# Search results

1. ### What is teh difference between a linear regression and a correlation between 2 variables?

You can use it for predictions (like hlsmith said, the concept you're looking for is called "extrapolation") but you're almost always going to be wrong. When you extrapolate from the regression line you're making the assumption (with no evidence) that if had gathered more data the same linear...
2. ### What is teh difference between a linear regression and a correlation between 2 variables?

The units of the regression weight would be the only difference for the case of the UNstandardized regression coefficient. If the variables are standardized then the regression coefficient and the Pearson correlation are the same.
3. ### Factor Analysis newbie seeks your help

Yes, you're kind of vastly overlooking some very important stuff: the issue that your observations are not independent. The theory of factor analysis (and most linear models) assumes that every person on which you collect data is independent from the next one. That is obviously not the case for...
4. ### Factor Analysis newbie seeks your help

So... if I understand this correctly the number of children are "nested" within their parents? So if Parent 1 has 3 children, then she/he will have 3 questionnaire responses (1 per child), if Parent 2 has 2 children, then 2 questionnaires and so on...?
5. ### calculating and interpreting SD from mean and SEM

What are "weighted and unweighted bases"? I guess this depends on what you mean by a "base" and these weighting issues but if all you're doing is re-arranging the formula for the standard error of the mean so that it looks like \sigma_{\bar{{x}}}\sqrt{n} = \sigma then, sure, I don't see any...
6. ### Hello

Welcome! Thank you for joining us!
7. ### Which test is appropriate for correlating categorical and continuous variables?

Would the point-biserial correlation or the biserial correlation be of any help?
8. ### Factor Analysis

Principal Component Analysis and Factor Analysis are related albeit different methods. In your particular instance, because you're only interested in dimension reduction and not in some mysterious latent variable that accounts for the correlations, Principal Components makes more sense. Just...
9. ### Factor Analysis

Well, if these are categories we're talking about as opposed to continuous variables, then Multiple Correspondance Analysis would be the correct way to go. But yeah, Factor Analysis or some other dimension-reduction technique would be appropriate. Although, in your case, I'd say Principal...
10. ### High significance of correlation coefficient with small sample size

There is no disagreeing with el spunky! Deliver agreement or I shall deliver ABSENCE OF CAKE
11. ### High significance of correlation coefficient with small sample size

Yes, they should be. In general, it is safe to say you should distrust **anything** that has such a small sample size. Small simulation example to exemplify: rr <- double(10000) for(i in 1:10000){ a<-rnorm(3) b<-rnorm(3) rr[i]<-cor(a,b) } > sum(abs(rr)>.9)/10000 [1] 0.2804 From a true...
12. ### Likert Type Items - Analysis

Technically you can as long as whatever you end up with is interpretable.
13. ### Hi all

Nice! You may be interested in combining that with some combinatronics? I know a lot of card games use discrete math of various types.
14. ### factor analysis

Communality <- shared item variance Common factor eigen values <- number of factors on a scale Uniqueness <- variance unique to each item. Honestly, just googling each term would give you a very good introduction to these concepts.
15. ### Hi all

Hi! Welcome to our board! I hope you find it useful. Although I haven't used them myself, I've heard the Coursera online classes for introduction to statistics are very good. Maybe that could help?
16. ### Variance of indivudial variables in multiple regression

To answer this kind of questions you'd need to use methods related to variable importance in OLS multiple regression like Dominance Analysis or the Pratt Index. Here's a good overview of these methods: https://onlinelibrary.wiley.com/doi/pdf/10.1002/wics.1346 Depending on which software you're...
17. ### Skewed Population distribution.

Not necessarily. The sample skewness is a statistic that's subject to sample-to-sample variability. The best option would be to pose a confidence interval on you value and assume the population skewness is somewhere around there.
18. ### The same SPSS models give DIFFERENT results

I'm just trying to understand if your question could be addressed by pointing out that if you are fitting two different models, there's no reason for any of the coefficients to remain the same, unless all the predictors are perfectly uncorrelated.
19. ### The same SPSS models give DIFFERENT results

So... do you end up with the exact same predictors in both models? Or does one model has more predictors than the other?
20. ### Invitation for a skype call

We usually prefer for questions and conversations to be posted on here. You never know if the same questions/problems that you have are also shared by other people. Keeping a public record of this would benefit you and any potential person who comes across it.