learning curve plotting graphpad prism 8

Note if strike_action='star' the entire table is converted to type character. One can choose to delete these values with strike_action='exclude', keep them with 'keep', or convert them to a trailing '*' (as they appear in Prism) with 'star'. To accommodate this, an option strike_action is available in read_pzfx. Prism allows user to exclude data by striking them out individual observations. To accommodate this, NAs are added if the columns are of different lengths. Prism does not require columns in a table to be of the same length.

For example, trailing _MEAN, _SD, _N are added if subcolumns represent mean, standard deviation and number of observations. It tries to infer the subcolumn types and adds appropriate suffix accordingly. To accommodate this, read_pzfx automatically adds _1, _2 etc to the original column name to account for sub columns if they represent replicates. On the other hand, I was thinking about using bootstrap, which theory I understand very basically, but I have never done it, and don't know if I can do it with this program.Prism allows subcolumns. How can I calculate a non-parametric CI, using Prism? I have the GraphPad Prism program, and it says that for calculating CI, you assume independent and gaussian distributed values. Then a lab mate told me that I could calculate the confidence interval for my controls, and take as 'different' from that any value that is outside the CI.

I tried a lot of ways to normalize the data (even one I had never heard before: using the arcsin of the percentage square root.!), but nothing works. I tried to use Friedman's test, but it really takes too long! (At least 30 minutes for every feature). My first idea was to do a 'repeated measures ANOVA', but unfortunately, my data are absolutely NOT normal distributed because they're 'binary' counts (with minimal value 0 and maximum value 15), and I have lots of ties. I observed a lot of features (between 6 and 12) in every embryo, every 24 hours for 5 days (24 hpf, 48 hpf, 72 hpf and 96 hpf) so I have a lot of data. In one type of experiment, I repeated this 6 times (6 independent replicates) in another type, I repeated this 9 times (9 independent replicates), so my n is quite low.

What I did is identifying if embryos had (yes = 1) or not (no = 0) malformations or delay in their development, within samples of 15 embryos.

Learning curve plotting graphpad prism 8