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QSCI week 1

by: Claira Notetaker

QSCI week 1 Q SCI 381

Claira Notetaker
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one week of notes
Introduction to Probability and Statisitics
Patrick C, Tobin
Class Notes




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This 3 page Class Notes was uploaded by Claira Notetaker on Friday January 8, 2016. The Class Notes belongs to Q SCI 381 at University of Washington taught by Patrick C, Tobin in Winter 2016. Since its upload, it has received 99 views. For similar materials see Introduction to Probability and Statisitics in Environmental Science at University of Washington.

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Date Created: 01/08/16
Chapter 1: Introduction to Statistics • Sections 1.1-1.3 Data • Def: sets of information through observation, can be created with counts, through measurements (whole numbers, decimals), or responses (categories) • How one analyzes different sets of data is where the hard part emerges o Each set (observation, counts, measurements, responses) in a scientific procedural way Statistics • Def: science is collecting, organizing, analyzing, and interpreting data for some purpose o Need to make a decision by looking at the data o Used to determine the cause and effect • Types of statistics o Descriptive: branch that involves the organization, summary, and display of data o Inferential: branch that involved data to draw conclusions Data collection • Census: Population level data: collection of all observations, measurement, or counts in a population o EX: § US government counts everyone o Parameter: the numerical description of a population characteristics § This value does not change because everyone in the count is recorded § EX: ú The average SAT scores for ALL incoming freshman at UW • Sample: a subset of the population o more feasible o statistics: the numerical sample characteristics § the parameter for a sample of data collected o EX: § The average SAT score for all of the students in a class that would be used to explain the average SAT score for the whole of UW Types of Data collection • Qualitative o Ordinal: scale of ordering or ranking observation, meaningful ranking § EX: college football team rating o Nominal: scale of grouping into a unique categories without meaningful ranking § EX: eye color o Dichotomous: a specialized nominal variable but with only 2 categories § EX: yes or no questions • Quantitative o Numerical data, very common data type § Integers, decimals/fractions, or continuous data Collecting Data • Experimental design: o The goal to collect data is to create a meaningful and useful manner o Observational study § Not changing any conditions o Experimental study § Exposure to different conditions • EX: perceived value of rare species o Used both observational and experimental studies • Control group o Receive no treatment of a neutral treatment o Early application of control group (1925) § Computing the effectiveness of insecticide ú Results of fly mortality in the presence of insecticide Sampling • Randomization (avoid bias) • Replication (ensure your data is meaningful) Census • “count everything/everyone” Systematic sampling • Pick a random number, then sample every time your reach that number o EX: roll a 5, count every fifth car color • Must set a rule ahead of time in order to get rid of the bias Systematic random sampling • Randomly select on a case-by-case bias o EX: flipping a coin for each subject § Heads you count the sample, tails you don’t count the sample • Make the rule first ^^ Stratified sampling • Individuals are randomly selected within groups or strata o Have similar characteristics believed to be important to the question § Then you sample within that strata ú so systematic or systematic random sampling • need to make sure you collect data across the range of the variable Cluster sampling • Population is divided into “groups” o Groups are often randomly selected o Individuals are randomly selected within each randomly selected group • Cluster sampling can be a cost effective means to get a representative view Chapter 2: Descriptive Statistics • All sections Graphing • Histogram o A graphical representation of the distribution of a set of data o Can used for both quantitative and qualitative data EX: the iris data set • Goal: collect the morphological variation of 3 species of the iris Cumulative proportions • Found through the frequency of the data point and all points preceding divided by the data points class • Can be percentiles Measures of position • Used to divide up data into statistically important quartiles • Used to understand the middle of the data o IQR = Q3 – Q1


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