FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationWed, 18 Nov 2009 05:11:35 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/18/t1258546390qzn3ifrvav1yr41.htm/, Retrieved Sun, 09 Nov 2025 02:34:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57438, Retrieved Sun, 09 Nov 2025 02:34:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact449

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-18 12:11:35] [830aa0f7fb5acd5849dbc0c6ad889830] [Current]
-    D        [Multiple Regression] [] [2009-12-15 15:01:33] [6ba840d2473f9a55d7b3e13093db69b8]
-    D          [Multiple Regression] [] [2009-12-21 09:25:37] [6ba840d2473f9a55d7b3e13093db69b8]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
149657	0
142773	0
133639	0
128332	0
120297	0
118632	0
155276	0
169316	0
167395	0
157939	0
149601	0
146310	0
141579	0
136473	0
129818	0
124226	0
116428	0
116440	0
147747	0
160069	0
163129	0
151108	0
141481	0
139174	0
134066	0
130104	0
123090	0
116598	0
109627	0
105428	0
137272	0
159836	0
155283	0
141514	0
131852	0
130691	0
128461	0
123066	0
117599	0
111599	0
105395	0
102334	0
131305	0
149033	0
144954	0
132404	0
122104	0
118755	0
116222	1
110924	1
103753	1
99983	1
93302	1
91496	1
119321	1
139261	1
133739	1
123913	1
113438	1
109416	1
109406	1
105645	1
101328	1
97686	1
93093	1
91382	1
122257	1
139183	1
139887	1
131822	1
116805	1
113706	1
113012	1
110452	1
107005	1
102841	1
98173	1
98181	1
137277	1
147579	1
146571	1
138920	1
130340	1
128140	1
127059	1
122860	1
117702	1
113537	1
108366	1
111078	1
150739	1
159129	1
157928	1
147768	1
137507	1
136919	1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57438&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57438&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57438&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 135682.416666667 -13898.7583333333X[t] -628.084027777662M1[t] -5258.07638888886M2[t] -11287.8187500000M3[t] -16163.6861111111M4[t] -22413.1784722222M5[t] -23611.2958333334M6[t] + 9682.21180555554M7[t] + 24974.3444444445M8[t] + 23174.9770833334M9[t] + 12753.3597222222M10[t] + 2486.49236111111M11[t] -15.6326388888899t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  135682.416666667 -13898.7583333333X[t] -628.084027777662M1[t] -5258.07638888886M2[t] -11287.8187500000M3[t] -16163.6861111111M4[t] -22413.1784722222M5[t] -23611.2958333334M6[t] +  9682.21180555554M7[t] +  24974.3444444445M8[t] +  23174.9770833334M9[t] +  12753.3597222222M10[t] +  2486.49236111111M11[t] -15.6326388888899t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57438&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  135682.416666667 -13898.7583333333X[t] -628.084027777662M1[t] -5258.07638888886M2[t] -11287.8187500000M3[t] -16163.6861111111M4[t] -22413.1784722222M5[t] -23611.2958333334M6[t] +  9682.21180555554M7[t] +  24974.3444444445M8[t] +  23174.9770833334M9[t] +  12753.3597222222M10[t] +  2486.49236111111M11[t] -15.6326388888899t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57438&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57438&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] = + 135682.416666667 -13898.7583333333X[t] -628.084027777662M1[t] -5258.07638888886M2[t] -11287.8187500000M3[t] -16163.6861111111M4[t] -22413.1784722222M5[t] -23611.2958333334M6[t] + 9682.21180555554M7[t] + 24974.3444444445M8[t] + 23174.9770833334M9[t] + 12753.3597222222M10[t] + 2486.49236111111M11[t] -15.6326388888899t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)135682.4166666673993.16078433.978700
X-13898.75833333333857.759822-3.60280.0005380.000269
M1-628.0840277776624675.032978-0.13430.8934560.446728
M2-5258.076388888864663.966549-1.12740.2628690.131434
M3-11287.81875000004653.93139-2.42540.0174860.008743
M4-16163.68611111114644.934187-3.47990.0008060.000403
M5-22413.17847222224636.980981-4.83366e-063e-06
M6-23611.29583333344630.077151-5.09952e-061e-06
M79682.211805555544624.2273992.09380.0393670.019683
M824974.34444444454619.4357285.40641e-060
M923174.97708333344615.7054345.02093e-061e-06
M1012753.35972222224613.0390912.76460.0070370.003518
M112486.492361111114611.4385450.53920.5912090.295605
t-15.632638888889970.152684-0.22280.8242160.412108

\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) & 135682.416666667 & 3993.160784 & 33.9787 & 0 & 0 \tabularnewline
X & -13898.7583333333 & 3857.759822 & -3.6028 & 0.000538 & 0.000269 \tabularnewline
M1 & -628.084027777662 & 4675.032978 & -0.1343 & 0.893456 & 0.446728 \tabularnewline
M2 & -5258.07638888886 & 4663.966549 & -1.1274 & 0.262869 & 0.131434 \tabularnewline
M3 & -11287.8187500000 & 4653.93139 & -2.4254 & 0.017486 & 0.008743 \tabularnewline
M4 & -16163.6861111111 & 4644.934187 & -3.4799 & 0.000806 & 0.000403 \tabularnewline
M5 & -22413.1784722222 & 4636.980981 & -4.8336 & 6e-06 & 3e-06 \tabularnewline
M6 & -23611.2958333334 & 4630.077151 & -5.0995 & 2e-06 & 1e-06 \tabularnewline
M7 & 9682.21180555554 & 4624.227399 & 2.0938 & 0.039367 & 0.019683 \tabularnewline
M8 & 24974.3444444445 & 4619.435728 & 5.4064 & 1e-06 & 0 \tabularnewline
M9 & 23174.9770833334 & 4615.705434 & 5.0209 & 3e-06 & 1e-06 \tabularnewline
M10 & 12753.3597222222 & 4613.039091 & 2.7646 & 0.007037 & 0.003518 \tabularnewline
M11 & 2486.49236111111 & 4611.438545 & 0.5392 & 0.591209 & 0.295605 \tabularnewline
t & -15.6326388888899 & 70.152684 & -0.2228 & 0.824216 & 0.412108 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57438&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]135682.416666667[/C][C]3993.160784[/C][C]33.9787[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-13898.7583333333[/C][C]3857.759822[/C][C]-3.6028[/C][C]0.000538[/C][C]0.000269[/C][/ROW]
[ROW][C]M1[/C][C]-628.084027777662[/C][C]4675.032978[/C][C]-0.1343[/C][C]0.893456[/C][C]0.446728[/C][/ROW]
[ROW][C]M2[/C][C]-5258.07638888886[/C][C]4663.966549[/C][C]-1.1274[/C][C]0.262869[/C][C]0.131434[/C][/ROW]
[ROW][C]M3[/C][C]-11287.8187500000[/C][C]4653.93139[/C][C]-2.4254[/C][C]0.017486[/C][C]0.008743[/C][/ROW]
[ROW][C]M4[/C][C]-16163.6861111111[/C][C]4644.934187[/C][C]-3.4799[/C][C]0.000806[/C][C]0.000403[/C][/ROW]
[ROW][C]M5[/C][C]-22413.1784722222[/C][C]4636.980981[/C][C]-4.8336[/C][C]6e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]M6[/C][C]-23611.2958333334[/C][C]4630.077151[/C][C]-5.0995[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M7[/C][C]9682.21180555554[/C][C]4624.227399[/C][C]2.0938[/C][C]0.039367[/C][C]0.019683[/C][/ROW]
