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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 20 Dec 2016 15:45:43 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/20/t14822467942n2s6sxa95hlerb.htm/, Retrieved Sun, 28 Apr 2024 00:42:08 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Sun, 28 Apr 2024 00:42:08 +0200

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	Big Analytics Cloud Computing Center
R Engine error message	Error in vif.default(mylm) : model contains fewer than 2 terms Calls: vif -> vif.default Execution halted

\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 time6 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Engine error message & Error in vif.default(mylm) : model contains fewer than 2 terms
Calls: vif -> vif.default
Execution halted
 \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]

6 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [ROW]

R Engine error message[/C][C]

Error in vif.default(mylm) : model contains fewer than 2 terms
Calls: vif -> vif.default
Execution halted

[/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	6 seconds
R Server	Big Analytics Cloud Computing Center
R Engine error message	Error in vif.default(mylm) : model contains fewer than 2 terms Calls: vif -> vif.default Execution halted

Multiple Linear Regression - Estimated Regression Equation
tevreden[t] = + 3.81219 + 0.0841827UTD[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
tevreden[t] =  +  3.81219 +  0.0841827UTD[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]tevreden[t] =  +  3.81219 +  0.0841827UTD[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
tevreden[t] = + 3.81219 + 0.0841827UTD[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)	+3.812	0.3627	+1.0510e+01	4.983e-20	2.491e-20
UTD	+0.08418	0.07898	+1.0660e+00	0.2881	0.144

\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) & +3.812 &  0.3627 & +1.0510e+01 &  4.983e-20 &  2.491e-20 \tabularnewline
UTD & +0.08418 &  0.07898 & +1.0660e+00 &  0.2881 &  0.144 \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]+3.812[/C][C] 0.3627[/C][C]+1.0510e+01[/C][C] 4.983e-20[/C][C] 2.491e-20[/C][/ROW]
[ROW][C]UTD[/C][C]+0.08418[/C][C] 0.07898[/C][C]+1.0660e+00[/C][C] 0.2881[/C][C] 0.144[/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)	+3.812	0.3627	+1.0510e+01	4.983e-20	2.491e-20
UTD	+0.08418	0.07898	+1.0660e+00	0.2881	0.144

Multiple Linear Regression - Regression Statistics
Multiple R	0.08345
R-squared	0.006963
Adjusted R-squared	0.0008333
F-TEST (value)	1.136
F-TEST (DF numerator)	1
F-TEST (DF denominator)	162
p-value	0.2881
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.6349
Sum Squared Residuals	65.3

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.08345 \tabularnewline
R-squared &  0.006963 \tabularnewline
Adjusted R-squared &  0.0008333 \tabularnewline
F-TEST (value) &  1.136 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 162 \tabularnewline
p-value &  0.2881 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.6349 \tabularnewline
Sum Squared Residuals &  65.3 \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] 0.08345[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.006963[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.0008333[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.136[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]162[/C][/ROW]
[ROW][C]p-value[/C][C] 0.2881[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.6349[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 65.3[/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	0.08345
R-squared	0.006963
Adjusted R-squared	0.0008333
F-TEST (value)	1.136
F-TEST (DF numerator)	1
F-TEST (DF denominator)	162
p-value	0.2881
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.6349
Sum Squared Residuals	65.3

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	3	4.233	-1.233
2	4	4.233	-0.2331
3	5	4.233	0.7669
4	4	4.149	-0.1489
5	4	4.233	-0.2331
6	5	4.233	0.7669
7	5	4.149	0.8511
8	5	4.233	0.7669
9	5	4.233	0.7669
10	5	4.065	0.9353
11	4	4.233	-0.2331
12	4	4.233	-0.2331
13	4	4.233	-0.2331
14	4	4.149	-0.1489
15	5	4.149	0.8511
16	4	4.233	-0.2331
17	4	4.149	-0.1489
18	5	4.149	0.8511
19	4	4.233	-0.2331
20	4	4.233	-0.2331
21	4	4.233	-0.2331
22	4	4.149	-0.1489
23	5	4.233	0.7669
24	5	4.149	0.8511
25	4	4.233	-0.2331
26	4	4.149	-0.1489
27	5	4.233	0.7669
28	5	4.233	0.7669
29	3	4.065	-1.065
30	5	4.149	0.8511
31	4	4.233	-0.2331
32	5	4.149	0.8511
33	4	4.233	-0.2331
34	5	4.233	0.7669
35	4	4.149	-0.1489
36	4	4.149	-0.1489
37	4	4.149	-0.1489
38	5	4.233	0.7669
39	4	4.149	-0.1489
40	5	4.149	0.8511
41	3	4.065	-1.065
42	5	4.233	0.7669
43	4	4.149	-0.1489
44	5	4.149	0.8511
45	5	4.149	0.8511
46	3	4.149	-1.149
47	5	4.233	0.7669
48	5	4.149	0.8511
49	4	4.233	-0.2331
50	3	4.233	-1.233
51	4	4.233	-0.2331
52	4	4.233	-0.2331
53	4	4.149	-0.1489
54	4	4.149	-0.1489
55	4	4.149	-0.1489
56	4	4.149	-0.1489
57	5	4.233	0.7669
58	4	4.233	-0.2331
59	4	4.233	-0.2331
60	4	4.233	-0.2331
61	4	4.233	-0.2331
62	4	4.149	-0.1489
63	5	3.981	1.019
64	4	4.233	-0.2331
65	4	4.149	-0.1489
66	4	4.149	-0.1489
67	4	4.233	-0.2331
68	4	4.233	-0.2331
69	3	4.233	-1.233
70	4	4.233	-0.2331
71	4	4.233	-0.2331
72	4	4.233	-0.2331
73	5	4.233	0.7669
74	4	4.233	-0.2331
75	4	4.233	-0.2331
76	5	4.233	0.7669
77	5	4.233	0.7669
78	3	4.149	-1.149
79	5	4.149	0.8511
80	4	4.065	-0.06474
81	5	4.233	0.7669
82	5	4.233	0.7669
83	5	4.149	0.8511
84	5	4.233	0.7669
85	4	4.233	-0.2331
86	4	4.233	-0.2331
87	2	4.149	-2.149
88	4	4.065	-0.06474
89	5	4.149	0.8511
90	5	4.149	0.8511
91	5	4.233	0.7669
92	4	4.233	-0.2331
93	4	4.233	-0.2331
94	4	4.233	-0.2331
95	5	4.233	0.7669
96	4	4.149	-0.1489
97	5	4.233	0.7669
98	4	4.233	-0.2331
99	4	4.149	-0.1489
100	4	4.149	-0.1489
101	5	4.233	0.7669
102	4	4.233	-0.2331
103	5	4.233	0.7669
104	4	4.233	-0.2331
105	4	4.233	-0.2331
106	4	4.149	-0.1489
107	3	4.149	-1.149
108	4	4.149	-0.1489
109	3	4.065	-1.065
110	5	4.149	0.8511
111	4	4.149	-0.1489
112	4	4.233	-0.2331
113	5	4.233	0.7669
114	4	4.233	-0.2331
115	4	4.233	-0.2331
116	4	4.233	-0.2331
117	4	4.149	-0.1489
118	4	4.233	-0.2331
119	4	4.233	-0.2331
120	5	4.149	0.8511
121	4	4.233	-0.2331
122	4	4.233	-0.2331
123	5	4.149	0.8511
124	4	4.233	-0.2331
125	4	4.233	-0.2331
126	3	4.233	-1.233
127	4	4.233	-0.2331
128	4	4.149	-0.1489
129	4	4.233	-0.2331
130	3	4.065	-1.065
131	4	4.233	-0.2331
132	5	4.065	0.9353
133	3	4.065	-1.065
134	4	4.233	-0.2331
135	5	4.233	0.7669
136	5	4.149	0.8511
137	4	4.233	-0.2331
138	4	4.233	-0.2331
139	4	4.233	-0.2331
140	3	4.233	-1.233
141	5	4.233	0.7669
142	4	4.233	-0.2331
143	5	4.233	0.7669
144	4	4.233	-0.2331
145	4	4.233	-0.2331
146	3	4.149	-1.149
147	4	4.233	-0.2331
148	4	4.233	-0.2331
149	4	4.233	-0.2331
150	3	4.233	-1.233
151	4	4.233	-0.2331
152	5	4.233	0.7669
153	3	4.233	-1.233
154	4	4.149	-0.1489
155	5	4.233	0.7669
156	4	4.233	-0.2331
157	5	4.149	0.8511
158	4	4.149	-0.1489
159	4	4.233	-0.2331
160	4	4.233	-0.2331
161	4	4.149	-0.1489
162	4	4.233	-0.2331
163	3	4.149	-1.149
164	4	4.233	-0.2331

