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Data X:
1958 3 315.71 1958 4 317.45 1958 5 317.5 1958 6 317.12 1958 7 315.86 1958 8 314.93 1958 9 313.2 1958 10 312.6 1958 11 313.33 1958 12 314.67 1959 1 315.62 1959 2 316.38 1959 3 316.71 1959 4 317.72 1959 5 318.29 1959 6 318.16 1959 7 316.55 1959 8 314.8 1959 9 313.84 1959 10 313.26 1959 11 314.8 1959 12 315.59 1960 1 316.43 1960 2 316.97 1960 3 317.58 1960 4 319.02 1960 5 320.02 1960 6 319.59 1960 7 318.18 1960 8 315.91 1960 9 314.16 1960 10 313.83 1960 11 315 1960 12 316.19 1961 1 316.93 1961 2 317.7 1961 3 318.54 1961 4 319.48 1961 5 320.58 1961 6 319.77 1961 7 318.58 1961 8 316.79 1961 9 314.8 1961 10 315.38 1961 11 316.1 1961 12 317.01 1962 1 317.94 1962 2 318.55 1962 3 319.68 1962 4 320.63 1962 5 321.01 1962 6 320.55 1962 7 319.58 1962 8 317.4 1962 9 316.26 1962 10 315.42 1962 11 316.69 1962 12 317.7 1963 1 318.74 1963 2 319.08 1963 3 319.86 1963 4 321.39 1963 5 322.24 1963 6 321.47 1963 7 319.74 1963 8 317.77 1963 9 316.21 1963 10 315.99 1963 11 317.12 1963 12 318.31 1964 1 319.57 1964 2 320.08 1964 3 320.75 1964 4 321.8 1964 5 322.24 1964 6 321.89 1964 7 320.44 1964 8 318.7 1964 9 316.7 1964 10 316.79 1964 11 317.79 1964 12 318.71 1965 1 319.44 1965 2 320.44 1965 3 320.89 1965 4 322.13 1965 5 322.16 1965 6 321.87 1965 7 321.39 1965 8 318.8 1965 9 317.81 1965 10 317.3 1965 11 318.87 1965 12 319.42 1966 1 320.62 1966 2 321.59 1966 3 322.39 1966 4 323.87 1966 5 324.01 1966 6 323.75 1966 7 322.4 1966 8 320.37 1966 9 318.64 1966 10 318.1 1966 11 319.78 1966 12 321.08 1967 1 322.06 1967 2 322.5 1967 3 323.04 1967 4 324.42 1967 5 325 1967 6 324.09 1967 7 322.55 1967 8 320.92 1967 9 319.31 1967 10 319.31 1967 11 320.72 1967 12 321.96 1968 1 322.57 1968 2 323.15 1968 3 323.89 1968 4 325.02 1968 5 325.57 1968 6 325.36 1968 7 324.14 1968 8 322.03 1968 9 320.41 1968 10 320.25 1968 11 321.31 1968 12 322.84 1969 1 324 1969 2 324.42 1969 3 325.64 1969 4 326.66 1969 5 327.34 1969 6 326.76 1969 7 325.88 1969 8 323.67 1969 9 322.38 1969 10 321.78 1969 11 322.85 1969 12 324.12 1970 1 325.03 1970 2 325.99 1970 3 326.87 1970 4 328.14 1970 5 328.07 1970 6 327.66 1970 7 326.35 1970 8 324.69 1970 9 323.1 1970 10 323.16 1970 11 323.98 1970 12 325.13 1971 1 326.17 1971 2 326.68 1971 3 327.18 1971 4 327.78 1971 5 328.92 1971 6 328.57 1971 7 327.34 1971 8 325.46 1971 9 323.36 1971 10 323.56 1971 11 324.8 1971 12 326.01 1972 1 326.77 1972 2 327.63 1972 3 327.75 1972 4 329.72 1972 5 330.07 1972 6 329.09 1972 7 328.05 1972 8 326.32 1972 9 324.93 1972 10 325.06 1972 11 326.5 1972 12 327.55 1973 1 328.55 1973 2 329.56 1973 3 330.3 1973 4 331.5 1973 5 332.48 1973 6 332.07 1973 7 330.87 1973 8 329.31 1973 9 327.51 1973 10 327.18 1973 11 328.16 1973 12 328.64 1974 1 329.35 1974 2 330.71 1974 3 331.48 1974 4 332.65 1974 5 333.15 1974 6 332.13 1974 7 330.99 1974 8 329.17 1974 9 327.41 1974 10 327.21 1974 11 328.34 1974 12 329.5 1975 1 330.68 1975 2 331.41 1975 3 331.85 1975 4 333.29 1975 5 333.91 1975 6 333.4 1975 7 331.74 1975 8 329.88 1975 9 328.57 1975 10 328.35 1975 11 329.33 1975 12 330.58 1976 1 331.66 1976 2 332.75 