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UC Irvine - BIOL - 97
Bio Sci 99: Part II Regulation of Gene Expression Prof. Ingrid Ruf iruf@uci.edu (please put reference to Bio 99 in subject) 3236 McGaugh Hall Office Hours: Fridays 11 am - noon, or by appointmentCENTRAL DOGMARegulation of Gene Expression Intro and Over
UC Irvine - BIOL - 97
Main Points: Lecture 1Elements of a promoter: -35 and -10 regions, site of transcription initiation, importance of DNA sequence and spacing between elements Prok. RNAP: subunits, sigma factor as specificity factor, core vs. holoenzyme Termination of tran
UC Irvine - BIOL - 97
Main Points: Lecture 2Different sigma factors recognize different consensus recognition sites in promoter, allows utilization of different promoters and transcription of different genes under different conditions Constitutive vs. regulated expression: ho
UC Irvine - BIOL - 97
Main Points: Lecture 7 Assembly and role of GTFs What does a minimal eukaryotic pol II promoter look like? Phosphorylation of CTD of Pol II TBP as a universal factor Characteristics of Pol I and III promoters PPE and common txn factorsTranscriptionFactor
UC Irvine - BIOL - 99
1BIO SCI 99 Problem Set #3 Zoran Rakonjac Monday 11am Section 1. What experiments or observations eliminated the possibility that the genetic code was a three-letter overlapping code? What experiment or observation eliminated the possibility that there ar
Binghamton - ACCT - 575
Shu-Ya Peng Tax Assignment 2Explanation 1: Clark and Lois Kent can claim both May & Billy, Jr., as dependent because of the qualifying child test. They meet the relationship test because they are Loiss children, they meet the Abode Test because they live
Binghamton - ACCT - 575
Chapter Two Problem Solutions7. Income tax Reg. 1. 702 1 (a) (8)Type of Regulation Related Code Section Regulation Number Regulation Paragraph Regulation Subparagraph 9. p. 2-6 In many Code sections, Congress has given to the Secretary or his delegate t
Binghamton - ACCT - 575
Chapter 4GrossIncome: ConceptsandInclusionsIndividual Income TaxesCopyright 2009 Cengage Learning Individual Income Taxes C4-1 C4-Gross Income (slide 1 of 3) Definition: Gross income includes all income from whatever source derived, unless specifical
Binghamton - ACCT - 575
Chapter 2WorkingwiththeTaxLawIndividual Income TaxesCopyright 2009 Cengage Learning Individual Income Taxes C2-1 C2-Statutory Sources of Tax Law(slide 1 of 2) Internal Revenue Code Codification of the Federal tax law provisions in a logical sequenc
Binghamton - ACCT - 575
1 Deductions are disallowed unless allowed by a specific provision in the tax law.status: correct (1.0) correct: true your answer: true feedback: Correct. 2 Generally, a closely-held family corporation cannot take a deduction for a salary paid to a famil
Binghamton - ACCT - 540
Shu-Ya Peng Student Assigned Article Summary IV - U.S. Is Said to Be Urging New Mergers in Banking Article Summary The Treasury plan to have a new package to encourage banks to buy or merge with rivals. With the credit crisis, many banks open to sell them
Binghamton - ACCT - 540
Shu-Ya Peng Student Assigned Article Summary: The Financial Crisis: Bailout Politics Article Summary: No matter which candidate wins the White House, he will face dire economic situation and huge budget deficit. For this fiscal year, the deficit hit 454.8
University of Alberta - ECON - ECON 399
Part 1Cross Sectional DataSimple Linear Regression Model Chapter 2 Multiple Regression Analysis Chapters 3 and 4 Advanced Regression Topics Chapter 6 Dummy Variables Chapter 7 Note: Appendices A, B, and C are additional review if needed.1. The Simple R
