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*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 19 Dec 2017 21:05:32 +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/2017/Dec/19/t1513714031wbk6dh9nqwqjq69.htm/, Retrieved Wed, 15 May 2024 12:57:04 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310414, Retrieved Wed, 15 May 2024 12:57:04 +0000

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Estimated Impact

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

-       [Multiple Regression] [] [2017-12-19 20:05:32] [deec28e763260dad9f228be262d61467] [Current]

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2815	0,9	56
2815	0,95	60
2815	0,89	59
2863	0,72	57
2863	0,72	60
2863	0,71	55
2864	0,81	57
2865	0,83	58
2865	0,73	55
2866	0,56	56
2866	0,56	55
2866	0,71	55
3154	0,73	56
3154	0,77	55
3154	0,7	58
3154	0,7	55
3154	0,95	66
3333	0,71	58
3333	0,93	60
3334	0,9	62
3334	0,9	62
3334	0,78	58
3471	0,8	56
3471	0,73	55
3471	0,7	57
3471	0,7	55
3471	0,73	61
772	0,31	56
772	0,31	57
772	0,41	59
772	0,35	58
772	0,35	56
772	0,35	57
772	0,41	59
772	0,4	59
776	0,3	56
776	0,3	57
776	0,3	59
776	0,3	55
776	0,3	58
776	0,3	55
776	0,3	59
776	0,3	57
890	0,32	57
890	0,31	56
890	0,4	57
890	0,4	53
890	0,4	56
891	0,38	56
2559	0,7	64
2560	0,6	58
2560	0,7	56
2560	0,7	59
2560	0,74	58
2561	0,78	54
2561	0,73	57
1890	0,55	55
1890	0,6	56
1890	0,56	54
1890	0,55	56
1890	0,7	58
1890	0,7	58
1890	0,5	65
1890	0,7	58
1891	0,5	57
1891	0,59	56
1892	0,54	56
1892	0,54	56
1892	0,54	57
1892	0,54	55
2757	0,7	59
2757	0,72	59
2757	0,72	57
2757	0,72	55
2757	0,7	60
2777	0,7	58

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=310414&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=310414&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310414&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
a[t] = + 1423.05 + 4673.56b[t] -36.4119c[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
a[t] =  +  1423.05 +  4673.56b[t] -36.4119c[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310414&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]a[t] =  +  1423.05 +  4673.56b[t] -36.4119c[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310414&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310414&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
a[t] = + 1423.05 + 4673.56b[t] -36.4119c[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)	+1423	1102	+1.2910e+00	0.2008	0.1004
b	+4674	244.4	+1.9120e+01	3.807e-30	1.903e-30
c	-36.41	19.75	-1.8440e+00	0.06925	0.03463

\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) & +1423 &  1102 & +1.2910e+00 &  0.2008 &  0.1004 \tabularnewline
b & +4674 &  244.4 & +1.9120e+01 &  3.807e-30 &  1.903e-30 \tabularnewline
c & -36.41 &  19.75 & -1.8440e+00 &  0.06925 &  0.03463 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310414&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]+1423[/C][C] 1102[/C][C]+1.2910e+00[/C][C] 0.2008[/C][C] 0.1004[/C][/ROW]
[ROW][C]b[/C][C]+4674[/C][C] 244.4[/C][C]+1.9120e+01[/C][C] 3.807e-30[/C][C] 1.903e-30[/C][/ROW]
[ROW][C]c[/C][C]-36.41[/C][C] 19.75[/C][C]-1.8440e+00[/C][C] 0.06925[/C][C] 0.03463[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310414&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310414&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)	+1423	1102	+1.2910e+00	0.2008	0.1004
b	+4674	244.4	+1.9120e+01	3.807e-30	1.903e-30
c	-36.41	19.75	-1.8440e+00	0.06925	0.03463

Multiple Linear Regression - Regression Statistics
Multiple R	0.9156
R-squared	0.8383
Adjusted R-squared	0.8338
F-TEST (value)	189.2
F-TEST (DF numerator)	2
F-TEST (DF denominator)	73
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	396.2
Sum Squared Residuals	1.146e+07

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9156 \tabularnewline
R-squared &  0.8383 \tabularnewline
Adjusted R-squared &  0.8338 \tabularnewline
F-TEST (value) &  189.2 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 73 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  396.2 \tabularnewline
Sum Squared Residuals &  1.146e+07 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310414&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9156[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.8383[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.8338[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 189.2[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]73[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 396.2[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.146e+07[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310414&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310414&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	0.9156
R-squared	0.8383
Adjusted R-squared	0.8338
F-TEST (value)	189.2
F-TEST (DF numerator)	2
F-TEST (DF denominator)	73
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	396.2
Sum Squared Residuals	1.146e+07