[ROW][C]M8[/C][C]24974.3444444445[/C][C]4619.435728[/C][C]5.4064[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]23174.9770833334[/C][C]4615.705434[/C][C]5.0209[/C][C]3e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M10[/C][C]12753.3597222222[/C][C]4613.039091[/C][C]2.7646[/C][C]0.007037[/C][C]0.003518[/C][/ROW]
[ROW][C]M11[/C][C]2486.49236111111[/C][C]4611.438545[/C][C]0.5392[/C][C]0.591209[/C][C]0.295605[/C][/ROW]
[ROW][C]t[/C][C]-15.6326388888899[/C][C]70.152684[/C][C]-0.2228[/C][C]0.824216[/C][C]0.412108[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57438&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57438&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)135682.4166666673993.16078433.978700
X-13898.75833333333857.759822-3.60280.0005380.000269
M1-628.0840277776624675.032978-0.13430.8934560.446728
M2-5258.076388888864663.966549-1.12740.2628690.131434
M3-11287.81875000004653.93139-2.42540.0174860.008743
M4-16163.68611111114644.934187-3.47990.0008060.000403
M5-22413.17847222224636.980981-4.83366e-063e-06
M6-23611.29583333344630.077151-5.09952e-061e-06
M79682.211805555544624.2273992.09380.0393670.019683
M824974.34444444454619.4357285.40641e-060
M923174.97708333344615.7054345.02093e-061e-06
M1012753.35972222224613.0390912.76460.0070370.003518
M112486.492361111114611.4385450.53920.5912090.295605
t-15.632638888889970.152684-0.22280.8242160.412108






Multiple Linear Regression - Regression Statistics
Multiple R0.895559876202289
R-squared0.80202749186346
Adjusted R-squared0.770641606427178
F-TEST (value)25.5537634422238
F-TEST (DF numerator)13
F-TEST (DF denominator)82
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9221.80981332463
Sum Squared Residuals6973425651.1167

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.895559876202289 \tabularnewline
R-squared & 0.80202749186346 \tabularnewline
Adjusted R-squared & 0.770641606427178 \tabularnewline
F-TEST (value) & 25.5537634422238 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 82 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9221.80981332463 \tabularnewline
Sum Squared Residuals & 6973425651.1167 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57438&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.895559876202289[/C][/ROW]
[ROW][C]R-squared[/C][C]0.80202749186346[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.770641606427178[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]25.5537634422238[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]82[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]9221.80981332463[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]6973425651.1167[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57438&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57438&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 R0.895559876202289
R-squared0.80202749186346
Adjusted R-squared0.770641606427178
F-TEST (value)25.5537634422238
F-TEST (DF numerator)13
F-TEST (DF denominator)82
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9221.80981332463
Sum Squared Residuals6973425651.1167






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1149657135038.69999999914618.3000000008
2142773130393.07512379.9250000000
3133639124347.79291.29999999995
4128332119456.2000000008875.79999999988
5120297113191.0757105.92500000001
6118632111977.3256654.67499999994
7155276145255.210020.8000000000
8169316160531.78784.30000000005
9167395158716.78678.30000000001
10157939148279.459659.54999999998
11149601137996.9511604.0500000000
12146310135494.82510815.175
13141579134851.1083333336727.89166666652
14136473130205.4833333336267.51666666664
15129818124160.1083333335657.89166666665
16124226119268.6083333334957.39166666666
17116428113003.4833333333424.51666666664
18116440111789.7333333334650.26666666665
19147747145067.6083333332679.39166666665
20160069160344.108333333-275.108333333362
21163129158529.1083333334599.89166666664
22151108148091.8583333333016.14166666665
23141481137809.3583333333671.64166666665
24139174135307.2333333333866.76666666666
25134066134663.516666667-597.516666666795
26130104130017.89166666786.1083333333254
27123090123972.516666667-882.516666666673
28116598119081.016666667-2483.01666666666
29109627112815.891666667-3188.89166666668
30105428111602.141666667-6174.14166666667
31137272144880.016666667-7608.01666666667
32159836160156.516666667-320.516666666682
33155283158341.516666667-3058.51666666668
34141514147904.266666667-6390.26666666667
35131852137621.766666667-5769.76666666667
36130691135119.641666667-4428.64166666666
37128461134475.925-6014.92500000012
38123066129830.3-6764.29999999999
39117599123784.925-6185.925
40111599118893.425-7294.42499999998
41105395112628.3-7233.3
42102334111414.55-9080.54999999999
43131305144692.425-13387.425
44149033159968.925-10935.925
45144954158153.925-13199.925
46132404147716.675-15312.675
47122104137434.175-15330.175
48118755134932.05-16177.0500000000
49116222120389.575000000-4167.57500000013
50110924115743.95-4819.95000000001
51103753109698.575-5945.575
5299983104807.075-4824.07499999999
539330298541.95-5239.95000000001
549149697328.2-5832.20000000001
55119321130606.075-11285.075
56139261145882.575-6621.57500000002
57133739144067.575-10328.5750000000
58123913133630.325-9717.32500000001
59113438123347.825-9909.82500000001
60109416120845.7-11429.7
61109406120201.983333333-10795.9833333334
62105645115556.358333333-9911.35833333332
63101328109510.983333333-8182.98333333333
6497686104619.483333333-6933.48333333331
659309398354.3583333333-5261.35833333333
669138297140.6083333333-5758.60833333333
67122257130418.483333333-8161.48333333333
68139183145694.983333333-6511.98333333334
69139887143879.983333333-3992.98333333334
70131822133442.733333333-1620.73333333333
71116805123160.233333333-6355.23333333333
72113706120658.108333333-6952.10833333332
73113012120014.391666667-7002.39166666677
74110452115368.766666667-4916.76666666665
75107005109323.391666667-2318.39166666665
76102841104431.891666667-1590.89166666663
779817398166.76666666676.23333333334313
789818196953.01666666661227.98333333335
79137277130230.8916666677046.10833333335
80147579145507.3916666672071.60833333333
