ECON 104 Lecture Notes - Lecture 7: Conditional Variance, Independent And Identically Distributed Random Variables, Bias Of An Estimator
Document Summary
During the pset, there was a common mistake in e2. 1. It starts by saying the table contains joint distribution - it gives you some frequencies of potential outcomes. Age from 25-34 then hourly earnings from 5-70. Numbers look too small to be hourly earnings and the total sum is 1. This is one big cdf - cumulative mass function since it contains discrete function but still sums to. For variance you need to look at the values of hourly earnings! Variance should be calculated using the values of ahe. An estimate is a formula that gives a guess of the true population value -> false the definition is for estimator, estimate is the outcome. Someone suggests using the 1st observation from a sample of n as an estimator of the population mean. Suppose estimator is y bar, and true pop value is mu y . For unbiasedness to occur, we need e(y bar) = mu y .