University of Alberta - ECON - ECON 399
3. Multiple Regression Analysis: Estimation-Although bivariate linear regressions are sometimes useful, they are often unrealistic -SLR.4, that all factors affecting y are uncorrelated with x, is often violated -MULTIPLE REGRESSION ANALYSIS allows us to
University of Alberta - ECON - ECON 399
3.2OLSFittedValuesandResidualsafterobtainingOLSestimates,wecanthen obtainfittedorpredictedvaluesfory: yi = 0 + 1 xi1 + 2 xi 2 + . + k xik(3.20)givenouractualandpredictedvaluesfory,asin thesimpleregressioncasewecanobtain residuals: u = y y (3.21)i i i
University of Alberta - ECON - ECON 399
5.ConsistencyWecannotalwaysachieveunbiasednessof estimators. Forexample,hatisnotanunbiased estimatorof Itisonlyconsistent Whereunbiasednesscannotbeachieved, consistencyistheminimumrequirementforan estimator ConsistencyrequiresMLR.1throughMLR.4,as wellasn
University of Alberta - ECON - ECON 399
6.4PredictionWehavealreadyseenhowtomake predictionsaboutourdependentvariable usingourOLSestimatesandvaluesfor ourindependentvariables However,theseestimatesaresubjectto samplingvariation aconfidenceintervalisthereforeoften preferabletoapointestimateAssu
University of Alberta - ECON - ECON 399
Oftenitisdesiroustoknowiftwodifferentgroups followthesameordifferentregressionfunctions OnewaytotestthisistouseDVstoallowfor ALLdifferentinterceptsandslopesforthetwo groups,andtestifallDVterms=0:7.4DVsandGroupsy = 0 + 1 x1 + 2 x2 + . + k xk + 0 DV + 1 D
University of Alberta - ECON - ECON 399
Wehavealreadyseenthathomoskedasticity existswhentheerrortermsvariance,conditional onallxvariables,isconstant: Homoskedasticityfailsifthevarianceoftheerror termvariesinthesample(ie:varieswiththex variables) WeusedHomoskedasticityforttests,Ftest, andconfide
University of Alberta - ECON - ECON 399
Beforetheexistenceofheteroskedasticityrobust statistics,oneneededtoknowtheformof heteroskedasticity HetwasthencorrectedusingWEIGHTED LEASTSQUARES(WLS) Thismethodisstillusefultoday,asif heteroskedasticitycanbecorrectlymodeled, WLSbecomesmoreefficientthanOL
University of Alberta - ECON - ECON 399
10. Basic Regressions with Times Series Data10.1 The Nature of Time Series Data 10.2 Examples of Time Series Regression Models 10.3 Finite Sample Properties of OLS Under Classical Assumptions 10.4 Functional Form, Dummy Variables, and Index Numbers 10.5
University of Alberta - ECON - ECON 399
Whereascrosssectionaldataneeded3 assumptionstomakeOLSunbiased,time seriesdataneedsonly2 Althoughthethirdassumptionismuch stronger Ifweomitavalidvariable,wecausebiasedas seenandcalculatedinChapter3 Nowallthatremainsistoderiveassumptions thatallowustotestth
University of Alberta - ECON - ECON 399
Chapter10coveredOLSpropertiesforfinite (small)sampletimeseriesdata IfourChapter10assumptionsfail,weneedto derivelargesampletimeseriesdataOLS properties forexample,ifstrictexogeneityfails(TS.3)10FurtherTimeSeriesOLSIssuesUnfortunatelylargesampleanalysisi
University of Alberta - ECON - ECON 399
Thefollowingautocorrelationcorrectionrequires allourregressorstobestrictlyexogenous inparticular,weshouldhavenolagged explanatoryvariables AssumethatourerrortermsfollowAR(1)SERIAL CORRELATION: assumingfromhereoninthateverythingis conditionalonX,wecancalcu
University of Alberta - ECON - ECON 299
1.4InterestRates Interestratesareimportantineconomics, astheyshowtheopportunitycostofa project. Differentinterestratesapplytodifferent situations. Differentinterestratesareavailableto differentpeople.1.4InterestRateExamples(Sept05) BankofCanadaRate:2.75%
