Register now to access 7 million high quality study materials (What's Course Hero?) Course Hero is the premier provider of high quality online educational resources. With millions of study documents, online tutors, digital flashcards and free courseware, Course Hero is helping students learn more efficiently and effectively. Whether you're interested in exploring new subjects or mastering key topics for your next exam, Course Hero has the tools you need to achieve your goals.
1 Page
Document Preview
EGM Name: 3400 / 3401 Quiz #10 The uniform slender rod AB with a length of 0.5 m and a mass of 2 kg is in equilibrium in the vertical position shown when end A is given a slight nudge causing the rod to rotate and counterclockwise hit the horizontal surface. Knowing that the coefficient of restitution between the knob at A and the horizontal surface is 0.50, determine the maximum angle of rebound, , of the rod.
Unformatted Document Excerpt
Coursehero >> Florida >> University of Florida >> EGM 3400Course Hero has millions of student submitted documents similar to the one
below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.
Course Hero has millions of student submitted documents similar to the one
below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.
EGM Name: 3400 / 3401 Quiz #10 The unifor...
Find millions of documents on Course Hero - Study Guides, Lecture Notes, Reference Materials, Practice Exams and more.
Course Hero has millions of course specific materials providing students with the best way to expand
their education.
Below is a small sample set of documents:
Below is a small sample set of documents:
University of Florida - EGM - 3400
Name: EGM 3401 Quiz #11 A uniform rod of mass m = 2 kg is bent into the shape shown and is suspended from a wire attached to its mass center G. The distance a is equal to 1.5 m. The bent rod is hit at A in a direction perpendicular to the plane conta
University of Florida - EGM - 3400
University of Florida - EGM - 3400
University of Florida - EGM - 3400
University of Florida - EGM - 3400
University of Florida - EGM - 3400
Name: EGM 3401 Quiz #12The uniform thin 5 lb disk spins at a constant rate of 2 = 6 rad/sec about the axis through point O. The disk is also rotating about the line AO at the constant rate of 1 = 3 rad/sec. Determine the net moment that is being ap
Allan Hancock College - COMP - 284
Chapter 22Managing peoplecomp284-Software Engineering1Objectives1. To describe simple models of human cognition and their relevance for software managers 2. To explain the key issues that determine the success or otherwise of team working 3
Allan Hancock College - COMP - 284
%!PS-Adobe-2.0 %Creator: dvips(k) 5.92b Copyright 2002 Radical Eye Software %Title: slides.dvi %Pages: 38 %PageOrder: Ascend %Orientation: Landscape %BoundingBox: 0 0 596 842 %DocumentFonts: CMBX12 CMR12 CMR5 CMR10 CMTI10 CMSY10 CMTI12 CMR9 %+ CMTT12
U. Houston - CUIN - 3111
Math Reading Science History English8 4 7 6 5Which subject do you like best?9 8 7 6 5 4 3 2 1 0
Oregon State - BA - 360
Overview of Financial Management Introduction Keys to Success Recitations Class Structure - Syllabus Text 2nd Preliminary Draft of Fin. Mgmt. Exams Open Book and Open Notes Expectations Attendance Preparation for ClassKeys to Success R
Oregon State - BA - 360
Chapter 1 - Financial Management Learning Objectives Describe the "Cycle of Money" Distinguish the four main areas of finance Discuss financial markets Discuss the financial management Identify the goal of finance Explain the interactions o
Oregon State - BA - 360
Chapter 3 Time Value of Money Part 2 Learning Objectives Explain and Illustrate an Annuity Determine Future Value of an Annuity Determine Present Value of an Annuity Present Value of Annuity Stream Adjust formulas for Annuity Due Distinguish
Oregon State - BA - 360
Chapter 5 Bonds and Bond Pricing Learning Objectives Apply the TVM Equations in bond pricing Understand the difference between annual bonds and semi-annual bonds Differentiate between different types of bonds Calculate yields on bonds Bond Fe
Oregon State - BA - 360
Chapter 6 Stocks and Stock Valuation Learning Objectives Explain features of common stock Understand contractual rights of ownership Define primary and secondary markets Explain efficient markets Calculate stock value based on dividends Calc