81146571143692.3916666672878.60833333334
82138920133255.1416666675664.85833333335
83130340122972.6416666677367.35833333335
84128140120470.5166666677669.48333333335
85127059119826.87232.19999999991
86122860115181.1757678.82500000003
87117702109135.88566.20000000003
88113537104244.39292.70000000004
8910836697979.17510386.8250000000
9011107896765.42514312.5750000000
91150739130043.320695.7000000000
92159129145319.813809.2000000000
93157928143504.814423.2000000000
94147768133067.5514700.4500000000
95137507122785.0514721.9500000000
96136919120282.92516636.0750000000

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 149657 & 135038.699999999 & 14618.3000000008 \tabularnewline
2 & 142773 & 130393.075 & 12379.9250000000 \tabularnewline
3 & 133639 & 124347.7 & 9291.29999999995 \tabularnewline
4 & 128332 & 119456.200000000 & 8875.79999999988 \tabularnewline
5 & 120297 & 113191.075 & 7105.92500000001 \tabularnewline
6 & 118632 & 111977.325 & 6654.67499999994 \tabularnewline
7 & 155276 & 145255.2 & 10020.8000000000 \tabularnewline
8 & 169316 & 160531.7 & 8784.30000000005 \tabularnewline
9 & 167395 & 158716.7 & 8678.30000000001 \tabularnewline
10 & 157939 & 148279.45 & 9659.54999999998 \tabularnewline
11 & 149601 & 137996.95 & 11604.0500000000 \tabularnewline
12 & 146310 & 135494.825 & 10815.175 \tabularnewline
13 & 141579 & 134851.108333333 & 6727.89166666652 \tabularnewline
14 & 136473 & 130205.483333333 & 6267.51666666664 \tabularnewline
15 & 129818 & 124160.108333333 & 5657.89166666665 \tabularnewline
16 & 124226 & 119268.608333333 & 4957.39166666666 \tabularnewline
17 & 116428 & 113003.483333333 & 3424.51666666664 \tabularnewline
18 & 116440 & 111789.733333333 & 4650.26666666665 \tabularnewline
19 & 147747 & 145067.608333333 & 2679.39166666665 \tabularnewline
20 & 160069 & 160344.108333333 & -275.108333333362 \tabularnewline
21 & 163129 & 158529.108333333 & 4599.89166666664 \tabularnewline
22 & 151108 & 148091.858333333 & 3016.14166666665 \tabularnewline
23 & 141481 & 137809.358333333 & 3671.64166666665 \tabularnewline
24 & 139174 & 135307.233333333 & 3866.76666666666 \tabularnewline
25 & 134066 & 134663.516666667 & -597.516666666795 \tabularnewline
26 & 130104 & 130017.891666667 & 86.1083333333254 \tabularnewline
27 & 123090 & 123972.516666667 & -882.516666666673 \tabularnewline
28 & 116598 & 119081.016666667 & -2483.01666666666 \tabularnewline
29 & 109627 & 112815.891666667 & -3188.89166666668 \tabularnewline
30 & 105428 & 111602.141666667 & -6174.14166666667 \tabularnewline
31 & 137272 & 144880.016666667 & -7608.01666666667 \tabularnewline
32 & 159836 & 160156.516666667 & -320.516666666682 \tabularnewline
33 & 155283 & 158341.516666667 & -3058.51666666668 \tabularnewline
34 & 141514 & 147904.266666667 & -6390.26666666667 \tabularnewline
35 & 131852 & 137621.766666667 & -5769.76666666667 \tabularnewline
36 & 130691 & 135119.641666667 & -4428.64166666666 \tabularnewline
37 & 128461 & 134475.925 & -6014.92500000012 \tabularnewline
38 & 123066 & 129830.3 & -6764.29999999999 \tabularnewline
39 & 117599 & 123784.925 & -6185.925 \tabularnewline
40 & 111599 & 118893.425 & -7294.42499999998 \tabularnewline
41 & 105395 & 112628.3 & -7233.3 \tabularnewline
42 & 102334 & 111414.55 & -9080.54999999999 \tabularnewline
43 & 131305 & 144692.425 & -13387.425 \tabularnewline
44 & 149033 & 159968.925 & -10935.925 \tabularnewline
45 & 144954 & 158153.925 & -13199.925 \tabularnewline
46 & 132404 & 147716.675 & -15312.675 \tabularnewline
47 & 122104 & 137434.175 & -15330.175 \tabularnewline
48 & 118755 & 134932.05 & -16177.0500000000 \tabularnewline
49 & 116222 & 120389.575000000 & -4167.57500000013 \tabularnewline
50 & 110924 & 115743.95 & -4819.95000000001 \tabularnewline
51 & 103753 & 109698.575 & -5945.575 \tabularnewline
52 & 99983 & 104807.075 & -4824.07499999999 \tabularnewline
53 & 93302 & 98541.95 & -5239.95000000001 \tabularnewline
54 & 91496 & 97328.2 & -5832.20000000001 \tabularnewline
55 & 119321 & 130606.075 & -11285.075 \tabularnewline
56 & 139261 & 145882.575 & -6621.57500000002 \tabularnewline
57 & 133739 & 144067.575 & -10328.5750000000 \tabularnewline
58 & 123913 & 133630.325 & -9717.32500000001 \tabularnewline
59 & 113438 & 123347.825 & -9909.82500000001 \tabularnewline
60 & 109416 & 120845.7 & -11429.7 \tabularnewline
61 & 109406 & 120201.983333333 & -10795.9833333334 \tabularnewline
62 & 105645 & 115556.358333333 & -9911.35833333332 \tabularnewline
63 & 101328 & 109510.983333333 & -8182.98333333333 \tabularnewline
64 & 97686 & 104619.483333333 & -6933.48333333331 \tabularnewline
65 & 93093 & 98354.3583333333 & -5261.35833333333 \tabularnewline
66 & 91382 & 97140.6083333333 & -5758.60833333333 \tabularnewline
67 & 122257 & 130418.483333333 & -8161.48333333333 \tabularnewline
68 & 139183 & 145694.983333333 & -6511.98333333334 \tabularnewline
69 & 139887 & 143879.983333333 & -3992.98333333334 \tabularnewline
70 & 131822 & 133442.733333333 & -1620.73333333333 \tabularnewline
71 & 116805 & 123160.233333333 & -6355.23333333333 \tabularnewline
72 & 113706 & 120658.108333333 & -6952.10833333332 \tabularnewline
73 & 113012 & 120014.391666667 & -7002.39166666677 \tabularnewline
74 & 110452 & 115368.766666667 & -4916.76666666665 \tabularnewline
75 & 107005 & 109323.391666667 & -2318.39166666665 \tabularnewline
76 & 102841 & 104431.891666667 & -1590.89166666663 \tabularnewline
77 & 98173 & 98166.7666666667 & 6.23333333334313 \tabularnewline
78 & 98181 & 96953.0166666666 & 1227.98333333335 \tabularnewline
79 & 137277 & 130230.891666667 & 7046.10833333335 \tabularnewline
80 & 147579 & 145507.391666667 & 2071.60833333333 \tabularnewline
81 & 146571 & 143692.391666667 & 2878.60833333334 \tabularnewline
82 & 138920 & 133255.141666667 & 5664.85833333335 \tabularnewline
83 & 130340 & 122972.641666667 & 7367.35833333335 \tabularnewline
84 & 128140 & 120470.516666667 & 7669.48333333335 \tabularnewline
85 & 127059 & 119826.8 & 7232.19999999991 \tabularnewline
86 & 122860 & 115181.175 & 7678.82500000003 \tabularnewline
87 & 117702 & 109135.8 & 8566.20000000003 \tabularnewline
88 & 113537 & 104244.3 & 9292.70000000004 \tabularnewline
89 & 108366 & 97979.175 & 10386.8250000000 \tabularnewline
90 & 111078 & 96765.425 & 14312.5750000000 \tabularnewline
91 & 150739 & 130043.3 & 20695.7000000000 \tabularnewline
