Samples vs. Distributions Distributions: Discrete Random Variable Distributions: Continuous Random Variable Another Situation: Sample of Data.

Slides:



Advertisements
Similar presentations
Practice Midterm Welcome to Pstat5E: Statistics with Economics and Business Applications Yuedong Wang Solution to Practice Midterm Exam.
Advertisements

Continuous Random Variables Chapter 5 Nutan S. Mishra Department of Mathematics and Statistics University of South Alabama.
AB 11 22 33 44 55 66 77 88 99 10  20  19  18  17  16  15  14  13  12  11  21  22  23  24  25  26  27  28.
EGR Defining Probabilities: Random Variables Examples: –Out of 100 heart catheterization procedures performed at a local hospital each year, the.
CORE 1 Patterns in Chance. Daily Starter Begin Handout.
Probability Theory STAT 312 STAT 312 Dr. Zakeia AlSaiary.
1 Def: Let and be random variables of the discrete type with the joint p.m.f. on the space S. (1) is called the mean of (2) is called the variance of (3)
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Discrete random variables Probability mass function Distribution function (Secs )
Assignment 2 Chapter 2: Problems  Due: March 1, 2004 Exam 1 April 1, 2004 – 6:30-8:30 PM Exam 2 May 13, 2004 – 6:30-8:30 PM Makeup.
2003/02/13 Chapter 4 1頁1頁 Chapter 4 : Multiple Random Variables 4.1 Vector Random Variables.
4. Convergence of random variables  Convergence in probability  Convergence in distribution  Convergence in quadratic mean  Properties  The law of.
Statistics Lecture 14. Example Consider a rv, X, with pdf Sketch pdf.
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Continuous random variables Uniform and Normal distribution (Sec. 3.1, )
The moment generating function of random variable X is given by Moment generating function.
Normal and Sampling Distributions A normal distribution is uniquely determined by its mean, , and variance,  2 The random variable Z = (X-  /  is.
Sampling Distributions  A statistic is random in value … it changes from sample to sample.  The probability distribution of a statistic is called a sampling.
Probability Distributions. A sample space is the set of all possible outcomes in a distribution. Distributions can be discrete or continuous.
L7.1b Continuous Random Variables CONTINUOUS RANDOM VARIABLES NORMAL DISTRIBUTIONS AD PROBABILITY DISTRIBUTIONS.
Starter The probability distribution of a discrete random variable X is given by: P(X = r) = 30kr for r = 3, 5, 7 P(X = r) = 0 otherwise What is the value.
Chapter 14 Monte Carlo Simulation Introduction Find several parameters Parameter follow the specific probability distribution Generate parameter.
Advanced Higher Statistics Data Analysis and Modelling Hypothesis Testing Statistical Inference AH.
AP STATISTICS LESSON COMPARING TWO PROPORTIONS.
Probability Distributions u Discrete Probability Distribution –Discrete vs. continuous random variables »discrete - only a countable number of values »continuous.
The Median of a Continuous Distribution
Sample Variability Consider the small population of integers {0, 2, 4, 6, 8} It is clear that the mean, μ = 4. Suppose we did not know the population mean.
Math b (Discrete) Random Variables, Binomial Distribution.
Probability and Statistics Dr. Saeid Moloudzadeh Random Variables/ Distribution Functions/ Discrete Random Variables. 1 Contents Descriptive.
Continuous and Discrete Probability Distributions Sections 7.1 and 8.1.
Discrete and Continuous Random Variables.  13 out of 21 students correctly identified the bottled water. How many correct identifications would you need.
Methodology Solving problems with known distributions 1.
The Binomial Distribution
Probability and Statistics Dr. Saeid Moloudzadeh Joint Distribution of Two Random Variables/ Independent Random Variables 1 Contents Descriptive.
Copyright © 2010 Pearson Addison-Wesley. All rights reserved. Chapter 3 Random Variables and Probability Distributions.
8-3: Probability and Probability Distributions English Casbarro Unit 8.
4.3 More Discrete Probability Distributions NOTES Coach Bridges.
Continuous Random Variables Lecture 22 Section Mon, Feb 25, 2008.
Discrete and Continuous Random Variables. Yesterday we calculated the mean number of goals for a randomly selected team in a randomly selected game.
This is a discrete distribution. Situations that can be modeled with the binomial distribution must have these 4 properties: Only two possible outcomes.
Central Limit Theorem Let X 1, X 2, …, X n be n independent, identically distributed random variables with mean  and standard deviation . For large n:
Copyright © 2010 Pearson Addison-Wesley. All rights reserved. Chapter 3 Random Variables and Probability Distributions.
Discrete Probability Distributions Chapter 4. § 4.3 More Discrete Probability Distributions.
Random Variables By: 1.
Chapter 9: Joint distributions and independence CIS 3033.
4.3 Probability Distributions of Continuous Random Variables: For any continuous r. v. X, there exists a function f(x), called the density function of.
Conditional Probability 423/what-is-your-favorite-data-analysis-cartoon 1.
Random Variable 2013.
Advanced Higher Statistics
Math a Discrete Random Variables
CH 23: Discrete random variables
4.3 Probability Distributions of Continuous Random Variables:
Cumulative distribution functions and expected values
Random Variable.
AP Statistics: Chapter 7
Means and Variances of Random Variables
In-Class Exercise: Discrete Distributions
اختر أي شخصية واجعلها تطير!
Probability & Statistics Probability Theory Mathematical Probability Models Event Relationships Distributions of Random Variables Continuous Random.
Expectation & Variance of a Discrete Random Variable
The Normal Probability Distribution Summary
Lecture Slides Elementary Statistics Twelfth Edition
Random Variable.
Exponential Probabilities
4.3 Probability Distributions of Continuous Random Variables:
Discrete Variables Classes
... DISCRETE random variables X, Y Joint Probability Mass Function y1
Chapter 14 Monte Carlo Simulation
Cumulative Distribution Function
Chapter 3 : Random Variables
Empirical Distributions
Presentation transcript:

Samples vs. Distributions Distributions: Discrete Random Variable Distributions: Continuous Random Variable Another Situation: Sample of Data

Distributions, Continuous (material continues) ab fX(x)fX(x) x x fX(x)fX(x) Probability given by height. Probability given by area. Probability Distributions. Continuous Random Variables: page 18  IT C

Distributions, Continuous Probability can also be given by cumulative functions. Business applications require the use of both types of probability information. (material continues)  Probability Distributions. Continuous Random Variables: page 19 IT C

Feedback: Privacy Policy Feedback
Do Not Sell My Personal Information
About project: SlidePlayer Terms of Service

© 2025 SlidePlayer.com. Inc. All rights reserved.