It depends on the statistical model. You might as well ask what R function you use for statistics. Answer from berf on reddit.com
Bookdown
bookdown.org › logan_kelly › r_practice › p09.html
Practice 9 Calculating Confidence Intervals in R | R Practices for Learning Statistics
# Calculate the mean and standard error l.model <- lm(mpg ~ 1, mtcars) # Calculate the confidence interval confint(l.model, level=0.95) ## 2.5 % 97.5 % ## (Intercept) 17.91768 22.26357 · Use R to complete the following activities (this is just for practice you do not need to turn anything in).
CRAN
cran.r-project.org › web › packages › interpretCI › vignettes › Confidence_interval_for_a_proportion.html
Confidence interval for a proportion
Therefore, the 99% confidence interval is 0.37 to 0.43. That is, we are 99% confident that the true proportion is in the range 0.37 to 0.43.
Which R command do people use for confidence intervals for a mean?
It depends on the statistical model. You might as well ask what R function you use for statistics. More on reddit.com
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March 28, 2022statistics - Mean and Confidence interval for Groups of Variable in R - Stack Overflow
Or, are you looking for one function to do it all for you? ... Just for the record: I am the author of the rcompanion package. There is no ci.mean function in this package. However, there is a groupwiseMean function that produces confidence intervals for groups, offering a few different methods. More on stackoverflow.com
How to calculate P-value and 95% confidence interval in R for cross-sectional table - Cross Validated
I am a newbie to statistics and R. Need someone's help to understand how to get a 95% confidence interval and p-value from the above data to test the null hypothesis that there is no difference in ... More on stats.stackexchange.com
How to generate the confidence interval for difference between control and test proportions? (My boss is adamant his solution is correct but I disagree..)
For users of old reddit: The table and code in the question might not work if you use old reddit (at least for some setups). This might save you some effort: Group | Converted | Did not convert | Control | 30 | 387 | Test | 59 | 465 code under 1: ctrl <- 30/(30 + 387) test <- 59/(59 + 465) sqrt(ctrl*(1-ctrl)/(30 + 387)) # 0.01265354 sqrt(test*(1-test)/(59 + 465)) # 0.01380879 and under 2: calculate the confidence interval of each proportion ctrl +/- 2 * 0.01265354 <-- 2 standard devs gives 95% conf interval test +/- 2 * 0.01380879 On the CI for the difference in proportion: The use of two confidence intervals in Step 3 is not correct. You can form an approximate confidence interval for the difference in proportion by adding the squares of the standard errors of each proportion and taking the square root (giving the s.e. of the difference), and using a z interval based off that. There are other approaches but that should work fine. Since you're using R, you can skip that and just use prop.test which by default gives the same chi-squared value as chisq.test (i.e. it also uses Yates' continuity correction, though doesn't say so in the output), but then it also gives an interval for the difference in proportion: conv<-matrix(c(30,59,387,465),nr=2) rownames(conv)<-c("Control","Test") colnames(conv)<-c("Converted","Did not convert") conv prop.test(conv) The output for the last two lines should look like this: > conv Converted Did not convert Control 30 387 Test 59 465 and > prop.test(conv) 2-sample test for equality of proportions with continuity correction data: conv X-squared = 4.0192, df = 1, p-value = 0.04498 alternative hypothesis: two.sided 95 percent confidence interval: -0.079515385 -0.001790562 sample estimates: prop 1 prop 2 0.07194245 0.11259542 so with those settings, the 95% CI does not overlap 0. You can turn off the continuity correction in the usual way (i.e. just as with chisq.test). More on reddit.com
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September 13, 2023Videos
04:08
Confidence Intervals in R - YouTube
Confidence Intervals for one Population Mean in R (Z & t ...
25:33
Confidence Intervals for means and proportions in R - YouTube
03:57
R & R Studio #12 The 95% Confidence Interval - YouTube
09:54
[R Beginners]: Calculate and visualise Confidence Intervals and ...
