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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 04 Dec 2017 15:00:32 +0100

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/04/t1512396074cugb0ltvciy95hp.htm/, Retrieved Tue, 14 May 2024 22:06:08 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=308512, Retrieved Tue, 14 May 2024 22:06:08 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

110

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

-       [Multiple Regression] [Dataset 2: Multip...] [2017-12-04 14:00:32] [b4c215a57d838e3992780a1253393c3b] [Current]

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Histogram

Boxplots

100	79	28
7.683	10.726	4.432
102	48	43
219	124	64
149	79	29
277	320	258
272	136	98
224	127	85
194	328	226
113	27	27
45	34	27
165	156	108
196	212	219
197	91	60
87	34	15
289	189	99
142	35	15
159	47	37
127	59	40
59	16	13
142	121	104
297	164	154
189	108	103
82	39	24
123	69	43
230	190	71
106	44	22
160	69	101
208	100	63
120	95	48
94	39	25
112	53	48
163	85	34
160	128	57
374	176	62
333	266	113
1.177	811	395
205	52	28
117	68	23
156	56	25
78	32	14
206	132	53
304	187	63
131	203	81
29	69	60
211	77	54
188	104	63
91	50	19
373	328	134
85	41	20
218	160	87
65	59	21
96	31	51
224	484	257
95	30	19
147	103	37
128	52	40
86	56	24
90	65	30
348	384	220
152	46	20
112	105	44
175	270	198
110	126	46
134	73	34
501	764	244
87	20	12
117	57	46
205	109	94
131	32	27

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	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 time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308512&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]

8 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=308512&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308512&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
geboorte[t] = + 123.94 + 0.386156inwijking[t] -0.153377uitwijking[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
geboorte[t] =  +  123.94 +  0.386156inwijking[t] -0.153377uitwijking[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308512&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]geboorte[t] =  +  123.94 +  0.386156inwijking[t] -0.153377uitwijking[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308512&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308512&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
geboorte[t] = + 123.94 + 0.386156inwijking[t] -0.153377uitwijking[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)	+123.9	13.46	+9.2060e+00	1.641e-13	8.203e-14
inwijking	+0.3862	0.1517	+2.5450e+00	0.01324	0.006621
uitwijking	-0.1534	0.3004	-5.1060e-01	0.6113	0.3056

\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) & +123.9 &  13.46 & +9.2060e+00 &  1.641e-13 &  8.203e-14 \tabularnewline
inwijking & +0.3862 &  0.1517 & +2.5450e+00 &  0.01324 &  0.006621 \tabularnewline
uitwijking & -0.1534 &  0.3004 & -5.1060e-01 &  0.6113 &  0.3056 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308512&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]+123.9[/C][C] 13.46[/C][C]+9.2060e+00[/C][C] 1.641e-13[/C][C] 8.203e-14[/C][/ROW]
[ROW][C]inwijking[/C][C]+0.3862[/C][C] 0.1517[/C][C]+2.5450e+00[/C][C] 0.01324[/C][C] 0.006621[/C][/ROW]
[ROW][C]uitwijking[/C][C]-0.1534[/C][C] 0.3004[/C][C]-5.1060e-01[/C][C] 0.6113[/C][C] 0.3056[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308512&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308512&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)	+123.9	13.46	+9.2060e+00	1.641e-13	8.203e-14
inwijking	+0.3862	0.1517	+2.5450e+00	0.01324	0.006621
uitwijking	-0.1534	0.3004	-5.1060e-01	0.6113	0.3056

Multiple Linear Regression - Regression Statistics
Multiple R	0.5115
R-squared	0.2616
Adjusted R-squared	0.2396
F-TEST (value)	11.87
F-TEST (DF numerator)	2
F-TEST (DF denominator)	67
p-value	3.865e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	79.77
Sum Squared Residuals	4.264e+05

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5115 \tabularnewline
R-squared &  0.2616 \tabularnewline
Adjusted R-squared &  0.2396 \tabularnewline
F-TEST (value) &  11.87 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 67 \tabularnewline
p-value &  3.865e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  79.77 \tabularnewline
Sum Squared Residuals &  4.264e+05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308512&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5115[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.2616[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2396[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 11.87[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]67[/C][/ROW]
[ROW][C]p-value[/C][C] 3.865e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 79.77[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 4.264e+05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308512&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308512&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	0.5115
R-squared	0.2616
Adjusted R-squared	0.2396
F-TEST (value)	11.87
F-TEST (DF numerator)	2
F-TEST (DF denominator)	67
p-value	3.865e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	79.77
Sum Squared Residuals	4.264e+05

