How to detect patterns between fields of a distribution in SPSS? Suppose I have the following simplified distribution:
time | value
1    | 2
2    | 4
3    | 8
4    | 16

1    | 1
2    | 3
3    | 9
4    | 27

1    | 40
2    | 20
3    | 10
4    | 5

1    | 12
2    | 1
3    | 99
4    | 23423

These are all part of the same dataset (so one x can have multiple values here, eg. time = 1 corresponds with value = 2,1,40,12). I separated them because the first 3 have an obvious pattern within their slope (2, 3 and 0.5) and the last does not have a pattern in its slope. Time is in days, and the values represent a quantity.
Now, how do I use SPSS to find the elements of a distribution that together form a pattern (any kind of pattern) in their slope? And how can I make it also include elements that almost follow this pattern, but have slight variations (a variation that I can set)?
Any feedback is appreciated. Thanks.
Update:
It looks like SPSS is not the best tool for the task. I am interested in the R language however.
If anyone could recommend any books regarding this field of pattern recognition in R, that would be great.
 A: I think I understand what you are after, but I might be wrong - just to clarify things in advance :)
If you want to find the "distribution" of your data, than R could do this easily, I have no idea about Spss. Though I am not sure about your are really after distributions, as those would only show the probabilities of certain values in your data series not dealing with the order, fitdistr from MASS and fitdist from fitdistrplus package will be your friend. Also, Vito Ricci's paper worths reading in the issue available on CRAN.
A small example assuming you have a data table (data) with your data, and would like to fit the first columns data to normal distribution:
library(fitdistrplus)
fitdist(data[,1],"norm")

If you would like to get the "slopes" of the pattern of a data in a row, as I suppose you are really after, than you need to set up some linear models based on your data. I am sure Spss can do the trick also, but in R look for lm and glm functions. See the manual of the lm to fit linear models.
A small example:
# make up a demo dataset from day 1 to day 10 with 10 values
data <- data.frame(time=1:10, data=c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14))

This would look like:
> data
   time data
1     1 4.17
2     2 5.58
3     3 5.18
4     4 6.11
5     5 4.50
6     6 4.61
7     7 5.17
8     8 4.53
9     9 5.33
10   10 5.14

And fit a simple model on it:
> lm(data)

Call:
lm(formula = data)

Coefficients:
(Intercept)         data  
     4.6613       0.1667  

Which shows the slope being around 0.16667.
