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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 10 Dec 2014 13:31:58 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/10/t1418221550xysnsw1xri2ce58.htm/, Retrieved Sun, 19 May 2024 16:29:33 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=265264, Retrieved Sun, 19 May 2024 16:29:33 +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] [] [2013-11-04 07:18:26] [0307e7a6407eb638caabc417e3a6b260]
-  MPD  [Multiple Regression] [Task 1.2 WS7] [2014-11-12 13:46:22] [805021881bfa5340347077d26b077617]
-   PD      [Multiple Regression] [Paper 6] [2014-12-10 13:31:58] [3e8c20c2e60277acd0ccfb10a62c3907] [Current]

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Dataseries X:

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Histogram

Boxplots

26 13 21 93 13
37 11 22 115 14
67 14 18 97 16
43 15 23 99 14
52 14 12 104 13
52 11 20 124 15
43 13 22 88 13
84 16 21 104 20
67 14 19 106 17
49 14 22 77 15
70 15 15 101 16
58 13 19 98 17
68 14 18 120 11
62 11 15 131 16
43 12 20 96 16
56 14 21 106 15
74 12 15 111 14
63 15 23 107 16
58 14 21 109 17
63 12 25 117 15
53 12 9 124 14
57 12 30 132 14
64 14 23 103 15
53 16 16 90 17
29 12 16 70 14
54 12 19 104 16
58 14 25 92 15
51 15 23 104 16
54 14 10 107 8
56 13 14 101 17
47 16 26 108 10
50 15 24 128 16
35 13 24 69 16
30 16 18 105 16
68 16 23 107 8
56 15 23 92 14
43 13 19 64 16
67 12 21 109 19
62 14 18 86 19
57 14 27 115 14
54 10 13 93 13
61 16 28 91 15
56 14 23 104 11
41 14 21 133 9
53 15 19 110 12
46 16 17 86 13
51 15 25 116 17
37 13 14 84 7
42 12 16 100 15
38 12 24 111 12
66 14 20 97 15
53 15 24 78 16
49 11 22 94 14
49 14 22 105 16
59 16 20 88 13
40 13 10 111 16
63 11 22 132 10
34 12 20 117 12
32 12 22 82 14
67 14 20 92 16
61 12 17 109 18
60 13 18 106 12

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265264&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265264&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265264&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Multiple Linear Regression - Estimated Regression Equation
AMS.I[t] = -20.427 + 2.10093STRESSTOT[t] -0.193905NUMERACYTOT[t] + 0.281792PERFECTIONISM.TOT[t] + 1.39385CONFSTATTOT[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
AMS.I[t] =  -20.427 +  2.10093STRESSTOT[t] -0.193905NUMERACYTOT[t] +  0.281792PERFECTIONISM.TOT[t] +  1.39385CONFSTATTOT[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265264&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]AMS.I[t] =  -20.427 +  2.10093STRESSTOT[t] -0.193905NUMERACYTOT[t] +  0.281792PERFECTIONISM.TOT[t] +  1.39385CONFSTATTOT[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265264&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265264&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
AMS.I[t] = -20.427 + 2.10093STRESSTOT[t] -0.193905NUMERACYTOT[t] + 0.281792PERFECTIONISM.TOT[t] + 1.39385CONFSTATTOT[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)	-20.427	19.2484	-1.061	0.293061	0.146531
STRESSTOT	2.10093	0.930031	2.259	0.0277262	0.0138631
NUMERACYTOT	-0.193905	0.329323	-0.5888	0.558321	0.279161
PERFECTIONISM.TOT	0.281792	0.093357	3.018	0.0037953	0.00189765
CONFSTATTOT	1.39385	0.529337	2.633	0.0108678	0.00543392