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=310414&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=310414&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310414&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	2815	3590	-775.2
2	2815	3678	-863.2
3	2815	3434	-619.2
4	2863	2713	150.5
5	2863	2603	259.7
6	2863	2739	124.4
7	2864	3133	-269.2
8	2865	3190	-325.2
9	2865	2832	32.91
10	2866	2001	864.8
11	2866	2038	828.4
12	2866	2739	127.4
13	3154	2796	358.3
14	3154	3019	135
15	3154	2583	571.4
16	3154	2692	462.1
17	3154	3460	-305.7
18	3333	2629	703.6
19	3333	3585	-251.7
20	3334	3372	-37.71
21	3334	3372	-37.71
22	3334	2957	377.5
23	3471	3123	348.2
24	3471	2832	638.9
25	3471	2619	851.9
26	3471	2692	779.1
27	3471	2614	857.4
28	772	832.8	-60.79
29	772	796.4	-24.37
30	772	1191	-418.9
31	772	946.9	-174.9
32	772	1020	-247.7
33	772	983.3	-211.3
34	772	1191	-418.9
35	772	1144	-372.2
36	776	786.1	-10.05
37	776	749.6	26.36
38	776	676.8	99.19
39	776	822.5	-46.46
40	776	713.2	62.77
41	776	822.5	-46.46
42	776	676.8	99.19
43	776	749.6	26.36
44	890	843.1	46.89
45	890	832.8	57.21
46	890	1217	-327
47	890	1363	-472.6
48	890	1253	-363.4
49	891	1160	-268.9
50	2559	2364	194.8
51	2560	2115	444.7
52	2560	2655	-95.47
53	2560	2546	13.76
54	2560	2770	-209.6
55	2561	3102	-541.2
56	2561	2759	-198.3
57	1890	1991	-100.9
58	1890	2188	-298.1
59	1890	2074	-184
60	1890	1954	-64.44
61	1890	2583	-692.6
62	1890	2583	-692.6
63	1890	1393	496.9
64	1890	2583	-692.6
65	1891	1684	206.7
66	1891	2141	-250.4
67	1892	1908	-15.7
68	1892	1908	-15.7
69	1892	1871	20.71
70	1892	1944	-52.12
71	2757	2546	210.8
72	2757	2640	117.3
73	2757	2713	44.47
74	2757	2785	-28.36
75	2757	2510	247.2
76	2777	2583	194.4

