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| Section | Weight | Objectives |
|---|---|---|
| Enterprise-wide Deployment | 6% | - Team formation and training - Team performance evaluation - Systems and change management - Organization objectives and deployment planning |
| Management and Leadership | 17% | - Performance metrics and balanced scorecard - Risk assessment and management - Coaching and mentoring - Communication and presentation skills - Team leadership and conflict resolution |
| Process Optimization | 20% | - Process control and SPC - Root cause analysis and corrective action - Value stream mapping - Cycle time reduction - Lean tools and waste elimination - Process capability analysis (Cp, Cpk, Pp, Ppk) - Design of Experiments (DOE) - Statistical process control charts (X-bar, R, S, I-MR, p, np, c, u) - Measurement system analysis (MSA/Gage R&R) |
| DFSS (Design for Six Sigma) | 12% | - Design for manufacturability - Design for reliability and FMEA - QFD (Quality Function Deployment) - IDDOV (Identify, Define, Design, Optimize, Verify) |
| DMAIC | 25% | - Measure: data collection, descriptive statistics, graphical analysis - Analyze: hypothesis testing, ANOVA, regression, chi-square - Improve: DOE, response surface methodology, optimization - Define: VOC, VOB, project charter, SIPOC - Control: control plans, SPC, feedback monitoring |
| Statistical Analysis | 20% | - Correlation and regression analysis - Descriptive statistics (mean, median, mode, std dev, variance) - Non-parametric tests - Probability distributions (Normal, Binomial, Poisson, Exponential) - Inferential statistics and confidence intervals - Hypothesis testing (t-test, F-test, chi-square test) - Analysis of variance (ANOVA) |
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NEW QUESTION # 268
In a Weibull distribution, which of the following describes the shape parameter?
Answer: D
Explanation:
The correct answer is A. The skewness and kurtosis of the distribution . In the CSSBB material, the Weibull shape parameter is identified as # , and the text states that the shape parameter is what gives the Weibull distribution its flexibility . By changing #, the distribution can model a wide variety of data patterns.
For example, when # = 1 , the Weibull becomes the exponential distribution; when # = 2 , it becomes the Rayleigh distribution; and when # is between about 3 and 4 , it approximates the normal distribution.
This means the shape parameter controls the form of the distribution, including how skewed it is and how the curve behaves. By contrast, the scale parameter determines the range or spread on the x-axis, and the location parameter defines a failure-free zone or shift along the x-axis. Therefore, among the listed choices, the best description of the shape parameter is the one tied to the distribution's shape characteristics, namely skewness and kurtosis , rather than scale, intercept, or mean.
NEW QUESTION # 269
What conclusion is most correct about the Experimental Design shown here with the response in the far right column?
Answer: D
NEW QUESTION # 270
Those people who have an interest in the outputs of a process are known as _____________________.
Answer: C
NEW QUESTION # 271
Which of the following techniques will create sample bias?
Answer: D
Explanation:
Collecting samples from the same location in each container, rack, or bin creates sample bias because it systematically favors one portion of the population and may fail to represent the whole. In the Measure Phase, valid data collection depends on obtaining samples that reflect the actual variation present in the population. If the same location is used repeatedly, hidden patterns such as settling, temperature gradients, stacking effects, handling differences, or location-specific defects may distort the sample. Stratified sampling across machines, shifts, or operators is usually done specifically to improve representativeness when meaningful subgroups exist. Collecting from various locations in a homogeneous population is also a sound approach because it reduces location bias. Acceptance sampling in continuous flow may be appropriate depending on the plan design and context, but the clearly biased method among the listed choices is repeatedly sampling from the same location. In Six Sigma, sampling bias is dangerous because it produces misleading conclusions and weakens all downstream analysis. Therefore, the correct answer is C, because fixed-location sampling can systematically misrepresent the true population.
NEW QUESTION # 272
If = 0.5, what is the critical value?
Answer: B
NEW QUESTION # 273
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