\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) & -20.427 & 19.2484 & -1.061 & 0.293061 & 0.146531 \tabularnewline
STRESSTOT & 2.10093 & 0.930031 & 2.259 & 0.0277262 & 0.0138631 \tabularnewline
NUMERACYTOT & -0.193905 & 0.329323 & -0.5888 & 0.558321 & 0.279161 \tabularnewline
PERFECTIONISM.TOT & 0.281792 & 0.093357 & 3.018 & 0.0037953 & 0.00189765 \tabularnewline
CONFSTATTOT & 1.39385 & 0.529337 & 2.633 & 0.0108678 & 0.00543392 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265264&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]-20.427[/C][C]19.2484[/C][C]-1.061[/C][C]0.293061[/C][C]0.146531[/C][/ROW]
[ROW][C]STRESSTOT[/C][C]2.10093[/C][C]0.930031[/C][C]2.259[/C][C]0.0277262[/C][C]0.0138631[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]-0.193905[/C][C]0.329323[/C][C]-0.5888[/C][C]0.558321[/C][C]0.279161[/C][/ROW]
[ROW][C]PERFECTIONISM.TOT[/C][C]0.281792[/C][C]0.093357[/C][C]3.018[/C][C]0.0037953[/C][C]0.00189765[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]1.39385[/C][C]0.529337[/C][C]2.633[/C][C]0.0108678[/C][C]0.00543392[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265264&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265264&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)	-20.427	19.2484	-1.061	0.293061	0.146531
STRESSTOT	2.10093	0.930031	2.259	0.0277262	0.0138631
NUMERACYTOT	-0.193905	0.329323	-0.5888	0.558321	0.279161
PERFECTIONISM.TOT	0.281792	0.093357	3.018	0.0037953	0.00189765
CONFSTATTOT	1.39385	0.529337	2.633	0.0108678	0.00543392

Multiple Linear Regression - Regression Statistics
Multiple R	0.478154
R-squared	0.228632
Adjusted R-squared	0.1745
F-TEST (value)	4.22366
F-TEST (DF numerator)	4
F-TEST (DF denominator)	57
p-value	0.00460203
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	10.8622
Sum Squared Residuals	6725.25

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.478154 \tabularnewline
R-squared & 0.228632 \tabularnewline
Adjusted R-squared & 0.1745 \tabularnewline
F-TEST (value) & 4.22366 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 57 \tabularnewline
p-value & 0.00460203 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 10.8622 \tabularnewline
Sum Squared Residuals & 6725.25 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265264&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.478154[/C][/ROW]
[ROW][C]R-squared[/C][C]0.228632[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.1745[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.22366[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]57[/C][/ROW]
[ROW][C]p-value[/C][C]0.00460203[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]10.8622[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]6725.25[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265264&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265264&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	0.478154
R-squared	0.228632
Adjusted R-squared	0.1745
F-TEST (value)	4.22366
F-TEST (DF numerator)	4
F-TEST (DF denominator)	57
p-value	0.00460203
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	10.8622
Sum Squared Residuals	6725.25

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	26	47.1398	-21.1398
2	37	50.3373	-13.3373
3	67	55.1312	11.8688
4	43	54.0385	-11.0385
5	52	54.0856	-2.08563
6	52	54.6551	-2.65514
7	43	45.537	-2.53697
8	84	66.2993	17.7007
9	67	58.8673	8.13271
10	49	47.3259	1.6741
11	70	58.941	11.059
12	58	54.512	3.48798
13	68	54.6432	13.3568
14	62	58.9911	3.00893
15	43	50.2597	-7.25975
16	56	55.6918	0.308225
17	74	52.6685	21.3315
18	63	59.0805	3.91946
19	58	59.3249	-1.32486
20	63	53.814	9.186
21	53	57.4952	-4.49518
22	57	55.6775	1.3225
23	64	54.4586	9.54141
24	53	59.1422	-6.1422
25	29	40.9211	-11.9211
26	54	52.708	1.29201
27	58	50.9711	7.02894
28	51	58.2352	-7.23517
29	54	48.3495	5.65045
30	56	56.3269	-0.326928
31	47	52.5184	-5.51843
32	50	64.8043	-14.8043
33	35	43.9767	-8.97667
34	30	61.5874	-31.5874
35	68	50.0306	17.9694
36	56	52.066	3.93405
37	43	43.5372	-0.537238
38	67	57.9107	9.0893
39	62	56.2131	5.78694
40	57	55.6706	1.32938
41	54	42.3883	11.6117
42	61	54.3094	6.69058
43	56	49.165	6.83504
44	41	54.937	-13.937
45	53	55.1261	-2.12612
46	46	52.2457	-6.24571
47	51	62.6227	-11.6227
48	37	37.5979	-0.597923
49	42	50.7687	-8.76869
50	38	48.1356	-10.1356
51	66	53.3496	12.6504
52	53	50.7147	2.28534
53	49	44.4197	4.58029
54	49	56.6099	-7.60993
55	59	52.2276	6.77242
56	40	58.5266	-18.5266
57	63	49.5524	13.4476
58	34	50.602	-16.602
59	32	43.1391	-11.1391
60	67	53.3344	13.6656
61	61	57.2925	3.70753
62	60	49.991	10.009

