Purdue - CS - 197

Purdue - CS - 197

Iowa State - CPRE - 585

C a c h e MemoriesALAN JAY SMITHUnwersity of California, Berkeley, Californm 94720Cache memories are used in modern, medium and high-speed CPUs to hold temporarily those portions of the contents of main memory which are {believed to be) currently

Iowa State - CPRE - 585

100IEEE TRANSACTIONS ON COMPUTERS,VOL. 48, NO. 2,FEBRUARY 1999Functional Implementation Techniques for CPU Cache MemoriesJih-Kwon Peir, Member, IEEE, Windsor W. Hsu, Student Member, IEEE, and Alan Jay Smith, Fellow, IEEEAbstractAs the perfo

Iowa State - CPRE - 585

Improving Direct-Mapped Cache Performance by the Addition of a Small Fully-Associative Cache and Prefetch BuffersNorman P. Jouppi Digital Equipment Corporation Western Research Lab 100 Hamilton Ave. Palo Alto, CA 94301AbstractProjections of compu

Washington - MEDCH - 401

What Is VAERS? The Vaccine Adverse Event Reporting System (VAERS) is a national program that monitors the safety of vaccines after they are licensed. VAERS is managed by the U.S. Centers for Disease Control and Prevention (CDC) and the U.S. Food and

Penn State - STAT - 200

ACTIVITY SET 4Exercise #55, Chapter 5, Utts and Heckard. For a statistics class project at a large northeastern university, students examined the relationship between x = body weight (in pounds) and y = time to chug a 12 ounce beverage (in seconds).

Penn State - STAT - 200

ACTIVITY SET 6 6.1 Situation: In 1998 the American Film Institute created a list of the top 100 American films ever made (http:/www.afi.com/tvevents/100years/movies.aspx). Suppose that you and a date gather to watch one of these movies and, to avoid

Penn State - STAT - 200

Standard Normal Cumulative Probability Tablez0Cumulative probabilities for NEGATIVE z-values are shown in the following table:z -3.0 -2.9 -2.8 -2.7 -2.6 -2.5 -2.4 -2.3 -2.2 -2.1 -2.0 -1.9 -1.8 -1.7 -1.6 -1.5 -1.4 -1.3 -1.2 -1.1 -1.0 -0.9 -0.8

Penn State - STAT - 200

ACTIVITY SET 7Activity 7.1 In each part, indicate, (1) whether the variable is discrete or continuous AND (2) whether it is binomial or not AND (3) if it is binomial, give values for n and p. a. Number of times a "head" is flipped in 10 flips of a c

Penn State - STAT - 200

ACTIVITY SET 3 Work with students sitting around you to create answers to these questions.Activity 2.1 In the Datasets folder of the website, click on the link for the dataset Class Survey (Class_Survey.MTW - Minitab File). Data are from n = 226 stu

Penn State - STAT - 200

Penn State - STAT - 200

Table A.1 of textbookThe table shows cumulative probabilities for the standard normal curve. Cumulative probabilities for NEGATIVE z-values are shown first. SCROLL DOWN to the 2nd page for POSITIVE zz -3.4 -3.3 -3.2 -3.1 -3.0 -2.9 -2.8 -2.7 -2.6 -2

Penn State - STAT - 200

ACTIVITY SET 12 P-value Guidelines: Keep this in mind: The method for finding the p-value is based on the alternative hypothesis. Minitab will provide the p-value but if doing by hand using Table A1 observe the following: For Ha: p po then p-value =

Penn State - STAT - 200

SOLUTIONS LAB ACTIVITY 9Activity 9.1 The term sampling frame refers to the group that actually had a chance to get into the sample. Ideally, this is the same as the population of interest, but sometimes it isnt. In the following situation, describe

Penn State - STAT - 200

ACTIVITY SET 13Activity 13.1 Use Table A.3 to estimate the p-value for each of the following hypothesis testing situations. Then use the p-value to make a conclusion about the hypotheses. (Note: The value given for t is the calculated value of the t

Penn State - STAT - 200

SOLUTIONS ACTIVITY SET 14Activity 14.1 For each of the following research questions does the situation or research question involve independent samples or paired data? a. Twenty-five people have their cholesterol measure before eating a Big Mac and

Penn State - STAT - 200

SOLUTIONS ACTIVITY 16 Activity 16.4 Scenario for this activity: Suppose that we are comparing the proportion of successes for two medical treatments. We will gather data to determine if the success rate for treatment 1 is greater than the success rat

Penn State - STAT - 200

SOLUTIONS ACTIVITY 15 One-way analysis of variance is a method for comparing several population means, when the data are from independent samples. It can be thought of as a tool for examining the relationship between a quantitative response variable