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=308512&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=308512&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308512&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	100	150.2	-50.15
2	7.683	127.4	-119.7
3	102	135.9	-33.88
4	219	162	56.99
5	149	150	-0.998
6	277	207.9	69.06
7	272	161.4	110.6
8	224	159.9	64.06
9	194	215.9	-21.94
10	113	130.2	-17.22
11	45	132.9	-87.93
12	165	167.6	-2.615
13	196	172.2	23.78
14	197	149.9	47.12
15	87	134.8	-47.77
16	289	181.7	107.3
17	142	135.2	6.846
18	159	136.4	22.59
19	127	140.6	-13.59
20	59	128.1	-69.12
21	142	154.7	-12.71
22	297	163.6	133.4
23	189	149.8	39.15
24	82	135.3	-53.32
25	123	144	-20.99
26	230	186.4	43.58
27	106	137.6	-31.56
28	160	135.1	24.91
29	208	152.9	55.11
30	120	153.3	-33.26
31	94	135.2	-41.17
32	112	137	-25.04
33	163	151.5	11.45
34	160	164.6	-4.625
35	374	182.4	191.6
36	333	209.3	123.7
37	1.177	376.5	-375.4
38	205	139.7	65.27
39	117	146.7	-29.67
40	156	141.7	14.27
41	78	134.1	-56.15
42	206	166.8	39.22
43	304	186.5	117.5
44	131	189.9	-58.91
45	29	141.4	-112.4
46	211	145.4	65.61
47	188	154.4	33.56
48	91	140.3	-49.33
49	373	230	143
50	85	136.7	-51.7
51	218	172.4	45.62
52	65	143.5	-78.5
53	96	128.1	-32.09
54	224	271.4	-47.42
55	95	132.6	-37.61
56	147	158	-11.04
57	128	137.9	-9.885
58	86	141.9	-55.88
59	90	144.4	-54.44
60	348	238.5	109.5
61	152	138.6	13.36
62	112	157.7	-45.74
63	175	197.8	-22.83
64	110	165.5	-55.54
65	134	146.9	-12.91
66	501	381.5	119.5
67	87	129.8	-42.82
68	117	138.9	-21.9
69	205	151.6	53.39
70	131	132.2	-1.155

