| paper MR | | *The author of this computation has been verified* | | R Software Module: /rwasp_multipleregression.wasp (opens new window with default values) | | Title produced by software: Multiple Regression | | Date of computation: Sun, 26 Dec 2010 22:10:23 +0000 | | | | Cite this page as follows: | | Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/26/t129340208496v1818pq5tc140.htm/, Retrieved Sun, 26 Dec 2010 23:21:36 +0100 | | | | BibTeX entries for LaTeX users: | @Manual{KEY,
author = {{YOUR NAME}},
publisher = {Office for Research Development and Education},
title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/26/t129340208496v1818pq5tc140.htm/},
year = {2010},
}
@Manual{R,
title = {R: A Language and Environment for Statistical Computing},
author = {{R Development Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2010},
note = {{ISBN} 3-900051-07-0},
url = {http://www.R-project.org},
}
| | | | Original text written by user: | | | | | IsPrivate? | | No (this computation is public) | | | | User-defined keywords: | | | | | Dataseries X: | | » Textbox « » Textfile « » CSV « | | 5,2
7,9
8,7
8,9
15,3
15,4
18,1
19,7
13
12,6
6,2
3,5
3,4
0
9,5
8,9
10,4
13,2
18,9
19
16,3
10,6
5,8
3,6
2,6
5
7,3
9,2
15,7
16,8
18,4
18,1
14,6
7,8
7,6
3,8
5,6
2,2
6,8
11,8
14,9
16,7
16,7
15,9
13,6
9,2
2,8
2,5
4,8
2,8
7,8
9
12,9
16,4
21,8
17,8
13,5
10
10,4
5,5
4
6,8
5,7
9,1
13,6
15
20,9
20,4
14
13,7
7,1
0,8
2,1
1,3
3,9
10,7
11,1
16,4
17,1
17,3
12,9
10,9
5,3
0,7
-0,2
6,5
8,6
8,5
13,3
16,2
17,5
21,2
14,8
10,3
7,3
5,1
4,4
6,2
7,7
9,3
15,6
16,3
16,6
17,4
15,3
9,7
3,7
4,6
5,4
3,1
7,9
10,1
15
15,6
19,7
18,1
17,7
10,7
6,2
4,2
4
5,9
7,1
10,5
15,1
16,8
15,3
18,4
16,1
11,3
7,9
5,6
3,4
4,8
6,5
8,5
15,1
15,7
18,7
19,2
12,9
14,4
6,2
3,3
4,6
7,2
7,8
9,9
13,6
17,1
17,8
18,6
14,7
10,5
8,6
4,4
2,3
2,8
8,8
10,7
13,9
19,3
19,5
20,4
15,3
7,9
8,3
4,5
3,2
5
6,6
11,1
12,8
16,3
17,4
18,9
15,8
11,7
6,4
2,9
4,7
2,4
7,2
10,7
13,4
18,5
18,3
16,8
16,6
14, etc... | | | | Output produced by software: | Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!
| Multiple Linear Regression - Estimated Regression Equation | | Temperatuur[t] = + 3.24210526315791 + 0.110847953216363M1[t] + 0.762134502923958M2[t] + 3.49342105263159M3[t] + 6.43470760233919M4[t] + 10.3809941520468M5[t] + 12.7672807017544M6[t] + 14.7885672514620M7[t] + 14.6898538011696M8[t] + 11.2711403508772M9[t] + 7.3924269005848M10[t] + 3.21371345029240M11[t] + 0.00371345029239764t + e[t] |
| Multiple Linear Regression - Ordinary Least Squares | | Variable | Parameter | S.D. | T-STAT
H0: parameter = 0 | 2-tail p-value | 1-tail p-value | | (Intercept) | 3.24210526315791 | 0.415835 | 7.7966 | 0 | 0 | | M1 | 0.110847953216363 | 0.520818 | 0.2128 | 0.831647 | 0.415824 | | M2 | 0.762134502923958 | 0.52077 | 1.4635 | 0.144721 | 0.07236 | | M3 | 3.49342105263159 | 0.520727 | 6.7087 | 0 | 0 | | M4 | 6.43470760233919 | 0.520689 | 12.3581 | 0 | 0 | | M5 | 10.3809941520468 | 0.520655 | 19.9383 | 0 | 0 | | M6 | 12.7672807017544 | 0.520626 | 24.523 | 0 | 0 | | M7 | 14.7885672514620 | 0.520601 | 28.4067 | 0 | 0 | | M8 | 14.6898538011696 | 0.52058 | 28.2182 | 0 | 0 | | M9 | 11.2711403508772 | 0.520564 | 21.6518 | 0 | 0 | | M10 | 7.3924269005848 | 0.520553 | 14.2011 | 0 | 0 | | M11 | 3.21371345029240 | 0.520546 | 6.1737 | 0 | 0 | | t | 0.00371345029239764 | 0.001536 | 2.4183 | 0.016383 | 0.008192 |
| Multiple Linear Regression - Regression Statistics | | Multiple R | 0.958336827443597 | | R-squared | 0.91840947483466 | | Adjusted R-squared | 0.914096319319311 | | F-TEST (value) | 212.932149459136 | | F-TEST (DF numerator) | 12 | | F-TEST (DF denominator) | 227 | | p-value | 0 | | Multiple Linear Regression - Residual Statistics | | Residual Standard Deviation | 1.64610468633684 | | Sum Squared Residuals | 615.092964912282 |
| Multiple Linear Regression - Actuals, Interpolation, and Residuals | | Time or Index | Actuals | Interpolation
Forecast | Residuals
