PDF4PRO ⚡AMP

Modern search engine that looking for books and documents around the web

Example: tourism industry

Lecture 4: Random Variables and Distributions

Lecture 4: RandomVariables and DistributionsGoals Working with Distributions in R Overview of discrete and continuousdistributions important in genetics/genomics Random VariablesRandom Variables ! "01-1A rv is any rule ( , function) that associatesa number with each outcome in the samplespaceTwo Types of Random Variables A discrete Random variable has acountable number of possible values A continuous Random variable takes allvalues in an interval of numbersProbability Distributions of RVsDiscreteLet X be a discrete rv. Then the probability mass function (pmf), f(x),of X is:!

The expected or mean value of a continuous rv X with pdf f(x) is: Discrete ... probability distribution - referred to as a sampling distribution •Let’s focus on the sampling distribution of the mean,! X . Behold The Power of the CLT •Let X 1,X 2

Fullscreen Download
Loading..

Tags:

  Distribution, Continuous

Information

Domain:

Source:

Link to this page:

Please notify us if you found a problem with this document:

Spam in document Broken preview Other abuse

Transcription of Lecture 4: Random Variables and Distributions

Documents from same domain

Lecture 10: Multiple Testing - UW Genome Sciences

www.gs.washington.edu

Why Multiple Testing Matters Genomics = Lots of Data = Lots of Hypothesis Tests A typical microarray experiment might result in performing 10000 separate hypothesis tests.

  Tests, Multiple, Testing, Hypothesis, Hypothesis tests, Multiple testing

DNA Isolation from Strawberries

www.gs.washington.edu

source for extracting DNA because they are easy to pulverize and contain enzymes called pectinases and cellulases that help to break down cell walls. And most important, strawberries have eight copies of each chromosome (they are octoploid), so there is a lot of DNA to isolate. The purpose of each ingredient in the procedure is as follows:

  Form, Isolation, Strawberries, Extracting, Dna isolation from strawberries

Lecture 10: Multiple Testing

www.gs.washington.edu

False Discovery Rate m 0 m-m 0 m V S R Called Significant U T m - R Not Called Significant True True Total Null Alternative V = # Type I errors [false positives] •False discovery rate (FDR) is designed to control the proportion of false positives among the set of rejected hypotheses (R)

  Rates, Discovery, False, False discovery rates

Lecture 7: Hypothesis Testing and ANOVA

www.gs.washington.edu

•Calculate a test statistic in the sample data that is ... candidate gene. We then divide these N individuals into ... Sum of MS F Squares Source of df Variation! SST G k"1! SST E N"k! SST G k"1 SST E N"k. Non-Parametric Alternative • Kruskal-Wallis …

  Samples, Candidate

I-9 Form: Instructions for Nonresident on H-1B or TN Visa

www.gs.washington.edu

I-9 Form: Instructions for Nonresident on H-1B or TN visa Instructions for both New Hires and Updating & Reverification For more detailed information about completing Form I-9, employers and employees should refer to the Handbook for Employers: Instructions for Completing Form I-9 (M-274). _____

  Form, Visa

Lecture 5: Estimation - University of Washington

www.gs.washington.edu

Bayesian Estimation: ÒSimpleÓ Example ¥I want to estimate the recombination fraction between locus A and B from 5 heterozygous (AaBb) parents. I examine 30 gametes for each and observe 4, 3, 5, 6, and 7 recombinant gametes in the Þve parents. What is the mle of the recombination fraction? ¥Tedious to show Bayesian analysis. LetÕs simplify ...

  Estimation

Lecture 9: Linear Regression - University of Washington

www.gs.washington.edu

Lecture 9: Linear Regression. Goals • Linear regression in R •Estimating parameters and hypothesis testing ... •Previous coding would result in colinearity •Solution is to set up a series of dummy variable. In general for k levels you need k-1 dummy variables x 1 …

  Lecture, Previous

1. Plasmid structure 2. Plasmid replication and copy ...

www.gs.washington.edu

Plasmid replication requires host DNA replication machinery. 2. Most wild plasmids carry genes needed for transfer and copy number ... Large or small region of homologous DNA cloned that will integrate into the chromosomal target. 5. Need a counter selection method to kill the donor cells 6. Screen for what you think is correct.

