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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 15 Dec 2017 20:20:10 +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/15/t1513365845h51wcg8tp4zkd2o.htm/, Retrieved Wed, 15 May 2024 16:09:29 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309808, Retrieved Wed, 15 May 2024 16:09:29 +0000

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-       [Multiple Regression] [Multiple regression] [2017-12-15 19:20:10] [b5977ab717675b0b3b579d30e37b73cc] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 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 time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309808&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]

9 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309808&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309808&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	9 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
a[t] = + 40.5404 -1.74893b[t] -0.617904c[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
a[t] =  +  40.5404 -1.74893b[t] -0.617904c[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309808&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]a[t] =  +  40.5404 -1.74893b[t] -0.617904c[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309808&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309808&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] = + 40.5404 -1.74893b[t] -0.617904c[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)	+40.54	4.163	+9.7370e+00	9.043e-16	4.522e-16
b	-1.749	1.697	-1.0310e+00	0.3054	0.1527
c	-0.6179	3.922	-1.5760e-01	0.8752	0.4376

\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) & +40.54 &  4.163 & +9.7370e+00 &  9.043e-16 &  4.522e-16 \tabularnewline
b & -1.749 &  1.697 & -1.0310e+00 &  0.3054 &  0.1527 \tabularnewline
c & -0.6179 &  3.922 & -1.5760e-01 &  0.8752 &  0.4376 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309808&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]+40.54[/C][C] 4.163[/C][C]+9.7370e+00[/C][C] 9.043e-16[/C][C] 4.522e-16[/C][/ROW]
[ROW][C]b[/C][C]-1.749[/C][C] 1.697[/C][C]-1.0310e+00[/C][C] 0.3054[/C][C] 0.1527[/C][/ROW]
[ROW][C]c[/C][C]-0.6179[/C][C] 3.922[/C][C]-1.5760e-01[/C][C] 0.8752[/C][C] 0.4376[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309808&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309808&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)	+40.54	4.163	+9.7370e+00	9.043e-16	4.522e-16
b	-1.749	1.697	-1.0310e+00	0.3054	0.1527
c	-0.6179	3.922	-1.5760e-01	0.8752	0.4376

Multiple Linear Regression - Regression Statistics
Multiple R	0.1088
R-squared	0.01183
Adjusted R-squared	-0.009885
F-TEST (value)	0.5448
F-TEST (DF numerator)	2
F-TEST (DF denominator)	91
p-value	0.5818
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	12.69
Sum Squared Residuals	1.465e+04

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.1088 \tabularnewline
R-squared &  0.01183 \tabularnewline
Adjusted R-squared & -0.009885 \tabularnewline
F-TEST (value) &  0.5448 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 91 \tabularnewline
p-value &  0.5818 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  12.69 \tabularnewline
Sum Squared Residuals &  1.465e+04 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309808&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.1088[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.01183[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.009885[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 0.5448[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]91[/C][/ROW]
[ROW][C]p-value[/C][C] 0.5818[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 12.69[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.465e+04[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309808&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309808&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	0.1088
R-squared	0.01183
Adjusted R-squared	-0.009885
F-TEST (value)	0.5448
F-TEST (DF numerator)	2
F-TEST (DF denominator)	91
p-value	0.5818
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	12.69
Sum Squared Residuals	1.465e+04

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=309808&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=309808&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309808&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	74	38.79	35.21
2	42	38.17	3.826
3	49	38.17	10.83
4	49	38.17	10.83
5	68	36.42	31.58
6	34	38.17	-4.174
7	47	38.17	8.826
8	42	36.42	5.575
9	57	38.17	18.83
10	41	38.17	2.826
11	38	38.17	-0.1736
12	35	38.17	-3.174
13	35	38.17	-3.174
14	45	38.17	6.826
15	25	38.79	-13.79
16	30	38.17	-8.174
17	49	38.17	10.83
18	58	38.17	19.83
19	59	38.17	20.83
20	19	36.42	-17.42
21	58	38.17	19.83
22	39	38.17	0.8264
23	63	38.17	24.83
24	33	38.17	-5.174
25	22	38.17	-16.17
26	30	38.17	-8.174
27	51	38.17	12.83
28	47	38.17	8.826
29	41	38.17	2.826
30	35	38.17	-3.174
31	36	38.17	-2.174
32	21	38.17	-17.17
33	23	38.17	-15.17
34	53	36.42	16.58
35	30	38.17	-8.174
36	51	38.17	12.83
37	54	36.42	17.58
38	33	38.17	-5.174
39	27	38.17	-11.17
40	42	38.17	3.826
41	59	38.17	20.83
42	30	38.17	-8.174
43	24	38.17	-14.17
44	39	38.79	0.2085
45	24	38.17	-14.17
46	42	38.17	3.826
47	22	38.17	-16.17
48	28	38.79	-10.79
49	27	38.17	-11.17
50	41	38.17	2.826
51	37	38.17	-1.174
52	23	38.17	-15.17
53	44	38.17	5.826
54	24	38.17	-14.17
55	36	38.17	-2.174
56	42	38.79	3.209
57	41	38.17	2.826
58	32	38.17	-6.174
59	35	38.17	-3.174
60	47	38.17	8.826
61	49	38.17	10.83
62	51	38.17	12.83
63	38	38.79	-0.7915
64	17	38.17	-21.17
65	45	38.17	6.826
66	29	38.17	-9.174
67	35	38.17	-3.174
68	47	38.17	8.826
69	39	38.17	0.8264
70	20	38.17	-18.17
71	30	38.17	-8.174
72	36	37.04	-1.043
73	20	38.17	-18.17
74	50	38.79	11.21
75	42	36.42	5.575
76	30	36.42	-6.425
77	59	38.17	20.83
78	45	38.17	6.826
79	45	38.17	6.826
80	22	38.17	-16.17
81	64	38.17	25.83
82	29	38.79	-9.791
83	25	38.17	-13.17
84	23	38.17	-15.17
85	18	38.79	-20.79
86	30	38.17	-8.174
87	36	37.04	-1.043
88	33	38.17	-5.174
89	25	38.17	-13.17
90	47	38.79	8.209
91	20	25.93	-5.931
92	25	38.17	-13.17
93	33	38.17	-5.174
94	27	36.42	-9.425

