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StatMax is a stats component that contains over 70 computational tools. The mathematical computations that are provided categorized as: Permutations Combinations, Factorials, Mean, Median, Weighted Average, Standard Deviation Quartile Deviation, Covariance, Inter-quartile Deviation, Kurtosis, Linear Correlation Population Variance, Rank Correlation, Regression, Skewness for both grouped and ungrouped data, Tailed test sampling, T Distribution, F Distribution Chi Square, Probability, Binomial Distribution, Geometric Distribution, Poisson Distribution, Normal/Gaussian Distribution. StatMax quickly performs complex calculations, and can be programmed to perform composite calculations very easily.

• Arithmetic Mean - The Arithmetic mean is the average of the scores in the population
• Arithmetic Mean Ungrouped Data - The sum of a set of data divided by the number of data. The method Arithmetic Mean Ungrouped Data computes the arithmetic mean of the given set of numbers.
• Bernoulli Distribution Function - Bernoulli Distribution Function the outcome a trial can only be either a success or failure, then the trial is a Bernoulli trial. The method Bernoulli Distribution Function finds the distribution function for the given Bernoulli Distribution.
• Bernoulli Distribution Mean - The Mean of the Bernoulli Distribution. The method Bernoulli Distribution Mean finds the mean of the given Bernoulli Distribution.
• Bernoulli Distribution Variance - The Variance of the Bernoulli Distribution. The method Bernoulli Distribution Variance finds the variance of the given Bernoulli distribution.
• Binomial Distribution Function - The distribution of a discrete random variable taking two values, usually 0 and 1. An experiment or trial that has exactly two possible results, often classified as 'success' or 'failure', is called a Bernoulli trial. If the probability of a success is p and the number of successes in a single experiment is the random variable X, then X is a Bernoulli variable (also called a binary variable) and is said to have a Bernoulli distribution with parameter p. The method Binomial Distribution Function finds the distribution function for the given binomial distribution.
• Binomial Distribution Mean - The Mean of the Binomial Distribution.  The method Binomial Distribution Mean finds the mean of a binomial distribution.
• Binomial Distribution Variance - The Variance of the Binomial Distribution.  The method Binomial Distribution Variance finds the variance of a binomial distribution.
• Circular Permutations - Circular Permutations is an arrangement of objects around a circle. The method Circular Permutations computes the circular permutation of the given number of objects.
• Coefficient Of Quartile Deviation Discrete Distribution - The Coefficient of quartile deviation discrete distribution is the discrete distribution of the Coefficient Of Quartile Deviation Ungrouped Data . The method Coefficient Of Quartile Deviation Discrete Dist computes the coefficient of quartile deviation of the given discrete frequency distribution.
• Coefficient Of Quartile Deviation Ungrouped Data - Coefficient Of Quartile Deviation is a measure of comparison of the dispersion between two or more data. The method Coefficient Of Quartile Deviation Ungrouped Data computes the coefficient of quartile deviation of the given set of numbers.
• Coefficient Of Variation - Coefficient of Variation is an attribute of a distribution: its standard deviation divided by its mean. The method Coefficient Of Variation computes the coefficient of variation of the given set of values.
• Combined Standard Deviation - Is a measure of the dispersion of a set of data from its mean. The more spread apart the data, the higher the deviation. Standard Deviation is calculated as the square root of variance. The method CombinedStandardDeviation computes the combined standard deviation of the two given sets of values.
• Correlation - A measure of linear association between two (ordered) lists.Two variables can be strongly correlated without having any causal relationship, and two variables can have a causal relationship and yet be uncorrelated.
• Correlation coefficient - Denoted as "r", a measure of the linear relationship between two variables. The absolute value of "r" rovides an indication of the strength of the relationship.The value of "r" varies between positive 1 and negative 1, with -1 or 1 indicating a perfect linear relationship, and r = 0 indicating no relationship. The sign of the correlation coefficient indicates whether the slope of the line is positive or negative when the two variables are plotted in a scatterplot.