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 26 & 47.1398 & -21.1398 \tabularnewline
2 & 37 & 50.3373 & -13.3373 \tabularnewline
3 & 67 & 55.1312 & 11.8688 \tabularnewline
4 & 43 & 54.0385 & -11.0385 \tabularnewline
5 & 52 & 54.0856 & -2.08563 \tabularnewline
6 & 52 & 54.6551 & -2.65514 \tabularnewline
7 & 43 & 45.537 & -2.53697 \tabularnewline
8 & 84 & 66.2993 & 17.7007 \tabularnewline
9 & 67 & 58.8673 & 8.13271 \tabularnewline
10 & 49 & 47.3259 & 1.6741 \tabularnewline
11 & 70 & 58.941 & 11.059 \tabularnewline
12 & 58 & 54.512 & 3.48798 \tabularnewline
13 & 68 & 54.6432 & 13.3568 \tabularnewline
14 & 62 & 58.9911 & 3.00893 \tabularnewline
15 & 43 & 50.2597 & -7.25975 \tabularnewline
16 & 56 & 55.6918 & 0.308225 \tabularnewline
17 & 74 & 52.6685 & 21.3315 \tabularnewline
18 & 63 & 59.0805 & 3.91946 \tabularnewline
19 & 58 & 59.3249 & -1.32486 \tabularnewline
20 & 63 & 53.814 & 9.186 \tabularnewline
21 & 53 & 57.4952 & -4.49518 \tabularnewline
22 & 57 & 55.6775 & 1.3225 \tabularnewline
23 & 64 & 54.4586 & 9.54141 \tabularnewline
24 & 53 & 59.1422 & -6.1422 \tabularnewline
25 & 29 & 40.9211 & -11.9211 \tabularnewline
26 & 54 & 52.708 & 1.29201 \tabularnewline
27 & 58 & 50.9711 & 7.02894 \tabularnewline
28 & 51 & 58.2352 & -7.23517 \tabularnewline
29 & 54 & 48.3495 & 5.65045 \tabularnewline
30 & 56 & 56.3269 & -0.326928 \tabularnewline
31 & 47 & 52.5184 & -5.51843 \tabularnewline
32 & 50 & 64.8043 & -14.8043 \tabularnewline
33 & 35 & 43.9767 & -8.97667 \tabularnewline
34 & 30 & 61.5874 & -31.5874 \tabularnewline
35 & 68 & 50.0306 & 17.9694 \tabularnewline
36 & 56 & 52.066 & 3.93405 \tabularnewline
37 & 43 & 43.5372 & -0.537238 \tabularnewline
38 & 67 & 57.9107 & 9.0893 \tabularnewline
39 & 62 & 56.2131 & 5.78694 \tabularnewline
40 & 57 & 55.6706 & 1.32938 \tabularnewline
41 & 54 & 42.3883 & 11.6117 \tabularnewline
42 & 61 & 54.3094 & 6.69058 \tabularnewline
43 & 56 & 49.165 & 6.83504 \tabularnewline
44 & 41 & 54.937 & -13.937 \tabularnewline
45 & 53 & 55.1261 & -2.12612 \tabularnewline
46 & 46 & 52.2457 & -6.24571 \tabularnewline
47 & 51 & 62.6227 & -11.6227 \tabularnewline
48 & 37 & 37.5979 & -0.597923 \tabularnewline
49 & 42 & 50.7687 & -8.76869 \tabularnewline
50 & 38 & 48.1356 & -10.1356 \tabularnewline
51 & 66 & 53.3496 & 12.6504 \tabularnewline
52 & 53 & 50.7147 & 2.28534 \tabularnewline
53 & 49 & 44.4197 & 4.58029 \tabularnewline
54 & 49 & 56.6099 & -7.60993 \tabularnewline
55 & 59 & 52.2276 & 6.77242 \tabularnewline
56 & 40 & 58.5266 & -18.5266 \tabularnewline
57 & 63 & 49.5524 & 13.4476 \tabularnewline
58 & 34 & 50.602 & -16.602 \tabularnewline
59 & 32 & 43.1391 & -11.1391 \tabularnewline
