0.4999
149	0.5281	0.9437	0.4719
150	0.6793	0.6414	0.3207
151	0.622	0.7561	0.378
152	0.5859	0.8282	0.4141
153	0.5307	0.9385	0.4693
154	0.6445	0.7109	0.3554
155	0.5873	0.8254	0.4127
156	0.5897	0.8205	0.4103
157	0.5227	0.9546	0.4773
158	0.4894	0.9787	0.5106
159	0.4164	0.8327	0.5836
160	0.4189	0.8379	0.5811
161	0.5956	0.8089	0.4044
162	0.6566	0.6869	0.3434
163	0.5888	0.8225	0.4112
164	0.6977	0.6047	0.3023
165	0.6191	0.7619	0.3809
166	0.7433	0.5134	0.2567
167	0.6552	0.6895	0.3448
168	0.5487	0.9026	0.4513
169	0.6521	0.6959	0.3479
170	0.5435	0.913	0.4565
171	0.3989	0.7978	0.6011
172	0.2984	0.5968	0.7016

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.7074 &  0.5853 &  0.2926 \tabularnewline
8 &  0.6007 &  0.7986 &  0.3993 \tabularnewline
9 &  0.6211 &  0.7577 &  0.3789 \tabularnewline
10 &  0.5359 &  0.9282 &  0.4641 \tabularnewline
11 &  0.4973 &  0.9947 &  0.5027 \tabularnewline
12 &  0.7253 &  0.5494 &  0.2747 \tabularnewline
13 &  0.6712 &  0.6576 &  0.3288 \tabularnewline
14 &  0.871 &  0.258 &  0.129 \tabularnewline
15 &  0.9314 &  0.1372 &  0.0686 \tabularnewline
16 &  0.907 &  0.1861 &  0.09303 \tabularnewline
17 &  0.8721 &  0.2557 &  0.1279 \tabularnewline
18 &  0.8818 &  0.2363 &  0.1182 \tabularnewline
19 &  0.8423 &  0.3154 &  0.1577 \tabularnewline
20 &  0.8319 &  0.3361 &  0.1681 \tabularnewline
21 &  0.8764 &  0.2473 &  0.1236 \tabularnewline
22 &  0.869 &  0.262 &  0.131 \tabularnewline
23 &  0.8362 &  0.3277 &  0.1638 \tabularnewline
24 &  0.7945 &  0.411 &  0.2055 \tabularnewline
25 &  0.7853 &  0.4294 &  0.2147 \tabularnewline
26 &  0.899 &  0.202 &  0.101 \tabularnewline
27 &  0.8739 &  0.2523 &  0.1261 \tabularnewline
28 &  0.8669 &  0.2661 &  0.1331 \tabularnewline
29 &  0.8321 &  0.3358 &  0.1679 \tabularnewline
30 &  0.8042 &  0.3917 &  0.1958 \tabularnewline
31 &  0.8784 &  0.2433 &  0.1216 \tabularnewline
32 &  0.8592 &  0.2815 &  0.1408 \tabularnewline
33 &  0.8252 &  0.3496 &  0.1748 \tabularnewline
34 &  0.8252 &  0.3496 &  0.1748 \tabularnewline
35 &  0.7962 &  0.4077 &  0.2039 \tabularnewline
36 &  0.7598 &  0.4805 &  0.2402 \tabularnewline
37 &  0.7265 &  0.547 &  0.2735 \tabularnewline
38 &  0.7125 &  0.575 &  0.2875 \tabularnewline
39 &  0.6785 &  0.6429 &  0.3215 \tabularnewline
40 &  0.6307 &  0.7386 &  0.3693 \tabularnewline
41 &  0.6889 &  0.6222 &  0.3111 \tabularnewline
42 &  0.6422 &  0.7156 &  0.3578 \tabularnewline
43 &  0.5956 &  0.8089 &  0.4044 \tabularnewline
44 &  0.5501 &  0.8999 &  0.4499 \tabularnewline
45 &  0.5004 &  0.9993 &  0.4996 \tabularnewline
46 &  0.4786 &  0.9572 &  0.5214 \tabularnewline
47 &  0.4807 &  0.9613 &  0.5193 \tabularnewline
48 &  0.4501 &  0.9002 &  0.5499 \tabularnewline
49 &  0.5524 &  0.8953 &  0.4476 \tabularnewline
50 &  0.5481 &  0.9038 &  0.4519 \tabularnewline
51 &  0.5339 &  0.9322 &  0.4661 \tabularnewline
52 &  0.4861 &  0.9723 &  0.5139 \tabularnewline
53 &  0.467 &  0.934 &  0.533 \tabularnewline
54 &  0.4337 &  0.8674 &  0.5663 \tabularnewline
55 &  0.4628 &  0.9256 &  0.5372 \tabularnewline
56 &  0.4199 &  0.8399 &  0.5801 \tabularnewline
57 &  0.4348 &  0.8697 &  0.5652 \tabularnewline
58 &  0.5576 &  0.8848 &  0.4424 \tabularnewline
59 &  0.5135 &  0.9729 &  0.4865 \tabularnewline
60 &  0.4756 &  0.9512 &  0.5244 \tabularnewline
61 &  0.4367 &  0.8733 &  0.5633 \tabularnewline
62 &  0.4139 &  0.8278 &  0.5861 \tabularnewline
63 &  0.3717 &  0.7435 &  0.6283 \tabularnewline
64 &  0.3316 &  0.6632 &  0.6684 \tabularnewline
65 &  0.3967 &  0.7935 &  0.6033 \tabularnewline
66 &  0.4165 &  0.833 &  0.5835 \tabularnewline
67 &  0.4941 &  0.9883 &  0.5059 \tabularnewline
68 &  0.4664 &  0.9328 &  0.5336 \tabularnewline
69 &  0.4265 &  0.853 &  0.5735 \tabularnewline
70 &  0.3961 &  0.7922 &  0.6039 \tabularnewline
71 &  0.3955 &  0.7911 &  0.6045 \tabularnewline
72 &  0.4531 &  0.9061 &  0.5469 \tabularnewline
73 &  0.4181 &  0.8363 &  0.5819 \tabularnewline
74 &  0.4653 &  0.9306 &  0.5347 \tabularnewline
75 &  0.4957 &  0.9913 &  0.5043 \tabularnewline
76 &  0.5414 &  0.9172 &  0.4586 \tabularnewline
77 &  0.5848 &  0.8303 &  0.4152 \tabularnewline
78 &  0.5466 &  0.9068 &  0.4534 \tabularnewline
79 &  0.5049 &  0.9902 &  0.4951 \tabularnewline
80 &  0.4896 &  0.9792 &  0.5104 \tabularnewline
81 &  0.4544 &  0.9088 &  0.5456 \tabularnewline
82 &  0.4223 &  0.8446 &  0.5777 \tabularnewline
83 &  0.3927 &  0.7853 &  0.6073 \tabularnewline
84 &  0.4204 &  0.8409 &  0.5796 \tabularnewline