92 & 159129 & 145319.8 & 13809.2000000000 \tabularnewline
93 & 157928 & 143504.8 & 14423.2000000000 \tabularnewline
94 & 147768 & 133067.55 & 14700.4500000000 \tabularnewline
95 & 137507 & 122785.05 & 14721.9500000000 \tabularnewline
96 & 136919 & 120282.925 & 16636.0750000000 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57438&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]149657[/C][C]135038.699999999[/C][C]14618.3000000008[/C][/ROW]
[ROW][C]2[/C][C]142773[/C][C]130393.075[/C][C]12379.9250000000[/C][/ROW]
[ROW][C]3[/C][C]133639[/C][C]124347.7[/C][C]9291.29999999995[/C][/ROW]
[ROW][C]4[/C][C]128332[/C][C]119456.200000000[/C][C]8875.79999999988[/C][/ROW]
[ROW][C]5[/C][C]120297[/C][C]113191.075[/C][C]7105.92500000001[/C][/ROW]
[ROW][C]6[/C][C]118632[/C][C]111977.325[/C][C]6654.67499999994[/C][/ROW]
[ROW][C]7[/C][C]155276[/C][C]145255.2[/C][C]10020.8000000000[/C][/ROW]
[ROW][C]8[/C][C]169316[/C][C]160531.7[/C][C]8784.30000000005[/C][/ROW]
[ROW][C]9[/C][C]167395[/C][C]158716.7[/C][C]8678.30000000001[/C][/ROW]
[ROW][C]10[/C][C]157939[/C][C]148279.45[/C][C]9659.54999999998[/C][/ROW]
[ROW][C]11[/C][C]149601[/C][C]137996.95[/C][C]11604.0500000000[/C][/ROW]
[ROW][C]12[/C][C]146310[/C][C]135494.825[/C][C]10815.175[/C][/ROW]
[ROW][C]13[/C][C]141579[/C][C]134851.108333333[/C][C]6727.89166666652[/C][/ROW]
[ROW][C]14[/C][C]136473[/C][C]130205.483333333[/C][C]6267.51666666664[/C][/ROW]
[ROW][C]15[/C][C]129818[/C][C]124160.108333333[/C][C]5657.89166666665[/C][/ROW]
[ROW][C]16[/C][C]124226[/C][C]119268.608333333[/C][C]4957.39166666666[/C][/ROW]
[ROW][C]17[/C][C]116428[/C][C]113003.483333333[/C][C]3424.51666666664[/C][/ROW]
[ROW][C]18[/C][C]116440[/C][C]111789.733333333[/C][C]4650.26666666665[/C][/ROW]
[ROW][C]19[/C][C]147747[/C][C]145067.608333333[/C][C]2679.39166666665[/C][/ROW]
[ROW][C]20[/C][C]160069[/C][C]160344.108333333[/C][C]-275.108333333362[/C][/ROW]
[ROW][C]21[/C][C]163129[/C][C]158529.108333333[/C][C]4599.89166666664[/C][/ROW]
[ROW][C]22[/C][C]151108[/C][C]148091.858333333[/C][C]3016.14166666665[/C][/ROW]
[ROW][C]23[/C][C]141481[/C][C]137809.358333333[/C][C]3671.64166666665[/C][/ROW]
[ROW][C]24[/C][C]139174[/C][C]135307.233333333[/C][C]3866.76666666666[/C][/ROW]
[ROW][C]25[/C][C]134066[/C][C]134663.516666667[/C][C]-597.516666666795[/C][/ROW]
[ROW][C]26[/C][C]130104[/C][C]130017.891666667[/C][C]86.1083333333254[/C][/ROW]
[ROW][C]27[/C][C]123090[/C][C]123972.516666667[/C][C]-882.516666666673[/C][/ROW]
[ROW][C]28[/C][C]116598[/C][C]119081.016666667[/C][C]-2483.01666666666[/C][/ROW]
[ROW][C]29[/C][C]109627[/C][C]112815.891666667[/C][C]-3188.89166666668[/C][/ROW]
[ROW][C]30[/C][C]105428[/C][C]111602.141666667[/C][C]-6174.14166666667[/C][/ROW]
[ROW][C]31[/C][C]137272[/C][C]144880.016666667[/C][C]-7608.01666666667[/C][/ROW]
[ROW][C]32[/C][C]159836[/C][C]160156.516666667[/C][C]-320.516666666682[/C][/ROW]
[ROW][C]33[/C][C]155283[/C][C]158341.516666667[/C][C]-3058.51666666668[/C][/ROW]
[ROW][C]34[/C][C]141514[/C][C]147904.266666667[/C][C]-6390.26666666667[/C][/ROW]
[ROW][C]35[/C][C]131852[/C][C]137621.766666667[/C][C]-5769.76666666667[/C][/ROW]
[ROW][C]36[/C][C]130691[/C][C]135119.641666667[/C][C]-4428.64166666666[/C][/ROW]
[ROW][C]37[/C][C]128461[/C][C]134475.925[/C][C]-6014.92500000012[/C][/ROW]
[ROW][C]38[/C][C]123066[/C][C]129830.3[/C][C]-6764.29999999999[/C][/ROW]
[ROW][C]39[/C][C]117599[/C][C]123784.925[/C][C]-6185.925[/C][/ROW]
[ROW][C]40[/C][C]111599[/C][C]118893.425[/C][C]-7294.42499999998[/C][/ROW]
[ROW][C]41[/C][C]105395[/C][C]112628.3[/C][C]-7233.3[/C][/ROW]
[ROW][C]42[/C][C]102334[/C][C]111414.55[/C][C]-9080.54999999999[/C][/ROW]
[ROW][C]43[/C][C]131305[/C][C]144692.425[/C][C]-13387.425[/C][/ROW]
[ROW][C]44[/C][C]149033[/C][C]159968.925[/C][C]-10935.925[/C][/ROW]
[ROW][C]45[/C][C]144954[/C][C]158153.925[/C][C]-13199.925[/C][/ROW]
[ROW][C]46[/C][C]132404[/C][C]147716.675[/C][C]-15312.675[/C][/ROW]
[ROW][C]47[/C][C]122104[/C][C]137434.175[/C][C]-15330.175[/C][/ROW]
[ROW][C]48[/C][C]118755[/C][C]134932.05[/C][C]-16177.0500000000[/C][/ROW]
[ROW][C]49[/C][C]116222[/C][C]120389.575000000[/C][C]-4167.57500000013[/C][/ROW]
[ROW][C]50[/C][C]110924[/C][C]115743.95[/C][C]-4819.95000000001[/C][/ROW]
[ROW][C]51[/C][C]103753[/C][C]109698.575[/C][C]-5945.575[/C][/ROW]
[ROW][C]52[/C][C]99983[/C][C]104807.075[/C][C]-4824.07499999999[/C][/ROW]
[ROW][C]53[/C][C]93302[/C][C]98541.95[/C][C]-5239.95000000001[/C][/ROW]
[ROW][C]54[/C][C]91496[/C][C]97328.2[/C][C]-5832.20000000001[/C][/ROW]
[ROW][C]55[/C][C]119321[/C][C]130606.075[/C][C]-11285.075[/C][/ROW]
[ROW][C]56[/C][C]139261[/C][C]145882.575[/C][C]-6621.57500000002[/C][/ROW]
[ROW][C]57[/C][C]133739[/C][C]144067.575[/C][C]-10328.5750000000[/C][/ROW]
[ROW][C]58[/C][C]123913[/C][C]133630.325[/C][C]-9717.32500000001[/C][/ROW]
[ROW][C]59[/C][C]113438[/C][C]123347.825[/C][C]-9909.82500000001[/C][/ROW]
[ROW][C]60[/C][C]109416[/C][C]120845.7[/C][C]-11429.7[/C][/ROW]
[ROW][C]61[/C][C]109406[/C][C]120201.983333333[/C][C]-10795.9833333334[/C][/ROW]
[ROW][C]62[/C][C]105645[/C][C]115556.358333333[/C][C]-9911.35833333332[/C][/ROW]
[ROW][C]63[/C][C]101328[/C][C]109510.983333333[/C][C]-8182.98333333333[/C][/ROW]
[ROW][C]64[/C][C]97686[/C][C]104619.483333333[/C][C]-6933.48333333331[/C][/ROW]
[ROW][C]65[/C][C]93093[/C][C]98354.3583333333[/C][C]-5261.35833333333[/C][/ROW]
[ROW][C]66[/C][C]91382[/C][C]97140.6083333333[/C][C]-5758.60833333333[/C][/ROW]
[ROW][C]67[/C][C]122257[/C][C]130418.483333333[/C][C]-8161.48333333333[/C][/ROW]
[ROW][C]68[/C][C]139183[/C][C]145694.983333333[/C][C]-6511.98333333334[/C][/ROW]
[ROW][C]69[/C][C]139887[/C][C]143879.983333333[/C][C]-3992.98333333334[/C][/ROW]
[ROW][C]70[/C][C]131822[/C][C]133442.733333333[/C][C]-1620.73333333333[/C][/ROW]
[ROW][C]71[/C][C]116805[/C][C]123160.233333333[/C][C]-6355.23333333333[/C][/ROW]
[ROW][C]72[/C][C]113706[/C][C]120658.108333333[/C][C]-6952.10833333332[/C][/ROW]
[ROW][C]73[/C][C]113012[/C][C]120014.391666667[/C][C]-7002.39166666677[/C][/ROW]
[ROW][C]74[/C][C]110452[/C][C]115368.766666667[/C][C]-4916.76666666665[/C][/ROW]
[ROW][C]75[/C][C]107005[/C][C]109323.391666667[/C][C]-2318.39166666665[/C][/ROW]
[ROW][C]76[/C][C]102841[/C][C]104431.891666667[/C][C]-1590.89166666663[/C][/ROW]
[ROW][C]77[/C][C]98173[/C][C]98166.7666666667[/C][C]6.23333333334313[/C][/ROW]
[ROW][C]78[/C][C]98181[/C][C]96953.0166666666[/C][C]1227.98333333335[/C][/ROW]
