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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 31 Oct 2013 11:12:20 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Oct/31/t13832323569gmz8nkzh3k9dhm.htm/, Retrieved Sun, 28 Apr 2024 23:48:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=221536, Retrieved Sun, 28 Apr 2024 23:48:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact63

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [WS7] [2013-10-31 15:12:20] [89bcd0abe8a03478ecfbd1bf2de33aa3] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	7	41	38	14	12	3
2	5	39	32	18	11	5
2	5	30	35	11	14	4
1	5	31	33	12	12	4
2	8	34	37	16	21	5
2	6	35	29	18	12	5
2	5	39	31	14	22	2
2	6	34	36	14	11	5
2	5	36	35	15	10	4
2	4	37	38	15	13	4
1	6	38	31	17	10	5
2	5	36	34	19	8	3
1	5	38	35	10	15	5
2	6	39	38	16	14	3
2	7	33	37	18	10	5
1	6	32	33	14	14	3
1	7	36	32	14	14	4
2	6	38	38	17	11	5
1	8	39	38	14	10	4
2	7	32	32	16	13	3
1	5	32	33	18	7	4
2	5	31	31	11	14	4
2	7	39	38	14	12	3
2	7	37	39	12	14	3
1	5	39	32	17	11	4
2	4	41	32	9	9	5
1	10	36	35	16	11	4
2	6	33	37	14	15	4
2	5	33	33	15	14	4
1	5	34	33	11	13	4
2	5	31	28	16	9	4
1	5	27	32	13	15	3
2	6	37	31	17	10	4
2	5	34	37	15	11	5
1	5	34	30	14	13	4
1	5	32	33	16	8	4
1	5	29	31	9	20	3
1	5	36	33	15	12	4
2	5	29	31	17	10	4
1	5	35	33	13	10	4
1	5	37	32	15	9	5
2	7	34	33	16	14	4
1	5	38	32	16	8	3
1	6	35	33	12	14	3
2	7	38	28	12	11	4
2	7	37	35	11	13	4
2	5	38	39	15	9	4
2	5	33	34	15	11	5
2	4	36	38	17	15	4
1	5	38	32	13	11	5
2	4	32	38	16	10	4
1	5	32	30	14	14	4
1	5	32	33	11	18	4
2	7	34	38	12	14	4
1	5	32	32	12	11	4
2	5	37	32	15	12	5
2	6	39	34	16	13	4
2	4	29	34	15	9	4
1	6	37	36	12	10	4
2	6	35	34	12	15	4
1	5	30	28	8	20	3
1	7	38	34	13	12	4
2	6	34	35	11	12	5
2	8	31	35	14	14	1
2	7	34	31	15	13	3
1	5	35	37	10	11	5
2	6	36	35	11	17	4
1	6	30	27	12	12	4
2	5	39	40	15	13	3
1	5	35	37	15	14	4
1	5	38	36	14	13	4
2	5	31	38	16	15	3
2	4	34	39	15	13	5
1	6	38	41	15	10	4
1	6	34	27	13	11	5
2	6	39	30	12	19	4
2	6	37	37	17	13	4
2	7	34	31	13	17	4
1	5	28	31	15	13	4
1	7	37	27	13	9	3
1	6	33	36	15	11	5
1	5	37	38	16	10	NA
2	5	35	37	15	9	5
1	4	37	33	16	12	4
2	8	32	34	15	12	4
2	8	33	31	14	13	5
1	5	38	39	15	13	4
2	5	33	34	14	12	4
2	6	29	32	13	15	3
2	4	33	33	7	22	4
2	5	31	36	17	13	4
2	5	36	32	13	15	3
2	5	35	41	15	13	5
2	5	32	28	14	15	5
2	6	29	30	13	10	5
2	6	39	36	16	11	4
2	5	37	35	12	16	4
2	6	35	31	14	11	4
1	5	37	34	17	11	4
1	7	32	36	15	10	4
2	5	38	36	17	10	4
1	6	37	35	12	16	4
2	6	36	37	16	12	5
1	6	32	28	11	11	4
2	4	33	39	15	16	4
1	5	40	32	9	19	3
2	5	38	35	16	11	5
1	7	41	39	15	16	4
1	6	36	35	10	15	3
2	9	43	42	10	24	2
2	6	30	34	15	14	5
2	6	31	33	11	15	4
2	5	32	41	13	11	5
1	6	32	33	14	15	1
2	5	37	34	18	12	5
1	8	37	32	16	10	5
2	7	33	40	14	14	3
2	5	34	40	14	13	4
2	7	33	35	14	9	5
2	6	38	36	14	15	5
2	6	33	37	12	15	3
2	9	31	27	14	14	4
2	7	38	39	15	11	5
2	6	37	38	15	8	4
2	5	33	31	15	11	4
2	5	31	33	13	11	4
1	6	39	32	17	8	5
2	6	44	39	17	10	4
2	7	33	36	19	11	5
2	5	35	33	15	13	4
1	5	32	33	13	11	4
1	5	28	32	9	20	4
2	6	40	37	15	10	4
1	4	27	30	15	15	3
1	5	37	38	15	12	4
2	7	32	29	16	14	5
1	5	28	22	11	23	3
1	7	34	35	14	14	4
2	7	30	35	11	16	3
2	6	35	34	15	11	4
1	5	31	35	13	12	3
2	8	32	34	15	10	3
1	5	30	34	16	14	5
2	5	30	35	14	12	5
1	5	31	23	15	12	5
2	6	40	31	16	11	5
2	4	32	27	16	12	5
1	5	36	36	11	13	4
1	5	32	31	12	11	4
1	7	35	32	9	19	4
2	6	38	39	16	12	5
2	7	42	37	13	17	5
1	10	34	38	16	9	4
2	6	35	39	12	12	4
2	8	35	34	9	19	4
2	4	33	31	13	18	5
2	5	36	32	13	15	3
2	6	32	37	14	14	4
2	7	33	36	19	11	5
2	7	34	32	13	9	5
2	6	32	35	12	18	5
2	6	34	36	13	16	5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time15 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 15 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221536&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]15 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221536&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221536&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time15 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Y_t[t] = + 4.86568 + 0.245042X_1t[t] -0.0726865X_2t[t] + 0.0141811X_3t[t] -0.0166972X_4t[t] + 0.0157767X_5t[t] -0.0714425X_6t[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y_t[t] =  +  4.86568 +  0.245042X_1t[t] -0.0726865X_2t[t] +  0.0141811X_3t[t] -0.0166972X_4t[t] +  0.0157767X_5t[t] -0.0714425X_6t[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221536&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y_t[t] =  +  4.86568 +  0.245042X_1t[t] -0.0726865X_2t[t] +  0.0141811X_3t[t] -0.0166972X_4t[t] +  0.0157767X_5t[t] -0.0714425X_6t[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221536&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221536&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Y_t[t] = + 4.86568 + 0.245042X_1t[t] -0.0726865X_2t[t] + 0.0141811X_3t[t] -0.0166972X_4t[t] + 0.0157767X_5t[t] -0.0714425X_6t[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.865680.9477365.1348.44843e-074.22421e-07
X_1t0.2450420.1311351.8690.06357420.0317871
X_2t-0.07268650.052241-1.3910.166120.08306
X_3t0.01418110.01926620.73610.4628150.231408
X_4t-0.01669720.0187078-0.89250.3735040.186752
X_5t0.01577670.03153260.50030.6175580.308779
X_6t-0.07144250.0228526-3.1260.002117060.00105853

\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) & 4.86568 & 0.947736 & 5.134 & 8.44843e-07 & 4.22421e-07 \tabularnewline
X_1t & 0.245042 & 0.131135 & 1.869 & 0.0635742 & 0.0317871 \tabularnewline
X_2t & -0.0726865 & 0.052241 & -1.391 & 0.16612 & 0.08306 \tabularnewline
X_3t & 0.0141811 & 0.0192662 & 0.7361 & 0.462815 & 0.231408 \tabularnewline
X_4t & -0.0166972 & 0.0187078 & -0.8925 & 0.373504 & 0.186752 \tabularnewline
X_5t & 0.0157767 & 0.0315326 & 0.5003 & 0.617558 & 0.308779 \tabularnewline
X_6t & -0.0714425 & 0.0228526 & -3.126 & 0.00211706 & 0.00105853 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221536&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]4.86568[/C][C]0.947736[/C][C]5.134[/C][C]8.44843e-07[/C][C]4.22421e-07[/C][/ROW]
[ROW][C]X_1t[/C][C]0.245042[/C][C]0.131135[/C][C]1.869[/C][C]0.0635742[/C][C]0.0317871[/C][/ROW]
[ROW][C]X_2t[/C][C]-0.0726865[/C][C]0.052241[/C][C]-1.391[/C][C]0.16612[/C][C]0.08306[/C][/ROW]
[ROW][C]X_3t[/C][C]0.0141811[/C][C]0.0192662[/C][C]0.7361[/C][C]0.462815[/C][C]0.231408[/C][/ROW]
[ROW][C]X_4t[/C][C]-0.0166972[/C][C]0.0187078[/C][C]-0.8925[/C][C]0.373504[/C][C]0.186752[/C][/ROW]
[ROW][C]X_5t[/C][C]0.0157767[/C][C]0.0315326[/C][C]0.5003[/C][C]0.617558[/C][C]0.308779[/C][/ROW]
[ROW][C]X_6t[/C][C]-0.0714425[/C][C]0.0228526[/C][C]-3.126[/C][C]0.00211706[/C][C]0.00105853[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221536&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221536&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.865680.9477365.1348.44843e-074.22421e-07
X_1t0.2450420.1311351.8690.06357420.0317871
X_2t-0.07268650.052241-1.3910.166120.08306
X_3t0.01418110.01926620.73610.4628150.231408
X_4t-0.01669720.0187078-0.89250.3735040.186752
X_5t0.01577670.03153260.50030.6175580.308779
X_6t-0.07144250.0228526-3.1260.002117060.00105853






Multiple Linear Regression - Regression Statistics
Multiple R0.362527
R-squared0.131426
Adjusted R-squared0.0975856
F-TEST (value)3.88369
F-TEST (DF numerator)6
F-TEST (DF denominator)154
p-value0.0012229
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.756165
Sum Squared Residuals88.055