Prediction Error | | 1 | 5.2 | 3.35666666666662 | 1.84333333333338 | | 2 | 7.9 | 4.01166666666661 | 3.88833333333339 | | 3 | 8.7 | 6.74666666666658 | 1.95333333333342 | | 4 | 8.9 | 9.69166666666671 | -0.79166666666671 | | 5 | 15.3 | 13.6416666666667 | 1.65833333333329 | | 6 | 15.4 | 16.0316666666666 | -0.631666666666627 | | 7 | 18.1 | 18.0566666666668 | 0.0433333333332414 | | 8 | 19.7 | 17.9616666666666 | 1.73833333333338 | | 9 | 13 | 14.5466666666667 | -1.54666666666666 | | 10 | 12.6 | 10.6716666666666 | 1.92833333333338 | | 11 | 6.2 | 6.4966666666667 | -0.296666666666691 | | 12 | 3.5 | 3.28666666666668 | 0.213333333333323 | | 13 | 3.4 | 3.40122807017544 | -0.00122807017544409 | | 14 | 0 | 4.05622807017544 | -4.05622807017544 | | 15 | 9.5 | 6.79122807017544 | 2.70877192982456 | | 16 | 8.9 | 9.73622807017544 | -0.836228070175437 | | 17 | 10.4 | 13.6862280701754 | -3.28622807017544 | | 18 | 13.2 | 16.0762280701754 | -2.87622807017544 | | 19 | 18.9 | 18.1012280701754 | 0.798771929824564 | | 20 | 19 | 18.0062280701754 | 0.993771929824557 | | 21 | 16.3 | 14.5912280701754 | 1.70877192982456 | | 22 | 10.6 | 10.7162280701754 | -0.116228070175443 | | 23 | 5.8 | 6.54122807017544 | -0.74122807017544 | | 24 | 3.6 | 3.33122807017544 | 0.268771929824558 | | 25 | 2.6 | 3.44578947368422 | -0.845789473684218 | | 26 | 5 | 4.10078947368421 | 0.899210526315787 | | 27 | 7.3 | 6.83578947368422 | 0.464210526315784 | | 28 | 9.2 | 9.7807894736842 | -0.58078947368421 | | 29 | 15.7 | 13.7307894736842 | 1.96921052631579 | | 30 | 16.8 | 16.1207894736842 | 0.679210526315786 | | 31 | 18.4 | 18.1457894736842 | 0.254210526315792 | | 32 | 18.1 | 18.0507894736842 | 0.049210526315787 | | 33 | 14.6 | 14.6357894736842 | -0.0357894736842123 | | 34 | 7.8 | 10.7607894736842 | -2.96078947368421 | | 35 | 7.6 | 6.58578947368421 | 1.01421052631579 | | 36 | 3.8 | 3.37578947368421 | 0.424210526315786 | | 37 | 5.6 | 3.49035087719299 | 2.10964912280701 | | 38 | 2.2 | 4.14535087719298 | -1.94535087719298 | | 39 | 6.8 | 6.88035087719299 | -0.0803508771929872 | | 40 | 11.8 | 9.82535087719298 | 1.97464912280702 | | 41 | 14.9 | 13.7753508771930 | 1.12464912280702 | | 42 | 16.7 | 16.165350877193 | 0.534649122807013 | | 43 | 16.7 | 18.1903508771930 | -1.49035087719298 | | 44 | 15.9 | 18.095350877193 | -2.19535087719298 | | 45 | 13.6 | 14.680350877193 | -1.08035087719298 | | 46 | 9.2 | 10.805350877193 | -1.60535087719299 | | 47 | 2.8 | 6.63035087719298 | -3.83035087719298 | | 48 | 2.5 | 3.42035087719298 | -0.920350877192985 | | 49 | 4.8 | 3.53491228070176 | 1.26508771929824 | | 50 | 2.8 | 4.18991228070176 | -1.38991228070176 | | 51 | 7.8 | 6.92491228070176 | 0.87508771929824 | | 52 | 9 | 9.86991228070175 | -0.869912280701752 | | 53 | 12.9 | 13.8199122807018 | -0.919912280701753 | | 54 | 16.4 | 16.2099122807018 | 0.190087719298241 | | 55 | 21.8 | 18.2349122807017 | 3.56508771929825 | | 56 | 17.8 | 18.1399122807018 | -0.339912280701757 | | 57 | 13.5 | 14.7249122807018 | -1.22491228070176 | | 58 | 10 | 10.8499122807018 | -0.849912280701758 | | 59 | 10.4 | 6.67491228070175 | 3.72508771929825 | | 60 | 5.5 | 3.46491228070176 | 2.03508771929824 | | 61 | 4 | 3.57947368421053 | 0.420526315789467 | | 62 | 6.8 | 4.23447368421053 | 2.56552631578947 | | 63 | 5.7 | 6.96947368421053 | -1.26947368421053 | | 64 | 9.1 | 9.91447368421052 | -0.814473684210524 | | 65 | 13.6 | 13.8644736842105 | -0.264473684210526 | | 66 | 15 | 16.2544736842105 | -1.25447368421053 | | 67 | 20.9 | 18.2794736842105 | 2.62052631578948 | | 68 | 20.4 | 18.1844736842105 | 2.21552631578947 | | 69 | 14 | 14.7694736842105 | -0.769473684210527 | | 70 | 13.7 | 10.8944736842105 | 2.80552631578947 | | 71 | 7.1 | 6.71947368421053 | 0.380526315789474 | | 72 | 0.8 | 3.50947368421053 | -2.70947368421053 | | 73 | 2.1 | 3.62403508771930 | -1.52403508771930 | | 74 | 1.3 | 4.2790350877193 | -2.9790350877193 | | 75 | 3.9 | 7.0140350877193 | -3.1140350877193 | | 76 | 10.7 | 9.9590350877193 | 0.740964912280703 | | 77 | 11.1 | 13.9090350877193 | -2.80903508771930 | | 78 | 16.4 | 16.2990350877193 | 0.100964912280697 | | 79 | 17.1 | 18.3240350877193 | -1.22403508771929 | | 80 | 17.3 | 18.2290350877193 | -0.9290350877193 | | 81 | 12.9 | 14.8140350877193 | -1.91403508771930 | | 82 | 10.9 | 10.9390350877193 | -0.0390350877193015 | | 83 | 5.3 | 6.7640350877193 | -1.4640350877193 | | 84 | 0.7 | 3.5540350877193 | -2.8540350877193 | | 85 | -0.2 | 3.66859649122807 | -3.86859649122807 | | 86 | 6.5 | 4.32359649122807 | 2.17640350877193 | | 87 | 8.6 | 7.05859649122807 | 1.54140350877192 | | 88 | 8.5 | 10.0035964912281 | -1.50359649122807 | | 