NORMSINV (mentioned in a comment) is the inverse of the CDF of the standard normal distribution. Using scipy, you can compute this with the ppf method of the scipy.stats.norm object. The acronym ppf stands for percent point function, which is another name for the quantile function. In [20]: from scipy.stats import norm In [21]: norm.ppf(0.95) … Read more
It’s not a bug. It’s not an incorrect result either. Probability density function’s value at some specific point does not give you probability; it is a measure of how dense the distribution is around that value. For continuous random variables, the probability at a given point is equal to zero. Instead of p(X = x), … Read more
The parametrization of truncnorm is complicated, so here is a function that translates the parametrization to something more intuitive: from scipy.stats import truncnorm def get_truncated_normal(mean=0, sd=1, low=0, upp=10): return truncnorm( (low – mean) / sd, (upp – mean) / sd, loc=mean, scale=sd) How to use it? Instance the generator with the parameters: mean, standard deviation, … Read more
Normality tests don’t do what most think they do. Shapiro’s test, Anderson Darling, and others are null hypothesis tests AGAINST the the assumption of normality. These should not be used to determine whether to use normal theory statistical procedures. In fact they are of virtually no value to the data analyst. Under what conditions are … Read more
There are many methods to generate Gaussian-distributed numbers from a regular RNG. The Box-Muller transform is commonly used. It correctly produces values with a normal distribution. The math is easy. You generate two (uniform) random numbers, and by applying an formula to them, you get two normally distributed random numbers. Return one, and save the … Read more
There is no standard deviation function C++, so you’d need to do write all the necessary functions yourself — Generate random numbers and calculate the standard deviation. double stDev(const vector<double>& data) { double mean = std::accumulate(data.begin(), data.end(), 0.0) / data.size(); double sqSum = std::inner_product(data.begin(), data.end(), data.begin(), 0.0); return std::sqrt(sqSum / data.size() – mean * mean); … Read more