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Arizona - HIST - 01
Austin J. Kelly NS202 Section 2 2-20-08 Research on Alfred Thayer Mahan Alfred Thayer Mahan was born in West Point, New York on September 27, 1840 where his father was currently a professor at the United States Military Academy. Mahan attended Columb
Arizona - CHEM - 01
WesolowskiThe Language of Chemistry Some things to MEMORIZE Today!1) MEMORIZE the correct symbol and spelling of the name for the first 36 elements on the periodic table. Be careful with Fluorine not Flourine, etc. Most symbols have one capital le
Arizona - CHEM - 01
-11Qualitative AnalysisAuthor: Austin Kelly Lab Partners: Daniel Swanson, Kyle Roney, Brian Zilliox Supervisor: Sveta Enman Course: Chem. 104a Section 70 Dates of Execution: Sept 26 and Oct 3 Date of Submission: Oct. 102IntroductionDuring the
Arizona - CHEM - 01
Synthesis of Zinc Iodide Author: Austin Kelly Lab Partner: Daniel Swanson Lab Instructor: Sveta Enman Chem 104b Section 70 Date work performed: September 5, 2007 Date work submitted: September 12, 2007CalculationsIn this lab we used a series of c
University of Texas - CHE - 350
CHAPTER 5: IMPERFECTIONS IN SOLIDSISSUES TO ADDRESS. What are the solidification mechanisms? What types of defects arise in solids? Can the number and type of defects be varied and controlled? How do defects affect material properties? Are defe
University of Texas - CHE - 350
CHAPTER 4: POLYMER STRUCTURESISSUES TO ADDRESS. What are the general structural and chemical characteristics of polymer molecules? What are some of the common polymeric materials, and how do they differ chemically? How is the crystalline state in
University of Texas - CHE - 350
Chapter 3: Structures of Metals & CeramicsISSUES TO ADDRESS. How do atoms assemble into solid structures? How does the density of a material depend on its structure? How do the crystal structures of ceramic materials differ from those for metals?
University of Texas - CHE - 350
CHAPTER 2: BONDING AND PROPERTIESISSUES TO ADDRESS. What promotes bonding? What types of bonds are there? What properties are inferred from bonding?Chapter 2 - 1Atomic Structure (Freshman Chem.) atom electrons 9.11 x 10-31 kg protons 1.67
University of Texas - CHE - 350
Chapter 1 - Introduction What is materials science? Why should we know about it? Materials drive our society Stone Age Bronze Age Iron Age Now? Silicon Age? Polymer Age?Chapter 1 - 1Example Hip Implant With age or certain illnesses joi
Arizona - MATH - 170
MAT 170 Precalculus Proportional Reasoning Homework Pathways to Calculus Fall 2008 portional Reasoning Homework Pathways to Calculus Fall 2008 1. Consider the following task: Orange Juice Problem Mixture A of orange juice contains 4 cups of OJ flavor
Arizona - MATH - 170
MAT 170 PrecalculusProportional Reasoning Homework Pathways to Calculus Fall 20081. Consider the following task: Orange Juice Problem Mixture A of orange juice contains 4 cups of OJ flavor and 2 cups of water. Mixture B of orange juice contains 5
Arizona - MATH - 170
Forming Useful ByproductsPrasun Mahantis Group 3: Richard Mulder Kelly Stapleton Asher Kurtzman Shane PerezIntroduction: Many chemical processes involve the production of byproducts that may have no purpose. Chemists try to reduce the production o
Arizona - AFS - 394
Pt = the total number of purple jellybeans in the jar Yt = the total number of yellow jellybeans in the jar T = the total number of jellybeans in the jarPs = the total number of purple jellybeans in the scoop Ys = the total number of yellow jellybe
Arizona - CHEM - 113
Lecture Quiz #1 (5 pts) _ CHM 113 Dr. Ron BriggsName: Recitation Section: _1. In your own words, how would you define the subject of chemistry? I would define the subject of chemistry as the study of how all things are made up.2. According to y
Arizona - AFS - 394
b = length of the base of the photograph h = length of the height of the photograph be = length of the base of the enlarged photograph he = length of the height of the enlarged photograph Note that all quantities are measured in inchesb/h is propor
