paper multi smoke

*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: Sat, 18 Dec 2010 14:46:09 +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/18/t1292683512doq0fxvou7egon2.htm/, Retrieved Sat, 18 Dec 2010 15:45:12 +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/18/t1292683512doq0fxvou7egon2.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 «

0 17.080 9 0 17.240 8 0 17.200 7 0 15.580 9 0 18.950 9 0 23.360 8 0 19.190 6 0 14.150 6 0 19.870 8 0 19.420 9 0 16.020 6 0 17.350 8 0 21.930 8 0 21.180 8 0 22.580 8 0 19.320 7 0 14.720 5 0 18.780 6 0 23.520 9 0 26.040 9 0 14.000 5 0 12.560 5 0 0.839 4 0 25.780 7 0 29.880 9 0 14.040 3 0 23.480 9 0 17.550 5 0 29.800 8 0 21.000 9 0 12.820 5 0 30.000 9 0 26.730 8 0 20.930 7 0 16.120 5 0 21.750 8 0 27.250 9 0 20.710 8 0 15.470 8 0 20.040 8 0 31.350 9 0 24.200 8 0 17.760 5 0 19.310 8 0 13.430 5 0 20.760 9 0 16.240 7 0 13.440 8 0 16.500 6 0 27.320 8 0 20.170 5 0 27.970 9 0 35.560 9 0 17.030 8 0 16.340 6 0 25.700 9 0 30.160 9 0 24.190 7 0 15.690 4 0 16.980 8 0 21.230 8 0 24.810 8 0 14.810 6 0 15.770 4 0 19.400 8 0 17.470 6 0 20.690 9 0 16.310 7 0 15.360 5 0 25.600 9 0 19.620 8 0 25.310 8 0 27.150 9 0 24.570 9 0 20.900 9 0 17.890 7 0 18.580 5 0 14.520 5 0 38.420 9 0 17.190 6 0 21.110 7 0 16.950 6 0 22.110 8 0 17.940 8 0 19.170 7 0 21. 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! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 25 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation FEV[t] = + 3.40917384291795 -2.14803366960063Smoke[t] + 2.32990431027801Age[t] + 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) 3.40917384291795 0.82212 4.1468 3.8e-05 1.9e-05 Smoke -2.14803366960063 0.81515 -2.6351 0.008611 0.004305 Age 2.32990431027801 0.082624 28.1989 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.759422388017405 R-squared 0.576722363422059 Adjusted R-squared 0.575421971297395 F-TEST (value) 443.498812768751 F-TEST (DF numerator) 2 F-TEST (DF denominator) 651 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.70449155674434 Sum Squared Residuals 21184.3367725498 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 17.08 24.3783126354201 -7.29831263542005 2 17.24 22.0484083251421 -4.80840832514206 3 17.2 19.7185040148640 -2.51850401486404 4 15.58 24.3783126354201 -8.79831263542008 5 18.95 24.3783126354201 -5.42831263542008 6 23.36 22.0484083251421 1.31159167485794 7 19.19 17.3885997045860 1.80140029541397 8 14.15 17.3885997045860 -3.23859970458604 9 19.87 22.0484083251421 -2.17840832514206 10 19.42 24.3783126354201 -4.95831263542008 11 16.02 17.3885997045860 -1.36859970458604 12 17.35 22.0484083251421 -4.69840832514206 13 21.93 22.0484083251421 -0.118408325142063 14 21.18 22.0484083251421 -0.868408325142063 15 22.58 22.0484083251421 0.531591674857936 16 19.32 19.7185040148640 -0.398504014864049 17 14.72 15.0586953943080 -0.338695394308020 18 18.78 17.3885997045860 1.39140029541397 19 23.52 24.3783126354201 -0.858312635420077 20 26.04 24.3783126354201 1.66168736457992 21 14 15.0586953943080 -1.05869539430802 22 12.56 15.0586953943080 -2.49869539430802 23 0.839 12.72879108403 -11.88979108403 24 25.78 19.7185040148640 6.06149598513595 25 29.88 24.3783126354201 5.50168736457992 26 14.04 10.398886773752 3.64111322624801 27 23.48 24.3783126354201 -0.898312635420077 28 17.55 15.0586953943080 2.49130460569198 29 29.8 22.0484083251421 7.75159167485794 30 21 24.3783126354201 -3.37831263542008 31 12.82 15.0586953943080 -2.23869539430802 32 30 24.3783126354201 5.62168736457992 33 26.73 22.0484083251421 4.68159167485794 34 20.93 19.7185040148640 1.21149598513595 35 16.12 15.0586953943080 1.06130460569198 36 21.75 22.0484083251421 -0.298408325142063 37 27.25 24.3783126354201 2.87168736457992 38 20.71 22.0484083251421 -1.33840832514206 39 15.47 22.0484083251421 -6.57840832514206 40 20.04 22.0484083251421 -2.00840832514206 41 31.35 24.3783126354201 6.97168736457992 42 24.2 22.0484083251421 2.15159167485794 43 17.76 15.0586953943080 2.70130460569198 44 19.31 22.0484083251421 -2.73840832514206 45 13.43 15.0586953943080 -1.62869539430802 46 20.76 24.3783126354201 -3.61831263542008 47 16.24 19.7185040148640 -3.47850401486405 48 13.44 22.0484083251421 -8.60840832514207 49 16.5 17.3885997045860 -0.888599704586036 50 27.32 22.0484083251421 5.27159167485794 51 20.17 15.0586953943080 5.11130460569198 52 27.97 24.3783126354201 3.59168736457992 53 35.56 24.3783126354201 11.1816873645799 54 17.03 22.0484083251421 -5.01840832514206 55 16.34 17.3885997045860 -1.04859970458604 56 25.7 24.3783126354201 1.32168736457992 57 30.16 24.3783126354201 5.78168736457992 58 24.19 19.7185040148640 4.47149598513595 59 15.69 12.72879108403 2.96120891596999 60 16.98 22.0484083251421 -5.06840832514206 61 21.23 22.0484083251421 -0.818408325142062 62 24.81 22.0484083251421 2.76159167485794 63 14.81 17.3885997045860 -2.57859970458603 64 15.77 12.72879108403 3.04120891596999 65 19.4 22.0484083251421 -2.64840832514206 66 17.47 17.3885997045860 0.0814002954139642 67 20.69 24.3783126354201 -3.68831263542008 68 16.31 19.7185040148640 -3.40850401486405 69 15.36 15.0586953943080 0.301304605691979 70 25.6 24.3783126354201 1.22168736457992 71 19.62 22.0484083251421 -2.42840832514206 72 25.31 22.0484083251421 3.26159167485794 73 27.15 24.3783126354201 2.77168736457992 74 24.57 24.3783126354201 0.191687364579923 75 20.9 24.3783126354201 -3.47831263542008 76 17.89 19.7185040148640 -1.82850401486405 77 18.58 15.0586953943080 3.52130460569198 78 14.52 15.0586953943080 -0.538695394308021 79 38.42 24.3783126354201 14.0416873645799 80 17.19 17.3885997045860 -0.198599704586034 81 21.11 19.7185040148640 1.39149598513595 82 16.95 17.3885997045860 -0.438599704586036 83 22.11 22.0484083251421 0.0615916748579372 84 17.94 22.0484083251421 -4.10840832514206 85 19.17 19.7185040148640 -0.548504014864047 86 21.44 22.0484083251421 -0.608408325142061 87 12.53 19.7185040148640 -7.18850401486405 88 26.59 24.3783126354201 2.21168736457992 89 15.8 15.0586953943080 0.74130460569198 90 21.26 24.3783126354201 -3.11831263542008 91 30.29 24.3783126354201 5.91168736457992 92 29.64 24.3783126354201 5.26168736457992 93 16.11 19.7185040148640 -3.60850401486405 94 22.15 22.0484083251421 0.101591674857936 95 23.88 22.0484083251421 1.83159167485794 96 21.96 24.3783126354201 -2.41831263542008 97 17.51 24.3783126354201 -6.86831263542008 98 21.65 24.3783126354201 -2.72831263542008 99 16.82 19.7185040148640 -2.89850401486405 100 15.23 22.0484083251421 -6.81840832514206 101 12.92 22.0484083251421 -9.12840832514206 102 16.49 19.7185040148640 -3.22850401486405 103 25.88 24.3783126354201 1.50168736457992 104 0.796 12.72879108403 -11.93279108403 105 25.74 24.3783126354201 1.36168736457992 106 19.79 17.3885997045860 2.40140029541396 107 23.54 22.0484083251421 1.49159167485794 108 17.18 17.3885997045860 -0.208599704586036 109 17.42 19.7185040148640 -2.29850401486405 110 16.03 19.7185040148640 -3.68850401486405 111 26.39 22.0484083251421 4.34159167485794 112 18.29 19.7185040148640 -1.42850401486405 113 20.84 19.7185040148640 1.12149598513595 114 22.2 19.7185040148640 2.48149598513595 115 14.73 19.7185040148640 -4.98850401486405 116 23.41 22.0484083251421 1.36159167485794 117 16.98 19.7185040148640 -2.73850401486405 118 11.96 15.0586953943080 -3.09869539430802 119 18.72 22.0484083251421 -3.32840832514206 120 22.19 19.7185040148640 2.47149598513595 121 24.2 24.3783126354201 -0.178312635420078 122 18.27 19.7185040148640 -1.44850401486405 123 14.61 19.7185040148640 -5.10850401486405 124 13.38 17.3885997045860 -4.00859970458604 125 20.9 22.0484083251421 -1.14840832514206 126 16.97 22.0484083251421 -5.07840832514206 127 15.62 22.0484083251421 -6.42840832514206 128 20.4 24.3783126354201 -3.97831263542008 129 16.09 19.7185040148640 -3.62850401486405 130 24.58 22.0484083251421 2.53159167485794 131 26.5 24.3783126354201 2.12168736457992 132 14.29 22.0484083251421 -7.75840832514206 133 16.75 22.0484083251421 -5.29840832514206 134 19.47 24.3783126354201 -4.90831263542008 135 20.69 22.0484083251421 -1.35840832514206 136 15.72 17.3885997045860 -1.66859970458604 137 13.48 17.3885997045860 -3.90859970458604 138 22.88 22.0484083251421 0.831591674857937 139 17.73 24.3783126354201 -6.64831263542008 140 0.791 15.0586953943080 -14.2676953943080 141 19.05 19.7185040148640 -0.668504014864048 142 24.63 24.3783126354201 0.251687364579922 143 14.31 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14.27 17.3885997045860 -3.11859970458604 301 18.26 19.7185040148640 -1.45850401486405 302 26.88 24.3783126354201 2.50168736457992 303 16.57 22.0484083251421 -5.47840832514206 304 16.72 17.3885997045860 -0.668599704586037 305 20.15 22.0484083251421 -1.89840832514206 306 23.71 19.7185040148640 3.99149598513595 307 21.15 15.0586953943080 6.09130460569198 308 23.28 22.0484083251421 1.23159167485794 309 14.95 19.7185040148640 -4.76850401486405 310 28.84 29.0381212559761 -0.198121255976105 311 23.28 26.7082169456981 -3.42821694569809 312 33.81 36.0278341868101 -2.21783418681015 313 21.7 29.0381212559761 -7.3381212559761 314 34.7 29.0381212559761 5.6618787440239 315 30.58 31.3680255662541 -0.788025566254122 316 18.11 26.7082169456981 -8.5982169456981 317 25.24 29.0381212559761 -3.79812125597611 318 26.42 26.7082169456981 -0.28821694569809 319 37.41 36.0278341868101 1.38216581318985 320 43.36 33.6979298765321 9.66207012346786 321 48.42 36.0278341868102 12.3921658131899 322 45.5 31.3680255662541 14.1319744337459 323 28.41 31.3680255662541 -2.95802556625412 324 31.66 26.7082169456981 4.95178305430191 325 38.16 33.6979298765321 4.46207012346786 326 25.61 26.7082169456981 -1.09821694569809 327 36.54 29.0381212559761 7.5018787440239 328 24.81 26.7082169456981 -1.89821694569809 329 26.65 29.0381212559761 -2.38812125597611 330 32.03 26.7082169456981 5.32178305430191 331 35.49 33.6979298765321 1.79207012346787 332 22.36 33.8798005172095 -11.5198005172095 333 32.22 29.0381212559761 3.18187874402389 334 31.11 26.7082169456981 4.40178305430191 335 34.9 29.0381212559761 5.86187874402389 336 31.47 33.6979298765321 -2.22792987653214 337 25.2 26.7082169456981 -1.50821694569809 338 22.92 26.7082169456981 -3.78821694569809 339 28.89 31.3680255662541 -2.47802556625412 340 22.46 26.7082169456981 -4.24821694569809 341 19.37 26.7082169456981 -7.33821694569809 342 26.46 26.7082169456981 -0.248216945698091 343 29.57 29.0381212559761 0.531878744023895 344 40.07 29.0381212559761 11.0318787440239 345 23.86 29.0381212559761 -5.1781212559761 346 32.51 26.7082169456981 5.80178305430191 347 27.62 29.0381212559761 -1.41812125597610 348 30.11 29.0381212559761 1.07187874402389 349 43.05 33.6979298765321 9.35207012346786 350 39.06 33.6979298765321 5.36207012346787 351 35.83 29.0381212559761 6.79187874402389 352 32.36 29.0381212559761 3.32187874402389 353 34.36 36.0278341868101 -1.66783418681015 354 30.58 29.0381212559761 1.54187874402389 355 30.07 26.7082169456981 3.36178305430191 356 34.89 26.7082169456981 8.1817830543019 357 28.64 26.7082169456981 1.93178305430191 358 34.28 33.8798005172095 0.400199482790486 359 28.19 33.6979298765321 -5.50792987653213 360 22.5 26.7082169456981 -4.20821694569809 361 46.83 36.0278341868102 10.8021658131898 362 23.52 26.7082169456981 -3.18821694569809 363 31.08 29.0381212559761 2.04187874402389 364 39.94 33.6979298765321 6.24207012346786 365 43.93 31.3680255662541 12.5619744337459 366 32.08 31.5498962069315 0.530103793068498 367 25.92 26.7082169456981 -0.78821694569809 368 31.93 33.6979298765321 -1.76792987653213 369 16.94 26.8900875863755 -9.95008758637547 370 39.57 33.8798005172095 5.69019948279048 371 23.46 29.0381212559761 -5.5781212559761 372 47.89 31.5498962069315 16.3401037930685 373 35.15 29.0381212559761 6.11187874402389 374 27.54 29.0381212559761 -1.49812125597611 375 27.2 26.7082169456981 0.491783054301908 376 24.63 29.0381212559761 -4.40812125597611 377 26.33 29.0381212559761 -2.70812125597611 378 30.48 26.7082169456981 3.77178305430191 379 31.11 29.0381212559761 2.07187874402389 380 37.45 33.6979298765321 3.75207012346787 381 23.84 29.2199918966535 -5.37999189665349 382 20.94 26.7082169456981 -5.76821694569809 383 31.83 26.7082169456981 5.12178305430191 384 30.74 33.8798005172095 -3.13980051720952 385 39.77 29.0381212559761 10.7318787440239 386 33.54 26.7082169456981 6.83178305430191 387 34.11 29.0381212559761 5.07187874402389 388 23.87 24.5601832760975 -0.690183276097458 389 31.71 29.0381212559761 2.67187874402390 390 38.87 33.6979298765321 5.17207012346786 