As another example, sometimes the presence of a compound can alter the response of the test to other compounds (a "matrix interference"); when this is detected by the laboratory, it will inflate its reporting limits accordingly.(By "practical" I mean a method that can reliably be coded in at least one generally available software environment like R, Python, SAS, etc.
I am fitting a GLM model (in R), and would like to get an estimation of the variability of the coefficients estimated by the model.Am I correct that an easy way to do this is by using the boot command from the boot package, then output the coefficients at each simulation, and at the end calculate their var?
I came across an example where standard deviation was being plotted on a Cartesian plot (standard 2D with X and Y axes.)
This seems like a valid thing to do but in this case the example only had a single line running across the graph to "indicate" standard deviation.
Precision is defined as:
p = true positives / (true positives + false positives)
Is it correct that, as true positives and false positives approach 0, the precision approaches 1?Same question for recall:
r = true positives / (true positives + false negatives)
I am currently implementing a statistical test where I need to calculate these values, and sometimes it happens that the denominator is 0, and I am wondering which value to return for this case.
The poission distribution is used to model arrivals in a system over time and thus may work well for network flows.)
Step 2: Identify a testing strategy which would let you ascertain the strength of evidence for your null model.
There was already a request for Mathematical Statistics Videos, but it explicitly asked from people for
videos that provide a rigorous
mathematical presentation of
statistics.org/#Statistics: series of short videos on introductory statistics
For those who don't know R, those are functions for generating mixed effects or multi-level models.If I have fixed effects in something like a repeated measures design what would a confidence interval around the predicted value (similar to mean) mean?
Assuming indpendant binomial trials, that means for each act there was a 1/104 probability of success.The distribution you should be looking at is the negative binomial distribution.
Imagine one method yields [1, 0.Which measure can I apply to *de*emphasize the between-sample variance in the series and emphasize the fact that method 1 always outperforms method 2?
I want to somehow analyse this database and produce some graphics showing the correlations between different keywords.I'm also outside my area of expertise, but assuming that you want to use R, here are a few thoughts.
> xtable(res$p.value, caption=res$method)
% latex table generated in R 2.
I am regressing two butterfly richness variables (summer and winter)
against a set of environmental variables separately.And, the Summer/Winter variable would be simply a single dummy variable (1,0).
grid(height = seq(60, 80, 5), weight = seq(100, 300, 50),
sex = c("Male","Female"))
height weight sex
1 60 100 Male
2 65 100 Male.30 80 100 Female
31 60 150 Female
You could then loop over each row in the resulting data frame to pull out records from your original data.
function  = simple_structure_criteria (my_pattern_table)
%Simple Structure Criteria
%Making Sense of Factor Analysis, page 132
disp('Simple Structure Criteria (Thurstone):');
disp('1.Each row of the factor matrix should contain at least one zero');
I have 20 runs with the same configuration for each of the two algorithms, using different initial random number seeds.In your case, you are comparing two simulators so you should just use a two-sampled t-test instead.
I need to summarize the responses to a survey (for management).All answers are categorical (on an ordinal scale, they are like "not at all", "rarely".
For an effect size analysis, I am noticing that there are differences between Cohen's d, Hedges's g and Hedges' g*.These effect sizes and Cliff's and other nonparametric effect sizes are discussed in detail in my book:
I'm doing this to analyze the evolution of the entropy in a software artifact (code, model, whatever).I'm not worried with the absolute values, but with the relative increase (or decrease) in entropy.
I need some help about repeated measurement ANOVA.I think I'll do two ANOVA, one for "before intervention", one for "during intervention", and I suppose that the ANOVA "before intervention" should not have a significant F-ratio test.
How to perform Student's t-test having only sample size, sample average and population average are known?
However, how do I compute for $s$ when only the sample size and sample average are known?It is reported that a random sample of $49$ workers in ACME South Factory had an annual income of $\$112$.