Arizona - AFS - 394
F = Cups of OJ flavor - measured in cups W = Cups of Water - measured in cups T = Total number of Cups in the mixture - measured in cupsMixture A tastes more orangey. I naturally approached this problem using a constant ratio. F (Cups of OJ flavor)
Arizona - MATH - 170
Sym Ops 1Homework Solutions Simplify by performing the appropriate operation 1. 3x 2 5x 2 Since these are like terms, they can be combined by adding (subtracting) their coefficients. 3x 2 5x 2 = 2x 22. 8x 2 y 2x 2 y + 4 x 2 y Since all three t
Arizona - PSYC - 101
Below is a study guide for Exam #1. Keep in mind that I am looking at your exam questions while writing the study guide. Be sure you know what the terms are, how they have been researched, and describe your answers in your own words. I decided to lim
ASU - IEE - 380
I+.05 method:Time between successive events of a Poisson process PDF: e-x for 0x Good for slow wear-out Has lack of memory E(X) = 1, V(x)= 12 e.g. X=distance until first crack, and need to find probability of no cracks in 10-mile stretch P(X>10)=10
ASU - IEE - 380
God I hate stats. In a factorial experiment, each possible combinations of the levels of the factors in each replicate of the experiment is investigated. An experiment with two factors (A, B) is represented by a flat sheet in 3-D surface plot. Factor
ASU - CSE - 360
A data abstraction is a named collection of data that describes a data object. For instance, in the procedural abstraction open, we can define a data abstraction called door. Like any data object, the data abstraction for door would encompass a set o
ASU - IEE - 380
I+.05 method: for kth %ile. Returns item place in array of ordered set. K as integer. N+1 method: . K in decimal form. For interpolators (e.g. 3.5), ipart + fpart(larger-smaller). Sample mean is just average Sample variance PDFs: P(a<x<b) =1) 2)CD
ASU - IEE - 380
I+.05 method:(i-0.5)n*100=k for kth %ile. Returns item placein array of ordered set. K as integer. N+1 method:kn+1= i. K in decimal form.For interpolators (e.g. 3.5), ipart + fpart(larger-smaller). Sample mean is just average Sample variance
ASU - PHY - 131
Ch. 21: Coulombs Law: F=kq1q2r2, k=140 = force between two point charges (in a vacuum). Electric field: E=F0q0F0=Eq0=ma Electric field of a point charge: E=qr40r2 REMEMBER: r=rr Electric field of a ring of charge: E=Exi=Qx40x2+a232i (for a ring with
ASU - PHY - 131
Ch. 21: Coulombs Law: Electric field: Electric field of a point charge: REMEMBER: Electric field of a ring of charge: (for a ring with symmetry on x axis, centered in yz plane). Field of a finite line of charge: With charge density , =Q/2a: thus for
ASU - IEE - 380
God I hate stats. In a factorial experiment, each possible combinations of the levels of the factors in each replicate of the experiment is investigated. An experiment with two factors (A, B) is represented by a flat sheet in 3-D surface plot. Factor
ASU - IEE - 380
God I hate stats. In a factorial experiment, each possible combinations of the levels of the factors in each replicate of the experiment is investigated. An experiment with two factors (A, B) is represented by a flat sheet in 3-D surface plot. Factor
ASU - IEE - 380
Inference on the Means of 2 Populations, Variances Known Assumptions: Both samples are random samples, independent, and are normal (if not normal, C.L.T. apply) E(Xbar1 Xbar2) = E(Xbar1) E(Xbar2) = 1-2 V(Xbar1Xbar2)=V(Xbar1)+V(Xbar2)=12n1+22n2Con
ASU - CSE - 240
Paradigms Imperative/procedural - expresses computation by fully-controlled/specified manipulation of named data in a step-wise fashion. Foundation of these is the stored program concept. Languages include Fortran, Algol, Pascal, C. Object-oriented -
ASU - IEE - 380
Inference on the Means of 2 Populations, Variances Known Assumptions: Both samples are random samples, independent, and are normal (if not normal, C.L.T. apply) E(Xbar1 Xbar2) = E(Xbar1) E(Xbar2) = 1-2 V(Xbar1-Xbar2)=V(Xbar1)+V(Xbar2)=12n1+22n2Z=