University of Alberta - ECON - ECON 299
2.2.2 ElasticitiesWe have already seen how the derivative, or the slope, can change as x and y change -even if a slope is constant, changes can have different impacts at different points -For example, given a linear demand for Xbox 360s, a $100 price inc
University of Alberta - ECON - ECON 299
2.A Optimization It can be argued that economists are involved in three practical areas: 1) Analyze what has happened 2) Specify where an economy is now 3) Advise on future decisions To achieve the third goal, often an economist must know what the best re
University of Alberta - ECON - ECON 299
Chapter3StatisticalReview Thischapterwillconductabriefreviewof statisticalconcepts.ItisNOTareplacement forastatisticscourse.Conceptswillinclude:Models Randomandnonrandomvariables Probabilities ExpectedValues Variance DistributionsandTables(Normal,t,F,ch
University of Alberta - ECON - ECON 299
TimeSeries,CrossSectional&Pooled Data 1.TimeSeriesDataOnelocationsdataacrosstime Yearly,monthly,quarterly(everythree months),weekly,daily,etc. ie:CanadianGDP,Enronstockvalue, yourheight,UofAtuition,worldpop.TimeSeries,CrossSectional&Pooled Data 2.CrossS
University of Alberta - ECON - ECON 281
Econ 281Intermediate MicroeconomicsConsumer Behavior Theory of Production and Cost Various Market StructuresLorne Priemaza, M.A. Lorne.priemaza@ualberta.caVarious material courtesy of Katharine Rockette and Wiley & Sons INC.Chapter 1What is economic
University of Alberta - ECON - ECON 281
Chapter 5 The Theory of Demand Thus far we have studied supply and demand and their equilibrium In this chapter we will see how the demand curve arises out of consumer theory Shifts in demand will be dissected and consumer choices will be investigated fu
University of Alberta - ECON - ECON 281
Theindividualsdemandcurvecanbeseenastheindividualswillingnesstopaycurve.Ontheotherhand,theindividualmustonlyactuallypaythe marketpricefor(all)theunitsconsumed. Forexample,youmaybewillingtopay$40forahaircut,but uponarrivingatthestylist,discoverthatthepri
University of Alberta - ECON - ECON 281
Isoquants Regions of ProductionDue to the law of diminishing marginal returns, increasing one input will eventually decrease total output When this occurs, in order to maintain a level of output (stay on the same isoquant), the other input will have to i
University of Alberta - ECON - ECON 281
Chapter 7: Costs and Cost MinimizationConsumers purchase GOODS to maximize their utility. This consumption depends upon a consumers INCOME and the PRICE of the goods Firms purchase INPUTS to produce OUTPUT This output depends upon the firms FUNDS and the
University of Alberta - ECON - ECON 281
Chapter 8: Cost CurvesIn the long run, a firms costs equal zero when zero production is undertaken As production (Q) increases, the firm must use more inputs, thus increasing its cost By minimizing costs, a firms long run cost curve is as follows:1KQ1
University of Alberta - ECON - ECON 281
Ch 12: Capturing SurplusAny firm with market power (such as a monopolist) has an incentive to capture (obtain) consumer surplus in order to increase its profits Consumer surplus can be captured though: -Price Discrimination -Tie-Ins -Advertising -Often c
Oregon State - ECON - 202
Washington - IE - 315
Chapter 2Section 2-1 2-1. Sample average:x=i =1 xi nn=i =1 xi 1212=673.1 = 56.09 12Sample standard deviation:i =1 xi = 673.1012i =1 22 xi =3916812s=n x i n i =1 2 xi i =1 n n 1=39168 ( 673.10) 212