85 &  0.38 &  0.7601 &  0.62 \tabularnewline
86 &  0.376 &  0.7519 &  0.624 \tabularnewline
87 &  0.3441 &  0.6883 &  0.6559 \tabularnewline
88 &  0.3099 &  0.6198 &  0.6901 \tabularnewline
89 &  0.2796 &  0.5592 &  0.7204 \tabularnewline
90 &  0.2497 &  0.4995 &  0.7503 \tabularnewline
91 &  0.4952 &  0.9904 &  0.5048 \tabularnewline
92 &  0.4565 &  0.913 &  0.5435 \tabularnewline
93 &  0.4237 &  0.8473 &  0.5763 \tabularnewline
94 &  0.3847 &  0.7695 &  0.6153 \tabularnewline
95 &  0.3445 &  0.689 &  0.6555 \tabularnewline
96 &  0.3362 &  0.6724 &  0.6638 \tabularnewline
97 &  0.2988 &  0.5976 &  0.7012 \tabularnewline
98 &  0.279 &  0.5579 &  0.721 \tabularnewline
99 &  0.2552 &  0.5104 &  0.7448 \tabularnewline
100 &  0.2222 &  0.4444 &  0.7778 \tabularnewline
101 &  0.4199 &  0.8398 &  0.5801 \tabularnewline
102 &  0.3943 &  0.7886 &  0.6057 \tabularnewline
103 &  0.3539 &  0.7077 &  0.6461 \tabularnewline
104 &  0.3287 &  0.6574 &  0.6713 \tabularnewline
105 &  0.3658 &  0.7317 &  0.6342 \tabularnewline
106 &  0.3856 &  0.7713 &  0.6144 \tabularnewline
107 &  0.4448 &  0.8896 &  0.5552 \tabularnewline
108 &  0.4022 &  0.8044 &  0.5978 \tabularnewline
109 &  0.3832 &  0.7663 &  0.6168 \tabularnewline
110 &  0.3429 &  0.6858 &  0.6571 \tabularnewline
111 &  0.6475 &  0.7049 &  0.3525 \tabularnewline
112 &  0.6731 &  0.6538 &  0.3269 \tabularnewline
113 &  0.7315 &  0.537 &  0.2685 \tabularnewline
114 &  0.6932 &  0.6136 &  0.3068 \tabularnewline
115 &  0.6674 &  0.6652 &  0.3326 \tabularnewline
116 &  0.6773 &  0.6455 &  0.3227 \tabularnewline
117 &  0.6359 &  0.7282 &  0.3641 \tabularnewline
118 &  0.592 &  0.8161 &  0.408 \tabularnewline
119 &  0.7653 &  0.4694 &  0.2347 \tabularnewline
120 &  0.7497 &  0.5006 &  0.2503 \tabularnewline
121 &  0.7129 &  0.5741 &  0.2871 \tabularnewline
122 &  0.6718 &  0.6565 &  0.3282 \tabularnewline
123 &  0.6999 &  0.6003 &  0.3001 \tabularnewline
124 &  0.6575 &  0.6851 &  0.3425 \tabularnewline
125 &  0.6204 &  0.7593 &  0.3796 \tabularnewline
126 &  0.6092 &  0.7815 &  0.3908 \tabularnewline
127 &  0.6008 &  0.7983 &  0.3992 \tabularnewline
128 &  0.5554 &  0.8892 &  0.4446 \tabularnewline
129 &  0.5537 &  0.8925 &  0.4463 \tabularnewline
130 &  0.5711 &  0.8577 &  0.4289 \tabularnewline
131 &  0.5388 &  0.9224 &  0.4612 \tabularnewline
132 &  0.4939 &  0.9878 &  0.5061 \tabularnewline
133 &  0.4862 &  0.9724 &  0.5138 \tabularnewline
134 &  0.4379 &  0.8758 &  0.5621 \tabularnewline
135 &  0.5088 &  0.9824 &  0.4912 \tabularnewline
136 &  0.4988 &  0.9977 &  0.5012 \tabularnewline
137 &  0.4498 &  0.8996 &  0.5502 \tabularnewline
138 &  0.4351 &  0.8703 &  0.5649 \tabularnewline
139 &  0.4111 &  0.8222 &  0.5889 \tabularnewline
140 &  0.5442 &  0.9115 &  0.4558 \tabularnewline
141 &  0.5281 &  0.9438 &  0.4719 \tabularnewline
142 &  0.4785 &  0.9571 &  0.5215 \tabularnewline
143 &  0.6441 &  0.7117 &  0.3559 \tabularnewline
144 &  0.6027 &  0.7947 &  0.3973 \tabularnewline
145 &  0.5696 &  0.8609 &  0.4304 \tabularnewline
146 &  0.5542 &  0.8916 &  0.4458 \tabularnewline
147 &  0.5552 &  0.8896 &  0.4448 \tabularnewline
148 &  0.5001 &  0.9999 &  0.4999 \tabularnewline
149 &  0.5281 &  0.9437 &  0.4719 \tabularnewline
150 &  0.6793 &  0.6414 &  0.3207 \tabularnewline
151 &  0.622 &  0.7561 &  0.378 \tabularnewline
152 &  0.5859 &  0.8282 &  0.4141 \tabularnewline
153 &  0.5307 &  0.9385 &  0.4693 \tabularnewline
154 &  0.6445 &  0.7109 &  0.3554 \tabularnewline
155 &  0.5873 &  0.8254 &  0.4127 \tabularnewline
156 &  0.5897 &  0.8205 &  0.4103 \tabularnewline
157 &  0.5227 &  0.9546 &  0.4773 \tabularnewline
158 &  0.4894 &  0.9787 &  0.5106 \tabularnewline
159 &  0.4164 &  0.8327 &  0.5836 \tabularnewline
160 &  0.4189 &  0.8379 &  0.5811 \tabularnewline
161 &  0.5956 &  0.8089 &  0.4044 \tabularnewline
162 &  0.6566 &  0.6869 &  0.3434 \tabularnewline
163 &  0.5888 &  0.8225 &  0.4112 \tabularnewline
164 &  0.6977 &  0.6047 &  0.3023 \tabularnewline
165 &  0.6191 &  0.7619 &  0.3809 \tabularnewline
166 &  0.7433 &  0.5134 &  0.2567 \tabularnewline
167 &  0.6552 &  0.6895 &  0.3448 \tabularnewline
168 &  0.5487 &  0.9026 &  0.4513 \tabularnewline
169 &  0.6521 &  0.6959 &  0.3479 \tabularnewline
170 &  0.5435 &  0.913 &  0.4565 \tabularnewline
171 &  0.3989 &  0.7978 &  0.6011 \tabularnewline
172 &  0.2984 &  0.5968 &  0.7016 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=6