89 | 13.3 | 13.9535964912281 | -0.653596491228068 | | 90 | 16.2 | 16.3435964912281 | -0.143596491228074 | | 91 | 17.5 | 18.3685964912281 | -0.868596491228065 | | 92 | 21.2 | 18.2735964912281 | 2.92640350877193 | | 93 | 14.8 | 14.8585964912281 | -0.0585964912280698 | | 94 | 10.3 | 10.9835964912281 | -0.683596491228073 | | 95 | 7.3 | 6.80859649122807 | 0.49140350877193 | | 96 | 5.1 | 3.59859649122807 | 1.50140350877193 | | 97 | 4.4 | 3.71315789473685 | 0.686842105263153 | | 98 | 6.2 | 4.36815789473684 | 1.83184210526316 | | 99 | 7.7 | 7.10315789473685 | 0.596842105263154 | | 100 | 9.3 | 10.0481578947368 | -0.748157894736839 | | 101 | 15.6 | 13.9981578947368 | 1.60184210526316 | | 102 | 16.3 | 16.3881578947368 | -0.0881578947368438 | | 103 | 16.6 | 18.4131578947368 | -1.81315789473683 | | 104 | 17.4 | 18.3181578947368 | -0.918157894736846 | | 105 | 15.3 | 14.9031578947368 | 0.396842105263158 | | 106 | 9.7 | 11.0281578947368 | -1.32815789473685 | | 107 | 3.7 | 6.85315789473684 | -3.15315789473684 | | 108 | 4.6 | 3.64315789473684 | 0.956842105263156 | | 109 | 5.4 | 3.75771929824562 | 1.64228070175438 | | 110 | 3.1 | 4.41271929824562 | -1.31271929824562 | | 111 | 7.9 | 7.14771929824562 | 0.752280701754383 | | 112 | 10.1 | 10.0927192982456 | 0.00728070175438869 | | 113 | 15 | 14.0427192982456 | 0.957280701754388 | | 114 | 15.6 | 16.4327192982456 | -0.832719298245617 | | 115 | 19.7 | 18.4577192982456 | 1.24228070175439 | | 116 | 18.1 | 18.3627192982456 | -0.262719298245615 | | 117 | 17.7 | 14.9477192982456 | 2.75228070175439 | | 118 | 10.7 | 11.0727192982456 | -0.372719298245618 | | 119 | 6.2 | 6.89771929824561 | -0.697719298245613 | | 120 | 4.2 | 3.68771929824562 | 0.512280701754384 | | 121 | 4 | 3.80228070175439 | 0.197719298245609 | | 122 | 5.9 | 4.45728070175439 | 1.44271929824561 | | 123 | 7.1 | 7.19228070175439 | -0.0922807017543897 | | 124 | 10.5 | 10.1372807017544 | 0.362719298245617 | | 125 | 15.1 | 14.0872807017544 | 1.01271929824562 | | 126 | 16.8 | 16.4772807017544 | 0.322719298245613 | | 127 | 15.3 | 18.5022807017544 | -3.20228070175438 | | 128 | 18.4 | 18.4072807017544 | -0.00728070175438963 | | 129 | 16.1 | 14.9922807017544 | 1.10771929824562 | | 130 | 11.3 | 11.1172807017544 | 0.182719298245612 | | 131 | 7.9 | 6.94228070175438 | 0.957719298245616 | | 132 | 5.6 | 3.73228070175439 | 1.86771929824561 | | 133 | 3.4 | 3.84684210526316 | -0.446842105263163 | | 134 | 4.8 | 4.50184210526316 | 0.298157894736841 | | 135 | 6.5 | 7.23684210526316 | -0.736842105263161 | | 136 | 8.5 | 10.1818421052632 | -1.68184210526315 | | 137 | 15.1 | 14.1318421052632 | 0.968157894736844 | | 138 | 15.7 | 16.5218421052632 | -0.821842105263161 | | 139 | 18.7 | 18.5468421052632 | 0.153157894736848 | | 140 | 19.2 | 18.4518421052632 | 0.74815789473684 | | 141 | 12.9 | 15.0368421052632 | -2.13684210526316 | | 142 | 14.4 | 11.1618421052632 | 3.23815789473684 | | 143 | 6.2 | 6.98684210526316 | -0.786842105263156 | | 144 | 3.3 | 3.77684210526316 | -0.476842105263162 | | 145 | 4.6 | 3.89140350877193 | 0.708596491228065 | | 146 | 7.2 | 4.54640350877193 | 2.65359649122807 | | 147 | 7.8 | 7.28140350877193 | 0.518596491228067 | | 148 | 9.9 | 10.2264035087719 | -0.326403508771926 | | 149 | 13.6 | 14.1764035087719 | -0.576403508771928 | | 150 | 17.1 | 16.5664035087719 | 0.53359649122807 | | 151 | 17.8 | 18.5914035087719 | -0.791403508771923 | | 152 | 18.6 | 18.4964035087719 | 0.103596491228070 | | 153 | 14.7 | 15.0814035087719 | -0.38140350877193 | | 154 | 10.5 | 11.2064035087719 | -0.706403508771932 | | 155 | 8.6 | 7.03140350877193 | 1.56859649122807 | | 156 | 4.4 | 3.82140350877193 | 0.57859649122807 | | 157 | 2.3 | 3.93596491228071 | -1.63596491228071 | | 158 | 2.8 | 4.5909649122807 | -1.79096491228070 | | 159 | 8.8 | 7.3259649122807 | 1.47403508771930 | | 160 | 10.7 | 10.2709649122807 | 0.429035087719301 | | 161 | 13.9 | 14.2209649122807 | -0.320964912280699 | | 162 | 19.3 | 16.6109649122807 | 2.68903508771930 | | 163 | 19.5 | 18.6359649122807 | 0.864035087719304 | | 164 | 20.4 | 18.5409649122807 | 1.85903508771930 | | 165 | 15.3 | 15.1259649122807 | 0.174035087719300 | | 166 | 7.9 | 11.2509649122807 | -3.3509649122807 | | 167 | 8.3 | 7.0759649122807 | 1.2240350877193 | | 168 | 4.5 | 3.8659649122807 | 0.634035087719298 | | 169 | 3.2 | 3.98052631578948 | -0.780526315789478 | | 170 | 5 | 4.63552631578947 | 0.364473684210526 | | 171 | 6.6 | 7.37052631578948 | -0.770526315789477 | | 172 | 11.1 | 10.3155263157895 | 0.78447368421053 | | 173 | 12.8 | 14.2655263157895 | -1.46552631578947 | | 174 | 16.3 | 16.6555263157895 | -0.355526315789475 | | 175 | 17.4 | 18.6805263157895 | -1.28052631578947 | | 176 | 18.9 | 18.5855263157895 | 0.314473684210523 | | 177 | 15.8 | 15.1705263157895 | 0.629473684210528 | | 178 | 11.7 | 11.2955263157895 | 0.404473684210523 | | 179 | 6.4 | 7.12052631578947 | -0.72052631578947 | | 180 | 2.9 | 3.91052631578948 | -1.01052631578948 | | 181 | 4.7 | 4.02508771929825 | 0.67491228070175 | | 182 | 2.4 | 4.68008771929824 | -2.28008771929824 | | 183 | 7.2 | 7.41508771929825 | -0.215087719298248 | | 184 | 10.7 | 10.3600877192982 | 0.339912280701758 | | 185 | 13.4 | 14.3100877192982 | -0.910087719298243 | | 186 | 18.5 | 16.7000877192982 | 1.79991228070175 | | 187 | 18.3 | 18.7250877192982 | -0.425087719298238 | | 188 | 16.8 | 18.6300877192982 | -1.83008771929825 | | 189 | 16.6 | 15.2150877192982 | 1.38491228070176 | | 190 | 14.1 | 11.3400877192982 | 2.75991228070175 | | 191 | 6.1 | 7.16508771929824 | -1.06508771929824 | | 192 | 3.5 | 3.95508771929825 | -0.455087719298246 | | 193 | 1.7 | 4.06964912280702 | -2.36964912280702 | | 194 | 2.3 | 4.72464912280702 | -2.42464912280702 | | 195 | 4.5 | 7.45964912280702 | -2.95964912280702 | | 196 | 9.3 | 10.4046491228070 | -1.10464912280701 | | 197 | 14.2 | 14.354649122807 | -0.154649122807015 | | 198 | 17.3 | 16.7446491228070 | 0.555350877192982 | | 199 | 23 | 18.769649122807 | 4.23035087719299 | | 200 | 16.3 | 18.6746491228070 | -2.37464912280702 | | 201 | 18.4 | 15.259649122807 | 3.14035087719298 | | 202 | 14.2 | 11.3846491228070 | 2.81535087719298 | | 203 | 9.1 | 7.20964912280702 | 1.89035087719298 | | 204 | 5.9 | 3.99964912280702 | 1.90035087719298 | | 205 | 7.2 | 4.11421052631579 | 3.08578947368421 | | 206 | 6.8 | 4.76921052631579 | 2.03078947368421 | | 207 | 8 | 7.5042105263158 | 0.495789473684208 | | 208 | 14.3 | 10.4492105263158 | 3.85078947368421 | | 209 | 14.6 | 14.3992105263158 | 0.200789473684214 | | 210 | 17.5 | 16.7892105263158 | 0.71078947368421 | | 211 | 17.2 | 18.8142105263158 | -1.61421052631578 | | 212 | 17.2 | 18.7192105263158 | -1.51921052631579 | | 213 | 14.1 | 15.3042105263158 | -1.20421052631579 | | 214 | 10.5 | 11.4292105263158 | -0.92921052631579 | | 215 | 6.8 | 7.25421052631579 | -0.454210526315787 | | 216 | 4.1 | 4.04421052631579 | 0.0557894736842105 | | 217 | 6.5 | 4.15877192982456 | 2.34122807017544 | | 218 | 6.1 | 4.81377192982456 | 1.28622807017544 | | 219 | 6.3 | 7.54877192982456 | -1.24877192982456 | | 220 | 9.3 | 10.4937719298246 | -1.19377192982456 | | 221 | 16.4 | 14.4437719298246 | 1.95622807017544 | | 222 | 16.1 | 16.8337719298246 | -0.733771929824561 | | 223 | 18 | 18.8587719298246 | -0.858771929824554 | | 224 | 17.6 | 18.7637719298246 | -1.16377192982456 | | 225 | 14 | 15.3487719298246 | -1.34877192982456 | | 226 | 10.5 | 11.4737719298246 | -0.973771929824562 | | 227 | 6.9 | 7.29877192982456 | -0.398771929824558 | | 228 | 2.8 | 4.08877192982456 | -1.28877192982456 | | 229 | 0.7 | 4.20333333333334 | -3.50333333333334 | | 230 | 3.6 | 4.85833333333333 | -1.25833333333333 | | 231 | 6.7 | 7.59333333333334 | -0.893333333333335 | | 232 | 12.5 | 10.5383333333333 | 1.96166666666667 | | 233 | 14.4 | 14.4883333333333 | -0.0883333333333292 | | 234 | 16.5 | 16.8783333333333 | -0.378333333333334 | | 235 | 18.7 | 18.9033333333333 | -0.203333333333326 | | 236 | 19.4 | 18.8083333333333 | 0.591666666666665 | | 237 | 15.8 | 15.3933333333333 | 0.406666666666669 | | 238 | 11.3 | 11.5183333333333 | -0.218333333333334 | | 239 | 9.7 | 7.34333333333333 | 2.35666666666667 | | 240 | 2.9 | 4.13333333333333 | -1.23333333333333 |
