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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 16 Dec 2014 13:58:58 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/16/t1418749589ps3f6rcwkzt1gx9.htm/, Retrieved Thu, 16 May 2024 15:32:08 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269846, Retrieved Thu, 16 May 2024 15:32:08 +0000

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Estimated Impact

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [] [2014-12-16 13:58:58] [baa7d013c3374cabca6c222951a47a9f] [Current]

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1	21	26	50	4	13	12	13	13	21	2	149	96	18	68	86	12,9
0	23	51	68	9	NA	NA	NA	NA	26	NA	152	75	7	55	62	7,4
1	22	57	62	4	8	8	13	16	22	NA	139	70	31	39	70	12,2
1	21	37	54	5	14	11	11	11	22	0	148	88	39	32	71	12,8
0	21	67	71	4	16	13	14	10	18	0	158	114	46	62	108	7,4
1	21	43	54	4	14	11	15	9	23	4	128	69	31	33	64	6,7
0	21	52	65	9	13	10	14	8	12	0	224	176	67	52	119	12,6
0	21	52	73	8	15	7	11	26	20	-1	159	114	35	62	97	14,8
1	23	43	52	11	13	10	13	10	22	0	105	121	52	77	129	13,3
1	22	84	84	4	20	15	16	10	21	1	159	110	77	76	153	11,1
0	25	67	42	4	17	12	14	8	19	0	167	158	37	41	78	8,2
1	21	49	66	6	15	12	14	13	22	3	165	116	32	48	80	11,4
0	23	70	65	4	16	10	15	11	15	-1	159	181	36	63	99	6,4
0	22	52	78	8	12	10	15	8	20	NA	119	77	38	30	68	10,6
1	21	58	73	4	17	14	13	12	19	4	176	141	69	78	147	12,0
0	21	68	75	4	11	6	14	24	18	1	54	35	21	19	40	6,3
0	25	62	72	11	16	12	11	21	15	0	91	80	26	31	57	11,3
1	21	43	66	4	16	14	12	5	20	-2	163	152	54	66	120	11,9
1	21	56	70	4	15	11	14	14	21	-4	124	97	36	35	71	9,3
1	20	56	61	6	13	8	13	11	21	NA	137	99	42	42	84	9,6
0	24	74	81	6	14	12	12	9	15	2	121	84	23	45	68	10,0
1	23	65	71	4	19	15	15	8	16	NA	153	68	34	21	55	6,4
1	21	63	69	8	16	13	15	17	23	2	148	101	112	25	137	13,8
1	24	58	71	5	17	11	14	18	21	-4	221	107	35	44	79	10,8
1	23	57	72	4	10	12	14	16	18	NA	188	88	47	69	116	13,8
0	21	63	68	9	15	7	12	23	25	2	149	112	47	54	101	11,7
1	22	53	70	4	14	11	12	9	9	2	244	171	37	74	111	10,9
0	20	57	68	7	14	7	12	14	30	0	148	137	109	80	189	16,1
0	18	51	61	10	16	12	15	13	20	NA	92	77	24	42	66	13,4
1	21	64	67	4	15	12	14	10	23	-3	150	66	20	61	81	9,9
0	22	53	76	4	17	13	16	8	16	2	153	93	22	41	63	11,5
1	22	29	70	7	14	9	12	10	16	0	94	105	23	46	69	8,3
0	21	54	60	12	16	11	12	19	19	4	156	131	32	39	71	11,7
0	23	51	77	4	NA	NA	NA	NA	25	NA	146	89	7	63	70	6,1
1	21	58	72	7	15	12	14	11	25	2	132	102	30	34	64	9,0
0	25	43	69	5	16	15	16	16	18	NA	161	161	92	51	143	9,7
1	22	51	71	8	16	12	15	12	23	2	105	120	43	42	85	10,8
1	22	53	62	5	10	6	12	11	21	NA	97	127	55	31	86	10,3
0	20	54	70	4	8	5	14	11	10	-4	151	77	16	39	55	10,4
0	21	56	64	9	17	13	13	10	14	3	131	108	49	20	69	12,7
0	21	61	58	7	14	11	14	13	22	NA	166	85	71	49	120	9,3
1	21	47	76	4	10	6	16	14	26	2	157	168	43	53	96	11,8
0	22	39	52	4	14	12	12	8	23	NA	111	48	29	31	60	5,9
1	21	48	59	4	12	10	14	11	23	NA	145	152	56	39	95	11,4
1	24	50	68	4	16	6	15	11	24	-1	162	75	46	54	100	13,0
1	22	35	76	4	16	12	13	13	24	-3	163	107	19	49	68	10,8
1	22	30	65	7	16	11	16	15	18	0	59	62	23	34	57	12,3
0	21	68	67	4	8	6	16	15	23	1	187	121	59	46	105	11,3
1	22	49	59	7	16	12	12	16	15	NA	109	124	30	55	85	11,8
0	19	61	69	4	15	12	12	12	19	NA	90	72	61	42	103	7,9
1	22	67	76	4	8	8	16	12	16	NA	105	40	7	50	57	12,7
0	23	47	63	4	13	10	12	17	25	NA	83	58	38	13	51	12,3
0	20	56	75	4	14	11	15	14	23	-3	116	97	32	37	69	11,6
0	20	50	63	8	13	7	12	15	17	NA	42	88	16	25	41	6,7
0	23	43	60	4	16	12	13	12	19	3	148	126	19	30	49	10,9
0	20	67	73	4	19	13	12	13	21	0	155	104	22	28	50	12,1