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  3 &  4.233 & -1.233 \tabularnewline
2 &  4 &  4.233 & -0.2331 \tabularnewline
3 &  5 &  4.233 &  0.7669 \tabularnewline
4 &  4 &  4.149 & -0.1489 \tabularnewline
5 &  4 &  4.233 & -0.2331 \tabularnewline
6 &  5 &  4.233 &  0.7669 \tabularnewline
7 &  5 &  4.149 &  0.8511 \tabularnewline
8 &  5 &  4.233 &  0.7669 \tabularnewline
9 &  5 &  4.233 &  0.7669 \tabularnewline
10 &  5 &  4.065 &  0.9353 \tabularnewline
11 &  4 &  4.233 & -0.2331 \tabularnewline
12 &  4 &  4.233 & -0.2331 \tabularnewline
13 &  4 &  4.233 & -0.2331 \tabularnewline
14 &  4 &  4.149 & -0.1489 \tabularnewline
15 &  5 &  4.149 &  0.8511 \tabularnewline
16 &  4 &  4.233 & -0.2331 \tabularnewline
17 &  4 &  4.149 & -0.1489 \tabularnewline
18 &  5 &  4.149 &  0.8511 \tabularnewline
19 &  4 &  4.233 & -0.2331 \tabularnewline
20 &  4 &  4.233 & -0.2331 \tabularnewline
21 &  4 &  4.233 & -0.2331 \tabularnewline
22 &  4 &  4.149 & -0.1489 \tabularnewline
23 &  5 &  4.233 &  0.7669 \tabularnewline
24 &  5 &  4.149 &  0.8511 \tabularnewline
25 &  4 &  4.233 & -0.2331 \tabularnewline
26 &  4 &  4.149 & -0.1489 \tabularnewline
27 &  5 &  4.233 &  0.7669 \tabularnewline
28 &  5 &  4.233 &  0.7669 \tabularnewline
29 &  3 &  4.065 & -1.065 \tabularnewline
30 &  5 &  4.149 &  0.8511 \tabularnewline
31 &  4 &  4.233 & -0.2331 \tabularnewline
32 &  5 &  4.149 &  0.8511 \tabularnewline
33 &  4 &  4.233 & -0.2331 \tabularnewline
34 &  5 &  4.233 &  0.7669 \tabularnewline
35 &  4 &  4.149 & -0.1489 \tabularnewline
36 &  4 &  4.149 & -0.1489 \tabularnewline
37 &  4 &  4.149 & -0.1489 \tabularnewline
38 &  5 &  4.233 &  0.7669 \tabularnewline
39 &  4 &  4.149 & -0.1489 \tabularnewline
40 &  5 &  4.149 &  0.8511 \tabularnewline
41 &  3 &  4.065 & -1.065 \tabularnewline
42 &  5 &  4.233 &  0.7669 \tabularnewline
43 &  4 &  4.149 & -0.1489 \tabularnewline
44 &  5 &  4.149 &  0.8511 \tabularnewline
45 &  5 &  4.149 &  0.8511 \tabularnewline
46 &  3 &  4.149 & -1.149 \tabularnewline
47 &  5 &  4.233 &  0.7669 \tabularnewline
48 &  5 &  4.149 &  0.8511 \tabularnewline
49 &  4 &  4.233 & -0.2331 \tabularnewline
50 &  3 &  4.233 & -1.233 \tabularnewline
51 &  4 &  4.233 & -0.2331 \tabularnewline
52 &  4 &  4.233 & -0.2331 \tabularnewline
53 &  4 &  4.149 & -0.1489 \tabularnewline
54 &  4 &  4.149 & -0.1489 \tabularnewline
55 &  4 &  4.149 & -0.1489 \tabularnewline
56 &  4 &  4.149 & -0.1489 \tabularnewline
57 &  5 &  4.233 &  0.7669 \tabularnewline
58 &  4 &  4.233 & -0.2331 \tabularnewline
59 &  4 &  4.233 & -0.2331 \tabularnewline
60 &  4 &  4.233 & -0.2331 \tabularnewline
61 &  4 &  4.233 & -0.2331 \tabularnewline
62 &  4 &  4.149 & -0.1489 \tabularnewline
63 &  5 &  3.981 &  1.019 \tabularnewline
64 &  4 &  4.233 & -0.2331 \tabularnewline
65 &  4 &  4.149 & -0.1489 \tabularnewline
66 &  4 &  4.149 & -0.1489 \tabularnewline
67 &  4 &  4.233 & -0.2331 \tabularnewline
68 &  4 &  4.233 & -0.2331 \tabularnewline
69 &  3 &  4.233 & -1.233 \tabularnewline
70 &  4 &  4.233 & -0.2331 \tabularnewline
71 &  4 &  4.233 & -0.2331 \tabularnewline
72 &  4 &  4.233 & -0.2331 \tabularnewline
73 &  5 &  4.233 &  0.7669 \tabularnewline
74 &  4 &  4.233 & -0.2331 \tabularnewline
75 &  4 &  4.233 & -0.2331 \tabularnewline
76 &  5 &  4.233 &  0.7669 \tabularnewline
77 &  5 &  4.233 &  0.7669 \tabularnewline
78 &  3 &  4.149 & -1.149 \tabularnewline
79 &  5 &  4.149 &  0.8511 \tabularnewline
80 &  4 &  4.065 & -0.06474 \tabularnewline
81 &  5 &  4.233 &  0.7669 \tabularnewline
82 &  5 &  4.233 &  0.7669 \tabularnewline
83 &  5 &  4.149 &  0.8511 \tabularnewline
84 &  5 &  4.233 &  0.7669 \tabularnewline
85 &  4 &  4.233 & -0.2331 \tabularnewline
86 &  4 &  4.233 & -0.2331 \tabularnewline
87 &  2 &  4.149 & -2.149 \tabularnewline
88 &  4 &  4.065 & -0.06474 \tabularnewline
89 &  5 &  4.149 &  0.8511 \tabularnewline
90 &  5 &  4.149 &  0.8511 \tabularnewline
91 &  5 &  4.233 &  0.7669 \tabularnewline
92 &  4 &  4.233 & -0.2331 \tabularnewline
93 &  4 &  4.233 & -0.2331 \tabularnewline
94 &  4 &  4.233 & -0.2331 \tabularnewline
95 &  5 &  4.233 &  0.7669 \tabularnewline
96 &  4 &  4.149 & -0.1489 \tabularnewline
97 &  5 &  4.233 &  0.7669 \tabularnewline
98 &  4 &  4.233 & -0.2331 \tabularnewline
99 &  4 &  4.149 & -0.1489 \tabularnewline
100 &  4 &  4.149 & -0.1489 \tabularnewline
101 &  5 &  4.233 &  0.7669 \tabularnewline
102 &  4 &  4.233 & -0.2331 \tabularnewline
103 &  5 &  4.233 &  0.7669 \tabularnewline
104 &  4 &  4.233 & -0.2331 \tabularnewline
105 &  4 &  4.233 & -0.2331 \tabularnewline
106 &  4 &  4.149 & -0.1489 \tabularnewline
107 &  3 &  4.149 & -1.149 \tabularnewline
108 &  4 &  4.149 & -0.1489 \tabularnewline
109 &  3 &  4.065 & -1.065 \tabularnewline
110 &  5 &  4.149 &  0.8511 \tabularnewline
111 &  4 &  4.149 & -0.1489 \tabularnewline
112 &  4 &  4.233 & -0.2331 \tabularnewline
113 &  5 &  4.233 &  0.7669 \tabularnewline
114 &  4 &  4.233 & -0.2331 \tabularnewline
115 &  4 &  4.233 & -0.2331 \tabularnewline
116 &  4 &  4.233 & -0.2331 \tabularnewline
117 &  4 &  4.149 & -0.1489 \tabularnewline
118 &  4 &  4.233 & -0.2331 \tabularnewline
119 &  4 &  4.233 & -0.2331 \tabularnewline
120 &  5 &  4.149 &  0.8511 \tabularnewline
121 &  4 &  4.233 & -0.2331 \tabularnewline
122 &  4 &  4.233 & -0.2331 \tabularnewline
123 &  5 &  4.149 &  0.8511 \tabularnewline
124 &  4 &  4.233 & -0.2331 \tabularnewline
125 &  4 &  4.233 & -0.2331 \tabularnewline
126 &  3 &  4.233 & -1.233 \tabularnewline
127 &  4 &  4.233 & -0.2331 \tabularnewline
128 &  4 &  4.149 & -0.1489 \tabularnewline
129 &  4 &  4.233 & -0.2331 \tabularnewline
130 &  3 &  4.065 & -1.065 \tabularnewline
131 &  4 &  4.233 & -0.2331 \tabularnewline
132 &  5 &  4.065 &  0.9353 \tabularnewline
133 &  3 &  4.065 & -1.065 \tabularnewline
134 &  4 &  4.233 & -0.2331 \tabularnewline
135 &  5 &  4.233 &  0.7669 \tabularnewline
136 &  5 &  4.149 &  0.8511 \tabularnewline
137 &  4 &  4.233 & -0.2331 \tabularnewline
138 &  4 &  4.233 & -0.2331 \tabularnewline
139 &  4 &  4.233 & -0.2331 \tabularnewline
140 &  3 &  4.233 & -1.233 \tabularnewline
141 &  5 &  4.233 &  0.7669 \tabularnewline
142 &  4 &  4.233 & -0.2331 \tabularnewline
143 &  5 &  4.233 &  0.7669 \tabularnewline
144 &  4 &  4.233 & -0.2331 \tabularnewline
145 &  4 &  4.233 & -0.2331 \tabularnewline
146 &  3 &  4.149 & -1.149 \tabularnewline
147 &  4 &  4.233 & -0.2331 \tabularnewline
148 &  4 &  4.233 & -0.2331 \tabularnewline
149 &  4 &  4.233 & -0.2331 \tabularnewline
150 &  3 &  4.233 & -1.233 \tabularnewline
151 &  4 &  4.233 & -0.2331 \tabularnewline
152 &  5 &  4.233 &  0.7669 \tabularnewline
153 &  3 &  4.233 & -1.233 \tabularnewline
154 &  4 &  4.149 & -0.1489 \tabularnewline
155 &  5 &  4.233 &  0.7669 \tabularnewline
156 &  4 &  4.233 & -0.2331 \tabularnewline
157 &  5 &  4.149 &  0.8511 \tabularnewline
158 &  4 &  4.149 & -0.1489 \tabularnewline
159 &  4 &  4.233 & -0.2331 \tabularnewline
160 &  4 &  4.233 & -0.2331 \tabularnewline
161 &  4 &  4.149 & -0.1489 \tabularnewline
162 &  4 &  4.233 & -0.2331 \tabularnewline
163 &  3 &  4.149 & -1.149 \tabularnewline
164 &  4 &  4.233 & -0.2331 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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] 3[/C][C] 4.233[/C][C]-1.233[/C][/ROW]
[ROW][C]2[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]3[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]4[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]5[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]6[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]7[/C][C] 5[/C][C] 4.149[/C][C] 0.8511[/C][/ROW]
[ROW][C]8[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]9[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]10[/C][C] 5[/C][C] 4.065[/C][C] 0.9353[/C][/ROW]
[ROW][C]11[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]12[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]13[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]14[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]15[/C][C] 5[/C][C] 4.149[/C][C] 0.8511[/C][/ROW]
[ROW][C]16[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]17[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]18[/C][C] 5[/C][C] 4.149[/C][C] 0.8511[/C][/ROW]
[ROW][C]19[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]20[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]21[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]22[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]23[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]24[/C][C] 5[/C][C] 4.149[/C][C] 0.8511[/C][/ROW]
[ROW][C]25[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]26[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]27[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]28[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]29[/C][C] 3[/C][C] 4.065[/C][C]-1.065[/C][/ROW]
[ROW][C]30[/C][C] 5[/C][C] 4.149[/C][C] 0.8511[/C][/ROW]
[ROW][C]31[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]32[/C][C] 5[/C][C] 4.149[/C][C] 0.8511[/C][/ROW]
[ROW][C]33[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]34[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]35[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]36[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]37[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]38[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]39[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]40[/C][C] 5[/C][C] 4.149[/C][C] 0.8511[/C][/ROW]
[ROW][C]41[/C][C] 3[/C][C] 4.065[/C][C]-1.065[/C][/ROW]
[ROW][C]42[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]43[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]44[/C][C] 5[/C][C] 4.149[/C][C] 0.8511[/C][/ROW]
[ROW][C]45[/C][C] 5[/C][C] 4.149[/C][C] 0.8511[/C][/ROW]
[ROW][C]46[/C][C] 3[/C][C] 4.149[/C][C]-1.149[/C][/ROW]
[ROW][C]47[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]48[/C][C] 5[/C][C] 4.149[/C][C] 0.8511[/C][/ROW]