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  2815 &  3590 & -775.2 \tabularnewline
2 &  2815 &  3678 & -863.2 \tabularnewline
3 &  2815 &  3434 & -619.2 \tabularnewline
4 &  2863 &  2713 &  150.5 \tabularnewline
5 &  2863 &  2603 &  259.7 \tabularnewline
6 &  2863 &  2739 &  124.4 \tabularnewline
7 &  2864 &  3133 & -269.2 \tabularnewline
8 &  2865 &  3190 & -325.2 \tabularnewline
9 &  2865 &  2832 &  32.91 \tabularnewline
10 &  2866 &  2001 &  864.8 \tabularnewline
11 &  2866 &  2038 &  828.4 \tabularnewline
12 &  2866 &  2739 &  127.4 \tabularnewline
13 &  3154 &  2796 &  358.3 \tabularnewline
14 &  3154 &  3019 &  135 \tabularnewline
15 &  3154 &  2583 &  571.4 \tabularnewline
16 &  3154 &  2692 &  462.1 \tabularnewline
17 &  3154 &  3460 & -305.7 \tabularnewline
18 &  3333 &  2629 &  703.6 \tabularnewline
19 &  3333 &  3585 & -251.7 \tabularnewline
20 &  3334 &  3372 & -37.71 \tabularnewline
21 &  3334 &  3372 & -37.71 \tabularnewline
22 &  3334 &  2957 &  377.5 \tabularnewline
23 &  3471 &  3123 &  348.2 \tabularnewline
24 &  3471 &  2832 &  638.9 \tabularnewline
25 &  3471 &  2619 &  851.9 \tabularnewline
26 &  3471 &  2692 &  779.1 \tabularnewline
27 &  3471 &  2614 &  857.4 \tabularnewline
28 &  772 &  832.8 & -60.79 \tabularnewline
29 &  772 &  796.4 & -24.37 \tabularnewline
30 &  772 &  1191 & -418.9 \tabularnewline
31 &  772 &  946.9 & -174.9 \tabularnewline
32 &  772 &  1020 & -247.7 \tabularnewline
33 &  772 &  983.3 & -211.3 \tabularnewline
34 &  772 &  1191 & -418.9 \tabularnewline
35 &  772 &  1144 & -372.2 \tabularnewline
36 &  776 &  786.1 & -10.05 \tabularnewline
37 &  776 &  749.6 &  26.36 \tabularnewline
38 &  776 &  676.8 &  99.19 \tabularnewline
39 &  776 &  822.5 & -46.46 \tabularnewline
40 &  776 &  713.2 &  62.77 \tabularnewline
41 &  776 &  822.5 & -46.46 \tabularnewline
42 &  776 &  676.8 &  99.19 \tabularnewline
43 &  776 &  749.6 &  26.36 \tabularnewline
44 &  890 &  843.1 &  46.89 \tabularnewline
45 &  890 &  832.8 &  57.21 \tabularnewline
46 &  890 &  1217 & -327 \tabularnewline
47 &  890 &  1363 & -472.6 \tabularnewline
48 &  890 &  1253 & -363.4 \tabularnewline
49 &  891 &  1160 & -268.9 \tabularnewline
50 &  2559 &  2364 &  194.8 \tabularnewline
51 &  2560 &  2115 &  444.7 \tabularnewline
52 &  2560 &  2655 & -95.47 \tabularnewline
53 &  2560 &  2546 &  13.76 \tabularnewline
54 &  2560 &  2770 & -209.6 \tabularnewline
55 &  2561 &  3102 & -541.2 \tabularnewline
56 &  2561 &  2759 & -198.3 \tabularnewline
57 &  1890 &  1991 & -100.9 \tabularnewline
58 &  1890 &  2188 & -298.1 \tabularnewline
59 &  1890 &  2074 & -184 \tabularnewline
60 &  1890 &  1954 & -64.44 \tabularnewline
61 &  1890 &  2583 & -692.6 \tabularnewline
62 &  1890 &  2583 & -692.6 \tabularnewline
63 &  1890 &  1393 &  496.9 \tabularnewline
64 &  1890 &  2583 & -692.6 \tabularnewline
65 &  1891 &  1684 &  206.7 \tabularnewline
66 &  1891 &  2141 & -250.4 \tabularnewline
67 &  1892 &  1908 & -15.7 \tabularnewline
68 &  1892 &  1908 & -15.7 \tabularnewline
69 &  1892 &  1871 &  20.71 \tabularnewline
70 &  1892 &  1944 & -52.12 \tabularnewline
71 &  2757 &  2546 &  210.8 \tabularnewline
72 &  2757 &  2640 &  117.3 \tabularnewline
73 &  2757 &  2713 &  44.47 \tabularnewline
74 &  2757 &  2785 & -28.36 \tabularnewline
75 &  2757 &  2510 &  247.2 \tabularnewline
76 &  2777 &  2583 &  194.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310414&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] 2815[/C][C] 3590[/C][C]-775.2[/C][/ROW]
[ROW][C]2[/C][C] 2815[/C][C] 3678[/C][C]-863.2[/C][/ROW]
[ROW][C]3[/C][C] 2815[/C][C] 3434[/C][C]-619.2[/C][/ROW]
[ROW][C]4[/C][C] 2863[/C][C] 2713[/C][C] 150.5[/C][/ROW]
[ROW][C]5[/C][C] 2863[/C][C] 2603[/C][C] 259.7[/C][/ROW]
[ROW][C]6[/C][C] 2863[/C][C] 2739[/C][C] 124.4[/C][/ROW]
[ROW][C]7[/C][C] 2864[/C][C] 3133[/C][C]-269.2[/C][/ROW]
[ROW][C]8[/C][C] 2865[/C][C] 3190[/C][C]-325.2[/C][/ROW]
[ROW][C]9[/C][C] 2865[/C][C] 2832[/C][C] 32.91[/C][/ROW]
[ROW][C]10[/C][C] 2866[/C][C] 2001[/C][C] 864.8[/C][/ROW]
[ROW][C]11[/C][C] 2866[/C][C] 2038[/C][C] 828.4[/C][/ROW]
[ROW][C]12[/C][C] 2866[/C][C] 2739[/C][C] 127.4[/C][/ROW]
[ROW][C]13[/C][C] 3154[/C][C] 2796[/C][C] 358.3[/C][/ROW]
[ROW][C]14[/C][C] 3154[/C][C] 3019[/C][C] 135[/C][/ROW]
[ROW][C]15[/C][C] 3154[/C][C] 2583[/C][C] 571.4[/C][/ROW]
[ROW][C]16[/C][C] 3154[/C][C] 2692[/C][C] 462.1[/C][/ROW]
[ROW][C]17[/C][C] 3154[/C][C] 3460[/C][C]-305.7[/C][/ROW]
[ROW][C]18[/C][C] 3333[/C][C] 2629[/C][C] 703.6[/C][/ROW]
[ROW][C]19[/C][C] 3333[/C][C] 3585[/C][C]-251.7[/C][/ROW]
[ROW][C]20[/C][C] 3334[/C][C] 3372[/C][C]-37.71[/C][/ROW]
[ROW][C]21[/C][C] 3334[/C][C] 3372[/C][C]-37.71[/C][/ROW]
[ROW][C]22[/C][C] 3334[/C][C] 2957[/C][C] 377.5[/C][/ROW]
