Here we expect a positive correlation.

So the correlation is a positive.22.

Slight high correlation, or puh, puh, sorry, positive correlation for high SES.

Correlations can range from positive one, which is a perfect positive correlation.

I don't see that strong positive correlation at Princeton.

What do you need for a strong, positive correlation?

So what you see here is a modest positive correlation.

That's different from the last lecture where we saw a positive correlation.

So, this is a strong positive correlation between working memory capacity and, and

So, to wrap this up, how do we know if a correlation is reliable or significant?

Correlation

Is there any correlation, positive or negative, between intelligence and tendency to be religious?

So, how do we untangle that positive correlation in trying to predict the outcome measure?

And what you see is a nice positive correlation between runs and batting average.4.

And it's standardized so that the correlation coefficient falls between positive one and negative one.

You, you said, hey look, I got this significant correlation and I got this significant effect when in fact in the population, that effect doesn't really exist.

So I've said that the alternative is that the correlation is greater than zero, or positive.

Okay, so. A correlation, this simply defining correlation.

It's either significant or it's not significant.

TNEF Correlation Key

That's called correlation.

We saw a nice correlation, positive correlation of .40 what I've done now with this plot is I've put the actual player names next to each individual dot.

Significant figures

Significant Building

How significant?

And what we see is a nice moderate to strong positive correlation between working memory capacity and SAT.

And, then, the alternative hypothesis is that, there's a positive correlation, say, between, say, working memory capacity, and

Here are three data sets in one, we have a strongly positive correlation in another, it's above zero, and in the third one, it will be a negative correlation.

So the closer you get to positive one, the stronger you get the negative one, the stronger the correlation.

Specifically the correlation analysis.

3 significant figures.

Highly significant relationship.

Significant problems remained.

That's quite significant!

You're not significant!

So you might observe a positive correlation between x and y, introduce a moderator and boom it could go away.

Remember from the lecture on correlation, then, that represents the covariance or correlation.

Positive seven times positive three.

What positive? Positive what? Pressure.

So the correlation is low.

It's an extraordinarily close correlation.

We could observe a correlation.

They're not statistically significant.

Okay. That's pretty significant.

This is statistically significant.