01:960:285 Lecture Notes - Lecture 2: Stratified Sampling, Convenience Sampling, Cluster Sampling

• School: Rutgers University
• Department: Statistics and Biostatistic
• Course: 01:960:285
• Professor: Shyam Moondra

Document Summary

The population includes all objects of interest whereas the sample is only a portion of the population. Parameters are associated with populations and statistics with samples. Parameters are usually denoted using greek letters (mu, sigma) while statistics are usually denoted using roman letters (x, s). There are several reasons why we don"t work with populations. They are usually large, and it is often impossible to get data for every object we"re studying. Sampling does not usually occur without cost, and the more items surveyed, the larger the cost. We compute statistics, and use them to estimate parameters. The computation is the first part of the statistics course (descriptive statistics) and the estimation is the second part (inferential statistics) There are a finite or countable number of choices available with discrete data. You can"t have 2. 63 people in the room. Length, weight, and time are all examples of continous variables. Since continuous variables are real numbers, we usually round them.