[ROW][C]79[/C][C]137277[/C][C]130230.891666667[/C][C]7046.10833333335[/C][/ROW]
[ROW][C]80[/C][C]147579[/C][C]145507.391666667[/C][C]2071.60833333333[/C][/ROW]
[ROW][C]81[/C][C]146571[/C][C]143692.391666667[/C][C]2878.60833333334[/C][/ROW]
[ROW][C]82[/C][C]138920[/C][C]133255.141666667[/C][C]5664.85833333335[/C][/ROW]
[ROW][C]83[/C][C]130340[/C][C]122972.641666667[/C][C]7367.35833333335[/C][/ROW]
[ROW][C]84[/C][C]128140[/C][C]120470.516666667[/C][C]7669.48333333335[/C][/ROW]
[ROW][C]85[/C][C]127059[/C][C]119826.8[/C][C]7232.19999999991[/C][/ROW]
[ROW][C]86[/C][C]122860[/C][C]115181.175[/C][C]7678.82500000003[/C][/ROW]
[ROW][C]87[/C][C]117702[/C][C]109135.8[/C][C]8566.20000000003[/C][/ROW]
[ROW][C]88[/C][C]113537[/C][C]104244.3[/C][C]9292.70000000004[/C][/ROW]
[ROW][C]89[/C][C]108366[/C][C]97979.175[/C][C]10386.8250000000[/C][/ROW]
[ROW][C]90[/C][C]111078[/C][C]96765.425[/C][C]14312.5750000000[/C][/ROW]
[ROW][C]91[/C][C]150739[/C][C]130043.3[/C][C]20695.7000000000[/C][/ROW]
[ROW][C]92[/C][C]159129[/C][C]145319.8[/C][C]13809.2000000000[/C][/ROW]
[ROW][C]93[/C][C]157928[/C][C]143504.8[/C][C]14423.2000000000[/C][/ROW]
[ROW][C]94[/C][C]147768[/C][C]133067.55[/C][C]14700.4500000000[/C][/ROW]
[ROW][C]95[/C][C]137507[/C][C]122785.05[/C][C]14721.9500000000[/C][/ROW]
[ROW][C]96[/C][C]136919[/C][C]120282.925[/C][C]16636.0750000000[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57438&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57438&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
1149657135038.69999999914618.3000000008
2142773130393.07512379.9250000000
3133639124347.79291.29999999995
4128332119456.2000000008875.79999999988
5120297113191.0757105.92500000001
6118632111977.3256654.67499999994
7155276145255.210020.8000000000
8169316160531.78784.30000000005
9167395158716.78678.30000000001
10157939148279.459659.54999999998
11149601137996.9511604.0500000000
12146310135494.82510815.175
13141579134851.1083333336727.89166666652
14136473130205.4833333336267.51666666664
15129818124160.1083333335657.89166666665
16124226119268.6083333334957.39166666666
17116428113003.4833333333424.51666666664
18116440111789.7333333334650.26666666665
19147747145067.6083333332679.39166666665
20160069160344.108333333-275.108333333362
21163129158529.1083333334599.89166666664
22151108148091.8583333333016.14166666665
23141481137809.3583333333671.64166666665
24139174135307.2333333333866.76666666666
25134066134663.516666667-597.516666666795
26130104130017.89166666786.1083333333254
27123090123972.516666667-882.516666666673
28116598119081.016666667-2483.01666666666
29109627112815.891666667-3188.89166666668
30105428111602.141666667-6174.14166666667
31137272144880.016666667-7608.01666666667
32159836160156.516666667-320.516666666682
33155283158341.516666667-3058.51666666668
34141514147904.266666667-6390.26666666667
35131852137621.766666667-5769.76666666667
36130691135119.641666667-4428.64166666666
37128461134475.925-6014.92500000012
38123066129830.3-6764.29999999999
39117599123784.925-6185.925
40111599118893.425-7294.42499999998
41105395112628.3-7233.3
42102334111414.55-9080.54999999999
43131305144692.425-13387.425
44149033159968.925-10935.925
45144954158153.925-13199.925
46132404147716.675-15312.675
47122104137434.175-15330.175
48118755134932.05-16177.0500000000
49116222120389.575000000-4167.57500000013
50110924115743.95-4819.95000000001
51103753109698.575-5945.575
5299983104807.075-4824.07499999999
539330298541.95-5239.95000000001
549149697328.2-5832.20000000001
55119321130606.075-11285.075
56139261145882.575-6621.57500000002
57133739144067.575-10328.5750000000
58123913133630.325-9717.32500000001
59113438123347.825-9909.82500000001
60109416120845.7-11429.7
61109406120201.983333333-10795.9833333334
62105645115556.358333333-9911.35833333332
63101328109510.983333333-8182.98333333333
6497686104619.483333333-6933.48333333331
659309398354.3583333333-5261.35833333333
669138297140.6083333333-5758.60833333333
67122257130418.483333333-8161.48333333333
68139183145694.983333333-6511.98333333334
69139887143879.983333333-3992.98333333334
70131822133442.733333333-1620.73333333333
71116805123160.233333333-6355.23333333333
72113706120658.108333333-6952.10833333332
73113012120014.391666667-7002.39166666677
74110452115368.766666667-4916.76666666665
75107005109323.391666667-2318.39166666665
76102841104431.891666667-1590.89166666663
779817398166.76666666676.23333333334313
789818196953.01666666661227.98333333335
79137277130230.8916666677046.10833333335
80147579145507.3916666672071.60833333333
81146571143692.3916666672878.60833333334
82138920133255.1416666675664.85833333335
83130340122972.6416666677367.35833333335
84128140120470.5166666677669.48333333335
85127059119826.87232.19999999991
86122860115181.1757678.82500000003
87117702109135.88566.20000000003
88113537104244.39292.70000000004
8910836697979.17510386.8250000000
9011107896765.42514312.5750000000
91150739130043.320695.7000000000
92159129145319.813809.2000000000
93157928143504.814423.2000000000
94147768133067.5514700.4500000000
95137507122785.0514721.9500000000
96136919120282.92516636.0750000000






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.01143745320635580.02287490641271160.988562546793644
180.00478123032222790.00956246064445580.995218769677772
190.001900762035953000.003801524071906010.998099237964047
200.001356097982754820.002712195965509650.998643902017245
210.0004570893061141440.0009141786122282880.999542910693886
220.0001573121973608850.000314624394721770.99984268780264
238.86230267848214e-050.0001772460535696430.999911376973215
244.36422064330728e-058.72844128661456e-050.999956357793567
252.78345214218111e-055.56690428436222e-050.999972165478578
261.07306623397564e-052.14613246795128e-050.99998926933766
274.31099631415487e-068.62199262830974e-060.999995689003686
281.52422585146163e-063.04845170292327e-060.999998475774148
295.3757564382403e-071.07515128764806e-060.999999462424356
304.22177611021321e-078.44355222042642e-070.999999577822389
311.29000709516074e-062.58001419032148e-060.999998709992905
327.88804929085506e-061.57760985817101e-050.999992111950709
335.35945646235033e-061.07189129247007e-050.999994640543538
346.33997704182202e-061.26799540836440e-050.999993660022958
351.15581817076452e-052.31163634152903e-050.999988441818292