Arizona - MATH - 223
VECTOR CALCULUS MATH 22301 Summer Session II, 2004Instructor: Leonid Friedlander Office: Math 716 Phone: 6212742 Office hours: MF, 11:3012:20 Text: W.McCallum, et al., "Multivariable Calculus", Third Edition Tests: There will be two mid-term tests
Arizona - MATH - 528
PROBLEM SET 1Let C be a closed, convex set in a Banach space E. We say that a point x C is an extremal point if it is not a middle point of any segment that lies in C, i.e. if y C, z C, and x = (y + z)/2 then y = z = x. In the following problems
Idaho - PSYC - 430
Assignment 4: Development of a Personality TestPsychology 430 50* points(10 points have already been awarded based on the item writing exercise)Due Thursday, Dec. 4 For this exercise, we will examine the validity evidence for the test we develope
Idaho - PSYC - 430
<?xml version="1.0" encoding="UTF-8"?> <Error><Code>NoSuchKey</Code><Message>The specified key does not exist.</Message><Key>e56bf5cad15fb2935171f5808d1047a03d826da1.pptx</Key><RequestId> D5FF2E8E523F3B0E</RequestId><HostId>FBXSpQHQJa1zj+1rilbaRk+ZSS
Idaho - PSYC - 430
Psychology 430Construct ValidityConstruct Validity Define the construct Establish a nomological network ".to establish its relation to other variables with which it should, theoretically, be associated positively, negatively, or practically not
Idaho - PSYC - 430
Psychology 430: Tests & MeasurementsContent V lidi C ValidityIntroduction to ValidityEvaluating what a test really measures Tests are not valid or invalid; we validate how the test is used"To call Test A valid or Test B invalid is illogical. Pa
Idaho - PSYC - 430
Classifying Decisions and Test UtilityWhenever we use a test to make decisions, there will be some errors Increasing the correlation between test g and criterion will reduce errors Can manipulate the kinds of correct or incorrect decisions by alter
Idaho - PSYC - 430
Testing in OrganizationsOverviewCompanies use psychological tests to improve the quality of their workforce Want predictors with high validity p g y coefficients (i.e., high correlation between predictor and criterion) Must also consider legal iss
Idaho - PSYC - 430
Psychology 430Introduction to Generalizability TheoryGeneralizability Theory vs. CTT How does it differ from CTT? Multiple sources of error can be estimated separately in a single analysis CTT can estimate separately only one source of error at
Idaho - PSYC - 430
Psychology 430Brief History of Psychological Testing1History of TestingBrass Instruments EraSir Francis GaltonRT and sensory discriminationJames McKeen CattellBodily energy and mental energy are inseparable2Modern Mental TestingAlfred
Idaho - PSYC - 430
Psychology 430Brief History of Psychological Testing1History of TestingBrass Instruments Era SirFrancis Galtonand sensory discrimination RT JamesMcKeen Cattell Bodilyenergy and mental energy are inseparable2Modern Mental Testi
Idaho - PSYC - 430
Psychology 430Norms and StandardsSetting Standards Norm-referenced tests e.g., personality tests Criterion-referenced tests e.g., certification/licensure exams Establishment of critical (or cut) score may be difficultNorms Selecting a no
Idaho - PSYC - 430
Psychology430ReliabilityClassicalTestTheory(CTT) Truescore=truelevelofability Differencebetweentruescoreandobservedscoreresultsfrom measurementerror X=T+eWhereX=observedscore,T=true score,ande=errorCTT(cont.)Assumesthaterrorsof
Idaho - PSYC - 430
vti_encoding:SR|utf8-nl vti_timelastmodified:TR|07 Nov 2008 18:11:14 -0000 vti_extenderversion:SR|4.0.2.8912 vti_backlinkinfo:VX|
Idaho - PSYC - 430
vti_encoding:SR|utf8-nl vti_timelastmodified:TR|09 Sep 2008 15:44:34 -0000 vti_extenderversion:SR|4.0.2.8912 vti_backlinkinfo:VX|
Idaho - PSYC - 430
vti_encoding:SR|utf8-nl vti_timelastmodified:TR|29 Sep 2008 22:45:14 -0000 vti_extenderversion:SR|4.0.2.8912 vti_backlinkinfo:VX|
Idaho - PSYC - 430
vti_encoding:SR|utf8-nl vti_timelastmodified:TR|06 Oct 2008 20:32:08 -0000 vti_extenderversion:SR|4.0.2.8912 vti_filesize:IR|23040 vti_title:SR|Assignment 1: Interpreting Test Scores vti_backlinkinfo:VX| vti_cacheddtm:TX|06 Oct 2008 21:32:08 -0000 vt
Idaho - PSYC - 430
vti_encoding:SR|utf8-nl vti_timelastmodified:TR|06 Oct 2008 17:01:02 -0000 vti_extenderversion:SR|4.0.2.8912 vti_backlinkinfo:VX|
Idaho - PSYC - 430
