Factorial Designs
Acquire the proficiency in crafting an array of full and fractional factorial designs through Minitab's user-friendly DOE interface. During this minitab training, concrete instances from real-world scenarios elucidate how the principles of randomization, replication, and blocking underpin meticulous experimental methodologies. This will equip you with the competence to aptly dissect the resultant data, thereby facilitating the efficient realization of experimental goals. This course in minitab training will help in building a grasp over factorial design concept.
Harness Minitab's dynamic and customizable graphical presentations to decipher and effectively convey the findings of your experiments. Subsequently, learn to leverage these findings to enhance both products and processes, identify pivotal factors influencing critical response variables, curtail process variability, and accelerate the progression of research and development initiatives.
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Topics Included:
Design of Factorial Experiments
Utilize the "Analyze Factorial Design" tool to assess a planned experiment. This tool accommodates the analysis of various factorial designs, including:
- 2-level factorial design
- General full factorial design
- Plackett-Burman design
- Split-plot design
Normal plot of the effects
- The normal probability plot of effects displays standardized effects in relation to a distribution fit line for the scenario where all effects are 0.
- Standardized effects are represented by t-statistics, assessing the null hypothesis that an effect is 0.
- Positive main effects elevate the response as factor settings shift from low to high values.
- Negative main effects reduce the response as factor settings shift from low to high values.
- Effects located farther from 0 on the x-axis exhibit greater magnitude.
- Effects distanced from 0 on the x-axis denote higher statistical significance.
Pareto chart of standardized effects
- The Pareto chart arranges the absolute values of standardized effects from the most significant effect to the least significant. Additionally, the chart includes a reference line to highlight the statistically significant effects.
Power and Sample Size
- Minitab computes the test's statistical power using the given difference and sample size. A power of 0.9 is generally regarded as satisfactory, signifying a 90% probability of identifying a discrepancy between the population mean and the target if a genuine difference exists. In instances of low power, there's a risk of failing to detect a difference, potentially leading to the incorrect conclusion that no difference exists. Typically, when the sample size diminishes or the difference becomes smaller, the test's ability to detect a difference weakens.
Main Effect, Interaction, and Cube Plots
- Employ a main effects plot to investigate variations between level means for one or multiple factors. A main effect arises when distinct levels of a factor put diverse impacts on the response. This plot visually represents the response mean for each factor level linked by a line.
- Utilize an Interaction Plot to illustrate how the connection between a categorical factor and a continuous response varies based on the values of another categorical factor. This plot portrays the means for different levels of one factor along the x-axis, with distinct lines representing each level of the other factor.
- Employ a Cube Plot when working with a two-level factorial design or a mixture design and you wish to depict the correlation between factors and a response. Each cube within the plot can illustrate three factors. In cases of just two factors, Minitab will present a square plot. The software generates as many cubes as needed to visualize up to eight factors. Notably, center points are depicted solely for designs encompassing five or fewer factors. Cube plots can show the following:
- The permutations of factor configurations along with either the mean data or the fitted mean corresponding to each arrangement.
- The permutations of factor settings, devoid of any associated response means.
Center Points
- Center points are experimental trials in which your variables are positioned precisely midway between the low and high settings, essentially at the center.
Overlaid Contour Plots
- Utilize it to visually pinpoint viable variables for multiple responses in a model.
- Note that settings suitable for one response might not be feasible for another response.
- Overlaid contour plots are employed to simultaneously assess responses.
- When generating an overlaid contour plot, you define lower and upper boundaries for each response.
- The plot illustrates contours representing these bounds against two (or three for mixtures) continuous factors on the axes.
- Remaining variables in the model are maintained at user-defined settings.
Multiple Response Optimization
- Response optimization aids in determining the optimal combination of variable configurations to optimize a single response or a group of responses.
- It proves valuable when assessing the influence of numerous variables on a response.
- It's essential to establish a model before employing the response optimizer
- For optimizing multiple responses, you need to fit a distinct model for each response.
- The response optimizer does not utilize the worksheet data.
- Minitab searches within the worksheet for stored model(s) to acquire the essential information.