[ROW][C]49[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]50[/C][C] 3[/C][C] 4.233[/C][C]-1.233[/C][/ROW]
[ROW][C]51[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]52[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]53[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]54[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]55[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]56[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]57[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]58[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]59[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]60[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]61[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]62[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]63[/C][C] 5[/C][C] 3.981[/C][C] 1.019[/C][/ROW]
[ROW][C]64[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]65[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]66[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]67[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]68[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]69[/C][C] 3[/C][C] 4.233[/C][C]-1.233[/C][/ROW]
[ROW][C]70[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]71[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]72[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]73[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]74[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]75[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]76[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]77[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]78[/C][C] 3[/C][C] 4.149[/C][C]-1.149[/C][/ROW]
[ROW][C]79[/C][C] 5[/C][C] 4.149[/C][C] 0.8511[/C][/ROW]
[ROW][C]80[/C][C] 4[/C][C] 4.065[/C][C]-0.06474[/C][/ROW]
[ROW][C]81[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]82[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]83[/C][C] 5[/C][C] 4.149[/C][C] 0.8511[/C][/ROW]
[ROW][C]84[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]85[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]86[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]87[/C][C] 2[/C][C] 4.149[/C][C]-2.149[/C][/ROW]
[ROW][C]88[/C][C] 4[/C][C] 4.065[/C][C]-0.06474[/C][/ROW]
[ROW][C]89[/C][C] 5[/C][C] 4.149[/C][C] 0.8511[/C][/ROW]
[ROW][C]90[/C][C] 5[/C][C] 4.149[/C][C] 0.8511[/C][/ROW]
[ROW][C]91[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]92[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]93[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]94[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]95[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]96[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]97[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]98[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]99[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]100[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]101[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]102[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]103[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]104[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]105[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]106[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]107[/C][C] 3[/C][C] 4.149[/C][C]-1.149[/C][/ROW]
[ROW][C]108[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]109[/C][C] 3[/C][C] 4.065[/C][C]-1.065[/C][/ROW]
[ROW][C]110[/C][C] 5[/C][C] 4.149[/C][C] 0.8511[/C][/ROW]
[ROW][C]111[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]112[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]113[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]114[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]115[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]116[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]117[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]118[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]119[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]120[/C][C] 5[/C][C] 4.149[/C][C] 0.8511[/C][/ROW]
[ROW][C]121[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]122[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]123[/C][C] 5[/C][C] 4.149[/C][C] 0.8511[/C][/ROW]
[ROW][C]124[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]125[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]126[/C][C] 3[/C][C] 4.233[/C][C]-1.233[/C][/ROW]
[ROW][C]127[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]128[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]129[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]130[/C][C] 3[/C][C] 4.065[/C][C]-1.065[/C][/ROW]
[ROW][C]131[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]132[/C][C] 5[/C][C] 4.065[/C][C] 0.9353[/C][/ROW]
[ROW][C]133[/C][C] 3[/C][C] 4.065[/C][C]-1.065[/C][/ROW]
[ROW][C]134[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]135[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]136[/C][C] 5[/C][C] 4.149[/C][C] 0.8511[/C][/ROW]
[ROW][C]137[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]138[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]139[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]140[/C][C] 3[/C][C] 4.233[/C][C]-1.233[/C][/ROW]
[ROW][C]141[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]142[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]143[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]144[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]145[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]146[/C][C] 3[/C][C] 4.149[/C][C]-1.149[/C][/ROW]
[ROW][C]147[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]148[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]149[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]150[/C][C] 3[/C][C] 4.233[/C][C]-1.233[/C][/ROW]
[ROW][C]151[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]152[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]153[/C][C] 3[/C][C] 4.233[/C][C]-1.233[/C][/ROW]
[ROW][C]154[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]155[/C][C] 5[/C][C] 4.233[/C][C] 0.7669[/C][/ROW]
[ROW][C]156[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]157[/C][C] 5[/C][C] 4.149[/C][C] 0.8511[/C][/ROW]
[ROW][C]158[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]159[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]160[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]161[/C][C] 4[/C][C] 4.149[/C][C]-0.1489[/C][/ROW]
[ROW][C]162[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/C][/ROW]
[ROW][C]163[/C][C] 3[/C][C] 4.149[/C][C]-1.149[/C][/ROW]
[ROW][C]164[/C][C] 4[/C][C] 4.233[/C][C]-0.2331[/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:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	3	4.233	-1.233
2	4	4.233	-0.2331
3	5	4.233	0.7669
4	4	4.149	-0.1489
5	4	4.233	-0.2331
6	5	4.233	0.7669
7	5	4.149	0.8511
8	5	4.233	0.7669
9	5	4.233	0.7669
10	5	4.065	0.9353
11	4	4.233	-0.2331
12	4	4.233	-0.2331
13	4	4.233	-0.2331
14	4	4.149	-0.1489
15	5	4.149	0.8511
16	4	4.233	-0.2331
17	4	4.149	-0.1489
18	5	4.149	0.8511
19	4	4.233	-0.2331
20	4	4.233	-0.2331
21	4	4.233	-0.2331
22	4	4.149	-0.1489
23	5	4.233	0.7669
24	5	4.149	0.8511
25	4	4.233	-0.2331
26	4	4.149	-0.1489
27	5	4.233	0.7669
28	5	4.233	0.7669
29	3	4.065	-1.065
30	5	4.149	0.8511
31	4	4.233	-0.2331
32	5	4.149	0.8511
33	4	4.233	-0.2331
34	5	4.233	0.7669
35	4	4.149	-0.1489
36	4	4.149	-0.1489
37	4	4.149	-0.1489
38	5	4.233	0.7669
39	4	4.149	-0.1489
40	5	4.149	0.8511
41	3	4.065	-1.065
42	5	4.233	0.7669
43	4	4.149	-0.1489
44	5	4.149	0.8511
45	5	4.149	0.8511
46	3	4.149	-1.149
47	5	4.233	0.7669
48	5	4.149	0.8511
49	4	4.233	-0.2331
50	3	4.233	-1.233
51	4	4.233	-0.2331
52	4	4.233	-0.2331
53	4	4.149	-0.1489
54	4	4.149	-0.1489
55	4	4.149	-0.1489
56	4	4.149	-0.1489
57	5	4.233	0.7669
58	4	4.233	-0.2331
59	4	4.233	-0.2331
60	4	4.233	-0.2331
61	4	4.233	-0.2331
62	4	4.149	-0.1489
63	5	3.981	1.019
64	4	4.233	-0.2331
65	4	4.149	-0.1489
66	4	4.149	-0.1489
67	4	4.233	-0.2331
68	4	4.233	-0.2331
69	3	4.233	-1.233
70	4	4.233	-0.2331
71	4	4.233	-0.2331
72	4	4.233	-0.2331
73	5	4.233	0.7669
74	4	4.233	-0.2331
75	4	4.233	-0.2331
76	5	4.233	0.7669
77	5	4.233	0.7669
78	3	4.149	-1.149
79	5	4.149	0.8511
80	4	4.065	-0.06474
81	5	4.233	0.7669
82	5	4.233	0.7669
83	5	4.149	0.8511
84	5	4.233	0.7669
85	4	4.233	-0.2331
86	4	4.233	-0.2331
87	2	4.149	-2.149
88	4	4.065	-0.06474
89	5	4.149	0.8511
90	5	4.149	0.8511
91	5	4.233	0.7669
92	4	4.233	-0.2331
93	4	4.233	-0.2331
94	4	4.233	-0.2331
95	5	4.233	0.7669
96	4	4.149	-0.1489
97	5	4.233	0.7669
98	4	4.233	-0.2331
99	4	4.149	-0.1489
100	4	4.149	-0.1489
101	5	4.233	0.7669
102	4	4.233	-0.2331
103	5	4.233	0.7669
104	4	4.233	-0.2331
105	4	4.233	-0.2331
106	4	4.149	-0.1489
107	3	4.149	-1.149
108	4	4.149	-0.1489
109	3	4.065	-1.065
110	5	4.149	0.8511
111	4	4.149	-0.1489
112	4	4.233	-0.2331
113	5	4.233	0.7669
114	4	4.233	-0.2331
115	4	4.233	-0.2331
116	4	4.233	-0.2331
117	4	4.149	-0.1489
118	4	4.233	-0.2331
119	4	4.233	-0.2331
120	5	4.149	0.8511
121	4	4.233	-0.2331
122	4	4.233	-0.2331
123	5	4.149	0.8511
124	4	4.233	-0.2331
125	4	4.233	-0.2331
126	3	4.233	-1.233
127	4	4.233	-0.2331
128	4	4.149	-0.1489
129	4	4.233	-0.2331
130	3	4.065	-1.065
131	4	4.233	-0.2331
132	5	4.065	0.9353
133	3	4.065	-1.065
134	4	4.233	-0.2331
135	5	4.233	0.7669
136	5	4.149	0.8511
137	4	4.233	-0.2331
138	4	4.233	-0.2331
139	4	4.233	-0.2331
140	3	4.233	-1.233
141	5	4.233	0.7669
142	4	4.233	-0.2331
143	5	4.233	0.7669
144	4	4.233	-0.2331
145	4	4.233	-0.2331
146	3	4.149	-1.149
147	4	4.233	-0.2331
148	4	4.233	-0.2331
149	4	4.233	-0.2331
150	3	4.233	-1.233
151	4	4.233	-0.2331
152	5	4.233	0.7669
153	3	4.233	-1.233
154	4	4.149	-0.1489
155	5	4.233	0.7669
156	4	4.233	-0.2331
157	5	4.149	0.8511
158	4	4.149	-0.1489
159	4	4.233	-0.2331
160	4	4.233	-0.2331
161	4	4.149	-0.1489
162	4	4.233	-0.2331
163	3	4.149	-1.149
164	4	4.233	-0.2331