[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]7[/C][C] 0.7074[/C][C] 0.5853[/C][C] 0.2926[/C][/ROW]
[ROW][C]8[/C][C] 0.6007[/C][C] 0.7986[/C][C] 0.3993[/C][/ROW]
[ROW][C]9[/C][C] 0.6211[/C][C] 0.7577[/C][C] 0.3789[/C][/ROW]
[ROW][C]10[/C][C] 0.5359[/C][C] 0.9282[/C][C] 0.4641[/C][/ROW]
[ROW][C]11[/C][C] 0.4973[/C][C] 0.9947[/C][C] 0.5027[/C][/ROW]
[ROW][C]12[/C][C] 0.7253[/C][C] 0.5494[/C][C] 0.2747[/C][/ROW]
[ROW][C]13[/C][C] 0.6712[/C][C] 0.6576[/C][C] 0.3288[/C][/ROW]
[ROW][C]14[/C][C] 0.871[/C][C] 0.258[/C][C] 0.129[/C][/ROW]
[ROW][C]15[/C][C] 0.9314[/C][C] 0.1372[/C][C] 0.0686[/C][/ROW]
[ROW][C]16[/C][C] 0.907[/C][C] 0.1861[/C][C] 0.09303[/C][/ROW]
[ROW][C]17[/C][C] 0.8721[/C][C] 0.2557[/C][C] 0.1279[/C][/ROW]
[ROW][C]18[/C][C] 0.8818[/C][C] 0.2363[/C][C] 0.1182[/C][/ROW]
[ROW][C]19[/C][C] 0.8423[/C][C] 0.3154[/C][C] 0.1577[/C][/ROW]
[ROW][C]20[/C][C] 0.8319[/C][C] 0.3361[/C][C] 0.1681[/C][/ROW]
[ROW][C]21[/C][C] 0.8764[/C][C] 0.2473[/C][C] 0.1236[/C][/ROW]
[ROW][C]22[/C][C] 0.869[/C][C] 0.262[/C][C] 0.131[/C][/ROW]
[ROW][C]23[/C][C] 0.8362[/C][C] 0.3277[/C][C] 0.1638[/C][/ROW]
[ROW][C]24[/C][C] 0.7945[/C][C] 0.411[/C][C] 0.2055[/C][/ROW]
[ROW][C]25[/C][C] 0.7853[/C][C] 0.4294[/C][C] 0.2147[/C][/ROW]
[ROW][C]26[/C][C] 0.899[/C][C] 0.202[/C][C] 0.101[/C][/ROW]
[ROW][C]27[/C][C] 0.8739[/C][C] 0.2523[/C][C] 0.1261[/C][/ROW]
[ROW][C]28[/C][C] 0.8669[/C][C] 0.2661[/C][C] 0.1331[/C][/ROW]
[ROW][C]29[/C][C] 0.8321[/C][C] 0.3358[/C][C] 0.1679[/C][/ROW]
[ROW][C]30[/C][C] 0.8042[/C][C] 0.3917[/C][C] 0.1958[/C][/ROW]
[ROW][C]31[/C][C] 0.8784[/C][C] 0.2433[/C][C] 0.1216[/C][/ROW]
[ROW][C]32[/C][C] 0.8592[/C][C] 0.2815[/C][C] 0.1408[/C][/ROW]
[ROW][C]33[/C][C] 0.8252[/C][C] 0.3496[/C][C] 0.1748[/C][/ROW]
[ROW][C]34[/C][C] 0.8252[/C][C] 0.3496[/C][C] 0.1748[/C][/ROW]
[ROW][C]35[/C][C] 0.7962[/C][C] 0.4077[/C][C] 0.2039[/C][/ROW]
[ROW][C]36[/C][C] 0.7598[/C][C] 0.4805[/C][C] 0.2402[/C][/ROW]
[ROW][C]37[/C][C] 0.7265[/C][C] 0.547[/C][C] 0.2735[/C][/ROW]
[ROW][C]38[/C][C] 0.7125[/C][C] 0.575[/C][C] 0.2875[/C][/ROW]
[ROW][C]39[/C][C] 0.6785[/C][C] 0.6429[/C][C] 0.3215[/C][/ROW]
[ROW][C]40[/C][C] 0.6307[/C][C] 0.7386[/C][C] 0.3693[/C][/ROW]
[ROW][C]41[/C][C] 0.6889[/C][C] 0.6222[/C][C] 0.3111[/C][/ROW]
[ROW][C]42[/C][C] 0.6422[/C][C] 0.7156[/C][C] 0.3578[/C][/ROW]
[ROW][C]43[/C][C] 0.5956[/C][C] 0.8089[/C][C] 0.4044[/C][/ROW]
[ROW][C]44[/C][C] 0.5501[/C][C] 0.8999[/C][C] 0.4499[/C][/ROW]
[ROW][C]45[/C][C] 0.5004[/C][C] 0.9993[/C][C] 0.4996[/C][/ROW]
[ROW][C]46[/C][C] 0.4786[/C][C] 0.9572[/C][C] 0.5214[/C][/ROW]
[ROW][C]47[/C][C] 0.4807[/C][C] 0.9613[/C][C] 0.5193[/C][/ROW]
[ROW][C]48[/C][C] 0.4501[/C][C] 0.9002[/C][C] 0.5499[/C][/ROW]