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  100 &  150.2 & -50.15 \tabularnewline
2 &  7.683 &  127.4 & -119.7 \tabularnewline
3 &  102 &  135.9 & -33.88 \tabularnewline
4 &  219 &  162 &  56.99 \tabularnewline
5 &  149 &  150 & -0.998 \tabularnewline
6 &  277 &  207.9 &  69.06 \tabularnewline
7 &  272 &  161.4 &  110.6 \tabularnewline
8 &  224 &  159.9 &  64.06 \tabularnewline
9 &  194 &  215.9 & -21.94 \tabularnewline
10 &  113 &  130.2 & -17.22 \tabularnewline
11 &  45 &  132.9 & -87.93 \tabularnewline
12 &  165 &  167.6 & -2.615 \tabularnewline
13 &  196 &  172.2 &  23.78 \tabularnewline
14 &  197 &  149.9 &  47.12 \tabularnewline
15 &  87 &  134.8 & -47.77 \tabularnewline
16 &  289 &  181.7 &  107.3 \tabularnewline
17 &  142 &  135.2 &  6.846 \tabularnewline
18 &  159 &  136.4 &  22.59 \tabularnewline
19 &  127 &  140.6 & -13.59 \tabularnewline
20 &  59 &  128.1 & -69.12 \tabularnewline
21 &  142 &  154.7 & -12.71 \tabularnewline
22 &  297 &  163.6 &  133.4 \tabularnewline
23 &  189 &  149.8 &  39.15 \tabularnewline
24 &  82 &  135.3 & -53.32 \tabularnewline
25 &  123 &  144 & -20.99 \tabularnewline
26 &  230 &  186.4 &  43.58 \tabularnewline
27 &  106 &  137.6 & -31.56 \tabularnewline
28 &  160 &  135.1 &  24.91 \tabularnewline
29 &  208 &  152.9 &  55.11 \tabularnewline
30 &  120 &  153.3 & -33.26 \tabularnewline
31 &  94 &  135.2 & -41.17 \tabularnewline
32 &  112 &  137 & -25.04 \tabularnewline
33 &  163 &  151.5 &  11.45 \tabularnewline
34 &  160 &  164.6 & -4.625 \tabularnewline
35 &  374 &  182.4 &  191.6 \tabularnewline
36 &  333 &  209.3 &  123.7 \tabularnewline
37 &  1.177 &  376.5 & -375.4 \tabularnewline
38 &  205 &  139.7 &  65.27 \tabularnewline
39 &  117 &  146.7 & -29.67 \tabularnewline
40 &  156 &  141.7 &  14.27 \tabularnewline
41 &  78 &  134.1 & -56.15 \tabularnewline
42 &  206 &  166.8 &  39.22 \tabularnewline
43 &  304 &  186.5 &  117.5 \tabularnewline
44 &  131 &  189.9 & -58.91 \tabularnewline
45 &  29 &  141.4 & -112.4 \tabularnewline
46 &  211 &  145.4 &  65.61 \tabularnewline
47 &  188 &  154.4 &  33.56 \tabularnewline
48 &  91 &  140.3 & -49.33 \tabularnewline
49 &  373 &  230 &  143 \tabularnewline
50 &  85 &  136.7 & -51.7 \tabularnewline
51 &  218 &  172.4 &  45.62 \tabularnewline
52 &  65 &  143.5 & -78.5 \tabularnewline
53 &  96 &  128.1 & -32.09 \tabularnewline
54 &  224 &  271.4 & -47.42 \tabularnewline
55 &  95 &  132.6 & -37.61 \tabularnewline
56 &  147 &  158 & -11.04 \tabularnewline
57 &  128 &  137.9 & -9.885 \tabularnewline
58 &  86 &  141.9 & -55.88 \tabularnewline
59 &  90 &  144.4 & -54.44 \tabularnewline
60 &  348 &  238.5 &  109.5 \tabularnewline
61 &  152 &  138.6 &  13.36 \tabularnewline
62 &  112 &  157.7 & -45.74 \tabularnewline
63 &  175 &  197.8 & -22.83 \tabularnewline
64 &  110 &  165.5 & -55.54 \tabularnewline
65 &  134 &  146.9 & -12.91 \tabularnewline
66 &  501 &  381.5 &  119.5 \tabularnewline
67 &  87 &  129.8 & -42.82 \tabularnewline
68 &  117 &  138.9 & -21.9 \tabularnewline
69 &  205 &  151.6 &  53.39 \tabularnewline
70 &  131 &  132.2 & -1.155 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308512&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] 100[/C][C] 150.2[/C][C]-50.15[/C][/ROW]
[ROW][C]2[/C][C] 7.683[/C][C] 127.4[/C][C]-119.7[/C][/ROW]
[ROW][C]3[/C][C] 102[/C][C] 135.9[/C][C]-33.88[/C][/ROW]
[ROW][C]4[/C][C] 219[/C][C] 162[/C][C] 56.99[/C][/ROW]
[ROW][C]5[/C][C] 149[/C][C] 150[/C][C]-0.998[/C][/ROW]
[ROW][C]6[/C][C] 277[/C][C] 207.9[/C][C] 69.06[/C][/ROW]
[ROW][C]7[/C][C] 272[/C][C] 161.4[/C][C] 110.6[/C][/ROW]
[ROW][C]8[/C][C] 224[/C][C] 159.9[/C][C] 64.06[/C][/ROW]
[ROW][C]9[/C][C] 194[/C][C] 215.9[/C][C]-21.94[/C][/ROW]
[ROW][C]10[/C][C] 113[/C][C] 130.2[/C][C]-17.22[/C][/ROW]
[ROW][C]11[/C][C] 45[/C][C] 132.9[/C][C]-87.93[/C][/ROW]
[ROW][C]12[/C][C] 165[/C][C] 167.6[/C][C]-2.615[/C][/ROW]
[ROW][C]13[/C][C] 196[/C][C] 172.2[/C][C] 23.78[/C][/ROW]
[ROW][C]14[/C][C] 197[/C][C] 149.9[/C][C] 47.12[/C][/ROW]
[ROW][C]15[/C][C] 87[/C][C] 134.8[/C][C]-47.77[/C][/ROW]
[ROW][C]16[/C][C] 289[/C][C] 181.7[/C][C] 107.3[/C][/ROW]
[ROW][C]17[/C][C] 142[/C][C] 135.2[/C][C] 6.846[/C][/ROW]
[ROW][C]18[/C][C] 159[/C][C] 136.4[/C][C] 22.59[/C][/ROW]
[ROW][C]19[/C][C] 127[/C][C] 140.6[/C][C]-13.59[/C][/ROW]
[ROW][C]20[/C][C] 59[/C][C] 128.1[/C][C]-69.12[/C][/ROW]