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.8221	0.3557	0.1779
6	0.8578	0.2843	0.1422
7	0.8517	0.2965	0.1483
8	0.8519	0.2962	0.1481
9	0.8362	0.3277	0.1638
10	0.7824	0.4353	0.2176
11	0.7282	0.5437	0.2718
12	0.666	0.6681	0.334
13	0.5979	0.8042	0.4021
14	0.5693	0.8614	0.4307
15	0.5373	0.9255	0.4627
16	0.4698	0.9397	0.5302
17	0.4425	0.885	0.5575
18	0.4197	0.8393	0.5803
19	0.3583	0.7165	0.6417
20	0.3003	0.6005	0.6997
21	0.247	0.4941	0.753
22	0.2286	0.4572	0.7714
23	0.2641	0.5281	0.7359
24	0.2528	0.5055	0.7472
25	0.2101	0.4202	0.7899
26	0.1945	0.389	0.8055
27	0.2197	0.4394	0.7803
28	0.2385	0.477	0.7615
29	0.4742	0.9484	0.5258
30	0.4878	0.9757	0.5122
31	0.446	0.8919	0.554
32	0.4559	0.9118	0.5441
33	0.4146	0.8292	0.5854
34	0.4231	0.8461	0.5769
35	0.3851	0.7703	0.6149
36	0.3471	0.6943	0.6529
37	0.3097	0.6194	0.6903
38	0.3165	0.633	0.6835
39	0.2805	0.561	0.7195
40	0.2963	0.5927	0.7037
41	0.4199	0.8397	0.5801
42	0.4208	0.8416	0.5792
43	0.3779	0.7558	0.6221
44	0.4033	0.8067	0.5967
45	0.4268	0.8537	0.5732
46	0.5704	0.8592	0.4296
47	0.5714	0.8572	0.4286
48	0.5964	0.8072	0.4036
49	0.5672	0.8655	0.4328
50	0.725	0.5499	0.275
51	0.6951	0.6098	0.3049
52	0.6632	0.6735	0.3368
53	0.6242	0.7516	0.3758
54	0.5836	0.8327	0.4164
55	0.542	0.9159	0.458
56	0.4998	0.9997	0.5002
57	0.5097	0.9806	0.4903
58	0.4751	0.9501	0.5249
59	0.4401	0.8801	0.5599
60	0.405	0.8101	0.595
61	0.3704	0.7407	0.6296
62	0.3315	0.6629	0.6685
63	0.395	0.79	0.605
64	0.3592	0.7185	0.6408
65	0.3224	0.6449	0.6776
66	0.2872	0.5743	0.7128
67	0.2558	0.5115	0.7442
68	0.2261	0.4522	0.7739
69	0.3382	0.6763	0.6618
70	0.3032	0.6065	0.6968
71	0.2699	0.5399	0.7301
72	0.2386	0.4771	0.7614
73	0.2551	0.5102	0.7449
74	0.2249	0.4499	0.7751
75	0.1968	0.3937	0.8032
76	0.2116	0.4232	0.7884
77	0.2265	0.453	0.7735
78	0.3155	0.6309	0.6845
79	0.3479	0.6958	0.6521
80	0.3113	0.6226	0.6887
81	0.3289	0.6578	0.6711
82	0.3472	0.6943	0.6528
83	0.3849	0.7698	0.6151
84	0.4054	0.8108	0.5946
85	0.3691	0.7382	0.6309
86	0.3337	0.6674	0.6663
87	0.7674	0.4652	0.2326
88	0.733	0.534	0.267
89	0.7689	0.4622	0.2311
90	0.805	0.39	0.195
91	0.8227	0.3545	0.1773
92	0.7959	0.4081	0.2041
93	0.7667	0.4665	0.2333
94	0.7352	0.5296	0.2648
95	0.7577	0.4846	0.2423
96	0.7229	0.5541	0.2771
97	0.7479	0.5041	0.2521
98	0.7145	0.5709	0.2855
99	0.6766	0.6468	0.3234
100	0.6365	0.7269	0.3635
101	0.6671	0.6658	0.3329
102	0.6288	0.7424	0.3712
103	0.6626	0.6749	0.3374
104	0.6236	0.7528	0.3764
105	0.583	0.8339	0.417
106	0.5391	0.9218	0.4609
107	0.6262	0.7476	0.3738
108	0.5822	0.8357	0.4178
109	0.6526	0.6948	0.3474
110	0.702	0.5961	0.298
111	0.66	0.68	0.34
112	0.6185	0.7629	0.3815
113	0.6607	0.6785	0.3393
114	0.6185	0.7629	0.3815
115	0.5745	0.8509	0.4255
116	0.5293	0.9414	0.4707
117	0.4809	0.9617	0.5191
118	0.4349	0.8697	0.5651
119	0.3894	0.7788	0.6106
120	0.4519	0.9039	0.5481
121	0.4051	0.8102	0.5949
122	0.3593	0.7185	0.6407
123	0.432	0.8639	0.568
124	0.3842	0.7683	0.6158
125	0.3377	0.6754	0.6623
126	0.445	0.89	0.555
127	0.3953	0.7905	0.6047
128	0.3456	0.6913	0.6544
129	0.2993	0.5985	0.7007
130	0.3488	0.6976	0.6512
131	0.301	0.6019	0.699
132	0.4197	0.8394	0.5803
133	0.4528	0.9057	0.5472
134	0.3981	0.7961	0.6019
135	0.4542	0.9084	0.5458
136	0.5575	0.885	0.4425
137	0.498	0.996	0.502
138	0.4378	0.8756	0.5622
139	0.3783	0.7566	0.6217
140	0.5061	0.9878	0.4939
141	0.5724	0.8553	0.4276
142	0.5067	0.9867	0.4934
143	0.5932	0.8137	0.4068
144	0.5239	0.9522	0.4761
145	0.4526	0.9052	0.5474
146	0.542	0.9159	0.458
147	0.4662	0.9323	0.5338
148	0.3899	0.7798	0.6101
149	0.316	0.6319	0.684
150	0.4495	0.8991	0.5505
151	0.3676	0.7352	0.6324
152	0.4569	0.9139	0.5431
153	0.6509	0.6983	0.3491
154	0.5488	0.9024	0.4512
155	0.6764	0.6473	0.3236
156	0.5597	0.8806	0.4403
157	0.8976	0.2048	0.1024
158	0.8736	0.2529	0.1264
159	0.7458	0.5084	0.2542

