confidence-interval

I am trying to calculate the confidence interval in R. Due to some special reasons, I have to do it with the functions in "bootstrap" package.(which means I can't use the functions in "boot" package.) Here is my code. And what I am doing is trying to calculate the Pearson correlation coefficient, and then apply the Bootstrap method (with B = 100) to obtain the estimate of the correlation coefficient. But I don't know how to construct the 95% confidence intervals. library(bootstrap) data('law') set.seed(1) theta <- function(ind) { cor(law[ind, 1], law[ind, 2], method = "pearson") } law.boot <-

I want to plot some confidence interval graphs in MATLAB but I don't have any idea at all how to do it. I have the data in a .xls file. Can someone give me a hint, or does anyone know commands for plotting CIs? I'm not sure what you meant by confidence intervals graph, but this is an example of how to plot a two-sided 95% CI of a normal distribution: alpha = 0.05; % significance level mu = 10; % mean sigma = 2; % std cutoff1 = norminv(alpha, mu, sigma); cutoff2 = norminv(1-alpha, mu, sigma); x = [linspace(mu-4*sigma,cutoff1), ... linspace(cutoff1,cutoff2), ... linspace(cutoff2,mu+4*sigma)]; y

I'd like a 95% confidence interval line above and below my data line - as opposed to vertical bars at each point. Is there a way that I can do this in gnuplot without plotting another line? Or do I need to plot another line and then label it appropriately? You can use the filledcurves style to fill the region of 95% confidence. Consider the example data file data.dat with the content: # x y ylow yhigh 1 3 2.6 3.5 2 5 4 6 3 4 3.2 4.3 4 3.5 3.3 3.7 and plot this with the script set style fill transparent solid 0.2 noborder plot 'data.dat' using 1:3:4 with filledcurves title '95% confidence', \ '

I have a collection of data over several studies. For each study I am interested about the mean of a variable by gender, and if this significantly differs. For each study I have the mean and 95% confidence intervals for both males and females. What I would like to do is something similar to this: I have used several flavours of dotplots (dotplot, dotplot2, Dotplot) but did not quite get there. Using Dotplot from Hmisc I managed to have one series and its errorbars, but I am at a loss on how to adding the second series. I used Dotplot and got the vertical ending of the error bars following

I have a 1-dimensional array of data: a = np.array([1,2,3,4,4,4,5,5,5,5,4,4,4,6,7,8]) for which I want to obtain the 68% confidence interval (ie: the 1 sigma ). The first comment in this answer states that this can be achieved using scipy.stats.norm.interval from the scipy.stats.norm function, via: from scipy import stats import numpy as np mean, sigma = np.mean(a), np.std(a) conf_int = stats.norm.interval(0.68, loc=mean, scale=sigma) But a comment in this post states that the actual correct way of obtaining the confidence interval is: conf_int = stats.norm.interval(0.68, loc=mean, scale=sigma

Here is an exercise from Introductory Statistics with R: With the rmr data set, plot metabolic rate versus body weight. Fit a linear regression model to the relation. According to the fitted model, what is the predicted metabolic rate for a body weight of 70 kg? Give a 95% confidence interval for the slope of the line. rmr data set is in the 'ISwR' package. It looks like this: > rmr body.weight metabolic.rate 1 49.9 1079 2 50.8 1146 3 51.8 1115 4 52.6 1161 5 57.6 1325 6 61.4 1351 7 62.3 1402 8 64.9 1365 9 43.1 870 10 48.1 1372 11 52.2 1132 12 53.5 1172 13 55.0 1034 14 55.0 1155 15 56.0 1392 16

How do I get the CI for one point on the regression line? I'm quite sure I should use confint() for that, but if I try this confint(model,param=value) it just gives me the same number as if I just type in confint(model) if I try without a value, it does not give me any values at all. What am I doing wrong? You want predict() instead of confint() . Also, as Joran noted, you'll need to be clear about whether you want the confidence interval or prediction interval for a given x. (A confidence interval expresses uncertainty about the expected value of y-values at a given x. A prediction interval