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1 parent 844c029 commit d98f03806e3bca40ab546dbddbcd8cfed7628046 @skalnik committed Sep 29, 2010
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+\documentclass[12pt]{article}
+\title{Summary}
+\author{Mike Skalnik}
+
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+\begin{document}
+\begin{flushright}{\large Summary\\ Mike Skalnik}\end{flushright}
+
+Darwinian evolution has been witnessed as a process that will accomplish an end goal, even if the outcome is not what could be expected. Due to this, it has become more and more common to simulate evolution to study various topics. I would like to investigate the evolution of a functioning timepiece out of parts. Wirt Atmar (1994) outlines the general idea of how such a program would be written. An initial population is generated, then replicated with random mutations introduced, the new population is judged by a set of criteria, and any unacceptable candidates are removed with acceptable candidates kept, and then the cycle starts again. This continues until a candidate reaches a set of predetermined rules that are used to define an acceptable solution.
+
+While there have been rather complex simulations, such as the evolution of highly divergent DNA sequences by Strope, Abel, Scott, and Moriyama (2009), studying the evolution of timepieces would give more insight into moderately complex sequences. I would create such a simulation, tweak the rules that define the relationship between the various watch parts, run the simulation, and see how many generations it takes before an acceptable watch is created.
+
+I predict that a basic affinity between parts and fitness based upon accuracy is all that is required to create a working timepiece. Exactly what the affinity rules would be are hard to determine at this point. However, the work of Hase, Khang, and Eom (2004), who simulated evolution to model hopping motions, can be used to determine good initial values. Once optimal rules have been discovered, the initial population can be changed as well to see how the better watches turn out. This would also be interesting since one could examine the difference watches created with this process and watches in real life. I predict that the watches created by such a process would be radically different than the ones we have made due to the restrictions that have been worked around. However, if these rules that model these restrictions are implemented, then I feel like the outcome would be relatively close to watches created by humans.
+
+\end{document}
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+Interential Statistics
+* Statistics used to draw conclusions about your data and how they can be applied to other groups
+Definitions
+* Population: All members of some predefined groups
+* Sample: Some subset of the population
+* Statistic: a numerical index (e.g., mean) based on sample data
+* Parameter: a numerical index based on population data
+Inferential Statistics
+* Sometimes we can study all members of a population, especially when they are a small group
+* Other times, we can't and so we need to sample from the population to whom we wish to generalize
+* Basically, alllows you to say how well your data apply to the larger population of which you want to generalize to
+* It relies on sampling distrbutions for making probabilitic statements about the populations based on sample data
+Remember Hypothesis testing?
+* What is the Null Hypothesis?
+ * No relationship
+* What is the Alternative Hypothesis?
+ * There is a relationship
+* What are the two possible outcomes we can have?
+ * We can reject null hypothesis
+ * Fail to reject
+Hypothesis Testing as Distributions
+* The Null Hypothesis suggests that our two sets of data come from the same population distribution
+* The Alternative Distribution suggests that they come from different populations
+* If they come from different populations we should be able to see the difference
+Alpha Level
+* The Alpha level: The probability of obtaining your particular result if the null hypothesis is really true
+* If you reject the null at Alpha = .05, then it means you believe the probability is very low (5 out of 100) that your research outcome is due to chance.
+Why .05?
+* In reality, it is an arbitrary number that developed over the years
+* But it still makes sense
+* Remember the Normal Distrbution
+One-Tailed vs Two-Tailed tests
+* Sometimes we are just interested in one groul being different from another
+* Other times we are interested in one group being not only different, but better or worse than another group
+
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+Inverse Pyramid
+
+1. Review General Idea
+2. Theoretical Underpinnings
+3. Hole (discuss)
+ - Why?
+ - How?
+4. Discuss Methodology
+5. Hypothesis
+ - Rationale
+
+Who they are?
+What they found?
+Why do I care?
+How does it apply to my research?

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