# Search results

1. ### F-Test for equality of variances- effect size scale?

Thanks Staassis! When calculating the power of the F test for variances we use the ratio as an expected effect size. And yes, for equal samples, the power of ratio=0.5 or ratio=2 is equal. So I thought of max(ratio, 1/ratio) or maybe max(ratio, 1/ratio)-1 I find it nice when you use the same...
2. ### F-Test for equality of variances- effect size scale?

The effect size for equality of variances is Ratio=variance1/variance2 I didn't find any "common" scale for this effect size. (although you may say the ratio explains itself...) I know that these definitions are not absolute, and may depend on the specific field. Do you know of a scale for the...

Any Idea?
4. ### Regression with no intercept - VIF

Thanks Jmyles, Sorry, I don't talk about a specific case but try to understand statistics. In R for example: model = lm(y~x1+x2+x3+0) should you do: vif( lm(y~x1+x2+x3+0) ) or vif (lm(y~x1+x2+x3) In: https://stats.stackexchange.com/questions/231252/high-vif-after-removing-intercept-in-r...
5. ### Regression with no intercept - VIF

Yes, I know that usually, you shouldn't remove the intercept. The question is for the rare cases when you decide to remove the intercept. how to calculate the VIF? How do you interpret the results in this case? What VIF value is suspicious for multicollinearity? What VIF value is dependently...
6. ### Regression with no intercept - VIF

Do you have any idea?
7. ### Regression with no intercept - VIF

What is the less problematic/"common practice" way to calculate the VIF when using the no intercept model? How do you interpret the results in this case? What VIF value is suspicious for multicollinearity? What VIF value is dependently multicollinearity...
8. ### Regression with no intercept - null assumption for

Why H0: y = epsilon, and not H0: y =b0+ epsilon? Clearly the zero Y-intercept is relevant to H1, but why also H0?
9. ### Regression with no intercept - null assumption for

Hi, In linear regression: H1: y = b0+b1x H0 y = b0 In linear regression with no intercept: H1: y = b1x What is the null assumption? H0: y = 0 or H0: y = b0?
10. ### Confidence interval - why not porbability

Thanks you Karabiner, this a very good explanation!
11. ### Confidence interval - why not porbability

Wikipedia: "the confidence level represents the frequency (i.e. the proportion) of possible confidence intervals that contain the true value of the unknown population parameter" Some say the following is incorrect: The chance that the true population value is in the confidence interval is the...