Which test would I use to determine this?

I feel like this is something I should be able to figure out, but I just can't seem to search for the correct terms.

- Thread starter DanMann
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Which test would I use to determine this?

I feel like this is something I should be able to figure out, but I just can't seem to search for the correct terms.

Normality assumption.

Equal standard variations assumption.

I believe that in the case you compare two groups, the

If your H0 assumption is that one printer type is faster, and you want to use H0: μ1 ≥ μ2, H1: μ1 < μ2 assumption you can only do it with one-tailed t-test

@DanMann can you confirm/deny my understanding of your question and provide clarification on what you want to show?

Dason is correct: I have one model of printer, I have quantity A of this model of printer and I want to print on each printer B times and I want to use this data to show that the printer model prints faster than 30 s.

I tried the two-sample T and it appeared to do what I think OBH thought I wanted to do (i.e. compare the performance of one printer to another), but this isn't what I wanted to do.

Hi Dan,

If you want to analysis the variance inside each printer and the variance between the printers I think you can use: "confidence interval for a variance "

But as I understand you just interested in the probability that one print on one printer will be less than 30s?

If so maybe you can just take all data and run the one-sample t-test? (I assume that B should be equal for all the printers, or similar)

In this way, you will get both variances between printers and between prints.

Generally, one of the one-sample t-test assumptions is "independent of observations", using the same printers several times has a dependency.

But I think it should be okay since you plan a good random spread.

@Dason what do you think?

Anyway, I think you should also analyze the variance between the printers, you probably want this to be small.

for example, if the result will be that in 98% of the prints are faster than 30 s, you don't want that for some users 100% of the prints are faster than 30 s and for some only 20% are faster. Maybe repeated ANOVA will give you the division of the sum of squares between printers and between prints (but you need to print the same pages for each printer)

If you want to analysis the variance inside each printer and the variance between the printers I think you can use: "confidence interval for a variance "

But as I understand you just interested in the probability that one print on one printer will be less than 30s?

If so maybe you can just take all data and run the one-sample t-test? (I assume that B should be equal for all the printers, or similar)

In this way, you will get both variances between printers and between prints.

Generally, one of the one-sample t-test assumptions is "independent of observations", using the same printers several times has a dependency.

But I think it should be okay since you plan a good random spread.

@Dason what do you think?

Anyway, I think you should also analyze the variance between the printers, you probably want this to be small.

for example, if the result will be that in 98% of the prints are faster than 30 s, you don't want that for some users 100% of the prints are faster than 30 s and for some only 20% are faster. Maybe repeated ANOVA will give you the division of the sum of squares between printers and between prints (but you need to print the same pages for each printer)

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