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MGSC 485 Final Exam Questions & 100% Verified Correct Answers with complete solutions (Latest update
MGSC 485 Final Exam Questions & 100% Verified Correct Answers with complete solutions (Latest update
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MGSC 485 Final ExamA visual indication of the type of relationship between two variables can usually be seen in a
scatter diagram, and we always recommend that you create one first to gain some
understanding of the nature of any potential relationship before conducting regression analysis.
Typically, the variables in question represent possible cause-and-effect relationships. For
example, if a manufacturer suspects that the percentage of an ingredient in an alloy is
influencing the ability to meet hardness specifications, a scatter diagram can easily show if
hardness seems to change when the percentage of the ingredient is changed. Scatter diagrams
can be plotted easily on a Microsoft Excel chart. - ANS-How do scatter diagrams assist in
finding solutions to quality problems?
Alpha represents producer's risk (Type I error). It indicates the probability that you will consider
a product bad when it really isn't (i.e., you will conclude that it deviates from target level
performance whereas in actuality it might not have deviated from that level). - ANS-What is the
importance of "alpha" values in statistical conclusions about whether your products are
acceptable to your customers or not?
Beta represents consumer's risk (Type II error). It indicates the probability that you will NOT
consider a product bad when it really is (i.e., you will conclude that it does not deviate from
target level performance whereas in actuality it might have deviated from that level). The lower
the beta value, the higher is the POWER that your conclusions are correct with respect to
customer service. - ANS-What is the importance of "beta" values in statistical conclusions about
whether your products are acceptable to your customers or not?
Multiple - ANS-________ regression is used for continuous X variables and continuous Y
variables
25 - ANS-For regression analysis, the number of observations needed is number of parameters
times _________.
150 - ANS-For regression analysis, a regression involving 5 X and 1 Y variable, at least ______
observations are needed.
R-Square - ANS-For regression analysis, the power of regression to predict the Y based on Xs
is indicated by _______.
Adjusted R-Square - ANS-For regression analysis, __________ _____________ is a better
indicator of prediction as it accounts for sample size.
t-statistic - ANS-For regression analysis, ______-________ of an x variable indicates if it is a
statistically significant predictor of Y (typically more than 1.96 indicating 95% confidence).
, p-value - ANS-For regression analysis, _____-_______ of an X variable indicates if it is a
statistically significant predictor of Y (typically p<0.05 indicates 95% confidence that this X is a
significant predictor.
Logistic - ANS-_________ regression is used when X's are continuous and Y is discrete
ANOVA - ANS-_________ is used when a dependent variable is continuous and independent
variables are discrete.
ANOVA - ANS-What type of regression should we use?
Quality material supplied from two suppliers; or order turnaround time corresponding to various
plant locations.
Chi-Squared Analysis - ANS-Use ________-___________ ___________ when both X's and Y
are discrete.
Categorical - ANS-Chi-Square Analysis is useful for analysis of _________ data.
Failure mode - ANS-They way in which a process can fail.
Effect - ANS-The impact on the process or customer requirements as a result of the failure.
Severity - ANS-The impact of the effect on the customer or process.
Root cause - ANS-The initiating source of the failure mode.
Occurrence (or frequency) - ANS-How often the failure is likely to occur.
Detection - ANS-The likelihood that the failure will be discovered in a timely manner, or before it
can reach the customer.
Risk priority number (RPN) - ANS-The value computed by multiplying the values assigned to
Severity, Occurrence and Detection
An approach for evaluating multiple options against each other, relative to a baseline option. -
ANS-What is a Pugh Matrix?
1. Identify requirements.
2. Develop a weight for each requirement.
3. List improve concepts.
4. Select one of the concepts as a baseline.
5. Evaluate each concept against the baseline for each of the criteria. Determine whether it is
better (+), the same (0), or worse(-).
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