| Goldfeld-Quandt test for Heteroskedasticity | | p-values | Alternative Hypothesis | | breakpoint index | greater | 2-sided | less | | 16 | 0.966801448374136 | 0.0663971032517283 | 0.0331985516258642 | | 17 | 0.958361662535931 | 0.0832766749281369 | 0.0416383374640685 | | 18 | 0.92784121463724 | 0.144317570725522 | 0.0721587853627609 | | 19 | 0.938635295226316 | 0.122729409547368 | 0.061364704773684 | | 20 | 0.911289896903644 | 0.177420206192712 | 0.0887101030963558 | | 21 | 0.969923866063754 | 0.0601522678724918 | 0.0300761339362459 | | 22 | 0.95161248710325 | 0.0967750257935008 | 0.0483875128967504 | | 23 | 0.929522951990387 | 0.140954096019226 | 0.0704770480096132 | | 24 | 0.906001152513194 | 0.187997694973612 | 0.0939988474868062 | | 25 | 0.867404835348352 | 0.265190329303297 | 0.132595164651648 | | 26 | 0.880511493484377 | 0.238977013031246 | 0.119488506515623 | | 27 | 0.840195447411421 | 0.319609105177159 | 0.159804552588579 | | 28 | 0.816724449710513 | 0.366551100578974 | 0.183275550289487 | | 29 | 0.89287477278187 | 0.21425045443626 | 0.10712522721813 | | 30 | 0.914314849552707 | 0.171370300894586 | 0.085685150447293 | | 31 | 0.885395577374952 | 0.229208845250097 | 0.114604422625048 | | 32 | 0.8547509354372 | 0.290498129125599 | 0.145249064562799 | | 33 | 0.815460403414015 | 0.369079193171971 | 0.184539596585985 | | 34 | 0.865052645791122 | 0.269894708417755 | 0.134947354208878 | | 35 | 0.862901370969365 | 0.27419725806127 | 0.137098629030635 | | 36 | 0.830960216847642 | 0.338079566304716 | 0.169039783152358 | | 37 | 0.846157668305807 | 0.307684663388385 | 0.153842331694193 | | 38 | 0.83637683259227 | 0.327246334815459 | 0.163623167407729 | | 39 | 0.808534028814853 | 0.382931942370293 | 0.191465971185146 | | 40 | 0.860090954365136 | 0.279818091269728 | 0.139909045634864 | | 41 | 0.84348040407202 | 0.313039191855961 | 0.156519595927981 | | 42 | 0.830981999145986 | 0.338036001708028 | 0.169018000854014 | | 43 | 0.81736515485511 | 0.365269690289778 | 0.182634845144889 | | 44 | 0.842141152712366 | 0.315717694575268 | 0.157858847287634 | | 45 | 0.812531168798667 | 0.374937662402666 | 0.187468831201333 | | 46 | 0.784635968911701 | 0.430728062176597 | 0.215364031088299 | | 47 | 0.853049891406001 | 0.293900217187999 | 0.146950108593999 | | 48 | 0.824573500390323 | 0.350852999219355 | 0.175426499609677 | | 49 | 0.810023927557926 | 0.379952144884149 | 0.189976072442074 | | 50 | 0.779924983257932 | 0.440150033484136 | 0.220075016742068 | | 51 | 0.747134303153265 | 0.50573139369347 | 0.252865696846735 | | 52 | 0.707965789413916 | 0.584068421172169 | 0.292034210586084 | | 53 | 0.668326548841298 | 0.663346902317404 | 0.331673451158702 | | 54 | 0.648310132529802 | 0.703379734940397 | 0.351689867470198 | | 55 | 0.810006181020157 | 0.379987637959686 | 0.189993818979843 | | 56 | 0.775996422655502 | 0.448007154688996 | 0.224003577344498 | | 57 | 0.745477479089562 | 0.509045041820875 | 0.254522520910438 | | 58 | 0.710766423904928 | 0.578467152190145 | 0.289233576095072 | | 59 | 0.885714720468203 | 0.228570559063594 | 0.114285279531797 | | 60 | 0.89602625366671 | 0.207947492666581 | 0.103973746333290 | | 61 | 0.875692255920417 | 0.248615488159166 | 0.124307744079583 | | 62 | 0.912783517070378 | 0.174432965859244 | 0.0872164829296221 | | 63 | 0.91495067739631 | 0.170098645207381 | 0.0850493226036907 | | 64 | 0.897917074376233 | 0.204165851247535 | 0.102082925623767 | | 65 | 0.876788650338223 | 0.246422699323555 | 0.123211349661777 | | 66 | 0.858801096461596 | 0.282397807076808 | 0.141198903538404 | | 67 | 0.880559202165764 | 0.238881595668473 | 0.119440797834236 | | 68 | 0.895047512892512 | 0.209904974214976 | 0.104952487107488 | | 69 | 0.87551401875831 | 0.248971962483379 | 0.124485981241689 | | 70 | 0.920369013063758 | 0.159261973872484 | 0.079630986936242 | | 71 | 0.904508254072854 | 0.190983491854293 | 0.0954917459271463 | | 72 | 0.929606251977624 | 0.140787496044751 | 0.0703937480223756 | | 73 | 0.930729142221579 | 0.138541715556843 | 0.0692708577784214 | | 74 | 0.949151693350066 | 0.101696613299869 | 0.0508483066499344 | | 75 | 0.969521634980565 | 0.0609567300388692 | 0.0304783650194346 | | 76 | 0.965401250275069 | 0.0691974994498624 | 0.0345987497249312 | | 77 | 0.973233728203388 | 0.0535325435932241 | 0.0267662717966121 | | 78 | 0.967845105491516 | 0.0643097890169672 | 0.0321548945084836 | | 79 | 0.964892723195884 | 0.0702145536082313 | 0.0351072768041157 | | 80 | 0.958005236558409 | 0.0839895268831827 | 0.0419947634415914 | | 81 | 0.956068209902575 | 0.08786358019485 | 0.043931790097425 | | 82 | 0.946533149405713 | 0.106933701188574 | 0.0534668505942871 | | 83 | 0.940609382524632 | 