[ROW][C]21[/C][C] 142[/C][C] 154.7[/C][C]-12.71[/C][/ROW]
[ROW][C]22[/C][C] 297[/C][C] 163.6[/C][C] 133.4[/C][/ROW]
[ROW][C]23[/C][C] 189[/C][C] 149.8[/C][C] 39.15[/C][/ROW]
[ROW][C]24[/C][C] 82[/C][C] 135.3[/C][C]-53.32[/C][/ROW]
[ROW][C]25[/C][C] 123[/C][C] 144[/C][C]-20.99[/C][/ROW]
[ROW][C]26[/C][C] 230[/C][C] 186.4[/C][C] 43.58[/C][/ROW]
[ROW][C]27[/C][C] 106[/C][C] 137.6[/C][C]-31.56[/C][/ROW]
[ROW][C]28[/C][C] 160[/C][C] 135.1[/C][C] 24.91[/C][/ROW]
[ROW][C]29[/C][C] 208[/C][C] 152.9[/C][C] 55.11[/C][/ROW]
[ROW][C]30[/C][C] 120[/C][C] 153.3[/C][C]-33.26[/C][/ROW]
[ROW][C]31[/C][C] 94[/C][C] 135.2[/C][C]-41.17[/C][/ROW]
[ROW][C]32[/C][C] 112[/C][C] 137[/C][C]-25.04[/C][/ROW]
[ROW][C]33[/C][C] 163[/C][C] 151.5[/C][C] 11.45[/C][/ROW]
[ROW][C]34[/C][C] 160[/C][C] 164.6[/C][C]-4.625[/C][/ROW]
[ROW][C]35[/C][C] 374[/C][C] 182.4[/C][C] 191.6[/C][/ROW]
[ROW][C]36[/C][C] 333[/C][C] 209.3[/C][C] 123.7[/C][/ROW]
[ROW][C]37[/C][C] 1.177[/C][C] 376.5[/C][C]-375.4[/C][/ROW]
[ROW][C]38[/C][C] 205[/C][C] 139.7[/C][C] 65.27[/C][/ROW]
[ROW][C]39[/C][C] 117[/C][C] 146.7[/C][C]-29.67[/C][/ROW]
[ROW][C]40[/C][C] 156[/C][C] 141.7[/C][C] 14.27[/C][/ROW]
[ROW][C]41[/C][C] 78[/C][C] 134.1[/C][C]-56.15[/C][/ROW]
[ROW][C]42[/C][C] 206[/C][C] 166.8[/C][C] 39.22[/C][/ROW]
[ROW][C]43[/C][C] 304[/C][C] 186.5[/C][C] 117.5[/C][/ROW]
[ROW][C]44[/C][C] 131[/C][C] 189.9[/C][C]-58.91[/C][/ROW]
[ROW][C]45[/C][C] 29[/C][C] 141.4[/C][C]-112.4[/C][/ROW]
[ROW][C]46[/C][C] 211[/C][C] 145.4[/C][C] 65.61[/C][/ROW]
[ROW][C]47[/C][C] 188[/C][C] 154.4[/C][C] 33.56[/C][/ROW]
[ROW][C]48[/C][C] 91[/C][C] 140.3[/C][C]-49.33[/C][/ROW]
[ROW][C]49[/C][C] 373[/C][C] 230[/C][C] 143[/C][/ROW]
[ROW][C]50[/C][C] 85[/C][C] 136.7[/C][C]-51.7[/C][/ROW]
[ROW][C]51[/C][C] 218[/C][C] 172.4[/C][C] 45.62[/C][/ROW]
[ROW][C]52[/C][C] 65[/C][C] 143.5[/C][C]-78.5[/C][/ROW]
[ROW][C]53[/C][C] 96[/C][C] 128.1[/C][C]-32.09[/C][/ROW]
[ROW][C]54[/C][C] 224[/C][C] 271.4[/C][C]-47.42[/C][/ROW]
[ROW][C]55[/C][C] 95[/C][C] 132.6[/C][C]-37.61[/C][/ROW]
[ROW][C]56[/C][C] 147[/C][C] 158[/C][C]-11.04[/C][/ROW]
[ROW][C]57[/C][C] 128[/C][C] 137.9[/C][C]-9.885[/C][/ROW]
[ROW][C]58[/C][C] 86[/C][C] 141.9[/C][C]-55.88[/C][/ROW]
[ROW][C]59[/C][C] 90[/C][C] 144.4[/C][C]-54.44[/C][/ROW]
[ROW][C]60[/C][C] 348[/C][C] 238.5[/C][C] 109.5[/C][/ROW]
[ROW][C]61[/C][C] 152[/C][C] 138.6[/C][C] 13.36[/C][/ROW]
[ROW][C]62[/C][C] 112[/C][C] 157.7[/C][C]-45.74[/C][/ROW]
[ROW][C]63[/C][C] 175[/C][C] 197.8[/C][C]-22.83[/C][/ROW]
[ROW][C]64[/C][C] 110[/C][C] 165.5[/C][C]-55.54[/C][/ROW]
[ROW][C]65[/C][C] 134[/C][C] 146.9[/C][C]-12.91[/C][/ROW]
[ROW][C]66[/C][C] 501[/C][C] 381.5[/C][C] 119.5[/C][/ROW]
[ROW][C]67[/C][C] 87[/C][C] 129.8[/C][C]-42.82[/C][/ROW]
[ROW][C]68[/C][C] 117[/C][C] 138.9[/C][C]-21.9[/C][/ROW]
[ROW][C]69[/C][C] 205[/C][C] 151.6[/C][C] 53.39[/C][/ROW]
[ROW][C]70[/C][C] 131[/C][C] 132.2[/C][C]-1.155[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308512&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308512&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	100	150.2	-50.15
2	7.683	127.4	-119.7
3	102	135.9	-33.88
4	219	162	56.99
5	149	150	-0.998
6	277	207.9	69.06
7	272	161.4	110.6
8	224	159.9	64.06
9	194	215.9	-21.94
10	113	130.2	-17.22
11	45	132.9	-87.93
12	165	167.6	-2.615
13	196	172.2	23.78
14	197	149.9	47.12
15	87	134.8	-47.77
16	289	181.7	107.3
17	142	135.2	6.846
18	159	136.4	22.59
19	127	140.6	-13.59
20	59	128.1	-69.12
21	142	154.7	-12.71
22	297	163.6	133.4
23	189	149.8	39.15
24	82	135.3	-53.32
25	123	144	-20.99
26	230	186.4	43.58
27	106	137.6	-31.56
28	160	135.1	24.91
29	208	152.9	55.11
30	120	153.3	-33.26
31	94	135.2	-41.17
32	112	137	-25.04
33	163	151.5	11.45
34	160	164.6	-4.625
35	374	182.4	191.6
36	333	209.3	123.7
37	1.177	376.5	-375.4
38	205	139.7	65.27
39	117	146.7	-29.67
40	156	141.7	14.27
41	78	134.1	-56.15
42	206	166.8	39.22
43	304	186.5	117.5
44	131	189.9	-58.91
45	29	141.4	-112.4
46	211	145.4	65.61
47	188	154.4	33.56
48	91	140.3	-49.33
49	373	230	143
50	85	136.7	-51.7
51	218	172.4	45.62
52	65	143.5	-78.5
53	96	128.1	-32.09
54	224	271.4	-47.42
55	95	132.6	-37.61
56	147	158	-11.04
57	128	137.9	-9.885
58	86	141.9	-55.88
59	90	144.4	-54.44
60	348	238.5	109.5
61	152	138.6	13.36
62	112	157.7	-45.74
63	175	197.8	-22.83
64	110	165.5	-55.54
65	134	146.9	-12.91
66	501	381.5	119.5
67	87	129.8	-42.82
68	117	138.9	-21.9
69	205	151.6	53.39
70	131	132.2	-1.155

