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WS6Q2nothing

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Sat, 17 Nov 2007 05:05:55 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2007/Nov/17/t1195300915ntxs10jkvcs2q83.htm/, Retrieved Sat, 17 Nov 2007 13:01:56 +0100

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

-183,9235445 0 -177,0726091 0 -228,6351091 0 -237,4476091 0 -127,7601091 0 -193,0101091 0 -220,6351091 0 -164,5101091 0 -268,3226091 0 -333,6976091 0 -34,26010911 0 -154,8851091 0 -97,74528053 0 101,1056549 0 2,543154874 0 -43,26934513 0 -163,5818451 0 -162,8318451 0 46,54315487 0 26,66815487 0 -107,1443451 0 42,48065487 0 76,91815487 0 196,2931549 0 201,4329835 0 12,28391886 0 -0,278581137 0 42,90891886 0 87,59641886 0 84,34641886 0 57,72141886 0 173,8464189 0 -185,9660811 0 47,65891886 0 89,09641886 0 -68,52858114 0 272,6112475 0 146,4621829 0 162,8996829 0 10,08718285 0 279,7746829 0 212,5246829 0 248,8996829 0 -41,97531715 0 -5,787817149 0 52,83718285 0 274,2746829 0 414,6496829 0 310,7895114 0 362,6404468 0 26,07794684 0 403,2654468 0 327,9529468 0 193,7029468 0 317,0779468 0 202,2029468 0 321,3904468 0 178,0154468 0 16,45294684 0 -68,17205316 0 -157,0322246 0 -76,18128917 0 -81,74378917 0 -134,5562892 0 77,13121083 0 199,8812108 0 105,2562108 0 198,3812108 0 262,5687108 0 196,1937108 0 11,63121083 0 -145,9937892 0 -166,8539606 0 -202,0030252 0 43,43447482 0 -113,3780252 0 -113,6905252 0 -155,9405252 0 -210,5655252 0 -124,4405252 0 -64,25302518 0 -298,6280252 0 -154,1905252 0 23,18447482 0 -249,6756966 0 118,1752388 0 -180,3872612 0 -79,19976119 0 -81,51226119 0 -246,7622612 0 -105,3872612 0 -319,2622612 0 -72,07476119 0 -90,44976119 0 -80,01226119 0 119,3627388 0 -53,49743261 0 -114,6464972 0 -155,2089972 0 -50,02149721 0 -196,3339972 0 -14,58399721 0 -82,20899721 0 17,91600279 0 -162,8964972 0 -132,2714972 0 -16,83399721 0 81,54100279 0 275,6808314 0 -32,46823322 0 17,96926678 0 27,15676678 0 -123,1557332 0 108,5942668 0 67,96926678 0 34,09426678 0 -13,71823322 0 -113,0932332 0 54,34426678 0 149,7192668 0 153,8590954 0 -28,28996923 0 238,1475308 0 50,33503077 0 8,022530771 0 -61,22746923 0 -140,8524692 0 -28,72746923 0 9,460030771 0 -121,9149692 0 41,52253077 0 115,8975308 0 27,03735936 0 -91,11170524 0 3,325794759 0 -29,48670524 0 -73,79920524 0 50,95079476 0 -86,67420524 0 -9,54920524 0 -66,36170524 0 73,26329476 0 -216,2992052 0 -128,9242052 0 -142,7843767 0 27,06655875 0 60,50405875 0 35,69155875 0 16,37905875 0 -64,87094125 0 115,5040587 0 -30,37094125 0 87,81655875 0 205,4415587 0 -64,12094125 0 -322,7459413 0 -139,6061127 0 35,24482274 0 -4,317677263 0 17,86982274 0 2,557322737 0 129,3073227 0 -16,31767726 0 164,8073227 0 21,99482274 0 138,6198227 0 87,05732274 0 51,43232274 0 -80,42784867 0 -105,1918797 1 5,245620328 1 68,43312033 1 -0,879379672 1 -105,1293797 1 -82,75437967 1 -132,6293797 1 102,5581203 1 23,18312033 1 -180,3793797 1 -267,0043797 1 30,13544892 1 23,98638432 1 90,42388432 1 31,61138432 1 81,29888432 1 25,04888432 1 -13,57611568 1 33,54888432 1 140,7363843 1 132,3613843 1 94,79888432 1 4,173884316 1