ASU - IEE - 380
Type I error: Rejecting H0 when it is true. Type II error: Failing to reject H0 when it is false. =P(type I error) = P(reject H0 when H0 is true) P-value is smallest level of significance that would lead to rejection of H0. 1) Parameter of interest 2
ASU - IEE - 380
I+.05 method: for kth %ile. Returns item place in array of ordered set. K as integer. N+1 method: . K in decimal form. For interpolators (e.g. 3.5), ipart + fpart(larger-smaller). Sample mean is just average Sample variance PDFs: P(a<x<b) =1) F(x)
ASU - IEE - 380
I+.05 method:(i-0.5)n*100=k for kth %ile. Returns item place in array of ordered set. K as integer.N+1 method: kn+1= i. K in decimal form. For interpolators (e.g. 3.5), ipart + fpart(larger-smaller). Sample mean is just average Sample variance s2=
ASU - IEE - 380
Type I error: Rejecting H0 when it is true. Type II error: Failing to reject H0 when it is false. =P(type I error) = P(reject H0 when H0 is true) P-value is smallest level of significance that would lead to rejection of H0. 1) Parameter of interest 2
ASU - IEE - 380
E(Y|x)=Y|x=0+1x Simple Linear Regression Model: Y=0+1x+ Assumes random, independent, mean 0, constant variance Method of Least Squares: Yi=0+1xi+i, i=1, 2, , n where:Null/Alt.: H0/1=/0 Test: Reject if: P-value is probability beyond f0 in F1,n-2 di
ASU - IEE - 380
Inference on the Means of 2 Populations, Variances Known Assumptions: Both samples are random samples, independent, and are normal (if not normal, C.L.T. apply) E(Xbar1 Xbar2) = E(Xbar1) E(Xbar2) = 1-2 V(Xbar1-Xbar2)=V(Xbar1)+V(Xbar2)= Alt. Hyp: !=
ASU - IEE - 380
E(Y|x)=Y|x=0+1x Simple Linear Regression Model: Y=0+1x+ Assumes random, independent, mean 0, constant variance Method of Least Squares: Yi=0+1xi+i, i=1, 2, , n where:Y|x0=y0=0+1x0, x0is some specified value VY|x0=21n+x0-x2Sxxse(Y|x0)=sqrt(V[Y|x0]
ASU - CSE - 360
Communicationproject initiation requirement gatheringPlanningestimating scheduling trackingModelinganalysis designConstructioncode testDeploymentdelivery support f eedbackClassic waterfall approach above Problems Real projects rarely
USF - PHY - 2049
hopkins (tlh982) HW02 criss (4908) This print-out should have 16 questions. Multiple-choice questions may continue on the next column or page nd all choices before answering. 001 (part 1 of 2) 10.0 points A skyrocket explodes 126 m above the grou
University of Texas - CH - 310n
Problem of the Day #04 Answer KeyDeadline: 3:00 p.m., Friday, 7/18/08 LATE WORK WILL NOT BE ACCEPTED OR GRADED!This problem is worth a total of 20 raw points. Compound A, a hydrocarbon having molecular formula C6H12, has the IR and 1H NMR spectra
University of Texas - CH - 310n
Problem of the Day #02 Answer KeyDeadline: 3:00 p.m., Wednesday, 7/16/08 LATE WORK WILL NOT BE ACCEPTED OR GRADED!This problem is worth a total of 20 raw points. Each part is worth 10 points. In each part below, an infrared spectrum is shown. To
University of Texas - CH - 310n
Problem of the Day #10 Answer KeyDeadline: 3:00 p.m., Wednesday, 7/30/08 LATE WORK WILL NOT BE ACCEPTED OR GRADED!This problem is worth a total of 20 raw points. In each part below, propose a sequence of reactions to synthesize the target compoun
University of Texas - CH - 310n
Problem of the Day #08 Answer KeyDeadline: 3:00 p.m., Monday, 7/28/08 LATE WORK WILL NOT BE ACCEPTED OR GRADED!This problem is worth a total of 20 raw points. In lecture, we discussed the Wolff-Kishner reduction, which occurs when an aldehyde or
University of Texas - CH - 310n
Problem of the Day #06 Answer KeyDeadline: 3:00 p.m., Tuesday, 7/22/08 LATE WORK WILL NOT BE ACCEPTED OR GRADED!This problem is worth a total of 20 raw points. Consider the overall transformation given below:H 3C OH C N O CH3+KOH+KCN
University of Texas - CH - 310M
Lecture 5Organic Chemistry IProf. Jonathan L. Sessler310/318M Pre-Health Professionals Unique numbers: 54410, 54435, 54440, 54445, and 54655Notes: Monday recitations start PAI 4.28 right after class. Also, homework due at the start of class on