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.196	0.3921	0.804
7	0.402	0.8039	0.598
8	0.3187	0.6374	0.6813
9	0.4797	0.9593	0.5203
10	0.3577	0.7154	0.6423
11	0.3305	0.6609	0.6695
12	0.237	0.4741	0.763
13	0.1656	0.3312	0.8344
14	0.1415	0.283	0.8585
15	0.09838	0.1968	0.9016
16	0.1075	0.2149	0.8925
17	0.07496	0.1499	0.925
18	0.05572	0.1114	0.9443
19	0.03459	0.06919	0.9654
20	0.02592	0.05184	0.9741
21	0.01549	0.03097	0.9845
22	0.05127	0.1025	0.9487
23	0.03971	0.07941	0.9603
24	0.02929	0.05857	0.9707
25	0.01856	0.03712	0.9814
26	0.0115	0.023	0.9885
27	0.007107	0.01421	0.9929
28	0.005326	0.01065	0.9947
29	0.004473	0.008945	0.9955
30	0.002928	0.005857	0.9971
31	0.001789	0.003577	0.9982
32	0.0009788	0.001958	0.999
33	0.0005295	0.001059	0.9995
34	0.0002787	0.0005574	0.9997
35	0.003893	0.007785	0.9961
36	0.004097	0.008195	0.9959
37	0.9883	0.0234	0.0117
38	0.9897	0.02058	0.01029
39	0.984	0.03194	0.01597
40	0.9769	0.0463	0.02315
41	0.9701	0.05973	0.02987
42	0.9626	0.0749	0.03745
43	0.9864	0.02724	0.01362
44	0.9848	0.03048	0.01524
45	0.9924	0.0152	0.007602
46	0.9942	0.01158	0.005792
47	0.9923	0.01534	0.007671
48	0.9881	0.02371	0.01186
49	0.9984	0.003185	0.001593
50	0.9972	0.005531	0.002765
51	0.9968	0.006396	0.003198
52	0.9965	0.007043	0.003522
53	0.9931	0.01372	0.006859
54	0.999	0.001957	0.0009787
55	0.9977	0.004546	0.002273
56	0.9948	0.01036	0.005179
57	0.9892	0.02166	0.01083
58	0.9811	0.03786	0.01893
59	0.9691	0.06173	0.03087
60	0.9737	0.05257	0.02629
61	0.9573	0.0854	0.0427
62	0.9265	0.147	0.0735
63	0.9858	0.02843	0.01421
64	0.9869	0.02616	0.01308

