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Notes 1/21/16-2/11/16

by: Maddi Caudill

Notes 1/21/16-2/11/16 STA 210

Marketplace > University of Kentucky > STA 210 > Notes 1 21 16 2 11 16
Maddi Caudill
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About this Document

Standard deviation, human inference, response variable, etc
Intro to Statistical Reasoning
Dr. William S. Rayens
Class Notes
Statistics, confounding, Math, STA




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This 3 page Class Notes was uploaded by Maddi Caudill on Thursday February 11, 2016. The Class Notes belongs to STA 210 at University of Kentucky taught by Dr. William S. Rayens in Spring 2016. Since its upload, it has received 15 views.


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Date Created: 02/11/16
Mean ---> add all and divide by how many you have   Median --> put the numbers in order smallest to largest, then find the middle number   Standard deviation --> subtract the mean from each observation  and square the difference Add all up and divide by n-1 and take square root         • S measures spread about the mean. It should only be used when the mean is used to describe the center • S is not food to use (nor is the mean) if the distribution of numbers in the data set is skewed • S is always nonnegative. It can be zero only when all the observations in the data set are the same   • If the results have a large enough difference for you to say the results were anything other than chance, they are statistically significant. • If the difference is not big enough to say the results are based on anything other than chance, the results are NOT statistically significant.     Important to Know: • Statistical science can quantify the risk you are taking in saying that an experiment has found a difference in treatments. Sort of. • Statistical science has other roles in experimentation as well - like offering a quantitative way to compare different types of designs     We Started More Simply The Human Inference Question • We asked: if controlled studies produce some of the purest data, what can still create problems? • Answer organized in an artificially simple way on the video by introducing confounding from: • Inadequate or improper comparison • Lack of randomization   Example: How far two different designs of paper airplanes fly when thrown   Response variable: linear flight distance of airplane Explanatory variable: design type Subjects: different airplane samples   Any Obvious Confounding? Lurking variables that could compromise this experiment: • Different "pilots" • Different weight of paper (perhaps)       Rats and Cage Preferences (Example)   Randomly assigned----> new diet                                   ---> standard diet • Measured weight gain in rats and compare   Results: rats on the top shelf tended to gain more weight regardless of which diet (new or standard) they were on • They were unable to make nay confirmations on results of diet due to the confounding in the shelves • The scientists never would of been able to anticipate it   *don’t underestimate the power of the placebo   Addressing Confounding Double Blindness Neither the person receiving the treatment nor the person evaluating the symptoms knows which treatment has been administered. This eliminates a potential source of blindness.   Other Problems Deal With: • Dropouts • Non-adherers • Generalizations   • When it is possible to achieve, randomization is critical to experimentation • In some cases, experiments aren't really experiments at all, but are observational studies that compare two groups of data that have already been collected • In other cases, new experimental data are compared to existing data • In still other cases, randomization is simply not possible for ethical or practical reasons • In all of these situations, the potential severity of confounding must be evaluated on a case-by-case basis   Quasi Experiments: studies that are unable to use randomization to evaluate effectiveness of interventions • This can make it difficult


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