Advanced Statistical Tables provides users with the opportunity to calculate PDF, CDF and inverse CDF for a variety of probability distributions directly on their Pocket PC device, including normal (Gaussian), Student's t, F, chi-square, exponential, beta, gamma, Poisson, binomial, negative binomial, geometric and hypergeometric. What sets Advanced Statistical Tables apart from similar programs is its intuitive user interface and inverse CDF calculation which is being extensively used for hypothesis test.The range of possible applications of Advanced Statistical Tables is virtually unlimited, such as design of experiments, engineering statistics, financial analysis, biostatistics, clinical research, probability calculations for lotteries and gambling operations.

Features:

• PDF, CDF and inverse CDF calculations for 12 probability distributions;
• Calculation history;
• Configurable decimal places for calculation results;
• Remember Last distribution and calculation type (PDF, CDF or inverse CDF);
• Embedded Greek font.

Supported Distributions

1. Continuous Distribution

• Normal Distribution - The normal distribution, also called the Gaussian distribution, is an important family of continuous probability distributions, applicable in many fields. Each member of the family may be defined by two parameters, location and scale: the mean μ and standard deviation σ, respectively. The standard normal distribution is the normal distribution with a mean of zero and a variance of one.
• t Distribution - The Student's t-distribution is a probability distribution that arises in the problem of estimating the mean of a normally distributed population when the sample size is small. It is the basis of the popular Student's t-tests for the statistical significance of the difference between two sample means, and for confidence intervals for the difference between two population means.
• F Distribution - The F distribution is a right-skewed distribution used most commonly in Analysis of Variance (i.e. ANOVA and MANOVA). The F distribution is a ratio of two Chi-square distributions divided by their respective degree of freedom, and a specific F distribution is denoted by the degrees of freedom for the numerator Chi-square ν1 and the degrees of freedom for the denominator Chi-square ν2.
• Chi-Square Distribution - The chi-square distribution is one of the most widely used theoretical probability distributions in inferential statistics, i.e. in statistical significance tests. The chi-square distribution has one parameter, its degrees of freedom ν. It has a positive skew; the skew is less with more degrees of freedom. As the degree of freedom increase, the chi square distribution approaches a normal distribution. The mean of a chi-square distribution is its degree of freedom ν.
• Exponentioal Distribution - The exponential distributions are a class of continuous probability distribution. They are often used to model the time interval between independent events that happen at a constant average rate. The exponential distribution is the only continuous memoryless random distribution.
• Beta Distribution - The beta distribution arises from a transformation of the F distribution and is typically used to model the distribution of order statistics. Because the beta distribution is bounded on both sides, it is often used for representing processes with natural lower and upper limits.
• Gamma Distribution - The gamma distribution is a two-parameter family of continuous probability distributions. It has a shape parameter α and a scale parameter β. If k is an integer then the distribution represents the sum of k exponentially distributed random variables, each of which has mean β.

2. Discrete Distribution

• Poisson Distribution - The Poisson distribution is a discrete probability distribution that expresses the probability of a number of events occurring in a fixed period of time if these events occur with a known average rate λ, and are independent of the time since the last event.
• Binomial Distribution - The binomial distribution is a discrete probability distribution that expresses the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p. Such a yes/no experiment is also called a Bernoulli experiment or Bernoulli trial. In fact, when n = 1, the binomial distribution is a Bernoulli distribution.
• Negative-Binomial Distribution - The Negative-Binomial distribution is a discrete probability distribution that expresses the number of trials required to obtain r success. Each of the independent trials yields success with probability p.
• Geometric Distribution - The Geometric Distribution is a discrete probability distribution that expresses the number of Bernoulli trials, with success probability of p, needed to get one success.
• Hypergeometric Distribution - The Hypergeometric Distribution is a discrete probability distribution. Suppose a population or collection consists of a finite number of items, say N, and there are M items of type 1 and the remaining N-M items are of type 2. Suppose n items are drawn at random without replacement, and denote by X the number of items of type 1 that are drawn. Then X follows Hypergeometric Distribution.