  Regions, Replication

Lecture 2: Descriptive Statistics and Exploratory Data ...

www.gs.washington.edu

Multivariate Data •Organize units into clusters •Descriptive, not inferential •Many approaches •“Clusters” always produced Clustering Data Reduction Approaches (PCA) •Reduce n-dimensional dataset into much smaller number •Finds a new (smaller) set of variables that retains most of the information in the total sample

  Data, Multivariate, Multivariate data

Related documents

Probability Distributions: Discrete vs. Continuous

www.casrilanka.com

A continuous probability distribution differs from a discrete probability distribution in several ways. The probability that a continuous random variable will assume a particular value is zero. As a result, a continuous probability distribution cannot be expressed in tabular form.

  Distribution, Continuous

21 The Exponential Distribution

mast.queensu.ca

distribution if it has probability density function f X(x|λ) = ˆ λe−λx for x>0 0 for x≤ 0, where λ>0 is called the rate of the distribution. In the study of continuous-time stochastic processes, the exponential distribution is usually used to model the time until something hap-pens in the process. The mean of the Exponential(λ ...

  Distribution, Continuous, Exponential, Exponential distribution

Chapter 5: JOINT PROBABILITY DISTRIBUTIONS Part 1 ...

homepage.stat.uiowa.edu

Mean from a Joint Distribution If Xand Y are continuous random variables with joint probability density function fXY(x;y), then E(X) = Z 1 1 xfX(x) dx = Z 1 1 Z 1 1 xfXY(x;y) dydx HINT: E(X) and V(X) can be obtained by rst calculating the marginal probability distribution of X, or fX(x). 22

  Distribution, Joint, Continuous, Joint distributions

Discrete and Continuous Random Variables

ocw.mit.edu

the probability distribution function, f(x), for continuous R.V. X. The total area under f(x) is 1.0 a b c . 15.063 Summer 2003 1818 The Uniform Distribution a) What is the mean transit time? X is uniform on [a,b] if X is equally likely to take any value in the range from a to b.

  Distribution, Continuous

Chapter 3 Continuous Random Variables

www.pnw.edu

Random variable Xis continuous if probability density function (pdf) fis continuous at all but a nite number of points and possesses the following properties: f(x) 0, for all x, R 1 1 f(x) dx= 1, P(a<X b) = R b a f(x) dx The (cumulative) distribution function (cdf) for random variable Xis F(x) = P(X x) = Z x 1 f(t) dt; and has properties lim x ...

  Distribution, Continuous

Univariate Distribution Relationships

www.math.wm.edu

verse cumulative distribution function of a continuous ran-dom variable can be expressed in closed form. For a dis-crete random variable, this property indicates that a variate can be generated in an O(1)algorithm that does not cycle through the support values or …

  Distribution, Continuous

Continuous Random Variables: The Uniform Distribution

resources.saylor.org

De nition 2: Uniform Distribution A continuous random ariablev V)(R that has equally likely outcomes over the domain, a<x<b. Often referred as the Rectangular distribution because the graph of the pdf has the form of a rectangle. Notation: X~U (a;b). The mean is = a+b 2 and the standard deviation is ˙= q (ba) 2 12 The probability density ...

  Distribution, Uniform, Variable, Continuous, Random, Continuous random variables, The uniform distribution

6 Jointly continuous random variables

www.math.arizona.edu

6 Jointly continuous random variables Again, we deviate from the order in the book for this chapter, so the subsec-tions in this chapter do not correspond to those in the text. 6.1 Joint density functions Recall that X is continuous if there is a function f(x) (the density) such that P(X ≤ t) = Z t −∞ fX(x)dx

  Continuous

Lecture 4: Poisson Approximation to Binomial Distribution ...

www.stat.purdue.edu

Binomial Distribution • For Binomial Distribution with large n, calculating the mass function is pretty nasty • So for those nasty “large” Binomials (n ≥100) and for small π (usually ≤0.01), we can use a Poisson with λ = nπ (≤20) to approximate it!

  Distribution

Related search queries

Continuous, Distribution, Exponential distribution, Exponential, Joint, Joint Distribution, Continuous Random Variables: The Uniform Distribution