Examples of F distribution in the following topics:
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- The notation for the F distribution is F∼Fdf(num),df(denom) where df(num) = dfbetween and df(denom) = dfwithin
- The mean for the F distribution is μ=df(denom)1df(num)
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- An F-test is any statistical test in which the test statistic has an F-distribution under the null hypothesis.
- An F-test is any statistical test in which the test statistic has an F-distribution under the null hypothesis.
- (Note that all populations involved must be assumed to be normally distributed.)
- The F-distribution exhibits the following properties, as illustrated in the above graph:
- The F-distribution is skewed to the right and begins at the x-axis, meaning that F-values are always positive.
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- The distribution for the test is the F distribution with 2 different degrees of freedom.
- The distribution for the hypothesis test is the F distribution with 2 different degrees of freedom.
- The populations from which the two samples are drawn are normally distributed.
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- An $F$-test for the null hypothesis that two normal populations have the same variance is sometimes used; although, it needs to be used with caution as it can be sensitive to the assumption that the variables have this distribution.
- This particular situation is of importance in mathematical statistics since it provides a basic exemplar case in which the $F$ distribution can be derived.
- Let $X_1, \dots, X_n$ and $Y_1, \dots, Y_m$ be independent and identically distributed samples from two populations which each have a normal distribution.
- It has an $F$-distribution with n−1 and m−1 degrees of freedom if the null hypothesis of equality of variances is true.
- These $F$-tests are generally not robust when there are violations of the assumption that each population follows the normal distribution, particularly for small alpha levels and unbalanced layouts.
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- A p-value can be computed from the F statistic using an F distribution, which has two associated parameters: df1 and df2.
- An F distribution with 3 and 323 degrees of freedom, corresponding to the F statistic for the baseball hypothesis test, is shown in Figure 5.29.
- If H0 is true and the model assumptions are satisfied, the statistic F follows an F distribution with parameters df1 = k−1 and df2 = n−k.
- The upper tail of the F distribution is used to represent the p-value.
- An F distribution with df1 = 3 and df2 = 323.
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- A gas of many molecules has a predictable distribution of molecular speeds, known as the Maxwell-Boltzmann distribution.
- Maxwell-Boltzmann distribution is a probability distribution.
- $f_\mathbf{v} (v_x, v_y, v_z) = \left(\frac{m}{2 \pi kT} \right)^{3/2} \exp \left[- \frac{m(v_x^2 + v_y^2 + v_z^2)}{2kT} \right]$,
- The Maxwell–Boltzmann distribution for the speed follows immediately from the distribution of the velocity vector, above.
- $f(v) = \sqrt{\left(\frac{m}{2 \pi kT}\right)^3}\, 4\pi v^2 \exp \left(\frac{-mv^2}{2kT}\right)$ for speed v.
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- This $F−statisticfollowstheF$-distribution with K−1, N−K degrees of freedom under the null hypothesis.
- The data were distributed as follows:
- Remember that the null hypothesis claims that the sorority groups are from the same normal distribution.
- The alternate hypothesis says that at least two of the sorority groups come from populations with different normal distributions.
- This chart shows example p-values for two F-statistics: p = 0.05 for F = 3.68, and p = 0.00239 for F = 9.27.
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- A continuous probability distribution is a probability distribution that has a probability density function.
- This requirement is stronger than simple continuity of the cumulative distribution function, and there is a special class of distributions—singular distributions, which are neither continuous nor discrete nor a mixture of those.
- An example is given by the Cantor distribution.
- For example, the uniform distribution on the interval [0,21] has probability density $f(x) = 2for0 \leq x \leq \frac{1}{2}andf(x) = 0$ elsewhere.
- The standard normal distribution has probability density function:
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- Let's suppose that we have an isotropic distribution of photons of a single energy E0 and a beam of electrons traveling along the x-axis with energy γmc2 and density N.
- Let's assume that there are many beams isotropically distributed, so we need to find the mean value of $j(E_f,\mu_f)$ over angle,
- so we find that the scattered photons have an energy distribution E−s where s=(p−1)/2.
- This power-law distribution is valid over a limited range of photon energies.
- If the initial photon distribution peaks at E¯ the power-law will work between 4γ12E¯ and $4 \gamma_2^2 {\bar E}
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- $\displaystyle{f _1 = \frac{1}{2l}, \hspace{2mm} f _2 = \frac{2}{2l}, \hspace{2mm} f _3 = \frac{3}{2l}, \cdots , \hspace{2mm} f _k = \frac{k}{2l} \cdots }$
- $ \displaystyle{f _1 = \frac{1}{2L}, \hspace{2mm} f _2 = \frac{2}{2L}, \hspace{2mm} f _3 = \frac{3}{2L}, \cdots , \hspace{2mm} f _k = \frac{k}{2L} \cdots }$
- The Fourier coefficients become more and more densely distributed, until, in the limit that L→∞ , the coefficient sequence cn becomes a continuous function.
- A function $f(t)isrelatedtoitsFouriertransformf(\omega)$ via:
- $\displaystyle{g(t) = \int _{-\infty} ^{\infty} G(f) e^{2\pi i f t} ~df}$