[ROW][C]49[/C][C] 0.5524[/C][C] 0.8953[/C][C] 0.4476[/C][/ROW]
[ROW][C]50[/C][C] 0.5481[/C][C] 0.9038[/C][C] 0.4519[/C][/ROW]
[ROW][C]51[/C][C] 0.5339[/C][C] 0.9322[/C][C] 0.4661[/C][/ROW]
[ROW][C]52[/C][C] 0.4861[/C][C] 0.9723[/C][C] 0.5139[/C][/ROW]
[ROW][C]53[/C][C] 0.467[/C][C] 0.934[/C][C] 0.533[/C][/ROW]
[ROW][C]54[/C][C] 0.4337[/C][C] 0.8674[/C][C] 0.5663[/C][/ROW]
[ROW][C]55[/C][C] 0.4628[/C][C] 0.9256[/C][C] 0.5372[/C][/ROW]
[ROW][C]56[/C][C] 0.4199[/C][C] 0.8399[/C][C] 0.5801[/C][/ROW]
[ROW][C]57[/C][C] 0.4348[/C][C] 0.8697[/C][C] 0.5652[/C][/ROW]
[ROW][C]58[/C][C] 0.5576[/C][C] 0.8848[/C][C] 0.4424[/C][/ROW]
[ROW][C]59[/C][C] 0.5135[/C][C] 0.9729[/C][C] 0.4865[/C][/ROW]
[ROW][C]60[/C][C] 0.4756[/C][C] 0.9512[/C][C] 0.5244[/C][/ROW]
[ROW][C]61[/C][C] 0.4367[/C][C] 0.8733[/C][C] 0.5633[/C][/ROW]
[ROW][C]62[/C][C] 0.4139[/C][C] 0.8278[/C][C] 0.5861[/C][/ROW]
[ROW][C]63[/C][C] 0.3717[/C][C] 0.7435[/C][C] 0.6283[/C][/ROW]
[ROW][C]64[/C][C] 0.3316[/C][C] 0.6632[/C][C] 0.6684[/C][/ROW]
[ROW][C]65[/C][C] 0.3967[/C][C] 0.7935[/C][C] 0.6033[/C][/ROW]
[ROW][C]66[/C][C] 0.4165[/C][C] 0.833[/C][C] 0.5835[/C][/ROW]
[ROW][C]67[/C][C] 0.4941[/C][C] 0.9883[/C][C] 0.5059[/C][/ROW]
[ROW][C]68[/C][C] 0.4664[/C][C] 0.9328[/C][C] 0.5336[/C][/ROW]
[ROW][C]69[/C][C] 0.4265[/C][C] 0.853[/C][C] 0.5735[/C][/ROW]
[ROW][C]70[/C][C] 0.3961[/C][C] 0.7922[/C][C] 0.6039[/C][/ROW]
[ROW][C]71[/C][C] 0.3955[/C][C] 0.7911[/C][C] 0.6045[/C][/ROW]
[ROW][C]72[/C][C] 0.4531[/C][C] 0.9061[/C][C] 0.5469[/C][/ROW]
[ROW][C]73[/C][C] 0.4181[/C][C] 0.8363[/C][C] 0.5819[/C][/ROW]
[ROW][C]74[/C][C] 0.4653[/C][C] 0.9306[/C][C] 0.5347[/C][/ROW]
[ROW][C]75[/C][C] 0.4957[/C][C] 0.9913[/C][C] 0.5043[/C][/ROW]
[ROW][C]76[/C][C] 0.5414[/C][C] 0.9172[/C][C] 0.4586[/C][/ROW]
[ROW][C]77[/C][C] 0.5848[/C][C] 0.8303[/C][C] 0.4152[/C][/ROW]
[ROW][C]78[/C][C] 0.5466[/C][C] 0.9068[/C][C] 0.4534[/C][/ROW]
[ROW][C]79[/C][C] 0.5049[/C][C] 0.9902[/C][C] 0.4951[/C][/ROW]
[ROW][C]80[/C][C] 0.4896[/C][C] 0.9792[/C][C] 0.5104[/C][/ROW]
[ROW][C]81[/C][C] 0.4544[/C][C] 0.9088[/C][C] 0.5456[/C][/ROW]
[ROW][C]82[/C][C] 0.4223[/C][C] 0.8446[/C][C] 0.5777[/C][/ROW]
[ROW][C]83[/C][C] 0.3927[/C][C] 0.7853[/C][C] 0.6073[/C][/ROW]
[ROW][C]84[/C][C] 0.4204[/C][C] 0.8409[/C][C] 0.5796[/C][/ROW]
[ROW][C]85[/C][C] 0.38[/C][C] 0.7601[/C][C] 0.62[/C][/ROW]
[ROW][C]86[/C][C] 0.376[/C][C] 0.7519[/C][C] 0.624[/C][/ROW]
[ROW][C]87[/C][C] 0.3441[/C][C] 0.6883[/C][C] 0.6559[/C][/ROW]
[ROW][C]88[/C][C] 0.3099[/C][C] 0.6198[/C][C] 0.6901[/C][/ROW]
[ROW][C]89[/C][C] 0.2796[/C][C] 0.5592[/C][C] 0.7204[/C][/ROW]
[ROW][C]90[/C][C] 0.2497[/C][C] 0.4995[/C][C] 0.7503[/C][/ROW]
