Professionals’ Assessment

The Cause and Effect diagram, used to identify potential causes of a problem, is commonly known by two other names: the Fishbone diagram due to its appearance, and the Ishikawa diagram after its inventor, Kaoru Ishikawa. A Histogram is a bar graph showing the frequency distribution of a dataset.

Measures of central tendency are statistical values that represent the center or typical value of a dataset. The Mean (average), Median (middle value), and Mode (most frequent value) are the most common measures of central tendency. Quartiles are measures of position that divide a dataset into four equal parts and can be used to understand the spread and also indicate the central tendency (the second quartile is the median). Therefore, all the listed options are considered measures of central tendency or related positional measures used to understand the center of data.

The DMAIC (Define, Measure, Analyze, Improve, Control) methodology is central to Six Sigma. The Analyze phase specifically focuses on identifying the root causes (the "Vital X's") of the problem defined in the Define phase and measured in the Measure phase. Statistical tools are used to validate the relationship between these causes and the effect (the "Y"). Communicating and signing off to close the project happens in the Control phase. Generating potential solutions and assessing failure modes are part of the Improve phase.

A well-formed problem statement in methodologies like Six Sigma should clearly articulate the issue without pre-supposing a cause or suggesting a solution. Including blame or a solution at this stage can narrow the focus prematurely and prevent the identification of the true root causes and potentially better solutions. While mentioning the extent of the pain (impact) is often a crucial part of the problem statement to convey its significance, assigning blame or including a solution are explicitly avoided.

DPMO is a key metric used in Six Sigma to measure the performance of a process. It represents the number of defects observed per one million opportunities for a defect to occur. This allows for a standardized way to compare the quality of different processes. "Defectives" refers to items that have one or more defects, while DPMO focuses on the number of individual defects.

The Measure phase in DMAIC is focused on collecting reliable data to understand the current performance of the process and the magnitude of the problem. This involves identifying what needs to be measured (the Project Ys), developing a plan for how to collect the data (Data Collection Strategy), ensuring the measurement system is accurate and reliable (Validate Measurement System or Gage R&R), and assessing the current performance baseline (Determine Process Capability). Therefore, all the listed activities are part of the Measure phase.

A biased sample is one where the selection method favors certain individuals or groups over others, leading to a sample that does not accurately reflect the characteristics of the overall population. This inevitably results in incorrect conclusions when trying to generalize findings from the sample to the population. Clustered, random, and stratified random sampling are all methods designed to obtain representative samples, although they use different techniques.

A deployment flowchart, also known as a cross-functional flowchart, is specifically designed to show the steps in a process and which person, group, or department is responsible for each step. This is represented by dividing the flowchart into swimlanes, with each lane representing a different role or department. A standard process flowchart just shows the sequence of steps.

BPR is a well-established management concept and stands for Business Process Re-engineering. It involves fundamentally rethinking and radically redesigning business processes to achieve dramatic improvements in critical contemporary measures of performance such as cost, quality, service, and speed.

In hypothesis testing, a Type I error occurs when you reject the null hypothesis (Ho) when it is actually true. This is also known as a false positive. A Type II error occurs when you fail to reject the null hypothesis when it is false.