University of Texas - CH - 310M
OHClH NH NN N M N N NH NH NO OO OO OO OOHOrganic Chemistry I310/318M Pre-Health Professionals, TIPS, ChemEs Unique numbers: 54110, 54655, 54435, 54440, and 54445Prof. Jonathan L. SesslerN N H HN HN NH H N NN NH N HN HN NH N
University of Texas - CH - 310M
Lecture 2Organic Chemistry IProf. Jonathan L. Sessler310/318M Pre-Health Professionals Unique numbers: 54410 (there was a typo in syllabus), 54435, 54440, 54445, and 54655NOTE: IF YOU MISSED WEDNESDAY, GO TO BLACKBOARD AND GET THE CLASS MATERIA
University of Texas - CH - 310M
Lecture 3Organic Chemistry IProf. Jonathan L. Sessler310/318M Pre-Health Professionals Unique numbers: 54410, 54435, 54440, 54445, and 54655Simplified View of Bonding Coulombs law of electric charge: Opposite charges attract each other Like
University of Texas - CH - 310M
Lecture 6Organic Chemistry IProf. Jonathan L. Sessler310/318M Pre-Health Professionals Unique numbers: 54410, 54435, 54440, 54445, and 54655Notes: 1st homework due at the start of class today. 2nd homework (on Blackboard) due in one week! Wel 2
University of Texas - CH - 310M
Lecture 4Organic Chemistry IProf. Jonathan L. Sessler310/318M Pre-Health Professionals Unique numbers: 54410, 54435, 54440, 54445, and 54655Apparent Exceptions to the Octet Rule Molecules that contain atoms of Group 3A elements, particularly
University of Texas - CH - 310M
Lecture 7Organic Chemistry IProf. Jonathan L. Sessler310/318M Pre-Health Professionals Unique numbers: 54410, 54435, 54440, 54445, and 54655Combining VB & MO Theories VB theory views bonding as arising from electron pairs localized between ad
University of Texas - CH - 310M
Lecture 8Organic Chemistry IProf. Jonathan L. Sessler310/318M Pre-Health Professionals Unique numbers: 54410, 54435, 54440, 54445, and 54655Note: Homework 2 will be due on the 24th, not this Wednesday. Note: Prof. Sesslers office hours will end
City University of Hong Kong - ABA - 102
LS22114 W1 Tutorial English Communication Skills for Business (2008/09 Semester A)Prepared by Joey MaToday Agenda Knowmore about Your tutor Your classmates Your course handbook Your textbook Your assignment Your examinationToday Agenda
City University of Hong Kong - ABA - 123
LS22114 Lecture 3Barriers to Effective CommunicationBarriers Climate of the organization Status within the organization Problems of communication overload Defensiveness2Climate ControlOrganizational climate established can determine
City University of Hong Kong - ABA - 123
Chapter28 UnemploymentandItsNaturalRateMULTIPLECHOICEiThenaturalrateofunemploymentis a. zeropercent. b. therateassociatedwiththehighestpossiblelevelofGDP. c. theamountofunemploymentthattheeconomynormallyexperiences. d. thedifferencebetweenthelong
City University of Hong Kong - ABA - 123
Topic 10 : Benchmarking Technology : ( ) Marshall BreedingDirector for Innovative Technologies and Research Vanderbilt University http:/staffweb.library.vanderbilt.edu/breedingRedefining Libraries: Web 2.0 and other ChallengesMay 2007 Xia
City University of Hong Kong - ABA - 123
PRINCIPLES OF MACROECONOMICSChapter 28 UnemploymentOverview In this chapter, we will examine broadly the labor market and see how full utilization of our labor resources improves the level of production and our standard of living. We will see how
City University of Hong Kong - ABA - 123
Mankiw Macroeconomics Chapter 15 (28)Unemployment and its Natural RateA Roadmap for Chapter 151. 2. 3. 4. Background Long Run vs. Short Run Unemployment Unemployment - Generally Speaking Determinants of Long-Run UnemploymentHow to think about l
City University of Hong Kong - ABA - 123
Mankiw Macroeconomics Chapter 15 (28)Unemployment and its Natural RateA Roadmap for Chapter 151. 2. 3. 4. Background Long Run vs. Short Run Unemployment Unemployment - Generally Speaking Determinants of Long-Run UnemploymentHow to think about l