60 & 67 & 53.3344 & 13.6656 \tabularnewline
61 & 61 & 57.2925 & 3.70753 \tabularnewline
62 & 60 & 49.991 & 10.009 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265264&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]26[/C][C]47.1398[/C][C]-21.1398[/C][/ROW]
[ROW][C]2[/C][C]37[/C][C]50.3373[/C][C]-13.3373[/C][/ROW]
[ROW][C]3[/C][C]67[/C][C]55.1312[/C][C]11.8688[/C][/ROW]
[ROW][C]4[/C][C]43[/C][C]54.0385[/C][C]-11.0385[/C][/ROW]
[ROW][C]5[/C][C]52[/C][C]54.0856[/C][C]-2.08563[/C][/ROW]
[ROW][C]6[/C][C]52[/C][C]54.6551[/C][C]-2.65514[/C][/ROW]
[ROW][C]7[/C][C]43[/C][C]45.537[/C][C]-2.53697[/C][/ROW]
[ROW][C]8[/C][C]84[/C][C]66.2993[/C][C]17.7007[/C][/ROW]
[ROW][C]9[/C][C]67[/C][C]58.8673[/C][C]8.13271[/C][/ROW]
[ROW][C]10[/C][C]49[/C][C]47.3259[/C][C]1.6741[/C][/ROW]
[ROW][C]11[/C][C]70[/C][C]58.941[/C][C]11.059[/C][/ROW]
[ROW][C]12[/C][C]58[/C][C]54.512[/C][C]3.48798[/C][/ROW]
[ROW][C]13[/C][C]68[/C][C]54.6432[/C][C]13.3568[/C][/ROW]
[ROW][C]14[/C][C]62[/C][C]58.9911[/C][C]3.00893[/C][/ROW]
[ROW][C]15[/C][C]43[/C][C]50.2597[/C][C]-7.25975[/C][/ROW]
[ROW][C]16[/C][C]56[/C][C]55.6918[/C][C]0.308225[/C][/ROW]
[ROW][C]17[/C][C]74[/C][C]52.6685[/C][C]21.3315[/C][/ROW]
[ROW][C]18[/C][C]63[/C][C]59.0805[/C][C]3.91946[/C][/ROW]
[ROW][C]19[/C][C]58[/C][C]59.3249[/C][C]-1.32486[/C][/ROW]
[ROW][C]20[/C][C]63[/C][C]53.814[/C][C]9.186[/C][/ROW]
[ROW][C]21[/C][C]53[/C][C]57.4952[/C][C]-4.49518[/C][/ROW]
[ROW][C]22[/C][C]57[/C][C]55.6775[/C][C]1.3225[/C][/ROW]
[ROW][C]23[/C][C]64[/C][C]54.4586[/C][C]9.54141[/C][/ROW]
[ROW][C]24[/C][C]53[/C][C]59.1422[/C][C]-6.1422[/C][/ROW]
[ROW][C]25[/C][C]29[/C][C]40.9211[/C][C]-11.9211[/C][/ROW]
[ROW][C]26[/C][C]54[/C][C]52.708[/C][C]1.29201[/C][/ROW]
[ROW][C]27[/C][C]58[/C][C]50.9711[/C][C]7.02894[/C][/ROW]
[ROW][C]28[/C][C]51[/C][C]58.2352[/C][C]-7.23517[/C][/ROW]
[ROW][C]29[/C][C]54[/C][C]48.3495[/C][C]5.65045[/C][/ROW]
[ROW][C]30[/C][C]56[/C][C]56.3269[/C][C]-0.326928[/C][/ROW]
[ROW][C]31[/C][C]47[/C][C]52.5184[/C][C]-5.51843[/C][/ROW]
[ROW][C]32[/C][C]50[/C][C]64.8043[/C][C]-14.8043[/C][/ROW]
[ROW][C]33[/C][C]35[/C][C]43.9767[/C][C]-8.97667[/C][/ROW]
[ROW][C]34[/C][C]30[/C][C]61.5874[/C][C]-31.5874[/C][/ROW]
[ROW][C]35[/C][C]68[/C][C]50.0306[/C][C]17.9694[/C][/ROW]
[ROW][C]36[/C][C]56[/C][C]52.066[/C][C]3.93405[/C][/ROW]
[ROW][C]37[/C][C]43[/C][C]43.5372[/C][C]-0.537238[/C][/ROW]
[ROW][C]38[/C][C]67[/C][C]57.9107[/C][C]9.0893[/C][/ROW]
[ROW][C]39[/C][C]62[/C][C]56.2131[/C][C]5.78694[/C][/ROW]
[ROW][C]40[/C][C]57[/C][C]55.6706[/C][C]1.32938[/C][/ROW]
[ROW][C]41[/C][C]54[/C][C]42.3883[/C][C]11.6117[/C][/ROW]
[ROW][C]42[/C][C]61[/C][C]54.3094[/C][C]6.69058[/C][/ROW]
[ROW][C]43[/C][C]56[/C][C]49.165[/C][C]6.83504[/C][/ROW]
