Displaying 5 results from an estimated 5 matches for "niesterok".
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nesterov
2009 Apr 28
2
correlation coefficient
Hello,
I would like to get a correlation coefficient (R-squared) for my model.
I don't know how to calculate it in R.
What I've done so far:
x<-8.5:32.5 #Vektor x
y<-c(NA ,5.88 , 6.95 , 7.2 , 7.66 , 8.02 , 8.44 , 9.06, 9.65, 10.22 ,
10.63 ,11.06, 11.37, 11.91 ,12.28, 12.69 ,13.07 , 13.5 , 13.3 ,14.14 , NA , NA , NA , NA , NA) #Vektor y
2009 Apr 08
2
Null-Hypothesis
Hello R users,
I've used the following help two compare two regression line slopes.
Wanted to test if they differ significantly:
Hi,
I've made a research about how to compare two regression line slopes
(of y versus x for 2 groups, "group" being a factor ) using R.
I knew the method based on the following statement :
t = (b1 - b2) / sb1,b2
where b1 and b2 are the two slope
2009 Apr 02
2
finding best fitting model
Hello,
Is there a function in r to find the best fitting model for a set of data?
I would like to know if my data are related exponentially,linearly or if there is a logarithmic correlation between my x and y values.
To get a better imagination I've added the graphics at the end of this mail
as an attachment.
Thanks, Ben
--
2009 Apr 01
1
Discriminant analyse
Hi everyone,
I intend to do a discriminant analyse for 2 measures(eye diameter and body length) and for different areas to show differences between those areas if there are any. The raw data (eye diameter, body length) make one cloud of points so it seems there aren't any differences between those areas.
If I take the mean values (eye diameter) for length classes, I nearly get
linear functions
2009 Apr 06
1
Comparing 2 slopes of 2 regression lines
Hello everyone,
I would like to test two regression slopes:do they differ significantly?The data and commands I've used so far:
x<-8.5:32.5 #Vektor x
y<-c( NA , NA , 5.67 , 6.53 , 6.83, 7.41 , 7.93 , 8.5 , 8.86, 9.46 , 9.82 , 10 ,10.35 , 10.7 ,11.03 ,11.37 ,11.61 ,11.84, 12.12, 12.39 ,12.67 ,12.96, 13.28 ,13.47, 13.65) #Vektor y
(regression<-lm(y~x))
summary(regression)