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.362527 \tabularnewline
R-squared & 0.131426 \tabularnewline
Adjusted R-squared & 0.0975856 \tabularnewline
F-TEST (value) & 3.88369 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 0.0012229 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.756165 \tabularnewline
Sum Squared Residuals & 88.055 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221536&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.362527[/C][/ROW]
[ROW][C]R-squared[/C][C]0.131426[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0975856[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.88369[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]0.0012229[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.756165[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]88.055[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221536&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221536&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.362527
R-squared0.131426
Adjusted R-squared0.0975856
F-TEST (value)3.88369
F-TEST (DF numerator)6
F-TEST (DF denominator)154
p-value0.0012229
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.756165
Sum Squared Residuals88.055






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
134.15745-1.15745
254.509190.490809
344.00671-0.00670532
443.96790.0320993
553.390761.60924
654.358430.641571
723.67691-1.67691
854.23570.764297
944.44067-0.440668
1044.26312-0.263117
1154.249640.750356
1234.66336-1.66336
1353.787891.21211
1434.09044-1.09044
1554.266690.733312
1633.79806-0.798063
1743.79880.201202
1854.306360.693637
1943.954240.0457581
2034.09011-1.09011
2144.43395-0.433954
2244.08768-0.0876752
2334.12909-1.12909
2433.90959-0.909587
2544.24837-0.248372
2654.611130.388866
2743.776530.223472
2843.919050.080945
2944.14575-0.14575
3043.923220.0767753
3144.57386-0.573863
3233.72932-0.729323
3344.48051-0.480505
3454.307470.692531
3544.02065-0.0206463
3644.33096-0.330959
3733.35406-0.354063
3844.08614-0.086136
3944.43974-0.439743
4044.18329-0.183287
4154.331340.668658
4244.03033-0.0303341
4334.43274-1.43274
4433.80905-0.809053
4544.32177-0.321765
4644.03204-0.0320421
4744.47368-0.473684
4854.343380.65662
4944.1376-0.137604
5054.171080.828916
5144.42232-0.422316
5243.920840.0791584
5343.537650.46235
5443.883740.116258
5544.07022-0.0702215
5654.362060.637944
5744.22867-0.228671
5844.50223-0.502227
5944.07309-0.073094
6043.965960.0340446
6133.40256-0.402559
6243.920870.0791255
6354.133630.866372
6413.85016-2.85016
6534.11939-1.11939
6653.997731.00227
6743.804780.195222
6843.981220.0187837
6934.1854-1.1854
7043.862280.137719
7143.977190.0228127
7233.97824-0.978236
7354.203880.796124
7444.05112-0.051119
7554.125160.87484
7643.80370.196302
7744.16599-0.165994
7843.802070.197929
7943.934640.0653606
8034.2379-1.2379
8153.992261.00774
82NANA0.535465
8355.18878-0.18878
8444.0397-0.0396967
8543.016750.98325
8654.942870.0571276
8744.25616-0.256161
8844.93004-0.93004
8932.520680.479317
9044.17029-0.170292
9145.10199-1.10199
9232.111980.888023
9354.127840.872165
9454.320650.679353
9555.33816-0.338162
9643.978860.0211357
9744.33337-0.33337
9844.18662-0.186616
9943.976830.0231679
10044.48389-0.483887
10143.661140.338864
10243.207480.792521
10355.04855-0.0485471
10443.975370.0246322
10544.5648-0.5648
10632.413360.586635
10754.625710.374285
10844.68684-0.686844
10934.05323-1.05323
11021.013820.986177
11154.910150.0898483
11243.180770.819235
11357.72662-2.72662
11410.3759920.624008
11554.057620.942384
11655.86772-0.867719
11733.09872-0.0987161
11843.308420.691582
11954.006660.993343
12055.8875-0.887502
12132.911050.0889523
12243.185430.814574
12355.47496-0.474956
12444.39347-0.393471
12544.30016-0.300162
12643.390010.609987
12755.44619-0.446195
12843.227720.772281
12955.24555-0.245554
13044.0693-0.069301
13143.323180.676815
13244.39131-0.391312
13344.86696-0.866957
13433.01683-0.016831
13543.068760.931239
13655.30738-0.307382
13732.720340.279655
13844.71845-0.718447
13933.29906-0.299055
14044.95028-0.950283
14134.18258-1.18258
14231.857241.14276
14354.196920.80308
14454.18220.817797
14554.435830.564171
14654.46310.5369
14754.90150.0985048
14844.08692-0.0869187
14943.348520.651479
15043.202450.797553
15153.815341.18466
15254.840960.159041
15344.0968-0.096797
15443.487480.512518
15542.934511.06549
15656.10199-1.10199
15732.976320.0236835
15843.227720.772281
15954.356910.643086
16053.692391.30761
16153.862711.13729
1625NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3 & 4.15745 & -1.15745 \tabularnewline
2 & 5 & 4.50919 & 0.490809 \tabularnewline
3 & 4 & 4.00671 & -0.00670532 \tabularnewline
4 & 4 & 3.9679 & 0.0320993 \tabularnewline
5 & 5 & 3.39076 & 1.60924 \tabularnewline
6 & 5 & 4.35843 & 0.641571 \tabularnewline
7 & 2 & 3.67691 & -1.67691 \tabularnewline
8 & 5 & 4.2357 & 0.764297 \tabularnewline
9 & 4 & 4.44067 & -0.440668 \tabularnewline
10 & 4 & 4.26312 & -0.263117 \tabularnewline
11 & 5 & 4.24964 & 0.750356 \tabularnewline
12 & 3 & 4.66336 & -1.66336 \tabularnewline
13 & 5 & 3.78789 & 1.21211 \tabularnewline
14 & 3 & 4.09044 & -1.09044 \tabularnewline
15 & 5 & 4.26669 & 0.733312 \tabularnewline
16 & 3 & 3.79806 & -0.798063 \tabularnewline
17 & 4 & 3.7988 & 0.201202 \tabularnewline
18 & 5 & 4.30636 & 0.693637 \tabularnewline
19 & 4 & 3.95424 & 0.0457581 \tabularnewline
20 & 3 & 4.09011 & -1.09011 \tabularnewline
21 & 4 & 4.43395 & -0.433954 \tabularnewline
22 & 4 & 4.08768 & -0.0876752 \tabularnewline
23 & 3 & 4.12909 & -1.12909 \tabularnewline
24 & 3 & 3.90959 & -0.909587 \tabularnewline
25 & 4 & 4.24837 & -0.248372 \tabularnewline
26 & 5 & 4.61113 & 0.388866 \tabularnewline
27 & 4 & 3.77653 & 0.223472 \tabularnewline
28 & 4 & 3.91905 & 0.080945 \tabularnewline
29 & 4 & 4.14575 & -0.14575 \tabularnewline
30 & 4 & 3.92322 & 0.0767753 \tabularnewline
31 & 4 & 4.57386 & -0.573863 \tabularnewline
32 & 3 & 3.72932 & -0.729323 \tabularnewline
33 & 4 & 4.48051 & -0.480505 \tabularnewline
34 & 5 & 4.30747 & 0.692531 \tabularnewline
35 & 4 & 4.02065 & -0.0206463 \tabularnewline
36 & 4 & 4.33096 & -0.330959 \tabularnewline
37 & 3 & 3.35406 & -0.354063 \tabularnewline
38 & 4 & 4.08614 & -0.086136 \tabularnewline
39 & 4 & 4.43974 & -0.439743 \tabularnewline
40 & 4 & 4.18329 & -0.183287 \tabularnewline
41 & 5 & 4.33134 & 0.668658 \tabularnewline
42 & 4 & 4.03033 & -0.0303341 \tabularnewline
43 & 3 & 4.43274 & -1.43274 \tabularnewline
44 & 3 & 3.80905 & -0.809053 \tabularnewline
45 & 4 & 4.32177 & -0.321765 \tabularnewline
46 & 4 & 4.03204 & -0.0320421 \tabularnewline
47 & 4 & 4.47368 & -0.473684 \tabularnewline
48 & 5 & 4.34338 & 0.65662 \tabularnewline
49 & 4 & 4.1376 & -0.137604 \tabularnewline
50 & 5 & 4.17108 & 0.828916 \tabularnewline
51 & 4 & 4.42232 & -0.422316 \tabularnewline
52 & 4 & 3.92084 & 0.0791584 \tabularnewline
53 & 4 & 3.53765 & 0.46235 \tabularnewline
54 & 4 & 3.88374 & 0.116258 \tabularnewline
55 & 4 & 4.07022 & -0.0702215 \tabularnewline
56 & 5 & 4.36206 & 0.637944 \tabularnewline
57 & 4 & 4.22867 & -0.228671 \tabularnewline
58 & 4 & 4.50223 & -0.502227 \tabularnewline
59 & 4 & 4.07309 & -0.073094 \tabularnewline
60 & 4 & 3.96596 & 0.0340446 \tabularnewline
61 & 3 & 3.40256 & -0.402559 \tabularnewline
62 & 4 & 3.92087 & 0.0791255 \tabularnewline
63 & 5 & 4.13363 & 0.866372 \tabularnewline
