Re: Spss 17 Serial Keygen Generator
Olegario Benford
The way this would happen is if the random number seed is the same on all the machines at the times when the sampling is done. The link @whuber provides gives some discussion of using the two available random number generators and how to set seeds in order to get different results and/or to be able to replicate results.
SPSS Statistics has two pseudo-random number generators, a multiplicative congruential (MC) generator, and a Mersenne Twister (MT) generator. The Mersenne Twister is a more advanced generator and is usually to be preferred, but to maintain easier compatibility with older versions, the MC generator is the default one when you install the program for the first time. In either case, there is a default seed that is reset each time you begin a session. In order to get results that differ when executing the same commands involving random number generation, you need to set the seed to something other than the default. You can set it to either "RANDOM" or to a specific number. Setting it to a specific number lets you replicate results later. RANDOM creates a seed based on the precise time on the computer's clock, and results following this will generally not be replicable, unless you precede commands that require random numbers with a SHOW SEED command, which will print out the current seed setting.
odk_central_to_spss_R.txt (11.0 KB)
Hello @Florian_May
Thank you for your views. I quite agree with the fact that if I have ODK central data in R then analysing the same in R would be a natural choice. As a matter of fact, I started by doing the same.
The next example changes the flips to 100. It also sets the seed for the random number generator. The seed determines the string of random numbers that will be generated. John got the fewest heads (49 out of 100) and Samuel got the most (58 out of 100).
Before you start the multiple imputation procedure, it is possible to set the starting point of the random number generator in SPSS at a fixed value of 950 (in R we use the seed for this). In this way you are able to reproduce results exactly. later. It is also a good idea to store the multiple imputed datasets.
Bootstrapping has a random component. Based on SPSS' random number generator random samples are drawn from your sample with replacement. Therefore, if you repeatedly run an analysis with bootstrapping you will get slightly different results (SE, p-values, confidence intervals) each time. If you want to get the same results each time you are running the analysis you can give the random number generator a start value with /seed..., followed by an integer number between 1 and 2,000,000,000.
Now you are set to run analyses. Please note that the PROCESS-macro forR uses a different random number generator than SPSS and SAS[1] and thattherefore the bootstrapping confidence intervals for the Indirecteffect(s) of X on Y are different from what the output shown in the book.Furthermore, does the current version of the PROCESS-macro for R acceptdata only in numeric format.[2] Thus, factors must be converted to numericform (e.g., 0 and 1) prior to their use in a PROCESS command. This canbe done using the following command in Rj (just copy-and-paste it).
I have set this program up to sample from populations with different means. (You canchange it to equal means if you like.) Therefore, you already know that the nullhypothesis is false, because you know the true population means. But that doesn't meanthat all of your resulting F values will be significant. But a nonsignificant Fis definitely a Type II error. I repeated this whole process 10 times on my own, andcollected the resulting 100 F values. The distribution of these F values canbe seen in the accompanying figureif you are interested. The critical value for 4 and 95 df is 2.49, so the area tothe left of that point represents beta. Using the material on power on p. 324 of the 3rdedition of the "Methods" book, I calculate beta to be 0.17 and power to be 0.83.How do those values compare to the results of my 100 replications? This demonstration canbe used in several ways. The best way would be for each student to enter and run theaccompanying program, generating 10 F's. By pooling across the entire class andplotting the results, you will get an idea of the kind of variation we routinely find.You'll actually see what we mean by power when you calculate the percentage oftimes you (correctly) rejecting the null hypothesis. I would then suggest that you modifythe program slightly and repeat the procedure. You could:
- Set mean2 to mean5 equal to 0.00, which would make the null true.
- Decrease the sample size
- increase the sample size
- Leave mean5 alone, but set the others to 0.00
- Omit groups 2 and 4 by putting an * in front of the lines that create them.
- Change the SPSS random number generator from Normal(1) to RV.Binom(5,.80), or the SAS random number generator from rannor(seed) to ranbin(seed, 5, .80). This would model scores on a 5 item true/false test where the probability of correct on any item is .80.
The SPSS is an integrated hybrid system that combines solar power with a standard military diesel generator to provide a continuous energy loop, said Tom Merrill, renewable energy storage lead with Army's Product Manager Force Sustainment Systems.
The system gathers and manages energy as it collects the sun's rays through photovoltaic panels and stores power in a battery. Once the battery power is depleted, the generator starts in order to maintain a seamless operation. A SPSS unit sits atop a military shelter or shipping container, known as a CONEX, to eliminate the need for additional ground footprint to deploy and operate the system.
"The goal is to minimize generator usage. We're looking to decrease the dependency on fuel resources. By harvesting alternative energy, we're able to reduce the amount of energy required from the generators and require fewer fuel convoys to save lives and money," said Rich Guiliano, program acquisition specialist with PdM FSS.
Suppose I have a random number generator and I want to check with a Chi Square Test whether its pdf is uniform or no. I can write a script that does that and I will run it several times. To my surprise, I get completely different results each times. Sometimes I will get a p-value of 0.3, sometimes 0.987 and other times 0.003. Which is the number I should take? Should I try to get an average of the p-values I get? How do I decide if this generator passes the test or not?
So what I try next is using a random number generator that I already "know" to be uniform and I run the test on that. And I keep getting absolutely varying results. Even if I increase the number of samples, it keeps varying a lot! From what I understad, the probability if seeing a very low p-value when drawing random samples from a uniform distribution should be low, and the probability of seeing a high p-value should be high. But this doesn't seem to happen.
As to what you should do with the p values and how you should interpret them/decide if the generator passes/fails, check out this other cross validated post. In short, you can just average them and interpret the result as one p value, but there are other more natural things that you can do that are typically better.
This article presents an extensive theoretical and empirical analysis of the pseudorandom number generators provided by subroutine libraries (NAG, CERN, IMSL, and ESSL), statistical and simulation software packages (GLIM, SAS, SPSS, DATASIM, ESSP, and LLRANDOMII), built-in functions of programming languages (APL, Turbo Pascal, Advanced BASIC, GW-BASIC, and QBASIC), and autoimplemented algorithms (Fishman & Moore, 1986; Wichmann & Hill, 1982; Van Es, Gill, & Van Putten, 1983). On the basis of these analyses, it is concluded that most of the built-in functions of the software packages that were tested can be used safely. In addition, it is concluded that the Wichmann and Hill algorithm is a good choice if only single-precision arithmetic is available, but that a prime-modulus multiplicative congruential generator with modulus 2(31)-1 and multiplier 16,807 is a better choice if double-precision arithmetic is available, and that the same generator with multiplier 62,089,911 or 742,938,285 is the best choice if extended-precision arithmetic is available. A Turbo Pascal and a VS FORTRAN program for the latter are given in the Appendixes.
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