Penn State - STAT - 200

sex actual ideal diffMale 215 190 25Female 155 135 20Male 195 155 40Female 145 130 15Female 110 100 10Male 155 170 -15Male 155 155 0Fe

UNC - EDUC - 001

UNC - WEEK - 148

UNC - WEEK - 148

UNC - WEEK - 148

UNC - WEEK - 148

UNC - WEEK - 148

UNC - WEEK - 148

N A T I O N A LC E N T E RF O RE D U C A T I O NS T A T I S T I C SNAEP 1996 MATHEMATICSReport Card for the Nation and the StatesU.S. DEPARTMENT OF EDUCATION OFFICE OF EDUCATIONAL RESEARCH AND IMPROVEMENTFindings from the National Asse

UNC - WEEK - 148

UNC - WEEK - 148

2. 1. 2. 3. 4. 5.List of Desirable Item Format CharacteristicsAvoid negatives in writing items. If correct answer falls on a continuum write incorrect foils on same continuum. Correct answer should be longest answer no more than 25% of the time.

UNC - WEEK - 148

UNC - WEEK - 148

A number of people who contacted ETS about this question made a distinction between a rotation and a revolution, arguing that in a year the earth makes 365 1/- rotations but only one revolution. Furthermore, they argued that in the Rolling Circle Que

UNC - ENVR - 230

ANALYSIS OF GENOMIC INFORMATIONlikely GTPases, as indicated by the activity of CIITA and HET-E [E. V. Koonin, L. Aravind, Trends Biochem. Sci. 25, 223 (2000)]. T. L. Beattie, W. Zhou, M. O. Robinson, L. Harrington, Curr. Biol. 8, 177 (1998). E. Diez

UNC - CHAPT - 210

Envr 210l lTues. and Thurs- 3 credit hours 8 to 9:30 amsnow days call me at 942 4880 or cell 919 614 4730lroom 2304lhttp:/www.unc.edu/courses/2005spring/envr/210/ 001/Envr210.htmlRich Kamens; 966 5452 kamens@unc.edu http:/airsite.unc.edu

Michigan - CHEM - 216

University of Michigan Chemistry 216HClass MeetingsSection 220 230 231 250 251 270 271 Day Tu Tu Tu W W Th Th Time 8:10 - 12 noon 1:10 - 5 PM 1:10 - 5 PM 1:10 - 5 PM 1:10 - 5 PM 1:10 - 5 PM 1:10 - 5 PM Room 2500 2411 2500 2411 2500 2411 2500Winte

Oakland University - XSOC - 73994

Interpreting and using heterogeneous choice & generalized ordered logit modelsRichard Williams, University of Notre Dame, July 2006, http:/www.nd.edu/~rwilliam/I.AbstractThe assumptions of the ordered logit/probit models estimated by ologit an

Oakland University - XSOC - 592

Opening day problemsHere are examples of the sorts of substantive problems we'll be addressing in this course. We'll go over what statistical techniques are and are not appropriate for such problems, and we'll show you how to use these techniques co

Oakland University - XSOC - 592

Introduction; Descriptive & Univariate StatisticsI. KEY CONCEPTS Population. Definitions: A. 1. 2. 3. B. The entire set of members in a group. EXAMPLES: All U.S. citizens; all Notre Dame Students. All values of a variable in a definable group (e.g.

Oakland University - XSOC - 592

Probability distributions(Notes are heavily adapted from Harnett, Ch. 3; Hayes, sections 2.14-2.19; see also Hayes, Appendix B.) I. Random variables (in general) A. So far we have focused on single events, or with a combination of events in an exper

Oakland University - XSOC - 592

ExpectationsExpectations. (See also Hays, Appendix B; Harnett, ch. 3). A. The expected value of a random variable is the arithmetic mean of that variable, i.e. E(X) = . As Hays notes, the idea of the expectation of a random variable began with proba

Oakland University - XSOC - 592

The Binomial DistributionA. It would be very tedious if, every time we had a slightly different problem, we had to determine the probability distributions from scratch. Luckily, there are enough similarities between certain types, or families, of ex

Oakland University - XSOC - 592

Estimation; Sampling; The T distributionI. Estimation A. In most statistical studies, the population parameters are unknown and must be estimated. Therefore, developing methods for estimating as accurately as possible the values of population parame

Oakland University - XSOC - 592

Confidence IntervalsI. Interval estimation.The particular value chosen as most likely for a population parameter is called the point estimate. Because of sampling error, we know the point estimate probably is not identical to the population parame

