Correlation Standard Error Estimate
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here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site correlation coefficient standard error About Us Learn more about Stack Overflow the company Business Learn more about standard error of estimate calculator hiring developers or posting ads with us Stack Overflow Questions Jobs Documentation Tags Users Badges Ask Question x Dismiss Join standard error of estimate anova table the Stack Overflow Community Stack Overflow is a community of 4.7 million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up How to compute P-value
Correlation Standard Deviation
and standard error from correlation analysis of R's cor() up vote 12 down vote favorite 2 I have data that contain 54 samples for each condition (x and y). I have computed the correlation the following way: > dat <- read.table("http://dpaste.com/1064360/plain/",header=TRUE) > cor(dat$x,dat$y) [1] 0.2870823 Is there a native way to produce SE of correlation in R's cor() functions above and p-value from T-test? As explained correlation confidence interval in this web (page 14.6) r correlation share|improve this question asked Apr 19 '13 at 4:55 neversaint 10.4k50150248 4 Perhaps you're looking for ?cor.test instead. –A Handcart And Mohair Apr 19 '13 at 4:59 add a comment| 2 Answers 2 active oldest votes up vote 20 down vote accepted I think that what you're looking for is simply the cor.test() function, which will return everything you're looking for except for the standard error of correlation. However, as you can see, the formula for that is very straightforward, and if you use cor.test, you have all the inputs required to calculate it. Using the data from the example (so you can compare it yourself with the results on page 14.6): > cor.test(mydf$X, mydf$Y) Pearson's product-moment correlation data: mydf$X and mydf$Y t = -5.0867, df = 10, p-value = 0.0004731 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.9568189 -0.5371871 sample estimates: cor -0.8492663 If you wanted to, you could also create a function like the following to include the standard error of the correlation coefficient. For convenience, here's the equation: r = the correlation estimate and n -
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Correlation T Test
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Correlation R Square
visualization. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top Standard error from correlation coefficient up http://stackoverflow.com/questions/16097453/how-to-compute-p-value-and-standard-error-from-correlation-analysis-of-rs-cor vote 2 down vote favorite 1 Many studies only report the relationship between two variables (e.g. linear or logistic equation), $n$, and $r^2$. I want to use these reported statistics to reproduce this relationship with its variation. Most statistical software will generate a parameter distribution from a mean and standard error. Assuming a normal distribution, can the standard error of the parameter estimates be calculated with just these three statistics? Essentially, can I get a standard error from $r^2$? http://stats.stackexchange.com/questions/73621/standard-error-from-correlation-coefficient Or will I need to do some kind of bootstrapping procedure to generate a distribution that has the same $r^2$ and then calculate the standard error? if so are there better ones for linear vs. nonlinear equations? distributions correlation normal-distribution variance share|improve this question edited Oct 23 '13 at 23:50 Nick Cox 28.2k35684 asked Oct 23 '13 at 18:53 janice 1112 Sorry for the typo, it should be correlation coefficient, not correction coefficient. –janice Oct 23 '13 at 19:05 Welcome to our site, Janice! We encourage you to continue improving your question, which includes editing it for typographical errors. More information is available at our help center. –whuber♦ Oct 23 '13 at 20:14 add a comment| 1 Answer 1 active oldest votes up vote 3 down vote If you look at the Wikipedia page for the Pearson product-moment correlation, you will find sections that describe how confidence intervals can be calculated. Typically, people will use Fisher's $z$-transformation (arctan) to turn the $r$ into a variable that is approximately normally distributed: $$ z_r = \frac 1 2 \ln \frac{1 + r}{1 - r} $$ Having applied this transformation, the standard error will be approximately $^1/_{\sqrt{(N-3)}}$. With this you can form whatever length confidence interval you like. Once you've found the confidence limits you want, you can back-transform them to the original $r$ scale (i.e., $[-1, 1]$) like so: $$ \tex
Ana-Maria ŠimundićEditor-in-ChiefDepartment of Medical Laboratory DiagnosticsUniversity Hospital "Sveti Duh"Sveti Duh 6410 000 Zagreb, CroatiaPhone: +385 1 http://www.biochemia-medica.com/content/standard-error-meaning-and-interpretation 3712-021e-mail address:editorial_office [at] biochemia-medica [dot] com Useful links Events Follow us on Facebook Home Standard error: meaning and interpretation Lessons in biostatistics Mary L. McHugh. Standard error: meaning and interpretation. Biochemia Medica 2008;18(1):7-13. http://dx.doi.org/10.11613/BM.2008.002 School of Nursing, University of Indianapolis, Indianapolis, Indiana, USA *Corresponding author: Mary [dot] standard error McHugh [at] uchsc [dot] edu Abstract Standard error statistics are a class of inferential statistics that function somewhat like descriptive statistics in that they permit the researcher to construct confidence intervals about the obtained sample statistic. The confidence interval so constructed provides an estimate of the interval in standard error of which the population parameter will fall. The two most commonly used standard error statistics are the standard error of the mean and the standard error of the estimate. The standard error of the mean permits the researcher to construct a confidence interval in which the population mean is likely to fall. The formula, (1-P) (most often P < 0.05) is the probability that the population mean will fall in the calculated interval (usually 95%). The Standard Error of the estimate is the other standard error statistic most commonly used by researchers. This statistic is used with the correlation measure, the Pearson R. It can allow the researcher to construct a confidence interval within which the true population correlation will fall. The computations derived from the r and the standard error of the estimate can be used to determine how precise an estimate of the population c