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  74 &  38.79 &  35.21 \tabularnewline
2 &  42 &  38.17 &  3.826 \tabularnewline
3 &  49 &  38.17 &  10.83 \tabularnewline
4 &  49 &  38.17 &  10.83 \tabularnewline
5 &  68 &  36.42 &  31.58 \tabularnewline
6 &  34 &  38.17 & -4.174 \tabularnewline
7 &  47 &  38.17 &  8.826 \tabularnewline
8 &  42 &  36.42 &  5.575 \tabularnewline
9 &  57 &  38.17 &  18.83 \tabularnewline
10 &  41 &  38.17 &  2.826 \tabularnewline
11 &  38 &  38.17 & -0.1736 \tabularnewline
12 &  35 &  38.17 & -3.174 \tabularnewline
13 &  35 &  38.17 & -3.174 \tabularnewline
14 &  45 &  38.17 &  6.826 \tabularnewline
15 &  25 &  38.79 & -13.79 \tabularnewline
16 &  30 &  38.17 & -8.174 \tabularnewline
17 &  49 &  38.17 &  10.83 \tabularnewline
18 &  58 &  38.17 &  19.83 \tabularnewline
19 &  59 &  38.17 &  20.83 \tabularnewline
20 &  19 &  36.42 & -17.42 \tabularnewline
21 &  58 &  38.17 &  19.83 \tabularnewline
22 &  39 &  38.17 &  0.8264 \tabularnewline
23 &  63 &  38.17 &  24.83 \tabularnewline
24 &  33 &  38.17 & -5.174 \tabularnewline
25 &  22 &  38.17 & -16.17 \tabularnewline
26 &  30 &  38.17 & -8.174 \tabularnewline
27 &  51 &  38.17 &  12.83 \tabularnewline
28 &  47 &  38.17 &  8.826 \tabularnewline
29 &  41 &  38.17 &  2.826 \tabularnewline
30 &  35 &  38.17 & -3.174 \tabularnewline
31 &  36 &  38.17 & -2.174 \tabularnewline
32 &  21 &  38.17 & -17.17 \tabularnewline
33 &  23 &  38.17 & -15.17 \tabularnewline
34 &  53 &  36.42 &  16.58 \tabularnewline
35 &  30 &  38.17 & -8.174 \tabularnewline
36 &  51 &  38.17 &  12.83 \tabularnewline
37 &  54 &  36.42 &  17.58 \tabularnewline
38 &  33 &  38.17 & -5.174 \tabularnewline
39 &  27 &  38.17 & -11.17 \tabularnewline
40 &  42 &  38.17 &  3.826 \tabularnewline
41 &  59 &  38.17 &  20.83 \tabularnewline
42 &  30 &  38.17 & -8.174 \tabularnewline
43 &  24 &  38.17 & -14.17 \tabularnewline
44 &  39 &  38.79 &  0.2085 \tabularnewline
45 &  24 &  38.17 & -14.17 \tabularnewline
46 &  42 &  38.17 &  3.826 \tabularnewline
47 &  22 &  38.17 & -16.17 \tabularnewline
48 &  28 &  38.79 & -10.79 \tabularnewline
49 &  27 &  38.17 & -11.17 \tabularnewline
50 &  41 &  38.17 &  2.826 \tabularnewline
51 &  37 &  38.17 & -1.174 \tabularnewline
52 &  23 &  38.17 & -15.17 \tabularnewline
53 &  44 &  38.17 &  5.826 \tabularnewline
54 &  24 &  38.17 & -14.17 \tabularnewline
55 &  36 &  38.17 & -2.174 \tabularnewline
56 &  42 &  38.79 &  3.209 \tabularnewline
57 &  41 &  38.17 &  2.826 \tabularnewline
58 &  32 &  38.17 & -6.174 \tabularnewline
59 &  35 &  38.17 & -3.174 \tabularnewline
60 &  47 &  38.17 &  8.826 \tabularnewline
61 &  49 &  38.17 &  10.83 \tabularnewline
62 &  51 &  38.17 &  12.83 \tabularnewline
63 &  38 &  38.79 & -0.7915 \tabularnewline
64 &  17 &  38.17 & -21.17 \tabularnewline
65 &  45 &  38.17 &  6.826 \tabularnewline
66 &  29 &  38.17 & -9.174 \tabularnewline
67 &  35 &  38.17 & -3.174 \tabularnewline
68 &  47 &  38.17 &  8.826 \tabularnewline
69 &  39 &  38.17 &  0.8264 \tabularnewline
70 &  20 &  38.17 & -18.17 \tabularnewline
71 &  30 &  38.17 & -8.174 \tabularnewline
72 &  36 &  37.04 & -1.043 \tabularnewline
73 &  20 &  38.17 & -18.17 \tabularnewline
74 &  50 &  38.79 &  11.21 \tabularnewline
75 &  42 &  36.42 &  5.575 \tabularnewline
76 &  30 &  36.42 & -6.425 \tabularnewline
77 &  59 &  38.17 &  20.83 \tabularnewline
78 &  45 &  38.17 &  6.826 \tabularnewline
79 &  45 &  38.17 &  6.826 \tabularnewline
80 &  22 &  38.17 & -16.17 \tabularnewline
81 &  64 &  38.17 &  25.83 \tabularnewline
82 &  29 &  38.79 & -9.791 \tabularnewline
83 &  25 &  38.17 & -13.17 \tabularnewline
84 &  23 &  38.17 & -15.17 \tabularnewline
85 &  18 &  38.79 & -20.79 \tabularnewline
86 &  30 &  38.17 & -8.174 \tabularnewline
87 &  36 &  37.04 & -1.043 \tabularnewline
88 &  33 &  38.17 & -5.174 \tabularnewline
89 &  25 &  38.17 & -13.17 \tabularnewline
90 &  47 &  38.79 &  8.209 \tabularnewline
91 &  20 &  25.93 & -5.931 \tabularnewline
92 &  25 &  38.17 & -13.17 \tabularnewline
93 &  33 &  38.17 & -5.174 \tabularnewline
94 &  27 &  36.42 & -9.425 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309808&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] 74[/C][C] 38.79[/C][C] 35.21[/C][/ROW]
[ROW][C]2[/C][C] 42[/C][C] 38.17[/C][C] 3.826[/C][/ROW]
[ROW][C]3[/C][C] 49[/C][C] 38.17[/C][C] 10.83[/C][/ROW]
[ROW][C]4[/C][C] 49[/C][C] 38.17[/C][C] 10.83[/C][/ROW]
[ROW][C]5[/C][C] 68[/C][C] 36.42[/C][C] 31.58[/C][/ROW]
[ROW][C]6[/C][C] 34[/C][C] 38.17[/C][C]-4.174[/C][/ROW]
[ROW][C]7[/C][C] 47[/C][C] 38.17[/C][C] 8.826[/C][/ROW]
[ROW][C]8[/C][C] 42[/C][C] 36.42[/C][C] 5.575[/C][/ROW]
[ROW][C]9[/C][C] 57[/C][C] 38.17[/C][C] 18.83[/C][/ROW]
[ROW][C]10[/C][C] 41[/C][C] 38.17[/C][C] 2.826[/C][/ROW]
[ROW][C]11[/C][C] 38[/C][C] 38.17[/C][C]-0.1736[/C][/ROW]
[ROW][C]12[/C][C] 35[/C][C] 38.17[/C][C]-3.174[/C][/ROW]
[ROW][C]13[/C][C] 35[/C][C] 38.17[/C][C]-3.174[/C][/ROW]
[ROW][C]14[/C][C] 45[/C][C] 38.17[/C][C] 6.826[/C][/ROW]
[ROW][C]15[/C][C] 25[/C][C] 38.79[/C][C]-13.79[/C][/ROW]
[ROW][C]16[/C][C] 30[/C][C] 38.17[/C][C]-8.174[/C][/ROW]
[ROW][C]17[/C][C] 49[/C][C] 38.17[/C][C] 10.83[/C][/ROW]
[ROW][C]18[/C][C] 58[/C][C] 38.17[/C][C] 19.83[/C][/ROW]
[ROW][C]19[/C][C] 59[/C][C] 38.17[/C][C] 20.83[/C][/ROW]
[ROW][C]20[/C][C] 19[/C][C] 36.42[/C][C]-17.42[/C][/ROW]
[ROW][C]21[/C][C] 58[/C][C] 38.17[/C][C] 19.83[/C][/ROW]
[ROW][C]22[/C][C] 39[/C][C] 38.17[/C][C] 0.8264[/C][/ROW]
[ROW][C]23[/C][C] 63[/C][C] 38.17[/C][C] 24.83[/C][/ROW]
[ROW][C]24[/C][C] 33[/C][C] 38.17[/C][C]-5.174[/C][/ROW]
[ROW][C]25[/C][C] 22[/C][C] 38.17[/C][C]-16.17[/C][/ROW]
[ROW][C]26[/C][C] 30[/C][C] 38.17[/C][C]-8.174[/C][/ROW]
[ROW][C]27[/C][C] 51[/C][C] 38.17[/C][C] 12.83[/C][/ROW]
