1 / 10

AP Statistics

AP Statistics . Section 5.1 Designing Samples. Objective: To be able to identify and use different sampling techniques. Observational Study : individuals are observed and variables of interest are measured. No influence on the responses.

tiara
Download Presentation

AP Statistics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. AP Statistics Section 5.1 Designing Samples

  2. Objective: To be able to identify and use different sampling techniques. Observational Study: individuals are observed and variables of interest are measured. No influence on the responses. Experiment: actively impose a treatment on a group in order to observe a response. Population: the entire group of individuals that we desire information about.

  3. Sample: a subset of the population that we actually examine in order to gather information about the population. Census: attempts to contact every individual in the entire population. Sampling Method: the process used to choose the sample. Sampling frame: the actual list of individuals from which the sample is actually selected.

  4. Types of Sampling Techniques: • Voluntary Response Sample: consists of individuals who choose themselves to respond to a general appeal. • Convenience Sample: sampling which chooses individuals that are easiest to reach. Biased: systematically favors certain outcomes.

  5. Simple Random Sample: (SRS) a sample of size n in which each individual has the same chance of being selected and each set of n individuals has the same chance of being selected. Steps: 1. Label subjects. Using the RNT: • <10 use digits 0 – 9 • 11 – 100 use digits 00 – 99 • 101 – 1000 use digits 000 – 999 (and so on) • Select x digits at a time. • Skip repeats and specified digits. • Stopping rules

  6. Ex. Select a sample of size 3 from students in this class using line 110 of the RNT. • Probability Sample: a sample chosen by chance. We must know what samples are possible and what chance each sample has. • Stratified Random Sample: • Divide the population into groups of similar individuals called strata. • Choose an SRS from each strata. • Combine all the SRSs to form the one sample

  7. Ex. Choose a stratified random sample of size 4 from a population that has 20 individuals (15M / 5F). Use line 120 from the RNT to do so. • Cluster Sample: • Divide the population into heterogeneous groups (clusters). • Assign each cluster a number. • Choose an SRS of the clusters. • Combine all the members of the randomly selected clusters to form the sample. Ex of clusters:

  8. Multistage Sample: restricts random selection by choosing the samples in stages. Uses multiple sampling techniques within the sampling process. Ex. • Systematic Random Sample: • Begin by finding k. • Think of the population in k groups. • Randomly select the first subject/unit by randomly selecting a number from 1 to k. • This subject/unit is the first member of the sample. Continue to add k to this first number to get the remaining members of the sample.

  9. Ex. Choose a Systematic Random Sample from a class of 24 students where n = 4. Types of Bias: • Undercoverage: occurs when some subgroup of the population is unintentionally left out of the sampling process. • Nonresponse: occurs when an individual chosen for the sample can’t be contacted or refuses to cooperate.

  10. Response bias: when the behavior of the interviewer or the respondent affects the results. • Wording of the Question: when the phrasing of the questions leads to biased results. *Larger samples give more accurate and less variable results. However, if the data is poorly collected there is no way to fix biased results.

More Related