How to distinguish dead fine roots from live one?

I'm doing a research project where we are testing different methods of fluorescent live/dead stains and we need to kill some strawberry and potato roots so we can stain them. The only method we know will work well is boiling them in 70-80 degree water, have any other ideas?

thanks!

Hi everybody,

Graphpad prism is able to calculate geometric means and geometric standard deviation.

- Is there any requirement for the data to have a normal distribution in order to calculate the geometric mean of the data ?

- Is it possible to calculate the geometric standard error of the mean instead of the geometric standard deviation? Or is this question completely mathematically dumb?

Thanks for your answer to a very statistic naive person...

Juliette

I received a mail few days ago ''Congratulations! Your nominated profile has been selected for the "International Research Award on New Science Inventions" under the category of "Young Scientist Award". Kindly complete your registration process as earlier as possible and use your submission ID for the further registration process''

Is this real or just a scam?

What is the difference between resistance and tolerance to a compound?

If there is bacteria that grow in a indirect contact of a toxic compound, when can we say if this bacteria resist or tolerance this compound?

For examining the association between two variables, say X and Y, using the Pearson correlation coefficient, the assumption commonly stated in text books is that both variables need to be normally distributed, or at least a reasonable approximation to that distribution.

On the other hand, the assumption for a parametric OLS regression model is that the residuals are normally distributed. In such a regression analysis, unless there is a very strong relationship between the independent and dependent variables (say X and Y, resp.) the distribution of the residuals is very close to that of the dependent variable, Y. (That is why the commonly stated assumption for regression is that the DV needs to be normally distributed.) So, in a situation where Y, (and hence the residuals) are sufficiently normal, but the predictor, X, is very non-normal (say skewness outside the range of plus and minus one), the parametric regression model would still satisfy the required criterion and so allow the standardised regression coefficient, beta, to be validly estimated.

However, the Pearson correlation coefficient is precisely the same as the standardised regression coefficient, beta, derived from a simple regression analysis. So there seems to be a conflict in the commonly stated assumptions regarding distributions for the two types of analyses, each of which allows the estimation of the same statistic (correlation or beta).

Does the above imply that it should be valid to use the Pearson correlation coefficient when only one of the two variables is normally distributed, but not the other?

Hi,

I am comparin 5 groups (1 control and 4 treatment) After using one way anova and rejecting null hypothesis, now I have to apply a proper post-hos test.

I am usind R and I already used TukeyHSD but some sources are saying that unequal sample sizes are not okay for TukeyHSD.

Does TukeyHSD function of R automatically correct the problem or should I use different function/test in R , if so, which package should I download?

I am writing a review paper. In my paper, can I use figures from "open access" journals/articles etc provided I cite them properly? (i.e. without have to go for requesting permission for using). Also, is there any way to obtain permission to use already published figures without incurring cost?

Thank you in advance for your answer.