[ROW][C]23[/C][C] 3471[/C][C] 3123[/C][C] 348.2[/C][/ROW]
[ROW][C]24[/C][C] 3471[/C][C] 2832[/C][C] 638.9[/C][/ROW]
[ROW][C]25[/C][C] 3471[/C][C] 2619[/C][C] 851.9[/C][/ROW]
[ROW][C]26[/C][C] 3471[/C][C] 2692[/C][C] 779.1[/C][/ROW]
[ROW][C]27[/C][C] 3471[/C][C] 2614[/C][C] 857.4[/C][/ROW]
[ROW][C]28[/C][C] 772[/C][C] 832.8[/C][C]-60.79[/C][/ROW]
[ROW][C]29[/C][C] 772[/C][C] 796.4[/C][C]-24.37[/C][/ROW]
[ROW][C]30[/C][C] 772[/C][C] 1191[/C][C]-418.9[/C][/ROW]
[ROW][C]31[/C][C] 772[/C][C] 946.9[/C][C]-174.9[/C][/ROW]
[ROW][C]32[/C][C] 772[/C][C] 1020[/C][C]-247.7[/C][/ROW]
[ROW][C]33[/C][C] 772[/C][C] 983.3[/C][C]-211.3[/C][/ROW]
[ROW][C]34[/C][C] 772[/C][C] 1191[/C][C]-418.9[/C][/ROW]
[ROW][C]35[/C][C] 772[/C][C] 1144[/C][C]-372.2[/C][/ROW]
[ROW][C]36[/C][C] 776[/C][C] 786.1[/C][C]-10.05[/C][/ROW]
[ROW][C]37[/C][C] 776[/C][C] 749.6[/C][C] 26.36[/C][/ROW]
[ROW][C]38[/C][C] 776[/C][C] 676.8[/C][C] 99.19[/C][/ROW]
[ROW][C]39[/C][C] 776[/C][C] 822.5[/C][C]-46.46[/C][/ROW]
[ROW][C]40[/C][C] 776[/C][C] 713.2[/C][C] 62.77[/C][/ROW]
[ROW][C]41[/C][C] 776[/C][C] 822.5[/C][C]-46.46[/C][/ROW]
[ROW][C]42[/C][C] 776[/C][C] 676.8[/C][C] 99.19[/C][/ROW]
[ROW][C]43[/C][C] 776[/C][C] 749.6[/C][C] 26.36[/C][/ROW]
[ROW][C]44[/C][C] 890[/C][C] 843.1[/C][C] 46.89[/C][/ROW]
[ROW][C]45[/C][C] 890[/C][C] 832.8[/C][C] 57.21[/C][/ROW]
[ROW][C]46[/C][C] 890[/C][C] 1217[/C][C]-327[/C][/ROW]
[ROW][C]47[/C][C] 890[/C][C] 1363[/C][C]-472.6[/C][/ROW]
[ROW][C]48[/C][C] 890[/C][C] 1253[/C][C]-363.4[/C][/ROW]
[ROW][C]49[/C][C] 891[/C][C] 1160[/C][C]-268.9[/C][/ROW]
[ROW][C]50[/C][C] 2559[/C][C] 2364[/C][C] 194.8[/C][/ROW]
[ROW][C]51[/C][C] 2560[/C][C] 2115[/C][C] 444.7[/C][/ROW]
[ROW][C]52[/C][C] 2560[/C][C] 2655[/C][C]-95.47[/C][/ROW]
[ROW][C]53[/C][C] 2560[/C][C] 2546[/C][C] 13.76[/C][/ROW]
[ROW][C]54[/C][C] 2560[/C][C] 2770[/C][C]-209.6[/C][/ROW]
[ROW][C]55[/C][C] 2561[/C][C] 3102[/C][C]-541.2[/C][/ROW]
[ROW][C]56[/C][C] 2561[/C][C] 2759[/C][C]-198.3[/C][/ROW]
[ROW][C]57[/C][C] 1890[/C][C] 1991[/C][C]-100.9[/C][/ROW]
[ROW][C]58[/C][C] 1890[/C][C] 2188[/C][C]-298.1[/C][/ROW]
[ROW][C]59[/C][C] 1890[/C][C] 2074[/C][C]-184[/C][/ROW]
[ROW][C]60[/C][C] 1890[/C][C] 1954[/C][C]-64.44[/C][/ROW]
[ROW][C]61[/C][C] 1890[/C][C] 2583[/C][C]-692.6[/C][/ROW]
[ROW][C]62[/C][C] 1890[/C][C] 2583[/C][C]-692.6[/C][/ROW]
[ROW][C]63[/C][C] 1890[/C][C] 1393[/C][C] 496.9[/C][/ROW]
[ROW][C]64[/C][C] 1890[/C][C] 2583[/C][C]-692.6[/C][/ROW]
[ROW][C]65[/C][C] 1891[/C][C] 1684[/C][C] 206.7[/C][/ROW]
[ROW][C]66[/C][C] 1891[/C][C] 2141[/C][C]-250.4[/C][/ROW]
[ROW][C]67[/C][C] 1892[/C][C] 1908[/C][C]-15.7[/C][/ROW]
[ROW][C]68[/C][C] 1892[/C][C] 1908[/C][C]-15.7[/C][/ROW]
[ROW][C]69[/C][C] 1892[/C][C] 1871[/C][C] 20.71[/C][/ROW]
[ROW][C]70[/C][C] 1892[/C][C] 1944[/C][C]-52.12[/C][/ROW]
[ROW][C]71[/C][C] 2757[/C][C] 2546[/C][C] 210.8[/C][/ROW]
[ROW][C]72[/C][C] 2757[/C][C] 2640[/C][C] 117.3[/C][/ROW]
[ROW][C]73[/C][C] 2757[/C][C] 2713[/C][C] 44.47[/C][/ROW]
[ROW][C]74[/C][C] 2757[/C][C] 2785[/C][C]-28.36[/C][/ROW]
[ROW][C]75[/C][C] 2757[/C][C] 2510[/C][C] 247.2[/C][/ROW]
[ROW][C]76[/C][C] 2777[/C][C] 2583[/C][C] 194.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310414&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310414&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	2815	3590	-775.2
2	2815	3678	-863.2
3	2815	3434	-619.2
4	2863	2713	150.5
5	2863	2603	259.7
6	2863	2739	124.4
7	2864	3133	-269.2
8	2865	3190	-325.2
9	2865	2832	32.91
10	2866	2001	864.8
11	2866	2038	828.4
12	2866	2739	127.4
13	3154	2796	358.3
14	3154	3019	135
15	3154	2583	571.4
16	3154	2692	462.1
17	3154	3460	-305.7
18	3333	2629	703.6
19	3333	3585	-251.7
20	3334	3372	-37.71
21	3334	3372	-37.71
22	3334	2957	377.5
23	3471	3123	348.2
24	3471	2832	638.9
25	3471	2619	851.9
26	3471	2692	779.1
27	3471	2614	857.4
28	772	832.8	-60.79
29	772	796.4	-24.37
30	772	1191	-418.9
31	772	946.9	-174.9
32	772	1020	-247.7
33	772	983.3	-211.3
34	772	1191	-418.9
35	772	1144	-372.2
36	776	786.1	-10.05
37	776	749.6	26.36
38	776	676.8	99.19
39	776	822.5	-46.46
40	776	713.2	62.77
41	776	822.5	-46.46
42	776	676.8	99.19
43	776	749.6	26.36
44	890	843.1	46.89
45	890	832.8	57.21
46	890	1217	-327
47	890	1363	-472.6
48	890	1253	-363.4
49	891	1160	-268.9
50	2559	2364	194.8
51	2560	2115	444.7
52	2560	2655	-95.47
53	2560	2546	13.76
54	2560	2770	-209.6
55	2561	3102	-541.2
56	2561	2759	-198.3
57	1890	1991	-100.9
58	1890	2188	-298.1
59	1890	2074	-184
60	1890	1954	-64.44
61	1890	2583	-692.6
62	1890	2583	-692.6
63	1890	1393	496.9
64	1890	2583	-692.6
65	1891	1684	206.7
66	1891	2141	-250.4
67	1892	1908	-15.7
68	1892	1908	-15.7
69	1892	1871	20.71
70	1892	1944	-52.12
71	2757	2546	210.8
72	2757	2640	117.3
73	2757	2713	44.47
74	2757	2785	-28.36
75	2757	2510	247.2
76	2777	2583	194.4