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 &  0.8221 &  0.3557 &  0.1779 \tabularnewline
6 &  0.8578 &  0.2843 &  0.1422 \tabularnewline
7 &  0.8517 &  0.2965 &  0.1483 \tabularnewline
8 &  0.8519 &  0.2962 &  0.1481 \tabularnewline
9 &  0.8362 &  0.3277 &  0.1638 \tabularnewline
10 &  0.7824 &  0.4353 &  0.2176 \tabularnewline
11 &  0.7282 &  0.5437 &  0.2718 \tabularnewline
12 &  0.666 &  0.6681 &  0.334 \tabularnewline
13 &  0.5979 &  0.8042 &  0.4021 \tabularnewline
14 &  0.5693 &  0.8614 &  0.4307 \tabularnewline
15 &  0.5373 &  0.9255 &  0.4627 \tabularnewline
16 &  0.4698 &  0.9397 &  0.5302 \tabularnewline
17 &  0.4425 &  0.885 &  0.5575 \tabularnewline
18 &  0.4197 &  0.8393 &  0.5803 \tabularnewline
19 &  0.3583 &  0.7165 &  0.6417 \tabularnewline
20 &  0.3003 &  0.6005 &  0.6997 \tabularnewline
21 &  0.247 &  0.4941 &  0.753 \tabularnewline
22 &  0.2286 &  0.4572 &  0.7714 \tabularnewline
23 &  0.2641 &  0.5281 &  0.7359 \tabularnewline
24 &  0.2528 &  0.5055 &  0.7472 \tabularnewline
25 &  0.2101 &  0.4202 &  0.7899 \tabularnewline
26 &  0.1945 &  0.389 &  0.8055 \tabularnewline
27 &  0.2197 &  0.4394 &  0.7803 \tabularnewline
28 &  0.2385 &  0.477 &  0.7615 \tabularnewline
29 &  0.4742 &  0.9484 &  0.5258 \tabularnewline
30 &  0.4878 &  0.9757 &  0.5122 \tabularnewline
31 &  0.446 &  0.8919 &  0.554 \tabularnewline
32 &  0.4559 &  0.9118 &  0.5441 \tabularnewline
33 &  0.4146 &  0.8292 &  0.5854 \tabularnewline
34 &  0.4231 &  0.8461 &  0.5769 \tabularnewline
35 &  0.3851 &  0.7703 &  0.6149 \tabularnewline
36 &  0.3471 &  0.6943 &  0.6529 \tabularnewline
37 &  0.3097 &  0.6194 &  0.6903 \tabularnewline
38 &  0.3165 &  0.633 &  0.6835 \tabularnewline
39 &  0.2805 &  0.561 &  0.7195 \tabularnewline
40 &  0.2963 &  0.5927 &  0.7037 \tabularnewline
41 &  0.4199 &  0.8397 &  0.5801 \tabularnewline
42 &  0.4208 &  0.8416 &  0.5792 \tabularnewline
43 &  0.3779 &  0.7558 &  0.6221 \tabularnewline
44 &  0.4033 &  0.8067 &  0.5967 \tabularnewline
45 &  0.4268 &  0.8537 &  0.5732 \tabularnewline
46 &  0.5704 &  0.8592 &  0.4296 \tabularnewline
47 &  0.5714 &  0.8572 &  0.4286 \tabularnewline
48 &  0.5964 &  0.8072 &  0.4036 \tabularnewline
49 &  0.5672 &  0.8655 &  0.4328 \tabularnewline
50 &  0.725 &  0.5499 &  0.275 \tabularnewline
51 &  0.6951 &  0.6098 &  0.3049 \tabularnewline
52 &  0.6632 &  0.6735 &  0.3368 \tabularnewline
53 &  0.6242 &  0.7516 &  0.3758 \tabularnewline
54 &  0.5836 &  0.8327 &  0.4164 \tabularnewline
55 &  0.542 &  0.9159 &  0.458 \tabularnewline
56 &  0.4998 &  0.9997 &  0.5002 \tabularnewline
57 &  0.5097 &  0.9806 &  0.4903 \tabularnewline
58 &  0.4751 &  0.9501 &  0.5249 \tabularnewline
59 &  0.4401 &  0.8801 &  0.5599 \tabularnewline
60 &  0.405 &  0.8101 &  0.595 \tabularnewline
61 &  0.3704 &  0.7407 &  0.6296 \tabularnewline
62 &  0.3315 &  0.6629 &  0.6685 \tabularnewline
63 &  0.395 &  0.79 &  0.605 \tabularnewline
64 &  0.3592 &  0.7185 &  0.6408 \tabularnewline
65 &  0.3224 &  0.6449 &  0.6776 \tabularnewline
66 &  0.2872 &  0.5743 &  0.7128 \tabularnewline
67 &  0.2558 &  0.5115 &  0.7442 \tabularnewline
68 &  0.2261 &  0.4522 &  0.7739 \tabularnewline
69 &  0.3382 &  0.6763 &  0.6618 \tabularnewline
70 &  0.3032 &  0.6065 &  0.6968 \tabularnewline
71 &  0.2699 &  0.5399 &  0.7301 \tabularnewline
72 &  0.2386 &  0.4771 &  0.7614 \tabularnewline
73 &  0.2551 &  0.5102 &  0.7449 \tabularnewline
74 &  0.2249 &  0.4499 &  0.7751 \tabularnewline
75 &  0.1968 &  0.3937 &  0.8032 \tabularnewline
76 &  0.2116 &  0.4232 &  0.7884 \tabularnewline
77 &  0.2265 &  0.453 &  0.7735 \tabularnewline
78 &  0.3155 &  0.6309 &  0.6845 \tabularnewline
79 &  0.3479 &  0.6958 &  0.6521 \tabularnewline
80 &  0.3113 &  0.6226 &  0.6887 \tabularnewline
81 &  0.3289 &  0.6578 &  0.6711 \tabularnewline
82 &  0.3472 &  0.6943 &  0.6528 \tabularnewline
83 &  0.3849 &  0.7698 &  0.6151 \tabularnewline
84 &  0.4054 &  0.8108 &  0.5946 \tabularnewline
85 &  0.3691 &  0.7382 &  0.6309 \tabularnewline
86 &  0.3337 &  0.6674 &  0.6663 \tabularnewline
87 &  0.7674 &  0.4652 &  0.2326 \tabularnewline
88 &  0.733 &  0.534 &  0.267 \tabularnewline
89 &  0.7689 &  0.4622 &  0.2311 \tabularnewline
90 &  0.805 &  0.39 &  0.195 \tabularnewline
91 &  0.8227 &  0.3545 &  0.1773 \tabularnewline
92 &  0.7959 &  0.4081 &  0.2041 \tabularnewline
93 &  0.7667 &  0.4665 &  0.2333 \tabularnewline
94 &  0.7352 &  0.5296 &  0.2648 \tabularnewline
95 &  0.7577 &  0.4846 &  0.2423 \tabularnewline
96 &  0.7229 &  0.5541 &  0.2771 \tabularnewline
97 &  0.7479 &  0.5041 &  0.2521 \tabularnewline
98 &  0.7145 &  0.5709 &  0.2855 \tabularnewline
99 &  0.6766 &  0.6468 &  0.3234 \tabularnewline
100 &  0.6365 &  0.7269 &  0.3635 \tabularnewline
101 &  0.6671 &  0.6658 &  0.3329 \tabularnewline
102 &  0.6288 &  0.7424 &  0.3712 \tabularnewline
103 &  0.6626 &  0.6749 &  0.3374 \tabularnewline
104 &  0.6236 &  0.7528 &  0.3764 \tabularnewline
105 &  0.583 &  0.8339 &  0.417 \tabularnewline
106 &  0.5391 &  0.9218 &  0.4609 \tabularnewline
107 &  0.6262 &  0.7476 &  0.3738 \tabularnewline
108 &  0.5822 &  0.8357 &  0.4178 \tabularnewline
109 &  0.6526 &  0.6948 &  0.3474 \tabularnewline
110 &  0.702 &  0.5961 &  0.298 \tabularnewline
111 &  0.66 &  0.68 &  0.34 \tabularnewline
112 &  0.6185 &  0.7629 &  0.3815 \tabularnewline
113 &  0.6607 &  0.6785 &  0.3393 \tabularnewline
114 &  0.6185 &  0.7629 &  0.3815 \tabularnewline
115 &  0.5745 &  0.8509 &  0.4255 \tabularnewline
116 &  0.5293 &  0.9414 &  0.4707 \tabularnewline
117 &  0.4809 &  0.9617 &  0.5191 \tabularnewline
118 &  0.4349 &  0.8697 &  0.5651 \tabularnewline
119 &  0.3894 &  0.7788 &  0.6106 \tabularnewline
120 &  0.4519 &  0.9039 &  0.5481 \tabularnewline
121 &  0.4051 &  0.8102 &  0.5949 \tabularnewline
122 &  0.3593 &  0.7185 &  0.6407 \tabularnewline
123 &  0.432 &  0.8639 &  0.568 \tabularnewline