0.118781234950736 | 0.0593906174753678 | | 84 | 0.9534016206576 | 0.0931967586848 | 0.0465983793424 | | 85 | 0.978943968280621 | 0.0421120634387573 | 0.0210560317193787 | | 86 | 0.98566880241268 | 0.0286623951746393 | 0.0143311975873196 | | 87 | 0.986004998456157 | 0.0279900030876863 | 0.0139950015438432 | | 88 | 0.984428320317327 | 0.0311433593653467 | 0.0155716796826733 | | 89 | 0.980999626842586 | 0.038000746314829 | 0.0190003731574145 | | 90 | 0.97705597588298 | 0.0458880482340406 | 0.0229440241170203 | | 91 | 0.97244362269628 | 0.0551127546074408 | 0.0275563773037204 | | 92 | 0.984207214957121 | 0.0315855700857577 | 0.0157927850428789 | | 93 | 0.98117496331085 | 0.0376500733783008 | 0.0188250366891504 | | 94 | 0.97692528675701 | 0.0461494264859789 | 0.0230747132429894 | | 95 | 0.972485017930796 | 0.0550299641384077 | 0.0275149820692039 | | 96 | 0.973757718868608 | 0.0524845622627833 | 0.0262422811313916 | | 97 | 0.969231233120737 | 0.0615375337585258 | 0.0307687668792629 | | 98 | 0.971923776804902 | 0.0561524463901968 | 0.0280762231950984 | | 99 | 0.96625062250875 | 0.0674987549824983 | 0.0337493774912492 | | 100 | 0.959961793487747 | 0.0800764130245052 | 0.0400382065122526 | | 101 | 0.961308670693208 | 0.0773826586135832 | 0.0386913293067916 | | 102 | 0.953231521721262 | 0.0935369565574756 | 0.0467684782787378 | | 103 | 0.954318454102412 | 0.0913630917951763 | 0.0456815458975881 | | 104 | 0.947271194372948 | 0.105457611254104 | 0.052728805627052 | | 105 | 0.93927834404234 | 0.121443311915320 | 0.0607216559576602 | | 106 | 0.93439908356542 | 0.131201832869160 | 0.0656009164345799 | | 107 | 0.959886999849382 | 0.0802260003012353 | 0.0401130001506176 | | 108 | 0.95505343706332 | 0.0898931258733594 | 0.0449465629366797 | | 109 | 0.95566423784433 | 0.0886715243113391 | 0.0443357621556696 | | 110 | 0.951795950799068 | 0.0964080984018642 | 0.0482040492009321 | | 111 | 0.94403105909185 | 0.111937881816300 | 0.0559689409081499 | | 112 | 0.933811349293008 | 0.132377301413984 | 0.0661886507069919 | | 113 | 0.925976805826768 | 0.148046388346465 | 0.0740231941732324 | | 114 | 0.917224588765658 | 0.165550822468685 | 0.0827754112343425 | | 115 | 0.910883761025333 | 0.178232477949335 | 0.0891162389746673 | | 116 | 0.894656960644428 | 0.210686078711143 | 0.105343039355572 | | 117 | 0.92368532856943 | 0.152629342861140 | 0.0763146714305701 | | 118 | 0.910788414032611 | 0.178423171934778 | 0.0892115859673888 | | 119 | 0.899657365502013 | 0.200685268995973 | 0.100342634497987 | | 120 | 0.883529930642239 | 0.232940138715522 | 0.116470069357761 | | 121 | 0.863757975440208 | 0.272484049119584 | 0.136242024559792 | | 122 | 0.858516567119919 | 0.282966865760162 | 0.141483432880081 | | 123 | 0.836477357593931 | 0.327045284812137 | 0.163522642406069 | | 124 | 0.813973856937268 | 0.372052286125464 | 0.186026143062732 | | 125 | 0.796976773090907 | 0.406046453818185 | 0.203023226909093 | | 126 | 0.771549248395977 | 0.456901503208046 | 0.228450751604023 | | 127 | 0.84367443269267 | 0.312651134614661 | 0.156325567307330 | | 128 | 0.819411314957685 | 0.36117737008463 | 0.180588685042315 | | 129 | 0.80410667687626 | 0.391786646247480 | 0.195893323123740 | | 130 | 0.778038038784797 | 0.443923922430407 | 0.221961961215203 | | 131 | 0.758284880357766 | 0.483430239284468 | 0.241715119642234 | | 132 | 0.764488980624938 | 0.471022038750124 | 0.235511019375062 | | 133 | 0.73550589469696 | 0.52898821060608 | 0.26449410530304 | | 134 | 0.703665922318228 | 0.592668155363543 | 0.296334077681772 | | 135 | 0.675315359065548 | 0.649369281868904 | 0.324684640934452 | | 136 | 0.686852036468676 | 0.626295927062649 | 0.313147963531324 | | 137 | 0.662967655691361 | 0.674064688617277 | 0.337032344308639 | | 138 | 0.644763301228837 | 0.710473397542327 | 0.355236698771164 | | 139 | 0.607661839798767 | 0.784676320402466 | 0.392338160201233 | | 140 | 0.579470304500788 | 0.841059390998423 | 0.420529695499212 | | 141 | 0.614408240450271 | 0.771183519099457 | 0.385591759549729 | | 142 | 0.706095690561165 | 0.58780861887767 | 0.293904309438835 | | 143 | 0.687794041175257 | 0.624411917649485 | 0.312205958824743 | | 144 | 0.655375606072958 | 0.689248787854084 | 0.344624393927042 | | 145 | 0.625354828371437 | 0.749290343257126 | 0.374645171628563 | | 146 | 0.690982444094302 | 0.618035111811397 | 0.309017555905698 | | 147 | 0.663468721973501 | 0.673062556052998 | 0.336531278026499 | | 148 | 0.63546300971147 | 0.729073980577061 | 0.364536990288530 | | 149 | 0.601508312523326 | 0.796983374953349 | 0.398491687476674 | | 150 | 0.566233720148529 | 0.867532559702942 | 0.433766279851471 | | 151 | 0.537451377525136 | 0.925097244949728 | 0.462548622474864 | | 152 | 0.500042547989032 | 0.999914904021935 | 0.499957452010967 | | 153 | 0.467152362746267 | 0.934304725492534 | 0.532847637253733 | | 154 | 0.436338689176787 | 0.872677378353574 | 0.563661310823213 | | 155 | 0.421929347022467 | 0.843858694044935 | 0.578070652977533 | | 156 | 0.386809972046223 | 0.773619944092445 | 0.613190027953777 | | 157 | 0.384097885224557 | 0.768195770449114 | 0.615902114775443 | | 158 | 0.38360399832648 | 0.76720799665296 | 0.61639600167352 | | 159 | 0.391211591333088 | 0.782423182666176 | 0.608788408666912 | | 160 | 0.356584655902011 | 0.713169311804022 | 0.643415344097989 | | 161 | 0.320147155252716 | 0.640294310505432 | 0.679852844747284 | | 162 | 0.361146700998804 | 0.722293401997608 | 0.638853299001196 | | 163 | 0.331099289714858 | 0.662198579429716 | 0.668900710285142 | | 164 | 0.368908586440743 | 0.737817172881485 | 0.631091413559257 | | 165 | 0.330512943566281 | 0.661025887132563 | 0.669487056433719 | | 166 | 0.478605400700216 | 0.957210801400432 | 0.521394599299784 | | 167 | 0.451143545729981 | 0.902287091459962 | 0.548856454270019 | | 168 | 0.417734807838686 | 0.835469615677373 | 0.582265192161314 | | 169 | 0.385362839147613 | 0.770725678295226 | 0.614637160852387 | | 170 | 0.350987724223388 | 0.701975448446777 | 0.649012275776612 | | 171 | 0.316920532319334 | 0.633841064638669 | 0.683079467680666 | | 172 | 0.28281526563254 | 0.56563053126508 | 0.71718473436746 | | 173 | 0.276491255645074 | 0.552982511290149 | 0.723508744354926 | | 174 | 0.246006645798632 | 0.492013291597263 | 0.753993354201368 | | 175 | 0.236664109738808 | 0.473328219477616 | 0.763335890261192 | | 176 | 0.217374601661243 | 0.434749203322485 | 0.782625398338757 | | 177 | 0.187594478671701 | 0.375188957343401 | 0.8124055213283 | | 178 | 0.160691114322711 | 0.321382228645421 | 0.839308885677289 | | 179 | 0.145194763340147 | 0.290389526680294 | 0.854805236659853 | | 180 | 0.128803448794366 | 0.257606897588732 | 0.871196551205634 | | 181 | 0.108378275548660 | 0.216756551097321 | 0.89162172445134 | | 182 | 0.124270196280126 | 0.248540392560252 | 0.875729803719874 | | 183 | 0.104001991331521 | 0.208003982663042 | 0.895998008668479 | | 184 | 0.0875863530463803 | 0.175172706092761 | 0.91241364695362 | | 185 | 0.0816034157336273 | 0.163206831467255 | 0.918396584266373 | | 186 | 0.0760018507097085 | 0.152003701419417 | 0.923998149290291 | | 187 | 0.064583826788377 | 0.129167653576754 | 0.935416173211623 | | 188 | 0.0592164906544292 | 0.118432981308858 | 0.94078350934557 | | 189 | 0.0494990407529691 | 0.0989980815059383 | 0.95050095924703 | | 190 | 0.0577081457084929 | 0.115416291416986 | 0.942291854291507 | | 191 | 0.0590665719869978 | 0.118133143973996 | 0.940933428013002 | | 192 | 0.0474792607068976 | 0.0949585214137952 | 0.952520739293102 | | 193 | 0.0731193980896507 | 0.146238796179301 | 0.92688060191035 | | 194 | 0.121607342090560 | 0.243214684181119 | 0.87839265790944 | | 195 | 0.177000282139716 | 0.354000564279432 | 0.822999717860284 | | 196 | 0.256774319200504 | 0.513548638401008 | 0.743225680799496 | | 197 | 0.25702783421598 | 0.51405566843196 | 0.74297216578402 | | 198 | 0.219849464434924 | 0.439698928869847 | 0.780150535565076 | | 199 | 0.38073503619347 | 0.76147007238694 | 0.61926496380653 | | 200 | 0.454067208899369 | 0.908134417798738 | 0.545932791100631 | | 201 | 0.519409225038096 | 0.961181549923807 | 0.480590774961904 | | 202 | 0.572780633103119 | 0.854438733793763 | 0.427219366896881 | | 203 | 0.525957696348047 | 0.948084607303906 | 0.474042303651953 | | 204 | 0.532698636701593 | 0.934602726596814 | 0.467301363298407 | | 205 | 0.646319791457684 | 0.707360417084633 | 0.353680208542316 | | 206 | 0.641228055210603 | 0.717543889578794 | 0.358771944789397 | | 207 | 0.611121728075972 | 0.777756543848057 | 0.388878271924028 | | 208 | 0.767301363900618 | 0.465397272198763 | 