Paper MR Time Series

*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, 11 Dec 2010 10:40:35 +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/11/t1292063965uyzbahwa5bhf4qa.htm/, Retrieved Sat, 11 Dec 2010 11:39:36 +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/11/t1292063965uyzbahwa5bhf4qa.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 «

17848 19592 21092 20899 25890 24965 22225 20977 22897 22785 22769 19637 20203 20450 23083 21738 26766 25280 22574 22729 21378 22902 24989 21116 15169 15846 20927 18273 22538 15596 14034 11366 14861 15149 13577 13026 13190 13196 15826 14733 16307 15703 14589 12043 15057 14053 12698 10888 10045 11549 13767 12434 13116 14211 12266 12602 15714 13742 12745 10491 10057 10900 11771 11992 11933 14504 11727 11477 13578 11555 11846 11397 10066 10269 14279 13870 13695 14420 11424 9704 12464 14301 13464 9893 11572 12380 16692 16052 16459 14761 13654 13480 18068 16560 14530 10650 11651 13735 13360 17818 20613 16231 13862 12004 17734 15034 12609 12320 10833 11350 13648 14890 16325 18045 15616 11926 16855 15083 12520 12355

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 13 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Pas[t] = + 17765.0666666667 -878.527777777783M1[t] + 54.2838383838423M2[t] + 2641.59545454547M3[t] + 2536.50707070707M4[t] + 4700.31868686869M5[t] + 3777.23030303032M6[t] + 1672.24191919192M7[t] + 375.453535353541M8[t] + 3474.76515151516M9[t] + 2800.07676767677M10[t] + 1927.88838383839M11[t] -69.5116161616161t + 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) 17765.0666666667 1143.005441 15.5424 0 0 M1 -878.527777777783 1417.702817 -0.6197 0.536783 0.268392 M2 54.2838383838423 1417.182837 0.0383 0.969517 0.484758 M3 2641.59545454547 1416.712213 1.8646 0.064979 0.032489 M4 2536.50707070707 1416.290997 1.791 0.076129 0.038064 M5 4700.31868686869 1415.919231 3.3196 0.001233 0.000617 M6 3777.23030303032 1415.596955 2.6683 0.00881 0.004405 M7 1672.24191919192 1415.324203 1.1815 0.240013 0.120007 M8 375.453535353541 1415.101003 0.2653 0.791274 0.395637 M9 3474.76515151516 1414.927378 2.4558 0.015667 0.007834 M10 2800.07676767677 1414.803348 1.9791 0.050371 0.025185 M11 1927.88838383839 1414.728924 1.3627 0.17583 0.087915 t -69.5116161616161 8.37822 -8.2967 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.696874486476466 R-squared 0.485634049901839 Adjusted R-squared 0.42794814895625 F-TEST (value) 8.41859175190675 F-TEST (DF numerator) 12 F-TEST (DF denominator) 107 p-value 5.10929076824596e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3163.37457068026 Sum Squared Residuals 1070742438.16364 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 17848 16817.0272727273 1030.97272727265 2 19592 17680.3272727273 1911.67272727273 3 21092 20198.1272727273 893.872727272738 4 20899 20023.5272727273 875.472727272716 5 25890 22117.8272727272 3772.17272727275 6 24965 21125.2272727273 3839.77272727273 7 22225 18950.7272727273 3274.27272727271 8 20977 17584.4272727273 3392.57272727273 9 22897 20614.2272727273 2282.77272727273 10 22785 19870.0272727273 2914.97272727272 11 22769 18928.3272727273 3840.67272727273 12 19637 16930.9272727273 2706.07272727273 13 20203 15982.8878787879 4220.11212121213 14 20450 16846.1878787879 3603.81212121212 15 23083 19363.9878787879 3719.01212121212 16 21738 19189.3878787879 2548.61212121212 17 26766 21283.6878787879 5482.31212121212 18 25280 20291.0878787879 4988.91212121212 19 22574 18116.5878787879 4457.41212121213 20 22729 16750.2878787879 5978.71212121212 21 21378 19780.0878787879 1597.91212121212 22 22902 19035.8878787879 3866.11212121212 23 24989 18094.1878787879 6894.81212121213 24 21116 16096.7878787879 5019.21212121213 25 15169 15148.7484848485 20.2515151515273 26 15846 16012.0484848485 -166.048484848485 27 20927 18529.8484848485 2397.15151515152 28 18273 18355.2484848485 -82.2484848484826 29 22538 