[ROW][C]28[/C][C] 47[/C][C] 38.17[/C][C] 8.826[/C][/ROW]
[ROW][C]29[/C][C] 41[/C][C] 38.17[/C][C] 2.826[/C][/ROW]
[ROW][C]30[/C][C] 35[/C][C] 38.17[/C][C]-3.174[/C][/ROW]
[ROW][C]31[/C][C] 36[/C][C] 38.17[/C][C]-2.174[/C][/ROW]
[ROW][C]32[/C][C] 21[/C][C] 38.17[/C][C]-17.17[/C][/ROW]
[ROW][C]33[/C][C] 23[/C][C] 38.17[/C][C]-15.17[/C][/ROW]
[ROW][C]34[/C][C] 53[/C][C] 36.42[/C][C] 16.58[/C][/ROW]
[ROW][C]35[/C][C] 30[/C][C] 38.17[/C][C]-8.174[/C][/ROW]
[ROW][C]36[/C][C] 51[/C][C] 38.17[/C][C] 12.83[/C][/ROW]
[ROW][C]37[/C][C] 54[/C][C] 36.42[/C][C] 17.58[/C][/ROW]
[ROW][C]38[/C][C] 33[/C][C] 38.17[/C][C]-5.174[/C][/ROW]
[ROW][C]39[/C][C] 27[/C][C] 38.17[/C][C]-11.17[/C][/ROW]
[ROW][C]40[/C][C] 42[/C][C] 38.17[/C][C] 3.826[/C][/ROW]
[ROW][C]41[/C][C] 59[/C][C] 38.17[/C][C] 20.83[/C][/ROW]
[ROW][C]42[/C][C] 30[/C][C] 38.17[/C][C]-8.174[/C][/ROW]
[ROW][C]43[/C][C] 24[/C][C] 38.17[/C][C]-14.17[/C][/ROW]
[ROW][C]44[/C][C] 39[/C][C] 38.79[/C][C] 0.2085[/C][/ROW]
[ROW][C]45[/C][C] 24[/C][C] 38.17[/C][C]-14.17[/C][/ROW]
[ROW][C]46[/C][C] 42[/C][C] 38.17[/C][C] 3.826[/C][/ROW]
[ROW][C]47[/C][C] 22[/C][C] 38.17[/C][C]-16.17[/C][/ROW]
[ROW][C]48[/C][C] 28[/C][C] 38.79[/C][C]-10.79[/C][/ROW]
[ROW][C]49[/C][C] 27[/C][C] 38.17[/C][C]-11.17[/C][/ROW]
[ROW][C]50[/C][C] 41[/C][C] 38.17[/C][C] 2.826[/C][/ROW]
[ROW][C]51[/C][C] 37[/C][C] 38.17[/C][C]-1.174[/C][/ROW]
[ROW][C]52[/C][C] 23[/C][C] 38.17[/C][C]-15.17[/C][/ROW]
[ROW][C]53[/C][C] 44[/C][C] 38.17[/C][C] 5.826[/C][/ROW]
[ROW][C]54[/C][C] 24[/C][C] 38.17[/C][C]-14.17[/C][/ROW]
[ROW][C]55[/C][C] 36[/C][C] 38.17[/C][C]-2.174[/C][/ROW]
[ROW][C]56[/C][C] 42[/C][C] 38.79[/C][C] 3.209[/C][/ROW]
[ROW][C]57[/C][C] 41[/C][C] 38.17[/C][C] 2.826[/C][/ROW]
[ROW][C]58[/C][C] 32[/C][C] 38.17[/C][C]-6.174[/C][/ROW]
[ROW][C]59[/C][C] 35[/C][C] 38.17[/C][C]-3.174[/C][/ROW]
[ROW][C]60[/C][C] 47[/C][C] 38.17[/C][C] 8.826[/C][/ROW]
[ROW][C]61[/C][C] 49[/C][C] 38.17[/C][C] 10.83[/C][/ROW]
[ROW][C]62[/C][C] 51[/C][C] 38.17[/C][C] 12.83[/C][/ROW]
[ROW][C]63[/C][C] 38[/C][C] 38.79[/C][C]-0.7915[/C][/ROW]
[ROW][C]64[/C][C] 17[/C][C] 38.17[/C][C]-21.17[/C][/ROW]
[ROW][C]65[/C][C] 45[/C][C] 38.17[/C][C] 6.826[/C][/ROW]
[ROW][C]66[/C][C] 29[/C][C] 38.17[/C][C]-9.174[/C][/ROW]
[ROW][C]67[/C][C] 35[/C][C] 38.17[/C][C]-3.174[/C][/ROW]
[ROW][C]68[/C][C] 47[/C][C] 38.17[/C][C] 8.826[/C][/ROW]
[ROW][C]69[/C][C] 39[/C][C] 38.17[/C][C] 0.8264[/C][/ROW]
[ROW][C]70[/C][C] 20[/C][C] 38.17[/C][C]-18.17[/C][/ROW]
[ROW][C]71[/C][C] 30[/C][C] 38.17[/C][C]-8.174[/C][/ROW]
[ROW][C]72[/C][C] 36[/C][C] 37.04[/C][C]-1.043[/C][/ROW]
[ROW][C]73[/C][C] 20[/C][C] 38.17[/C][C]-18.17[/C][/ROW]
[ROW][C]74[/C][C] 50[/C][C] 38.79[/C][C] 11.21[/C][/ROW]
[ROW][C]75[/C][C] 42[/C][C] 36.42[/C][C] 5.575[/C][/ROW]
[ROW][C]76[/C][C] 30[/C][C] 36.42[/C][C]-6.425[/C][/ROW]
[ROW][C]77[/C][C] 59[/C][C] 38.17[/C][C] 20.83[/C][/ROW]
[ROW][C]78[/C][C] 45[/C][C] 38.17[/C][C] 6.826[/C][/ROW]
[ROW][C]79[/C][C] 45[/C][C] 38.17[/C][C] 6.826[/C][/ROW]
[ROW][C]80[/C][C] 22[/C][C] 38.17[/C][C]-16.17[/C][/ROW]
[ROW][C]81[/C][C] 64[/C][C] 38.17[/C][C] 25.83[/C][/ROW]
[ROW][C]82[/C][C] 29[/C][C] 38.79[/C][C]-9.791[/C][/ROW]
[ROW][C]83[/C][C] 25[/C][C] 38.17[/C][C]-13.17[/C][/ROW]
[ROW][C]84[/C][C] 23[/C][C] 38.17[/C][C]-15.17[/C][/ROW]
[ROW][C]85[/C][C] 18[/C][C] 38.79[/C][C]-20.79[/C][/ROW]
[ROW][C]86[/C][C] 30[/C][C] 38.17[/C][C]-8.174[/C][/ROW]
[ROW][C]87[/C][C] 36[/C][C] 37.04[/C][C]-1.043[/C][/ROW]
[ROW][C]88[/C][C] 33[/C][C] 38.17[/C][C]-5.174[/C][/ROW]
[ROW][C]89[/C][C] 25[/C][C] 38.17[/C][C]-13.17[/C][/ROW]
[ROW][C]90[/C][C] 47[/C][C] 38.79[/C][C] 8.209[/C][/ROW]
[ROW][C]91[/C][C] 20[/C][C] 25.93[/C][C]-5.931[/C][/ROW]
[ROW][C]92[/C][C] 25[/C][C] 38.17[/C][C]-13.17[/C][/ROW]
[ROW][C]93[/C][C] 33[/C][C] 38.17[/C][C]-5.174[/C][/ROW]
[ROW][C]94[/C][C] 27[/C][C] 36.42[/C][C]-9.425[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309808&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309808&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	74	38.79	35.21
2	42	38.17	3.826
3	49	38.17	10.83
4	49	38.17	10.83
5	68	36.42	31.58
6	34	38.17	-4.174
7	47	38.17	8.826
8	42	36.42	5.575
9	57	38.17	18.83
10	41	38.17	2.826
11	38	38.17	-0.1736
12	35	38.17	-3.174
13	35	38.17	-3.174
14	45	38.17	6.826
15	25	38.79	-13.79
16	30	38.17	-8.174
17	49	38.17	10.83
18	58	38.17	19.83
19	59	38.17	20.83
20	19	36.42	-17.42
21	58	38.17	19.83
22	39	38.17	0.8264
23	63	38.17	24.83
24	33	38.17	-5.174
25	22	38.17	-16.17
26	30	38.17	-8.174
27	51	38.17	12.83
28	47	38.17	8.826
29	41	38.17	2.826
30	35	38.17	-3.174
31	36	38.17	-2.174
32	21	38.17	-17.17
33	23	38.17	-15.17
34	53	36.42	16.58
35	30	38.17	-8.174
36	51	38.17	12.83
37	54	36.42	17.58
38	33	38.17	-5.174
39	27	38.17	-11.17
40	42	38.17	3.826
41	59	38.17	20.83
42	30	38.17	-8.174
43	24	38.17	-14.17
44	39	38.79	0.2085
45	24	38.17	-14.17
46	42	38.17	3.826
47	22	38.17	-16.17
48	28	38.79	-10.79
49	27	38.17	-11.17
50	41	38.17	2.826
51	37	38.17	-1.174
52	23	38.17	-15.17
53	44	38.17	5.826
54	24	38.17	-14.17
55	36	38.17	-2.174
56	42	38.79	3.209
57	41	38.17	2.826
58	32	38.17	-6.174
59	35	38.17	-3.174
60	47	38.17	8.826
61	49	38.17	10.83
62	51	38.17	12.83
63	38	38.79	-0.7915
64	17	38.17	-21.17
65	45	38.17	6.826
66	29	38.17	-9.174
67	35	38.17	-3.174
68	47	38.17	8.826
69	39	38.17	0.8264
70	20	38.17	-18.17
71	30	38.17	-8.174
72	36	37.04	-1.043
73	20	38.17	-18.17
74	50	38.79	11.21
75	42	36.42	5.575
76	30	36.42	-6.425
77	59	38.17	20.83
78	45	38.17	6.826
79	45	38.17	6.826
80	22	38.17	-16.17
81	64	38.17	25.83
82	29	38.79	-9.791
83	25	38.17	-13.17
84	23	38.17	-15.17
85	18	38.79	-20.79
86	30	38.17	-8.174
87	36	37.04	-1.043
88	33	38.17	-5.174
89	25	38.17	-13.17
90	47	38.79	8.209
91	20	25.93	-5.931
92	25	38.17	-13.17
93	33	38.17	-5.174
94	27	36.42	-9.425