361.56168324166436e-053.12336648332871e-050.999984383167583
371.03441869014352e-052.06883738028704e-050.999989655813099
386.55582412887952e-061.31116482577590e-050.999993444175871
396.94131684383077e-061.38826336876615e-050.999993058683156
405.76943268076598e-061.15388653615320e-050.99999423056732
417.9469684321937e-061.58939368643874e-050.999992053031568
425.37487911006237e-061.07497582201247e-050.99999462512089
433.60864935193846e-067.21729870387693e-060.999996391350648
441.94759840748235e-063.8951968149647e-060.999998052401593
452.60559910207784e-065.21119820415568e-060.999997394400898
464.00576148215528e-068.01152296431056e-060.999995994238518
478.78417916030895e-061.75683583206179e-050.99999121582084
482.46502682070830e-054.93005364141661e-050.999975349731793
499.78593663348462e-050.0001957187326696920.999902140633665
500.00039127505006840.00078255010013680.999608724949932
510.001007855117883900.002015710235767810.998992144882116
520.005039292766997680.01007858553399540.994960707233002
530.02045179540286860.04090359080573720.979548204597131
540.05609447379290380.1121889475858080.943905526207096
550.0455082150059280.0910164300118560.954491784994072
560.1285262523636280.2570525047272560.871473747636372
570.1428826486944960.2857652973889910.857117351305504
580.1249687641268500.2499375282536990.87503123587315
590.1330316030025970.2660632060051940.866968396997403
600.123349602752070.246699205504140.87665039724793
610.1245325018581310.2490650037162630.875467498141869
620.1300695362676810.2601390725353620.869930463732319
630.1824307378222840.3648614756445680.817569262177716
640.3331493399766960.6662986799533920.666850660023304
650.6546033235815010.6907933528369980.345396676418499
660.7650113271691560.4699773456616880.234988672830844
670.9372469883862760.1255060232274480.0627530116137239
680.9364148033457770.1271703933084460.0635851966542232
690.9734505404447560.05309891911048730.0265494595552436
700.9980990605046170.003801878990765140.00190093949538257
710.9965157725890260.006968454821948540.00348422741097427
720.9957974659741070.008405068051785780.00420253402589289
730.9967749061966440.006450187606712750.00322509380335638
740.9954754325329070.009049134934186670.00452456746709334
750.9920526875889970.01589462482200520.00794731241100259
760.9849016100318570.03019677993628560.0150983899681428
770.9710743798898580.05785124022028360.0289256201101418
780.9601899607169850.0796200785660310.0398100392830155
790.9685239268607180.06295214627856310.0314760731392816

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.0114374532063558 & 0.0228749064127116 & 0.988562546793644 \tabularnewline
18 & 0.0047812303222279 & 0.0095624606444558 & 0.995218769677772 \tabularnewline
19 & 0.00190076203595300 & 0.00380152407190601 & 0.998099237964047 \tabularnewline
20 & 0.00135609798275482 & 0.00271219596550965 & 0.998643902017245 \tabularnewline
21 & 0.000457089306114144 & 0.000914178612228288 & 0.999542910693886 \tabularnewline
22 & 0.000157312197360885 & 0.00031462439472177 & 0.99984268780264 \tabularnewline
23 & 8.86230267848214e-05 & 0.000177246053569643 & 0.999911376973215 \tabularnewline
24 & 4.36422064330728e-05 & 8.72844128661456e-05 & 0.999956357793567 \tabularnewline
25 & 2.78345214218111e-05 & 5.56690428436222e-05 & 0.999972165478578 \tabularnewline
26 & 1.07306623397564e-05 & 2.14613246795128e-05 & 0.99998926933766 \tabularnewline
27 & 4.31099631415487e-06 & 8.62199262830974e-06 & 0.999995689003686 \tabularnewline
28 & 1.52422585146163e-06 & 3.04845170292327e-06 & 0.999998475774148 \tabularnewline
29 & 5.3757564382403e-07 & 1.07515128764806e-06 & 0.999999462424356 \tabularnewline
30 & 4.22177611021321e-07 & 8.44355222042642e-07 & 0.999999577822389 \tabularnewline
31 & 1.29000709516074e-06 & 2.58001419032148e-06 & 0.999998709992905 \tabularnewline
32 & 7.88804929085506e-06 & 1.57760985817101e-05 & 0.999992111950709 \tabularnewline
33 & 5.35945646235033e-06 & 1.07189129247007e-05 & 0.999994640543538 \tabularnewline
34 & 6.33997704182202e-06 & 1.26799540836440e-05 & 0.999993660022958 \tabularnewline
35 & 1.15581817076452e-05 & 2.31163634152903e-05 & 0.999988441818292 \tabularnewline
36 & 1.56168324166436e-05 & 3.12336648332871e-05 & 0.999984383167583 \tabularnewline
37 & 1.03441869014352e-05 & 2.06883738028704e-05 & 0.999989655813099 \tabularnewline
38 & 6.55582412887952e-06 & 1.31116482577590e-05 & 0.999993444175871 \tabularnewline
39 & 6.94131684383077e-06 & 1.38826336876615e-05 & 0.999993058683156 \tabularnewline
40 & 5.76943268076598e-06 & 1.15388653615320e-05 & 0.99999423056732 \tabularnewline
41 & 7.9469684321937e-06 & 1.58939368643874e-05 & 0.999992053031568 \tabularnewline
42 & 5.37487911006237e-06 & 1.07497582201247e-05 & 0.99999462512089 \tabularnewline
43 & 3.60864935193846e-06 & 7.21729870387693e-06 & 0.999996391350648 \tabularnewline
44 & 1.94759840748235e-06 & 3.8951968149647e-06 & 0.999998052401593 \tabularnewline
45 & 2.60559910207784e-06 & 5.21119820415568e-06 & 0.999997394400898 \tabularnewline
46 & 4.00576148215528e-06 & 8.01152296431056e-06 & 0.999995994238518 \tabularnewline
47 & 8.78417916030895e-06 & 1.75683583206179e-05 & 0.99999121582084 \tabularnewline
48 & 2.46502682070830e-05 & 4.93005364141661e-05 & 0.999975349731793 \tabularnewline
49 & 9.78593663348462e-05 & 0.000195718732669692 & 0.999902140633665 \tabularnewline
50 & 0.0003912750500684 & 0.0007825501001368 & 0.999608724949932 \tabularnewline
51 & 0.00100785511788390 & 0.00201571023576781 & 0.998992144882116 \tabularnewline
52 & 0.00503929276699768 & 0.0100785855339954 & 0.994960707233002 \tabularnewline
53 & 0.0204517954028686 & 0.0409035908057372 & 0.979548204597131 \tabularnewline
54 & 0.0560944737929038 & 0.112188947585808 & 0.943905526207096 \tabularnewline
55 & 0.045508215005928 & 0.091016430011856 & 0.954491784994072 \tabularnewline
56 & 0.128526252363628 & 0.257052504727256 & 0.871473747636372 \tabularnewline
57 & 0.142882648694496 & 0.285765297388991 & 0.857117351305504 \tabularnewline
58 & 0.124968764126850 & 0.249937528253699 & 0.87503123587315 \tabularnewline
59 & 0.133031603002597 & 0.266063206005194 & 0.866968396997403 \tabularnewline
60 & 0.12334960275207 & 0.24669920550414 & 0.87665039724793 \tabularnewline
61 & 0.124532501858131 & 0.249065003716263 & 0.875467498141869 \tabularnewline