vti_encoding:SR|utf8-nl vti_timelastmodified:TR|22 Sep 2005 01:00:10 -0000 vti_extenderversion:SR|4.0.2.8912 vti_cacheddtm:TX|22 Sep 2005 01:00:10 -0000 vti_filesize:IR|59904 vti_cachedlinkinfo:VX| vti_cachedsvcrellinks:VX| vti_cachedtitle:SR|Psychol
Idaho - PSYC - 430
vti_encoding:SR|utf8-nl vti_timelastmodified:TR|01 Sep 2008 21:07:56 -0000 vti_extenderversion:SR|4.0.2.8912 vti_backlinkinfo:VX|
Idaho - PSYC - 430
vti_encoding:SR|utf8-nl vti_timelastmodified:TR|08 Oct 2007 19:48:54 -0000 vti_extenderversion:SR|4.0.2.8912 vti_cacheddtm:TX|08 Oct 2007 19:48:54 -0000 vti_filesize:IR|32256 vti_cachedlinkinfo:VX| vti_cachedsvcrellinks:VX| vti_cachedtitle:SR|Psychol
Idaho - PSYC - 430
<?xml version="1.0" encoding="UTF-8"?> <Error><Code>NoSuchKey</Code><Message>The specified key does not exist.</Message><Key>0866125bfdc0334b185ed406a8a0a0a839643c12.ppt</Key><RequestId>3 9BDE73B6A335A99</RequestId><HostId>uXRGwOjtPMg4nnH5y1S7VDcReeH
Idaho - PSYC - 430
vti_encoding:SR|utf8-nl vti_timelastmodified:TR|14 Sep 2005 23:15:52 -0000 vti_extenderversion:SR|4.0.2.8912 vti_cacheddtm:TX|14 Sep 2005 23:15:52 -0000 vti_filesize:IR|83968 vti_cachedlinkinfo:VX| vti_cachedsvcrellinks:VX| vti_cachedtitle:SR|Psychol
Idaho - PSYC - 430
vti_encoding:SR|utf8-nl vti_timelastmodified:TR|20 Aug 2008 21:47:24 -0000 vti_extenderversion:SR|4.0.2.8912 vti_cacheddtm:TX|20 Aug 2008 21:47:24 -0000 vti_filesize:IR|53248 vti_cachedlinkinfo:VX|H|mailto:dss@uidaho.edu H|mailto:tthorste@uidaho.edu
Georgia Tech - CS - 1050
Rosen 1.3Propositional Functions Propositional functions (or predicates) are propositions that contain variables. Ex: Let P(x) denote x > 3 P(x) has no truth value until the variable x is bound by either assigning it a value or by quantifying
Georgia Tech - CS - 1050
Rosen 1.4, 1.5Basic Definitions Set - Collection of objects, usually denoted by capital letter Member, element - Object in a set, usually denoted by lower case letter Set Membership - a A denotes that a is an element of set A Cardinality of a
Georgia Tech - CS - 1050
More Functions and SetsRosen 1.6Inverse Image Let f be a function from set A to set B. Let S be a subset of B. We define the inverse image of S to be the subset of A containing all pre-images of all elements of S. f-1(S) = {aA | f(a) S}A Ba
Georgia Tech - CS - 1050
Sequences & SummationsCS 1050 Rosen 1.7Sequence A sequence is a discrete structure used to represent an ordered list. A sequence is a function from a subset of the set of integers (usually either the set {0,1,2,. . .} or {1,2, 3,. . .}to a set S
Georgia Tech - CS - 1050
Doing SumsWhat isk =02n-kar n+1 - a ar k = ,r 1 k =0 r -1nk =0(n1 k 2)1- ( ) = 1- 1 21 n +1 2Let a = 1 and r = 1/2=1 - 2-n -1 = -1 2 - n -1 n +1 1 - 2 2 n +1 -1 2 2 2n +1 - 1 = n 2What if k starts at 1?k =1
Georgia Tech - CS - 1050
Mathematical InductionRosen 3.2Basics The Well-Ordering Property - Every nonempty set of nonnegative integers has a least element. Many theorems state that P(n) is true for all positive integers. For example, P(n) could be the statement that th
Georgia Tech - CS - 1050
Induction PracticeCS1050(2 j + 1) = 3n 2 whenever n Prove that j =n2 n 1is a positive integer. Proof: Basic Case: Let n = 1, then2 (1) 1 j =1 (2 j + 1) = ( 2 j + 1) = 3 = 3(1)j 112=3(2 j + 1) = 3n 2 whenever n Prove that j =n2
Georgia Tech - CS - 1050
Problems to Solve Involving Inductionar n +1 - a k ar = r - 1 , r 1? k =0nProof by Induction Basic Step: Does it work for n=0? ark =00k= ar = a0ar 0+1 - a ar - a a (r - 1) = = =a r -1 r -1 r -1Inductive StepAssume that for r 1
Georgia Tech - CS - 1050
PracticeCS1050 Spring 2002Flipping a coin 4 times. How many different combinations of heads and tails are there? C1:HHHHHHHHTTTTTTTT C2:HHHHTTTTHHHHTTTT C3:HHTTHHTTHHTTHHTT C4:HTHTHTHTHTHTHTHT 16: How do we compute without counting? Product rule:
E. Kentucky - ITTC - 412
1/14/2003Amplifier Saturation.doc1/7Amplifier SaturationNote that the ideal amplifier transfer function:vo (t ) = A vi (t ) vois an equation of a line (with slope = Avo and y-intercept = 0).voAvoviThis ideal transfer function implie
E. Kentucky - ITTC - 412
10/17/2003BJT Biasing using a Current Source1/4BJT Biasing using a Current SourceAnother way to bias a BJT small signal amplifier is to use one voltage source and one current source. This biasing scheme has a number of important advantages: 1.
