STP 231 Lecture covering 1.3
STP 231 Lecture covering 1.3 STP 231
Popular in Statistics for Biosciences
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This 2 page Class Notes was uploaded by Andrej Sodoma on Saturday September 3, 2016. The Class Notes belongs to STP 231 at Arizona State University taught by Dr. Ye Zhang in Fall 2016. Since its upload, it has received 6 views. For similar materials see Statistics for Biosciences in Statistics at Arizona State University.
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Date Created: 09/03/16
STP 231 lecture covering 1.3 I.) Sample vs. Population, Descriptive vs. Inferential statistics A.) Population: Collection of all the individuals who are in consideration for the study. Denoted by the variable N. B.) Sample: Part of the population that is sampled and the experiment is ran on them. Denoted by the variable n. C.) Example: Concussions are a rising issue in the NFL. In order to study the effect concussions have on cognitive ability 100 football players were randomly selected. i. The population would be the all of the players in the national football league. While the sample would, be the 100 football players used in the study. D.) Descriptive statistics: It is a method for summarizing data that only uses given information no predictions are made, it basically lists information given, it lists facts. E.) Inferential statistics: It is a method for summarizing data that makes predictions about a population. i. example: A study was conducted on students in order to see how TV affected their grades. They only recorded the results. - Descriptive statistics ii. Example: Based off last year’s grades you predict that you will do the same if not better next year. - Inferential statistics II.) Types of sampling A.) Simple Random Sampling: Every subject in the population has an equal chance of being chosen to participate in the study. i. Just because one person is chosen does not change the odds of you being chosen. ii. How to make a simple random sample from a number table. 1.) Start on at a point on a table 2.) Go by row or column 3.) The numbers must be within the parameters of the sample and population. 0001-9999 people 4.) All the numbers must have the same number of digits. 0001, 0002, …0100,…1204. 5.) No repeating numbers. 6.) Stop once you reach your required sample amount. B.) Random Cluster Sampling: a technique that divides the population into clusters and then the clusters are randomly selected. i. Example: Number of people in Nathan Hale elementary that use mechanical pencils 1.) People in Nathan Hale Elementary 2.) Population is divided in clusters, which could be classrooms. 3.) Then 10 clusters (classrooms) are randomly selected. 4.) Then the people in each cluster are analyzed. C.) Stratified Random Sampling: a technique that divides the population into strata (multiple stratum) a sample is then taken from each stratum. i. This is different from random cluster sampling because each stratum represents a characteristic. In addition, all of the groups have to be represented in the sample. ii. Example: Number of people at a Rihanna concert that are senior citizens. 1.) Population: people at the concert. 2.) Number of people from each stratum that must be sampled, (n x ((number of subjects in stratum)/ (N))): (20 people for each age range, teens, 20’s, 30’s, middle aged, senior citizens) x ((number of subjects in each stratum of teens, 20’s, 30’s, middle aged, senior)/ (people at the concert))). 3.) Number of subjects in each stratum: You need 10 teens, 5 20’s, 1 30’s, 3 middle aged, and 1 senior citizens in your stratified random sample. If you did it right they all should add up to the desired sample total. 4.) Randomly select the subjects in each age range using simple random sampling. D.) Determining sampling techniques i. How the population is divided. Is it just one general population or is made of groups? 1.) If it is made of groups it is Simple random sampling ii. How are the groups divided? Randomly or with structure? 1.) Random is Random cluster sampling. With structure is a stratified random sampling. III.) Error and biases A.) sampling error: when the sample does not represent the population. B.) Sampling bias: when the individuals are not randomly chosen. C.) Response bias: when a question is poorly worded resulting in a dishonest answer. D.) Non-response bias: The person does not respond to your survey. E.) Missing data: observations are missing de to the targeted group changing, leaving, or dying.
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