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.219	0.438	0.781
7	0.1145	0.229	0.8855
8	0.3758	0.7516	0.6242
9	0.3963	0.7926	0.6037
10	0.3022	0.6043	0.6978
11	0.2428	0.4856	0.7572
12	0.2142	0.4284	0.7858
13	0.1796	0.3592	0.8204
14	0.1243	0.2486	0.8757
15	0.6748	0.6505	0.3252
16	0.6611	0.6777	0.3389
17	0.6132	0.7737	0.3868
18	0.6648	0.6704	0.3352
19	0.719	0.5619	0.281
20	0.8867	0.2267	0.1133
21	0.9028	0.1943	0.09716
22	0.8748	0.2505	0.1252
23	0.9256	0.1487	0.07437
24	0.9173	0.1654	0.0827
25	0.9486	0.1028	0.0514
26	0.9448	0.1104	0.05519
27	0.9396	0.1208	0.06041
28	0.9256	0.1488	0.0744
29	0.9029	0.1943	0.09714
30	0.8814	0.2371	0.1186
31	0.8541	0.2917	0.1459
32	0.896	0.208	0.104
33	0.9144	0.1711	0.08556
34	0.9206	0.1588	0.07942
35	0.9093	0.1814	0.09071
36	0.9098	0.1804	0.09018
37	0.9228	0.1544	0.07718
38	0.9047	0.1906	0.09531
39	0.9011	0.1978	0.09892
40	0.8769	0.2462	0.1231
41	0.9303	0.1394	0.06972
42	0.9187	0.1627	0.08134
43	0.9248	0.1504	0.07522
44	0.9069	0.1863	0.09315
45	0.9123	0.1753	0.08767
46	0.8913	0.2174	0.1087
47	0.9062	0.1877	0.09383
48	0.9051	0.1897	0.09487
49	0.8967	0.2065	0.1033
50	0.8708	0.2584	0.1292
51	0.8367	0.3267	0.1633
52	0.847	0.306	0.153
53	0.8223	0.3554	0.1777
54	0.8257	0.3486	0.1743
55	0.7835	0.4329	0.2165
56	0.7398	0.5205	0.2602
57	0.6937	0.6126	0.3063
58	0.6455	0.7091	0.3545
59	0.5868	0.8263	0.4132
60	0.5686	0.8627	0.4314
61	0.5726	0.8549	0.4274
62	0.6056	0.7888	0.3944
63	0.5434	0.9132	0.4566
64	0.6266	0.7468	0.3734
65	0.6001	0.7998	0.3999
66	0.554	0.892	0.446
67	0.4877	0.9755	0.5123
68	0.4799	0.9597	0.5201
69	0.4201	0.8402	0.5799
70	0.4515	0.903	0.5485
71	0.395	0.79	0.605
72	0.3408	0.6816	0.6592
73	0.3765	0.753	0.6235
74	0.3923	0.7846	0.6077
75	0.3472	0.6943	0.6528
76	0.2944	0.5888	0.7056
77	0.4875	0.9749	0.5125
78	0.4741	0.9483	0.5259
79	0.4763	0.9526	0.5237
80	0.4485	0.897	0.5515
81	0.9657	0.06858	0.03429
82	0.9457	0.1085	0.05427
83	0.9118	0.1764	0.0882
84	0.8749	0.2503	0.1251
85	0.9948	0.01043	0.005217
86	0.9839	0.03214	0.01607
87	0.9827	0.03469	0.01734
88	0.9664	0.06727	0.03364