[ROW][C]91[/C][C] 0.4952[/C][C] 0.9904[/C][C] 0.5048[/C][/ROW]
[ROW][C]92[/C][C] 0.4565[/C][C] 0.913[/C][C] 0.5435[/C][/ROW]
[ROW][C]93[/C][C] 0.4237[/C][C] 0.8473[/C][C] 0.5763[/C][/ROW]
[ROW][C]94[/C][C] 0.3847[/C][C] 0.7695[/C][C] 0.6153[/C][/ROW]
[ROW][C]95[/C][C] 0.3445[/C][C] 0.689[/C][C] 0.6555[/C][/ROW]
[ROW][C]96[/C][C] 0.3362[/C][C] 0.6724[/C][C] 0.6638[/C][/ROW]
[ROW][C]97[/C][C] 0.2988[/C][C] 0.5976[/C][C] 0.7012[/C][/ROW]
[ROW][C]98[/C][C] 0.279[/C][C] 0.5579[/C][C] 0.721[/C][/ROW]
[ROW][C]99[/C][C] 0.2552[/C][C] 0.5104[/C][C] 0.7448[/C][/ROW]
[ROW][C]100[/C][C] 0.2222[/C][C] 0.4444[/C][C] 0.7778[/C][/ROW]
[ROW][C]101[/C][C] 0.4199[/C][C] 0.8398[/C][C] 0.5801[/C][/ROW]
[ROW][C]102[/C][C] 0.3943[/C][C] 0.7886[/C][C] 0.6057[/C][/ROW]
[ROW][C]103[/C][C] 0.3539[/C][C] 0.7077[/C][C] 0.6461[/C][/ROW]
[ROW][C]104[/C][C] 0.3287[/C][C] 0.6574[/C][C] 0.6713[/C][/ROW]
[ROW][C]105[/C][C] 0.3658[/C][C] 0.7317[/C][C] 0.6342[/C][/ROW]
[ROW][C]106[/C][C] 0.3856[/C][C] 0.7713[/C][C] 0.6144[/C][/ROW]
[ROW][C]107[/C][C] 0.4448[/C][C] 0.8896[/C][C] 0.5552[/C][/ROW]
[ROW][C]108[/C][C] 0.4022[/C][C] 0.8044[/C][C] 0.5978[/C][/ROW]
[ROW][C]109[/C][C] 0.3832[/C][C] 0.7663[/C][C] 0.6168[/C][/ROW]
[ROW][C]110[/C][C] 0.3429[/C][C] 0.6858[/C][C] 0.6571[/C][/ROW]
[ROW][C]111[/C][C] 0.6475[/C][C] 0.7049[/C][C] 0.3525[/C][/ROW]
[ROW][C]112[/C][C] 0.6731[/C][C] 0.6538[/C][C] 0.3269[/C][/ROW]
[ROW][C]113[/C][C] 0.7315[/C][C] 0.537[/C][C] 0.2685[/C][/ROW]
[ROW][C]114[/C][C] 0.6932[/C][C] 0.6136[/C][C] 0.3068[/C][/ROW]
[ROW][C]115[/C][C] 0.6674[/C][C] 0.6652[/C][C] 0.3326[/C][/ROW]
[ROW][C]116[/C][C] 0.6773[/C][C] 0.6455[/C][C] 0.3227[/C][/ROW]
[ROW][C]117[/C][C] 0.6359[/C][C] 0.7282[/C][C] 0.3641[/C][/ROW]
[ROW][C]118[/C][C] 0.592[/C][C] 0.8161[/C][C] 0.408[/C][/ROW]
[ROW][C]119[/C][C] 0.7653[/C][C] 0.4694[/C][C] 0.2347[/C][/ROW]
[ROW][C]120[/C][C] 0.7497[/C][C] 0.5006[/C][C] 0.2503[/C][/ROW]
[ROW][C]121[/C][C] 0.7129[/C][C] 0.5741[/C][C] 0.2871[/C][/ROW]
[ROW][C]122[/C][C] 0.6718[/C][C] 0.6565[/C][C] 0.3282[/C][/ROW]
[ROW][C]123[/C][C] 0.6999[/C][C] 0.6003[/C][C] 0.3001[/C][/ROW]
[ROW][C]124[/C][C] 0.6575[/C][C] 0.6851[/C][C] 0.3425[/C][/ROW]
[ROW][C]125[/C][C] 0.6204[/C][C] 0.7593[/C][C] 0.3796[/C][/ROW]
[ROW][C]126[/C][C] 0.6092[/C][C] 0.7815[/C][C] 0.3908[/C][/ROW]
[ROW][C]127[/C][C] 0.6008[/C][C] 0.7983[/C][C] 0.3992[/C][/ROW]
[ROW][C]128[/C][C] 0.5554[/C][C] 0.8892[/C][C] 0.4446[/C][/ROW]
[ROW][C]129[/C][C] 0.5537[/C][C] 0.8925[/C][C] 0.4463[/C][/ROW]
[ROW][C]130[/C][C] 0.5711[/C][C] 0.8577[/C][C] 0.4289[/C][/ROW]
[ROW][C]131[/C][C] 0.5388[/C][C] 0.9224[/C][C] 0.4612[/C][/ROW]