[ROW][C]44[/C][C]41[/C][C]54.937[/C][C]-13.937[/C][/ROW]
[ROW][C]45[/C][C]53[/C][C]55.1261[/C][C]-2.12612[/C][/ROW]
[ROW][C]46[/C][C]46[/C][C]52.2457[/C][C]-6.24571[/C][/ROW]
[ROW][C]47[/C][C]51[/C][C]62.6227[/C][C]-11.6227[/C][/ROW]
[ROW][C]48[/C][C]37[/C][C]37.5979[/C][C]-0.597923[/C][/ROW]
[ROW][C]49[/C][C]42[/C][C]50.7687[/C][C]-8.76869[/C][/ROW]
[ROW][C]50[/C][C]38[/C][C]48.1356[/C][C]-10.1356[/C][/ROW]
[ROW][C]51[/C][C]66[/C][C]53.3496[/C][C]12.6504[/C][/ROW]
[ROW][C]52[/C][C]53[/C][C]50.7147[/C][C]2.28534[/C][/ROW]
[ROW][C]53[/C][C]49[/C][C]44.4197[/C][C]4.58029[/C][/ROW]
[ROW][C]54[/C][C]49[/C][C]56.6099[/C][C]-7.60993[/C][/ROW]
[ROW][C]55[/C][C]59[/C][C]52.2276[/C][C]6.77242[/C][/ROW]
[ROW][C]56[/C][C]40[/C][C]58.5266[/C][C]-18.5266[/C][/ROW]
[ROW][C]57[/C][C]63[/C][C]49.5524[/C][C]13.4476[/C][/ROW]
[ROW][C]58[/C][C]34[/C][C]50.602[/C][C]-16.602[/C][/ROW]
[ROW][C]59[/C][C]32[/C][C]43.1391[/C][C]-11.1391[/C][/ROW]
[ROW][C]60[/C][C]67[/C][C]53.3344[/C][C]13.6656[/C][/ROW]
[ROW][C]61[/C][C]61[/C][C]57.2925[/C][C]3.70753[/C][/ROW]
[ROW][C]62[/C][C]60[/C][C]49.991[/C][C]10.009[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265264&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265264&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	26	47.1398	-21.1398
2	37	50.3373	-13.3373
3	67	55.1312	11.8688
4	43	54.0385	-11.0385
5	52	54.0856	-2.08563
6	52	54.6551	-2.65514
7	43	45.537	-2.53697
8	84	66.2993	17.7007
9	67	58.8673	8.13271
10	49	47.3259	1.6741
11	70	58.941	11.059
12	58	54.512	3.48798
13	68	54.6432	13.3568
14	62	58.9911	3.00893
15	43	50.2597	-7.25975
16	56	55.6918	0.308225
17	74	52.6685	21.3315
18	63	59.0805	3.91946
19	58	59.3249	-1.32486
20	63	53.814	9.186
21	53	57.4952	-4.49518
22	57	55.6775	1.3225
23	64	54.4586	9.54141
24	53	59.1422	-6.1422
25	29	40.9211	-11.9211
26	54	52.708	1.29201
27	58	50.9711	7.02894
28	51	58.2352	-7.23517
29	54	48.3495	5.65045
30	56	56.3269	-0.326928
31	47	52.5184	-5.51843
32	50	64.8043	-14.8043
33	35	43.9767	-8.97667
34	30	61.5874	-31.5874
35	68	50.0306	17.9694
36	56	52.066	3.93405
37	43	43.5372	-0.537238
38	67	57.9107	9.0893
39	62	56.2131	5.78694
40	57	55.6706	1.32938
41	54	42.3883	11.6117
42	61	54.3094	6.69058
43	56	49.165	6.83504
44	41	54.937	-13.937
45	53	55.1261	-2.12612
46	46	52.2457	-6.24571
47	51	62.6227	-11.6227
48	37	37.5979	-0.597923
49	42	50.7687	-8.76869
50	38	48.1356	-10.1356
51	66	53.3496	12.6504
52	53	50.7147	2.28534
53	49	44.4197	4.58029
54	49	56.6099	-7.60993
55	59	52.2276	6.77242
56	40	58.5266	-18.5266
57	63	49.5524	13.4476
58	34	50.602	-16.602
59	32	43.1391	-11.1391
60	67	53.3344	13.6656
61	61	57.2925	3.70753
62	60	49.991	10.009

