Tag: ANOVA

“All models are wrong but some are useful.” – George Box The statistic R2 is useful for interpreting the results of certain statistical analyses; it represents the percentage of variation in a response variable that is explained by its relationship with one or more predictor variables. Common Use of R2 When looking at a simple […]

Just as people follow weather predictions to know if they should carry an umbrella, organizations use forecasting to predict and prepare for future events. Across industries, companies attempt to determine what will happen – they forecast for product or raw material prices, market demand, exchange rates, and numerous other key metrics. Based on these forecasts, […]

Six Sigma, 6s, is nothing more, and nothing less, than the use of science to solve problems. Scientific evidence that a breakthrough medical solution is genuine is statistical evidence. Though altruism and evidence influence medical treatments, economic pressure drives improvement. Multi-million dollar savings created by “beating heart” or “off-pump” coronary artery bypass outcomes are a […]

In 1920, Sir Ronald A. Fisher invented a statistical way to compare data sets. Fisher called his method the analysis of variance, which was later dubbed an ANOVA. This method eventually evolved into Six Sigma data set comparisons. The F ratio is the probability information produced by an ANOVA. It was named for Fisher. The […]

The analysis of variance (ANOVA) procedure is conducted during the Analyze phase of a Six Sigma project. Assessing results from an ANOVA table can present a challenge making it difficult to understand precisely what conclusions to draw. However, there is an easy way for Master Black Belts to explain to their charges the ANOVA procedure. […]

Three methods used to dissolve a powder in water are compared by the time (in minutes) it takes until the powder is fully dissolved. The results are summarized in the following table: It is thought that the population means of the three methods m1, m2 and m3 are not all equal (i.e., at least one m […]

Six Sigma projects in various industries often deal with experiments whose outcomes are not continuous variable data, but ordered categorical data. Analysis of variables (ANOVA) is a technique used to analyze continuous experimental data, but is not adequate for analyzing categorical experimental outcomes. Fortunately, many other methods have been developed to deal with categorical experiments, […]

Much of the Six Sigma DMAIC methodology is concerned with finding differences: Do people do a certain job the same way or are there differences? Will a particular change make a difference in the output? Are there differences in where and when a problem occurs? In most cases, the answer to all these questions is […]