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 &  0.196 &  0.3921 &  0.804 \tabularnewline
7 &  0.402 &  0.8039 &  0.598 \tabularnewline
8 &  0.3187 &  0.6374 &  0.6813 \tabularnewline
9 &  0.4797 &  0.9593 &  0.5203 \tabularnewline
10 &  0.3577 &  0.7154 &  0.6423 \tabularnewline
11 &  0.3305 &  0.6609 &  0.6695 \tabularnewline
12 &  0.237 &  0.4741 &  0.763 \tabularnewline
13 &  0.1656 &  0.3312 &  0.8344 \tabularnewline
14 &  0.1415 &  0.283 &  0.8585 \tabularnewline
15 &  0.09838 &  0.1968 &  0.9016 \tabularnewline
16 &  0.1075 &  0.2149 &  0.8925 \tabularnewline
17 &  0.07496 &  0.1499 &  0.925 \tabularnewline
18 &  0.05572 &  0.1114 &  0.9443 \tabularnewline
19 &  0.03459 &  0.06919 &  0.9654 \tabularnewline
20 &  0.02592 &  0.05184 &  0.9741 \tabularnewline
21 &  0.01549 &  0.03097 &  0.9845 \tabularnewline
22 &  0.05127 &  0.1025 &  0.9487 \tabularnewline
23 &  0.03971 &  0.07941 &  0.9603 \tabularnewline
24 &  0.02929 &  0.05857 &  0.9707 \tabularnewline
25 &  0.01856 &  0.03712 &  0.9814 \tabularnewline
26 &  0.0115 &  0.023 &  0.9885 \tabularnewline
27 &  0.007107 &  0.01421 &  0.9929 \tabularnewline
28 &  0.005326 &  0.01065 &  0.9947 \tabularnewline
29 &  0.004473 &  0.008945 &  0.9955 \tabularnewline
30 &  0.002928 &  0.005857 &  0.9971 \tabularnewline
31 &  0.001789 &  0.003577 &  0.9982 \tabularnewline
32 &  0.0009788 &  0.001958 &  0.999 \tabularnewline
33 &  0.0005295 &  0.001059 &  0.9995 \tabularnewline
34 &  0.0002787 &  0.0005574 &  0.9997 \tabularnewline
35 &  0.003893 &  0.007785 &  0.9961 \tabularnewline
36 &  0.004097 &  0.008195 &  0.9959 \tabularnewline
37 &  0.9883 &  0.0234 &  0.0117 \tabularnewline
38 &  0.9897 &  0.02058 &  0.01029 \tabularnewline
39 &  0.984 &  0.03194 &  0.01597 \tabularnewline
40 &  0.9769 &  0.0463 &  0.02315 \tabularnewline
41 &  0.9701 &  0.05973 &  0.02987 \tabularnewline
42 &  0.9626 &  0.0749 &  0.03745 \tabularnewline
43 &  0.9864 &  0.02724 &  0.01362 \tabularnewline
44 &  0.9848 &  0.03048 &  0.01524 \tabularnewline
45 &  0.9924 &  0.0152 &  0.007602 \tabularnewline
46 &  0.9942 &  0.01158 &  0.005792 \tabularnewline
47 &  0.9923 &  0.01534 &  0.007671 \tabularnewline
48 &  0.9881 &  0.02371 &  0.01186 \tabularnewline
49 &  0.9984 &  0.003185 &  0.001593 \tabularnewline
50 &  0.9972 &  0.005531 &  0.002765 \tabularnewline
51 &  0.9968 &  0.006396 &  0.003198 \tabularnewline
52 &  0.9965 &  0.007043 &  0.003522 \tabularnewline
53 &  0.9931 &  0.01372 &  0.006859 \tabularnewline
54 &  0.999 &  0.001957 &  0.0009787 \tabularnewline
55 &  0.9977 &  0.004546 &  0.002273 \tabularnewline
56 &  0.9948 &  0.01036 &  0.005179 \tabularnewline
57 &  0.9892 &  0.02166 &  0.01083 \tabularnewline
58 &  0.9811 &  0.03786 &  0.01893 \tabularnewline
59 &  0.9691 &  0.06173 &  0.03087 \tabularnewline
60 &  0.9737 &  0.05257 &  0.02629 \tabularnewline
61 &  0.9573 &  0.0854 &  0.0427 \tabularnewline
62 &  0.9265 &  0.147 &  0.0735 \tabularnewline
63 &  0.9858 &  0.02843 &  0.01421 \tabularnewline
64 &  0.9869 &  0.02616 &  0.01308 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308512&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]6[/C][C] 0.196[/C][C] 0.3921[/C][C] 0.804[/C][/ROW]
[ROW][C]7[/C][C] 0.402[/C][C] 0.8039[/C][C] 0.598[/C][/ROW]
[ROW][C]8[/C][C] 0.3187[/C][C] 0.6374[/C][C] 0.6813[/C][/ROW]
[ROW][C]9[/C][C] 0.4797[/C][C] 0.9593[/C][C] 0.5203[/C][/ROW]
[ROW][C]10[/C][C] 0.3577[/C][C] 0.7154[/C][C] 0.6423[/C][/ROW]
[ROW][C]11[/C][C] 0.3305[/C][C] 0.6609[/C][C] 0.6695[/C][/ROW]
[ROW][C]12[/C][C] 0.237[/C][C] 0.4741[/C][C] 0.763[/C][/ROW]
[ROW][C]13[/C][C] 0.1656[/C][C] 0.3312[/C][C] 0.8344[/C][/ROW]
[ROW][C]14[/C][C] 0.1415[/C][C] 0.283[/C][C] 0.8585[/C][/ROW]
[ROW][C]15[/C][C] 0.09838[/C][C] 0.1968[/C][C] 0.9016[/C][/ROW]
[ROW][C]16[/C][C] 0.1075[/C][C] 0.2149[/C][C] 0.8925[/C][/ROW]
[ROW][C]17[/C][C] 0.07496[/C][C] 0.1499[/C][C] 0.925[/C][/ROW]
[ROW][C]18[/C][C] 0.05572[/C][C] 0.1114[/C][C] 0.9443[/C][/ROW]
[ROW][C]19[/C][C] 0.03459[/C][C] 0.06919[/C][C] 0.9654[/C][/ROW]
[ROW][C]20[/C][C] 0.02592[/C][C] 0.05184[/C][C] 0.9741[/C][/ROW]
[ROW][C]21[/C][C] 0.01549[/C][C] 0.03097[/C][C] 0.9845[/C][/ROW]
[ROW][C]22[/C][C] 0.05127[/C][C] 0.1025[/C][C] 0.9487[/C][/ROW]
[ROW][C]23[/C][C] 0.03971[/C][C] 0.07941[/C][C] 0.9603[/C][/ROW]
[ROW][C]24[/C][C] 0.02929[/C][C] 0.05857[/C][C] 0.9707[/C][/ROW]