391 26.46 33.6979298765321 -7.23792987653213 392 25.04 26.7082169456981 -1.66821694569809 393 35.87 29.0381212559761 6.83187874402389 394 38.45 29.0381212559761 9.4118787440239 395 29.71 31.3680255662541 -1.65802556625412 396 28.91 26.7082169456981 2.20178305430191 397 18.23 26.7082169456981 -8.4782169456981 398 24.17 29.0381212559761 -4.8681212559761 399 21.75 26.7082169456981 -4.95821694569809 400 27.35 29.0381212559761 -1.68812125597610 401 42.73 36.0278341868101 6.70216581318985 402 29.76 33.6979298765321 -3.93792987653213 403 38.35 29.2199918966535 9.13000810334651 404 40.65 29.0381212559761 11.6118787440239 405 23.18 29.0381212559761 -5.8581212559761 406 35.96 29.0381212559761 6.9218787440239 407 33.95 36.0278341868101 -2.07783418681015 408 27.51 31.3680255662541 -3.85802556625412 409 26.73 26.7082169456981 0.0217830543019091 410 25.56 31.3680255662541 -5.80802556625412 411 25.42 29.0381212559761 -3.61812125597610 412 26.08 26.7082169456981 -0.628216945698093 413 23.54 29.0381212559761 -5.4981212559761 414 25.99 31.5498962069315 -5.5598962069315 415 14.58 26.7082169456981 -12.1282169456981 416 37.95 26.7082169456981 11.2417830543019 417 24.91 29.0381212559761 -4.12812125597611 418 30.6 33.6979298765321 -3.09792987653213 419 25.45 26.7082169456981 -1.25821694569809 420 29.93 29.0381212559761 0.891878744023894 421 33.05 26.7082169456981 6.34178305430191 422 47.56 31.5498962069315 16.0101037930685 423 37.74 29.0381212559761 8.7018787440239 424 28.55 26.7082169456981 1.84178305430191 425 29.88 29.0381212559761 0.841878744023894 426 24.98 29.0381212559761 -4.05812125597611 427 31.69 36.0278341868101 -4.33783418681015 428 28.87 29.0381212559761 -0.168121255976104 429 27.04 33.6979298765321 -6.65792987653214 430 35.15 29.0381212559761 6.11187874402389 431 34.25 29.0381212559761 5.21187874402389 432 22.87 26.7082169456981 -3.83821694569809 433 24.34 33.6979298765321 -9.35792987653214 434 23.65 26.7082169456981 -3.05821694569809 435 30.86 31.5498962069315 -0.689896206931501 436 26.96 26.7082169456981 0.251783054301909 437 28.68 31.3680255662541 -2.68802556625412 438 28.13 26.7082169456981 1.42178305430191 439 43.09 33.8798005172095 9.21019948279049 440 32.55 31.3680255662541 1.18197443374588 441 34.13 24.5601832760975 9.56981672390254 442 45.93 29.0381212559761 16.8918787440239 443 41.11 36.0278341868101 5.08216581318985 444 19.16 31.3680255662541 -12.2080255662541 445 18.58 26.7082169456981 -8.1282169456981 446 29.75 24.5601832760975 5.18981672390254 447 33.5 26.7082169456981 6.79178305430191 448 29.01 26.7082169456981 2.30178305430191 449 22.41 31.3680255662541 -8.95802556625412 450 42.25 33.6979298765321 8.55207012346786 451 32.23 29.0381212559761 3.19187874402389 452 52.24 31.3680255662541 20.8719744337459 453 40.73 29.0381212559761 11.6918787440239 454 40.8 31.3680255662541 9.43197443374588 455 26.06 29.0381212559761 -2.97812125597611 456 31.69 26.8900875863755 4.79991241362453 457 44.11 31.3680255662541 12.7419744337459 458 37.91 31.3680255662541 6.54197443374588 459 30.89 33.6979298765321 -2.80792987653213 460 24.65 29.0381212559761 -4.38812125597611 461 33.43 29.2199918966535 4.21000810334651 462 32 26.7082169456981 5.29178305430191 463 29.13 31.3680255662541 -2.23802556625412 464 48.77 33.6979298765321 15.0720701234679 465 23.58 26.7082169456981 -3.12821694569809 466 32.79 31.3680255662541 1.42197443374588 467 25.81 26.7082169456981 -0.898216945698093 468 23.47 31.3680255662541 -7.89802556625412 469 26.91 26.7082169456981 0.201783054301909 470 28.27 29.0381212559761 -0.768121255976105 471 18.73 26.7082169456981 -7.97821694569809 472 37.51 29.2199918966535 8.29000810334651 473 25.38 36.0278341868102 -10.6478341868102 474 27.58 26.7082169456981 0.871783054301907 475 30.5 26.7082169456981 3.79178305430191 476 30.79 31.3680255662541 -0.578025566254121 477 22.01 26.7082169456981 -4.69821694569809 478 18.58 26.7082169456981 -8.1282169456981 479 22.16 31.5498962069315 -9.3898962069315 480 34.03 31.3680255662541 2.66197443374588 481 35.01 31.3680255662541 3.64197443374588 482 25.78 29.0381212559761 -3.25812125597610 483 30.78 31.5498962069315 -0.7698962069315 484 31.86 