124 &  0.3842 &  0.7683 &  0.6158 \tabularnewline
125 &  0.3377 &  0.6754 &  0.6623 \tabularnewline
126 &  0.445 &  0.89 &  0.555 \tabularnewline
127 &  0.3953 &  0.7905 &  0.6047 \tabularnewline
128 &  0.3456 &  0.6913 &  0.6544 \tabularnewline
129 &  0.2993 &  0.5985 &  0.7007 \tabularnewline
130 &  0.3488 &  0.6976 &  0.6512 \tabularnewline
131 &  0.301 &  0.6019 &  0.699 \tabularnewline
132 &  0.4197 &  0.8394 &  0.5803 \tabularnewline
133 &  0.4528 &  0.9057 &  0.5472 \tabularnewline
134 &  0.3981 &  0.7961 &  0.6019 \tabularnewline
135 &  0.4542 &  0.9084 &  0.5458 \tabularnewline
136 &  0.5575 &  0.885 &  0.4425 \tabularnewline
137 &  0.498 &  0.996 &  0.502 \tabularnewline
138 &  0.4378 &  0.8756 &  0.5622 \tabularnewline
139 &  0.3783 &  0.7566 &  0.6217 \tabularnewline
140 &  0.5061 &  0.9878 &  0.4939 \tabularnewline
141 &  0.5724 &  0.8553 &  0.4276 \tabularnewline
142 &  0.5067 &  0.9867 &  0.4934 \tabularnewline
143 &  0.5932 &  0.8137 &  0.4068 \tabularnewline
144 &  0.5239 &  0.9522 &  0.4761 \tabularnewline
145 &  0.4526 &  0.9052 &  0.5474 \tabularnewline
146 &  0.542 &  0.9159 &  0.458 \tabularnewline
147 &  0.4662 &  0.9323 &  0.5338 \tabularnewline
148 &  0.3899 &  0.7798 &  0.6101 \tabularnewline
149 &  0.316 &  0.6319 &  0.684 \tabularnewline
150 &  0.4495 &  0.8991 &  0.5505 \tabularnewline
151 &  0.3676 &  0.7352 &  0.6324 \tabularnewline
152 &  0.4569 &  0.9139 &  0.5431 \tabularnewline
153 &  0.6509 &  0.6983 &  0.3491 \tabularnewline
154 &  0.5488 &  0.9024 &  0.4512 \tabularnewline
155 &  0.6764 &  0.6473 &  0.3236 \tabularnewline
156 &  0.5597 &  0.8806 &  0.4403 \tabularnewline
157 &  0.8976 &  0.2048 &  0.1024 \tabularnewline
158 &  0.8736 &  0.2529 &  0.1264 \tabularnewline
159 &  0.7458 &  0.5084 &  0.2542 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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] 0.8221[/C][C] 0.3557[/C][C] 0.1779[/C][/ROW]
[ROW][C]6[/C][C] 0.8578[/C][C] 0.2843[/C][C] 0.1422[/C][/ROW]
[ROW][C]7[/C][C] 0.8517[/C][C] 0.2965[/C][C] 0.1483[/C][/ROW]
[ROW][C]8[/C][C] 0.8519[/C][C] 0.2962[/C][C] 0.1481[/C][/ROW]
[ROW][C]9[/C][C] 0.8362[/C][C] 0.3277[/C][C] 0.1638[/C][/ROW]
[ROW][C]10[/C][C] 0.7824[/C][C] 0.4353[/C][C] 0.2176[/C][/ROW]
[ROW][C]11[/C][C] 0.7282[/C][C] 0.5437[/C][C] 0.2718[/C][/ROW]
[ROW][C]12[/C][C] 0.666[/C][C] 0.6681[/C][C] 0.334[/C][/ROW]
[ROW][C]13[/C][C] 0.5979[/C][C] 0.8042[/C][C] 0.4021[/C][/ROW]
[ROW][C]14[/C][C] 0.5693[/C][C] 0.8614[/C][C] 0.4307[/C][/ROW]
[ROW][C]15[/C][C] 0.5373[/C][C] 0.9255[/C][C] 0.4627[/C][/ROW]
[ROW][C]16[/C][C] 0.4698[/C][C] 0.9397[/C][C] 0.5302[/C][/ROW]
[ROW][C]17[/C][C] 0.4425[/C][C] 0.885[/C][C] 0.5575[/C][/ROW]
[ROW][C]18[/C][C] 0.4197[/C][C] 0.8393[/C][C] 0.5803[/C][/ROW]
[ROW][C]19[/C][C] 0.3583[/C][C] 0.7165[/C][C] 0.6417[/C][/ROW]
[ROW][C]20[/C][C] 0.3003[/C][C] 0.6005[/C][C] 0.6997[/C][/ROW]
[ROW][C]21[/C][C] 0.247[/C][C] 0.4941[/C][C] 0.753[/C][/ROW]
[ROW][C]22[/C][C] 0.2286[/C][C] 0.4572[/C][C] 0.7714[/C][/ROW]
[ROW][C]23[/C][C] 0.2641[/C][C] 0.5281[/C][C] 0.7359[/C][/ROW]
[ROW][C]24[/C][C] 0.2528[/C][C] 0.5055[/C][C] 0.7472[/C][/ROW]
[ROW][C]25[/C][C] 0.2101[/C][C] 0.4202[/C][C] 0.7899[/C][/ROW]
[ROW][C]26[/C][C] 0.1945[/C][C] 0.389[/C][C] 0.8055[/C][/ROW]
[ROW][C]27[/C][C] 0.2197[/C][C] 0.4394[/C][C] 0.7803[/C][/ROW]
[ROW][C]28[/C][C] 0.2385[/C][C] 0.477[/C][C] 0.7615[/C][/ROW]
[ROW][C]29[/C][C] 0.4742[/C][C] 0.9484[/C][C] 0.5258[/C][/ROW]
[ROW][C]30[/C][C] 0.4878[/C][C] 0.9757[/C][C] 0.5122[/C][/ROW]
[ROW][C]31[/C][C] 0.446[/C][C] 0.8919[/C][C] 0.554[/C][/ROW]
[ROW][C]32[/C][C] 0.4559[/C][C] 0.9118[/C][C] 0.5441[/C][/ROW]
[ROW][C]33[/C][C] 0.4146[/C][C] 0.8292[/C][C] 0.5854[/C][/ROW]
[ROW][C]34[/C][C] 0.4231[/C][C] 0.8461[/C][C] 0.5769[/C][/ROW]
[ROW][C]35[/C][C] 0.3851[/C][C] 0.7703[/C][C] 0.6149[/C][/ROW]
[ROW][C]36[/C][C] 0.3471[/C][C] 0.6943[/C][C] 0.6529[/C][/ROW]
[ROW][C]37[/C][C] 0.3097[/C][C] 0.6194[/C][C] 0.6903[/C][/ROW]
[ROW][C]38[/C][C] 0.3165[/C][C] 0.633[/C][C] 0.6835[/C][/ROW]
[ROW][C]39[/C][C] 0.2805[/C][C] 0.561[/C][C] 0.7195[/C][/ROW]
[ROW][C]40[/C][C] 0.2963[/C][C] 0.5927[/C][C] 0.7037[/C][/ROW]
[ROW][C]41[/C][C] 0.4199[/C][C] 0.8397[/C][C] 0.5801[/C][/ROW]
[ROW][C]42[/C][C] 0.4208[/C][C] 0.8416[/C][C] 0.5792[/C][/ROW]
[ROW][C]43[/C][C] 0.3779[/C][C] 0.7558[/C][C] 0.6221[/C][/ROW]
[ROW][C]44[/C][C] 0.4033[/C][C] 0.8067[/C][C] 0.5967[/C][/ROW]
[ROW][C]45[/C][C] 0.4268[/C][C] 0.8537[/C][C] 0.5732[/C][/ROW]
[ROW][C]46[/C][C] 0.5704[/C][C] 0.8592[/C][C] 0.4296[/C][/ROW]
[ROW][C]47[/C][C] 0.5714[/C][C] 0.8572[/C][C] 0.4286[/C][/ROW]
[ROW][C]48[/C][C] 0.5964[/C][C] 0.8072[/C][C] 0.4036[/C][/ROW]
[ROW][C]49[/C][C] 0.5672[/C][C] 0.8655[/C][C] 0.4328[/C][/ROW]
[ROW][C]50[/C][C] 0.725[/C][C] 0.5499[/C][C] 0.275[/C][/ROW]
[ROW][C]51[/C][C] 0.6951[/C][C] 0.6098[/C][C] 0.3049[/C][/ROW]
[ROW][C]52[/C][C] 0.6632[/C][C] 0.6735[/C][C] 0.3368[/C][/ROW]
[ROW][C]53[/C][C] 0.6242[/C][C] 0.7516[/C][C] 0.3758[/C][/ROW]
[ROW][C]54[/C][C] 0.5836[/C][C] 0.8327[/C][C] 0.4164[/C][/ROW]
[ROW][C]55[/C][C] 0.542[/C][C] 0.9159[/C][C] 0.458[/C][/ROW]
[ROW][C]56[/C][C] 0.4998[/C][C] 0.9997[/C][C] 0.5002[/C][/ROW]
[ROW][C]57[/C][C] 0.5097[/C][C] 0.9806[/C][C] 0.4903[/C][/ROW]
[ROW][C]58[/C][C] 0.4751[/C][C] 0.9501[/C][C] 0.5249[/C][/ROW]
[ROW][C]59[/C][C] 0.4401[/C][C] 0.8801[/C][C] 0.5599[/C][/ROW]
[ROW][C]60[/C][C] 0.405[/C][C] 0.8101[/C][C] 0.595[/C][/ROW]
[ROW][C]61[/C][C] 0.3704[/C][C] 0.7407[/C][C] 0.6296[/C][/ROW]
[ROW][C]62[/C][C] 0.3315[/C][C] 0.6629[/C][C] 0.6685[/C][/ROW]
[ROW][C]63[/C][C] 0.395[/C][C] 0.79[/C][C] 0.605[/C][/ROW]
[ROW][C]64[/C][C] 0.3592[/C][C] 0.7185[/C][C] 0.6408[/C][/ROW]
[ROW][C]65[/C][C] 0.3224[/C][C] 0.6449[/C][C] 0.6776[/C][/ROW]
[ROW][C]66[/C][C] 0.2872[/C][C] 0.5743[/C][C] 0.7128[/C][/ROW]
[ROW][C]67[/C][C] 0.2558[/C][C] 0.5115[/C][C] 0.7442[/C][/ROW]
