statistics

问题 I am reading a statistics textbook Introduction to Statistics for Engineers by Sheldon Ross, p.275 and trying to re-do its examples on paper and in Octave. I am not able to replicate many Bayes calculations in Octave when it comes to the integration part. Please advise how to go about replicating below calculation in Octave? Below is a simple Bayes estimator example which naturally becomes a symbolic integration problem, where I often encounter difficulty doing in Octave. [ Clarification :

问题 I am reading a statistics textbook Introduction to Statistics for Engineers by Sheldon Ross, p.275 and trying to re-do its examples on paper and in Octave. I am not able to replicate many Bayes calculations in Octave when it comes to the integration part. Please advise how to go about replicating below calculation in Octave? Below is a simple Bayes estimator example which naturally becomes a symbolic integration problem, where I often encounter difficulty doing in Octave. [ Clarification :

问题 I am reading a statistics textbook Introduction to Statistics for Engineers by Sheldon Ross, p.275 and trying to re-do its examples on paper and in Octave. I am not able to replicate many Bayes calculations in Octave when it comes to the integration part. Please advise how to go about replicating below calculation in Octave? Below is a simple Bayes estimator example which naturally becomes a symbolic integration problem, where I often encounter difficulty doing in Octave. [ Clarification :

问题 I am reading a statistics textbook Introduction to Statistics for Engineers by Sheldon Ross, p.275 and trying to re-do its examples on paper and in Octave. I am not able to replicate many Bayes calculations in Octave when it comes to the integration part. Please advise how to go about replicating below calculation in Octave? Below is a simple Bayes estimator example which naturally becomes a symbolic integration problem, where I often encounter difficulty doing in Octave. [ Clarification :

问题 I have some data that I have to test to see if it comes from a Weibull distribution with unknown parameters. In R I could use https://cran.r-project.org/web/packages/KScorrect/index.html but I can't find anything in Python. Using scipy.stats I can fit parameters with: scipy.stats.weibull_min.fit(values) However in order to turn this into a test I think I need to perform some Monte-Carlo simulation (e.g. https://en.m.wikipedia.org/wiki/Lilliefors_test) I am not sure what to do exactly. How can

问题 I have some data that I have to test to see if it comes from a Weibull distribution with unknown parameters. In R I could use https://cran.r-project.org/web/packages/KScorrect/index.html but I can't find anything in Python. Using scipy.stats I can fit parameters with: scipy.stats.weibull_min.fit(values) However in order to turn this into a test I think I need to perform some Monte-Carlo simulation (e.g. https://en.m.wikipedia.org/wiki/Lilliefors_test) I am not sure what to do exactly. How can

问题 import pandas as pd import numpy as np df = pd.read_csv("test.txt", sep='\t', names= ["ElementID","Load"]) df.insert(0, 'count', df.groupby('ElementID').cumcount()) df2 = df.pivot(index='count',columns='ElementID', values='Load') df2_norm = df2.apply(lambda x: (x / x.abs().max())) allowableCorr = df2_norm.corr(method = 'pearson') slope = allowableCorr * (df2_norm.std().values / df2_norm.std().values[:, np.newaxis]) I have a lot of datasets which I use the .corr method to get the r^2 value.

问题 I have the following plot that looks like: I'm trying to add points in another layer using according to the following code found here My data frame i'm importing looks like this: residue_id residue_num per_ddg pg9_seq_bool A 96 0.00437094862845686 FALSE A 97 -0.0026333752377897 FALSE A 98 -0.00453070737795937 FALSE A 99 -0.0130564560942629 TRUE A 100 -0.00578979380922636 FALSE A 101 -0.0128130535970934 FALSE A 102 -0.0264740211311766 FALSE A 103 -0.183036342874782 FALSE A 104 -0

问题 Actually there are 2 questions, one is more advanced than the other. Q1: I am looking for a method that similar to corrplot() but can deal with factors. I originally tried to use chisq.test() then calculate the p-value and Cramer's V as correlation, but there too many columns to figure out. So could anyone tell me if there is a quick way to create a "corrplot" that each cell contains the value of Cramer's V , while the colour is rendered by p-value . Or any other kind of similar plot.

问题 Actually there are 2 questions, one is more advanced than the other. Q1: I am looking for a method that similar to corrplot() but can deal with factors. I originally tried to use chisq.test() then calculate the p-value and Cramer's V as correlation, but there too many columns to figure out. So could anyone tell me if there is a quick way to create a "corrplot" that each cell contains the value of Cramer's V , while the colour is rendered by p-value . Or any other kind of similar plot.