Estimation

• Up to this point we have assumed that the parameters of the population of interest are unknown.
• When dealing with methods such as normal distribution, the population mean, µx and standard deviation σx were given.
•  In estimation, there is a complete change in the type of problem with which we are concerned.
• Now we have to get used to dealing with problems where the population parameter of interest is either completely unknown or is given as a hypothetical(assumption) value. Solving these problem involves statistical inference.
• The general concepts of estimation
  1. θ represent any parameter of interest. e.g. the parameter whose value is unknown and must be estimated.
  2. Ô sample statistic that will be used to estimate unknown θ. 
  3. σÔ represents the standard error sample statistic,Ô. This measures the variability or error associated with all possible values of Ô as an estimate of θ
• There are two possibilities of determining population parameters - we can either calculate it exactly or we can estimate it.