confidence-interval

问题 I am new to python, and trying to use the lmfit package to check my own calculations, however I am unsure (1) as to how to include the errors for data (sig) for the following test (and 2) of an error I get with conf_interval2d shown below): import numpy as np from lmfit import Parameters, Minimizer, conf_interval, conf_interval2d, minimize, printfuncs x=np.array([ 0.18, 0.26, 1.14, 0.63, 0.3 , 0.22, 1.16, 0.62, 0.84,0.44, 1.24, 0.89, 1.2 , 0.62, 0.86, 0.45, 1.17, 0.59, 0.85, 0.44]) data=np

问题 I recently started to use Python and I can't understand how to plot a confidence interval for a given datum (or set of data). I already have a function that computes, given a set of measurements, a higher and lower bound depending on the confidence level that I pass to it, but I don't know how to use those two values to plot a confidence interval. I know that this question have been already asked here but I did not find the answers useful. 回答1: There are several ways to accomplish what you

问题 I recently started to use Python and I can't understand how to plot a confidence interval for a given datum (or set of data). I already have a function that computes, given a set of measurements, a higher and lower bound depending on the confidence level that I pass to it, but I don't know how to use those two values to plot a confidence interval. I know that this question have been already asked here but I did not find the answers useful. 回答1: There are several ways to accomplish what you

问题 I am trying to get the five types of bootstrap intervals for linear and quantile regression. I was able to bootstrap and find the 5 boostrap intervals (Quantile,Normal,Basic,Studentized and BCa) for the linear regression using Boot from car and boot.ci from boot . When i tried to do the same for quantile regression using rq from quantreg , it throws up an error. Here is the sample code Creating the model library(car) library(quantreg) library(boot) newdata = Prestige[,c(1:4)] education.c =

问题 I'm trying to recreate a plot from An Introduction to Statistical Learning and I'm having trouble figuring out how to calculate the confidence interval for a probability prediction. Specifically, I'm trying to recreate the right-hand panel of this figure (figure 7.1) which is predicting the probability that wage>250 based on a degree 4 polynomial of age with associated 95% confidence intervals. The wage data is here if anyone cares. I can predict and plot the predicted probabilities fine with

问题 First, I must admit that my statistics knowledge is rusty at best: even when it was shining new, it's not a discipline I particularly liked, which means I had a hard time making sense of it. Nevertheless, I took a look at how the barplot graphs were calculating error bars, and was surprised to find a "confidence interval" (CI) used instead of (the more common) standard deviation. Researching more CI led me to this wikipedia article which seems to say that, basically, a CI is computed as: Or,

问题 I have a dataframe which looks like this: df <- data.frame ( time = rep(c("2010", "2011", "2012", "2013", "2014"),4), age = rep(c("40-44", "45-49", "50-54", "55-59", "60-64"),4), weight = rep(c(0.38, 0.23, 0.19, 0.12, 0.08),4), ethnic = rep(c(rep("M",5),rep("NM",5)),2), gender = c(rep("M",10), rep("F",10)), pop = round((runif(10, min = 10000, max = 99999)), digits = 0), count = round((runif(10, min = 100, max = 999)), digits = 0) ) df$rate = df$count / df$pop I want to calculate the direct

问题 I am trying to calculate the mean and confidence interval(95%) of a column "Force" in a large dataset. I need the result by using the groupby function by grouping different "Classes". When I calculate the mean and put it in the new dataframe, it gives me NaN values for all rows. I'm not sure if I'm going the correct way. Is there any easier way to do this? This is the sample dataframe: df=pd.DataFrame({ 'Class': ['A1','A1','A1','A2','A3','A3'], 'Force': [50,150,100,120,140,160] }, columns=[

问题 I am trying to calculate the mean and confidence interval(95%) of a column "Force" in a large dataset. I need the result by using the groupby function by grouping different "Classes". When I calculate the mean and put it in the new dataframe, it gives me NaN values for all rows. I'm not sure if I'm going the correct way. Is there any easier way to do this? This is the sample dataframe: df=pd.DataFrame({ 'Class': ['A1','A1','A1','A2','A3','A3'], 'Force': [50,150,100,120,140,160] }, columns=[

问题 I am trying to calculate the mean and confidence interval(95%) of a column "Force" in a large dataset. I need the result by using the groupby function by grouping different "Classes". When I calculate the mean and put it in the new dataframe, it gives me NaN values for all rows. I'm not sure if I'm going the correct way. Is there any easier way to do this? This is the sample dataframe: df=pd.DataFrame({ 'Class': ['A1','A1','A1','A2','A3','A3'], 'Force': [50,150,100,120,140,160] }, columns=[