20449.5484848485 2088.45151515152 30 15596 19456.9484848485 -3860.94848484848 31 14034 17282.4484848485 -3248.44848484848 32 11366 15916.1484848485 -4550.14848484848 33 14861 18945.9484848485 -4084.94848484848 34 15149 18201.7484848485 -3052.74848484848 35 13577 17260.0484848485 -3683.04848484848 36 13026 15262.6484848485 -2236.64848484848 37 13190 14314.6090909091 -1124.60909090908 38 13196 15177.9090909091 -1981.90909090909 39 15826 17695.7090909091 -1869.70909090909 40 14733 17521.1090909091 -2788.10909090909 41 16307 19615.4090909091 -3308.40909090909 42 15703 18622.8090909091 -2919.80909090909 43 14589 16448.3090909091 -1859.30909090909 44 12043 15082.0090909091 -3039.00909090909 45 15057 18111.8090909091 -3054.80909090909 46 14053 17367.6090909091 -3314.60909090909 47 12698 16425.9090909091 -3727.90909090909 48 10888 14428.5090909091 -3540.50909090908 49 10045 13480.4696969697 -3435.46969696969 50 11549 14343.7696969697 -2794.7696969697 51 13767 16861.5696969697 -3094.5696969697 52 12434 16686.9696969697 -4252.9696969697 53 13116 18781.2696969697 -5665.2696969697 54 14211 17788.6696969697 -3577.6696969697 55 12266 15614.1696969697 -3348.16969696969 56 12602 14247.8696969697 -1645.86969696970 57 15714 17277.6696969697 -1563.66969696970 58 13742 16533.4696969697 -2791.46969696970 59 12745 15591.7696969697 -2846.76969696970 60 10491 13594.3696969697 -3103.36969696969 61 10057 12646.3303030303 -2589.33030303029 62 10900 13509.6303030303 -2609.63030303030 63 11771 16027.4303030303 -4256.43030303031 64 11992 15852.8303030303 -3860.8303030303 65 11933 17947.1303030303 -6014.1303030303 66 14504 16954.5303030303 -2450.53030303031 67 11727 14780.0303030303 -3053.0303030303 68 11477 13413.7303030303 -1936.73030303030 69 13578 16443.5303030303 -2865.53030303030 70 11555 15699.3303030303 -4144.3303030303 71 11846 14757.6303030303 -2911.6303030303 72 11397 12760.2303030303 -1363.2303030303 73 10066 11812.1909090909 -1746.1909090909 74 10269 12675.4909090909 -2406.49090909091 75 14279 15193.2909090909 -914.290909090911 76 13870 15018.6909090909 -1148.69090909091 77 13695 17112.9909090909 -3417.99090909091 78 14420 16120.3909090909 -1700.39090909091 79 11424 13945.8909090909 -2521.89090909091 80 9704 12579.5909090909 -2875.59090909091 81 12464 15609.3909090909 -3145.39090909091 82 14301 14865.1909090909 -564.190909090909 83 13464 13923.4909090909 -459.49090909091 84 9893 11926.0909090909 -2033.09090909091 85 11572 10978.0515151515 593.948484848494 86 12380 11841.3515151515 538.648484848482 87 16692 14359.1515151515 2332.84848484848 88 16052 14184.5515151515 1867.44848484848 89 16459 16278.8515151515 180.148484848486 90 14761 15286.2515151515 -525.251515151519 91 13654 13111.7515151515 542.248484848486 92 13480 11745.4515151515 1734.54848484848 93 18068 14775.2515151515 3292.74848484848 94 16560 14031.0515151515 2528.94848484848 95 14530 13089.3515151515 1440.64848484848 96 10650 11091.9515151515 -441.951515151513 97 11651 10143.9121212121 1507.08787878789 98 13735 11007.2121212121 2727.78787878788 99 13360 13525.0121212121 -165.012121212124 100 17818 13350.4121212121 4467.58787878788 101 20613 15444.7121212121 5168.28787878788 102 16231 14452.1121212121 1778.88787878787 103 13862 12277.6121212121 1584.38787878788 104 12004 10911.3121212121 1092.68787878788 105 17734 13941.1121212121 3792.88787878788 106 15034 13196.9121212121 1837.08787878788 107 12609 12255.2121212121 353.787878787878 108 12320 10257.8121212121 2062.18787878788 109 10833 9309.77272727272 1523.22727272728 110 11350 10173.0727272727 1176.92727272727 111 13648 12690.8727272727 957.12727272727 112 14890 12516.2727272727 2373.72727272727 113 16325 14610.5727272727 1714.42727272727 114 18045 13617.9727272727 4427.02727272727 115 15616 11443.4727272727 4172.52727272727 116 11926 10077.1727272727 1848.82727272727 117 16855 13106.9727272727 3748.02727272727 118 