STAT 325 INTRO TO STATISTICS Kansas State

Review Basic Terminologies Population and Sample Parameter and Statistic Discrete and Continuous Random Variables Quantitative and Qualitative variables Means and Proportions Review Ctd Graphical Techniques Histograms Bar charts Scatter plot

Syllabus for Stat 706 Fall 2004 Course: Basic Elements of Statistical Theory Class Time: MWF 1:30-2:20pm in 108 Bluemont Hall Prerequisite: MATH 205, 210, or 220 and STAT 320 or equiv. Instructor: Haiyan Wang, Department of Statistics Ofce: 108E Di

STAT 725 Notes Random Number Generation Continued What if R/Splus doesnt have a function for that distribution? Although R and Splus can generate data from many dierent distributions, certainly most of the common ones, you may need to generate data f

STAT 725 Notes More Examples of Gibbs Sampling Modern Bayesian Methods Consider the general hierarchical Bayes model: X| = f (x|), | = h(|), (). (1) (2) (3) With this model, we can exert control over the prior h(|) by modifying the pdf of the

STAT 510: Handout 4 Moment-Generating Functions The material on this handout is not covered in the text. We will discuss moment generating functions in general in this handout. Later moment generating functions will be discussed for specic special di

STAT 725 Notes Simulations Part I Generating Data according to a Specied Model Often, we want to compare two competing methods for analyzing data. For example, suppose that a researcher will be collecting data according to a completely randomized de

STAT 725 Notes Optimization Part I Many statistical problems involve optimization. For example, maximum likelihood estimation involves maximizing a likelihood function while least squares involves minimizing the sum of squared residuals. The discuss

STAT 510, FALL 2006 CALCULUS EXERCISES 1. Compute the following limit: x2 4 x2 x 2 lim 2. Are there real values of x for which f (x) = x2 + 2x + 2 is equal to zero? Justify your answer. 3. For a {0, 1} and > 0, compute the following: / 0 xa d

STAT 725 Notes Optimization An Example Consider the problem of nding the MLE of the shape parameter of a gamma distribution with scale parameter equal to 1. That is, we want the MLE of based on a random sample X1 , . . . , Xn from a Gamma(, 1) dist