Statistics 300: Elementary Statistics

Slides:



Advertisements
Similar presentations
Descriptive statistics Experiment  Data  Sample Statistics Experiment  Data  Sample Statistics Sample mean Sample mean Sample variance Sample variance.
Advertisements

Lesson #17 Sampling Distributions. The mean of a sampling distribution is called the expected value of the statistic. The standard deviation of a sampling.
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Statistical inference.
Introduction to Normal Distributions and the Standard Distribution
Chapter 8: Confidence Intervals
Statistics 1: Elementary Statistics Section 5-4. Review of the Requirements for a Binomial Distribution Fixed number of trials All trials are independent.
Statistics 300: Elementary Statistics Section 6-2.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Lecture Slides Elementary Statistics Eleventh Edition and the Triola Statistics Series by.
Unit 5 Section 5-4 – Day : The Binomial Distribution  The mean, variance, and standard deviation of a variable that has the binomial distribution.
Section Copyright © 2014, 2012, 2010 Pearson Education, Inc. Lecture Slides Elementary Statistics Twelfth Edition and the Triola Statistics Series.
Normal Probability Distributions Chapter 5. § 5.4 Sampling Distributions and the Central Limit Theorem.
Normal Probability Distributions
Copyright © 1998, Triola, Elementary Statistics Addison Wesley Longman 1 Binomial Experiments Section 4-3 & Section 4-4 M A R I O F. T R I O L A Copyright.
7.3 and 7.4 Extra Practice Quiz: TOMORROW THIS REVIEW IS ON MY TEACHER WEB PAGE!!!
Statistics 300: Elementary Statistics Section 6-5.
Basic Business Statistics, 11e © 2009 Prentice-Hall, Inc.. Chap 7-1 Developing a Sampling Distribution Assume there is a population … Population size N=4.
Section 6-5 The Central Limit Theorem. THE CENTRAL LIMIT THEOREM Given: 1.The random variable x has a distribution (which may or may not be normal) with.
8 Sampling Distribution of the Mean Chapter8 p Sampling Distributions Population mean and standard deviation,  and   unknown Maximal Likelihood.
1 Continuous Probability Distributions Continuous Random Variables & Probability Distributions Dr. Jerrell T. Stracener, SAE Fellow Leadership in Engineering.
§ 5.3 Normal Distributions: Finding Values. Probability and Normal Distributions If a random variable, x, is normally distributed, you can find the probability.
1 Chapter 5. Section 5-5. Triola, Elementary Statistics, Eighth Edition. Copyright Addison Wesley Longman M ARIO F. T RIOLA E IGHTH E DITION E LEMENTARY.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Section 5-5 Poisson Probability Distributions.
Statistics. A two-dimensional random variable with a uniform distribution.
Chapter 7 Statistical Inference: Estimating a Population Mean.
Statistics 300: Elementary Statistics Sections 7-2, 7-3, 7-4, 7-5.
Summarizing Risk Analysis Results To quantify the risk of an output variable, 3 properties must be estimated: A measure of central tendency (e.g. µ ) A.
STATISTIC & INFORMATION THEORY (CSNB134) MODULE 7B PROBABILITY DISTRIBUTIONS FOR RANDOM VARIABLES ( POISSON DISTRIBUTION)
Section 7.2 P1 Means and Variances of Random Variables AP Statistics.
1 Sampling distributions The probability distribution of a statistic is called a sampling distribution. : the sampling distribution of the mean.
A study involving stress is done on a college campus among the students. The stress scores are known to follow a uniform distribution with the lowest stress.
1 1 Slide Continuous Probability Distributions n The Uniform Distribution  a b   n The Normal Distribution n The Exponential Distribution.
Normal Probability Distributions Chapter 5. § 5.2 Normal Distributions: Finding Probabilities.
Lesson 8 - R Chapter 8 Review. Objectives Summarize the chapter Define the vocabulary used Complete all objectives Successfully answer any of the review.
Sampling Distributions Chapter 9 Central Limit Theorem.
Chapter 2 Section 2.4A Worksheet. 1. Section 2.4A Worksheet 2.
Created by Tom Wegleitner, Centreville, Virginia Section 4-5 The Poisson Distribution.
Central Limit Theorem-CLT
Central Limit Theorem Section 5-5
Statistical Reasoning for everyday life
Continuous Random Variables
Sec. 7-5: Central Limit Theorem
عمل الطالبة : هايدى محمد عبد المنعم حسين
الأستاذ المساعد بقسم المناهج وطرق التدريس
Continuous Random Variables
Elementary Statistics: Picturing The World
Statistics 1: Elementary Statistics
Distribution of the Sample Proportion
Sampling Distribution
Sampling Distribution
Lecture Slides Elementary Statistics Twelfth Edition
Introduction to Probability & Statistics The Central Limit Theorem
POINT ESTIMATOR OF PARAMETERS
Lecture Slides Elementary Statistics Twelfth Edition
Sampling Distributions
Statistics 1: Elementary Statistics
Chapter 3: Averages and Variation
Statistics 300: Elementary Statistics
Sampling Distribution of a Sample Proportion
The mean value of land and buildings per acre for farms is $1300, with a standard deviation of $250. A random sample of size 36 is drawn. 1) What is the.
Section Means and Variances of Random Variables
Section Means and Variances of Random Variables
The Central Limit Theorem
Elementary Statistics
Sampling Distribution of a Sample Proportion
C.2.10 Sample Questions.
Sampling Distributions
C.2.8 Sample Questions.
C.2.8 Sample Questions.
PROBABILITY AND STATISTICS
Presentation transcript:

Statistics 300: Elementary Statistics Section 6-4

Sampling Distributions Given: X has mean = m and standard deviation = s For a specified sample size “n” How many samples are possible? What is the distribution for means of all of these samples?

Sampling Distributions Consider 400,000 acres of grazing land for cattle Select 80 acres at random How many samples are possible? Measure the biomass for each What is the distribution of the st.dev. of all of these samples?

The Uniform Distribution X ~ U[a,b] The total area = 1 Likelihood a b

The Uniform Distribution X ~ U[100,500] Likelihood 100 500

The Uniform Distribution X ~ U[100,500] P(2 values are both < 140) Likelihood 100 500

If X ~ U[a,b] then Likelihood a c d b

If X ~ U[100,500] then P(x1 < 140 and x2 < 140) = P(x1 < 140) P(x1 < 140) = (0.1)(0.1) = 0.01 100 140 500