64 & 1 & 3.85016 & -2.85016 \tabularnewline
65 & 3 & 4.11939 & -1.11939 \tabularnewline
66 & 5 & 3.99773 & 1.00227 \tabularnewline
67 & 4 & 3.80478 & 0.195222 \tabularnewline
68 & 4 & 3.98122 & 0.0187837 \tabularnewline
69 & 3 & 4.1854 & -1.1854 \tabularnewline
70 & 4 & 3.86228 & 0.137719 \tabularnewline
71 & 4 & 3.97719 & 0.0228127 \tabularnewline
72 & 3 & 3.97824 & -0.978236 \tabularnewline
73 & 5 & 4.20388 & 0.796124 \tabularnewline
74 & 4 & 4.05112 & -0.051119 \tabularnewline
75 & 5 & 4.12516 & 0.87484 \tabularnewline
76 & 4 & 3.8037 & 0.196302 \tabularnewline
77 & 4 & 4.16599 & -0.165994 \tabularnewline
78 & 4 & 3.80207 & 0.197929 \tabularnewline
79 & 4 & 3.93464 & 0.0653606 \tabularnewline
80 & 3 & 4.2379 & -1.2379 \tabularnewline
81 & 5 & 3.99226 & 1.00774 \tabularnewline
82 & NA & NA & 0.535465 \tabularnewline
83 & 5 & 5.18878 & -0.18878 \tabularnewline
84 & 4 & 4.0397 & -0.0396967 \tabularnewline
85 & 4 & 3.01675 & 0.98325 \tabularnewline
86 & 5 & 4.94287 & 0.0571276 \tabularnewline
87 & 4 & 4.25616 & -0.256161 \tabularnewline
88 & 4 & 4.93004 & -0.93004 \tabularnewline
89 & 3 & 2.52068 & 0.479317 \tabularnewline
90 & 4 & 4.17029 & -0.170292 \tabularnewline
91 & 4 & 5.10199 & -1.10199 \tabularnewline
92 & 3 & 2.11198 & 0.888023 \tabularnewline
93 & 5 & 4.12784 & 0.872165 \tabularnewline
94 & 5 & 4.32065 & 0.679353 \tabularnewline
95 & 5 & 5.33816 & -0.338162 \tabularnewline
96 & 4 & 3.97886 & 0.0211357 \tabularnewline
97 & 4 & 4.33337 & -0.33337 \tabularnewline
98 & 4 & 4.18662 & -0.186616 \tabularnewline
99 & 4 & 3.97683 & 0.0231679 \tabularnewline
100 & 4 & 4.48389 & -0.483887 \tabularnewline
101 & 4 & 3.66114 & 0.338864 \tabularnewline
102 & 4 & 3.20748 & 0.792521 \tabularnewline
103 & 5 & 5.04855 & -0.0485471 \tabularnewline
104 & 4 & 3.97537 & 0.0246322 \tabularnewline
105 & 4 & 4.5648 & -0.5648 \tabularnewline
106 & 3 & 2.41336 & 0.586635 \tabularnewline
107 & 5 & 4.62571 & 0.374285 \tabularnewline
108 & 4 & 4.68684 & -0.686844 \tabularnewline
109 & 3 & 4.05323 & -1.05323 \tabularnewline
110 & 2 & 1.01382 & 0.986177 \tabularnewline
111 & 5 & 4.91015 & 0.0898483 \tabularnewline
112 & 4 & 3.18077 & 0.819235 \tabularnewline
113 & 5 & 7.72662 & -2.72662 \tabularnewline
114 & 1 & 0.375992 & 0.624008 \tabularnewline
115 & 5 & 4.05762 & 0.942384 \tabularnewline
116 & 5 & 5.86772 & -0.867719 \tabularnewline
117 & 3 & 3.09872 & -0.0987161 \tabularnewline
118 & 4 & 3.30842 & 0.691582 \tabularnewline
119 & 5 & 4.00666 & 0.993343 \tabularnewline
120 & 5 & 5.8875 & -0.887502 \tabularnewline
121 & 3 & 2.91105 & 0.0889523 \tabularnewline
122 & 4 & 3.18543 & 0.814574 \tabularnewline
123 & 5 & 5.47496 & -0.474956 \tabularnewline
124 & 4 & 4.39347 & -0.393471 \tabularnewline
125 & 4 & 4.30016 & -0.300162 \tabularnewline
126 & 4 & 3.39001 & 0.609987 \tabularnewline
127 & 5 & 5.44619 & -0.446195 \tabularnewline
128 & 4 & 3.22772 & 0.772281 \tabularnewline
129 & 5 & 5.24555 & -0.245554 \tabularnewline
130 & 4 & 4.0693 & -0.069301 \tabularnewline
131 & 4 & 3.32318 & 0.676815 \tabularnewline
132 & 4 & 4.39131 & -0.391312 \tabularnewline
133 & 4 & 4.86696 & -0.866957 \tabularnewline
134 & 3 & 3.01683 & -0.016831 \tabularnewline
135 & 4 & 3.06876 & 0.931239 \tabularnewline
136 & 5 & 5.30738 & -0.307382 \tabularnewline
137 & 3 & 2.72034 & 0.279655 \tabularnewline
138 & 4 & 4.71845 & -0.718447 \tabularnewline
139 & 3 & 3.29906 & -0.299055 \tabularnewline
140 & 4 & 4.95028 & -0.950283 \tabularnewline
141 & 3 & 4.18258 & -1.18258 \tabularnewline
142 & 3 & 1.85724 & 1.14276 \tabularnewline
143 & 5 & 4.19692 & 0.80308 \tabularnewline
144 & 5 & 4.1822 & 0.817797 \tabularnewline
145 & 5 & 4.43583 & 0.564171 \tabularnewline
146 & 5 & 4.4631 & 0.5369 \tabularnewline
147 & 5 & 4.9015 & 0.0985048 \tabularnewline
148 & 4 & 4.08692 & -0.0869187 \tabularnewline
149 & 4 & 3.34852 & 0.651479 \tabularnewline
150 & 4 & 3.20245 & 0.797553 \tabularnewline
151 & 5 & 3.81534 & 1.18466 \tabularnewline
152 & 5 & 4.84096 & 0.159041 \tabularnewline
153 & 4 & 4.0968 & -0.096797 \tabularnewline
154 & 4 & 3.48748 & 0.512518 \tabularnewline
155 & 4 & 2.93451 & 1.06549 \tabularnewline
156 & 5 & 6.10199 & -1.10199 \tabularnewline
157 & 3 & 2.97632 & 0.0236835 \tabularnewline
158 & 4 & 3.22772 & 0.772281 \tabularnewline
159 & 5 & 4.35691 & 0.643086 \tabularnewline
160 & 5 & 3.69239 & 1.30761 \tabularnewline
161 & 5 & 3.86271 & 1.13729 \tabularnewline
162 & 5 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221536&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]3[/C][C]4.15745[/C][C]-1.15745[/C][/ROW]
[ROW][C]2[/C][C]5[/C][C]4.50919[/C][C]0.490809[/C][/ROW]
[ROW][C]3[/C][C]4[/C][C]4.00671[/C][C]-0.00670532[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]3.9679[/C][C]0.0320993[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]3.39076[/C][C]1.60924[/C][/ROW]
[ROW][C]6[/C][C]5[/C][C]4.35843[/C][C]0.641571[/C][/ROW]
[ROW][C]7[/C][C]2[/C][C]3.67691[/C][C]-1.67691[/C][/ROW]
[ROW][C]8[/C][C]5[/C][C]4.2357[/C][C]0.764297[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]4.44067[/C][C]-0.440668[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]4.26312[/C][C]-0.263117[/C][/ROW]
[ROW][C]11[/C][C]5[/C][C]4.24964[/C][C]0.750356[/C][/ROW]
[ROW][C]12[/C][C]3[/C][C]4.66336[/C][C]-1.66336[/C][/ROW]
[ROW][C]13[/C][C]5[/C][C]3.78789[/C][C]1.21211[/C][/ROW]
[ROW][C]14[/C][C]3[/C][C]4.09044[/C][C]-1.09044[/C][/ROW]
[ROW][C]15[/C][C]5[/C][C]4.26669[/C][C]0.733312[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]3.79806[/C][C]-0.798063[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]3.7988[/C][C]0.201202[/C][/ROW]
[ROW][C]18[/C][C]5[/C][C]4.30636[/C][C]0.693637[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]3.95424[/C][C]0.0457581[/C][/ROW]
[ROW][C]20[/C][C]3[/C][C]4.09011[/C][C]-1.09011[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]4.43395[/C][C]-0.433954[/C][/ROW]
[ROW][C]22[/C][C]4[/C][C]4.08768[/C][C]-0.0876752[/C][/ROW]
[ROW][C]23[/C][C]3[/C][C]4.12909[/C][C]-1.12909[/C][/ROW]
[ROW][C]24[/C][C]3[/C][C]3.90959[/C][C]-0.909587[/C][/ROW]
[ROW][C]25[/C][C]4[/C][C]4.24837[/C][C]-0.248372[/C][/ROW]
[ROW][C]26[/C][C]5[/C][C]4.61113[/C][C]0.388866[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]3.77653[/C][C]0.223472[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]3.91905[/C][C]0.080945[/C][/ROW]
[ROW][C]29[/C][C]4[/C][C]4.14575[/C][C]-0.14575[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]3.92322[/C][C]0.0767753[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]4.57386[/C][C]-0.573863[/C][/ROW]
[ROW][C]32[/C][C]3[/C][C]3.72932[/C][C]-0.729323[/C][/ROW]
[ROW][C]33[/C][C]4[/C][C]4.48051[/C][C]-0.480505[/C][/ROW]
[ROW][C]34[/C][C]5[/C][C]4.30747[/C][C]0.692531[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]4.02065[/C][C]-0.0206463[/C][/ROW]
[ROW][C]36[/C][C]4[/C][C]4.33096[/C][C]-0.330959[/C][/ROW]
[ROW][C]37[/C][C]3[/C][C]3.35406[/C][C]-0.354063[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]4.08614[/C][C]-0.086136[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]4.43974[/C][C]-0.439743[/C][/ROW]
[ROW][C]40[/C][C]4[/C][C]4.18329[/C][C]-0.183287[/C][/ROW]
[ROW][C]41[/C][C]5[/C][C]4.33134[/C][C]0.668658[/C][/ROW]
[ROW][C]42[/C][C]4[/C][C]4.03033[/C][C]-0.0303341[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]4.43274[/C][C]-1.43274[/C][/ROW]