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.412812	0.825624	0.587188
9	0.254963	0.509926	0.745037
10	0.143598	0.287196	0.856402
11	0.0820699	0.16414	0.91793
12	0.0738148	0.14763	0.926185
13	0.404105	0.80821	0.595895
14	0.303166	0.606332	0.696834
15	0.225979	0.451958	0.774021
16	0.156719	0.313438	0.843281
17	0.428451	0.856901	0.571549
18	0.346283	0.692566	0.653717
19	0.289449	0.578898	0.710551
20	0.36768	0.735361	0.63232
21	0.385208	0.770416	0.614792
22	0.308815	0.617629	0.691185
23	0.287817	0.575634	0.712183
24	0.306508	0.613016	0.693492
25	0.288552	0.577103	0.711448
26	0.225389	0.450778	0.774611
27	0.199509	0.399019	0.800491
28	0.198535	0.39707	0.801465
29	0.169672	0.339344	0.830328
30	0.129769	0.259538	0.870231
31	0.106755	0.21351	0.893245
32	0.181768	0.363535	0.818232
33	0.184189	0.368377	0.815811
34	0.660678	0.678643	0.339322
35	0.780968	0.438065	0.219032
36	0.724025	0.55195	0.275975
37	0.678887	0.642227	0.321113
38	0.656626	0.686749	0.343374
39	0.599925	0.800149	0.400075
40	0.518855	0.962291	0.481145
41	0.529709	0.940582	0.470291
42	0.459765	0.91953	0.540235
43	0.401561	0.803121	0.598439
44	0.410072	0.820144	0.589928
45	0.324124	0.648248	0.675876
46	0.267952	0.535904	0.732048
47	0.298012	0.596023	0.701988
48	0.219674	0.439348	0.780326
49	0.162261	0.324523	0.837739
50	0.178308	0.356616	0.821692
51	0.166104	0.332208	0.833896
52	0.103047	0.206094	0.896953
53	0.0649944	0.129989	0.935006
54	0.123535	0.247069	0.876465