62 & 0.130069536267681 & 0.260139072535362 & 0.869930463732319 \tabularnewline
63 & 0.182430737822284 & 0.364861475644568 & 0.817569262177716 \tabularnewline
64 & 0.333149339976696 & 0.666298679953392 & 0.666850660023304 \tabularnewline
65 & 0.654603323581501 & 0.690793352836998 & 0.345396676418499 \tabularnewline
66 & 0.765011327169156 & 0.469977345661688 & 0.234988672830844 \tabularnewline
67 & 0.937246988386276 & 0.125506023227448 & 0.0627530116137239 \tabularnewline
68 & 0.936414803345777 & 0.127170393308446 & 0.0635851966542232 \tabularnewline
69 & 0.973450540444756 & 0.0530989191104873 & 0.0265494595552436 \tabularnewline
70 & 0.998099060504617 & 0.00380187899076514 & 0.00190093949538257 \tabularnewline
71 & 0.996515772589026 & 0.00696845482194854 & 0.00348422741097427 \tabularnewline
72 & 0.995797465974107 & 0.00840506805178578 & 0.00420253402589289 \tabularnewline
73 & 0.996774906196644 & 0.00645018760671275 & 0.00322509380335638 \tabularnewline
74 & 0.995475432532907 & 0.00904913493418667 & 0.00452456746709334 \tabularnewline
75 & 0.992052687588997 & 0.0158946248220052 & 0.00794731241100259 \tabularnewline
76 & 0.984901610031857 & 0.0301967799362856 & 0.0150983899681428 \tabularnewline
77 & 0.971074379889858 & 0.0578512402202836 & 0.0289256201101418 \tabularnewline
78 & 0.960189960716985 & 0.079620078566031 & 0.0398100392830155 \tabularnewline
79 & 0.968523926860718 & 0.0629521462785631 & 0.0314760731392816 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57438&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]17[/C][C]0.0114374532063558[/C][C]0.0228749064127116[/C][C]0.988562546793644[/C][/ROW]
[ROW][C]18[/C][C]0.0047812303222279[/C][C]0.0095624606444558[/C][C]0.995218769677772[/C][/ROW]
[ROW][C]19[/C][C]0.00190076203595300[/C][C]0.00380152407190601[/C][C]0.998099237964047[/C][/ROW]
[ROW][C]20[/C][C]0.00135609798275482[/C][C]0.00271219596550965[/C][C]0.998643902017245[/C][/ROW]
[ROW][C]21[/C][C]0.000457089306114144[/C][C]0.000914178612228288[/C][C]0.999542910693886[/C][/ROW]
[ROW][C]22[/C][C]0.000157312197360885[/C][C]0.00031462439472177[/C][C]0.99984268780264[/C][/ROW]
[ROW][C]23[/C][C]8.86230267848214e-05[/C][C]0.000177246053569643[/C][C]0.999911376973215[/C][/ROW]
[ROW][C]24[/C][C]4.36422064330728e-05[/C][C]8.72844128661456e-05[/C][C]0.999956357793567[/C][/ROW]
[ROW][C]25[/C][C]2.78345214218111e-05[/C][C]5.56690428436222e-05[/C][C]0.999972165478578[/C][/ROW]
[ROW][C]26[/C][C]1.07306623397564e-05[/C][C]2.14613246795128e-05[/C][C]0.99998926933766[/C][/ROW]
[ROW][C]27[/C][C]4.31099631415487e-06[/C][C]8.62199262830974e-06[/C][C]0.999995689003686[/C][/ROW]
[ROW][C]28[/C][C]1.52422585146163e-06[/C][C]3.04845170292327e-06[/C][C]0.999998475774148[/C][/ROW]
[ROW][C]29[/C][C]5.3757564382403e-07[/C][C]1.07515128764806e-06[/C][C]0.999999462424356[/C][/ROW]
[ROW][C]30[/C][C]4.22177611021321e-07[/C][C]8.44355222042642e-07[/C][C]0.999999577822389[/C][/ROW]
[ROW][C]31[/C][C]1.29000709516074e-06[/C][C]2.58001419032148e-06[/C][C]0.999998709992905[/C][/ROW]
[ROW][C]32[/C][C]7.88804929085506e-06[/C][C]1.57760985817101e-05[/C][C]0.999992111950709[/C][/ROW]
[ROW][C]33[/C][C]5.35945646235033e-06[/C][C]1.07189129247007e-05[/C][C]0.999994640543538[/C][/ROW]
[ROW][C]34[/C][C]6.33997704182202e-06[/C][C]1.26799540836440e-05[/C][C]0.999993660022958[/C][/ROW]
[ROW][C]35[/C][C]1.15581817076452e-05[/C][C]2.31163634152903e-05[/C][C]0.999988441818292[/C][/ROW]
[ROW][C]36[/C][C]1.56168324166436e-05[/C][C]3.12336648332871e-05[/C][C]0.999984383167583[/C][/ROW]
[ROW][C]37[/C][C]1.03441869014352e-05[/C][C]2.06883738028704e-05[/C][C]0.999989655813099[/C][/ROW]
[ROW][C]38[/C][C]6.55582412887952e-06[/C][C]1.31116482577590e-05[/C][C]0.999993444175871[/C][/ROW]
[ROW][C]39[/C][C]6.94131684383077e-06[/C][C]1.38826336876615e-05[/C][C]0.999993058683156[/C][/ROW]
[ROW][C]40[/C][C]5.76943268076598e-06[/C][C]1.15388653615320e-05[/C][C]0.99999423056732[/C][/ROW]
[ROW][C]41[/C][C]7.9469684321937e-06[/C][C]1.58939368643874e-05[/C][C]0.999992053031568[/C][/ROW]
[ROW][C]42[/C][C]5.37487911006237e-06[/C][C]1.07497582201247e-05[/C][C]0.99999462512089[/C][/ROW]
[ROW][C]43[/C][C]3.60864935193846e-06[/C][C]7.21729870387693e-06[/C][C]0.999996391350648[/C][/ROW]
[ROW][C]44[/C][C]1.94759840748235e-06[/C][C]3.8951968149647e-06[/C][C]0.999998052401593[/C][/ROW]
[ROW][C]45[/C][C]2.60559910207784e-06[/C][C]5.21119820415568e-06[/C][C]0.999997394400898[/C][/ROW]
[ROW][C]46[/C][C]4.00576148215528e-06[/C][C]8.01152296431056e-06[/C][C]0.999995994238518[/C][/ROW]
[ROW][C]47[/C][C]8.78417916030895e-06[/C][C]1.75683583206179e-05[/C][C]0.99999121582084[/C][/ROW]
[ROW][C]48[/C][C]2.46502682070830e-05[/C][C]4.93005364141661e-05[/C][C]0.999975349731793[/C][/ROW]
[ROW][C]49[/C][C]9.78593663348462e-05[/C][C]0.000195718732669692[/C][C]0.999902140633665[/C][/ROW]
[ROW][C]50[/C][C]0.0003912750500684[/C][C]0.0007825501001368[/C][C]0.999608724949932[/C][/ROW]
[ROW][C]51[/C][C]0.00100785511788390[/C][C]0.00201571023576781[/C][C]0.998992144882116[/C][/ROW]
[ROW][C]52[/C][C]0.00503929276699768[/C][C]0.0100785855339954[/C][C]0.994960707233002[/C][/ROW]
[ROW][C]53[/C][C]0.0204517954028686[/C][C]0.0409035908057372[/C][C]0.979548204597131[/C][/ROW]
[ROW][C]54[/C][C]0.0560944737929038[/C][C]0.112188947585808[/C][C]0.943905526207096[/C][/ROW]
[ROW][C]55[/C][C]0.045508215005928[/C][C]0.091016430011856[/C][C]0.954491784994072[/C][/ROW]
[ROW][C]56[/C][C]0.128526252363628[/C][C]0.257052504727256[/C][C]0.871473747636372[/C][/ROW]
[ROW][C]57[/C][C]0.142882648694496[/C][C]0.285765297388991[/C][C]0.857117351305504[/C][/ROW]
[ROW][C]58[/C][C]0.124968764126850[/C][C]0.249937528253699[/C][C]0.87503123587315[/C][/ROW]
[ROW][C]59[/C][C]0.133031603002597[/C][C]0.266063206005194[/C][C]0.866968396997403[/C][/ROW]
[ROW][C]60[/C][C]0.12334960275207[/C][C]0.24669920550414[/C][C]0.87665039724793[/C][/ROW]
[ROW][C]61[/C][C]0.124532501858131[/C][C]0.249065003716263[/C][C]0.875467498141869[/C][/ROW]
[ROW][C]62[/C][C]0.130069536267681[/C][C]0.260139072535362[/C][C]0.869930463732319[/C][/ROW]
[ROW][C]63[/C][C]0.182430737822284[/C][C]0.364861475644568[/C][C]0.817569262177716[/C][/ROW]
[ROW][C]64[/C][C]0.333149339976696[/C][C]0.666298679953392[/C][C]0.666850660023304[/C][/ROW]
[ROW][C]65[/C][C]0.654603323581501[/C][C]0.690793352836998[/C][C]0.345396676418499[/C][/ROW]