[ROW][C]44[/C][C]3[/C][C]3.80905[/C][C]-0.809053[/C][/ROW]
[ROW][C]45[/C][C]4[/C][C]4.32177[/C][C]-0.321765[/C][/ROW]
[ROW][C]46[/C][C]4[/C][C]4.03204[/C][C]-0.0320421[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]4.47368[/C][C]-0.473684[/C][/ROW]
[ROW][C]48[/C][C]5[/C][C]4.34338[/C][C]0.65662[/C][/ROW]
[ROW][C]49[/C][C]4[/C][C]4.1376[/C][C]-0.137604[/C][/ROW]
[ROW][C]50[/C][C]5[/C][C]4.17108[/C][C]0.828916[/C][/ROW]
[ROW][C]51[/C][C]4[/C][C]4.42232[/C][C]-0.422316[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]3.92084[/C][C]0.0791584[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]3.53765[/C][C]0.46235[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]3.88374[/C][C]0.116258[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]4.07022[/C][C]-0.0702215[/C][/ROW]
[ROW][C]56[/C][C]5[/C][C]4.36206[/C][C]0.637944[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]4.22867[/C][C]-0.228671[/C][/ROW]
[ROW][C]58[/C][C]4[/C][C]4.50223[/C][C]-0.502227[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]4.07309[/C][C]-0.073094[/C][/ROW]
[ROW][C]60[/C][C]4[/C][C]3.96596[/C][C]0.0340446[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]3.40256[/C][C]-0.402559[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]3.92087[/C][C]0.0791255[/C][/ROW]
[ROW][C]63[/C][C]5[/C][C]4.13363[/C][C]0.866372[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]3.85016[/C][C]-2.85016[/C][/ROW]
[ROW][C]65[/C][C]3[/C][C]4.11939[/C][C]-1.11939[/C][/ROW]
[ROW][C]66[/C][C]5[/C][C]3.99773[/C][C]1.00227[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]3.80478[/C][C]0.195222[/C][/ROW]
[ROW][C]68[/C][C]4[/C][C]3.98122[/C][C]0.0187837[/C][/ROW]
[ROW][C]69[/C][C]3[/C][C]4.1854[/C][C]-1.1854[/C][/ROW]
[ROW][C]70[/C][C]4[/C][C]3.86228[/C][C]0.137719[/C][/ROW]
[ROW][C]71[/C][C]4[/C][C]3.97719[/C][C]0.0228127[/C][/ROW]
[ROW][C]72[/C][C]3[/C][C]3.97824[/C][C]-0.978236[/C][/ROW]
[ROW][C]73[/C][C]5[/C][C]4.20388[/C][C]0.796124[/C][/ROW]
[ROW][C]74[/C][C]4[/C][C]4.05112[/C][C]-0.051119[/C][/ROW]
[ROW][C]75[/C][C]5[/C][C]4.12516[/C][C]0.87484[/C][/ROW]
[ROW][C]76[/C][C]4[/C][C]3.8037[/C][C]0.196302[/C][/ROW]
[ROW][C]77[/C][C]4[/C][C]4.16599[/C][C]-0.165994[/C][/ROW]
[ROW][C]78[/C][C]4[/C][C]3.80207[/C][C]0.197929[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]3.93464[/C][C]0.0653606[/C][/ROW]
[ROW][C]80[/C][C]3[/C][C]4.2379[/C][C]-1.2379[/C][/ROW]
[ROW][C]81[/C][C]5[/C][C]3.99226[/C][C]1.00774[/C][/ROW]
[ROW][C]82[/C][C]NA[/C][C]NA[/C][C]0.535465[/C][/ROW]
[ROW][C]83[/C][C]5[/C][C]5.18878[/C][C]-0.18878[/C][/ROW]
[ROW][C]84[/C][C]4[/C][C]4.0397[/C][C]-0.0396967[/C][/ROW]
[ROW][C]85[/C][C]4[/C][C]3.01675[/C][C]0.98325[/C][/ROW]
[ROW][C]86[/C][C]5[/C][C]4.94287[/C][C]0.0571276[/C][/ROW]
[ROW][C]87[/C][C]4[/C][C]4.25616[/C][C]-0.256161[/C][/ROW]
[ROW][C]88[/C][C]4[/C][C]4.93004[/C][C]-0.93004[/C][/ROW]
[ROW][C]89[/C][C]3[/C][C]2.52068[/C][C]0.479317[/C][/ROW]
[ROW][C]90[/C][C]4[/C][C]4.17029[/C][C]-0.170292[/C][/ROW]
[ROW][C]91[/C][C]4[/C][C]5.10199[/C][C]-1.10199[/C][/ROW]
[ROW][C]92[/C][C]3[/C][C]2.11198[/C][C]0.888023[/C][/ROW]
[ROW][C]93[/C][C]5[/C][C]4.12784[/C][C]0.872165[/C][/ROW]
[ROW][C]94[/C][C]5[/C][C]4.32065[/C][C]0.679353[/C][/ROW]
[ROW][C]95[/C][C]5[/C][C]5.33816[/C][C]-0.338162[/C][/ROW]
[ROW][C]96[/C][C]4[/C][C]3.97886[/C][C]0.0211357[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]4.33337[/C][C]-0.33337[/C][/ROW]
[ROW][C]98[/C][C]4[/C][C]4.18662[/C][C]-0.186616[/C][/ROW]
[ROW][C]99[/C][C]4[/C][C]3.97683[/C][C]0.0231679[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]4.48389[/C][C]-0.483887[/C][/ROW]
[ROW][C]101[/C][C]4[/C][C]3.66114[/C][C]0.338864[/C][/ROW]
[ROW][C]102[/C][C]4[/C][C]3.20748[/C][C]0.792521[/C][/ROW]
[ROW][C]103[/C][C]5[/C][C]5.04855[/C][C]-0.0485471[/C][/ROW]
[ROW][C]104[/C][C]4[/C][C]3.97537[/C][C]0.0246322[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]4.5648[/C][C]-0.5648[/C][/ROW]
[ROW][C]106[/C][C]3[/C][C]2.41336[/C][C]0.586635[/C][/ROW]
[ROW][C]107[/C][C]5[/C][C]4.62571[/C][C]0.374285[/C][/ROW]
[ROW][C]108[/C][C]4[/C][C]4.68684[/C][C]-0.686844[/C][/ROW]
[ROW][C]109[/C][C]3[/C][C]4.05323[/C][C]-1.05323[/C][/ROW]
[ROW][C]110[/C][C]2[/C][C]1.01382[/C][C]0.986177[/C][/ROW]
[ROW][C]111[/C][C]5[/C][C]4.91015[/C][C]0.0898483[/C][/ROW]
[ROW][C]112[/C][C]4[/C][C]3.18077[/C][C]0.819235[/C][/ROW]
[ROW][C]113[/C][C]5[/C][C]7.72662[/C][C]-2.72662[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0.375992[/C][C]0.624008[/C][/ROW]
[ROW][C]115[/C][C]5[/C][C]4.05762[/C][C]0.942384[/C][/ROW]
[ROW][C]116[/C][C]5[/C][C]5.86772[/C][C]-0.867719[/C][/ROW]
[ROW][C]117[/C][C]3[/C][C]3.09872[/C][C]-0.0987161[/C][/ROW]
[ROW][C]118[/C][C]4[/C][C]3.30842[/C][C]0.691582[/C][/ROW]
[ROW][C]119[/C][C]5[/C][C]4.00666[/C][C]0.993343[/C][/ROW]
[ROW][C]120[/C][C]5[/C][C]5.8875[/C][C]-0.887502[/C][/ROW]
[ROW][C]121[/C][C]3[/C][C]2.91105[/C][C]0.0889523[/C][/ROW]
[ROW][C]122[/C][C]4[/C][C]3.18543[/C][C]0.814574[/C][/ROW]
[ROW][C]123[/C][C]5[/C][C]5.47496[/C][C]-0.474956[/C][/ROW]
[ROW][C]124[/C][C]4[/C][C]4.39347[/C][C]-0.393471[/C][/ROW]
[ROW][C]125[/C][C]4[/C][C]4.30016[/C][C]-0.300162[/C][/ROW]
[ROW][C]126[/C][C]4[/C][C]3.39001[/C][C]0.609987[/C][/ROW]
[ROW][C]127[/C][C]5[/C][C]5.44619[/C][C]-0.446195[/C][/ROW]
[ROW][C]128[/C][C]4[/C][C]3.22772[/C][C]0.772281[/C][/ROW]
[ROW][C]129[/C][C]5[/C][C]5.24555[/C][C]-0.245554[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]4.0693[/C][C]-0.069301[/C][/ROW]
[ROW][C]131[/C][C]4[/C][C]3.32318[/C][C]0.676815[/C][/ROW]
[ROW][C]132[/C][C]4[/C][C]4.39131[/C][C]-0.391312[/C][/ROW]
[ROW][C]133[/C][C]4[/C][C]4.86696[/C][C]-0.866957[/C][/ROW]
[ROW][C]134[/C][C]3[/C][C]3.01683[/C][C]-0.016831[/C][/ROW]
[ROW][C]135[/C][C]4[/C][C]3.06876[/C][C]0.931239[/C][/ROW]
[ROW][C]136[/C][C]5[/C][C]5.30738[/C][C]-0.307382[/C][/ROW]
[ROW][C]137[/C][C]3[/C][C]2.72034[/C][C]0.279655[/C][/ROW]
[ROW][C]138[/C][C]4[/C][C]4.71845[/C][C]-0.718447[/C][/ROW]
[ROW][C]139[/C][C]3[/C][C]3.29906[/C][C]-0.299055[/C][/ROW]
[ROW][C]140[/C][C]4[/C][C]4.95028[/C][C]-0.950283[/C][/ROW]
[ROW][C]141[/C][C]3[/C][C]4.18258[/C][C]-1.18258[/C][/ROW]
[ROW][C]142[/C][C]3[/C][C]1.85724[/C][C]1.14276[/C][/ROW]
[ROW][C]143[/C][C]5[/C][C]4.19692[/C][C]0.80308[/C][/ROW]
[ROW][C]144[/C][C]5[/C][C]4.1822[/C][C]0.817797[/C][/ROW]
[ROW][C]145[/C][C]5[/C][C]4.43583[/C][C]0.564171[/C][/ROW]
[ROW][C]146[/C][C]5[/C][C]4.4631[/C][C]0.5369[/C][/ROW]
[ROW][C]147[/C][C]5[/C][C]4.9015[/C][C]0.0985048[/C][/ROW]
[ROW][C]148[/C][C]4[/C][C]4.08692[/C][C]-0.0869187[/C][/ROW]
[ROW][C]149[/C][C]4[/C][C]3.34852[/C][C]0.651479[/C][/ROW]
[ROW][C]150[/C][C]4[/C][C]3.20245[/C][C]0.797553[/C][/ROW]
[ROW][C]151[/C][C]5[/C][C]3.81534[/C][C]1.18466[/C][/ROW]
[ROW][C]152[/C][C]5[/C][C]4.84096[/C][C]0.159041[/C][/ROW]
[ROW][C]153[/C][C]4[/C][C]4.0968[/C][C]-0.096797[/C][/ROW]
[ROW][C]154[/C][C]4[/C][C]3.48748[/C][C]0.512518[/C][/ROW]
[ROW][C]155[/C][C]4[/C][C]2.93451[/C][C]1.06549[/C][/ROW]
[ROW][C]156[/C][C]5[/C][C]6.10199[/C][C]-1.10199[/C][/ROW]
[ROW][C]157[/C][C]3[/C][C]2.97632[/C][C]0.0236835[/C][/ROW]
[ROW][C]158[/C][C]4[/C][C]3.22772[/C][C]0.772281[/C][/ROW]
[ROW][C]159[/C][C]5[/C][C]4.35691[/C][C]0.643086[/C][/ROW]
[ROW][C]160[/C][C]5[/C][C]3.69239[/C][C]1.30761[/C][/ROW]
[ROW][C]161[/C][C]5[/C][C]3.86271[/C][C]1.13729[/C][/ROW]
