Agenda of Week VII. Sampling Distribution Objective : Understanding the standard normal distribution Understanding the sampling distribution Week 6 1 Random.

Slides:



Advertisements
Similar presentations
Agenda of Week V. Dispersion & RV Objective : Understanding the descriptive statistics Understanding the random variable and probability Week 4 1 Graphs.
Advertisements

Agenda of Week V Review of Week IV Inference on MV Mean Vector One population Two populations Multi-populations: MANOVA.
THE CENTRAL LIMIT THEOREM
Comparing Two Population Parameters
Exponential Distribution. = mean interval between consequent events = rate = mean number of counts in the unit interval > 0 X = distance between events.
Sections 7-1 and 7-2 Review and Preview and Estimating a Population Proportion.
Sampling: Final and Initial Sample Size Determination
Sampling Distributions (§ )
Samples vs. Distributions Distributions: Discrete Random Variable Distributions: Continuous Random Variable Another Situation: Sample of Data.
Chap 9-1 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chapter 9 Estimation: Additional Topics Statistics for Business and Economics.
8-5 Testing a Claim About a Standard Deviation or Variance This section introduces methods for testing a claim made about a population standard deviation.
1 Inference About a Population Variance Sometimes we are interested in making inference about the variability of processes. Examples: –Investors use variance.
Probability and the Sampling Distribution Quantitative Methods in HPELS 440:210.
Hypothesis Testing and T-Tests. Hypothesis Tests Related to Differences Copyright © 2009 Pearson Education, Inc. Chapter Tests of Differences One.
Education 793 Class Notes T-tests 29 October 2003.
Section 7-4 Estimating a Population Mean: σ Not Known.
Topic 5 Statistical inference: point and interval estimate
Basic Business Statistics, 11e © 2009 Prentice-Hall, Inc. Chap 8-1 Confidence Interval Estimation.
Chapter 7 Estimates and Sample Sizes
Measures of Dispersion CUMULATIVE FREQUENCIES INTER-QUARTILE RANGE RANGE MEAN DEVIATION VARIANCE and STANDARD DEVIATION STATISTICS: DESCRIBING VARIABILITY.
Normal Distributions Z Transformations Central Limit Theorem Standard Normal Distribution Z Distribution Table Confidence Intervals Levels of Significance.
CHAPTER 11 DAY 1. Assumptions for Inference About a Mean  Our data are a simple random sample (SRS) of size n from the population.  Observations from.
Determination of Sample Size: A Review of Statistical Theory
Estimating the Population Mean Income of Lexus Owners Sample Mean + Margin of Error Called a Confidence Interval To Compute Margin of Error, One of Two.
Sections 7-1 and 7-2 Review and Preview and Estimating a Population Proportion.
Copyright © Cengage Learning. All rights reserved. 13 Linear Correlation and Regression Analysis.
381 Continuous Probability Distributions (The Normal Distribution-II) QSCI 381 – Lecture 17 (Larson and Farber, Sect )
Section 8-5 Testing a Claim about a Mean: σ Not Known.
§ 5.3 Normal Distributions: Finding Values. Probability and Normal Distributions If a random variable, x, is normally distributed, you can find the probability.
1 BA 275 Quantitative Business Methods Quiz #2 Sampling Distribution of a Statistic Statistical Inference: Confidence Interval Estimation Introduction.
Chapter 10: Confidence Intervals
: An alternative representation of level of significance. - normal distribution applies. - α level of significance (e.g. 5% in two tails) determines the.
26134 Business Statistics Tutorial 12: REVISION THRESHOLD CONCEPT 5 (TH5): Theoretical foundation of statistical inference:
Review Normal Distributions –Draw a picture. –Convert to standard normal (if necessary) –Use the binomial tables to look up the value. –In the case of.
Chapters 6 & 7 Overview Created by Erin Hodgess, Houston, Texas.
1 Chapter 6. Section 6-3. Triola, Elementary Statistics, Eighth Edition. Copyright Addison Wesley Longman M ARIO F. T RIOLA E IGHTH E DITION E LEMENTARY.
Point Estimates point estimate A point estimate is a single number determined from a sample that is used to estimate the corresponding population parameter.
Confidence Intervals of the Mean 2 nd part of ‘estimate of the mean’ presentation.
AGENDA Review In-Class Group Problems Review. Homework #3 Due on Thursday Do the first problem correctly Difference between what should happen over the.
Ch 8 Estimating with Confidence 8.1: Confidence Intervals.
MATB344 Applied Statistics I. Experimental Designs for Small Samples II. Statistical Tests of Significance III. Small Sample Test Statistics Chapter 10.
Lecture 4 Confidence Intervals. Lecture Summary Last lecture, we talked about summary statistics and how “good” they were in estimating the parameters.
Correlation. u Definition u Formula Positive Correlation r =
10.1 – Estimating with Confidence. Recall: The Law of Large Numbers says the sample mean from a large SRS will be close to the unknown population mean.
Unit 4 Review. Starter Write the characteristics of the binomial setting. What is the difference between the binomial setting and the geometric setting?
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.
Summary of t-Test for Testing a Single Population Mean (m)
Inference for the Mean of a Population
Sampling Distributions
Math 4030 – 10b Inferences Concerning Variances: Hypothesis Testing
4.3 Probability Distributions of Continuous Random Variables:
Estimating the Population Mean Income of Lexus Owners
Chapter 7 Sampling Distributions.
Probability and the Sampling Distribution
Confidence Interval (CI) for the Mean When σ Is Known
Chapter 7 Sampling Distributions.
Sampling Distribution
Sampling Distribution
Chapter 6 Confidence Intervals.
POPULATION (of “units”)
Sampling Distribution Models
8.3 – Estimating a Population Mean
Chapter 7 Sampling Distributions.
4.3 Probability Distributions of Continuous Random Variables:
AGENDA: DG minutes Begin Part 2 Unit 1 Lesson 11.
Chapter 7 Sampling Distributions.
Chapter 8: Confidence Intervals
Day 13 AGENDA: DG minutes Begin Part 2 Unit 1 Lesson 11.
Chapter 7 Sampling Distributions.
Presentation transcript:

