Six Sigma Yellow Belt Trivia Quiz!

Critical to Quality parameters are derived from the Voice of Customer. The Voice of Customer refers to the feedback, needs, and expectations of the customers. By understanding and analyzing the Voice of Customer, organizations can identify the key quality parameters that are most important to their customers. This information helps in setting quality targets, designing products or services, and making improvements to meet customer expectations and enhance customer satisfaction.

The statement is true because Six Sigma is a statistical measure used to indicate the level of quality in a process. It represents that there are six standard deviations between the process mean and the nearest specification limit, with three standard deviations below the mean and three above it. This means that the process is highly capable and has very low variation, leading to a high level of quality and customer satisfaction.

Quality and Critical-to-Quality (CTQ's) are subjective terms that are defined by the customer. The customer is the ultimate judge of whether a product or service meets their expectations and requirements. Their feedback and satisfaction are crucial in determining the quality of a product or service. The management team, line supervisor, and design team may have their own perspectives and criteria for quality, but ultimately it is the customer's perception that matters the most.

If the area under the curve is 1, it means that the entire distribution is accounted for and there are no gaps or missing values. In such a case, the mean, median, and mode values would be the same. The mean represents the average value of the distribution, the median represents the middle value, and the mode represents the most frequently occurring value. When the area under the curve is 1, it indicates a symmetrical distribution, where the mean, median, and mode all coincide at the center of the distribution. Therefore, the statement "The mean, median, and mode values would be same" is true.

The given answer suggests that there is a strong negative correlation between defect density and productivity in subsequent releases of the project. This means that as the defect density increases, the productivity decreases. The strong negative correlation implies that there is a clear and consistent inverse relationship between these two variables, indicating that as one variable increases, the other consistently decreases.

Hypothesis tests are statistical tests that assist in decision making based on data inferences. These tests involve formulating a null hypothesis and an alternative hypothesis, collecting data, and then using statistical methods to determine whether there is enough evidence to reject the null hypothesis in favor of the alternative hypothesis. This helps in making decisions about the population parameter being tested, such as determining if a new treatment is effective or if there is a significant difference between two groups.

A control chart is a statistical tool used to monitor and control a process over time. It helps in determining whether the process is stable or not. A stable process is one that is consistent and predictable, with variation that is within acceptable limits. By analyzing the data plotted on a control chart, we can identify any patterns or trends that may indicate instability in the process. Therefore, the control chart would help us determine whether the process is stable or not.

All of the options listed - performance, outcome of the process, and operational efficiency - can be measured against a benchmark in a process. Performance refers to how well a task or activity is executed, while the outcome of the process assesses the end result or achievement. Operational efficiency evaluates the effectiveness and productivity of the process. Therefore, all three options can be used as criteria to measure against a benchmark in order to evaluate and improve the performance of a process.

Variance is a statistical measure that helps to understand the spread of variation in a dataset. It calculates the average of the squared differences between each data point and the mean. A higher variance indicates a wider spread of data points, while a lower variance indicates a narrower spread. Therefore, variance is a useful measure for understanding the extent to which data points deviate from the mean and provides insights into the overall variability of the dataset.

When an observation is above the upper control limit, it indicates that the process has experienced a special cause variation. Special cause variations are sporadic and unpredictable events that occur outside the normal range of process variation. They are often caused by factors that are not inherent to the process and can be identified and eliminated. This observation suggests that there is a specific reason or event that caused the process to deviate from its usual performance, and it needs to be investigated and addressed to bring the process back under control.

The Kano Model is a Six Sigma tool that qualifies the Voice of Customer. It is used to categorize customer requirements into different categories based on their impact on customer satisfaction. The model helps in understanding customer needs and preferences, allowing organizations to prioritize and focus on the most critical requirements. It helps in identifying the features that delight customers and those that are basic expectations. By using the Kano Model, organizations can align their products and services with customer expectations, leading to improved customer satisfaction and loyalty.

The given regression model includes two independent variables, X1 and X2, which indicates that it is a multi linear regression model. In multi linear regression, the dependent variable is predicted based on multiple independent variables. Therefore, the given equation Y= -0.1320 X1 + 0.16 X2 + 23.200 represents a multi linear regression model.

