Sentences with phrase «like confidence intervals»

What is the justification for adjusting past values, and is there any way to convey the increasing level of statistical uncertainty in the USHCN values, like confidence intervals or error bars on charts?

I also think, rumbling around under the covers, there are fundamental misunderstandings about notions like confidence interval which we statisticians just have not done a good job of educating about.

Not exact matches

TLDR; in statistics, a mean value is meaningless unless you know the confidence interval for a given error probability (any poll saying that candidate Y will get X votes actually tells, in the fine print, something like «we are 95 % sure that the candidate will receive at least X-error votes and at most X + error votes»)

It seems like with a bit of math (confidence intervals etc.) you could derive this info.

There are accepted methods by which one can manage uncertainty — statistical analysis, confidence intervals, and the like.

What I want to say is only that empirical evidence of the type that F+G 06 or any other of these analyses of climate sensitivity or related variables is information on the likelihood function (or equivalently conditional probability), and that this information alone can not provide any PDFs of confidence intervals for the climate sensitivity or a functionally related parameter like Y. To get a PDF or a confidence interval, a prior must be assumed and plausible alternative priors give in this case significantly different results.

Ie, Confidence intervals that are unrealistically small, you can call this an artifical reduction in variance if you like.

I think that I've posted up on Foster and Stine on the calculation of «honest confidence intervals» in stepwise regression (it's a phrase that I like and use).

All three methods agreed that the effect of climate change was positive, making precipitation events like this about 40 % more likely, with a provisional 2.5 - 97.5 % confidence interval of 5 - 80 %.

Peter317 Fixed yours too: Moral of the story: on spans too short to have a significant confidence interval you can put your start points and end points wherever you like, and the trend line changes.

There's a reason science relies on things like 95 % confidence intervals instead of the ol' eyeball when looking at data.

By placing stations at what looks like relatively even intervals along major routes — as well as big box stores like Target and Walmart — and by prioritizing multiple charge points at each location, and faster rates of charge (assuming your car can take it), this network will genuinely make a huge difference in the level of confidence a driver might have as to whether they can find a charge.

The GMST trend for the last 40 or so years has converged to something like a mean of +0.2 ºC / century, with considerable spread to the associated «95 % confidence interval».

What I am doing is almost like comparing the 99 % confidence intervals of 2 variables, and saying that they are statistically significantly different, if there is no overlap.

Performed various statistics like power, inter and intra subject calculations, confidence interval and other point estimates.