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….
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 ….
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….
I can’t seem to find any python libraries that do multiple regression. The only things I find only do simple regression. I need to regress my dependent variable (y) against several independent variables (x1, x2, x3, etc.).
For example, with this data:
print ‘y x1 x2 x3 x4 x5 x6 x7’
for t in texts:
print “….
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.
….
I notice that
In [30]: np.mean([1, 2, 3])
Out[30]: 2.0
In [31]: np.average([1, 2, 3])
Out[31]: 2.0
However, there should be some differences, since after all they are two different functions.
What are the differences between them?
….
Is there a convenient way to calculate percentiles for a sequence or single-dimensional numpy array?
I am looking for something similar to Excel’s percentile function.
I looked in NumPy’s statistics reference, and couldn’t find this. All I could find is the median (50th percentile), but not something more specific.
….
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….