0.232698636099382 | | 209 | 0.711667064341894 | 0.576665871316212 | 0.288332935658106 | | 210 | 0.68747633152497 | 0.62504733695006 | 0.31252366847503 | | 211 | 0.632666134887754 | 0.734667730224492 | 0.367333865112246 | | 212 | 0.579259057894382 | 0.841481884211236 | 0.420740942105618 | | 213 | 0.50952012925988 | 0.98095974148024 | 0.49047987074012 | | 214 | 0.431626335154715 | 0.86325267030943 | 0.568373664845285 | | 215 | 0.386527329923259 | 0.773054659846518 | 0.613472670076741 | | 216 | 0.334376975943354 | 0.668753951886709 | 0.665623024056646 | | 217 | 0.84708189611961 | 0.305836207760779 | 0.152918103880390 | | 218 | 0.934512795011747 | 0.130974409976505 | 0.0654872049882527 | | 219 | 0.8956536019399 | 0.208692796120199 | 0.104346398060099 | | 220 | 0.91737411614291 | 0.165251767714180 | 0.0826258838570902 | | 221 | 0.985493081645055 | 0.0290138367098902 | 0.0145069183549451 | | 222 | 0.972419398926828 | 0.0551612021463441 | 0.0275806010731720 | | 223 | 0.94078171014044 | 0.118436579719121 | 0.0592182898595604 | | 224 | 0.859951468334833 | 0.280097063330333 | 0.140048531665167 |
| Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity | | Description | # significant tests | % significant tests | OK/NOK | | 1% type I error level | 0 | 0 | OK | | 5% type I error level | 10 | 0.0478468899521531 | OK | | 10% type I error level | 39 | 0.186602870813397 | NOK |
| | | | Charts produced by software: |  | | http://www.freestatistics.org/blog/date/2010/Dec/26/t129340208496v1818pq5tc140/109qxk1293401408.png (open in new window) | | http://www.freestatistics.org/blog/date/2010/Dec/26/t129340208496v1818pq5tc140/109qxk1293401408.ps (open in new window) |
 | | http://www.freestatistics.org/blog/date/2010/Dec/26/t129340208496v1818pq5tc140/12pi81293401408.png (open in new window) | | http://www.freestatistics.org/blog/date/2010/Dec/26/t129340208496v1818pq5tc140/12pi81293401408.ps (open in new window) |
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 | | http://www.freestatistics.org/blog/date/2010/Dec/26/t129340208496v1818pq5tc140/3dgzt1293401408.png (open in new window) | | http://www.freestatistics.org/blog/date/2010/Dec/26/t129340208496v1818pq5tc140/3dgzt1293401408.ps (open in new window) |
 | | http://www.freestatistics.org/blog/date/2010/Dec/26/t129340208496v1818pq5tc140/4dgzt1293401408.png (open in new window) | | http://www.freestatistics.org/blog/date/2010/Dec/26/t129340208496v1818pq5tc140/4dgzt1293401408.ps (open in new window) |
 | | http://www.freestatistics.org/blog/date/2010/Dec/26/t129340208496v1818pq5tc140/5dgzt1293401408.png (open in new window) | | http://www.freestatistics.org/blog/date/2010/Dec/26/t129340208496v1818pq5tc140/5dgzt1293401408.ps (open in new window) |
 | | http://www.freestatistics.org/blog/date/2010/Dec/26/t129340208496v1818pq5tc140/66phw1293401408.png (open in new window) | | http://www.freestatistics.org/blog/date/2010/Dec/26/t129340208496v1818pq5tc140/66phw1293401408.ps (open in new window) |
 | | http://www.freestatistics.org/blog/date/2010/Dec/26/t129340208496v1818pq5tc140/7ghyz1293401408.png (open in new window) | | http://www.freestatistics.org/blog/date/2010/Dec/26/t129340208496v1818pq5tc140/7ghyz1293401408.ps (open in new window) |
 | | http://www.freestatistics.org/blog/date/2010/Dec/26/t129340208496v1818pq5tc140/8ghyz1293401408.png (open in new window) | | http://www.freestatistics.org/blog/date/2010/Dec/26/t129340208496v1818pq5tc140/8ghyz1293401408.ps (open in new window) |
 | | http://www.freestatistics.org/blog/date/2010/Dec/26/t129340208496v1818pq5tc140/9ghyz1293401408.png (open in new window) | | http://www.freestatistics.org/blog/date/2010/Dec/26/t129340208496v1818pq5tc140/9ghyz1293401408.ps (open in new window) |
| | | | 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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$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$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)
a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$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')
}
| | |
Copyright
This work is licensed under a
Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.
Software written by Ed van Stee & Patrick Wessa
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