If the project is delivering 10 defects on average, it means that the current mean performance is outside of the specified limits. Therefore, the appropriate action would be to shift the mean performance within the specification limits. This can be achieved by identifying and addressing the root causes of the defects, making necessary improvements, and ensuring that the project meets the target of less than 4 defects. Changing the specification limits or shifting the limits to target may not address the underlying issues causing the defects, so shifting the mean performance is the most suitable action in this scenario.

The geometric mean is not a measure of central tendency because it is used to calculate the average of a set of numbers that are multiplied together, rather than added together like in the case of the arithmetic mean, median, and mode. The geometric mean is commonly used in situations where the values being averaged are proportional or have a multiplicative relationship, such as calculating average growth rates or average rates of return.

The cost of change management and external consultancy is not included in the cost of quality. The cost of quality refers to the expenses incurred in order to prevent defects and failures, as well as the costs associated with appraising and evaluating the quality of products or services. Change management and external consultancy, although important aspects of business operations, are not directly related to the cost of quality.

The distance between Quartile1 (Q1) and Quartile3 (Q3) is called the Inter Quartile Range (IQR) and it helps to understand the spread of process variation. The IQR represents the range of values that the middle 50% of the data falls within, excluding outliers. It is a measure of dispersion that is not affected by extreme values, making it useful in identifying the variability within the central portion of the data. The width of the IQR represents the extent of this spread, providing insight into the overall range of values within which the majority of the data falls.

Standard deviation is a widely used measure of variability in a data set or population. It provides information about how spread out the values are from the mean. A higher standard deviation indicates a greater dispersion of data points, while a lower standard deviation suggests that the data points are closer to the mean. Therefore, standard deviation is a reliable measure to assess the variability or dispersion of a dataset.

The correct answer is 99.976700%. In statistical terms, a process operating at +/- 3 Sigma level means that it has a defect rate of 0.27%. To calculate the percentage of perfection, we subtract the defect rate from 100%. Therefore, the percentage of perfection in this case is 100% - 0.27% = 99.976700%.

A Pareto Chart is a graph that helps to identify and prioritize problems to be solved. It is based on the Pareto principle, which states that a small number of causes are responsible for a large percentage of the problems or issues. The chart displays the frequency or count of each problem or issue in descending order, along with a cumulative percentage line. This allows the user to easily identify the most significant problems that should be addressed first. The Pareto Chart is a useful tool in problem-solving and decision-making processes.

In Control chart interpretations, 7 Shewart Rules are used to interpret for a stable process. These rules help in identifying any patterns or trends that may indicate that the process is out of control. By applying these rules, any deviations from the expected pattern can be detected and appropriate actions can be taken to bring the process back to stability. These rules are an essential tool in quality control and process improvement.

The Kano Model is a tool used to analyze customer preferences and prioritize product features. It categorizes customer requirements into four categories: Delighters, Satisfiers, Must-Be, and Indifferent. Delighters are unexpected features that exceed customer expectations, Satisfiers are features that meet customer expectations, and Must-Be are basic requirements that must be fulfilled. The model does not depict objections, which are concerns or issues raised by customers regarding a product or service.

Wockhardt is not an exponent of Quality evolution. Juran, Philip Crossby, and Deming are well-known figures in the field of quality management and have made significant contributions to the evolution of quality practices. However, Wockhardt is a pharmaceutical company and not an individual or organization associated with quality management. Therefore, it can be concluded that Wockhardt is not an exponent of Quality evolution.

The correct answer is False. If special causes are assigned in the process variations, the UCL and LCL would not be recomputed without removing those outliers. In statistical process control, special causes are considered to be non-random and indicate that the process is out of control. In such cases, it is important to identify and remove these special causes before recalculating the control limits. This ensures that the control limits accurately represent the variation caused by common causes and help in detecting any further deviations from the desired process performance.

In a normal distribution, approximately 99% of the data points fall within three standard deviations on both the left and right sides of the mean. This means that the remaining 1% of the data points are outliers that fall outside this range. This is a commonly used rule in statistics known as the 68-95-99.7 rule, which states that approximately 68% of the data falls within one standard deviation, 95% falls within two standard deviations, and 99.7% falls within three standard deviations of the mean.