15083 12362.7727272727 2720.22727272727 119 12520 11421.0727272727 1098.92727272727 120 12355 9423.67272727273 2931.32727272727 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.00818438454552234 0.0163687690910447 0.991815615454478 17 0.00191871134144153 0.00383742268288306 0.998081288658558 18 0.000843664412573123 0.00168732882514625 0.999156335587427 19 0.000304198990097864 0.000608397980195729 0.999695801009902 20 0.000118282251684662 0.000236564503369325 0.999881717748315 21 0.000876826611342441 0.00175365322268488 0.999123173388658 22 0.000445569174432122 0.000891138348864245 0.999554430825568 23 0.000800458291199748 0.00160091658239950 0.9991995417088 24 0.000860538718411284 0.00172107743682257 0.999139461281589 25 0.0802174725886553 0.160434945177311 0.919782527411345 26 0.249016051761472 0.498032103522943 0.750983948238528 27 0.298282228599881 0.596564457199763 0.701717771400119 28 0.364340381301364 0.728680762602727 0.635659618698636 29 0.651688023262953 0.696623953474094 0.348311976737047 30 0.984401426748668 0.0311971465026634 0.0155985732513317 31 0.997414081562938 0.00517183687412452 0.00258591843706226 32 0.999832663873947 0.000334672252106273 0.000167336126053136 33 0.999862099930116 0.000275800139767683 0.000137900069883841 34 0.99990451631522 0.000190967369559642 9.5483684779821e-05 35 0.99997778196126 4.44360774804318e-05 2.22180387402159e-05 36 0.9999840038822 3.19922355998216e-05 1.59961177999108e-05 37 0.999988032334065 2.39353318695689e-05 1.19676659347844e-05 38 0.999985799264255 2.84014714892668e-05 1.42007357446334e-05 39 0.999986963840785 2.60723184297822e-05 1.30361592148911e-05 40 0.999978566033436 4.28679331270899e-05 2.14339665635450e-05 41 0.999982625231477 3.47495370451106e-05 1.73747685225553e-05 42 0.999973962572518 5.20748549638256e-05 2.60374274819128e-05 43 0.999978121836116 4.37563277688306e-05 2.18781638844153e-05 44 0.999968417134956 6.3165730087111e-05 3.15828650435555e-05 45 0.999949272263952 0.000101455472096070 5.07277360480351e-05 46 0.999920315631397 0.000159368737206339 7.96843686031697e-05 47 0.999896914230415 0.000206171539170233 0.000103085769585116 48 0.999854358519346 0.000291282961307989 0.000145641480653994 49 0.999765990836591 0.000468018326817560 0.000234009163408780 50 0.999698710924231 0.000602578151537009 0.000301289075768504 51 0.999580920500627 0.000838158998746189 0.000419079499373094 52 0.999350270472505 0.00129945905499026 0.000649729527495128 53 0.999232401664142 0.00153519667171529 0.000767598335857645 54 0.998807875821734 0.00238424835653175 0.00119212417826587 55 0.998225640670093 0.00354871865981345 0.00177435932990673 56 0.998779519754862 0.00244096049027526 0.00122048024513763 57 0.999186046144065 0.00162790771187072 0.000813953855935362 58 0.998928741950917 0.00214251609816625 0.00107125804908312 59 0.998668991552664 0.00266201689467289 0.00133100844733644 60 0.998155097069281 0.00368980586143771 0.00184490293071886 61 0.997806767423186 0.00438646515362794 0.00219323257681397 62 0.997301961815947 0.00539607636810513 0.00269803818405256 63 0.99607113982475 0.00785772035050139 0.00392886017525070 64 0.995940189732812 0.00811962053437671 0.00405981026718836 65 0.997415024088687 0.00516995182262609 0.00258497591131304 66 0.996822166067957 0.00635566786408571 0.00317783393204285 67 0.995626522773585 0.00874695445283037 0.00437347722641518 68 0.995125046655314 0.00974990668937144 0.00487495334468572 69 0.994497927068464 0.0110041458630731 0.00550207293153656 70 0.994541777168753 0.0109164456624937 0.00545822283124683 71 0.992058663413326 0.0158826731733471 0.00794133658667354 72 0.9919915165329 0.0160169669341984 0.00800848346709921 73 0.990920861891885 0.0181582762162294 0.00907913810811468 74 0.989364381402027 0.0212712371959462 0.0106356185979731 75 0.98911087871045 0.0217782425791011 0.0108891212895505 76 0.990229106668796 0.0195417866624073 