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 &  0.219 &  0.438 &  0.781 \tabularnewline
7 &  0.1145 &  0.229 &  0.8855 \tabularnewline
8 &  0.3758 &  0.7516 &  0.6242 \tabularnewline
9 &  0.3963 &  0.7926 &  0.6037 \tabularnewline
10 &  0.3022 &  0.6043 &  0.6978 \tabularnewline
11 &  0.2428 &  0.4856 &  0.7572 \tabularnewline
12 &  0.2142 &  0.4284 &  0.7858 \tabularnewline
13 &  0.1796 &  0.3592 &  0.8204 \tabularnewline
14 &  0.1243 &  0.2486 &  0.8757 \tabularnewline
15 &  0.6748 &  0.6505 &  0.3252 \tabularnewline
16 &  0.6611 &  0.6777 &  0.3389 \tabularnewline
17 &  0.6132 &  0.7737 &  0.3868 \tabularnewline
18 &  0.6648 &  0.6704 &  0.3352 \tabularnewline
19 &  0.719 &  0.5619 &  0.281 \tabularnewline
20 &  0.8867 &  0.2267 &  0.1133 \tabularnewline
21 &  0.9028 &  0.1943 &  0.09716 \tabularnewline
22 &  0.8748 &  0.2505 &  0.1252 \tabularnewline
23 &  0.9256 &  0.1487 &  0.07437 \tabularnewline
24 &  0.9173 &  0.1654 &  0.0827 \tabularnewline
25 &  0.9486 &  0.1028 &  0.0514 \tabularnewline
26 &  0.9448 &  0.1104 &  0.05519 \tabularnewline
27 &  0.9396 &  0.1208 &  0.06041 \tabularnewline
28 &  0.9256 &  0.1488 &  0.0744 \tabularnewline
29 &  0.9029 &  0.1943 &  0.09714 \tabularnewline
30 &  0.8814 &  0.2371 &  0.1186 \tabularnewline
31 &  0.8541 &  0.2917 &  0.1459 \tabularnewline
32 &  0.896 &  0.208 &  0.104 \tabularnewline
33 &  0.9144 &  0.1711 &  0.08556 \tabularnewline
34 &  0.9206 &  0.1588 &  0.07942 \tabularnewline
35 &  0.9093 &  0.1814 &  0.09071 \tabularnewline
36 &  0.9098 &  0.1804 &  0.09018 \tabularnewline
37 &  0.9228 &  0.1544 &  0.07718 \tabularnewline
38 &  0.9047 &  0.1906 &  0.09531 \tabularnewline
39 &  0.9011 &  0.1978 &  0.09892 \tabularnewline
40 &  0.8769 &  0.2462 &  0.1231 \tabularnewline
41 &  0.9303 &  0.1394 &  0.06972 \tabularnewline
42 &  0.9187 &  0.1627 &  0.08134 \tabularnewline
43 &  0.9248 &  0.1504 &  0.07522 \tabularnewline
44 &  0.9069 &  0.1863 &  0.09315 \tabularnewline
45 &  0.9123 &  0.1753 &  0.08767 \tabularnewline
46 &  0.8913 &  0.2174 &  0.1087 \tabularnewline
47 &  0.9062 &  0.1877 &  0.09383 \tabularnewline
48 &  0.9051 &  0.1897 &  0.09487 \tabularnewline
49 &  0.8967 &  0.2065 &  0.1033 \tabularnewline
50 &  0.8708 &  0.2584 &  0.1292 \tabularnewline
51 &  0.8367 &  0.3267 &  0.1633 \tabularnewline
52 &  0.847 &  0.306 &  0.153 \tabularnewline
53 &  0.8223 &  0.3554 &  0.1777 \tabularnewline
54 &  0.8257 &  0.3486 &  0.1743 \tabularnewline
55 &  0.7835 &  0.4329 &  0.2165 \tabularnewline
56 &  0.7398 &  0.5205 &  0.2602 \tabularnewline
57 &  0.6937 &  0.6126 &  0.3063 \tabularnewline
58 &  0.6455 &  0.7091 &  0.3545 \tabularnewline
59 &  0.5868 &  0.8263 &  0.4132 \tabularnewline
60 &  0.5686 &  0.8627 &  0.4314 \tabularnewline
61 &  0.5726 &  0.8549 &  0.4274 \tabularnewline
62 &  0.6056 &  0.7888 &  0.3944 \tabularnewline
63 &  0.5434 &  0.9132 &  0.4566 \tabularnewline
64 &  0.6266 &  0.7468 &  0.3734 \tabularnewline
65 &  0.6001 &  0.7998 &  0.3999 \tabularnewline
66 &  0.554 &  0.892 &  0.446 \tabularnewline
67 &  0.4877 &  0.9755 &  0.5123 \tabularnewline
68 &  0.4799 &  0.9597 &  0.5201 \tabularnewline
69 &  0.4201 &  0.8402 &  0.5799 \tabularnewline
70 &  0.4515 &  0.903 &  0.5485 \tabularnewline
71 &  0.395 &  0.79 &  0.605 \tabularnewline
72 &  0.3408 &  0.6816 &  0.6592 \tabularnewline
73 &  0.3765 &  0.753 &  0.6235 \tabularnewline
74 &  0.3923 &  0.7846 &  0.6077 \tabularnewline
75 &  0.3472 &  0.6943 &  0.6528 \tabularnewline
76 &  0.2944 &  0.5888 &  0.7056 \tabularnewline
77 &  0.4875 &  0.9749 &  0.5125 \tabularnewline
78 &  0.4741 &  0.9483 &  0.5259 \tabularnewline
79 &  0.4763 &  0.9526 &  0.5237 \tabularnewline
80 &  0.4485 &  0.897 &  0.5515 \tabularnewline
81 &  0.9657 &  0.06858 &  0.03429 \tabularnewline
82 &  0.9457 &  0.1085 &  0.05427 \tabularnewline
83 &  0.9118 &  0.1764 &  0.0882 \tabularnewline