29.2199918966535 2.64000810334651 485 16.65 26.7082169456981 -10.0582169456981 486 20.81 29.0381212559761 -8.2281212559761 487 29.74 29.0381212559761 0.701878744023893 488 32.97 31.5498962069315 1.42010379306850 489 40.73 31.3680255662541 9.36197443374587 490 44.48 33.6979298765321 10.7820701234679 491 39.84 33.6979298765321 6.14207012346787 492 22.5 26.7082169456981 -4.20821694569809 493 27.52 31.3680255662541 -3.84802556625412 494 23.04 29.2199918966535 -6.17999189665349 495 36.8 36.0278341868101 0.772165813189849 496 31.02 26.8900875863755 4.12991241362453 497 28.62 26.7082169456981 1.91178305430191 498 26.77 31.5498962069315 -4.7798962069315 499 30.23 29.0381212559761 1.19187874402389 500 36.81 29.0381212559761 7.7718787440239 501 32.55 33.6979298765321 -1.14792987653214 502 36.92 31.3680255662541 5.55197443374588 503 23.56 26.7082169456981 -3.14821694569809 504 45.91 26.7082169456981 19.2017830543019 505 30.82 31.3680255662541 -0.54802556625412 506 32.97 31.5498962069315 1.42010379306850 507 32.58 29.0381212559761 3.54187874402389 508 22.16 26.7082169456981 -4.54821694569809 509 32.47 29.0381212559761 3.43187874402389 510 43.24 29.0381212559761 14.2018787440239 511 23.62 29.0381212559761 -5.4181212559761 512 25.63 29.0381212559761 -3.40812125597611 513 32.06 29.0381212559761 3.02187874402390 514 35.85 36.0278341868101 -0.177834186810147 515 47.2 31.3680255662541 15.8319744337459 516 33.31 33.6979298765321 -0.387929876532133 517 50.83 33.6979298765321 17.1320701234679 518 34.98 24.5601832760975 10.4198167239025 519 24.17 31.3680255662541 -7.19802556625412 520 23.64 26.7082169456981 -3.06821694569809 521 23.41 26.7082169456981 -3.29821694569809 522 27.59 29.2199918966535 -1.62999189665349 523 29.53 26.8900875863755 2.63991241362453 524 32.31 31.3680255662541 0.941974433745882 525 30.78 29.0381212559761 1.74187874402390 526 33.69 29.0381212559761 4.65187874402389 527 35.29 31.3680255662541 3.92197443374588 528 28.66 31.3680255662541 -2.70802556625412 529 28.91 36.0278341868101 -7.11783418681015 530 30.22 29.0381212559761 1.18187874402389 531 31.27 26.7082169456981 4.56178305430191 532 28.66 29.0381212559761 -0.378121255976105 533 26.05 31.3680255662541 -5.31802556625412 534 30.56 33.6979298765321 -3.13792987653214 535 25.69 31.3680255662541 -5.67802556625412 536 25.01 29.0381212559761 -4.0281212559761 537 33.2 29.0381212559761 4.1618787440239 538 21.23 29.0381212559761 -7.8081212559761 539 37.8 36.0278341868101 1.77216581318985 540 38.47 29.0381212559761 9.4318787440239 541 37.85 31.5498962069315 6.3001037930685 542 39.24 31.3680255662541 7.87197443374588 543 21.32 26.7082169456981 -5.38821694569809 544 27.52 31.3680255662541 -3.84802556625412 545 24.49 33.6979298765321 -9.20792987653214 546 34.56 26.7082169456981 7.85178305430191 547 30.73 26.7082169456981 4.02178305430191 548 26.88 26.7082169456981 0.171783054301908 549 33.29 26.7082169456981 6.58178305430191 550 42.71 36.0278341868101 6.68216581318985 551 35.3 31.3680255662541 3.93197443374588 552 29.28 29.0381212559761 0.241878744023896 553 26.89 29.0381212559761 -2.14812125597610 554 23.32 31.3680255662541 -8.04802556625412 555 29.34 36.0278341868101 -6.68783418681015 556 22.76 33.8798005172095 -11.1198005172095 557 31.1 26.7082169456981 4.39178305430191 558 28.94 29.0381212559761 -0.0981212559761037 559 46.37 26.8900875863755 19.4799124136245 560 24.35 26.7082169456981 -2.35821694569809 561 28.38 26.7082169456981 1.67178305430191 562 30.35 31.3680255662541 -1.01802556625412 563 48.31 31.3680255662541 16.9419744337459 564 28.12 29.0381212559761 -0.918121255976104 565 27.14 31.3680255662541 -4.22802556625412 566 30.86 26.7082169456981 4.15178305430191 567 35.19 31.3680255662541 3.82197443374588 568 42.32 33.6979298765321 8.62207012346786 569 27.7 26.7082169456981 0.991783054301908 570 33.41 31.3680255662541 2.04197443374588 571 30.9 26.7082169456981 4.19178305430191 572 25.31 33.6979298765321 -8.38792987653214 573 28.22 31.3680255662541 -3.14802556625412 574 30.38 24.5601832760975 5.81981672390254 575 29.35 31.3680255662541 -2.01802556625412 576 25.68 26.7082169456981 -1.02821694569809 577 23.87 29.0381212559761 -5.1681212559761 578 24.99 31.3680255662541 -6.37802556625412 579 41.3 29.0381212559761 12.2618787440239 580 30.01 31.3680255662541 -1.35802556625412 581 31.32 26.7082169456981 4.61178305430191 582 35.77 33.6979298765321 2.07207012346787 583 32.22 31.3680255662541 0.851974433745879 584 32.8 29.0381212559761 3.76187874402389 585 26.59 29.0381212559761 -2.44812125597611 586 28.22 29.0381212559761 -0.818121255976106 587 21.4 29.0381212559761 -7.6381212559761 588 42.03 31.3680255662541 10.6619744337459 589 29.97 36.0278341868101 -6.05783418681015 590 31.2 26.8900875863755 4.30991241362453 591 25.62 29.0381212559761 -3.41812125597610 592 30.82 31.3680255662541 -0.54802556625412 593 38.06 36.0278341868101 2.03216581318985 594 33.39 26.8900875863755 6.49991241362453 595 31.52 31.5498962069315 -0.0298962069315012 596 24.58 29.0381212559761 -4.45812125597611 597 23.91 26.7082169456981 -2.79821694569809 598 31.41 33.6979298765321 -2.28792987653213 599 25.79 31.3680255662541 -5.57802556625412 600 31.04 26.8900875863755 4.14991241362453 601 40.45 31.5498962069315 8.9001037930685 602 47.63 33.8798005172095 13.7501994827905 603 21 26.7082169456981 -5.70821694569809 604 30.69 26.8900875863755 3.79991241362453 605 27.85 29.0381212559761 -1.18812125597610 606 42.84 38.3577384970882 4.48226150291184 607 45.06 36.2097048274875 8.85029517251247 608 29.06 45.3474514279222 -16.2874514279222 609 51.02 47.6773557382002 3.34264426179978 610 35.19 45.5293220685996 -10.3393220685996 611 36.88 38.5396091377655 -1.65960913776554 612 44.29 43.0175471176442 1.27245288235581 613 42.79 38.3577384970882 4.43226150291184 614 45 38.3577384970882 6.64226150291184 615 26.35 38.3577384970882 -12.0077384970882 616 26.79 36.2097048274875 -9.41970482748753 617 21.98 36.2097048274875 -14.2297048274875 618 33.45 45.5293220685996 -12.0793220685996 619 30.82 45.3474514279222 -14.5274514279222 620 33.87 40.6876428073662 -6.81764280736618 621 30.82 40.8695134480436 -10.0495134480436 622 29.03 38.5396091377655 -9.50960913776554 623 30.04 36.2097048274875 -6.16970482748753 624 57.93 38.3577384970882 19.5722615029118 625 39.85 38.3577384970882 1.49226150291184 626 42.2 45.3474514279222 -3.1474514279222 627 47.24 43.0175471176442 4.22245288235581 628 37.31 38.3577384970882 -1.04773849708816 629 34.06 40.8695134480436 -6.80951344804356 630 35 43.0175471176442 -8.01754711764419 631 36.74 40.6876428073662 -3.94764280736618 632 56.33 43.0175471176442 13.3124528823558 633 31.22 36.2097048274875 -4.98970482748753 634 33.3 36.2097048274875 -2.90970482748753 635 26.08 38.5396091377655 -12.4596091377655 636 36.45 40.6876428073662 -4.23764280736617 637 37.99 36.2097048274875 1.78029517251247 638 40.86 43.1994177583216 -2.33941775832157 639 28.87 38.3577384970882 -9.48773849708816 640 40.7 38.5396091377655 2.16039086223446 641 39.6 43.0175471176442 -3.41754711764419 642 42.99 40.6876428073662 2.30235719263382 643 29.81 40.6876428073662 -10.8776428073662 644 22.64 36.2097048274875 -13.5697048274875 645 44.04 43.1994177583216 0.840582241678429 646 22.78 36.2097048274875 -13.4297048274875 647 45.04 40.6876428073662 4.35235719263382 648 56.38 43.0175471176442 13.3624528823558 649 48.72 38.5396091377655 10.1803908622345 650 42.7 38.5396091377655 4.16039086223446 651 37.27 36.2097048274875 1.06029517251247 652 28.53 45.3474514279222 -16.8174514279222 653 27.95 38.5396091377655 -10.5896091377655 654 32.11 38.3577384970882 -6.24773849708816 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.224005280655267 0.448010561310534 0.775994719344733 7 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8.32503634425303e-07 0.999999583748183 545 6.79268736461574e-07 1.35853747292315e-06 0.999999320731264 546 6.79969091841151e-07 1.35993818368230e-06 0.999999320030908 547 5.04110505036983e-07 1.00822101007397e-06 0.999999495889495 548 3.62588332825274e-07 7.25176665650549e-07 0.999999637411667 549 3.18615813572692e-07 6.37231627145383e-07 0.999999681384186 550 3.68676550001864e-07 7.37353100003727e-07 0.99999963132345 551 2.85653552055930e-07 5.71307104111859e-07 0.999999714346448 552 1.98175290983513e-07 3.96350581967025e-07 0.99999980182471 553 