Skip to main content
뒤로

Introduction to Confidence Intervals quiz #1

컨트롤 버튼이 '내비게이션' 모드로 변경되었습니다.
1/10

이 집합의 용어 (10)

  • What is a confidence interval, and how does it improve upon using a single point estimate when estimating a population parameter?
    A confidence interval is a range of values, derived from sample data, that is likely to contain the true value of a population parameter. It improves upon a single point estimate by providing a range that accounts for sampling variability, giving a more informative and reliable estimate.
  • What does the confidence level (such as 95%) represent in the context of confidence intervals?
    The confidence level, such as 95%, represents the probability that the constructed confidence interval contains the true population parameter. It indicates how confident we are that the interval includes the parameter.
  • How is the value of alpha (α) related to the confidence level, and what does it represent?
    Alpha (α) is calculated as 1 minus the confidence level (α = 1 − c). It represents the total probability that the true parameter falls outside the confidence interval, split equally between the two tails of the distribution.
  • How do you calculate the endpoints of a confidence interval given a point estimate and a margin of error?
    The endpoints of a confidence interval are calculated by subtracting and adding the margin of error to the point estimate: (point estimate − margin of error, point estimate + margin of error).
  • What is the margin of error in a confidence interval, and how is it typically determined?
    The margin of error is the maximum likely amount of error in the estimate, representing the distance from the point estimate to the endpoints of the confidence interval. It is typically determined using a critical value (such as a z-score) and the standard error of the estimate.
  • What are the critical z-values for common confidence levels such as 90%, 95%, and 99%?
    The critical z-values for common confidence levels are: 1.645 for 90%, 1.96 for 95%, and 2.576 for 99% confidence intervals.
  • How would you interpret a 95% confidence interval for a parameter y, given endpoints of 2 and 6?
    You would interpret it as: 'We are 95% confident that the true value of the parameter y lies between 2 and 6.'
  • What is a confidence interval and why is it preferred over a single point estimate when estimating a population parameter?
    A confidence interval is a range of values likely to contain the true population parameter, providing a more informative estimate than a single point by accounting for sampling variability.
  • How is the value of alpha (α) related to the confidence level, and what does it represent in a confidence interval?
    Alpha (α) is calculated as 1 minus the confidence level (α = 1 − c) and represents the total probability that the true parameter falls outside the confidence interval, split equally between the two tails.
  • What are the critical z-values for the common confidence levels of 90%, 95%, and 99%?
    The critical z-values are 1.645 for 90%, 1.96 for 95%, and 2.576 for 99% confidence intervals.