Examples of using Probabilities in English and their translations into Thai
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Colloquial
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Ecclesiastic
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Ecclesiastic
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Computer
Conditional Probabilities, 516.
Probabilities are always between 0 and 1.
He can't even calculate probabilities.
Probabilities less than 0.5, it's more unlikely than not.
And then in the y-axis you will have the probabilities.
I'm extrapolating probabilities as we speak.
But we're going to represent beliefs with probabilities.
Probabilities greater than 0.5 say, hey, it's more likely than not.
So once again we just have to multiply all these probabilities.
That gives significant probabilities for the trade to be successful.
Picking up from last class, we're still dealing with probabilities.
Getting pregnant the first time: probabilities the first month and tips.
And we merely evaluate by considering all those probabilities.
And probabilities of 0.5 say, hey, it's just as likely to happen or as to not happen.
And in the next video, we will interpret z-scores and probabilities a little bit more.
So if we solved all the probabilities that a random variable can take, or we're summing over all of the values, this is going to sum up to 1.
And I know in this other world-- I will just draw it so we can figure out maybe other probabilities.
I draw a coin at random with the probabilities shown, and then I flip it twice, that same coin.
So there're important lessons in what we just learned, the key thing is we talked about conditional probabilities.
And we have to start introducing different interest rates and probabilities, and we will get to that eventually.
The lottery is an excellent example, of course-- an excellent test-case of people's ability to compute probabilities.
The committee will base its findings on the balance of probabilities and will state its reasons for those findings.
Now, I'm not saying that any of these risks are particularly likely, but when there's so much at stake, even small probabilities matter a great deal.
Rather, researchers intentionally select people with unequal probabilities of inclusion in order to reduce cost and increase accuracy.
The Lexis diagram1 is commonly used to illustrate the usual method for computing death probabilities and other demographic measures.
For example, parity-specific birth probabilities may exceed one for certain years when many postponed births are made up.
The machine crunches data, calculates probabilities, gets 80 percent of the way, 90 percent, making it easier for analysis and decision-making of the human party.
When researchers intentionally select people with different probabilities of inclusion, then adjustments are needed to undo the distortions caused by the sampling process.
However, they strongly disagreed on the standard of such proof,“i.e., whether a balance of probabilities is sufficient or whether corruption must be established to the more demanding standard of‘clear and convincing evidence' of corruption.”5.
The Director calculated an 88 percent probability that an EEG.