Billions - 3x04 "Hell of a Ride" - Episode Discussion by NicholasCajun in Billions

[–]multi-mod 0 points1 point  (0 children)

He played Captain Winters in Band of Brothers. OP was just joking about it.

Statistic tests! by gaatjesprikker in AskStatistics

[–]multi-mod 1 point2 points  (0 children)

The explanation of your experiment is rather confusing. Can you briefly summarize your response and explanatory variables?

I have run an experiment but I need help deciding what statistical analyses to run. by terusama in AskStatistics

[–]multi-mod 1 point2 points  (0 children)

A multinomial test would be appropriate here. This is also really easy to simulate if you want a little more freedom.

Tips for IP by tehforsh in labrats

[–]multi-mod 4 points5 points  (0 children)

If you are having trouble keeping track of your beads, consider switching to magnetic protein A beads. I find them much easier and quicker to work with.

As for your protocol in general, you appear to be doing your cell lysis and some of your washes with a buffer that contains SDS. Ionic detergents are generally pretty stringent, especially when you want to use your protein in downstream applications. I would consider re-optimizing your buffers by switching to a non-ionic detergent (such as NP-40 or Triton), and playing around with your salt concentrations.

Not sure about what kind of analysis fits my data. Pathway dataset by keithwaits in AskStatistics

[–]multi-mod 1 point2 points  (0 children)

This is referred to as path analysis, and there are a few packages in R that deal with this.

Interpreting fixed effects by irishrapist in AskStatistics

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The coefficient in multiple logistic regression is the change in log odds of being in the reference group of your response variable for every "unit" change in your predictor variable, while holding all other variables constant.

The null hypothesis for each variable is that the adjusted coefficient is 0 (signifying that the variable is no better a predictor than guessing). The p-value is expressing the probability of getting your coefficient or greater given that the null hypothesis is true.

Remember that this p-value is derived from a coefficient that was itself the result of holding all other variables constant. This means including or excluding other variables from your model can actually change the p-value. So although other variables may not be significant, they themselves could be helping another variable to be significant.

Graphing Help by mF_Huffy in AskStatistics

[–]multi-mod 0 points1 point  (0 children)

Do you expect some sort of monotonic relationship between the variables? Perhaps it would be better to make a scatter plot of two of the variables, and then include the third as a style on the points - such as using a color gradient for intensity or changing the size of the point based on the magnitude.

It's difficult to give more specific feedback without knowing anything about your experiment.

Is Uncertainty measurement too high in this report? by [deleted] in labrats

[–]multi-mod 4 points5 points  (0 children)

If you are provided a level of 2.95 ± 0.97 PPB, that generally means that the sample value is 2.95, and the true population value most likely falls somewhere between 1.98 and 3.92.

There are many different ways to calculate uncertainty, and many different factors that could effect it. This could include the natural variability between the samples you provided, the limits of the machine doing the detection, technical variation in sample preparation, and the number of samples tested. For more specific feedback you should consider consulting with an expert in this field, or contacting the facility that did the analysis for more information.

How are histones specifically isolated? by ZingoMonkey in labrats

[–]multi-mod 1 point2 points  (0 children)

Histones are proteins. Did you mean the DNA wrapped around histones?

Statistics question by DBrainz in labrats

[–]multi-mod 4 points5 points  (0 children)

This is referred to as survival analysis. It measures if there is a difference in time between groups until an event occurs, such as death. Any stats program, such as R, will have packages to analyze it.

If you didn't record when they died, you indeed could do a chi-squared test.

What statistical fallacy is this? by berserkerscientist in AskStatistics

[–]multi-mod 1 point2 points  (0 children)

I would also consider this a form of ecological fallacy - making conclusion about groups by studying only those groups.

Narrowing down the number of tests of association - is it appropriate? by bioinformaticsnewb in AskStatistics

[–]multi-mod 0 points1 point  (0 children)

Bonferroni and Holm-Bonferroni don't require independence. If your comparisons are dependent, you will lose some power with these methods, but your type I error still won't exceed the set alpha.

Narrowing down the number of tests of association - is it appropriate? by bioinformaticsnewb in AskStatistics

[–]multi-mod 1 point2 points  (0 children)

There are less conservative correction methods if you are worried about false negatives. Examples would include Holm-Bonferroni and FDR.

In general, you want your analysis to be decided before you see your data. That's why I suggest a less conservative correction above.

Interpreting multinomial logistic regression- parameter estimates- Expected Bs far too high by daisysneal in AskStatistics

[–]multi-mod 1 point2 points  (0 children)

There is nothing inherently wrong with large ORs. They just indicate a probability close to 0 or 1 (depending on if it's negative or positive respectively).

They could be a symptom though of other model problems, such as a poor fit or quasi-separation (when your predictor variable almost completely correlates with the classes of the response variable). Without having more information I wouldn't be able to provide you with any more specific feedback.

Variables on same scale? by sozialwissenschaft97 in AskStatistics

[–]multi-mod 0 points1 point  (0 children)

It depends on the test you are performing.

For your specific example you would be fine with ordinal regression, but there is no universal answer for all cases.

Papers regarding promoter definition for HOMER by testyporklet in bioinformatics

[–]multi-mod 4 points5 points  (0 children)

You should really define your promoter region and settings beforehand. If you keep changing your settings after seeing the results of your analysis, you are increasing the likelyhood of false positives. It's a form of p-hackimg because of this.

Can the p value from a paired t-test be presented alongside the fold change between two groups, rather than the difference between the two groups? by torontolife997 in AskStatistics

[–]multi-mod 0 points1 point  (0 children)

but that just seems inherently wrong to me

Just to toss out an example in genomics, sample difference is often calculated with a regression, but the magnitude reported as log fold change. I wouldn't say this is appropriate in all instances, so take into consideration what is normal in your field.

Deciding on a non-parametric test comparing multiple groups over 3 different variables? by jubjub_05 in AskStatistics

[–]multi-mod 2 points3 points  (0 children)

If I am understanding this correctly, your response is satisfaction (ordinal), and your main independent variable is class type (unordered factor). If so, ordinal logistic regression would indeed be a good starting point.

[Opinion] 'Sticky' community discussions? by mind_bomber in FuturologyModerators

[–]multi-mod 0 points1 point  (0 children)

We did this for a bit in the past. I would be cool with it.

Likert Scale Statistics by peaches124810 in AskStatistics

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You should decide on a hypothesis first. When you figure out what question you want answered, it then becomes easier to decide on a statistical test to answer it. In general, you want these types of analysis to be hypothesis driven. If you perform multiple tests to look for anything statistically significant in your data, you are very likely to be giving meaning to noise instead of some real effect.

Question regarding how to approach data analysis - pretest/posttest design by [deleted] in AskStatistics

[–]multi-mod 0 points1 point  (0 children)

If you only care about how the two variables affect test score performance, it would be better to split it into two analysis: one for understanding, and the other for logical thinking. If this is done, you could do a mixed design ANOVA for both.

If you wanted to better understand if there was any interaction between your two explanatory variables, a mixed effects linear regression would be a good starting point. You have paired data points, which you can control for as random effects in the model.