Using MS Excel for One-way Analysis of Variance - One way ANOVA example
(Page 3 of 4 )
Let's have an example of one way analysis of variance. Say you are an engineer in charge of five machines manufacturing a specific type of resistor with a standard of 100 ohms. To maintain quality, you are conducting a study to see whether each of those machines are capable of producing its standard specifications with all other factors held constant (same machine model, same machine settings, same machine input parameters like pressure, voltage, etc). If there is a substantial difference in the quality of output of those machines, a machine replacement will be recommended.
Of course, hunches and rough analysis are definitely not recommended for this type of improvement project, as it involves buying another machine as a replacement. Carelessness and shallow analysis can lead to the wrong judgment on the part of the management, which leads to wasting company resources.
An experimental study following the classic scientific method implementing "one way analysis of variance" is one of the best methods for coming up with the right decision for a project like this. Okay, once the problem has been clearly defined, you will then formulate a sampling plan for your experiment. A typical sampling plan could look like this:
There is only one factor involved (the machines) and one experimental response to be measured (resistance output of those resistor products), for which the groups are the "machines." Based on the above sampling plan, the engineer needs to gather 20 random pieces of resistors (10 pieces from the day shift and 10 pieces from the night shift). After the samples have been gathered, measurement will then be initiated. A example of measurement data laid out in MS Excel is shown below:
This table is ready for "One way ANOVA analysis" using Excel. You will need to formulate your hypothesis:
Null Hypothesis: The means of resistance measurements from all machines are statistically the same.
Alternative Hypothesis: The means of resistance measurements from all machines are NOT the same.
Rejection Criteria: If P value < 0.05, reject null hypothesis.
The value of 0.05 is the alpha, which is based on a percentage confidence level = 1- alpha. Industry standard confidence level is 95 percent, meaning you risk a five percent chance of accepting the null hypothesis when in reality it is false.
Deciding on an acceptable number for this value calls for consultation with the management; if the project involves a higher cost (involving millions of dollars for investment), then a higher percentage confidence level will be desired, such as a 99.5% confidence level (an alpha of 100%-99.5%= 0.005).
If you need to increase the percentage of confidence level of the study, you'll have to increase the sample sizes to be declared in the sampling plan. For example, instead of gathering 20 samples, this can be increased to 50 samples per machine for greater experimental accuracy.
Next: Filling in the data >>
More BrainDump Articles
More By Codex-M