[ROW][C]25[/C][C] 0.01856[/C][C] 0.03712[/C][C] 0.9814[/C][/ROW]
[ROW][C]26[/C][C] 0.0115[/C][C] 0.023[/C][C] 0.9885[/C][/ROW]
[ROW][C]27[/C][C] 0.007107[/C][C] 0.01421[/C][C] 0.9929[/C][/ROW]
[ROW][C]28[/C][C] 0.005326[/C][C] 0.01065[/C][C] 0.9947[/C][/ROW]
[ROW][C]29[/C][C] 0.004473[/C][C] 0.008945[/C][C] 0.9955[/C][/ROW]
[ROW][C]30[/C][C] 0.002928[/C][C] 0.005857[/C][C] 0.9971[/C][/ROW]
[ROW][C]31[/C][C] 0.001789[/C][C] 0.003577[/C][C] 0.9982[/C][/ROW]
[ROW][C]32[/C][C] 0.0009788[/C][C] 0.001958[/C][C] 0.999[/C][/ROW]
[ROW][C]33[/C][C] 0.0005295[/C][C] 0.001059[/C][C] 0.9995[/C][/ROW]
[ROW][C]34[/C][C] 0.0002787[/C][C] 0.0005574[/C][C] 0.9997[/C][/ROW]
[ROW][C]35[/C][C] 0.003893[/C][C] 0.007785[/C][C] 0.9961[/C][/ROW]
[ROW][C]36[/C][C] 0.004097[/C][C] 0.008195[/C][C] 0.9959[/C][/ROW]
[ROW][C]37[/C][C] 0.9883[/C][C] 0.0234[/C][C] 0.0117[/C][/ROW]
[ROW][C]38[/C][C] 0.9897[/C][C] 0.02058[/C][C] 0.01029[/C][/ROW]
[ROW][C]39[/C][C] 0.984[/C][C] 0.03194[/C][C] 0.01597[/C][/ROW]
[ROW][C]40[/C][C] 0.9769[/C][C] 0.0463[/C][C] 0.02315[/C][/ROW]
[ROW][C]41[/C][C] 0.9701[/C][C] 0.05973[/C][C] 0.02987[/C][/ROW]
[ROW][C]42[/C][C] 0.9626[/C][C] 0.0749[/C][C] 0.03745[/C][/ROW]
[ROW][C]43[/C][C] 0.9864[/C][C] 0.02724[/C][C] 0.01362[/C][/ROW]
[ROW][C]44[/C][C] 0.9848[/C][C] 0.03048[/C][C] 0.01524[/C][/ROW]
[ROW][C]45[/C][C] 0.9924[/C][C] 0.0152[/C][C] 0.007602[/C][/ROW]
[ROW][C]46[/C][C] 0.9942[/C][C] 0.01158[/C][C] 0.005792[/C][/ROW]
[ROW][C]47[/C][C] 0.9923[/C][C] 0.01534[/C][C] 0.007671[/C][/ROW]
[ROW][C]48[/C][C] 0.9881[/C][C] 0.02371[/C][C] 0.01186[/C][/ROW]
[ROW][C]49[/C][C] 0.9984[/C][C] 0.003185[/C][C] 0.001593[/C][/ROW]
[ROW][C]50[/C][C] 0.9972[/C][C] 0.005531[/C][C] 0.002765[/C][/ROW]
[ROW][C]51[/C][C] 0.9968[/C][C] 0.006396[/C][C] 0.003198[/C][/ROW]
[ROW][C]52[/C][C] 0.9965[/C][C] 0.007043[/C][C] 0.003522[/C][/ROW]
[ROW][C]53[/C][C] 0.9931[/C][C] 0.01372[/C][C] 0.006859[/C][/ROW]
[ROW][C]54[/C][C] 0.999[/C][C] 0.001957[/C][C] 0.0009787[/C][/ROW]
[ROW][C]55[/C][C] 0.9977[/C][C] 0.004546[/C][C] 0.002273[/C][/ROW]
[ROW][C]56[/C][C] 0.9948[/C][C] 0.01036[/C][C] 0.005179[/C][/ROW]
[ROW][C]57[/C][C] 0.9892[/C][C] 0.02166[/C][C] 0.01083[/C][/ROW]
[ROW][C]58[/C][C] 0.9811[/C][C] 0.03786[/C][C] 0.01893[/C][/ROW]
[ROW][C]59[/C][C] 0.9691[/C][C] 0.06173[/C][C] 0.03087[/C][/ROW]
[ROW][C]60[/C][C] 0.9737[/C][C] 0.05257[/C][C] 0.02629[/C][/ROW]
[ROW][C]61[/C][C] 0.9573[/C][C] 0.0854[/C][C] 0.0427[/C][/ROW]
[ROW][C]62[/C][C] 0.9265[/C][C] 0.147[/C][C] 0.0735[/C][/ROW]
[ROW][C]63[/C][C] 0.9858[/C][C] 0.02843[/C][C] 0.01421[/C][/ROW]
[ROW][C]64[/C][C] 0.9869[/C][C] 0.02616[/C][C] 0.01308[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308512&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308512&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
6	0.196	0.3921	0.804
7	0.402	0.8039	0.598
8	0.3187	0.6374	0.6813
9	0.4797	0.9593	0.5203
10	0.3577	0.7154	0.6423
11	0.3305	0.6609	0.6695
12	0.237	0.4741	0.763
13	0.1656	0.3312	0.8344
14	0.1415	0.283	0.8585
15	0.09838	0.1968	0.9016
16	0.1075	0.2149	0.8925
17	0.07496	0.1499	0.925
18	0.05572	0.1114	0.9443
19	0.03459	0.06919	0.9654
20	0.02592	0.05184	0.9741
21	0.01549	0.03097	0.9845
22	0.05127	0.1025	0.9487
23	0.03971	0.07941	0.9603
24	0.02929	0.05857	0.9707
25	0.01856	0.03712	0.9814
26	0.0115	0.023	0.9885
27	0.007107	0.01421	0.9929
28	0.005326	0.01065	0.9947
29	0.004473	0.008945	0.9955
30	0.002928	0.005857	0.9971
31	0.001789	0.003577	0.9982
32	0.0009788	0.001958	0.999
33	0.0005295	0.001059	0.9995
34	0.0002787	0.0005574	0.9997
35	0.003893	0.007785	0.9961
36	0.004097	0.008195	0.9959
37	0.9883	0.0234	0.0117
38	0.9897	0.02058	0.01029
39	0.984	0.03194	0.01597
40	0.9769	0.0463	0.02315
41	0.9701	0.05973	0.02987
42	0.9626	0.0749	0.03745
43	0.9864	0.02724	0.01362
44	0.9848	0.03048	0.01524
45	0.9924	0.0152	0.007602
46	0.9942	0.01158	0.005792
47	0.9923	0.01534	0.007671
48	0.9881	0.02371	0.01186
49	0.9984	0.003185	0.001593
50	0.9972	0.005531	0.002765
51	0.9968	0.006396	0.003198
52	0.9965	0.007043	0.003522
53	0.9931	0.01372	0.006859
54	0.999	0.001957	0.0009787
55	0.9977	0.004546	0.002273
56	0.9948	0.01036	0.005179
57	0.9892	0.02166	0.01083
58	0.9811	0.03786	0.01893
59	0.9691	0.06173	0.03087
60	0.9737	0.05257	0.02629
61	0.9573	0.0854	0.0427
62	0.9265	0.147	0.0735
63	0.9858	0.02843	0.01421
64	0.9869	0.02616	0.01308

