Chi - square.

Slides:



Advertisements
Similar presentations
McGraw-Hill/Irwin Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. Chi-Square Tests Chapter 12.
Advertisements

Estimating a Population Variance
A Model to Evaluate Recreational Management Measures Objective I – Stock Assessment Analysis Create a model to distribute estimated landings (A + B1 fish)
1 Power 14 Goodness of Fit & Contingency Tables. 2 II. Goodness of Fit & Chi Square u Rolling a Fair Die u The Multinomial Distribution u Experiment:
Confidence intervals. Population mean Assumption: sample from normal distribution.
Probability Densities
1 The Chi squared table provides critical value for various right hand tail probabilities ‘A’. The form of the probabilities that appear in the Chi-Squared.
1 Power 14 Goodness of Fit & Contingency Tables. 2 Outline u I. Parting Shots On the Linear Probability Model u II. Goodness of Fit & Chi Square u III.Contingency.
1 Power 14 Goodness of Fit & Contingency Tables. 2 Outline u I. Projects u II. Goodness of Fit & Chi Square u III.Contingency Tables.
Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Chapter 4 Continuous Random Variables and Probability Distributions.
Some Continuous Probability Distributions Asmaa Yaseen.
LECTURE UNIT 4.3 Normal Random Variables and Normal Probability Distributions.
Moment Generating Functions
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Chapter 11 Inferences About Population Variances n Inference about a Population Variance n.
Chapter 10 The Normal and t Distributions. The Normal Distribution A random variable Z (-∞ ∞) is said to have a standard normal distribution if its probability.
Two Variable Statistics
Chapter 16 – Categorical Data Analysis Math 22 Introductory Statistics.
Estimating a Population Standard Deviation. Chi-Square Distribution.
SampleFK ChiMerge Discretization Statistical approach to Data Discretization Applies the Chi.
Statistics 300: Elementary Statistics Section 11-2.
LOGISTIC REGRESSION Binary dependent variable (pass-fail) Odds ratio: p/(1-p) eg. 1/9 means 1 time in 10 pass, 9 times fail Log-odds ratio: y = ln[p/(1-p)]
1 Copyright © 2015 Elsevier Inc. All rights reserved. Chapter 4 Sampling Distributions.
Section 6.4 Inferences for Variances. Chi-square probability densities.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Chapter 12 Tests of Goodness of Fit and Independence n Goodness of Fit Test: A Multinomial.
Chi Square Test for Goodness of Fit Determining if our sample fits the way it should be.
MathematicalMarketing Slide 4b.1 Distributions Chapter 4: Part b – The Multivariate Normal Distribution We will be discussing  The Multivariate Normal.
ПЕЧЕНЬ 9. Закладка печени в период эмбрионального развития.
Random Variables By: 1.
Jacek Wallusch _________________________________ Statistics for International Business Lecture 8: Distributions and Densities.
Chi Square Chi square is employed to test the difference between an actual sample and another hypothetical or previously established distribution such.
ChiMerge Discretization
Inference concerning two population variances
Theme 8. Major probability distributions
Capital Budgeting in the Chemical Industry
3. Random Variables (Fig.3.1)
The Exponential and Gamma Distributions
Other confidence intervals
Chapter 18 Chi-Square Tests
Chapter 12 Chi-Square Tests.
Math 4030 – 10b Inferences Concerning Variances: Hypothesis Testing
Ch4.2 Cumulative Distribution Functions
Distribution functions
Probability and Distributions
Statistics for Business and Economics (13e)
Goodness-of-Fit Tests
Chapter 8 – Continuous Probability Distributions
NORMAL PROBABILITY DISTRIBUTIONS
LESSON 17: THE F-DISTRIBUTION
Part IV Significantly Different Using Inferential Statistics
Chi Squared Test.
Econ 3790: Business and Economics Statistics
Statistics By Tony Cai.
More about Normal Distributions
POINT ESTIMATOR OF PARAMETERS
Statistical Process Control
Chapter 13 – Applications of the Chi-Square Statistic
Lecture 3. The Multinomial Distribution
LESSON 16: THE CHI-SQUARE DISTRIBUTION
Chi2 (A.K.A X2).
5. Functions of a Random Variable
... DISCRETE random variables X, Y Joint Probability Mass Function y1
Confidence Intervals for Proportions and Variances
6-1: Operations on Functions (+ – x ÷)
5. Functions of a Random Variable
3. Random Variables Let (, F, P) be a probability model for an experiment, and X a function that maps every to a unique point.
6.3 Sampling Distributions
Introduction to Probability Distributions
Chapter 3 : Random Variables
Introduction to Probability Distributions
Chapter Outline Goodness of Fit test Test of Independence.
Presentation transcript:

Chi - square

Distributions of functions of r.v.s X – has a probability density function f(x) We define Y = g(X), where g(.) is monotonic function What is the distribution of Y ?

Examples

2 with 1 degree of freedom (d.f.) What is the distribution of Y ?

We introduce V = | X |

f(x) x f(v) v

Y ~ 2(1 d.f.)

f(y) y

2(n d.f.) 2(n d.f.) = 2(1 d.f.)* 2(1 d.f.) …* 2(1 d.f.) n times

Y ~ 2(n d.f.)

2 and multinomial distribution Multinomial distribution – K possible outcomes of an experiment probabilities: p1, p2, …, pK, p1+p2+ …+pK=1 N - experiments

For large N Becomes 2(K-1 d.f.)