0	23	62	63	4	19	14	14	7	18	0	125	148	48	45	93	13,3
1	21	57	70	4	14	12	14	8	27	0	116	146	23	35	58	10,1
1	22	41	75	7	15	6	11	16	21	NA	128	80	26	28	54	5,7
1	21	54	66	12	13	14	10	20	13	3	138	97	33	41	74	14,3
0	21	45	63	4	10	10	12	14	8	NA	49	25	9	6	15	8,0
0	19	48	63	4	16	12	11	10	29	NA	96	99	24	45	69	13,3
0	22	61	64	4	15	11	16	16	28	0	164	118	34	73	107	9,3
0	21	56	70	5	11	10	14	11	23	2	162	58	48	17	65	12,5
0	21	41	75	15	9	7	14	26	21	-1	99	63	18	40	58	7,6
1	21	43	61	5	16	12	15	9	19	NA	202	139	43	64	107	15,9
1	21	53	60	10	12	7	15	15	19	3	186	50	33	37	70	9,2
1	21	44	62	9	12	12	14	12	20	NA	66	60	28	25	53	9,1
1	21	66	73	8	14	12	13	21	18	NA	183	152	71	65	136	11,1
0	22	58	61	4	14	10	11	20	19	NA	214	142	26	100	126	13,0
1	22	46	66	5	13	10	16	20	17	2	188	94	67	28	95	14,5
0	18	37	64	4	15	12	12	10	19	NA	104	66	34	35	69	12,2
1	21	51	59	9	17	12	15	15	25	2	177	127	80	56	136	12,3
1	23	51	64	4	14	12	14	10	19	NA	126	67	29	29	58	11,4
0	19	56	60	10	11	8	15	16	22	NA	76	90	16	43	59	8,8
1	19	66	56	4	9	10	14	9	23	NA	99	75	59	59	118	14,6
0	23	45	66	7	NA	NA	NA	NA	26	NA	157	96	58	52	110	7,3
0	21	37	78	4	7	5	13	17	14	-2	139	128	32	50	82	12,6
0	21	59	53	6	13	10	6	10	28	NA	78	41	47	3	50	NA
0	21	42	67	7	15	10	12	19	16	0	162	146	43	59	102	13,0
0	21	38	59	5	12	12	12	13	24	-2	108	69	38	27	65	12,6
0	20	66	66	4	15	11	14	8	20	0	159	186	29	61	90	13,2
1	19	34	68	4	14	9	14	11	12	NA	74	81	36	28	64	9,9
0	21	53	71	4	16	12	15	9	24	6	110	85	32	51	83	7,7
0	19	49	66	4	14	11	11	12	22	0	96	54	35	35	70	10,5
1	19	55	73	4	13	10	13	10	12	NA	116	46	21	29	50	13,4
1	19	49	72	4	16	12	14	9	22	-2	87	106	29	48	77	10,9
1	20	59	71	6	13	10	16	14	20	1	97	34	12	25	37	4,3
1	19	40	59	10	16	9	13	14	10	0	127	60	37	44	81	10,3
0	19	58	64	7	16	11	14	10	23	NA	106	95	37	64	101	11,8
1	19	60	66	4	16	12	16	8	17	NA	80	57	47	32	79	11,2
1	20	63	78	4	10	7	11	13	22	2	74	62	51	20	71	11,4
0	19	56	68	7	12	11	13	9	24	NA	91	36	32	28	60	8,6
0	18	54	73	4	12	12	13	14	18	NA	133	56	21	34	55	13,2
0	19	52	62	8	12	6	15	8	21	NA	74	54	13	31	44	12,6
0	21	34	65	11	12	9	12	16	20	2	114	64	14	26	40	5,6
1	18	69	68	6	19	15	13	14	20	NA	140	76	-2	58	56	9,9
0	18	32	65	14	14	10	12	14	22	-3	95	98	20	23	43	8,8
0	19	48	60	5	13	11	14	8	19	NA	98	88	24	21	45	7,7
0	21	67	71	4	16	12	14	11	20	1	121	35	11	21	32	9,0
0	20	58	65	8	15	12	16	11	26	NA	126	102	23	33	56	7,3
0	24	57	68	9	12	12	15	13	23	NA	98	61	24	16	40	11,4
0	22	42	64	4	8	11	14	12	24	NA	95	80	14	20	34	13,6
1	21	64	74	4	10	9	13	13	21	NA	110	49	52	37	89	7,9
1	21	58	69	5	16	11	14	9	21	NA	70	78	15	35	50	10,7
1	19	66	76	4	16	12	15	10	19	NA	102	90	23	33	56	10,3
0	19	26	68	5	10	12	14	12	8	NA	86	45	19	27	46	8,3
0	20	61	72	4	18	14	12	11	17	-4	130	55	35	41	76	9,6
1	18	52	67	4	12	8	7	13	20	NA	96	96	24	40	64	14,2
0	19	51	63	7	16	10	12	17	11	NA	102	43	39	35	74	8,5
1	19	55	59	10	10	9	15	15	8	NA	100	52	29	28	57	13,5
1	20	50	73	4	14	10	12	15	15	NA	94	60	13	32	45	4,9
1	21	60	66	5	12	9	13	14	18	1	52	54	8	22	30	6,4
1	18	56	62	4	11	10	11	10	18	NA	98	51	18	44	62	9,6
1	19	63	69	4	15	12	14	15	19	0	118	51	24	27	51	11,6
0	19	61	66	4	7	11	13	14	19	NA	99	38	19	17	36	11,1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net
R Engine error message	Error in if (gqarr[mypoint - kp3 + 1, 2] < 0.01) numsignificant1 <- numsignificant1 + : missing value where TRUE/FALSE needed In addition: Warning messages: 1: In pf(gq, df[1], df[2], lower.tail = FALSE) : NaNs produced 2: In pf(gq, df[1], df[2]) : NaNs produced 3: In pf(gq, df[1], df[2], lower.tail = FALSE) : NaNs produced 4: In pf(gq, df[1], df[2]) : NaNs produced Execution halted