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	5.184e-06	1.037e-05	1
7	2.413e-06	4.826e-06	1
8	3.864e-07	7.728e-07	1
9	1.391e-08	2.782e-08	1
10	3.252e-09	6.505e-09	1
11	2.991e-10	5.982e-10	1
12	1.423e-11	2.845e-11	1
13	3.118e-05	6.236e-05	1
14	0.0001131	0.0002262	0.9999
15	0.0003469	0.0006938	0.9997
16	0.0003843	0.0007686	0.9996
17	0.0006099	0.00122	0.9994
18	0.002574	0.005147	0.9974
19	0.006099	0.0122	0.9939
20	0.006356	0.01271	0.9936
21	0.005643	0.01129	0.9944
22	0.007218	0.01444	0.9928
23	0.02158	0.04316	0.9784
24	0.06302	0.126	0.937
25	0.1638	0.3276	0.8362
26	0.4165	0.833	0.5835
27	0.6182	0.7635	0.3818
28	0.9988	0.00249	0.001245
29	0.9996	0.0008535	0.0004268
30	0.9999	0.0002136	0.0001068
31	0.9999	0.0002382	0.0001191
32	0.9999	0.0002458	0.0001229
33	0.9998	0.0003285	0.0001642
34	0.9999	0.000178	8.899e-05
35	0.9999	0.000107	5.351e-05
36	0.9999	0.0002095	0.0001047
37	0.9998	0.0004117	0.0002058
38	0.9996	0.0007478	0.0003739
39	0.9994	0.001285	0.0006424
40	0.9988	0.002324	0.001162
41	0.9981	0.003851	0.001926
42	0.9968	0.006415	0.003207
43	0.9946	0.01086	0.005431
44	0.9911	0.01782	0.008908
45	0.9865	0.02709	0.01354
46	0.9857	0.02857	0.01429
47	0.9877	0.02466	0.01233
48	0.9892	0.02167	0.01084
49	0.9907	0.01864	0.009319
50	0.9863	0.02744	0.01372
51	0.9903	0.01933	0.009666
52	0.9857	0.02851	0.01425
53	0.9774	0.04522	0.02261
54	0.9655	0.06897	0.03449
55	0.9597	0.08068	0.04034
56	0.9397	0.1205	0.06027
57	0.9106	0.1788	0.08942
58	0.8851	0.2298	0.1149
59	0.8393	0.3213	0.1607
60	0.7779	0.4442	0.2221
61	0.8811	0.2378	0.1189
62	0.9719	0.0562	0.0281
63	0.9545	0.09092	0.04546
64	1	8.59e-05	4.295e-05
65	1	8.751e-05	4.376e-05
66	1	1.266e-06	6.332e-07
67	1	1.351e-05	6.756e-06
68	0.9999	0.000133	6.652e-05
69	0.9995	0.0009782	0.0004891
70	1	2.654e-05	1.327e-05