Ubc
whitlockschluter3e.zoology.ubc.ca › RExamples › Rcode_Chapter_12.html
R code for example in Chapter 12: Comparing two means
A paired t-test can be done either on differences you have already calculated (d here) or by using the paired = TRUE argument with the measurements from the two groups. ... ## ## One Sample t-test ## ## data: blackbird$d ## t = 1.2714, df = 12, p-value = 0.2277 ## alternative hypothesis: true mean is not equal to 0 ## 95 percent confidence interval: ## -0.04007695 0.15238464 ## sample estimates: ## mean of x ## 0.05615385
R-bloggers
r-bloggers.com › r bloggers › calculate confidence intervals in r
Calculate Confidence Intervals in R | R-bloggers
November 18, 2021 - The code below demonstrates how to compute a 95% confidence interval for the genuine difference in population means. n1 <- 20 xbar1 <- 250 s1 <- 13 n2 <- 20 xbar2 <- 280 s2 <- 11.9 · Now we need to calculate the pooled variance of the above data.
Chemistry LibreTexts
chem.libretexts.org › bookshelves › analytical chemistry › chemometrics using r (harvey) › 6: uncertainty of data
6.4: Using R to Find Confidence Intervals - Chemistry LibreTexts
June 6, 2021 - The confidence interval for a population’s mean, \(\mu\), given an experimental mean, \(\bar{x}\), for \(n\) samples is defined as · \[\mu = \bar{x} \pm \frac {z \sigma} {\sqrt{n}} \nonumber\] if we know the population's standard deviation, \(\sigma\), and as · \[\mu = \bar{x} \pm \frac {t s} {\sqrt{n}} \nonumber\] if we assume that the sample's standard deviation, \(s\), is a reasonable predictor of the population's standard deviation.
Rcompanion
rcompanion.org › handbook › H_02.html
R Handbook: Confidence Intervals for Proportions
They want to determine the difference ... and calculate a confidence interval for that difference. The following are the data: Seras Victoria Integra Hellsing Experience Count Experience Count Yes 7 Yes 13 No 14 No 4 ---------- ----- ---------- ----- Total 21 Total 17 · Two functions in the PropCIs package can determine a confidence interval for a difference for in independent proportions. ... “Confidence Limits” in Mangiafico, S.S. 2015a. An R Companion ...
Reddit
reddit.com › r/rlanguage › which r command do people use for confidence intervals for a mean?
r/Rlanguage on Reddit: Which R command do people use for confidence intervals for a mean?
March 28, 2022 -
If you use R to calculate confidence intervals for a mean, which command do you use and to what degree does it depend on particular properties of the data? Also, if you care to share, what discipline are you in? Thanks.
Top answer 1 of 3
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It depends on the statistical model. You might as well ask what R function you use for statistics.
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I usually just use t.test(). Call t.test() on a numeric vector and it will return the mean and a confidence interval on the mean based on the t-distribution. (Of course it also carries out a t-test that the mean is equal to 0, which I ignore.) The smean.cl.normal() and smean.cl.boot() functions in the Hmisc package will return CIs based on the t distribution and bootstrap, respectively. I sometimes use those if I want to work with a vector instead of a list.
Uw-statistics
uw-statistics.github.io › Stat311Tutorial › confidence-intervals.html
6.1 Confidence Intervals | Introduction to Statistics with R
There’s no function in base R that will just compute a confidence interval, but we can use the z.test and t.test functions to do what we need here (at least for means – we can’t use this for proportions).
Introduction to Econometrics with R
econometrics-with-r.org › 5.2-cifrc.html
5.2 Confidence Intervals for Regression Coefficients | Introduction to Econometrics with R
The confidence level is set to \(95\%\) by default but can be modified by setting the argument level, see ?confint. # compute 95% confidence interval for coefficients in 'linear_model' confint(linear_model) #> 2.5 % 97.5 % #> (Intercept) 680.32312 717.542775 #> STR -3.22298 -1.336636 · Let us check if the calculation is done as we expect it to be for \(\beta_1\), the coefficient on STR.
GeeksforGeeks
geeksforgeeks.org › r language › how-to-find-confidence-intervals-in-r
How to Find Confidence Intervals in R? - GeeksforGeeks
July 24, 2025 - We use the t.test() function to find the confidence interval of the sample mean in R programming language.
Optimumsportsperformance
optimumsportsperformance.com › blog › calculating-confidence-intervals-in-r
Calculating Confidence Intervals in R | Patrick Ward, PhD
January 31, 2024 - ... Multiply by a t-critical value specific to the level of confidence of interest and the degrees of freedom (DF) for the single sample, DF = N – 1 ... We collect data on 30 participants on a special test of strength and observe a mean of 40 and standard deviation of 10.