1.50983327959869e-07 3.01966655919739e-07 0.999999849016672 554 2.19651187637419e-07 4.39302375274838e-07 0.999999780348812 555 2.25587532853954e-07 4.51175065707909e-07 0.999999774412467 556 5.22189813739082e-07 1.04437962747816e-06 0.999999477810186 557 3.86164610333787e-07 7.72329220667575e-07 0.99999961383539 558 2.70325313448619e-07 5.40650626897239e-07 0.999999729674687 559 4.16307701037113e-06 8.32615402074226e-06 0.99999583692299 560 3.43209973407071e-06 6.86419946814142e-06 0.999996567900266 561 2.42743031351807e-06 4.85486062703615e-06 0.999997572569686 562 1.74367636408373e-06 3.48735272816745e-06 0.999998256323636 563 1.41254093614290e-05 2.82508187228579e-05 0.999985874590639 564 1.04320966935263e-05 2.08641933870525e-05 0.999989567903307 565 9.0889606717351e-06 1.81779213434702e-05 0.999990911039328 566 6.85516776137524e-06 1.37103355227505e-05 0.999993144832239 567 5.34845701985704e-06 1.06969140397141e-05 0.99999465154298 568 7.61820077215778e-06 1.52364015443156e-05 0.999992381799228 569 5.37310403434504e-06 1.07462080686901e-05 0.999994626895966 570 3.86175680234180e-06 7.72351360468361e-06 0.999996138243198 571 2.91194299190137e-06 5.82388598380274e-06 0.999997088057008 572 3.81568083019934e-06 7.63136166039868e-06 0.99999618431917 573 2.9723940984905e-06 5.944788196981e-06 0.999997027605901 574 2.46332705560505e-06 4.9266541112101e-06 0.999997536672944 575 1.78289271058762e-06 3.56578542117524e-06 0.99999821710729 576 1.29122903778350e-06 2.58245807556700e-06 0.999998708770962 577 1.28796546875458e-06 2.57593093750915e-06 0.999998712034531 578 1.43583340555354e-06 2.87166681110708e-06 0.999998564166594 579 3.20114760575371e-06 6.40229521150742e-06 0.999996798852394 580 2.25487299414909e-06 4.50974598829818e-06 0.999997745127006 581 1.67750599185342e-06 3.35501198370685e-06 0.999998322494008 582 1.18418291091558e-06 2.36836582183115e-06 0.99999881581709 583 7.91057412793163e-07 1.58211482558633e-06 0.999999208942587 584 5.72086741710234e-07 1.14417348342047e-06 0.999999427913258 585 4.19112308824283e-07 8.38224617648566e-07 0.999999580887691 586 2.82144516372721e-07 5.64289032745443e-07 0.999999717855484 587 4.18903352824000e-07 8.37806705648001e-07 0.999999581096647 588 7.7197831098351e-07 1.54395662196702e-06 0.99999922802169 589 6.96397015343906e-07 1.39279403068781e-06 0.999999303602985 590 5.15450744728335e-07 1.03090148945667e-06 0.999999484549255 591 4.15232471107832e-07 8.30464942215664e-07 0.999999584767529 592 2.71333579739753e-07 5.42667159479505e-07 0.99999972866642 593 1.85211604049681e-07 3.70423208099362e-07 0.999999814788396 594 1.70057541967935e-07 3.40115083935871e-07 0.999999829942458 595 1.095944573254e-07 2.191889146508e-07 0.999999890405543 596 9.69511656063983e-08 1.93902331212797e-07 0.999999903048834 597 8.16165241215533e-08 1.63233048243107e-07 0.999999918383476 598 5.58708218992233e-08 1.11741643798447e-07 0.999999944129178 599 6.4873075124506e-08 1.29746150249012e-07 0.999999935126925 600 4.22647520907566e-08 8.45295041815131e-08 0.999999957735248 601 6.96888576264537e-08 1.39377715252907e-07 0.999999930311142 602 6.74631042910096e-07 1.34926208582019e-06 0.999999325368957 603 1.00068232200259e-06 2.00136464400517e-06 0.999998999317678 604 7.06015032706993e-07 1.41203006541399e-06 0.999999293984967 605 5.65958907173896e-07 1.13191781434779e-06 0.999999434041093 606 4.28785344292759e-07 8.57570688585517e-07 0.999999571214656 607 1.34672819034605e-06 2.69345638069210e-06 0.99999865327181 608 8.13088729109817e-06 1.62617745821963e-05 0.999991869112709 609 7.74399434919587e-06 1.54879886983917e-05 0.99999225600565 610 7.0621130540273e-06 1.41242261080546e-05 0.999992937886946 611 5.19858823671557e-06 1.03971764734311e-05 0.999994801411763 612 3.57571444055096e-06 7.15142888110193e-06 0.99999642428556 613 2.86522693827850e-06 5.73045387655699e-06 0.999997134773062 614 3.29381451280524e-06 6.58762902561049e-06 0.999996706185487 615 8.31463976178052e-06 1.66292795235610e-05 0.999991685360238 616 7.94769105648919e-06 1.58953821129784e-05 0.999992052308944 617 1.95948460675747e-05 3.91896921351494e-05 0.999980405153932 618 1.94056763989127e-05 