0.00977089333120367 77 0.993200672667942 0.0135986546641165 0.00679932733205827 78 0.992027849060926 0.0159443018781484 0.0079721509390742 79 0.99246799077802 0.0150640184439606 0.00753200922198032 80 0.992545628150344 0.0149087436993121 0.00745437184965606 81 0.999151493622438 0.00169701275512416 0.00084850637756208 82 0.99916100150604 0.00167799698792169 0.000838998493960847 83 0.998772534026897 0.00245493194620558 0.00122746597310279 84 0.998891221352352 0.00221755729529662 0.00110877864764831 85 0.99862947267147 0.00274105465706078 0.00137052732853039 86 0.99825767131596 0.00348465736808016 0.00174232868404008 87 0.999324343043245 0.00135131391350896 0.000675656956754481 88 0.999196721108208 0.00160655778358434 0.000803278891792172 89 0.999345073759949 0.00130985248010228 0.000654926240051142 90 0.999658767015484 0.000682465969032083 0.000341232984516042 91 0.999643220720453 0.000713558559094867 0.000356779279547434 92 0.999397618002257 0.00120476399548691 0.000602381997743457 93 0.99909925271642 0.00180149456716083 0.000900747283580413 94 0.998506225881276 0.00298754823744759 0.00149377411872379 95 0.99769085465754 0.00461829068491866 0.00230914534245933 96 0.997782378825144 0.0044352423497124 0.0022176211748562 97 0.995179844031834 0.00964031193633185 0.00482015596816592 98 0.993274387927807 0.0134512241443856 0.00672561207219282 99 0.98539852261549 0.0292029547690191 0.0146014773845096 100 0.985883979880737 0.0282320402385253 0.0141160201192626 101 0.999372705380494 0.00125458923901273 0.000627294619506364 102 0.99916843884505 0.00166312230989766 0.00083156115494883 103 0.999880302660346 0.000239394679308196 0.000119697339654098 104 0.998616946890175 0.00276610621964979 0.00138305310982490 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 67 0.752808988764045 NOK 5% type I error level 84 0.943820224719101 NOK 10% type I error level 84 0.943820224719101 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292063965uyzbahwa5bhf4qa/10jbq41292064021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292063965uyzbahwa5bhf4qa/10jbq41292064021.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292063965uyzbahwa5bhf4qa/1data1292064021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292063965uyzbahwa5bhf4qa/1data1292064021.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292063965uyzbahwa5bhf4qa/2data1292064021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292063965uyzbahwa5bhf4qa/2data1292064021.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292063965uyzbahwa5bhf4qa/351sv1292064021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292063965uyzbahwa5bhf4qa/351sv1292064021.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292063965uyzbahwa5bhf4qa/451sv1292064021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292063965uyzbahwa5bhf4qa/451sv1292064021.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292063965uyzbahwa5bhf4qa/5yb9y1292064021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292063965uyzbahwa5bhf4qa/5yb9y1292064021.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292063965uyzbahwa5bhf4qa/6yb9y1292064021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292063965uyzbahwa5bhf4qa/6yb9y1292064021.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292063965uyzbahwa5bhf4qa/79kqj1292064021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292063965uyzbahwa5bhf4qa/79kqj1292064021.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292063965uyzbahwa5bhf4qa/89kqj1292064021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292063965uyzbahwa5bhf4qa/89kqj1292064021.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292063965uyzbahwa5bhf4qa/99kqj1292064021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292063965uyzbahwa5bhf4qa/99kqj1292064021.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

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by