Western Sydney University
westernsydney.edu.au › mesh › mesh › support_and_resources › test_standard_page › confidence_intervals
Confidence Intervals with R Commander | Western Sydney University
February 9, 2025 - The videos on this page show you how to use R Commander to determine a confidence interval for the population parameter from a sample of data and how to determine the sample size required for a given confidence level. Instructions for constructing a confidence interval. Calculating the sample size required for a given confidence level.
Scribbr
scribbr.com › home › understanding confidence intervals | easy examples & formulas
Understanding Confidence Intervals | Easy Examples & Formulas
June 22, 2023 - You can find a distribution that matches the shape of your data and use that distribution to calculate the confidence interval. You can perform a transformation on your data to make it fit a normal distribution, and then find the confidence interval for the transformed data. Performing data transformations is very common in statistics, for example, when data follows a logarithmic curve but we want to use it alongside linear data. You just have to remember to do the reverse transformation on your data when you calculate the upper and lower bounds of the confidence interval.
Statology
statology.org › home › how to calculate a binomial confidence interval in r
How to Calculate a Binomial Confidence Interval in R
March 15, 2022 - One way to calculate the 95% binomial confidence interval is to use the prop.test() function in base R:
Top answer 1 of 5
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Assuming you just want the usual non-pooled t confidence interval for each group you can do
require(dplyr)
alpha <- 0.05
data %>%
group_by(Group, Time) %>%
summarize(mean = mean(mean_PctPasses),
lower = mean(mean_PctPasses) - qt(1- alpha/2, (n() - 1))*sd(mean_PctPasses)/sqrt(n()),
upper = mean(mean_PctPasses) + qt(1- alpha/2, (n() - 1))*sd(mean_PctPasses)/sqrt(n()))
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Doing this with R is easy, too.
Another way, using CI() from Rmisc:
library(dplyr)
library(Rmisc)
library(ggplot2)
data <-
data %>%
group_by(Group, Time) %>%
dplyr::summarise(avg_PctPasses = mean(mean_PctPasses),
uci_PctPasses = CI(mean_PctPasses)[1],
lci_PctPasses = CI(mean_PctPasses)[3]) %>%
mutate(Time = Time %>% as.factor())
Admittedly, I'm not a big fan of the "magic numbers" after the call to CI().
Plotting the data is equally simple.
data %>%
ggplot(aes(x = Group, y = avg_PctPasses, fill = Time)) +
geom_bar(stat = "identity", position = "dodge") +
geom_errorbar(aes(ymin = lci_PctPasses, ymax = uci_PctPasses), position = "dodge")
University of Wisconsin
pages.stat.wisc.edu › ~yandell › st571 › R › append7.pdf pdf
7.6.2 Appendix: Using R to Find Confidence Intervals
The tinterval command of R is a useful one for finding confidence intervals for the mean
Intro to GIS
mgimond.github.io › Stats-in-R › CI.html
Basic stats explained (in R) - Confidence intervals
The idea of a confidence interval, CI, is a natural extension of the standard error. It allows us to define a level of confidence in our population parameter estimate gleaned from a sample. For example, if we wanted to be 95% confident that the range of mean TV hours per week computed from our sample encompasses the true mean value for all households in the population we would compute this interval by adding and subtracting \(1.96\; SE\) to/from the sample mean.
UCLA Statistics
stat.ucla.edu › ~rgould › 110as02 › bsci pdf
Finding Confidence Intervals with R Data
plot show that this wasn’t such a great assumption to begin with: So at best, the confidence intervals from above are approximate. The approximation, however, ... A bootstrap interval might be helpful. Here are the steps involved. 1. From our sample of size 10, draw a new sample, WITH replacement, of size 10. 2. Calculate ...
Cyclismo
cyclismo.org › tutorial › R › confidence.html
9. Calculating Confidence Intervals — R Tutorial
Our level of certainty about the true mean is 95% in predicting that the true mean is within the interval between 4.06 and 5.94 assuming that the original random variable is normally distributed, and the samples are independent. We now look at an example where we have a univariate data set and want to find the 95% confidence interval for the mean.