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.412812 & 0.825624 & 0.587188 \tabularnewline
9 & 0.254963 & 0.509926 & 0.745037 \tabularnewline
10 & 0.143598 & 0.287196 & 0.856402 \tabularnewline
11 & 0.0820699 & 0.16414 & 0.91793 \tabularnewline
12 & 0.0738148 & 0.14763 & 0.926185 \tabularnewline
13 & 0.404105 & 0.80821 & 0.595895 \tabularnewline
14 & 0.303166 & 0.606332 & 0.696834 \tabularnewline
15 & 0.225979 & 0.451958 & 0.774021 \tabularnewline
16 & 0.156719 & 0.313438 & 0.843281 \tabularnewline
17 & 0.428451 & 0.856901 & 0.571549 \tabularnewline
18 & 0.346283 & 0.692566 & 0.653717 \tabularnewline
19 & 0.289449 & 0.578898 & 0.710551 \tabularnewline
20 & 0.36768 & 0.735361 & 0.63232 \tabularnewline
21 & 0.385208 & 0.770416 & 0.614792 \tabularnewline
22 & 0.308815 & 0.617629 & 0.691185 \tabularnewline
23 & 0.287817 & 0.575634 & 0.712183 \tabularnewline
24 & 0.306508 & 0.613016 & 0.693492 \tabularnewline
25 & 0.288552 & 0.577103 & 0.711448 \tabularnewline
26 & 0.225389 & 0.450778 & 0.774611 \tabularnewline
27 & 0.199509 & 0.399019 & 0.800491 \tabularnewline
28 & 0.198535 & 0.39707 & 0.801465 \tabularnewline
29 & 0.169672 & 0.339344 & 0.830328 \tabularnewline
30 & 0.129769 & 0.259538 & 0.870231 \tabularnewline
31 & 0.106755 & 0.21351 & 0.893245 \tabularnewline
32 & 0.181768 & 0.363535 & 0.818232 \tabularnewline
33 & 0.184189 & 0.368377 & 0.815811 \tabularnewline
34 & 0.660678 & 0.678643 & 0.339322 \tabularnewline
35 & 0.780968 & 0.438065 & 0.219032 \tabularnewline
36 & 0.724025 & 0.55195 & 0.275975 \tabularnewline
37 & 0.678887 & 0.642227 & 0.321113 \tabularnewline
38 & 0.656626 & 0.686749 & 0.343374 \tabularnewline
39 & 0.599925 & 0.800149 & 0.400075 \tabularnewline
40 & 0.518855 & 0.962291 & 0.481145 \tabularnewline
41 & 0.529709 & 0.940582 & 0.470291 \tabularnewline
42 & 0.459765 & 0.91953 & 0.540235 \tabularnewline
43 & 0.401561 & 0.803121 & 0.598439 \tabularnewline
44 & 0.410072 & 0.820144 & 0.589928 \tabularnewline
45 & 0.324124 & 0.648248 & 0.675876 \tabularnewline
46 & 0.267952 & 0.535904 & 0.732048 \tabularnewline
47 & 0.298012 & 0.596023 & 0.701988 \tabularnewline
48 & 0.219674 & 0.439348 & 0.780326 \tabularnewline
49 & 0.162261 & 0.324523 & 0.837739 \tabularnewline
50 & 0.178308 & 0.356616 & 0.821692 \tabularnewline
51 & 0.166104 & 0.332208 & 0.833896 \tabularnewline
52 & 0.103047 & 0.206094 & 0.896953 \tabularnewline
53 & 0.0649944 & 0.129989 & 0.935006 \tabularnewline
54 & 0.123535 & 0.247069 & 0.876465 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265264&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.412812[/C][C]0.825624[/C][C]0.587188[/C][/ROW]
[ROW][C]9[/C][C]0.254963[/C][C]0.509926[/C][C]0.745037[/C][/ROW]
[ROW][C]10[/C][C]0.143598[/C][C]0.287196[/C][C]0.856402[/C][/ROW]
[ROW][C]11[/C][C]0.0820699[/C][C]0.16414[/C][C]0.91793[/C][/ROW]
[ROW][C]12[/C][C]0.0738148[/C][C]0.14763[/C][C]0.926185[/C][/ROW]
[ROW][C]13[/C][C]0.404105[/C][C]0.80821[/C][C]0.595895[/C][/ROW]
[ROW][C]14[/C][C]0.303166[/C][C]0.606332[/C][C]0.696834[/C][/ROW]
[ROW][C]15[/C][C]0.225979[/C][C]0.451958[/C][C]0.774021[/C][/ROW]
[ROW][C]16[/C][C]0.156719[/C][C]0.313438[/C][C]0.843281[/C][/ROW]
[ROW][C]17[/C][C]0.428451[/C][C]0.856901[/C][C]0.571549[/C][/ROW]
[ROW][C]18[/C][C]0.346283[/C][C]0.692566[/C][C]0.653717[/C][/ROW]
[ROW][C]19[/C][C]0.289449[/C][C]0.578898[/C][C]0.710551[/C][/ROW]
[ROW][C]20[/C][C]0.36768[/C][C]0.735361[/C][C]0.63232[/C][/ROW]
[ROW][C]21[/C][C]0.385208[/C][C]0.770416[/C][C]0.614792[/C][/ROW]
[ROW][C]22[/C][C]0.308815[/C][C]0.617629[/C][C]0.691185[/C][/ROW]
[ROW][C]23[/C][C]0.287817[/C][C]0.575634[/C][C]0.712183[/C][/ROW]
[ROW][C]24[/C][C]0.306508[/C][C]0.613016[/C][C]0.693492[/C][/ROW]
[ROW][C]25[/C][C]0.288552[/C][C]0.577103[/C][C]0.711448[/C][/ROW]
[ROW][C]26[/C][C]0.225389[/C][C]0.450778[/C][C]0.774611[/C][/ROW]
[ROW][C]27[/C][C]0.199509[/C][C]0.399019[/C][C]0.800491[/C][/ROW]
[ROW][C]28[/C][C]0.198535[/C][C]0.39707[/C][C]0.801465[/C][/ROW]
[ROW][C]29[/C][C]0.169672[/C][C]0.339344[/C][C]0.830328[/C][/ROW]
[ROW][C]30[/C][C]0.129769[/C][C]0.259538[/C][C]0.870231[/C][/ROW]