[ROW][C]132[/C][C] 0.4939[/C][C] 0.9878[/C][C] 0.5061[/C][/ROW]
[ROW][C]133[/C][C] 0.4862[/C][C] 0.9724[/C][C] 0.5138[/C][/ROW]
[ROW][C]134[/C][C] 0.4379[/C][C] 0.8758[/C][C] 0.5621[/C][/ROW]
[ROW][C]135[/C][C] 0.5088[/C][C] 0.9824[/C][C] 0.4912[/C][/ROW]
[ROW][C]136[/C][C] 0.4988[/C][C] 0.9977[/C][C] 0.5012[/C][/ROW]
[ROW][C]137[/C][C] 0.4498[/C][C] 0.8996[/C][C] 0.5502[/C][/ROW]
[ROW][C]138[/C][C] 0.4351[/C][C] 0.8703[/C][C] 0.5649[/C][/ROW]
[ROW][C]139[/C][C] 0.4111[/C][C] 0.8222[/C][C] 0.5889[/C][/ROW]
[ROW][C]140[/C][C] 0.5442[/C][C] 0.9115[/C][C] 0.4558[/C][/ROW]
[ROW][C]141[/C][C] 0.5281[/C][C] 0.9438[/C][C] 0.4719[/C][/ROW]
[ROW][C]142[/C][C] 0.4785[/C][C] 0.9571[/C][C] 0.5215[/C][/ROW]
[ROW][C]143[/C][C] 0.6441[/C][C] 0.7117[/C][C] 0.3559[/C][/ROW]
[ROW][C]144[/C][C] 0.6027[/C][C] 0.7947[/C][C] 0.3973[/C][/ROW]
[ROW][C]145[/C][C] 0.5696[/C][C] 0.8609[/C][C] 0.4304[/C][/ROW]
[ROW][C]146[/C][C] 0.5542[/C][C] 0.8916[/C][C] 0.4458[/C][/ROW]
[ROW][C]147[/C][C] 0.5552[/C][C] 0.8896[/C][C] 0.4448[/C][/ROW]
[ROW][C]148[/C][C] 0.5001[/C][C] 0.9999[/C][C] 0.4999[/C][/ROW]
[ROW][C]149[/C][C] 0.5281[/C][C] 0.9437[/C][C] 0.4719[/C][/ROW]
[ROW][C]150[/C][C] 0.6793[/C][C] 0.6414[/C][C] 0.3207[/C][/ROW]
[ROW][C]151[/C][C] 0.622[/C][C] 0.7561[/C][C] 0.378[/C][/ROW]
[ROW][C]152[/C][C] 0.5859[/C][C] 0.8282[/C][C] 0.4141[/C][/ROW]
[ROW][C]153[/C][C] 0.5307[/C][C] 0.9385[/C][C] 0.4693[/C][/ROW]
[ROW][C]154[/C][C] 0.6445[/C][C] 0.7109[/C][C] 0.3554[/C][/ROW]
[ROW][C]155[/C][C] 0.5873[/C][C] 0.8254[/C][C] 0.4127[/C][/ROW]
[ROW][C]156[/C][C] 0.5897[/C][C] 0.8205[/C][C] 0.4103[/C][/ROW]
[ROW][C]157[/C][C] 0.5227[/C][C] 0.9546[/C][C] 0.4773[/C][/ROW]
[ROW][C]158[/C][C] 0.4894[/C][C] 0.9787[/C][C] 0.5106[/C][/ROW]
[ROW][C]159[/C][C] 0.4164[/C][C] 0.8327[/C][C] 0.5836[/C][/ROW]
[ROW][C]160[/C][C] 0.4189[/C][C] 0.8379[/C][C] 0.5811[/C][/ROW]
[ROW][C]161[/C][C] 0.5956[/C][C] 0.8089[/C][C] 0.4044[/C][/ROW]
[ROW][C]162[/C][C] 0.6566[/C][C] 0.6869[/C][C] 0.3434[/C][/ROW]
[ROW][C]163[/C][C] 0.5888[/C][C] 0.8225[/C][C] 0.4112[/C][/ROW]
[ROW][C]164[/C][C] 0.6977[/C][C] 0.6047[/C][C] 0.3023[/C][/ROW]
[ROW][C]165[/C][C] 0.6191[/C][C] 0.7619[/C][C] 0.3809[/C][/ROW]
[ROW][C]166[/C][C] 0.7433[/C][C] 0.5134[/C][C] 0.2567[/C][/ROW]
[ROW][C]167[/C][C] 0.6552[/C][C] 0.6895[/C][C] 0.3448[/C][/ROW]
[ROW][C]168[/C][C] 0.5487[/C][C] 0.9026[/C][C] 0.4513[/C][/ROW]
[ROW][C]169[/C][C] 0.6521[/C][C] 0.6959[/C][C] 0.3479[/C][/ROW]
[ROW][C]170[/C][C] 0.5435[/C][C] 0.913[/C][C] 0.4565[/C][/ROW]
[ROW][C]171[/C][C] 0.3989[/C][C] 0.7978[/C][C] 0.6011[/C][/ROW]
[ROW][C]172[/C][C] 0.2984[/C][C] 0.5968[/C][C] 0.7016[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=6

