Ellistat Data Analysis offers a Multi X sub-menu which is activated as soon as a response and several Xs are selected. This sub-menu contains tools to provide statistical proof of the correlation between a Y response and several Xs.
Ellistat Data Analysis has two tools:
⇒ Multivari
⇒ The BOB VS WOW
These tools are presented in the following examples.
The dataset used in these examples is shown on the next page:
Independent Data 🇺🇸/ Données indépendantes🇫🇷
Example 1: Multivari
Visit Multvari method is a technique used in quality control and variation analysis to identify sources of variability in industrial processes or complex systems. This method is particularly effective for relationships between cyclical data.
- In the example, we want to plot the multivariate of Y="Force" as a function of several Xs: X1="temperature", X2="supplier" and X3="Month".
- Click on the "Inferential statistics".
- In the zone 1Select the Y column Y="Force" and the X columns X1="Temperature", X2="Supplier" and X3="Month".
- In zone 2, select the type of analysis. By default, the proposed analyses are multi-variate and BOB vs WOW 📝: choose "BOB vs WOW".
- In zone 3the multi-variable graph is obtained: the ordinate shows "force" and each graph color represents an X variable.
💡 The resulting graphs can be modified by changing the order of the variables using the following menus.
Example 2: BOB vs WOW
The BOB (best of the best) VS WOW (worst of the worst) method compares a response Y with the extreme values of the "X" variables. This makes it easy to find the optimum configuration that maximizes or minimizes Y.
- In the example, we want to plot the multivariate of Y="Force" as a function of several Xs: X1="temperature", X2="supplier" and X3="Month".
- Click on the "Inferential statistics".
- In the zone 1Select the Y column Y="Force" and the X columns X1="Temperature", X2="Supplier" and X3="Month".
- In zone 2, select the type of analysis. By default, the proposed analyses are multi-variate and BOB vs WOW 📝: choose "BOB vs WOW".
- In zone 3the multivariate graph is obtained: the ordinate shows "strength" and each graph color represents an X variable.