M.com.( Account and Finance ) with 2 Years Experience Accounting fields.
When to Use One-Way, Single Factor ANOVA
In a manufacturing or service environment, you might wonder if changing a formula, process or material might deliver a better product at a lower cost. Saving a penny a pound on five million pounds a month can really add up. Saving ten minutes of wait time in hospital might add $100,000 to the bottom line and deliver better patient outcomes. Comparing two or more drug formulations might pinpoint the best drug for a desired result.
How can you compare the old formula with a new one and be certain that you have an opportunity to improve? Use one-way ANOVA (also known as single factor ANOVA) to determine if there's a statistically significant difference between two or more alternatives.
One-Way ANOVA Example
Imagine that you manufacture paper bags and that you want to improve the tensile strength of the bag. You suspect that changing the concentration of hardwood in the bag will change the tensile strength. You measure the tensile strength in pounds per square inch (PSI). So, you decide to test this at 5%, 10%, 15% and 20% hardwood concentration levels. These "levels" are also called "treatments."
Since we are only evaluating a single factor (hardwood concentration) this is called one-way ANOVA.
The null hypothesis is that the means are equal:
H0: Mean1 = Mean2 = Mean3 = Mean4
The alternate hypothesis is that at least one of the means are different:
Ha: At least one of the means is different
To conduct the one-way ANOVA test, you need to randomize the trials (assumption #1). Imagine that we've conducted these trials at each of the four levels of hardwood concentration.
You'll find the results of these trials in the ANOVA test data provided with the QI Macros at c:\qimacros\testdata\anova.xls.
Select the data with your mouse and click on the QI Macros Menu to choose:
Anova and Analysis Tools - Anova: Single factor.
The QI Macros will prompt you for the significance level you desire.
While the default is
In a manufacturing or service environment, you might wonder if changing a formula, process or material might deliver a better product at a lower cost. Saving a penny a pound on five million pounds a month can really add up. Saving ten minutes of wait time in hospital might add $100,000 to the bottom line and deliver better patient outcomes. Comparing two or more drug formulations might pinpoint the best drug for a desired result.
How can you compare the old formula with a new one and be certain that you have an opportunity to improve? Use one-way ANOVA (also known as single factor ANOVA) to determine if there's a statistically significant difference between two or more alternatives.
One-Way ANOVA Example
Imagine that you manufacture paper bags and that you want to improve the tensile strength of the bag. You suspect that changing the concentration of hardwood in the bag will change the tensile strength. You measure the tensile strength in pounds per square inch (PSI). So, you decide to test this at 5%, 10%, 15% and 20% hardwood concentration levels. These "levels" are also called "treatments."
Since we are only evaluating a single factor (hardwood concentration) this is called one-way ANOVA.
The null hypothesis is that the means are equal:
H0: Mean1 = Mean2 = Mean3 = Mean4
The alternate hypothesis is that at least one of the means are different:
Ha: At least one of the means is different
To conduct the one-way ANOVA test, you need to randomize the trials (assumption #1). Imagine that we've conducted these trials at each of the four levels of hardwood concentration.
You'll find the results of these trials in the ANOVA test data provided with the QI Macros at c:\qimacros\testdata\anova.xls.
Select the data with your mouse and click on the QI Macros Menu to choose:
Anova and Analysis Tools - Anova: Single factor.
The QI Macros will prompt you for the significance level you desire.
While the default is