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 &  5.184e-06 &  1.037e-05 &  1 \tabularnewline
7 &  2.413e-06 &  4.826e-06 &  1 \tabularnewline
8 &  3.864e-07 &  7.728e-07 &  1 \tabularnewline
9 &  1.391e-08 &  2.782e-08 &  1 \tabularnewline
10 &  3.252e-09 &  6.505e-09 &  1 \tabularnewline
11 &  2.991e-10 &  5.982e-10 &  1 \tabularnewline
12 &  1.423e-11 &  2.845e-11 &  1 \tabularnewline
13 &  3.118e-05 &  6.236e-05 &  1 \tabularnewline
14 &  0.0001131 &  0.0002262 &  0.9999 \tabularnewline
15 &  0.0003469 &  0.0006938 &  0.9997 \tabularnewline
16 &  0.0003843 &  0.0007686 &  0.9996 \tabularnewline
17 &  0.0006099 &  0.00122 &  0.9994 \tabularnewline
18 &  0.002574 &  0.005147 &  0.9974 \tabularnewline
19 &  0.006099 &  0.0122 &  0.9939 \tabularnewline
20 &  0.006356 &  0.01271 &  0.9936 \tabularnewline
21 &  0.005643 &  0.01129 &  0.9944 \tabularnewline
22 &  0.007218 &  0.01444 &  0.9928 \tabularnewline
23 &  0.02158 &  0.04316 &  0.9784 \tabularnewline
24 &  0.06302 &  0.126 &  0.937 \tabularnewline
25 &  0.1638 &  0.3276 &  0.8362 \tabularnewline
26 &  0.4165 &  0.833 &  0.5835 \tabularnewline
27 &  0.6182 &  0.7635 &  0.3818 \tabularnewline
28 &  0.9988 &  0.00249 &  0.001245 \tabularnewline
29 &  0.9996 &  0.0008535 &  0.0004268 \tabularnewline
30 &  0.9999 &  0.0002136 &  0.0001068 \tabularnewline
31 &  0.9999 &  0.0002382 &  0.0001191 \tabularnewline
32 &  0.9999 &  0.0002458 &  0.0001229 \tabularnewline
33 &  0.9998 &  0.0003285 &  0.0001642 \tabularnewline
34 &  0.9999 &  0.000178 &  8.899e-05 \tabularnewline
35 &  0.9999 &  0.000107 &  5.351e-05 \tabularnewline
36 &  0.9999 &  0.0002095 &  0.0001047 \tabularnewline
37 &  0.9998 &  0.0004117 &  0.0002058 \tabularnewline
38 &  0.9996 &  0.0007478 &  0.0003739 \tabularnewline
39 &  0.9994 &  0.001285 &  0.0006424 \tabularnewline
40 &  0.9988 &  0.002324 &  0.001162 \tabularnewline
41 &  0.9981 &  0.003851 &  0.001926 \tabularnewline
42 &  0.9968 &  0.006415 &  0.003207 \tabularnewline
43 &  0.9946 &  0.01086 &  0.005431 \tabularnewline
44 &  0.9911 &  0.01782 &  0.008908 \tabularnewline
45 &  0.9865 &  0.02709 &  0.01354 \tabularnewline
46 &  0.9857 &  0.02857 &  0.01429 \tabularnewline
47 &  0.9877 &  0.02466 &  0.01233 \tabularnewline
48 &  0.9892 &  0.02167 &  0.01084 \tabularnewline
49 &  0.9907 &  0.01864 &  0.009319 \tabularnewline
50 &  0.9863 &  0.02744 &  0.01372 \tabularnewline
51 &  0.9903 &  0.01933 &  0.009666 \tabularnewline
52 &  0.9857 &  0.02851 &  0.01425 \tabularnewline
53 &  0.9774 &  0.04522 &  0.02261 \tabularnewline
54 &  0.9655 &  0.06897 &  0.03449 \tabularnewline
55 &  0.9597 &  0.08068 &  0.04034 \tabularnewline
56 &  0.9397 &  0.1205 &  0.06027 \tabularnewline
57 &  0.9106 &  0.1788 &  0.08942 \tabularnewline
58 &  0.8851 &  0.2298 &  0.1149 \tabularnewline
59 &  0.8393 &  0.3213 &  0.1607 \tabularnewline
60 &  0.7779 &  0.4442 &  0.2221 \tabularnewline
61 &  0.8811 &  0.2378 &  0.1189 \tabularnewline
62 &  0.9719 &  0.0562 &  0.0281 \tabularnewline
63 &  0.9545 &  0.09092 &  0.04546 \tabularnewline
64 &  1 &  8.59e-05 &  4.295e-05 \tabularnewline
65 &  1 &  8.751e-05 &  4.376e-05 \tabularnewline
66 &  1 &  1.266e-06 &  6.332e-07 \tabularnewline
67 &  1 &  1.351e-05 &  6.756e-06 \tabularnewline
68 &  0.9999 &  0.000133 &  6.652e-05 \tabularnewline
69 &  0.9995 &  0.0009782 &  0.0004891 \tabularnewline
70 &  1 &  2.654e-05 &  1.327e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310414&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] 5.184e-06[/C][C] 1.037e-05[/C][C] 1[/C][/ROW]
[ROW][C]7[/C][C] 2.413e-06[/C][C] 4.826e-06[/C][C] 1[/C][/ROW]
[ROW][C]8[/C][C] 3.864e-07[/C][C] 7.728e-07[/C][C] 1[/C][/ROW]
[ROW][C]9[/C][C] 1.391e-08[/C][C] 2.782e-08[/C][C] 1[/C][/ROW]
[ROW][C]10[/C][C] 3.252e-09[/C][C] 6.505e-09[/C][C] 1[/C][/ROW]
[ROW][C]11[/C][C] 2.991e-10[/C][C] 5.982e-10[/C][C] 1[/C][/ROW]
[ROW][C]12[/C][C] 1.423e-11[/C][C] 2.845e-11[/C][C] 1[/C][/ROW]
[ROW][C]13[/C][C] 3.118e-05[/C][C] 6.236e-05[/C][C] 1[/C][/ROW]
[ROW][C]14[/C][C] 0.0001131[/C][C] 0.0002262[/C][C] 0.9999[/C][/ROW]
[ROW][C]15[/C][C] 0.0003469[/C][C] 0.0006938[/C][C] 0.9997[/C][/ROW]
[ROW][C]16[/C][C] 0.0003843[/C][C] 0.0007686[/C][C] 0.9996[/C][/ROW]
[ROW][C]17[/C][C] 0.0006099[/C][C] 0.00122[/C][C] 0.9994[/C][/ROW]
[ROW][C]18[/C][C] 0.002574[/C][C] 0.005147[/C][C] 0.9974[/C][/ROW]
[ROW][C]19[/C][C] 0.006099[/C][C] 0.0122[/C][C] 0.9939[/C][/ROW]
[ROW][C]20[/C][C] 0.006356[/C][C] 0.01271[/C][C] 0.9936[/C][/ROW]
[ROW][C]21[/C][C] 0.005643[/C][C] 0.01129[/C][C] 0.9944[/C][/ROW]
[ROW][C]22[/C][C] 0.007218[/C][C] 0.01444[/C][C] 0.9928[/C][/ROW]
[ROW][C]23[/C][C] 0.02158[/C][C] 0.04316[/C][C] 0.9784[/C][/ROW]
[ROW][C]24[/C][C] 0.06302[/C][C] 0.126[/C][C] 0.937[/C][/ROW]
[ROW][C]25[/C][C] 0.1638[/C][C] 0.3276[/C][C] 0.8362[/C][/ROW]
[ROW][C]26[/C][C] 0.4165[/C][C] 0.833[/C][C] 0.5835[/C][/ROW]