The normal distribution would be suited for understanding the process behavior in this data set because it is commonly used to model continuous data and is characterized by a bell-shaped curve. Since the data represents effort variance in % for different project releases, which is a continuous variable, the normal distribution would provide a good fit for analyzing the variability of the data and identifying any patterns or trends.

As the given statement states that there is a negative correlation between review effort (hrs) and defect rate (no. of defects per hour), it means that as the defect rate increases, the review effort hours decrease. This implies that when there are more defects per hour, less time is required for reviewing and addressing them. Therefore, the correct answer is "As Defect Rate increases, Review Effort Hrs decreases."

If a process has a CPk value less than 1, it indicates that the process is not capable. CPk is a measure of process capability, specifically the ability of a process to consistently produce output within specified limits. A CPk value less than 1 suggests that the process is not able to meet the required specifications consistently, indicating that it is not capable. Therefore, the correct answer is "Not Capable".

If a Team Lead encounters a positively skewed normal distribution curve for Defect Leakage Rate, it means that the process is not stable. A positively skewed distribution indicates that there are more defects occurring at higher values, which suggests that there may be underlying issues or factors causing the defects. Therefore, the correct conclusion from the given options is that the process needs corrective action over defects to address and improve the high defect rate.

The statement "If P value is >=0.5, then the process is said to be Normal" suggests that the statistics being used in this context is inferential. Inferential statistics involves making inferences or drawing conclusions about a population based on a sample. In this case, the P value is likely being used to make an inference about the normality of a process based on the observed data.

A scatter diagram is a tool that indicates a relationship between X and Y variables by plotting data points on a graph. It provides a visual representation of the correlation between the variables, allowing for the identification of patterns or trends. The scatter diagram also calculates a correlation coefficient, which quantifies the strength and direction of the relationship between the variables. Therefore, a scatter diagram is the correct answer as it fulfills both criteria mentioned in the question.

When the P-value is greater than or equal to 0.05, it means that the observed data is not statistically significant enough to reject the null hypothesis. Therefore, the correct outcome of hypothesis testing in this case would be to accept the null hypothesis. This implies that there is not enough evidence to support the alternative hypothesis and any observed differences or relationships in the data can be attributed to chance or random variation.

Ratio and Interval Scale of measurements are based on variable and continuous type of data. In these scales, the data consists of numerical values that can take on any value within a certain range, and the intervals between the values are equal. This means that the data can be measured and compared using mathematical operations such as addition, subtraction, multiplication, and division. Additionally, the data in these scales can have a true zero point, which allows for the calculation of ratios between values. Therefore, variable and continuous data are the basis for ratio and interval scales of measurements.

The given information suggests that the data points are within the control limits (UCL & LCL), indicating that the process is stable and under statistical control. Additionally, the fact that 10 out of 11 data points are on one side of the mean value suggests that the process is exhibiting a common cause variation. Common causes refer to the inherent variability within a process that is expected and can be attributed to the system itself. Therefore, the process is likely experiencing common causes rather than special causes, assignable causes, or outliers.

A strong negative correlation means that as one variable increases, the other variable decreases. In this case, the design and code review effort is high, which means that more effort is being put into reviewing the design and code. As a result, there are less number of defects injected in the UAT phase. This indicates that the more effort put into the review, the fewer defects are found, showing a strong negative correlation between review effort and number of defects.

The correct answer is "A table to collect raw voices of Customers." This means that Voice of Customer is a method or system used to gather and document the feedback, opinions, and preferences of customers. It involves creating a table or database where customer feedback can be collected in its raw form, without any interpretation or analysis. This information can then be used to understand customer needs and expectations, and make improvements to products or services.

The Pareto Graph is used to represent data on a nominal scale of measurement. Nominal scale is the lowest level of measurement where data is categorized into distinct categories or labels without any specific order or ranking. In a Pareto Graph, the categories are represented by bars or columns, with the height of each bar indicating the frequency or count of each category. This type of graph is commonly used to show the relative importance or frequency of different categories in a dataset.

The I-mR Chart, also known as the Individuals and Moving Range Chart, is the appropriate type of control chart for outlier analysis when the sample value of subgroup size is n=1. This chart is used to monitor continuous data and is particularly useful when the subgroup size is small or variable. It helps identify any individual data points that fall outside of the expected range, indicating potential outliers in the process. The chart consists of individual data points plotted over time, along with the moving range between consecutive data points, allowing for the detection of both systematic and random variation in the process.

