Probability density vs mass

For a continuous random variable, we model density, not mass.

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I'll just point out one little technical detail that turns out to be not important, but we need to state it.
Just to be truthful, this value on this axis is not actual probability.
That's what's called probability density probabilities.
We're going right with a big piece.
Usually, if we're being strict, we're going to be that sloppy on the slides coming up big piece of true probabilities and some upto one.
So, for example, I got this coin I toss this coin on, decide with heads or tails some of the probability of it being a head, plus the probability of it being a tail equals one because it's one or the other.
How many times I do it? Add them all up.
Always add up to one, because X here is a continuous value, not discreet heads and tails is a discreet thing because there's a continuous value could actually take an infinite number of possible values along the scale.
Okay, so it's a scaled, continuous value, and what that means is that we can't make these probably density some 21 What sums to one is the area under this thing? Not that just not the sum of the heights.
Okay, So into the integral, integral integration, the area sums to one.
It's not important because it's just proportional to problems.
So we're going to use the word probability a little bit.
Sloppily, we might mean density, but for this course, that's not an important distinction.

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