[ROW][C]27[/C][C] 0.6182[/C][C] 0.7635[/C][C] 0.3818[/C][/ROW]
[ROW][C]28[/C][C] 0.9988[/C][C] 0.00249[/C][C] 0.001245[/C][/ROW]
[ROW][C]29[/C][C] 0.9996[/C][C] 0.0008535[/C][C] 0.0004268[/C][/ROW]
[ROW][C]30[/C][C] 0.9999[/C][C] 0.0002136[/C][C] 0.0001068[/C][/ROW]
[ROW][C]31[/C][C] 0.9999[/C][C] 0.0002382[/C][C] 0.0001191[/C][/ROW]
[ROW][C]32[/C][C] 0.9999[/C][C] 0.0002458[/C][C] 0.0001229[/C][/ROW]
[ROW][C]33[/C][C] 0.9998[/C][C] 0.0003285[/C][C] 0.0001642[/C][/ROW]
[ROW][C]34[/C][C] 0.9999[/C][C] 0.000178[/C][C] 8.899e-05[/C][/ROW]
[ROW][C]35[/C][C] 0.9999[/C][C] 0.000107[/C][C] 5.351e-05[/C][/ROW]
[ROW][C]36[/C][C] 0.9999[/C][C] 0.0002095[/C][C] 0.0001047[/C][/ROW]
[ROW][C]37[/C][C] 0.9998[/C][C] 0.0004117[/C][C] 0.0002058[/C][/ROW]
[ROW][C]38[/C][C] 0.9996[/C][C] 0.0007478[/C][C] 0.0003739[/C][/ROW]
[ROW][C]39[/C][C] 0.9994[/C][C] 0.001285[/C][C] 0.0006424[/C][/ROW]
[ROW][C]40[/C][C] 0.9988[/C][C] 0.002324[/C][C] 0.001162[/C][/ROW]
[ROW][C]41[/C][C] 0.9981[/C][C] 0.003851[/C][C] 0.001926[/C][/ROW]
[ROW][C]42[/C][C] 0.9968[/C][C] 0.006415[/C][C] 0.003207[/C][/ROW]
[ROW][C]43[/C][C] 0.9946[/C][C] 0.01086[/C][C] 0.005431[/C][/ROW]
[ROW][C]44[/C][C] 0.9911[/C][C] 0.01782[/C][C] 0.008908[/C][/ROW]
[ROW][C]45[/C][C] 0.9865[/C][C] 0.02709[/C][C] 0.01354[/C][/ROW]
[ROW][C]46[/C][C] 0.9857[/C][C] 0.02857[/C][C] 0.01429[/C][/ROW]
[ROW][C]47[/C][C] 0.9877[/C][C] 0.02466[/C][C] 0.01233[/C][/ROW]
[ROW][C]48[/C][C] 0.9892[/C][C] 0.02167[/C][C] 0.01084[/C][/ROW]
[ROW][C]49[/C][C] 0.9907[/C][C] 0.01864[/C][C] 0.009319[/C][/ROW]
[ROW][C]50[/C][C] 0.9863[/C][C] 0.02744[/C][C] 0.01372[/C][/ROW]
[ROW][C]51[/C][C] 0.9903[/C][C] 0.01933[/C][C] 0.009666[/C][/ROW]
[ROW][C]52[/C][C] 0.9857[/C][C] 0.02851[/C][C] 0.01425[/C][/ROW]
[ROW][C]53[/C][C] 0.9774[/C][C] 0.04522[/C][C] 0.02261[/C][/ROW]
[ROW][C]54[/C][C] 0.9655[/C][C] 0.06897[/C][C] 0.03449[/C][/ROW]
[ROW][C]55[/C][C] 0.9597[/C][C] 0.08068[/C][C] 0.04034[/C][/ROW]
[ROW][C]56[/C][C] 0.9397[/C][C] 0.1205[/C][C] 0.06027[/C][/ROW]
[ROW][C]57[/C][C] 0.9106[/C][C] 0.1788[/C][C] 0.08942[/C][/ROW]
[ROW][C]58[/C][C] 0.8851[/C][C] 0.2298[/C][C] 0.1149[/C][/ROW]
[ROW][C]59[/C][C] 0.8393[/C][C] 0.3213[/C][C] 0.1607[/C][/ROW]
[ROW][C]60[/C][C] 0.7779[/C][C] 0.4442[/C][C] 0.2221[/C][/ROW]
[ROW][C]61[/C][C] 0.8811[/C][C] 0.2378[/C][C] 0.1189[/C][/ROW]
[ROW][C]62[/C][C] 0.9719[/C][C] 0.0562[/C][C] 0.0281[/C][/ROW]
[ROW][C]63[/C][C] 0.9545[/C][C] 0.09092[/C][C] 0.04546[/C][/ROW]
[ROW][C]64[/C][C] 1[/C][C] 8.59e-05[/C][C] 4.295e-05[/C][/ROW]
[ROW][C]65[/C][C] 1[/C][C] 8.751e-05[/C][C] 4.376e-05[/C][/ROW]
[ROW][C]66[/C][C] 1[/C][C] 1.266e-06[/C][C] 6.332e-07[/C][/ROW]
[ROW][C]67[/C][C] 1[/C][C] 1.351e-05[/C][C] 6.756e-06[/C][/ROW]
[ROW][C]68[/C][C] 0.9999[/C][C] 0.000133[/C][C] 6.652e-05[/C][/ROW]
[ROW][C]69[/C][C] 0.9995[/C][C] 0.0009782[/C][C] 0.0004891[/C][/ROW]
[ROW][C]70[/C][C] 1[/C][C] 2.654e-05[/C][C] 1.327e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310414&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310414&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	5.184e-06	1.037e-05	1
7	2.413e-06	4.826e-06	1
8	3.864e-07	7.728e-07	1
9	1.391e-08	2.782e-08	1
10	3.252e-09	6.505e-09	1
11	2.991e-10	5.982e-10	1
12	1.423e-11	2.845e-11	1
13	3.118e-05	6.236e-05	1
14	0.0001131	0.0002262	0.9999
15	0.0003469	0.0006938	0.9997
16	0.0003843	0.0007686	0.9996
17	0.0006099	0.00122	0.9994
18	0.002574	0.005147	0.9974
19	0.006099	0.0122	0.9939
20	0.006356	0.01271	0.9936
21	0.005643	0.01129	0.9944
22	0.007218	0.01444	0.9928
23	0.02158	0.04316	0.9784
24	0.06302	0.126	0.937
25	0.1638	0.3276	0.8362
26	0.4165	0.833	0.5835
27	0.6182	0.7635	0.3818
28	0.9988	0.00249	0.001245
29	0.9996	0.0008535	0.0004268
30	0.9999	0.0002136	0.0001068
31	0.9999	0.0002382	0.0001191
32	0.9999	0.0002458	0.0001229
33	0.9998	0.0003285	0.0001642
34	0.9999	0.000178	8.899e-05
35	0.9999	0.000107	5.351e-05
36	0.9999	0.0002095	0.0001047
37	0.9998	0.0004117	0.0002058
38	0.9996	0.0007478	0.0003739
39	0.9994	0.001285	0.0006424
40	0.9988	0.002324	0.001162
41	0.9981	0.003851	0.001926
42	0.9968	0.006415	0.003207
43	0.9946	0.01086	0.005431
44	0.9911	0.01782	0.008908
45	0.9865	0.02709	0.01354
46	0.9857	0.02857	0.01429
47	0.9877	0.02466	0.01233
48	0.9892	0.02167	0.01084
49	0.9907	0.01864	0.009319
50	0.9863	0.02744	0.01372
51	0.9903	0.01933	0.009666
52	0.9857	0.02851	0.01425
53	0.9774	0.04522	0.02261
54	0.9655	0.06897	0.03449
55	0.9597	0.08068	0.04034
56	0.9397	0.1205	0.06027
57	0.9106	0.1788	0.08942
58	0.8851	0.2298	0.1149
59	0.8393	0.3213	0.1607
60	0.7779	0.4442	0.2221
61	0.8811	0.2378	0.1189
62	0.9719	0.0562	0.0281
63	0.9545	0.09092	0.04546
64	1	8.59e-05	4.295e-05
65	1	8.751e-05	4.376e-05
66	1	1.266e-06	6.332e-07
67	1	1.351e-05	6.756e-06
68	0.9999	0.000133	6.652e-05
69	0.9995	0.0009782	0.0004891
70	1	2.654e-05	1.327e-05