Regression analysis is a statistical technique used to determine the nature of the relationship between variables. It helps us make predictions by identifying the strength and direction of the relationship between a dependent variable and one or more independent variables. By analyzing the data and calculating the regression equation, we can estimate the value of the dependent variable based on the values of the independent variables. Therefore, regression analysis is a suitable method for determining the nature of the relationship and making predictions.

The Kano Model is not a Process Improvement Methodology. It is a customer satisfaction model developed by Noriaki Kano in the 1980s. The Kano Model helps in understanding and categorizing customer preferences and requirements. It classifies customer needs into five categories: must-be, one-dimensional, attractive, indifferent, and reverse. This model is used to prioritize and improve product features based on customer satisfaction. While Lean, Six Sigma, and Theory of Constraints are all process improvement methodologies that focus on eliminating waste, reducing variation, and optimizing processes, the Kano Model is not directly related to process improvement.

The probability value of the area under the curve between +1.0 and +2.0 standard deviation units in a normal distribution is 0.4772. This means that there is a 47.72% chance that a randomly selected value from the distribution falls within this range.

The process is none of the above because the organization specification limits and control limits are not within the same range. The organization specification limits are +5% or -5%, while the control limits are +6% and -2%. This means that the process is not stable, capable, or within control according to both sets of limits.

In a normal distribution (a bell-shaped curve), approximately 68.27% of the data falls within one standard deviation (σ) of the mean in both directions. This means that within +/- 1σ from the mean, you would find about 68.27% of the data.

A data set is said to have a normal distribution if the p-value is greater than or equal to 0.05. This means that there is not enough evidence to reject the null hypothesis that the data follows a normal distribution. In other words, the data is likely to be normally distributed if the p-value is greater than or equal to 0.05.

The median cannot be calculated for open-ended classes because it requires the data to be arranged in order. Open-ended classes do not have specific values, so it is not possible to determine the exact position of the median.

A negatively skewed normal distribution curve indicates that the majority of the data points are concentrated towards the right side of the curve, with a few extreme values towards the left side. In the context of project management, if the effort variance of a project shows a negatively skewed normal distribution curve, it means that most of the actual effort expended on the project is less than the expected effort. This suggests that the project is proactively finishing ahead of time, as the actual effort is less than anticipated.

A box plot is a type of graph that provides a visual representation of the distribution of data points in a dataset. It consists of a box that represents the interquartile range (IQR) of the data, with a line inside the box indicating the median. The "whiskers" extend from the box to show the range of the data, excluding outliers. By analyzing the box plot, we can easily identify the spread, skewness, and presence of outliers in the non-parametric distribution of data points. Therefore, a box plot is the most suitable graph for analyzing the stability of a non-parametric distribution.

Box plots would be the most suitable technique for comparing the on-time delivery (%) of Team Leads across 4 teams. Box plots provide a visual representation of the distribution of data, including the median, quartiles, and any outliers. By using box plots, the Project Manager can easily compare the on-time delivery performance of each Team Lead and identify any variations or outliers among the teams. This technique allows for a quick and effective comparison of the data, helping the Project Manager make informed decisions and take appropriate actions to improve on-time delivery across the teams.

The inputs to a process are referred to as the x variables because they are the independent variables that can be manipulated or controlled. These inputs are also considered the "causes" that create the effect or the outcome of the process. The process output is a function of its inputs, meaning that the result or output of the process is determined by the values or characteristics of the inputs.

The mean helps to understand process behavior for parametric distribution because it provides a measure of central tendency. It represents the average value of a set of data, indicating the typical value around which the data points are clustered. By calculating the mean, we can determine the average behavior of the process and identify any deviations or trends. This information is useful in analyzing and predicting the behavior of the process in relation to the parametric distribution.

The Box Plot Graph is a useful tool for identifying outliers in a dataset and differentiating before and after improvements. It allows us to visually analyze the distribution of data, identify any extreme values or outliers that may exist, and compare the distribution before and after certain improvements or interventions have been made. It is particularly suited for time-ordered data and parametric distribution, making it a valuable tool in data analysis and decision-making processes.