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
R Engine error message & Error in if (gqarr[mypoint - kp3 + 1, 2] < 0.01) numsignificant1 <- numsignificant1 +  : 
  missing value where TRUE/FALSE needed
In addition: Warning messages:
1: In pf(gq, df[1], df[2], lower.tail = FALSE) : NaNs produced
2: In pf(gq, df[1], df[2]) : NaNs produced
3: In pf(gq, df[1], df[2], lower.tail = FALSE) : NaNs produced
4: In pf(gq, df[1], df[2]) : NaNs produced
Execution halted
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269846&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[ROW][C]R Engine error message[/C][C]Error in if (gqarr[mypoint - kp3 + 1, 2] < 0.01) numsignificant1 <- numsignificant1 +  : 
  missing value where TRUE/FALSE needed
In addition: Warning messages:
1: In pf(gq, df[1], df[2], lower.tail = FALSE) : NaNs produced
2: In pf(gq, df[1], df[2]) : NaNs produced
3: In pf(gq, df[1], df[2], lower.tail = FALSE) : NaNs produced
4: In pf(gq, df[1], df[2]) : NaNs produced
Execution halted
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269846&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269846&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net
R Engine error message	Error in if (gqarr[mypoint - kp3 + 1, 2] < 0.01) numsignificant1 <- numsignificant1 + : missing value where TRUE/FALSE needed In addition: Warning messages: 1: In pf(gq, df[1], df[2], lower.tail = FALSE) : NaNs produced 2: In pf(gq, df[1], df[2]) : NaNs produced 3: In pf(gq, df[1], df[2], lower.tail = FALSE) : NaNs produced 4: In pf(gq, df[1], df[2]) : NaNs produced Execution halted

Parameters (Session):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 17 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '6'
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code