[ROW][C]31[/C][C]0.106755[/C][C]0.21351[/C][C]0.893245[/C][/ROW]
[ROW][C]32[/C][C]0.181768[/C][C]0.363535[/C][C]0.818232[/C][/ROW]
[ROW][C]33[/C][C]0.184189[/C][C]0.368377[/C][C]0.815811[/C][/ROW]
[ROW][C]34[/C][C]0.660678[/C][C]0.678643[/C][C]0.339322[/C][/ROW]
[ROW][C]35[/C][C]0.780968[/C][C]0.438065[/C][C]0.219032[/C][/ROW]
[ROW][C]36[/C][C]0.724025[/C][C]0.55195[/C][C]0.275975[/C][/ROW]
[ROW][C]37[/C][C]0.678887[/C][C]0.642227[/C][C]0.321113[/C][/ROW]
[ROW][C]38[/C][C]0.656626[/C][C]0.686749[/C][C]0.343374[/C][/ROW]
[ROW][C]39[/C][C]0.599925[/C][C]0.800149[/C][C]0.400075[/C][/ROW]
[ROW][C]40[/C][C]0.518855[/C][C]0.962291[/C][C]0.481145[/C][/ROW]
[ROW][C]41[/C][C]0.529709[/C][C]0.940582[/C][C]0.470291[/C][/ROW]
[ROW][C]42[/C][C]0.459765[/C][C]0.91953[/C][C]0.540235[/C][/ROW]
[ROW][C]43[/C][C]0.401561[/C][C]0.803121[/C][C]0.598439[/C][/ROW]
[ROW][C]44[/C][C]0.410072[/C][C]0.820144[/C][C]0.589928[/C][/ROW]
[ROW][C]45[/C][C]0.324124[/C][C]0.648248[/C][C]0.675876[/C][/ROW]
[ROW][C]46[/C][C]0.267952[/C][C]0.535904[/C][C]0.732048[/C][/ROW]
[ROW][C]47[/C][C]0.298012[/C][C]0.596023[/C][C]0.701988[/C][/ROW]
[ROW][C]48[/C][C]0.219674[/C][C]0.439348[/C][C]0.780326[/C][/ROW]
[ROW][C]49[/C][C]0.162261[/C][C]0.324523[/C][C]0.837739[/C][/ROW]
[ROW][C]50[/C][C]0.178308[/C][C]0.356616[/C][C]0.821692[/C][/ROW]
[ROW][C]51[/C][C]0.166104[/C][C]0.332208[/C][C]0.833896[/C][/ROW]
[ROW][C]52[/C][C]0.103047[/C][C]0.206094[/C][C]0.896953[/C][/ROW]
[ROW][C]53[/C][C]0.0649944[/C][C]0.129989[/C][C]0.935006[/C][/ROW]
[ROW][C]54[/C][C]0.123535[/C][C]0.247069[/C][C]0.876465[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265264&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265264&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.412812	0.825624	0.587188
9	0.254963	0.509926	0.745037
10	0.143598	0.287196	0.856402
11	0.0820699	0.16414	0.91793
12	0.0738148	0.14763	0.926185
13	0.404105	0.80821	0.595895
14	0.303166	0.606332	0.696834
15	0.225979	0.451958	0.774021
16	0.156719	0.313438	0.843281
17	0.428451	0.856901	0.571549
18	0.346283	0.692566	0.653717
19	0.289449	0.578898	0.710551
20	0.36768	0.735361	0.63232
21	0.385208	0.770416	0.614792
22	0.308815	0.617629	0.691185
23	0.287817	0.575634	0.712183
24	0.306508	0.613016	0.693492
25	0.288552	0.577103	0.711448
26	0.225389	0.450778	0.774611
27	0.199509	0.399019	0.800491
28	0.198535	0.39707	0.801465
29	0.169672	0.339344	0.830328
30	0.129769	0.259538	0.870231
31	0.106755	0.21351	0.893245
32	0.181768	0.363535	0.818232
33	0.184189	0.368377	0.815811
34	0.660678	0.678643	0.339322
35	0.780968	0.438065	0.219032
36	0.724025	0.55195	0.275975
37	0.678887	0.642227	0.321113
38	0.656626	0.686749	0.343374
39	0.599925	0.800149	0.400075
40	0.518855	0.962291	0.481145
41	0.529709	0.940582	0.470291
42	0.459765	0.91953	0.540235
43	0.401561	0.803121	0.598439
44	0.410072	0.820144	0.589928
45	0.324124	0.648248	0.675876
46	0.267952	0.535904	0.732048
47	0.298012	0.596023	0.701988
48	0.219674	0.439348	0.780326
49	0.162261	0.324523	0.837739
50	0.178308	0.356616	0.821692
51	0.166104	0.332208	0.833896
52	0.103047	0.206094	0.896953
53	0.0649944	0.129989	0.935006
54	0.123535	0.247069	0.876465

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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265264&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265264&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265264&T=6

As an alternative you can also use a QR Code:

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

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

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

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

Parameters (R input):

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

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- 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'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
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[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1/numgqtests,6))
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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code