1976 3 333.46 1976 4 334.78 1976 5 334.79 1976 6 334.05 1976 7 332.95 1976 8 330.64 1976 9 328.96 1976 10 328.77 1976 11 330.18 1976 12 331.65 1977 1 332.69 1977 2 333.23 1977 3 334.97 1977 4 336.03 1977 5 336.82 1977 6 336.1 1977 7 334.79 1977 8 332.53 1977 9 331.19 1977 10 331.21 1977 11 332.35 1977 12 333.47 1978 1 335.09 1978 2 335.26 1978 3 336.62 1978 4 337.77 1978 5 338 1978 6 337.98 1978 7 336.48 1978 8 334.37 1978 9 332.33 1978 10 332.4 1978 11 333.76 1978 12 334.83 1979 1 336.21 1979 2 336.64 1979 3 338.13 1979 4 338.96 1979 5 339.02 1979 6 339.2 1979 7 337.6 1979 8 335.56 1979 9 333.93 1979 10 334.12 1979 11 335.26 1979 12 336.77 1980 1 337.8 1980 2 338.28 1980 3 340.04 1980 4 340.86 1980 5 341.47 1980 6 341.26 1980 7 339.34 1980 8 337.45 1980 9 336.1 1980 10 336.05 1980 11 337.21 1980 12 338.29 1981 1 339.36 1981 2 340.51 1981 3 341.57 1981 4 342.56 1981 5 343.01 1981 6 342.52 1981 7 340.71 1981 8 338.51 1981 9 336.96 1981 10 337.13 1981 11 338.58 1981 12 339.91 1982 1 340.92 1982 2 341.69 1982 3 342.87 1982 4 343.83 1982 5 344.3 1982 6 343.42 1982 7 341.85 1982 8 339.82 1982 9 337.98 1982 10 338.09 1982 11 339.24 1982 12 340.67 1983 1 341.42 1983 2 342.67 1983 3 343.45 1983 4 345.08 1983 5 345.76 1983 6 345.32 1983 7 343.93 1983 8 342.08 1983 9 340 1983 10 340.12 1983 11 341.35 1983 12 342.89 1984 1 343.87 1984 2 344.59 1984 3 345.29 1984 4 346.59 1984 5 347.36 1984 6 346.8 1984 7 345.37 1984 8 343.06 1984 9 341.24 1984 10 341.54 1984 11 342.9 1984 12 344.36 1985 1 345.08 1985 2 345.89 1985 3 347.49 1985 4 348.02 1985 5 348.75 1985 6 348.19 1985 7 346.49 1985 8 344.7 1985 9 343.04 1985 10 342.92 1985 11 344.22 1985 12 345.61 1986 1 346.42 1986 2 346.95 1986 3 347.88 1986 4 349.57 1986 5 350.35 1986 6 349.7 1986 7 347.78 1986 8 345.89 1986 9 344.88 1986 10 344.34 1986 11 345.67 1986 12 346.89 1987 1 348.2 1987 2 348.55 1987 3 349.56 1987 4 351.12 1987 5 351.84 1987 6 351.45 1987 7 349.77 1987 8 347.62 1987 9 346.37 1987 10 346.48 1987 11 347.8 1987 12 349.03 1988 1 350.23 1988 2 351.58 1988 3 352.22 1988 4 353.53 1988 5 354.14 1988 6 353.64 1988 7 352.53 1988 8 350.42 1988 9 348.84 1988 10 348.94 1988 11 349.99 1988 12 351.29 1989 1 352.72 1989 2 353.1 1989 3 353.64 1989 4 355.43 1989 5 355.7 1989 6 355.11 1989 7 353.79 1989 8 351.42 1989 9 349.83 1989 10 350.1 1989 11 351.26 1989 12 352.66 1990 1 353.63 1990 2 354.72 1990 3 355.49 1990 4 356.1 1990 5 357.08 1990 6 356.11 1990 7 354.67 1990 8 352.67 1990 9 351.05 1990 10 351.36 1990 11 352.81 1990 12 354.21 1991 1 354.87 1991 2 355.67 1991 3 357 1991 4 358.4 1991 5 359 1991 6 357.99 1991 7 355.96 1991 8 353.78 1991 9 352.2 1991 10 352.22 1991 11 353.7 1991 12 354.98 1992 1 356.08 1992 2 356.84 1992 3 357.73 1992 4 358.91 1992 5 359.45 1992 6 359.19 1992 7 356.72 1992 8 354.77 1992 9 352.8 1992 10 353.21 1992 11 354.15 1992 12 355.39 1993 1 356.76 1993 2 357.17 1993 3 358.26 1993 4 359.17 1993 5 360.07 1993 6 359.41 1993 7 357.36 1993 8 355.29 1993 9 353.96 1993 10 354.03 1993 11 355.27 1993 12 356.7 1994 1 358.05 1994 2 358.8 1994 3 359.67 1994 4 361.13 1994 5 361.48 1994 6 360.6 1994 