Fix Python – How to calculate probability in a normal distribution given mean & standard deviation?

How to calculate probability in normal distribution given mean, std in Python? I can always explicitly code my own function according to the definition like the OP in this question did: Calculating Probability of a Random Variable in a Distribution in Python
Just wondering if there is a library function call will allow you to do this. In my imagin….

Fix Python – How do I calculate r-squared using Python and Numpy?

I’m using Python and Numpy to calculate a best fit polynomial of arbitrary degree. I pass a list of x values, y values, and the degree of the polynomial I want to fit (linear, quadratic, etc.).
This much works, but I also want to calculate r (coefficient of correlation) and r-squared(coefficient of determination). I am comparing my results with ….

Fix Python – Statistics: combinations in Python

I need to compute combinatorials (nCr) in Python but cannot find the function to do that in math, numpy or stat libraries. Something like a function of the type:
comb = calculate_combinations(n, r)

I need the number of possible combinations, not the actual combinations, so itertools.combinations does not interest me.
Finally, I want to avoid usi….

Fix Python – Compute a confidence interval from sample data

I have sample data which I would like to compute a confidence interval for, assuming a normal distribution.
I have found and installed the numpy and scipy packages and have gotten numpy to return a mean and standard deviation (numpy.mean(data) with data being a list). Any advice on getting a sample confidence interval would be much appreciated.

Fix Python – How to normalize a NumPy array to a unit vector?

I would like to convert a NumPy array to a unit vector. More specifically, I am looking for an equivalent version of this normalisation function:
def normalize(v):
norm = np.linalg.norm(v)
if norm == 0:
return v
return v / norm

This function handles the situation where vector v has the norm value of 0.
Is there any similar fun….