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	14	0.2373	NOK
5% type I error level	35	0.59322	NOK
10% type I error level	44	0.745763	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 14 &  0.2373 & NOK \tabularnewline
5% type I error level & 35 & 0.59322 & NOK \tabularnewline
10% type I error level & 44 & 0.745763 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308512&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]14[/C][C] 0.2373[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]35[/C][C]0.59322[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]44[/C][C]0.745763[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308512&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308512&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	14	0.2373	NOK
5% type I error level	35	0.59322	NOK
10% type I error level	44	0.745763	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 15.936, df1 = 2, df2 = 65, p-value = 2.334e-06
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 28.611, df1 = 4, df2 = 63, p-value = 1.453e-13
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 34.046, df1 = 2, df2 = 65, p-value = 7.67e-11

\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 = 15.936, df1 = 2, df2 = 65, p-value = 2.334e-06
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 28.611, df1 = 4, df2 = 63, p-value = 1.453e-13
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 34.046, df1 = 2, df2 = 65, p-value = 7.67e-11
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308512&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 = 15.936, df1 = 2, df2 = 65, p-value = 2.334e-06
[/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 = 28.611, df1 = 4, df2 = 63, p-value = 1.453e-13
[/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 = 34.046, df1 = 2, df2 = 65, p-value = 7.67e-11
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308512&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308512&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 = 15.936, df1 = 2, df2 = 65, p-value = 2.334e-06
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 28.611, df1 = 4, df2 = 63, p-value = 1.453e-13
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 34.046, df1 = 2, df2 = 65, p-value = 7.67e-11

Variance Inflation Factors (Multicollinearity)
> vif inwijking uitwijking 5.407165 5.407165

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
 inwijking uitwijking 
  5.407165   5.407165 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308512&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
 inwijking uitwijking 
  5.407165   5.407165 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308512&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308512&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 inwijking uitwijking 5.407165 5.407165

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par6 = 12 ;

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):

par6 <- '12'
par5 <- ''
par4 <- ''
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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