[ROW][C]68[/C][C] 0.2261[/C][C] 0.4522[/C][C] 0.7739[/C][/ROW]
[ROW][C]69[/C][C] 0.3382[/C][C] 0.6763[/C][C] 0.6618[/C][/ROW]
[ROW][C]70[/C][C] 0.3032[/C][C] 0.6065[/C][C] 0.6968[/C][/ROW]
[ROW][C]71[/C][C] 0.2699[/C][C] 0.5399[/C][C] 0.7301[/C][/ROW]
[ROW][C]72[/C][C] 0.2386[/C][C] 0.4771[/C][C] 0.7614[/C][/ROW]
[ROW][C]73[/C][C] 0.2551[/C][C] 0.5102[/C][C] 0.7449[/C][/ROW]
[ROW][C]74[/C][C] 0.2249[/C][C] 0.4499[/C][C] 0.7751[/C][/ROW]
[ROW][C]75[/C][C] 0.1968[/C][C] 0.3937[/C][C] 0.8032[/C][/ROW]
[ROW][C]76[/C][C] 0.2116[/C][C] 0.4232[/C][C] 0.7884[/C][/ROW]
[ROW][C]77[/C][C] 0.2265[/C][C] 0.453[/C][C] 0.7735[/C][/ROW]
[ROW][C]78[/C][C] 0.3155[/C][C] 0.6309[/C][C] 0.6845[/C][/ROW]
[ROW][C]79[/C][C] 0.3479[/C][C] 0.6958[/C][C] 0.6521[/C][/ROW]
[ROW][C]80[/C][C] 0.3113[/C][C] 0.6226[/C][C] 0.6887[/C][/ROW]
[ROW][C]81[/C][C] 0.3289[/C][C] 0.6578[/C][C] 0.6711[/C][/ROW]
[ROW][C]82[/C][C] 0.3472[/C][C] 0.6943[/C][C] 0.6528[/C][/ROW]
[ROW][C]83[/C][C] 0.3849[/C][C] 0.7698[/C][C] 0.6151[/C][/ROW]
[ROW][C]84[/C][C] 0.4054[/C][C] 0.8108[/C][C] 0.5946[/C][/ROW]
[ROW][C]85[/C][C] 0.3691[/C][C] 0.7382[/C][C] 0.6309[/C][/ROW]
[ROW][C]86[/C][C] 0.3337[/C][C] 0.6674[/C][C] 0.6663[/C][/ROW]
[ROW][C]87[/C][C] 0.7674[/C][C] 0.4652[/C][C] 0.2326[/C][/ROW]
[ROW][C]88[/C][C] 0.733[/C][C] 0.534[/C][C] 0.267[/C][/ROW]
[ROW][C]89[/C][C] 0.7689[/C][C] 0.4622[/C][C] 0.2311[/C][/ROW]
[ROW][C]90[/C][C] 0.805[/C][C] 0.39[/C][C] 0.195[/C][/ROW]
[ROW][C]91[/C][C] 0.8227[/C][C] 0.3545[/C][C] 0.1773[/C][/ROW]
[ROW][C]92[/C][C] 0.7959[/C][C] 0.4081[/C][C] 0.2041[/C][/ROW]
[ROW][C]93[/C][C] 0.7667[/C][C] 0.4665[/C][C] 0.2333[/C][/ROW]
[ROW][C]94[/C][C] 0.7352[/C][C] 0.5296[/C][C] 0.2648[/C][/ROW]
[ROW][C]95[/C][C] 0.7577[/C][C] 0.4846[/C][C] 0.2423[/C][/ROW]
[ROW][C]96[/C][C] 0.7229[/C][C] 0.5541[/C][C] 0.2771[/C][/ROW]
[ROW][C]97[/C][C] 0.7479[/C][C] 0.5041[/C][C] 0.2521[/C][/ROW]
[ROW][C]98[/C][C] 0.7145[/C][C] 0.5709[/C][C] 0.2855[/C][/ROW]
[ROW][C]99[/C][C] 0.6766[/C][C] 0.6468[/C][C] 0.3234[/C][/ROW]
[ROW][C]100[/C][C] 0.6365[/C][C] 0.7269[/C][C] 0.3635[/C][/ROW]
[ROW][C]101[/C][C] 0.6671[/C][C] 0.6658[/C][C] 0.3329[/C][/ROW]
[ROW][C]102[/C][C] 0.6288[/C][C] 0.7424[/C][C] 0.3712[/C][/ROW]
[ROW][C]103[/C][C] 0.6626[/C][C] 0.6749[/C][C] 0.3374[/C][/ROW]
[ROW][C]104[/C][C] 0.6236[/C][C] 0.7528[/C][C] 0.3764[/C][/ROW]
[ROW][C]105[/C][C] 0.583[/C][C] 0.8339[/C][C] 0.417[/C][/ROW]
[ROW][C]106[/C][C] 0.5391[/C][C] 0.9218[/C][C] 0.4609[/C][/ROW]
[ROW][C]107[/C][C] 0.6262[/C][C] 0.7476[/C][C] 0.3738[/C][/ROW]
[ROW][C]108[/C][C] 0.5822[/C][C] 0.8357[/C][C] 0.4178[/C][/ROW]
[ROW][C]109[/C][C] 0.6526[/C][C] 0.6948[/C][C] 0.3474[/C][/ROW]
[ROW][C]110[/C][C] 0.702[/C][C] 0.5961[/C][C] 0.298[/C][/ROW]
[ROW][C]111[/C][C] 0.66[/C][C] 0.68[/C][C] 0.34[/C][/ROW]
[ROW][C]112[/C][C] 0.6185[/C][C] 0.7629[/C][C] 0.3815[/C][/ROW]
[ROW][C]113[/C][C] 0.6607[/C][C] 0.6785[/C][C] 0.3393[/C][/ROW]
[ROW][C]114[/C][C] 0.6185[/C][C] 0.7629[/C][C] 0.3815[/C][/ROW]
[ROW][C]115[/C][C] 0.5745[/C][C] 0.8509[/C][C] 0.4255[/C][/ROW]
[ROW][C]116[/C][C] 0.5293[/C][C] 0.9414[/C][C] 0.4707[/C][/ROW]
[ROW][C]117[/C][C] 0.4809[/C][C] 0.9617[/C][C] 0.5191[/C][/ROW]
[ROW][C]118[/C][C] 0.4349[/C][C] 0.8697[/C][C] 0.5651[/C][/ROW]
[ROW][C]119[/C][C] 0.3894[/C][C] 0.7788[/C][C] 0.6106[/C][/ROW]
[ROW][C]120[/C][C] 0.4519[/C][C] 0.9039[/C][C] 0.5481[/C][/ROW]
[ROW][C]121[/C][C] 0.4051[/C][C] 0.8102[/C][C] 0.5949[/C][/ROW]
[ROW][C]122[/C][C] 0.3593[/C][C] 0.7185[/C][C] 0.6407[/C][/ROW]
[ROW][C]123[/C][C] 0.432[/C][C] 0.8639[/C][C] 0.568[/C][/ROW]
[ROW][C]124[/C][C] 0.3842[/C][C] 0.7683[/C][C] 0.6158[/C][/ROW]
[ROW][C]125[/C][C] 0.3377[/C][C] 0.6754[/C][C] 0.6623[/C][/ROW]
[ROW][C]126[/C][C] 0.445[/C][C] 0.89[/C][C] 0.555[/C][/ROW]
[ROW][C]127[/C][C] 0.3953[/C][C] 0.7905[/C][C] 0.6047[/C][/ROW]
[ROW][C]128[/C][C] 0.3456[/C][C] 0.6913[/C][C] 0.6544[/C][/ROW]
[ROW][C]129[/C][C] 0.2993[/C][C] 0.5985[/C][C] 0.7007[/C][/ROW]
[ROW][C]130[/C][C] 0.3488[/C][C] 0.6976[/C][C] 0.6512[/C][/ROW]
[ROW][C]131[/C][C] 0.301[/C][C] 0.6019[/C][C] 0.699[/C][/ROW]
[ROW][C]132[/C][C] 0.4197[/C][C] 0.8394[/C][C] 0.5803[/C][/ROW]
[ROW][C]133[/C][C] 0.4528[/C][C] 0.9057[/C][C] 0.5472[/C][/ROW]
[ROW][C]134[/C][C] 0.3981[/C][C] 0.7961[/C][C] 0.6019[/C][/ROW]
[ROW][C]135[/C][C] 0.4542[/C][C] 0.9084[/C][C] 0.5458[/C][/ROW]
[ROW][C]136[/C][C] 0.5575[/C][C] 0.885[/C][C] 0.4425[/C][/ROW]
[ROW][C]137[/C][C] 0.498[/C][C] 0.996[/C][C] 0.502[/C][/ROW]
[ROW][C]138[/C][C] 0.4378[/C][C] 0.8756[/C][C] 0.5622[/C][/ROW]
[ROW][C]139[/C][C] 0.3783[/C][C] 0.7566[/C][C] 0.6217[/C][/ROW]
[ROW][C]140[/C][C] 0.5061[/C][C] 0.9878[/C][C] 0.4939[/C][/ROW]
[ROW][C]141[/C][C] 0.5724[/C][C] 0.8553[/C][C] 0.4276[/C][/ROW]
[ROW][C]142[/C][C] 0.5067[/C][C] 0.9867[/C][C] 0.4934[/C][/ROW]
[ROW][C]143[/C][C] 0.5932[/C][C] 0.8137[/C][C] 0.4068[/C][/ROW]
[ROW][C]144[/C][C] 0.5239[/C][C] 0.9522[/C][C] 0.4761[/C][/ROW]
[ROW][C]145[/C][C] 0.4526[/C][C] 0.9052[/C][C] 0.5474[/C][/ROW]
[ROW][C]146[/C][C] 0.542[/C][C] 0.9159[/C][C] 0.458[/C][/ROW]
[ROW][C]147[/C][C] 0.4662[/C][C] 0.9323[/C][C] 0.5338[/C][/ROW]
[ROW][C]148[/C][C] 0.3899[/C][C] 0.7798[/C][C] 0.6101[/C][/ROW]
[ROW][C]149[/C][C] 0.316[/C][C] 0.6319[/C][C] 0.684[/C][/ROW]
[ROW][C]150[/C][C] 0.4495[/C][C] 0.8991[/C][C] 0.5505[/C][/ROW]
[ROW][C]151[/C][C] 0.3676[/C][C] 0.7352[/C][C] 0.6324[/C][/ROW]
[ROW][C]152[/C][C] 0.4569[/C][C] 0.9139[/C][C] 0.5431[/C][/ROW]
[ROW][C]153[/C][C] 0.6509[/C][C] 0.6983[/C][C] 0.3491[/C][/ROW]
[ROW][C]154[/C][C] 0.5488[/C][C] 0.9024[/C][C] 0.4512[/C][/ROW]
[ROW][C]155[/C][C] 0.6764[/C][C] 0.6473[/C][C] 0.3236[/C][/ROW]
[ROW][C]156[/C][C] 0.5597[/C][C] 0.8806[/C][C] 0.4403[/C][/ROW]
[ROW][C]157[/C][C] 0.8976[/C][C] 0.2048[/C][C] 0.1024[/C][/ROW]
[ROW][C]158[/C][C] 0.8736[/C][C] 0.2529[/C][C] 0.1264[/C][/ROW]
[ROW][C]159[/C][C] 0.7458[/C][C] 0.5084[/C][C] 0.2542[/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:

