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Week 1 Lecture Notes


Week 1 Lecture Notes Stat 239

Statistics for the Biological and Physical Sciences
Diane Lovett

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About this Document

Statistics for biological and physical science. To be taken with Statistical Design for many science majors at SCSU. Lecture Notes
Statistics for the Biological and Physical Sciences
Diane Lovett
Class Notes
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This 4 page Class Notes was uploaded by an elite notetaker on Monday August 31, 2015. The Class Notes belongs to Stat 239 at St. Cloud State University taught by Diane Lovett in Summer 2015. Since its upload, it has received 44 views. For similar materials see Statistics for the Biological and Physical Sciences in Statistics at St. Cloud State University.


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Date Created: 08/31/15
Statistics for Bio and Aug 24Aug 28 Notes by Phys Science Brook Hoffman Section 1 1 The Structure of Data 0 Data 0 Set of measurements taken on a set of individual units 0 Usually data is stored and presented in a dataset Comprised of variables measured on cases 0 Everywhere pertains to wide varieties of topics 0 Cases and Variables 0 Obtain information about casesunits 0 Variable is any characteristic that is recorded for each case 0 Each Case row in dataset 0 Each Variabe Column in data set Categorical vs Quantitative o Categorical variable divides cases into groups 0 Quantitative variable measures numerical quantities for each case Explanatory and Response 0 Explanatory variable predicts the response variable Statistics for Bio and Aug 24Aug 28 Notes by Phys Science Brook Hoffman Section 12 Sampling from a Population 0 Sample vs Population 0 Population all individuals or objects of interest 0 Sample all the cases that data was collected on A subset of the population 0 Statistical inference using data from a sample to gain information about the population 0 Sampling Bias 0 Occurs when the method of selecting a sample causes the sample to differ from the population in some relevant way 0 We cannot trust generalizations made from a sample with a sampling bias 0 Humans are TERRIBLE at picking unbiased samples 0 Random Sampling 0 Avoid sampling bias Like names in a hat Use Technology Averages from random samples are centered around the correct number 0 ONLY random samples can be trusted when making generalizations to the populations 0 Simple Random Sample 0 Each unit has the same chance of being selected regardless of other units chosen for the sample Realities of Sampling 0 Random is ideal but often unfeasible o Generalizations are limited to the population that was sampled from NonRandom Samples Statistics for Bio and Aug 24Aug 28 Notes by Phys Science Brook Hoffman o Targeting a specific group 0 Bad Methods 0 Sampling units based on something obviously related to other variable you are studying 0 Volunteer Bias People who choose to participate are probably not representative of the entire population 0 Poor Question wording o Context of presentation 0 Inaccurate Responses 0 Always think critically about how data was collected Recognize that not all forms of data collection lead to valid inferences Section 13 Experiments and Observational Studies 0 Association and Causation 0 Associated values of one variable tend to be related to values of the other variable 0 Causally associated changing the value of the explanatory variable influences the value of the response variable Confounding Variable 0 Associated with both the explanatory variable and the response variable 0 When present causal association cannot be determined A study in which the researcher actively controls one or more of the explanatory variables Statistics for Bio and Aug 24Aug 28 Notes by Phys Science Brook Hoffman


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