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	35	0.5385	NOK
5% type I error level	51	0.784615	NOK
10% type I error level	55	0.846154	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 & 35 &  0.5385 & NOK \tabularnewline
5% type I error level & 51 & 0.784615 & NOK \tabularnewline
10% type I error level & 55 & 0.846154 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310414&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]35[/C][C] 0.5385[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]51[/C][C]0.784615[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]55[/C][C]0.846154[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310414&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310414&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	35	0.5385	NOK
5% type I error level	51	0.784615	NOK
10% type I error level	55	0.846154	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 13.65, df1 = 2, df2 = 71, p-value = 9.64e-06
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 6.455, df1 = 4, df2 = 69, p-value = 0.0001795
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.1898, df1 = 2, df2 = 71, p-value = 0.8275

\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 = 13.65, df1 = 2, df2 = 71, p-value = 9.64e-06
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 6.455, df1 = 4, df2 = 69, p-value = 0.0001795
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.1898, df1 = 2, df2 = 71, p-value = 0.8275
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310414&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 = 13.65, df1 = 2, df2 = 71, p-value = 9.64e-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 = 6.455, df1 = 4, df2 = 69, p-value = 0.0001795
[/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 = 0.1898, df1 = 2, df2 = 71, p-value = 0.8275
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310414&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310414&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 = 13.65, df1 = 2, df2 = 71, p-value = 9.64e-06
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 6.455, df1 = 4, df2 = 69, p-value = 0.0001795
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.1898, df1 = 2, df2 = 71, p-value = 0.8275

Variance Inflation Factors (Multicollinearity)
> vif b c 1.082266 1.082266

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       b        c 
1.082266 1.082266 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310414&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
       b        c 
1.082266 1.082266 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310414&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310414&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 b c 1.082266 1.082266

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

PNG link

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Parameters (Session):

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