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BIO 305: Exam 1 Review

by: Emily.nicole

BIO 305: Exam 1 Review BIO 305

Marketplace > Syracuse University > Biology > BIO 305 > BIO 305 Exam 1 Review
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(Exam covers lectures from weeks 2-6) Full study guide on all topics covered: Advanced statistics, limiting factors in biology II, distribution and diversity, common biological principles that o...
Integrative Biology Lab
Dr. Mark Ritchie, Dr. Kate Lewis, & Dr. Eleanor Maine
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This 5 page Study Guide was uploaded by Emily.nicole on Thursday December 3, 2015. The Study Guide belongs to BIO 305 at Syracuse University taught by Dr. Mark Ritchie, Dr. Kate Lewis, & Dr. Eleanor Maine in Spring 2014. Since its upload, it has received 187 views. For similar materials see Integrative Biology Lab in Biology at Syracuse University.

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Date Created: 12/03/15
POPULATION VS. SAMPLE A POPULATION​IS ACOLLECTION OF DATAWHOSE PROPERTIES ARE ANALYZED. THE POPULATION ISTHE COMPLETE COLLECTION​TO BE STUDIED, IT CONTAINALL SUBJECTS OF INTEREST. A SAMPLE​IS PART​OF THE POPULATION OF INTEREST, SUB-COLLECTION​SELECTED FROM A POPULATION. THE NORMAL DISTRIBUTION Example: Changing μ​shifts the distribution left or right. Changing σ increases or decreases the spread. Normal distribution is defined by its mean and standard dev. MEAN μ: mean=AVERAGE Excel Syntax:Mean: AVERAGE(starting cell: ending cell) Median = middle value Excel : MEDIAN(starting cell: ending cell) Mode= most often Excel : MODE(starting cell: ending cell) Standard Deviation- quantifies variability σ: standard deviation -GIVES AN IDEA OF HOW CLOSE THE ENTIRE SET OF DATA IS TO THE AVERAGE VALUE STDEV(starting cell: ending cell) Standard Error of the Mean -quantifies the percision of the mean STDEV(starting cell: ending cell)/SQRT(number) SEx = s/ sq rt. of n s= standard deviationn= sample size SD VS. SEM THE SD​QUANTIFIES VARIBILITY — HOW MUCH THE VALUES VARY FROM ONE ANOTHER. THE SEM​QUANTIFIES HOW PRECISELY YOU KNOW THE TRUE MEAN OF THE POPULATION. IT TAKES INTO ACCOUNT BOTH THE VALUE OF THE SD AND THE SAMPLE SIZE. THE ​EM​, BY DEFINITION, IS ALWAYS SMALLER THAN TSD.​ THE SEM​GETS SMALLER AS YOUR SAMPLES GET LARGER. THE ​D​DOES NOT CHANGE PREDICTABLY AS YOU ACQUIRE MORE DATA. What’s hypothesis is a proposeexplanatiofor aphenomenon​. testable Most of the time a hypothesis is written like this: "If _____[I do this] _____, then _____[this]_____ will happen." Logic : Assume A. If A, then B. Not B. Therefore, Not A. Steps of hypothesis testing 1. Initial observation 2. Define your hypothesis (null H0, alternative/experimental Ha) 3. Do experiments to test your hypothesis (including Information gathering) 4. Record observation 5. Do statistics (calculate the p-value of what you observed) 6. Conclusion (accept/ reject your hypothesis) Types of T. TEST independent​ t.testindependent samples -- compares means between two independent groups. Paired t.tes: paired (dependent) samples -- compares means between two related groups . e.g.: 1) measurements were taken from the same group twice (repeated measures) 2) measurements are joined, for example, comparing the IQ of older and younger brothers The T-Test The t­test assesses whether the means of two groups are ​ statisticall different  from each other.   ANOVA=ANalysis Of VAriance compares means between more than two independent groups (there’s extension) Just an extension of the t-test One-way ANOVA (single factor): an ANOVA with only two groups, is mathematically equivalent to a t-test Two-way ANOVA (multiple factors) Chi-square test- goodness of fit compares proportions between two or more groups- frequencies only categorical variable Example: Limiting Factor: Limiting means the factthat effects the growth or development of an organism, population, or  process     


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