Washington - IE - 315
Chapter 2Section 2-1 2-1. Sample average:12x=i =1 xi nn=i =1 xi 1212=673.1 = 56.09 12Sample standard deviation:i =1 xi = 673.10i =1 22 xi =3916812s=n x i n i =1 2 xi i =1 n n 1=39168 (673.10)212 = 1
Washington - IE - 315
Chapter 3 Section 3-2 3-1. Continuous 3-2. Discrete 3-3. Continuous 3-4. Discrete 3-5. Discrete 3-6. Continuous 3-7. Discrete Section 3-3 3-8. a) Engineers with at least 36 months of full-time employment. b) Samples of cement blocks with compressive stren
Washington - IE - 315
Section 3-10 3-117. a) E(X) = 300(0.4) = 120, V(X) = 300(0.4)(0.6) = 72 and X = Then, P ( X 90) P Z 72 .90 120 = P ( Z 3.54) = 0.0002 72 70 120 90 120 = P(5.89 < Z 3.54) <Z b) P (70 < X 90) P 72 72 = 0.0002 0 = 0.0002 3-1
Washington - IE - 315
CHAPTER 4 Section 4-2 2n Xi 1 2n 1 X1 = E i =1 = E Xi = ( 2 n ) = E 2n 2 n i =1 2 n 4-1.()E X2()n Xi 1 n 1 = E i =1 = E X i = ( n ) = , n n i =1 n X1 and X2 are unbiased estimators of .The varia
Washington - IE - 315
CHAPTER 5 Section 5-2 5-1. a) 1) The parameter of interest is the difference in fill volume, 1 2 2) H0 : 1 2 = 0 or 1 = 2 3) H1 : 1 2 0 or 1 2 4) = 0.05 5) The test statistic is z0 = ( x1 x2 ) 02 1 2 +2 n1 n 26) Reject H0 if z0 < z/
Washington - IE - 315
CHAPTER 6 Note to Instructor: For computer exercises, the procedure Regression under Stat in Minitab can be used for the regression analysis except for computing confidence intervals on the regressor variables.Sections 6-2 6-1. a) The regression eq
Washington - IE - 315
CHAPTER 7 Notes to the Instructor: The normal probability plots are constructed in Minitab using the command Normality Test under Basic Statistics. The analysis of variance is carried out using ANOVA under Stat in Minitab. In cases where there are ex
Washington - IE - 315
Chapter 8Note to the Instructor: Some of the revised control charts provided in the solutions retain the removed points only as place holders. Other revised control charts have been created by removing the out-of-control point from the worksheet and
Washington - EE - 453
ABSTRACT INTRODUCTION DESIGN PROCEDURE PRESENTATION, DISCUSSION, AND ANALYSIS OF THE RESULTS CONCLUSIONS
Washington - EE - 453
Topic focus on how protection failures lead to cascading failures and blackouts. Hidden Failure Model looks promising An approach for modeling the protection system in transmission network reliability evaluation When protection systems are out of ord
Washington - EE - 453
Lower Limit Mean8.17 0.4 0.3 0.76 8.15 0.44 0.2 0.67Upper Limit 6.79 0.38 0.27 0.72 9.55 0.42 0.33 0.81 Upper Limit 7.51 0.43 0.19 0.65 8.79 0.45 0.22 0.69Variance5.32 0.08 0.11 0.17 6.38 0.12 0.19 0.23Short Players 1.38 PTS 0.02 FG% 0.03 3P%
Washington - EE - 453
Unit IRF9520 MTP3055 LM311 MUR420 .1uF ceramic cap 10uF, 50V cap 100uF, 35V cap 1000uF, 25V cap LM555 SG3524 7805 5V reg 7808C 8V reg GAL22V10 100k 3/4 turn pot 50K 3/4 turn pot 1kohm resistor 10kohm resistorCost/Unit ($) 1.50 1.00 0.60 0.80 0.20 0
Washington - EE - 453
Integrated Power System Vulnerability Analysis Considering Protection FailuresXingbinYu,Studenr Member, IEEE, and Chanan Singh, Fellow, IEEEAbstract-Protection system failure is one of the main causes of cascading outages. This paper proposes an
Washington - EE - 453
Time A B C V7 0.100000000E-04 0.200000000E-04 0.300000000E-04 0.400000000E-04 0.500000000E-04 0.600000000E-04 0.700000000E-04 0.800000000E-04 0.900000000E-04 0.100000000E-03 0.110000000E-03 0.120000000E-03 0.130000000E-03 0.140000000E-03 0.150000000E