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
7	0.7074	0.5853	0.2926
8	0.6007	0.7986	0.3993
9	0.6211	0.7577	0.3789
10	0.5359	0.9282	0.4641
11	0.4973	0.9947	0.5027
12	0.7253	0.5494	0.2747
13	0.6712	0.6576	0.3288
14	0.871	0.258	0.129
15	0.9314	0.1372	0.0686
16	0.907	0.1861	0.09303
17	0.8721	0.2557	0.1279
18	0.8818	0.2363	0.1182
19	0.8423	0.3154	0.1577
20	0.8319	0.3361	0.1681
21	0.8764	0.2473	0.1236
22	0.869	0.262	0.131
23	0.8362	0.3277	0.1638
24	0.7945	0.411	0.2055
25	0.7853	0.4294	0.2147
26	0.899	0.202	0.101
27	0.8739	0.2523	0.1261
28	0.8669	0.2661	0.1331
29	0.8321	0.3358	0.1679
30	0.8042	0.3917	0.1958
31	0.8784	0.2433	0.1216
32	0.8592	0.2815	0.1408
33	0.8252	0.3496	0.1748
34	0.8252	0.3496	0.1748
35	0.7962	0.4077	0.2039
36	0.7598	0.4805	0.2402
37	0.7265	0.547	0.2735
38	0.7125	0.575	0.2875
39	0.6785	0.6429	0.3215
40	0.6307	0.7386	0.3693
41	0.6889	0.6222	0.3111
42	0.6422	0.7156	0.3578
43	0.5956	0.8089	0.4044
44	0.5501	0.8999	0.4499
45	0.5004	0.9993	0.4996
46	0.4786	0.9572	0.5214
47	0.4807	0.9613	0.5193
48	0.4501	0.9002	0.5499
49	0.5524	0.8953	0.4476
50	0.5481	0.9038	0.4519
51	0.5339	0.9322	0.4661
52	0.4861	0.9723	0.5139
53	0.467	0.934	0.533
54	0.4337	0.8674	0.5663
55	0.4628	0.9256	0.5372
56	0.4199	0.8399	0.5801
57	0.4348	0.8697	0.5652
58	0.5576	0.8848	0.4424
59	0.5135	0.9729	0.4865
60	0.4756	0.9512	0.5244
61	0.4367	0.8733	0.5633
62	0.4139	0.8278	0.5861
63	0.3717	0.7435	0.6283
64	0.3316	0.6632	0.6684
65	0.3967	0.7935	0.6033
66	0.4165	0.833	0.5835
67	0.4941	0.9883	0.5059
68	0.4664	0.9328	0.5336
69	0.4265	0.853	0.5735
70	0.3961	0.7922	0.6039
71	0.3955	0.7911	0.6045
72	0.4531	0.9061	0.5469
73	0.4181	0.8363	0.5819
74	0.4653	0.9306	0.5347
75	0.4957	0.9913	0.5043
76	0.5414	0.9172	0.4586
77	0.5848	0.8303	0.4152
78	0.5466	0.9068	0.4534
79	0.5049	0.9902	0.4951
80	0.4896	0.9792	0.5104
81	0.4544	0.9088	0.5456
82	0.4223	0.8446	0.5777
83	0.3927	0.7853	0.6073
84	0.4204	0.8409	0.5796
85	0.38	0.7601	0.62
86	0.376	0.7519	0.624
87	0.3441	0.6883	0.6559
88	0.3099	0.6198	0.6901
89	0.2796	0.5592	0.7204
90	0.2497	0.4995	0.7503
91	0.4952	0.9904	0.5048
92	0.4565	0.913	0.5435
93	0.4237	0.8473	0.5763
94	0.3847	0.7695	0.6153
95	0.3445	0.689	0.6555
96	0.3362	0.6724	0.6638
97	0.2988	0.5976	0.7012
98	0.279	0.5579	0.721
99	0.2552	0.5104	0.7448
100	0.2222	0.4444	0.7778
101	0.4199	0.8398	0.5801
102	0.3943	0.7886	0.6057
103	0.3539	0.7077	0.6461
104	0.3287	0.6574	0.6713
105	0.3658	0.7317	0.6342
106	0.3856	0.7713	0.6144
107	0.4448	0.8896	0.5552
108	0.4022	0.8044	0.5978
109	0.3832	0.7663	0.6168
110	0.3429	0.6858	0.6571
111	0.6475	0.7049	0.3525
112	0.6731	0.6538	0.3269
113	0.7315	0.537	0.2685
114	0.6932	0.6136	0.3068
115	0.6674	0.6652	0.3326
116	0.6773	0.6455	0.3227
117	0.6359	0.7282	0.3641
118	0.592	0.8161	0.408
119	0.7653	0.4694	0.2347
120	0.7497	0.5006	0.2503
121	0.7129	0.5741	0.2871
122	0.6718	0.6565	0.3282
123	0.6999	0.6003	0.3001
124	0.6575	0.6851	0.3425
125	0.6204	0.7593	0.3796
126	0.6092	0.7815	0.3908
127	0.6008	0.7983	0.3992
128	0.5554	0.8892	0.4446
129	0.5537	0.8925	0.4463
130	0.5711	0.8577	0.4289
131	0.5388	0.9224	0.4612
132	0.4939	0.9878	0.5061
133	0.4862	0.9724	0.5138
134	0.4379	0.8758	0.5621
135	0.5088	0.9824	0.4912
136	0.4988	0.9977	0.5012
137	0.4498	0.8996	0.5502
138	0.4351	0.8703	0.5649
139	0.4111	0.8222	0.5889
140	0.5442	0.9115	0.4558
141	0.5281	0.9438	0.4719
142	0.4785	0.9571	0.5215
143	0.6441	0.7117	0.3559
144	0.6027	0.7947	0.3973
145	0.5696	0.8609	0.4304
146	0.5542	0.8916	0.4458
147	0.5552	0.8896	0.4448
148	0.5001	0.9999	0.4999
149	0.5281	0.9437	0.4719
150	0.6793	0.6414	0.3207
151	0.622	0.7561	0.378
152	0.5859	0.8282	0.4141
153	0.5307	0.9385	0.4693
154	0.6445	0.7109	0.3554
155	0.5873	0.8254	0.4127
156	0.5897	0.8205	0.4103
157	0.5227	0.9546	0.4773
158	0.4894	0.9787	0.5106
159	0.4164	0.8327	0.5836
160	0.4189	0.8379	0.5811
161	0.5956	0.8089	0.4044
162	0.6566	0.6869	0.3434
163	0.5888	0.8225	0.4112
164	0.6977	0.6047	0.3023
165	0.6191	0.7619	0.3809
166	0.7433	0.5134	0.2567
167	0.6552	0.6895	0.3448
168	0.5487	0.9026	0.4513
169	0.6521	0.6959	0.3479
170	0.5435	0.913	0.4565
171	0.3989	0.7978	0.6011
172	0.2984	0.5968	0.7016