[ROW][C]162[/C][C]5[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221536&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221536&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
134.15745-1.15745
254.509190.490809
344.00671-0.00670532
443.96790.0320993
553.390761.60924
654.358430.641571
723.67691-1.67691
854.23570.764297
944.44067-0.440668
1044.26312-0.263117
1154.249640.750356
1234.66336-1.66336
1353.787891.21211
1434.09044-1.09044
1554.266690.733312
1633.79806-0.798063
1743.79880.201202
1854.306360.693637
1943.954240.0457581
2034.09011-1.09011
2144.43395-0.433954
2244.08768-0.0876752
2334.12909-1.12909
2433.90959-0.909587
2544.24837-0.248372
2654.611130.388866
2743.776530.223472
2843.919050.080945
2944.14575-0.14575
3043.923220.0767753
3144.57386-0.573863
3233.72932-0.729323
3344.48051-0.480505
3454.307470.692531
3544.02065-0.0206463
3644.33096-0.330959
3733.35406-0.354063
3844.08614-0.086136
3944.43974-0.439743
4044.18329-0.183287
4154.331340.668658
4244.03033-0.0303341
4334.43274-1.43274
4433.80905-0.809053
4544.32177-0.321765
4644.03204-0.0320421
4744.47368-0.473684
4854.343380.65662
4944.1376-0.137604
5054.171080.828916
5144.42232-0.422316
5243.920840.0791584
5343.537650.46235
5443.883740.116258
5544.07022-0.0702215
5654.362060.637944
5744.22867-0.228671
5844.50223-0.502227
5944.07309-0.073094
6043.965960.0340446
6133.40256-0.402559
6243.920870.0791255
6354.133630.866372
6413.85016-2.85016
6534.11939-1.11939
6653.997731.00227
6743.804780.195222
6843.981220.0187837
6934.1854-1.1854
7043.862280.137719
7143.977190.0228127
7233.97824-0.978236
7354.203880.796124
7444.05112-0.051119
7554.125160.87484
7643.80370.196302
7744.16599-0.165994
7843.802070.197929
7943.934640.0653606
8034.2379-1.2379
8153.992261.00774
82NANA0.535465
8355.18878-0.18878
8444.0397-0.0396967
8543.016750.98325
8654.942870.0571276
8744.25616-0.256161
8844.93004-0.93004
8932.520680.479317
9044.17029-0.170292
9145.10199-1.10199
9232.111980.888023
9354.127840.872165
9454.320650.679353
9555.33816-0.338162
9643.978860.0211357
9744.33337-0.33337
9844.18662-0.186616
9943.976830.0231679
10044.48389-0.483887
10143.661140.338864
10243.207480.792521
10355.04855-0.0485471
10443.975370.0246322
10544.5648-0.5648
10632.413360.586635
10754.625710.374285
10844.68684-0.686844
10934.05323-1.05323
11021.013820.986177
11154.910150.0898483
11243.180770.819235
11357.72662-2.72662
11410.3759920.624008
11554.057620.942384
11655.86772-0.867719
11733.09872-0.0987161
11843.308420.691582
11954.006660.993343
12055.8875-0.887502
12132.911050.0889523
12243.185430.814574
12355.47496-0.474956
12444.39347-0.393471
12544.30016-0.300162
12643.390010.609987
12755.44619-0.446195
12843.227720.772281
12955.24555-0.245554
13044.0693-0.069301
13143.323180.676815
13244.39131-0.391312
13344.86696-0.866957
13433.01683-0.016831
13543.068760.931239
13655.30738-0.307382
13732.720340.279655
13844.71845-0.718447
13933.29906-0.299055
14044.95028-0.950283
14134.18258-1.18258
14231.857241.14276
14354.196920.80308
14454.18220.817797
14554.435830.564171
14654.46310.5369
14754.90150.0985048
14844.08692-0.0869187
14943.348520.651479
15043.202450.797553
15153.815341.18466
15254.840960.159041
15344.0968-0.096797
15443.487480.512518
15542.934511.06549
15656.10199-1.10199
15732.976320.0236835
15843.227720.772281
15954.356910.643086
16053.692391.30761
16153.862711.13729
1625NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.2320060.4640120.767994
110.1503970.3007930.849603
120.9517030.09659460.0482973
130.9782620.04347570.0217379
140.975850.04830090.0241504
150.9601420.0797150.0398575
160.9898320.02033540.0101677
170.9845530.03089340.0154467
180.9833910.0332170.0166085
190.9774810.04503750.0225187
200.9869050.02619080.0130954
210.9833090.03338190.016691
220.9746220.05075510.0253776
230.978680.04264040.0213202
240.9768350.0463310.0231655
250.9664370.06712630.0335631
260.9705650.05887030.0294352
270.9585450.08290950.0414547
280.9431630.1136730.0568367
290.9229990.1540020.0770012
300.8979370.2041250.102063
310.8785190.2429630.121481
320.8787410.2425170.121259
330.8521930.2956150.147807
340.8571080.2857840.142892
350.8215390.3569220.178461
360.7876150.424770.212385
370.7513560.4972890.248644
380.7043170.5913650.295683
390.6613310.6773370.338669
400.6105670.7788670.389433
410.6017140.7965720.398286
420.5494310.9011390.450569
430.6617730.6764530.338227
440.6621520.6756950.337848
450.617550.76490.38245
460.5661750.867650.433825
470.5277940.9444120.472206
480.5315090.9369810.468491
490.4822660.9645330.517734
500.5055440.9889130.494456
510.4651130.9302250.534887
520.4170590.8341190.582941
530.3870760.7741520.612924
540.3408080.6816160.659192
550.2960910.5921820.703909
560.298680.5973610.70132
570.260.520.74
580.2336160.4672320.766384
590.1977940.3955880.802206
600.1657470.3314940.834253
610.1443310.2886630.855669
620.1183120.2366250.881688
630.1284630.2569270.871537
640.6365810.7268380.363419
650.6730540.6538920.326946
660.7029620.5940760.297038
670.665380.6692390.33462
680.6237690.7524630.376231
690.6900230.6199530.309977
700.6482810.7034370.351719
710.6037760.7924480.396224
720.6342240.7315510.365776
730.6433210.7133590.356679
740.5997180.8005640.400282
750.6235170.7529670.376483
760.5831210.8337580.416879
770.547550.90490.45245
780.5102250.979550.489775
790.4657630.9315250.534237
800.5351430.9297150.464857
810.5756920.8486160.424308
820.5563150.8873690.443685
830.5144550.9710910.485545
840.4757750.951550.524225
850.5146440.9707130.485356
860.4680930.9361860.531907
870.428280.8565590.57172
880.4577770.9155550.542223
890.433690.867380.56631
900.4012080.8024150.598792
910.4628860.9257730.537114
920.4768620.9537230.523138
930.4888450.9776910.511155
940.4801480.9602960.519852
950.4506050.901210.549395
960.4039530.8079070.596047
970.3735390.7470770.626461
980.3353730.6707460.664627
990.2927760.5855520.707224
1000.2844270.5688540.715573
1010.2541680.5083360.745832
1020.2496150.499230.750385
1030.2131080.4262160.786892
1040.1806590.3613190.819341
1050.1639030.3278060.836097
1060.1468870.2937740.853113
1070.1248850.249770.875115
1080.1152180.2304370.884782
1090.1643620.3287240.835638
1100.1779020.3558040.822098
1110.1472970.2945940.852703
1120.168470.3369390.83153
1130.797090.4058210.20291
1140.7697710.4604580.230229
1150.765410.469180.23459
1160.804540.3909210.19546
1170.7668830.4662330.233117
1180.7850760.4298470.214924
1190.7791930.4416140.220807
1200.8123940.3752110.187606
1210.7789360.4421280.221064
1220.7686960.4626080.231304
1230.7335850.532830.266415
1240.6992210.6015580.300779
1250.6478410.7043190.352159
1260.6232890.7534210.376711
1270.6445330.7109330.355467
1280.6037650.792470.396235
1290.5785220.8429570.421478
1300.5182970.9634060.481703
1310.5063310.9873390.493669
1320.5139380.9721240.486062
1330.5634850.873030.436515
1340.5285460.9429080.471454
1350.5005940.9988110.499406
1360.5856150.8287690.414385
1370.5241960.9516090.475804
1380.5489480.9021040.451052
1390.5035560.9928870.496444
1400.6093520.7812960.390648
1410.809220.381560.19078
1420.7678110.4643770.232189
1430.7334860.5330290.266514
1440.6747750.6504510.325225
1450.5855350.8289310.414465
1460.491360.982720.50864
1470.3880240.7760490.611976
1480.2956820.5913640.704318
1490.2262350.4524690.773765
1500.1545530.3091050.845447
1510.2732380.5464770.726762
1520.1419470.2838950.858053

