# Deriving the Mean of a Geometric Distribution

An important topic in statistics.

An important topic in statistics.

We wish to emphasize that the data sets presented so far are associated with a single random variable. This means that each sample refers to a single, specific population or population characteristic. The technical name for such data is univariate. Data which derives from two separate populations or population characteristics is called bivariate.

Statistics is often introduced using quantitative data such as height, weight, waiting time, etc. But there is a wide, wide world of very important data of a different type; qualitative or categorical data. As the name suggests, categorical data is expressed, as a random variable, in terms of categories or cells; two or more information Read more about Categorical Data[…]

Introduction Chi-Square Tests are generally applied to categorical data. Yet the Chi-square distribution is a continuous random variable. How can this be ? The answer may be found in the history of statistical methods. Just as de Moivre and Laplace sought for and found the normal approximation to the binomial, Karl Pearson sought for and Read more about Testing Categorical Data[…]

The conditional probability of event B given that event A has occurred, P(B/A), was introduced in Chances Are…?. Here we provide additional examples of conditional probability with a special emphasis on applications of the general multiplication rule P(A and B) = P(A) x P(B/A), An extension of the general multiplication rule {P(A and B) = Read more about Conditional Probability: Part 2[…]

Here we outline some of the theory behind the chi-square, t- and F-distributions. Recall from Inferential Statistics that the chi-squared test uses the chi-squared statistic to determine whether or not two population characteristics are related in some way. While a large value for the chi-squared statistic suggests that the two characteristics are not independent, a Read more about Some Distribution Theory[…]

Math_ This page is under construction. In Inferential Statistics we saw that the methods of data interpretation depend, among other things, on the number of samples involved …. are different depending on the size, n, of the sample(s) under consideration. Here we examine situations where the sample size is large; that is, where n > Read more about Single Sample Inferences[…]

As we saw in Statistics Defined, inferential statistics may be defined as the science of data interpretation. Data interpretation involves drawing one or more conclusions about a population underlying a data set accompanied by statements about the reliability of such conclusions. The process begins with a claim about some numerical feature of a population (a Read more about Inferential Statistics[…]