Text written by user:

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 compuational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 7 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation y[t] = + 1.77500390610615e-11 -6.88731608018628e-09x[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) 1.77500390610615e-11 11.348159 0 1 0.5 x -6.88731608018628e-09 32.787788 0 1 0.5 Multiple Linear Regression - Regression Statistics Multiple R 1.52391567063411e-11 R-squared 2.32231897120422e-22 Adjusted R-squared -0.00526315789473686 F-TEST (value) 4.41240604528802e-20 F-TEST (DF numerator) 1 F-TEST (DF denominator) 190 p-value 0.999999999832619 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 147.526070314389 Sum Squared Residuals 4135148.87025712 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 -183.9235445 1.77635683940025e-11 -183.923544500018 2 -177.0726091 1.77351466845721e-11 -177.072609100018 3 -228.6351091 1.77351466845721e-11 -228.635109100018 4 -237.4476091 1.77351466845721e-11 -237.447609100018 5 -127.7601091 1.77635683940025e-11 -127.760109100018 6 -193.0101091 1.77351466845721e-11 -193.010109100018 7 -220.6351091 1.77351466845721e-11 -220.635109100018 8 -164.5101091 1.77351466845721e-11 -164.510109100018 9 -268.3226091 1.77351466845721e-11 -268.322609100018 10 -333.6976091 1.77919901034329e-11 -333.697609100018 11 -34.26010911 1.77493575392873e-11 -34.2601091100178 12 -154.8851091 1.77351466845721e-11 -154.885109100018 13 -97.74528053 1.77635683940025e-11 -97.7452805300178 14 101.1056549 1.77635683940025e-11 101.105654899982 15 2.543154874 1.77489134500775e-11 2.54315487398225 16 -43.26934513 1.77493575392873e-11 -43.2693451300177 17 -163.5818451 1.77351466845721e-11 -163.581845100018 18 -162.8318451 1.77351466845721e-11 -162.831845100018 19 46.54315487 1.77493575392873e-11 46.5431548699823 20 26.66815487 1.77564629666449e-11 26.6681548699822 21 -107.1443451 1.77635683940025e-11 -107.144345100018 22 42.48065487 1.77493575392873e-11 42.4806548699823 23 76.91815487 1.77635683940025e-11 76.9181548699822 24 196.2931549 1.77351466845721e-11 196.293154899982 25 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248.899682899982 44 -41.97531715 1.77493575392873e-11 -41.9753171500178 45 -5.787817149 1.7750245717707e-11 -5.78781714901775 46 52.83718285 1.77635683940025e-11 52.8371828499822 47 274.2746829 1.77919901034329e-11 274.274682899982 48 414.6496829 1.77919901034329e-11 414.649682899982 49 310.7895114 1.77919901034329e-11 310.789511399982 50 362.6404468 1.77919901034329e-11 362.640446799982 51 26.07794684 1.77564629666449e-11 26.0779468399822 52 403.2654468 1.77919901034329e-11 403.265446799982 53 327.9529468 1.77919901034329e-11 327.952946799982 54 193.7029468 1.77351466845721e-11 193.702946799982 55 317.0779468 1.77919901034329e-11 317.077946799982 56 202.2029468 1.77351466845721e-11 202.202946799982 57 321.3904468 1.77919901034329e-11 321.390446799982 58 178.0154468 1.77351466845721e-11 178.015446799982 59 16.45294684 1.77493575392873e-11 16.4529468399822 60 -68.17205316 1.77493575392873e-11 -68.1720531600178 61 -157.0322246 1.77351466845721e-11 -157.032224600018 62 -76.18128917 1.77493575392873e-11 -76.1812891700177 63 -81.74378917 1.77635683940025e-11 -81.7437891700178 64 -134.5562892 1.77635683940025e-11 -134.556289200018 65 77.13121083 1.77635683940025e-11 77.1312108299822 66 199.8812108 1.77351466845721e-11 199.881210799982 67 105.2562108 1.77635683940025e-11 105.256210799982 68 198.3812108 1.77351466845721e-11 198.381210799982 69 262.5687108 1.77919901034329e-11 262.568710799982 70 196.1937108 1.77351466845721e-11 196.193710799982 71 11.63121083 1.77475811824479e-11 11.6312108299823 72 -145.9937892 1.77351466845721e-11 -145.993789200018 73 -166.8539606 1.77351466845721e-11 -166.853960600018 74 -202.0030252 1.77351466845721e-11 -202.003025200018 75 43.43447482 1.77493575392873e-11 43.4344748199822 76 -113.3780252 1.77635683940025e-11 -113.378025200018 77 -113.6905252 1.77635683940025e-11 -113.690525200018 78 -155.9405252 1.77351466845721e-11 -155.940525200018 79 -210.5655252 1.77351466845721e-11 -210.565525200018 80 -124.4405252 1.77635683940025e-11 -124.440525200018 81 -64.25302518 1.77493575392873e-11 -64.2530251800177 82 -298.6280252 1.77919901034329e-11 -298.628025200018 83 -154.1905252 1.77351466845721e-11 -154.190525200018 84 23.18447482 1.77529102529661e-11 23.1844748199822 85 -249.6756966 1.77351466845721e-11 -249.675696600018 86 118.1752388 1.77351466845721e-11 118.175238799982 87 -180.3872612 1.77351466845721e-11 -180.387261200018 88 -79.19976119 1.77635683940025e-11 -79.1997611900178 89 -81.51226119 1.77635683940025e-11 -81.5122611900178 90 -246.7622612 1.77351466845721e-11 -246.762261200018 91 -105.3872612 1.77635683940025e-11 -105.387261200018 92 -319.2622612 1.77919901034329e-11 -319.262261200018 93 -72.07476119 1.77493575392873e-11 -72.0747611900178 94 -90.44976119 1.77635683940025e-11 -90.4497611900178 95 -80.01226119 1.77635683940025e-11 -80.0122611900178 96 119.3627388 1.77351466845721e-11 119.362738799982 97 -53.49743261 1.77493575392873e-11 -53.4974326100177 98 -114.6464972 1.77635683940025e-11 -114.646497200018 99 -155.2089972 1.77351466845721e-11 -155.208997200018 100 -50.02149721 1.77493575392873e-11 -50.0214972100177 101 -196.3339972 1.77351466845721e-11 -196.333997200018 102 -14.58399721 1.77493575392873e-11 -14.5839972100177 103 -82.20899721 1.77635683940025e-11 -82.2089972100178 104 17.91600279 1.77529102529661e-11 17.9160027899822 105 -162.8964972 1.77351466845721e-11 -162.896497200018 106 -132.2714972 1.77635683940025e-11 -132.271497200018 107 -16.83399721 1.77493575392873e-11 -16.8339972100177 108 81.54100279 1.77635683940025e-11 81.5410027899822 109 275.6808314 1.77919901034329e-11 275.680831399982 110 -32.46823322 1.77493575392873e-11 -32.4682332200178 111 17.96926678 1.77493575392873e-11 17.9692667799823 112 27.15676678 1.77564629666449e-11 27.1567667799822 113 -123.1557332 1.77635683940025e-11 -123.155733200018 114 108.5942668 1.77635683940025e-11 108.594266799982 115 67.96926678 1.77635683940025e-11 67.9692667799822 116 34.09426678 1.77493575392873e-11 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-91.1117052400178 135 3.325794759 1.77506898069169e-11 3.32579475898225 136 -29.48670524 1.77493575392873e-11 -29.4867052400177 137 -73.79920524 1.77493575392873e-11 -73.7992052400178 138 50.95079476 1.77422521119297e-11 50.9507947599823 139 -86.67420524 1.77635683940025e-11 -86.6742052400178 140 -9.54920524 1.77493575392873e-11 -9.54920524001775 141 -66.36170524 1.77493575392873e-11 -66.3617052400178 142 73.26329476 1.77635683940025e-11 73.2632947599822 143 -216.2992052 1.77351466845721e-11 -216.299205200018 144 -128.9242052 1.77635683940025e-11 -128.924205200018 145 -142.7843767 1.77635683940025e-11 -142.784376700018 146 27.06655875 1.77529102529661e-11 27.0665587499822 147 60.50405875 1.77564629666449e-11 60.5040587499822 148 35.69155875 1.77493575392873e-11 35.6915587499822 149 16.37905875 1.77529102529661e-11 16.3790587499822 150 -64.87094125 1.77493575392873e-11 -64.8709412500178 151 115.5040587 1.77635683940025e-11 115.504058699982 152 -30.37094125 1.77493575392873e-11 -30.3709412500178 153 87.81655875 1.77635683940025e-11 87.8165587499822 154 205.4415587 1.77351466845721e-11 205.441558699982 155 -64.12094125 1.77493575392873e-11 -64.1209412500178 156 -322.7459413 1.77919901034329e-11 -322.745941300018 157 -139.6061127 1.77635683940025e-11 -139.606112700018 158 35.24482274 1.77493575392873e-11 35.2448227399822 159 -4.317677263 1.77484693608676e-11 -4.31767726301775 160 17.86982274 1.77493575392873e-11 17.8698227399823 161 2.557322737 1.77480252716578e-11 2.55732273698225 162 129.3073227 1.77351466845721e-11 129.307322699982 163 -16.31767726 1.77493575392873e-11 -16.3176772600177 164 164.8073227 1.77351466845721e-11 164.807322699982 165 21.99482274 1.77564629666449e-11 21.9948227399822 166 138.6198227 1.77351466845721e-11 138.619822699982 167 87.05732274 1.77635683940025e-11 87.0573227399822 168 51.43232274 1.77422521119297e-11 51.4323227399823 169 -80.42784867 1.77635683940025e-11 -80.4278486700178 170 -105.1918797 -6.86955559103808e-09 -105.191879693130 171 5.245620328 -6.86956447282228e-09 5.24562033486956 172 68.43312033 -6.86955559103808e-09 68.4331203368695 173 -0.879379672 -6.86956491691149e-09 -0.879379665130435 174 -105.1293797 -6.86955559103808e-09 -105.129379693130 175 -82.75437967 -6.86955559103808e-09 -82.7543796631305 176 -132.6293797 -6.86958401274751e-09 -132.629379693130 177 102.5581203 -6.86955559103808e-09 102.558120306870 178 23.18312033 -6.86956624917912e-09 23.1831203368696 179 -180.3793797 -6.86955559103808e-09 -180.379379693130 180 -267.0043797 -6.86952716932865e-09 -267.004379693130 181 30.13544892 -6.86956624917912e-09 30.1354489268696 182 23.98638432 -6.86956624917912e-09 23.9863843268696 183 90.42388432 -6.86955559103808e-09 90.4238843268696 184 31.61138432 -6.86956624917912e-09 31.6113843268696 185 81.29888432 -6.86955559103808e-09 81.2988843268696 186 25.04888432 -6.86956624917912e-09 25.0488843268696 187 -13.57611568 -6.86956269646544e-09 -13.5761156731304 188 33.54888432 -6.8695698018928e-09 33.5488843268696 189 140.7363843 -6.86955559103808e-09 140.736384306870 190 132.3613843 -6.86955559103808e-09 132.361384306870 191 94.79888432 -6.86955559103808e-09 94.7988843268696 192 4.173884316 -6.86956269646544e-09 4.17388432286956

