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The core ANOVA analysis consists of a series of calculations. The data is collected in tabular form. Then Each treatment group is summarized by the number of experimental units, two sums, a mean and a variance. The treatment group summaries are combined to provide totals for the number of units and the sums.
The following example uses data from Chambers et al. [17] on daily readings of ozone for May 1 to September 30, 1973, in New York City. The data are in the R data set airquality, and the analysis is included in the documentation for the R function kruskal.test. Boxplots of ozone values by month are shown in the figure.
This can also be called what-if analysis or backsolving. It can either be attempted through trial and improvement or more logical means. Basic goal seeking functionality is built into most modern spreadsheet packages such as Microsoft Excel. According to O'Brien and Marakas, [1] optimization analysis is a more complex extension of goal-seeking ...
A what if chart (Whif chart, WHIF analysis, etc.) is a visual tool for modeling the outcome of a combination of different factors.The table can represent actual results or predicted outcome based on combinations of parameters.
Such data might represent the temperature at different locations (two-way data) sampled over different times (leading to three-way data) Four-way data, using the same spreadsheet analogy, can be represented as a file folder full of separate workbooks. Five-way data and six-way data can be represented by similarly higher levels of data aggregation.
Decision tables are a concise visual representation for specifying which actions to perform depending on given conditions. Decision table is the term used for a Control table or State-transition table in the field of Business process modeling; they are usually formatted as the transpose of the way they are formatted in Software engineering.
Pivot table, in spreadsheet software, cross-tabulates sampling data with counts (contingency table) and/or sums. TPL Tables is a tool for generating and printing crosstabs. The iterative proportional fitting procedure essentially manipulates contingency tables to match altered joint distributions or marginal sums.
Variation varies between 0 and 1. Variation is 0 if and only if all cases belong to a single category. Variation is 1 if and only if cases are evenly divided across all categories. [1] In particular, the value of these standardized indices does not depend on the number of categories or number of samples.