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Jan
25
comment How to tell the probability of failure if there were no failures?
If there are few or no failures then $\Theta \sim U(0,0.1)$ will produce almost the same result as $\Theta \sim U(0,1)$ i.e. Beta$(1,1)$, and the latter is easier to handle
Jan
21
comment How to find third quartile from the mean and the standard deviation
Less than $93.8$ (which is $\sqrt3$ standard deviations above the mean), but the actual value depends on the distribution.
Jan
12
comment Density estimation for streams of Data
You could store your data as counts in intervals, where the intervals are substantially narrower than the bandwith you are using for smoothing your kernel. Then you just need to remember as many numbers as there are intervals.
Jan
10
comment Measuring “almost the same” when significantly different
@Glen_b: I am looking for a summary measurement of "closeness" which is insensitive to scale (e.g. multiplying all the data by $10$) and can be used for meaningful comparisons with other splits of the same population by different factors and for comparisons with other cases for example when the success rates change.
Jan
10
comment Measuring “almost the same” when significantly different
There are other similar tables (such as gender, age, and ethnic origin) which show different disparities as well has having a variety of distributions between the size of the largest group and of the others. I was wondering if there was some sensible summary statistic that I had missed which like the $\chi^2$ statistic gave a greater weight to disparities in the larger groups but which was not so dependent on the total number of individuals as the $\chi^2$ statistic. For example the $\chi^2$ statistic divided by the total number of individuals would be unitless.
Jan
9
comment How to find $\displaystyle \dfrac{d}{dt} \left [\int_t^\infty xf(x)~dx \right ] $ (when $f(x)$ is a probability density function)
Use the fundamental theorem of calculus
Jan
9
comment How to find $\displaystyle \dfrac{d}{dt} \left [\int_t^\infty xf(x)~dx \right ] $ (when $f(x)$ is a probability density function)
Do you mean $\displaystyle \dfrac{d}{dt} \left [\int_t^\infty xf(x)~dx \right ]$? Perhaps $-tf(t).$ Or do you mean $\displaystyle \dfrac{d}{dt} \left [\dfrac{\int_t^\infty xf(x)~dx}{1-F(t)} \right ]$?
Jan
8
comment Definitions of quantiles in R
Wikipedia gives the formulae
Jan
3
comment Explanation of formula for median closest point to origin of N samples from unit ball
I suspect it may be suggesting that in high dimensions, points to predict are effectively a long way from the training data, as if on the edge of a sphere, so you are not really interpolating but rather extrapolating, and so uncertainties are much greater. But I do not really know.
Dec
25
comment When to use a normal approximation of a Bernoulli distribution
@Victor - I used the MathJax version of TeX. See meta.math.stackexchange.com/questions/5020/… for some tips
Nov
28
comment Using kFold in a regression problem?
It rather depends on what you mean by regression. $k$-fold cross validation is method to help decide the hyperparameters of your method (at an extreme level deciding the method itself) while trying to avoid over-fitting. And you should not be using the test set to compare methods: you should instead be using the results of the cross validation.
Nov
23
comment Area within a given number of standard deviations from given mean
It depends on the shape of the distribution.
Nov
23
comment Rearrange boxplot with ggplot
It may depend on whether you use reshape or reshape2 (you should have mentioned this in your question). As for displaying results, you can show how many 1s and 0s there are or what proportion are 1s.
Nov
19
comment Fast way to compute central moments of a Poisson random variable?
What do you know? The moment generating function? Stirling numbers of the second kind? Moving from moments about $0$ to moments about the mean?
Nov
18
comment Can mean plus one standard deviation exceed maximum value?
Indeed - my minor point is that this curiosity is a result of what standard deviations represent for strongly non-symmetric distributions rather than a result of taking a sample. But in general, I think your answer is excellent
Nov
18
comment Can mean plus one standard deviation exceed maximum value?
Pedantically, you and R are using the $n-1$ sample standard deviation calculation. If the population is $1,5,5,5$ then its standard deviation is $\sqrt{3} \gt 1$ so your example is still valid.
Nov
16
comment Inconsistency between R and SAS for MLE on Weibull
The difference between the two sets of parameters is much smaller than the bracketed estimates of the errors in these estimates. So they are close.
Nov
16
comment Polynomial regression P value is getting altered
Almost certainly yes if you are going to use the raw polynomials in the final equation, probably yes even if not. And the 1st (linear) and 0th (constant) degrees too.
Nov
16
comment Polynomial regression P value is getting altered
I am not a fan of polynomial regression without a theoretical justification, though it might sometimes work for interpolation (not extrapolation) as a way of drawing a smooth curve. If you must go down this route, you might consider using the orthogonal polynomials to decide which is the highest degree you are going to take into account, and then re-regress on the raw polynomials of that degree and smaller to get coefficients which can easily be implemented, even though the reported statistics about the coefficients of the re-regressed raw polynomials are meaningless.
Nov
11
comment Creating condifence intervals ? Help with statistics intervals.
So which are you finding difficult in the calculation of $\bar{x} + z s / \sqrt{n}$? A sample of $50$ should be big enough to assume a normal distribution is a reasonable approximation of the distribution of the sample mean. If you would rather use a $t$-distrubution, see an earlier answer. (Your use of $\mu$ is not quite correct, but that should not stop you calculating the confidence interval)