#Cdf of a normal distribution pdf#

Is the measure of how spread out a normally distributed set of data is. The normal distribution is always symmetrical about the mean. Many everyday data sets typically follow a normal distribution: for example, the heights of adult humans, the scores on a test given to a large class, errors in measurements. It has a shape often referred to as a 'bell curve.' Set.seed ( 27 ) X 3 1 - cdf ( Z, z_stat ) # p-value for one-sided test # H_0: mu >= 3 vs H_A: mu < 3 cdf ( Z, z_stat ) # example: calculating a 88 percent Z CI for a mean # same `x` as before, still assume `sigma = 2` # lower-bound mean ( x ) - quantile ( Z, 1 - 0.12 / 2 ) * 2 / sqrt ( nx ) # upper-bound mean ( x ) + quantile ( Z, 1 - 0.12 / 2 ) * 2 / sqrt ( nx ) # equivalent to mean ( x ) + c ( -1, 1 ) * quantile ( Z, 1 - 0.12 / 2 ) * 2 / sqrt ( nx ) # also equivalent to mean ( x ) + quantile ( Z, 0.12 / 2 ) * 2 / sqrt ( nx ) mean ( x ) + quantile ( Z, 1 - 0.12 / 2 ) * 2 / sqrt ( nx ) # generating random samples and plugging in ks.test() set.seed ( 27 ) # generate a random sample ns <- random ( Normal ( 3, 7 ), 26 ) # test if sample is Normal(3, 7) ks.test ( ns, pnorm, mean = 3, sd = 7 ) # test if sample is gamma(8, 3) using base R pgamma() ks.test ( ns, pgamma, shape = 8, rate = 3 ) # MISC # note that the cdf() and quantile() functions are inverses cdf ( X, quantile ( X, 0.

Geometric: Create a Geometric distribution.

#Cdf of a normal distribution pdf#

geom_auc: Fill out area under the curve for a plotted PDF.

fit_mle.Poisson: Fit an Poisson distribution to data.

fit_mle.Normal: Fit a Normal distribution to data.

fit_mle.LogNormal: Fit a Log Normal distribution to data.

fit_mle.Geometric: Fit a Geometric distribution to data.

fit_mle.Gamma: Fit a Gamma distribution to data.

fit_mle.Exponential: Fit an Exponential distribution to data.

fit_mle.Binomial: Fit a Binomial distribution to data.

fit_mle.Bernoulli: Fit a Bernoulli distribution to data.

Exponential: Create an Exponential distribution.

distributions3-package: distributions3: Probability Distributions as S3 Objects.

ChiSquare: Create a Chi-Square distribution.

cdf.Weibull: Evaluate the cumulative distribution function of a Weibull.

cdf.Uniform: Evaluate the cumulative distribution function of a continuous.

cdf.Tukey: Evaluate the cumulative distribution function of a Tukey.

cdf.StudentsT: Evaluate the cumulative distribution function of a StudentsT.

cdf.RevWeibull: Evaluate the cumulative distribution function of an.

cdf.Poisson: Evaluate the cumulative distribution function of a Poisson.

cdf.Normal: Evaluate the cumulative distribution function of a Normal.

cdf.NegativeBinomial: Evaluate the cumulative distribution function of a negative.

cdf.LogNormal: Evaluate the cumulative distribution function of a LogNormal.

cdf.Logistic: Evaluate the cumulative distribution function of a Logistic.

cdf.HyperGeometric: Evaluate the cumulative distribution function of a.

cdf.Gumbel: Evaluate the cumulative distribution function of a Gumbel.

cdf.GP: Evaluate the cumulative distribution function of a GP.

cdf.GEV: Evaluate the cumulative distribution function of a GEV.

cdf.Geometric: Evaluate the cumulative distribution function of a Geometric.

cdf.Gamma: Evaluate the cumulative distribution function of a Gamma.

cdf.Frechet: Evaluate the cumulative distribution function of a Frechet.

cdf.FisherF: Evaluate the cumulative distribution function of an F.

cdf.Exponential: Evaluate the cumulative distribution function of an.

cdf.Erlang: Evaluate the cumulative distribution function of an Erlang.

cdf.ChiSquare: Evaluate the cumulative distribution function of a chi square.

cdf.Cauchy: Evaluate the cumulative distribution function of a Cauchy.

cdf.Categorical: Evaluate the cumulative distribution function of a.

cdf.Binomial: Evaluate the cumulative distribution function of a Binomial.

cdf.Beta: Evaluate the cumulative distribution function of a Beta.cdf.Bernoulli: Evaluate the cumulative distribution function of a Bernoulli.cdf: Evaluate the probability density of a probability.Categorical: Create a Categorical distribution.Binomial: Create a Binomial distribution.Bernoulli: Create a Bernoulli distribution.