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195300915ntxs10jkvcs2q83/1nkjz1195301144.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195300915ntxs10jkvcs2q83/1nkjz1195301144.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195300915ntxs10jkvcs2q83/2l6eo1195301144.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195300915ntxs10jkvcs2q83/2l6eo1195301144.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195300915ntxs10jkvcs2q83/3t0sv1195301144.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195300915ntxs10jkvcs2q83/3t0sv1195301144.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195300915ntxs10jkvcs2q83/4g8tn1195301144.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195300915ntxs10jkvcs2q83/4g8tn1195301144.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195300915ntxs10jkvcs2q83/579g61195301144.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195300915ntxs10jkvcs2q83/579g61195301144.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195300915ntxs10jkvcs2q83/623n01195301144.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195300915ntxs10jkvcs2q83/623n01195301144.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195300915ntxs10jkvcs2q83/7oidp1195301144.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195300915ntxs10jkvcs2q83/7oidp1195301144.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195300915ntxs10jkvcs2q83/81pae1195301144.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195300915ntxs10jkvcs2q83/81pae1195301144.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195300915ntxs10jkvcs2q83/92lh81195301144.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/17/t1195300915ntxs10jkvcs2q83/92lh81195301144.ps (open in new window)

Parameters:

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

library(lattice) 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)) 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$resid, main='Residual Normal Q-Q Plot') grid() dev.off() (myerror <- as.ts(mysum$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() 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')

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Software written by Ed van Stee & Patrick Wessa

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