84 &  0.8749 &  0.2503 &  0.1251 \tabularnewline
85 &  0.9948 &  0.01043 &  0.005217 \tabularnewline
86 &  0.9839 &  0.03214 &  0.01607 \tabularnewline
87 &  0.9827 &  0.03469 &  0.01734 \tabularnewline
88 &  0.9664 &  0.06727 &  0.03364 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309808&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] 0.219[/C][C] 0.438[/C][C] 0.781[/C][/ROW]
[ROW][C]7[/C][C] 0.1145[/C][C] 0.229[/C][C] 0.8855[/C][/ROW]
[ROW][C]8[/C][C] 0.3758[/C][C] 0.7516[/C][C] 0.6242[/C][/ROW]
[ROW][C]9[/C][C] 0.3963[/C][C] 0.7926[/C][C] 0.6037[/C][/ROW]
[ROW][C]10[/C][C] 0.3022[/C][C] 0.6043[/C][C] 0.6978[/C][/ROW]
[ROW][C]11[/C][C] 0.2428[/C][C] 0.4856[/C][C] 0.7572[/C][/ROW]
[ROW][C]12[/C][C] 0.2142[/C][C] 0.4284[/C][C] 0.7858[/C][/ROW]
[ROW][C]13[/C][C] 0.1796[/C][C] 0.3592[/C][C] 0.8204[/C][/ROW]
[ROW][C]14[/C][C] 0.1243[/C][C] 0.2486[/C][C] 0.8757[/C][/ROW]
[ROW][C]15[/C][C] 0.6748[/C][C] 0.6505[/C][C] 0.3252[/C][/ROW]
[ROW][C]16[/C][C] 0.6611[/C][C] 0.6777[/C][C] 0.3389[/C][/ROW]
[ROW][C]17[/C][C] 0.6132[/C][C] 0.7737[/C][C] 0.3868[/C][/ROW]
[ROW][C]18[/C][C] 0.6648[/C][C] 0.6704[/C][C] 0.3352[/C][/ROW]
[ROW][C]19[/C][C] 0.719[/C][C] 0.5619[/C][C] 0.281[/C][/ROW]
[ROW][C]20[/C][C] 0.8867[/C][C] 0.2267[/C][C] 0.1133[/C][/ROW]
[ROW][C]21[/C][C] 0.9028[/C][C] 0.1943[/C][C] 0.09716[/C][/ROW]
[ROW][C]22[/C][C] 0.8748[/C][C] 0.2505[/C][C] 0.1252[/C][/ROW]
[ROW][C]23[/C][C] 0.9256[/C][C] 0.1487[/C][C] 0.07437[/C][/ROW]
[ROW][C]24[/C][C] 0.9173[/C][C] 0.1654[/C][C] 0.0827[/C][/ROW]
[ROW][C]25[/C][C] 0.9486[/C][C] 0.1028[/C][C] 0.0514[/C][/ROW]
[ROW][C]26[/C][C] 0.9448[/C][C] 0.1104[/C][C] 0.05519[/C][/ROW]
[ROW][C]27[/C][C] 0.9396[/C][C] 0.1208[/C][C] 0.06041[/C][/ROW]
[ROW][C]28[/C][C] 0.9256[/C][C] 0.1488[/C][C] 0.0744[/C][/ROW]
[ROW][C]29[/C][C] 0.9029[/C][C] 0.1943[/C][C] 0.09714[/C][/ROW]
[ROW][C]30[/C][C] 0.8814[/C][C] 0.2371[/C][C] 0.1186[/C][/ROW]
[ROW][C]31[/C][C] 0.8541[/C][C] 0.2917[/C][C] 0.1459[/C][/ROW]
[ROW][C]32[/C][C] 0.896[/C][C] 0.208[/C][C] 0.104[/C][/ROW]
[ROW][C]33[/C][C] 0.9144[/C][C] 0.1711[/C][C] 0.08556[/C][/ROW]
[ROW][C]34[/C][C] 0.9206[/C][C] 0.1588[/C][C] 0.07942[/C][/ROW]
[ROW][C]35[/C][C] 0.9093[/C][C] 0.1814[/C][C] 0.09071[/C][/ROW]
[ROW][C]36[/C][C] 0.9098[/C][C] 0.1804[/C][C] 0.09018[/C][/ROW]
[ROW][C]37[/C][C] 0.9228[/C][C] 0.1544[/C][C] 0.07718[/C][/ROW]
[ROW][C]38[/C][C] 0.9047[/C][C] 0.1906[/C][C] 0.09531[/C][/ROW]
[ROW][C]39[/C][C] 0.9011[/C][C] 0.1978[/C][C] 0.09892[/C][/ROW]
[ROW][C]40[/C][C] 0.8769[/C][C] 0.2462[/C][C] 0.1231[/C][/ROW]
[ROW][C]41[/C][C] 0.9303[/C][C] 0.1394[/C][C] 0.06972[/C][/ROW]
[ROW][C]42[/C][C] 0.9187[/C][C] 0.1627[/C][C] 0.08134[/C][/ROW]
[ROW][C]43[/C][C] 0.9248[/C][C] 0.1504[/C][C] 0.07522[/C][/ROW]
[ROW][C]44[/C][C] 0.9069[/C][C] 0.1863[/C][C] 0.09315[/C][/ROW]
[ROW][C]45[/C][C] 0.9123[/C][C] 0.1753[/C][C] 0.08767[/C][/ROW]
[ROW][C]46[/C][C] 0.8913[/C][C] 0.2174[/C][C] 0.1087[/C][/ROW]
[ROW][C]47[/C][C] 0.9062[/C][C] 0.1877[/C][C] 0.09383[/C][/ROW]
[ROW][C]48[/C][C] 0.9051[/C][C] 0.1897[/C][C] 0.09487[/C][/ROW]
[ROW][C]49[/C][C] 0.8967[/C][C] 0.2065[/C][C] 0.1033[/C][/ROW]
[ROW][C]50[/C][C] 0.8708[/C][C] 0.2584[/C][C] 0.1292[/C][/ROW]
[ROW][C]51[/C][C] 0.8367[/C][C] 0.3267[/C][C] 0.1633[/C][/ROW]
[ROW][C]52[/C][C] 0.847[/C][C] 0.306[/C][C] 0.153[/C][/ROW]
[ROW][C]53[/C][C] 0.8223[/C][C] 0.3554[/C][C] 0.1777[/C][/ROW]
[ROW][C]54[/C][C] 0.8257[/C][C] 0.3486[/C][C] 0.1743[/C][/ROW]
[ROW][C]55[/C][C] 0.7835[/C][C] 0.4329[/C][C] 0.2165[/C][/ROW]
[ROW][C]56[/C][C] 0.7398[/C][C] 0.5205[/C][C] 0.2602[/C][/ROW]
[ROW][C]57[/C][C] 0.6937[/C][C] 0.6126[/C][C] 0.3063[/C][/ROW]
[ROW][C]58[/C][C] 0.6455[/C][C] 0.7091[/C][C] 0.3545[/C][/ROW]
[ROW][C]59[/C][C] 0.5868[/C][C] 0.8263[/C][C] 0.4132[/C][/ROW]
[ROW][C]60[/C][C] 0.5686[/C][C] 0.8627[/C][C] 0.4314[/C][/ROW]
[ROW][C]61[/C][C] 0.5726[/C][C] 0.8549[/C][C] 0.4274[/C][/ROW]