• Covariance - Covariance measures the strength of the correlation between two or more sets of random variables. The covariance for two random variables x and y.  The method Covariance computes the covariance between two random variables.
• Critical Region - The critical region CR, or rejection region RR, is a set of values of the test statistic for which the null hypothesis is rejected in a hypothesis test; that is, the sample space for the test statistic is partitioned into two regions; one region (the critical region) will lead us to reject the null hypothesis H0', the other not. So, if the observed value of the test statistic is a member of the critical region, we conclude 'reject H0'; if it is not a member of the critical region then we conclude 'do not reject H0.
• Critical Value(s) - The critical value(s) for a hypothesis test is a threshold to which the value of the test statistic in a sample is compared to determine whether or not the null hypothesis is rejected. The critical value for any hypothesis test depends on the significance level at which the test is carried out, and whether the test is one-sided or two-sided.
• Exponential Distribution Mean - The Mean of the Exponential Distribution. The method Exponential Distribution Mean finds the mean of the given Exponential Distribution.
• Exponential Distribution Variance - The Mean of the Exponential Distribution. The method Exponential Distribution Variance finds the variance of the given Exponential Distribution.
• Factorial - The product of a given positive integer multiplied by all lesser positive integers. The method Factorial computes the factorial of the given number.
• Geometric Distribution - Geometric distributions model (some) discrete random variables. Typically, a Geometric random variable is the number of trials required to obtain the first failure, for example, the number of tosses of a coin untill the first 'tail' is obtained, or a process where components from a production line are tested, in turn, until the first defective item is found.
• Geometric Mean - Geometric mean is the 'n'th positive root of the product of 'n' positive given values. The method GeometricMean computes the geometric mean of the given set of numbers.
• Linear Correlation Coefficient - The Linear Correlation Coefficient measures the strength and the direction of a linear relationship between two variables.  The method Linear Correlation Coefficient computes the linear correlation coefficient between two attributes.
• Lower quartile - The 25th percentile, calculated by ordering the data from smallest to largest and finding the value which lies 25% of the way up through the data.
• Median - The median of a population is the point that divides the distribution of scores in half.
• Mean Deviation Ungrouped Data - Is obtained by adding the raw scores and dividing the sum by the number of items. The method MeanDeviationUngroupedData computes the mean deviation of the given set of numbers.
• Negative Binomial Distribution Function - Negative Binomial Distribution is a discrete probability distribution. It arises as the probability distribution of the number of failures in a sequence of Bernoulli trials needed to get a specified (non-random) number of successes. The method Negative Binomial Distribution Function finds the distribution function for the given Negative Binomial Distribution.
• Permutations - Permutations is an arrangement of objects in different orders. The method Permutations computes the number of permutations for the given event.
• Poisson distribution - A distribution often used to express probabilities concerning the number of events per unit. For example, the number of computer malfunctions per year, or the number of bubbles per square yard in a sheet of glass, might follow a Poisson distribution. The distribution is fully characterized by its mean, usually expressed in terms of a rate.
• Probability - A probability provides a quantatative description of the likely occurrence of a particular event. Probability is conventionally expressed on a scale from 0 to 1; a rare event has a probability close to 0, a very common event has a probability close to 1.
• Rank Correlation Coefficient - Rank correlation is the study of relationships between different rankings on the same set of items. A rank correlation coefficient measures the correspondence between two rankings and assesses its significance. The method Rank Correlation Coefficient computes the rank correlation coefficient between two independent attributes.
• Standard Deviation - The standard deviation is one of several indices of variability that statisticians use to characterize the dispersion among the measures in a given population. To calculate the standard deviation of a population it is first necessary to calculate that population's variance. Numerically, the standard deviation is the square root of the variance.
• T-Distribution Single Mean - The method T-Distribution Single Mean tests the null hypothesis H0, that is, whether the sample mean does not deviate significantly from the population mean for small samples.
• Weighted Average - Weighted Average multiplies each data point by an arbitrary 'weight' and divides it by the sum of the weights (arithmetic mean). The method WeightedAverage computes the weighted average of the given data.