** Platform:** x86_64-w64-mingw32/圆4 (64-bit)Īttached base packages: stats, graphics, grDevices, utils, datasets, methods and base McClave J.T., Dietrich F.H., Statistics, 4th edition, 1988.
#F test stata code#
This can be easily executed in R as a two-tailed test as shown in the following code block:įreedman D.A., Robert Pisani, Roger Purves.
The following figure shows the observed \(P\) values in both tails. With such a high \(P\)-value, we cannot reject the null and therefore can state that for all intents and purposes, the variances between both populations are the same (i.e. the observed variability between both \(s\) can be explain by chance alone). Since \(H_a\) represents both sides of the distribution, we double the probability to give us the chance of getting a test statistic as great or as small as ours, so for a two-tailed test, \(P=0.42\). The probability of getting an \(F\) as large as ours is about 0.21 (or 21%). The \(F\) values associated with a probability of 0.025 and 0.975 (associated with rejection regions for a two-tailed \(\alpha\) of 0.05) are displayed on the curve in grey dashed vertical lines. pf(2.12, 7, 5,lower.tail=FALSE) where the values 7 and 5 are the \(degrees\ of\ freedom\) for the reference sample and contaminated site sample respectively). Now that we have the shape of the \(F\)-distribution defined, we can look up the probability of getting an \(F\) statistic as extreme as ours (an F-distribution table can be used, or the value can be computed exactly using the function pf(), e.g. This requires that we compute the two \(df\)’s from the samples to define the shape of the \(F\) distribution: \[ Next, we must determine where the \(F\) statistic lies along the \(F\)-distribution curve. \] and the alternate hypothesis states that the ratio differs from 1 (i.e. the variances differ), \[ The null hypothesis states that the ratio equal 1, \[
Now, you'll be "in the realms" of the Stata Longitudinal-Data Panel-Data Reference Manual.The method is simple it consists of taking the ratio between the larger population variance, \(\sigma_1^2\), and the smaller population variance, \(\sigma_2^2\), then looking up the ratio on an \(F\)-distribution curve. Just to give you an example, and going directly to the F-tests under xtreg, please type "help streg", then click on " xtreg" on the top of the page. If - as I understood - you are starting to delve into longitudinal studies, please don't underestimate the Stata Manual, There you will have most of the background information you're longing for. Meanwhile, until the admnistration perform the changes demanded by you, please use your name and family name inside your message.Īlso as Carlo underlined, you gave the impression your question was mostly related to the background knowledge of panel data, and that you can easily get just by reading - now quoting Carlo - "decent textbooks". As Carlo underlined, please re-register with you name and family name (just click on the "contact us" button below to the right and give this information.
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