[ROW][C]66[/C][C]0.765011327169156[/C][C]0.469977345661688[/C][C]0.234988672830844[/C][/ROW]
[ROW][C]67[/C][C]0.937246988386276[/C][C]0.125506023227448[/C][C]0.0627530116137239[/C][/ROW]
[ROW][C]68[/C][C]0.936414803345777[/C][C]0.127170393308446[/C][C]0.0635851966542232[/C][/ROW]
[ROW][C]69[/C][C]0.973450540444756[/C][C]0.0530989191104873[/C][C]0.0265494595552436[/C][/ROW]
[ROW][C]70[/C][C]0.998099060504617[/C][C]0.00380187899076514[/C][C]0.00190093949538257[/C][/ROW]
[ROW][C]71[/C][C]0.996515772589026[/C][C]0.00696845482194854[/C][C]0.00348422741097427[/C][/ROW]
[ROW][C]72[/C][C]0.995797465974107[/C][C]0.00840506805178578[/C][C]0.00420253402589289[/C][/ROW]
[ROW][C]73[/C][C]0.996774906196644[/C][C]0.00645018760671275[/C][C]0.00322509380335638[/C][/ROW]
[ROW][C]74[/C][C]0.995475432532907[/C][C]0.00904913493418667[/C][C]0.00452456746709334[/C][/ROW]
[ROW][C]75[/C][C]0.992052687588997[/C][C]0.0158946248220052[/C][C]0.00794731241100259[/C][/ROW]
[ROW][C]76[/C][C]0.984901610031857[/C][C]0.0301967799362856[/C][C]0.0150983899681428[/C][/ROW]
[ROW][C]77[/C][C]0.971074379889858[/C][C]0.0578512402202836[/C][C]0.0289256201101418[/C][/ROW]
[ROW][C]78[/C][C]0.960189960716985[/C][C]0.079620078566031[/C][C]0.0398100392830155[/C][/ROW]
[ROW][C]79[/C][C]0.968523926860718[/C][C]0.0629521462785631[/C][C]0.0314760731392816[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57438&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57438&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
170.01143745320635580.02287490641271160.988562546793644
180.00478123032222790.00956246064445580.995218769677772
190.001900762035953000.003801524071906010.998099237964047
200.001356097982754820.002712195965509650.998643902017245
210.0004570893061141440.0009141786122282880.999542910693886
220.0001573121973608850.000314624394721770.99984268780264
238.86230267848214e-050.0001772460535696430.999911376973215
244.36422064330728e-058.72844128661456e-050.999956357793567
252.78345214218111e-055.56690428436222e-050.999972165478578
261.07306623397564e-052.14613246795128e-050.99998926933766
274.31099631415487e-068.62199262830974e-060.999995689003686
281.52422585146163e-063.04845170292327e-060.999998475774148
295.3757564382403e-071.07515128764806e-060.999999462424356
304.22177611021321e-078.44355222042642e-070.999999577822389
311.29000709516074e-062.58001419032148e-060.999998709992905
327.88804929085506e-061.57760985817101e-050.999992111950709
335.35945646235033e-061.07189129247007e-050.999994640543538
346.33997704182202e-061.26799540836440e-050.999993660022958
351.15581817076452e-052.31163634152903e-050.999988441818292
361.56168324166436e-053.12336648332871e-050.999984383167583
371.03441869014352e-052.06883738028704e-050.999989655813099
386.55582412887952e-061.31116482577590e-050.999993444175871
396.94131684383077e-061.38826336876615e-050.999993058683156
405.76943268076598e-061.15388653615320e-050.99999423056732
417.9469684321937e-061.58939368643874e-050.999992053031568
425.37487911006237e-061.07497582201247e-050.99999462512089
433.60864935193846e-067.21729870387693e-060.999996391350648
441.94759840748235e-063.8951968149647e-060.999998052401593
452.60559910207784e-065.21119820415568e-060.999997394400898
464.00576148215528e-068.01152296431056e-060.999995994238518
478.78417916030895e-061.75683583206179e-050.99999121582084
482.46502682070830e-054.93005364141661e-050.999975349731793
499.78593663348462e-050.0001957187326696920.999902140633665
500.00039127505006840.00078255010013680.999608724949932
510.001007855117883900.002015710235767810.998992144882116
520.005039292766997680.01007858553399540.994960707233002
530.02045179540286860.04090359080573720.979548204597131
540.05609447379290380.1121889475858080.943905526207096
550.0455082150059280.0910164300118560.954491784994072
560.1285262523636280.2570525047272560.871473747636372
570.1428826486944960.2857652973889910.857117351305504
580.1249687641268500.2499375282536990.87503123587315
590.1330316030025970.2660632060051940.866968396997403
600.123349602752070.246699205504140.87665039724793
610.1245325018581310.2490650037162630.875467498141869
620.1300695362676810.2601390725353620.869930463732319
630.1824307378222840.3648614756445680.817569262177716
640.3331493399766960.6662986799533920.666850660023304
650.6546033235815010.6907933528369980.345396676418499
660.7650113271691560.4699773456616880.234988672830844
670.9372469883862760.1255060232274480.0627530116137239
680.9364148033457770.1271703933084460.0635851966542232
690.9734505404447560.05309891911048730.0265494595552436
700.9980990605046170.003801878990765140.00190093949538257
710.9965157725890260.006968454821948540.00348422741097427
720.9957974659741070.008405068051785780.00420253402589289
730.9967749061966440.006450187606712750.00322509380335638
740.9954754325329070.009049134934186670.00452456746709334
750.9920526875889970.01589462482200520.00794731241100259
760.9849016100318570.03019677993628560.0150983899681428
770.9710743798898580.05785124022028360.0289256201101418
780.9601899607169850.0796200785660310.0398100392830155
790.9685239268607180.06295214627856310.0314760731392816






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level390.619047619047619NOK
5% type I error level440.698412698412698NOK
10% type I error level490.777777777777778NOK

\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 & 39 & 0.619047619047619 & NOK \tabularnewline
5% type I error level & 44 & 0.698412698412698 & NOK \tabularnewline
10% type I error level & 49 & 0.777777777777778 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57438&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]39[/C][C]0.619047619047619[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]44[/C][C]0.698412698412698[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]49[/C][C]0.777777777777778[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57438&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57438&T=6

As an alternative you can also use a QR Code:  
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 testsOK/NOK
1% type I error level390.619047619047619NOK
5% type I error level440.698412698412698NOK
10% type I error level490.777777777777778NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], 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,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}