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.232006 & 0.464012 & 0.767994 \tabularnewline
11 & 0.150397 & 0.300793 & 0.849603 \tabularnewline
12 & 0.951703 & 0.0965946 & 0.0482973 \tabularnewline
13 & 0.978262 & 0.0434757 & 0.0217379 \tabularnewline
14 & 0.97585 & 0.0483009 & 0.0241504 \tabularnewline
15 & 0.960142 & 0.079715 & 0.0398575 \tabularnewline
16 & 0.989832 & 0.0203354 & 0.0101677 \tabularnewline
17 & 0.984553 & 0.0308934 & 0.0154467 \tabularnewline
18 & 0.983391 & 0.033217 & 0.0166085 \tabularnewline
19 & 0.977481 & 0.0450375 & 0.0225187 \tabularnewline
20 & 0.986905 & 0.0261908 & 0.0130954 \tabularnewline
21 & 0.983309 & 0.0333819 & 0.016691 \tabularnewline
22 & 0.974622 & 0.0507551 & 0.0253776 \tabularnewline
23 & 0.97868 & 0.0426404 & 0.0213202 \tabularnewline
24 & 0.976835 & 0.046331 & 0.0231655 \tabularnewline
25 & 0.966437 & 0.0671263 & 0.0335631 \tabularnewline
26 & 0.970565 & 0.0588703 & 0.0294352 \tabularnewline
27 & 0.958545 & 0.0829095 & 0.0414547 \tabularnewline
28 & 0.943163 & 0.113673 & 0.0568367 \tabularnewline
29 & 0.922999 & 0.154002 & 0.0770012 \tabularnewline
30 & 0.897937 & 0.204125 & 0.102063 \tabularnewline
31 & 0.878519 & 0.242963 & 0.121481 \tabularnewline
32 & 0.878741 & 0.242517 & 0.121259 \tabularnewline
33 & 0.852193 & 0.295615 & 0.147807 \tabularnewline
34 & 0.857108 & 0.285784 & 0.142892 \tabularnewline
35 & 0.821539 & 0.356922 & 0.178461 \tabularnewline
36 & 0.787615 & 0.42477 & 0.212385 \tabularnewline
37 & 0.751356 & 0.497289 & 0.248644 \tabularnewline
38 & 0.704317 & 0.591365 & 0.295683 \tabularnewline
39 & 0.661331 & 0.677337 & 0.338669 \tabularnewline
40 & 0.610567 & 0.778867 & 0.389433 \tabularnewline
41 & 0.601714 & 0.796572 & 0.398286 \tabularnewline
42 & 0.549431 & 0.901139 & 0.450569 \tabularnewline
43 & 0.661773 & 0.676453 & 0.338227 \tabularnewline
44 & 0.662152 & 0.675695 & 0.337848 \tabularnewline
45 & 0.61755 & 0.7649 & 0.38245 \tabularnewline
46 & 0.566175 & 0.86765 & 0.433825 \tabularnewline
47 & 0.527794 & 0.944412 & 0.472206 \tabularnewline
48 & 0.531509 & 0.936981 & 0.468491 \tabularnewline
49 & 0.482266 & 0.964533 & 0.517734 \tabularnewline
50 & 0.505544 & 0.988913 & 0.494456 \tabularnewline
51 & 0.465113 & 0.930225 & 0.534887 \tabularnewline
52 & 0.417059 & 0.834119 & 0.582941 \tabularnewline
53 & 0.387076 & 0.774152 & 0.612924 \tabularnewline
54 & 0.340808 & 0.681616 & 0.659192 \tabularnewline
55 & 0.296091 & 0.592182 & 0.703909 \tabularnewline
56 & 0.29868 & 0.597361 & 0.70132 \tabularnewline
57 & 0.26 & 0.52 & 0.74 \tabularnewline
58 & 0.233616 & 0.467232 & 0.766384 \tabularnewline
59 & 0.197794 & 0.395588 & 0.802206 \tabularnewline
60 & 0.165747 & 0.331494 & 0.834253 \tabularnewline
61 & 0.144331 & 0.288663 & 0.855669 \tabularnewline
62 & 0.118312 & 0.236625 & 0.881688 \tabularnewline
63 & 0.128463 & 0.256927 & 0.871537 \tabularnewline
64 & 0.636581 & 0.726838 & 0.363419 \tabularnewline
65 & 0.673054 & 0.653892 & 0.326946 \tabularnewline
66 & 0.702962 & 0.594076 & 0.297038 \tabularnewline
67 & 0.66538 & 0.669239 & 0.33462 \tabularnewline
68 & 0.623769 & 0.752463 & 0.376231 \tabularnewline
69 & 0.690023 & 0.619953 & 0.309977 \tabularnewline
70 & 0.648281 & 0.703437 & 0.351719 \tabularnewline
71 & 0.603776 & 0.792448 & 0.396224 \tabularnewline
72 & 0.634224 & 0.731551 & 0.365776 \tabularnewline
73 & 0.643321 & 0.713359 & 0.356679 \tabularnewline
74 & 0.599718 & 0.800564 & 0.400282 \tabularnewline
75 & 0.623517 & 0.752967 & 0.376483 \tabularnewline
76 & 0.583121 & 0.833758 & 0.416879 \tabularnewline
77 & 0.54755 & 0.9049 & 0.45245 \tabularnewline
78 & 0.510225 & 0.97955 & 0.489775 \tabularnewline
79 & 0.465763 & 0.931525 & 0.534237 \tabularnewline
80 & 0.535143 & 0.929715 & 0.464857 \tabularnewline
81 & 0.575692 & 0.848616 & 0.424308 \tabularnewline
82 & 0.556315 & 0.887369 & 0.443685 \tabularnewline
83 & 0.514455 & 0.971091 & 0.485545 \tabularnewline
84 & 0.475775 & 0.95155 & 0.524225 \tabularnewline
85 & 0.514644 & 0.970713 & 0.485356 \tabularnewline
86 & 0.468093 & 0.936186 & 0.531907 \tabularnewline
87 & 0.42828 & 0.856559 & 0.57172 \tabularnewline
88 & 0.457777 & 0.915555 & 0.542223 \tabularnewline
89 & 0.43369 & 0.86738 & 0.56631 \tabularnewline
90 & 0.401208 & 0.802415 & 0.598792 \tabularnewline
91 & 0.462886 & 0.925773 & 0.537114 \tabularnewline
92 & 0.476862 & 0.953723 & 0.523138 \tabularnewline
93 & 0.488845 & 0.977691 & 0.511155 \tabularnewline
94 & 0.480148 & 0.960296 & 0.519852 \tabularnewline
95 & 0.450605 & 0.90121 & 0.549395 \tabularnewline
96 & 0.403953 & 0.807907 & 0.596047 \tabularnewline
97 & 0.373539 & 0.747077 & 0.626461 \tabularnewline
98 & 0.335373 & 0.670746 & 0.664627 \tabularnewline
99 & 0.292776 & 0.585552 & 0.707224 \tabularnewline
100 & 0.284427 & 0.568854 & 0.715573 \tabularnewline
101 & 0.254168 & 0.508336 & 0.745832 \tabularnewline
102 & 0.249615 & 0.49923 & 0.750385 \tabularnewline
103 & 0.213108 & 0.426216 & 0.786892 \tabularnewline
104 & 0.180659 & 0.361319 & 0.819341 \tabularnewline
105 & 0.163903 & 0.327806 & 0.836097 \tabularnewline
106 & 0.146887 & 0.293774 & 0.853113 \tabularnewline
107 & 0.124885 & 0.24977 & 0.875115 \tabularnewline
108 & 0.115218 & 0.230437 & 0.884782 \tabularnewline
109 & 0.164362 & 0.328724 & 0.835638 \tabularnewline
110 & 0.177902 & 0.355804 & 0.822098 \tabularnewline
111 & 0.147297 & 0.294594 & 0.852703 \tabularnewline
112 & 0.16847 & 0.336939 & 0.83153 \tabularnewline
113 & 0.79709 & 0.405821 & 0.20291 \tabularnewline
114 & 0.769771 & 0.460458 & 0.230229 \tabularnewline
115 & 0.76541 & 0.46918 & 0.23459 \tabularnewline
116 & 0.80454 & 0.390921 & 0.19546 \tabularnewline
117 & 0.766883 & 0.466233 & 0.233117 \tabularnewline
118 & 0.785076 & 0.429847 & 0.214924 \tabularnewline
119 & 0.779193 & 0.441614 & 0.220807 \tabularnewline
120 & 0.812394 & 0.375211 & 0.187606 \tabularnewline
121 & 0.778936 & 0.442128 & 0.221064 \tabularnewline
122 & 0.768696 & 0.462608 & 0.231304 \tabularnewline
123 & 0.733585 & 0.53283 & 0.266415 \tabularnewline
124 & 0.699221 & 0.601558 & 0.300779 \tabularnewline
125 & 0.647841 & 0.704319 & 0.352159 \tabularnewline
126 & 0.623289 & 0.753421 & 0.376711 \tabularnewline
127 & 0.644533 & 0.710933 & 0.355467 \tabularnewline
128 & 0.603765 & 0.79247 & 0.396235 \tabularnewline
129 & 0.578522 & 0.842957 & 0.421478 \tabularnewline
130 & 0.518297 & 0.963406 & 0.481703 \tabularnewline
131 & 0.506331 & 0.987339 & 0.493669 \tabularnewline
132 & 0.513938 & 0.972124 & 0.486062 \tabularnewline
133 & 0.563485 & 0.87303 & 0.436515 \tabularnewline
134 & 0.528546 & 0.942908 & 0.471454 \tabularnewline
135 & 0.500594 & 0.998811 & 0.499406 \tabularnewline
136 & 0.585615 & 0.828769 & 0.414385 \tabularnewline
137 & 0.524196 & 0.951609 & 0.475804 \tabularnewline
138 & 0.548948 & 0.902104 & 0.451052 \tabularnewline
139 & 0.503556 & 0.992887 & 0.496444 \tabularnewline
140 & 0.609352 & 0.781296 & 0.390648 \tabularnewline
141 & 0.80922 & 0.38156 & 0.19078 \tabularnewline
142 & 0.767811 & 0.464377 & 0.232189 \tabularnewline
143 & 0.733486 & 0.533029 & 0.266514 \tabularnewline
144 & 0.674775 & 0.650451 & 0.325225 \tabularnewline
145 & 0.585535 & 0.828931 & 0.414465 \tabularnewline
146 & 0.49136 & 0.98272 & 0.50864 \tabularnewline
147 & 0.388024 & 0.776049 & 0.611976 \tabularnewline
148 & 0.295682 & 0.591364 & 0.704318 \tabularnewline