Oakland University - XSOC - 592

Using Stata for Confidence IntervalsAll of the confidence interval problems we have discussed so far can be solved in Stata via either (a) statistical calculator functions, where you provide Stata with the necessary summary statistics for means, sta

Oakland University - XSOC - 592

Introduction to Hypothesis TestingI. Terms, Concepts.A. In general, we do not know the true value of population parameters - they must be estimated. However, we do have hypotheses about what the true values are. B. The major purpose of hypothesis

Oakland University - XSOC - 592

Sampling Distributions and One Sample TestsSo far, we have only talked about hypothesis testing in a very limited set of situations. We will now expand our discussion to cover a much broader array of cases. We begin with single sample tests: Hypothe

Oakland University - XSOC - 592

Using Stata for Two Sample TestsAll of the two sample problems we have discussed so far can be solved in Stata via either (a) statistical calculator functions, where you provide Stata with the necessary summary statistics for means, standard deviati

Oakland University - XSOC - 592

Categorical Data AnalysisRelated topics/headings: Categorical data analysis; or, Nonparametric statistics; or, chi-square tests for the analysis of categorical data. OVERVIEW For our hypothesis testing so far, we have been using parametric statistic

Oakland University - XSOC - 592

One-Way Analysis of VarianceNote: Much of the math here is tedious but straightforward. We'll skim over it in class but you should be sure to ask questions if you don't understand it. I. Overview A. We have previously compared two populations, testi

Oakland University - XSOC - 592

Multiple/Post Hoc Group Comparisons in ANOVANote: We may just go over this quickly in class. The key thing to understand is that, when trying to identify where differences are between groups, there are different ways of adjusting the probability est

Oakland University - XSOC - 592

Using Stata for One-Way Analysis of VarianceWe have previously shown how the following one-way ANOVA problem can be solved using SPSS. We will now approach it using Stata. See the related handouts for the underlying theory and formulas. Problem: A f

Oakland University - XSOC - 592

Using Stata for Two-Way Analysis of VarianceWe have previously shown how the following two-way ANOVA problem can be solved using SPSS. We will now approach it using Stata. Problem. A consumer research firm wants to compare three brands of radial tir

Oakland University - XSOC - 592

Using SPSS for OLS RegressionIntroduction. This handout summarizes most of the points we cover in Stats I about using SPSS for OLS regression, along with a few additional points. It assumes understanding of the statistical concepts that are presente

Oakland University - XSOC - 592

Bivariate Regression - Part IIUsually I present concepts and formulas first, and then work through examples. For variety, I will present the example first, and then give the rationale and procedures for working through it. Data are collected from 20

Oakland University - XSOC - 592

Standardized CoefficientsTask. How do you decide which of the Xs are most important for determining Y? In this handout, we discuss one possible (and controversial) answer to this question - the standardized regression coefficients. Formulas. First,

Oakland University - XSOC - 592

Supplemental Notes on Standardized CoefficientsNOTE: Long and Freeses spostado programs are used in this handout; specifically, the listcoef command, which is part of spostado, is used. Use the findit command to locate and install spostado. See Long

Oakland University - XSOC - 592

Analytic Strategies: Simultaneous, Hierarchical, and Stepwise RegressionThis discussion borrows heavily from Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, by Jacob and Patricia Cohen (1975 edition). The simultaneous m

Oakland University - XSOC - 63993

Logic of Scientific Inference/ What is Causality?[NOTE: Toolbook files from Soc 513 will be used when presenting this material] (Adapted from ch. 2 of Stinchcomb, Constructing Social Theories; and, Ch. 1 of Cook and Campbell, Quasi-Experimentation)

Oakland University - XSOC - 63993

The Logic of Causal Order[NOTE: Toolbook files from Soc 513 will be used when presenting this material] * First, go over the handout on variable names and conventions * Adapted from Davis, The Logic of Causal OrderTwo variable case (p. 9): Variabl

Oakland University - XSOC - 63993

COMMENTS ON VARIABLE NAMING AND CODING1.Do not confuse the name of a variable with the possible values that the variablecan assume. Remember, in order for something to be a variable, it must be able to assume at least two different values. For

Oakland University - XSOC - 63993

FIGURES FOR "THE LOGIC OF CAUSAL ORDER" Figure 1. Family income +)> Alienation <), * * * - * * * * + * .)> Informedness )444444444444444444444444444444444444444444444444444444444444444 Figure 2. Family income), Alienation <), * * * - * * * * + * .)>

Oakland University - XSOC - 63993

Suppressor EffectsEXAMPLE 1: Head Start is a program designed to give students from disadvantaged backgrounds a "head start" in schooling that will hopefully lead to greater academic achievement. For the variable HEAD START, let 1 = participates in