There are two categories of sampling processes. Random Non-random

1. Statistics Section 1.2Identify different methods for selecting a sampleSimulate a random process In order to learn about a characteristic of a population, a small group of individuals is selected and studied. The small group is called a sample. The process of choosing a sample is called sampling. There are two categories of sampling processes. • Random • Non-random Review: quantitative and qualitative variables, population parameter, sample statistic, four levels of measurement https://www.youtube.com/watch?v=tgtCJrbvM44

2. Random Sampling methods. • Simple Random- every individual in the population has an equal chance of being selected. (Every individual in the population is numbered and a random number generator is used to select the sample.) Example: A company builds 250 CD players in a day. They want to inspect 15 of the players for defects each day. Create a simple random list of the 15 players to be inspected. Use a TI 84 to generate a list of 15 numbers. MATH PRB randINT(x,y,z) x=lowest possible number y = largest possible number z= number of values to be generated

3. Random samples continued… 2. Stratified Random – Groups or classes inside a population that share a common characteristic call strata are selected. Individuals within each stratum are them selected using simple random sampling. Example: A population is made up of students in grades 9 -12. Devise a procedure to obtain a sample of 20 students using a stratified random process.

4. Random samples continued… 3. Systematic Sampling – The elements of the population are arranged in some natural sequential order. Then, from a random starting point, we select every kth element for the sample. Example: A class of students has 75 members. Devise a procedure to select a sample 10 students using systematic sampling.

5. Random samples continued… 4. Cluster Sampling – begin by dividing the population into demographic sections. Randomly select several sections and include every member from the selected sections. Example: A population consists of a town of 1200 people. Devise a procedure to select a sample using cluster sampling.

6. Random samples continued… 5. Multistage Sampling – First, a cluster of individual is selected. Second, The cluster is stratified according to some common factor. Third, each stratum is broken down into smaller clusters. Finally, each small clusters are chosen randomly and each individual is chosen for the sample. Example: Devise a multistage sample from population of a particular state.

7. Non-Random Sampling Convenience Sampling- individuals are selected for a sample because they are easy to access. (This method can be very biased and may not yield accurate results.) Example: Devise a procedure for picking a sample of the students in the 12th grade using convenience sampling.

8. Errors in sampling • Undercoverage – results from omitting population members from the sample frame.(list of individuals from which the sample is actually selected) • Sampling Error - is the difference between measurements from a sample and measurements from the entire population. • Nonsampling Error – is the result or poor sample design, sloppy data collection, bias in questioning, … (avoidable errors.) • Guided exercise page 14

9. assignment Page 18 Problems 1-5, 7, 8, 11, 13, 16, 19, 20