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.8221	0.3557	0.1779
6	0.8578	0.2843	0.1422
7	0.8517	0.2965	0.1483
8	0.8519	0.2962	0.1481
9	0.8362	0.3277	0.1638
10	0.7824	0.4353	0.2176
11	0.7282	0.5437	0.2718
12	0.666	0.6681	0.334
13	0.5979	0.8042	0.4021
14	0.5693	0.8614	0.4307
15	0.5373	0.9255	0.4627
16	0.4698	0.9397	0.5302
17	0.4425	0.885	0.5575
18	0.4197	0.8393	0.5803
19	0.3583	0.7165	0.6417
20	0.3003	0.6005	0.6997
21	0.247	0.4941	0.753
22	0.2286	0.4572	0.7714
23	0.2641	0.5281	0.7359
24	0.2528	0.5055	0.7472
25	0.2101	0.4202	0.7899
26	0.1945	0.389	0.8055
27	0.2197	0.4394	0.7803
28	0.2385	0.477	0.7615
29	0.4742	0.9484	0.5258
30	0.4878	0.9757	0.5122
31	0.446	0.8919	0.554
32	0.4559	0.9118	0.5441
33	0.4146	0.8292	0.5854
34	0.4231	0.8461	0.5769
35	0.3851	0.7703	0.6149
36	0.3471	0.6943	0.6529
37	0.3097	0.6194	0.6903
38	0.3165	0.633	0.6835
39	0.2805	0.561	0.7195
40	0.2963	0.5927	0.7037
41	0.4199	0.8397	0.5801
42	0.4208	0.8416	0.5792
43	0.3779	0.7558	0.6221
44	0.4033	0.8067	0.5967
45	0.4268	0.8537	0.5732
46	0.5704	0.8592	0.4296
47	0.5714	0.8572	0.4286
48	0.5964	0.8072	0.4036
49	0.5672	0.8655	0.4328
50	0.725	0.5499	0.275
51	0.6951	0.6098	0.3049
52	0.6632	0.6735	0.3368
53	0.6242	0.7516	0.3758
54	0.5836	0.8327	0.4164
55	0.542	0.9159	0.458
56	0.4998	0.9997	0.5002
57	0.5097	0.9806	0.4903
58	0.4751	0.9501	0.5249
59	0.4401	0.8801	0.5599
60	0.405	0.8101	0.595
61	0.3704	0.7407	0.6296
62	0.3315	0.6629	0.6685
63	0.395	0.79	0.605
64	0.3592	0.7185	0.6408
65	0.3224	0.6449	0.6776
66	0.2872	0.5743	0.7128
67	0.2558	0.5115	0.7442
68	0.2261	0.4522	0.7739
69	0.3382	0.6763	0.6618
70	0.3032	0.6065	0.6968
71	0.2699	0.5399	0.7301
72	0.2386	0.4771	0.7614
73	0.2551	0.5102	0.7449
74	0.2249	0.4499	0.7751
75	0.1968	0.3937	0.8032
76	0.2116	0.4232	0.7884
77	0.2265	0.453	0.7735
78	0.3155	0.6309	0.6845
79	0.3479	0.6958	0.6521
80	0.3113	0.6226	0.6887
81	0.3289	0.6578	0.6711
82	0.3472	0.6943	0.6528
83	0.3849	0.7698	0.6151
84	0.4054	0.8108	0.5946
85	0.3691	0.7382	0.6309
86	0.3337	0.6674	0.6663
87	0.7674	0.4652	0.2326
88	0.733	0.534	0.267
89	0.7689	0.4622	0.2311
90	0.805	0.39	0.195
91	0.8227	0.3545	0.1773
92	0.7959	0.4081	0.2041
93	0.7667	0.4665	0.2333
94	0.7352	0.5296	0.2648
95	0.7577	0.4846	0.2423
96	0.7229	0.5541	0.2771
97	0.7479	0.5041	0.2521
98	0.7145	0.5709	0.2855
99	0.6766	0.6468	0.3234
100	0.6365	0.7269	0.3635
101	0.6671	0.6658	0.3329
102	0.6288	0.7424	0.3712
103	0.6626	0.6749	0.3374
104	0.6236	0.7528	0.3764
105	0.583	0.8339	0.417
106	0.5391	0.9218	0.4609
107	0.6262	0.7476	0.3738
108	0.5822	0.8357	0.4178
109	0.6526	0.6948	0.3474
110	0.702	0.5961	0.298
111	0.66	0.68	0.34
112	0.6185	0.7629	0.3815
113	0.6607	0.6785	0.3393
114	0.6185	0.7629	0.3815
115	0.5745	0.8509	0.4255
116	0.5293	0.9414	0.4707
117	0.4809	0.9617	0.5191
118	0.4349	0.8697	0.5651
119	0.3894	0.7788	0.6106
120	0.4519	0.9039	0.5481
121	0.4051	0.8102	0.5949
122	0.3593	0.7185	0.6407
123	0.432	0.8639	0.568
124	0.3842	0.7683	0.6158
125	0.3377	0.6754	0.6623
126	0.445	0.89	0.555
127	0.3953	0.7905	0.6047
128	0.3456	0.6913	0.6544
129	0.2993	0.5985	0.7007
130	0.3488	0.6976	0.6512
131	0.301	0.6019	0.699
132	0.4197	0.8394	0.5803
133	0.4528	0.9057	0.5472
134	0.3981	0.7961	0.6019
135	0.4542	0.9084	0.5458
136	0.5575	0.885	0.4425
137	0.498	0.996	0.502
138	0.4378	0.8756	0.5622
139	0.3783	0.7566	0.6217
140	0.5061	0.9878	0.4939
141	0.5724	0.8553	0.4276
142	0.5067	0.9867	0.4934
143	0.5932	0.8137	0.4068
144	0.5239	0.9522	0.4761
145	0.4526	0.9052	0.5474
146	0.542	0.9159	0.458
147	0.4662	0.9323	0.5338
148	0.3899	0.7798	0.6101
149	0.316	0.6319	0.684
150	0.4495	0.8991	0.5505
151	0.3676	0.7352	0.6324
152	0.4569	0.9139	0.5431
153	0.6509	0.6983	0.3491
154	0.5488	0.9024	0.4512
155	0.6764	0.6473	0.3236
156	0.5597	0.8806	0.4403
157	0.8976	0.2048	0.1024
158	0.8736	0.2529	0.1264
159	0.7458	0.5084	0.2542

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=6

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

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 = 3.5938, df1 = 2, df2 = 160, p-value = 0.02973
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 3.5938, df1 = 2, df2 = 160, p-value = 0.02973
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 3.5938, df1 = 2, df2 = 160, p-value = 0.02973

\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 = 3.5938, df1 = 2, df2 = 160, p-value = 0.02973
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 3.5938, df1 = 2, df2 = 160, p-value = 0.02973
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 3.5938, df1 = 2, df2 = 160, p-value = 0.02973
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=7

[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 = 3.5938, df1 = 2, df2 = 160, p-value = 0.02973
[/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 = 3.5938, df1 = 2, df2 = 160, p-value = 0.02973
[/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 = 3.5938, df1 = 2, df2 = 160, p-value = 0.02973
[/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:

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 3.5938, df1 = 2, df2 = 160, p-value = 0.02973
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 3.5938, df1 = 2, df2 = 160, p-value = 0.02973
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 3.5938, df1 = 2, df2 = 160, p-value = 0.02973

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

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 <- ''
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 (par5=='') par5 <- 0
par5 <- as.numeric(par5)
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=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(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 - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,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*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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