149 & 0.226235 & 0.452469 & 0.773765 \tabularnewline
150 & 0.154553 & 0.309105 & 0.845447 \tabularnewline
151 & 0.273238 & 0.546477 & 0.726762 \tabularnewline
152 & 0.141947 & 0.283895 & 0.858053 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221536&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]10[/C][C]0.232006[/C][C]0.464012[/C][C]0.767994[/C][/ROW]
[ROW][C]11[/C][C]0.150397[/C][C]0.300793[/C][C]0.849603[/C][/ROW]
[ROW][C]12[/C][C]0.951703[/C][C]0.0965946[/C][C]0.0482973[/C][/ROW]
[ROW][C]13[/C][C]0.978262[/C][C]0.0434757[/C][C]0.0217379[/C][/ROW]
[ROW][C]14[/C][C]0.97585[/C][C]0.0483009[/C][C]0.0241504[/C][/ROW]
[ROW][C]15[/C][C]0.960142[/C][C]0.079715[/C][C]0.0398575[/C][/ROW]
[ROW][C]16[/C][C]0.989832[/C][C]0.0203354[/C][C]0.0101677[/C][/ROW]
[ROW][C]17[/C][C]0.984553[/C][C]0.0308934[/C][C]0.0154467[/C][/ROW]
[ROW][C]18[/C][C]0.983391[/C][C]0.033217[/C][C]0.0166085[/C][/ROW]
[ROW][C]19[/C][C]0.977481[/C][C]0.0450375[/C][C]0.0225187[/C][/ROW]
[ROW][C]20[/C][C]0.986905[/C][C]0.0261908[/C][C]0.0130954[/C][/ROW]
[ROW][C]21[/C][C]0.983309[/C][C]0.0333819[/C][C]0.016691[/C][/ROW]
[ROW][C]22[/C][C]0.974622[/C][C]0.0507551[/C][C]0.0253776[/C][/ROW]
[ROW][C]23[/C][C]0.97868[/C][C]0.0426404[/C][C]0.0213202[/C][/ROW]
[ROW][C]24[/C][C]0.976835[/C][C]0.046331[/C][C]0.0231655[/C][/ROW]
[ROW][C]25[/C][C]0.966437[/C][C]0.0671263[/C][C]0.0335631[/C][/ROW]
[ROW][C]26[/C][C]0.970565[/C][C]0.0588703[/C][C]0.0294352[/C][/ROW]
[ROW][C]27[/C][C]0.958545[/C][C]0.0829095[/C][C]0.0414547[/C][/ROW]
[ROW][C]28[/C][C]0.943163[/C][C]0.113673[/C][C]0.0568367[/C][/ROW]
[ROW][C]29[/C][C]0.922999[/C][C]0.154002[/C][C]0.0770012[/C][/ROW]
[ROW][C]30[/C][C]0.897937[/C][C]0.204125[/C][C]0.102063[/C][/ROW]
[ROW][C]31[/C][C]0.878519[/C][C]0.242963[/C][C]0.121481[/C][/ROW]
[ROW][C]32[/C][C]0.878741[/C][C]0.242517[/C][C]0.121259[/C][/ROW]
[ROW][C]33[/C][C]0.852193[/C][C]0.295615[/C][C]0.147807[/C][/ROW]
[ROW][C]34[/C][C]0.857108[/C][C]0.285784[/C][C]0.142892[/C][/ROW]
[ROW][C]35[/C][C]0.821539[/C][C]0.356922[/C][C]0.178461[/C][/ROW]
[ROW][C]36[/C][C]0.787615[/C][C]0.42477[/C][C]0.212385[/C][/ROW]
[ROW][C]37[/C][C]0.751356[/C][C]0.497289[/C][C]0.248644[/C][/ROW]
[ROW][C]38[/C][C]0.704317[/C][C]0.591365[/C][C]0.295683[/C][/ROW]
[ROW][C]39[/C][C]0.661331[/C][C]0.677337[/C][C]0.338669[/C][/ROW]
[ROW][C]40[/C][C]0.610567[/C][C]0.778867[/C][C]0.389433[/C][/ROW]
[ROW][C]41[/C][C]0.601714[/C][C]0.796572[/C][C]0.398286[/C][/ROW]
[ROW][C]42[/C][C]0.549431[/C][C]0.901139[/C][C]0.450569[/C][/ROW]
[ROW][C]43[/C][C]0.661773[/C][C]0.676453[/C][C]0.338227[/C][/ROW]
[ROW][C]44[/C][C]0.662152[/C][C]0.675695[/C][C]0.337848[/C][/ROW]
[ROW][C]45[/C][C]0.61755[/C][C]0.7649[/C][C]0.38245[/C][/ROW]
[ROW][C]46[/C][C]0.566175[/C][C]0.86765[/C][C]0.433825[/C][/ROW]
[ROW][C]47[/C][C]0.527794[/C][C]0.944412[/C][C]0.472206[/C][/ROW]
[ROW][C]48[/C][C]0.531509[/C][C]0.936981[/C][C]0.468491[/C][/ROW]
[ROW][C]49[/C][C]0.482266[/C][C]0.964533[/C][C]0.517734[/C][/ROW]
[ROW][C]50[/C][C]0.505544[/C][C]0.988913[/C][C]0.494456[/C][/ROW]
[ROW][C]51[/C][C]0.465113[/C][C]0.930225[/C][C]0.534887[/C][/ROW]
[ROW][C]52[/C][C]0.417059[/C][C]0.834119[/C][C]0.582941[/C][/ROW]
[ROW][C]53[/C][C]0.387076[/C][C]0.774152[/C][C]0.612924[/C][/ROW]
[ROW][C]54[/C][C]0.340808[/C][C]0.681616[/C][C]0.659192[/C][/ROW]
[ROW][C]55[/C][C]0.296091[/C][C]0.592182[/C][C]0.703909[/C][/ROW]
[ROW][C]56[/C][C]0.29868[/C][C]0.597361[/C][C]0.70132[/C][/ROW]
[ROW][C]57[/C][C]0.26[/C][C]0.52[/C][C]0.74[/C][/ROW]
[ROW][C]58[/C][C]0.233616[/C][C]0.467232[/C][C]0.766384[/C][/ROW]
[ROW][C]59[/C][C]0.197794[/C][C]0.395588[/C][C]0.802206[/C][/ROW]
[ROW][C]60[/C][C]0.165747[/C][C]0.331494[/C][C]0.834253[/C][/ROW]
[ROW][C]61[/C][C]0.144331[/C][C]0.288663[/C][C]0.855669[/C][/ROW]
[ROW][C]62[/C][C]0.118312[/C][C]0.236625[/C][C]0.881688[/C][/ROW]
[ROW][C]63[/C][C]0.128463[/C][C]0.256927[/C][C]0.871537[/C][/ROW]
[ROW][C]64[/C][C]0.636581[/C][C]0.726838[/C][C]0.363419[/C][/ROW]
[ROW][C]65[/C][C]0.673054[/C][C]0.653892[/C][C]0.326946[/C][/ROW]
[ROW][C]66[/C][C]0.702962[/C][C]0.594076[/C][C]0.297038[/C][/ROW]
[ROW][C]67[/C][C]0.66538[/C][C]0.669239[/C][C]0.33462[/C][/ROW]
[ROW][C]68[/C][C]0.623769[/C][C]0.752463[/C][C]0.376231[/C][/ROW]
[ROW][C]69[/C][C]0.690023[/C][C]0.619953[/C][C]0.309977[/C][/ROW]
[ROW][C]70[/C][C]0.648281[/C][C]0.703437[/C][C]0.351719[/C][/ROW]
[ROW][C]71[/C][C]0.603776[/C][C]0.792448[/C][C]0.396224[/C][/ROW]
[ROW][C]72[/C][C]0.634224[/C][C]0.731551[/C][C]0.365776[/C][/ROW]
[ROW][C]73[/C][C]0.643321[/C][C]0.713359[/C][C]0.356679[/C][/ROW]
[ROW][C]74[/C][C]0.599718[/C][C]0.800564[/C][C]0.400282[/C][/ROW]
[ROW][C]75[/C][C]0.623517[/C][C]0.752967[/C][C]0.376483[/C][/ROW]
[ROW][C]76[/C][C]0.583121[/C][C]0.833758[/C][C]0.416879[/C][/ROW]
[ROW][C]77[/C][C]0.54755[/C][C]0.9049[/C][C]0.45245[/C][/ROW]
[ROW][C]78[/C][C]0.510225[/C][C]0.97955[/C][C]0.489775[/C][/ROW]
[ROW][C]79[/C][C]0.465763[/C][C]0.931525[/C][C]0.534237[/C][/ROW]
[ROW][C]80[/C][C]0.535143[/C][C]0.929715[/C][C]0.464857[/C][/ROW]
[ROW][C]81[/C][C]0.575692[/C][C]0.848616[/C][C]0.424308[/C][/ROW]
[ROW][C]82[/C][C]0.556315[/C][C]0.887369[/C][C]0.443685[/C][/ROW]
[ROW][C]83[/C][C]0.514455[/C][C]0.971091[/C][C]0.485545[/C][/ROW]
[ROW][C]84[/C][C]0.475775[/C][C]0.95155[/C][C]0.524225[/C][/ROW]
[ROW][C]85[/C][C]0.514644[/C][C]0.970713[/C][C]0.485356[/C][/ROW]
[ROW][C]86[/C][C]0.468093[/C][C]0.936186[/C][C]0.531907[/C][/ROW]
[ROW][C]87[/C][C]0.42828[/C][C]0.856559[/C][C]0.57172[/C][/ROW]
[ROW][C]88[/C][C]0.457777[/C][C]0.915555[/C][C]0.542223[/C][/ROW]
[ROW][C]89[/C][C]0.43369[/C][C]0.86738[/C][C]0.56631[/C][/ROW]
[ROW][C]90[/C][C]0.401208[/C][C]0.802415[/C][C]0.598792[/C][/ROW]
[ROW][C]91[/C][C]0.462886[/C][C]0.925773[/C][C]0.537114[/C][/ROW]
[ROW][C]92[/C][C]0.476862[/C][C]0.953723[/C][C]0.523138[/C][/ROW]
[ROW][C]93[/C][C]0.488845[/C][C]0.977691[/C][C]0.511155[/C][/ROW]
[ROW][C]94[/C][C]0.480148[/C][C]0.960296[/C][C]0.519852[/C][/ROW]
[ROW][C]95[/C][C]0.450605[/C][C]0.90121[/C][C]0.549395[/C][/ROW]
[ROW][C]96[/C][C]0.403953[/C][C]0.807907[/C][C]0.596047[/C][/ROW]
[ROW][C]97[/C][C]0.373539[/C][C]0.747077[/C][C]0.626461[/C][/ROW]
[ROW][C]98[/C][C]0.335373[/C][C]0.670746[/C][C]0.664627[/C][/ROW]
[ROW][C]99[/C][C]0.292776[/C][C]0.585552[/C][C]0.707224[/C][/ROW]
[ROW][C]100[/C][C]0.284427[/C][C]0.568854[/C][C]0.715573[/C][/ROW]
[ROW][C]101[/C][C]0.254168[/C][C]0.508336[/C][C]0.745832[/C][/ROW]
[ROW][C]102[/C][C]0.249615[/C][C]0.49923[/C][C]0.750385[/C][/ROW]
[ROW][C]103[/C][C]0.213108[/C][C]0.426216[/C][C]0.786892[/C][/ROW]
[ROW][C]104[/C][C]0.180659[/C][C]0.361319[/C][C]0.819341[/C][/ROW]
[ROW][C]105[/C][C]0.163903[/C][C]0.327806[/C][C]0.836097[/C][/ROW]
[ROW][C]106[/C][C]0.146887[/C][C]0.293774[/C][C]0.853113[/C][/ROW]
[ROW][C]107[/C][C]0.124885[/C][C]0.24977[/C][C]0.875115[/C][/ROW]
[ROW][C]108[/C][C]0.115218[/C][C]0.230437[/C][C]0.884782[/C][/ROW]
[ROW][C]109[/C][C]0.164362[/C][C]0.328724[/C][C]0.835638[/C][/ROW]
[ROW][C]110[/C][C]0.177902[/C][C]0.355804[/C][C]0.822098[/C][/ROW]
[ROW][C]111[/C][C]0.147297[/C][C]0.294594[/C][C]0.852703[/C][/ROW]
[ROW][C]112[/C][C]0.16847[/C][C]0.336939[/C][C]0.83153[/C][/ROW]
[ROW][C]113[/C][C]0.79709[/C][C]0.405821[/C][C]0.20291[/C][/ROW]
[ROW][C]114[/C][C]0.769771[/C][C]0.460458[/C][C]0.230229[/C][/ROW]
[ROW][C]115[/C][C]0.76541[/C][C]0.46918[/C][C]0.23459[/C][/ROW]
