normal-distribution

问题 I have read the documentation on qnorm() but could not really understand what it does. Can you please answer in detail what qnorm() does? note-Please excuse me for asking such a simple question. I am a beginner at R. 回答1: The function qnorm() aims to find the boundary value, A in P(X < A) , given the probability P. For example, suppose you want to find the 85th percentile of a normal distribution whose mean is 70 and whose standard deviation is 3. Then you ask for: qnorm(0.85,mean=70,sd=3) [1

问题 Can anyone give me a way to do a qq plot in Seaborn as a test for normality of data? Or failing that, at least in matplotlib. Thanks in advance 回答1: After reading the wikipedia article, I understand that the Q-Q plot is a plot of the quantiles of two distributions against each other. numpy.percentile allows to obtain the percentile of a distribution. Hence you can call numpy.percentile on each of the distributions and plot the results against each other. import numpy as np import matplotlib

问题 I am trying to use a truncated normal distribution with scipy in Python3. I want to do something simple: plot the pdf of a truncated normal centered at 0.5 and ranging from 0 to 1. I have the following code line from scipy import truncnorm import matplotlib.pyplot as plt plt.plot([truncnorm.pdf(p,0,1, loc=0.5) for p in np.arange(0,1.1,0.1)]) However, this does not give the nice bell-shaped probability distribution function I would expect. Rather, it equals 0 before 0.5, and I cannot figure

问题 I am trying to use a truncated normal distribution with scipy in Python3. I want to do something simple: plot the pdf of a truncated normal centered at 0.5 and ranging from 0 to 1. I have the following code line from scipy import truncnorm import matplotlib.pyplot as plt plt.plot([truncnorm.pdf(p,0,1, loc=0.5) for p in np.arange(0,1.1,0.1)]) However, this does not give the nice bell-shaped probability distribution function I would expect. Rather, it equals 0 before 0.5, and I cannot figure

问题 I'm trying to transform my dataset to a normal distribution. 0 8.298511e-03 1 3.055319e-01 2 6.938647e-02 3 2.904091e-02 4 7.422441e-02 5 6.074046e-02 6 9.265747e-04 7 7.521846e-02 8 5.960521e-02 9 7.405019e-04 10 3.086551e-02 11 5.444835e-02 12 2.259236e-02 13 4.691038e-02 14 6.463911e-02 15 2.172805e-02 16 8.210005e-02 17 2.301189e-02 18 4.073898e-07 19 4.639910e-02 20 1.662777e-02 21 8.662539e-02 22 4.436425e-02 23 4.557591e-02 24 3.499897e-02 25 2.788340e-02 26 1.707958e-02 27 1.506404e

问题 G'day, I am trying to compute the lower tail probability for the bivariate Normal distribution given by the following formula for 2 random variables (X1, X2): Where X1 = -1.23, X2 = -2.75 and rho = 0.65. I am very curious how to solve this problem? The first term it's just calculations but how would you attack the integrals? Can someone provide me with some code or hints or if it's possible a solution? X's are log normal distributed random variables. Furthermore; how would extend it to

问题 I have been playing with pandas lately and I now I tried to replace NaN value inside a dataframe with different random value of normal distribution. Assuming I have this CSV file without header 0 0 343 1 483 2 101 3 NaN 4 NaN 5 NaN My expected result should be something like this 0 0 343 1 483 2 101 3 randomnumber1 4 randomnumber2 5 randomnumber3 But instead I got the following : 0 0 343 1 483 2 101 3 randomnumber1 4 randomnumber1 5 randomnumber1 # all NaN filled with same number My code so

问题 I have been playing with pandas lately and I now I tried to replace NaN value inside a dataframe with different random value of normal distribution. Assuming I have this CSV file without header 0 0 343 1 483 2 101 3 NaN 4 NaN 5 NaN My expected result should be something like this 0 0 343 1 483 2 101 3 randomnumber1 4 randomnumber2 5 randomnumber3 But instead I got the following : 0 0 343 1 483 2 101 3 randomnumber1 4 randomnumber1 5 randomnumber1 # all NaN filled with same number My code so

问题 I have been playing with pandas lately and I now I tried to replace NaN value inside a dataframe with different random value of normal distribution. Assuming I have this CSV file without header 0 0 343 1 483 2 101 3 NaN 4 NaN 5 NaN My expected result should be something like this 0 0 343 1 483 2 101 3 randomnumber1 4 randomnumber2 5 randomnumber3 But instead I got the following : 0 0 343 1 483 2 101 3 randomnumber1 4 randomnumber1 5 randomnumber1 # all NaN filled with same number My code so

问题 I am given the log10 mean and log10 standard deviation of a log-normal distribution. I want to get a random number from this log-normal distribution. Can this be accomplished with numpy.random.lognormal, even though that function's inputs are the mean and standard of the underlying normal distribution (I do not have that)? Also, will the random number that I get back from the function be log10, natural log, or regular? 回答1: Wikipedia says that the parameters of lognormal distribution are