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Engineering Statistics and Data-Driven Decision Making Flashcards

7 cards from real BSE practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

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  1. A manufacturing process produces components with a mean diameter of 50 mm and a standard deviation of 0.5 mm. What percentage of parts fall within ±2 standard deviations of the mean under a normal distribution?

    Answer: 95.45%

    The empirical rule states that ±2 standard deviations from the mean captures approximately 95.45% of data in a normal distribution.

  2. In hypothesis testing, a Type II error occurs when:

    Answer: A false null hypothesis is not rejected

    A Type II error (false negative) occurs when the null hypothesis is false but the test fails to reject it.

  3. Which control chart is most appropriate for monitoring the proportion of defective items in a production batch?

    Answer: p-chart

    The p-chart monitors the proportion (fraction) of defective items in samples, making it the correct choice for attribute data expressed as a proportion.

  4. An engineering manager wants to determine the relationship between machine age (years) and maintenance cost ($). Which statistical method is most appropriate?

    Answer: Simple linear regression

    Simple linear regression models the relationship between one independent variable (machine age) and one dependent variable (maintenance cost).

  5. The coefficient of variation (CV) is defined as:

    Answer: Standard deviation divided by mean, expressed as a percentage

    CV = (Standard Deviation / Mean) × 100%, and it measures relative variability, allowing comparison across datasets with different units or scales.

  6. In a factorial experiment with 3 factors each at 2 levels, how many treatment combinations are required for a full factorial design?

    Answer: 8

    A full 2³ factorial design requires 2³ = 8 treatment combinations, one for each unique combination of factor levels.

  7. Which probability distribution is most commonly used to model the time between failures of a system assuming a constant failure rate?

    Answer: Exponential distribution

    The exponential distribution models time between events in a Poisson process, making it ideal for systems with a constant (memoryless) failure rate.