The source of variability within process is identified

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Reference no: EM132187065

1. Analysis of valiance (ANOVA) is a statistical technique by which the source of variability within a process is identified. ANOVA is widely used in industry to help identify the source of potential problems in a production process and identify whether variation in measured output values is due to variability between various manufacturing processes or within them.

a. True

b. False

2. Two-way ANOVA provides for the analysis of two populations with a single treatment. This assists in determining whether there are differences in such things as the quality of materials coming from two suppliers, the warranty returns from two different areas, the differences between two processes producing the same product, or an y other combination of inputs to a single treatment.

a. True

b. False

3. Degrees of freedom (df) are the number of independent comparisons available to evaluate the data. It is necessary to determine the degrees of freedom for treatment, within, and total.

a. True

b. False

4. The level of significance applied to the analysis can be a very subjective choice where no specific standards exist. It is important that some standard for selection of the significance level be implemented and applied to analysis uniformly throughout.

a. True

b. False

5. Multivariate ANOVA (a.k.a. MANOVA) can be used for the evaluation of process data and in designed experiments. It extends analysis of variance methods to handle cases where there is more than one dependent variable and where the dependent variables cannot simply be combined. It identifies whether changes in the independent variables have a significant effect on the dependent variables. It also seeks to identify the interactions among the independent variables and the association between dependent variables.

a. True

b. False

6. Accepting or rejecting the null hypothesis in the ANOVA analysis implies that there is difference in means and the exact nature of this difference is specified. Linear contrasts can provide additional understanding and also provide for a graphical representation of the data. A contrast is the sum of the high level mean minus the sum of the low-level mean.

a. True

b. False

7. Design of experiments (DOE) is one of the most powerful tools available for the design, characterization, and improvement of products and services. DOE is a group of techniques used to organize and evaluate testing so that it provides the most valuable data and makes efficient use of assets.

a. True

b. False

8. Full factorial designs are the best-designed experiments to use when all main effects and interactions are critical, with ANOVA provide the most comprehensive information for fact-based decision-making, are the most costly, time- consuming, and resource consuming of all available designed experiments.

a. True

b. False

9. Always evaluate the data to analyze existing processes. Be sure the data you are collecting and using is relevant to the current process. Be cautious about using historical data from a process when configuration changes or supplier changes have occurred.

a. True

b. False

10. Linear contrasts will provide a measure of effects for significant treatments. Use the critical process metrics (CSF) as measures of effectiveness, quality cost schedule, and/or risk.

a. True

b. False

Reference no: EM132187065

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