Homeworks 1 PhD Course.

Slides:



Advertisements
Similar presentations
SOME GENERAL PROBLEMS.
Advertisements

Point Estimation Notes of STAT 6205 by Dr. Fan.
Discrete Uniform Distribution
Chapter 7. Statistical Estimation and Sampling Distributions
Review for Midterm Including response to student’s questions Feb 26.
Jump to first page STATISTICAL INFERENCE Statistical Inference uses sample data and statistical procedures to: n Estimate population parameters; or n Test.
The Mean Square Error (MSE):. Now, Examples: 1) 2)
Chapter 6 Introduction to Sampling Distributions
Fall 2006 – Fundamentals of Business Statistics 1 Chapter 6 Introduction to Sampling Distributions.
Estimation of parameters. Maximum likelihood What has happened was most likely.
Statistics Lecture 15. Percentile for Normal Distributions The 100p th percentile of the N( ,  2 ) distribution is  +  (p)  Where  (p) is.
Lesson #17 Sampling Distributions. The mean of a sampling distribution is called the expected value of the statistic. The standard deviation of a sampling.
1 Inference About a Population Variance Sometimes we are interested in making inference about the variability of processes. Examples: –Investors use variance.
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Statistical inference.
1 More about the Sampling Distribution of the Sample Mean and introduction to the t-distribution Presentation 3.
Confidence Intervals for Means. point estimate – using a single value (or point) to approximate a population parameter. –the sample mean is the best point.
POLYNOMIALS Unit 4. The Laws of Exponents Let m and n be positive integers and a and b be real numbers with a 0 and b 0 when they are the divisors  a.
ENGR 610 Applied Statistics Fall Week 4 Marshall University CITE Jack Smith.
Properties of Estimators Statistics: 1.Sufficiency 2.Un-biased 3.Resistance 4.Efficiency Parameters:Describe the population Describe samples. But we use.
1 Standard error Estimated standard error,s,. 2 Example 1 While measuring the thermal conductivity of Armco iron, using a temperature of 100F and a power.
The final exam solutions. Part I, #1, Central limit theorem Let X1,X2, …, Xn be a sequence of i.i.d. random variables each having mean μ and variance.
A course is designed to increase mathematical comprehension. In order to evaluate the effectiveness of the course, students are given a test before and.
5. Maximum Likelihood –II Prof. Yuille. Stat 231. Fall 2004.
Lecture 4 Confidence Intervals. Lecture Summary Last lecture, we talked about summary statistics and how “good” they were in estimating the parameters.
Week 21 Order Statistics The order statistics of a set of random variables X 1, X 2,…, X n are the same random variables arranged in increasing order.
ESTIMATION OF THE MEAN. 2 INTRO :: ESTIMATION Definition The assignment of plausible value(s) to a population parameter based on a value of a sample statistic.
Week 21 Statistical Model A statistical model for some data is a set of distributions, one of which corresponds to the true unknown distribution that produced.
Statistics -Continuous probability distribution 2013/11/18.
Stat 223 Introduction to the Theory of Statistics
Sampling and Sampling Distributions
Ch5.4 Central Limit Theorem
Statistical Estimation
The hypergeometric and negative binomial distributions
Tatiana Varatnitskaya Belаrussian State University, Minsk
STATISTICS POINT ESTIMATION
STATISTICAL INFERENCE
Stat 223 Introduction to the Theory of Statistics
Chapter 19: Unbiased estimators
Factoring Sums and Differences
Parameter Estimation 主講人:虞台文.
Chapter Six Normal Curves and Sampling Probability Distributions
Sampling Distributions for a Proportion
Module 22: Proportions: One Sample
Chapter 5 Statistical Models in Simulation
Observation 1 2 ….. i …… n ……….M 1 X11 X21 Xi1 Xn1 M.1
Inferential Statistics and Probability a Holistic Approach
Probability & Statistics Probability Theory Mathematical Probability Models Event Relationships Distributions of Random Variables Continuous Random.
Sampling Distribution
Sampling Distribution
POINT ESTIMATOR OF PARAMETERS
Lecture 2 Interval Estimation
In-Class Exercise: The Exponential Distribution
Lecture 5 b Faten alamri.
From Simulations to the Central Limit Theorem
Basic Concepts PhD Course.
Measures of Dispersion (Spread)
Бази от данни и СУБД Основни понятия инж. Ангел Ст. Ангелов.
Stat 223 Introduction to the Theory of Statistics
Chapter 7: Introduction to Sampling Distributions
6.3 Sampling Distributions
Determination of Sample Size
C.2.10 Sample Questions.
Homework #4.
C.2.8 Sample Questions.
C.2.8 Sample Questions.
Statistical Model A statistical model for some data is a set of distributions, one of which corresponds to the true unknown distribution that produced.
Statistical Model A statistical model for some data is a set of distributions, one of which corresponds to the true unknown distribution that produced.
STA 291 Summer 2008 Lecture 12 Dustin Lueker.
STA 291 Spring 2008 Lecture 12 Dustin Lueker.
Observation 1 2 ….. i …… n ……….M 1 X11 X21 Xi1 Xn1 M.1
Presentation transcript:

Homeworks 1 PhD Course

Let X₁,X₂,...,Xn a statistical sample from the Poisson distribution, where the parameter is n. Show that the sample mean is sufficient statistics. 1. Let X₁,X₂,...,Xn a statistical sample from the Exponential distribution, where the parameter is n. Show that the sample mean is efficient statistics for 1/ n. 2. Let X₁,X₂,...,Xn a statistical sample from the N(m,s) distribution. If Mn denote the sample mean statistics, prove that Tn=(n/(n+1))* Mn is consistent estimation the of the parameter m. 3.