Quantitative Methods: Application
Reading 10. Sampling and Estimation
Learning Outcome Statements
g. identify and describe desirable properties of an estimator;
h. distinguish between a point estimate and a confidence interval estimate of a population parameter;
CFA Curriculum, 2020, Volume 1
Subject 5. Estimators
The single estimate of an unknown population parameter calculated as a sample mean is called a point estimate of the mean. The formula used to compute the point estimate is called an estimator. The specific value calculated from sample observations using an estimator is called an estimate. For example, the sample mean is a point estimate of the population mean. Suppose two samples are taken from a population and the sample means are 16 and 21 respectively. Therefore, 16 and 21 are two estimates of the population mean. Note that an estimator will yield different estimates as repeated samples are taken from the sample population.
A confidence interval is an interval for which one can assert with a given probability 1 - α, called the degree of confidence, that it will contain the parameter it is intended to estimate. This interval is often referred to as the (1 - α)% confidence interval for the parameter, where α is referred to as the level of significance. The end points of a confidence interval are called the lower and upper confidence limits.
For example, suppose that a 95% confidence interval for the population mean is 20 to 40. This means that:
- There is a 95% probability that the population mean lies in the range of 20 to 40.
- "95%" is the degree of confidence.
- "5%" is the level of significance.
- 20 and 40 are the lower and higher confidence limits, respectively.
User Contributed Comments 6You need to log in first to add your comment.
level of significance = 1-degree of confidence
Note: strictly speaking we really can't say there's a "95% probablility" of the mean being between 20-40. See wikipedia.org/Confidence_intervals for a detail description. But for the exam, I guess it's probably not a big deal.
Tha above is correct, a confidence interval does not imply a probability statement of the estimated parameter being inside it (this is a given -- it is) nor does it give a probability of statement of the true mean. You cannot technically say the mean has a 95% probability of being inside a confidence interval. This is WRONG. The mean is either inside or outside the interval, there is no middle ground. THE TRUE MEAN IS NOT A RANDOM VARIABLE. What is being said is that 95% of all CONFIDENCE INTERVALS (note: the interval(S****)) contain the true mean. Its very subtle.
UnEfCo: Unbiased, Efficiency, Consistency
Unbiased: Mean = Intended Parameter
Efficiency: Least variance among all parameters
Consistency: Converges towards the actual value as the sample size increases
you are the best Sahilb7