Popular in Introduction to Statistics
Popular in Statistics
verified elite notetaker
This 3 page Class Notes was uploaded by Debra Tee on Friday September 30, 2016. The Class Notes belongs to STATS 250 at University of Michigan taught by Brenda Gunderson in Fall 2016. Since its upload, it has received 3 views. For similar materials see Introduction to Statistics in Statistics at University of Michigan.
Reviews for Lecture 4
Report this Material
What is Karma?
Karma is the currency of StudySoup.
You can buy or earn more Karma at anytime and redeem it for class notes, study guides, flashcards, and more!
Date Created: 09/30/16
Lecture 4: Gathering Useful Data for Examining Relationships 6.1 Speaking the Language of Research Studies Definitions: - Observational Studies: The researchers simply observe or measure the participants (about opinions, behaviors, or outcomes) and do not assign any treatments or conditions. Participants are not asked to do anything differently. - Experiments: The researchers manipulate something and measure the effect of the manipulation on some outcome of interest. Often participants are randomly assigned to the various conditions or treatments. Most studies, either observational or experimental, are interested in learning of the effect of one variable (called the explanatory variable) on another variable (called the response or outcome variable). - A confounding variable is a variable that both affects the response variable and also is related to the explanatory variable. The effect of a confounding variable on the response variable cannot be separated from the effect of the explanatory variable. - Confounding variables might be measured and accounted for in the analysis, or they could be unmeasured lurking variables. Confounding variables are especially a problem in observational studies. Randomized experiments help control the influence of confounding variables. - Since the sample is just a part of the population there will be some uncertainty about the estimates and decisions we make. To measure and quantify that uncertainty we turn to PROBABILITY Example: A researcher at the University of Michigan believes that the number of times a student visits the Student Health Center (SHC) is strongly correlated with the student’s type of diet and their amount of weekly exercise. The researcher selected a simple random sample of 100 students from a total of 3,568 students that visited SHC last month and first recorded the number of visits made to the SHC for each selected student over the previous 6 months. After recording the number of visits, he looked into their records and classified each student according to the type of diet (Home-‐Cooked Food / Fast Food) and the amount of exercise (None / Twice a Week / Everyday). a. Is this an observational study or a randomized experiment? An observational study (recorded the variables, not assigned to a level). b. What are the explanatory and response variables? Response = number of visits (quantitative and discrete) - Explanatory = type of diet and amount of exercise (here both categorical) .
Are you sure you want to buy this material for
You're already Subscribed!
Looks like you've already subscribed to StudySoup, you won't need to purchase another subscription to get this material. To access this material simply click 'View Full Document'