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=&T=7

[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=&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=7

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 5.9439, df1 = 2, df2 = 173, p-value = 0.003187
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.7204, df1 = 6, df2 = 169, p-value = 0.119
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 4.7754, df1 = 2, df2 = 173, p-value = 0.009578

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 5.9439, df1 = 2, df2 = 173, p-value = 0.003187
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.7204, df1 = 6, df2 = 169, p-value = 0.119
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.7754, df1 = 2, df2 = 173, p-value = 0.009578
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 5.9439, df1 = 2, df2 = 173, p-value = 0.003187
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.7204, df1 = 6, df2 = 169, p-value = 0.119
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.7754, df1 = 2, df2 = 173, p-value = 0.009578
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=8

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 5.9439, df1 = 2, df2 = 173, p-value = 0.003187
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.7204, df1 = 6, df2 = 169, p-value = 0.119
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 4.7754, df1 = 2, df2 = 173, p-value = 0.009578

Variance Inflation Factors (Multicollinearity)
> vif Relative_Advantage Perceived_Ease_of_Use System_Quality 1.382208 1.415201 1.361465

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
   Relative_Advantage Perceived_Ease_of_Use        System_Quality 
             1.382208              1.415201              1.361465 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
   Relative_Advantage Perceived_Ease_of_Use        System_Quality 
             1.382208              1.415201              1.361465 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=9

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif Relative_Advantage Perceived_Ease_of_Use System_Quality 1.382208 1.415201 1.361465

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Parameters (Session):

par1 = 11111112additive1212additive12121212FALSETRUE121212FALSETRUE12110111111 ; par2 = Do not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal Dummies221212TripleTriple12SingleTriple0.00.00.00.30.3Do not include Seasonal DummiesDo not include Seasonal DummiesgreygreygreynoDo not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal DummiesDo not include Seasonal Dummies ; par3 = No Linear TrendNo Linear TrendNo Linear TrendNo Linear TrendNo Linear Trend3TRUEBFGSadditivemultiplicativemultiplicativemultiplicative11100No Linear TrendNo Linear TrendFALSEFALSEFALSE512No Linear TrendNo Linear TrendNo Linear TrendNo Linear TrendNo Linear TrendNo Linear TrendNo Linear TrendNo Linear Trend ; par4 = 00TRUE121212121211111UnknownUnknownUnknown ; par5 = 001212121212 ; par6 = 12121212120333312121212121212121212 ; par7 = 11111 ; par8 = 02222 ; par9 = 11111 ; par10 = FALSE ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = 12 ;

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

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$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.row.start(a)
a<-table.element(a, mywarning)
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,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code