Agenda of Week VII. Sampling Distribution Objective : Understanding the standard normal distribution Understanding the sampling distribution Week 6 1 Random variable Normal dist. Definition Properties Example Estimation Standard Normal Sampling Dist. 32 Definition Properties

Review of Week VI Objective : Understanding the random variable and probability Definition Probability Random variable 1 Definition Properties Normal distribution Normal Dist. 2

Standard Normal Distribution o A most useful modification of ND Eq. 7.4 Characteristics: Table 7.3 pdf. and cdf.: Figure 7.9

Sampling Distribution o So far, under the assumption that all parameters of population is known to us, the probability of a value has been calculated

Sampling Distribution o Definition When a population is normally distributed,, how is the means of samples from the population distributed? o N vs. n N: the number of obs. in population (Population size) n: the number of obs. in a sample (Sample size)

Sampling Distribution o Possible number of samples from a population Example: N=50, n=5 o Sampling variation Figure 8.1 and Table in the top of p.296

Sampling Distribution o When 100 samples of 10 obs. are drawn, their sample means and s.d. are expressed as: o r.v. is defined if it has one of and can be calculated

Sampling Distribution o In this case, r.v. is and Z for becomes Figure in p.298 o CLT for a mean P.299

Sampling Distribution o Example Height of female college students: Probability that sample mean is greater than 160 and less than 163 when drawing 100 students

Sampling Distribution

o Estimation or o Point estimation Estimation of only with a value of Weakness ???

Sampling Distribution o Interval estimation Significant level ( ) and critical value under a s.l. ( )

Sampling Distribution o Interval estimation Significant level ( ) and critical value under a s.l. ( )

Sampling Distribution o Interval estimation Significant level ( ) and critical value under a s.l. ( )