[ROW][C]62[/C][C] 0.6056[/C][C] 0.7888[/C][C] 0.3944[/C][/ROW]
[ROW][C]63[/C][C] 0.5434[/C][C] 0.9132[/C][C] 0.4566[/C][/ROW]
[ROW][C]64[/C][C] 0.6266[/C][C] 0.7468[/C][C] 0.3734[/C][/ROW]
[ROW][C]65[/C][C] 0.6001[/C][C] 0.7998[/C][C] 0.3999[/C][/ROW]
[ROW][C]66[/C][C] 0.554[/C][C] 0.892[/C][C] 0.446[/C][/ROW]
[ROW][C]67[/C][C] 0.4877[/C][C] 0.9755[/C][C] 0.5123[/C][/ROW]
[ROW][C]68[/C][C] 0.4799[/C][C] 0.9597[/C][C] 0.5201[/C][/ROW]
[ROW][C]69[/C][C] 0.4201[/C][C] 0.8402[/C][C] 0.5799[/C][/ROW]
[ROW][C]70[/C][C] 0.4515[/C][C] 0.903[/C][C] 0.5485[/C][/ROW]
[ROW][C]71[/C][C] 0.395[/C][C] 0.79[/C][C] 0.605[/C][/ROW]
[ROW][C]72[/C][C] 0.3408[/C][C] 0.6816[/C][C] 0.6592[/C][/ROW]
[ROW][C]73[/C][C] 0.3765[/C][C] 0.753[/C][C] 0.6235[/C][/ROW]
[ROW][C]74[/C][C] 0.3923[/C][C] 0.7846[/C][C] 0.6077[/C][/ROW]
[ROW][C]75[/C][C] 0.3472[/C][C] 0.6943[/C][C] 0.6528[/C][/ROW]
[ROW][C]76[/C][C] 0.2944[/C][C] 0.5888[/C][C] 0.7056[/C][/ROW]
[ROW][C]77[/C][C] 0.4875[/C][C] 0.9749[/C][C] 0.5125[/C][/ROW]
[ROW][C]78[/C][C] 0.4741[/C][C] 0.9483[/C][C] 0.5259[/C][/ROW]
[ROW][C]79[/C][C] 0.4763[/C][C] 0.9526[/C][C] 0.5237[/C][/ROW]
[ROW][C]80[/C][C] 0.4485[/C][C] 0.897[/C][C] 0.5515[/C][/ROW]
[ROW][C]81[/C][C] 0.9657[/C][C] 0.06858[/C][C] 0.03429[/C][/ROW]
[ROW][C]82[/C][C] 0.9457[/C][C] 0.1085[/C][C] 0.05427[/C][/ROW]
[ROW][C]83[/C][C] 0.9118[/C][C] 0.1764[/C][C] 0.0882[/C][/ROW]
[ROW][C]84[/C][C] 0.8749[/C][C] 0.2503[/C][C] 0.1251[/C][/ROW]
[ROW][C]85[/C][C] 0.9948[/C][C] 0.01043[/C][C] 0.005217[/C][/ROW]
[ROW][C]86[/C][C] 0.9839[/C][C] 0.03214[/C][C] 0.01607[/C][/ROW]
[ROW][C]87[/C][C] 0.9827[/C][C] 0.03469[/C][C] 0.01734[/C][/ROW]
[ROW][C]88[/C][C] 0.9664[/C][C] 0.06727[/C][C] 0.03364[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309808&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309808&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	0.219	0.438	0.781
7	0.1145	0.229	0.8855
8	0.3758	0.7516	0.6242
9	0.3963	0.7926	0.6037
10	0.3022	0.6043	0.6978
11	0.2428	0.4856	0.7572
12	0.2142	0.4284	0.7858
13	0.1796	0.3592	0.8204
14	0.1243	0.2486	0.8757
15	0.6748	0.6505	0.3252
16	0.6611	0.6777	0.3389
17	0.6132	0.7737	0.3868
18	0.6648	0.6704	0.3352
19	0.719	0.5619	0.281
20	0.8867	0.2267	0.1133
21	0.9028	0.1943	0.09716
22	0.8748	0.2505	0.1252
23	0.9256	0.1487	0.07437
24	0.9173	0.1654	0.0827
25	0.9486	0.1028	0.0514
26	0.9448	0.1104	0.05519
27	0.9396	0.1208	0.06041
28	0.9256	0.1488	0.0744
29	0.9029	0.1943	0.09714
30	0.8814	0.2371	0.1186
31	0.8541	0.2917	0.1459
32	0.896	0.208	0.104
33	0.9144	0.1711	0.08556
34	0.9206	0.1588	0.07942
35	0.9093	0.1814	0.09071
36	0.9098	0.1804	0.09018
37	0.9228	0.1544	0.07718
38	0.9047	0.1906	0.09531
39	0.9011	0.1978	0.09892
40	0.8769	0.2462	0.1231
41	0.9303	0.1394	0.06972
42	0.9187	0.1627	0.08134
43	0.9248	0.1504	0.07522
44	0.9069	0.1863	0.09315
45	0.9123	0.1753	0.08767
46	0.8913	0.2174	0.1087
47	0.9062	0.1877	0.09383
48	0.9051	0.1897	0.09487
49	0.8967	0.2065	0.1033
50	0.8708	0.2584	0.1292
51	0.8367	0.3267	0.1633
52	0.847	0.306	0.153
53	0.8223	0.3554	0.1777
54	0.8257	0.3486	0.1743
55	0.7835	0.4329	0.2165
56	0.7398	0.5205	0.2602
57	0.6937	0.6126	0.3063
58	0.6455	0.7091	0.3545
59	0.5868	0.8263	0.4132
60	0.5686	0.8627	0.4314
61	0.5726	0.8549	0.4274
62	0.6056	0.7888	0.3944
63	0.5434	0.9132	0.4566
64	0.6266	0.7468	0.3734
65	0.6001	0.7998	0.3999
66	0.554	0.892	0.446
67	0.4877	0.9755	0.5123
68	0.4799	0.9597	0.5201
69	0.4201	0.8402	0.5799
70	0.4515	0.903	0.5485
71	0.395	0.79	0.605
72	0.3408	0.6816	0.6592
73	0.3765	0.753	0.6235
74	0.3923	0.7846	0.6077
75	0.3472	0.6943	0.6528
76	0.2944	0.5888	0.7056
77	0.4875	0.9749	0.5125
78	0.4741	0.9483	0.5259
79	0.4763	0.9526	0.5237
80	0.4485	0.897	0.5515
81	0.9657	0.06858	0.03429
82	0.9457	0.1085	0.05427
83	0.9118	0.1764	0.0882
84	0.8749	0.2503	0.1251
85	0.9948	0.01043	0.005217
86	0.9839	0.03214	0.01607
87	0.9827	0.03469	0.01734
88	0.9664	0.06727	0.03364