E. Kentucky - ITTC - 412
11/10/2003Current Steering Circuits1/3Current Steering CircuitsA current mirror may consist of many MOSFET current sources!VDD VDD VDD VDDRIrefRL1IL1 =IrefRL2IL2=Iref RL3IL3=IrefQrefQ1Q2Q3This circuit is particularly u
E. Kentucky - ITTC - 412
2/3/2003Example An Inverting Network.doc1/2Example: An Inverting NetworkNow let's determine the complex transfer function of this circuit:R2CviR1v1-ideal v2+voNote this circuit uses the inverting configuration, so that:T (
E. Kentucky - ITTC - 412
Example: D.C. Analysis of a BJT CircuitConsider again this circuit from lecture: 10.7 V1.0 K Q: What is iB, iC, iE andalso VCE, VCB, VBE ?5.7 V 10 K = 99A: I don't know ! But, we can find out, IF we complete each of the five steps required
E. Kentucky - ITTC - 412
9/24/2003Example The Input Bias Current1/4Example: The Input Bias CurrentQ: How do input bias currents IB 1 and IB 2 affect amplifieroperation?A: Consider both inverting and non-inverting configurations.Inverting ConfigurationR2 i2vi
E. Kentucky - ITTC - 412
9/22/2003Full Power Bandwidth1/5Full-Power BandwidthConsider now the case where the input to an op-amp circuit is sinusoidal, with frequency . The output will thus likewise be sinusoidal, e.g.: vo (t ) = Vo sint where Vo is the magnitude of t
E. Kentucky - ITTC - 412
12/4/2003Large Signal Operation of MOSFET Diff Pair1/6Large-Signal Operation of the MOSFET Differential PairConsider now the MOSFET differential pair:Figure 7.1 The basic MOS differential-pairNote:v S 1 = vS 2 = vSandiD 1 + iD 2 = II
E. Kentucky - ITTC - 412
9/29/2003Real Op Amp Input and Output Resistance1/4Real Op-Amp Input and Output ResistancesThe input resistances of real op-amps are very large, but of course not infinite! Typical values of input resistances range from several hundred Kilo Oh
E. Kentucky - ITTC - 412
9/25/2003Reducing the Effect of Input Bias Current1/5Reducing the Effect of Input Bias CurrentWe found that the input bias current will cause an offset in the output voltage. There is a solution to this problem-place a resistor (R3) on the non
E. Kentucky - ITTC - 412
1/27/2003Ri and Ro of the Inverting Amplifier.doc1/6Inverting AmplifierWe can use the concept of the virtual short to easily determine the input resistance of the inverting amplifier. Recall that the input resistance of an amplifier is:Ri an
E. Kentucky - ITTC - 412
2/5/2003Superposition and OpAmp Circuits.doc1/4Superposition and Op-Amp CircuitsConsider this op-amp circuit:v1R1R2-v2R3ideal+voR4The easiest way to analyze this circuit is to apply superposition! Recall that op-amp circui
E. Kentucky - ITTC - 412
9/30/2003The Small Signal Equaton Matrix1/2The Small-Signal Equation MatrixWe can summarize our small-signal equations with the smallsignal equation matrix. Note this matrix relates the small-signal BJT parameters vbe , ib , ic , and ie . Colu
E. Kentucky - ITTC - 412
9/4/2003The Weighted Summer1/2The Weighted SummerConsider an inverting amplifier with multiple inputs!v1 v2 v3R1 R2 R3i1 i2 i3i-Rfideal+voFrom KCL, we can conclude that the currents are related as: i= and because of virtual g