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Is, a difference in predictive validity.

And that obviously alerts the child to their unusual nature.

So I think, conceivably, those could be predictive.

My mobile phone was ringing with vibrating alerts all the time.

So the question is, what happens to the predictive validity or the, the predictive value of X after you add in the mediator.

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In fact, very prescient on your part. That's a very predictive question.

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And if he alerts the enemy to our whereabouts it will make the crossing even more dangerous.

We're going to be predictive we're going to know what you're likely to have.

And for the problems we discuss in this course we'll get extremely accurate predictive power.

And with computation and algorithms and predictive models, we will be able to see the future.

And scientific theory means quantifiable relying on underlying generic principles that can be made into a predictive framework.

Now describe to me the predictive Gaussian and give me the new mu and the new sigma square.

That is if the test is predictive of how well the officers do on the job, or how well the soldier does in officer training, if it's predictive for one group but not for the other, then we see evidence of bias.

It can be predictive from some constant plus some predictor variable x times the regression coefficient for x plus error.

If its homo scadastic then those distances are not a function of X, they are not related to the predictive variable.

The best method we have right now is to use a predictive controller, which is we have a model of the car.

And then you do this full model and the question is what happens to the predictive validity, or the predictive value of X after adding in M. So to get full mediation, what we'd like to see, is first in these two, the first two simple regression equations.

Because what that question assumes is that our external world is predictive of our happiness levels, when in reality, if I know everything about your external world,

Today they're developing analytical tools to do predictive modeling so that before you pick up the phone, you can guess or predict what this phone call is about.

The technologies that are coming, high performance computing, analytics, big data that everyone's talking about, will allow us to build predictive models for each of us as individual patients.

One of the problems, when you get too much chatter, it's hard to process all the packets, so you have to prioritize, and that's where the predictive model helps you.

This is something you repeat again and again until your hypothesis becomes consistent with all observations and has so much predictive power that it becomes what one calls a theory.

And we will actually try to get to the point where we have a predictive model where we can understand, when cancer happens, what's actually happening in there and which treatment will treat that cancer.

So practicing throwing things has been shown to stimulate the frontal and parietal lobes, which have to do with visual acuity, 3D understanding, and structural problem solving, so it helps develop their visualization skills and their predictive ability.

We'll be able to establish a lot of performance guarantees for fundamental algorithms but at the same time we'll have good predictive power what the theory advocates will in fact be algorithms that are known to be best in practice.

You can run multiple predictive models one with a blown tire and one without a blown tire and then when the one with the blown tire comes along, then this model all of a sudden becomes the more plausible to explain what's happening.

In a theory you repeat the process of refining the hypothesis through prediction, experiment, observation or creating new hypothesis again going through the circle again and again and again until your hypothesis becomes a theory that is a predictive and comprehensive description of some aspect of reality.

And there are a couple of these that have strong predictive relationships, one of which is education, where you see the states with the fewest years of schooling per adult are in red, and you see this part of the country, the kind of Appalachians region, is less educated.

And then, I also wasn't really shining in a certain area that I wanted to be, and you know, you look at those scores, and it wasn't bad, but it was not certainly predictive that I would one day make my living out of the artful arrangement of words.

The fact that we can derive more knowledge by joining related information together and spotting correlations can inform and enrich numerous aspects of everyday life, either in real time, such as traffic or financial conditions, in short term evolutions, such as medical or meteorological, or in predictive situations, such as business, crime, or disease trends.

On the other hand we don't want to throw out the baby with the bath water, we want to retain predictive power and this turns out this definition turns out for the problems we're going to talk about in this course, to be the sweet spot for reasoning about algorithms okay worst case analysis using the asymptotic running time.

So, just to reiterate on the previous slide we saw that if we fit some set of parameters, say theta 0, theta 1, and so on, to some training set, then the performance of the fitted model on the training set is not predictive of how well the hypothesis we generalized the new examples is because these parameters would fit to the training set.