[ROW][C]116[/C][C]0.80454[/C][C]0.390921[/C][C]0.19546[/C][/ROW]
[ROW][C]117[/C][C]0.766883[/C][C]0.466233[/C][C]0.233117[/C][/ROW]
[ROW][C]118[/C][C]0.785076[/C][C]0.429847[/C][C]0.214924[/C][/ROW]
[ROW][C]119[/C][C]0.779193[/C][C]0.441614[/C][C]0.220807[/C][/ROW]
[ROW][C]120[/C][C]0.812394[/C][C]0.375211[/C][C]0.187606[/C][/ROW]
[ROW][C]121[/C][C]0.778936[/C][C]0.442128[/C][C]0.221064[/C][/ROW]
[ROW][C]122[/C][C]0.768696[/C][C]0.462608[/C][C]0.231304[/C][/ROW]
[ROW][C]123[/C][C]0.733585[/C][C]0.53283[/C][C]0.266415[/C][/ROW]
[ROW][C]124[/C][C]0.699221[/C][C]0.601558[/C][C]0.300779[/C][/ROW]
[ROW][C]125[/C][C]0.647841[/C][C]0.704319[/C][C]0.352159[/C][/ROW]
[ROW][C]126[/C][C]0.623289[/C][C]0.753421[/C][C]0.376711[/C][/ROW]
[ROW][C]127[/C][C]0.644533[/C][C]0.710933[/C][C]0.355467[/C][/ROW]
[ROW][C]128[/C][C]0.603765[/C][C]0.79247[/C][C]0.396235[/C][/ROW]
[ROW][C]129[/C][C]0.578522[/C][C]0.842957[/C][C]0.421478[/C][/ROW]
[ROW][C]130[/C][C]0.518297[/C][C]0.963406[/C][C]0.481703[/C][/ROW]
[ROW][C]131[/C][C]0.506331[/C][C]0.987339[/C][C]0.493669[/C][/ROW]
[ROW][C]132[/C][C]0.513938[/C][C]0.972124[/C][C]0.486062[/C][/ROW]
[ROW][C]133[/C][C]0.563485[/C][C]0.87303[/C][C]0.436515[/C][/ROW]
[ROW][C]134[/C][C]0.528546[/C][C]0.942908[/C][C]0.471454[/C][/ROW]
[ROW][C]135[/C][C]0.500594[/C][C]0.998811[/C][C]0.499406[/C][/ROW]
[ROW][C]136[/C][C]0.585615[/C][C]0.828769[/C][C]0.414385[/C][/ROW]
[ROW][C]137[/C][C]0.524196[/C][C]0.951609[/C][C]0.475804[/C][/ROW]
[ROW][C]138[/C][C]0.548948[/C][C]0.902104[/C][C]0.451052[/C][/ROW]
[ROW][C]139[/C][C]0.503556[/C][C]0.992887[/C][C]0.496444[/C][/ROW]
[ROW][C]140[/C][C]0.609352[/C][C]0.781296[/C][C]0.390648[/C][/ROW]
[ROW][C]141[/C][C]0.80922[/C][C]0.38156[/C][C]0.19078[/C][/ROW]
[ROW][C]142[/C][C]0.767811[/C][C]0.464377[/C][C]0.232189[/C][/ROW]
[ROW][C]143[/C][C]0.733486[/C][C]0.533029[/C][C]0.266514[/C][/ROW]
[ROW][C]144[/C][C]0.674775[/C][C]0.650451[/C][C]0.325225[/C][/ROW]
[ROW][C]145[/C][C]0.585535[/C][C]0.828931[/C][C]0.414465[/C][/ROW]
[ROW][C]146[/C][C]0.49136[/C][C]0.98272[/C][C]0.50864[/C][/ROW]
[ROW][C]147[/C][C]0.388024[/C][C]0.776049[/C][C]0.611976[/C][/ROW]
[ROW][C]148[/C][C]0.295682[/C][C]0.591364[/C][C]0.704318[/C][/ROW]
[ROW][C]149[/C][C]0.226235[/C][C]0.452469[/C][C]0.773765[/C][/ROW]
[ROW][C]150[/C][C]0.154553[/C][C]0.309105[/C][C]0.845447[/C][/ROW]
[ROW][C]151[/C][C]0.273238[/C][C]0.546477[/C][C]0.726762[/C][/ROW]
[ROW][C]152[/C][C]0.141947[/C][C]0.283895[/C][C]0.858053[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221536&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221536&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.2320060.4640120.767994
110.1503970.3007930.849603
120.9517030.09659460.0482973
130.9782620.04347570.0217379
140.975850.04830090.0241504
150.9601420.0797150.0398575
160.9898320.02033540.0101677
170.9845530.03089340.0154467
180.9833910.0332170.0166085
190.9774810.04503750.0225187
200.9869050.02619080.0130954
210.9833090.03338190.016691
220.9746220.05075510.0253776
230.978680.04264040.0213202
240.9768350.0463310.0231655
250.9664370.06712630.0335631
260.9705650.05887030.0294352
270.9585450.08290950.0414547
280.9431630.1136730.0568367
290.9229990.1540020.0770012
300.8979370.2041250.102063
310.8785190.2429630.121481
320.8787410.2425170.121259
330.8521930.2956150.147807
340.8571080.2857840.142892
350.8215390.3569220.178461
360.7876150.424770.212385
370.7513560.4972890.248644
380.7043170.5913650.295683
390.6613310.6773370.338669
400.6105670.7788670.389433
410.6017140.7965720.398286
420.5494310.9011390.450569
430.6617730.6764530.338227
440.6621520.6756950.337848
450.617550.76490.38245
460.5661750.867650.433825
470.5277940.9444120.472206
480.5315090.9369810.468491
490.4822660.9645330.517734
500.5055440.9889130.494456
510.4651130.9302250.534887
520.4170590.8341190.582941
530.3870760.7741520.612924
540.3408080.6816160.659192
550.2960910.5921820.703909
560.298680.5973610.70132
570.260.520.74
580.2336160.4672320.766384
590.1977940.3955880.802206
600.1657470.3314940.834253
610.1443310.2886630.855669
620.1183120.2366250.881688
630.1284630.2569270.871537
640.6365810.7268380.363419
650.6730540.6538920.326946
660.7029620.5940760.297038
670.665380.6692390.33462
680.6237690.7524630.376231
690.6900230.6199530.309977
700.6482810.7034370.351719
710.6037760.7924480.396224
720.6342240.7315510.365776
730.6433210.7133590.356679
740.5997180.8005640.400282
750.6235170.7529670.376483
760.5831210.8337580.416879
770.547550.90490.45245
780.5102250.979550.489775
790.4657630.9315250.534237
800.5351430.9297150.464857
810.5756920.8486160.424308
820.5563150.8873690.443685
830.5144550.9710910.485545
840.4757750.951550.524225
850.5146440.9707130.485356
860.4680930.9361860.531907
870.428280.8565590.57172
880.4577770.9155550.542223
890.433690.867380.56631
900.4012080.8024150.598792
910.4628860.9257730.537114
920.4768620.9537230.523138
930.4888450.9776910.511155
940.4801480.9602960.519852
950.4506050.901210.549395
960.4039530.8079070.596047
970.3735390.7470770.626461
980.3353730.6707460.664627
990.2927760.5855520.707224
1000.2844270.5688540.715573
1010.2541680.5083360.745832
1020.2496150.499230.750385
1030.2131080.4262160.786892
1040.1806590.3613190.819341
1050.1639030.3278060.836097
1060.1468870.2937740.853113
1070.1248850.249770.875115
1080.1152180.2304370.884782
1090.1643620.3287240.835638
1100.1779020.3558040.822098
1110.1472970.2945940.852703
1120.168470.3369390.83153
1130.797090.4058210.20291
1140.7697710.4604580.230229
1150.765410.469180.23459
1160.804540.3909210.19546
1170.7668830.4662330.233117
1180.7850760.4298470.214924
1190.7791930.4416140.220807
1200.8123940.3752110.187606
1210.7789360.4421280.221064
1220.7686960.4626080.231304
1230.7335850.532830.266415
1240.6992210.6015580.300779
1250.6478410.7043190.352159
1260.6232890.7534210.376711
1270.6445330.7109330.355467
1280.6037650.792470.396235
1290.5785220.8429570.421478
1300.5182970.9634060.481703
1310.5063310.9873390.493669
1320.5139380.9721240.486062
1330.5634850.873030.436515
1340.5285460.9429080.471454
1350.5005940.9988110.499406
1360.5856150.8287690.414385
1370.5241960.9516090.475804
1380.5489480.9021040.451052
1390.5035560.9928870.496444
1400.6093520.7812960.390648
1410.809220.381560.19078
1420.7678110.4643770.232189
1430.7334860.5330290.266514
1440.6747750.6504510.325225
1450.5855350.8289310.414465
1460.491360.982720.50864
1470.3880240.7760490.611976
1480.2956820.5913640.704318
1490.2262350.4524690.773765
1500.1545530.3091050.845447
1510.2732380.5464770.726762
1520.1419470.2838950.858053






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level100.0699301NOK
10% type I error level160.111888NOK

\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 & 10 & 0.0699301 & NOK \tabularnewline
10% type I error level & 16 & 0.111888 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=221536&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]10[/C][C]0.0699301[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.111888[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=221536&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=221536&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level100.0699301NOK
10% type I error level160.111888NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '7'
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')
}