7 359.2 1994 8 357.23 1994 9 355.42 1994 10 355.89 1994 11 357.41 1994 12 358.74 1995 1 359.73 1995 2 360.61 1995 3 361.6 1995 4 363.05 1995 5 363.62 1995 6 363.03 1995 7 361.55 1995 8 358.94 1995 9 357.93 1995 10 357.8 1995 11 359.22 1995 12 360.42 1996 1 361.83 1996 2 362.94 1996 3 363.91 1996 4 364.28 1996 5 364.93 1996 6 364.7 1996 7 363.31 1996 8 361.15 1996 9 359.41 1996 10 359.34 1996 11 360.62 1996 12 361.96 1997 1 362.81 1997 2 363.87 1997 3 364.25 1997 4 366.02 1997 5 366.47 1997 6 365.37 1997 7 364.1 1997 8 361.89 1997 9 360.05 1997 10 360.49 1997 11 362.21 1997 12 364.12 1998 1 365 1998 2 365.82 1998 3 366.95 1998 4 368.42 1998 5 369.33 1998 6 368.78 1998 7 367.59 1998 8 365.81 1998 9 363.83 1998 10 364.18 1998 11 365.36 1998 12 366.88 1999 1 367.97 1999 2 368.83 1999 3 369.46 1999 4 370.77 1999 5 370.66 1999 6 370.1 1999 7 369.1 1999 8 366.7 1999 9 364.61 1999 10 365.17 1999 11 366.51 1999 12 367.86 2000 1 369.07 2000 2 369.32 2000 3 370.38 2000 4 371.63 2000 5 371.32 2000 6 371.51 2000 7 369.69 2000 8 368.18 2000 9 366.87 2000 10 366.94 2000 11 368.27 2000 12 369.62 2001 1 370.47 2001 2 371.44 2001 3 372.39 2001 4 373.32 2001 5 373.77 2001 6 373.13 2001 7 371.51 2001 8 369.59 2001 9 368.12 2001 10 368.38 2001 11 369.64 2001 12 371.11 2002 1 372.38 2002 2 373.08 2002 3 373.87 2002 4 374.93 2002 5 375.58 2002 6 375.44 2002 7 373.91 2002 8 371.77 2002 9 370.72 2002 10 370.5 2002 11 372.19 2002 12 373.71 2003 1 374.92 2003 2 375.63 2003 3 376.51 2003 4 377.75 2003 5 378.54 2003 6 378.21 2003 7 376.65 2003 8 374.28 2003 9 373.12 2003 10 373.1 2003 11 374.67 2003 12 375.97 2004 1 377.03 2004 2 377.87 2004 3 378.88 2004 4 380.42 2004 5 380.62 2004 6 379.66 2004 7 377.48 2004 8 376.07 2004 9 374.1 2004 10 374.47 2004 11 376.15 2004 12 377.51 2005 1 378.43 2005 2 379.7 2005 3 380.91 2005 4 382.2 2005 5 382.45 2005 6 382.14 2005 7 380.6 2005 8 378.6 2005 9 376.72 2005 10 376.98 2005 11 378.29 2005 12 380.07 2006 1 381.36 2006 2 382.19 2006 3 382.65 2006 4 384.65 2006 5 384.94 2006 6 384.01 2006 7 382.15 2006 8 380.33 2006 9 378.81 2006 10 379.06 2006 11 380.17 2006 12 381.85 2007 1 382.88 2007 2 383.77 2007 3 384.42 2007 4 386.36 2007 5 386.53 2007 6 386.01 2007 7 384.45 2007 8 381.96 2007 9 380.81 2007 10 381.09 2007 11 382.37 2007 12 383.84 2008 1 385.42 2008 2 385.72 2008 3 385.96 2008 4 387.18 2008 5 388.5 2008 6 387.88 2008 7 386.38 2008 8 384.15 2008 9 383.07 2008 10 382.98 2008 11 384.11 2008 12 385.54 2009 1 386.92 2009 2 387.41 2009 3 388.77 2009 4 389.46 2009 5 390.18 2009 6 389.43 2009 7 387.74 2009 8 385.91 2009 9 384.77 2009 10 384.38 2009 11 385.99 2009 12 387.26 2010 1 388.45 2010 2 389.7 2010 3 391.08 2010 4 392.46 2010 5 392.96 2010 6 392.03 2010 7 390.13 2010 8 388.15 2010 9 386.8 2010 10 387.18 2010 11 388.59
Names of X columns:
year month CO2
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
No Linear Trend
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
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') }
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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