3.88113527978254e-05 0.999980594323601 619 6.68355304767217e-05 0.000133671060953443 0.999933164469523 620 6.34759651560524e-05 0.000126951930312105 0.999936524034844 621 6.2764050630464e-05 0.000125528101260928 0.99993723594937 622 6.02635038318965e-05 0.000120527007663793 0.999939736496168 623 4.20955156312038e-05 8.41910312624077e-05 0.999957904484369 624 0.00205178381816151 0.00410356763632302 0.997948216181838 625 0.00162838445763273 0.00325676891526546 0.998371615542367 626 0.00116548654440398 0.00233097308880795 0.998834513455596 627 0.00100542117551044 0.00201084235102087 0.99899457882449 628 0.000685570203771314 0.00137114040754263 0.999314429796229 629 0.00056733095885397 0.00113466191770794 0.999432669041146 630 0.00057665577745642 0.00115331155491284 0.999423344222544 631 0.000365019075524495 0.000730038151048991 0.999634980924476 632 0.00218281892408722 0.00436563784817443 0.997817181075913 633 0.00140469641030336 0.00280939282060673 0.998595303589697 634 0.000865744438887625 0.00173148887777525 0.999134255561112 635 0.00157448958248415 0.00314897916496830 0.998425510417516 636 0.000946794173998231 0.00189358834799646 0.999053205826002 637 0.000720918265352534 0.00144183653070507 0.999279081734647 638 0.000441747185138203 0.000883494370276406 0.999558252814862 639 0.000320519566026279 0.000641039132052557 0.999679480433974 640 0.000214864080010874 0.000429728160021748 0.99978513591999 641 0.000111625695775769 0.000223251391551539 0.999888374304224 642 7.70762187391493e-05 0.000154152437478299 0.99992292378126 643 8.38401484224457e-05 0.000167680296844891 0.999916159851578 644 0.000178882667784682 0.000357765335569363 0.999821117332215 645 8.1067597763373e-05 0.000162135195526746 0.999918932402237 646 0.000462074729180715 0.00092414945836143 0.99953792527082 647 0.000273167220658122 0.000546334441316243 0.999726832779342 648 0.0605986419289735 0.121197283857947 0.939401358071026 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 597 0.92846034214619 NOK 5% type I error level 625 0.972006220839813 NOK 10% type I error level 637 0.990668740279938 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292683512doq0fxvou7egon2/10iyaq1292683540.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292683512doq0fxvou7egon2/10iyaq1292683540.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292683512doq0fxvou7egon2/1tfvw1292683540.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292683512doq0fxvou7egon2/1tfvw1292683540.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292683512doq0fxvou7egon2/2tfvw1292683540.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292683512doq0fxvou7egon2/2tfvw1292683540.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292683512doq0fxvou7egon2/346cz1292683540.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292683512doq0fxvou7egon2/346cz1292683540.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292683512doq0fxvou7egon2/446cz1292683540.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292683512doq0fxvou7egon2/446cz1292683540.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292683512doq0fxvou7egon2/546cz1292683540.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292683512doq0fxvou7egon2/546cz1292683540.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292683512doq0fxvou7egon2/6110q1292683540.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292683512doq0fxvou7egon2/6110q1292683540.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292683512doq0fxvou7egon2/7ca0b1292683540.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292683512doq0fxvou7egon2/7ca0b1292683540.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292683512doq0fxvou7egon2/8ca0b1292683540.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292683512doq0fxvou7egon2/8ca0b1292683540.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292683512doq0fxvou7egon2/9ca0b1292683540.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292683512doq0fxvou7egon2/9ca0b1292683540.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$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') }

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