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	3	0.0361446	OK
10% type I error level	5	0.060241	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 3 & 0.0361446 & OK \tabularnewline
10% type I error level & 5 & 0.060241 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309808&T=7

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]3[/C][C]0.0361446[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]5[/C][C]0.060241[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309808&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309808&T=7

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	3	0.0361446	OK
10% type I error level	5	0.060241	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.91409, df1 = 2, df2 = 89, p-value = 0.4046
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.33827, df1 = 4, df2 = 87, p-value = 0.8515
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.91409, df1 = 2, df2 = 89, p-value = 0.4046

\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 = 0.91409, df1 = 2, df2 = 89, p-value = 0.4046
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.33827, df1 = 4, df2 = 87, p-value = 0.8515
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.91409, df1 = 2, df2 = 89, p-value = 0.4046
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309808&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 = 0.91409, df1 = 2, df2 = 89, p-value = 0.4046
[/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 = 0.33827, df1 = 4, df2 = 87, p-value = 0.8515
[/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 = 0.91409, df1 = 2, df2 = 89, p-value = 0.4046
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309808&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309808&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 = 0.91409, df1 = 2, df2 = 89, p-value = 0.4046
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.33827, df1 = 4, df2 = 87, p-value = 0.8515
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.91409, df1 = 2, df2 = 89, p-value = 0.4046

Variance Inflation Factors (Multicollinearity)
> vif b c 1.000049 1.000049

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       b        c 
1.000049 1.000049 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309808&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
       b        c 
1.000049 1.000049 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309808&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309808&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 1.000049 1.000049

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;

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

par6 <- '12'
par5 <- '0'
par4 <- '0'
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